id
float64 706
1.8k
| title
stringlengths 1
343
| abstract
stringlengths 6
6.09k
| categories
stringlengths 5
125
| processed_abstract
stringlengths 2
5.96k
| tokenized_abstract
stringlengths 8
8.74k
| centroid
stringlengths 2.1k
2.17k
|
|---|---|---|---|---|---|---|
1,803.04467
|
Witnessing Planetary Systems in the Making with the Next Generation Very
Large Array
|
The discovery of thousands of exoplanets over the last couple of decades has
shown that the birth of planets is a very efficient process in nature. Theories
invoke a multitude of mechanisms to describe the assembly of planets in the
disks around pre-main-sequence stars, but observational constraints have been
sparse on account of insufficient sensitivity and resolution. Understanding how
planets form and interact with their parental disk is crucial also to
illuminate the main characteristics of a large portion of the full population
of planets that is inaccessible to current and near-future observations. This
White Paper describes some of the main issues for our current understanding of
the formation and evolution of planets, and the critical contribution expected
in this field by the Next Generation Very Large Array.
|
astro-ph.EP
|
the discovery of thousands of exoplanets over the last couple of decades has shown that the birth of planets is a very efficient process in nature theories invoke a multitude of mechanisms to describe the assembly of planets in the disks around premainsequence stars but observational constraints have been sparse on account of insufficient sensitivity and resolution understanding how planets form and interact with their parental disk is crucial also to illuminate the main characteristics of a large portion of the full population of planets that is inaccessible to current and nearfuture observations this white paper describes some of the main issues for our current understanding of the formation and evolution of planets and the critical contribution expected in this field by the next generation very large array
|
[['the', 'discovery', 'of', 'thousands', 'of', 'exoplanets', 'over', 'the', 'last', 'couple', 'of', 'decades', 'has', 'shown', 'that', 'the', 'birth', 'of', 'planets', 'is', 'a', 'very', 'efficient', 'process', 'in', 'nature', 'theories', 'invoke', 'a', 'multitude', 'of', 'mechanisms', 'to', 'describe', 'the', 'assembly', 'of', 'planets', 'in', 'the', 'disks', 'around', 'premainsequence', 'stars', 'but', 'observational', 'constraints', 'have', 'been', 'sparse', 'on', 'account', 'of', 'insufficient', 'sensitivity', 'and', 'resolution', 'understanding', 'how', 'planets', 'form', 'and', 'interact', 'with', 'their', 'parental', 'disk', 'is', 'crucial', 'also', 'to', 'illuminate', 'the', 'main', 'characteristics', 'of', 'a', 'large', 'portion', 'of', 'the', 'full', 'population', 'of', 'planets', 'that', 'is', 'inaccessible', 'to', 'current', 'and', 'nearfuture', 'observations', 'this', 'white', 'paper', 'describes', 'some', 'of', 'the', 'main', 'issues', 'for', 'our', 'current', 'understanding', 'of', 'the', 'formation', 'and', 'evolution', 'of', 'planets', 'and', 'the', 'critical', 'contribution', 'expected', 'in', 'this', 'field', 'by', 'the', 'next', 'generation', 'very', 'large', 'array']]
|
[-0.11452597232437256, 0.1347909614250966, -0.0572464414605065, 0.08018087191847201, -0.11748208846984198, -0.03112314624377177, 0.04344050247982523, 0.32275064240093343, -0.20596431016747374, -0.34357690418255515, 0.1263108377925164, -0.23878664886433398, -0.13139601305374526, 0.20173666977643734, -0.06383020927751204, 0.009296963489759946, 0.1315146809847647, -0.03996103546933227, -0.003944392516132211, -0.2792301155423047, 0.326108151988592, 0.08637777705735061, 0.1416323141643261, 0.03412950344682031, 0.06604215151492099, -0.07327074989370885, -0.06134457921871217, -0.044727371790941106, -0.146710784575248, 0.11644775353670411, 0.2427798023854848, 0.1606795078059804, 0.28300575027788, -0.44893305799996597, -0.22992959596012952, 0.07599066592229065, 0.1754534480833172, 0.07277939417326706, -0.06714543534053519, -0.22617135359178064, 0.10202968502107979, -0.17049881680577528, -0.15109134877639008, -0.024108186094963457, 0.0702879793871034, 0.03938249297692664, -0.21214772924986391, 0.02617163510581122, 0.08995702180254739, 0.05615757351188222, -0.10480608175748785, -0.11339613244945213, 0.0016042962761275703, 0.15804150971962372, 0.06093919356658262, 0.01234221077629627, 0.12148108271640012, -0.1865087314072298, -0.0725049266602582, 0.40428527331505393, -0.07861889461128158, -0.08718070966006053, 0.2703085023458698, -0.23874410040662042, -0.15201590416108957, 0.13299377007206203, 0.19565161986247404, 0.1262844492921431, -0.17333110666731955, 0.05502538569044191, 0.006193532286488335, 0.16558653011452407, 0.03638173058971006, 0.07160771328199189, 0.38654434736236, 0.2273386953784211, 0.059752546795607486, 0.07731313140538987, -0.13422038232056366, -0.10807753389235586, -0.22584767411171924, -0.15447503562972997, -0.16049405548801587, 0.05383347319730092, -0.04747493276443038, -0.15440142865895723, 0.40722220618590654, 0.20204624039479313, 0.19813835183231276, 0.006297403997450601, 0.3051121277749189, 0.05511908921880604, 0.1220678853442223, 0.035710079482669244, 0.2869592479958101, 0.16903940834345121, 0.1056817598428097, -0.23070033252224675, 0.12256159967728308, -0.05467820381636557]
|
1,803.04468
|
Independent indistinguishable quantum light sources on a reconfigurable
photonic integrated circuit
|
We report a compact, scalable, quantum photonic integrated circuit realised
by combining multiple, independent InGaAs/GaAs quantum-light-emitting-diodes
(QLEDs) with a silicon oxynitride waveguide circuit. Each waveguide joining the
circuit can then be excited by a separate, independently electrically contacted
QLED. We show that the emission from neighbouring QLEDs can be independently
tuned to degeneracy using the Stark Effect and that the resulting photon
streams are indistinguishable. This enables on-chip Hong-Ou-Mandel-type
interference, as required for many photonic quantum information processing
schemes.
|
physics.app-ph physics.optics quant-ph
|
we report a compact scalable quantum photonic integrated circuit realised by combining multiple independent ingaasgaas quantumlightemittingdiodes qleds with a silicon oxynitride waveguide circuit each waveguide joining the circuit can then be excited by a separate independently electrically contacted qled we show that the emission from neighbouring qleds can be independently tuned to degeneracy using the stark effect and that the resulting photon streams are indistinguishable this enables onchip hongoumandeltype interference as required for many photonic quantum information processing schemes
|
[['we', 'report', 'a', 'compact', 'scalable', 'quantum', 'photonic', 'integrated', 'circuit', 'realised', 'by', 'combining', 'multiple', 'independent', 'ingaasgaas', 'quantumlightemittingdiodes', 'qleds', 'with', 'a', 'silicon', 'oxynitride', 'waveguide', 'circuit', 'each', 'waveguide', 'joining', 'the', 'circuit', 'can', 'then', 'be', 'excited', 'by', 'a', 'separate', 'independently', 'electrically', 'contacted', 'qled', 'we', 'show', 'that', 'the', 'emission', 'from', 'neighbouring', 'qleds', 'can', 'be', 'independently', 'tuned', 'to', 'degeneracy', 'using', 'the', 'stark', 'effect', 'and', 'that', 'the', 'resulting', 'photon', 'streams', 'are', 'indistinguishable', 'this', 'enables', 'onchip', 'hongoumandeltype', 'interference', 'as', 'required', 'for', 'many', 'photonic', 'quantum', 'information', 'processing', 'schemes']]
|
[-0.14317629461248332, 0.19828239884466314, -0.026544709976475973, -0.05681809675073824, -0.03312493549235977, -0.27134367279732263, 0.05587363882491795, 0.4942101300335847, -0.25822162375045127, -0.31471143131598067, 0.001236507022836938, -0.28342153867467856, -0.09113220028349389, 0.2479102889266916, -0.016030574611459788, 0.09042719679359251, 0.049248065578584105, -0.10351918635364526, 0.002386339859535488, -0.16601817602984226, 0.24678359039497968, 0.007456734598888896, 0.3183881390887575, -0.013983227888671443, 0.11011246552404302, -0.013002584201809114, 0.04679697713790796, 0.007024021583418243, -0.04443365474062142, 0.13398894201367137, 0.28441806458450186, 0.01179187848500964, 0.21119347207385522, -0.4894039070663544, -0.18699751939409628, 0.0637984530998764, 0.223443761951505, 0.17722125914998543, -0.08489724835500312, -0.3368332781590139, 0.053382376867095724, -0.1562266896914643, -0.04477389513825377, -0.0022421379048281754, -0.057766146217592254, -0.02109940455127985, -0.2259330075377455, -0.03528359735933825, -0.004777209375065585, -0.03731050499929832, 0.06958662199143034, -0.029323979328649167, -0.03680401911529211, 0.08599482861777338, -0.17240569711877748, -0.014505028160503851, 0.24658122311274594, -0.002457329264591233, -0.219192369052997, 0.3111147243732539, -0.07543608912219031, -0.16467244194474262, 0.10473939311953309, -0.09364949682882677, -0.05773132834105919, 0.11929535247289981, 0.13028699908859265, 0.041704993436444335, -0.1623752095306722, 0.0486309115382401, 0.00995296742528295, 0.23242338818426317, 0.12716030319316837, 0.14513975724231643, 0.2726144734531259, 0.13946658440041715, 0.015268287396816632, 0.211592309591838, -0.06906757718071532, -0.025688740950173292, -0.25026768815587275, -0.175747903672835, -0.23570130529025426, 0.09040554030201374, -0.07832561104945714, -0.13594377376377964, 0.35491442361560005, 0.11588051741142781, 0.15746340804542294, -0.029954368182911705, 0.3497408935561394, 0.14122686747503346, 0.12359383643473475, 0.071609605044031, 0.24869409448897037, 0.16173266153185603, 0.042710309997439765, -0.25388978940482515, 0.0010363489138678862, -0.0745015046141373]
|
1,803.04469
|
An Introduction to Image Synthesis with Generative Adversarial Nets
|
There has been a drastic growth of research in Generative Adversarial Nets
(GANs) in the past few years. Proposed in 2014, GAN has been applied to various
applications such as computer vision and natural language processing, and
achieves impressive performance. Among the many applications of GAN, image
synthesis is the most well-studied one, and research in this area has already
demonstrated the great potential of using GAN in image synthesis. In this
paper, we provide a taxonomy of methods used in image synthesis, review
different models for text-to-image synthesis and image-to-image translation,
and discuss some evaluation metrics as well as possible future research
directions in image synthesis with GAN.
|
cs.CV
|
there has been a drastic growth of research in generative adversarial nets gans in the past few years proposed in 2014 gan has been applied to various applications such as computer vision and natural language processing and achieves impressive performance among the many applications of gan image synthesis is the most wellstudied one and research in this area has already demonstrated the great potential of using gan in image synthesis in this paper we provide a taxonomy of methods used in image synthesis review different models for texttoimage synthesis and imagetoimage translation and discuss some evaluation metrics as well as possible future research directions in image synthesis with gan
|
[['there', 'has', 'been', 'a', 'drastic', 'growth', 'of', 'research', 'in', 'generative', 'adversarial', 'nets', 'gans', 'in', 'the', 'past', 'few', 'years', 'proposed', 'in', '2014', 'gan', 'has', 'been', 'applied', 'to', 'various', 'applications', 'such', 'as', 'computer', 'vision', 'and', 'natural', 'language', 'processing', 'and', 'achieves', 'impressive', 'performance', 'among', 'the', 'many', 'applications', 'of', 'gan', 'image', 'synthesis', 'is', 'the', 'most', 'wellstudied', 'one', 'and', 'research', 'in', 'this', 'area', 'has', 'already', 'demonstrated', 'the', 'great', 'potential', 'of', 'using', 'gan', 'in', 'image', 'synthesis', 'in', 'this', 'paper', 'we', 'provide', 'a', 'taxonomy', 'of', 'methods', 'used', 'in', 'image', 'synthesis', 'review', 'different', 'models', 'for', 'texttoimage', 'synthesis', 'and', 'imagetoimage', 'translation', 'and', 'discuss', 'some', 'evaluation', 'metrics', 'as', 'well', 'as', 'possible', 'future', 'research', 'directions', 'in', 'image', 'synthesis', 'with', 'gan']]
|
[0.01970851349584553, -0.05537184039045365, -0.027899236888240236, 0.03957148295199208, -0.06321558870648572, -0.14019668516660228, -0.05876170384052526, 0.519063019932786, -0.23788583319347967, -0.3682686914689839, 0.11538612751884027, -0.2548778309239023, -0.2286801955602858, 0.25455023317572173, -0.2015161689909158, 0.1777179743208145, 0.08540112358611932, -0.0017284086065413858, -0.06725789220424779, -0.3020035498860984, 0.23016889962376258, 0.02701544196028775, 0.3863829776234583, 0.054736895508073063, 0.09340241129740799, -0.054240890718791464, 0.015500405807861922, -0.022393025534757232, -0.08053811723681449, 0.1984628950194413, 0.3697460918604044, 0.24460635800628844, 0.35315153742243255, -0.42899975209676344, -0.30414081477654087, 0.08414360357089601, 0.1385470295790583, 0.14110570070291056, -0.2173704670212411, -0.27673969851858027, 0.09294136761583029, -0.17069010973113394, 0.06049507685484142, -0.10286875054295765, 0.050397430119369556, -0.004234711607008181, -0.18667885489706293, -0.04964651006760947, 0.1093196692639415, 0.1345055714813531, -0.05616407560979213, -0.1761520916953578, 0.01784531378353852, 0.17319426109262426, 0.0917818525831702, 0.10775041855752059, 0.0994718640470245, -0.19249147924951304, -0.20668603202601502, 0.3839115200888947, -0.07545962969029169, -0.14289588240346948, 0.20709820181438957, -0.049164496197732624, -0.22403455764021074, 0.03076662874349111, 0.23571597354982152, 0.12555314567145132, -0.16506020878569796, 0.10605643722420952, -0.012233029893992485, 0.11886002987178915, 0.09407533801784483, 0.025037643491128168, 0.20633028638378265, 0.3120468139750968, 0.018780538602411714, 0.1720383531678844, -0.1209862530829573, -0.08106438208151387, -0.16752915460308757, -0.14477438891494493, -0.1712801898757547, 0.012294610116391554, -0.014631925098819085, -0.13873264925776546, 0.4602560098154829, 0.2210578519697173, 0.19636579086884445, -0.006750183812248597, 0.31178054859468696, 0.015040622875649789, 0.15557271831694547, 0.019291294152301592, 0.22358404932284104, 0.07709524278964745, 0.14912188095478512, -0.0927265155248754, 0.08682379585801872, -0.018716257475658294]
|
1,803.0447
|
Conservative cosmology: combining data with allowance for unknown
systematics
|
When combining data sets to perform parameter inference, the results will be
unreliable if there are unknown systematics in data or models. Here we
introduce a flexible methodology, BACCUS: BAyesian Conservative Constraints and
Unknown Systematics, which deals in a conservative way with the problem of data
combination, for any degree of tension between experiments. We introduce
hyperparameters that describe a bias in each model parameter for each class of
experiments. A conservative posterior for the model parameters is then obtained
by marginalization both over these unknown shifts and over the width of their
prior. We contrast this approach with an existing hyperparameter method in
which each individual likelihood is scaled, comparing the performance of each
approach and their combination in application to some idealized models. Using
only these rescaling hyperparameters is not a suitable approach for the current
observational situation, in which internal null tests of the errors are passed,
and yet different experiments prefer models that are in poor agreement. The
possible existence of large shift systematics cannot be constrained with a
small number of data sets, leading to extended tails on the conservative
posterior distributions. We illustrate our method with the case of the $H_0$
tension between results from the cosmic distance ladder and physical
measurements that rely on the standard cosmological model.
|
astro-ph.CO astro-ph.IM
|
when combining data sets to perform parameter inference the results will be unreliable if there are unknown systematics in data or models here we introduce a flexible methodology baccus bayesian conservative constraints and unknown systematics which deals in a conservative way with the problem of data combination for any degree of tension between experiments we introduce hyperparameters that describe a bias in each model parameter for each class of experiments a conservative posterior for the model parameters is then obtained by marginalization both over these unknown shifts and over the width of their prior we contrast this approach with an existing hyperparameter method in which each individual likelihood is scaled comparing the performance of each approach and their combination in application to some idealized models using only these rescaling hyperparameters is not a suitable approach for the current observational situation in which internal null tests of the errors are passed and yet different experiments prefer models that are in poor agreement the possible existence of large shift systematics cannot be constrained with a small number of data sets leading to extended tails on the conservative posterior distributions we illustrate our method with the case of the h_0 tension between results from the cosmic distance ladder and physical measurements that rely on the standard cosmological model
|
[['when', 'combining', 'data', 'sets', 'to', 'perform', 'parameter', 'inference', 'the', 'results', 'will', 'be', 'unreliable', 'if', 'there', 'are', 'unknown', 'systematics', 'in', 'data', 'or', 'models', 'here', 'we', 'introduce', 'a', 'flexible', 'methodology', 'baccus', 'bayesian', 'conservative', 'constraints', 'and', 'unknown', 'systematics', 'which', 'deals', 'in', 'a', 'conservative', 'way', 'with', 'the', 'problem', 'of', 'data', 'combination', 'for', 'any', 'degree', 'of', 'tension', 'between', 'experiments', 'we', 'introduce', 'hyperparameters', 'that', 'describe', 'a', 'bias', 'in', 'each', 'model', 'parameter', 'for', 'each', 'class', 'of', 'experiments', 'a', 'conservative', 'posterior', 'for', 'the', 'model', 'parameters', 'is', 'then', 'obtained', 'by', 'marginalization', 'both', 'over', 'these', 'unknown', 'shifts', 'and', 'over', 'the', 'width', 'of', 'their', 'prior', 'we', 'contrast', 'this', 'approach', 'with', 'an', 'existing', 'hyperparameter', 'method', 'in', 'which', 'each', 'individual', 'likelihood', 'is', 'scaled', 'comparing', 'the', 'performance', 'of', 'each', 'approach', 'and', 'their', 'combination', 'in', 'application', 'to', 'some', 'idealized', 'models', 'using', 'only', 'these', 'rescaling', 'hyperparameters', 'is', 'not', 'a', 'suitable', 'approach', 'for', 'the', 'current', 'observational', 'situation', 'in', 'which', 'internal', 'null', 'tests', 'of', 'the', 'errors', 'are', 'passed', 'and', 'yet', 'different', 'experiments', 'prefer', 'models', 'that', 'are', 'in', 'poor', 'agreement', 'the', 'possible', 'existence', 'of', 'large', 'shift', 'systematics', 'can', 'not', 'be', 'constrained', 'with', 'a', 'small', 'number', 'of', 'data', 'sets', 'leading', 'to', 'extended', 'tails', 'on', 'the', 'conservative', 'posterior', 'distributions', 'we', 'illustrate', 'our', 'method', 'with', 'the', 'case', 'of', 'the', 'h_0', 'tension', 'between', 'results', 'from', 'the', 'cosmic', 'distance', 'ladder', 'and', 'physical', 'measurements', 'that', 'rely', 'on', 'the', 'standard', 'cosmological', 'model']]
|
[-0.06494309546176864, 0.06716937928759699, -0.0769845912492899, 0.07907451627353683, -0.09622918936142395, -0.13437842272308675, 0.07871561280971523, 0.4166743888931219, -0.2616637375855515, -0.33798974025486916, 0.09833528671446148, -0.25281991349143346, -0.09551290892779307, 0.23044882050260554, -0.05365615957524887, 0.06634293071449149, 0.11482384355392221, 0.01353694889261279, -0.08334930065808124, -0.2612364566863276, 0.3199736296784046, 0.06796960152122516, 0.25953731061268037, -0.01975208722531449, 0.09332598048630496, -0.022109921086514585, -0.050964149551162886, 0.06413694637892552, -0.14328315074627998, 0.12452553168729727, 0.21827037599447743, 0.14905417558498854, 0.2715392396395463, -0.38579800165323325, -0.2234206245345778, 0.15055216268181454, 0.11246710480623986, 0.12694906503203066, -0.0170940590231831, -0.2744734018510424, 0.05675576032615851, -0.16595564523101297, -0.0848371847121175, -0.09819667271749918, -0.038705685665441115, 0.04355290563517185, -0.3159377321211058, 0.11642774088243239, 0.023612924781086488, 0.03696698643873597, -0.05647795009318479, -0.13380997010376738, -0.0022209823304830596, 0.10685772901728512, 0.068297021153253, 0.012573806121728795, 0.11126944640824614, -0.108509894698685, -0.09543644617647366, 0.3509617973108191, -0.0657629579795676, -0.2683257072646886, 0.17407560411754042, -0.10464777640435238, -0.16742079172196778, 0.08815117426823045, 0.17028635455577007, 0.09789466240611144, -0.15842051985707448, 0.09150654164354079, -0.024330622251253833, 0.17053145696084165, 0.01207162297958898, -0.021183833555991186, 0.20200090535028384, 0.17137995767840292, 0.06220141484423779, 0.07933955350782462, -0.11997222258644395, -0.07040518029771616, -0.32775072555036044, -0.06698462128704197, -0.16314274225015798, -0.016673438460972204, -0.12592708647737094, -0.1655808088278972, 0.3698670913920153, 0.22629981801092494, 0.24593417844626791, 0.08199936776844308, 0.3305534846279337, 0.0791265333916039, 0.054098261532178796, 0.0494608958468361, 0.2324061364830244, 0.07311506260641265, 0.011094046157763101, -0.1681129226493541, 0.11345468236696582, -0.02752716117971685]
|
1,803.04471
|
Tensor and Matrix models: a one-night stand or a lifetime romance?
|
The spectra of energy eigenstates of free tensor and matrix models are
organized by Kronecker coefficients and Littlewood-Richardson numbers,
respectively. Exploiting recent results in combinatorics for Kronecker
coefficients, we derive a formula that relates Kronecker coefficients with a
hook shape with Littlewood-Richardson numbers. This formula has a natural
translation into physics: the eigenstates of the hook sector of tensor models
are in one-to-one correspondence with fluctuations of 1/2-BPS states in
multi-matrix models. We then conjecture the duality between both sectors.
Finally, we study the Hagedorn behaviour of tensor models with finite rank of
the symmetry group and, using similar arguments, suggest that the second (high
energy) phase could be entirely described by multi-matrix models.
|
hep-th math-ph math.MP
|
the spectra of energy eigenstates of free tensor and matrix models are organized by kronecker coefficients and littlewoodrichardson numbers respectively exploiting recent results in combinatorics for kronecker coefficients we derive a formula that relates kronecker coefficients with a hook shape with littlewoodrichardson numbers this formula has a natural translation into physics the eigenstates of the hook sector of tensor models are in onetoone correspondence with fluctuations of 12bps states in multimatrix models we then conjecture the duality between both sectors finally we study the hagedorn behaviour of tensor models with finite rank of the symmetry group and using similar arguments suggest that the second high energy phase could be entirely described by multimatrix models
|
[['the', 'spectra', 'of', 'energy', 'eigenstates', 'of', 'free', 'tensor', 'and', 'matrix', 'models', 'are', 'organized', 'by', 'kronecker', 'coefficients', 'and', 'littlewoodrichardson', 'numbers', 'respectively', 'exploiting', 'recent', 'results', 'in', 'combinatorics', 'for', 'kronecker', 'coefficients', 'we', 'derive', 'a', 'formula', 'that', 'relates', 'kronecker', 'coefficients', 'with', 'a', 'hook', 'shape', 'with', 'littlewoodrichardson', 'numbers', 'this', 'formula', 'has', 'a', 'natural', 'translation', 'into', 'physics', 'the', 'eigenstates', 'of', 'the', 'hook', 'sector', 'of', 'tensor', 'models', 'are', 'in', 'onetoone', 'correspondence', 'with', 'fluctuations', 'of', '12bps', 'states', 'in', 'multimatrix', 'models', 'we', 'then', 'conjecture', 'the', 'duality', 'between', 'both', 'sectors', 'finally', 'we', 'study', 'the', 'hagedorn', 'behaviour', 'of', 'tensor', 'models', 'with', 'finite', 'rank', 'of', 'the', 'symmetry', 'group', 'and', 'using', 'similar', 'arguments', 'suggest', 'that', 'the', 'second', 'high', 'energy', 'phase', 'could', 'be', 'entirely', 'described', 'by', 'multimatrix', 'models']]
|
[-0.1291616871246349, 0.1885070525060751, -0.11935074614736725, 0.07804080391029027, -0.07051162313457512, -0.14338033206417763, 0.0026303932230037296, 0.3184143977582847, -0.2767477079617317, -0.26042489894551407, 0.0738358280872681, -0.2608299079624805, -0.19737984431221298, 0.12089567874321355, -0.04385307758205097, 0.026684508491470887, 0.05172277546156884, 0.012406107021920514, -0.11089601360257356, -0.21399632123658402, 0.33875726144597457, 0.017814256054790395, 0.27164328450581116, 0.06426068178884554, 0.08397811868129984, 0.002381922231921763, -0.03740502546312647, -0.017189590766895236, -0.11762859225500347, 0.16932427553866433, 0.25011559927189775, 0.10230178778313034, 0.13196501932351998, -0.43218279455118536, -0.1440154934943278, 0.12905957511413776, 0.1416634169086536, 0.07670489406180486, -0.007404811737503399, -0.24075505143366427, 0.05683719325686086, -0.23747291905282622, -0.12451561997857011, -0.12554268278140707, 0.011486548825224306, 0.004917281955529593, -0.27141291553383334, 0.13524468672837606, 0.041215265292282165, 0.07342869855398149, -0.07575859682044227, -0.1760241454405906, -0.03124844551437714, 0.09030536675351884, 0.07491288907629878, -0.06725217789419714, 0.04662272188988138, -0.1812621943485155, -0.15752192990641975, 0.3480413162737693, -0.0760465628296962, -0.2386233476889238, 0.1328191964053794, -0.18186290402847685, -0.1757651005642848, 0.06437146470967778, 0.10701050632224794, 0.093693556039637, -0.07551814489099279, 0.13783299436302818, -0.12010991220615738, 0.10730796259942285, 0.0940279324228565, -0.007528870774988542, 0.22984305790445783, 0.02513711583359461, -0.04609122087264009, 0.19000775633449257, 0.02920064088488161, -0.11634917790887125, -0.31377651725421873, -0.14359371793832174, -0.1920139657480544, 0.051323135650978145, -0.16420722382693048, -0.15677442738230815, 0.4200092607030743, 0.055535735693155674, 0.2327278990951048, 0.12269207694273639, 0.20385773001206026, 0.15137532458787686, 0.0889861180545076, 0.026066016928668608, 0.15949943794035598, 0.2536661946541515, 0.058228359766010394, -0.1647991383060958, -0.014964855691671306, 0.2073265194143332]
|
1,803.04472
|
Dynamical Similarity
|
We examine "dynamical similarities" in the Lagrangian framework. These are
symmetries of an intrinsically determined physical system under which
observables remain unaffected, but the extraneous information is changed. We
establish three central results in this context: i) Given a system with such a
symmetry there exists a system of invariants which form a subalgebra of phase
space, whose evolution is autonomous; ii) this subalgebra of autonomous
observables evolves as a contact system, in which the friction-like term
describes evolution along the direction of similarity; iii) the contact
Hamiltonian and one-form are invariants, and reproduce the dynamics of the
invariants. As the subalgebra of invariants is smaller than phase space,
dynamics is determined only in terms of this smaller space. We show how to
obtain the contact system from the symplectic system, and the embedding which
inverts the process. These results are then illustrated in the case of
homogeneous Lagrangians, including flat cosmologies minimally coupled to
matter; the n-body problem and homogeneous, anisotropic cosmology.
|
gr-qc hep-th
|
we examine dynamical similarities in the lagrangian framework these are symmetries of an intrinsically determined physical system under which observables remain unaffected but the extraneous information is changed we establish three central results in this context i given a system with such a symmetry there exists a system of invariants which form a subalgebra of phase space whose evolution is autonomous ii this subalgebra of autonomous observables evolves as a contact system in which the frictionlike term describes evolution along the direction of similarity iii the contact hamiltonian and oneform are invariants and reproduce the dynamics of the invariants as the subalgebra of invariants is smaller than phase space dynamics is determined only in terms of this smaller space we show how to obtain the contact system from the symplectic system and the embedding which inverts the process these results are then illustrated in the case of homogeneous lagrangians including flat cosmologies minimally coupled to matter the nbody problem and homogeneous anisotropic cosmology
|
[['we', 'examine', 'dynamical', 'similarities', 'in', 'the', 'lagrangian', 'framework', 'these', 'are', 'symmetries', 'of', 'an', 'intrinsically', 'determined', 'physical', 'system', 'under', 'which', 'observables', 'remain', 'unaffected', 'but', 'the', 'extraneous', 'information', 'is', 'changed', 'we', 'establish', 'three', 'central', 'results', 'in', 'this', 'context', 'i', 'given', 'a', 'system', 'with', 'such', 'a', 'symmetry', 'there', 'exists', 'a', 'system', 'of', 'invariants', 'which', 'form', 'a', 'subalgebra', 'of', 'phase', 'space', 'whose', 'evolution', 'is', 'autonomous', 'ii', 'this', 'subalgebra', 'of', 'autonomous', 'observables', 'evolves', 'as', 'a', 'contact', 'system', 'in', 'which', 'the', 'frictionlike', 'term', 'describes', 'evolution', 'along', 'the', 'direction', 'of', 'similarity', 'iii', 'the', 'contact', 'hamiltonian', 'and', 'oneform', 'are', 'invariants', 'and', 'reproduce', 'the', 'dynamics', 'of', 'the', 'invariants', 'as', 'the', 'subalgebra', 'of', 'invariants', 'is', 'smaller', 'than', 'phase', 'space', 'dynamics', 'is', 'determined', 'only', 'in', 'terms', 'of', 'this', 'smaller', 'space', 'we', 'show', 'how', 'to', 'obtain', 'the', 'contact', 'system', 'from', 'the', 'symplectic', 'system', 'and', 'the', 'embedding', 'which', 'inverts', 'the', 'process', 'these', 'results', 'are', 'then', 'illustrated', 'in', 'the', 'case', 'of', 'homogeneous', 'lagrangians', 'including', 'flat', 'cosmologies', 'minimally', 'coupled', 'to', 'matter', 'the', 'nbody', 'problem', 'and', 'homogeneous', 'anisotropic', 'cosmology']]
|
[-0.19985091823804552, 0.11453444212241215, -0.09133712859664318, 0.046699750785374576, -0.05061933421101307, -0.14346131591620553, -0.045157838972039704, 0.3299705710524887, -0.3005560871350802, -0.2907789725591743, 0.09501132518667271, -0.268175883532896, -0.1627484404029231, 0.16343245167353768, -0.04024011387198341, 0.0029683482284697652, 0.06333524667360803, 0.05370383479695287, -0.12202054978079774, -0.21683110913298909, 0.40253470176430933, 0.0013063525741039976, 0.2184748124971163, -0.018349631111340404, 0.12821844070749872, -0.02362108010044493, -0.018458022347485725, 0.03749340398135692, -0.1366261385504065, 0.06906256045891898, 0.19927271820341974, 0.07971126585627443, 0.1740956968410752, -0.3751998330277899, -0.19461786743407739, 0.12182868492041081, 0.15373016921003446, 0.10503345362819229, 0.002729732018289802, -0.31223215844819285, 0.04013751919718425, -0.16110722942883435, -0.15915050910660095, -0.07882351005747, 0.023491356269016495, -0.012296684097750055, -0.21069793423539188, 0.06594191661584506, 0.06861075033213046, 0.05519955348726248, -0.09390819179139849, -0.03595489804187298, -0.07026492340341667, 0.12050168968142907, 0.016785798839863357, 0.0354309311369434, 0.1406959676836543, -0.13806698463165823, -0.06785124354350146, 0.43330569955466053, -0.05540832353263388, -0.2447894730164521, 0.15875631401978113, -0.1397838748025885, -0.1574837699406809, 0.10407511354852582, 0.12471037197492423, 0.11511461115927875, -0.1424931322732952, 0.12711999054421083, -0.027752124384456983, 0.11795310880646018, 0.005485902697510698, 0.030717341684981997, 0.21165064811432288, 0.15555957691783964, 0.05530577528924664, 0.11375531386526908, 0.013858615135076953, -0.13993314935602932, -0.3399517422004894, -0.17103732583838646, -0.13337630665992484, 0.0640837965930235, -0.09539751898735888, -0.1639097032217206, 0.40130875319773845, 0.11922994307585152, 0.1957669712494533, 0.03738963143740055, 0.25104347048427317, 0.11875673856420522, 0.0497204750930559, 0.08162833687969329, 0.2406275342275714, 0.14088086003689038, 0.06762462955516724, -0.22450082241328992, 0.020556718486709737, 0.08425422994551346]
|
1,803.04473
|
Performance of a highly sensitive, 19-element, dual-polarization,
cryogenic L-band Phased Array Feed on the Green Bank Telescope
|
A new 1.4 GHz 19-element, dual-polarization, cryogenic phased array feed
(PAF) radio astronomy receiver has been developed for the Robert C. Byrd Green
Bank Telescope (GBT) as part of FLAG (Focal L-band Array for the GBT) project.
Commissioning observations of calibrator radio sources show that this receiver
has the lowest reported beamformed system temperature ($T_{\rm sys}$)
normalized by aperture efficiency ($\eta$) of any phased array receiver to
date. The measured $T_{\rm sys}/\eta$ is $25.4 \pm 2.5$ K near 1350 MHz for the
boresight beam, which is comparable to the performance of the current 1.4 GHz
cryogenic single feed receiver on the GBT. The degradation in $T_{\rm
sys}/\eta$ at $\sim$ 4 arcmin (required for Nyquist sampling) and $\sim$ 8
arcmin offsets from the boresight is, respectively, $\sim$ 1\% and $\sim$ 20\%
of the boresight value. The survey speed of the PAF with seven formed beams is
larger by a factor between 2.1 and 7 compared to a single beam system depending
on the observing application. The measured performance, both in frequency and
offset from boresight, qualitatively agree with predictions from a rigorous
electromagnetic model of the PAF. The astronomical utility of the receiver is
demonstrated by observations of the pulsar B0329+54 and an extended HII region,
the Rosette Nebula. The enhanced survey speed with the new PAF receiver will
enable the GBT to carry out exciting new science, such as more efficient
observations of diffuse, extended neutral hydrogen emission from galactic
in-flows and searches for Fast Radio Bursts.
|
astro-ph.IM
|
a new 14 ghz 19element dualpolarization cryogenic phased array feed paf radio astronomy receiver has been developed for the robert c byrd green bank telescope gbt as part of flag focal lband array for the gbt project commissioning observations of calibrator radio sources show that this receiver has the lowest reported beamformed system temperature t_rm sys normalized by aperture efficiency eta of any phased array receiver to date the measured t_rm syseta is 254 pm 25 k near 1350 mhz for the boresight beam which is comparable to the performance of the current 14 ghz cryogenic single feed receiver on the gbt the degradation in t_rm syseta at sim 4 arcmin required for nyquist sampling and sim 8 arcmin offsets from the boresight is respectively sim 1 and sim 20 of the boresight value the survey speed of the paf with seven formed beams is larger by a factor between 21 and 7 compared to a single beam system depending on the observing application the measured performance both in frequency and offset from boresight qualitatively agree with predictions from a rigorous electromagnetic model of the paf the astronomical utility of the receiver is demonstrated by observations of the pulsar b032954 and an extended hii region the rosette nebula the enhanced survey speed with the new paf receiver will enable the gbt to carry out exciting new science such as more efficient observations of diffuse extended neutral hydrogen emission from galactic inflows and searches for fast radio bursts
|
[['a', 'new', '14', 'ghz', '19element', 'dualpolarization', 'cryogenic', 'phased', 'array', 'feed', 'paf', 'radio', 'astronomy', 'receiver', 'has', 'been', 'developed', 'for', 'the', 'robert', 'c', 'byrd', 'green', 'bank', 'telescope', 'gbt', 'as', 'part', 'of', 'flag', 'focal', 'lband', 'array', 'for', 'the', 'gbt', 'project', 'commissioning', 'observations', 'of', 'calibrator', 'radio', 'sources', 'show', 'that', 'this', 'receiver', 'has', 'the', 'lowest', 'reported', 'beamformed', 'system', 'temperature', 't_rm', 'sys', 'normalized', 'by', 'aperture', 'efficiency', 'eta', 'of', 'any', 'phased', 'array', 'receiver', 'to', 'date', 'the', 'measured', 't_rm', 'syseta', 'is', '254', 'pm', '25', 'k', 'near', '1350', 'mhz', 'for', 'the', 'boresight', 'beam', 'which', 'is', 'comparable', 'to', 'the', 'performance', 'of', 'the', 'current', '14', 'ghz', 'cryogenic', 'single', 'feed', 'receiver', 'on', 'the', 'gbt', 'the', 'degradation', 'in', 't_rm', 'syseta', 'at', 'sim', '4', 'arcmin', 'required', 'for', 'nyquist', 'sampling', 'and', 'sim', '8', 'arcmin', 'offsets', 'from', 'the', 'boresight', 'is', 'respectively', 'sim', '1', 'and', 'sim', '20', 'of', 'the', 'boresight', 'value', 'the', 'survey', 'speed', 'of', 'the', 'paf', 'with', 'seven', 'formed', 'beams', 'is', 'larger', 'by', 'a', 'factor', 'between', '21', 'and', '7', 'compared', 'to', 'a', 'single', 'beam', 'system', 'depending', 'on', 'the', 'observing', 'application', 'the', 'measured', 'performance', 'both', 'in', 'frequency', 'and', 'offset', 'from', 'boresight', 'qualitatively', 'agree', 'with', 'predictions', 'from', 'a', 'rigorous', 'electromagnetic', 'model', 'of', 'the', 'paf', 'the', 'astronomical', 'utility', 'of', 'the', 'receiver', 'is', 'demonstrated', 'by', 'observations', 'of', 'the', 'pulsar', 'b032954', 'and', 'an', 'extended', 'hii', 'region', 'the', 'rosette', 'nebula', 'the', 'enhanced', 'survey', 'speed', 'with', 'the', 'new', 'paf', 'receiver', 'will', 'enable', 'the', 'gbt', 'to', 'carry', 'out', 'exciting', 'new', 'science', 'such', 'as', 'more', 'efficient', 'observations', 'of', 'diffuse', 'extended', 'neutral', 'hydrogen', 'emission', 'from', 'galactic', 'inflows', 'and', 'searches', 'for', 'fast', 'radio', 'bursts']]
|
[-0.12471176389918416, 0.08253289597276456, 0.015825537816450304, -0.008187898704592063, -0.09142172570834506, -0.171761561076784, 0.06506106127813763, 0.41414195843497104, -0.15749592087326608, -0.358611324928342, 0.13421206695208213, -0.2966879068588724, -0.03906566441957174, 0.2579176268255224, 0.013351428987605649, -0.005038877681125792, 0.058611300317760635, -0.09951475641633174, -0.04622900880046417, -0.17803714378702465, 0.15200347044239088, 0.19941778431209375, 0.26092952092757865, -0.017201251072847114, 0.14069937228210916, -0.08512836228866054, -0.06833325880864749, -0.0800786686034835, -0.08962853432799885, 0.026486784056285205, 0.2877016302606813, 0.15393283656627244, 0.18908015988501056, -0.3308911681669403, -0.15536952913187596, 0.03772714109521131, 0.11834387231751212, 0.01538372393459918, -0.005599497110708807, -0.33598120929958414, 0.07838212482973325, -0.19084341676278535, -0.17103723986549493, 0.12168405545989469, 0.013787410765582202, 0.06571420247533492, -0.21853486807560737, 0.030863987859718653, -0.053355066886418785, 0.11143177353672455, -0.06661923986821607, -0.19179152832746657, 0.02360839528193203, 0.08525679789350502, -0.06783237782691852, 0.1358857148250907, 0.15752323132188878, -0.07919360398771051, -0.04892145249688504, 0.3444435664927777, -0.10011239810466614, -0.016591628862316816, 0.1427723481101269, -0.24151252168135678, -0.13834162151098858, 0.2408623200841248, 0.16487253041823907, 0.039159086001656795, -0.16563378831856332, 0.018800738277578992, 0.03012483321434381, 0.2685700786642122, 0.1312306173478386, 0.06033502557143873, 0.27226670364545164, 0.1373754034248865, 0.07925829637477326, 0.14257941697344031, -0.31447383487331015, 0.0025020645199609654, -0.24029786524419883, -0.10103717694268087, -0.15543272039301845, 0.10011042188472893, -0.09904323369460608, -0.012764199260308653, 0.371619394368359, 0.10668300549708763, 0.12639067256728148, 0.06974881555533455, 0.34646865170144914, 0.05360420112384065, 0.13023898504455858, 0.09310925157398593, 0.31645747000465585, 0.16130477086146724, 0.157957103550054, -0.20112392969182408, -0.034807302046339125, -0.07467638875977896]
|
1,803.04474
|
Predicting Crime Using Spatial Features
|
Our study aims to build a machine learning model for crime prediction using
geospatial features for different categories of crime. The reverse geocoding
technique is applied to retrieve open street map (OSM) spatial data. This study
also proposes finding hotpoints extracted from crime hotspots area found by
Hierarchical Density-Based Spatial Clustering of Applications with Noise
(HDBSCAN). A spatial distance feature is then computed based on the position of
different hotpoints for various types of crime and this value is used as a
feature for classifiers. We test the engineered features in crime data from
Royal Canadian Mounted Police of Halifax, NS. We observed a significant
performance improvement in crime prediction using the new generated spatial
features.
|
cs.AI cs.CY
|
our study aims to build a machine learning model for crime prediction using geospatial features for different categories of crime the reverse geocoding technique is applied to retrieve open street map osm spatial data this study also proposes finding hotpoints extracted from crime hotspots area found by hierarchical densitybased spatial clustering of applications with noise hdbscan a spatial distance feature is then computed based on the position of different hotpoints for various types of crime and this value is used as a feature for classifiers we test the engineered features in crime data from royal canadian mounted police of halifax ns we observed a significant performance improvement in crime prediction using the new generated spatial features
|
[['our', 'study', 'aims', 'to', 'build', 'a', 'machine', 'learning', 'model', 'for', 'crime', 'prediction', 'using', 'geospatial', 'features', 'for', 'different', 'categories', 'of', 'crime', 'the', 'reverse', 'geocoding', 'technique', 'is', 'applied', 'to', 'retrieve', 'open', 'street', 'map', 'osm', 'spatial', 'data', 'this', 'study', 'also', 'proposes', 'finding', 'hotpoints', 'extracted', 'from', 'crime', 'hotspots', 'area', 'found', 'by', 'hierarchical', 'densitybased', 'spatial', 'clustering', 'of', 'applications', 'with', 'noise', 'hdbscan', 'a', 'spatial', 'distance', 'feature', 'is', 'then', 'computed', 'based', 'on', 'the', 'position', 'of', 'different', 'hotpoints', 'for', 'various', 'types', 'of', 'crime', 'and', 'this', 'value', 'is', 'used', 'as', 'a', 'feature', 'for', 'classifiers', 'we', 'test', 'the', 'engineered', 'features', 'in', 'crime', 'data', 'from', 'royal', 'canadian', 'mounted', 'police', 'of', 'halifax', 'ns', 'we', 'observed', 'a', 'significant', 'performance', 'improvement', 'in', 'crime', 'prediction', 'using', 'the', 'new', 'generated', 'spatial', 'features']]
|
[-0.020505354269979556, -0.019074570324288255, -0.12336199963873808, 0.10042134890733123, -0.10038815475510139, -0.1447745842793793, 0.04769028334780351, 0.3815259529898564, -0.27511671990495207, -0.33635521146648545, 0.08745292136078014, -0.2994939962957512, -0.14294074481483876, 0.21243036775073657, -0.12518466830564043, 0.0581037756413009, 0.06827458257280421, 0.030654533161667354, -0.028041866113709516, -0.23866866671137119, 0.32524725845443053, 0.05619510271374071, 0.41907556850071015, 0.016265330133600195, 0.11071598083148465, -0.023401419158306038, -0.11445091759148789, 0.03146861768286806, -0.09693954144441114, 0.18901427255804537, 0.3457230117555969, 0.19668216886521692, 0.30032039154320955, -0.36609624177544264, -0.24055247371117666, 0.06599025815937734, 0.12529199266307905, 0.09271527390126466, -0.06177924410542147, -0.3999472303446709, 0.07874226481592479, -0.14692985069766445, -0.07224542057354069, -0.07626979184549368, 0.01651044844715088, 0.02429389724050436, -0.2677949612915287, 0.07224670516121764, 0.0010865993487338226, 0.15940428549717917, -0.04592015481913429, -0.0815862722024975, 0.004067960065552606, 0.2205280981071522, 0.035299637061829696, 0.01887311439618076, 0.13834647643134781, -0.13859078520092002, -0.1487930974261345, 0.35901517594200477, -0.07378316048957538, -0.15788381603922239, 0.1666396138719855, -0.05829421715124657, -0.1674515101976954, 0.08970148362660486, 0.2843088148357837, 0.06507329371545398, -0.18707499265866845, -0.027117544013635142, -0.0409813404017896, 0.1945389842322472, 0.08029720114478678, -0.04398178350049676, 0.18703098114163272, 0.2557392139355407, 0.03225375078781916, 0.14011169033913398, -0.23247369050187172, -0.049077492072492054, -0.1827194034407863, -0.10244999931749414, -0.1728174123390202, -0.010048350045588194, -0.11844970046513481, -0.14254757228487155, 0.4189515065875623, 0.22587713771581389, 0.20932145463302732, 0.028706316891322404, 0.28851827028158467, 0.018899818000040557, 0.09372783201060404, 0.08325046873978224, 0.11592647903843929, -0.0035140022672222634, 0.16876189706830733, -0.13947178059453635, 0.08591633213667624, 0.05088956314629238]
|
1,803.04475
|
Accuracy-Reliability Cost Function for Empirical Variance Estimation
|
In this paper we focus on the problem of assigning uncertainties to
single-point predictions. We introduce a cost function that encodes the
trade-off between accuracy and reliability in probabilistic forecast. We derive
analytic formula for the case of forecasts of continuous scalar variables
expressed in terms of Gaussian distributions. The Accuracy-Reliability cost
function can be used to empirically estimate the variance in heteroskedastic
regression problems (input dependent noise), by solving a two-objective
optimization problem. The simple philosophy behind this strategy is that
predictions based on the estimated variances should be both accurate and
reliable (i.e. statistical consistent with observations). We show several
examples with synthetic data, where the underlying hidden noise function can be
accurately recovered, both in one and multi-dimensional problems. The practical
implementation of the method has been done using a Neural Network and, in the
one-dimensional case, with a simple polynomial fit.
|
stat.ML cs.LG
|
in this paper we focus on the problem of assigning uncertainties to singlepoint predictions we introduce a cost function that encodes the tradeoff between accuracy and reliability in probabilistic forecast we derive analytic formula for the case of forecasts of continuous scalar variables expressed in terms of gaussian distributions the accuracyreliability cost function can be used to empirically estimate the variance in heteroskedastic regression problems input dependent noise by solving a twoobjective optimization problem the simple philosophy behind this strategy is that predictions based on the estimated variances should be both accurate and reliable ie statistical consistent with observations we show several examples with synthetic data where the underlying hidden noise function can be accurately recovered both in one and multidimensional problems the practical implementation of the method has been done using a neural network and in the onedimensional case with a simple polynomial fit
|
[['in', 'this', 'paper', 'we', 'focus', 'on', 'the', 'problem', 'of', 'assigning', 'uncertainties', 'to', 'singlepoint', 'predictions', 'we', 'introduce', 'a', 'cost', 'function', 'that', 'encodes', 'the', 'tradeoff', 'between', 'accuracy', 'and', 'reliability', 'in', 'probabilistic', 'forecast', 'we', 'derive', 'analytic', 'formula', 'for', 'the', 'case', 'of', 'forecasts', 'of', 'continuous', 'scalar', 'variables', 'expressed', 'in', 'terms', 'of', 'gaussian', 'distributions', 'the', 'accuracyreliability', 'cost', 'function', 'can', 'be', 'used', 'to', 'empirically', 'estimate', 'the', 'variance', 'in', 'heteroskedastic', 'regression', 'problems', 'input', 'dependent', 'noise', 'by', 'solving', 'a', 'twoobjective', 'optimization', 'problem', 'the', 'simple', 'philosophy', 'behind', 'this', 'strategy', 'is', 'that', 'predictions', 'based', 'on', 'the', 'estimated', 'variances', 'should', 'be', 'both', 'accurate', 'and', 'reliable', 'ie', 'statistical', 'consistent', 'with', 'observations', 'we', 'show', 'several', 'examples', 'with', 'synthetic', 'data', 'where', 'the', 'underlying', 'hidden', 'noise', 'function', 'can', 'be', 'accurately', 'recovered', 'both', 'in', 'one', 'and', 'multidimensional', 'problems', 'the', 'practical', 'implementation', 'of', 'the', 'method', 'has', 'been', 'done', 'using', 'a', 'neural', 'network', 'and', 'in', 'the', 'onedimensional', 'case', 'with', 'a', 'simple', 'polynomial', 'fit']]
|
[-0.04735193461723005, 0.015002118842352906, -0.09883713638474648, 0.12223054832348781, -0.0812256282822798, -0.1491820831741077, 0.05041874208190064, 0.39625066887432087, -0.2707552029347021, -0.31259147378215046, 0.11476666291612976, -0.2354700576720966, -0.180971876142899, 0.2096742560501702, -0.10133645274375642, 0.110534755419293, 0.07682996271998693, 0.009432067451093139, -0.08808717607704845, -0.2601628252482947, 0.2847081942292991, 0.05274220905913454, 0.2672115099655154, 0.009173495337664563, 0.13712595946223397, 0.020917381447765768, -0.054076741591820285, 0.03445723878313503, -0.10295859989825355, 0.13688625616648803, 0.25682779514398946, 0.167369534337518, 0.3039724227888251, -0.41065664862334317, -0.2343750628384037, 0.14478298850978413, 0.13134790582504743, 0.09043941695482419, 0.00492192908455359, -0.25403983687606846, 0.05577644270589291, -0.15787825166868666, -0.08666525781639696, -0.09878854953502822, -0.03673474401714177, 0.034082444766277656, -0.34302387946000734, 0.10899083725720023, -0.006920823707736822, 0.04406976230934055, -0.048952008789961435, -0.13211051493918705, 0.022220583457965404, 0.12190514567798043, 0.03233184645877271, 0.02742730959693694, 0.10640788124227482, -0.10814229694854778, -0.12368178716941555, 0.36740615958761835, -0.09865921237117921, -0.29396819367280436, 0.11512089860399202, -0.09799690614777824, -0.16187534035675022, 0.07918482737214719, 0.2145741212621538, 0.09862337769785275, -0.16565123871502388, 0.06319963436787172, -0.026109551613141473, 0.1621388166400316, 0.010382143767654069, -0.005864846549229696, 0.16240311894378262, 0.18620234426473165, 0.02478541899472475, 0.15684305500488133, -0.08735158565751691, -0.10538621366463809, -0.28992697965198505, -0.09103158388693879, -0.217782365416901, -0.0033414737683617407, -0.13686154925017036, -0.14213447433172, 0.39651331811263746, 0.19898554175193162, 0.21208620457602795, 0.12974708316000438, 0.34300516050360685, 0.17514525945201362, -0.00209449772228254, 0.07819170850982321, 0.18658200129357283, 0.10917034068067248, 0.05687559275247622, -0.18145364311769502, 0.13335846375816296, 0.028423586447994847]
|
1,803.04476
|
A subdirect decomposition of a semigroup of all fuzzy sets in a
semigroup
|
In this paper we give a subdirect decomposition of semigroups $({\mathfrak
F}(S); \circ )$, where $S$ is a semigroup, ${\mathfrak F}(S)$ is the set of all
fuzzy sets in $S$, and the operation $\circ$ on ${\mathfrak F}(S)$ is defined
by the following way: for $f, g\in {\mathfrak F}(S)$ and $s\in S$, $(f\circ
g)(s)=\vee _{{x, y\in S}\atop{s=xy}}(f(x)\wedge g(y))$ if $s\in S^2$, and
$(f\circ g)(s)=0$ otherwise.
|
math.GR
|
in this paper we give a subdirect decomposition of semigroups mathfrak fs circ where s is a semigroup mathfrak fs is the set of all fuzzy sets in s and the operation circ on mathfrak fs is defined by the following way for f gin mathfrak fs and sin s fcirc gsvee _x yin satopsxyfxwedge gy if sin s2 and fcirc gs0 otherwise
|
[['in', 'this', 'paper', 'we', 'give', 'a', 'subdirect', 'decomposition', 'of', 'semigroups', 'mathfrak', 'fs', 'circ', 'where', 's', 'is', 'a', 'semigroup', 'mathfrak', 'fs', 'is', 'the', 'set', 'of', 'all', 'fuzzy', 'sets', 'in', 's', 'and', 'the', 'operation', 'circ', 'on', 'mathfrak', 'fs', 'is', 'defined', 'by', 'the', 'following', 'way', 'for', 'f', 'gin', 'mathfrak', 'fs', 'and', 'sin', 's', 'fcirc', 'gsvee', '_x', 'yin', 'satopsxyfxwedge', 'gy', 'if', 'sin', 's2', 'and', 'fcirc', 'gs0', 'otherwise']]
|
[-0.22228804572174946, 0.13394173469084006, 0.0012076212093234061, -0.04229906629382943, -0.0566195626432697, -0.17140756561420858, 0.03672280633860889, 0.4186757439747453, -0.37548134513199327, -0.10495374028105289, 0.05994415664657329, -0.33202436541517577, -0.06118426394338409, 0.2198316359659657, -0.13231168770386528, -0.034564320805172126, 0.02467177570797503, 0.10775955207645893, -0.14210215129399634, -0.189849632049057, 0.2985638504847884, -0.13549788640812038, 0.13281487726295987, -0.02507408318730692, 0.12121133081770191, -0.06523332014718713, 0.09609045786783099, -0.10755583920205633, -0.26584411805185176, -0.002253408745552103, 0.2817333236336708, 0.15571893306914716, 0.2809857522447904, -0.33367739639555416, -0.07662868365490189, 0.2039074055016196, 0.12545193838110816, -0.254271787032485, 0.06300895615131594, -0.2451248375698924, 0.1871239427787562, -0.1372202873385201, -0.07045199741454174, -0.06482334818380574, 0.2961206597896914, -0.003993027800849328, -0.38973023064124085, 0.010918834200128912, 0.18775560772046446, 0.08310583059598381, 0.019775468142082295, -0.1699442792373399, -0.14162631874981646, -0.051465045598646, -0.10175375697435811, 0.22854857603476073, 0.07063812483878186, 0.02306429426341007, -0.021748503235479198, 0.3824854184873402, -0.041372059596081576, -0.1828251645589868, 0.04085157629257689, -0.23957769672852008, -0.10585546634780864, 0.0486357569694519, -0.024361403724954774, 0.2301911946075658, -0.05122906512891253, 0.31022238462561896, -0.15136783845567456, 0.0974109888387223, 0.1318953348789364, 0.031672916023914394, 0.08089658903966969, 0.14139687904777626, 0.09041793150827289, 0.0281517261484017, 0.002651632301664601, 0.1807502848173802, -0.3960173108614981, -0.17167227421887218, -0.09485705990615921, 0.20473576782581707, -0.04875513254592079, -0.10313794836401939, 0.40787598825991156, 0.06328287447492281, 0.1581091746687889, 0.06365367195879419, 0.13021430907150108, 0.07707991637119752, -0.019322182765851417, 0.12286300829146057, 0.0019689480774104594, 0.19081170866653943, -0.07386548431434979, -0.1845100433798507, -0.01419435798500975, 0.18480530253921945]
|
1,803.04477
|
Correction by Projection: Denoising Images with Generative Adversarial
Networks
|
Generative adversarial networks (GANs) transform low-dimensional latent
vectors into visually plausible images. If the real dataset contains only clean
images, then ostensibly, the manifold learned by the GAN should contain only
clean images. In this paper, we propose to denoise corrupted images by finding
the nearest point on the GAN manifold, recovering latent vectors by minimizing
distances in image space. We first demonstrate that given a corrupted version
of an image that truly lies on the GAN manifold, we can approximately recover
the latent vector and denoise the image, obtaining significantly higher
quality, comparing with BM3D. Next, we demonstrate that latent vectors
recovered from noisy images exhibit a consistent bias. By subtracting this bias
before projecting back to image space, we improve denoising results even
further. Finally, even for unseen images, our method performs better at
denoising better than BM3D. Notably, the basic version of our method (without
bias correction) requires no prior knowledge on the noise variance. To achieve
the highest possible denoising quality, the best performing signal processing
based methods, such as BM3D, require an estimate of the blur kernel.
|
cs.CV
|
generative adversarial networks gans transform lowdimensional latent vectors into visually plausible images if the real dataset contains only clean images then ostensibly the manifold learned by the gan should contain only clean images in this paper we propose to denoise corrupted images by finding the nearest point on the gan manifold recovering latent vectors by minimizing distances in image space we first demonstrate that given a corrupted version of an image that truly lies on the gan manifold we can approximately recover the latent vector and denoise the image obtaining significantly higher quality comparing with bm3d next we demonstrate that latent vectors recovered from noisy images exhibit a consistent bias by subtracting this bias before projecting back to image space we improve denoising results even further finally even for unseen images our method performs better at denoising better than bm3d notably the basic version of our method without bias correction requires no prior knowledge on the noise variance to achieve the highest possible denoising quality the best performing signal processing based methods such as bm3d require an estimate of the blur kernel
|
[['generative', 'adversarial', 'networks', 'gans', 'transform', 'lowdimensional', 'latent', 'vectors', 'into', 'visually', 'plausible', 'images', 'if', 'the', 'real', 'dataset', 'contains', 'only', 'clean', 'images', 'then', 'ostensibly', 'the', 'manifold', 'learned', 'by', 'the', 'gan', 'should', 'contain', 'only', 'clean', 'images', 'in', 'this', 'paper', 'we', 'propose', 'to', 'denoise', 'corrupted', 'images', 'by', 'finding', 'the', 'nearest', 'point', 'on', 'the', 'gan', 'manifold', 'recovering', 'latent', 'vectors', 'by', 'minimizing', 'distances', 'in', 'image', 'space', 'we', 'first', 'demonstrate', 'that', 'given', 'a', 'corrupted', 'version', 'of', 'an', 'image', 'that', 'truly', 'lies', 'on', 'the', 'gan', 'manifold', 'we', 'can', 'approximately', 'recover', 'the', 'latent', 'vector', 'and', 'denoise', 'the', 'image', 'obtaining', 'significantly', 'higher', 'quality', 'comparing', 'with', 'bm3d', 'next', 'we', 'demonstrate', 'that', 'latent', 'vectors', 'recovered', 'from', 'noisy', 'images', 'exhibit', 'a', 'consistent', 'bias', 'by', 'subtracting', 'this', 'bias', 'before', 'projecting', 'back', 'to', 'image', 'space', 'we', 'improve', 'denoising', 'results', 'even', 'further', 'finally', 'even', 'for', 'unseen', 'images', 'our', 'method', 'performs', 'better', 'at', 'denoising', 'better', 'than', 'bm3d', 'notably', 'the', 'basic', 'version', 'of', 'our', 'method', 'without', 'bias', 'correction', 'requires', 'no', 'prior', 'knowledge', 'on', 'the', 'noise', 'variance', 'to', 'achieve', 'the', 'highest', 'possible', 'denoising', 'quality', 'the', 'best', 'performing', 'signal', 'processing', 'based', 'methods', 'such', 'as', 'bm3d', 'require', 'an', 'estimate', 'of', 'the', 'blur', 'kernel']]
|
[0.009998787737868543, -0.03569199433463886, -0.059961707318447285, 0.10830144476078983, -0.11248654059062783, -0.15787687217963828, 0.03338600709988956, 0.4896036490061126, -0.2886574380765004, -0.3241286516511956, 0.08443272210962068, -0.2829584081728871, -0.19391398064716678, 0.17136131730928486, -0.17804133486140847, 0.05332056945957407, 0.1382593067956495, 0.04889907557798734, -0.12153269034704184, -0.3352828893188776, 0.3197517925694298, 0.07763785474734115, 0.306451781888257, -0.10614117177825032, 0.15844443655357912, 0.00721804583496363, -0.045246381321883564, -0.014465320968276381, -0.01058190483992483, 0.13717324224653765, 0.27111770579550454, 0.18011904903687537, 0.28407265779378077, -0.414256175754817, -0.2552173677678373, 0.12220422886465522, 0.13936921456767307, 0.1208288991050782, -0.060360467193806853, -0.3834410179886138, 0.11034166005153495, -0.04534346002901157, 0.042666718223040086, -0.16878200258744963, -0.08957870694895653, -0.07812366593797988, -0.30766533592019923, 0.11380019196012355, 0.10863309999375226, 0.02204626966808324, -0.09262175828852988, -0.15431363675820098, -0.003751554628731785, 0.15556451402717159, -0.004995252427921354, 0.11557288396563688, 0.14037831325421704, -0.17929546334022775, -0.09353609001622178, 0.3487890800256487, -0.09832583820253193, -0.2343795154428245, 0.1275219247039346, -0.10049960821749294, -0.10816293556990286, 0.16762820822647312, 0.19141988206787833, 0.10221903601272421, -0.14547783811832524, 0.006233265473453615, -0.06886827029034516, 0.19782147189979007, 0.07626461710494298, -0.0039257098751777145, 0.13890130356578465, 0.17608204525051085, 0.07128182439527546, 0.17122983885033485, -0.1980602832876944, 0.026756047206907788, -0.22253135265654547, -0.09276198804798116, -0.29258980388896394, 0.011144689767836379, -0.15933095012875567, -0.14493937071944987, 0.40726644041241855, 0.26775924792349504, 0.2851497275028173, 0.09696564298368744, 0.3821272527399872, 0.03278085764223751, 0.08693506710927237, 0.08568465881622754, 0.1599655202589929, 0.041111458742806875, 0.0476598603405304, -0.11492650061564336, 0.09109444901256651, 0.052458865410689226]
|
1,803.04478
|
Bridge type classification: supervised learning on a modified NBI
dataset
|
A key phase in the bridge design process is the selection of the structural
system. Due to budget and time constraints, engineers typically rely on
engineering judgment and prior experience when selecting a structural system,
often considering a limited range of design alternatives. The objective of this
study was to explore the suitability of supervised machine learning as a
preliminary design aid that provides guidance to engineers with regards to the
statistically optimal bridge type to choose, ultimately improving the
likelihood of optimized design, design standardization, and reduced maintenance
costs. In order to devise this supervised learning system, data for over
600,000 bridges from the National Bridge Inventory database were analyzed. Key
attributes for determining the bridge structure type were identified through
three feature selection techniques. Potentially useful attributes like seismic
intensity and historic data on the cost of materials (steel and concrete) were
then added from the US Geological Survey (USGS) database and Engineering News
Record. Decision tree, Bayes network and Support Vector Machines were used for
predicting the bridge design type. Due to state-to-state variations in material
availability, material costs, and design codes, supervised learning models
based on the complete data set did not yield favorable results. Supervised
learning models were then trained and tested using 10-fold cross validation on
data for each state. Inclusion of seismic data improved the model performance
noticeably. The data was then resampled to reduce the bias of the models
towards more common design types, and the supervised learning models thus
constructed showed further improvements in performance. The average recall and
precision for the state models was 88.6% and 88.0% using Decision Trees, 84.0%
and 83.7% using Bayesian Networks, and 80.8% and 75.6% using SVM.
|
stat.ML cs.LG stat.AP
|
a key phase in the bridge design process is the selection of the structural system due to budget and time constraints engineers typically rely on engineering judgment and prior experience when selecting a structural system often considering a limited range of design alternatives the objective of this study was to explore the suitability of supervised machine learning as a preliminary design aid that provides guidance to engineers with regards to the statistically optimal bridge type to choose ultimately improving the likelihood of optimized design design standardization and reduced maintenance costs in order to devise this supervised learning system data for over 600000 bridges from the national bridge inventory database were analyzed key attributes for determining the bridge structure type were identified through three feature selection techniques potentially useful attributes like seismic intensity and historic data on the cost of materials steel and concrete were then added from the us geological survey usgs database and engineering news record decision tree bayes network and support vector machines were used for predicting the bridge design type due to statetostate variations in material availability material costs and design codes supervised learning models based on the complete data set did not yield favorable results supervised learning models were then trained and tested using 10fold cross validation on data for each state inclusion of seismic data improved the model performance noticeably the data was then resampled to reduce the bias of the models towards more common design types and the supervised learning models thus constructed showed further improvements in performance the average recall and precision for the state models was 886 and 880 using decision trees 840 and 837 using bayesian networks and 808 and 756 using svm
|
[['a', 'key', 'phase', 'in', 'the', 'bridge', 'design', 'process', 'is', 'the', 'selection', 'of', 'the', 'structural', 'system', 'due', 'to', 'budget', 'and', 'time', 'constraints', 'engineers', 'typically', 'rely', 'on', 'engineering', 'judgment', 'and', 'prior', 'experience', 'when', 'selecting', 'a', 'structural', 'system', 'often', 'considering', 'a', 'limited', 'range', 'of', 'design', 'alternatives', 'the', 'objective', 'of', 'this', 'study', 'was', 'to', 'explore', 'the', 'suitability', 'of', 'supervised', 'machine', 'learning', 'as', 'a', 'preliminary', 'design', 'aid', 'that', 'provides', 'guidance', 'to', 'engineers', 'with', 'regards', 'to', 'the', 'statistically', 'optimal', 'bridge', 'type', 'to', 'choose', 'ultimately', 'improving', 'the', 'likelihood', 'of', 'optimized', 'design', 'design', 'standardization', 'and', 'reduced', 'maintenance', 'costs', 'in', 'order', 'to', 'devise', 'this', 'supervised', 'learning', 'system', 'data', 'for', 'over', '600000', 'bridges', 'from', 'the', 'national', 'bridge', 'inventory', 'database', 'were', 'analyzed', 'key', 'attributes', 'for', 'determining', 'the', 'bridge', 'structure', 'type', 'were', 'identified', 'through', 'three', 'feature', 'selection', 'techniques', 'potentially', 'useful', 'attributes', 'like', 'seismic', 'intensity', 'and', 'historic', 'data', 'on', 'the', 'cost', 'of', 'materials', 'steel', 'and', 'concrete', 'were', 'then', 'added', 'from', 'the', 'us', 'geological', 'survey', 'usgs', 'database', 'and', 'engineering', 'news', 'record', 'decision', 'tree', 'bayes', 'network', 'and', 'support', 'vector', 'machines', 'were', 'used', 'for', 'predicting', 'the', 'bridge', 'design', 'type', 'due', 'to', 'statetostate', 'variations', 'in', 'material', 'availability', 'material', 'costs', 'and', 'design', 'codes', 'supervised', 'learning', 'models', 'based', 'on', 'the', 'complete', 'data', 'set', 'did', 'not', 'yield', 'favorable', 'results', 'supervised', 'learning', 'models', 'were', 'then', 'trained', 'and', 'tested', 'using', '10fold', 'cross', 'validation', 'on', 'data', 'for', 'each', 'state', 'inclusion', 'of', 'seismic', 'data', 'improved', 'the', 'model', 'performance', 'noticeably', 'the', 'data', 'was', 'then', 'resampled', 'to', 'reduce', 'the', 'bias', 'of', 'the', 'models', 'towards', 'more', 'common', 'design', 'types', 'and', 'the', 'supervised', 'learning', 'models', 'thus', 'constructed', 'showed', 'further', 'improvements', 'in', 'performance', 'the', 'average', 'recall', 'and', 'precision', 'for', 'the', 'state', 'models', 'was', '886', 'and', '880', 'using', 'decision', 'trees', '840', 'and', '837', 'using', 'bayesian', 'networks', 'and', '808', 'and', '756', 'using', 'svm']]
|
[-0.02020288371119371, 0.007929570210027569, -0.06134460364296662, 0.06379355758606009, -0.12476369627282446, -0.1714064602651146, 0.12382165313506803, 0.42056328431102086, -0.25314031945250876, -0.3763828475378348, 0.13006304774486216, -0.27385421365446655, -0.1144017234167203, 0.20848891523696403, -0.10482228186837418, 0.11076451431966472, 0.12546453253145556, 0.011855475359318897, -0.05226023486012045, -0.27794942303217207, 0.28058141172848683, 0.11338457316886766, 0.3699670132143912, -0.0137523707540719, 0.07978770712511085, 0.010821646403975196, -0.07924134054399551, -0.017295003067768107, -0.11147336221287371, 0.15283386391758463, 0.32520964280133774, 0.20367410829363403, 0.31978474710721205, -0.39516488166611824, -0.20707383241458194, 0.08653971108489056, 0.0870217627759838, 0.06102209409091172, -0.013006637044053224, -0.2999367948245019, 0.049911408099768594, -0.15128314481153984, -0.04175563524386364, -0.11227910000878775, -0.029270404584851143, 0.018639233338990056, -0.2772616324296092, 0.005355286284792402, 0.023210578259363016, 0.10502225000754739, -0.08202047136666729, -0.18277829058397338, -0.01097130861004795, 0.14912350300391095, 0.005661146116177948, 0.037796164385178486, 0.1545875621431157, -0.14873998162851848, -0.14080388725451226, 0.3526432637706505, -0.041051797527103216, -0.1552679910958606, 0.19782483857207608, -0.00764592227043835, -0.1394149901555704, 0.12058111040147867, 0.26744312383890884, 0.04672351290871462, -0.20610408524530385, -0.016664141089554732, 0.035247463622322, 0.19043246688113172, 0.04435902749620054, -0.031925202966155815, 0.17852972100814143, 0.2626985434074523, -0.00022479610068472565, 0.12422603926479367, -0.12245102802246928, -0.058658332858277575, -0.21885230783777682, -0.1394501255778926, -0.1506576184587923, -0.020336742416230204, -0.10049824296511876, -0.12941239319712003, 0.36733614926718516, 0.2000990546967319, 0.1597231872918923, 0.07458083383104902, 0.28548290749354593, 0.025694856740559487, 0.10325779736616668, 0.07568687675883028, 0.22825207074002712, 0.06289214609233755, 0.12624846706092555, -0.18009003119667866, 0.136890276835518, -0.006554440393753297]
|
1,803.04479
|
Machine Learning Harnesses Molecular Dynamics to Discover New $\mu$
Opioid Chemotypes
|
Computational chemists typically assay drug candidates by virtually screening
compounds against crystal structures of a protein despite the fact that some
targets, like the $\mu$ Opioid Receptor and other members of the GPCR family,
traverse many non-crystallographic states. We discover new conformational
states of $\mu OR$ with molecular dynamics simulation and then machine learn
ligand-structure relationships to predict opioid ligand function. These
artificial intelligence models identified a novel $\mu$ opioid chemotype.
|
q-bio.BM stat.ML
|
computational chemists typically assay drug candidates by virtually screening compounds against crystal structures of a protein despite the fact that some targets like the mu opioid receptor and other members of the gpcr family traverse many noncrystallographic states we discover new conformational states of mu or with molecular dynamics simulation and then machine learn ligandstructure relationships to predict opioid ligand function these artificial intelligence models identified a novel mu opioid chemotype
|
[['computational', 'chemists', 'typically', 'assay', 'drug', 'candidates', 'by', 'virtually', 'screening', 'compounds', 'against', 'crystal', 'structures', 'of', 'a', 'protein', 'despite', 'the', 'fact', 'that', 'some', 'targets', 'like', 'the', 'mu', 'opioid', 'receptor', 'and', 'other', 'members', 'of', 'the', 'gpcr', 'family', 'traverse', 'many', 'noncrystallographic', 'states', 'we', 'discover', 'new', 'conformational', 'states', 'of', 'mu', 'or', 'with', 'molecular', 'dynamics', 'simulation', 'and', 'then', 'machine', 'learn', 'ligandstructure', 'relationships', 'to', 'predict', 'opioid', 'ligand', 'function', 'these', 'artificial', 'intelligence', 'models', 'identified', 'a', 'novel', 'mu', 'opioid', 'chemotype']]
|
[-0.05848689475096762, 0.18185916203739388, -0.038784342924399036, 0.0947105421019452, -0.08208059039232986, -0.21149854234286716, 0.1421210203319788, 0.37037542342607466, -0.30688112729362077, -0.302499807626009, -0.018186485724956063, -0.3412058019212314, -0.24170697085625892, 0.13117119222546794, -0.023589976876974107, 0.03613957539866013, 0.07284195643982717, 0.014483714729015317, 0.08724298669757055, -0.20882411443495325, 0.23685987569125636, 0.028261086976687822, 0.25657853139564396, 0.018532921400453362, 0.09475542246696672, -0.050321160755785445, 0.027355526979746563, -0.05923173675879038, -0.1654640992909013, 0.15643600770106006, 0.3134807026037119, 0.16014773387994086, 0.3074846408729042, -0.39848559626511165, -0.26816168537895596, 0.14015096952540002, 0.12084660998412541, 0.1361939421455775, -0.08518557373567351, -0.3265083117543587, 0.07016269315499812, -0.1488543956607048, -0.12222236464731395, -0.14595744444523007, 0.0358824811676251, 0.07418424955851931, -0.20747385941711918, 0.0409208029974252, -0.06046313123245325, 0.1385403862223029, -0.11971406995185783, -0.21078273208113388, -0.06592894232432757, 0.12283340464678726, 0.08672162795971547, 0.03497118975501508, 0.26985197459081456, -0.14591684751212597, -0.17164442214748954, 0.36503718829564086, 0.009894366485864989, -0.19484352895856968, 0.2749846313813967, -0.07126862618939153, -0.2191011460231883, 0.14515121980969395, 0.17593747736536897, 0.09651765817044569, -0.18877659719437362, 0.018601033209623502, 0.03156578629942877, 0.20294528060725758, 0.06112581683721926, 0.023501687663208162, 0.20739180650562047, 0.18123026182875038, 2.8867952226262006e-05, 0.08798196687047104, -0.09608349175791123, -0.09012518085406294, -0.14965852657333018, -0.19410696868435479, -0.1440196662342974, 0.060659153352025895, -0.0326571675179626, -0.1894158510385231, 0.35219789780676364, 0.08544685405546001, 0.1334137383050152, 0.05034975535901529, 0.15543991622648068, -0.08078130906713861, 0.12298042682398642, -0.016957357765308448, 0.1046358122644181, 0.05415473107381591, 0.02932632339985243, -0.25816380507312714, 0.1471514682185703, -0.02350923109001347]
|
1,803.0448
|
Coulomb interaction rules timescales in potassium ion channel tunneling
|
Assuming that the selectivity filter of KcsA potassium ion channel may
exhibit quantum coherence, we extend a previous model by Vaziri and Plenio
(2010) to take into account Coulomb repulsion between potassium ions. We show
that typical ion transit timescales are determined by this interaction, which
imposes optimal input/output parameter ranges. Also, as observed in other
examples of quantum tunneling in biological systems, addition of moderate noise
helps coherent ion transport.
