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1,803.00367 | A Benchmark Problem in Transportation Networks | In this note, we propose a case study of freeway traffic flow modeled as a
hybrid system. We describe two general classes of networks that model flow
along a freeway with merging onramps. The admission rate of traffic flow from
each onramp is metered via a control input. Both classes of networks are easily
scaled to accommodate arbitrary state dimension. The model is discrete-time and
possesses piecewise-affine dynamics. Moreover, we present several control
objectives that are especially relevant for traffic flow management. The
proposed model is flexible and extensible and offers a benchmark for evaluating
tools and techniques developed for hybrid systems.
| cs.SY | in this note we propose a case study of freeway traffic flow modeled as a hybrid system we describe two general classes of networks that model flow along a freeway with merging onramps the admission rate of traffic flow from each onramp is metered via a control input both classes of networks are easily scaled to accommodate arbitrary state dimension the model is discretetime and possesses piecewiseaffine dynamics moreover we present several control objectives that are especially relevant for traffic flow management the proposed model is flexible and extensible and offers a benchmark for evaluating tools and techniques developed for hybrid systems | [['in', 'this', 'note', 'we', 'propose', 'a', 'case', 'study', 'of', 'freeway', 'traffic', 'flow', 'modeled', 'as', 'a', 'hybrid', 'system', 'we', 'describe', 'two', 'general', 'classes', 'of', 'networks', 'that', 'model', 'flow', 'along', 'a', 'freeway', 'with', 'merging', 'onramps', 'the', 'admission', 'rate', 'of', 'traffic', 'flow', 'from', 'each', 'onramp', 'is', 'metered', 'via', 'a', 'control', 'input', 'both', 'classes', 'of', 'networks', 'are', 'easily', 'scaled', 'to', 'accommodate', 'arbitrary', 'state', 'dimension', 'the', 'model', 'is', 'discretetime', 'and', 'possesses', 'piecewiseaffine', 'dynamics', 'moreover', 'we', 'present', 'several', 'control', 'objectives', 'that', 'are', 'especially', 'relevant', 'for', 'traffic', 'flow', 'management', 'the', 'proposed', 'model', 'is', 'flexible', 'and', 'extensible', 'and', 'offers', 'a', 'benchmark', 'for', 'evaluating', 'tools', 'and', 'techniques', 'developed', 'for', 'hybrid', 'systems']] | [-0.17850247841766653, 0.05689490869568259, -0.08104606581094, 0.04724178478824672, -0.031785305402259906, -0.21670217672819456, 0.013294622354556387, 0.38959687900747736, -0.2735698225693412, -0.2661283222191474, 0.14220919265918544, -0.23451664095160132, -0.16930474381061847, 0.24270707597800842, -0.08939363454085063, 0.09667221239457528, 0.08056647886919296, 0.002199815054351062, 0.0014115754319765769, -0.19147543320614918, 0.31179016025499534, -0.021870951533463655, 0.3330421485737258, 0.025914432759414994, 0.1305790783828307, 0.0071899974787169516, -0.0009105165384015909, 0.06303107888236933, -0.09633568313966309, 0.16081741054122353, 0.27710955168175344, 0.15440033790061944, 0.26636320400033514, -0.4102814864622904, -0.2846634173742952, 0.09558784116205632, 0.10924784400883843, 0.10393621689439111, -0.031161597217707074, -0.2523738270803519, 0.1166757442121047, -0.23162648559767096, -0.09615645533930921, -0.09985218731248204, -0.04114975269390818, 0.06535054351358364, -0.32163672318097714, 0.028077466705558347, 0.01760218812463184, 0.04222476636698725, -0.05408067203254676, -0.047059174811960584, -0.01703350731701243, 0.16145689716430234, 0.006040587069411927, -0.01617276692972975, 0.13779605156742036, -0.11784363884761856, -0.1501216516766113, 0.40092492851830436, -0.03828786110605899, -0.22099803253898725, 0.1924391869711233, -0.0011380830501187959, -0.13800715309837103, 0.08071086916378607, 0.2901305552142873, 0.11589103838994953, -0.21483688053273248, 0.018346019456729146, -0.05009196701404803, 0.1474712887121474, -0.0015873243397686119, -0.028813934329709988, 0.14931696845108972, 0.27458727683908507, 0.11693082899585658, 0.18356272714285582, -0.07586596358348341, -0.1571543914284192, -0.26251784692901897, -0.12947070420327067, -0.0872788691879524, -0.015722141991478995, -0.06511229310839839, -0.10699579225140898, 0.41016898013870506, 0.15370560779838877, 0.17587479783276863, 0.11408673476783887, 0.34636938644061777, 0.09750718734222556, 0.023916490814265084, 0.1487234014351213, 0.13225545154030746, 0.06399725019639614, 0.15391362142548257, -0.1922151614768047, 0.07573704012012218, 0.0509246417786926] |
1,803.00368 | An Event-based Diffusion LMS Strategy | We consider a wireless sensor network consists of cooperative nodes, each of
them keep adapting to streaming data to perform a least-mean-squares
estimation, and also maintain information exchange among neighboring nodes in
order to improve performance. For the sake of reducing communication overhead,
prolonging batter life while preserving the benefits of diffusion cooperation,
we propose an energy-efficient diffusion strategy that adopts an event-based
communication mechanism, which allow nodes to cooperate with neighbors only
when necessary. We also study the performance of the proposed algorithm, and
show that its network mean error and MSD are bounded in steady state. Numerical
results demonstrate that the proposed method can effectively reduce the network
energy consumption without sacrificing steady-state network MSD performance
significantly.