|
physics.bio-ph quant-ph
|
assuming that the selectivity filter of kcsa potassium ion channel may exhibit quantum coherence we extend a previous model by vaziri and plenio 2010 to take into account coulomb repulsion between potassium ions we show that typical ion transit timescales are determined by this interaction which imposes optimal inputoutput parameter ranges also as observed in other examples of quantum tunneling in biological systems addition of moderate noise helps coherent ion transport
|
[['assuming', 'that', 'the', 'selectivity', 'filter', 'of', 'kcsa', 'potassium', 'ion', 'channel', 'may', 'exhibit', 'quantum', 'coherence', 'we', 'extend', 'a', 'previous', 'model', 'by', 'vaziri', 'and', 'plenio', '2010', 'to', 'take', 'into', 'account', 'coulomb', 'repulsion', 'between', 'potassium', 'ions', 'we', 'show', 'that', 'typical', 'ion', 'transit', 'timescales', 'are', 'determined', 'by', 'this', 'interaction', 'which', 'imposes', 'optimal', 'inputoutput', 'parameter', 'ranges', 'also', 'as', 'observed', 'in', 'other', 'examples', 'of', 'quantum', 'tunneling', 'in', 'biological', 'systems', 'addition', 'of', 'moderate', 'noise', 'helps', 'coherent', 'ion', 'transport']]
|
[-0.12447365717962385, 0.22469717963332578, -0.029428632218124612, 0.05966349542639884, 0.022918851094852602, -0.2424913652174707, 0.07145074806176126, 0.40732710286974905, -0.24151685372261064, -0.3024805753159204, -0.03022173642114337, -0.2657956549066252, -0.11486413118961666, 0.17308802102093718, -0.04755568474398127, 0.03631708939958896, 0.08113788710907102, -0.05816520379989275, -0.021086089133418032, -0.2028321639967284, 0.24579138520306776, 0.07349589218917701, 0.25174482744187116, 0.09917250767591343, 0.10850427881315616, 0.02995777891815773, 0.03578218073318047, 0.005898494739085436, -0.15119910311456936, 0.05555301528157933, 0.24744752117382762, 0.024941155341054712, 0.23827363975079996, -0.45722294481737275, -0.30519673506330164, 0.0567782427583422, 0.17551959431771788, 0.16648293486630011, -0.05895353135253702, -0.28587108734729033, -0.001565359892057521, -0.16822406775983317, -0.037746378845934354, -0.07617661878466606, 0.024685291373836144, 0.07106995686023895, -0.30944766189370837, 0.12287498538540344, 0.0916013694634395, 0.06781601743506534, -0.07760681822629911, -0.12393318356147834, -0.02406128780317626, 0.07665402089644756, -0.011845127830747515, -0.06294483483236815, 0.2031639779751588, -0.0522567316191271, -0.08907840017096273, 0.3246147134208253, -0.07661499785525458, -0.12385958463211344, 0.18601871679087967, -0.16616500745128307, -0.06916038714615362, 0.16857915867918305, 0.14756011149979065, 0.023129870562947224, -0.17822952129478967, 0.05910249740866545, -0.03448320896131918, 0.19876428878295493, 0.11587886062251138, 0.15535271409898996, 0.17658958951277393, 0.14513906938955187, 0.014158220339699516, 0.14329213975475535, -0.1441147596974458, -0.12256451040073964, -0.24266561966256373, -0.12193908040810908, -0.13182005636127933, 0.05042816082547818, -0.04724323408258247, -0.09286953805546676, 0.34660559886542613, 0.2023675914670873, 0.20168829723926526, -0.04160344461478027, 0.23754973730870654, 0.11758674679689908, 0.04274571200034448, 0.012735388371428208, 0.26251089981357967, 0.13407900230293826, 0.1100667306887252, -0.3153597076024328, 0.08903833563173456, 0.0012580555381386407]
|
1,803.04481
|
Irreproducibility; Nothing is More Predictable
|
The increasing ease of data capture and storage has led to a corresponding
increase in the choice of data, the type of analysis performed on that data,
and the complexity of the analysis performed. The main contribution of this
paper is to show that the subjective choice of data and analysis methodology
substantially impacts the identification of factors and outcomes of
observational studies. This subjective variability of inference is at the heart
of recent discussions around irreproducibility in scientific research. To
demonstrate this subjective variability, data is taken from an existing study,
where interest centres on understanding the factors associated with a young
adult's propensity to fall into the category of `not in employment, education
or training' (NEET). A fully probabilistic analysis is performed, set in a
Bayesian framework and implemented using Reversible Jump Markov chain Monte
Carlo (RJMCMC). The results show that different techniques lead to different
inference but that models consisting of different factors often have the same
predictive performance, whether the analysis is frequentist or Bayesian, making
inference problematic. We demonstrate how the use of prior distributions in
Bayesian techniques can be used to as a tool for assessing a factor's
importance.
|
stat.AP
|
the increasing ease of data capture and storage has led to a corresponding increase in the choice of data the type of analysis performed on that data and the complexity of the analysis performed the main contribution of this paper is to show that the subjective choice of data and analysis methodology substantially impacts the identification of factors and outcomes of observational studies this subjective variability of inference is at the heart of recent discussions around irreproducibility in scientific research to demonstrate this subjective variability data is taken from an existing study where interest centres on understanding the factors associated with a young adults propensity to fall into the category of not in employment education or training neet a fully probabilistic analysis is performed set in a bayesian framework and implemented using reversible jump markov chain monte carlo rjmcmc the results show that different techniques lead to different inference but that models consisting of different factors often have the same predictive performance whether the analysis is frequentist or bayesian making inference problematic we demonstrate how the use of prior distributions in bayesian techniques can be used to as a tool for assessing a factors importance
|
[['the', 'increasing', 'ease', 'of', 'data', 'capture', 'and', 'storage', 'has', 'led', 'to', 'a', 'corresponding', 'increase', 'in', 'the', 'choice', 'of', 'data', 'the', 'type', 'of', 'analysis', 'performed', 'on', 'that', 'data', 'and', 'the', 'complexity', 'of', 'the', 'analysis', 'performed', 'the', 'main', 'contribution', 'of', 'this', 'paper', 'is', 'to', 'show', 'that', 'the', 'subjective', 'choice', 'of', 'data', 'and', 'analysis', 'methodology', 'substantially', 'impacts', 'the', 'identification', 'of', 'factors', 'and', 'outcomes', 'of', 'observational', 'studies', 'this', 'subjective', 'variability', 'of', 'inference', 'is', 'at', 'the', 'heart', 'of', 'recent', 'discussions', 'around', 'irreproducibility', 'in', 'scientific', 'research', 'to', 'demonstrate', 'this', 'subjective', 'variability', 'data', 'is', 'taken', 'from', 'an', 'existing', 'study', 'where', 'interest', 'centres', 'on', 'understanding', 'the', 'factors', 'associated', 'with', 'a', 'young', 'adults', 'propensity', 'to', 'fall', 'into', 'the', 'category', 'of', 'not', 'in', 'employment', 'education', 'or', 'training', 'neet', 'a', 'fully', 'probabilistic', 'analysis', 'is', 'performed', 'set', 'in', 'a', 'bayesian', 'framework', 'and', 'implemented', 'using', 'reversible', 'jump', 'markov', 'chain', 'monte', 'carlo', 'rjmcmc', 'the', 'results', 'show', 'that', 'different', 'techniques', 'lead', 'to', 'different', 'inference', 'but', 'that', 'models', 'consisting', 'of', 'different', 'factors', 'often', 'have', 'the', 'same', 'predictive', 'performance', 'whether', 'the', 'analysis', 'is', 'frequentist', 'or', 'bayesian', 'making', 'inference', 'problematic', 'we', 'demonstrate', 'how', 'the', 'use', 'of', 'prior', 'distributions', 'in', 'bayesian', 'techniques', 'can', 'be', 'used', 'to', 'as', 'a', 'tool', 'for', 'assessing', 'a', 'factors', 'importance']]
|
[-0.02459767130203545, 0.03653805531831518, -0.11539216260545147, 0.07983770191979905, -0.09377609706268861, -0.09570531876136859, 0.08071904063332253, 0.390006329673223, -0.2429698775976132, -0.3307696590080666, 0.1274098370329459, -0.2517508237622678, -0.12469919933579289, 0.20575217762484382, -0.09457760308272181, 0.050418607575986055, 0.12677417938143778, 0.018988878179628115, -0.05365266212193558, -0.248280191502701, 0.301773279118471, 0.0933120537835818, 0.35519624246696296, 0.019664258423906105, 0.07241892611476569, 0.00228337071215113, -0.09110302316884582, 0.01692814220650456, -0.11434910117233625, 0.1665831635837467, 0.30191531275148664, 0.22693976448992123, 0.3531678343555914, -0.3998610897610585, -0.2170755319106273, 0.09661682482737188, 0.1392103034358185, 0.07209254965090599, -0.03135030280010631, -0.2830508068777048, 0.053346027044627146, -0.182594889937303, -0.06676324151504116, -0.1011039430824801, -0.014633425017102407, 0.003976467088199197, -0.27811900638760284, 0.05191734195328676, 0.047601031065912534, 0.11184635660014092, -0.030057406400402005, -0.1347056830325761, 0.009219512559521274, 0.1528032291919375, 0.10910351030492725, 0.006754987064796762, 0.1418793484210395, -0.13288792536641733, -0.1639031980932953, 0.35119072815331703, -0.02332933312998368, -0.1924083136451932, 0.20677738697148668, -0.12004589138027184, -0.19136384751659652, 0.11233153398005435, 0.21204192010709683, 0.09324052971907151, -0.17262603082479192, 0.042942904364257954, 0.02632425262433035, 0.18949188137940393, -0.01792722296160765, -0.017997583545123538, 0.1789258091746328, 0.21933661464315193, -0.020400911248408447, 0.12448792387850774, -0.09692315835392294, -0.10907897236159979, -0.25494613314572817, -0.1391648186896092, -0.15628269970751343, 0.022592135160065925, -0.07680054834099093, -0.17556992193910842, 0.3775948577441084, 0.2151326508803089, 0.1788005816600978, 0.041985535418662505, 0.2980995916212216, 0.07280876723286779, 0.07161495140395485, 0.036794342301212826, 0.20563910710297, 0.07648961826299246, 0.07227269711020665, -0.1955150950461244, 0.1433197191492535, -0.03256474732188508]
|
1,803.04482
|
Critical behavior of quasi-two-dimensional weak itinerant ferromagnet
trigonal chromium telluride Cr$_{0.62}$Te
|
The critical properties of flux-grown single-crystalline
quasi-two-dimensional weak itinerant ferromagnet Cr$_{0.62}$Te were
investigated by bulk dc magnetization around the paramagnetic (PM) to
ferromagnetic (FM) phase transition. Critical exponents $\beta = 0.315(7)$ with
a critical temperature $T_c = 230.6(3)$ K and $\gamma = 1.81(2)$ with $T_c =
229.1(1)$ K are obtained by the Kouvel-Fisher method whereas $\delta = 6.35(4)$
is obtained by a critical isotherm analysis at $T_c = 230$ K. With these
obtained exponents, the magnetization-field-temperature curves collapse into
two independent curves following a single scaling equation
$M|\frac{T-T_c}{T_c}|^{-\beta} = f_\pm(H|\frac{T-T_c}{T_c}|^{-\beta\delta})$
around $T_c$, suggesting the reliability of the obtained exponents.
Additionally, the determined exponents of Cr$_{0.62}$Te exhibit an Ising-like
behavior with a change from short-range order to long-range order in the nature
of magnetic interaction and with an extension from 2D to 3D on cooling through
$T_c$.
|
cond-mat.str-el
|
the critical properties of fluxgrown singlecrystalline quasitwodimensional weak itinerant ferromagnet cr_062te were investigated by bulk dc magnetization around the paramagnetic pm to ferromagnetic fm phase transition critical exponents beta 03157 with a critical temperature t_c 23063 k and gamma 1812 with t_c 22911 k are obtained by the kouvelfisher method whereas delta 6354 is obtained by a critical isotherm analysis at t_c 230 k with these obtained exponents the magnetizationfieldtemperature curves collapse into two independent curves following a single scaling equation mfractt_ct_cbeta f_pmhfractt_ct_cbetadelta around t_c suggesting the reliability of the obtained exponents additionally the determined exponents of cr_062te exhibit an isinglike behavior with a change from shortrange order to longrange order in the nature of magnetic interaction and with an extension from 2d to 3d on cooling through t_c
|
[['the', 'critical', 'properties', 'of', 'fluxgrown', 'singlecrystalline', 'quasitwodimensional', 'weak', 'itinerant', 'ferromagnet', 'cr_062te', 'were', 'investigated', 'by', 'bulk', 'dc', 'magnetization', 'around', 'the', 'paramagnetic', 'pm', 'to', 'ferromagnetic', 'fm', 'phase', 'transition', 'critical', 'exponents', 'beta', '03157', 'with', 'a', 'critical', 'temperature', 't_c', '23063', 'k', 'and', 'gamma', '1812', 'with', 't_c', '22911', 'k', 'are', 'obtained', 'by', 'the', 'kouvelfisher', 'method', 'whereas', 'delta', '6354', 'is', 'obtained', 'by', 'a', 'critical', 'isotherm', 'analysis', 'at', 't_c', '230', 'k', 'with', 'these', 'obtained', 'exponents', 'the', 'magnetizationfieldtemperature', 'curves', 'collapse', 'into', 'two', 'independent', 'curves', 'following', 'a', 'single', 'scaling', 'equation', 'mfractt_ct_cbeta', 'f_pmhfractt_ct_cbetadelta', 'around', 't_c', 'suggesting', 'the', 'reliability', 'of', 'the', 'obtained', 'exponents', 'additionally', 'the', 'determined', 'exponents', 'of', 'cr_062te', 'exhibit', 'an', 'isinglike', 'behavior', 'with', 'a', 'change', 'from', 'shortrange', 'order', 'to', 'longrange', 'order', 'in', 'the', 'nature', 'of', 'magnetic', 'interaction', 'and', 'with', 'an', 'extension', 'from', '2d', 'to', '3d', 'on', 'cooling', 'through', 't_c']]
|
[-0.16329739123298057, 0.23141936065022034, -0.059321978044780815, -0.04034841023212345, -0.01584921268869407, -0.18332886078956823, 0.1302231257927627, 0.3354037618841822, -0.22568428198506763, -0.2831269127666981, 0.0013886034933172843, -0.388049630525191, -0.059205798303308195, 0.21279529861582458, 0.12919047988435514, 0.07391186224004208, -0.10591146775159585, 0.0402195411926705, -0.15383685771991645, -0.1962072139390367, 0.28934250136237005, -0.03031624502633229, 0.27853045473452503, 0.060436560982099374, -0.01486530471910638, -0.01635988476909389, 0.1576201526047893, 0.05154467462600509, -0.24936983857904216, -0.01324838558057985, 0.23809004026948097, -0.12086598254305606, 0.1471616540735295, -0.3165454073647453, -0.23760084395319964, 0.0360328550084902, 0.14506891331046604, 0.019381890165781186, -0.0064910505446105085, -0.3175969732509664, 0.09932170944550067, -0.06623436092237427, -0.16723833283532627, -0.09505711080629595, -0.011792985860966453, 0.016247266185785498, -0.2884133258326487, 0.16675071331310617, 0.06893506383115411, 0.17890906450915928, -0.0910805568353019, -0.1502114541844026, -0.053711973415487564, 0.053933740501130416, 0.06823667978640831, 0.11552058567662439, 0.14337479269270562, -0.09428132219963577, -0.07528754277251014, 0.32129159686732883, -0.059664665586661454, -0.018512952076761853, 0.12270714336436642, -0.21121980732091325, -0.07408044974450484, 0.239314339820234, 0.08107312481411663, 0.04452150827658571, -0.15508352170028716, 0.057672695713771954, 0.0524150341641435, 0.21269551658625752, 0.028872971099701304, -0.037696367597269, 0.2339672334992504, 0.1625868032613292, -0.01325278541781316, 0.14554164549605228, -0.09958670782187495, -0.08279686286460516, -0.2234490942209959, -0.12123501395675823, -0.2105899967391752, 0.10189773714697097, -0.17461302767922401, -0.16300613346532727, 0.32880235610391234, 0.14000189601451404, 0.23418658219416402, 0.004797710088458805, 0.19556379441964664, 0.12531832712394578, 0.039856719722863566, 0.0966422871825062, 0.2289039307035321, 0.17422672884330953, 0.1688768531018791, -0.3009966828029079, 0.053604416597311286, 0.08768531657016475]
|
1,803.04483
|
Pathwise moderate deviations for option pricing
|
We provide a unifying treatment of pathwise moderate deviations for models
commonly used in financial applications, and for related integrated
functionals. Suitable scaling allows us to transfer these results into
small-time, large-time and tail asymptotics for diffusions, as well as for
option prices and realised variances. In passing, we highlight some intuitive
relationships between moderate deviations rate functions and their large
deviations counterparts; these turn out to be useful for numerical purposes, as
large deviations rate functions are often difficult to compute.
|
q-fin.MF math.PR q-fin.PR
|
we provide a unifying treatment of pathwise moderate deviations for models commonly used in financial applications and for related integrated functionals suitable scaling allows us to transfer these results into smalltime largetime and tail asymptotics for diffusions as well as for option prices and realised variances in passing we highlight some intuitive relationships between moderate deviations rate functions and their large deviations counterparts these turn out to be useful for numerical purposes as large deviations rate functions are often difficult to compute
|
[['we', 'provide', 'a', 'unifying', 'treatment', 'of', 'pathwise', 'moderate', 'deviations', 'for', 'models', 'commonly', 'used', 'in', 'financial', 'applications', 'and', 'for', 'related', 'integrated', 'functionals', 'suitable', 'scaling', 'allows', 'us', 'to', 'transfer', 'these', 'results', 'into', 'smalltime', 'largetime', 'and', 'tail', 'asymptotics', 'for', 'diffusions', 'as', 'well', 'as', 'for', 'option', 'prices', 'and', 'realised', 'variances', 'in', 'passing', 'we', 'highlight', 'some', 'intuitive', 'relationships', 'between', 'moderate', 'deviations', 'rate', 'functions', 'and', 'their', 'large', 'deviations', 'counterparts', 'these', 'turn', 'out', 'to', 'be', 'useful', 'for', 'numerical', 'purposes', 'as', 'large', 'deviations', 'rate', 'functions', 'are', 'often', 'difficult', 'to', 'compute']]
|
[-0.02904592610884276, 0.026795642490203424, -0.10635373373402328, 0.25124462152740396, -0.08373444258780559, -0.15608872287827202, 0.08593231066777485, 0.4014410203686211, -0.2804900369175324, -0.276241903443162, 0.14180951107509163, -0.2793839969730825, -0.11549660207976292, 0.28414613497585467, -0.069740172486934, 0.11449086792176454, 0.026400316739454865, -0.09279175646987846, -0.05472412042138053, -0.18974878516320776, 0.22853919097063383, 0.07989762629177875, 0.2833991249478081, 0.040683451449362244, 0.08309901619284618, -0.009771208037485981, -0.030578357900106688, 0.028318727667239016, -0.17129017412662506, 0.13818274501983713, 0.33284222058691515, 0.08545014188375051, 0.26512834488800385, -0.4505343948740785, -0.15524347404744931, 0.1430841817906717, 0.1263292154564136, 0.10213191710162635, -0.024556729601832424, -0.2542430897379612, 0.0484540062649857, -0.19533763746390256, -0.17813071350726048, -0.21254511359261302, 0.0475687880137163, 0.10288984674347065, -0.34054905215914294, 0.1346268023076047, 0.009833595929127867, 0.04791220075382692, -0.006942333637242682, -0.15315964933280357, 0.046408401231463184, 0.19062958948495912, 0.13893216354318144, -0.09457470287949346, 0.12610457547963028, -0.12945690096878423, -0.10091950683059489, 0.3519490335999829, -0.09922904112353557, -0.22952788519650336, 0.23520441815859025, -0.1468555582232955, -0.1359360432422652, 0.10834196596065672, 0.23289125418381357, 0.09546978787587183, -0.19758364652487925, 0.04086770387877506, 0.04133873462972299, 0.09072544324927305, 0.07170052593006049, 0.09445478421885793, 0.20171491018774715, 0.08875922452113251, 0.07805028549827091, 0.14061338606818646, -0.037436034507686046, -0.1757469459766204, -0.3442492881312785, -0.13686893336338604, -0.12075748610332972, 0.09638496313425826, -0.16357394963681765, -0.16200697043251883, 0.3014750963832229, 0.18760044053878364, 0.18463336828550914, 0.14118043485483744, 0.19792005426545695, 0.17027721413916613, 0.03817172498135578, 0.06001442300564632, 0.2005314120086529, 0.1186113405700137, 0.11512669469457029, -0.12209853763328637, 0.1288931475804619, 0.027745079226428417]
|
1,803.04484
|
Adaptive two-stage sequential double sampling
|
In many surveys inexpensive auxiliary variables are available that can help
us to make more precise estimation about the main variable. Using auxiliary
variable has been extended by regression estimators for rare and cluster
populations. In conventional regression estimator it is assumed that the mean
of auxiliary variable in the population is known. In many surveys we don't have
such wide information about auxiliary variable. In this paper we present a
multi-phase variant of two-stage sequential sampling based on an inexpensive
auxiliary variable associated with the survey variable in the form of double
sampling. The auxiliary variable will be used in both design and estimation
stage. The population mean is estimated by a modified regression-type estimator
with two different coefficient. Results will be investigated using some
simulations following Median and Thompson (2004).
|
math.ST stat.TH
|
in many surveys inexpensive auxiliary variables are available that can help us to make more precise estimation about the main variable using auxiliary variable has been extended by regression estimators for rare and cluster populations in conventional regression estimator it is assumed that the mean of auxiliary variable in the population is known in many surveys we dont have such wide information about auxiliary variable in this paper we present a multiphase variant of twostage sequential sampling based on an inexpensive auxiliary variable associated with the survey variable in the form of double sampling the auxiliary variable will be used in both design and estimation stage the population mean is estimated by a modified regressiontype estimator with two different coefficient results will be investigated using some simulations following median and thompson 2004
|
[['in', 'many', 'surveys', 'inexpensive', 'auxiliary', 'variables', 'are', 'available', 'that', 'can', 'help', 'us', 'to', 'make', 'more', 'precise', 'estimation', 'about', 'the', 'main', 'variable', 'using', 'auxiliary', 'variable', 'has', 'been', 'extended', 'by', 'regression', 'estimators', 'for', 'rare', 'and', 'cluster', 'populations', 'in', 'conventional', 'regression', 'estimator', 'it', 'is', 'assumed', 'that', 'the', 'mean', 'of', 'auxiliary', 'variable', 'in', 'the', 'population', 'is', 'known', 'in', 'many', 'surveys', 'we', 'dont', 'have', 'such', 'wide', 'information', 'about', 'auxiliary', 'variable', 'in', 'this', 'paper', 'we', 'present', 'a', 'multiphase', 'variant', 'of', 'twostage', 'sequential', 'sampling', 'based', 'on', 'an', 'inexpensive', 'auxiliary', 'variable', 'associated', 'with', 'the', 'survey', 'variable', 'in', 'the', 'form', 'of', 'double', 'sampling', 'the', 'auxiliary', 'variable', 'will', 'be', 'used', 'in', 'both', 'design', 'and', 'estimation', 'stage', 'the', 'population', 'mean', 'is', 'estimated', 'by', 'a', 'modified', 'regressiontype', 'estimator', 'with', 'two', 'different', 'coefficient', 'results', 'will', 'be', 'investigated', 'using', 'some', 'simulations', 'following', 'median', 'and', 'thompson', '2004']]
|
[-0.04423685403537203, 0.08389399426199486, -0.12426467794064207, 0.10623178330504081, -0.12924883616241542, -0.15980029251482902, 0.04260048264140179, 0.44953510093248705, -0.2568379062559512, -0.3282108114270324, 0.18114552151316524, -0.23501067158102876, -0.13412474923428486, 0.2050874758173119, -0.12780107733807666, 0.06851665172937581, 0.06002348838338741, 0.006257008902277007, -0.03778474914901325, -0.29230587827888405, 0.26641049816724705, 0.06624453478125912, 0.22029656333369063, -0.11334833717079494, 0.13307967796131517, 0.07758804818855203, -0.11471550585702062, 0.031196457305640884, -0.11635052637520306, 0.11921519875371207, 0.2562744911338086, 0.14325910263560768, 0.35678365358828823, -0.3463216417409113, -0.23731048080089473, 0.1092446695238874, 0.174593526337324, 0.08648084465301398, -0.03639935857978399, -0.23253243861924575, 0.03494391017452334, -0.16787911286916246, -0.09982996255293432, -0.07980960779682253, -0.04455265027442665, 0.06362975869948666, -0.3195245861513258, 0.08832641485655172, -0.02109102653974498, 0.054383069474362965, -0.033685734015748356, -0.18522953054035138, 0.03415282225857178, 0.07578205517369718, 0.017124173331963408, 0.03871114900385058, 0.09668220481729474, -0.11695889023548896, -0.08243743004277349, 0.28457557647065684, -0.09147854305355753, -0.21723289214864824, 0.18199277089584168, -0.11397338429973884, -0.19797998420880036, 0.11233846158244308, 0.19149108507465853, 0.1306850113200419, -0.2311798844726361, 0.032862005030796296, -0.04179914045909589, 0.17846252049573444, -0.0009670290499784504, 0.011246065535074607, 0.17493174376431853, 0.16202129696665163, 0.05501848720737281, 0.13082035065445147, -0.150678056611937, -0.09946448578866142, -0.2650562352148082, -0.13742432895973747, -0.1871004176139126, 0.023668896332806486, -0.12876564133738144, -0.1451433958039464, 0.33994612149774295, 0.20968481897955027, 0.16297351674593025, 0.006454767937997751, 0.2721225267706552, 0.12394916600632397, 0.06929567475295202, 0.06821922311968537, 0.20544202437833353, 0.13853985968991323, 0.05205961184533821, -0.15177452359277982, 0.12943819833030415, 0.015234301510535068]
|
1,803.04485
|
Poisson Kernel-Based Clustering on the Sphere: Convergence Properties,
Identifiability, and a Method of Sampling
|
Many applications of interest involve data that can be analyzed as unit
vectors on a d-dimensional sphere. Specific examples include text mining, in
particular clustering of documents, biology, astronomy and medicine among
others. Previous work has proposed a clustering method using mixtures of
Poisson kernel-based distributions (PKBD) on the sphere. We prove
identifiability of mixtures of the aforementioned model, convergence of the
associated EM-type algorithm and study its operational characteristics.
Furthermore, we propose an empirical densities distance plot for estimating the
number of clusters in a PKBD model. Finally, we propose a method to simulate
data from Poisson kernel-based densities and exemplify our methods via
application on real data sets and simulation experiments.
|
stat.ME
|
many applications of interest involve data that can be analyzed as unit vectors on a ddimensional sphere specific examples include text mining in particular clustering of documents biology astronomy and medicine among others previous work has proposed a clustering method using mixtures of poisson kernelbased distributions pkbd on the sphere we prove identifiability of mixtures of the aforementioned model convergence of the associated emtype algorithm and study its operational characteristics furthermore we propose an empirical densities distance plot for estimating the number of clusters in a pkbd model finally we propose a method to simulate data from poisson kernelbased densities and exemplify our methods via application on real data sets and simulation experiments
|
[['many', 'applications', 'of', 'interest', 'involve', 'data', 'that', 'can', 'be', 'analyzed', 'as', 'unit', 'vectors', 'on', 'a', 'ddimensional', 'sphere', 'specific', 'examples', 'include', 'text', 'mining', 'in', 'particular', 'clustering', 'of', 'documents', 'biology', 'astronomy', 'and', 'medicine', 'among', 'others', 'previous', 'work', 'has', 'proposed', 'a', 'clustering', 'method', 'using', 'mixtures', 'of', 'poisson', 'kernelbased', 'distributions', 'pkbd', 'on', 'the', 'sphere', 'we', 'prove', 'identifiability', 'of', 'mixtures', 'of', 'the', 'aforementioned', 'model', 'convergence', 'of', 'the', 'associated', 'emtype', 'algorithm', 'and', 'study', 'its', 'operational', 'characteristics', 'furthermore', 'we', 'propose', 'an', 'empirical', 'densities', 'distance', 'plot', 'for', 'estimating', 'the', 'number', 'of', 'clusters', 'in', 'a', 'pkbd', 'model', 'finally', 'we', 'propose', 'a', 'method', 'to', 'simulate', 'data', 'from', 'poisson', 'kernelbased', 'densities', 'and', 'exemplify', 'our', 'methods', 'via', 'application', 'on', 'real', 'data', 'sets', 'and', 'simulation', 'experiments']]
|
[-0.03189980156451195, -0.0035848769951592877, -0.08883760443276113, 0.0758944808459329, -0.06185575855921048, -0.10230554343329172, 0.06195993447961571, 0.404956184231114, -0.26025728379200036, -0.29778875541025857, 0.1031887617805419, -0.30212789495383297, -0.19264811667328416, 0.23024256803044998, -0.07861123920569348, 0.08420929648265293, 0.09564549771036852, 0.031170906448686444, -0.06229001481027284, -0.27525266807375326, 0.3190568374922952, 0.035692425830675674, 0.3322946281940047, 0.0038395371615282587, 0.11544339990490829, 0.01454091500407001, -0.0867713232239423, 0.019823798104315192, -0.1437582617129649, 0.16680960653628735, 0.27424913708766635, 0.22318130660474905, 0.29813091762716304, -0.3818856506998575, -0.25826007043858906, 0.13545823051501904, 0.15153987030288926, 0.062089321111236606, -0.0715063049862799, -0.2785930891630349, 0.03838108167857737, -0.16192304238945515, -0.07592467826094713, -0.16255412472260966, -0.008231973030126176, 0.0792598572626777, -0.2719127271861375, 0.09056949623239537, 0.040463599590332924, 0.07291668427789265, -0.06214390073059681, -0.15813686195467305, 0.051280200269559886, 0.08613730441392944, 0.03942606762641, -0.02223595765813648, 0.12475490222290696, -0.09341479956197578, -0.16091177820622385, 0.36618750481992157, -0.028107688401989336, -0.2449883245904972, 0.22167547871904061, -0.07685022820458487, -0.1910206816905925, 0.06338340817187149, 0.27505812898848775, 0.128785184097921, -0.1594263751160454, 0.10012779595447821, -0.06254381195616883, 0.12172226803712882, 0.053698150648710294, -0.050657381851075066, 0.15614494151301547, 0.1872928967460285, 0.04461456174901393, 0.16626196981906924, -0.15127496381535674, -0.09678825716328648, -0.2652854482655892, -0.13755039288033102, -0.23778928710600813, -0.024879453787664034, -0.14489823330334295, -0.16885963330666223, 0.3604387864470482, 0.20264954569234056, 0.21933985927754696, 0.06771701182026903, 0.3094498791966822, 0.057863193190748896, 0.012624625221465353, 0.03710438494847433, 0.09780970346266488, 0.12741718111509406, 0.08358532804072843, -0.14442881568217533, 0.06053255238519931, 0.040795096166868196]
|
1,803.04486
|
Olivine-rich asteroids in the near-Earth space
|
In the framework of a 30-night spectroscopic survey of small near-Earth
asteroids (NEAs) we present new results regarding the identification of
olivine-rich objects. The following NEAs were classified as A-type using
visible spectra obtained with 3.6 m NTT telescope: (293726) 2007 RQ17, (444584)
2006 UK, 2012 NP, 2014 YS34, 2015 HB117, 2015 LH, 2015 TB179, 2015 TW144. We
determined a relative abundance of $5.4\% $ (8 out of 147 observed targets)
A-types at hundred meter size range of NEAs population. The ratio is at least
five times larger compared with the previously known A-types, which represent
less than $\sim1\%$ of NEAs taxonomically classified. By taking into account
that part of our targets may not be confirmed as olivine-rich asteroids by
their near-infrared spectra, or they can have a nebular origin, our result
provides an upper-limit estimation of mantle fragments at size ranges bellow
300m. Our findings are compared with the "battered-to-bits" scenario, claiming
that at small sizes the olivine-rich objects should be more abundant when
compared with basaltic and iron ones.
|
astro-ph.EP
|
in the framework of a 30night spectroscopic survey of small nearearth asteroids neas we present new results regarding the identification of olivinerich objects the following neas were classified as atype using visible spectra obtained with 36 m ntt telescope 293726 2007 rq17 444584 2006 uk 2012 np 2014 ys34 2015 hb117 2015 lh 2015 tb179 2015 tw144 we determined a relative abundance of 54 8 out of 147 observed targets atypes at hundred meter size range of neas population the ratio is at least five times larger compared with the previously known atypes which represent less than sim1 of neas taxonomically classified by taking into account that part of our targets may not be confirmed as olivinerich asteroids by their nearinfrared spectra or they can have a nebular origin our result provides an upperlimit estimation of mantle fragments at size ranges bellow 300m our findings are compared with the batteredtobits scenario claiming that at small sizes the olivinerich objects should be more abundant when compared with basaltic and iron ones
|
[['in', 'the', 'framework', 'of', 'a', '30night', 'spectroscopic', 'survey', 'of', 'small', 'nearearth', 'asteroids', 'neas', 'we', 'present', 'new', 'results', 'regarding', 'the', 'identification', 'of', 'olivinerich', 'objects', 'the', 'following', 'neas', 'were', 'classified', 'as', 'atype', 'using', 'visible', 'spectra', 'obtained', 'with', '36', 'm', 'ntt', 'telescope', '293726', '2007', 'rq17', '444584', '2006', 'uk', '2012', 'np', '2014', 'ys34', '2015', 'hb117', '2015', 'lh', '2015', 'tb179', '2015', 'tw144', 'we', 'determined', 'a', 'relative', 'abundance', 'of', '54', '8', 'out', 'of', '147', 'observed', 'targets', 'atypes', 'at', 'hundred', 'meter', 'size', 'range', 'of', 'neas', 'population', 'the', 'ratio', 'is', 'at', 'least', 'five', 'times', 'larger', 'compared', 'with', 'the', 'previously', 'known', 'atypes', 'which', 'represent', 'less', 'than', 'sim1', 'of', 'neas', 'taxonomically', 'classified', 'by', 'taking', 'into', 'account', 'that', 'part', 'of', 'our', 'targets', 'may', 'not', 'be', 'confirmed', 'as', 'olivinerich', 'asteroids', 'by', 'their', 'nearinfrared', 'spectra', 'or', 'they', 'can', 'have', 'a', 'nebular', 'origin', 'our', 'result', 'provides', 'an', 'upperlimit', 'estimation', 'of', 'mantle', 'fragments', 'at', 'size', 'ranges', 'bellow', '300m', 'our', 'findings', 'are', 'compared', 'with', 'the', 'batteredtobits', 'scenario', 'claiming', 'that', 'at', 'small', 'sizes', 'the', 'olivinerich', 'objects', 'should', 'be', 'more', 'abundant', 'when', 'compared', 'with', 'basaltic', 'and', 'iron', 'ones']]
|
[-0.02410599758764671, 0.14583367677428977, -0.06356222575287455, 0.04677949038539183, -0.032676586346926384, -0.07971016763310347, 0.04958844495364552, 0.35083572034035687, -0.12346928464668161, -0.45052055366372007, 0.11604369401581793, -0.32542670922215033, -0.04500972975178489, 0.21686852625021652, -0.10304525237160254, -0.0315974201225997, 0.13155234695458495, -0.08588404051270061, 0.026123408487743426, -0.32197663552989686, 0.20273725065165518, 0.10559378413993993, 0.13352327619430535, -0.058295047927108413, 0.05622054777622963, -0.07075532788977675, -0.07455239864933935, -0.0005901280836844296, -0.1403597356837065, 0.07967908332928414, 0.25214532466304507, 0.10185378847747856, 0.18846282049122712, -0.3244906068658051, -0.17523456044836397, 0.06681632184964367, 0.1135401974333832, 0.03959929326083511, 0.019933389711916095, -0.34300536360238787, 0.12032388795965485, -0.18457046952619224, -0.16965516063226357, 0.031108518096369615, 0.10107828120799353, 0.0023940406506158043, -0.2047106055137904, 0.07088387175896986, 0.035655193113490885, 0.15811237088340965, -0.14427399245142844, -0.26475973556871, -0.06553920497756266, 0.10039711987215777, 0.022945557719513034, -0.00782355394283807, 0.13197011326624786, -0.029026721598383803, -0.08078504511681588, 0.3951978742474725, -0.04837811029827445, 0.007504833537704884, 0.2338164842156809, -0.17709752742594442, -0.14191127539271062, 0.21414076584978434, 0.16486996596372508, 0.15344946583521824, -0.1558825673224724, 0.005425371128056337, -0.08108580764455385, 0.23386970466662269, 0.10679434165098235, 0.0516801679431698, 0.25005356558043257, 0.15376909755847146, -0.0013963569299844296, 0.07947491308072617, -0.22088065886615504, -0.0026231966392131325, -0.2330896716784709, -0.09937775131388747, -0.1443055325284635, 0.04805486618770035, -0.08893251396209126, -0.03744292618368423, 0.33125427952441183, 0.10068053820803877, 0.22882303688169012, 0.044872091064281666, 0.2201468400940623, 0.02942817417907627, 0.12613407871396692, 0.10116628905388046, 0.33550820330169445, 0.0760086438899154, 0.10205593716746532, -0.12938205333085304, 0.09822747734034006, -0.0032424817341871514]
|
1,803.04487
|
Integrated machine learning pipeline for aberrant biomarker enrichment
(i-mAB): characterizing clusters of differentiation within a compendium of
systemic lupus erythematosus patients
|
Clusters of differentiation (CD) are cell surface biomarkers that denote key
biological differences between cell types and disease state. CD-targeting
therapeutic monoclonal antibodies (mAB) afford rich trans-disease repositioning
opportunities. Within a compendium of systemic lupus erythematous (SLE)
patients, we applied the Integrated machine learning pipeline for aberrant
biomarker enrichment (i-mAB) to profile de novo gene expression features
affecting CD20, CD22 and CD30 gene aberrance. First, a novel relief-based
algorithm identified interdependent features(p=681) predicting
treatment-na\"ive SLE patients (balanced accuracy=0.822). We then compiled
CD-associated expression profiles using regularized logistic regression and
pathway enrichment analyses. On an independent general cell line model system
data, we replicated associations (in silico) of BCL7A(padj=1.69e-9) and
STRBP(padj=4.63e-8) with CD22; NCOA2(padj=7.00e-4), ATN1(padj=1.71e-2), and
HOXC4(padj=3.34e-2) with CD30; and PHOSPHO1, a phosphatase linked to bone
mineralization, with both CD22(padj=4.37e-2) and CD30(padj=7.40e-3). Utilizing
carefully aggregated secondary data and leveraging a priori hypotheses, i-mAB
fostered robust biomarker profiling among interdependent biological features.
|
q-bio.MN
|
clusters of differentiation cd are cell surface biomarkers that denote key biological differences between cell types and disease state cdtargeting therapeutic monoclonal antibodies mab afford rich transdisease repositioning opportunities within a compendium of systemic lupus erythematous sle patients we applied the integrated machine learning pipeline for aberrant biomarker enrichment imab to profile de novo gene expression features affecting cd20 cd22 and cd30 gene aberrance first a novel reliefbased algorithm identified interdependent featuresp681 predicting treatmentnaive sle patients balanced accuracy0822 we then compiled cdassociated expression profiles using regularized logistic regression and pathway enrichment analyses on an independent general cell line model system data we replicated associations in silico of bcl7apadj169e9 and strbppadj463e8 with cd22 ncoa2padj700e4 atn1padj171e2 and hoxc4padj334e2 with cd30 and phospho1 a phosphatase linked to bone mineralization with both cd22padj437e2 and cd30padj740e3 utilizing carefully aggregated secondary data and leveraging a priori hypotheses imab fostered robust biomarker profiling among interdependent biological features
|
[['clusters', 'of', 'differentiation', 'cd', 'are', 'cell', 'surface', 'biomarkers', 'that', 'denote', 'key', 'biological', 'differences', 'between', 'cell', 'types', 'and', 'disease', 'state', 'cdtargeting', 'therapeutic', 'monoclonal', 'antibodies', 'mab', 'afford', 'rich', 'transdisease', 'repositioning', 'opportunities', 'within', 'a', 'compendium', 'of', 'systemic', 'lupus', 'erythematous', 'sle', 'patients', 'we', 'applied', 'the', 'integrated', 'machine', 'learning', 'pipeline', 'for', 'aberrant', 'biomarker', 'enrichment', 'imab', 'to', 'profile', 'de', 'novo', 'gene', 'expression', 'features', 'affecting', 'cd20', 'cd22', 'and', 'cd30', 'gene', 'aberrance', 'first', 'a', 'novel', 'reliefbased', 'algorithm', 'identified', 'interdependent', 'featuresp681', 'predicting', 'treatmentnaive', 'sle', 'patients', 'balanced', 'accuracy0822', 'we', 'then', 'compiled', 'cdassociated', 'expression', 'profiles', 'using', 'regularized', 'logistic', 'regression', 'and', 'pathway', 'enrichment', 'analyses', 'on', 'an', 'independent', 'general', 'cell', 'line', 'model', 'system', 'data', 'we', 'replicated', 'associations', 'in', 'silico', 'of', 'bcl7apadj169e9', 'and', 'strbppadj463e8', 'with', 'cd22', 'ncoa2padj700e4', 'atn1padj171e2', 'and', 'hoxc4padj334e2', 'with', 'cd30', 'and', 'phospho1', 'a', 'phosphatase', 'linked', 'to', 'bone', 'mineralization', 'with', 'both', 'cd22padj437e2', 'and', 'cd30padj740e3', 'utilizing', 'carefully', 'aggregated', 'secondary', 'data', 'and', 'leveraging', 'a', 'priori', 'hypotheses', 'imab', 'fostered', 'robust', 'biomarker', 'profiling', 'among', 'interdependent', 'biological', 'features']]
|
[-0.033684254080678024, 0.02559384426102483, -0.03922543825341784, 0.08295469961806455, -0.09636722985570403, -0.23241041830712647, 0.12999032121716123, 0.4007081624797799, -0.24453986169431696, -0.2550226477623889, 0.02163689566165858, -0.291447565523964, -0.23957827917095556, 0.15892209787676675, -0.10843485911115722, 0.04243095381058414, 0.11632765247839305, -0.03385073461774454, 0.10847924004759658, -0.22918876951249936, 0.2529793592120493, 0.04940621483591244, 0.29285809552212333, -0.037136515214418374, 0.12182106103599918, -0.0354494056252103, -0.07408336543471017, -0.00929925878087736, -0.14959477713168642, 0.1997549568402588, 0.36918825473615224, 0.26313178040034085, 0.32608876252462243, -0.4283117377622561, -0.2536933073336541, 0.11869597492235091, 0.18738826581234622, 0.07204302518176987, -0.04645589593476898, -0.302296874903594, 0.05709861742621852, -0.1452552264416943, -0.07516135296530345, -0.0793706695575648, 0.008023556776937436, 0.03392099540779424, -0.302537575053672, 0.15414102882443165, -0.05795169333489894, 0.19872339031038186, -0.16409127754300382, -0.1555696340210764, -0.07367317055733028, 0.18180256615795026, 0.01433754208846949, 0.02378887640379341, 0.243546277676201, -0.10836995792906788, -0.1321046413257577, 0.2650660212631478, -0.011306092360014603, -0.15744255942461843, 0.1958532393063334, -0.05547269703939409, -0.20647678135851907, 0.10372860219936367, 0.20930084080618777, 0.058190725817853076, -0.25589495088735764, -0.06029330745558113, 0.06768356147927768, 0.18011180320613537, 0.10757795640683705, -0.06761161576397365, 0.17460941793207452, 0.24357329918403234, -0.03203548760167464, 0.06289149087917228, -0.1394519476929087, -0.04120014040293454, -0.17313180642315384, -0.17268098465071738, -0.05440928699857922, -0.0019652568788098342, -0.14506568794516198, -0.1986964377221849, 0.3501370881894025, 0.06394566591497454, 0.13041589330885361, 0.06841753216488569, 0.23289010119906656, -0.02754773868417198, 0.09916756410772602, 0.01374096850949255, 0.04411061655617121, 0.08926847473313418, 0.07232207767700663, -0.2559706842893678, 0.21380550344474614, -0.013007945568075009]
|
1,803.04488
|
Concept2vec: Metrics for Evaluating Quality of Embeddings for
Ontological Concepts
|
Although there is an emerging trend towards generating embeddings for
primarily unstructured data and, recently, for structured data, no systematic
suite for measuring the quality of embeddings has been proposed yet. This
deficiency is further sensed with respect to embeddings generated for
structured data because there are no concrete evaluation metrics measuring the
quality of the encoded structure as well as semantic patterns in the embedding
space. In this paper, we introduce a framework containing three distinct tasks
concerned with the individual aspects of ontological concepts: (i) the
categorization aspect, (ii) the hierarchical aspect, and (iii) the relational
aspect. Then, in the scope of each task, a number of intrinsic metrics are
proposed for evaluating the quality of the embeddings. Furthermore, w.r.t. this
framework, multiple experimental studies were run to compare the quality of the
available embedding models. Employing this framework in future research can
reduce misjudgment and provide greater insight about quality comparisons of
embeddings for ontological concepts. We positioned our sampled data and code at
https://github.com/alshargi/Concept2vec under GNU General Public License v3.0.
|
cs.CL cs.AI
|
although there is an emerging trend towards generating embeddings for primarily unstructured data and recently for structured data no systematic suite for measuring the quality of embeddings has been proposed yet this deficiency is further sensed with respect to embeddings generated for structured data because there are no concrete evaluation metrics measuring the quality of the encoded structure as well as semantic patterns in the embedding space in this paper we introduce a framework containing three distinct tasks concerned with the individual aspects of ontological concepts i the categorization aspect ii the hierarchical aspect and iii the relational aspect then in the scope of each task a number of intrinsic metrics are proposed for evaluating the quality of the embeddings furthermore wrt this framework multiple experimental studies were run to compare the quality of the available embedding models employing this framework in future research can reduce misjudgment and provide greater insight about quality comparisons of embeddings for ontological concepts we positioned our sampled data and code at httpsgithubcomalshargiconcept2vec under gnu general public license v30
|
[['although', 'there', 'is', 'an', 'emerging', 'trend', 'towards', 'generating', 'embeddings', 'for', 'primarily', 'unstructured', 'data', 'and', 'recently', 'for', 'structured', 'data', 'no', 'systematic', 'suite', 'for', 'measuring', 'the', 'quality', 'of', 'embeddings', 'has', 'been', 'proposed', 'yet', 'this', 'deficiency', 'is', 'further', 'sensed', 'with', 'respect', 'to', 'embeddings', 'generated', 'for', 'structured', 'data', 'because', 'there', 'are', 'no', 'concrete', 'evaluation', 'metrics', 'measuring', 'the', 'quality', 'of', 'the', 'encoded', 'structure', 'as', 'well', 'as', 'semantic', 'patterns', 'in', 'the', 'embedding', 'space', 'in', 'this', 'paper', 'we', 'introduce', 'a', 'framework', 'containing', 'three', 'distinct', 'tasks', 'concerned', 'with', 'the', 'individual', 'aspects', 'of', 'ontological', 'concepts', 'i', 'the', 'categorization', 'aspect', 'ii', 'the', 'hierarchical', 'aspect', 'and', 'iii', 'the', 'relational', 'aspect', 'then', 'in', 'the', 'scope', 'of', 'each', 'task', 'a', 'number', 'of', 'intrinsic', 'metrics', 'are', 'proposed', 'for', 'evaluating', 'the', 'quality', 'of', 'the', 'embeddings', 'furthermore', 'wrt', 'this', 'framework', 'multiple', 'experimental', 'studies', 'were', 'run', 'to', 'compare', 'the', 'quality', 'of', 'the', 'available', 'embedding', 'models', 'employing', 'this', 'framework', 'in', 'future', 'research', 'can', 'reduce', 'misjudgment', 'and', 'provide', 'greater', 'insight', 'about', 'quality', 'comparisons', 'of', 'embeddings', 'for', 'ontological', 'concepts', 'we', 'positioned', 'our', 'sampled', 'data', 'and', 'code', 'at', 'httpsgithubcomalshargiconcept2vec', 'under', 'gnu', 'general', 'public', 'license', 'v30']]
|
[-0.06407103325224164, 0.021652026188837745, -0.03757223179240878, 0.0954020709550342, -0.08971118164044196, -0.11940090968192829, 0.027446958127719582, 0.43752294699150013, -0.2527154239831288, -0.3532840063101666, 0.1032578636073868, -0.2988385735496857, -0.11307464769605353, 0.20200889945740816, -0.09248332365384573, 0.05909661647750635, 0.10322971364504771, 0.029267658970912252, -0.07865316585591871, -0.2405626542968514, 0.35057969348817375, 0.06503215418977804, 0.3690543769605759, 0.04654955486410346, 0.08567173020881223, -0.017982256738738173, -0.10246807378528348, 0.04927303002204482, -0.09419460357163462, 0.21478886683363835, 0.3167940686288671, 0.24454980034709536, 0.28516899345501867, -0.38637026952784187, -0.21411705253419677, 0.07624988600267152, 0.13203957192182564, 0.08573711842616126, -0.08245560207883317, -0.3113078093748389, 0.11122477847936084, -0.15054637987861097, -0.042440892373497, -0.1046082616132291, 0.009914961828233815, -0.017433488956851765, -0.24797621552295435, -0.0012190333982079016, 0.06425350412088474, 0.11455304890526513, -0.06739609960515074, -0.12551161630137694, 0.02711089328469741, 0.19688142998133265, 0.048909474763147144, 0.04060879532473883, 0.08862273215593479, -0.14044937788780693, -0.13386054388974186, 0.40908107458683796, -0.0318976107936331, -0.24810471868605455, 0.17285081160906163, -0.05690335291518555, -0.18537312389130708, 0.05726976563046737, 0.20738929333878584, 0.066068314435768, -0.1674449731076992, 0.048823814964287976, -0.03289613678292344, 0.19367823103238221, 0.06455229347534047, 0.04966809775584749, 0.19101643086561793, 0.23280766385276344, -0.01085603094223693, 0.14100430070858644, -0.062368707174187625, -0.06988904365699071, -0.26543981272147193, -0.1590479274853698, -0.14238873067058924, -0.016758705159606185, -0.0940212235282003, -0.15491413261009263, 0.40030039046121213, 0.18039013684129698, 0.18264276825020767, 0.0569552226056684, 0.32852736612340444, 0.013469799512571984, 0.07249819393393535, 0.07521943102228708, 0.1774877042138163, 0.011387686028462872, 0.105060028525963, -0.11979832780154455, 0.1082999422268759, 0.025398862692177553]
|
1,803.04489
|
Probabilistic and Regularized Graph Convolutional Networks
|
This paper explores the recently proposed Graph Convolutional Network
architecture proposed in (Kipf & Welling, 2016) The key points of their work is
summarized and their results are reproduced. Graph regularization and
alternative graph convolution approaches are explored. I find that explicit
graph regularization was correctly rejected by (Kipf & Welling, 2016). I
attempt to improve the performance of GCN by approximating a k-step transition
matrix in place of the normalized graph laplacian, but I fail to find positive
results. Nonetheless, the performance of several configurations of this GCN
variation is shown for the Cora, Citeseer, and Pubmed datasets.
|
cs.LG stat.ML
|
this paper explores the recently proposed graph convolutional network architecture proposed in kipf welling 2016 the key points of their work is summarized and their results are reproduced graph regularization and alternative graph convolution approaches are explored i find that explicit graph regularization was correctly rejected by kipf welling 2016 i attempt to improve the performance of gcn by approximating a kstep transition matrix in place of the normalized graph laplacian but i fail to find positive results nonetheless the performance of several configurations of this gcn variation is shown for the cora citeseer and pubmed datasets
|
[['this', 'paper', 'explores', 'the', 'recently', 'proposed', 'graph', 'convolutional', 'network', 'architecture', 'proposed', 'in', 'kipf', 'welling', '2016', 'the', 'key', 'points', 'of', 'their', 'work', 'is', 'summarized', 'and', 'their', 'results', 'are', 'reproduced', 'graph', 'regularization', 'and', 'alternative', 'graph', 'convolution', 'approaches', 'are', 'explored', 'i', 'find', 'that', 'explicit', 'graph', 'regularization', 'was', 'correctly', 'rejected', 'by', 'kipf', 'welling', '2016', 'i', 'attempt', 'to', 'improve', 'the', 'performance', 'of', 'gcn', 'by', 'approximating', 'a', 'kstep', 'transition', 'matrix', 'in', 'place', 'of', 'the', 'normalized', 'graph', 'laplacian', 'but', 'i', 'fail', 'to', 'find', 'positive', 'results', 'nonetheless', 'the', 'performance', 'of', 'several', 'configurations', 'of', 'this', 'gcn', 'variation', 'is', 'shown', 'for', 'the', 'cora', 'citeseer', 'and', 'pubmed', 'datasets']]
|
[-0.05781456152308425, -0.015309699330978171, -0.06530519005285633, 0.07726315737308301, -0.0993532166989117, -0.14174335401927687, 0.02324564040748921, 0.43074211286208064, -0.212438411602616, -0.3404776561121965, 0.07272799691118147, -0.29375643523640393, -0.2571472480278654, 0.10524301338441593, -0.14487954769667583, 0.0821612609992015, 0.14091344730275654, 0.014774926640477377, -0.02633884131725993, -0.3140757821680925, 0.30341903448162466, 0.06134351448530389, 0.2938745919922271, 0.07246883580288321, 0.09755358240595952, -0.07285635371106802, -0.08139342545128438, 0.022746374651780064, -0.13695993407225276, 0.1357927922490677, 0.26797574733566376, 0.18088880247561281, 0.27648821100592613, -0.39413224494795207, -0.22004939593643563, 0.09682287466616284, 0.1222296875239033, 0.04277486871995786, 0.0012886499429179221, -0.3127504805890248, 0.11766485902528788, -0.18832630848454446, 0.0022154972725307817, -0.09948576873712321, 0.03387733460547998, 0.008750297782040135, -0.2726410259780733, 0.019833136863584063, 0.12013386194738224, 0.008556438681967173, 0.00445268603915483, -0.18700750702608032, 0.003443378033404498, 0.11720437908511531, 0.004334455936878305, 0.0659796610395376, 0.07546391020788207, -0.0876070029947177, -0.16251802048885944, 0.31904614148373456, -0.04181917769757589, -0.14375786715158329, 0.1324277956461169, -0.049808831827849454, -0.18335466915933588, 0.0712668152055559, 0.15456938910653295, 0.08059430920720562, -0.1524933691318963, 0.07621824044851855, -0.04694741226963161, 0.09925030137306635, 0.05272804036467653, -0.03691368916830451, 0.0885596626783846, 0.1884633675313641, 0.013119783245276698, 0.11180982781938978, -0.07733080489080899, -0.06074705827001905, -0.2249341850533851, -0.10557471218605324, -0.2621896567360796, -0.021753176741935696, -0.10635060295684638, -0.13497008538904562, 0.44579324647592206, 0.19792519344650594, 0.22557856301901882, 0.09142240461024466, 0.25561385444778295, 0.06113661823736638, 0.057947920131276254, 0.14880644682237937, 0.22172632282743826, 0.09601440889876067, 0.11369182326898132, -0.164671200356379, 0.0759932441758736, 0.1115337039353614]
|
1,803.0449
|
Prethermal quantum many-body Kapitza phases of periodically driven spin
systems
|
As realized by Kapitza long ago, a rigid pendulum can be stabilized upside
down by periodically driving its suspension point with tuned amplitude and
frequency. While this dynamical stabilization is feasible in a variety of
instances in systems with few degrees of freedom, it is natural to search for
generalizations to multi-particle systems. In particular, a fundamental
question is whether, by periodically driving a single parameter in a many-body
system, one can stabilize an otherwise unstable phase of matter against all
possible fluctuations of its microscopic degrees of freedom. In this work we
show that such stabilization occurs in experimentally realizable quantum
many-body systems: a periodic modulation of a transverse magnetic field can
make ferromagnetic spin systems with long-range interactions stably trapped
around unstable paramagnetic configurations as well as in other unconventional
dynamical phases with no equilibrium counterparts. We demonstrate that these
quantum Kapitza phases have a long lifetime and can be observed in current
experiments with trapped ions.
|
cond-mat.stat-mech
|
as realized by kapitza long ago a rigid pendulum can be stabilized upside down by periodically driving its suspension point with tuned amplitude and frequency while this dynamical stabilization is feasible in a variety of instances in systems with few degrees of freedom it is natural to search for generalizations to multiparticle systems in particular a fundamental question is whether by periodically driving a single parameter in a manybody system one can stabilize an otherwise unstable phase of matter against all possible fluctuations of its microscopic degrees of freedom in this work we show that such stabilization occurs in experimentally realizable quantum manybody systems a periodic modulation of a transverse magnetic field can make ferromagnetic spin systems with longrange interactions stably trapped around unstable paramagnetic configurations as well as in other unconventional dynamical phases with no equilibrium counterparts we demonstrate that these quantum kapitza phases have a long lifetime and can be observed in current experiments with trapped ions
|
[['as', 'realized', 'by', 'kapitza', 'long', 'ago', 'a', 'rigid', 'pendulum', 'can', 'be', 'stabilized', 'upside', 'down', 'by', 'periodically', 'driving', 'its', 'suspension', 'point', 'with', 'tuned', 'amplitude', 'and', 'frequency', 'while', 'this', 'dynamical', 'stabilization', 'is', 'feasible', 'in', 'a', 'variety', 'of', 'instances', 'in', 'systems', 'with', 'few', 'degrees', 'of', 'freedom', 'it', 'is', 'natural', 'to', 'search', 'for', 'generalizations', 'to', 'multiparticle', 'systems', 'in', 'particular', 'a', 'fundamental', 'question', 'is', 'whether', 'by', 'periodically', 'driving', 'a', 'single', 'parameter', 'in', 'a', 'manybody', 'system', 'one', 'can', 'stabilize', 'an', 'otherwise', 'unstable', 'phase', 'of', 'matter', 'against', 'all', 'possible', 'fluctuations', 'of', 'its', 'microscopic', 'degrees', 'of', 'freedom', 'in', 'this', 'work', 'we', 'show', 'that', 'such', 'stabilization', 'occurs', 'in', 'experimentally', 'realizable', 'quantum', 'manybody', 'systems', 'a', 'periodic', 'modulation', 'of', 'a', 'transverse', 'magnetic', 'field', 'can', 'make', 'ferromagnetic', 'spin', 'systems', 'with', 'longrange', 'interactions', 'stably', 'trapped', 'around', 'unstable', 'paramagnetic', 'configurations', 'as', 'well', 'as', 'in', 'other', 'unconventional', 'dynamical', 'phases', 'with', 'no', 'equilibrium', 'counterparts', 'we', 'demonstrate', 'that', 'these', 'quantum', 'kapitza', 'phases', 'have', 'a', 'long', 'lifetime', 'and', 'can', 'be', 'observed', 'in', 'current', 'experiments', 'with', 'trapped', 'ions']]
|
[-0.21510426438876293, 0.27846764206511787, -0.06773414992640943, 0.023848654635678156, -0.010299389780668905, -0.183332496781031, 0.03568620640351056, 0.38108917380794416, -0.3006940594487736, -0.28500167672879667, 0.095322749693878, -0.23653261810716875, -0.13829060315898378, 0.20466322158814743, -0.019122472675751498, 0.056521733753447594, 0.026932842039602733, 0.018329301712823647, -0.046665346656659466, -0.20943536214624187, 0.25458085940237035, 0.012478914872579183, 0.2149524822708149, 0.025300095601698512, 0.07657732416834659, -0.00334623555430217, 0.143953258608052, 0.0430665189786902, -0.09605665297041363, 0.007030896971341949, 0.26539775163942725, -0.018228836050081364, 0.23501617937061772, -0.43338148459885856, -0.2533770662457126, 0.10877266688178638, 0.18148578903725687, 0.18709475524744032, -0.05588835814811067, -0.2964437129561725, 0.04381921551916631, -0.17756036321087829, -0.19368795580494236, -0.14546976646748166, 0.04346729349418186, 0.021951361353957217, -0.2271137086282127, 0.07984024814605338, 0.06966566727474718, 0.08256680617085511, -0.047049156760609466, -0.02041182367339051, -0.03275707402008837, 0.08449351335963551, 0.020597082901008007, 0.03130998681504397, 0.15824269434823743, -0.1335282417873811, -0.16683888868809496, 0.39952690090468657, -0.06872626118327263, -0.18852258800183358, 0.2216456782031781, -0.12587944453647332, -0.08113821961407391, 0.1533534440135806, 0.1423268415935853, 0.10410368310817687, -0.12988677255607228, 0.05346909211289559, -0.008611573859662661, 0.21599655342233404, 0.06815548615278266, 0.0657993749980434, 0.3136586848201242, 0.2026866158959602, 0.07842653174006012, 0.15571608221482006, -0.037801121542491635, -0.16009835738713601, -0.2241708389336958, -0.11079482129868706, -0.18477339281425256, 0.07417356671103563, -0.029221492599653454, -0.1596401877724712, 0.3811540508923947, 0.14460117318052448, 0.18522526551647195, -0.05808470861945278, 0.27487336791196615, 0.09115053646035094, 0.05745653868887175, 0.026050850154492277, 0.2830828564554898, 0.1197053150774665, 0.07445061308003774, -0.24013496983251623, 0.012384749243164775, 0.003553383192926083]
|
1,803.04491
|
An Algorithm for the Tropical Realizability Problem for Families of
Curves
|
Given a tropical fan curve $\Sigma$ and a family of algebraic curves $X
\rightarrow \mathbb{A}^k$ we define the realization locus $\mathop{Real}_\Sigma
\subseteq \mathbb{A}^k$ as the set of fibers $X_a$ whose tropicalization is
$\Sigma$. We produce an algorithm that describes the Zariski closure of
$\mathop{Real}_\Sigma$ by imposing algebraic conditions for each ray of
$\Sigma$.
|
math.AG
|
given a tropical fan curve sigma and a family of algebraic curves x rightarrow mathbbak we define the realization locus mathopreal_sigma subseteq mathbbak as the set of fibers x_a whose tropicalization is sigma we produce an algorithm that describes the zariski closure of mathopreal_sigma by imposing algebraic conditions for each ray of sigma
|
[['given', 'a', 'tropical', 'fan', 'curve', 'sigma', 'and', 'a', 'family', 'of', 'algebraic', 'curves', 'x', 'rightarrow', 'mathbbak', 'we', 'define', 'the', 'realization', 'locus', 'mathopreal_sigma', 'subseteq', 'mathbbak', 'as', 'the', 'set', 'of', 'fibers', 'x_a', 'whose', 'tropicalization', 'is', 'sigma', 'we', 'produce', 'an', 'algorithm', 'that', 'describes', 'the', 'zariski', 'closure', 'of', 'mathopreal_sigma', 'by', 'imposing', 'algebraic', 'conditions', 'for', 'each', 'ray', 'of', 'sigma']]
|
[-0.21077452950617848, 0.04560650393221865, -0.11113649226871192, 0.04154406898134552, -0.08863632000672321, -0.12176180658998954, 0.0579569457103407, 0.31912628941091836, -0.3495355309443731, -0.12515404683482997, 0.04679682375132745, -0.2596514820322102, -0.1451791184863039, 0.22480262717341676, -0.16873601097248347, 0.0019545784567062763, 0.014781637372923833, 0.06547765473962999, -0.1109501811922254, -0.2398313876679715, 0.37232757703054187, -0.06972198177348166, 0.19000366233362287, -0.023168292590508274, 0.18013872323082944, 0.007234491343956952, 0.029650676447679016, 0.004222005770048674, -0.2031425180779051, 0.09889875605757184, 0.2844736626116084, 0.21925754169476966, 0.08225336018036686, -0.3336040712710397, -0.139541790966748, 0.27003094756647067, 0.10540653902160771, -0.05194979392941676, 0.06055693631516952, -0.23977561167203912, 0.11726525582957502, -0.1024654334885817, -0.22892351925153942, -0.030620091247792337, 0.0850752026353981, 0.044027665118668596, -0.2383273812809813, -0.10404500474824625, 0.10242990280191104, 0.18008466287716932, 0.0010396636104868615, -0.1258557100241602, -0.11883898100852236, -0.03711910554956572, -0.07915302664132826, 0.16141300046743423, 0.10830996023016233, -0.08484575937629915, -0.05420622521755742, 0.3871932148273724, -0.07704135914351426, -0.22227790487893656, 0.04286564344211536, -0.14692824187816358, -0.14556734811733751, 0.22096524555601327, 0.10251204837478843, 0.08020611377615555, -0.054347972714287394, 0.21829032231300302, -0.1632644861534822, 0.06919840547968359, 0.0868143137982663, -0.046080269421651665, 0.1536033314415345, 0.15904862153362118, 0.05764832468155552, 0.10617928780551415, -0.06027444278565692, 0.03469482086160604, -0.4565453507444438, -0.16106560948651796, -0.07020410354955268, 0.1685381238987925, -0.11437588358068537, -0.21739038830513463, 0.38607962502568377, -0.028197378102763026, 0.2518965331289698, 0.10238897917317409, 0.18028210751328835, 0.0806561846827618, -0.045549328311109076, 0.07965685676454622, 0.07324039095136173, 0.20618182627995515, -0.08364678174257278, -0.18153540755384692, 0.015992385360375776, 0.1990233538672328]
|
1,803.04492
|
Compressible fluids and active potentials
|
We consider a class of one dimensional compressible systems with degenerate
diffusion coefficients. We establish the fact that the solutions remain smooth
as long as the diffusion coefficients do not vanish, and give local and global
existence results. The models include the barotropic compressible Navier-Stokes
equations, shallow water systems and the lubrication approximation of slender
jets. In all these models the momentum equation is forced by the gradient of a
solution-dependent potential: the active potential. The method of proof uses
the Bresch-Desjardins entropy and the analysis of the evolution of the active
potential.
|
math.AP
|
we consider a class of one dimensional compressible systems with degenerate diffusion coefficients we establish the fact that the solutions remain smooth as long as the diffusion coefficients do not vanish and give local and global existence results the models include the barotropic compressible navierstokes equations shallow water systems and the lubrication approximation of slender jets in all these models the momentum equation is forced by the gradient of a solutiondependent potential the active potential the method of proof uses the breschdesjardins entropy and the analysis of the evolution of the active potential
|
[['we', 'consider', 'a', 'class', 'of', 'one', 'dimensional', 'compressible', 'systems', 'with', 'degenerate', 'diffusion', 'coefficients', 'we', 'establish', 'the', 'fact', 'that', 'the', 'solutions', 'remain', 'smooth', 'as', 'long', 'as', 'the', 'diffusion', 'coefficients', 'do', 'not', 'vanish', 'and', 'give', 'local', 'and', 'global', 'existence', 'results', 'the', 'models', 'include', 'the', 'barotropic', 'compressible', 'navierstokes', 'equations', 'shallow', 'water', 'systems', 'and', 'the', 'lubrication', 'approximation', 'of', 'slender', 'jets', 'in', 'all', 'these', 'models', 'the', 'momentum', 'equation', 'is', 'forced', 'by', 'the', 'gradient', 'of', 'a', 'solutiondependent', 'potential', 'the', 'active', 'potential', 'the', 'method', 'of', 'proof', 'uses', 'the', 'breschdesjardins', 'entropy', 'and', 'the', 'analysis', 'of', 'the', 'evolution', 'of', 'the', 'active', 'potential']]
|
[-0.15288717270342875, 0.09301531007108481, -0.06663593388688953, 0.05469742064166855, -0.038844103251745844, -0.14079001184512416, -0.043357793155693165, 0.2611273004592437, -0.3018457788731093, -0.24133337594811682, 0.13398910297774305, -0.2698455107924731, -0.12824370087954498, 0.15444046531363553, 0.01175650456191405, 0.09621513024498911, 0.06018740033892834, -0.011203145436213716, -0.05466388802945047, -0.22442832953833125, 0.3527221915251373, -0.01476973592830093, 0.22589693368291078, 0.02693332211397912, 0.1369956405225979, -0.03279779408046085, -0.02360652249974563, 0.03246563809048182, -0.16875917720901032, 0.08882231727930838, 0.20182020858326566, 0.007923681835603455, 0.27874172652066237, -0.45218966538896377, -0.2852164030773565, 0.09417537810361903, 0.13103429196179722, 0.14224064537404996, -0.04261575983432324, -0.2222176403839789, 0.039002826085577355, -0.15417020257724368, -0.1972153604577255, -0.10009549160325981, -0.01900371469800239, 0.12544832142758305, -0.24022909546392443, 0.17495480846770553, 0.13704895308119772, 0.01717508288667254, -0.17210136840889312, -0.09532491419889519, -0.04474653154814049, 0.10958055382513482, 0.0721991514349255, -0.02442274220874938, 0.09834245289675891, -0.17983320360579694, -0.05736547162371647, 0.39255045686403045, -0.11839146502113537, -0.27574801159789786, 0.2476891474393399, -0.12920797443644993, -0.07089320454827469, 0.1455663055556057, 0.20516945027402075, 0.15737339279756113, -0.12090842831505301, 0.10334004591087527, -0.07836744083982447, 0.13431765210976743, 0.020176737154226587, -0.03590405573967197, 0.17088904205466743, 0.14616253265969292, 0.08536691488141356, 0.08004756461393656, -0.0885169535689309, -0.1321177221075429, -0.34214518599860044, -0.17504191726101967, -0.1345380869828185, 0.06013563008483565, -0.09236126383905724, -0.21687933501949452, 0.3665449076494121, 0.11029754510420152, 0.16745309927500784, 0.06574373989892394, 0.28700788181943254, 0.14025159993806205, 0.021078690982666678, 0.12405589535331313, 0.27265610867787315, 0.17162102694957235, 0.16663587769574445, -0.2290918710464409, 0.039692147503323526, 0.16416872820169057]
|
1,803.04493
|
Particle Identification In Camera Image Sensors Using Computer Vision
|
We present a deep learning, computer vision algorithm constructed for the
purposes of identifying and classifying charged particles in camera image
sensors. We apply our algorithm to data collected by the Distributed Electronic
Cosmic-ray Observatory (DECO), a global network of smartphones that monitors
camera image sensors for the signatures of cosmic rays and other energetic
particles, such as those produced by radioactive decays. The algorithm, whose
core component is a convolutional neural network, achieves classification
performance comparable to human quality across four distinct DECO event
topologies. We apply our model to the entire DECO data set and determine a
selection that achieves $\ge90\%$ purity for all event types. In particular, we
estimate a purity of $95\%$ when applied to cosmic-ray muons. The automated
classification is run on the public DECO data set in real time in order to
provide classified particle interaction images to users of the app and other
interested members of the public.
|
astro-ph.IM physics.data-an
|
we present a deep learning computer vision algorithm constructed for the purposes of identifying and classifying charged particles in camera image sensors we apply our algorithm to data collected by the distributed electronic cosmicray observatory deco a global network of smartphones that monitors camera image sensors for the signatures of cosmic rays and other energetic particles such as those produced by radioactive decays the algorithm whose core component is a convolutional neural network achieves classification performance comparable to human quality across four distinct deco event topologies we apply our model to the entire deco data set and determine a selection that achieves ge90 purity for all event types in particular we estimate a purity of 95 when applied to cosmicray muons the automated classification is run on the public deco data set in real time in order to provide classified particle interaction images to users of the app and other interested members of the public
|
[['we', 'present', 'a', 'deep', 'learning', 'computer', 'vision', 'algorithm', 'constructed', 'for', 'the', 'purposes', 'of', 'identifying', 'and', 'classifying', 'charged', 'particles', 'in', 'camera', 'image', 'sensors', 'we', 'apply', 'our', 'algorithm', 'to', 'data', 'collected', 'by', 'the', 'distributed', 'electronic', 'cosmicray', 'observatory', 'deco', 'a', 'global', 'network', 'of', 'smartphones', 'that', 'monitors', 'camera', 'image', 'sensors', 'for', 'the', 'signatures', 'of', 'cosmic', 'rays', 'and', 'other', 'energetic', 'particles', 'such', 'as', 'those', 'produced', 'by', 'radioactive', 'decays', 'the', 'algorithm', 'whose', 'core', 'component', 'is', 'a', 'convolutional', 'neural', 'network', 'achieves', 'classification', 'performance', 'comparable', 'to', 'human', 'quality', 'across', 'four', 'distinct', 'deco', 'event', 'topologies', 'we', 'apply', 'our', 'model', 'to', 'the', 'entire', 'deco', 'data', 'set', 'and', 'determine', 'a', 'selection', 'that', 'achieves', 'ge90', 'purity', 'for', 'all', 'event', 'types', 'in', 'particular', 'we', 'estimate', 'a', 'purity', 'of', '95', 'when', 'applied', 'to', 'cosmicray', 'muons', 'the', 'automated', 'classification', 'is', 'run', 'on', 'the', 'public', 'deco', 'data', 'set', 'in', 'real', 'time', 'in', 'order', 'to', 'provide', 'classified', 'particle', 'interaction', 'images', 'to', 'users', 'of', 'the', 'app', 'and', 'other', 'interested', 'members', 'of', 'the', 'public']]
|
[-0.08176356062214435, 0.06379940054324976, -0.067097089239759, 0.09245815455713975, -0.059733800263470645, -0.1420097938003986, 0.022338557955143706, 0.4139011094419213, -0.2342259387981582, -0.3784220052638103, 0.06299327076533688, -0.33239191052104744, -0.0875270340592425, 0.22709219507470896, -0.06489187395865363, 0.05472083789842239, 0.14795123426963322, 0.048775988941076334, 0.013583121032079126, -0.3034134225484419, 0.2663162786811409, 0.07626017266956261, 0.30784514950370634, -0.020606584456906496, 0.14091989648597944, 0.017764829126392285, -0.07454186661906734, -0.008195641488381117, -0.07653372209905057, 0.12872896342108922, 0.3100723219349793, 0.2202124906848971, 0.2201807318593968, -0.40099571526727895, -0.1810167112278861, 0.1253062895644311, 0.13819535201197708, 0.04983048864856495, -0.07480733374257681, -0.3590534072998282, 0.11847544582439708, -0.17271490558976604, -0.059357631854809724, -0.058386912493809284, -0.0157409582624224, 0.021410389531210258, -0.28187468524586845, 0.0017028160623044936, -0.015380161346840466, 0.07895253579186687, -0.0836707140899614, -0.07586214788463692, 0.009401116659260028, 0.1839699377421087, 0.02574307050226353, 0.03568919057135361, 0.20713823948617927, -0.16821614402805926, -0.15180612977461092, 0.35867683151615903, -0.03755065510971713, -0.1387257358488663, 0.20319380604831333, -0.08208474016736274, -0.16588898281893366, 0.14373334575075558, 0.26981844883654027, 0.09523671849739629, -0.20484259200795302, 0.01876277960504272, -0.01767900532909802, 0.16442066937830155, 0.02245700293728574, 0.005295562748373909, 0.16144606618773094, 0.23416575884095744, 0.05907392007595098, 0.1223435080164209, -0.1925606734059636, 0.0013872234471287432, -0.23714877558870592, -0.140864478886829, -0.1879951112802358, 0.005497464625158819, -0.09161619202486336, -0.14780726287795182, 0.432562526230785, 0.18723926416223313, 0.16048992283388294, 0.047640070843611625, 0.33156232868907853, -0.039713947278373365, 0.08616635012410145, 0.10239917689982753, 0.21046682062618172, 0.010721160806473586, 0.1393663291131485, -0.1555936599045224, 0.060125620957076936, 0.05881777267401143]
|
1,803.04494
|
Gradient Augmented Information Retrieval with Autoencoders and Semantic
Hashing
|
This paper will explore the use of autoencoders for semantic hashing in the
context of Information Retrieval. This paper will summarize how to efficiently
train an autoencoder in order to create meaningful and low-dimensional
encodings of data. This paper will demonstrate how computing and storing the
closest encodings to an input query can help speed up search time and improve
the quality of our search results. The novel contributions of this paper
involve using the representation of the data learned by an auto-encoder in
order to augment our search query in various ways. I present and evaluate the
new gradient search augmentation (GSA) approach, as well as the more well-known
pseudo-relevance-feedback (PRF) adjustment. I find that GSA helps to improve
the performance of the TF-IDF based information retrieval system, and PRF
combined with GSA works best overall for the systems compared in this paper.
|
cs.IR cs.LG stat.ML
|
this paper will explore the use of autoencoders for semantic hashing in the context of information retrieval this paper will summarize how to efficiently train an autoencoder in order to create meaningful and lowdimensional encodings of data this paper will demonstrate how computing and storing the closest encodings to an input query can help speed up search time and improve the quality of our search results the novel contributions of this paper involve using the representation of the data learned by an autoencoder in order to augment our search query in various ways i present and evaluate the new gradient search augmentation gsa approach as well as the more wellknown pseudorelevancefeedback prf adjustment i find that gsa helps to improve the performance of the tfidf based information retrieval system and prf combined with gsa works best overall for the systems compared in this paper
|
[['this', 'paper', 'will', 'explore', 'the', 'use', 'of', 'autoencoders', 'for', 'semantic', 'hashing', 'in', 'the', 'context', 'of', 'information', 'retrieval', 'this', 'paper', 'will', 'summarize', 'how', 'to', 'efficiently', 'train', 'an', 'autoencoder', 'in', 'order', 'to', 'create', 'meaningful', 'and', 'lowdimensional', 'encodings', 'of', 'data', 'this', 'paper', 'will', 'demonstrate', 'how', 'computing', 'and', 'storing', 'the', 'closest', 'encodings', 'to', 'an', 'input', 'query', 'can', 'help', 'speed', 'up', 'search', 'time', 'and', 'improve', 'the', 'quality', 'of', 'our', 'search', 'results', 'the', 'novel', 'contributions', 'of', 'this', 'paper', 'involve', 'using', 'the', 'representation', 'of', 'the', 'data', 'learned', 'by', 'an', 'autoencoder', 'in', 'order', 'to', 'augment', 'our', 'search', 'query', 'in', 'various', 'ways', 'i', 'present', 'and', 'evaluate', 'the', 'new', 'gradient', 'search', 'augmentation', 'gsa', 'approach', 'as', 'well', 'as', 'the', 'more', 'wellknown', 'pseudorelevancefeedback', 'prf', 'adjustment', 'i', 'find', 'that', 'gsa', 'helps', 'to', 'improve', 'the', 'performance', 'of', 'the', 'tfidf', 'based', 'information', 'retrieval', 'system', 'and', 'prf', 'combined', 'with', 'gsa', 'works', 'best', 'overall', 'for', 'the', 'systems', 'compared', 'in', 'this', 'paper']]
|
[-0.015549221107820895, -0.0061517471055397635, -0.08045563618554831, 0.06799973144563944, -0.13294066120935538, -0.10196936401453885, 0.07381814356056413, 0.39039933155258216, -0.3097037834512604, -0.3632680130178122, 0.07727514304972899, -0.26877060014806636, -0.19499993185558312, 0.17390838182381504, -0.1214102952239605, 0.07147107347585521, 0.10047282513647855, 0.04797357470090208, -0.10074969833892594, -0.307354483466882, 0.329620933065751, 0.10506032472411236, 0.2917288734375701, 0.00393248340961608, 0.0690565833046876, 0.004113009684699607, -0.08423333097627009, -0.036663294059934314, -0.10505742563604958, 0.22267661824614018, 0.30900505872710343, 0.2462966757650663, 0.28392714246378076, -0.4046603014783843, -0.1944093180794295, 0.0920209787733905, 0.194318596757232, 0.13447719688651028, -0.06251588883743396, -0.31537542046679484, 0.06788048583043778, -0.1647759495234651, -0.001015391168217142, -0.19275849877198883, -0.04967540405996769, 0.020187801411180022, -0.28616428079565503, -0.02745456747398093, 0.09393591241718928, -0.0034532070410652803, -0.041688658241703584, -0.09722619459368419, 0.06596463658583149, 0.15989774817574795, 0.015940083122414933, 0.09887950504166107, 0.06894467009143526, -0.1466140047434072, -0.1937723520140235, 0.3945835135304011, -0.09497711648578411, -0.19674938131560135, 0.1834622333624068, -0.026972828025871014, -0.1502197944018599, 0.07015307750748274, 0.25433093164168247, 0.10925555299219376, -0.19177967102846483, 0.006081478642153235, -0.01170129099717507, 0.21865613548853596, 0.05315471422969253, 0.046119109125827416, 0.1365672521882567, 0.23435053303577266, 0.07087786597284404, 0.19116563177537305, -0.13581456156721064, -0.03664646179812563, -0.2402275881292038, -0.18988106412800043, -0.18683483116849736, -0.06878116775395257, -0.1036724110024654, -0.11062435381817859, 0.41605983545899494, 0.3278090555279457, 0.20876362042540644, 0.08509214043656325, 0.35531949802868434, 0.046499444451834356, 0.06170718648008533, 0.08156060417658174, 0.19617249091461927, 0.013040191511838483, 0.1571859160333976, -0.19783286416864063, 0.07399527647962365, 0.04350952296094461]
|
1,803.04495
|
Phase Based Alignment and Improved Projection Matching of Parallel Beam
Tomography Data
|
Tomography is an imaging technique that works by reconstructing a scene from
acquired data in the form of line integrals of the imaging domain. A
fundamental underlying assumption in the reconstruction procedure is the
precise alignment of the data values, i.e. the relationship between the data
values and the paths of the lines of integration is accurately known. In many
applications, e.g. electron and X-ray tomography, it is necessary to establish
this relationship using software alignment techniques or image registration due
to misalignment when rotating the physical specimen. Unfortunately, highly
accurate software alignment is still a challenge to achieve in many cases, and
improper alignment results in severe loss in the imaging resolution. In this
article, we develop a new approach that considers the alignment problem through
a completely different lens, as a problem of recovering phase shifts in Fourier
domain {within} the reconstruction algorithm. The recovery of these phase
shifts serves as the data alignment, which is done by calculating discrepancies
between the misaligned data and the current reconstruction. In the development
of the approach, we investigate proper selection of parameters, and we show
that it is fairly flexible and surprisingly accurate. Finally, the analysis of
our approach provides insight into why projection matching alignment by
cross-correlation can be improved through low pass filtering, which we
demonstrate. Our methods are validated in a wide range of examples and
settings.
|
math.NA
|
tomography is an imaging technique that works by reconstructing a scene from acquired data in the form of line integrals of the imaging domain a fundamental underlying assumption in the reconstruction procedure is the precise alignment of the data values ie the relationship between the data values and the paths of the lines of integration is accurately known in many applications eg electron and xray tomography it is necessary to establish this relationship using software alignment techniques or image registration due to misalignment when rotating the physical specimen unfortunately highly accurate software alignment is still a challenge to achieve in many cases and improper alignment results in severe loss in the imaging resolution in this article we develop a new approach that considers the alignment problem through a completely different lens as a problem of recovering phase shifts in fourier domain within the reconstruction algorithm the recovery of these phase shifts serves as the data alignment which is done by calculating discrepancies between the misaligned data and the current reconstruction in the development of the approach we investigate proper selection of parameters and we show that it is fairly flexible and surprisingly accurate finally the analysis of our approach provides insight into why projection matching alignment by crosscorrelation can be improved through low pass filtering which we demonstrate our methods are validated in a wide range of examples and settings
|
[['tomography', 'is', 'an', 'imaging', 'technique', 'that', 'works', 'by', 'reconstructing', 'a', 'scene', 'from', 'acquired', 'data', 'in', 'the', 'form', 'of', 'line', 'integrals', 'of', 'the', 'imaging', 'domain', 'a', 'fundamental', 'underlying', 'assumption', 'in', 'the', 'reconstruction', 'procedure', 'is', 'the', 'precise', 'alignment', 'of', 'the', 'data', 'values', 'ie', 'the', 'relationship', 'between', 'the', 'data', 'values', 'and', 'the', 'paths', 'of', 'the', 'lines', 'of', 'integration', 'is', 'accurately', 'known', 'in', 'many', 'applications', 'eg', 'electron', 'and', 'xray', 'tomography', 'it', 'is', 'necessary', 'to', 'establish', 'this', 'relationship', 'using', 'software', 'alignment', 'techniques', 'or', 'image', 'registration', 'due', 'to', 'misalignment', 'when', 'rotating', 'the', 'physical', 'specimen', 'unfortunately', 'highly', 'accurate', 'software', 'alignment', 'is', 'still', 'a', 'challenge', 'to', 'achieve', 'in', 'many', 'cases', 'and', 'improper', 'alignment', 'results', 'in', 'severe', 'loss', 'in', 'the', 'imaging', 'resolution', 'in', 'this', 'article', 'we', 'develop', 'a', 'new', 'approach', 'that', 'considers', 'the', 'alignment', 'problem', 'through', 'a', 'completely', 'different', 'lens', 'as', 'a', 'problem', 'of', 'recovering', 'phase', 'shifts', 'in', 'fourier', 'domain', 'within', 'the', 'reconstruction', 'algorithm', 'the', 'recovery', 'of', 'these', 'phase', 'shifts', 'serves', 'as', 'the', 'data', 'alignment', 'which', 'is', 'done', 'by', 'calculating', 'discrepancies', 'between', 'the', 'misaligned', 'data', 'and', 'the', 'current', 'reconstruction', 'in', 'the', 'development', 'of', 'the', 'approach', 'we', 'investigate', 'proper', 'selection', 'of', 'parameters', 'and', 'we', 'show', 'that', 'it', 'is', 'fairly', 'flexible', 'and', 'surprisingly', 'accurate', 'finally', 'the', 'analysis', 'of', 'our', 'approach', 'provides', 'insight', 'into', 'why', 'projection', 'matching', 'alignment', 'by', 'crosscorrelation', 'can', 'be', 'improved', 'through', 'low', 'pass', 'filtering', 'which', 'we', 'demonstrate', 'our', 'methods', 'are', 'validated', 'in', 'a', 'wide', 'range', 'of', 'examples', 'and', 'settings']]
|
[-0.09254223717700528, 0.023938640275446232, -0.0935107239215817, 0.06932837982996083, -0.06823702466006026, -0.09399161847895417, 0.03387668114155531, 0.42150311531580015, -0.2861459585514082, -0.3593855858092075, 0.13821527643392428, -0.23079235970811998, -0.16657163764086916, 0.21165306479366416, -0.07778936701209244, 0.04950970957048095, 0.11015687017580089, -0.03211527626313593, -0.10333168168106804, -0.1865720209601062, 0.28633745216676565, 0.052567295651154027, 0.3259207645633622, 0.033358383226289375, 0.10169309289144028, 0.05119693579719119, -0.05859135660828779, 0.017343559843523152, -0.09949967085998176, 0.14500835120409686, 0.27543634615709456, 0.16954987046268324, 0.24820520083986872, -0.38154079095339, -0.21679254495684291, 0.06735493180230664, 0.1569027010991071, 0.1241176408280254, -0.07875158567305492, -0.2603898499687405, 0.06610089057509828, -0.09746196219791232, -0.07891154812203477, -0.08525470951167138, -0.017114348328955795, -0.010705621087032815, -0.2947006724531884, 0.08437237210733735, 0.047291860223302375, 0.06334196630379428, -0.07564496969875029, -0.0614345534471795, 0.034800120987726946, 0.15637699310609604, 0.03132585310478411, 0.052926423892115604, 0.09189814667055464, -0.15274206048647024, -0.08660530094361013, 0.4021231585323973, -0.02349887804669814, -0.22663911157895042, 0.1805834433833218, -0.12227187036718849, -0.12145207797217628, 0.15130012529983144, 0.16144039992486006, 0.12249741706207556, -0.1622800047411305, 0.04742471422916582, -0.0046404505993806475, 0.1633090072189984, 0.05578944229460119, 0.0015207524001395896, 0.18806588609055008, 0.19499669268919642, 0.06179241344241587, 0.13076917828976825, -0.16127584701727915, -0.05536133289620604, -0.25728264852254856, -0.13678614953823853, -0.19988743706160914, -0.008246480615343898, -0.07294712960473312, -0.15022855064548227, 0.38820198516098214, 0.1945914461378656, 0.22497205794181513, 0.0065478006185477845, 0.39155061745449254, 0.07164131562711427, 0.07456666601862272, 0.012766518221115289, 0.24624047109543387, 0.11067353940325911, 0.08575538256327095, -0.19884083521977552, 0.08340627937577665, 0.0051628145409505005]
|
1,803.04496
|
Observation of proton-tagged, central (semi)exclusive production of
high-mass lepton pairs in pp collisions at 13 TeV with the CMS-TOTEM
precision proton spectrometer
|
The process pp$\to\ell^+\ell^-$p$^{(*)}$, with $\ell^+\ell^-$ a muon or an
electron pair produced at midrapidity with mass larger than 110 GeV, has been
observed for the first time at the LHC in pp collisions at $\sqrt{s} =$ 13 TeV.
One of the two scattered protons is measured in the CMS-TOTEM precision proton
spectrometer (CT-PPS), which operated for the first time in 2016. The second
proton either remains intact or is excited and then dissociates into a low-mass
state p$^{*}$, which is undetected. The measurement is based on an integrated
luminosity of 9.4 fb$^{-1}$ collected during standard, high-luminosity LHC
operation. A total of 12 $\mu^+\mu^-$ and 8 e$^+$e$^-$ pairs with
$m(\ell^{+}\ell^{-}) >$ 110 GeV, and matching forward proton kinematics, are
observed, with expected backgrounds of 1.49 $\pm$ 0.07 (stat) $\pm$ 0.53 (syst)
and 2.36 $\pm$ 0.09 (stat) $\pm$ 0.47 (syst), respectively. This corresponds to
an excess of more than five standard deviations over the expected background.
The present result constitutes the first observation of proton-tagged
$\gamma\gamma$ collisions at the electroweak scale. This measurement also
demonstrates that CT-PPS performs according to the design specifications.
|
hep-ex
|
the process pptoellellp with ellell a muon or an electron pair produced at midrapidity with mass larger than 110 gev has been observed for the first time at the lhc in pp collisions at sqrts 13 tev one of the two scattered protons is measured in the cmstotem precision proton spectrometer ctpps which operated for the first time in 2016 the second proton either remains intact or is excited and then dissociates into a lowmass state p which is undetected the measurement is based on an integrated luminosity of 94 fb1 collected during standard highluminosity lhc operation a total of 12 mumu and 8 ee pairs with mellell 110 gev and matching forward proton kinematics are observed with expected backgrounds of 149 pm 007 stat pm 053 syst and 236 pm 009 stat pm 047 syst respectively this corresponds to an excess of more than five standard deviations over the expected background the present result constitutes the first observation of protontagged gammagamma collisions at the electroweak scale this measurement also demonstrates that ctpps performs according to the design specifications
|
[['the', 'process', 'pptoellellp', 'with', 'ellell', 'a', 'muon', 'or', 'an', 'electron', 'pair', 'produced', 'at', 'midrapidity', 'with', 'mass', 'larger', 'than', '110', 'gev', 'has', 'been', 'observed', 'for', 'the', 'first', 'time', 'at', 'the', 'lhc', 'in', 'pp', 'collisions', 'at', 'sqrts', '13', 'tev', 'one', 'of', 'the', 'two', 'scattered', 'protons', 'is', 'measured', 'in', 'the', 'cmstotem', 'precision', 'proton', 'spectrometer', 'ctpps', 'which', 'operated', 'for', 'the', 'first', 'time', 'in', '2016', 'the', 'second', 'proton', 'either', 'remains', 'intact', 'or', 'is', 'excited', 'and', 'then', 'dissociates', 'into', 'a', 'lowmass', 'state', 'p', 'which', 'is', 'undetected', 'the', 'measurement', 'is', 'based', 'on', 'an', 'integrated', 'luminosity', 'of', '94', 'fb1', 'collected', 'during', 'standard', 'highluminosity', 'lhc', 'operation', 'a', 'total', 'of', '12', 'mumu', 'and', '8', 'ee', 'pairs', 'with', 'mellell', '110', 'gev', 'and', 'matching', 'forward', 'proton', 'kinematics', 'are', 'observed', 'with', 'expected', 'backgrounds', 'of', '149', 'pm', '007', 'stat', 'pm', '053', 'syst', 'and', '236', 'pm', '009', 'stat', 'pm', '047', 'syst', 'respectively', 'this', 'corresponds', 'to', 'an', 'excess', 'of', 'more', 'than', 'five', 'standard', 'deviations', 'over', 'the', 'expected', 'background', 'the', 'present', 'result', 'constitutes', 'the', 'first', 'observation', 'of', 'protontagged', 'gammagamma', 'collisions', 'at', 'the', 'electroweak', 'scale', 'this', 'measurement', 'also', 'demonstrates', 'that', 'ctpps', 'performs', 'according', 'to', 'the', 'design', 'specifications']]
|
[-0.049275185635950056, 0.21879935943306816, -0.07897353278588777, 0.07969231913851367, 0.025480494438539034, -0.12114808805540762, -0.0009895818094896563, 0.34412482459661103, -0.15698483772312768, -0.39154398395537826, 0.0008291777155604108, -0.3938687587588352, 0.14186441777589714, 0.1743607816212721, 0.025335951518835657, 0.06660413149333859, 0.09275373187189721, -0.01040539306477975, -0.07337117541086792, -0.2120973497134565, 0.1649396357917106, 0.12860902208613895, 0.24865269930086145, 0.0719949368671593, 0.08817632393885172, 0.030836803667605092, -0.033241165870284965, -0.12171491021774664, -0.12643701554820141, 0.056829752077908906, 0.24491416669640123, 0.042485705559078796, 0.16015629070392667, -0.2892400387853468, -0.019371448410938016, 0.15080977593335496, 0.15215550585897208, -0.011475013140825776, -0.012865400945226462, -0.31352633932384394, 0.16570639231184361, -0.1962652877446706, -0.08577469413531787, 0.09091807521569233, 0.048355729590133255, -0.12555432791090282, -0.32440081429878237, 0.15911725371938573, -0.044986425051505095, 0.0741602443849226, -0.0446590064448636, -0.22097563511023752, -0.07112063651793968, -0.052334464779307824, 0.05426384050298435, 0.16635179128774077, 0.16528652432127355, -0.05902664893369932, -0.18506710434143067, 0.3845850765115797, -0.054546144268229485, -0.07001411570315012, 0.14364411934584084, -0.223227166952197, -0.13706404808448303, 0.22224997100814925, 0.21347306213752068, 0.07125266370926536, -0.22741310064165227, 0.036360584027107574, 0.007063751082635869, 0.2646956065325014, 0.09596339727246786, 0.02396909980495371, 0.20903280044321793, 0.22904116353270004, 0.022592061984557973, 0.038567671333281796, -0.21211078856535004, -0.006805825149798292, -0.3826452113581442, -0.09494069005437669, -0.07517439941415237, 0.10389975038301831, -0.04237420694069874, 0.007882982297763649, 0.31705558080078855, 0.08888865594678469, 0.292094850896905, 0.02778182295151055, 0.27607891115617783, 0.11056950038541002, 0.03870065499354933, 0.11205723881096669, 0.33635277924059476, 0.1451151810828491, 0.17849658058606363, -0.1811219336187775, 0.04092016266027497, -0.01595257046648254]
|
1,803.04497
|
Automated software vulnerability detection with machine learning
|
Thousands of security vulnerabilities are discovered in production software
each year, either reported publicly to the Common Vulnerabilities and Exposures
database or discovered internally in proprietary code. Vulnerabilities often
manifest themselves in subtle ways that are not obvious to code reviewers or
the developers themselves. With the wealth of open source code available for
analysis, there is an opportunity to learn the patterns of bugs that can lead
to security vulnerabilities directly from data. In this paper, we present a
data-driven approach to vulnerability detection using machine learning,
specifically applied to C and C++ programs. We first compile a large dataset of
hundreds of thousands of open-source functions labeled with the outputs of a
static analyzer. We then compare methods applied directly to source code with
methods applied to artifacts extracted from the build process, finding that
source-based models perform better. We also compare the application of deep
neural network models with more traditional models such as random forests and
find the best performance comes from combining features learned by deep models
with tree-based models. Ultimately, our highest performing model achieves an
area under the precision-recall curve of 0.49 and an area under the ROC curve
of 0.87.
|
cs.SE cs.LG stat.ML
|
thousands of security vulnerabilities are discovered in production software each year either reported publicly to the common vulnerabilities and exposures database or discovered internally in proprietary code vulnerabilities often manifest themselves in subtle ways that are not obvious to code reviewers or the developers themselves with the wealth of open source code available for analysis there is an opportunity to learn the patterns of bugs that can lead to security vulnerabilities directly from data in this paper we present a datadriven approach to vulnerability detection using machine learning specifically applied to c and c programs we first compile a large dataset of hundreds of thousands of opensource functions labeled with the outputs of a static analyzer we then compare methods applied directly to source code with methods applied to artifacts extracted from the build process finding that sourcebased models perform better we also compare the application of deep neural network models with more traditional models such as random forests and find the best performance comes from combining features learned by deep models with treebased models ultimately our highest performing model achieves an area under the precisionrecall curve of 049 and an area under the roc curve of 087
|
[['thousands', 'of', 'security', 'vulnerabilities', 'are', 'discovered', 'in', 'production', 'software', 'each', 'year', 'either', 'reported', 'publicly', 'to', 'the', 'common', 'vulnerabilities', 'and', 'exposures', 'database', 'or', 'discovered', 'internally', 'in', 'proprietary', 'code', 'vulnerabilities', 'often', 'manifest', 'themselves', 'in', 'subtle', 'ways', 'that', 'are', 'not', 'obvious', 'to', 'code', 'reviewers', 'or', 'the', 'developers', 'themselves', 'with', 'the', 'wealth', 'of', 'open', 'source', 'code', 'available', 'for', 'analysis', 'there', 'is', 'an', 'opportunity', 'to', 'learn', 'the', 'patterns', 'of', 'bugs', 'that', 'can', 'lead', 'to', 'security', 'vulnerabilities', 'directly', 'from', 'data', 'in', 'this', 'paper', 'we', 'present', 'a', 'datadriven', 'approach', 'to', 'vulnerability', 'detection', 'using', 'machine', 'learning', 'specifically', 'applied', 'to', 'c', 'and', 'c', 'programs', 'we', 'first', 'compile', 'a', 'large', 'dataset', 'of', 'hundreds', 'of', 'thousands', 'of', 'opensource', 'functions', 'labeled', 'with', 'the', 'outputs', 'of', 'a', 'static', 'analyzer', 'we', 'then', 'compare', 'methods', 'applied', 'directly', 'to', 'source', 'code', 'with', 'methods', 'applied', 'to', 'artifacts', 'extracted', 'from', 'the', 'build', 'process', 'finding', 'that', 'sourcebased', 'models', 'perform', 'better', 'we', 'also', 'compare', 'the', 'application', 'of', 'deep', 'neural', 'network', 'models', 'with', 'more', 'traditional', 'models', 'such', 'as', 'random', 'forests', 'and', 'find', 'the', 'best', 'performance', 'comes', 'from', 'combining', 'features', 'learned', 'by', 'deep', 'models', 'with', 'treebased', 'models', 'ultimately', 'our', 'highest', 'performing', 'model', 'achieves', 'an', 'area', 'under', 'the', 'precisionrecall', 'curve', 'of', '049', 'and', 'an', 'area', 'under', 'the', 'roc', 'curve', 'of', '087']]
|
[-0.06597507912536255, -0.01557812850601616, -0.07332279330538115, 0.07443821093341405, -0.10159473159265789, -0.2020499590224871, 0.07334025581590738, 0.4217053976173353, -0.29052324990290357, -0.3601896219058997, 0.1020793945768453, -0.33837904701376276, -0.13526422844146085, 0.24460322599072537, -0.0984181337260801, 0.09181368494973133, 0.11879875160979503, 0.0072551084249609646, -0.05172270446759413, -0.31464907697006833, 0.3045264631862555, 0.08844756477746425, 0.2868509016709985, 0.0071211653806250354, 0.07282361792308759, -0.04053925520580525, -0.053661834990965544, -0.004375053111656635, -0.07080258013420554, 0.15749878860713745, 0.3061247089051242, 0.25301825162964037, 0.2681874519500957, -0.3984775286299562, -0.20224922973041734, 0.08573082697605997, 0.12306481457992711, 0.1001242568500039, -0.02041656985451794, -0.29475604730535937, 0.10263242604648412, -0.20304689268026538, -0.07253142815839377, -0.11418453483804684, -0.026752102177006878, 0.0235227580656147, -0.2328251831990789, 0.0017209889276383558, 0.009324333073119775, 0.12841570921679676, -0.02652822929667309, -0.13102392758703743, -0.020951340552372386, 0.16256395162017356, 0.057380592669660196, 0.07373834589750251, 0.13205262958636563, -0.14848535957939057, -0.15990380207258936, 0.3427585875733069, -0.08535283936112366, -0.15514527058412292, 0.21770499348452296, -0.024845265508206052, -0.15931786981533574, 0.12427722448318482, 0.2397105173805183, 0.09365696449072636, -0.2099077679026364, -0.003611806336780916, -0.0045233928019914665, 0.2411149809980116, 0.015061974328192364, -0.01937646312507415, 0.21750471192515558, 0.17512600334987957, -0.020067295447612803, 0.1708232018452213, -0.11770357434741324, -0.03997421879062636, -0.22818094737018751, -0.08924789361848562, -0.11707969111412607, 0.006170773634765264, -0.06895268736895165, -0.17515331790572317, 0.3971776860164046, 0.2545965316874737, 0.15714368723318772, 0.07431387924765603, 0.33179604756937486, -0.0035937785693077426, 0.1578912091808337, 0.14734064817024076, 0.17150350511450357, -0.007735681850604262, 0.09530538115962033, -0.12144064548486525, 0.11727292583126461, -0.028667212436869365]
|
1,803.04498
|
The signaling signature of the neurotensin type 1 receptor with
endogenous ligands
|
The human neurotensin 1 receptor (hNTS1) is a G protein-coupled receptor
involved in many physiological functions, including analgesia, hypothermia, and
hypotension. To gain a better understanding of which signaling pathways or
combination of pathways are linked to NTS1 activation and function, we
investigated the ability of activated hNTS1, which was stably expressed by
CHO-K1 cells, to directly engage G proteins, activate second messenger cascades
and recruit \b{eta}-arrestins. Using BRET-based biosensors, we found that
neurotensin (NT), NT(8-13) and neuromedin N (NN) activated the G{\alpha}q-,
G{\alpha}i1-, G{\alpha}oA-, and G{\alpha}13-protein signaling pathways as well
as the recruitment of \b{eta}-arrestins 1 and 2. Using pharmacological
inhibitors, we further demonstrated that all three ligands stimulated the
production of inositol phosphate and modulation of cAMP accumulation along with
ERK1/2 activation. Interestingly, despite the functional coupling to
G{\alpha}i1 and G{\alpha}oA, NT was found to produce higher levels of cAMP in
the presence of pertussis toxin, supporting that hNTS1 activation leads to cAMP
accumulation in a G{\alpha}s-dependent manner. Additionally, we demonstrated
that the full activation of ERK1/2 required signaling through both a
PTX-sensitive Gi/o-c-Src signaling pathway and PLCb-DAG-PKC-Raf-1- dependent
pathway downstream of Gq. Finally, the whole-cell integrated signatures
monitored by the cell-based surface plasmon resonance and changes in the
electrical impedance of a confluent cell monolayer led to identical phenotypic
responses between the three ligands. The characterization of the hNTS1-mediated
cellular signaling network will be helpful to accelerate the validation of
potential NTS1 biased ligands with an improved therapeutic/adverse effect
profile.
|
q-bio.CB q-bio.MN q-bio.SC
|
the human neurotensin 1 receptor hnts1 is a g proteincoupled receptor involved in many physiological functions including analgesia hypothermia and hypotension to gain a better understanding of which signaling pathways or combination of pathways are linked to nts1 activation and function we investigated the ability of activated hnts1 which was stably expressed by chok1 cells to directly engage g proteins activate second messenger cascades and recruit betaarrestins using bretbased biosensors we found that neurotensin nt nt813 and neuromedin n nn activated the galphaq galphai1 galphaoa and galpha13protein signaling pathways as well as the recruitment of betaarrestins 1 and 2 using pharmacological inhibitors we further demonstrated that all three ligands stimulated the production of inositol phosphate and modulation of camp accumulation along with erk12 activation interestingly despite the functional coupling to galphai1 and galphaoa nt was found to produce higher levels of camp in the presence of pertussis toxin supporting that hnts1 activation leads to camp accumulation in a galphasdependent manner additionally we demonstrated that the full activation of erk12 required signaling through both a ptxsensitive giocsrc signaling pathway and plcbdagpkcraf1 dependent pathway downstream of gq finally the wholecell integrated signatures monitored by the cellbased surface plasmon resonance and changes in the electrical impedance of a confluent cell monolayer led to identical phenotypic responses between the three ligands the characterization of the hnts1mediated cellular signaling network will be helpful to accelerate the validation of potential nts1 biased ligands with an improved therapeuticadverse effect profile
|
[['the', 'human', 'neurotensin', '1', 'receptor', 'hnts1', 'is', 'a', 'g', 'proteincoupled', 'receptor', 'involved', 'in', 'many', 'physiological', 'functions', 'including', 'analgesia', 'hypothermia', 'and', 'hypotension', 'to', 'gain', 'a', 'better', 'understanding', 'of', 'which', 'signaling', 'pathways', 'or', 'combination', 'of', 'pathways', 'are', 'linked', 'to', 'nts1', 'activation', 'and', 'function', 'we', 'investigated', 'the', 'ability', 'of', 'activated', 'hnts1', 'which', 'was', 'stably', 'expressed', 'by', 'chok1', 'cells', 'to', 'directly', 'engage', 'g', 'proteins', 'activate', 'second', 'messenger', 'cascades', 'and', 'recruit', 'betaarrestins', 'using', 'bretbased', 'biosensors', 'we', 'found', 'that', 'neurotensin', 'nt', 'nt813', 'and', 'neuromedin', 'n', 'nn', 'activated', 'the', 'galphaq', 'galphai1', 'galphaoa', 'and', 'galpha13protein', 'signaling', 'pathways', 'as', 'well', 'as', 'the', 'recruitment', 'of', 'betaarrestins', '1', 'and', '2', 'using', 'pharmacological', 'inhibitors', 'we', 'further', 'demonstrated', 'that', 'all', 'three', 'ligands', 'stimulated', 'the', 'production', 'of', 'inositol', 'phosphate', 'and', 'modulation', 'of', 'camp', 'accumulation', 'along', 'with', 'erk12', 'activation', 'interestingly', 'despite', 'the', 'functional', 'coupling', 'to', 'galphai1', 'and', 'galphaoa', 'nt', 'was', 'found', 'to', 'produce', 'higher', 'levels', 'of', 'camp', 'in', 'the', 'presence', 'of', 'pertussis', 'toxin', 'supporting', 'that', 'hnts1', 'activation', 'leads', 'to', 'camp', 'accumulation', 'in', 'a', 'galphasdependent', 'manner', 'additionally', 'we', 'demonstrated', 'that', 'the', 'full', 'activation', 'of', 'erk12', 'required', 'signaling', 'through', 'both', 'a', 'ptxsensitive', 'giocsrc', 'signaling', 'pathway', 'and', 'plcbdagpkcraf1', 'dependent', 'pathway', 'downstream', 'of', 'gq', 'finally', 'the', 'wholecell', 'integrated', 'signatures', 'monitored', 'by', 'the', 'cellbased', 'surface', 'plasmon', 'resonance', 'and', 'changes', 'in', 'the', 'electrical', 'impedance', 'of', 'a', 'confluent', 'cell', 'monolayer', 'led', 'to', 'identical', 'phenotypic', 'responses', 'between', 'the', 'three', 'ligands', 'the', 'characterization', 'of', 'the', 'hnts1mediated', 'cellular', 'signaling', 'network', 'will', 'be', 'helpful', 'to', 'accelerate', 'the', 'validation', 'of', 'potential', 'nts1', 'biased', 'ligands', 'with', 'an', 'improved', 'therapeuticadverse', 'effect', 'profile']]
|
[-0.10393365309243273, 0.15749279759051818, 0.01071278076297624, 0.02263343503839015, -0.032478255486140216, -0.17911998185992045, 0.11082912787772699, 0.37784557836333604, -0.2508776142916695, -0.2740951997236101, -0.002739718245831928, -0.2701870419845538, -0.26642086818676003, 0.16548769553517312, -0.03409651957765449, -0.015314853579359024, 0.036012728303044576, 0.02309676246131937, 0.08360091233209149, -0.18236151588806174, 0.19987490856903098, 0.1037658514413275, 0.2799599739157607, 0.09698912369668906, 0.11365217236521365, -0.0370389110874893, 0.023406540251699963, -0.039464453807177925, -0.10168279453008396, 0.14473606985905355, 0.2749351246798956, 0.11166954734749018, 0.27133819740570625, -0.47617270542902457, -0.24977629226642584, 0.0928468321161304, 0.17014961867027928, 0.07722980052122935, -0.040072873561626796, -0.24172913731689927, 0.0869595516741471, -0.1409006475157808, -0.05871505384825528, -0.06233082345674652, -0.001101127545568913, 0.07706749566177956, -0.28991022387999377, 0.09794269662170456, 0.015583544878561586, 0.08448794953574378, -0.09572242583579274, -0.12863861724193407, -0.10424495328677305, 0.17542195125603968, 0.044927284170316294, 0.043930678654311626, 0.24471360304307468, -0.11581407714529394, -0.12916172753234584, 0.2782826468116689, -0.0324550373309422, -0.16145802512927607, 0.210679153427807, -0.1149399613272236, -0.09832863997277605, 0.1710601642130016, 0.14257200154034608, 0.0831764978671845, -0.18009703711903446, -0.04000911505142528, 0.08751319792308726, 0.16779231041055742, 0.13820684278245576, -0.01433461985863973, 0.12222621788288056, 0.1974629347830783, -0.0012010257453739025, 0.123572451007343, -0.08420406339291532, -0.03944719208411539, -0.2006899511226042, -0.18076916809440854, -0.10754097292380708, 0.06387682186030909, -0.03538178404074825, -0.1336762869281198, 0.38519865129647296, 0.06767703857939837, 0.17042806897084656, 0.034421305854878015, 0.18809592281163195, 0.017310149741956892, 0.11822264148613772, -0.02078322965985237, 0.15632198419112825, 0.11695252312855657, 0.08210629930132064, -0.3070982019652106, 0.13272906963176634, -0.02376212218093104]
|
1,803.04499
|
On finite-population Bayesian inferences for $2^K$ factorial designs
with binary outcomes
|
Inspired by the pioneering work of Rubin (1978), we employ the potential
outcomes framework to develop a finite-population Bayesian causal inference
framework for randomized controlled $2^K$ factorial designs with binary
outcomes, which are common in medical research. As demonstrated by simulated
and empirical examples, the proposed framework corrects the well-known variance
over-estimation issue of the classic "Neymanian" inference framework, under
various settings.
|
stat.ME
|
inspired by the pioneering work of rubin 1978 we employ the potential outcomes framework to develop a finitepopulation bayesian causal inference framework for randomized controlled 2k factorial designs with binary outcomes which are common in medical research as demonstrated by simulated and empirical examples the proposed framework corrects the wellknown variance overestimation issue of the classic neymanian inference framework under various settings
|
[['inspired', 'by', 'the', 'pioneering', 'work', 'of', 'rubin', '1978', 'we', 'employ', 'the', 'potential', 'outcomes', 'framework', 'to', 'develop', 'a', 'finitepopulation', 'bayesian', 'causal', 'inference', 'framework', 'for', 'randomized', 'controlled', '2k', 'factorial', 'designs', 'with', 'binary', 'outcomes', 'which', 'are', 'common', 'in', 'medical', 'research', 'as', 'demonstrated', 'by', 'simulated', 'and', 'empirical', 'examples', 'the', 'proposed', 'framework', 'corrects', 'the', 'wellknown', 'variance', 'overestimation', 'issue', 'of', 'the', 'classic', 'neymanian', 'inference', 'framework', 'under', 'various', 'settings']]
|
[-0.01958606743644322, -0.009790524660098937, -0.07794258579792034, 0.10956884752358159, -0.09010049280890774, -0.15960310943304531, 0.07716463903348232, 0.3454407150106084, -0.22629550067077, -0.3548746015200572, 0.07017270519000088, -0.2028835315497652, -0.2573840347298932, 0.2239888676952931, -0.16606673745498543, 0.14263989062859647, 0.07309054820272591, -0.06250948218568679, -0.03132745779059347, -0.30128449655228084, 0.28998098662686383, 0.10717606099863206, 0.36475278193792027, -0.03693062045720346, 0.09923103782198121, 0.09801664868099315, -0.07300794286082589, 0.06210920741901763, -0.14639176085831657, 0.1459352248949149, 0.31039405680982574, 0.2325791031002037, 0.41973546291551284, -0.39570492710317334, -0.26957029616460204, 0.09109532504883264, 0.06868047338925422, 0.09865696070858487, -0.061025138396108826, -0.2948179294509182, 0.025479966158708258, -0.19773760233299748, -0.07843689360625801, -0.12813757762130756, -0.04645622533655936, -0.04340550706030861, -0.32822180419198926, 0.10385289209924879, 0.08657103585409781, 0.08987887902185321, 0.008821089885332771, -0.20026088540532416, 0.12810556478460408, 0.060031351610837926, 0.07392060071710617, 0.010022634392484062, 0.11861420639099614, -0.02100184296948775, -0.22871337432203995, 0.2897766091890873, 0.016087874106761452, -0.16530512619553314, 0.13206276419242063, -0.037244659119976625, -0.16221421620162624, 0.02196667623525906, 0.18745157513166627, 0.13317004673984728, -0.2060396198724066, 0.08374063279986713, -0.040278743381702134, 0.0651058263798815, 0.05917220287806084, -0.0834568535924078, 0.14038322199766914, 0.17393337121053087, -0.01924835085388153, 0.1577559726748375, -0.0900597132426957, -0.1628098064252446, -0.26335674688603067, -0.08183223920153274, -0.1481204189194907, -0.016961559923666138, -0.08452122643446332, -0.14825969442514883, 0.3402441631281568, 0.20472704509513512, 0.14130530966776272, 0.07921920350429812, 0.31735729964366916, 0.029421799288566915, 0.00028030625394275113, 0.07046482911814124, 0.1797155594423173, 0.15220153505252976, 0.045217338258460645, -0.18375450070016086, 0.12352085092495527, 0.03986766770662319]
|
1,803.045
|
The hypotensive effect of activated apelin receptor is correlated with
\b{eta}-arrestin recruitment
|
The apelinergic system is an important player in the regulation of both
vascular tone and cardiovascular function, making this physiological system an
attractive target for drug development for hypertension, heart failure and
ischemic heart disease. Indeed, apelin exerts a positive inotropic effect in
humans whilst reducing peripheral vascular resistance. In this study, we
investigated the signaling pathways through which apelin exerts its hypotensive
action. We synthesized a series of apelin-13 analogs whereby the C-terminal
Phe13 residue was replaced by natural or unnatural amino acids. In HEK293 cells
expressing APJ, we evaluated the relative efficacy of these compounds to
activate G{\alpha}i1 and G{\alpha}oA G-proteins, recruit \b{eta}-arrestins 1
and 2 (\b{eta}arrs), and inhibit cAMP production. Calculating the transduction
ratio for each pathway allowed us to identify several analogs with distinct
signaling profiles. Furthermore, we found that these analogs delivered i.v. to
Sprague-Dawley rats exerted a wide range of hypotensive responses. Indeed, two
compounds lost their ability to lower blood pressure, while other analogs
significantly reduced blood pressure as apelin-13. Interestingly, analogs that
did not lower blood pressure were less effective at recruiting \b{eta}arrs.
Finally, using Spearman correlations, we established that the hypotensive
response was significantly correlated with \b{eta}arr recruitment but not with
G protein- dependent signaling. In conclusion, our results demonstrated that
the \b{eta}arr recruitment potency is involved in the hypotensive efficacy of
activated APJ.
|
q-bio.MN q-bio.BM q-bio.CB q-bio.QM
|
the apelinergic system is an important player in the regulation of both vascular tone and cardiovascular function making this physiological system an attractive target for drug development for hypertension heart failure and ischemic heart disease indeed apelin exerts a positive inotropic effect in humans whilst reducing peripheral vascular resistance in this study we investigated the signaling pathways through which apelin exerts its hypotensive action we synthesized a series of apelin13 analogs whereby the cterminal phe13 residue was replaced by natural or unnatural amino acids in hek293 cells expressing apj we evaluated the relative efficacy of these compounds to activate galphai1 and galphaoa gproteins recruit betaarrestins 1 and 2 betaarrs and inhibit camp production calculating the transduction ratio for each pathway allowed us to identify several analogs with distinct signaling profiles furthermore we found that these analogs delivered iv to spraguedawley rats exerted a wide range of hypotensive responses indeed two compounds lost their ability to lower blood pressure while other analogs significantly reduced blood pressure as apelin13 interestingly analogs that did not lower blood pressure were less effective at recruiting betaarrs finally using spearman correlations we established that the hypotensive response was significantly correlated with betaarr recruitment but not with g protein dependent signaling in conclusion our results demonstrated that the betaarr recruitment potency is involved in the hypotensive efficacy of activated apj
|
[['the', 'apelinergic', 'system', 'is', 'an', 'important', 'player', 'in', 'the', 'regulation', 'of', 'both', 'vascular', 'tone', 'and', 'cardiovascular', 'function', 'making', 'this', 'physiological', 'system', 'an', 'attractive', 'target', 'for', 'drug', 'development', 'for', 'hypertension', 'heart', 'failure', 'and', 'ischemic', 'heart', 'disease', 'indeed', 'apelin', 'exerts', 'a', 'positive', 'inotropic', 'effect', 'in', 'humans', 'whilst', 'reducing', 'peripheral', 'vascular', 'resistance', 'in', 'this', 'study', 'we', 'investigated', 'the', 'signaling', 'pathways', 'through', 'which', 'apelin', 'exerts', 'its', 'hypotensive', 'action', 'we', 'synthesized', 'a', 'series', 'of', 'apelin13', 'analogs', 'whereby', 'the', 'cterminal', 'phe13', 'residue', 'was', 'replaced', 'by', 'natural', 'or', 'unnatural', 'amino', 'acids', 'in', 'hek293', 'cells', 'expressing', 'apj', 'we', 'evaluated', 'the', 'relative', 'efficacy', 'of', 'these', 'compounds', 'to', 'activate', 'galphai1', 'and', 'galphaoa', 'gproteins', 'recruit', 'betaarrestins', '1', 'and', '2', 'betaarrs', 'and', 'inhibit', 'camp', 'production', 'calculating', 'the', 'transduction', 'ratio', 'for', 'each', 'pathway', 'allowed', 'us', 'to', 'identify', 'several', 'analogs', 'with', 'distinct', 'signaling', 'profiles', 'furthermore', 'we', 'found', 'that', 'these', 'analogs', 'delivered', 'iv', 'to', 'spraguedawley', 'rats', 'exerted', 'a', 'wide', 'range', 'of', 'hypotensive', 'responses', 'indeed', 'two', 'compounds', 'lost', 'their', 'ability', 'to', 'lower', 'blood', 'pressure', 'while', 'other', 'analogs', 'significantly', 'reduced', 'blood', 'pressure', 'as', 'apelin13', 'interestingly', 'analogs', 'that', 'did', 'not', 'lower', 'blood', 'pressure', 'were', 'less', 'effective', 'at', 'recruiting', 'betaarrs', 'finally', 'using', 'spearman', 'correlations', 'we', 'established', 'that', 'the', 'hypotensive', 'response', 'was', 'significantly', 'correlated', 'with', 'betaarr', 'recruitment', 'but', 'not', 'with', 'g', 'protein', 'dependent', 'signaling', 'in', 'conclusion', 'our', 'results', 'demonstrated', 'that', 'the', 'betaarr', 'recruitment', 'potency', 'is', 'involved', 'in', 'the', 'hypotensive', 'efficacy', 'of', 'activated', 'apj']]
|
[-0.09100256158255485, 0.14312832531528483, -0.022658444101112746, 0.05189149754522281, -0.05682828003827865, -0.16174610742045759, 0.08439791992809362, 0.36174087111335507, -0.21383483369429987, -0.2576784597379025, 0.043840682298047294, -0.2861662173021376, -0.24200762438078663, 0.19508258472566675, -0.09748498719102903, -0.029374011638019424, 0.015735727603760522, 0.03488610130865301, 0.04977808699499224, -0.23214125379519623, 0.20769807904149257, 0.07669975569201895, 0.29336192131708333, 0.07853890190153409, 0.09837197486167581, -0.020571320583700054, 0.004712834402920992, -0.0079247874493937, -0.12446945983000975, 0.11117575273666287, 0.2848805211034003, 0.12461585936867675, 0.3140903942245625, -0.44255214959174116, -0.23894682643982956, 0.10910896124551073, 0.14276138764575438, 0.07310763099198186, -0.07373592944785291, -0.24981377971093127, 0.10586498093695562, -0.1577832724650991, -0.06533019569328177, -0.052493342573789306, 0.036856971377839776, 0.01800112433543333, -0.2561861334333771, 0.1451159351341868, 0.005963496619213435, 0.12862598175774806, -0.14464319729796724, -0.14954283943391083, -0.07987285977704181, 0.17999804424144825, 0.060080843860126826, 0.017763696465136432, 0.2604017820357232, -0.14216470623075544, -0.07425649993548117, 0.3199091877692562, -0.02764825090449347, -0.16365565018180026, 0.23126771081038794, -0.14005151291586737, -0.1287207234880417, 0.15869602854537698, 0.13957329387297837, 0.0857485152887633, -0.1711958049030574, -0.08011951056231797, 0.05260608793985943, 0.1852615810245293, 0.14366477614303477, -0.04172372555351494, 0.1529258980511505, 0.15840292257522276, -0.020018317882476426, 0.12275782804125615, -0.06611259111228276, -0.044476448998364365, -0.18261744987837553, -0.17286943538559418, -0.08825737361689577, 0.04508676849837115, -0.04531936286849342, -0.12501792641787432, 0.3572999640774835, 0.12356500188976735, 0.14001966903066274, 0.05063846413422567, 0.2267933009419738, 0.018357122348508337, 0.11300664131690652, -0.0017762236606120784, 0.1802852404799974, 0.08679042635640569, 0.08601753393549333, -0.3070935120808274, 0.182078137576023, 0.00323508155236247]
|
1,803.04501
|
Phenomenology of fermion production during axion inflation
|
We study the production of fermions through a derivative coupling with a
pseudoscalar inflaton and the effects of the produced fermions on the scalar
primordial perturbations. We present analytic results for the modification of
the scalar power spectrum due to the produced fermions, and we estimate the
amplitude of the non-Gaussianities in the equilateral regime. Remarkably, we
find a regime where the effect of the fermions gives the dominant contribution
to the scalar spectrum while the amplitude of the bispectrum is small and in
agreement with observation. We also note the existence of a regime in which the
backreaction of the fermions on the evolution of the zero-mode of the inflaton
can lead to inflation even if the potential of the inflaton is steep and does
not satisfy the slow-roll conditions.
|
astro-ph.CO hep-ph
|
we study the production of fermions through a derivative coupling with a pseudoscalar inflaton and the effects of the produced fermions on the scalar primordial perturbations we present analytic results for the modification of the scalar power spectrum due to the produced fermions and we estimate the amplitude of the nongaussianities in the equilateral regime remarkably we find a regime where the effect of the fermions gives the dominant contribution to the scalar spectrum while the amplitude of the bispectrum is small and in agreement with observation we also note the existence of a regime in which the backreaction of the fermions on the evolution of the zeromode of the inflaton can lead to inflation even if the potential of the inflaton is steep and does not satisfy the slowroll conditions
|
[['we', 'study', 'the', 'production', 'of', 'fermions', 'through', 'a', 'derivative', 'coupling', 'with', 'a', 'pseudoscalar', 'inflaton', 'and', 'the', 'effects', 'of', 'the', 'produced', 'fermions', 'on', 'the', 'scalar', 'primordial', 'perturbations', 'we', 'present', 'analytic', 'results', 'for', 'the', 'modification', 'of', 'the', 'scalar', 'power', 'spectrum', 'due', 'to', 'the', 'produced', 'fermions', 'and', 'we', 'estimate', 'the', 'amplitude', 'of', 'the', 'nongaussianities', 'in', 'the', 'equilateral', 'regime', 'remarkably', 'we', 'find', 'a', 'regime', 'where', 'the', 'effect', 'of', 'the', 'fermions', 'gives', 'the', 'dominant', 'contribution', 'to', 'the', 'scalar', 'spectrum', 'while', 'the', 'amplitude', 'of', 'the', 'bispectrum', 'is', 'small', 'and', 'in', 'agreement', 'with', 'observation', 'we', 'also', 'note', 'the', 'existence', 'of', 'a', 'regime', 'in', 'which', 'the', 'backreaction', 'of', 'the', 'fermions', 'on', 'the', 'evolution', 'of', 'the', 'zeromode', 'of', 'the', 'inflaton', 'can', 'lead', 'to', 'inflation', 'even', 'if', 'the', 'potential', 'of', 'the', 'inflaton', 'is', 'steep', 'and', 'does', 'not', 'satisfy', 'the', 'slowroll', 'conditions']]
|
[-0.19206109755132206, 0.19936446153082465, -0.12470000188438692, 0.08855133398990538, -0.06138347232721405, -0.0866005579436212, -0.0050080169777862215, 0.2643963175306793, -0.17943722380055044, -0.26505101196297254, 0.0424061439698562, -0.2817241574694466, -0.13786153203340332, 0.1553580064241213, 0.02645301336834904, 0.010784812196947009, 0.04654690715526977, 0.052890666742730914, -0.021618602141553096, -0.24179557360040435, 0.3671447241826353, 0.07248389923532728, 0.21440657008553052, 0.10474674106497346, 0.043787099883130946, -0.04119702740679033, 0.016646649544962634, -0.04111703129783841, -0.12282855662282642, 0.08223190048055454, 0.11068181965519222, 0.025210288110658355, 0.19993513924929932, -0.4019218536221799, -0.1887061174919843, 0.1845438011658897, 0.15939882132651298, 0.1483750947020621, -0.05836119563389882, -0.2696675719224087, 0.0604000449436539, -0.15177861253467909, -0.12900546737697743, -0.058039446986984204, -0.03293558295446498, -0.06091448054195716, -0.34428987424209256, 0.1282083941141467, 0.02317607231555464, -0.029027608079295462, -0.05499934389270159, -0.058195989230605025, -0.056138909382759845, 0.06039470203846466, 0.15147429234383783, 0.008331647187201235, 0.1323201529902072, -0.21260658678605812, -0.041169229826449154, 0.42510954056731387, -0.1944365712302161, -0.16967788719721422, 0.10781878102499678, -0.20208090437462425, -0.1234800637493493, 0.09026081353951793, 0.14829178044629346, 0.07955298574556036, -0.07694625290366643, 0.17246348638165715, 0.028691375485927095, 0.16156499220755036, 0.05964371208670258, 0.08102634752417856, 0.27139812558605014, 0.0940607410805826, 0.035509813505843396, 0.15222351742599102, -0.08751219242921397, -0.08234521776494634, -0.3837280353488585, -0.10339871611497685, -0.16616995695210596, 0.054367055249234256, -0.1156055819316952, -0.2009141651686025, 0.487746617650144, 0.14681811921497565, 0.2031028690243154, 0.061722590389860554, 0.2730147341365805, 0.15150249334903548, 0.06594182620578595, 0.05936156118329515, 0.3423385580127191, 0.1455051837318385, 0.1267010142852531, -0.2835529494163303, -0.025537869782819776, 0.01549856052271851]
|
1,803.04502
|
The Besicovitch covering property in the Heisenberg group revisited
|
The Besicovitch covering property (BCP) is known to be one of the fundamental
tools in measure theory, and more generally, a usefull property for numerous
purposes in analysis and geometry. We prove both sufficient and necessary
criteria for the validity of BCP in the first Heisenberg group equipped with a
homogeneous distance. Beyond recovering all previously known results about the
validity or non validity of BCP in this setting, we get simple descriptions of
new large classes of homogeneous distances satisfying BCP. We also obtain a
full characterization of rotationally invariant distances for which BCP holds
in the first Heisenberg group under mild regularity assumptions about their
unit sphere.
|
math.MG
|
the besicovitch covering property bcp is known to be one of the fundamental tools in measure theory and more generally a usefull property for numerous purposes in analysis and geometry we prove both sufficient and necessary criteria for the validity of bcp in the first heisenberg group equipped with a homogeneous distance beyond recovering all previously known results about the validity or non validity of bcp in this setting we get simple descriptions of new large classes of homogeneous distances satisfying bcp we also obtain a full characterization of rotationally invariant distances for which bcp holds in the first heisenberg group under mild regularity assumptions about their unit sphere
|
[['the', 'besicovitch', 'covering', 'property', 'bcp', 'is', 'known', 'to', 'be', 'one', 'of', 'the', 'fundamental', 'tools', 'in', 'measure', 'theory', 'and', 'more', 'generally', 'a', 'usefull', 'property', 'for', 'numerous', 'purposes', 'in', 'analysis', 'and', 'geometry', 'we', 'prove', 'both', 'sufficient', 'and', 'necessary', 'criteria', 'for', 'the', 'validity', 'of', 'bcp', 'in', 'the', 'first', 'heisenberg', 'group', 'equipped', 'with', 'a', 'homogeneous', 'distance', 'beyond', 'recovering', 'all', 'previously', 'known', 'results', 'about', 'the', 'validity', 'or', 'non', 'validity', 'of', 'bcp', 'in', 'this', 'setting', 'we', 'get', 'simple', 'descriptions', 'of', 'new', 'large', 'classes', 'of', 'homogeneous', 'distances', 'satisfying', 'bcp', 'we', 'also', 'obtain', 'a', 'full', 'characterization', 'of', 'rotationally', 'invariant', 'distances', 'for', 'which', 'bcp', 'holds', 'in', 'the', 'first', 'heisenberg', 'group', 'under', 'mild', 'regularity', 'assumptions', 'about', 'their', 'unit', 'sphere']]
|
[-0.1171827338477853, 0.05170687033641024, -0.09834162338098529, 0.10677372800066658, -0.07774297030505921, -0.13961952752561882, 0.03714632190477718, 0.3833819122882475, -0.2639965395007385, -0.23134931348640164, 0.15087688591976334, -0.19005882227366125, -0.08903034937294593, 0.22324917939440306, -0.07265208925573377, 0.057531473696778676, 0.0269035303478189, 0.06567696190157205, -0.12523265989121357, -0.24123358714539522, 0.3373926571707045, 0.0025328880634133215, 0.2872635368855858, 0.0625093336310024, 0.07352982979660871, 0.06316827666527088, -0.0022660611739911893, 0.0630292864430935, -0.2225875791606553, 0.11917036189323872, 0.21539478985712343, 0.09322534158714799, 0.25546497477372304, -0.3845711547813011, -0.21495454864354308, 0.12023135210780445, 0.06866175630941578, 0.06592368756083412, -0.01956096472746774, -0.27839231925136454, 0.15364671958644593, -0.11696637147576164, -0.21486286315297998, -0.07289293915921942, 0.017399366927939818, 0.02635429615214672, -0.248237102044695, 0.08591074084007574, 0.14357989674451155, 0.1039311080196992, -0.11085927321515772, -0.05979046464786617, 0.021427357193390163, 0.1567872076214146, 0.012809564784028216, -0.0400129511243228, 0.04023051774556484, -0.06110196586040387, -0.07854796937504492, 0.4136999399837004, -0.027806287281078482, -0.2143256874293636, 0.21941571497281484, -0.18253323570782437, -0.19873825900700934, 0.07837081512162855, 0.11169954931893207, 0.1233057179637766, -0.18103520380794455, 0.10646998252422728, -0.09741463011062336, 0.15320152867534156, 0.11162554219775243, 0.06852166721524688, 0.09923823261548073, 0.15258394728559965, 0.14242034079079782, 0.12802553090689822, -0.0031232189669043097, -0.08457567980683861, -0.34533458649086846, -0.1849779810579152, -0.15598987902561628, 0.08873454813841183, -0.1199274979989149, -0.1670033583212473, 0.31373973301859626, 0.09406103736746611, 0.15405314800184255, 0.10448374562584924, 0.22901669587222262, 0.01638862950067301, 0.05839067939937904, 0.05483431891546747, 0.21159393318134165, 0.2242916917274578, 0.031000777602691307, -0.08489874657670703, 0.05073126015432794, 0.0917378722032139]
|
1,803.04503
|
Improved Neymanian analysis for $2^K$ factorial designs with binary
outcomes
|
$2^K$ factorial designs are widely adopted by statisticians and the broader
scientific community. In this short note, under the potential outcomes
framework (Neyman, 1923; Rubin, 1974), we adopt the partial identification
approach and derive the sharp lower bound of the sampling variance of the
estimated factorial effects, which leads to an "improved" Neymanian variance
estimator that mitigates the over-estimation issue suffered by the classic
Neymanian variance estimator by Dasgupta et al. (2015).
|
stat.ME
|
2k factorial designs are widely adopted by statisticians and the broader scientific community in this short note under the potential outcomes framework neyman 1923 rubin 1974 we adopt the partial identification approach and derive the sharp lower bound of the sampling variance of the estimated factorial effects which leads to an improved neymanian variance estimator that mitigates the overestimation issue suffered by the classic neymanian variance estimator by dasgupta et al 2015
|
[['2k', 'factorial', 'designs', 'are', 'widely', 'adopted', 'by', 'statisticians', 'and', 'the', 'broader', 'scientific', 'community', 'in', 'this', 'short', 'note', 'under', 'the', 'potential', 'outcomes', 'framework', 'neyman', '1923', 'rubin', '1974', 'we', 'adopt', 'the', 'partial', 'identification', 'approach', 'and', 'derive', 'the', 'sharp', 'lower', 'bound', 'of', 'the', 'sampling', 'variance', 'of', 'the', 'estimated', 'factorial', 'effects', 'which', 'leads', 'to', 'an', 'improved', 'neymanian', 'variance', 'estimator', 'that', 'mitigates', 'the', 'overestimation', 'issue', 'suffered', 'by', 'the', 'classic', 'neymanian', 'variance', 'estimator', 'by', 'dasgupta', 'et', 'al', '2015']]
|
[-0.026788825466711488, 0.044347166939284456, -0.11146227060817182, 0.1411766015339203, -0.094212418085792, -0.1186736392814459, 0.0879009434429463, 0.2881310188935863, -0.21137101518494697, -0.3733957723662671, 0.09579068717721384, -0.23738056912811267, -0.1946314667713725, 0.17682613508400713, -0.21941283588401145, 0.07210162677155393, 0.02965040620054222, -0.09065495180483493, -0.04176874026436255, -0.3542833969566143, 0.2465478876967811, 0.18239759317495757, 0.3623322802466444, 0.0038292232420644723, 0.07802243202200367, 0.06231437347984562, -0.09589734114059764, 0.009548456149382724, -0.20347899844364292, 0.12009653362393793, 0.23690259917121795, 0.16608852186861137, 0.40571563235587543, -0.32585064684907494, -0.1880367779586878, 0.12217186853134383, 0.08395485474546957, 0.09014569846880881, -0.01248263381077979, -0.2990873529989686, 0.01288161271562179, -0.2142010652201457, -0.09980857440192874, -0.023481710060069518, -0.0034830912158617545, -0.025349030723898776, -0.31058330471730894, 0.17919977168397358, 0.12392979707025613, 0.06649602068419982, 0.049524388308378145, -0.26126174185386236, 0.08726732293143868, 0.023466187341707863, 0.07975672983512697, 0.020196417322343525, 0.06655451773096704, -0.01871204093266796, -0.13965208373782742, 0.2506042756285751, -0.03155270947516126, -0.13404298462491068, 0.10326645133965132, -0.09655079667249487, -0.13529897994238935, 0.10990871007864673, 0.17317892376255864, 0.0764637886443072, -0.1851276503681826, 0.10197627000969886, -0.03182706350667609, 0.09304949739533994, 0.10770917769857785, -0.05356331146968943, 0.09724565947221385, 0.08998068198302968, 0.06999609352917308, 0.11761025026175452, -0.10296632889852238, -0.0734808317413101, -0.22710275813005865, -0.08412366290980976, -0.18770516241460833, -0.0027374060979733863, -0.09901761209120094, -0.15510679108370418, 0.33977380697615445, 0.2040142954275426, 0.12057712026095639, 0.07736473688500053, 0.27516591619162983, 0.129997683973569, -0.016231212968705222, 0.09075109937111847, 0.22722320948701558, 0.16523456912060888, 0.06100836510045661, -0.19882862410870278, 0.14263200710734558, 0.08058990054026556]
|
1,803.04504
|
Neutrino flavor transformations in supernovae as a probe for nonstandard
neutrino-scalar interactions
|
We explore the possibility of probing the nonstandard interactions between
the neutrino and a hypothetical massive scalar or pseudoscalar via neutrino
flavor transformation in supernovae. We find that in the ultrarelativistic
limit, the effective interaction between the neutrinos vanishes if neutrinos
are Dirac fermions but not if they are Majorana fermions. The impact of the new
neutrino interaction upon the flavor transformation above the neutrinosphere is
calculated in the context of the multi-angle "neutrino bulb model". We find
that the addition of the nonstandard neutrino self-interaction (NSSI) to the
ordinary V-A self-interaction between neutrinos is capable of dramatically
altering the collective oscillations when its strength is comparable to the
standard, V-A, interaction. The effect of flavor-preserving (FP) NSSI is
generally to suppress flavor transformation, while the flavor-violating (FV)
interactions are found to promote flavor transformations. If the neutrino
signal from a Galactic supernova can be sufficiently well understood, supernova
neutrinos can provide complimentary constraints on scalar/pseudoscalar
interactions of neutrinos as well as distinguishing whether the neutrino is a
Majorana or Dirac fermion.
|
astro-ph.HE hep-ph
|
we explore the possibility of probing the nonstandard interactions between the neutrino and a hypothetical massive scalar or pseudoscalar via neutrino flavor transformation in supernovae we find that in the ultrarelativistic limit the effective interaction between the neutrinos vanishes if neutrinos are dirac fermions but not if they are majorana fermions the impact of the new neutrino interaction upon the flavor transformation above the neutrinosphere is calculated in the context of the multiangle neutrino bulb model we find that the addition of the nonstandard neutrino selfinteraction nssi to the ordinary va selfinteraction between neutrinos is capable of dramatically altering the collective oscillations when its strength is comparable to the standard va interaction the effect of flavorpreserving fp nssi is generally to suppress flavor transformation while the flavorviolating fv interactions are found to promote flavor transformations if the neutrino signal from a galactic supernova can be sufficiently well understood supernova neutrinos can provide complimentary constraints on scalarpseudoscalar interactions of neutrinos as well as distinguishing whether the neutrino is a majorana or dirac fermion
|
[['we', 'explore', 'the', 'possibility', 'of', 'probing', 'the', 'nonstandard', 'interactions', 'between', 'the', 'neutrino', 'and', 'a', 'hypothetical', 'massive', 'scalar', 'or', 'pseudoscalar', 'via', 'neutrino', 'flavor', 'transformation', 'in', 'supernovae', 'we', 'find', 'that', 'in', 'the', 'ultrarelativistic', 'limit', 'the', 'effective', 'interaction', 'between', 'the', 'neutrinos', 'vanishes', 'if', 'neutrinos', 'are', 'dirac', 'fermions', 'but', 'not', 'if', 'they', 'are', 'majorana', 'fermions', 'the', 'impact', 'of', 'the', 'new', 'neutrino', 'interaction', 'upon', 'the', 'flavor', 'transformation', 'above', 'the', 'neutrinosphere', 'is', 'calculated', 'in', 'the', 'context', 'of', 'the', 'multiangle', 'neutrino', 'bulb', 'model', 'we', 'find', 'that', 'the', 'addition', 'of', 'the', 'nonstandard', 'neutrino', 'selfinteraction', 'nssi', 'to', 'the', 'ordinary', 'va', 'selfinteraction', 'between', 'neutrinos', 'is', 'capable', 'of', 'dramatically', 'altering', 'the', 'collective', 'oscillations', 'when', 'its', 'strength', 'is', 'comparable', 'to', 'the', 'standard', 'va', 'interaction', 'the', 'effect', 'of', 'flavorpreserving', 'fp', 'nssi', 'is', 'generally', 'to', 'suppress', 'flavor', 'transformation', 'while', 'the', 'flavorviolating', 'fv', 'interactions', 'are', 'found', 'to', 'promote', 'flavor', 'transformations', 'if', 'the', 'neutrino', 'signal', 'from', 'a', 'galactic', 'supernova', 'can', 'be', 'sufficiently', 'well', 'understood', 'supernova', 'neutrinos', 'can', 'provide', 'complimentary', 'constraints', 'on', 'scalarpseudoscalar', 'interactions', 'of', 'neutrinos', 'as', 'well', 'as', 'distinguishing', 'whether', 'the', 'neutrino', 'is', 'a', 'majorana', 'or', 'dirac', 'fermion']]
|
[-0.11023586891932673, 0.31815551966970224, -0.022906589998286975, 0.22905928321347876, -0.11569495267386354, -0.14691188663980642, 0.05795682493467317, 0.307430712961007, -0.24288846437581058, -0.2876531158162411, -0.013120639800543486, -0.31696524696768436, -0.08672849017975115, 0.13077571283162776, 0.08905783493397859, -0.05536218298711478, 0.053132793497223794, 0.031243241390891557, -0.1090222027492547, -0.23017841587817772, 0.32448480054627843, 0.07695530948424062, 0.2190941680956437, 0.09312232967661044, 0.06750121345841001, -0.04155306596049042, -0.031608926527591986, -0.09640294745521144, -0.01730614744900417, -0.01733844731068022, 0.15597443086373333, 0.10211890195712967, 0.10123251731572457, -0.42629270961638105, -0.21678591388694637, 0.20418641538370055, 0.20365059448590198, 0.11434188724067712, -0.093115874147178, -0.34577966579994146, 0.04021102226566697, -0.20001491039807281, -0.118098275465241, -0.040049190015727, -0.07595307661252919, -0.018951778045955634, -0.3079049716010516, 0.08544276826164288, -0.02052022837140316, -0.03578756015130514, 0.003851425961921598, -0.12278403882424585, -0.03154665301012438, 0.06672102554520554, 0.16741933878091553, -0.021407159695098568, 0.10692676016925509, -0.20475538750179112, -0.05364753677967264, 0.49045404476359983, -0.10180497185332528, -0.18617336447675562, 0.17495147747983938, -0.14466183653729425, -0.07479130064386355, 0.10002985126799145, 0.15348498526213386, 0.027720139537894622, -0.17452322715410468, 0.14038966595483565, -0.0805560039372595, 0.1340107473444605, 0.03255291737421134, 0.032121871705427574, 0.3136853531807076, 0.19197670074834944, 0.08360988386570974, -0.03440660079683957, -0.10585655207437095, -0.014648405263785186, -0.3441558460986545, -0.09673404057786344, -0.1279921067342552, 0.08126117331173804, -0.07864652822857796, -0.12951388198687414, 0.41543017661328924, 0.13479599723224203, 0.13826624959772235, -0.03425943579113241, 0.26173158631210625, 0.08502554295037679, 0.08128409251993046, 0.028652228677623667, 0.37304268184735245, 0.156247059164872, 0.08648434942073817, -0.32830178483827904, 0.013300715271535134, 0.10519883775444658]
|
1,803.04505
|
Exploring circular polarization in the CMB due to conventional sources
of cosmic birefringence
|
The circular polarization of the cosmic microwave background (CMB) is usually
taken to be zero since it is not generated by Thomson scattering. Here we
explore the actual level of circular polarization in the CMB generated by
conventional cosmological sources of birefringence. We consider two classes of
mechanisms for birefringence. One is alignment of the matter to produce an
anisotropic susceptibility tensor: the hydrogen spins can be aligned either by
density perturbations or CMB anisotropies themselves. The other is anisotropy
of the radiation field coupled to the non-linear response of the medium to
electromagnetic fields: this can occur either via photon-photon scattering
(non-linear response of the vacuum); atomic hyperpolarizability (non-linear
response of neutral atoms); or plasma delay (non-linear response of free
electrons). The strongest effect comes from photon-photon scattering from
recombination at a level of $\sim 10^{-14}$ K. Our results are consistent with
a negligible circular polarization of the CMB in comparison with the linear
polarization or the sensitivity of current and near-term experiments.
|
astro-ph.CO
|
the circular polarization of the cosmic microwave background cmb is usually taken to be zero since it is not generated by thomson scattering here we explore the actual level of circular polarization in the cmb generated by conventional cosmological sources of birefringence we consider two classes of mechanisms for birefringence one is alignment of the matter to produce an anisotropic susceptibility tensor the hydrogen spins can be aligned either by density perturbations or cmb anisotropies themselves the other is anisotropy of the radiation field coupled to the nonlinear response of the medium to electromagnetic fields this can occur either via photonphoton scattering nonlinear response of the vacuum atomic hyperpolarizability nonlinear response of neutral atoms or plasma delay nonlinear response of free electrons the strongest effect comes from photonphoton scattering from recombination at a level of sim 1014 k our results are consistent with a negligible circular polarization of the cmb in comparison with the linear polarization or the sensitivity of current and nearterm experiments
|
[['the', 'circular', 'polarization', 'of', 'the', 'cosmic', 'microwave', 'background', 'cmb', 'is', 'usually', 'taken', 'to', 'be', 'zero', 'since', 'it', 'is', 'not', 'generated', 'by', 'thomson', 'scattering', 'here', 'we', 'explore', 'the', 'actual', 'level', 'of', 'circular', 'polarization', 'in', 'the', 'cmb', 'generated', 'by', 'conventional', 'cosmological', 'sources', 'of', 'birefringence', 'we', 'consider', 'two', 'classes', 'of', 'mechanisms', 'for', 'birefringence', 'one', 'is', 'alignment', 'of', 'the', 'matter', 'to', 'produce', 'an', 'anisotropic', 'susceptibility', 'tensor', 'the', 'hydrogen', 'spins', 'can', 'be', 'aligned', 'either', 'by', 'density', 'perturbations', 'or', 'cmb', 'anisotropies', 'themselves', 'the', 'other', 'is', 'anisotropy', 'of', 'the', 'radiation', 'field', 'coupled', 'to', 'the', 'nonlinear', 'response', 'of', 'the', 'medium', 'to', 'electromagnetic', 'fields', 'this', 'can', 'occur', 'either', 'via', 'photonphoton', 'scattering', 'nonlinear', 'response', 'of', 'the', 'vacuum', 'atomic', 'hyperpolarizability', 'nonlinear', 'response', 'of', 'neutral', 'atoms', 'or', 'plasma', 'delay', 'nonlinear', 'response', 'of', 'free', 'electrons', 'the', 'strongest', 'effect', 'comes', 'from', 'photonphoton', 'scattering', 'from', 'recombination', 'at', 'a', 'level', 'of', 'sim', '1014', 'k', 'our', 'results', 'are', 'consistent', 'with', 'a', 'negligible', 'circular', 'polarization', 'of', 'the', 'cmb', 'in', 'comparison', 'with', 'the', 'linear', 'polarization', 'or', 'the', 'sensitivity', 'of', 'current', 'and', 'nearterm', 'experiments']]
|
[-0.1336929714292992, 0.2287170262788117, -0.01271363259463503, 0.03256981616780139, -0.07455475201330534, -0.0817664899102344, -0.06007044202443666, 0.3825623344959373, -0.28605979518601443, -0.27287724077497133, 0.03437095700916493, -0.3501391904513783, -0.054473168340159535, 0.18834321376464397, 0.09539656780898662, 0.018424658002559005, -0.025488034706341284, -0.0086768195657741, -0.012951331386319929, -0.23058408944407568, 0.3135496754277882, 0.1432798206522243, 0.2837546019979594, 0.041043916796674816, 0.07839073901059015, 0.016676945891870163, -0.03458757809130475, 0.05327833913374556, -0.04836442542683671, 0.08033568779341649, 0.214921497564075, 0.0598218467046802, 0.15518010777943744, -0.45890572192374524, -0.21303251978271165, 0.10936268757483582, 0.07882442601059177, 0.17943625715447636, -0.041780907419373896, -0.27478759116266016, -0.008317805131721288, -0.1163258638130728, -0.12002158773035129, -0.027816386466062198, -0.024179959703072133, 0.009886700265313976, -0.29051411977015074, 0.1212480123692175, 0.0353402654397608, 0.015722299831572965, -0.05540038409313505, -0.07444096115565427, -0.029699201325079562, 0.03121667702131473, 0.06097348363113181, 0.03392641683621616, 0.19169988241841698, -0.16066725894087563, -0.10468324219024308, 0.4145310305445114, -0.14645250747548189, -0.1295009496076623, 0.13021027500082444, -0.21987904824789262, -0.051830720725730514, 0.18299898875184448, 0.18949157983155512, 0.08592435183775861, -0.13672192084671156, 0.08842931079597315, 0.023117492988381942, 0.19524543911286787, 0.10741326782040343, 0.054739733545319746, 0.3102510317524032, 0.10816077163539509, 0.03452023301591597, 0.1202950069298201, -0.11105923826817604, 0.024100034470704, -0.2870068385311198, -0.06124743125855741, -0.17398590089117244, 0.11002988744075992, -0.08062165243911029, -0.1599559598476711, 0.37241444225059595, 0.1455621179306834, 0.16840218501939244, -0.016267526494452685, 0.3604099387189419, 0.13202389962644678, 0.05244898287261405, 0.01318456106988689, 0.37407955794776904, 0.16953022070030296, 0.07303444782226551, -0.28572083167197954, 0.03792337314282521, -0.05594497025382092]
|
1,803.04506
|
Graph Algorithms for Topology Identification using Power Grid Probing
|
To perform any meaningful optimization task, power distribution operators
need to know the topology and line impedances of their electric networks.