| eess.SP cs.DC | we consider a wireless sensor network consists of cooperative nodes each of them keep adapting to streaming data to perform a leastmeansquares estimation and also maintain information exchange among neighboring nodes in order to improve performance for the sake of reducing communication overhead prolonging batter life while preserving the benefits of diffusion cooperation we propose an energyefficient diffusion strategy that adopts an eventbased communication mechanism which allow nodes to cooperate with neighbors only when necessary we also study the performance of the proposed algorithm and show that its network mean error and msd are bounded in steady state numerical results demonstrate that the proposed method can effectively reduce the network energy consumption without sacrificing steadystate network msd performance significantly | [['we', 'consider', 'a', 'wireless', 'sensor', 'network', 'consists', 'of', 'cooperative', 'nodes', 'each', 'of', 'them', 'keep', 'adapting', 'to', 'streaming', 'data', 'to', 'perform', 'a', 'leastmeansquares', 'estimation', 'and', 'also', 'maintain', 'information', 'exchange', 'among', 'neighboring', 'nodes', 'in', 'order', 'to', 'improve', 'performance', 'for', 'the', 'sake', 'of', 'reducing', 'communication', 'overhead', 'prolonging', 'batter', 'life', 'while', 'preserving', 'the', 'benefits', 'of', 'diffusion', 'cooperation', 'we', 'propose', 'an', 'energyefficient', 'diffusion', 'strategy', 'that', 'adopts', 'an', 'eventbased', 'communication', 'mechanism', 'which', 'allow', 'nodes', 'to', 'cooperate', 'with', 'neighbors', 'only', 'when', 'necessary', 'we', 'also', 'study', 'the', 'performance', 'of', 'the', 'proposed', 'algorithm', 'and', 'show', 'that', 'its', 'network', 'mean', 'error', 'and', 'msd', 'are', 'bounded', 'in', 'steady', 'state', 'numerical', 'results', 'demonstrate', 'that', 'the', 'proposed', 'method', 'can', 'effectively', 'reduce', 'the', 'network', 'energy', 'consumption', 'without', 'sacrificing', 'steadystate', 'network', 'msd', 'performance', 'significantly']] | [-0.17326780163951344, 0.022532053653129092, -0.004138230859159918, -0.005377177315787807, -0.07564910146865551, -0.21353704442459523, 0.11476445775597467, 0.421141879996127, -0.29952201032537523, -0.3185172333713558, 0.05803046626693932, -0.25939133124804853, -0.19289213637584599, 0.1146612662064322, -0.12628829486320836, 0.05625282076406451, 0.12064857208327848, 0.08083003636852096, -0.012193704365310655, -0.3170457046106063, 0.2466818955786116, 0.12200024391335072, 0.37379296551166513, 0.05536528024723816, 0.12943389949929443, 0.008555823098473504, -0.009915432044289123, -0.010869707397216837, -0.08761249235094973, 0.11924111124885789, 0.2664083533178447, 0.15587353466425913, 0.3424524601896183, -0.4796681948621773, -0.2551071452493858, 0.14260653419009708, 0.19170597131300907, 0.109952419239562, -0.021758453784336856, -0.2916339246587733, 0.13042032557847452, -0.2293468382904085, -0.058919916821296436, -0.1157485911685784, -0.07379024832162186, 0.05013399462805571, -0.29511381119047686, 0.06409951011695647, 0.01756806765244958, -0.0022613935456690143, -0.06182744661918289, -0.06015906727604442, -0.024799248014213675, 0.2089341538793001, 0.011090914775041264, -0.027669754091421365, 0.1464060656173881, -0.12711581289847146, -0.13938817828035346, 0.34414429083798015, -0.017829148691755295, -0.22907895377099166, 0.15796878502380607, -0.03289092059436617, -0.0743705689851826, 0.13311871892764723, 0.23919307453542063, 0.052980483783866765, -0.1683552277466071, -0.0010265498792050185, 0.05701645098476655, 0.197287399258654, 0.01268992927990917, 0.07456141268692405, 0.07544039860979448, 0.24394291811188587, 0.1773424596502051, 0.126170130862886, -0.11519684462824845, -0.13389554943384255, -0.19196427334494637, -0.17331250187135855, -0.20999830405985526, -0.027482375072441615, -0.1295086356602319, -0.0725367703530306, 0.3927913319272131, 0.21967942197017876, 0.1523396126349788, 0.15795275079721774, 0.3963533285768482, 0.040887621998928994, 0.06551905309828787, 0.18880952248286645, 0.20704255702952712, 0.029973984230309725, 0.14353755085879022, -0.2738257927654343, 0.11722131296439822, 0.01870139371798575] |
1,803.00369 | Two-qubit causal structures and the geometry of positive qubit-maps | We study quantum causal inference in a set-up proposed by Ried et al. [Nat.