Nevertheless, distribution grids currently lack a comprehensive metering
infrastructure. Although smart inverters are widely used for control purposes,
they have been recently advocated as the means for an active data acquisition
paradigm: Reading the voltage deviations induced by intentionally perturbing
inverter injections, the system operator can potentially recover the electric
grid topology. Adopting inverter probing for feeder processing, a suite of
graph-based topology identification algorithms is developed here. If the grid
is probed at all leaf nodes but voltage data are metered at all nodes, the
entire feeder topology can be successfully recovered. When voltage data are
collected only at probing buses, the operator can find a reduced feeder
featuring key properties and similarities to the actual feeder. To handle
modeling inaccuracies and load non-stationarity, noisy probing data need to be
preprocessed. If the suggested guidelines on the magnitude and duration of
probing are followed, the recoverability guarantees carry over from the
noiseless to the noisy setup with high probability.
|
math.OC
|
to perform any meaningful optimization task power distribution operators need to know the topology and line impedances of their electric networks nevertheless distribution grids currently lack a comprehensive metering infrastructure although smart inverters are widely used for control purposes they have been recently advocated as the means for an active data acquisition paradigm reading the voltage deviations induced by intentionally perturbing inverter injections the system operator can potentially recover the electric grid topology adopting inverter probing for feeder processing a suite of graphbased topology identification algorithms is developed here if the grid is probed at all leaf nodes but voltage data are metered at all nodes the entire feeder topology can be successfully recovered when voltage data are collected only at probing buses the operator can find a reduced feeder featuring key properties and similarities to the actual feeder to handle modeling inaccuracies and load nonstationarity noisy probing data need to be preprocessed if the suggested guidelines on the magnitude and duration of probing are followed the recoverability guarantees carry over from the noiseless to the noisy setup with high probability
|
[['to', 'perform', 'any', 'meaningful', 'optimization', 'task', 'power', 'distribution', 'operators', 'need', 'to', 'know', 'the', 'topology', 'and', 'line', 'impedances', 'of', 'their', 'electric', 'networks', 'nevertheless', 'distribution', 'grids', 'currently', 'lack', 'a', 'comprehensive', 'metering', 'infrastructure', 'although', 'smart', 'inverters', 'are', 'widely', 'used', 'for', 'control', 'purposes', 'they', 'have', 'been', 'recently', 'advocated', 'as', 'the', 'means', 'for', 'an', 'active', 'data', 'acquisition', 'paradigm', 'reading', 'the', 'voltage', 'deviations', 'induced', 'by', 'intentionally', 'perturbing', 'inverter', 'injections', 'the', 'system', 'operator', 'can', 'potentially', 'recover', 'the', 'electric', 'grid', 'topology', 'adopting', 'inverter', 'probing', 'for', 'feeder', 'processing', 'a', 'suite', 'of', 'graphbased', 'topology', 'identification', 'algorithms', 'is', 'developed', 'here', 'if', 'the', 'grid', 'is', 'probed', 'at', 'all', 'leaf', 'nodes', 'but', 'voltage', 'data', 'are', 'metered', 'at', 'all', 'nodes', 'the', 'entire', 'feeder', 'topology', 'can', 'be', 'successfully', 'recovered', 'when', 'voltage', 'data', 'are', 'collected', 'only', 'at', 'probing', 'buses', 'the', 'operator', 'can', 'find', 'a', 'reduced', 'feeder', 'featuring', 'key', 'properties', 'and', 'similarities', 'to', 'the', 'actual', 'feeder', 'to', 'handle', 'modeling', 'inaccuracies', 'and', 'load', 'nonstationarity', 'noisy', 'probing', 'data', 'need', 'to', 'be', 'preprocessed', 'if', 'the', 'suggested', 'guidelines', 'on', 'the', 'magnitude', 'and', 'duration', 'of', 'probing', 'are', 'followed', 'the', 'recoverability', 'guarantees', 'carry', 'over', 'from', 'the', 'noiseless', 'to', 'the', 'noisy', 'setup', 'with', 'high', 'probability']]
|
[-0.14581787863936665, 0.047475934934944494, -0.034149511672237384, 0.05302340805000569, -0.09384974182418739, -0.20455910980917555, 0.07530248811718035, 0.39518971003216397, -0.2872005909043287, -0.37130647497663266, 0.13032731091608782, -0.2793606347918963, -0.08071582904407107, 0.2420981582080513, -0.09777455821952355, 0.08696048160325427, 0.06580736098209015, -0.009617474358477398, -0.023252000893949847, -0.22884314355970417, 0.24554354380507495, 0.10426373325561003, 0.3419916593284444, 0.0024279526213585342, 0.08082959670007475, -0.03653538918755537, -0.0376650829045668, 0.04158199061922606, -0.03204545922428164, 0.05078174502367555, 0.31585370973318566, 0.15077786738804197, 0.2655847198973045, -0.4876804285327703, -0.2388652633484556, 0.13913531888029357, 0.11266142084875572, 0.07155087781627534, -0.03300572696170199, -0.27074121889138586, 0.12490845313141359, -0.15967871362436048, -0.09575846697234687, -0.10551990075448518, -0.0014102851730148766, 0.06001673238763575, -0.3167830577855981, -0.004117347590769358, -0.013677827024688297, 0.08402662074131613, -0.00983283833796107, -0.06651853818834878, -0.07391250494208948, 0.17946707936086131, -0.014997771357119247, -0.005681414218897319, 0.20580237376045704, -0.08030840499295715, -0.10952588846104333, 0.34364609603476787, 0.04174502610578845, -0.1956959447787447, 0.11972871593467233, -0.09854553514854848, -0.09917178589951, 0.09514633923491143, 0.1989065709881464, 0.05194072168319131, -0.1946201504711274, 0.05698773606026864, 0.030052336992809126, 0.1785544888605235, 0.04338455904089943, 0.016268695175865773, 0.21813635148998164, 0.1829342871688265, 0.11500624926807101, 0.08801161154759893, -0.10822778983036499, -0.06028360853007691, -0.2387073178780754, -0.05111747670732825, -0.2003338672645205, 0.0409945587174792, -0.0780977984026553, -0.1341674761301246, 0.4116148840203799, 0.16055840079142655, 0.160677699154778, 0.040132634192499336, 0.3929800323020276, 0.10655691016797306, 0.09416867771896004, 0.11273217658372081, 0.18532841194109753, 0.074514928545278, 0.16954091327902937, -0.18337742843117194, 0.12558616999824895, -0.0002766359875297678]
|
1,803.04507
|
Thermoelectric current in topological insulator nanowires with
impurities
|
In this paper we consider charge current generated by maintaining a
temperature difference over a nanowire at zero voltage bias. For topological
insulator nanowires in a perpendicular magnetic field the current can change
sign as the temperature of one end is increased. Here we study how this
thermoelectric current sign reversal depends on magnetic field and how
impurities affect the size of the thermoelectric current. We consider both
scalar and magnetic impurities and show that their influence on the current are
quite similar, although the magnetic impurities seem to be more effective in
reducing the effect. For moderate impurity concentration the sign reversal
persists.
|
cond-mat.mes-hall
|
in this paper we consider charge current generated by maintaining a temperature difference over a nanowire at zero voltage bias for topological insulator nanowires in a perpendicular magnetic field the current can change sign as the temperature of one end is increased here we study how this thermoelectric current sign reversal depends on magnetic field and how impurities affect the size of the thermoelectric current we consider both scalar and magnetic impurities and show that their influence on the current are quite similar although the magnetic impurities seem to be more effective in reducing the effect for moderate impurity concentration the sign reversal persists
|
[['in', 'this', 'paper', 'we', 'consider', 'charge', 'current', 'generated', 'by', 'maintaining', 'a', 'temperature', 'difference', 'over', 'a', 'nanowire', 'at', 'zero', 'voltage', 'bias', 'for', 'topological', 'insulator', 'nanowires', 'in', 'a', 'perpendicular', 'magnetic', 'field', 'the', 'current', 'can', 'change', 'sign', 'as', 'the', 'temperature', 'of', 'one', 'end', 'is', 'increased', 'here', 'we', 'study', 'how', 'this', 'thermoelectric', 'current', 'sign', 'reversal', 'depends', 'on', 'magnetic', 'field', 'and', 'how', 'impurities', 'affect', 'the', 'size', 'of', 'the', 'thermoelectric', 'current', 'we', 'consider', 'both', 'scalar', 'and', 'magnetic', 'impurities', 'and', 'show', 'that', 'their', 'influence', 'on', 'the', 'current', 'are', 'quite', 'similar', 'although', 'the', 'magnetic', 'impurities', 'seem', 'to', 'be', 'more', 'effective', 'in', 'reducing', 'the', 'effect', 'for', 'moderate', 'impurity', 'concentration', 'the', 'sign', 'reversal', 'persists']]
|
[-0.19247769330104347, 0.21815019163589638, 0.009684817637137782, 0.04788898498764656, -0.054212047843835674, -0.13307938795053184, 0.06307741604821505, 0.4035872731966755, -0.26573906172192296, -0.3345525238591318, 0.04150333214448228, -0.27435450254079813, -0.10170365625526756, 0.19423264181778693, -0.014885327828009255, -0.05193177108258869, -0.043620842947767906, 0.022953795860163294, -0.12759442431091045, -0.24726998847864495, 0.30690226020389955, 0.015211446510735326, 0.34054874368638804, 0.152111458287646, -0.0023693101829849184, 0.0018645999057648273, 0.07792414934374392, 0.11538265618415047, -0.0958769625311526, -0.004775541997962416, 0.1915850802503813, -0.08807575836097105, 0.20531655116848505, -0.48713077157019424, -0.167626698107387, 0.0785308478695627, 0.15150319611599955, 0.1797745707220971, -0.12408907656502449, -0.21979778317528634, 0.1037015319452621, -0.13888259775614223, -0.08812280397306985, -0.05706363879681493, 0.00825132447733687, 0.010003024257737426, -0.23889486788091466, 0.10081192382131121, 0.07211293720264131, 0.10575739051609372, -0.06855698665066694, -0.114481736968558, -0.04220869022537954, 0.07661999183143892, 0.11369324124937591, 0.07431663943302388, 0.2526547671250927, -0.17335811191542932, -0.09389622073929506, 0.30755870938838387, -0.10997666239451903, -0.17716030879483485, 0.13763996759483305, -0.2120427499220778, -0.06364519894570829, 0.08664169321015763, 0.1629393842483226, 0.09512608784000175, -0.1178683184284287, 0.10126626790924302, 0.006430452438787772, 0.1458283257133399, 0.040833533074384414, 0.039040842689036466, 0.2987774559117567, 0.18335416699231316, 0.11702931690244721, 0.14779565173375886, -0.12541409332502204, -0.01422344298603443, -0.22201437384893113, -0.17909171170769975, -0.19677602670316657, 0.0928449041944427, -0.07240982495582451, -0.1924570663429152, 0.47105311892496854, 0.27053199652492854, 0.18680507885829473, -0.04085345549258189, 0.2730858192707484, 0.17444224234630104, 0.06278273151390469, 0.044610602349096626, 0.25381956695659585, 0.15206525605753995, 0.16977311818873903, -0.356748883272163, 0.10852223839342165, -0.03262736027066309]
|
1,803.04508
|
Construction of a class of scalar quantum field theory models in any
dimensions
|
It is observed that certain convex envelopes of Wightman type functionals
corresponding to scalar, stochastically positive quantum fields consist of
Wightman type functionals only .This leads to the construction of a large
classes of not quasi-free scalar quantum field theory models obeying all
Wightman axioms in any dimensions
|
math-ph hep-th math.MP quant-ph
|
it is observed that certain convex envelopes of wightman type functionals corresponding to scalar stochastically positive quantum fields consist of wightman type functionals only this leads to the construction of a large classes of not quasifree scalar quantum field theory models obeying all wightman axioms in any dimensions
|
[['it', 'is', 'observed', 'that', 'certain', 'convex', 'envelopes', 'of', 'wightman', 'type', 'functionals', 'corresponding', 'to', 'scalar', 'stochastically', 'positive', 'quantum', 'fields', 'consist', 'of', 'wightman', 'type', 'functionals', 'only', 'this', 'leads', 'to', 'the', 'construction', 'of', 'a', 'large', 'classes', 'of', 'not', 'quasifree', 'scalar', 'quantum', 'field', 'theory', 'models', 'obeying', 'all', 'wightman', 'axioms', 'in', 'any', 'dimensions']]
|
[-0.13107699837807255, 0.20791018947056727, -0.07392554547792922, 0.12473809197157, -0.07304573123110458, -0.15846593616879545, -0.06357438219129108, 0.2767088299539561, -0.20116992865223438, -0.21799261778748283, -0.03827534948989827, -0.22311250446364284, -0.14868337335065007, 0.18434952205764907, -0.035080492069634296, 0.052551032868602, 0.01798467306070961, 0.08073446641598518, -0.09678323678963352, -0.32247376525386545, 0.42191610517329536, -0.07737603584246244, 0.26505459043740603, -0.00011204865101414423, 0.11111277089609455, 0.022127741453005, 0.040267402733055256, 0.06319568908656947, -0.12242891046313768, 0.10557719354013291, 0.2674065755403869, 0.13873678027691008, 0.27324512872534495, -0.404067103479368, -0.22093766975679804, 0.16877849776453027, 0.022742047168624897, 0.0856151154730469, 0.04742286451316128, -0.2619102507984887, 0.08014299435308203, -0.14205246444362274, -0.16338186868233606, -0.10827508405782282, -0.016666140142964043, -0.016527226311154664, -0.2677035578235518, 0.10462119230457272, 0.0833230996237641, 0.02780111333898579, -0.1193019545947512, -0.09651704434266624, -0.02791825107609232, 0.029949267831398174, 0.024394884079811163, 0.02353070147364633, 0.15083361327803382, -0.16113903357472736, -0.15304310538825425, 0.2915192033397034, -0.04927701875567436, -0.2956076072684179, 0.1911932572062748, -0.189752632616243, -0.12701277230371488, 0.1367814951033021, 0.10762364756859218, 0.1637702442628021, -0.1948646545254936, 0.2425343767584612, -0.04330362398953488, 0.14097697181083882, 0.12412553726850699, 0.1152964999297789, 0.21403201413340867, -0.02310113668985044, 0.058295453617271654, 0.10542993890703656, 0.04958434073099246, -0.19228129086938375, -0.46827736248572666, -0.19841989838520627, -0.1802600823599884, 0.18632403039888837, -0.10976754426277087, -0.3113229953451082, 0.35632189736982883, 0.08541737299916956, 0.08345321571687236, 0.10464657382787361, 0.182546372176148, 0.1666474880379004, 0.13464572748246914, 0.037879070140964664, 0.18462007290994129, 0.26090050500351936, 0.08112614929268602, -0.14066790991152325, -0.040746730500056096, 0.1348623536954013]
|
1,803.04509
|
Sorting by Swaps with Noisy Comparisons
|
We study sorting of permutations by random swaps if each comparison gives the
wrong result with some fixed probability $p<1/2$. We use this process as
prototype for the behaviour of randomized, comparison-based optimization
heuristics in the presence of noisy comparisons. As quality measure, we compute
the expected fitness of the stationary distribution. To measure the runtime, we
compute the minimal number of steps after which the average fitness
approximates the expected fitness of the stationary distribution.
We study the process where in each round a random pair of elements at
distance at most $r$ are compared. We give theoretical results for the extreme
cases $r=1$ and $r=n$, and experimental results for the intermediate cases. We
find a trade-off between faster convergence (for large $r$) and better quality
of the solution after convergence (for small $r$).
|
cs.NE math.PR
|
we study sorting of permutations by random swaps if each comparison gives the wrong result with some fixed probability p12 we use this process as prototype for the behaviour of randomized comparisonbased optimization heuristics in the presence of noisy comparisons as quality measure we compute the expected fitness of the stationary distribution to measure the runtime we compute the minimal number of steps after which the average fitness approximates the expected fitness of the stationary distribution we study the process where in each round a random pair of elements at distance at most r are compared we give theoretical results for the extreme cases r1 and rn and experimental results for the intermediate cases we find a tradeoff between faster convergence for large r and better quality of the solution after convergence for small r
|
[['we', 'study', 'sorting', 'of', 'permutations', 'by', 'random', 'swaps', 'if', 'each', 'comparison', 'gives', 'the', 'wrong', 'result', 'with', 'some', 'fixed', 'probability', 'p12', 'we', 'use', 'this', 'process', 'as', 'prototype', 'for', 'the', 'behaviour', 'of', 'randomized', 'comparisonbased', 'optimization', 'heuristics', 'in', 'the', 'presence', 'of', 'noisy', 'comparisons', 'as', 'quality', 'measure', 'we', 'compute', 'the', 'expected', 'fitness', 'of', 'the', 'stationary', 'distribution', 'to', 'measure', 'the', 'runtime', 'we', 'compute', 'the', 'minimal', 'number', 'of', 'steps', 'after', 'which', 'the', 'average', 'fitness', 'approximates', 'the', 'expected', 'fitness', 'of', 'the', 'stationary', 'distribution', 'we', 'study', 'the', 'process', 'where', 'in', 'each', 'round', 'a', 'random', 'pair', 'of', 'elements', 'at', 'distance', 'at', 'most', 'r', 'are', 'compared', 'we', 'give', 'theoretical', 'results', 'for', 'the', 'extreme', 'cases', 'r1', 'and', 'rn', 'and', 'experimental', 'results', 'for', 'the', 'intermediate', 'cases', 'we', 'find', 'a', 'tradeoff', 'between', 'faster', 'convergence', 'for', 'large', 'r', 'and', 'better', 'quality', 'of', 'the', 'solution', 'after', 'convergence', 'for', 'small', 'r']]
|
[-0.1011725875540395, 0.0586947749575807, -0.07509709625263457, 0.07754946557335508, 0.00456058218109387, -0.1479555435444194, 0.09990387995278946, 0.3881039326562098, -0.24326583917373445, -0.29260141993010486, 0.0908241385611257, -0.29292171785125026, -0.11873845633343552, 0.1672754284546331, -0.07225431767809722, 0.06627335942057133, 0.06955278677244982, 0.05841665879857761, -0.06373983461375314, -0.28772125765681267, 0.26391627369448545, 0.06456565583745638, 0.24999862140113557, -0.00664844612625462, 0.07600612888043677, 0.025709900338013, -0.022598363588460617, 0.012423911539129525, -0.16856334143214755, 0.07506564790382982, 0.20471208681938824, 0.17110600857133115, 0.3082874267913953, -0.3888563310727477, -0.11409939420172359, 0.14938529011000085, 0.14110809604775298, 0.09576105250614798, -0.052364300598424895, -0.21766134246631905, 0.12698691493779835, -0.12798196256022762, -0.12606980806492546, -0.05305658899402867, 0.05506692618968103, 0.08052291432309344, -0.33341483633309876, 0.05388756159701626, 0.034717919643002526, 0.03186662821638122, -0.0192507132905087, -0.14847327014431358, 0.01323127067061486, 0.1458320580560852, 0.075996744232166, 0.03325897472807103, 0.10354947055931445, -0.10795691992087221, -0.11650943966858365, 0.35850284506601315, -0.08873923998326064, -0.19340894589269603, 0.17001973692655425, -0.15933468467728407, -0.1277982961144988, 0.10922753223254673, 0.17898530571687002, 0.138277932063297, -0.09042134026444897, 0.04667168072881867, -0.05123666582836045, 0.1141824232681689, 0.07411351800999708, 0.04417972713708877, 0.1190070472261006, 0.15862899731361754, 0.11794359947002872, 0.1734217009300159, -0.08415642509029972, -0.1041349548743003, -0.34170195012939747, -0.15844290230975108, -0.19572271791079807, 0.045880586840212345, -0.18937238685547425, -0.15680766302264398, 0.39478009600468256, 0.15383696564714666, 0.2329129915184307, 0.15419284379954828, 0.27800622438253075, 0.10503349748302113, -0.017120671850042762, 0.09259682766817234, 0.16583691664423827, 0.05769947198116117, 0.04680063745903748, -0.21444954476146785, 0.11568694194709814, 0.06331484328817438]
|
1,803.0451
|
Discontinuity propagation in delay differential-algebraic equations
|
The propagation of primary discontinuities in initial value problems for
linear delay differential-algebraic equations (DDAEs) is discussed. Based on
the (quasi-) Weierstra{\ss} form for regular matrix pencil, a complete
characterization of the different propagation types is given and algebraic
criteria in terms of the matrices are developed. The analysis, which is based
on the method of steps, takes into account all possible inhomogeneities and
history functions and thus serves as the worst-case scenario. Moreover, it
reveals possible hidden delays in the DDAE. The new classification for DDAEs is
compared to existing approaches in the literature and the impact of splicing
conditions on the classification is studied.
|
math.DS
|
the propagation of primary discontinuities in initial value problems for linear delay differentialalgebraic equations ddaes is discussed based on the quasi weierstrass form for regular matrix pencil a complete characterization of the different propagation types is given and algebraic criteria in terms of the matrices are developed the analysis which is based on the method of steps takes into account all possible inhomogeneities and history functions and thus serves as the worstcase scenario moreover it reveals possible hidden delays in the ddae the new classification for ddaes is compared to existing approaches in the literature and the impact of splicing conditions on the classification is studied
|
[['the', 'propagation', 'of', 'primary', 'discontinuities', 'in', 'initial', 'value', 'problems', 'for', 'linear', 'delay', 'differentialalgebraic', 'equations', 'ddaes', 'is', 'discussed', 'based', 'on', 'the', 'quasi', 'weierstrass', 'form', 'for', 'regular', 'matrix', 'pencil', 'a', 'complete', 'characterization', 'of', 'the', 'different', 'propagation', 'types', 'is', 'given', 'and', 'algebraic', 'criteria', 'in', 'terms', 'of', 'the', 'matrices', 'are', 'developed', 'the', 'analysis', 'which', 'is', 'based', 'on', 'the', 'method', 'of', 'steps', 'takes', 'into', 'account', 'all', 'possible', 'inhomogeneities', 'and', 'history', 'functions', 'and', 'thus', 'serves', 'as', 'the', 'worstcase', 'scenario', 'moreover', 'it', 'reveals', 'possible', 'hidden', 'delays', 'in', 'the', 'ddae', 'the', 'new', 'classification', 'for', 'ddaes', 'is', 'compared', 'to', 'existing', 'approaches', 'in', 'the', 'literature', 'and', 'the', 'impact', 'of', 'splicing', 'conditions', 'on', 'the', 'classification', 'is', 'studied']]
|
[-0.1219962003862574, 0.0221015814691782, -0.07259751913093385, 0.07781471459838074, -0.07759126566040019, -0.10883571192001303, 0.0043375657871365545, 0.33881943392611685, -0.2549354296532415, -0.25948644621475114, 0.1306538932513268, -0.2501017124968625, -0.16138518860097975, 0.2266262019141799, -0.058742212608367914, 0.08572218371998697, 0.05437804414181127, 0.06405101968418984, -0.10926649016660771, -0.27570962806542715, 0.355008538333433, 0.033463861261095317, 0.2963774424401068, 0.049309573273750996, 0.11532921936096889, 0.026535980319160792, -0.06801038508170418, 0.013893732612597801, -0.11399967833976483, 0.06924667334671886, 0.2371663724027929, 0.15880031619841853, 0.24344863994490532, -0.42453402381922517, -0.22322458258297825, 0.08954676025147949, 0.11738740559667349, 0.10590878342655266, -0.012517846889039945, -0.26244330776944047, 0.09033359263447069, -0.09784246881137647, -0.09329508846359594, -0.03646163235507196, 0.027119927910999174, 0.038902097860617296, -0.28114122134589015, 0.07234025315514632, 0.06822899873368442, 0.028597447888127396, -0.08498007713794886, -0.13323287992472096, 0.005639148082229353, 0.12733149096990626, 0.03138750054474388, -0.03885529575692046, 0.06383854125271596, -0.09520377190284697, -0.09829551129646244, 0.39760879954944056, -0.03562372716897655, -0.23201979077199386, 0.1754895606876484, -0.06407756841065185, -0.11914754433867832, 0.13116901690317762, 0.1889008028255332, 0.12843224903612974, -0.1524976101637419, 0.07788625961346995, -0.010825969154636066, 0.13485648224041574, 0.06267550490530474, 0.022892209448452508, 0.13849171098499072, 0.20012175889597053, 0.05540362403595022, 0.1343103509928499, -0.05882373053270082, -0.11115825368157987, -0.31633720473342, -0.13547605055694778, -0.1400725576416811, 0.0028468489281034894, -0.10421787063491397, -0.1876767523453704, 0.4463319679522621, 0.10851622978668837, 0.1831744434311986, 0.038572150877942996, 0.30351410817709706, 0.13692003528343602, 0.043418600834549924, 0.04302596980705857, 0.208152437618091, 0.14599681470897935, 0.06730670587026648, -0.19662126085854004, 0.11895295340079992, 0.1066506051436244]
|
1,803.04511
|
On the continuity of entropy of Lorenz maps
|
We consider a one parameter family of Lorenz maps indexed by their point of
discontinuity $p$ and constructed from a pair of bilipschitz functions. We
prove that their topological entropies vary continuously as a function of $p$
and discuss Milnor's monotonicity conjecture in this setting.
|
math.DS
|
we consider a one parameter family of lorenz maps indexed by their point of discontinuity p and constructed from a pair of bilipschitz functions we prove that their topological entropies vary continuously as a function of p and discuss milnors monotonicity conjecture in this setting
|
[['we', 'consider', 'a', 'one', 'parameter', 'family', 'of', 'lorenz', 'maps', 'indexed', 'by', 'their', 'point', 'of', 'discontinuity', 'p', 'and', 'constructed', 'from', 'a', 'pair', 'of', 'bilipschitz', 'functions', 'we', 'prove', 'that', 'their', 'topological', 'entropies', 'vary', 'continuously', 'as', 'a', 'function', 'of', 'p', 'and', 'discuss', 'milnors', 'monotonicity', 'conjecture', 'in', 'this', 'setting']]
|
[-0.19407336397303476, 0.12039010856921474, -0.09698841508684887, 0.0837334630260658, -0.013637849196998609, -0.1695259032667511, 0.09198823041386074, 0.3158850750161542, -0.35981084267712304, -0.23937650809271468, 0.04828931358901577, -0.2861404586738596, -0.16474238766564264, 0.1821060888055298, -0.09076056399693092, 0.08398513611820009, -0.013658938825958304, 0.005754489555127091, -0.12973765540971524, -0.24460173996113654, 0.41352994557884004, -0.09828755665156576, 0.18383016408317618, 0.036379973052276506, 0.10070246101046602, 0.029471164755523204, -0.03954196304289831, 0.01940500773489475, -0.21631331049090174, 0.10163240664535099, 0.2317559242869417, 0.13772589678669142, 0.2731227925254239, -0.3046204637322161, -0.21819108425018688, 0.18574612885713576, 0.07062863966243135, -0.0004906034976657894, -0.018370035213107865, -0.2850503321737051, 0.08693688957331082, -0.1622047763110863, -0.1906675250476433, -0.06837049233209756, 0.05260401863811744, 0.0745123529392812, -0.2357821766494049, 0.030803729862802557, 0.11944071157938904, 0.11631164492314888, -0.04394477108079526, -0.07508079221265183, -0.11173256632561485, 0.08521348959248927, 0.04922673054194699, 0.0806843661185768, 0.11936360645211405, -0.0764510424548967, -0.11651791611479388, 0.3250432700164513, -0.07550197035695115, -0.2488509726193216, 0.12525998351888523, -0.17749133810607923, -0.1915504274165465, 0.06270903402732478, 0.14953385832616026, 0.1366680279581083, -0.06272126274804274, 0.16773332671727986, -0.0893327274463243, 0.08599015129018678, 0.1365882991709643, 0.0315616253659957, 0.171690815480219, 0.0061172952875494955, 0.11538879381906655, 0.24573136787447664, 0.020053434340904158, -0.04068150037589172, -0.35035984971457057, -0.19892198093649413, -0.19047360610630776, 0.09165171376532978, -0.1310632410090572, -0.22413986954424117, 0.3721748087969091, 0.0994541332953506, 0.28134877334038416, 0.11410630041112503, 0.15747095685866144, 0.123547649774183, 0.0065469413064420225, 0.035208838433027266, 0.14188544485304092, 0.10743581939281689, 0.0422015603321294, -0.11255750239749128, 0.01888745164291726, 0.17361287876135773]
|
1,803.04512
|
Predicting polarisation hemisphere switch in C60 caused by the motion of
an internal point charge with an electrostatic and quantum chemistry
solutions
|
A classical electrostatic solution for polarisation charge on the interface
of a dielectric cavity interacting with an internal point charge is presented.
This solution comes from the Gauss electrostatic potential as a sum of two
terms, the cavity and the point charge, expanded with Legendre polynomials.
Subsequent application of the Dietrich-Newman boundary conditions defines the
problem of emptiness in the inside and isotropic dielectric medium on the
outside of the spherical interface to obtain an equation which describes the
surface charge density on the interface. These results are compared with
quantum chemical calculations using density functional theory for neutral C60
fullerenes. Comparison showed that there was good qualitative agreement between
the classical electrostatic theory and quantum calculations. The polarisation
effect that occurs as a result of the motion of the trapped particle inside the
C60 molecule shows potential for a polarisable nanoswitch which might be used
in nanotechnology as electronic component.
|
physics.chem-ph
|
a classical electrostatic solution for polarisation charge on the interface of a dielectric cavity interacting with an internal point charge is presented this solution comes from the gauss electrostatic potential as a sum of two terms the cavity and the point charge expanded with legendre polynomials subsequent application of the dietrichnewman boundary conditions defines the problem of emptiness in the inside and isotropic dielectric medium on the outside of the spherical interface to obtain an equation which describes the surface charge density on the interface these results are compared with quantum chemical calculations using density functional theory for neutral c60 fullerenes comparison showed that there was good qualitative agreement between the classical electrostatic theory and quantum calculations the polarisation effect that occurs as a result of the motion of the trapped particle inside the c60 molecule shows potential for a polarisable nanoswitch which might be used in nanotechnology as electronic component
|
[['a', 'classical', 'electrostatic', 'solution', 'for', 'polarisation', 'charge', 'on', 'the', 'interface', 'of', 'a', 'dielectric', 'cavity', 'interacting', 'with', 'an', 'internal', 'point', 'charge', 'is', 'presented', 'this', 'solution', 'comes', 'from', 'the', 'gauss', 'electrostatic', 'potential', 'as', 'a', 'sum', 'of', 'two', 'terms', 'the', 'cavity', 'and', 'the', 'point', 'charge', 'expanded', 'with', 'legendre', 'polynomials', 'subsequent', 'application', 'of', 'the', 'dietrichnewman', 'boundary', 'conditions', 'defines', 'the', 'problem', 'of', 'emptiness', 'in', 'the', 'inside', 'and', 'isotropic', 'dielectric', 'medium', 'on', 'the', 'outside', 'of', 'the', 'spherical', 'interface', 'to', 'obtain', 'an', 'equation', 'which', 'describes', 'the', 'surface', 'charge', 'density', 'on', 'the', 'interface', 'these', 'results', 'are', 'compared', 'with', 'quantum', 'chemical', 'calculations', 'using', 'density', 'functional', 'theory', 'for', 'neutral', 'c60', 'fullerenes', 'comparison', 'showed', 'that', 'there', 'was', 'good', 'qualitative', 'agreement', 'between', 'the', 'classical', 'electrostatic', 'theory', 'and', 'quantum', 'calculations', 'the', 'polarisation', 'effect', 'that', 'occurs', 'as', 'a', 'result', 'of', 'the', 'motion', 'of', 'the', 'trapped', 'particle', 'inside', 'the', 'c60', 'molecule', 'shows', 'potential', 'for', 'a', 'polarisable', 'nanoswitch', 'which', 'might', 'be', 'used', 'in', 'nanotechnology', 'as', 'electronic', 'component']]
|
[-0.13180213971373936, 0.10079587192914914, -0.08115131461061537, 0.011611454092587034, 0.010264537151282033, -0.1147532530183283, 0.03140571868357559, 0.3512483393214643, -0.23856133624911308, -0.2732445223505298, 0.008415989056229591, -0.2888779599964619, -0.12214844016823917, 0.17139319578884168, 0.018688672720842684, 0.030778786805458366, 0.03077683156899487, 0.025765722749444345, -0.05177980968418221, -0.14019082736689598, 0.278528192245091, 0.05810421215525518, 0.25649812595297894, 0.13416401433137556, 0.07313711620556812, 0.011606925708474592, 0.030080693506946168, 0.037277435281624395, -0.12499460526324886, 0.11258025295023497, 0.23087848755065352, -0.001527260619526108, 0.22361600580625235, -0.48318499435360235, -0.20639762552455068, 0.021526962987457713, 0.10849559226616597, 0.13626551970063397, -0.11823877024376998, -0.2566934674667815, 0.026444094048347324, -0.1429922565445304, -0.1981151350370298, -0.025405752727140983, 0.002539859190583229, 0.0621203412146618, -0.24166899492343266, 0.10309871960935804, 0.025966020422056316, 0.04811914939268414, -0.11848970811658849, -0.12095074360414097, -0.04658851030593117, 0.07118179804335038, 0.02474052746935437, 0.06614322654282054, 0.2129692486859858, -0.12766555743447194, -0.07884367390846213, 0.3992420972635349, -0.07678683874197305, -0.2061188733826081, 0.19655846236584087, -0.14256247336044908, -0.04169399043234686, 0.15616419417162736, 0.09345306274170677, 0.08449370554337898, -0.13684875059872867, 0.12266061119618826, -0.03376272585941478, 0.17650881400875126, 0.10943460296839476, 0.026336863593072243, 0.24676400523787986, 0.138675068084461, 0.036293329273660975, 0.16511919719555104, -0.07173530026959876, -0.11610242475755513, -0.3167820111041268, -0.20982554223698874, -0.21146724644582718, 0.057090376856115954, -0.11174113107816083, -0.22275746466436733, 0.3746441306018581, 0.0860381543659605, 0.13273996680043637, -0.037589625033239524, 0.27198059648585815, 0.12940883934885886, 0.04515653279920419, 0.019214683199922245, 0.2532728975669791, 0.18877626550694307, 0.11061612226224193, -0.26093150984185437, 0.024354286091402173, 0.07351833612347643]
|
1,803.04513
|
Effects of Topology Knowledge and Relay Depth on Asynchronous Consensus
|
Consider a point-to-point message-passing network. We are interested in the
asynchronous crash-tolerant consensus problem in incomplete networks. We study
the feasibility and efficiency of approximate consensus under different
restrictions on topology knowledge and the relay depth, i.e., the maximum
number of hops any message can be relayed. These two constraints are common in
large-scale networks, and are used to avoid memory overload and network
congestion respectively. Specifically, for different values of integers k, k ,
we consider that each node knows all its neighbors of at most k-hop distance
(k-hop topology knowledge), and the relay depth is k . We consider both
directed and undirected graphs. More concretely, we answer the following main
question in asynchronous systems:
What is a tight condition on the underlying communication graphs for
achieving approximate consensus if each node has only a k-hop topology
knowledge and relay depth k?
To prove that the necessary conditions presented in the paper are also
sufficient, we have developed algorithms that achieve consensus in graphs
satisfying those conditions:
-The first class of algorithms requires k-hop topology knowledge and relay
depth k. Unlike prior algorithms, these algorithms do not flood the network,
and each node does not need the full topology knowledge. We show how the
convergence time and the message complexity of those algorithms is affected by
k, providing the respective upper bounds.
-The second set of algorithms requires only one-hop neighborhood knowledge,
i.e., immediate incoming and outgoing neighbors, but needs to flood the network
(i.e., relay depth is n, where n is the number of nodes). One result that may
be of independent interest is a topology discovery mechanism to learn and
"estimate" the topology in asynchronous directed networks with crash faults.
|
cs.DC
|
consider a pointtopoint messagepassing network we are interested in the asynchronous crashtolerant consensus problem in incomplete networks we study the feasibility and efficiency of approximate consensus under different restrictions on topology knowledge and the relay depth ie the maximum number of hops any message can be relayed these two constraints are common in largescale networks and are used to avoid memory overload and network congestion respectively specifically for different values of integers k k we consider that each node knows all its neighbors of at most khop distance khop topology knowledge and the relay depth is k we consider both directed and undirected graphs more concretely we answer the following main question in asynchronous systems what is a tight condition on the underlying communication graphs for achieving approximate consensus if each node has only a khop topology knowledge and relay depth k to prove that the necessary conditions presented in the paper are also sufficient we have developed algorithms that achieve consensus in graphs satisfying those conditions the first class of algorithms requires khop topology knowledge and relay depth k unlike prior algorithms these algorithms do not flood the network and each node does not need the full topology knowledge we show how the convergence time and the message complexity of those algorithms is affected by k providing the respective upper bounds the second set of algorithms requires only onehop neighborhood knowledge ie immediate incoming and outgoing neighbors but needs to flood the network ie relay depth is n where n is the number of nodes one result that may be of independent interest is a topology discovery mechanism to learn and estimate the topology in asynchronous directed networks with crash faults
|
[['consider', 'a', 'pointtopoint', 'messagepassing', 'network', 'we', 'are', 'interested', 'in', 'the', 'asynchronous', 'crashtolerant', 'consensus', 'problem', 'in', 'incomplete', 'networks', 'we', 'study', 'the', 'feasibility', 'and', 'efficiency', 'of', 'approximate', 'consensus', 'under', 'different', 'restrictions', 'on', 'topology', 'knowledge', 'and', 'the', 'relay', 'depth', 'ie', 'the', 'maximum', 'number', 'of', 'hops', 'any', 'message', 'can', 'be', 'relayed', 'these', 'two', 'constraints', 'are', 'common', 'in', 'largescale', 'networks', 'and', 'are', 'used', 'to', 'avoid', 'memory', 'overload', 'and', 'network', 'congestion', 'respectively', 'specifically', 'for', 'different', 'values', 'of', 'integers', 'k', 'k', 'we', 'consider', 'that', 'each', 'node', 'knows', 'all', 'its', 'neighbors', 'of', 'at', 'most', 'khop', 'distance', 'khop', 'topology', 'knowledge', 'and', 'the', 'relay', 'depth', 'is', 'k', 'we', 'consider', 'both', 'directed', 'and', 'undirected', 'graphs', 'more', 'concretely', 'we', 'answer', 'the', 'following', 'main', 'question', 'in', 'asynchronous', 'systems', 'what', 'is', 'a', 'tight', 'condition', 'on', 'the', 'underlying', 'communication', 'graphs', 'for', 'achieving', 'approximate', 'consensus', 'if', 'each', 'node', 'has', 'only', 'a', 'khop', 'topology', 'knowledge', 'and', 'relay', 'depth', 'k', 'to', 'prove', 'that', 'the', 'necessary', 'conditions', 'presented', 'in', 'the', 'paper', 'are', 'also', 'sufficient', 'we', 'have', 'developed', 'algorithms', 'that', 'achieve', 'consensus', 'in', 'graphs', 'satisfying', 'those', 'conditions', 'the', 'first', 'class', 'of', 'algorithms', 'requires', 'khop', 'topology', 'knowledge', 'and', 'relay', 'depth', 'k', 'unlike', 'prior', 'algorithms', 'these', 'algorithms', 'do', 'not', 'flood', 'the', 'network', 'and', 'each', 'node', 'does', 'not', 'need', 'the', 'full', 'topology', 'knowledge', 'we', 'show', 'how', 'the', 'convergence', 'time', 'and', 'the', 'message', 'complexity', 'of', 'those', 'algorithms', 'is', 'affected', 'by', 'k', 'providing', 'the', 'respective', 'upper', 'bounds', 'the', 'second', 'set', 'of', 'algorithms', 'requires', 'only', 'onehop', 'neighborhood', 'knowledge', 'ie', 'immediate', 'incoming', 'and', 'outgoing', 'neighbors', 'but', 'needs', 'to', 'flood', 'the', 'network', 'ie', 'relay', 'depth', 'is', 'n', 'where', 'n', 'is', 'the', 'number', 'of', 'nodes', 'one', 'result', 'that', 'may', 'be', 'of', 'independent', 'interest', 'is', 'a', 'topology', 'discovery', 'mechanism', 'to', 'learn', 'and', 'estimate', 'the', 'topology', 'in', 'asynchronous', 'directed', 'networks', 'with', 'crash', 'faults']]
|
[-0.2217281674739165, 0.05264555892150796, -0.0036008216057580415, 0.04305396919181663, -0.12743317339972243, -0.22774854739665562, 0.1264813764706908, 0.4341790472282797, -0.2910610740522999, -0.3130417215433104, 0.09165029333539453, -0.25590496656632694, -0.16000673425956036, 0.09674447229014815, -0.09109283025510252, 0.04925996564471713, 0.09499439375693018, 0.14312863257773062, -0.00703864368768607, -0.3103036804129667, 0.30068745271877406, 0.03439991773727039, 0.2709330161612362, 0.05051258815917292, 0.06433167893535306, 0.01768910411641279, -0.011179802117783392, 0.01952417765030677, -0.13012671902854614, 0.08039020200000732, 0.307358387114599, 0.226898139162031, 0.29431335930217134, -0.4610361383060829, -0.21369847052413574, 0.17934475502937186, 0.13502783611943575, 0.10046420232161668, 0.03692912637702551, -0.2592028079394264, 0.16151048781569877, -0.11088086796448902, -0.02417702949110498, -0.01053987905702633, -0.0031867001522728738, 0.0516688156475029, -0.29241508608584865, -0.019840994060591872, 0.09989826722443765, 0.03501564015851914, -0.013365152680417457, -0.10839353865078578, -0.009538203038618707, 0.18575651693844786, -0.022677308160946784, 0.008535311862013917, 0.09390035585780192, -0.12202872222302653, -0.1626528094102404, 0.3318952661278453, 0.022914149478104308, -0.1875770954165033, 0.1610222688759677, -0.07866180471644335, -0.1675650283416852, 0.12313907600073176, 0.22291252430089162, 0.11396297129161709, -0.1587321347695716, 0.08461069413510329, -0.055590183029928845, 0.15568041747662154, 0.0720945168550767, 0.058154917100827534, 0.11456879963008489, 0.1563300254314243, 0.17972096675224206, 0.1119483242476861, -0.06600773080371003, -0.10315483483545324, -0.2438422320344558, -0.09777850111870505, -0.22535956086358055, 0.022693259943815786, -0.14330247822585054, -0.10324054867623032, 0.3767880408278277, 0.17887430160317952, 0.1915128641061696, 0.11978636347896796, 0.3390752889466941, 0.05307681341549251, 0.016755359848393193, 0.21613364600649115, 0.17162067662587666, 0.10708095991145158, 0.08858626786493927, -0.14525739554654732, 0.17415956134003, 0.06339528525730344]
|
1,803.04514
|
Similar but Different: Exploiting Users' Congruity for Recommendation
Systems
|
The pervasive use of social media provides massive data about individuals'
online social activities and their social relations. The building block of most
existing recommendation systems is the similarity between users with social
relations, i.e., friends. While friendship ensures some homophily, the
similarity of a user with her friends can vary as the number of friends
increases. Research from sociology suggests that friends are more similar than
strangers, but friends can have different interests. Exogenous information such
as comments and ratings may help discern different degrees of agreement (i.e.,
congruity) among similar users. In this paper, we investigate if users'
congruity can be incorporated into recommendation systems to improve it's
performance. Experimental results demonstrate the effectiveness of embedding
congruity related information into recommendation systems.
|
cs.SI cs.IR
|
the pervasive use of social media provides massive data about individuals online social activities and their social relations the building block of most existing recommendation systems is the similarity between users with social relations ie friends while friendship ensures some homophily the similarity of a user with her friends can vary as the number of friends increases research from sociology suggests that friends are more similar than strangers but friends can have different interests exogenous information such as comments and ratings may help discern different degrees of agreement ie congruity among similar users in this paper we investigate if users congruity can be incorporated into recommendation systems to improve its performance experimental results demonstrate the effectiveness of embedding congruity related information into recommendation systems
|
[['the', 'pervasive', 'use', 'of', 'social', 'media', 'provides', 'massive', 'data', 'about', 'individuals', 'online', 'social', 'activities', 'and', 'their', 'social', 'relations', 'the', 'building', 'block', 'of', 'most', 'existing', 'recommendation', 'systems', 'is', 'the', 'similarity', 'between', 'users', 'with', 'social', 'relations', 'ie', 'friends', 'while', 'friendship', 'ensures', 'some', 'homophily', 'the', 'similarity', 'of', 'a', 'user', 'with', 'her', 'friends', 'can', 'vary', 'as', 'the', 'number', 'of', 'friends', 'increases', 'research', 'from', 'sociology', 'suggests', 'that', 'friends', 'are', 'more', 'similar', 'than', 'strangers', 'but', 'friends', 'can', 'have', 'different', 'interests', 'exogenous', 'information', 'such', 'as', 'comments', 'and', 'ratings', 'may', 'help', 'discern', 'different', 'degrees', 'of', 'agreement', 'ie', 'congruity', 'among', 'similar', 'users', 'in', 'this', 'paper', 'we', 'investigate', 'if', 'users', 'congruity', 'can', 'be', 'incorporated', 'into', 'recommendation', 'systems', 'to', 'improve', 'its', 'performance', 'experimental', 'results', 'demonstrate', 'the', 'effectiveness', 'of', 'embedding', 'congruity', 'related', 'information', 'into', 'recommendation', 'systems']]
|
[-0.12004340891895306, 0.06529609486554569, -0.03637457885798217, 0.12001268175100127, -0.23192219994398916, -0.20524179809276136, 0.11302696182786848, 0.4376334157441893, -0.21753331701620482, -0.365649915292799, 0.004683493748269675, -0.44295227949717836, -0.23862332355741772, 0.12736819362391777, -0.1258668740704325, -0.04272167689135043, 0.09010279780740084, 0.14237056430354114, -0.023849529766237304, -0.3372013732610691, 0.31710966708769484, 0.04619135770283925, 0.315078787644574, 0.06496159563727316, 0.01629693249349422, 0.02703279732621365, -0.10719427581305706, 0.03551149710772499, -0.0756851094956214, 0.18194144482033386, 0.37749471132373136, 0.2740063857880511, 0.38233889264595367, -0.40242745037070443, -0.22703720608185377, 0.07598346707245876, 0.17932294952427788, 0.042227187807927086, -0.040770044684200736, -0.3869583769250781, 0.07228823733936635, -0.24070144469489255, -0.02421264333515278, -0.061632873047323476, -0.0272867015383165, 0.07651534405745371, -0.22251752523645277, 0.03540405102690021, 0.014049888122826815, 0.1028360997461864, 0.002866025038348462, -0.11782425956710452, -0.03975167659471833, 0.2823838310395067, 0.127576239894317, -0.06494587733628109, 0.19220853529705276, -0.16914387636314776, -0.1677078348697133, 0.43756295327517775, 0.029187326658786567, -0.14181025246638926, 0.2212792703987009, -0.04817656262387191, -0.12115745388103777, 0.019735416205930372, 0.2776932330847147, 0.026045577743300987, -0.18880637301762979, -0.072952627973263, -0.10595207944166876, 0.220362460458531, 0.08318427762001633, 0.08225813490706411, 0.2156571199565435, 0.1433186522579842, 0.07482161462464701, 0.04166904674005514, 0.06065144126243409, -0.09806877419713043, -0.08537428956779261, -0.15302424549431196, -0.14851117271539424, 0.032316568307578564, -0.18752851791941794, -0.06538476073934187, 0.36702118067431355, 0.21417714843259855, 0.16405924916597864, 0.07222385492904353, 0.24190972206152736, -0.011696931615006179, 0.09853147844701345, 0.11540972468097724, 0.15001273302163276, 0.00821889228668184, 0.1706540007463416, -0.15293857254906587, 0.19993133678649283, -0.024687899918776127]
|
1,803.04515
|
Unveiling the physical conditions of the youngest disks: A warm embedded
disk in L1527
|
[Abridged] Protoplanetary disks have been studied extensively, both
physically and chemically, to understand the environment in which planets form.
However, the first steps of planet formation are likely to occur already when
the protostar and disk are still embedded in their natal envelope. The initial
conditions for planet formation may thus be provided by these young embedded
disks, of which the physical and chemical structure is poorly characterized. We
aim to constrain the midplane temperature structure, one of the critical
unknowns, of the embedded disk around L1527. In particular, we set out to
determine whether there is an extended cold outer region where CO is frozen
out, as is the case for Class II disks. We use archival ALMA data to directly
observe the midplane of the near edge-on L1527 disk. Optically thick $^{13}$CO
($J=2-1$) and C$^{18}$O ($J=2-1$) emission is observed throughout the disk and
inner envelope, while N$_2$D$^+ (J=3-2$), which can only be abundant when CO is
frozen out, is not detected. Both CO isotopologues have brightness temperatures
$\gtrsim$ 25 K along the midplane. Disk and envelope emission can be
disentangled kinematically, because the largest velocities are reached in the
disk. A power law radial temperature profile constructed using the highest
midplane temperature at these velocities suggest that the temperature is above
20 K out to at least 75 AU, and possibly throughout the entire 125 AU disk.
Radiative transfer models show that a model without CO freeze-out in the disk
matches the C$^{18}$O observations better than a model with the CO snowline at
$\sim$70 AU. In addition, there is no evidence for a large (order of magnitude)
depletion of CO. The disk around L1527 is likely to be warm enough to have CO
present in the gas phase throughout the disk, suggesting that young embedded
disks can indeed be warmer than the more evolved Class II disks.
|
astro-ph.SR astro-ph.GA
|
abridged protoplanetary disks have been studied extensively both physically and chemically to understand the environment in which planets form however the first steps of planet formation are likely to occur already when the protostar and disk are still embedded in their natal envelope the initial conditions for planet formation may thus be provided by these young embedded disks of which the physical and chemical structure is poorly characterized we aim to constrain the midplane temperature structure one of the critical unknowns of the embedded disk around l1527 in particular we set out to determine whether there is an extended cold outer region where co is frozen out as is the case for class ii disks we use archival alma data to directly observe the midplane of the near edgeon l1527 disk optically thick 13co j21 and c18o j21 emission is observed throughout the disk and inner envelope while n_2d j32 which can only be abundant when co is frozen out is not detected both co isotopologues have brightness temperatures gtrsim 25 k along the midplane disk and envelope emission can be disentangled kinematically because the largest velocities are reached in the disk a power law radial temperature profile constructed using the highest midplane temperature at these velocities suggest that the temperature is above 20 k out to at least 75 au and possibly throughout the entire 125 au disk radiative transfer models show that a model without co freezeout in the disk matches the c18o observations better than a model with the co snowline at sim70 au in addition there is no evidence for a large order of magnitude depletion of co the disk around l1527 is likely to be warm enough to have co present in the gas phase throughout the disk suggesting that young embedded disks can indeed be warmer than the more evolved class ii disks
|
[['abridged', 'protoplanetary', 'disks', 'have', 'been', 'studied', 'extensively', 'both', 'physically', 'and', 'chemically', 'to', 'understand', 'the', 'environment', 'in', 'which', 'planets', 'form', 'however', 'the', 'first', 'steps', 'of', 'planet', 'formation', 'are', 'likely', 'to', 'occur', 'already', 'when', 'the', 'protostar', 'and', 'disk', 'are', 'still', 'embedded', 'in', 'their', 'natal', 'envelope', 'the', 'initial', 'conditions', 'for', 'planet', 'formation', 'may', 'thus', 'be', 'provided', 'by', 'these', 'young', 'embedded', 'disks', 'of', 'which', 'the', 'physical', 'and', 'chemical', 'structure', 'is', 'poorly', 'characterized', 'we', 'aim', 'to', 'constrain', 'the', 'midplane', 'temperature', 'structure', 'one', 'of', 'the', 'critical', 'unknowns', 'of', 'the', 'embedded', 'disk', 'around', 'l1527', 'in', 'particular', 'we', 'set', 'out', 'to', 'determine', 'whether', 'there', 'is', 'an', 'extended', 'cold', 'outer', 'region', 'where', 'co', 'is', 'frozen', 'out', 'as', 'is', 'the', 'case', 'for', 'class', 'ii', 'disks', 'we', 'use', 'archival', 'alma', 'data', 'to', 'directly', 'observe', 'the', 'midplane', 'of', 'the', 'near', 'edgeon', 'l1527', 'disk', 'optically', 'thick', '13co', 'j21', 'and', 'c18o', 'j21', 'emission', 'is', 'observed', 'throughout', 'the', 'disk', 'and', 'inner', 'envelope', 'while', 'n_2d', 'j32', 'which', 'can', 'only', 'be', 'abundant', 'when', 'co', 'is', 'frozen', 'out', 'is', 'not', 'detected', 'both', 'co', 'isotopologues', 'have', 'brightness', 'temperatures', 'gtrsim', '25', 'k', 'along', 'the', 'midplane', 'disk', 'and', 'envelope', 'emission', 'can', 'be', 'disentangled', 'kinematically', 'because', 'the', 'largest', 'velocities', 'are', 'reached', 'in', 'the', 'disk', 'a', 'power', 'law', 'radial', 'temperature', 'profile', 'constructed', 'using', 'the', 'highest', 'midplane', 'temperature', 'at', 'these', 'velocities', 'suggest', 'that', 'the', 'temperature', 'is', 'above', '20', 'k', 'out', 'to', 'at', 'least', '75', 'au', 'and', 'possibly', 'throughout', 'the', 'entire', '125', 'au', 'disk', 'radiative', 'transfer', 'models', 'show', 'that', 'a', 'model', 'without', 'co', 'freezeout', 'in', 'the', 'disk', 'matches', 'the', 'c18o', 'observations', 'better', 'than', 'a', 'model', 'with', 'the', 'co', 'snowline', 'at', 'sim70', 'au', 'in', 'addition', 'there', 'is', 'no', 'evidence', 'for', 'a', 'large', 'order', 'of', 'magnitude', 'depletion', 'of', 'co', 'the', 'disk', 'around', 'l1527', 'is', 'likely', 'to', 'be', 'warm', 'enough', 'to', 'have', 'co', 'present', 'in', 'the', 'gas', 'phase', 'throughout', 'the', 'disk', 'suggesting', 'that', 'young', 'embedded', 'disks', 'can', 'indeed', 'be', 'warmer', 'than', 'the', 'more', 'evolved', 'class', 'ii', 'disks']]
|
[-0.05613881626379643, 0.10814712164682617, -0.002934716014239732, 0.04801162034099843, -0.061041688994869205, -0.0768728358987538, 0.001985742037657733, 0.44986649582156474, -0.21934536756907325, -0.30469966894369815, 0.1028313117661637, -0.23932169765402778, -0.0042463166689693105, 0.12635305681805395, 0.007197359132917272, -0.040764313384212034, 0.03139887310709189, -0.1163123471899377, -0.040400585303577354, -0.2398415008871771, 0.29727974225967385, 0.07660674047731707, 0.06955573630962268, 0.016860007924407505, -0.008053436947302308, -0.22022263741295509, 0.005187816691093605, -0.04120763101310599, -0.17675274348111644, 0.013905164468649197, 0.27187132927593877, 0.08096861655278637, 0.19847762181565262, -0.4085299304376046, -0.23895853582142723, 0.05837338321170134, 0.2048058833122181, 0.04605914604764215, 0.007932161618976155, -0.23530678739424638, 0.11297008972232604, -0.14952606235293245, -0.1937029136760721, 0.037819254881186974, 0.07063283155819938, -0.05304798214548662, -0.236655106715644, 0.09030601916711138, 0.07001797353900052, 0.11919843259231341, -0.1123358871890262, -0.1507380374867023, -0.14663601242846727, 0.0482863539128721, -0.007702023002641535, 0.0921684383869798, 0.2593019442364549, -0.09668745733172147, 0.03201568015123202, 0.3903983787800755, -0.10549572058420245, -0.053522330744969614, 0.2772373901423923, -0.2654136445276849, -0.15883655865860555, 0.2282280169388213, 0.11392489879227825, 0.14180281031233033, -0.13614212588829436, 0.016334775332127877, -0.06136225724613903, 0.2361806167446342, 0.09956721928193683, 0.03083198354165085, 0.4013142760505472, 0.08999633269450812, 0.05628619042811403, 0.14167913369356705, -0.2332796249312611, -0.1289798869701663, -0.19781293358328272, -0.15521012278519342, -0.15943327386679676, 0.06222610532847334, -0.10214201746434824, -0.0835094085908088, 0.2548144815940241, 0.11005367648081016, 0.2616350928496292, -0.0040533573978541355, 0.31909934089076025, 0.06862520588744427, 0.12448900263277289, 0.19193984404459452, 0.2960946761526794, 0.15481787097706642, 0.1290386814155396, -0.22412276994282512, 0.13921079653447863, -0.04399635327503712]
|
1,803.04516
|
Explicit inverse of tridiagonal matrix with applications in
autoregressive modeling
|
We present the explicit inverse of a class of symmetric tridiagonal matrices
which is almost Toeplitz, except that the first and last diagonal elements are
different from the rest. This class of tridiagonal matrices are of special
interest in complex statistical models which uses the first order
autoregression to induce dependence in the covariance structure, for instance,
in econometrics or spatial modeling. They also arise in interpolation problems
using the cubic spline. We show that the inverse can be expressed as a linear
combination of Chebyshev polynomials of the second kind and present results on
the properties of the inverse, such as bounds on the row sums, the trace of the
inverse and its square, and their limits as the order of the matrix increases.
|
math.NA
|
we present the explicit inverse of a class of symmetric tridiagonal matrices which is almost toeplitz except that the first and last diagonal elements are different from the rest this class of tridiagonal matrices are of special interest in complex statistical models which uses the first order autoregression to induce dependence in the covariance structure for instance in econometrics or spatial modeling they also arise in interpolation problems using the cubic spline we show that the inverse can be expressed as a linear combination of chebyshev polynomials of the second kind and present results on the properties of the inverse such as bounds on the row sums the trace of the inverse and its square and their limits as the order of the matrix increases
|
[['we', 'present', 'the', 'explicit', 'inverse', 'of', 'a', 'class', 'of', 'symmetric', 'tridiagonal', 'matrices', 'which', 'is', 'almost', 'toeplitz', 'except', 'that', 'the', 'first', 'and', 'last', 'diagonal', 'elements', 'are', 'different', 'from', 'the', 'rest', 'this', 'class', 'of', 'tridiagonal', 'matrices', 'are', 'of', 'special', 'interest', 'in', 'complex', 'statistical', 'models', 'which', 'uses', 'the', 'first', 'order', 'autoregression', 'to', 'induce', 'dependence', 'in', 'the', 'covariance', 'structure', 'for', 'instance', 'in', 'econometrics', 'or', 'spatial', 'modeling', 'they', 'also', 'arise', 'in', 'interpolation', 'problems', 'using', 'the', 'cubic', 'spline', 'we', 'show', 'that', 'the', 'inverse', 'can', 'be', 'expressed', 'as', 'a', 'linear', 'combination', 'of', 'chebyshev', 'polynomials', 'of', 'the', 'second', 'kind', 'and', 'present', 'results', 'on', 'the', 'properties', 'of', 'the', 'inverse', 'such', 'as', 'bounds', 'on', 'the', 'row', 'sums', 'the', 'trace', 'of', 'the', 'inverse', 'and', 'its', 'square', 'and', 'their', 'limits', 'as', 'the', 'order', 'of', 'the', 'matrix', 'increases']]
|
[-0.07884969365457073, 0.07857261266186834, -0.015928272016346456, 0.06676585822179913, -0.07631770560145378, -0.06873101235926152, -0.020982173170894385, 0.36954167354106904, -0.3399736121147871, -0.24431255628168583, 0.1876650391742587, -0.27002966984547677, -0.19657002996094525, 0.16790944872610272, -0.0017877709455788135, 0.07387550134025514, 0.010727906702086329, 0.07114466438814998, -0.17892016946244985, -0.24018748856149613, 0.34695810087770224, 0.04127185923233628, 0.2068643780387938, 0.011854906668886542, 0.11024288187921047, 0.008049576837569475, -0.05540383844450116, -0.02055157308280468, -0.051258185357437466, 0.14408225652761758, 0.2691116583937837, 0.10676674674358219, 0.2382983402442187, -0.40373753852769734, -0.1568575590327382, 0.1671840282008052, 0.12185091821104288, 0.07501215662620962, -0.03151732595823705, -0.2450653206408024, 0.0685062046488747, -0.1546506019346416, -0.10831523997988551, -0.05885570776090026, -0.0007870786264538765, 0.0757284572944045, -0.31966326060006395, 0.09813140487461351, 0.12233317947993055, 0.05068644899502397, -0.018642246973700823, -0.18994616056233646, 0.032341341610997915, 0.12101948537118733, 0.04084616150148213, -0.07142917380109429, 0.05479583695903421, -0.09384702585451304, -0.09657116413488984, 0.3943421217054129, -0.07422894693072886, -0.22797315584123135, 0.14022145158797503, -0.14904750690609217, -0.1397910396710504, 0.0884591813320294, 0.21745789533108473, 0.13092426311352756, -0.10494673779653385, 0.13579431865923106, -0.09514648565649986, 0.13587297004740687, 0.06605772402882576, 0.018794114764779806, 0.13847285724431277, 0.0848454357162118, 0.07254875414073467, 0.16274519784841687, -0.06390792641043663, -0.09269506554678082, -0.32960951966047286, -0.1637151996381581, -0.22460483963787556, 0.026779502773541027, -0.1601485745976679, -0.2196627718275413, 0.42985788962244986, 0.11244611852988601, 0.21416272515431045, 0.07233296092785894, 0.22482775141298772, 0.16945734280813485, 0.08577272603847086, 0.033086306928656994, 0.19151846821606158, 0.19510470443218947, 0.07646646823734045, -0.1698940114530269, 0.08422500889748336, 0.12928025638312102]
|
1,803.04517
|
T-duality and high-derivative gravity theories: the BTZ black
hole/string paradigm
|
We show that the temperature and entropy of a BTZ black hole are invariant
under T-duality to next to leading order in $M_\star^{-2}$, $M_\star$ being the
scale suppressing higher-curvature/derivative terms in the Lagrangian. We work
in the framework of a two-parameter family of theories exhibiting T-duality,
which includes (but goes beyond) String Theory. Interestingly enough, the
AdS/CFT correspondence enforces quantization conditions on these parameters. In
the particular case of bosonic/heterotic string theory, our results extend
those of a classical paper by Horowitz and Welch. For generic (albeit
quantized) values of the parameters, it suggests that T-duality might be an
interesting tool to constrain consistent low-energy effective actions while
entailing physical equivalences outside String Theory. Moreover, it generates a
new family of regular asymptotically flat black string solutions in
three-dimensions.
|
hep-th gr-qc
|
we show that the temperature and entropy of a btz black hole are invariant under tduality to next to leading order in m_star2 m_star being the scale suppressing highercurvaturederivative terms in the lagrangian we work in the framework of a twoparameter family of theories exhibiting tduality which includes but goes beyond string theory interestingly enough the adscft correspondence enforces quantization conditions on these parameters in the particular case of bosonicheterotic string theory our results extend those of a classical paper by horowitz and welch for generic albeit quantized values of the parameters it suggests that tduality might be an interesting tool to constrain consistent lowenergy effective actions while entailing physical equivalences outside string theory moreover it generates a new family of regular asymptotically flat black string solutions in threedimensions
|
[['we', 'show', 'that', 'the', 'temperature', 'and', 'entropy', 'of', 'a', 'btz', 'black', 'hole', 'are', 'invariant', 'under', 'tduality', 'to', 'next', 'to', 'leading', 'order', 'in', 'm_star2', 'm_star', 'being', 'the', 'scale', 'suppressing', 'highercurvaturederivative', 'terms', 'in', 'the', 'lagrangian', 'we', 'work', 'in', 'the', 'framework', 'of', 'a', 'twoparameter', 'family', 'of', 'theories', 'exhibiting', 'tduality', 'which', 'includes', 'but', 'goes', 'beyond', 'string', 'theory', 'interestingly', 'enough', 'the', 'adscft', 'correspondence', 'enforces', 'quantization', 'conditions', 'on', 'these', 'parameters', 'in', 'the', 'particular', 'case', 'of', 'bosonicheterotic', 'string', 'theory', 'our', 'results', 'extend', 'those', 'of', 'a', 'classical', 'paper', 'by', 'horowitz', 'and', 'welch', 'for', 'generic', 'albeit', 'quantized', 'values', 'of', 'the', 'parameters', 'it', 'suggests', 'that', 'tduality', 'might', 'be', 'an', 'interesting', 'tool', 'to', 'constrain', 'consistent', 'lowenergy', 'effective', 'actions', 'while', 'entailing', 'physical', 'equivalences', 'outside', 'string', 'theory', 'moreover', 'it', 'generates', 'a', 'new', 'family', 'of', 'regular', 'asymptotically', 'flat', 'black', 'string', 'solutions', 'in', 'threedimensions']]
|
[-0.13897041920461647, 0.15469644890115541, -0.0944673037821693, 0.13660547832795789, -0.08765452216002381, -0.1779212782931854, 0.03677720254442344, 0.2867004499061122, -0.20189686159100678, -0.27684170339119785, 0.05824014921081326, -0.261566433623167, -0.1823972321950668, 0.18691786184966092, -0.12018074367069713, 0.052716959337265015, -0.01039848535133171, 0.07277752182978604, -0.12487582905432357, -0.25149795892722315, 0.3410406336289949, 0.06576091625417273, 0.2849183773976706, 0.011884750426462334, 0.07779533836051881, -0.014607680530937773, 0.005437284726039728, 0.060077863257555736, -0.1669847478223678, 0.10042794597970825, 0.24522016137572272, 0.08449869189367053, 0.1397304486572033, -0.4039750553830157, -0.22989781148776056, 0.0458041827512225, 0.16028368570620105, 0.1802382421142067, -0.035583297874490244, -0.25338709085906247, 0.10322665285558388, -0.20439230138805534, -0.16379096754457032, -0.11170187270978377, 0.016632984222341624, -0.07550700455358887, -0.2505086155696994, 0.06847857768413618, 0.09476431738954806, -0.011976428119288314, -0.09279028276935042, -0.0034145821987222585, -0.03367792185218561, 0.07017571428450682, 0.1501102468312075, 0.06426228623512009, 0.12221465442359211, -0.14092698395400058, -0.10537444777594554, 0.35541410000887624, -0.06118752277873841, -0.19269651269155835, 0.16950908139170634, -0.14152527735420753, -0.1920437656439072, 0.06883166112478763, 0.08619374925610683, 0.17409308342261254, -0.13648166233998915, 0.19826922333514732, -0.047471885875214836, 0.13540106078024053, 0.1251060897521379, 0.0836685318425889, 0.30030482204719666, 0.08995405157163207, 0.055929439208869426, 0.14928410152002933, -0.005439996292504172, -0.1606554649754091, -0.3768866403501422, -0.11245667313166435, -0.09089876057225324, 0.09223542489954048, -0.14559731096567932, -0.1860739116941298, 0.3569463529136209, 0.14692760810084154, 0.19836546082995712, 0.09871379831632127, 0.18314471929555848, 0.08840937314218739, 0.060908515800884556, 0.07877315565692408, 0.2596195542238771, 0.14454337049068677, 0.056056285505219995, -0.21165210982814195, -0.06311524802789328, 0.16037323469636106]
|
1,803.04518
|
On multivariate modifications of Cramer Lundberg risk model with
constant intensities
|
The paper considers very general multivariate modifications of
Cramer-Lundberg risk model. The claims can be of different types and can arrive
in groups. The groups arrival processes within a type have constant
intensities. The counting groups processes are dependent multivariate compound
Poisson processes of type I. We allow empty groups and show that in that case
we can find stochastically equivalent Cramer-Lundberg model with non-empty
groups.
The investigated model generalizes the risk model with common shocks, the
Poisson risk process of order k, the Poisson negative binomial, the
Polya-Aeppli, the Polya-Aeppli of order k among others. All of them with one or
more types of polices.
The relations between the numerical characteristics and distributions of the
components of the risk processes are proven to be corollaries of the
corresponding formulae of the Cramer-Lundberg risk model.
|
math.PR
|
the paper considers very general multivariate modifications of cramerlundberg risk model the claims can be of different types and can arrive in groups the groups arrival processes within a type have constant intensities the counting groups processes are dependent multivariate compound poisson processes of type i we allow empty groups and show that in that case we can find stochastically equivalent cramerlundberg model with nonempty groups the investigated model generalizes the risk model with common shocks the poisson risk process of order k the poisson negative binomial the polyaaeppli the polyaaeppli of order k among others all of them with one or more types of polices the relations between the numerical characteristics and distributions of the components of the risk processes are proven to be corollaries of the corresponding formulae of the cramerlundberg risk model
|
[['the', 'paper', 'considers', 'very', 'general', 'multivariate', 'modifications', 'of', 'cramerlundberg', 'risk', 'model', 'the', 'claims', 'can', 'be', 'of', 'different', 'types', 'and', 'can', 'arrive', 'in', 'groups', 'the', 'groups', 'arrival', 'processes', 'within', 'a', 'type', 'have', 'constant', 'intensities', 'the', 'counting', 'groups', 'processes', 'are', 'dependent', 'multivariate', 'compound', 'poisson', 'processes', 'of', 'type', 'i', 'we', 'allow', 'empty', 'groups', 'and', 'show', 'that', 'in', 'that', 'case', 'we', 'can', 'find', 'stochastically', 'equivalent', 'cramerlundberg', 'model', 'with', 'nonempty', 'groups', 'the', 'investigated', 'model', 'generalizes', 'the', 'risk', 'model', 'with', 'common', 'shocks', 'the', 'poisson', 'risk', 'process', 'of', 'order', 'k', 'the', 'poisson', 'negative', 'binomial', 'the', 'polyaaeppli', 'the', 'polyaaeppli', 'of', 'order', 'k', 'among', 'others', 'all', 'of', 'them', 'with', 'one', 'or', 'more', 'types', 'of', 'polices', 'the', 'relations', 'between', 'the', 'numerical', 'characteristics', 'and', 'distributions', 'of', 'the', 'components', 'of', 'the', 'risk', 'processes', 'are', 'proven', 'to', 'be', 'corollaries', 'of', 'the', 'corresponding', 'formulae', 'of', 'the', 'cramerlundberg', 'risk', 'model']]
|
[-0.0938097129981206, 0.0980574282958211, -0.0883242897413395, 0.112253127980579, -0.04239566368395808, -0.14524419393597376, 0.07810897884148828, 0.38390613358329845, -0.29258765394419983, -0.25224273768081157, 0.07566700967684112, -0.2975757837433506, -0.13711146778013145, 0.17084902754501888, -0.07737264672673687, -0.0011428353100739143, 0.0014836486601443202, 0.059154814466213186, -0.0046093846947230675, -0.27910214228797015, 0.3635178469052469, 0.04667296382702059, 0.26915957929773465, -0.02296277679571951, 0.06801766164393888, -0.0041842331661394346, -0.06401298992611744, 0.008201118083498268, -0.11016486928166598, 0.11984320736352216, 0.2614177338296065, 0.0895604582744892, 0.2830737952798329, -0.394725639483443, -0.21909289889175582, 0.17008176740879813, 0.06934197947244954, 0.028422074571803763, 0.02914033602627805, -0.262377993841828, 0.05949982748414321, -0.18219439672582127, -0.13880326261881876, -0.022898927006732534, -0.008154173416120035, 0.09954773848531423, -0.30058108971074776, 0.0984055578462883, 0.08634689413356009, 0.005391647232075533, -0.07385099006049266, -0.17473944255788984, -0.024373099069904397, 0.14736648479743894, 0.08384893474716659, -0.10380276727869554, 0.10878841114678868, -0.08082537236450998, -0.18029210709587293, 0.37675883095987417, -0.04953681949733032, -0.20780142921678446, 0.18827551259762712, -0.17953117511890554, -0.17053507526784584, 0.1282982716716274, 0.17109664250682627, 0.1131796533483322, -0.17896429708020556, 0.08494621446847708, -0.034250765052382594, 0.07802038990237095, 0.07142993142241957, -0.019197515577198593, 0.1392673497536668, 0.10980626065973882, 0.046658660291588466, 0.12913308450779706, -0.05271100278930842, -0.11530215136223922, -0.3214289852314525, -0.15165524132963684, -0.06791948866944209, 0.03641435136432173, -0.1529621049844772, -0.16704938224585797, 0.34686621559990777, 0.1293515689170885, 0.177437738398159, 0.10352014688628346, 0.22413651171243854, 0.15933732623579325, 0.020148820902600333, 0.07681964884891554, 0.10845831538964477, 0.15007167958688958, 0.012333531089071013, -0.1279772514925787, 0.15955828413436257, 0.04253367374271706]
|
1,803.04519
|
Analytical results for the quantum non-Markovianity of spin ensembles
undergoing pure dephasing dynamics
|
We study analytically the non-Markovianity of a spin ensemble, with arbitrary
number of spins and spin quantum number, undergoing a pure dephasing dynamics.
The system is considered as a part of a larger spin ensemble of any geometry
with pairwise interactions. We derive exact formulas for the reduced dynamics
of the system and for its non-Markovianity as assessed by the witness of
Lorenzo \emph{et al.} [Phys.~Rev.~A \textbf{88}, 020102(R) (2013)]. The
non-Markovianity is further investigated in the thermodynamic limit when the
environment's size goes to infinity. In this limit and for finite-size systems,
we find that the Markovian's character of the system's dynamics crucially
depends on the range of the interactions. We also show that, when the system
and its environment are initially in a separable state, the appearance of
non-Markovianity is independent of the entanglement generation between the
system and its environment.
|
quant-ph
|
we study analytically the nonmarkovianity of a spin ensemble with arbitrary number of spins and spin quantum number undergoing a pure dephasing dynamics the system is considered as a part of a larger spin ensemble of any geometry with pairwise interactions we derive exact formulas for the reduced dynamics of the system and for its nonmarkovianity as assessed by the witness of lorenzo emphet al physreva textbf88 020102r 2013 the nonmarkovianity is further investigated in the thermodynamic limit when the environments size goes to infinity in this limit and for finitesize systems we find that the markovians character of the systems dynamics crucially depends on the range of the interactions we also show that when the system and its environment are initially in a separable state the appearance of nonmarkovianity is independent of the entanglement generation between the system and its environment
|
[['we', 'study', 'analytically', 'the', 'nonmarkovianity', 'of', 'a', 'spin', 'ensemble', 'with', 'arbitrary', 'number', 'of', 'spins', 'and', 'spin', 'quantum', 'number', 'undergoing', 'a', 'pure', 'dephasing', 'dynamics', 'the', 'system', 'is', 'considered', 'as', 'a', 'part', 'of', 'a', 'larger', 'spin', 'ensemble', 'of', 'any', 'geometry', 'with', 'pairwise', 'interactions', 'we', 'derive', 'exact', 'formulas', 'for', 'the', 'reduced', 'dynamics', 'of', 'the', 'system', 'and', 'for', 'its', 'nonmarkovianity', 'as', 'assessed', 'by', 'the', 'witness', 'of', 'lorenzo', 'emphet', 'al', 'physreva', 'textbf88', '020102r', '2013', 'the', 'nonmarkovianity', 'is', 'further', 'investigated', 'in', 'the', 'thermodynamic', 'limit', 'when', 'the', 'environments', 'size', 'goes', 'to', 'infinity', 'in', 'this', 'limit', 'and', 'for', 'finitesize', 'systems', 'we', 'find', 'that', 'the', 'markovians', 'character', 'of', 'the', 'systems', 'dynamics', 'crucially', 'depends', 'on', 'the', 'range', 'of', 'the', 'interactions', 'we', 'also', 'show', 'that', 'when', 'the', 'system', 'and', 'its', 'environment', 'are', 'initially', 'in', 'a', 'separable', 'state', 'the', 'appearance', 'of', 'nonmarkovianity', 'is', 'independent', 'of', 'the', 'entanglement', 'generation', 'between', 'the', 'system', 'and', 'its', 'environment']]
|
[-0.16146690211529638, 0.16270890715738415, -0.07929421825884732, 0.0317803670401216, 0.054200902113902334, -0.10702217617241422, 0.046530236425670855, 0.31614012535205704, -0.2247620449840679, -0.28600983537654295, 0.07369714120736491, -0.2725523398843997, -0.14014167179756448, 0.19318450860025244, 0.0017771308368331165, 0.042902495856116135, 0.058599386597052217, 0.051166600567546294, -0.046428390514396185, -0.24630269832275015, 0.32116325598708373, 0.06562515170765869, 0.2519794513177497, 0.061054126788855446, 0.12006519056719576, 0.04931960330206029, 0.050004903755500804, 0.026119276108418372, -0.11944038451020524, 0.059502526014673374, 0.1815716980457808, 0.11075899379245535, 0.24805131783140888, -0.37695509941706845, -0.20163874280579547, 0.10122181787915158, 0.11244622471491338, 0.16790605924944293, 0.022419860470108688, -0.32482008795346407, 0.015207014553552719, -0.21785103837490505, -0.1313749535227234, -0.06870913706349989, 0.09339744889619925, 0.016547794899645637, -0.2518906371654771, 0.09425594257946285, 0.1166712402560953, 0.05986616458720693, -0.039443470919934095, -0.044217564464474085, -0.01724929706001958, 0.14075949659500048, -0.023605551135463927, -0.008660469489859034, 0.1558363821009894, -0.1293835272890324, -0.11180138309545656, 0.3373587692787223, -0.06720455510769674, -0.2173013942289754, 0.22910392881826835, -0.1522596857233092, -0.09906635771721027, 0.07117433097966491, 0.15169766913531413, 0.11140884434579747, -0.14447144489402633, 0.11154878631371225, -0.03531503786682977, 0.1728857814182415, 0.004020050105288731, 0.09097011214954422, 0.1767708798474137, 0.15023171700741964, 0.050211056723450935, 0.20575392129996145, -0.06898306036394433, -0.1371512825816295, -0.2621224403021368, -0.18118110479678007, -0.24231414074666058, 0.08825587309196803, -0.08398981213333095, -0.139537562725154, 0.38464955422789493, 0.17409727954246262, 0.18220378352817915, 0.03938681842653569, 0.23272751498940988, 0.1311388491629788, -0.007770588908820076, 0.07684508933692324, 0.24154171530580057, 0.16004610468686314, 0.07155564125191341, -0.3382907470569332, 0.0674253513125029, 0.033643573147726924]
|
1,803.0452
|
Is the security of quantum cryptography guaranteed by the laws of
physics?
|
It is often claimed that the security of quantum key distribution (QKD) is
guaranteed by the laws of physics. However, this claim is content-free if the
underlying theoretical definition of QKD is not actually compatible with the
laws of physics. This paper observes that (1) the laws of physics pose serious
obstacles to the security of QKD and (2) the same laws are ignored in all QKD
"security proofs".
|
quant-ph cs.CR
|
it is often claimed that the security of quantum key distribution qkd is guaranteed by the laws of physics however this claim is contentfree if the underlying theoretical definition of qkd is not actually compatible with the laws of physics this paper observes that 1 the laws of physics pose serious obstacles to the security of qkd and 2 the same laws are ignored in all qkd security proofs
|
[['it', 'is', 'often', 'claimed', 'that', 'the', 'security', 'of', 'quantum', 'key', 'distribution', 'qkd', 'is', 'guaranteed', 'by', 'the', 'laws', 'of', 'physics', 'however', 'this', 'claim', 'is', 'contentfree', 'if', 'the', 'underlying', 'theoretical', 'definition', 'of', 'qkd', 'is', 'not', 'actually', 'compatible', 'with', 'the', 'laws', 'of', 'physics', 'this', 'paper', 'observes', 'that', '1', 'the', 'laws', 'of', 'physics', 'pose', 'serious', 'obstacles', 'to', 'the', 'security', 'of', 'qkd', 'and', '2', 'the', 'same', 'laws', 'are', 'ignored', 'in', 'all', 'qkd', 'security', 'proofs']]
|
[-0.16049079166516414, 0.09228162382565, -0.1607608972254264, 0.08584090755389565, -0.021866906701368007, -0.249233758323552, 0.025658154892666703, 0.2727741861496778, -0.25592125141445327, -0.3181944465922082, 0.09063440676853883, -0.23013367940096513, -0.11898558799122624, 0.22056873763829252, -0.21813929176834576, 0.15337966170216746, -0.011637749390073997, -0.01836424134671688, 0.010964248175321914, -0.2647606225355583, 0.3709267983213067, 0.04391503918005208, 0.33698340454686654, 0.07460499804138261, 0.08603442619171213, -0.0438650893732248, -0.031093205695095306, -0.06613398907596574, -0.1111715128108164, 0.10606549107743536, 0.29114608329666014, 0.1815386692281155, 0.32566190116545735, -0.3749772126731627, -0.1901496363502434, 0.10463538289289265, 0.13735402430243351, 0.09085822132799555, -0.04654255935319645, -0.2874603787615128, 0.0968255916993846, -0.19910241775762508, -0.18523233657127575, 0.0011941499610924546, -0.0019352513653061846, 0.03641080671941916, -0.17461771526894368, 0.02434303514275919, 0.1532269321820315, 0.05991160920273293, 0.055397465116317, -0.052745386492460966, 0.01007147625033908, 0.14679547548567987, 0.0667925384169554, -0.0328302947078448, 0.1312277554671335, -0.16188873403349563, -0.16282272470348022, 0.4240962490439415, 0.08299102032940615, -0.19380285344360507, 0.15472774128155672, -0.15984023642479717, -0.1907055594424224, 0.09419198422898155, 0.08676916340311222, 0.004047032637412057, -0.17048230982276008, 0.10730568570793633, -0.08010382713366519, 0.22379280719906092, 0.010444942950818907, 0.11350857851999428, 0.19484240638420863, 0.12794856973649824, 0.09030893156031038, -0.02050214307382703, -0.031223454502383795, -0.1997583417345167, -0.3838107704102774, -0.17482850519145893, -0.28031291515628043, 0.10324392332511385, -0.03177711329921936, -0.10006599573363714, 0.34199557261651053, 0.24384957312277572, 0.0791639471974443, 0.038104689912870526, 0.39437796131652947, 0.10151285149143352, 0.06180803683202933, 0.11681101538638092, 0.3047973068211885, 0.09820165997713476, 0.1249245445463149, -0.09194310916149441, 0.21326896409942386, -0.019027662066304508]
|
1,803.04521
|
A note on some sub-Gaussian random variables
|
In [8] the author of this paper continued the research on the complex-valued
discrete random variables $X_l(m,N)$ ($0\le l\le N-1$, $1\le M\le N)$ recently
introduced and studied in [24]. Here we extend our results by considering
$X_l(m,N)$ as sub-Gaussian random variables. Our investigation is motivated by
the known fact thatthe so-called Restricted Isometry Property (RIP) introduced
in [4] holds with high probability for any matrix generated by a sub-Gaussian
random variable. Notice that sensing matrices with the RIP play a crucial role
in Theory of compressive sensing.
Our main results concern the proofs of the lower and upper bound estimates of
the expected values of the random variables $|X_l(m,N)|$, $|U_l(m,N)|$ and
$|V_l(m,N)|$, where $U_l(m,N)$ and $U_l(m,N)$ are the real and the imaginary
part of $X_l(m,N)$, respectively. These estimates are also given in terms of
related sub-Gaussian norm $\Vert \cdot\Vert_{\psi_2}$ considered in [28].
Moreover, we prove a refinement of the mentioned upper bound estimates for the
real and the imaginary part of $X_l(m,N)$.
|
math.PR
|
in 8 the author of this paper continued the research on the complexvalued discrete random variables x_lmn 0le lle n1 1le mle n recently introduced and studied in 24 here we extend our results by considering x_lmn as subgaussian random variables our investigation is motivated by the known fact thatthe socalled restricted isometry property rip introduced in 4 holds with high probability for any matrix generated by a subgaussian random variable notice that sensing matrices with the rip play a crucial role in theory of compressive sensing our main results concern the proofs of the lower and upper bound estimates of the expected values of the random variables x_lmn u_lmn and v_lmn where u_lmn and u_lmn are the real and the imaginary part of x_lmn respectively these estimates are also given in terms of related subgaussian norm vert cdotvert_psi_2 considered in 28 moreover we prove a refinement of the mentioned upper bound estimates for the real and the imaginary part of x_lmn
|
[['in', '8', 'the', 'author', 'of', 'this', 'paper', 'continued', 'the', 'research', 'on', 'the', 'complexvalued', 'discrete', 'random', 'variables', 'x_lmn', '0le', 'lle', 'n1', '1le', 'mle', 'n', 'recently', 'introduced', 'and', 'studied', 'in', '24', 'here', 'we', 'extend', 'our', 'results', 'by', 'considering', 'x_lmn', 'as', 'subgaussian', 'random', 'variables', 'our', 'investigation', 'is', 'motivated', 'by', 'the', 'known', 'fact', 'thatthe', 'socalled', 'restricted', 'isometry', 'property', 'rip', 'introduced', 'in', '4', 'holds', 'with', 'high', 'probability', 'for', 'any', 'matrix', 'generated', 'by', 'a', 'subgaussian', 'random', 'variable', 'notice', 'that', 'sensing', 'matrices', 'with', 'the', 'rip', 'play', 'a', 'crucial', 'role', 'in', 'theory', 'of', 'compressive', 'sensing', 'our', 'main', 'results', 'concern', 'the', 'proofs', 'of', 'the', 'lower', 'and', 'upper', 'bound', 'estimates', 'of', 'the', 'expected', 'values', 'of', 'the', 'random', 'variables', 'x_lmn', 'u_lmn', 'and', 'v_lmn', 'where', 'u_lmn', 'and', 'u_lmn', 'are', 'the', 'real', 'and', 'the', 'imaginary', 'part', 'of', 'x_lmn', 'respectively', 'these', 'estimates', 'are', 'also', 'given', 'in', 'terms', 'of', 'related', 'subgaussian', 'norm', 'vert', 'cdotvert_psi_2', 'considered', 'in', '28', 'moreover', 'we', 'prove', 'a', 'refinement', 'of', 'the', 'mentioned', 'upper', 'bound', 'estimates', 'for', 'the', 'real', 'and', 'the', 'imaginary', 'part', 'of', 'x_lmn']]
|
[-0.109843468745832, 0.15680591045352427, -0.031687722444680363, 0.035284813250536506, -0.02214777854600956, -0.13196889283019841, 0.033357276404797104, 0.30708880685501216, -0.22013956896949166, -0.25175837896798986, 0.15470473082614658, -0.2747889961006108, -0.1891076764647172, 0.1838978972446509, -0.09568438235408575, 0.09300945562924047, 0.013093147345859072, 0.0733680000979893, -0.06392011634270828, -0.30921906478101424, 0.29455607442578635, 0.0005440098083121066, 0.2184367240399342, 0.021918615939454263, 0.06576649534302006, 0.06327464614896486, -0.06889274098291438, -0.0341300648117947, -0.14424225745704755, 0.12392687880591727, 0.2302135380667559, 0.11504149395765827, 0.2820406420827375, -0.3676353207867454, -0.17440851471785987, 0.16322259143680357, 0.1535971402277395, 0.006755060605959307, -0.06602572947986401, -0.2882201191014755, 0.12507673258956822, -0.10778634864815238, -0.13503579457249978, -0.03920322827422147, 0.021986201541463597, 0.04877816699734478, -0.3301269936014505, 0.09682005802180288, 0.14733461671514633, 0.05346064951223096, -0.09659185020762108, -0.21966702920841688, 0.05069627570746154, 0.08729109515997101, 0.056321249252347196, 0.023825950452028226, 0.06220651900434435, -0.07010492976170658, -0.0926696683507216, 0.3189320217771745, -0.049432512780547326, -0.20015623833498228, 0.0983773936453906, -0.17010832295799339, -0.1866559137225799, 0.06371818563354242, 0.13750355661216174, 0.11508645997700855, -0.07728568716003612, 0.16750842756924353, -0.07868992448177027, 0.11971966013809816, 0.07800798827886396, 0.05660350618721998, 0.0565023746371113, 0.06899817541577154, 0.07763230708942558, 0.13518696624831938, -0.031135660835530217, -0.07070160040527768, -0.33632133662214747, -0.14779003799722315, -0.268024722970328, 0.06663719525069768, -0.19212515494205573, -0.14564992360747564, 0.32045493070151865, 0.1284092600046663, 0.21245685283684676, 0.11346453397609987, 0.23472687172535692, 0.11381688107655424, 0.008023846437518271, 0.07926359818959088, 0.18563684145775364, 0.21967595132351245, 0.08483795711469853, -0.1356811390268495, 0.07530875710527533, 0.09817254480975246]
|
1,803.04522
|
Some limit laws for quantum walks with applications to a version of the
Parrondo paradox
|
A quantum walker moves on the integers with four extra degrees of freedom,
performing a coin-shift operation to alter its internal state and position at
discrete units of time. The time evolution is described by a unitary process.
We focus on finding the limit probability law for the position of the walker
and study it by means of Fourier analysis. The quantum walker exhibits both
localization and a ballistic behavior. Our two results are given as limit
theorems for a 2-period time-dependent walk and they describe the location of
the walker after it has repeated the unitary process a large number of times.
The theorems give an analytical tool to study some of the Parrondo type
behavior in a quantum game which was studied by J. Rajendran and C. Benjamin by
means of very nice numerical simulations [1]. With our analytical tools at hand
we can easily explore the "phase space" of parameters of one of the games,
similar to the winning game in their papers. We include numerical evidence that
our two games, similar to theirs, exhibit a Parrondo type paradox.
|
quant-ph math.PR
|
a quantum walker moves on the integers with four extra degrees of freedom performing a coinshift operation to alter its internal state and position at discrete units of time the time evolution is described by a unitary process we focus on finding the limit probability law for the position of the walker and study it by means of fourier analysis the quantum walker exhibits both localization and a ballistic behavior our two results are given as limit theorems for a 2period timedependent walk and they describe the location of the walker after it has repeated the unitary process a large number of times the theorems give an analytical tool to study some of the parrondo type behavior in a quantum game which was studied by j rajendran and c benjamin by means of very nice numerical simulations 1 with our analytical tools at hand we can easily explore the phase space of parameters of one of the games similar to the winning game in their papers we include numerical evidence that our two games similar to theirs exhibit a parrondo type paradox
|
[['a', 'quantum', 'walker', 'moves', 'on', 'the', 'integers', 'with', 'four', 'extra', 'degrees', 'of', 'freedom', 'performing', 'a', 'coinshift', 'operation', 'to', 'alter', 'its', 'internal', 'state', 'and', 'position', 'at', 'discrete', 'units', 'of', 'time', 'the', 'time', 'evolution', 'is', 'described', 'by', 'a', 'unitary', 'process', 'we', 'focus', 'on', 'finding', 'the', 'limit', 'probability', 'law', 'for', 'the', 'position', 'of', 'the', 'walker', 'and', 'study', 'it', 'by', 'means', 'of', 'fourier', 'analysis', 'the', 'quantum', 'walker', 'exhibits', 'both', 'localization', 'and', 'a', 'ballistic', 'behavior', 'our', 'two', 'results', 'are', 'given', 'as', 'limit', 'theorems', 'for', 'a', '2period', 'timedependent', 'walk', 'and', 'they', 'describe', 'the', 'location', 'of', 'the', 'walker', 'after', 'it', 'has', 'repeated', 'the', 'unitary', 'process', 'a', 'large', 'number', 'of', 'times', 'the', 'theorems', 'give', 'an', 'analytical', 'tool', 'to', 'study', 'some', 'of', 'the', 'parrondo', 'type', 'behavior', 'in', 'a', 'quantum', 'game', 'which', 'was', 'studied', 'by', 'j', 'rajendran', 'and', 'c', 'benjamin', 'by', 'means', 'of', 'very', 'nice', 'numerical', 'simulations', '1', 'with', 'our', 'analytical', 'tools', 'at', 'hand', 'we', 'can', 'easily', 'explore', 'the', 'phase', 'space', 'of', 'parameters', 'of', 'one', 'of', 'the', 'games', 'similar', 'to', 'the', 'winning', 'game', 'in', 'their', 'papers', 'we', 'include', 'numerical', 'evidence', 'that', 'our', 'two', 'games', 'similar', 'to', 'theirs', 'exhibit', 'a', 'parrondo', 'type', 'paradox']]
|
[-0.12671905256793653, 0.12348206548185219, -0.1426354444364487, 0.04829278695973126, -0.020565957041940967, -0.17125699763026767, 0.09634663567245418, 0.3478636608062857, -0.2180290843684288, -0.29160549183947276, 0.1139453488533277, -0.2591988782460148, -0.1450488940961865, 0.17400589844119319, -0.06103630628158951, 0.03515459619654157, 0.05944467057571721, 0.04693257743852902, -0.013585542122195397, -0.24090357943957683, 0.286964502195874, 0.04859390538198698, 0.21975741569979956, 0.013673646891366135, 0.11324149548032357, 0.025819892577636595, -0.03711763365729326, 0.03805390495060202, -0.15522185500438013, 0.046720862427201554, 0.20246999356278234, 0.09470334220455717, 0.2945919865587963, -0.41966115840510027, -0.1864516684117866, 0.08702511144660476, 0.1070694173068755, 0.132914703006815, -0.03020561698088371, -0.31702451841926294, 0.06596310518023388, -0.14906084232124917, -0.16653266564032154, -0.05714448709658332, 0.04481601094549203, 0.026717815074802104, -0.2296101415140081, 0.043777954777527744, 0.09806653052614744, 0.05075914132825578, 0.0001498050441018702, -0.07411757066912784, 0.006820964378700977, 0.1568379417629089, 0.020426567359274325, -0.006473168295947063, 0.11354045699561499, -0.12373329227975471, -0.17599340386493184, 0.3629683364614927, -0.05662268546836976, -0.18092046077594737, 0.18817660293557284, -0.15553718484688592, -0.126494069763804, 0.09571277491172045, 0.10233670552852898, 0.12971716059343047, -0.1166846342899358, 0.09339738376950749, -0.06092336192438945, 0.12525953823772584, 0.0642577380801227, 0.03356118205721228, 0.16571032181793813, 0.12566094191034854, 0.04887777771440577, 0.1311100219487012, -0.04780812460211368, -0.16941668672322485, -0.2956127089793017, -0.15834422154321195, -0.20297141482626493, 0.07938148607872941, -0.1182992163564314, -0.14652617726068512, 0.41951242850537707, 0.11851322696124877, 0.19234188451066367, 0.07613306485942323, 0.22931596926459785, 0.13643531562266512, -0.0012242278033990969, 0.06138171896323027, 0.18835303318401855, 0.12878470782458123, 0.12258628225828731, -0.22760742270639836, 0.058915874323915714, 0.08717481687520914]
|
1,803.04523
|
Event-based Moving Object Detection and Tracking
|
Event-based vision sensors, such as the Dynamic Vision Sensor (DVS), are
ideally suited for real-time motion analysis. The unique properties encompassed
in the readings of such sensors provide high temporal resolution, superior
sensitivity to light and low latency. These properties provide the grounds to
estimate motion extremely reliably in the most sophisticated scenarios but they
come at a price - modern event-based vision sensors have extremely low
resolution and produce a lot of noise. Moreover, the asynchronous nature of the
event stream calls for novel algorithms.
This paper presents a new, efficient approach to object tracking with
asynchronous cameras. We present a novel event stream representation which
enables us to utilize information about the dynamic (temporal) component of the
event stream, and not only the spatial component, at every moment of time. This
is done by approximating the 3D geometry of the event stream with a parametric
model; as a result, the algorithm is capable of producing the
motion-compensated event stream (effectively approximating egomotion), and
without using any form of external sensors in extremely low-light and noisy
conditions without any form of feature tracking or explicit optical flow
computation. We demonstrate our framework on the task of independent motion
detection and tracking, where we use the temporal model inconsistencies to
locate differently moving objects in challenging situations of very fast
motion.
|
cs.CV
|
eventbased vision sensors such as the dynamic vision sensor dvs are ideally suited for realtime motion analysis the unique properties encompassed in the readings of such sensors provide high temporal resolution superior sensitivity to light and low latency these properties provide the grounds to estimate motion extremely reliably in the most sophisticated scenarios but they come at a price modern eventbased vision sensors have extremely low resolution and produce a lot of noise moreover the asynchronous nature of the event stream calls for novel algorithms this paper presents a new efficient approach to object tracking with asynchronous cameras we present a novel event stream representation which enables us to utilize information about the dynamic temporal component of the event stream and not only the spatial component at every moment of time this is done by approximating the 3d geometry of the event stream with a parametric model as a result the algorithm is capable of producing the motioncompensated event stream effectively approximating egomotion and without using any form of external sensors in extremely lowlight and noisy conditions without any form of feature tracking or explicit optical flow computation we demonstrate our framework on the task of independent motion detection and tracking where we use the temporal model inconsistencies to locate differently moving objects in challenging situations of very fast motion
|
[['eventbased', 'vision', 'sensors', 'such', 'as', 'the', 'dynamic', 'vision', 'sensor', 'dvs', 'are', 'ideally', 'suited', 'for', 'realtime', 'motion', 'analysis', 'the', 'unique', 'properties', 'encompassed', 'in', 'the', 'readings', 'of', 'such', 'sensors', 'provide', 'high', 'temporal', 'resolution', 'superior', 'sensitivity', 'to', 'light', 'and', 'low', 'latency', 'these', 'properties', 'provide', 'the', 'grounds', 'to', 'estimate', 'motion', 'extremely', 'reliably', 'in', 'the', 'most', 'sophisticated', 'scenarios', 'but', 'they', 'come', 'at', 'a', 'price', 'modern', 'eventbased', 'vision', 'sensors', 'have', 'extremely', 'low', 'resolution', 'and', 'produce', 'a', 'lot', 'of', 'noise', 'moreover', 'the', 'asynchronous', 'nature', 'of', 'the', 'event', 'stream', 'calls', 'for', 'novel', 'algorithms', 'this', 'paper', 'presents', 'a', 'new', 'efficient', 'approach', 'to', 'object', 'tracking', 'with', 'asynchronous', 'cameras', 'we', 'present', 'a', 'novel', 'event', 'stream', 'representation', 'which', 'enables', 'us', 'to', 'utilize', 'information', 'about', 'the', 'dynamic', 'temporal', 'component', 'of', 'the', 'event', 'stream', 'and', 'not', 'only', 'the', 'spatial', 'component', 'at', 'every', 'moment', 'of', 'time', 'this', 'is', 'done', 'by', 'approximating', 'the', '3d', 'geometry', 'of', 'the', 'event', 'stream', 'with', 'a', 'parametric', 'model', 'as', 'a', 'result', 'the', 'algorithm', 'is', 'capable', 'of', 'producing', 'the', 'motioncompensated', 'event', 'stream', 'effectively', 'approximating', 'egomotion', 'and', 'without', 'using', 'any', 'form', 'of', 'external', 'sensors', 'in', 'extremely', 'lowlight', 'and', 'noisy', 'conditions', 'without', 'any', 'form', 'of', 'feature', 'tracking', 'or', 'explicit', 'optical', 'flow', 'computation', 'we', 'demonstrate', 'our', 'framework', 'on', 'the', 'task', 'of', 'independent', 'motion', 'detection', 'and', 'tracking', 'where', 'we', 'use', 'the', 'temporal', 'model', 'inconsistencies', 'to', 'locate', 'differently', 'moving', 'objects', 'in', 'challenging', 'situations', 'of', 'very', 'fast', 'motion']]
|
[-0.11757373788330534, 0.033167335962538014, -0.10184315551199358, 0.047139824950665404, -0.1183979976890524, -0.1493070321072909, 0.01605158408545487, 0.4259018812582574, -0.26739354712586977, -0.37883201264273586, 0.12653106066837524, -0.24313933295832776, -0.1396946125862252, 0.1992057840477421, -0.13543094284607585, 0.09349441915216052, 0.12897777136267077, 0.059066051264050075, -0.017972911269647406, -0.16838076847436076, 0.22591648436236111, 0.061987499639772896, 0.28320261320437895, -0.019175368783445182, 0.1841343879068683, 0.04273343774786388, -0.05282302011672238, 0.009257519507611339, -0.030532295154195956, 0.15083359309937805, 0.2897103917636824, 0.1593487734363655, 0.25595511689104816, -0.4600102731803252, -0.2262811957466924, 0.07972014410823414, 0.13852786937442776, 0.1031027270675457, -0.094019645645643, -0.32012564202093263, 0.10427682187027214, -0.16416778920340055, -0.09101029073565521, -0.10027446398215199, 0.01849746841154146, 0.01804357517341321, -0.27414535041686827, 0.05638276218986985, 0.05913644240512936, 0.056374893780924716, -0.043122650002425704, -0.017533764804565262, 0.06805332341622984, 0.17406209429027514, 0.002893210044236515, 0.014147124108752575, 0.16030175627436785, -0.1866997025885873, -0.1097452298166569, 0.38813361144513525, -0.02872762995327569, -0.19581263309757393, 0.23938942324920473, -0.10442876105450771, -0.1386830830015242, 0.15324862582439727, 0.21946579415234738, 0.14565122244794937, -0.18019345067864792, 0.01519940045625183, 0.02412183908533982, 0.18389386935741642, 0.04781955323520709, 0.07379566842944107, 0.2281831991096789, 0.20634456773669543, 0.08760242114076391, 0.10500547006641599, -0.17738734112248164, -0.06232401515695859, -0.25293665037448093, -0.1173588870622387, -0.16218167193054053, -0.014699515075335512, -0.10233062346934192, -0.16896648118272423, 0.37390252409756863, 0.23717426567507738, 0.22962476676647467, 0.04060053627313623, 0.39073452930897473, 0.07064423663753339, 0.07361532035707073, 0.08834021927931728, 0.18203926473877138, 0.027804081025731814, 0.16947364613456142, -0.17664828257561152, 0.09602501296268945, 0.037052719707770104]
|
1,803.04524
|
A note on non-inclusion "interval" root solvers
|
In this note we give comments on recent interval extensions of the
King-Ostrowski multipoint methods for solving nonlinear equations published in
at least five scientific journals offering faulty "self-validated" methods. We
show that they do not possess essential feature of genuine interval methods --
inclusion property.
|
math.NA
|
in this note we give comments on recent interval extensions of the kingostrowski multipoint methods for solving nonlinear equations published in at least five scientific journals offering faulty selfvalidated methods we show that they do not possess essential feature of genuine interval methods inclusion property
|
[['in', 'this', 'note', 'we', 'give', 'comments', 'on', 'recent', 'interval', 'extensions', 'of', 'the', 'kingostrowski', 'multipoint', 'methods', 'for', 'solving', 'nonlinear', 'equations', 'published', 'in', 'at', 'least', 'five', 'scientific', 'journals', 'offering', 'faulty', 'selfvalidated', 'methods', 'we', 'show', 'that', 'they', 'do', 'not', 'possess', 'essential', 'feature', 'of', 'genuine', 'interval', 'methods', 'inclusion', 'property']]
|
[-0.12157604123633571, 0.008366481671759556, -0.07968213535888596, 0.08200396958272904, -0.11200754883764096, -0.16202274588651436, 0.018272377810505933, 0.44608540744195846, -0.18949245297631553, -0.29427009214495503, 0.07073620134466437, -0.3221085222308026, -0.16171817596308713, 0.2309404092023236, -0.08634760663952938, 0.05218137705395388, 0.1425673425804044, 0.022123004591395688, -0.1064762624473376, -0.3644522583155438, 0.2937873697428163, -0.00847057240127131, 0.22069833286886298, 0.08627842526969522, 0.0911870750620268, -0.012396779658576084, -0.14442025551702395, 0.06115970153058337, -0.1523233293943164, 0.13119189526755795, 0.35068908979206587, 0.17101964583013987, 0.35364599924447926, -0.41441928778320203, -0.18873035976240887, 0.11903214517461005, 0.1461564801893262, 0.11196492491073387, 0.005840601656737543, -0.23341591201376083, 0.0849207297703901, -0.15917264272697096, -0.12520305682406868, -0.11463969142371139, 0.010292018831404315, -0.00043010978071495544, -0.21076346437858287, 0.10511094996662335, 0.12005010832014472, 0.12923419852416182, -0.017471696336775325, -0.17265114346302526, 0.07023474735446102, 0.09662677230161809, 0.04107162581085293, -0.05145223244894729, 0.04810204007121366, -0.0989231048747464, -0.17747774177913142, 0.3321711192297381, -0.0415139457557437, -0.20616675131456103, 0.21161266131499826, -0.12441779223952962, -0.25691761106772476, 0.10292468869755435, 0.16524932201042078, 0.11877576736083557, -0.14013646495376908, 0.09410857320438291, -0.03834814189521726, 0.13128860173530357, 0.08700170638689468, 0.08562674269426701, 0.15371149594267441, 0.07628801012368397, 0.08072009003114734, 0.052816135925240815, 0.022221851552468398, -0.06278208681100676, -0.33775605700996725, -0.09879481665746764, -0.1059004082885939, 0.029583860367745008, -0.03220361085651919, -0.21477196980700936, 0.37088427003906216, 0.22697424706678057, 0.10444917663834383, 0.03066440405280784, 0.22676330908786418, 0.08757711492132308, 0.09533137507563413, 0.10944091496166101, 0.2141540414805329, 0.058620735418138115, 0.15246968544196599, -0.11015200047472189, 0.05542584077920765, 0.1243592694856573]
|
1,803.04525
|
Well-posedness of Hamilton-Jacobi equations in population dynamics and
applications to large deviations
|
We prove Freidlin-Wentzell type large deviation principles for various
rescaled models in populations dynamics that have immigration and possibly
harvesting: birth-death processes, Galton-Watson trees, epidemic SI models, and
prey-predator models.
The proofs are carried out using a general analytic approach based on the
well-posedness of a class of associated Hamilton-Jacobi equations. The notable
feature for these Hamilton-Jacobi equations is that the Hamiltonian can be
discontinuous at the boundary. We prove a well-posedness result for a large
class of Hamilton-Jacobi equations corresponding to one-dimensional models, and
give partial results for the multi-dimensional setting.
|
math.PR
|
we prove freidlinwentzell type large deviation principles for various rescaled models in populations dynamics that have immigration and possibly harvesting birthdeath processes galtonwatson trees epidemic si models and preypredator models the proofs are carried out using a general analytic approach based on the wellposedness of a class of associated hamiltonjacobi equations the notable feature for these hamiltonjacobi equations is that the hamiltonian can be discontinuous at the boundary we prove a wellposedness result for a large class of hamiltonjacobi equations corresponding to onedimensional models and give partial results for the multidimensional setting
|
[['we', 'prove', 'freidlinwentzell', 'type', 'large', 'deviation', 'principles', 'for', 'various', 'rescaled', 'models', 'in', 'populations', 'dynamics', 'that', 'have', 'immigration', 'and', 'possibly', 'harvesting', 'birthdeath', 'processes', 'galtonwatson', 'trees', 'epidemic', 'si', 'models', 'and', 'preypredator', 'models', 'the', 'proofs', 'are', 'carried', 'out', 'using', 'a', 'general', 'analytic', 'approach', 'based', 'on', 'the', 'wellposedness', 'of', 'a', 'class', 'of', 'associated', 'hamiltonjacobi', 'equations', 'the', 'notable', 'feature', 'for', 'these', 'hamiltonjacobi', 'equations', 'is', 'that', 'the', 'hamiltonian', 'can', 'be', 'discontinuous', 'at', 'the', 'boundary', 'we', 'prove', 'a', 'wellposedness', 'result', 'for', 'a', 'large', 'class', 'of', 'hamiltonjacobi', 'equations', 'corresponding', 'to', 'onedimensional', 'models', 'and', 'give', 'partial', 'results', 'for', 'the', 'multidimensional', 'setting']]
|
[-0.10267022864212302, 0.06951628596921776, -0.10020211761635121, 0.1279418954699118, -0.067432547823283, -0.17713715866936938, 0.028505837089260636, 0.29851626712100016, -0.28966949321329594, -0.20857200786754812, 0.13690777411355395, -0.293097123078516, -0.1621191900719524, 0.18461180213109954, -0.06258477888110539, 0.08488710371412984, 0.12882426819946294, -0.04593155879284377, -0.04007010654364105, -0.21189538956715967, 0.3750508672395802, -0.05054266670270843, 0.23210111283697188, 0.006598728243261576, 0.15191702226824735, -0.03165969970312132, -0.000277818808251101, 0.04390451284226678, -0.18605999965502237, 0.07337550065510542, 0.27270936831764236, 0.06733061845981232, 0.28772174942550127, -0.4505767757680429, -0.27423770087467425, 0.1308869178376525, 0.10720430967732049, 0.17071046977830323, -0.030828639886433095, -0.3166052461905486, 0.05631573300343007, -0.12277852912919353, -0.20122911217217537, -0.06197891485593889, 0.01668860278416263, 0.08765831561354191, -0.30209938763721805, 0.11359370693706138, 0.09813839522109408, 0.05134387337855752, -0.12718375596096335, -0.10592244913087874, -0.04612638016418635, 0.04514141397489964, 0.021833142609861883, -0.07343781815902532, 0.07954463176935425, -0.09656930187433634, -0.13802817502342488, 0.324890504307721, -0.0851292124944334, -0.26932279855701263, 0.20630771551868352, -0.09469289848904895, -0.2080907964643658, 0.10869805781555403, 0.2302916535662244, 0.18980392912002889, -0.21851272726148044, 0.13690264744534786, -0.029655028639472617, 0.07819304984466567, 0.023876048128485032, -0.020199019635860484, 0.11153409079871261, 0.18048290040789414, 0.08289802935146524, 0.09764787585236659, -0.0142162990226137, -0.20194805205455454, -0.3383533147609104, -0.13530615006533006, -0.1288679777847036, 0.12450481226424807, -0.17525999650277538, -0.1916553740511122, 0.3385623637662755, 0.16991852907180463, 0.14954463141443936, 0.18644615193783387, 0.1696138349564179, 0.20364943870505237, -0.008875872079363984, 0.06060177022490003, 0.17009547451996934, 0.1793495756422129, 0.13106816511793548, -0.17462633149799608, 0.08024336177471053, 0.18226017656169183]
|
1,803.04526
|
Kepler Data Validation I -- Architecture, Diagnostic Tests, and Data
Products for Vetting Transiting Planet Candidates
|
The Kepler Mission was designed to identify and characterize transiting
planets in the Kepler Field of View and to determine their occurrence rates.
Emphasis was placed on identification of Earth-size planets orbiting in the
Habitable Zone of their host stars. Science data were acquired for a period of
four years. Long-cadence data with 29.4 min sampling were obtained for ~200,000
individual stellar targets in at least one observing quarter in the primary
Kepler Mission. Light curves for target stars are extracted in the Kepler
Science Data Processing Pipeline, and are searched for transiting planet
signatures. A Threshold Crossing Event is generated in the transit search for
targets where the transit detection threshold is exceeded and transit
consistency checks are satisfied. These targets are subjected to further
scrutiny in the Data Validation (DV) component of the Pipeline. Transiting
planet candidates are characterized in DV, and light curves are searched for
additional planets after transit signatures are modeled and removed. A suite of
diagnostic tests is performed on all candidates to aid in discrimination
between genuine transiting planets and instrumental or astrophysical false
positives. Data products are generated per target and planet candidate to
document and display transiting planet model fit and diagnostic test results.
These products are exported to the Exoplanet Archive at the NASA Exoplanet
Science Institute, and are available to the community. We describe the DV
architecture and diagnostic tests, and provide a brief overview of the data
products. Transiting planet modeling and the search for multiple planets on
individual targets are described in a companion paper. The final revision of
the Kepler Pipeline code base is available to the general public through
GitHub. The Kepler Pipeline has also been modified to support the TESS Mission
which will commence in 2018.
|
astro-ph.EP astro-ph.IM
|
the kepler mission was designed to identify and characterize transiting planets in the kepler field of view and to determine their occurrence rates emphasis was placed on identification of earthsize planets orbiting in the habitable zone of their host stars science data were acquired for a period of four years longcadence data with 294 min sampling were obtained for 200000 individual stellar targets in at least one observing quarter in the primary kepler mission light curves for target stars are extracted in the kepler science data processing pipeline and are searched for transiting planet signatures a threshold crossing event is generated in the transit search for targets where the transit detection threshold is exceeded and transit consistency checks are satisfied these targets are subjected to further scrutiny in the data validation dv component of the pipeline transiting planet candidates are characterized in dv and light curves are searched for additional planets after transit signatures are modeled and removed a suite of diagnostic tests is performed on all candidates to aid in discrimination between genuine transiting planets and instrumental or astrophysical false positives data products are generated per target and planet candidate to document and display transiting planet model fit and diagnostic test results these products are exported to the exoplanet archive at the nasa exoplanet science institute and are available to the community we describe the dv architecture and diagnostic tests and provide a brief overview of the data products transiting planet modeling and the search for multiple planets on individual targets are described in a companion paper the final revision of the kepler pipeline code base is available to the general public through github the kepler pipeline has also been modified to support the tess mission which will commence in 2018
|
[['the', 'kepler', 'mission', 'was', 'designed', 'to', 'identify', 'and', 'characterize', 'transiting', 'planets', 'in', 'the', 'kepler', 'field', 'of', 'view', 'and', 'to', 'determine', 'their', 'occurrence', 'rates', 'emphasis', 'was', 'placed', 'on', 'identification', 'of', 'earthsize', 'planets', 'orbiting', 'in', 'the', 'habitable', 'zone', 'of', 'their', 'host', 'stars', 'science', 'data', 'were', 'acquired', 'for', 'a', 'period', 'of', 'four', 'years', 'longcadence', 'data', 'with', '294', 'min', 'sampling', 'were', 'obtained', 'for', '200000', 'individual', 'stellar', 'targets', 'in', 'at', 'least', 'one', 'observing', 'quarter', 'in', 'the', 'primary', 'kepler', 'mission', 'light', 'curves', 'for', 'target', 'stars', 'are', 'extracted', 'in', 'the', 'kepler', 'science', 'data', 'processing', 'pipeline', 'and', 'are', 'searched', 'for', 'transiting', 'planet', 'signatures', 'a', 'threshold', 'crossing', 'event', 'is', 'generated', 'in', 'the', 'transit', 'search', 'for', 'targets', 'where', 'the', 'transit', 'detection', 'threshold', 'is', 'exceeded', 'and', 'transit', 'consistency', 'checks', 'are', 'satisfied', 'these', 'targets', 'are', 'subjected', 'to', 'further', 'scrutiny', 'in', 'the', 'data', 'validation', 'dv', 'component', 'of', 'the', 'pipeline', 'transiting', 'planet', 'candidates', 'are', 'characterized', 'in', 'dv', 'and', 'light', 'curves', 'are', 'searched', 'for', 'additional', 'planets', 'after', 'transit', 'signatures', 'are', 'modeled', 'and', 'removed', 'a', 'suite', 'of', 'diagnostic', 'tests', 'is', 'performed', 'on', 'all', 'candidates', 'to', 'aid', 'in', 'discrimination', 'between', 'genuine', 'transiting', 'planets', 'and', 'instrumental', 'or', 'astrophysical', 'false', 'positives', 'data', 'products', 'are', 'generated', 'per', 'target', 'and', 'planet', 'candidate', 'to', 'document', 'and', 'display', 'transiting', 'planet', 'model', 'fit', 'and', 'diagnostic', 'test', 'results', 'these', 'products', 'are', 'exported', 'to', 'the', 'exoplanet', 'archive', 'at', 'the', 'nasa', 'exoplanet', 'science', 'institute', 'and', 'are', 'available', 'to', 'the', 'community', 'we', 'describe', 'the', 'dv', 'architecture', 'and', 'diagnostic', 'tests', 'and', 'provide', 'a', 'brief', 'overview', 'of', 'the', 'data', 'products', 'transiting', 'planet', 'modeling', 'and', 'the', 'search', 'for', 'multiple', 'planets', 'on', 'individual', 'targets', 'are', 'described', 'in', 'a', 'companion', 'paper', 'the', 'final', 'revision', 'of', 'the', 'kepler', 'pipeline', 'code', 'base', 'is', 'available', 'to', 'the', 'general', 'public', 'through', 'github', 'the', 'kepler', 'pipeline', 'has', 'also', 'been', 'modified', 'to', 'support', 'the', 'tess', 'mission', 'which', 'will', 'commence', 'in', '2018']]
|
[-0.10262980180942415, 0.11906083643178085, -0.07854545292004382, 0.07332732216678305, -0.1478931736380455, -0.13297315098964035, 0.09147104864727948, 0.35665669609881834, -0.1511359836299001, -0.41031486199760475, 0.139337123995599, -0.3664112340703556, -0.06825639971624423, 0.2655719792285347, -0.10053119023872917, 0.10320808521148188, 0.20226429481129482, -0.010202408083200245, 0.02896060635278333, -0.3619131792890074, 0.23331502637720655, 0.07676478818199303, 0.09457130349968394, -0.06616283627545895, 0.006658170612653309, -0.025551360092534083, -0.1364805387491113, -0.0834029362766608, -0.18328527229908242, 0.05090211945396931, 0.3034982163186364, 0.24239341217995788, 0.19298121631017898, -0.3317249585970661, -0.20826165169184033, 0.0717459318653859, 0.10878664829404643, 0.01978915551195339, -0.04626251095929821, -0.3260840370091737, 0.09570093329976734, -0.16996974699926395, -0.14162491876089767, -0.03453951442896465, 0.0831819770332228, 0.027329525030026697, -0.26561247586306147, 0.000801527378215512, -0.004606744378154511, 0.20218229452179975, -0.15103792207401678, -0.14572036785531267, -0.0656155136107684, 0.1344308362123939, -0.0049861335607838805, 0.022832618145563574, 0.12973457002615213, -0.09835707728690404, -0.05431537231277317, 0.40982038731533676, -0.0767334352031331, -0.029498154417873193, 0.1992145849720532, -0.17032806293429706, -0.1515100331815942, 0.11755775678296868, 0.23509566592967954, 0.12402218356824238, -0.22286071668519344, -0.02441826709727051, 0.040200611933657565, 0.16285188382807225, 0.1071410826590767, 0.03154519610031195, 0.39157601418165006, 0.16452811327316377, 0.03342576043529608, 0.10121718951706672, -0.26326975381808765, -0.04598084755229113, -0.22562290050989584, -0.15248280169190329, -0.15401128426868763, -0.03905888290398862, -0.018943480489562643, -0.0932001114746377, 0.3833407759156129, 0.15544496052132997, 0.1125836453371847, -0.010065274224186009, 0.3122167995052846, 0.040992384642515686, 0.10490248258344627, 0.057997448423284116, 0.3025097497643214, 0.06914441209375756, 0.08396219956203785, -0.16780452417330943, 0.08746356138487486, -0.028592431247700802]
|
1,803.04527
|
Rise of the First Super-Massive Stars
|
We use high resolution adaptive mesh refinement simulations to model the
formation of massive metal-free stars in the early Universe. By applying
Lyman-Werner (LW) backgrounds of 100 J$_{21}$ and 1000 J$_{21}$ respectively we
construct environments conducive to the formation of massive stars. We find
that only in the case of the higher LW backgrounds are super-critical accretion
rates realised that are necessary for super-massive star (SMS) formation. Mild
fragmentation is observed for both backgrounds. Violent dynamical interactions
between the stars that form in the more massive halo formed (1000 J$_{21}$
background) results in the eventual expulsion of the two most massive stars
from the halo. In the smaller mass halo (100 J$_{21}$ background) mergers of
stars occur before any multibody interactions and a single massive Pop III star
is left at the centre of the halo at the end of our simulation. Feedback from
the very massive Pop III stars is not effective in generating a large HII
region with ionising photons absorbed within a few thousand AU of the star. In
all cases a massive black hole seed is the expected final fate of the most
massive objects. The seed of the massive Pop III star which remained at the
centre of the less massive halo experiences steady accretion rates of almost
$10^{-2}$ M$_{\odot}$/yr and if these rates continue could potentially
experience super-Eddington accretion rates in the immediate aftermath of
collapsing into a black hole.
|
astro-ph.GA
|
we use high resolution adaptive mesh refinement simulations to model the formation of massive metalfree stars in the early universe by applying lymanwerner lw backgrounds of 100 j_21 and 1000 j_21 respectively we construct environments conducive to the formation of massive stars we find that only in the case of the higher lw backgrounds are supercritical accretion rates realised that are necessary for supermassive star sms formation mild fragmentation is observed for both backgrounds violent dynamical interactions between the stars that form in the more massive halo formed 1000 j_21 background results in the eventual expulsion of the two most massive stars from the halo in the smaller mass halo 100 j_21 background mergers of stars occur before any multibody interactions and a single massive pop iii star is left at the centre of the halo at the end of our simulation feedback from the very massive pop iii stars is not effective in generating a large hii region with ionising photons absorbed within a few thousand au of the star in all cases a massive black hole seed is the expected final fate of the most massive objects the seed of the massive pop iii star which remained at the centre of the less massive halo experiences steady accretion rates of almost 102 m_odotyr and if these rates continue could potentially experience supereddington accretion rates in the immediate aftermath of collapsing into a black hole
|
[['we', 'use', 'high', 'resolution', 'adaptive', 'mesh', 'refinement', 'simulations', 'to', 'model', 'the', 'formation', 'of', 'massive', 'metalfree', 'stars', 'in', 'the', 'early', 'universe', 'by', 'applying', 'lymanwerner', 'lw', 'backgrounds', 'of', '100', 'j_21', 'and', '1000', 'j_21', 'respectively', 'we', 'construct', 'environments', 'conducive', 'to', 'the', 'formation', 'of', 'massive', 'stars', 'we', 'find', 'that', 'only', 'in', 'the', 'case', 'of', 'the', 'higher', 'lw', 'backgrounds', 'are', 'supercritical', 'accretion', 'rates', 'realised', 'that', 'are', 'necessary', 'for', 'supermassive', 'star', 'sms', 'formation', 'mild', 'fragmentation', 'is', 'observed', 'for', 'both', 'backgrounds', 'violent', 'dynamical', 'interactions', 'between', 'the', 'stars', 'that', 'form', 'in', 'the', 'more', 'massive', 'halo', 'formed', '1000', 'j_21', 'background', 'results', 'in', 'the', 'eventual', 'expulsion', 'of', 'the', 'two', 'most', 'massive', 'stars', 'from', 'the', 'halo', 'in', 'the', 'smaller', 'mass', 'halo', '100', 'j_21', 'background', 'mergers', 'of', 'stars', 'occur', 'before', 'any', 'multibody', 'interactions', 'and', 'a', 'single', 'massive', 'pop', 'iii', 'star', 'is', 'left', 'at', 'the', 'centre', 'of', 'the', 'halo', 'at', 'the', 'end', 'of', 'our', 'simulation', 'feedback', 'from', 'the', 'very', 'massive', 'pop', 'iii', 'stars', 'is', 'not', 'effective', 'in', 'generating', 'a', 'large', 'hii', 'region', 'with', 'ionising', 'photons', 'absorbed', 'within', 'a', 'few', 'thousand', 'au', 'of', 'the', 'star', 'in', 'all', 'cases', 'a', 'massive', 'black', 'hole', 'seed', 'is', 'the', 'expected', 'final', 'fate', 'of', 'the', 'most', 'massive', 'objects', 'the', 'seed', 'of', 'the', 'massive', 'pop', 'iii', 'star', 'which', 'remained', 'at', 'the', 'centre', 'of', 'the', 'less', 'massive', 'halo', 'experiences', 'steady', 'accretion', 'rates', 'of', 'almost', '102', 'm_odotyr', 'and', 'if', 'these', 'rates', 'continue', 'could', 'potentially', 'experience', 'supereddington', 'accretion', 'rates', 'in', 'the', 'immediate', 'aftermath', 'of', 'collapsing', 'into', 'a', 'black', 'hole']]
|
[-0.10198120275360406, 0.17873376454779152, -0.00930393418401748, 0.15510885500530633, -0.06490363549812049, -0.040946383732153986, 0.04838069118618555, 0.3463636639733183, -0.1162090383981102, -0.3463161157728252, 0.06162888499497736, -0.28023893009847567, 0.0007172118718499097, 0.18348063550538274, -0.002218788234300919, -0.08173286503030902, 0.08359362405450557, -0.006698726643847649, -0.052672409120023696, -0.30497524443169327, 0.3574760603359348, 0.09917051863059465, 0.15304410153747364, -0.044899696985385935, 0.049955606371286654, -0.11238034160499918, -0.04304132464251055, -0.06508929901198471, -0.13524307045585282, 0.0010951203649263767, 0.206670698780819, 0.18799402316937508, 0.2772620086386993, -0.4230044472516688, -0.20690152437329465, 0.06297589824495489, 0.23151507568782417, 0.12007101272159824, -0.16540080138169658, -0.22241280028939106, 0.14657712653200378, -0.2533004065419121, -0.15027795125773744, 0.1120930667473319, 0.040241950236261384, -0.00368206520082267, -0.26674035406016383, 0.17182328830454874, 0.04768333148634973, -0.04360946782685075, -0.08557468673443201, -0.01720328619545799, -0.10293621761957183, 0.07248251842901159, 0.03794478793121417, 0.05376314843306318, 0.24502625206872125, -0.209073441497729, -0.017178812534824582, 0.3989444298046183, -0.06625540950384891, -0.04097176787011341, 0.2818072953111476, -0.268364312581055, -0.15835942182753046, 0.19716848513225915, 0.1858627589151137, 0.20394886329462278, -0.1471575556472891, 0.0011780706268004675, 0.04080458082874321, 0.1692156060756642, 0.09695581140631013, 0.04260085336370709, 0.3821693566505466, 0.16416352277550594, -0.007349061616400446, 0.07392981742859929, -0.14774277154652213, -0.06705014159216277, -0.23663249536085937, -0.09980829926385244, -0.07401912426033405, 0.13180274635823927, -0.17916442348674322, -0.10934333979167928, 0.2749306812193797, 0.11224462363426968, 0.18916230729852124, 0.03377259975660324, 0.26820340491752376, 0.05410233289210552, 0.1019069708556221, 0.14785507365654743, 0.30841370136477053, 0.16716671030538283, 0.09000264476270463, -0.2356402414211593, 0.023660682498919384, -0.0011896653923648016]
|
1,803.04528
|
On sufficient conditions for mixed monotonicity
|
Mixed monotone systems form an important class of nonlinear systems that have
recently received attention in the abstraction-based control design area.
Slightly different definitions exist in the literature, and it remains a
challenge to verify mixed monotonicity of a system in general. In this paper,
we first clarify the relation between different existing definitions of mixed
monotone systems, and then give two sufficient conditions for mixed monotone
functions defined on Euclidean space. These sufficient conditions are more
general than the ones from the existing control literature, and they suggest
that mixed monotonicity is a very generic property. Some discussions are
provided on the computational usefulness of the proposed sufficient conditions.
|
math.OC cs.SY
|
mixed monotone systems form an important class of nonlinear systems that have recently received attention in the abstractionbased control design area slightly different definitions exist in the literature and it remains a challenge to verify mixed monotonicity of a system in general in this paper we first clarify the relation between different existing definitions of mixed monotone systems and then give two sufficient conditions for mixed monotone functions defined on euclidean space these sufficient conditions are more general than the ones from the existing control literature and they suggest that mixed monotonicity is a very generic property some discussions are provided on the computational usefulness of the proposed sufficient conditions
|
[['mixed', 'monotone', 'systems', 'form', 'an', 'important', 'class', 'of', 'nonlinear', 'systems', 'that', 'have', 'recently', 'received', 'attention', 'in', 'the', 'abstractionbased', 'control', 'design', 'area', 'slightly', 'different', 'definitions', 'exist', 'in', 'the', 'literature', 'and', 'it', 'remains', 'a', 'challenge', 'to', 'verify', 'mixed', 'monotonicity', 'of', 'a', 'system', 'in', 'general', 'in', 'this', 'paper', 'we', 'first', 'clarify', 'the', 'relation', 'between', 'different', 'existing', 'definitions', 'of', 'mixed', 'monotone', 'systems', 'and', 'then', 'give', 'two', 'sufficient', 'conditions', 'for', 'mixed', 'monotone', 'functions', 'defined', 'on', 'euclidean', 'space', 'these', 'sufficient', 'conditions', 'are', 'more', 'general', 'than', 'the', 'ones', 'from', 'the', 'existing', 'control', 'literature', 'and', 'they', 'suggest', 'that', 'mixed', 'monotonicity', 'is', 'a', 'very', 'generic', 'property', 'some', 'discussions', 'are', 'provided', 'on', 'the', 'computational', 'usefulness', 'of', 'the', 'proposed', 'sufficient', 'conditions']]
|
[-0.12299850052540105, 0.06915765103833771, -0.07051995305175131, 0.10608403733719818, -0.07706819537138059, -0.17509839939038185, -0.0117211286094971, 0.370911437146027, -0.2510531505908478, -0.27012647723101757, 0.15807979817883197, -0.2185177606953816, -0.19069199809296564, 0.2243212595920671, -0.13166171870279042, 0.08766184937293557, 0.040753134701993654, 0.0035526370735500345, -0.13433073405282234, -0.27898904300210153, 0.37393968537957833, -0.04538794627243822, 0.3115913829681548, 0.06396579872393472, 0.06280135598710992, -0.03165727369275621, -0.02382688956885514, 0.049211244212876246, -0.15967767747947206, 0.14815819371830333, 0.2570874507454309, 0.16925091389143332, 0.32148088445378975, -0.4269358417696574, -0.21034852978773416, 0.14358461633620953, 0.07791723304597492, 0.08002478607337583, -0.07853552628459345, -0.24362705945968627, 0.08302482226598923, -0.14472294216552242, -0.08988757848062298, -0.0941396029412069, 0.024701711785217576, 0.02286691606383432, -0.2700212949717587, 0.028113723220303654, 0.10176085377620026, 0.07207122283395041, -0.09332350884479555, -0.145061620215843, -0.011332068427211859, 0.10576217207549647, 0.012485442164523358, -0.019809512112459, 0.042252201836725525, -0.09623550702182745, -0.07857331368670037, 0.3440160663375123, 0.011410473773271819, -0.264053828235377, 0.24123944901353256, -0.0954894856274636, -0.1957142689222978, 0.07657177327835762, 0.1751693879846822, 0.14956611018966545, -0.17477092764086344, 0.048037198328264905, -0.08787304205946964, 0.12388052360832014, 0.06295467335519127, 0.09835891293213618, 0.15392384579331106, 0.1214782670775259, 0.1267566130157899, 0.16947588682682677, 0.03684880986640399, -0.14112087179144675, -0.32678269408643246, -0.13159375815144317, -0.13510160136290572, -0.012278270602903582, -0.042000482197115406, -0.14140463653338056, 0.35009206720754843, 0.15695728405632756, 0.131035149296407, 0.09160586279943925, 0.272508492693305, 0.11254280646045861, 0.031180276795798406, 0.04440846337767487, 0.23457537518353835, 0.14263321850448846, 0.11356043573045595, -0.17962800167998383, 0.10508192747154019, 0.037479951766065576]
|
1,803.04529
|
The $r$-derangement numbers
|
The classical derangement numbers count fixed point-free permutations. In
this paper we study the enumeration problem of generalized derangements, when
some of the elements are restricted to be in distinct cycles in the cycle
decomposition. We find exact formula, combinatorial relations for these numbers
as well as analytic and asymptotic description. Moreover, we study deeper
number theoretical properties, like modularity, $p$-adic valuations, and
diophantine problems.
|
math.NT math.CO
|
the classical derangement numbers count fixed pointfree permutations in this paper we study the enumeration problem of generalized derangements when some of the elements are restricted to be in distinct cycles in the cycle decomposition we find exact formula combinatorial relations for these numbers as well as analytic and asymptotic description moreover we study deeper number theoretical properties like modularity padic valuations and diophantine problems
|
[['the', 'classical', 'derangement', 'numbers', 'count', 'fixed', 'pointfree', 'permutations', 'in', 'this', 'paper', 'we', 'study', 'the', 'enumeration', 'problem', 'of', 'generalized', 'derangements', 'when', 'some', 'of', 'the', 'elements', 'are', 'restricted', 'to', 'be', 'in', 'distinct', 'cycles', 'in', 'the', 'cycle', 'decomposition', 'we', 'find', 'exact', 'formula', 'combinatorial', 'relations', 'for', 'these', 'numbers', 'as', 'well', 'as', 'analytic', 'and', 'asymptotic', 'description', 'moreover', 'we', 'study', 'deeper', 'number', 'theoretical', 'properties', 'like', 'modularity', 'padic', 'valuations', 'and', 'diophantine', 'problems']]
|
[-0.18175065038869015, 0.07537613737468536, -0.09422201436872665, 0.15431059244124648, -0.12027730265847192, -0.08716713568338981, 0.09880196035373956, 0.30384873088735803, -0.3383778868171458, -0.31543977923977834, 0.08240907945526907, -0.2434740910020012, -0.22442114449177797, 0.20645073405466974, -0.13265598381941135, 0.05925946443413312, 0.04470615874474438, 0.07043531156646518, -0.06099937149276509, -0.2639272858030521, 0.30245903697437965, -0.044585263695066366, 0.22349898108782676, 0.009762496440313184, 0.01195677239447832, 0.04950773266788858, -0.02335283587185236, 0.045210352253455384, -0.20589512831244905, 0.10314661234151572, 0.3033513008401944, 0.15485469256885923, 0.2145087047551687, -0.4357529525573437, -0.10604047977293914, 0.20550997426303533, 0.1692720360122621, 0.07501430623233318, 0.013328598267756975, -0.1703251608217565, 0.1067694200704304, -0.16116280903180058, -0.15930277468779913, -0.11049506194316423, 0.05202107885852456, 0.12125626891994705, -0.2226694849606317, 0.018345191286733516, 0.09119652785683194, 0.15589699497303136, -0.038840397551225925, -0.19067785810106075, 0.05807453364205475, 0.10064640391053847, 0.06156872188529143, -0.04626526554974799, 0.02108196002216293, -0.11798710080508429, -0.16246303781293905, 0.3583386447423926, 0.03692005331842945, -0.20914311941999655, 0.11983545707682004, -0.15870573901786253, -0.2661616649836875, 0.07832151680754927, 0.11912189075866571, 0.1693329176471497, -0.06051178458258689, 0.09811796633204302, -0.17389992392407014, 0.04712249567203868, 0.1739080235003852, 0.06321817284736496, 0.14942519435963522, 0.05883157056613037, 0.05579117494993485, 0.20497602980691365, 0.041268537394129313, -0.1043589811772108, -0.297885072331589, -0.1551762617748374, -0.16467193479363162, 0.10798968074198526, -0.16331226343530802, -0.22237912291804185, 0.3897194289244138, 0.10574869730133707, 0.17015446912712204, 0.1766866261569353, 0.2576922917595276, 0.12373341039324609, -0.022966038863747738, 0.03005158712084477, 0.09392318842583336, 0.2261022023570079, -0.004164489014790608, -0.15804400658234954, -0.00884046798500304, 0.20958125268897185]
|
1,803.0453
|
Phase transitions and magnetization of the mixed-spin Ising-Heisenberg
double sawtooth frustrated ladder
|
The mixed spin-(1,1/2) Ising-Heisenberg double sawtooth ladder containing
mixture of both spin-1 and spin-1/2 nodal atoms, and the spin-1/2 interstitial
dimers is approximately solved by the transfer-matrix method. Here, we study in
detail the ground-state phase diagrams, also influences of the bilinear
exchange coupling on the rungs and cyclic four-spin exchange interaction in
square plaquette of each block on the magnetization and magnetic susceptibility
of the suggested ladder at low temperature. Such a double sawtooth ladder may
be found in a Shastry-Sutherland Lattice-type. In spite of odd and even blocks
spin ordering are different from each other, but due to the commutation
relation between all different block Hamiltonians, phase diagrams,
magnetization behavior and thermodynamic properties of the model are the same
for odd and even blocks. We show that at low temperature, both exchange
couplings can change the quality and quantity of the magnetization plateaus
versus the magnetic field changes. Specially, we find a new magnetization
plateau M/Ms = 5/6 for this model. Besides, we examine the magnetic
susceptibility and specific heat of the model in detail. It is proven that
behaviors of the magnetization and the magnetic susceptibility coincide at low
temperature. The specific heat displays diverse temperature dependencies, which
include a Schottky-type peak at a special temperature interval. We observe that
with increase of the bilinear exchange coupling on the rungs, second peak
temperature dependence grows.
|
cond-mat.stat-mech cond-mat.str-el
|
the mixed spin112 isingheisenberg double sawtooth ladder containing mixture of both spin1 and spin12 nodal atoms and the spin12 interstitial dimers is approximately solved by the transfermatrix method here we study in detail the groundstate phase diagrams also influences of the bilinear exchange coupling on the rungs and cyclic fourspin exchange interaction in square plaquette of each block on the magnetization and magnetic susceptibility of the suggested ladder at low temperature such a double sawtooth ladder may be found in a shastrysutherland latticetype in spite of odd and even blocks spin ordering are different from each other but due to the commutation relation between all different block hamiltonians phase diagrams magnetization behavior and thermodynamic properties of the model are the same for odd and even blocks we show that at low temperature both exchange couplings can change the quality and quantity of the magnetization plateaus versus the magnetic field changes specially we find a new magnetization plateau mms 56 for this model besides we examine the magnetic susceptibility and specific heat of the model in detail it is proven that behaviors of the magnetization and the magnetic susceptibility coincide at low temperature the specific heat displays diverse temperature dependencies which include a schottkytype peak at a special temperature interval we observe that with increase of the bilinear exchange coupling on the rungs second peak temperature dependence grows
|
[['the', 'mixed', 'spin112', 'isingheisenberg', 'double', 'sawtooth', 'ladder', 'containing', 'mixture', 'of', 'both', 'spin1', 'and', 'spin12', 'nodal', 'atoms', 'and', 'the', 'spin12', 'interstitial', 'dimers', 'is', 'approximately', 'solved', 'by', 'the', 'transfermatrix', 'method', 'here', 'we', 'study', 'in', 'detail', 'the', 'groundstate', 'phase', 'diagrams', 'also', 'influences', 'of', 'the', 'bilinear', 'exchange', 'coupling', 'on', 'the', 'rungs', 'and', 'cyclic', 'fourspin', 'exchange', 'interaction', 'in', 'square', 'plaquette', 'of', 'each', 'block', 'on', 'the', 'magnetization', 'and', 'magnetic', 'susceptibility', 'of', 'the', 'suggested', 'ladder', 'at', 'low', 'temperature', 'such', 'a', 'double', 'sawtooth', 'ladder', 'may', 'be', 'found', 'in', 'a', 'shastrysutherland', 'latticetype', 'in', 'spite', 'of', 'odd', 'and', 'even', 'blocks', 'spin', 'ordering', 'are', 'different', 'from', 'each', 'other', 'but', 'due', 'to', 'the', 'commutation', 'relation', 'between', 'all', 'different', 'block', 'hamiltonians', 'phase', 'diagrams', 'magnetization', 'behavior', 'and', 'thermodynamic', 'properties', 'of', 'the', 'model', 'are', 'the', 'same', 'for', 'odd', 'and', 'even', 'blocks', 'we', 'show', 'that', 'at', 'low', 'temperature', 'both', 'exchange', 'couplings', 'can', 'change', 'the', 'quality', 'and', 'quantity', 'of', 'the', 'magnetization', 'plateaus', 'versus', 'the', 'magnetic', 'field', 'changes', 'specially', 'we', 'find', 'a', 'new', 'magnetization', 'plateau', 'mms', '56', 'for', 'this', 'model', 'besides', 'we', 'examine', 'the', 'magnetic', 'susceptibility', 'and', 'specific', 'heat', 'of', 'the', 'model', 'in', 'detail', 'it', 'is', 'proven', 'that', 'behaviors', 'of', 'the', 'magnetization', 'and', 'the', 'magnetic', 'susceptibility', 'coincide', 'at', 'low', 'temperature', 'the', 'specific', 'heat', 'displays', 'diverse', 'temperature', 'dependencies', 'which', 'include', 'a', 'schottkytype', 'peak', 'at', 'a', 'special', 'temperature', 'interval', 'we', 'observe', 'that', 'with', 'increase', 'of', 'the', 'bilinear', 'exchange', 'coupling', 'on', 'the', 'rungs', 'second', 'peak', 'temperature', 'dependence', 'grows']]
|
[-0.19482517775198413, 0.22644122649293952, 0.00788497477605742, 0.06734967066256108, -0.04417686438032053, -0.16116547268827647, 0.05932383076287806, 0.3921807597946203, -0.2633704135136982, -0.2955974722445208, 0.05533826120745489, -0.3298837426190351, -0.09959905573663339, 0.15512398161539717, 0.09442172176512327, -0.03723296758283898, -0.026525546425321685, 0.0800373417116078, -0.13208047103587822, -0.23501470269653782, 0.27041332229011844, 0.0036868831626906792, 0.3058287811769164, 0.09192959483946768, 0.07101548619215511, 0.03567423110639077, 0.11419961939561735, 0.027982034556370737, -0.12230298426952946, 0.009701486460800181, 0.18455546900208442, -0.07418052311412301, 0.12885812628554746, -0.40325446582772656, -0.16765357572318665, 0.08020390634371649, 0.14839635836986192, 0.1284873400325579, -0.018365151587348544, -0.22011510734056586, -0.01266711504282717, -0.16657304360699482, -0.11925157215723169, -0.12061062716727149, -0.0257879874252587, 0.03781925882215382, -0.2633962713075301, 0.12983855873124267, 0.06595275355585017, 0.12032357017132186, -0.07744657043531734, -0.1424761924170581, -0.0884716565975107, 0.08433810822944678, 0.03930970547886951, 0.038721812017147104, 0.11240549565863175, -0.11567883038768208, -0.10549408549828546, 0.31681319221542403, -0.07054422746539642, -0.11851810312301427, 0.14425322142150662, -0.1980073423871549, -0.1380961833722758, 0.11194421507629447, 0.09790768764404342, 0.07107041846542303, -0.14828234689314296, 0.08819175130748683, 0.0027067362041610582, 0.15962679816270453, 0.045564201507242476, 0.035371700637165604, 0.24427305616383002, 0.1339474867389328, 0.012015269172022997, 0.22361789118998074, -0.11837551221436111, -0.14846991685050898, -0.25009484166312257, -0.14380482007802714, -0.19024811383585, 0.019173717578834064, -0.15453446534139695, -0.1939243237007951, 0.42002921818144257, 0.1425258612455359, 0.19330144062046462, -0.015359659087926247, 0.2311051517913717, 0.12646295513449, 0.08622144791913026, 0.07427983136185022, 0.20653512616362832, 0.18322267327437153, 0.12970683532705457, -0.32474317715007356, 0.061081041115241756, 0.031070903401066947]
|
1,803.04531
|
Twisted argyle quivers and Higgs bundles
|
Ordinarily, quiver varieties are constructed as moduli spaces of quiver
representations in the category of vector spaces. It is also natural to
consider quiver representations in a richer category, namely that of vector
bundles on some complex variety equipped with a fixed sheaf that twists the
morphisms. Representations of A-type quivers in this twisted category --- known
in the literature as "holomorphic chains" --- have practical use in questions
concerning the topology of the moduli space of Higgs bundles. In that problem,
the variety is a Riemann surface of genus at least 2, and the twist is its
canonical line bundle. We extend the treatment of twisted A-type quiver
representations to any genus using the Hitchin stability condition induced by
Higgs bundles and computing their deformation theory. We then focus in
particular on so-called "argyle quivers", where the rank labelling alternates
between 1 and integers $r_i\geq1$. We give explicit geometric identifications
of moduli spaces of twisted representations of argyle quivers on $\mathbb{P}^1$
using invariant theory for a non-reductive action via Euclidean reduction on
polynomials. This leads to a stratification of the moduli space by change of
bundle type, which we identify with "collision manifolds" of invariant zeroes
of polynomials. We also relate the present work to Bradlow-Daskalopoulos
stability and Thaddeus' pullback maps to stable tuples. We apply our results to
computing $\mathbb{Q}$-Betti numbers of low-rank twisted Higgs bundle moduli
spaces on $\mathbb{P}^1$, where the Higgs fields take values in an arbitrary
ample line bundle. Our results agree with conjectural Poincar\'e series arising
from the ADHM recursion formula.
|
math.AG math.DG math.RT
|
ordinarily quiver varieties are constructed as moduli spaces of quiver representations in the category of vector spaces it is also natural to consider quiver representations in a richer category namely that of vector bundles on some complex variety equipped with a fixed sheaf that twists the morphisms representations of atype quivers in this twisted category known in the literature as holomorphic chains have practical use in questions concerning the topology of the moduli space of higgs bundles in that problem the variety is a riemann surface of genus at least 2 and the twist is its canonical line bundle we extend the treatment of twisted atype quiver representations to any genus using the hitchin stability condition induced by higgs bundles and computing their deformation theory we then focus in particular on socalled argyle quivers where the rank labelling alternates between 1 and integers r_igeq1 we give explicit geometric identifications of moduli spaces of twisted representations of argyle quivers on mathbbp1 using invariant theory for a nonreductive action via euclidean reduction on polynomials this leads to a stratification of the moduli space by change of bundle type which we identify with collision manifolds of invariant zeroes of polynomials we also relate the present work to bradlowdaskalopoulos stability and thaddeus pullback maps to stable tuples we apply our results to computing mathbbqbetti numbers of lowrank twisted higgs bundle moduli spaces on mathbbp1 where the higgs fields take values in an arbitrary ample line bundle our results agree with conjectural poincare series arising from the adhm recursion formula
|
[['ordinarily', 'quiver', 'varieties', 'are', 'constructed', 'as', 'moduli', 'spaces', 'of', 'quiver', 'representations', 'in', 'the', 'category', 'of', 'vector', 'spaces', 'it', 'is', 'also', 'natural', 'to', 'consider', 'quiver', 'representations', 'in', 'a', 'richer', 'category', 'namely', 'that', 'of', 'vector', 'bundles', 'on', 'some', 'complex', 'variety', 'equipped', 'with', 'a', 'fixed', 'sheaf', 'that', 'twists', 'the', 'morphisms', 'representations', 'of', 'atype', 'quivers', 'in', 'this', 'twisted', 'category', 'known', 'in', 'the', 'literature', 'as', 'holomorphic', 'chains', 'have', 'practical', 'use', 'in', 'questions', 'concerning', 'the', 'topology', 'of', 'the', 'moduli', 'space', 'of', 'higgs', 'bundles', 'in', 'that', 'problem', 'the', 'variety', 'is', 'a', 'riemann', 'surface', 'of', 'genus', 'at', 'least', '2', 'and', 'the', 'twist', 'is', 'its', 'canonical', 'line', 'bundle', 'we', 'extend', 'the', 'treatment', 'of', 'twisted', 'atype', 'quiver', 'representations', 'to', 'any', 'genus', 'using', 'the', 'hitchin', 'stability', 'condition', 'induced', 'by', 'higgs', 'bundles', 'and', 'computing', 'their', 'deformation', 'theory', 'we', 'then', 'focus', 'in', 'particular', 'on', 'socalled', 'argyle', 'quivers', 'where', 'the', 'rank', 'labelling', 'alternates', 'between', '1', 'and', 'integers', 'r_igeq1', 'we', 'give', 'explicit', 'geometric', 'identifications', 'of', 'moduli', 'spaces', 'of', 'twisted', 'representations', 'of', 'argyle', 'quivers', 'on', 'mathbbp1', 'using', 'invariant', 'theory', 'for', 'a', 'nonreductive', 'action', 'via', 'euclidean', 'reduction', 'on', 'polynomials', 'this', 'leads', 'to', 'a', 'stratification', 'of', 'the', 'moduli', 'space', 'by', 'change', 'of', 'bundle', 'type', 'which', 'we', 'identify', 'with', 'collision', 'manifolds', 'of', 'invariant', 'zeroes', 'of', 'polynomials', 'we', 'also', 'relate', 'the', 'present', 'work', 'to', 'bradlowdaskalopoulos', 'stability', 'and', 'thaddeus', 'pullback', 'maps', 'to', 'stable', 'tuples', 'we', 'apply', 'our', 'results', 'to', 'computing', 'mathbbqbetti', 'numbers', 'of', 'lowrank', 'twisted', 'higgs', 'bundle', 'moduli', 'spaces', 'on', 'mathbbp1', 'where', 'the', 'higgs', 'fields', 'take', 'values', 'in', 'an', 'arbitrary', 'ample', 'line', 'bundle', 'our', 'results', 'agree', 'with', 'conjectural', 'poincare', 'series', 'arising', 'from', 'the', 'adhm', 'recursion', 'formula']]
|
[-0.18860570194013418, 0.09302389330492587, -0.07802560283429921, 0.09189529736386612, -0.14172012175899, -0.13456637972965838, -0.009412944510113447, 0.36142633578367533, -0.3168066660221666, -0.1986393290106207, 0.09190648088976741, -0.20392134435195475, -0.19503501386381686, 0.1691419197805226, -0.17404722339165163, -0.010498310246213805, 0.06542891948577016, 0.07380950861424208, -0.1365442559896037, -0.32503938960051165, 0.4965940141286701, -0.04488327131443657, 0.25004556678328665, 0.024191458119545133, 0.1162486620284617, 0.03059060648328159, -0.0033176067061722278, -0.05297867819503881, -0.14093376885165343, 0.19413415959710256, 0.3018923878725618, 0.053272404943592845, 0.12116499990192824, -0.3821224803030491, -0.15389025784633123, 0.21234395935805514, 0.14172134649567306, 0.022667637984734028, 0.020342250044923276, -0.2746140596140176, 0.07673161590466043, -0.15050690916180612, -0.13947078161872922, -0.12756710071861743, 0.06125638586329296, -0.00014901220984756946, -0.220853425713256, -0.05423804908269085, 0.01240713548148051, 0.16322482059430332, -0.1224985293657519, -0.10698466833308339, -0.12338981475308537, 0.056040777718648314, 0.047508873248938474, 0.07748089945316315, 0.10756312447972596, -0.14971049766428768, -0.15474888223968447, 0.34049146608263253, -0.07528871348965913, -0.25618124797253405, 0.11643415923416614, -0.11589887135662139, -0.19602771771978586, 0.15482819652371108, 0.11190547593217343, 0.17048405061487573, 0.049848146706353874, 0.17501934632379562, -0.13034862597100436, 0.038192878944333646, 0.1104114287449047, -0.009308803709223866, 0.18093301443010568, 0.10162468579690903, 0.03288856143690646, 0.1324088180474937, -0.013572193069383502, -0.09126727689336986, -0.38486090479791163, -0.2119916337132454, -0.057548325120471415, 0.13696457239147275, -0.1318160401803907, -0.18581932378839702, 0.39729578472860155, 0.07640484227379785, 0.2674719956833869, 0.14605487065343187, 0.2098281622864306, 0.031054573632776736, 0.07355458458606154, 0.00012509835697710513, 0.15708320452889893, 0.2525757464822382, -0.0008781699617975391, -0.11265529947378673, -0.07926414064504206, 0.24935590026527643]
|
1,803.04532
|
Minimising the expectation value of the procurement cost in electricity
markets based on the prediction error of energy consumption
|
In this paper, we formulate a method for minimising the expectation value of
the procurement cost of electricity in two popular spot markets: {\it
day-ahead} and {\it intra-day}, under the assumption that expectation value of
unit prices and the distributions of prediction errors for the electricity
demand traded in two markets are known. The expectation value of the total
electricity cost is minimised over two parameters that change the amounts of
electricity. Two parameters depend only on the expected unit prices of
electricity and the distributions of prediction errors for the electricity
demand traded in two markets. That is, even if we do not know the predictions
for the electricity demand, we can determine the values of two parameters that
minimise the expectation value of the procurement cost of electricity in two
popular spot markets. We demonstrate numerically that the estimate of two
parameters often results in a small variance of the total electricity cost, and
illustrate the usefulness of the proposed procurement method through the
analysis of actual data.
|
q-fin.EC q-fin.GN
|
in this paper we formulate a method for minimising the expectation value of the procurement cost of electricity in two popular spot markets it dayahead and it intraday under the assumption that expectation value of unit prices and the distributions of prediction errors for the electricity demand traded in two markets are known the expectation value of the total electricity cost is minimised over two parameters that change the amounts of electricity two parameters depend only on the expected unit prices of electricity and the distributions of prediction errors for the electricity demand traded in two markets that is even if we do not know the predictions for the electricity demand we can determine the values of two parameters that minimise the expectation value of the procurement cost of electricity in two popular spot markets we demonstrate numerically that the estimate of two parameters often results in a small variance of the total electricity cost and illustrate the usefulness of the proposed procurement method through the analysis of actual data
|
[['in', 'this', 'paper', 'we', 'formulate', 'a', 'method', 'for', 'minimising', 'the', 'expectation', 'value', 'of', 'the', 'procurement', 'cost', 'of', 'electricity', 'in', 'two', 'popular', 'spot', 'markets', 'it', 'dayahead', 'and', 'it', 'intraday', 'under', 'the', 'assumption', 'that', 'expectation', 'value', 'of', 'unit', 'prices', 'and', 'the', 'distributions', 'of', 'prediction', 'errors', 'for', 'the', 'electricity', 'demand', 'traded', 'in', 'two', 'markets', 'are', 'known', 'the', 'expectation', 'value', 'of', 'the', 'total', 'electricity', 'cost', 'is', 'minimised', 'over', 'two', 'parameters', 'that', 'change', 'the', 'amounts', 'of', 'electricity', 'two', 'parameters', 'depend', 'only', 'on', 'the', 'expected', 'unit', 'prices', 'of', 'electricity', 'and', 'the', 'distributions', 'of', 'prediction', 'errors', 'for', 'the', 'electricity', 'demand', 'traded', 'in', 'two', 'markets', 'that', 'is', 'even', 'if', 'we', 'do', 'not', 'know', 'the', 'predictions', 'for', 'the', 'electricity', 'demand', 'we', 'can', 'determine', 'the', 'values', 'of', 'two', 'parameters', 'that', 'minimise', 'the', 'expectation', 'value', 'of', 'the', 'procurement', 'cost', 'of', 'electricity', 'in', 'two', 'popular', 'spot', 'markets', 'we', 'demonstrate', 'numerically', 'that', 'the', 'estimate', 'of', 'two', 'parameters', 'often', 'results', 'in', 'a', 'small', 'variance', 'of', 'the', 'total', 'electricity', 'cost', 'and', 'illustrate', 'the', 'usefulness', 'of', 'the', 'proposed', 'procurement', 'method', 'through', 'the', 'analysis', 'of', 'actual', 'data']]
|
[-0.11133010854725452, 0.07991531797794296, -0.05054953042143846, 0.1290963110219523, -0.04274523734827252, -0.08069960624527406, 0.1251264584962936, 0.4064052285516963, -0.2690605693798074, -0.32597912625574016, 0.1634631857727928, -0.3323234871796825, -0.09762799331322586, 0.22440556062673053, -0.12966032154748544, 0.061213862526946126, 0.055102477524676084, 0.026478804395917585, 0.012453430897884947, -0.3178234255160479, 0.26613374671844, 0.062337848582469366, 0.3463442418998217, 0.06313062026815983, 0.1215484737754142, -0.027808169765836176, -0.023984715632875176, 0.018172871310483008, -0.09635961321375149, 0.1960339529056321, 0.24363526274066638, 0.12160501875366797, 0.36973317108281395, -0.4539670056937372, -0.15198080743293224, 0.17874410253305756, -0.017088977462502524, -0.02184790704974576, 0.006836534608566367, -0.12144510529606659, 0.001675666748162578, -0.20598364477285513, -0.08521271986229455, -0.07183750868939301, -0.008491347645245053, 0.04739026448360699, -0.33610275621690294, 0.06026337429237388, -0.022668632097980555, 0.010115520664326407, -0.09234608596683863, -0.15490520829334856, -0.08534886209732469, 0.1483335480970495, 0.15602604120093233, -0.10110065838842489, 0.1449087611563942, -0.1712825559065951, -0.13262537489919102, 0.40901962693999794, -0.08598037412088802, -0.17552900104509556, 0.03675099333529087, -0.1484497630639988, -0.11256923974929925, 0.09043923988938332, 0.21695592957959675, 0.0637491693121453, -0.2020356262486209, 0.04614777013741653, -0.047651429200435386, 0.17963535776498782, 0.03291347792563851, -0.010059211496263742, 0.1990588525091024, 0.14127868986414635, 0.13307366989046227, 0.11158466354861636, -0.10108663497136577, -0.18938173382159543, -0.27526786529308406, -0.14847920849261914, -0.22023779130814708, 0.020737302176712546, -0.13476062283323914, -0.14267327801379212, 0.41138727923526486, 0.20946252893875628, 0.1635111089439734, 0.10017904435517266, 0.3542535471456016, 0.16020430123762172, 0.011440485299510115, 0.12053549904924105, 0.21195605083313934, -0.049731381538817114, 0.13820956981234142, -0.23510076445527375, 0.1535683935699875, 0.004760491042671834]
|
1,803.04533
|
A note on $p$-adic locally analytic functions with application to
behavior of the $p$-adic valuations of Stirling numbers
|
The aim of this paper is to prove conjectures concerning $p$-adic valuations
of Stirling numbers of the second kind $S(n,k)$, $n,k\in\mathbb{N}_+$, stated
by Amdeberhan, Manna and Moll and Berrizbeitia et al., where $p$ is a prime
number. The proof is based on elementary facts from $p$-adic analysis.
|
math.NT
|
the aim of this paper is to prove conjectures concerning padic valuations of stirling numbers of the second kind snk nkinmathbbn_ stated by amdeberhan manna and moll and berrizbeitia et al where p is a prime number the proof is based on elementary facts from padic analysis
|
[['the', 'aim', 'of', 'this', 'paper', 'is', 'to', 'prove', 'conjectures', 'concerning', 'padic', 'valuations', 'of', 'stirling', 'numbers', 'of', 'the', 'second', 'kind', 'snk', 'nkinmathbbn_', 'stated', 'by', 'amdeberhan', 'manna', 'and', 'moll', 'and', 'berrizbeitia', 'et', 'al', 'where', 'p', 'is', 'a', 'prime', 'number', 'the', 'proof', 'is', 'based', 'on', 'elementary', 'facts', 'from', 'padic', 'analysis']]
|
[-0.19009173578686184, 0.08159587246676286, -0.13252513663222393, 0.06380534768621955, -0.15273378788390093, -0.09138926452853613, 0.0754653220401249, 0.15554076079279183, -0.24233813205113014, -0.34527268471817174, 0.053463541054063375, -0.2678717243174712, -0.18535050087634267, 0.23621723831424282, -0.18848992795166042, 0.018560467003327277, -0.05687762518516845, 0.05257791384226746, 0.06341952627731694, -0.37891908180382516, 0.387001848883099, 0.0018503831078608831, 0.19097192771732807, 0.07019677087664604, 0.031356451681090726, 0.0336962829137014, -0.06500008716765378, -0.07162803498407205, -0.20456746855957642, 0.15835144550849994, 0.28386313089480003, 0.09673342283090783, 0.31374913820376, -0.3607792546351751, -0.07212868767480056, 0.16509769598229063, 0.06526408884189247, -0.01358965372459756, 0.005206067759233216, -0.2534926308525933, 0.13725927837141272, -0.11994326118793752, -0.15118125373911526, -0.0494380972865555, 0.09987426321539614, 0.1260214490815997, -0.3024590343857805, 0.047660650271508426, 0.17630473292536206, 0.18845269398556816, -0.02891276437892682, -0.19531045200096236, 0.06995267665220631, -0.04133233428518805, 0.0629012617851711, 0.019560017633355326, -0.006487823153535525, -0.0333912270091888, -0.18197542217870552, 0.3517810401817163, 0.042149920844369464, -0.10810711693225636, 0.0962986442933066, -0.14899721699249413, -0.2572906473341087, 0.06464644115832117, 0.057094582046071686, 0.17321946666472487, -0.016444072561959424, 0.15570407246705145, -0.24005452557984325, 0.13681252718799644, 0.18310222453955147, -0.07003958639171388, 0.11729280071902193, 0.06484844150642553, -0.021771704819467334, 0.18223496907287173, 0.04128102517376343, -0.056053472223639905, -0.3345576138339109, -0.23647721213185124, -0.2847040874676572, 0.12199153958354145, -0.024085516295680363, -0.09590349238779809, 0.34999621899591554, 0.10954139686913954, 0.15448686020034882, 0.15440456683023107, 0.22204584326181148, 0.0857355451832215, -0.060942470541016924, 0.05650077219017678, 0.08353747525769803, 0.2651521041420185, 0.06208868466524614, -0.07912710868921648, 0.043919926540305215, 0.2609432076724867]
|
1,803.04534
|
Anomaly Detection in Road Networks Using Sliding-Window Tensor
Factorization
|
Anomaly detection in road networks is vital for traffic management and
emergency response. However, existing approaches do not directly address
multiple anomaly types. We propose a tensor-based spatio-temporal model for
detecting multiple types of anomalies in road networks. First, we represent
network traffic data as a 3rd-order tensor. Next, we acquire spatial and
multi-scale temporal patterns of traffic variations via a novel,
computationally efficient tensor factorization algorithm: sliding window tensor
factorization. Then, from the factorization results, we can identify different
anomaly types by measuring deviations from different spatial and temporal
patterns. Finally, we discover path-level anomalies by formulating anomalous
path inference as a linear program that solves for the best matched paths of
anomalous links. We evaluate the proposed methods via both synthetic
experiments and case studies based on a real-world vehicle trajectory dataset,
demonstrating advantages of our approach over baselines.
|
physics.soc-ph cs.SI
|
anomaly detection in road networks is vital for traffic management and emergency response however existing approaches do not directly address multiple anomaly types we propose a tensorbased spatiotemporal model for detecting multiple types of anomalies in road networks first we represent network traffic data as a 3rdorder tensor next we acquire spatial and multiscale temporal patterns of traffic variations via a novel computationally efficient tensor factorization algorithm sliding window tensor factorization then from the factorization results we can identify different anomaly types by measuring deviations from different spatial and temporal patterns finally we discover pathlevel anomalies by formulating anomalous path inference as a linear program that solves for the best matched paths of anomalous links we evaluate the proposed methods via both synthetic experiments and case studies based on a realworld vehicle trajectory dataset demonstrating advantages of our approach over baselines
|
[['anomaly', 'detection', 'in', 'road', 'networks', 'is', 'vital', 'for', 'traffic', 'management', 'and', 'emergency', 'response', 'however', 'existing', 'approaches', 'do', 'not', 'directly', 'address', 'multiple', 'anomaly', 'types', 'we', 'propose', 'a', 'tensorbased', 'spatiotemporal', 'model', 'for', 'detecting', 'multiple', 'types', 'of', 'anomalies', 'in', 'road', 'networks', 'first', 'we', 'represent', 'network', 'traffic', 'data', 'as', 'a', '3rdorder', 'tensor', 'next', 'we', 'acquire', 'spatial', 'and', 'multiscale', 'temporal', 'patterns', 'of', 'traffic', 'variations', 'via', 'a', 'novel', 'computationally', 'efficient', 'tensor', 'factorization', 'algorithm', 'sliding', 'window', 'tensor', 'factorization', 'then', 'from', 'the', 'factorization', 'results', 'we', 'can', 'identify', 'different', 'anomaly', 'types', 'by', 'measuring', 'deviations', 'from', 'different', 'spatial', 'and', 'temporal', 'patterns', 'finally', 'we', 'discover', 'pathlevel', 'anomalies', 'by', 'formulating', 'anomalous', 'path', 'inference', 'as', 'a', 'linear', 'program', 'that', 'solves', 'for', 'the', 'best', 'matched', 'paths', 'of', 'anomalous', 'links', 'we', 'evaluate', 'the', 'proposed', 'methods', 'via', 'both', 'synthetic', 'experiments', 'and', 'case', 'studies', 'based', 'on', 'a', 'realworld', 'vehicle', 'trajectory', 'dataset', 'demonstrating', 'advantages', 'of', 'our', 'approach', 'over', 'baselines']]
|
[-0.13361687456911547, 0.03859529949903013, -0.08476154879932721, 0.08036653654970202, -0.12569383663250833, -0.15900832977177284, 0.04443113382984983, 0.4305215042797809, -0.2783262331917195, -0.31759196650633154, 0.11166459195652699, -0.2648607314901149, -0.2570095373489333, 0.16517480839529333, -0.058404840061652426, 0.11200955643645547, 0.10455489462796361, -0.0035320437936019813, -0.06387009272675512, -0.19452686303671687, 0.30401797202529907, 0.0007172586488808301, 0.3821587650144988, 0.07296122490025475, 0.1450859302240391, 0.022844199462734322, -0.11377963295589524, 0.06813265287056433, -0.031831711559015215, 0.13145261856476306, 0.30854561548442283, 0.17996378004689642, 0.22409756423357266, -0.45518065388611656, -0.28185632634670177, 0.1139612126573656, 0.13719778772152907, 0.10810151583587764, -0.003502298619578335, -0.3251265366644275, 0.11459783645650597, -0.16567036197751972, -0.03815450191048654, -0.16344318721866144, -0.03210524274762852, -0.022883593555579475, -0.3021740226854458, 0.0749032279585508, -0.0037242991697862215, 0.060595151730515856, -0.03708386201087835, -0.08859587342029186, 0.07041055973823981, 0.1756484260491974, 0.057795350354805534, -0.053938354642942866, 0.12680950555091736, -0.14698932271450552, -0.2266077470093807, 0.3729187677554945, -0.09917484273587465, -0.18602084955662912, 0.1792021690379062, -0.023245936794642437, -0.17104622304723566, 0.10347418527032995, 0.24842027611601172, 0.1295298499885801, -0.2019296720993857, -0.01782744102913173, -0.051018526110507494, 0.12907100958974227, 0.06224412644935891, -0.01329339058501712, 0.203313938036393, 0.23105052508519475, 0.07413868470850618, 0.10883845426373395, -0.1679373592722844, -0.07307117669810112, -0.2437508619714079, -0.11085950612447205, -0.1535403511837019, -0.053550741866339124, -0.12977564397907043, -0.1153884520506526, 0.42740087373618113, 0.2292798782751651, 0.1995382390529122, 0.10534218463597568, 0.36463076816475454, 0.037773815952142346, 0.10003132977334457, 0.08799882948808108, 0.12783050718007227, 0.025139894570943944, 0.15856697651349563, -0.21836428441345057, 0.07422019767984483, 0.06763996833155975]
|
1,803.04535
|
Computational methods in cardiovascular mechanics
|
The introduction of computational models in cardiovascular sciences has been
progressively bringing new and unique tools for the investigation of the
physiopathology. Together with the dramatic improvement of imaging and
measuring devices on one side, and of computational architectures on the other
one, mathematical and numerical models have provided a new, clearly
noninvasive, approach for understanding not only basic mechanisms but also
patient-specific conditions, and for supporting the design and the development
of new therapeutic options. The terminology in silico is, nowadays, commonly
accepted for indicating this new source of knowledge added to traditional in
vitro and in vivo investigations. The advantages of in silico methodologies are
basically the low cost in terms of infrastructures and facilities, the reduced
invasiveness and, in general, the intrinsic predictive capabilities based on
the use of mathematical models. The disadvantages are generally identified in
the distance between the real cases and their virtual counterpart required by
the conceptual modeling that can be detrimental for the reliability of
numerical simulations.
|
physics.comp-ph math.NA
|
the introduction of computational models in cardiovascular sciences has been progressively bringing new and unique tools for the investigation of the physiopathology together with the dramatic improvement of imaging and measuring devices on one side and of computational architectures on the other one mathematical and numerical models have provided a new clearly noninvasive approach for understanding not only basic mechanisms but also patientspecific conditions and for supporting the design and the development of new therapeutic options the terminology in silico is nowadays commonly accepted for indicating this new source of knowledge added to traditional in vitro and in vivo investigations the advantages of in silico methodologies are basically the low cost in terms of infrastructures and facilities the reduced invasiveness and in general the intrinsic predictive capabilities based on the use of mathematical models the disadvantages are generally identified in the distance between the real cases and their virtual counterpart required by the conceptual modeling that can be detrimental for the reliability of numerical simulations
|
[['the', 'introduction', 'of', 'computational', 'models', 'in', 'cardiovascular', 'sciences', 'has', 'been', 'progressively', 'bringing', 'new', 'and', 'unique', 'tools', 'for', 'the', 'investigation', 'of', 'the', 'physiopathology', 'together', 'with', 'the', 'dramatic', 'improvement', 'of', 'imaging', 'and', 'measuring', 'devices', 'on', 'one', 'side', 'and', 'of', 'computational', 'architectures', 'on', 'the', 'other', 'one', 'mathematical', 'and', 'numerical', 'models', 'have', 'provided', 'a', 'new', 'clearly', 'noninvasive', 'approach', 'for', 'understanding', 'not', 'only', 'basic', 'mechanisms', 'but', 'also', 'patientspecific', 'conditions', 'and', 'for', 'supporting', 'the', 'design', 'and', 'the', 'development', 'of', 'new', 'therapeutic', 'options', 'the', 'terminology', 'in', 'silico', 'is', 'nowadays', 'commonly', 'accepted', 'for', 'indicating', 'this', 'new', 'source', 'of', 'knowledge', 'added', 'to', 'traditional', 'in', 'vitro', 'and', 'in', 'vivo', 'investigations', 'the', 'advantages', 'of', 'in', 'silico', 'methodologies', 'are', 'basically', 'the', 'low', 'cost', 'in', 'terms', 'of', 'infrastructures', 'and', 'facilities', 'the', 'reduced', 'invasiveness', 'and', 'in', 'general', 'the', 'intrinsic', 'predictive', 'capabilities', 'based', 'on', 'the', 'use', 'of', 'mathematical', 'models', 'the', 'disadvantages', 'are', 'generally', 'identified', 'in', 'the', 'distance', 'between', 'the', 'real', 'cases', 'and', 'their', 'virtual', 'counterpart', 'required', 'by', 'the', 'conceptual', 'modeling', 'that', 'can', 'be', 'detrimental', 'for', 'the', 'reliability', 'of', 'numerical', 'simulations']]
|
[-0.04054709400544397, 0.046143193228068057, -0.06369392345545859, 0.06327637942314784, -0.07674528589676612, -0.14022039359424082, 0.041300748294594174, 0.3669857239668689, -0.23677951929715985, -0.3158923375374842, 0.12937133770839598, -0.2574263303909769, -0.1794780498382862, 0.2676505854114799, -0.09241782572040926, 0.06318122336694902, 0.07980894106837762, -0.0045867035557266055, -0.03161632556009686, -0.22564371153655485, 0.27098675144100903, 0.0905275127151981, 0.3244176268259563, 0.09840478677666042, 0.07662530055712545, -0.024336806461555748, -0.0673957901279919, 0.025258645353626822, -0.10432849190271178, 0.1824852536752454, 0.2753127427793116, 0.18085713019767044, 0.3263156579512103, -0.48614630715285495, -0.26018339152470593, 0.0551399058711512, 0.13032298241656773, 0.07886691882453217, -0.06816019752934552, -0.2574312617525277, 0.07441465134722487, -0.14703186989830033, -0.121872468063485, -0.10415489992400824, 0.009325593011098823, 0.043095236039357034, -0.2216653261225807, 0.041578231196071125, 0.014627738072105297, 0.11558279375227667, -0.06918177419977771, -0.15360822223294412, 0.010789127216856109, 0.1674334766639953, 0.04891287017238849, -0.004385064608735464, 0.10553143870244484, -0.17730454114129449, -0.15849061348481194, 0.3807114166429085, 0.015260470982799987, -0.19919650647334936, 0.25898469101478594, -0.09891988184848209, -0.1520488207972404, 0.09358370542617106, 0.17395185372968228, 0.06721085301890015, -0.17362195782068118, 0.04454387657052593, 0.05866812614201591, 0.14183374593693127, 0.024201248470046444, 0.05735760468531048, 0.1889473995528888, 0.2349123865473879, 6.557012433981204e-05, 0.08776145346325016, -0.06511484656806626, -0.1032088983622266, -0.267826748550472, -0.1944314379546019, -0.14226785393306848, -0.01615758178883059, -0.07098616986367255, -0.13189027889733906, 0.40133745374339747, 0.18419695599856445, 0.09751241481380264, 0.01206949166598639, 0.35732630405166166, 0.06178842065855861, 0.10790219489540394, 0.005539541524585064, 0.24851228426233296, 0.0793107730342166, 0.11670578158142544, -0.1971601376752937, 0.12676410984656797, -0.021035372875858008]
|
1,803.04536
|
Low-cost orbital-based linear-scaling \emph{ab initio} molecular
dynamics for weakly-interacting systems
|
Within the framework of linear-scaling Kohn-Sham density functional theory, a
robust method for maintaining compact localized orbitals close to the ground
state is coupled with nuclear dynamics. This allows to obviate the commonly
employed optimization of the one-electron density matrix and thus create an
efficient orbital-only molecular dynamics method for weakly-interacting
systems. An application to liquid water demonstrates that the low computational
overhead of the method makes it well-suited for routine simulations whereas its
linear-scaling complexity allows to extend first-principle dynamical studies of
molecular systems to previously inaccessible length scales.
|
physics.comp-ph physics.chem-ph quant-ph
|
within the framework of linearscaling kohnsham density functional theory a robust method for maintaining compact localized orbitals close to the ground state is coupled with nuclear dynamics this allows to obviate the commonly employed optimization of the oneelectron density matrix and thus create an efficient orbitalonly molecular dynamics method for weaklyinteracting systems an application to liquid water demonstrates that the low computational overhead of the method makes it wellsuited for routine simulations whereas its linearscaling complexity allows to extend firstprinciple dynamical studies of molecular systems to previously inaccessible length scales
|
[['within', 'the', 'framework', 'of', 'linearscaling', 'kohnsham', 'density', 'functional', 'theory', 'a', 'robust', 'method', 'for', 'maintaining', 'compact', 'localized', 'orbitals', 'close', 'to', 'the', 'ground', 'state', 'is', 'coupled', 'with', 'nuclear', 'dynamics', 'this', 'allows', 'to', 'obviate', 'the', 'commonly', 'employed', 'optimization', 'of', 'the', 'oneelectron', 'density', 'matrix', 'and', 'thus', 'create', 'an', 'efficient', 'orbitalonly', 'molecular', 'dynamics', 'method', 'for', 'weaklyinteracting', 'systems', 'an', 'application', 'to', 'liquid', 'water', 'demonstrates', 'that', 'the', 'low', 'computational', 'overhead', 'of', 'the', 'method', 'makes', 'it', 'wellsuited', 'for', 'routine', 'simulations', 'whereas', 'its', 'linearscaling', 'complexity', 'allows', 'to', 'extend', 'firstprinciple', 'dynamical', 'studies', 'of', 'molecular', 'systems', 'to', 'previously', 'inaccessible', 'length', 'scales']]
|
[-0.0940360304278632, 0.06992933279608325, -0.10486065751562516, 0.08967423563719623, -0.01791278102124731, -0.13175314262431737, 0.057991267206509496, 0.3527548527862463, -0.25902113227380646, -0.31010040524933075, 0.013132544353397356, -0.22129453027963691, -0.1411895172153082, 0.1800561528859867, 0.017868592370198003, 0.1363471644975814, 0.043667041546561654, -0.03451523776424842, -0.10756759841978136, -0.17864979435172346, 0.2307901690610581, 0.13354001712416194, 0.2899901500515019, 0.053576742992219, 0.07561156602007234, 0.015009260811429058, 0.043135717661223476, 0.006333308873905076, -0.10787574905787349, 0.17724653902825796, 0.3089204842224717, 0.08563271063548099, 0.30696983589894244, -0.5059839244311054, -0.23685126491925781, 0.008718773037091725, 0.14074238346268733, 0.18673754858577418, -0.0242275960639947, -0.26070218109008336, 0.05576024394896295, -0.18739552836503007, -0.20369981008892257, -0.17972687596920878, -0.0021765945246443154, -0.0012960079129293767, -0.25746569461706614, 0.11809163212569224, -0.055893853300626184, 0.014279345671335856, -0.06742711289423621, -0.11030770962436993, 0.014117540843370888, 0.07740152479899633, -0.03204777395197501, 0.03822425771504641, 0.18905619386997488, -0.07590541730169206, -0.06345236053586834, 0.3970302836762534, -0.04450109976524901, -0.20744629334399683, 0.2777244547692438, -0.07268562952263488, -0.15654138319918678, 0.2281759456731379, 0.12131153498776256, 0.14489833374714686, -0.1498698678714896, 0.10715769278629321, 0.010455291599242224, 0.25258063978205125, -0.05387226974125951, 0.051985516807892256, 0.17180669904272589, 0.23684959953857793, 0.10038130171597004, 0.10168371938893364, -0.0919556025415659, -0.16852243097964675, -0.17809102919159664, -0.1654642371978197, -0.22544516417611804, -0.015518997909708156, -0.044534498458152584, -0.19511268626277645, 0.3557871856726706, 0.18481862373236152, 0.09176725613263746, 0.04416425705163015, 0.32633648218794004, 0.09439503697730187, 0.07456031771273249, 0.0793234535997423, 0.18106859537462394, 0.1964116007652289, 0.04646982077198724, -0.30806349402604005, 0.0277437684022718, 0.04308362194957833]
|
1,803.04537
|
Massive Multiple Input Massive Multiple Output for 5G Wireless
Backhauling
|
In this paper, we propose a new technique for the future fifth generation
cellular network wireless backhauling. We show that hundreds of bits per second
per Hertz (bits per second per Hz) of spectral efficiency can be attained at a
high carrier frequency (such as 26 GHz) between large antenna arrays deployed
along structures (such as lamp posts) that are close and roughly parallel to
each other. Hundreds of data streams are spatially multiplexed through a short
range and line of sight massive multiple input massive multiple output
propagation channel thanks to a new low complexity spatial multiplexing scheme,
called block discrete Fourier transform based spatial multiplexing with maximum
ratio transmission. Its performance in real and existing environments is
assessed using accurate ray-tracing tools and antenna models. In the best
simulated scenario, 1.6 kbits per second per Hz of spectral efficiency is
attained, corresponding to 80% of Singular Value Decomposition performance,
with a transmitter and a receiver that are 200 and 10,000 times less complex,
respectively.
|
cs.IT math.IT
|
in this paper we propose a new technique for the future fifth generation cellular network wireless backhauling we show that hundreds of bits per second per hertz bits per second per hz of spectral efficiency can be attained at a high carrier frequency such as 26 ghz between large antenna arrays deployed along structures such as lamp posts that are close and roughly parallel to each other hundreds of data streams are spatially multiplexed through a short range and line of sight massive multiple input massive multiple output propagation channel thanks to a new low complexity spatial multiplexing scheme called block discrete fourier transform based spatial multiplexing with maximum ratio transmission its performance in real and existing environments is assessed using accurate raytracing tools and antenna models in the best simulated scenario 16 kbits per second per hz of spectral efficiency is attained corresponding to 80 of singular value decomposition performance with a transmitter and a receiver that are 200 and 10000 times less complex respectively
|
[['in', 'this', 'paper', 'we', 'propose', 'a', 'new', 'technique', 'for', 'the', 'future', 'fifth', 'generation', 'cellular', 'network', 'wireless', 'backhauling', 'we', 'show', 'that', 'hundreds', 'of', 'bits', 'per', 'second', 'per', 'hertz', 'bits', 'per', 'second', 'per', 'hz', 'of', 'spectral', 'efficiency', 'can', 'be', 'attained', 'at', 'a', 'high', 'carrier', 'frequency', 'such', 'as', '26', 'ghz', 'between', 'large', 'antenna', 'arrays', 'deployed', 'along', 'structures', 'such', 'as', 'lamp', 'posts', 'that', 'are', 'close', 'and', 'roughly', 'parallel', 'to', 'each', 'other', 'hundreds', 'of', 'data', 'streams', 'are', 'spatially', 'multiplexed', 'through', 'a', 'short', 'range', 'and', 'line', 'of', 'sight', 'massive', 'multiple', 'input', 'massive', 'multiple', 'output', 'propagation', 'channel', 'thanks', 'to', 'a', 'new', 'low', 'complexity', 'spatial', 'multiplexing', 'scheme', 'called', 'block', 'discrete', 'fourier', 'transform', 'based', 'spatial', 'multiplexing', 'with', 'maximum', 'ratio', 'transmission', 'its', 'performance', 'in', 'real', 'and', 'existing', 'environments', 'is', 'assessed', 'using', 'accurate', 'raytracing', 'tools', 'and', 'antenna', 'models', 'in', 'the', 'best', 'simulated', 'scenario', '16', 'kbits', 'per', 'second', 'per', 'hz', 'of', 'spectral', 'efficiency', 'is', 'attained', 'corresponding', 'to', '80', 'of', 'singular', 'value', 'decomposition', 'performance', 'with', 'a', 'transmitter', 'and', 'a', 'receiver', 'that', 'are', '200', 'and', '10000', 'times', 'less', 'complex', 'respectively']]
|
[-0.18562917131369552, 0.09059857968619676, 0.036565046900524256, -0.000813031626629632, -0.0705918780140893, -0.18902512321372078, 0.07937852573280885, 0.42074843408001295, -0.24747616436377348, -0.3489576208332262, 0.11356000140020399, -0.2620624003830893, -0.09594159882448344, 0.22638032814005324, -0.05357729947104691, 0.03875943560276674, 0.0754946913171829, 0.03658967553245195, -0.0386692551343354, -0.2529851322354053, 0.19796765420911272, 0.1287083412571081, 0.3183682226556275, -0.010078007349721998, 0.13302848964764075, -0.01562486081215811, -0.03828158030201925, -0.06764160278772217, -0.04000468169673345, 0.11498278076339692, 0.2778369746985889, 0.15719911625401217, 0.29002276950597156, -0.3948580821231288, -0.23550908099221773, 0.07415869057806859, 0.17034543525141452, 0.044473034803789245, -0.012846889165061784, -0.22328872898458896, 0.1591404272669068, -0.19787819175952934, -0.06004670305817152, 0.02139105329132942, -0.030279195544882172, 0.07481767837496767, -0.2812416375648365, 0.09399658515600555, -0.06689784906423056, 0.039168394647583545, -0.011571996514994022, -0.111160386440692, 0.013484332017622978, 0.11419888794231396, -0.06941524162434086, 0.01911153796925602, 0.1230681110983462, -0.03858355742862381, -0.11645645898183336, 0.37681528540743314, -0.09181007070837512, -0.17828414924246405, 0.17302280059960917, -0.16270089408025812, -0.0932431228498154, 0.20513872593832033, 0.23224879652878874, 0.11638290255543697, -0.13338058833972585, -0.04205948817533166, 0.0278998080904719, 0.27205260077362364, 0.1532973952827055, 0.10957803737921977, 0.22412339610563792, 0.1921372240246554, 0.06314449861129544, 0.12808141046335517, -0.19608539473233852, -0.056212596538482255, -0.22535983524748113, -0.14357289983615693, -0.22985123044247346, 0.032308790532602785, -0.17200089715851094, -0.06815563120419182, 0.3684767426086226, 0.14349373672101423, 0.18738825469984707, 0.09141709023794856, 0.3929725288671542, 0.0857553806097011, 0.10442361378402684, 0.093766650959221, 0.16420843138034086, 0.10446114493646176, 0.14351854545165257, -0.13109674367191781, -0.05167970423732536, -0.03878255766474204]
|
1,803.04538
|
Detection of a population of carbon-enhanced metal-poor stars in the
Sculptor dwarf spheroidal galaxy
|
The study of the chemical abundances of metal-poor stars in dwarf galaxies
provides a venue to constrain paradigms of chemical enrichment and galaxy
formation. Here we present metallicity and carbon abundance measurements of 100
stars in Sculptor from medium-resolution (R ~ 2000) spectra taken with the
Magellan/Michigan Fiber System mounted on the Magellan-Clay 6.5m telescope at
Las Campanas Observatory. We identify 24 extremely metal-poor star candidates
([Fe/H] < -3.0) and 21 carbon-enhanced metal-poor (CEMP) star candidates. Eight
carbon-enhanced stars are classified with at least 2$\sigma$ confidence and
five are confirmed as such with follow-up R~6000 observations using the
Magellan Echellette Spectrograph on the Magellan-Baade 6.5m telescope. We
measure a CEMP fraction of 36% for stars below [Fe/H] = -3.0, indicating that
the prevalence of carbon-enhanced stars in Sculptor is similar to that of the
halo (~43%) after excluding likely CEMP-s and CEMP-r/s stars from our sample.
However, we do not detect that any CEMP stars are strongly enhanced in carbon
(e.g., [C/Fe] > 1.0). The existence of a large number of CEMP stars both in the
halo and in Sculptor suggests that some halo CEMP stars may have originated
from accreted early analogs of dwarf galaxies.
|
astro-ph.GA
|
the study of the chemical abundances of metalpoor stars in dwarf galaxies provides a venue to constrain paradigms of chemical enrichment and galaxy formation here we present metallicity and carbon abundance measurements of 100 stars in sculptor from mediumresolution r 2000 spectra taken with the magellanmichigan fiber system mounted on the magellanclay 65m telescope at las campanas observatory we identify 24 extremely metalpoor star candidates feh 30 and 21 carbonenhanced metalpoor cemp star candidates eight carbonenhanced stars are classified with at least 2sigma confidence and five are confirmed as such with followup r6000 observations using the magellan echellette spectrograph on the magellanbaade 65m telescope we measure a cemp fraction of 36 for stars below feh 30 indicating that the prevalence of carbonenhanced stars in sculptor is similar to that of the halo 43 after excluding likely cemps and cemprs stars from our sample however we do not detect that any cemp stars are strongly enhanced in carbon eg cfe 10 the existence of a large number of cemp stars both in the halo and in sculptor suggests that some halo cemp stars may have originated from accreted early analogs of dwarf galaxies
|
[['the', 'study', 'of', 'the', 'chemical', 'abundances', 'of', 'metalpoor', 'stars', 'in', 'dwarf', 'galaxies', 'provides', 'a', 'venue', 'to', 'constrain', 'paradigms', 'of', 'chemical', 'enrichment', 'and', 'galaxy', 'formation', 'here', 'we', 'present', 'metallicity', 'and', 'carbon', 'abundance', 'measurements', 'of', '100', 'stars', 'in', 'sculptor', 'from', 'mediumresolution', 'r', '2000', 'spectra', 'taken', 'with', 'the', 'magellanmichigan', 'fiber', 'system', 'mounted', 'on', 'the', 'magellanclay', '65m', 'telescope', 'at', 'las', 'campanas', 'observatory', 'we', 'identify', '24', 'extremely', 'metalpoor', 'star', 'candidates', 'feh', '30', 'and', '21', 'carbonenhanced', 'metalpoor', 'cemp', 'star', 'candidates', 'eight', 'carbonenhanced', 'stars', 'are', 'classified', 'with', 'at', 'least', '2sigma', 'confidence', 'and', 'five', 'are', 'confirmed', 'as', 'such', 'with', 'followup', 'r6000', 'observations', 'using', 'the', 'magellan', 'echellette', 'spectrograph', 'on', 'the', 'magellanbaade', '65m', 'telescope', 'we', 'measure', 'a', 'cemp', 'fraction', 'of', '36', 'for', 'stars', 'below', 'feh', '30', 'indicating', 'that', 'the', 'prevalence', 'of', 'carbonenhanced', 'stars', 'in', 'sculptor', 'is', 'similar', 'to', 'that', 'of', 'the', 'halo', '43', 'after', 'excluding', 'likely', 'cemps', 'and', 'cemprs', 'stars', 'from', 'our', 'sample', 'however', 'we', 'do', 'not', 'detect', 'that', 'any', 'cemp', 'stars', 'are', 'strongly', 'enhanced', 'in', 'carbon', 'eg', 'cfe', '10', 'the', 'existence', 'of', 'a', 'large', 'number', 'of', 'cemp', 'stars', 'both', 'in', 'the', 'halo', 'and', 'in', 'sculptor', 'suggests', 'that', 'some', 'halo', 'cemp', 'stars', 'may', 'have', 'originated', 'from', 'accreted', 'early', 'analogs', 'of', 'dwarf', 'galaxies']]
|
[-0.03797503349054922, 0.13236415584532651, -0.07541347394760757, 0.05717779344242768, -0.16144215649405874, -0.04937632013287844, 0.13223756133598222, 0.4189343636280111, -0.023596022122037347, -0.42294639294372177, -0.02146081836816138, -0.37309498684139464, 0.00019429430258289683, 0.1962700944341456, -0.0908139613076084, -0.09833167371869146, 0.1612276309169829, -0.11672911071706153, 0.008726215534916645, -0.3892691820781892, 0.2638131994656555, 0.03785043631811963, 0.10447630283291115, -0.14395777046855987, 0.014563138265143202, -0.21521652187952156, -0.09378130558429588, -0.05579683440493625, -0.15876447637178398, -0.04254692798987499, 0.3099668809025017, 0.14957339547974355, 0.19108059498145005, -0.27721671643830725, -0.1369986019867682, 0.021480311559152386, 0.23933006377538107, -0.004679771310878986, -0.16129783882408438, -0.2915946320918296, 0.1006587938744153, -0.15716632438521266, -0.2126693429025524, 0.10213791678814432, 0.019268359818266637, 0.07152604476087947, -0.21285726261113072, 0.12545928479286877, -0.01739049739453025, 0.2147910797710077, -0.14029408031870386, -0.23403329773213596, -0.10931222781012352, 0.039412596592123754, -0.04989900711694664, 0.0077079955832203804, 0.1411426020493604, -0.09553446847438345, 0.05303945229144972, 0.46266744373590535, -0.19398668182023962, 0.14062506645303513, 0.24846533466622975, -0.2706119187166475, -0.29238052597922803, 0.07072906416757958, 0.16227021454973634, 0.18807551319740168, -0.22182316573931596, 0.0048747190185310565, -0.007221350005062307, 0.2137230282834687, 0.0959495470654317, 0.08278817684044976, 0.3656433523481429, 0.12201372915098727, 0.016500819358043373, -0.0310399778338432, -0.3261283889525029, 0.019731235778183524, -0.16633895014446834, -0.16032359316818298, -0.06084261755020168, 0.10428317926992621, -0.1680537368341883, -0.11483544493289129, 0.3070601661938257, 0.08913645925821921, 0.20404918722964394, 0.03632653164019057, 0.2623845337620688, 0.02052197834423192, 0.18029597014522775, 0.09634918148135059, 0.3299206668269974, 0.25407262148423343, 0.08535246778077188, -0.26376687032194973, 0.09466984804417372, -0.019380876436068905]
|
1,803.04539
|
Modular Linear Optical Circuits
|
We propose and demonstrate a modular architecture for reconfigurable on-chip
linear-optical circuits. Each module contains 10 independent phase-controlled
Mach-Zehnder interferometers; several such modules can be connected to each
other to build large reconfigurable interferometers. With this architecture,
large interferometers are easier to build and characterize than with
traditional, bespoke, monolithic designs. We demonstrate our approach by
fabricating three modules in the form of UV-written silica-on-silicon chips. We
characterize these chips, connect them to each other, and implement a wide
range of linear optical transformations. We envisage that this architecture
will enable many future experiments in quantum optics.
|
quant-ph physics.optics
|
we propose and demonstrate a modular architecture for reconfigurable onchip linearoptical circuits each module contains 10 independent phasecontrolled machzehnder interferometers several such modules can be connected to each other to build large reconfigurable interferometers with this architecture large interferometers are easier to build and characterize than with traditional bespoke monolithic designs we demonstrate our approach by fabricating three modules in the form of uvwritten silicaonsilicon chips we characterize these chips connect them to each other and implement a wide range of linear optical transformations we envisage that this architecture will enable many future experiments in quantum optics
|
[['we', 'propose', 'and', 'demonstrate', 'a', 'modular', 'architecture', 'for', 'reconfigurable', 'onchip', 'linearoptical', 'circuits', 'each', 'module', 'contains', '10', 'independent', 'phasecontrolled', 'machzehnder', 'interferometers', 'several', 'such', 'modules', 'can', 'be', 'connected', 'to', 'each', 'other', 'to', 'build', 'large', 'reconfigurable', 'interferometers', 'with', 'this', 'architecture', 'large', 'interferometers', 'are', 'easier', 'to', 'build', 'and', 'characterize', 'than', 'with', 'traditional', 'bespoke', 'monolithic', 'designs', 'we', 'demonstrate', 'our', 'approach', 'by', 'fabricating', 'three', 'modules', 'in', 'the', 'form', 'of', 'uvwritten', 'silicaonsilicon', 'chips', 'we', 'characterize', 'these', 'chips', 'connect', 'them', 'to', 'each', 'other', 'and', 'implement', 'a', 'wide', 'range', 'of', 'linear', 'optical', 'transformations', 'we', 'envisage', 'that', 'this', 'architecture', 'will', 'enable', 'many', 'future', 'experiments', 'in', 'quantum', 'optics']]
|
[-0.17483913822137973, 0.11033030129553865, -0.027932348584314596, -0.044494521360095476, -0.08922392817348548, -0.26452273550952216, -0.04861758531493701, 0.5145840195497287, -0.2677054655181302, -0.304007005028081, 0.07419893532252111, -0.22971947034148826, -0.2150937540399045, 0.2910905634773146, -0.06785201930834614, 0.0784882958510353, 0.0750455226408337, -0.08967109082132271, -0.05556789738337327, -0.22597684006485128, 0.2483000963513461, 0.02133274862912397, 0.3050635133764332, -0.03519405118314568, 0.14097692105035806, -0.013822605482965093, -0.017054824677017546, -0.03121876169341741, -0.06389831712947422, 0.17051735543405888, 0.3588593988535331, 0.12662853221653556, 0.2733322436508444, -0.49862944580537755, -0.15314790028502645, 0.0920875784989024, 0.10495443313296154, 0.11440085069175579, -0.022455555799695635, -0.2737465122925867, 0.06536431588681377, -0.24275221781248285, -0.11032643189327311, -0.13932914526613438, -0.046753769894082524, 0.05985378930777235, -0.2117577036266628, -0.13620572810099682, -0.03662785487345506, -0.02561419382307333, 0.03289503387185102, -0.08888150495717052, 0.07736688504504405, 0.08137851009702253, -0.22612407934112647, -0.027066060653935697, 0.18180707698572696, -0.07756011626537956, -0.19506538146626703, 0.3167964921851364, -0.025359103166451437, -0.1849967137569581, 0.1993386936854072, -0.11267337135336909, -0.12479990546245934, 0.039312717586410105, 0.19544087398374818, 0.07041296531859133, -0.16206631194032037, -0.022563936599008513, -0.0007754068380010497, 0.19664597415278867, 0.07616255759530384, 0.13562155826036462, 0.24227289361015117, 0.26398324520451966, 0.057985318123156535, 0.19778740026786462, -0.09015445304421238, -0.007514708963482036, -0.2695881526379548, -0.19516211315106177, -0.14106498985100038, 0.02294760422070616, -0.05888436434261823, -0.13971898672122932, 0.3809775615509446, 0.2401582941763372, 0.1569500262416976, 0.06011044103220182, 0.360118060161542, 0.035988075266227354, 0.23666986863407277, 0.03543139102187046, 0.21236176756102612, 0.08877803315408528, 0.07037252945268584, -0.1309167825540086, -0.04263894703988101, -0.04668531070863012]
|
1,803.0454
|
Constraints on light Dark Matter fermions from relic density
consideration and Tsallis statistics
|
The cold dark matter fermions with mass MeV scale, pair produced inside the
supernova SN1987A core, can freely stream away from the supernovae and hence
contributes to its energy loss rate. Similar type of DM fermions(having similar
kind of coupling to the standard model photon), produced from some other
sources earlier, could have contributed to the relic density of the Universe.
Working in a theory with an effective dark matter-photon coupling (inversely
proportional to the scale $\Lambda$) in the formalism of Tsallis statistics, we
find the dark matter contribution to the relic density and obtain a upper bound
on $\Lambda$ using the experimental bound on the relic density for cold
non-baryonic matter i.e. $\Omega h^2 = 0.1186 \pm 0.0020 $. The upper bound
obtained from the relic density is shown with the lower bound obtained from the
Raffelt's criterion on the emissibity rate of the supernovae SN1987A energy
loss $\dot{\varepsilon}(e^+ e^- \to \chi \overline{\chi}) \le
10^{19}~\rm{erg~g^{-1}s^{-1}}$ and the optical depth criteria on the free
streaming of the dark matter fermion (produced inside the supernovae core). As
the deformation parameter $q$ changes from $1.0$ (undeformed scenario) to
$1.1$(deformed scenario), the relic density bound on $\Lambda$ is found to vary
from $ \sim 4.9 \times 10^7 $ TeV to $1.6 \times 10^8$ TeV for a fermion dark
matter($\chi$) of mass $m_\chi = 30~\rm{MeV}$, which is almost $10$ times more
than the lower bound obtained from the SN1987A energy loss rate and the optical
depth criteria.
\noindent {{\bf Keywords}: Dark matter, Relic density, Supernova cooling,
Tsallis statistics, free-streaming, } }
|
hep-ph astro-ph.HE
|
the cold dark matter fermions with mass mev scale pair produced inside the supernova sn1987a core can freely stream away from the supernovae and hence contributes to its energy loss rate similar type of dm fermionshaving similar kind of coupling to the standard model photon produced from some other sources earlier could have contributed to the relic density of the universe working in a theory with an effective dark matterphoton coupling inversely proportional to the scale lambda in the formalism of tsallis statistics we find the dark matter contribution to the relic density and obtain a upper bound on lambda using the experimental bound on the relic density for cold nonbaryonic matter ie omega h2 01186 pm 00020 the upper bound obtained from the relic density is shown with the lower bound obtained from the raffelts criterion on the emissibity rate of the supernovae sn1987a energy loss dotvarepsilone e to chi overlinechi le 1019rmergg1s1 and the optical depth criteria on the free streaming of the dark matter fermion produced inside the supernovae core as the deformation parameter q changes from 10 undeformed scenario to 11deformed scenario the relic density bound on lambda is found to vary from sim 49 times 107 tev to 16 times 108 tev for a fermion dark matterchi of mass m_chi 30rmmev which is almost 10 times more than the lower bound obtained from the sn1987a energy loss rate and the optical depth criteria noindent bf keywords dark matter relic density supernova cooling tsallis statistics freestreaming
|
[['the', 'cold', 'dark', 'matter', 'fermions', 'with', 'mass', 'mev', 'scale', 'pair', 'produced', 'inside', 'the', 'supernova', 'sn1987a', 'core', 'can', 'freely', 'stream', 'away', 'from', 'the', 'supernovae', 'and', 'hence', 'contributes', 'to', 'its', 'energy', 'loss', 'rate', 'similar', 'type', 'of', 'dm', 'fermionshaving', 'similar', 'kind', 'of', 'coupling', 'to', 'the', 'standard', 'model', 'photon', 'produced', 'from', 'some', 'other', 'sources', 'earlier', 'could', 'have', 'contributed', 'to', 'the', 'relic', 'density', 'of', 'the', 'universe', 'working', 'in', 'a', 'theory', 'with', 'an', 'effective', 'dark', 'matterphoton', 'coupling', 'inversely', 'proportional', 'to', 'the', 'scale', 'lambda', 'in', 'the', 'formalism', 'of', 'tsallis', 'statistics', 'we', 'find', 'the', 'dark', 'matter', 'contribution', 'to', 'the', 'relic', 'density', 'and', 'obtain', 'a', 'upper', 'bound', 'on', 'lambda', 'using', 'the', 'experimental', 'bound', 'on', 'the', 'relic', 'density', 'for', 'cold', 'nonbaryonic', 'matter', 'ie', 'omega', 'h2', '01186', 'pm', '00020', 'the', 'upper', 'bound', 'obtained', 'from', 'the', 'relic', 'density', 'is', 'shown', 'with', 'the', 'lower', 'bound', 'obtained', 'from', 'the', 'raffelts', 'criterion', 'on', 'the', 'emissibity', 'rate', 'of', 'the', 'supernovae', 'sn1987a', 'energy', 'loss', 'dotvarepsilone', 'e', 'to', 'chi', 'overlinechi', 'le', '1019rmergg1s1', 'and', 'the', 'optical', 'depth', 'criteria', 'on', 'the', 'free', 'streaming', 'of', 'the', 'dark', 'matter', 'fermion', 'produced', 'inside', 'the', 'supernovae', 'core', 'as', 'the', 'deformation', 'parameter', 'q', 'changes', 'from', '10', 'undeformed', 'scenario', 'to', '11deformed', 'scenario', 'the', 'relic', 'density', 'bound', 'on', 'lambda', 'is', 'found', 'to', 'vary', 'from', 'sim', '49', 'times', '107', 'tev', 'to', '16', 'times', '108', 'tev', 'for', 'a', 'fermion', 'dark', 'matterchi', 'of', 'mass', 'm_chi', '30rmmev', 'which', 'is', 'almost', '10', 'times', 'more', 'than', 'the', 'lower', 'bound', 'obtained', 'from', 'the', 'sn1987a', 'energy', 'loss', 'rate', 'and', 'the', 'optical', 'depth', 'criteria', 'noindent', 'bf', 'keywords', 'dark', 'matter', 'relic', 'density', 'supernova', 'cooling', 'tsallis', 'statistics', 'freestreaming']]
|
[-0.09767733907162929, 0.2021653457019563, -0.09293402033308368, 0.13047028180225143, -0.07352078236034999, -0.09136406614323009, 0.050283844649055054, 0.2800879938848561, -0.2128472802034564, -0.3993016278989552, 0.011415406980467807, -0.30417274136275635, 0.05759059237965123, 0.189264570273646, 0.04189815684584886, 0.0049640468794558594, 0.010867980818863438, 0.07848453682419829, -0.04546406413581161, -0.25009398899216695, 0.2621452860384139, 0.10732094128186753, 0.2348940346960635, 0.07443211402850927, 0.044138232176591685, -0.08340109486922957, -0.029704167266774047, -0.10477745354645046, -0.2102588669980922, 0.07571230772608957, 0.1534639935465539, 0.11278827911906983, 0.13839885074698718, -0.3945653620767636, -0.21514237407007117, 0.16993385248962545, 0.15948904814679543, 0.04477866317881949, -0.042505295668381786, -0.32438438108236695, 0.06620413882583456, -0.24524893507849974, -0.13211870436236017, 0.07284163808960374, 0.018665303205155203, -0.024662937100625552, -0.25822067205206356, 0.20458770684502373, -0.0495009898522916, -0.08078611358359922, -0.09127512988257488, -0.17119240585989395, -0.04318549536687673, -0.07431225392959828, 0.0758769230098372, 0.04078112189184691, 0.21658237638299307, -0.20035314189888476, -0.007921279196677058, 0.3923793400515665, -0.1353959710460436, -0.07815690472167172, 0.16937994252943786, -0.14841692659576408, -0.09601122969532477, 0.1641801223702744, 0.1363361368378525, 0.06560359709537358, -0.10228371155662004, 0.11436444268939773, -0.04886973253009959, 0.2241454930388231, 0.094827635001895, 0.061283038399331594, 0.27270091755590475, 0.12349382730021492, 0.07827501188458867, 0.049402762340147004, -0.13772124537270608, -0.038604488179324284, -0.33567320841715714, -0.0942674811721354, -0.17866471844732945, 0.07460898633513859, -0.12347986237847315, -0.0809716507754602, 0.2964479469190367, 0.09841460820485945, 0.223892043142381, 0.030206856429581645, 0.2912727705848052, 0.11318508224761244, 0.05871591096875505, 0.10559471576955536, 0.29999138706053136, 0.17858222157305076, 0.09101587628037287, -0.22267204853820569, -0.011826389791734029, 0.039323625817024685]
|
1,803.04541
|
Probing secret interactions of eV-scale sterile neutrinos with the
diffuse supernova neutrino background
|
Sterile neutrinos with mass in the eV-scale and large mixings of order
$\theta_0\simeq 0.1$ could explain some anomalies found in short-baseline
neutrino oscillation data. Here, we revisit a neutrino portal scenario in which
eV-scale sterile neutrinos have self-interactions via a new gauge vector boson
$\phi$. Their production in the early Universe via mixing with active neutrinos
can be suppressed by the induced effective potential in the sterile sector. We
study how different cosmological observations can constrain this model, in
terms of the mass of the new gauge boson, $M_\phi$, and its coupling to sterile
neutrinos, $g_s$. Then, we explore how to probe part of the allowed parameter
space of this particular model with future observations of the diffuse
supernova neutrino background by the Hyper-Kamiokande and DUNE detectors. For
$M_\phi \sim 5-10$~keV and $g_s \sim 10^{-4}-10^{-2}$, as allowed by
cosmological constraints, we find that interactions of diffuse supernova
neutrinos with relic sterile neutrinos on their way to the Earth would result
in significant dips in the neutrino spectrum which would produce unique
features in the event spectra observed in these detectors.
|
hep-ph astro-ph.HE
|
sterile neutrinos with mass in the evscale and large mixings of order theta_0simeq 01 could explain some anomalies found in shortbaseline neutrino oscillation data here we revisit a neutrino portal scenario in which evscale sterile neutrinos have selfinteractions via a new gauge vector boson phi their production in the early universe via mixing with active neutrinos can be suppressed by the induced effective potential in the sterile sector we study how different cosmological observations can constrain this model in terms of the mass of the new gauge boson m_phi and its coupling to sterile neutrinos g_s then we explore how to probe part of the allowed parameter space of this particular model with future observations of the diffuse supernova neutrino background by the hyperkamiokande and dune detectors for m_phi sim 510kev and g_s sim 104102 as allowed by cosmological constraints we find that interactions of diffuse supernova neutrinos with relic sterile neutrinos on their way to the earth would result in significant dips in the neutrino spectrum which would produce unique features in the event spectra observed in these detectors
|
[['sterile', 'neutrinos', 'with', 'mass', 'in', 'the', 'evscale', 'and', 'large', 'mixings', 'of', 'order', 'theta_0simeq', '01', 'could', 'explain', 'some', 'anomalies', 'found', 'in', 'shortbaseline', 'neutrino', 'oscillation', 'data', 'here', 'we', 'revisit', 'a', 'neutrino', 'portal', 'scenario', 'in', 'which', 'evscale', 'sterile', 'neutrinos', 'have', 'selfinteractions', 'via', 'a', 'new', 'gauge', 'vector', 'boson', 'phi', 'their', 'production', 'in', 'the', 'early', 'universe', 'via', 'mixing', 'with', 'active', 'neutrinos', 'can', 'be', 'suppressed', 'by', 'the', 'induced', 'effective', 'potential', 'in', 'the', 'sterile', 'sector', 'we', 'study', 'how', 'different', 'cosmological', 'observations', 'can', 'constrain', 'this', 'model', 'in', 'terms', 'of', 'the', 'mass', 'of', 'the', 'new', 'gauge', 'boson', 'm_phi', 'and', 'its', 'coupling', 'to', 'sterile', 'neutrinos', 'g_s', 'then', 'we', 'explore', 'how', 'to', 'probe', 'part', 'of', 'the', 'allowed', 'parameter', 'space', 'of', 'this', 'particular', 'model', 'with', 'future', 'observations', 'of', 'the', 'diffuse', 'supernova', 'neutrino', 'background', 'by', 'the', 'hyperkamiokande', 'and', 'dune', 'detectors', 'for', 'm_phi', 'sim', '510kev', 'and', 'g_s', 'sim', '104102', 'as', 'allowed', 'by', 'cosmological', 'constraints', 'we', 'find', 'that', 'interactions', 'of', 'diffuse', 'supernova', 'neutrinos', 'with', 'relic', 'sterile', 'neutrinos', 'on', 'their', 'way', 'to', 'the', 'earth', 'would', 'result', 'in', 'significant', 'dips', 'in', 'the', 'neutrino', 'spectrum', 'which', 'would', 'produce', 'unique', 'features', 'in', 'the', 'event', 'spectra', 'observed', 'in', 'these', 'detectors']]
|
[-0.0996601332808902, 0.3207508257197414, -0.016701963653738543, 0.20084091992762626, -0.11382013413659688, -0.1283664245590276, 0.05561550414741206, 0.3174083415189935, -0.21254320803759152, -0.3573527694263563, 0.018996792846403536, -0.3030173472290099, -0.030151283577560608, 0.14480822776605548, 0.05247127399623769, -0.019661151193012094, 0.06715318445290579, -0.025044903989007185, -0.05585245180266321, -0.24873934208555928, 0.30285653441249494, 0.13422551699107585, 0.13975442168250804, 0.06303778947877318, 0.07517024101858553, -0.10112945961521408, -0.06728534610498457, -0.11055774322054714, -0.11903618562023718, 0.01736286882696122, 0.20773018812894037, 0.15370984833789, 0.09695355483856281, -0.40510879061575045, -0.22626361854936325, 0.23418088449430574, 0.2042481180087939, 0.05181990310015786, -0.09737106597677976, -0.3613706172324259, 0.033315821387057125, -0.23329457991844949, -0.13985447523017627, -0.030009659950220802, -0.07774866287539292, -0.0799738953407729, -0.29098728522346173, 0.09699924023189145, -0.08574043007765236, -0.07069818850986494, -0.03556084294098445, -0.12881939471621254, -0.008022663711606624, -0.0017831225804048043, 0.2071396571428934, -0.028237104846435136, 0.11980592744039924, -0.18284266216904133, -0.08839736070078096, 0.431803306158861, -0.17667288129028016, -0.09439606520518994, 0.12968269703836913, -0.21345869704362982, -0.1586949667846524, 0.10661873377875201, 0.19557467777449145, 0.028501397244601028, -0.18366234290658096, 0.14358906596040552, -0.07736726042257841, 0.17061194187867867, 0.037608081560748415, 0.047947521765430864, 0.35238094464485537, 0.1954468360714427, 0.1023113213823562, -0.06046195759232463, -0.14845238702095961, 0.04433627243741681, -0.3719545093406637, -0.10496117088256934, -0.06532561308938448, 0.07812664478590618, -0.1132750089794746, -0.08152145749041238, 0.4107942591296436, 0.16576596936627225, 0.20707048889894725, -0.04310178596984241, 0.2603272586129606, 0.024244168106669444, 0.06209907591812489, 0.003615156134757, 0.37219812324893004, 0.14889263253601778, 0.13122580983733248, -0.2654687840512345, -0.03484068826808693, 0.03785495925434558]
|
1,803.04542
|
Symmetries of Chimera States
|
Symmetry broken states arise naturally in oscillatory networks. In this
Letter, we investigate chaotic attractors in an ensemble of four mean-coupled
Stuart-Landau oscillators with two oscillators being synchronized. We report
that these states with partially broken symmetry, so-called chimera states,
have different set-wise symmetries in the incoherent oscillators, and in
particular some are and some are not invariant under a permutation symmetry on
average. This allows for a classification of different chimera states in small
networks. We conclude our report with a discussion of related states in
spatially extended systems, which seem to inherit the symmetry properties of
their counterparts in small networks.
|
nlin.CD
|
symmetry broken states arise naturally in oscillatory networks in this letter we investigate chaotic attractors in an ensemble of four meancoupled stuartlandau oscillators with two oscillators being synchronized we report that these states with partially broken symmetry socalled chimera states have different setwise symmetries in the incoherent oscillators and in particular some are and some are not invariant under a permutation symmetry on average this allows for a classification of different chimera states in small networks we conclude our report with a discussion of related states in spatially extended systems which seem to inherit the symmetry properties of their counterparts in small networks
|
[['symmetry', 'broken', 'states', 'arise', 'naturally', 'in', 'oscillatory', 'networks', 'in', 'this', 'letter', 'we', 'investigate', 'chaotic', 'attractors', 'in', 'an', 'ensemble', 'of', 'four', 'meancoupled', 'stuartlandau', 'oscillators', 'with', 'two', 'oscillators', 'being', 'synchronized', 'we', 'report', 'that', 'these', 'states', 'with', 'partially', 'broken', 'symmetry', 'socalled', 'chimera', 'states', 'have', 'different', 'setwise', 'symmetries', 'in', 'the', 'incoherent', 'oscillators', 'and', 'in', 'particular', 'some', 'are', 'and', 'some', 'are', 'not', 'invariant', 'under', 'a', 'permutation', 'symmetry', 'on', 'average', 'this', 'allows', 'for', 'a', 'classification', 'of', 'different', 'chimera', 'states', 'in', 'small', 'networks', 'we', 'conclude', 'our', 'report', 'with', 'a', 'discussion', 'of', 'related', 'states', 'in', 'spatially', 'extended', 'systems', 'which', 'seem', 'to', 'inherit', 'the', 'symmetry', 'properties', 'of', 'their', 'counterparts', 'in', 'small', 'networks']]
|
[-0.2392031461201316, 0.24284182810900828, -0.0690339571504178, 0.05145346338395029, 0.009423065254939538, -0.16418531779473758, 0.02637887438393983, 0.4058311752103013, -0.24111744042431169, -0.24364783094428918, 0.10778529539375621, -0.2535114677866302, -0.21395802230336794, 0.10416144002959424, -0.07615695292057068, 0.04536400842891239, 0.05043334871366182, 0.020324874651052205, -0.0678642232349946, -0.20467805894383906, 0.3711944128313631, -0.044627861695035416, 0.32733183562317314, -0.041557495010327364, 0.045693281693292745, -0.0835052787545411, 0.04009396035918126, -0.007972434550003312, -0.06705764422882816, 0.0822445294909937, 0.23846291489533933, 0.05687084559844259, 0.20390471077396297, -0.46464858507262724, -0.20649352252446845, 0.13737564503817873, 0.16434257963042267, 0.18999224338182888, -0.041404283474263386, -0.3622778744856809, 0.045314727305416386, -0.16982599709402113, -0.1446305101408678, -0.13758978468831629, -0.021998158153876953, 0.02750603447887389, -0.18306603394540064, 0.09007231350166395, 0.08415049351934417, 0.08684319120548739, -0.05827311324651865, -0.042344327824299824, -0.054248759027698316, 0.09978000051113685, 0.017252687127420716, -0.0793344155912671, 0.06707250502636182, -0.11638710685713473, -0.15875365698764868, 0.3577376017210019, -0.039477205624993364, -0.2144259620759198, 0.24050154518701283, -0.1341143517444531, -0.25450315497194725, 0.07888255931674411, 0.17465349433911673, 0.09696046361361868, -0.12557770221439354, 0.025476215447198747, -0.05937214693346737, 0.14048442109967307, 0.051616746704002806, 0.12254923084002062, 0.19531798309774376, 0.1272185784639042, 0.07416925292836465, 0.18710620147997842, -0.016780279912328458, -0.1600357217887672, -0.28393513707937124, -0.05827718952501777, -0.14896728884081775, 0.06470849424359211, -0.04049100094062833, -0.1640730340024639, 0.47050157313545543, 0.10930539999047623, 0.24065601579187548, 0.03764842506201353, 0.1588587632959745, 0.07166311554450944, 0.07354794649690316, 0.07023719697734158, 0.25956516590839085, 0.15509796002194942, 0.05934381399614116, -0.2248051463507627, -0.006073203850902763, -0.012278179802438793]
|
1,803.04543
|
Possible Bright Starspots on TRAPPIST-1
|
The M8V star TRAPPIST-1 hosts seven roughly Earth-sized planets and is a
promising target for exoplanet characterization. Kepler/K2 Campaign 12
observations of TRAPPIST-1 in the optical show an apparent rotational
modulation with a 3.3 day period, though that rotational signal is not readily
detected in the Spitzer light curve at 4.5 $\mu$m. If the rotational modulation
is due to starspots, persistent dark spots can be excluded from the lack of
photometric variability in the Spitzer light curve. We construct a photometric
model for rotational modulation due to photospheric bright spots on TRAPPIST-1
which is consistent with both the Kepler and Spitzer light curves. The
maximum-likelihood model with three spots has typical spot sizes of
$R_\mathrm{spot}/R_\star \approx 0.004$ at temperature $T_\mathrm{spot} \gtrsim
5300 \pm 200$ K. We also find that large flares are observed more often when
the brightest spot is facing the observer, suggesting a correlation between the
position of the bright spots and flare events. In addition, these flares may
occur preferentially when the spots are increasing in brightness, which
suggests that the 3.3 d periodicity may not be a rotational signal, but rather
a characteristic timescale of active regions.
|
astro-ph.SR astro-ph.EP
|
the m8v star trappist1 hosts seven roughly earthsized planets and is a promising target for exoplanet characterization keplerk2 campaign 12 observations of trappist1 in the optical show an apparent rotational modulation with a 33 day period though that rotational signal is not readily detected in the spitzer light curve at 45 mum if the rotational modulation is due to starspots persistent dark spots can be excluded from the lack of photometric variability in the spitzer light curve we construct a photometric model for rotational modulation due to photospheric bright spots on trappist1 which is consistent with both the kepler and spitzer light curves the maximumlikelihood model with three spots has typical spot sizes of r_mathrmspotr_star approx 0004 at temperature t_mathrmspot gtrsim 5300 pm 200 k we also find that large flares are observed more often when the brightest spot is facing the observer suggesting a correlation between the position of the bright spots and flare events in addition these flares may occur preferentially when the spots are increasing in brightness which suggests that the 33 d periodicity may not be a rotational signal but rather a characteristic timescale of active regions
|
[['the', 'm8v', 'star', 'trappist1', 'hosts', 'seven', 'roughly', 'earthsized', 'planets', 'and', 'is', 'a', 'promising', 'target', 'for', 'exoplanet', 'characterization', 'keplerk2', 'campaign', '12', 'observations', 'of', 'trappist1', 'in', 'the', 'optical', 'show', 'an', 'apparent', 'rotational', 'modulation', 'with', 'a', '33', 'day', 'period', 'though', 'that', 'rotational', 'signal', 'is', 'not', 'readily', 'detected', 'in', 'the', 'spitzer', 'light', 'curve', 'at', '45', 'mum', 'if', 'the', 'rotational', 'modulation', 'is', 'due', 'to', 'starspots', 'persistent', 'dark', 'spots', 'can', 'be', 'excluded', 'from', 'the', 'lack', 'of', 'photometric', 'variability', 'in', 'the', 'spitzer', 'light', 'curve', 'we', 'construct', 'a', 'photometric', 'model', 'for', 'rotational', 'modulation', 'due', 'to', 'photospheric', 'bright', 'spots', 'on', 'trappist1', 'which', 'is', 'consistent', 'with', 'both', 'the', 'kepler', 'and', 'spitzer', 'light', 'curves', 'the', 'maximumlikelihood', 'model', 'with', 'three', 'spots', 'has', 'typical', 'spot', 'sizes', 'of', 'r_mathrmspotr_star', 'approx', '0004', 'at', 'temperature', 't_mathrmspot', 'gtrsim', '5300', 'pm', '200', 'k', 'we', 'also', 'find', 'that', 'large', 'flares', 'are', 'observed', 'more', 'often', 'when', 'the', 'brightest', 'spot', 'is', 'facing', 'the', 'observer', 'suggesting', 'a', 'correlation', 'between', 'the', 'position', 'of', 'the', 'bright', 'spots', 'and', 'flare', 'events', 'in', 'addition', 'these', 'flares', 'may', 'occur', 'preferentially', 'when', 'the', 'spots', 'are', 'increasing', 'in', 'brightness', 'which', 'suggests', 'that', 'the', '33', 'd', 'periodicity', 'may', 'not', 'be', 'a', 'rotational', 'signal', 'but', 'rather', 'a', 'characteristic', 'timescale', 'of', 'active', 'regions']]
|
[-0.12871411342540923, 0.21146887710027548, -0.0925904727422647, 0.08885308108372594, -0.10951333567401562, -0.13247145763130258, 0.07568373107018747, 0.42559531621792646, -0.17780399393714058, -0.3801513059981286, 0.1163594899643656, -0.2987574916530065, -0.07497799916626521, 0.23953506194865676, -0.10718897770272282, -0.041656083057797146, 0.0888886748660336, -0.05350749418213372, -0.01780394852166335, -0.24922632671912354, 0.19712095971776064, 0.04731146459314953, 0.14255512517596047, -0.011521859616829605, 0.021163431221986356, -0.09783728281528782, -0.046552789776503456, -0.0635972688043559, -0.10259828920701423, 0.021965742828176607, 0.22370598884545778, 0.06200861218835045, 0.16325573429647616, -0.3191663624223558, -0.2547735954080795, 0.12046904521550805, 0.1681484577437572, 0.021010800569057072, 0.0005150050562824906, -0.2630899515392249, 0.06635531142511696, -0.10958248697881581, -0.17109496721988002, 0.048771173530627814, 0.1388932063648349, 0.005249530552497143, -0.2236295028308791, 0.11477714191451446, 0.021496203230329292, 0.19311611339999313, -0.14763614586455914, -0.0895216266979419, -0.10675721323590666, 0.024716310991804612, 0.04638640037442129, 0.09875486760336176, 0.12801919097977657, -0.08183422633422115, -0.031236180664217544, 0.40138999220949634, -0.11772907227425584, 0.007754350849796855, 0.19603145942486153, -0.25175423828993365, -0.14043307219996615, 0.22573882268948686, 0.14252974596564416, 0.12051436130607886, -0.14601881348077705, -0.05829134113777419, -0.015994818153712287, 0.27079237774310133, 0.0646838785257565, 0.08498489129278197, 0.38610199834934616, 0.11918998438071597, 0.06302177018200161, 0.07677667588950259, -0.33654606473513893, -0.034566002917628756, -0.22585336518875526, -0.06401251154710297, -0.1345652720435749, 0.04446656600878298, -0.11001491000133298, -0.08584675347560612, 0.3892000540746031, 0.10954459849375463, 0.21185754917355049, 0.017891482726015425, 0.30222590382472075, 0.07183126744779723, 0.11349013251372275, 0.1340664757782761, 0.323553162843738, 0.11399369336271697, 0.09114407245611782, -0.2259794146693484, 0.07933731850177522, -0.0636686084401811]
|
1,803.04544
|
Optimal $H_2$ Decentralized Control of Cone Causal Spatially Invariant
Systems
|
This paper presents an explicit solution to decentralized control of a class
of spatially invariant systems. The problem of optimal $H_2$ decentralized
control for cone causal systems is formulated. Using Parseval's identity, the
optimal $H_2$ decentralized control problem is transformed into an infinite
number of model matching problems with a specific structure that can be solved
efficiently. In addition, the closed-form expression (explicit formula) of the
decentralized controller is derived for the first time. In particular, it is
shown that the optimal decentralized controller is given by a specific positive
feedback scheme. A constructive procedure to obtain the state-space
representation of the decentralized controller is provided. A numerical example
is given and compared with previous works which demonstrate the effectiveness
of the proposed method.
|
cs.SY
|
this paper presents an explicit solution to decentralized control of a class of spatially invariant systems the problem of optimal h_2 decentralized control for cone causal systems is formulated using parsevals identity the optimal h_2 decentralized control problem is transformed into an infinite number of model matching problems with a specific structure that can be solved efficiently in addition the closedform expression explicit formula of the decentralized controller is derived for the first time in particular it is shown that the optimal decentralized controller is given by a specific positive feedback scheme a constructive procedure to obtain the statespace representation of the decentralized controller is provided a numerical example is given and compared with previous works which demonstrate the effectiveness of the proposed method
|
[['this', 'paper', 'presents', 'an', 'explicit', 'solution', 'to', 'decentralized', 'control', 'of', 'a', 'class', 'of', 'spatially', 'invariant', 'systems', 'the', 'problem', 'of', 'optimal', 'h_2', 'decentralized', 'control', 'for', 'cone', 'causal', 'systems', 'is', 'formulated', 'using', 'parsevals', 'identity', 'the', 'optimal', 'h_2', 'decentralized', 'control', 'problem', 'is', 'transformed', 'into', 'an', 'infinite', 'number', 'of', 'model', 'matching', 'problems', 'with', 'a', 'specific', 'structure', 'that', 'can', 'be', 'solved', 'efficiently', 'in', 'addition', 'the', 'closedform', 'expression', 'explicit', 'formula', 'of', 'the', 'decentralized', 'controller', 'is', 'derived', 'for', 'the', 'first', 'time', 'in', 'particular', 'it', 'is', 'shown', 'that', 'the', 'optimal', 'decentralized', 'controller', 'is', 'given', 'by', 'a', 'specific', 'positive', 'feedback', 'scheme', 'a', 'constructive', 'procedure', 'to', 'obtain', 'the', 'statespace', 'representation', 'of', 'the', 'decentralized', 'controller', 'is', 'provided', 'a', 'numerical', 'example', 'is', 'given', 'and', 'compared', 'with', 'previous', 'works', 'which', 'demonstrate', 'the', 'effectiveness', 'of', 'the', 'proposed', 'method']]
|
[-0.15738710907434353, -0.03132557270146208, -0.06704616444485803, 0.03074081620434299, -0.10380435986594568, -0.19158465606949082, 0.0451639122038614, 0.3790868539663573, -0.3200386246786483, -0.2761394848348573, 0.13742239312869647, -0.18429972128867503, -0.19180344568655616, 0.20272237498260615, -0.11101337823445999, 0.11062269291705301, 0.039702929841775085, 0.044341524967157674, -0.028745018843291028, -0.24732308054996294, 0.2951578645133263, 0.05349590437817237, 0.28446449950382474, 0.01907330992886977, 0.20454996050290403, -0.023731410976559404, -0.008875110165982117, 0.03336265233003594, -0.09379630446616624, 0.14051697376717423, 0.30511399464223593, 0.17076462273666215, 0.3123548118145986, -0.37814233982334694, -0.17644730795385136, 0.10397417595489852, 0.17227712438832368, 0.1366087843639581, -0.08838920428177281, -0.2767420066220145, 0.12471443954329457, -0.20016730779541597, -0.09995374879274037, -0.06578185658473822, -0.021852040558212226, -0.01325524685511658, -0.3770907543315139, 0.05281080097343112, 0.03925606239241578, 0.0004470987365611138, -0.12329305242747068, -0.06266091205179691, 0.005805617979457302, 0.13151754090006673, -0.024622260268980397, 0.011328760905364048, 0.10425830233350186, -0.0637651521490977, -0.16848721079933907, 0.3564130325140732, -0.0011943727243511426, -0.27064176295384496, 0.09528396939370601, -0.03931675829939665, -0.11624418711665296, 0.1517817313377295, 0.1831039312265573, 0.17251777011270245, -0.20868519795805052, 0.10717492659093678, -0.11895448189499157, 0.1728525789033982, -0.0013836533311874636, -0.01601275697260374, 0.11056846294612173, 0.17964210440292053, 0.18851042737913948, 0.18648733509114884, 0.02601862625939952, -0.1402183418770531, -0.29613967928960316, -0.1311380562838167, -0.17784203475175966, -0.022074782805922892, -0.0939741925966143, -0.12932929883390898, 0.3610931640377232, 0.11942929013715795, 0.1538434365704175, 0.11040125024643907, 0.35414106990661354, 0.20999539305170578, -0.0018112664148511907, 0.09761913364843255, 0.18436629564741686, 0.1054298223386849, 0.08581724884964886, -0.26718639925632987, 0.1055296044364842, 0.10483602703278584]
|
1,803.04545
|
Coverings of rational ruled normal surfaces
|
In this work we use arithmetic, geometric, and combinatorial techniques to
compute the cohomology of Weil divisors of a special class of normal surfaces,
the so-called rational ruled toric surfaces. These computations are used to
study the topology of cyclic coverings of such surfaces ramified along Q-normal
crossing divisors.
|
math.AG
|
in this work we use arithmetic geometric and combinatorial techniques to compute the cohomology of weil divisors of a special class of normal surfaces the socalled rational ruled toric surfaces these computations are used to study the topology of cyclic coverings of such surfaces ramified along qnormal crossing divisors
|
[['in', 'this', 'work', 'we', 'use', 'arithmetic', 'geometric', 'and', 'combinatorial', 'techniques', 'to', 'compute', 'the', 'cohomology', 'of', 'weil', 'divisors', 'of', 'a', 'special', 'class', 'of', 'normal', 'surfaces', 'the', 'socalled', 'rational', 'ruled', 'toric', 'surfaces', 'these', 'computations', 'are', 'used', 'to', 'study', 'the', 'topology', 'of', 'cyclic', 'coverings', 'of', 'such', 'surfaces', 'ramified', 'along', 'qnormal', 'crossing', 'divisors']]
|
[-0.23142186295697276, 0.05992302794617658, -0.13480814208029485, 0.143122424150589, -0.13910313319338827, -0.1610272862777418, 0.04121974303520152, 0.3009755617121653, -0.3790458296235575, -0.21170340341572857, 0.07524322100905512, -0.2092365813394058, -0.19158693100800928, 0.21617156193040463, -0.23940778908566857, 0.027831970371914153, -0.01279716225036857, 0.016371469526570672, -0.14057474295660374, -0.37148772643841044, 0.4550184382467854, -0.041566736502002696, 0.20947509593501382, 0.09395206026609379, 0.04433993381277031, 0.015421036412293206, -0.0045186451028044125, -0.030372189091784612, -0.24240915414554123, 0.2022409658042752, 0.3771390040614167, -0.012372996197177135, 0.10769738933565665, -0.4603562601366822, -0.13908719218203, 0.2584202465582259, 0.10030725939503433, 0.027162496456686332, 0.037661591981898765, -0.1770225502550602, 0.038609165555740496, -0.10800298873563202, -0.2435685497879678, -0.11957752091658054, -0.04021498834600254, 0.08068829926434068, -0.11276324979048602, -0.09406280958530855, 0.10161356383706538, 0.25331950178179813, 0.01256729020946184, -0.10633639827826802, -0.09003776562761287, 0.03222846255960817, 0.040861385593954615, -0.011172518053339148, 0.0997428632720269, -0.09378309836326053, -0.13047075889320398, 0.35227586849763687, -0.0023263256607235086, -0.21798150224269044, 0.11133000682279164, -0.10630096400118604, -0.2003893158208503, 0.17664672610378462, 0.12818000271764338, 0.23820743162413033, 0.02760933245513208, 0.16990605389998695, -0.1217619389229055, -0.009895373511185147, 0.1671334521902003, -0.048605287580617836, 0.1585640374343006, 0.042372608262741446, 0.02869814512680988, 0.12887552094512753, -0.04665290483520651, -0.10996569904061604, -0.36844205544615277, -0.23929590743263157, -0.13046514075629564, 0.14968170104928466, -0.092925712532466, -0.26460861042141914, 0.4341085566093727, 0.01719890029302665, 0.17384136298063155, 0.14599373920022377, 0.2325287315310264, 0.005841079472127009, 0.07044495505337812, 0.029516587623071914, 0.08803084138448217, 0.23588670223799288, -0.10447255056351423, -0.11615347343601515, -0.03039655866747608, 0.22027578305605114]
|
1,803.04546
|
The topological biquandle of a link
|
To every oriented link $L$, we associate a topologically defined biquandle
$\widehat{\mathcal{B}}_{L}$, which we call the topological biquandle of $L$.
The construction of $\widehat{\mathcal{B}}_{L}$ is similar to the topological
description of the fundamental quandle given by Matveev. We find a presentation
of the topological biquandle and explain how it is related to the fundamental
biquandle of the link.
|
math.GT
|
to every oriented link l we associate a topologically defined biquandle widehatmathcalb_l which we call the topological biquandle of l the construction of widehatmathcalb_l is similar to the topological description of the fundamental quandle given by matveev we find a presentation of the topological biquandle and explain how it is related to the fundamental biquandle of the link
|
[['to', 'every', 'oriented', 'link', 'l', 'we', 'associate', 'a', 'topologically', 'defined', 'biquandle', 'widehatmathcalb_l', 'which', 'we', 'call', 'the', 'topological', 'biquandle', 'of', 'l', 'the', 'construction', 'of', 'widehatmathcalb_l', 'is', 'similar', 'to', 'the', 'topological', 'description', 'of', 'the', 'fundamental', 'quandle', 'given', 'by', 'matveev', 'we', 'find', 'a', 'presentation', 'of', 'the', 'topological', 'biquandle', 'and', 'explain', 'how', 'it', 'is', 'related', 'to', 'the', 'fundamental', 'biquandle', 'of', 'the', 'link']]
|
[-0.2734761127337281, 0.15813263608807965, -0.09712606926249075, 0.10008217920299753, -0.14302084654835717, -0.16271815310964094, 0.07487187747444425, 0.32917763365964803, -0.34596670852209044, -0.28757453792370924, 0.0051705859950743616, -0.2266615157547806, -0.26343110795798047, 0.15984442273786822, -0.1834373894214098, -0.018604641717891873, -0.02873676872280027, 0.0910197073078182, -0.08025796036235988, -0.21371145679482392, 0.38173675124666523, -0.03518030441565705, 0.2058685907728172, 0.0837478309404105, 0.07250973485809352, -0.009980405198543199, -0.0843156162715916, 0.027561297440635308, -0.22282589755819313, 0.1511888419211443, 0.25180807935872246, 0.06474271546384054, 0.06497349824556815, -0.3196376180333443, -0.10050635374448445, 0.09676208289706015, 0.08543164045217314, 0.007046027772178475, -0.008260677086322435, -0.29298593025721076, 0.18426940320724888, -0.21011251310950943, -0.10902341599077252, -0.061225195515102575, 0.051508464717439244, 0.006421459439609732, -0.11915221935903121, -0.08602081365617258, 0.09683301033718246, 0.05811875461534198, 0.015942323894705623, 0.01014170968638999, -0.10072757781017572, 0.15499907178205571, -0.008524384657253645, 0.10335910136512082, 0.053318539974238126, -0.12654884791533863, -0.2318607181077823, 0.4496312476029353, -0.013451189085442041, -0.2160454425362072, 0.1868057327311752, -0.11228403732612995, -0.20928965128093427, 0.13989288635951067, 0.05208948485752834, 0.08200593961269728, -0.05120349796821496, 0.1157988756552056, -0.1616152932235439, 0.09562911026711975, 0.07895718373557818, 0.04017884937846767, 0.21866906633866684, 0.08808553522768696, 0.10780750844527834, 0.20253119344956108, -0.016052232256957462, -0.033874922149282484, -0.3291562833723479, -0.29882640545058947, -0.15583664115651377, 0.10664165547184114, -0.026532480298556038, -0.2091221350710839, 0.4723490252425628, 0.11000830567042742, 0.21374214706676348, 0.0696769923192083, 0.24025993455766834, 0.11897791729175619, 0.08694338000246457, 0.04083106881340167, 0.08961631038359233, 0.20271371751109005, 0.006727043239931975, -0.18413342261088214, -0.006486186865783695, 0.20628749206662178]
|
1,803.04547
|
Analysis of spectral clustering algorithms for community detection: the
general bipartite setting
|
We consider spectral clustering algorithms for community detection under a
general bipartite stochastic block model (SBM). A modern spectral clustering
algorithm consists of three steps: (1) regularization of an appropriate
adjacency or Laplacian matrix (2) a form of spectral truncation and (3) a
k-means type algorithm in the reduced spectral domain. We focus on the
adjacency-based spectral clustering and for the first step, propose a new
data-driven regularization that can restore the concentration of the adjacency
matrix even for the sparse networks. This result is based on recent work on
regularization of random binary matrices, but avoids using unknown population
level parameters, and instead estimates the necessary quantities from the data.
We also propose and study a novel variation of the spectral truncation step and
show how this variation changes the nature of the misclassification rate in a
general SBM. We then show how the consistency results can be extended to models
beyond SBMs, such as inhomogeneous random graph models with approximate
clusters, including a graphon clustering problem, as well as general
sub-Gaussian biclustering. A theme of the paper is providing a better
understanding of the analysis of spectral methods for community detection and
establishing consistency results, under fairly general clustering models and
for a wide regime of degree growths, including sparse cases where the average
expected degree grows arbitrarily slowly.
|
math.ST cs.SI stat.ML stat.TH
|
we consider spectral clustering algorithms for community detection under a general bipartite stochastic block model sbm a modern spectral clustering algorithm consists of three steps 1 regularization of an appropriate adjacency or laplacian matrix 2 a form of spectral truncation and 3 a kmeans type algorithm in the reduced spectral domain we focus on the adjacencybased spectral clustering and for the first step propose a new datadriven regularization that can restore the concentration of the adjacency matrix even for the sparse networks this result is based on recent work on regularization of random binary matrices but avoids using unknown population level parameters and instead estimates the necessary quantities from the data we also propose and study a novel variation of the spectral truncation step and show how this variation changes the nature of the misclassification rate in a general sbm we then show how the consistency results can be extended to models beyond sbms such as inhomogeneous random graph models with approximate clusters including a graphon clustering problem as well as general subgaussian biclustering a theme of the paper is providing a better understanding of the analysis of spectral methods for community detection and establishing consistency results under fairly general clustering models and for a wide regime of degree growths including sparse cases where the average expected degree grows arbitrarily slowly
|
[['we', 'consider', 'spectral', 'clustering', 'algorithms', 'for', 'community', 'detection', 'under', 'a', 'general', 'bipartite', 'stochastic', 'block', 'model', 'sbm', 'a', 'modern', 'spectral', 'clustering', 'algorithm', 'consists', 'of', 'three', 'steps', '1', 'regularization', 'of', 'an', 'appropriate', 'adjacency', 'or', 'laplacian', 'matrix', '2', 'a', 'form', 'of', 'spectral', 'truncation', 'and', '3', 'a', 'kmeans', 'type', 'algorithm', 'in', 'the', 'reduced', 'spectral', 'domain', 'we', 'focus', 'on', 'the', 'adjacencybased', 'spectral', 'clustering', 'and', 'for', 'the', 'first', 'step', 'propose', 'a', 'new', 'datadriven', 'regularization', 'that', 'can', 'restore', 'the', 'concentration', 'of', 'the', 'adjacency', 'matrix', 'even', 'for', 'the', 'sparse', 'networks', 'this', 'result', 'is', 'based', 'on', 'recent', 'work', 'on', 'regularization', 'of', 'random', 'binary', 'matrices', 'but', 'avoids', 'using', 'unknown', 'population', 'level', 'parameters', 'and', 'instead', 'estimates', 'the', 'necessary', 'quantities', 'from', 'the', 'data', 'we', 'also', 'propose', 'and', 'study', 'a', 'novel', 'variation', 'of', 'the', 'spectral', 'truncation', 'step', 'and', 'show', 'how', 'this', 'variation', 'changes', 'the', 'nature', 'of', 'the', 'misclassification', 'rate', 'in', 'a', 'general', 'sbm', 'we', 'then', 'show', 'how', 'the', 'consistency', 'results', 'can', 'be', 'extended', 'to', 'models', 'beyond', 'sbms', 'such', 'as', 'inhomogeneous', 'random', 'graph', 'models', 'with', 'approximate', 'clusters', 'including', 'a', 'graphon', 'clustering', 'problem', 'as', 'well', 'as', 'general', 'subgaussian', 'biclustering', 'a', 'theme', 'of', 'the', 'paper', 'is', 'providing', 'a', 'better', 'understanding', 'of', 'the', 'analysis', 'of', 'spectral', 'methods', 'for', 'community', 'detection', 'and', 'establishing', 'consistency', 'results', 'under', 'fairly', 'general', 'clustering', 'models', 'and', 'for', 'a', 'wide', 'regime', 'of', 'degree', 'growths', 'including', 'sparse', 'cases', 'where', 'the', 'average', 'expected', 'degree', 'grows', 'arbitrarily', 'slowly']]
|
[-0.06063392434840683, 0.034564356284218695, -0.08777296324048868, 0.09547355330591513, -0.06720715069677681, -0.13719859761820938, 0.03954663088449954, 0.37953186980025333, -0.27741542926735496, -0.31055749658368187, 0.15099613348085603, -0.22745024536270647, -0.1992102736580736, 0.12363608661742712, -0.08066151026578154, 0.07537324566140094, 0.08934819533129815, 0.047501324294981634, -0.09209405519279905, -0.22804167446460766, 0.3343791231026196, 0.07104258538985794, 0.2679221706591885, 0.016357609719059175, 0.08216544287689348, 0.03571843361820687, -0.05176974037789147, 0.04207125929221795, -0.10732049162897138, 0.1432065890377337, 0.23747514502940084, 0.17539317389389247, 0.31615380403501064, -0.3767123591218313, -0.253948371746744, 0.14353075598324225, 0.1642020976889233, 0.11425228524339301, -0.04960041535721922, -0.24392163926915436, 0.08835735307815908, -0.15011904877597276, -0.09326811617409642, -0.08330511012410914, -0.01883473330897025, 0.028935637276894837, -0.33700783553279257, 0.11404089748274683, 0.10019202251886865, 0.022053827413103796, -0.04163222921726463, -0.14173081103124952, 0.06482264839895917, 0.09748798256041483, 0.01675248986178882, 0.00038288182867373424, 0.09811675665392117, -0.1343289973865136, -0.1153546432477676, 0.3598085489928384, -0.07774380177883855, -0.18886376490858806, 0.17319236587817696, -0.07780802714511413, -0.2027149332962422, 0.07034320141696794, 0.21421387810260056, 0.1493124010295353, -0.13798774280902845, 0.08219655052453163, -0.03876575941473923, 0.17928621985060586, 0.024460398659787396, 0.026905636629089714, 0.13167907019696115, 0.2001386386538136, 0.1257177743737967, 0.14273392396809703, -0.10526755582541228, -0.05677928234641017, -0.2596884394445541, -0.09506257814937272, -0.22925491112877022, 0.02093313701969402, -0.2100690686562633, -0.19775353894060985, 0.4563369761083529, 0.14245244669410484, 0.24541560692754996, 0.12096664177423173, 0.28234368257690223, 0.10789028626115231, 0.03165232911811803, 0.08089998626912182, 0.1614512717933394, 0.1445984201590446, 0.0602631567935036, -0.17368685401743278, 0.08223733408958651, 0.08822603742168708]
|
1,803.04548
|
Taking Turing by Surprise? Designing Digital Computers for
morally-loaded contexts
|
There is much to learn from what Turing hastily dismissed as Lady Lovelace s
objection. Digital computers can indeed surprise us. Just like a piece of art,
algorithms can be designed in such a way as to lead us to question our
understanding of the world, or our place within it. Some humans do lose the
capacity to be surprised in that way. It might be fear, or it might be the
comfort of ideological certainties. As lazy normative animals, we do need to be
able to rely on authorities to simplify our reasoning: that is ok. Yet the
growing sophistication of systems designed to free us from the constraints of
normative engagement may take us past a point of no-return. What if, through
lack of normative exercise, our moral muscles became so atrophied as to leave
us unable to question our social practices? This paper makes two distinct
normative claims:
1. Decision-support systems should be designed with a view to regularly
jolting us out of our moral torpor.
2. Without the depth of habit to somatically anchor model certainty, a
computer s experience of something new is very different from that which in
humans gives rise to non-trivial surprises. This asymmetry has key
repercussions when it comes to the shape of ethical agency in artificial moral
agents. The worry is not just that they would be likely to leap morally ahead
of us, unencumbered by habits. The main reason to doubt that the moral
trajectories of humans v. autonomous systems might remain compatible stems from
the asymmetry in the mechanisms underlying moral change. Whereas in humans
surprises will continue to play an important role in waking us to the need for
moral change, cognitive processes will rule when it comes to machines. This
asymmetry will translate into increasingly different moral outlooks, to the
point of likely unintelligibility. The latter prospect is enough to doubt the
desirability of autonomous moral agents.
|
cs.CY cs.HC
|
there is much to learn from what turing hastily dismissed as lady lovelace s objection digital computers can indeed surprise us just like a piece of art algorithms can be designed in such a way as to lead us to question our understanding of the world or our place within it some humans do lose the capacity to be surprised in that way it might be fear or it might be the comfort of ideological certainties as lazy normative animals we do need to be able to rely on authorities to simplify our reasoning that is ok yet the growing sophistication of systems designed to free us from the constraints of normative engagement may take us past a point of noreturn what if through lack of normative exercise our moral muscles became so atrophied as to leave us unable to question our social practices this paper makes two distinct normative claims 1 decisionsupport systems should be designed with a view to regularly jolting us out of our moral torpor 2 without the depth of habit to somatically anchor model certainty a computer s experience of something new is very different from that which in humans gives rise to nontrivial surprises this asymmetry has key repercussions when it comes to the shape of ethical agency in artificial moral agents the worry is not just that they would be likely to leap morally ahead of us unencumbered by habits the main reason to doubt that the moral trajectories of humans v autonomous systems might remain compatible stems from the asymmetry in the mechanisms underlying moral change whereas in humans surprises will continue to play an important role in waking us to the need for moral change cognitive processes will rule when it comes to machines this asymmetry will translate into increasingly different moral outlooks to the point of likely unintelligibility the latter prospect is enough to doubt the desirability of autonomous moral agents
|
[['there', 'is', 'much', 'to', 'learn', 'from', 'what', 'turing', 'hastily', 'dismissed', 'as', 'lady', 'lovelace', 's', 'objection', 'digital', 'computers', 'can', 'indeed', 'surprise', 'us', 'just', 'like', 'a', 'piece', 'of', 'art', 'algorithms', 'can', 'be', 'designed', 'in', 'such', 'a', 'way', 'as', 'to', 'lead', 'us', 'to', 'question', 'our', 'understanding', 'of', 'the', 'world', 'or', 'our', 'place', 'within', 'it', 'some', 'humans', 'do', 'lose', 'the', 'capacity', 'to', 'be', 'surprised', 'in', 'that', 'way', 'it', 'might', 'be', 'fear', 'or', 'it', 'might', 'be', 'the', 'comfort', 'of', 'ideological', 'certainties', 'as', 'lazy', 'normative', 'animals', 'we', 'do', 'need', 'to', 'be', 'able', 'to', 'rely', 'on', 'authorities', 'to', 'simplify', 'our', 'reasoning', 'that', 'is', 'ok', 'yet', 'the', 'growing', 'sophistication', 'of', 'systems', 'designed', 'to', 'free', 'us', 'from', 'the', 'constraints', 'of', 'normative', 'engagement', 'may', 'take', 'us', 'past', 'a', 'point', 'of', 'noreturn', 'what', 'if', 'through', 'lack', 'of', 'normative', 'exercise', 'our', 'moral', 'muscles', 'became', 'so', 'atrophied', 'as', 'to', 'leave', 'us', 'unable', 'to', 'question', 'our', 'social', 'practices', 'this', 'paper', 'makes', 'two', 'distinct', 'normative', 'claims', '1', 'decisionsupport', 'systems', 'should', 'be', 'designed', 'with', 'a', 'view', 'to', 'regularly', 'jolting', 'us', 'out', 'of', 'our', 'moral', 'torpor', '2', 'without', 'the', 'depth', 'of', 'habit', 'to', 'somatically', 'anchor', 'model', 'certainty', 'a', 'computer', 's', 'experience', 'of', 'something', 'new', 'is', 'very', 'different', 'from', 'that', 'which', 'in', 'humans', 'gives', 'rise', 'to', 'nontrivial', 'surprises', 'this', 'asymmetry', 'has', 'key', 'repercussions', 'when', 'it', 'comes', 'to', 'the', 'shape', 'of', 'ethical', 'agency', 'in', 'artificial', 'moral', 'agents', 'the', 'worry', 'is', 'not', 'just', 'that', 'they', 'would', 'be', 'likely', 'to', 'leap', 'morally', 'ahead', 'of', 'us', 'unencumbered', 'by', 'habits', 'the', 'main', 'reason', 'to', 'doubt', 'that', 'the', 'moral', 'trajectories', 'of', 'humans', 'v', 'autonomous', 'systems', 'might', 'remain', 'compatible', 'stems', 'from', 'the', 'asymmetry', 'in', 'the', 'mechanisms', 'underlying', 'moral', 'change', 'whereas', 'in', 'humans', 'surprises', 'will', 'continue', 'to', 'play', 'an', 'important', 'role', 'in', 'waking', 'us', 'to', 'the', 'need', 'for', 'moral', 'change', 'cognitive', 'processes', 'will', 'rule', 'when', 'it', 'comes', 'to', 'machines', 'this', 'asymmetry', 'will', 'translate', 'into', 'increasingly', 'different', 'moral', 'outlooks', 'to', 'the', 'point', 'of', 'likely', 'unintelligibility', 'the', 'latter', 'prospect', 'is', 'enough', 'to', 'doubt', 'the', 'desirability', 'of', 'autonomous', 'moral', 'agents']]
|
[-0.05392896562553237, 0.10203083206746875, -0.15354703898961752, 0.11106095681123418, -0.2098513812499491, -0.181801043226081, 0.1132892846368174, 0.38112856641370285, -0.2821139147775453, -0.337855803292562, 0.08776183730418785, -0.27329014082312914, -0.17251002183771613, 0.17696260602862723, -0.1817876486987616, -0.0006806460041729058, 0.05171952989798521, 0.049661535619132294, 0.02953666853622977, -0.288525668179252, 0.2919908831456429, 0.0928827695084342, 0.2481949695441813, 0.0642306063403537, 0.040503865136538725, -0.027820475833205512, -0.006019550134363211, 0.005189793554578294, -0.05716412673373901, 0.13056059458002167, 0.35070593617650847, 0.22882250856390746, 0.3646018973225581, -0.4644683073054447, -0.18384022742130848, 0.14681923584953796, 0.1441407979733005, 0.09980674363064583, 0.03172022977231323, -0.3096550632633962, 0.06561797595588778, -0.1748763733511873, -0.1533292294538665, -0.09700990083028066, 0.026565594133724127, -0.04496098936323153, -0.2084356688329515, -0.02446689355783517, 0.1076461485401971, 0.05738833411677508, -0.026159046823746444, -0.0859983474297496, -0.004053285584508879, 0.22130493096487996, 0.0872426204514111, 0.03752727703887628, 0.15544207596693083, -0.14970196762463014, -0.13891169650785695, 0.4104082866216355, 0.03331215532896129, -0.14867635500141382, 0.23114209002756428, -0.12963263938946748, -0.15368695541399557, 0.09536506772872753, 0.1732873764345922, 0.02153613601300031, -0.13914073108409367, 0.0009453450667463267, -0.02622147013220146, 0.18246117446954255, 0.06506831551904199, -0.012577341876818273, 0.2717769708710632, 0.14323754529086785, 0.07225891567119588, 0.02385032670579745, 0.013406785264499604, -0.13150093798108445, -0.23367342618161854, -0.15567771773139008, -0.09055758550122663, 0.12402945068156772, -0.009782423021063233, -0.1386196318687816, 0.3061722305053428, 0.2601944275959821, 0.1563684685351856, 0.023934439022596395, 0.26066701737972864, 0.030667175540123253, 0.12540142429692888, 0.0328387373893842, 0.22794955866295646, 0.009920223976877644, 0.15236825388283337, -0.14771088321982628, 0.1720306097789835, -0.02954427413126433]
|
1,803.04549
|
Supersymmetrising the GSY Soliton
|
We supersymmetrise the Hopfion studied in a previous work. This soliton
represents a closed semilocal vortex string in $U(1)$ gauge theory. It carries
nonzero Hopf number due to the additional winding of a phase modulus as one
moves along the closed string. We study this solution in $\mathcal{N}= 2$
supersymmetric QED with two flavours. As a preliminary exercise we compactify
one space dimension and consider a straight vortex with periodic boundary
conditions. It turns out to be 1/2-BPS saturated. An additional winding along
the string can be introduced and it does not spoil the BPS nature of the
object. Next, we consider a ring-like vortex in a non-compact space and show
that the circumference of the ring $L$ can be stabilised once the previously
mentioned winding along the string is introduced. Of course the ring-like
vortex is not BPS but its energy becomes close to the BPS bound if $L$ is
large, which can be guaranteed in the case that we have a large value of the
angular momentum $J$. Thus we arrive at the concept of asymptotically
BPS-saturated solitons. BPS saturation is achieved in the limit $J\rightarrow
\infty$.
|
hep-th
|
we supersymmetrise the hopfion studied in a previous work this soliton represents a closed semilocal vortex string in u1 gauge theory it carries nonzero hopf number due to the additional winding of a phase modulus as one moves along the closed string we study this solution in mathcaln 2 supersymmetric qed with two flavours as a preliminary exercise we compactify one space dimension and consider a straight vortex with periodic boundary conditions it turns out to be 12bps saturated an additional winding along the string can be introduced and it does not spoil the bps nature of the object next we consider a ringlike vortex in a noncompact space and show that the circumference of the ring l can be stabilised once the previously mentioned winding along the string is introduced of course the ringlike vortex is not bps but its energy becomes close to the bps bound if l is large which can be guaranteed in the case that we have a large value of the angular momentum j thus we arrive at the concept of asymptotically bpssaturated solitons bps saturation is achieved in the limit jrightarrow infty
|
[['we', 'supersymmetrise', 'the', 'hopfion', 'studied', 'in', 'a', 'previous', 'work', 'this', 'soliton', 'represents', 'a', 'closed', 'semilocal', 'vortex', 'string', 'in', 'u1', 'gauge', 'theory', 'it', 'carries', 'nonzero', 'hopf', 'number', 'due', 'to', 'the', 'additional', 'winding', 'of', 'a', 'phase', 'modulus', 'as', 'one', 'moves', 'along', 'the', 'closed', 'string', 'we', 'study', 'this', 'solution', 'in', 'mathcaln', '2', 'supersymmetric', 'qed', 'with', 'two', 'flavours', 'as', 'a', 'preliminary', 'exercise', 'we', 'compactify', 'one', 'space', 'dimension', 'and', 'consider', 'a', 'straight', 'vortex', 'with', 'periodic', 'boundary', 'conditions', 'it', 'turns', 'out', 'to', 'be', '12bps', 'saturated', 'an', 'additional', 'winding', 'along', 'the', 'string', 'can', 'be', 'introduced', 'and', 'it', 'does', 'not', 'spoil', 'the', 'bps', 'nature', 'of', 'the', 'object', 'next', 'we', 'consider', 'a', 'ringlike', 'vortex', 'in', 'a', 'noncompact', 'space', 'and', 'show', 'that', 'the', 'circumference', 'of', 'the', 'ring', 'l', 'can', 'be', 'stabilised', 'once', 'the', 'previously', 'mentioned', 'winding', 'along', 'the', 'string', 'is', 'introduced', 'of', 'course', 'the', 'ringlike', 'vortex', 'is', 'not', 'bps', 'but', 'its', 'energy', 'becomes', 'close', 'to', 'the', 'bps', 'bound', 'if', 'l', 'is', 'large', 'which', 'can', 'be', 'guaranteed', 'in', 'the', 'case', 'that', 'we', 'have', 'a', 'large', 'value', 'of', 'the', 'angular', 'momentum', 'j', 'thus', 'we', 'arrive', 'at', 'the', 'concept', 'of', 'asymptotically', 'bpssaturated', 'solitons', 'bps', 'saturation', 'is', 'achieved', 'in', 'the', 'limit', 'jrightarrow', 'infty']]
|
[-0.19922640470408456, 0.1868495093690134, -0.08287442088491781, 0.08694969287829563, -0.09099265314064546, -0.15106797752231754, 0.013904610021094048, 0.3334062821749184, -0.21142065668695423, -0.24553620248722532, 0.10184800370581566, -0.2323191479333829, -0.09914675606513228, 0.12544371559353615, -0.07674436712213767, 0.01034157661069431, 0.02930042262943018, 0.0994804252829728, -0.055044345706750555, -0.25572291681809084, 0.30470604808488616, -0.0010800611365724493, 0.2409921173289943, 0.04975573329880794, 0.07244419843259035, -0.025786888377898665, 0.020535008789646248, 0.05677025162900685, -0.16390794707086767, 0.064365347384453, 0.2127492796067917, 0.02680897430107786, 0.1813048196355344, -0.42374238062393726, -0.18138063794603107, 0.13899782334735233, 0.2102454099884237, 0.14929090820871313, 0.029347948498393176, -0.25142856536398606, 0.10972950617886251, -0.1530896301957823, -0.20679253366191885, -0.049730031103728506, 0.0457675091321112, -0.04090181353905079, -0.2302297825565335, 0.026238573577117036, 0.0642532775001157, 0.013591142458762362, -0.03580682572019262, -0.056645642654359264, -0.09759147078163488, 0.0656765864357579, 0.09374794638999023, 0.10893998053984312, 0.10735856265411096, -0.15505987384588157, -0.11799556181012165, 0.33785867473238673, -0.05716369464185345, -0.23811551959071525, 0.10997455287047184, -0.14497334280614274, -0.14045862096149928, 0.16463600160743766, 0.06690393017713354, 0.14965515411532823, -0.06019970511761371, 0.16342357506084862, -0.11940671383248021, 0.15802470668312654, 0.13252673259684963, 0.03251265535357768, 0.27453642178366267, 0.13152635329102397, 0.10012971456473688, 0.17665810856189598, -0.09273730250563295, -0.12295610867514616, -0.3742213716937436, -0.156577147936717, -0.16580802450331036, 0.10827193875250742, -0.06524064665065558, -0.17479598769060675, 0.3640877508863846, 0.07915887203087252, 0.21977868777633244, 0.015105756539876065, 0.2228729110211134, 0.11779424780363798, 0.09081286268072232, 0.10937899107386964, 0.2307632925523514, 0.1161675370056872, 0.11338990862683329, -0.22484917460236126, -0.10033767234790263, 0.13839965815059643]
|
1,803.0455
|
Ergodicity in Stationary Graph Processes: A Weak Law of Large Numbers
|
For stationary signals in time the weak law of large numbers (WLLN) states
that ensemble and realization averages are within e of each other with a
probability of order O(1/Ne^2) when considering N signal components. The graph
WLLN introduced in this paper shows that the same is essentially true for
signals supported on graphs. However, the notions of stationarity, ensemble
mean, and realization mean are different. Recent papers have defined graph
stationary signals as those that satisfy a form of invariance with respect to
graph diffusion. The ensemble mean of a graph stationary signal is not a
constant but a node-varying signal whose structure depends on the spectral
properties of the graph. The realization average of a graph signal is defined
here as an average of successive weighted averages of local signal values with
signal values of neighboring nodes. The graph WLLN shows that this two
node-varying signals are within e of each other with probability of order
O(1/Ne^2) in at least some nodes. In stationary time signals, the realization
average is not only a consistent estimator of the ensemble mean but also
optimal in terms of mean squared error (MSE). This is not true of graph
signals. Optimal MSE graph filter designs are also presented. An example
problem concerning the estimation of the mean of a Gaussian random field is
presented.
|
eess.SP math.ST stat.TH
|
for stationary signals in time the weak law of large numbers wlln states that ensemble and realization averages are within e of each other with a probability of order o1ne2 when considering n signal components the graph wlln introduced in this paper shows that the same is essentially true for signals supported on graphs however the notions of stationarity ensemble mean and realization mean are different recent papers have defined graph stationary signals as those that satisfy a form of invariance with respect to graph diffusion the ensemble mean of a graph stationary signal is not a constant but a nodevarying signal whose structure depends on the spectral properties of the graph the realization average of a graph signal is defined here as an average of successive weighted averages of local signal values with signal values of neighboring nodes the graph wlln shows that this two nodevarying signals are within e of each other with probability of order o1ne2 in at least some nodes in stationary time signals the realization average is not only a consistent estimator of the ensemble mean but also optimal in terms of mean squared error mse this is not true of graph signals optimal mse graph filter designs are also presented an example problem concerning the estimation of the mean of a gaussian random field is presented
|
[['for', 'stationary', 'signals', 'in', 'time', 'the', 'weak', 'law', 'of', 'large', 'numbers', 'wlln', 'states', 'that', 'ensemble', 'and', 'realization', 'averages', 'are', 'within', 'e', 'of', 'each', 'other', 'with', 'a', 'probability', 'of', 'order', 'o1ne2', 'when', 'considering', 'n', 'signal', 'components', 'the', 'graph', 'wlln', 'introduced', 'in', 'this', 'paper', 'shows', 'that', 'the', 'same', 'is', 'essentially', 'true', 'for', 'signals', 'supported', 'on', 'graphs', 'however', 'the', 'notions', 'of', 'stationarity', 'ensemble', 'mean', 'and', 'realization', 'mean', 'are', 'different', 'recent', 'papers', 'have', 'defined', 'graph', 'stationary', 'signals', 'as', 'those', 'that', 'satisfy', 'a', 'form', 'of', 'invariance', 'with', 'respect', 'to', 'graph', 'diffusion', 'the', 'ensemble', 'mean', 'of', 'a', 'graph', 'stationary', 'signal', 'is', 'not', 'a', 'constant', 'but', 'a', 'nodevarying', 'signal', 'whose', 'structure', 'depends', 'on', 'the', 'spectral', 'properties', 'of', 'the', 'graph', 'the', 'realization', 'average', 'of', 'a', 'graph', 'signal', 'is', 'defined', 'here', 'as', 'an', 'average', 'of', 'successive', 'weighted', 'averages', 'of', 'local', 'signal', 'values', 'with', 'signal', 'values', 'of', 'neighboring', 'nodes', 'the', 'graph', 'wlln', 'shows', 'that', 'this', 'two', 'nodevarying', 'signals', 'are', 'within', 'e', 'of', 'each', 'other', 'with', 'probability', 'of', 'order', 'o1ne2', 'in', 'at', 'least', 'some', 'nodes', 'in', 'stationary', 'time', 'signals', 'the', 'realization', 'average', 'is', 'not', 'only', 'a', 'consistent', 'estimator', 'of', 'the', 'ensemble', 'mean', 'but', 'also', 'optimal', 'in', 'terms', 'of', 'mean', 'squared', 'error', 'mse', 'this', 'is', 'not', 'true', 'of', 'graph', 'signals', 'optimal', 'mse', 'graph', 'filter', 'designs', 'are', 'also', 'presented', 'an', 'example', 'problem', 'concerning', 'the', 'estimation', 'of', 'the', 'mean', 'of', 'a', 'gaussian', 'random', 'field', 'is', 'presented']]
|
[-0.16034198448601686, 0.1158697494994902, -0.08198826618873599, 0.03563229250298305, -0.04201449731236525, -0.10893474742770196, 0.031708680841521446, 0.3861266606910662, -0.25260048951445657, -0.2920622606753816, 0.11456373881030066, -0.28968862329863687, -0.15237775262635708, 0.14795779925771058, -0.07724902251819996, 0.06566821745588888, 0.07235168746397407, 0.14161535471635447, -0.044820515079085126, -0.23058461747833378, 0.26999820415777237, 0.06427500467874449, 0.27651908183258705, -0.023684596603693948, 0.12862671022726732, 0.017337576958181507, -0.02453532901656052, 0.05307352326768027, -0.09089945464101302, 0.08160222827229352, 0.22994886957299474, 0.1283678343794732, 0.27646485959200867, -0.3586547694719312, -0.19998175296932458, 0.19724231486801397, 0.11580995839851146, 0.0748296048830856, -0.013815413756211373, -0.26612606899474156, 0.1167874352244491, -0.10067171374843879, -0.058334538984027774, 0.014985344554853244, 0.030497279414504936, 0.08856816491143862, -0.3126807723017092, 0.10421591108342082, 0.07284921513955024, 0.06326365057823502, -0.02118759953882545, -0.15487733295356684, 0.009473345902833072, 0.1251167431538554, 0.021663513290695845, 0.029594525127586993, 0.11317974795502696, -0.109243537675039, -0.13968898678113792, 0.3448260203575377, -0.08795784247340635, -0.22071561339332468, 0.10505390182476151, -0.1548273937438022, -0.12784858750793235, 0.1413235266280191, 0.16537839218720116, 0.08981822437506873, -0.14535096915053808, 0.07030420420977118, -0.0524251015790717, 0.16049934252249923, 0.049316689121240584, 0.07249155764214017, 0.1524555206637491, 0.11869546640558507, 0.1325989274961268, 0.11603669844282029, -0.07362767640839923, -0.09451230937477455, -0.3263190252246039, -0.12529162889007817, -0.2741265835166401, 0.02933732030869578, -0.14936162293663735, -0.20377536319534886, 0.4235109263808805, 0.11848972877275876, 0.21780945126559925, 0.11912519778124989, 0.2669262914399786, 0.143228192278184, 0.017844497339419004, 0.10516246357636357, 0.2025999919496826, 0.17336868373677133, 0.020552964365660127, -0.15143321267625487, 0.09080132970067842, 0.04971535750029778]
|
1,803.04551
|
Multi-Sensor Conflict Measurement and Information Fusion
|
In sensing applications where multiple sensors observe the same scene, fusing
sensor outputs can provide improved results. However, if some of the sensors
are providing lower quality outputs, the fused results can be degraded. In this
work, a multi-sensor conflict measure is proposed which estimates multi-sensor
conflict by representing each sensor output as interval-valued information and
examines the sensor output overlaps on all possible n-tuple sensor
combinations. The conflict is based on the sizes of the intervals and how many
sensors output values lie in these intervals. In this work, conflict is defined
in terms of how little the output from multiple sensors overlap. That is, high
degrees of overlap mean low sensor conflict, while low degrees of overlap mean
high conflict. This work is a preliminary step towards a robust conflict and
sensor fusion framework. In addition, a sensor fusion algorithm is proposed
based on a weighted sum of sensor outputs, where the weights for each sensor
diminish as the conflict measure increases. The proposed methods can be
utilized to (1) assess a measure of multi-sensor conflict, and (2) improve
sensor output fusion by lessening weighting for sensors with high conflict.
Using this measure, a simulated example is given to explain the mechanics of
calculating the conflict measure, and stereo camera 3D outputs are analyzed and
fused. In the stereo camera case, the sensor output is corrupted by additive
impulse noise, DC offset, and Gaussian noise. Impulse noise is common in
sensors due to intermittent interference, a DC offset a sensor bias or
registration error, and Gaussian noise represents a sensor output with low SNR.
The results show that sensor output fusion based on the conflict measure shows
improved accuracy over a simple averaging fusion strategy.
|
eess.SP cs.AI
|
in sensing applications where multiple sensors observe the same scene fusing sensor outputs can provide improved results however if some of the sensors are providing lower quality outputs the fused results can be degraded in this work a multisensor conflict measure is proposed which estimates multisensor conflict by representing each sensor output as intervalvalued information and examines the sensor output overlaps on all possible ntuple sensor combinations the conflict is based on the sizes of the intervals and how many sensors output values lie in these intervals in this work conflict is defined in terms of how little the output from multiple sensors overlap that is high degrees of overlap mean low sensor conflict while low degrees of overlap mean high conflict this work is a preliminary step towards a robust conflict and sensor fusion framework in addition a sensor fusion algorithm is proposed based on a weighted sum of sensor outputs where the weights for each sensor diminish as the conflict measure increases the proposed methods can be utilized to 1 assess a measure of multisensor conflict and 2 improve sensor output fusion by lessening weighting for sensors with high conflict using this measure a simulated example is given to explain the mechanics of calculating the conflict measure and stereo camera 3d outputs are analyzed and fused in the stereo camera case the sensor output is corrupted by additive impulse noise dc offset and gaussian noise impulse noise is common in sensors due to intermittent interference a dc offset a sensor bias or registration error and gaussian noise represents a sensor output with low snr the results show that sensor output fusion based on the conflict measure shows improved accuracy over a simple averaging fusion strategy
|
[['in', 'sensing', 'applications', 'where', 'multiple', 'sensors', 'observe', 'the', 'same', 'scene', 'fusing', 'sensor', 'outputs', 'can', 'provide', 'improved', 'results', 'however', 'if', 'some', 'of', 'the', 'sensors', 'are', 'providing', 'lower', 'quality', 'outputs', 'the', 'fused', 'results', 'can', 'be', 'degraded', 'in', 'this', 'work', 'a', 'multisensor', 'conflict', 'measure', 'is', 'proposed', 'which', 'estimates', 'multisensor', 'conflict', 'by', 'representing', 'each', 'sensor', 'output', 'as', 'intervalvalued', 'information', 'and', 'examines', 'the', 'sensor', 'output', 'overlaps', 'on', 'all', 'possible', 'ntuple', 'sensor', 'combinations', 'the', 'conflict', 'is', 'based', 'on', 'the', 'sizes', 'of', 'the', 'intervals', 'and', 'how', 'many', 'sensors', 'output', 'values', 'lie', 'in', 'these', 'intervals', 'in', 'this', 'work', 'conflict', 'is', 'defined', 'in', 'terms', 'of', 'how', 'little', 'the', 'output', 'from', 'multiple', 'sensors', 'overlap', 'that', 'is', 'high', 'degrees', 'of', 'overlap', 'mean', 'low', 'sensor', 'conflict', 'while', 'low', 'degrees', 'of', 'overlap', 'mean', 'high', 'conflict', 'this', 'work', 'is', 'a', 'preliminary', 'step', 'towards', 'a', 'robust', 'conflict', 'and', 'sensor', 'fusion', 'framework', 'in', 'addition', 'a', 'sensor', 'fusion', 'algorithm', 'is', 'proposed', 'based', 'on', 'a', 'weighted', 'sum', 'of', 'sensor', 'outputs', 'where', 'the', 'weights', 'for', 'each', 'sensor', 'diminish', 'as', 'the', 'conflict', 'measure', 'increases', 'the', 'proposed', 'methods', 'can', 'be', 'utilized', 'to', '1', 'assess', 'a', 'measure', 'of', 'multisensor', 'conflict', 'and', '2', 'improve', 'sensor', 'output', 'fusion', 'by', 'lessening', 'weighting', 'for', 'sensors', 'with', 'high', 'conflict', 'using', 'this', 'measure', 'a', 'simulated', 'example', 'is', 'given', 'to', 'explain', 'the', 'mechanics', 'of', 'calculating', 'the', 'conflict', 'measure', 'and', 'stereo', 'camera', '3d', 'outputs', 'are', 'analyzed', 'and', 'fused', 'in', 'the', 'stereo', 'camera', 'case', 'the', 'sensor', 'output', 'is', 'corrupted', 'by', 'additive', 'impulse', 'noise', 'dc', 'offset', 'and', 'gaussian', 'noise', 'impulse', 'noise', 'is', 'common', 'in', 'sensors', 'due', 'to', 'intermittent', 'interference', 'a', 'dc', 'offset', 'a', 'sensor', 'bias', 'or', 'registration', 'error', 'and', 'gaussian', 'noise', 'represents', 'a', 'sensor', 'output', 'with', 'low', 'snr', 'the', 'results', 'show', 'that', 'sensor', 'output', 'fusion', 'based', 'on', 'the', 'conflict', 'measure', 'shows', 'improved', 'accuracy', 'over', 'a', 'simple', 'averaging', 'fusion', 'strategy']]
|
[-0.10462429983628985, 0.09467042949316758, -0.04931880005404924, 0.015344123934064618, -0.06181814127604928, -0.1712754229294584, 0.04875270580347199, 0.429096040429502, -0.2689332384924414, -0.3444905446693042, 0.10079554926956118, -0.275687909399513, -0.1508769998448586, 0.13105142084299182, -0.21018236576644944, 0.09155680056632365, 0.13101455717483854, 0.09062453272652274, -0.023147043546715276, -0.21384125214414054, 0.257719649001956, 0.09690481459215698, 0.34351386381021154, 0.005825246925196426, 0.17363042801686315, 0.03287949923422668, -0.08306327152701957, 0.05792866930534304, -0.026816234852762112, 0.13395174355684206, 0.293599925798166, 0.1305032247865657, 0.2867681861429991, -0.39784149009773334, -0.23464503775931608, 0.10834697859348788, 0.11168407047336162, 0.03952777006814276, -0.04894894330668221, -0.3195043630932072, 0.1094824657694336, -0.1754483830748379, -0.007548769901892761, -0.0074028963367503385, -0.014564717318278571, 0.04867997747145833, -0.35668166039855537, 0.05891209630577952, -0.014510088607519605, 0.057939265103433085, -0.07021183531020707, -0.11260162037878398, 0.038347782753732455, 0.17517089073380734, -0.05182003265632907, 0.028932565132818, 0.1905850940757149, -0.13263394422739913, -0.13410848714021856, 0.32870246971006595, -0.02719008694370423, -0.22761181226642704, 0.15431592850588938, -0.09425080781932337, -0.11239965935369535, 0.1206254488601956, 0.17099830332919566, 0.05986619475443656, -0.1524678377145804, -7.382427954329146e-05, 0.012410413009805754, 0.18092774835186815, 0.09097952149866817, 0.05665809454279546, 0.18039776760766782, 0.18520601001772574, 0.12673138361566177, 0.1267065496673931, -0.1669310362196693, -0.040066399233689924, -0.2831309387490368, -0.09150695154939872, -0.229491141737919, -0.02035508874539312, -0.11185445588768399, -0.1174619170295185, 0.34680757881547825, 0.2323931112199152, 0.24229404785125175, 0.04850147480351156, 0.4102639275804188, 0.1152539381145691, 0.05454226524750816, 0.023494009726813862, 0.21953181866590885, 0.12111091959932721, 0.08655619830418806, -0.16524350135169358, 0.08738448828189449, -0.016030670789424134]
|
1,803.04552
|
Text Data Mining from the Author's Perspective: Whose Text, Whose
Mining, and to Whose Benefit?
|
Given the many technical, social, and policy shifts in access to scholarly
content since the early days of text data mining, it is time to expand the
conversation about text data mining from concerns of the researcher wishing to
mine data to include concerns of researcher-authors about how their data are
mined, by whom, for what purposes, and to whose benefits.
|
cs.DL
|
given the many technical social and policy shifts in access to scholarly content since the early days of text data mining it is time to expand the conversation about text data mining from concerns of the researcher wishing to mine data to include concerns of researcherauthors about how their data are mined by whom for what purposes and to whose benefits
|
[['given', 'the', 'many', 'technical', 'social', 'and', 'policy', 'shifts', 'in', 'access', 'to', 'scholarly', 'content', 'since', 'the', 'early', 'days', 'of', 'text', 'data', 'mining', 'it', 'is', 'time', 'to', 'expand', 'the', 'conversation', 'about', 'text', 'data', 'mining', 'from', 'concerns', 'of', 'the', 'researcher', 'wishing', 'to', 'mine', 'data', 'to', 'include', 'concerns', 'of', 'researcherauthors', 'about', 'how', 'their', 'data', 'are', 'mined', 'by', 'whom', 'for', 'what', 'purposes', 'and', 'to', 'whose', 'benefits']]
|
[-0.026799529201040666, 0.03932970938816046, -0.09257657348643988, 0.10707190969066384, -0.2567037856128688, -0.11903514601290226, 0.13363510198929968, 0.38810622120896976, -0.3152901425142772, -0.3652141998366763, 0.1543345500472545, -0.3961129870265722, -0.06553134052470948, 0.1829293723528584, -0.1378007052776714, 0.019640283767754833, 0.08932354510761797, 0.12032007072120905, 0.054978131246753034, -0.35456875429178275, 0.3439197943235437, 0.07430376283203562, 0.31344108247819047, 0.06392284835843991, 0.017854148845071905, -0.018766732304356992, -0.15163412756907443, -0.10444760088964054, -0.10182167918731769, 0.1946155958653738, 0.44711186103522776, 0.353758838151892, 0.3465174788919588, -0.4385423142152528, -0.16568179194194574, 0.07127876566567769, 0.1296525723223264, 0.0966693484884066, -0.011103829586257536, -0.3608557967624317, 0.11061509086478812, -0.149269694990168, -0.061279888187224664, -0.08045638911426067, 0.07578796967864036, 0.021643625056215872, -0.216711447481066, 0.011382455945325394, 0.04515209652793904, 0.138746501179412, 0.011627927189692855, -0.09079721697295705, 0.006965279361853997, 0.2593056625686586, 0.18927778745613372, 0.026846129280359794, 0.1278247270267457, -0.17164256943700215, -0.13525219336152078, 0.43860006164759396, 0.030902566147657733, -0.11177747911618402, 0.12415151189391811, -0.08887114874087274, -0.15833968521716693, 0.08707303580207129, 0.24179668238696952, 0.016100486383462946, -0.24979157171522579, -0.03841714219694647, 0.031035515153780578, 0.19171721693128346, 0.10490783766532938, -0.0018308098738392194, 0.17725582489122946, 0.16433482682332395, 0.03899648610095028, 0.05071508052448432, -0.0017476323060691356, -0.0812737084304293, -0.18908210790250451, -0.09905804538478453, -0.17288323607062922, 0.022586953220888974, -0.06884018453565659, -0.1270197772110502, 0.36319008643428485, 0.2660638962251445, 0.15670195148947338, -0.013307867851108313, 0.2807296064992746, -0.05528176355486115, 0.11467289772893613, 0.08240344766139364, 0.09351604755502194, 0.005314533618123581, 0.2925319322384894, -0.10384693413895245, 0.13196280354944367, -0.07047320399433374]
|
1,803.04553
|
Luby--Veli\v{c}kovi\'c--Wigderson revisited: Improved correlation bounds
and pseudorandom generators for depth-two circuits
|
We study correlation bounds and pseudorandom generators for depth-two
circuits that consist of a $\mathsf{SYM}$-gate (computing an arbitrary
symmetric function) or $\mathsf{THR}$-gate (computing an arbitrary linear
threshold function) that is fed by $S$ $\mathsf{AND}$ gates. Such circuits were
considered in early influential work on unconditional derandomization of Luby,
Veli\v{c}kovi\'c, and Wigderson [LVW93], who gave the first non-trivial PRG
with seed length $2^{O(\sqrt{\log(S/\varepsilon)})}$ that $\varepsilon$-fools
these circuits.
In this work we obtain the first strict improvement of [LVW93]'s seed length:
we construct a PRG that $\varepsilon$-fools size-$S$
$\{\mathsf{SYM},\mathsf{THR}\} \circ\mathsf{AND}$ circuits over $\{0,1\}^n$
with seed length \[ 2^{O(\sqrt{\log S })} + \mathrm{polylog}(1/\varepsilon), \]
an exponential (and near-optimal) improvement of the $\varepsilon$-dependence
of [LVW93]. The above PRG is actually a special case of a more general PRG
which we establish for constant-depth circuits containing multiple
$\mathsf{SYM}$ or $\mathsf{THR}$ gates, including as a special case
$\{\mathsf{SYM},\mathsf{THR}\} \circ \mathsf{AC^0}$ circuits. These more
general results strengthen previous results of Viola [Vio06] and essentially
strengthen more recent results of Lovett and Srinivasan [LS11].
Our improved PRGs follow from improved correlation bounds, which are
transformed into PRGs via the Nisan--Wigderson "hardness versus randomness"
paradigm [NW94]. The key to our improved correlation bounds is the use of a
recent powerful \emph{multi-switching} lemma due to H{\aa}stad [H{\aa}s14].
|
cs.CC
|
we study correlation bounds and pseudorandom generators for depthtwo circuits that consist of a mathsfsymgate computing an arbitrary symmetric function or mathsfthrgate computing an arbitrary linear threshold function that is fed by s mathsfand gates such circuits were considered in early influential work on unconditional derandomization of luby velivckovic and wigderson lvw93 who gave the first nontrivial prg with seed length 2osqrtlogsvarepsilon that varepsilonfools these circuits in this work we obtain the first strict improvement of lvw93s seed length we construct a prg that varepsilonfools sizes mathsfsymmathsfthr circmathsfand circuits over 01n with seed length 2osqrtlog s mathrmpolylog1varepsilon an exponential and nearoptimal improvement of the varepsilondependence of lvw93 the above prg is actually a special case of a more general prg which we establish for constantdepth circuits containing multiple mathsfsym or mathsfthr gates including as a special case mathsfsymmathsfthr circ mathsfac0 circuits these more general results strengthen previous results of viola vio06 and essentially strengthen more recent results of lovett and srinivasan ls11 our improved prgs follow from improved correlation bounds which are transformed into prgs via the nisanwigderson hardness versus randomness paradigm nw94 the key to our improved correlation bounds is the use of a recent powerful emphmultiswitching lemma due to haastad haas14
|
[['we', 'study', 'correlation', 'bounds', 'and', 'pseudorandom', 'generators', 'for', 'depthtwo', 'circuits', 'that', 'consist', 'of', 'a', 'mathsfsymgate', 'computing', 'an', 'arbitrary', 'symmetric', 'function', 'or', 'mathsfthrgate', 'computing', 'an', 'arbitrary', 'linear', 'threshold', 'function', 'that', 'is', 'fed', 'by', 's', 'mathsfand', 'gates', 'such', 'circuits', 'were', 'considered', 'in', 'early', 'influential', 'work', 'on', 'unconditional', 'derandomization', 'of', 'luby', 'velivckovic', 'and', 'wigderson', 'lvw93', 'who', 'gave', 'the', 'first', 'nontrivial', 'prg', 'with', 'seed', 'length', '2osqrtlogsvarepsilon', 'that', 'varepsilonfools', 'these', 'circuits', 'in', 'this', 'work', 'we', 'obtain', 'the', 'first', 'strict', 'improvement', 'of', 'lvw93s', 'seed', 'length', 'we', 'construct', 'a', 'prg', 'that', 'varepsilonfools', 'sizes', 'mathsfsymmathsfthr', 'circmathsfand', 'circuits', 'over', '01n', 'with', 'seed', 'length', '2osqrtlog', 's', 'mathrmpolylog1varepsilon', 'an', 'exponential', 'and', 'nearoptimal', 'improvement', 'of', 'the', 'varepsilondependence', 'of', 'lvw93', 'the', 'above', 'prg', 'is', 'actually', 'a', 'special', 'case', 'of', 'a', 'more', 'general', 'prg', 'which', 'we', 'establish', 'for', 'constantdepth', 'circuits', 'containing', 'multiple', 'mathsfsym', 'or', 'mathsfthr', 'gates', 'including', 'as', 'a', 'special', 'case', 'mathsfsymmathsfthr', 'circ', 'mathsfac0', 'circuits', 'these', 'more', 'general', 'results', 'strengthen', 'previous', 'results', 'of', 'viola', 'vio06', 'and', 'essentially', 'strengthen', 'more', 'recent', 'results', 'of', 'lovett', 'and', 'srinivasan', 'ls11', 'our', 'improved', 'prgs', 'follow', 'from', 'improved', 'correlation', 'bounds', 'which', 'are', 'transformed', 'into', 'prgs', 'via', 'the', 'nisanwigderson', 'hardness', 'versus', 'randomness', 'paradigm', 'nw94', 'the', 'key', 'to', 'our', 'improved', 'correlation', 'bounds', 'is', 'the', 'use', 'of', 'a', 'recent', 'powerful', 'emphmultiswitching', 'lemma', 'due', 'to', 'haastad', 'haas14']]
|
[-0.14254754628272276, 0.08451322104312047, -0.06262023042078939, 0.06378050571803905, -0.04210132148736003, -0.17282810420498948, 0.08653152239212132, 0.34300174748340806, -0.2088495478376869, -0.32150949645214355, 0.07509319082409102, -0.2490696558322, -0.16124699702281708, 0.2939007239298547, -0.07105157968260675, 0.08474473703836838, 0.018420785916252777, -0.023254884770242394, -0.06734879716084553, -0.3369870225439657, 0.26430337816466587, 0.07963702364333465, 0.2293541659345175, 0.005474475698525106, 0.048102359438396906, 0.01651085562911908, -0.034045226678794636, -0.017651091877289197, -0.13560400461758215, 0.14485082128034024, 0.23891738201440363, 0.15864555152890447, 0.25170672109739467, -0.43017860288355325, -0.15386828559113072, 0.12919385498133742, 0.13170515690357065, 0.11575355713260821, -0.0755989776397665, -0.24232077700147345, 0.1340967990844357, -0.1622645603836665, -0.06900885987964173, -0.03140618241197624, 0.06166481213378055, 0.0483332851448782, -0.31428250148099246, 0.012129869158533249, 0.189857908171489, 0.02621740415362273, 0.03600267902947962, -0.1445431436941662, 0.06274348588732, 0.10776516387065115, -0.05569970891120137, 0.09349436467132213, 0.07647434810104851, -0.09626626808088846, -0.19858755269964165, 0.2647387132759892, -0.04722691874101676, -0.1410291874039263, 0.1421747602622158, -0.06410548467045793, -0.16160688233807438, 0.09037464307646707, 0.17128434061561457, 0.13685923913118922, -0.08590591673650984, 0.08712497513235055, -0.11625477882450098, 0.19067690533440007, 0.12212679061557648, 0.07130532928647801, 0.0767346935623049, 0.08861696522089314, 0.0911641899665183, 0.2032066153370069, 0.03940976725937287, -0.05005347995333128, -0.2869541548889074, -0.15916598590339054, -0.17416674001679683, 0.10450856509211646, -0.13135782952104108, -0.16910474627038238, 0.36383721230367383, 0.12102235845484577, 0.18401696688270422, 0.14989465765864843, 0.2692026902696343, 0.11103638255352072, 0.08848574840218075, 0.12555141651638813, 0.17863927733480603, 0.1615123123405143, 0.005131406852192563, -0.15362109969482646, 0.09967103688900901, 0.0851003924118621]
|
1,803.04554
|
Reconstruction Algorithm Design for Mitigating the Orientation Dependent
Conspicuity of Fiber-Like signals in Digital Breast Tomosynthesis
|
There are a number of clinically relevant tasks in digital breast
tomosynthesis (DBT) involving the detection and visual assessment of fiber-like
structures such as Cooper's ligaments, blood vessels, and spiculated lesions.
Such structures can exhibit orientation dependent variations in conspicuity.
This study demonstrates the presence of in-plane orientation-dependent signal
conspicuity for fiber-like signals in DBT and shows how reconstruction
algorithm design can mitigate this phenomenon. We uncover a tradeoff between
minimizing orientation-dependence and preserving depth resolution that is
dictated by the regularization strength employed in reconstruction.
|
physics.med-ph
|
there are a number of clinically relevant tasks in digital breast tomosynthesis dbt involving the detection and visual assessment of fiberlike structures such as coopers ligaments blood vessels and spiculated lesions such structures can exhibit orientation dependent variations in conspicuity this study demonstrates the presence of inplane orientationdependent signal conspicuity for fiberlike signals in dbt and shows how reconstruction algorithm design can mitigate this phenomenon we uncover a tradeoff between minimizing orientationdependence and preserving depth resolution that is dictated by the regularization strength employed in reconstruction
|
[['there', 'are', 'a', 'number', 'of', 'clinically', 'relevant', 'tasks', 'in', 'digital', 'breast', 'tomosynthesis', 'dbt', 'involving', 'the', 'detection', 'and', 'visual', 'assessment', 'of', 'fiberlike', 'structures', 'such', 'as', 'coopers', 'ligaments', 'blood', 'vessels', 'and', 'spiculated', 'lesions', 'such', 'structures', 'can', 'exhibit', 'orientation', 'dependent', 'variations', 'in', 'conspicuity', 'this', 'study', 'demonstrates', 'the', 'presence', 'of', 'inplane', 'orientationdependent', 'signal', 'conspicuity', 'for', 'fiberlike', 'signals', 'in', 'dbt', 'and', 'shows', 'how', 'reconstruction', 'algorithm', 'design', 'can', 'mitigate', 'this', 'phenomenon', 'we', 'uncover', 'a', 'tradeoff', 'between', 'minimizing', 'orientationdependence', 'and', 'preserving', 'depth', 'resolution', 'that', 'is', 'dictated', 'by', 'the', 'regularization', 'strength', 'employed', 'in', 'reconstruction']]
|
[-0.10541025303742464, 0.0431989832657992, -0.037855067892986184, 0.06259040983037695, -0.05888567356091431, -0.12174835762228159, -0.019051040917196695, 0.4316053241929587, -0.2496186542982126, -0.34302161331014597, 0.07521158812348457, -0.2802092369426699, -0.2611163945594693, 0.1971980247208301, -0.152615131311776, 0.05368119371671449, 0.0442098557291662, -0.029459723767221852, -0.03337168318548185, -0.1519873728058027, 0.22216675022945684, 0.030609632846693893, 0.3443153413341326, 0.0816213897374623, 0.11219641491770745, 0.026758657724541775, -0.05187649557586102, 0.02939573524396538, -0.0954612080094247, 0.10199944581836462, 0.34960767196162656, 0.14298666855868172, 0.23731942892567637, -0.39164041223771434, -0.22372621262774747, 0.11841587050653556, 0.17362052658024957, 0.09292130003518918, -0.07575671439442565, -0.30195781695930396, 0.08807007273768677, -0.061141501903972204, -0.025867939493892825, -0.1156670261064873, -0.027261718179044478, 0.016132838133832112, -0.2607535478713758, 0.14431619856725722, 0.004593354132136001, 0.13552837578908486, -0.07665298778344604, -0.06430748548796948, -0.003843247101587408, 0.1733116534203017, 0.031419956889551355, 0.02758046627209029, 0.20421945544726708, -0.193287366809433, -0.08368993435493287, 0.36682269783581006, -0.004241309906629955, -0.22601691901406465, 0.1511764571618508, -0.08682301177040619, -0.09638941722976811, 0.14646223911686856, 0.18075327821185483, 0.07467233258683015, -0.11668783412041032, -0.020049829931025775, 0.028630945689099678, 0.21770868097596308, 0.13363686123150675, 0.04508377635194098, 0.16851899705498535, 0.2250792851767448, 0.04294193550286924, 0.1808196929639534, -0.2051702970310169, 0.016230751464472098, -0.21206558133297435, -0.13336336945786195, -0.13942449642071392, -0.018999100673724623, -0.09748297898468616, -0.188052576270831, 0.38966156967422544, 0.14409971954848835, 0.20452176694319968, -0.01650278065353632, 0.34069521558635374, 0.0196831911952947, 0.09107745954876437, -0.048428560470176095, 0.22151906859567938, 0.08870014554053983, 0.07842965294771334, -0.2649675151593435, 0.11672348729334772, 0.034135346640558804]
|
1,803.04555
|
Reactive Proximity Data Structures for Graphs
|
We consider data structures for graphs where we maintain a subset of the
nodes called sites, and allow proximity queries, such as asking for the closest
site to a query node, and update operations that enable or disable nodes as
sites. We refer to a data structure that can efficiently react to such updates
as reactive. We present novel reactive proximity data structures for graphs of
polynomial expansion, i.e., the class of graphs with small separators, such as
planar graphs and road networks. Our data structures can be used in several
logistical problems and geographic information systems dealing with real-time
data, such as emergency dispatching. We experimentally compare our data
structure to Dijkstra's algorithm in a system emulating random queries in a
real road network.
|
cs.DS
|
we consider data structures for graphs where we maintain a subset of the nodes called sites and allow proximity queries such as asking for the closest site to a query node and update operations that enable or disable nodes as sites we refer to a data structure that can efficiently react to such updates as reactive we present novel reactive proximity data structures for graphs of polynomial expansion ie the class of graphs with small separators such as planar graphs and road networks our data structures can be used in several logistical problems and geographic information systems dealing with realtime data such as emergency dispatching we experimentally compare our data structure to dijkstras algorithm in a system emulating random queries in a real road network
|
[['we', 'consider', 'data', 'structures', 'for', 'graphs', 'where', 'we', 'maintain', 'a', 'subset', 'of', 'the', 'nodes', 'called', 'sites', 'and', 'allow', 'proximity', 'queries', 'such', 'as', 'asking', 'for', 'the', 'closest', 'site', 'to', 'a', 'query', 'node', 'and', 'update', 'operations', 'that', 'enable', 'or', 'disable', 'nodes', 'as', 'sites', 'we', 'refer', 'to', 'a', 'data', 'structure', 'that', 'can', 'efficiently', 'react', 'to', 'such', 'updates', 'as', 'reactive', 'we', 'present', 'novel', 'reactive', 'proximity', 'data', 'structures', 'for', 'graphs', 'of', 'polynomial', 'expansion', 'ie', 'the', 'class', 'of', 'graphs', 'with', 'small', 'separators', 'such', 'as', 'planar', 'graphs', 'and', 'road', 'networks', 'our', 'data', 'structures', 'can', 'be', 'used', 'in', 'several', 'logistical', 'problems', 'and', 'geographic', 'information', 'systems', 'dealing', 'with', 'realtime', 'data', 'such', 'as', 'emergency', 'dispatching', 'we', 'experimentally', 'compare', 'our', 'data', 'structure', 'to', 'dijkstras', 'algorithm', 'in', 'a', 'system', 'emulating', 'random', 'queries', 'in', 'a', 'real', 'road', 'network']]
|
[-0.16844532781839372, 0.037912528328597546, 0.000626670103520155, 0.0887589371651411, -0.1302866718955338, -0.16668565630540252, 0.12392964101023972, 0.43883055363595486, -0.3234360075965524, -0.3476874699033797, 0.13329737222846597, -0.32642643454670905, -0.1713174499515444, 0.16058246722631156, -0.07344447779934854, 0.08073007383197546, 0.09859700030460954, 0.06663404211774468, 0.015945986930280924, -0.2321730319391936, 0.3049613692611456, 0.04923131500277668, 0.22598673766851426, 0.03713181604072452, 0.074890884174034, 0.014799510149285197, 0.022486646600067616, 0.07818872347753494, -0.0648773962823907, 0.10395380316465162, 0.341629682328552, 0.2032717670351267, 0.2443795773126185, -0.498039677798748, -0.20175370788760483, 0.1361846881401725, 0.12490688683278858, 0.1260193142220378, -0.016604419354349375, -0.2948656808882952, 0.08455184859968722, -0.15295915074646474, -0.06085045030340552, -0.11362979666516185, 0.008267316803336143, 0.07062803093343974, -0.3131945008235052, -0.04162517259269953, -0.007290851240977645, 0.0393620490655303, -0.009603306267410516, -0.035074024031870064, 0.0002544778250157833, 0.1822639680635184, -0.07144791988749057, 0.01770818408485502, 0.1436195237711072, -0.09986316594481469, -0.21768086056411268, 0.4297763815224171, -0.006694980079308153, -0.17595075912773608, 0.17945180352404713, -0.045178127452731136, -0.198211062066257, 0.05515621785819531, 0.2722063540890813, 0.13345592387020588, -0.15765490126702936, 0.01573918998427689, -0.04336658269353211, 0.12858692859858276, 0.05942436282336712, 0.02227406941354275, 0.1364644700586796, 0.20917857132479548, 0.14468310536444187, 0.16029150414746254, -0.08155476942658424, -0.06581426984816789, -0.20406758493930102, -0.14219352060183882, -0.20060948241502047, -0.024050386771559715, -0.14289005573885516, -0.21275774974003434, 0.3724235995709896, 0.1885023991242051, 0.2583560836985707, 0.08526626314781606, 0.28130878087878225, -0.004610362687148154, 0.12380168286710977, 0.16936283784755507, 0.07186492548882961, 0.02271906663477421, 0.1308822785578668, -0.12260451137274504, 0.1300992140211165, 0.024265561755746602]
|
1,803.04556
|
Measuring Conflict in a Multi-Source Environment as a Normal Measure
|
In a multi-source environment, each source has its own credibility. If there
is no external knowledge about credibility then we can use the information
provided by the sources to assess their credibility. In this paper, we propose
a way to measure conflict in a multi-source environment as a normal measure. We
examine our algorithm using three simulated examples of increasing conflict and
one experimental example. The results demonstrate that the proposed measure can
represent conflict in a meaningful way similar to what a human might expect and
from it we can identify conflict within our sources.
|
eess.SP cs.AI cs.CV eess.IV
|
in a multisource environment each source has its own credibility if there is no external knowledge about credibility then we can use the information provided by the sources to assess their credibility in this paper we propose a way to measure conflict in a multisource environment as a normal measure we examine our algorithm using three simulated examples of increasing conflict and one experimental example the results demonstrate that the proposed measure can represent conflict in a meaningful way similar to what a human might expect and from it we can identify conflict within our sources
|
[['in', 'a', 'multisource', 'environment', 'each', 'source', 'has', 'its', 'own', 'credibility', 'if', 'there', 'is', 'no', 'external', 'knowledge', 'about', 'credibility', 'then', 'we', 'can', 'use', 'the', 'information', 'provided', 'by', 'the', 'sources', 'to', 'assess', 'their', 'credibility', 'in', 'this', 'paper', 'we', 'propose', 'a', 'way', 'to', 'measure', 'conflict', 'in', 'a', 'multisource', 'environment', 'as', 'a', 'normal', 'measure', 'we', 'examine', 'our', 'algorithm', 'using', 'three', 'simulated', 'examples', 'of', 'increasing', 'conflict', 'and', 'one', 'experimental', 'example', 'the', 'results', 'demonstrate', 'that', 'the', 'proposed', 'measure', 'can', 'represent', 'conflict', 'in', 'a', 'meaningful', 'way', 'similar', 'to', 'what', 'a', 'human', 'might', 'expect', 'and', 'from', 'it', 'we', 'can', 'identify', 'conflict', 'within', 'our', 'sources']]
|
[-0.06592236595558158, 0.04047017991251778, -0.116284454241395, 0.12658381327006887, -0.12005449343511525, -0.09315544363441101, 0.13150405581230493, 0.4428719861898571, -0.24114490494442484, -0.3583714935715155, 0.10220394238422159, -0.3056172452440175, -0.16677974734921008, 0.16274529691630354, -0.12382076875898444, -0.004748034121803357, 0.07097421842505962, 0.09678680977473657, -0.011054918822386147, -0.19747207110049203, 0.30478970711798564, 0.058412718559945155, 0.27195133057587856, 0.08038678259860414, 0.1068814972046918, -0.052724193592439406, -0.06910934263335851, 0.04895505084520361, -0.08753121757073738, 0.1331075334940882, 0.26516384297428885, 0.21647760060538226, 0.31638716789893806, -0.3889168490616915, -0.22823739379722005, 0.1418738704038939, 0.1484236828594779, 0.11959625390106036, -0.0839347996613166, -0.3217705159525697, 0.1152407382966582, -0.20444199389506443, -0.09649988724171028, -0.07105386085459031, -0.009497007694638645, -0.0314765887305839, -0.24755112105049193, 0.03254273873365795, 0.02661761324755692, 0.01591739310727765, -0.04394063697084979, -0.04740100023142683, 0.009282344142169071, 0.17530478397384286, 0.015622389587709526, 0.04133293231401088, 0.11365919095108741, -0.09642060790065443, -0.1665527824710201, 0.38747428986728966, -0.07471726224321174, -0.2296799865337865, 0.23236636909617422, -0.13827326096361503, -0.1511072518818158, 0.017982779419980943, 0.1779073431971483, 0.053523669087250404, -0.19704598704023132, -0.0011024690090077154, -0.06953871461640422, 0.21702644258039072, 0.021998071618630394, 0.012695504848428149, 0.236110990071514, 0.168827916320879, 0.05287656873891441, 0.16661766008716464, -0.08244022899695362, -0.05200255294524444, -0.2992087778596518, -0.14639507764756368, -0.15470695657859324, 0.0253370476954539, -0.08006445379063128, -0.07966579234804765, 0.3686324059720694, 0.3019685870628261, 0.19730182329173354, 0.03448524759733118, 0.32726698568634066, 0.06810625380239799, 0.05503482983355449, 0.07608181069220639, 0.22326157503509117, 0.05072639975211738, 0.055116805694221206, -0.14214266460961275, 0.11733557555999141, -0.07711376423200515]
|
1,803.04557
|
Topological horseshoes for surface homeomorphisms
|
In this work we develop a new criterion for the existence of topological
horseshoes for surface homeomorphisms in the isotopy class of the identity.
Based on our previous work on forcing theory, this new criterion is purely
topological and can be expressed in terms of equivariant Brouwer foliations and
transverse trajectories. We then apply this new tool in the study of the
dynamics of homeomorphisms of surfaces with zero genus and null topological
entropy and we obtain several applications. For homeomorphisms of the open
annulus $\mathbb{A}$ with zero topological entropy, we show that rotation
numbers exists for all points with nonempty omega limits, and that if
$\mathbb{A}$ is a generalized region of instability then it admits a single
rotation vector. We also offer a new proof of a recent result of Passegi,
Potrie and Sambarino, showing that zero entropy dissipative homeomorphisms of
the annulus having as an atractor a circloid have a single rotation number.
Our work also studies homeomorphisms of the sphere without horseshoes. For
these maps we present a structure theorem in terms of fixed point free
invariant sub-annuli, as well as a very restricted description of all possible
dynamical behavior in the transitive subsets. This description ensures, for
instance, that transitive sets can contain at most $2$ distinct periodic orbits
and that, in many cases, the restriction of the homeomorphism to the transitive
set must be an extension of an odometer. In particular, we show that any
nontrivial and stable transitive subset of a dissipative diffeomorphism of the
plane is always infinitely renormalizable in the sense of
Bonatti-Gambaudo-Lion-Tresser.
|
math.DS
|
in this work we develop a new criterion for the existence of topological horseshoes for surface homeomorphisms in the isotopy class of the identity based on our previous work on forcing theory this new criterion is purely topological and can be expressed in terms of equivariant brouwer foliations and transverse trajectories we then apply this new tool in the study of the dynamics of homeomorphisms of surfaces with zero genus and null topological entropy and we obtain several applications for homeomorphisms of the open annulus mathbba with zero topological entropy we show that rotation numbers exists for all points with nonempty omega limits and that if mathbba is a generalized region of instability then it admits a single rotation vector we also offer a new proof of a recent result of passegi potrie and sambarino showing that zero entropy dissipative homeomorphisms of the annulus having as an atractor a circloid have a single rotation number our work also studies homeomorphisms of the sphere without horseshoes for these maps we present a structure theorem in terms of fixed point free invariant subannuli as well as a very restricted description of all possible dynamical behavior in the transitive subsets this description ensures for instance that transitive sets can contain at most 2 distinct periodic orbits and that in many cases the restriction of the homeomorphism to the transitive set must be an extension of an odometer in particular we show that any nontrivial and stable transitive subset of a dissipative diffeomorphism of the plane is always infinitely renormalizable in the sense of bonattigambaudoliontresser
|
[['in', 'this', 'work', 'we', 'develop', 'a', 'new', 'criterion', 'for', 'the', 'existence', 'of', 'topological', 'horseshoes', 'for', 'surface', 'homeomorphisms', 'in', 'the', 'isotopy', 'class', 'of', 'the', 'identity', 'based', 'on', 'our', 'previous', 'work', 'on', 'forcing', 'theory', 'this', 'new', 'criterion', 'is', 'purely', 'topological', 'and', 'can', 'be', 'expressed', 'in', 'terms', 'of', 'equivariant', 'brouwer', 'foliations', 'and', 'transverse', 'trajectories', 'we', 'then', 'apply', 'this', 'new', 'tool', 'in', 'the', 'study', 'of', 'the', 'dynamics', 'of', 'homeomorphisms', 'of', 'surfaces', 'with', 'zero', 'genus', 'and', 'null', 'topological', 'entropy', 'and', 'we', 'obtain', 'several', 'applications', 'for', 'homeomorphisms', 'of', 'the', 'open', 'annulus', 'mathbba', 'with', 'zero', 'topological', 'entropy', 'we', 'show', 'that', 'rotation', 'numbers', 'exists', 'for', 'all', 'points', 'with', 'nonempty', 'omega', 'limits', 'and', 'that', 'if', 'mathbba', 'is', 'a', 'generalized', 'region', 'of', 'instability', 'then', 'it', 'admits', 'a', 'single', 'rotation', 'vector', 'we', 'also', 'offer', 'a', 'new', 'proof', 'of', 'a', 'recent', 'result', 'of', 'passegi', 'potrie', 'and', 'sambarino', 'showing', 'that', 'zero', 'entropy', 'dissipative', 'homeomorphisms', 'of', 'the', 'annulus', 'having', 'as', 'an', 'atractor', 'a', 'circloid', 'have', 'a', 'single', 'rotation', 'number', 'our', 'work', 'also', 'studies', 'homeomorphisms', 'of', 'the', 'sphere', 'without', 'horseshoes', 'for', 'these', 'maps', 'we', 'present', 'a', 'structure', 'theorem', 'in', 'terms', 'of', 'fixed', 'point', 'free', 'invariant', 'subannuli', 'as', 'well', 'as', 'a', 'very', 'restricted', 'description', 'of', 'all', 'possible', 'dynamical', 'behavior', 'in', 'the', 'transitive', 'subsets', 'this', 'description', 'ensures', 'for', 'instance', 'that', 'transitive', 'sets', 'can', 'contain', 'at', 'most', '2', 'distinct', 'periodic', 'orbits', 'and', 'that', 'in', 'many', 'cases', 'the', 'restriction', 'of', 'the', 'homeomorphism', 'to', 'the', 'transitive', 'set', 'must', 'be', 'an', 'extension', 'of', 'an', 'odometer', 'in', 'particular', 'we', 'show', 'that', 'any', 'nontrivial', 'and', 'stable', 'transitive', 'subset', 'of', 'a', 'dissipative', 'diffeomorphism', 'of', 'the', 'plane', 'is', 'always', 'infinitely', 'renormalizable', 'in', 'the', 'sense', 'of', 'bonattigambaudoliontresser']]
|
[-0.19576207539592128, 0.12150270248832795, -0.10681477517744795, 0.05427303142140797, -0.0570976232162202, -0.12056471923672402, 0.04844406584652815, 0.3166729421988999, -0.2855068070126393, -0.2187565322276857, 0.10071922914380504, -0.2794351334310343, -0.16399707885799705, 0.22778626681434114, -0.12511197482301412, 0.05196594972940749, 0.05048079739763231, 0.07701711415753479, -0.07187943422740166, -0.2217228004004732, 0.3741951806322175, -0.06290952865884128, 0.2174876978392586, 0.07547352680255177, 0.10442409117280094, -0.014403247082395777, 0.025171066600383634, 0.06371489291289043, -0.17387228338350225, 0.09857724074469988, 0.22658509602009205, 0.09403930272153263, 0.21684943701155077, -0.35650744931301254, -0.2094160096578532, 0.15724182148293706, 0.12729515631752022, 0.06643974502364369, -0.056763097914280046, -0.25817615836532604, 0.13090358234956453, -0.15193749897558864, -0.17434622823887647, -0.11098905514331411, 0.06627071105081457, 0.00736903599264093, -0.22767938667358395, 0.019450073403800956, 0.1732423512811151, 0.1339068756083657, -0.05977378371497798, -0.022025245475557412, -0.07770676407346988, 0.12274155356642222, 0.03754506968523481, 0.07587011184104889, 0.08607975705544889, -0.06018016702071528, -0.12447189538266713, 0.3692082530268058, -0.09661555894449397, -0.26619781941313164, 0.1961347375215477, -0.1589988312078821, -0.2032841580271257, 0.11122261676466244, 0.1261297839835054, 0.14901058657768868, -0.09930530901303414, 0.14505327239873592, -0.12707104477379108, 0.1282798760283624, 0.09011970980977096, 0.029931078878764047, 0.19909398399355935, 0.12132358866388608, 0.15011173680063214, 0.15863755694409318, -0.026946011627661897, -0.04860562747294547, -0.36256307940422794, -0.17453755952367117, -0.13492846215489876, 0.1010256458738826, -0.08234660730177196, -0.2233084761890934, 0.39754954633821193, 0.10195805955291382, 0.205136220072349, 0.09183205311741056, 0.24391384419472345, 0.07270215602745873, 0.03530771216442845, 0.10213452119766173, 0.17247171222290264, 0.13278782369608805, -0.026365566938927085, -0.15532980001339478, -0.01131595320617309, 0.14242001031235527]
|
1,803.04558
|
Empirical Wavelet-based Estimation for Non-linear Additive Regression
Models
|
Additive regression models are actively researched in the statistical field
because of their usefulness in the analysis of responses determined by
non-linear relationships with multivariate predictors. In this kind of
statistical models, the response depends linearly on unknown functions of
predictor variables and typically, the goal of the analysis is to make
inference about these functions.
In this paper, we consider the problem of Additive Regression with random
designs from a novel viewpoint: we propose an estimator based on an orthogonal
projection onto a multiresolution space using empirical wavelet coefficients
that are fully data driven. In this setting, we derive a mean-square consistent
estimator based on periodic wavelets on the interval $[0,1]$. For construction
of the estimator, we assume that the joint distribution of predictors is
non-zero and bounded on its support; We also assume that the functions belong
to a Sobolev space and integrate to zero over the $[0,1]$ interval, which
guarantees model identifiability and convergence of the proposed method.
Moreover, we provide the $\mathbb{L}_{2}$ risk analysis of the estimator and
derive its convergence rate.
Theoretically, we show that this approach achieves good convergence rates
when the dimensionality of the problem is relatively low and the set of unknown
functions is sufficiently smooth. In this approach, the results are obtained
without the assumption of an equispaced design, a condition that is typically
assumed in most wavelet-based procedures.
Finally, we show practical results obtained from simulated data,
demonstrating the potential applicability of our method in the problem of
additive regression models with random designs.
|
stat.AP
|
additive regression models are actively researched in the statistical field because of their usefulness in the analysis of responses determined by nonlinear relationships with multivariate predictors in this kind of statistical models the response depends linearly on unknown functions of predictor variables and typically the goal of the analysis is to make inference about these functions in this paper we consider the problem of additive regression with random designs from a novel viewpoint we propose an estimator based on an orthogonal projection onto a multiresolution space using empirical wavelet coefficients that are fully data driven in this setting we derive a meansquare consistent estimator based on periodic wavelets on the interval 01 for construction of the estimator we assume that the joint distribution of predictors is nonzero and bounded on its support we also assume that the functions belong to a sobolev space and integrate to zero over the 01 interval which guarantees model identifiability and convergence of the proposed method moreover we provide the mathbbl_2 risk analysis of the estimator and derive its convergence rate theoretically we show that this approach achieves good convergence rates when the dimensionality of the problem is relatively low and the set of unknown functions is sufficiently smooth in this approach the results are obtained without the assumption of an equispaced design a condition that is typically assumed in most waveletbased procedures finally we show practical results obtained from simulated data demonstrating the potential applicability of our method in the problem of additive regression models with random designs
|
[['additive', 'regression', 'models', 'are', 'actively', 'researched', 'in', 'the', 'statistical', 'field', 'because', 'of', 'their', 'usefulness', 'in', 'the', 'analysis', 'of', 'responses', 'determined', 'by', 'nonlinear', 'relationships', 'with', 'multivariate', 'predictors', 'in', 'this', 'kind', 'of', 'statistical', 'models', 'the', 'response', 'depends', 'linearly', 'on', 'unknown', 'functions', 'of', 'predictor', 'variables', 'and', 'typically', 'the', 'goal', 'of', 'the', 'analysis', 'is', 'to', 'make', 'inference', 'about', 'these', 'functions', 'in', 'this', 'paper', 'we', 'consider', 'the', 'problem', 'of', 'additive', 'regression', 'with', 'random', 'designs', 'from', 'a', 'novel', 'viewpoint', 'we', 'propose', 'an', 'estimator', 'based', 'on', 'an', 'orthogonal', 'projection', 'onto', 'a', 'multiresolution', 'space', 'using', 'empirical', 'wavelet', 'coefficients', 'that', 'are', 'fully', 'data', 'driven', 'in', 'this', 'setting', 'we', 'derive', 'a', 'meansquare', 'consistent', 'estimator', 'based', 'on', 'periodic', 'wavelets', 'on', 'the', 'interval', '01', 'for', 'construction', 'of', 'the', 'estimator', 'we', 'assume', 'that', 'the', 'joint', 'distribution', 'of', 'predictors', 'is', 'nonzero', 'and', 'bounded', 'on', 'its', 'support', 'we', 'also', 'assume', 'that', 'the', 'functions', 'belong', 'to', 'a', 'sobolev', 'space', 'and', 'integrate', 'to', 'zero', 'over', 'the', '01', 'interval', 'which', 'guarantees', 'model', 'identifiability', 'and', 'convergence', 'of', 'the', 'proposed', 'method', 'moreover', 'we', 'provide', 'the', 'mathbbl_2', 'risk', 'analysis', 'of', 'the', 'estimator', 'and', 'derive', 'its', 'convergence', 'rate', 'theoretically', 'we', 'show', 'that', 'this', 'approach', 'achieves', 'good', 'convergence', 'rates', 'when', 'the', 'dimensionality', 'of', 'the', 'problem', 'is', 'relatively', 'low', 'and', 'the', 'set', 'of', 'unknown', 'functions', 'is', 'sufficiently', 'smooth', 'in', 'this', 'approach', 'the', 'results', 'are', 'obtained', 'without', 'the', 'assumption', 'of', 'an', 'equispaced', 'design', 'a', 'condition', 'that', 'is', 'typically', 'assumed', 'in', 'most', 'waveletbased', 'procedures', 'finally', 'we', 'show', 'practical', 'results', 'obtained', 'from', 'simulated', 'data', 'demonstrating', 'the', 'potential', 'applicability', 'of', 'our', 'method', 'in', 'the', 'problem', 'of', 'additive', 'regression', 'models', 'with', 'random', 'designs']]
|
[-0.06968158879134012, 0.018255522065531504, -0.1074173857905264, 0.06028954389296091, -0.045972411903024715, -0.1116615059569214, 0.06038218623487367, 0.4140522523570483, -0.2776335845852645, -0.24299099281114914, 0.14807892966406846, -0.24683032290438028, -0.20056646235340356, 0.20978098041396295, -0.10218356217085348, 0.10351063195784671, 0.0567171856152319, 0.02835851752551687, -0.09056074876571074, -0.27957889267360775, 0.32617876678338553, 0.04505555306407293, 0.29940908848982684, -0.021905348482211744, 0.1413167646570923, 0.015261846759968884, -0.06485195963012833, 0.0023567834270909046, -0.1370228821030228, 0.14948260875218952, 0.23636818304026302, 0.15173516766072845, 0.34449490201549504, -0.3687730206719299, -0.20147588325370613, 0.11959137823652681, 0.10779957704412875, 0.064509859836136, -0.03406533900342192, -0.24945262570289792, 0.10175062718803034, -0.135489778296186, -0.09164621174247481, -0.10143528562511214, -0.04189111232000411, 0.053714831664014286, -0.34871538434183136, 0.0957455986121433, 0.07583786269620531, 0.06453680892394284, -0.09351529800113086, -0.13093873637262732, 0.02995177186974214, 0.07163846093620903, 0.06974081512848575, 0.011804371473185101, 0.08766488077148325, -0.09284469061696124, -0.0958157673140797, 0.3304701098845905, -0.07766084603559203, -0.2840850481264592, 0.1728281329503268, -0.14665487095049956, -0.14310011202762798, 0.10310785631707862, 0.2279320163535427, 0.1153183738998042, -0.15512095009945892, 0.09981934161543732, -0.06119426939449471, 0.16851036600341404, 0.004713918345496352, 0.011546075221253779, 0.13134212777161544, 0.17436169433557816, 0.0912819751666668, 0.14383643166543403, -0.09835273220192817, -0.07482202098829539, -0.31263209040841367, -0.08839271466468193, -0.22848035185414506, -0.015523713639860342, -0.13170919715627152, -0.19709698511214238, 0.38966924973624195, 0.19213392110688127, 0.20410496185856367, 0.127464413034682, 0.29249084957024657, 0.14200260137822196, 0.011496581983253889, 0.08942001498685637, 0.20179311511396453, 0.13759130111521506, 0.031170715344840602, -0.1712983350432661, 0.11020649742063751, 0.038026798996094915]
|
1,803.04559
|
Weighted Bayesian Bootstrap for Scalable Bayes
|
We develop a weighted Bayesian Bootstrap (WBB) for machine learning and
statistics. WBB provides uncertainty quantification by sampling from a high
dimensional posterior distribution. WBB is computationally fast and scalable
using only off-theshelf optimization software such as TensorFlow. We provide
regularity conditions which apply to a wide range of machine learning and
statistical models. We illustrate our methodology in regularized regression,
trend filtering and deep learning. Finally, we conclude with directions for
future research.
|
stat.ME
|
we develop a weighted bayesian bootstrap wbb for machine learning and statistics wbb provides uncertainty quantification by sampling from a high dimensional posterior distribution wbb is computationally fast and scalable using only offtheshelf optimization software such as tensorflow we provide regularity conditions which apply to a wide range of machine learning and statistical models we illustrate our methodology in regularized regression trend filtering and deep learning finally we conclude with directions for future research
|
[['we', 'develop', 'a', 'weighted', 'bayesian', 'bootstrap', 'wbb', 'for', 'machine', 'learning', 'and', 'statistics', 'wbb', 'provides', 'uncertainty', 'quantification', 'by', 'sampling', 'from', 'a', 'high', 'dimensional', 'posterior', 'distribution', 'wbb', 'is', 'computationally', 'fast', 'and', 'scalable', 'using', 'only', 'offtheshelf', 'optimization', 'software', 'such', 'as', 'tensorflow', 'we', 'provide', 'regularity', 'conditions', 'which', 'apply', 'to', 'a', 'wide', 'range', 'of', 'machine', 'learning', 'and', 'statistical', 'models', 'we', 'illustrate', 'our', 'methodology', 'in', 'regularized', 'regression', 'trend', 'filtering', 'and', 'deep', 'learning', 'finally', 'we', 'conclude', 'with', 'directions', 'for', 'future', 'research']]
|
[0.031592139206524636, -0.004076837775977077, -0.1176993047088586, 0.1104884380049928, -0.13462609724405952, -0.1964274600488008, 0.03662841283791774, 0.4977616522259809, -0.29818980373140125, -0.3143716463441583, 0.13808526810277505, -0.21182516844615945, -0.15843176573587306, 0.24636892377826813, -0.11097108284829536, 0.15583783015608788, 0.13056888966191862, -0.0569212142707673, -0.09208497691685585, -0.23757477922717463, 0.24614602309097913, 0.06747902701003163, 0.3527308554715804, -0.0029522909505947216, 0.13639580728360326, 0.060258298841732984, -0.032036353550801006, -0.00255068699432839, -0.14441951335922223, 0.21409278286218242, 0.352521473377311, 0.2668706643697176, 0.4092012149457996, -0.3366401730407331, -0.2511354524078244, 0.08907231360019462, 0.16527154577291897, 0.09682095418778576, -0.08175615939261341, -0.26962543812555234, 0.05194219205185544, -0.225988307766415, -0.03791553717786195, -0.22938193295566314, -0.05906623356497368, 0.018524831255293778, -0.33963552835032446, 0.08243063020494741, 0.0225245954116454, 0.1235061274161814, -0.009519899389832406, -0.14681079385588197, 0.07975585259987998, 0.04975479088622976, 0.01803813819660226, 0.023116367408137366, 0.15165525524423937, -0.13755138828924532, -0.1485446276822807, 0.28585402132641224, -0.052162616882580205, -0.19924749159631697, 0.19004249249308094, -0.014475492301170487, -0.22759227781254496, 0.05157242166318905, 0.29651648458093405, 0.11065243992551758, -0.17579394399591192, 0.051380318649289373, 0.019198557007994905, 0.11058594801186307, -0.03117347732689735, -0.0744506848686551, 0.18793815051243212, 0.27823420410120003, 0.07793071073782001, 0.1424648256255062, -0.16430986910504666, -0.09059625592183422, -0.2631035798757866, -0.1325353816327815, -0.1711068047349014, -0.008047777907671156, -0.15576180696056374, -0.1917794160167309, 0.33687556485922354, 0.2623519150098169, 0.15629066690500523, 0.17084816916424125, 0.31832499162772215, 0.08783878118580056, 0.0430738409049809, 0.16470686048961472, 0.17348275396570162, 0.09724917175009141, 0.09930065961403621, -0.10139459525423779, 0.0516123842901072, -0.002322427450200996]
|
1,803.0456
|
How far is the Borel map from being surjective in quasianalytic
ultradifferentiable classes?
|
The Borel map takes germs at 0 of smooth functions to the sequence of
iterated partial derivatives at 0. In the literature, it is well known that the
restriction of this mapping to the germs of quasianalytic ultradifferentiable
classes which are strictly containing the real analytic functions can never be
onto the corresponding sequence space. In this paper, we are interested in
studying how large the image of the Borel map is and we investigate the size
and the structure of this image by using different approaches (Baire
residuality, prevalence and lineability). We give an answer to this question in
the very general setting of quasianalytic ultradifferentiable classes defined
by weight matrices, which contains as particular cases the classes defined by a
single weight sequence or by a weight function.
|
math.FA
|
the borel map takes germs at 0 of smooth functions to the sequence of iterated partial derivatives at 0 in the literature it is well known that the restriction of this mapping to the germs of quasianalytic ultradifferentiable classes which are strictly containing the real analytic functions can never be onto the corresponding sequence space in this paper we are interested in studying how large the image of the borel map is and we investigate the size and the structure of this image by using different approaches baire residuality prevalence and lineability we give an answer to this question in the very general setting of quasianalytic ultradifferentiable classes defined by weight matrices which contains as particular cases the classes defined by a single weight sequence or by a weight function
|
[['the', 'borel', 'map', 'takes', 'germs', 'at', '0', 'of', 'smooth', 'functions', 'to', 'the', 'sequence', 'of', 'iterated', 'partial', 'derivatives', 'at', '0', 'in', 'the', 'literature', 'it', 'is', 'well', 'known', 'that', 'the', 'restriction', 'of', 'this', 'mapping', 'to', 'the', 'germs', 'of', 'quasianalytic', 'ultradifferentiable', 'classes', 'which', 'are', 'strictly', 'containing', 'the', 'real', 'analytic', 'functions', 'can', 'never', 'be', 'onto', 'the', 'corresponding', 'sequence', 'space', 'in', 'this', 'paper', 'we', 'are', 'interested', 'in', 'studying', 'how', 'large', 'the', 'image', 'of', 'the', 'borel', 'map', 'is', 'and', 'we', 'investigate', 'the', 'size', 'and', 'the', 'structure', 'of', 'this', 'image', 'by', 'using', 'different', 'approaches', 'baire', 'residuality', 'prevalence', 'and', 'lineability', 'we', 'give', 'an', 'answer', 'to', 'this', 'question', 'in', 'the', 'very', 'general', 'setting', 'of', 'quasianalytic', 'ultradifferentiable', 'classes', 'defined', 'by', 'weight', 'matrices', 'which', 'contains', 'as', 'particular', 'cases', 'the', 'classes', 'defined', 'by', 'a', 'single', 'weight', 'sequence', 'or', 'by', 'a', 'weight', 'function']]
|
[-0.1199204395740078, 0.055086783435791195, -0.04144264909152228, 0.08854794392290596, -0.057232493912586224, -0.06396062174776139, -0.00013270253100647377, 0.37690414663117666, -0.3390337615381353, -0.20171800140076532, 0.10614030266610476, -0.24950875063211872, -0.16032638696846194, 0.18081295705364586, -0.09386976819951087, 0.04280157577729999, 0.025447331719960158, 0.06017526819800528, -0.09729815439786763, -0.27552009042030057, 0.4618630523632209, -0.04092651224122024, 0.19355831794584027, 0.01973388039936813, 0.10041581069190915, 0.0037200079980091406, -0.021760532546501895, -0.0072214281805617455, -0.15570186667996144, 0.1150644375405346, 0.2991741391382395, 0.15131286474374625, 0.29622317073472704, -0.3279784600322063, -0.15823331529346224, 0.17476540377739674, 0.11278142981732694, 0.015622923478412513, 0.001488601484407599, -0.28508779430367914, 0.12296456749407718, -0.10862745578812721, -0.11563294696836518, -0.07793748496959989, 0.046588567798384106, 0.059381573697408804, -0.27334802181218176, 0.0191432673125886, 0.1218876589814989, 0.08085533486715016, -0.05982919914934497, -0.08881586615193206, -0.026952266164768774, 0.14467506477645098, 0.0144146097844616, 0.1058131959119167, 0.08565591056831182, -0.1137070396393448, -0.08941722901251453, 0.3550555631518364, -0.06610746861507114, -0.255559263471514, 0.17921084547415375, -0.19936705422945894, -0.1583290326767243, 0.10087785134091973, 0.12328229672991886, 0.18272349758503528, -0.10411132632154757, 0.15698343455084027, -0.10654757240285667, 0.10441618280198711, 0.10260491596105006, 0.0210462234251631, 0.1481347417458892, 0.09966193814224635, 0.06205865306367811, 0.19061728765238792, -0.001384602261420626, -0.04297158743899602, -0.3123232501057478, -0.1468577206510907, -0.16665164065332366, 0.0938914437420093, -0.07362754733129315, -0.21198013532333648, 0.43865675372787966, 0.08004037296542754, 0.24435591952015573, 0.12854233290462827, 0.25561120096020973, 0.14338770466408907, 0.06912891304192062, 0.029305109972707355, 0.12457970536958712, 0.09624631273954247, 0.018509586449139394, -0.12436370175654211, 0.06566301904811273, 0.13852361640892924]
|
1,803.04561
|
Optimizing power oscillations in an ellipsometric system
|
Ellipsometry is a powerful and well-established optical technique used in the
characterisation of materials. It works by combining the components of
elliptically polarized light in order to draw information about the optical
system. We propose an ellipsometric experimental set up to study polarization
interference in the total internal reflection regime for Gaussian laser beams.
The relative phase between orthogonal states can be measured as a power
oscillation of the optical beam transmitted through a dielectric block and
whose orthogonal components are then mixed by a polarizer. We show under which
conditions the plane wave analysis is valid and when the power oscillation can
be optimized to reproduce a full pattern of oscillation and to simulate quarter
and half wave plates.
|
physics.optics
|
ellipsometry is a powerful and wellestablished optical technique used in the characterisation of materials it works by combining the components of elliptically polarized light in order to draw information about the optical system we propose an ellipsometric experimental set up to study polarization interference in the total internal reflection regime for gaussian laser beams the relative phase between orthogonal states can be measured as a power oscillation of the optical beam transmitted through a dielectric block and whose orthogonal components are then mixed by a polarizer we show under which conditions the plane wave analysis is valid and when the power oscillation can be optimized to reproduce a full pattern of oscillation and to simulate quarter and half wave plates
|
[['ellipsometry', 'is', 'a', 'powerful', 'and', 'wellestablished', 'optical', 'technique', 'used', 'in', 'the', 'characterisation', 'of', 'materials', 'it', 'works', 'by', 'combining', 'the', 'components', 'of', 'elliptically', 'polarized', 'light', 'in', 'order', 'to', 'draw', 'information', 'about', 'the', 'optical', 'system', 'we', 'propose', 'an', 'ellipsometric', 'experimental', 'set', 'up', 'to', 'study', 'polarization', 'interference', 'in', 'the', 'total', 'internal', 'reflection', 'regime', 'for', 'gaussian', 'laser', 'beams', 'the', 'relative', 'phase', 'between', 'orthogonal', 'states', 'can', 'be', 'measured', 'as', 'a', 'power', 'oscillation', 'of', 'the', 'optical', 'beam', 'transmitted', 'through', 'a', 'dielectric', 'block', 'and', 'whose', 'orthogonal', 'components', 'are', 'then', 'mixed', 'by', 'a', 'polarizer', 'we', 'show', 'under', 'which', 'conditions', 'the', 'plane', 'wave', 'analysis', 'is', 'valid', 'and', 'when', 'the', 'power', 'oscillation', 'can', 'be', 'optimized', 'to', 'reproduce', 'a', 'full', 'pattern', 'of', 'oscillation', 'and', 'to', 'simulate', 'quarter', 'and', 'half', 'wave', 'plates']]
|
[-0.13494042129411052, 0.15473744862744448, -0.09717145425577958, 0.015494006698524269, -0.06334087203722447, -0.13830885716791574, 0.05568232275254559, 0.4288223643787205, -0.27526781195386624, -0.2751632141958301, 0.11309329716508122, -0.25886754180925586, -0.122096156544103, 0.24173411286125582, 0.01163463043048978, 0.0730863540355737, 0.019519845485532036, -0.030656231049215422, -0.04868229472776875, -0.1594889300293289, 0.28786041686932246, 0.04967909261273841, 0.33344093022557597, -0.015633734667305058, 0.11128525862974735, 0.058323504555543575, -0.021105010896765936, 0.00610767292479674, -0.0854441277362639, 0.09408184993226314, 0.24795412872141848, 0.07118355315178633, 0.16163344555534423, -0.41898574503138664, -0.2192850442059959, 0.08700343926126758, 0.12244872931187274, 0.14263899300228028, -0.003072184401874741, -0.2533488881580221, 0.028038065717555582, -0.1160150718409568, -0.16400106579919035, -0.0608376508190607, -0.02764484993337343, 0.0631740092280476, -0.2950962803869819, 0.018635910376906394, 0.008869789248092275, 0.05583593603999664, -0.028193362839131927, -0.07717199629890577, -0.029300767429716264, 0.07968661675622571, -0.012697331052428732, 0.038641388757969256, 0.10418224113915736, -0.07254016307415441, -0.07710881106904707, 0.3802479612369401, -0.0823981238732813, -0.16466289649251847, 0.10908234023760693, -0.18043958837612686, 0.00300662584680443, 0.16256420320132747, 0.19981045105184117, 0.07631350956701984, -0.1448112476797784, -0.0040035060786370496, -0.04169452918358729, 0.22156862345097275, 0.1289346138597466, 0.05958210042444989, 0.24886437033613523, 0.1361177834032181, 0.05561929350842547, 0.170258888459648, -0.12593865567275012, -0.014664576769185563, -0.2716200443217531, -0.11504465913943325, -0.16772714761997728, 0.03951111736605526, -0.03396985937470163, -0.11774520482091853, 0.446239909902215, 0.09028097137343138, 0.14728136254707352, -0.01693930561984113, 0.3574394736283769, 0.12487912558923805, 0.057286232754510516, 0.023072742905545358, 0.28725260952487586, 0.2122272525021496, 0.09315142577979714, -0.23292161262361333, 0.01779648879698167, -0.023365847770279894]
|
1,803.04562
|
Bias in OLAP Queries: Detection, Explanation, and Removal
|
On line analytical processing (OLAP) is an essential element of
decision-support systems. OLAP tools provide insights and understanding needed
for improved decision making. However, the answers to OLAP queries can be
biased and lead to perplexing and incorrect insights. In this paper, we propose
HypDB, a system to detect, explain, and to resolve bias in decision-support
queries. We give a simple definition of a \emph{biased query}, which performs a
set of independence tests on the data to detect bias. We propose a novel
technique that gives explanations for bias, thus assisting an analyst in
understanding what goes on. Additionally, we develop an automated method for
rewriting a biased query into an unbiased query, which shows what the analyst
intended to examine. In a thorough evaluation on several real datasets we show
both the quality and the performance of our techniques, including the
completely automatic discovery of the revolutionary insights from a famous 1973
discrimination case.
|
cs.DB
|
on line analytical processing olap is an essential element of decisionsupport systems olap tools provide insights and understanding needed for improved decision making however the answers to olap queries can be biased and lead to perplexing and incorrect insights in this paper we propose hypdb a system to detect explain and to resolve bias in decisionsupport queries we give a simple definition of a emphbiased query which performs a set of independence tests on the data to detect bias we propose a novel technique that gives explanations for bias thus assisting an analyst in understanding what goes on additionally we develop an automated method for rewriting a biased query into an unbiased query which shows what the analyst intended to examine in a thorough evaluation on several real datasets we show both the quality and the performance of our techniques including the completely automatic discovery of the revolutionary insights from a famous 1973 discrimination case
|
[['on', 'line', 'analytical', 'processing', 'olap', 'is', 'an', 'essential', 'element', 'of', 'decisionsupport', 'systems', 'olap', 'tools', 'provide', 'insights', 'and', 'understanding', 'needed', 'for', 'improved', 'decision', 'making', 'however', 'the', 'answers', 'to', 'olap', 'queries', 'can', 'be', 'biased', 'and', 'lead', 'to', 'perplexing', 'and', 'incorrect', 'insights', 'in', 'this', 'paper', 'we', 'propose', 'hypdb', 'a', 'system', 'to', 'detect', 'explain', 'and', 'to', 'resolve', 'bias', 'in', 'decisionsupport', 'queries', 'we', 'give', 'a', 'simple', 'definition', 'of', 'a', 'emphbiased', 'query', 'which', 'performs', 'a', 'set', 'of', 'independence', 'tests', 'on', 'the', 'data', 'to', 'detect', 'bias', 'we', 'propose', 'a', 'novel', 'technique', 'that', 'gives', 'explanations', 'for', 'bias', 'thus', 'assisting', 'an', 'analyst', 'in', 'understanding', 'what', 'goes', 'on', 'additionally', 'we', 'develop', 'an', 'automated', 'method', 'for', 'rewriting', 'a', 'biased', 'query', 'into', 'an', 'unbiased', 'query', 'which', 'shows', 'what', 'the', 'analyst', 'intended', 'to', 'examine', 'in', 'a', 'thorough', 'evaluation', 'on', 'several', 'real', 'datasets', 'we', 'show', 'both', 'the', 'quality', 'and', 'the', 'performance', 'of', 'our', 'techniques', 'including', 'the', 'completely', 'automatic', 'discovery', 'of', 'the', 'revolutionary', 'insights', 'from', 'a', 'famous', '1973', 'discrimination', 'case']]
|
[-0.07954879963880876, -0.041280185519158234, -0.12152338281346413, 0.11382672702591237, -0.16453108744538547, -0.16402796500168934, 0.11989789355097205, 0.3786018606541412, -0.23109094656447504, -0.3395011717961593, 0.098006609868101, -0.2708353588732516, -0.1525139584320948, 0.2389520681463182, -0.13591319279648462, 0.025050243574448607, 0.08495368101549419, 0.031197165124089602, -0.028030559470477914, -0.2788876865381169, 0.26418951372978844, 0.07363279579908817, 0.2836837163195014, 0.07958196556534279, 0.06186182023851539, 0.016881584623743864, -0.09914182598986844, 0.0251044326510232, -0.13997768887263676, 0.14984350988162112, 0.3099423539159553, 0.24523085768201522, 0.3169000748569122, -0.42479779741230916, -0.15844655391085868, 0.0680013004807202, 0.16788473512142799, 0.1159910575522438, -0.09165343184737706, -0.30168568224240966, 0.06864784190933183, -0.14368073305858953, -0.078245113237894, -0.164074420983383, 0.0014815790784339627, -0.05164460552774684, -0.3283195831425875, -0.01821052788189074, 0.10981254649421128, 0.06759505337346103, -0.023052430144728873, -0.07505257600879103, 0.11683788881017282, 0.17169111259523537, 0.0033648514389907095, 0.0287834564284361, 0.09753177292016033, -0.1294989541109482, -0.1814875041932932, 0.3698266002156034, -0.005749452070336087, -0.18412752210029534, 0.1987981594578883, -0.05932948587828183, -0.17409823677584216, 0.08616605278273876, 0.2191601507909983, 0.07171855379174186, -0.18928082376890876, 0.027224078949453164, -0.01932231128645166, 0.21779481665678807, 0.025314794312074007, 0.007271282606933556, 0.2322395083981312, 0.21811996070160108, 0.06275796227069426, 0.14892574652724042, -0.03759490143410958, -0.0335987088128368, -0.24338963623581963, -0.1927314548178307, -0.11719687487295066, 0.05514795608915292, -0.08335839939262497, -0.21934660827716837, 0.38926748735467337, 0.27859479979636126, 0.20969285243326896, 0.05514244791483144, 0.34336969862667865, 0.07090183077487783, 0.03990364320842283, 0.046139733675359326, 0.1507659376298029, 0.03719934888710207, 0.11041673662612675, -0.17039584585498752, 0.10911416652228369, 0.023590401137042066]
|
1,803.04563
|
On The Nature of ultra-faint Dwarf Galaxy Candidates II: The case of
Cetus II
|
We obtained deep Gemini GMOS-S $g,r$ photometry of the ultra-faint dwarf
galaxy candidate Cetus II with the aim of providing stronger constraints on its
size, luminosity and stellar population. Cetus II is an important object in the
size-luminosity plane as it occupies the transition zone between dwarf galaxies
and star clusters. All known objects smaller than Cetus II ($r_h \sim 20$ pc)
are reported to be star clusters, while most larger objects are likely dwarf
galaxies. We found a prominent excess of main-sequence stars in the
colour-magnitude diagram of Cetus II, best described by a single stellar
population with an age of 11.2 Gyr, metallicity of [Fe/H] = $-1.28$ dex, an
[$\alpha$/Fe] = 0.0 dex at a heliocentric distance of 26.3$\pm$1.2 kpc. As well
as being spatially located within the Sagittarius dwarf tidal stream, these
properties are well matched to the Sagittarius galaxy's Population B stars.
Interestingly, like our recent findings on the ultra-faint dwarf galaxy
candidate Tucana V, the stellar field in the direction of Cetus II shows no
evidence of a concentrated overdensity despite tracing the main sequence for
over six magnitudes. These results strongly support the picture that Cetus II
is not an ultra-faint stellar system in the Milky Way halo, but made up of
stars from the Sagittarius tidal stream.
|
astro-ph.GA
|
we obtained deep gemini gmoss gr photometry of the ultrafaint dwarf galaxy candidate cetus ii with the aim of providing stronger constraints on its size luminosity and stellar population cetus ii is an important object in the sizeluminosity plane as it occupies the transition zone between dwarf galaxies and star clusters all known objects smaller than cetus ii r_h sim 20 pc are reported to be star clusters while most larger objects are likely dwarf galaxies we found a prominent excess of mainsequence stars in the colourmagnitude diagram of cetus ii best described by a single stellar population with an age of 112 gyr metallicity of feh 128 dex an alphafe 00 dex at a heliocentric distance of 263pm12 kpc as well as being spatially located within the sagittarius dwarf tidal stream these properties are well matched to the sagittarius galaxys population b stars interestingly like our recent findings on the ultrafaint dwarf galaxy candidate tucana v the stellar field in the direction of cetus ii shows no evidence of a concentrated overdensity despite tracing the main sequence for over six magnitudes these results strongly support the picture that cetus ii is not an ultrafaint stellar system in the milky way halo but made up of stars from the sagittarius tidal stream
|
[['we', 'obtained', 'deep', 'gemini', 'gmoss', 'gr', 'photometry', 'of', 'the', 'ultrafaint', 'dwarf', 'galaxy', 'candidate', 'cetus', 'ii', 'with', 'the', 'aim', 'of', 'providing', 'stronger', 'constraints', 'on', 'its', 'size', 'luminosity', 'and', 'stellar', 'population', 'cetus', 'ii', 'is', 'an', 'important', 'object', 'in', 'the', 'sizeluminosity', 'plane', 'as', 'it', 'occupies', 'the', 'transition', 'zone', 'between', 'dwarf', 'galaxies', 'and', 'star', 'clusters', 'all', 'known', 'objects', 'smaller', 'than', 'cetus', 'ii', 'r_h', 'sim', '20', 'pc', 'are', 'reported', 'to', 'be', 'star', 'clusters', 'while', 'most', 'larger', 'objects', 'are', 'likely', 'dwarf', 'galaxies', 'we', 'found', 'a', 'prominent', 'excess', 'of', 'mainsequence', 'stars', 'in', 'the', 'colourmagnitude', 'diagram', 'of', 'cetus', 'ii', 'best', 'described', 'by', 'a', 'single', 'stellar', 'population', 'with', 'an', 'age', 'of', '112', 'gyr', 'metallicity', 'of', 'feh', '128', 'dex', 'an', 'alphafe', '00', 'dex', 'at', 'a', 'heliocentric', 'distance', 'of', '263pm12', 'kpc', 'as', 'well', 'as', 'being', 'spatially', 'located', 'within', 'the', 'sagittarius', 'dwarf', 'tidal', 'stream', 'these', 'properties', 'are', 'well', 'matched', 'to', 'the', 'sagittarius', 'galaxys', 'population', 'b', 'stars', 'interestingly', 'like', 'our', 'recent', 'findings', 'on', 'the', 'ultrafaint', 'dwarf', 'galaxy', 'candidate', 'tucana', 'v', 'the', 'stellar', 'field', 'in', 'the', 'direction', 'of', 'cetus', 'ii', 'shows', 'no', 'evidence', 'of', 'a', 'concentrated', 'overdensity', 'despite', 'tracing', 'the', 'main', 'sequence', 'for', 'over', 'six', 'magnitudes', 'these', 'results', 'strongly', 'support', 'the', 'picture', 'that', 'cetus', 'ii', 'is', 'not', 'an', 'ultrafaint', 'stellar', 'system', 'in', 'the', 'milky', 'way', 'halo', 'but', 'made', 'up', 'of', 'stars', 'from', 'the', 'sagittarius', 'tidal', 'stream']]
|
[-0.062427690344249116, 0.04749475232401633, -0.07324701272198374, 0.10008287273283377, -0.16019212483688836, -0.058508946404459125, 0.05614388737247118, 0.44277169474210787, -0.11170565206678894, -0.3956401853853069, -0.04770012927336521, -0.324904919242248, -0.026633093389572125, 0.20706498875311394, -0.08359759754801425, -0.09335253775032608, 0.07872344779487372, -0.049299369179930594, -0.04057822317012996, -0.34108945401606117, 0.26282171907578705, 0.022514330977833303, 0.0694901648571194, -0.18065564516298385, 0.01397526218396969, -0.1341634209331796, -0.07127959627500334, -0.0472630844098379, -0.16295034897319063, -0.023916163531979503, 0.2529902623611021, 0.14378616520561172, 0.2282846597678765, -0.318168458519989, -0.16979835358706538, 0.009745114658941555, 0.2901449881701496, 0.016087501129947637, -0.14832596591634067, -0.3281722357595931, 0.09950363073250836, -0.19432244760313147, -0.21903784402924198, 0.170903193455419, 0.10301657964009792, 0.021009914894804572, -0.1710952818073703, 0.19197313693601425, 0.05466671875772901, 0.1548831729390504, -0.14151789777486262, -0.15773011998205017, -0.0989889857343782, 0.060491374699328256, -0.001450262581973703, 0.13734675699434468, 0.20760458319390554, -0.11432572611457541, 0.020200132677591985, 0.4332708216744577, -0.08890845432803884, 0.09938365405191495, 0.25520526821685346, -0.21839450088757756, -0.15738343550510245, 0.041418407373880674, 0.10402222416006135, 0.1269531305184173, -0.1990244198001268, 0.03168277820339564, -0.031950350934683715, 0.21851827724013584, 0.029020410853160008, 0.08002059617351798, 0.3824806853652142, 0.09894840037935718, 0.09771443618311419, 0.004937691738171353, -0.3047594219574184, -0.06570900526224366, -0.20495117111881903, -0.09115708191074669, -0.11411064738734356, 0.04878954169947612, -0.20106785464797378, -0.1381574696993623, 0.31662027214066746, 0.08288295303073588, 0.2628074187984838, 0.0668973216689876, 0.28163703533895845, 0.043202278792407914, 0.19448818796853892, 0.17389859066905355, 0.3011657794301897, 0.2137343514880598, 0.02891044503450835, -0.23895106719082918, 0.1080298072739032, -0.01979412255208427]
|
1,803.04564
|
NASA's Asteroid Grand Challenge: Strategy, Results and Lessons Learned
|
Beginning in 2012, NASA utilized a strategic process to identify broad
societal questions, or grand challenges, that are well suited to the aerospace
sector and align with national priorities. This effort generated NASA's first
grand challenge, the Asteroid Grand Challenge, a large scale effort using
multidisciplinary collaborations and innovative engagement mechanisms focused
on finding and addressing asteroid threats to human populations. In April 2010,
President Barack Obama announced a mission to send humans to an asteroid by
2025. This resulted in the agency's Asteroid Redirect Mission to leverage and
maximize existing robotic and human efforts to capture and reroute an asteroid,
with the goal of eventual human exploration. The AGC, initiated in 2013,
complemented ARM by expanding public participation, partnerships, and other
approaches to find, understand, and overcome these potentially harmful
asteroids. This paper describes a selection of AGC activities implemented from
2013 to 2017 and their results, excluding those conducted by NASA's Near Earth
Object Observations Program and other organizations. The strategic development
of the initiative is outlined as well as initial successes, strengths, and
weaknesses resulting from the first four years of AGC activities and
approaches. Finally, we describe lesson learned and areas for continued work
and study. The AGC lessons learned and strategies could inform the work of
other agencies and organizations seeking to conduct a global scientific
investigation with matrixed organizational support, multiple strategic
partners, and numerous internal and external open innovation approaches and
audiences.
|
astro-ph.IM
|
beginning in 2012 nasa utilized a strategic process to identify broad societal questions or grand challenges that are well suited to the aerospace sector and align with national priorities this effort generated nasas first grand challenge the asteroid grand challenge a large scale effort using multidisciplinary collaborations and innovative engagement mechanisms focused on finding and addressing asteroid threats to human populations in april 2010 president barack obama announced a mission to send humans to an asteroid by 2025 this resulted in the agencys asteroid redirect mission to leverage and maximize existing robotic and human efforts to capture and reroute an asteroid with the goal of eventual human exploration the agc initiated in 2013 complemented arm by expanding public participation partnerships and other approaches to find understand and overcome these potentially harmful asteroids this paper describes a selection of agc activities implemented from 2013 to 2017 and their results excluding those conducted by nasas near earth object observations program and other organizations the strategic development of the initiative is outlined as well as initial successes strengths and weaknesses resulting from the first four years of agc activities and approaches finally we describe lesson learned and areas for continued work and study the agc lessons learned and strategies could inform the work of other agencies and organizations seeking to conduct a global scientific investigation with matrixed organizational support multiple strategic partners and numerous internal and external open innovation approaches and audiences
|
[['beginning', 'in', '2012', 'nasa', 'utilized', 'a', 'strategic', 'process', 'to', 'identify', 'broad', 'societal', 'questions', 'or', 'grand', 'challenges', 'that', 'are', 'well', 'suited', 'to', 'the', 'aerospace', 'sector', 'and', 'align', 'with', 'national', 'priorities', 'this', 'effort', 'generated', 'nasas', 'first', 'grand', 'challenge', 'the', 'asteroid', 'grand', 'challenge', 'a', 'large', 'scale', 'effort', 'using', 'multidisciplinary', 'collaborations', 'and', 'innovative', 'engagement', 'mechanisms', 'focused', 'on', 'finding', 'and', 'addressing', 'asteroid', 'threats', 'to', 'human', 'populations', 'in', 'april', '2010', 'president', 'barack', 'obama', 'announced', 'a', 'mission', 'to', 'send', 'humans', 'to', 'an', 'asteroid', 'by', '2025', 'this', 'resulted', 'in', 'the', 'agencys', 'asteroid', 'redirect', 'mission', 'to', 'leverage', 'and', 'maximize', 'existing', 'robotic', 'and', 'human', 'efforts', 'to', 'capture', 'and', 'reroute', 'an', 'asteroid', 'with', 'the', 'goal', 'of', 'eventual', 'human', 'exploration', 'the', 'agc', 'initiated', 'in', '2013', 'complemented', 'arm', 'by', 'expanding', 'public', 'participation', 'partnerships', 'and', 'other', 'approaches', 'to', 'find', 'understand', 'and', 'overcome', 'these', 'potentially', 'harmful', 'asteroids', 'this', 'paper', 'describes', 'a', 'selection', 'of', 'agc', 'activities', 'implemented', 'from', '2013', 'to', '2017', 'and', 'their', 'results', 'excluding', 'those', 'conducted', 'by', 'nasas', 'near', 'earth', 'object', 'observations', 'program', 'and', 'other', 'organizations', 'the', 'strategic', 'development', 'of', 'the', 'initiative', 'is', 'outlined', 'as', 'well', 'as', 'initial', 'successes', 'strengths', 'and', 'weaknesses', 'resulting', 'from', 'the', 'first', 'four', 'years', 'of', 'agc', 'activities', 'and', 'approaches', 'finally', 'we', 'describe', 'lesson', 'learned', 'and', 'areas', 'for', 'continued', 'work', 'and', 'study', 'the', 'agc', 'lessons', 'learned', 'and', 'strategies', 'could', 'inform', 'the', 'work', 'of', 'other', 'agencies', 'and', 'organizations', 'seeking', 'to', 'conduct', 'a', 'global', 'scientific', 'investigation', 'with', 'matrixed', 'organizational', 'support', 'multiple', 'strategic', 'partners', 'and', 'numerous', 'internal', 'and', 'external', 'open', 'innovation', 'approaches', 'and', 'audiences']]
|
[-0.04739696716795824, 0.087279301474361, -0.06993538690330141, 0.061192116986542505, -0.15534828060126604, -0.10155050169594367, 0.07080510272566198, 0.3801297972941411, -0.21452886265499063, -0.4100158354502265, 0.1293966557860899, -0.2981413254874344, -0.17826380956469948, 0.16466907937247932, -0.1573048371189673, 0.07785900981173295, 0.10105786641067707, -0.06527229445250783, 0.05830752085872335, -0.30405701670067836, 0.2633908142730938, 0.13393704724564617, 0.24095893839076787, 0.014730920899706353, 0.08168572162868877, -0.022078443291702724, -0.1176924052346936, -0.053076597456937706, -0.13717689161290275, 0.18340415685015002, 0.3843895852861206, 0.24738323621592728, 0.3723610571386026, -0.42711642646316844, -0.1720062904010973, 0.07665949672198377, 0.0802473924039597, 0.019717198051915782, -0.028673950837584634, -0.40201671985408466, 0.0031676126190093384, -0.25205636837438017, -0.17393279268539377, -0.05999442277059933, 0.03488629922943832, 0.0012679739924687735, -0.23144941474115396, -0.0660525169859429, 0.024623681073558338, 0.14221340586028272, -0.10721274348907173, -0.1369117811936572, -0.01878229041090783, 0.2311895466771587, 0.12577206871025715, 0.07352817769179722, 0.1786613513372189, -0.15082129751461237, -0.18809032608058399, 0.3932878538073988, 0.012362764031819984, -0.016661719962985825, 0.23447118478141404, -0.09491575291251153, -0.16055732983274057, 0.05974679287843907, 0.2592356594279653, 0.05226533832967657, -0.18833524023941836, 0.024514253758248176, 0.03975014599674076, 0.15034709466932372, 0.047229281251960506, -0.019401711679822884, 0.27403286488587575, 0.1947481323105964, 0.07815068264437072, 0.07622821453274932, -0.06571925145758808, -0.09845999598370067, -0.18365028998085706, -0.11875907952325936, -0.10888258756056209, -0.002737606313821132, 0.03834471580524246, -0.05742677193166328, 0.36636903669348597, 0.18910411169543126, 0.12748673775804645, -0.009775124973531406, 0.29820437517658877, -0.07146733048351916, 0.07750309243731733, 0.07464415999422899, 0.25987009160776287, -0.02171073282580674, 0.17602729111735732, -0.18403138239484498, 0.0850853206474381, -0.013262998877179284]
|
1,803.04565
|
Learning to recognize Abnormalities in Chest X-Rays with Location-Aware
Dense Networks
|
Chest X-ray is the most common medical imaging exam used to assess multiple
pathologies. Automated algorithms and tools have the potential to support the
reading workflow, improve efficiency, and reduce reading errors. With the
availability of large scale data sets, several methods have been proposed to
classify pathologies on chest X-ray images. However, most methods report
performance based on random image based splitting, ignoring the high
probability of the same patient appearing in both training and test set. In
addition, most methods fail to explicitly incorporate the spatial information
of abnormalities or utilize the high resolution images. We propose a novel
approach based on location aware Dense Networks (DNetLoc), whereby we
incorporate both high-resolution image data and spatial information for
abnormality classification. We evaluate our method on the largest data set
reported in the community, containing a total of 86,876 patients and 297,541
chest X-ray images. We achieve (i) the best average AUC score for published
training and test splits on the single benchmarking data set (ChestX-Ray14),
and (ii) improved AUC scores when the pathology location information is
explicitly used. To foster future research we demonstrate the limitations of
the current benchmarking setup and provide new reference patient-wise splits
for the used data sets. This could support consistent and meaningful
benchmarking of future methods on the largest publicly available data sets.
|
cs.CV cs.AI
|
chest xray is the most common medical imaging exam used to assess multiple pathologies automated algorithms and tools have the potential to support the reading workflow improve efficiency and reduce reading errors with the availability of large scale data sets several methods have been proposed to classify pathologies on chest xray images however most methods report performance based on random image based splitting ignoring the high probability of the same patient appearing in both training and test set in addition most methods fail to explicitly incorporate the spatial information of abnormalities or utilize the high resolution images we propose a novel approach based on location aware dense networks dnetloc whereby we incorporate both highresolution image data and spatial information for abnormality classification we evaluate our method on the largest data set reported in the community containing a total of 86876 patients and 297541 chest xray images we achieve i the best average auc score for published training and test splits on the single benchmarking data set chestxray14 and ii improved auc scores when the pathology location information is explicitly used to foster future research we demonstrate the limitations of the current benchmarking setup and provide new reference patientwise splits for the used data sets this could support consistent and meaningful benchmarking of future methods on the largest publicly available data sets
|
[['chest', 'xray', 'is', 'the', 'most', 'common', 'medical', 'imaging', 'exam', 'used', 'to', 'assess', 'multiple', 'pathologies', 'automated', 'algorithms', 'and', 'tools', 'have', 'the', 'potential', 'to', 'support', 'the', 'reading', 'workflow', 'improve', 'efficiency', 'and', 'reduce', 'reading', 'errors', 'with', 'the', 'availability', 'of', 'large', 'scale', 'data', 'sets', 'several', 'methods', 'have', 'been', 'proposed', 'to', 'classify', 'pathologies', 'on', 'chest', 'xray', 'images', 'however', 'most', 'methods', 'report', 'performance', 'based', 'on', 'random', 'image', 'based', 'splitting', 'ignoring', 'the', 'high', 'probability', 'of', 'the', 'same', 'patient', 'appearing', 'in', 'both', 'training', 'and', 'test', 'set', 'in', 'addition', 'most', 'methods', 'fail', 'to', 'explicitly', 'incorporate', 'the', 'spatial', 'information', 'of', 'abnormalities', 'or', 'utilize', 'the', 'high', 'resolution', 'images', 'we', 'propose', 'a', 'novel', 'approach', 'based', 'on', 'location', 'aware', 'dense', 'networks', 'dnetloc', 'whereby', 'we', 'incorporate', 'both', 'highresolution', 'image', 'data', 'and', 'spatial', 'information', 'for', 'abnormality', 'classification', 'we', 'evaluate', 'our', 'method', 'on', 'the', 'largest', 'data', 'set', 'reported', 'in', 'the', 'community', 'containing', 'a', 'total', 'of', '86876', 'patients', 'and', '297541', 'chest', 'xray', 'images', 'we', 'achieve', 'i', 'the', 'best', 'average', 'auc', 'score', 'for', 'published', 'training', 'and', 'test', 'splits', 'on', 'the', 'single', 'benchmarking', 'data', 'set', 'chestxray14', 'and', 'ii', 'improved', 'auc', 'scores', 'when', 'the', 'pathology', 'location', 'information', 'is', 'explicitly', 'used', 'to', 'foster', 'future', 'research', 'we', 'demonstrate', 'the', 'limitations', 'of', 'the', 'current', 'benchmarking', 'setup', 'and', 'provide', 'new', 'reference', 'patientwise', 'splits', 'for', 'the', 'used', 'data', 'sets', 'this', 'could', 'support', 'consistent', 'and', 'meaningful', 'benchmarking', 'of', 'future', 'methods', 'on', 'the', 'largest', 'publicly', 'available', 'data', 'sets']]
|
[-0.025093634990941083, -0.05251462118224339, -0.04589576200569271, 0.10745379366375797, -0.11829193024105743, -0.1396993572680587, 0.06664957126192407, 0.4262363924602166, -0.1928098363275298, -0.330861107542376, 0.13774067526971578, -0.319397724558249, -0.10111085037965703, 0.23885163128132497, -0.13501354159120846, 0.10560295229135273, 0.15855496164802205, 0.0531794181460175, -0.0338013633901954, -0.30933471336595525, 0.30135127425475744, 0.059205467554253585, 0.4105256696812672, 0.05236994196316911, 0.10152781787093455, -0.011302912848095897, -0.09361320958524054, 0.01620467317316237, -0.08649003719339583, 0.16702901544967888, 0.30736086135217494, 0.2542866885709072, 0.2990860797581263, -0.4274548240552802, -0.20627081682136572, 0.0933018719645029, 0.12609795650142594, 0.09194085406662295, -0.03284857558287304, -0.3204868570346078, 0.09300179664235682, -0.1243121634790708, -0.022123800292653845, -0.12752487504461762, -0.05285515185123002, -0.002931997665938913, -0.2752063050187928, 0.07425080870765202, -0.0342259767802791, 0.11800830471414833, -0.10518649816534423, -0.13909883488080713, 0.03682692908177819, 0.20429034959249853, 0.0078532258027293, 0.04681871490448354, 0.14567072557311062, -0.14649274307614862, -0.16291160269164046, 0.35454731590516114, -0.030354410987731856, -0.17285453430441028, 0.19959258696666723, -0.09140766469266164, -0.15022025998241756, 0.10979353929303774, 0.23459228305060656, 0.10800460941764876, -0.17403875590749823, -0.021891613892726, 0.013153015550465212, 0.20101714630636874, 0.058540827453717456, 0.004907790612036346, 0.166710235717826, 0.216596522134058, -0.012701018237073076, 0.11721531204012044, -0.21752576149333644, 0.02038996258254992, -0.22595102812719975, -0.12586756650499417, -0.18956583049340567, -0.04197547171444316, -0.08662503836146468, -0.1612313228420093, 0.389630383459441, 0.2607117308914627, 0.17690404573745958, 0.03523867378393701, 0.3485689307178516, -0.003836436567567994, 0.11476349685375296, 0.06807401442874668, 0.17589278930505534, 0.0006889389494621973, 0.08644068420121293, -0.19363013558824538, 0.0823247092690558, -0.00039328939020804584]
|
1,803.04566
|
Compact Convolutional Neural Networks for Classification of Asynchronous
Steady-state Visual Evoked Potentials
|
Steady-State Visual Evoked Potentials (SSVEPs) are neural oscillations from
the parietal and occipital regions of the brain that are evoked from flickering
visual stimuli. SSVEPs are robust signals measurable in the
electroencephalogram (EEG) and are commonly used in brain-computer interfaces
(BCIs). However, methods for high-accuracy decoding of SSVEPs usually require
hand-crafted approaches that leverage domain-specific knowledge of the stimulus
signals, such as specific temporal frequencies in the visual stimuli and their
relative spatial arrangement. When this knowledge is unavailable, such as when
SSVEP signals are acquired asynchronously, such approaches tend to fail. In
this paper, we show how a compact convolutional neural network (Compact-CNN),
which only requires raw EEG signals for automatic feature extraction, can be
used to decode signals from a 12-class SSVEP dataset without the need for any
domain-specific knowledge or calibration data. We report across subject mean
accuracy of approximately 80% (chance being 8.3%) and show this is
substantially better than current state-of-the-art hand-crafted approaches
using canonical correlation analysis (CCA) and Combined-CCA. Furthermore, we
analyze our Compact-CNN to examine the underlying feature representation,
discovering that the deep learner extracts additional phase and amplitude
related features associated with the structure of the dataset. We discuss how
our Compact-CNN shows promise for BCI applications that allow users to freely
gaze/attend to any stimulus at any time (e.g., asynchronous BCI) as well as
provides a method for analyzing SSVEP signals in a way that might augment our
understanding about the basic processing in the visual cortex.
|
cs.LG q-bio.NC stat.ML
|
steadystate visual evoked potentials ssveps are neural oscillations from the parietal and occipital regions of the brain that are evoked from flickering visual stimuli ssveps are robust signals measurable in the electroencephalogram eeg and are commonly used in braincomputer interfaces bcis however methods for highaccuracy decoding of ssveps usually require handcrafted approaches that leverage domainspecific knowledge of the stimulus signals such as specific temporal frequencies in the visual stimuli and their relative spatial arrangement when this knowledge is unavailable such as when ssvep signals are acquired asynchronously such approaches tend to fail in this paper we show how a compact convolutional neural network compactcnn which only requires raw eeg signals for automatic feature extraction can be used to decode signals from a 12class ssvep dataset without the need for any domainspecific knowledge or calibration data we report across subject mean accuracy of approximately 80 chance being 83 and show this is substantially better than current stateoftheart handcrafted approaches using canonical correlation analysis cca and combinedcca furthermore we analyze our compactcnn to examine the underlying feature representation discovering that the deep learner extracts additional phase and amplitude related features associated with the structure of the dataset we discuss how our compactcnn shows promise for bci applications that allow users to freely gazeattend to any stimulus at any time eg asynchronous bci as well as provides a method for analyzing ssvep signals in a way that might augment our understanding about the basic processing in the visual cortex
|
[['steadystate', 'visual', 'evoked', 'potentials', 'ssveps', 'are', 'neural', 'oscillations', 'from', 'the', 'parietal', 'and', 'occipital', 'regions', 'of', 'the', 'brain', 'that', 'are', 'evoked', 'from', 'flickering', 'visual', 'stimuli', 'ssveps', 'are', 'robust', 'signals', 'measurable', 'in', 'the', 'electroencephalogram', 'eeg', 'and', 'are', 'commonly', 'used', 'in', 'braincomputer', 'interfaces', 'bcis', 'however', 'methods', 'for', 'highaccuracy', 'decoding', 'of', 'ssveps', 'usually', 'require', 'handcrafted', 'approaches', 'that', 'leverage', 'domainspecific', 'knowledge', 'of', 'the', 'stimulus', 'signals', 'such', 'as', 'specific', 'temporal', 'frequencies', 'in', 'the', 'visual', 'stimuli', 'and', 'their', 'relative', 'spatial', 'arrangement', 'when', 'this', 'knowledge', 'is', 'unavailable', 'such', 'as', 'when', 'ssvep', 'signals', 'are', 'acquired', 'asynchronously', 'such', 'approaches', 'tend', 'to', 'fail', 'in', 'this', 'paper', 'we', 'show', 'how', 'a', 'compact', 'convolutional', 'neural', 'network', 'compactcnn', 'which', 'only', 'requires', 'raw', 'eeg', 'signals', 'for', 'automatic', 'feature', 'extraction', 'can', 'be', 'used', 'to', 'decode', 'signals', 'from', 'a', '12class', 'ssvep', 'dataset', 'without', 'the', 'need', 'for', 'any', 'domainspecific', 'knowledge', 'or', 'calibration', 'data', 'we', 'report', 'across', 'subject', 'mean', 'accuracy', 'of', 'approximately', '80', 'chance', 'being', '83', 'and', 'show', 'this', 'is', 'substantially', 'better', 'than', 'current', 'stateoftheart', 'handcrafted', 'approaches', 'using', 'canonical', 'correlation', 'analysis', 'cca', 'and', 'combinedcca', 'furthermore', 'we', 'analyze', 'our', 'compactcnn', 'to', 'examine', 'the', 'underlying', 'feature', 'representation', 'discovering', 'that', 'the', 'deep', 'learner', 'extracts', 'additional', 'phase', 'and', 'amplitude', 'related', 'features', 'associated', 'with', 'the', 'structure', 'of', 'the', 'dataset', 'we', 'discuss', 'how', 'our', 'compactcnn', 'shows', 'promise', 'for', 'bci', 'applications', 'that', 'allow', 'users', 'to', 'freely', 'gazeattend', 'to', 'any', 'stimulus', 'at', 'any', 'time', 'eg', 'asynchronous', 'bci', 'as', 'well', 'as', 'provides', 'a', 'method', 'for', 'analyzing', 'ssvep', 'signals', 'in', 'a', 'way', 'that', 'might', 'augment', 'our', 'understanding', 'about', 'the', 'basic', 'processing', 'in', 'the', 'visual', 'cortex']]
|
[-0.05334610856399745, 0.03562460932209934, -0.07897157317272736, 0.0724879065236276, -0.14147730847368392, -0.19386450658502524, 0.015890144427314616, 0.46404488462908766, -0.2596059987367487, -0.30918412772007287, 0.08357573125545902, -0.28762036807252545, -0.24874037876821012, 0.21107938821205233, -0.12528600394457517, 0.05360381028048671, 0.10113345095868617, 0.10238159976903456, -0.04880286942077072, -0.18017772636462556, 0.25278755016490784, 0.015734406014585315, 0.3099777524516661, -0.013916942990890899, 0.08874480387937554, -0.031269741306893924, -0.06320023421165073, -0.04734857076704272, -0.016619770492875226, 0.14479005086300856, 0.3749884736825762, 0.21438443490137335, 0.2808113387102451, -0.4554323462636206, -0.25480561465822127, 0.10464435743183327, 0.14406754991245396, 0.12472398897166816, -0.044656555688606554, -0.3604568350742046, 0.11521104918576063, -0.11708588583908454, 0.021290208027150037, -0.1450710719673599, -0.000366455388596519, 0.03195198881823125, -0.2985316341996304, 0.09342163815757129, 0.08174884505920259, 0.12964184078222157, -0.0671052743006237, -0.08528513457327161, 0.027105403226156385, 0.19916243495159963, 0.019131761647898843, 0.07225508668168674, 0.17959487692878387, -0.17712078819342664, -0.11937123398970115, 0.3226529152626977, -0.048309792634475796, -0.2043665120083026, 0.2391517339748572, -0.08611927274039682, -0.12409072215649694, 0.09820220837850553, 0.21569352089274108, 0.06792417780240832, -0.181879016366541, -0.03290991908956871, 0.04077119525108072, 0.24208773877481268, 0.07986963525655921, 0.0383193068064504, 0.20480423584703064, 0.20102160593203494, -0.005445895139741187, 0.10935202462037205, -0.13002017924371426, -0.002007867451067324, -0.22137565715018076, -0.07266871705761471, -0.17086521424295634, -0.019718766425412395, -0.09045002859532776, -0.11299280035252005, 0.42959542587445476, 0.23763475426547698, 0.19102984802881753, 0.08975938293417444, 0.3444771216454084, 0.031167512510926174, 0.12816004837200107, 0.07764276518743225, 0.1916700544107874, 0.015645430417570435, 0.14797248803232432, -0.18233177982128912, 0.12578042987695753, -0.001677967013921512]
|
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.