Phys. 11, 414 (2015)] in which a common-cause scenario can be mixed with a
cause-effect scenario, and for which it was found that quantum mechanics can
bring an advantage in distinguishing the two scenarios: Whereas in classical
statistics, interventions such as randomized trials are needed, a quantum
observational scheme can be enough to detect the causal structure if the common
cause results from a maximally entangled state. We analyze this setup in terms
of the geometry of unital positive but not completely positive qubit-maps,
arising from the mixture of qubit-channels and steering maps. We find the range
of mixing parameters that can generate given correlations, and prove a quantum
advantage in a more general setup, allowing arbitrary unital channels and
initial states with fully mixed reduced states. This is achieved by
establishing new bounds on signed singular values of sums of matrices. Based on
the geometry, we quantify and identify the origin of the quantum advantage
depending on the observed correlations, and discuss how additional constraints
can lead to a unique solution of the problem.
| quant-ph | we study quantum causal inference in a setup proposed by ried et al nat phys 11 414 2015 in which a commoncause scenario can be mixed with a causeeffect scenario and for which it was found that quantum mechanics can bring an advantage in distinguishing the two scenarios whereas in classical statistics interventions such as randomized trials are needed a quantum observational scheme can be enough to detect the causal structure if the common cause results from a maximally entangled state we analyze this setup in terms of the geometry of unital positive but not completely positive qubitmaps arising from the mixture of qubitchannels and steering maps we find the range of mixing parameters that can generate given correlations and prove a quantum advantage in a more general setup allowing arbitrary unital channels and initial states with fully mixed reduced states this is achieved by establishing new bounds on signed singular values of sums of matrices based on the geometry we quantify and identify the origin of the quantum advantage depending on the observed correlations and discuss how additional constraints can lead to a unique solution of the problem | [['we', 'study', 'quantum', 'causal', 'inference', 'in', 'a', 'setup', 'proposed', 'by', 'ried', 'et', 'al', 'nat', 'phys', '11', '414', '2015', 'in', 'which', 'a', 'commoncause', 'scenario', 'can', 'be', 'mixed', 'with', 'a', 'causeeffect', 'scenario', 'and', 'for', 'which', 'it', 'was', 'found', 'that', 'quantum', 'mechanics', 'can', 'bring', 'an', 'advantage', 'in', 'distinguishing', 'the', 'two', 'scenarios', 'whereas', 'in', 'classical', 'statistics', 'interventions', 'such', 'as', 'randomized', 'trials', 'are', 'needed', 'a', 'quantum', 'observational', 'scheme', 'can', 'be', 'enough', 'to', 'detect', 'the', 'causal', 'structure', 'if', 'the', 'common', 'cause', 'results', 'from', 'a', 'maximally', 'entangled', 'state', 'we', 'analyze', 'this', 'setup', 'in', 'terms', 'of', 'the', 'geometry', 'of', 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1,803.0037 | Exploiting the Potential of Standard Convolutional Autoencoders for
Image Restoration by Evolutionary Search | Researchers have applied deep neural networks to image restoration tasks, in
which they proposed various network architectures, loss functions, and training
methods. In particular, adversarial training, which is employed in recent
studies, seems to be a key ingredient to success. In this paper, we show that
simple convolutional autoencoders (CAEs) built upon only standard network
components, i.e., convolutional layers and skip connections, can outperform the
state-of-the-art methods which employ adversarial training and sophisticated
loss functions. The secret is to employ an evolutionary algorithm to
automatically search for good architectures. Training optimized CAEs by
minimizing the $\ell_2$ loss between reconstructed images and their ground
truths using the ADAM optimizer is all we need. Our experimental results show
that this approach achieves 27.8 dB peak signal to noise ratio (PSNR) on the
CelebA dataset and 40.4 dB on the SVHN dataset, compared to 22.8 dB and 33.0 dB
provided by the former state-of-the-art methods, respectively.
| cs.NE | researchers have applied deep neural networks to image restoration tasks in which they proposed various network architectures loss functions and training methods in particular adversarial training which is employed in recent studies seems to be a key ingredient to success in this paper we show that simple convolutional autoencoders caes built upon only standard network components ie convolutional layers and skip connections can outperform the stateoftheart methods which employ adversarial training and sophisticated loss functions the secret is to employ an evolutionary algorithm to automatically search for good architectures training optimized caes by minimizing the ell_2 loss between reconstructed images and their ground truths using the adam optimizer is all we need our experimental results show that this approach achieves 278 db peak signal to noise ratio psnr on the celeba dataset and 404 db on the svhn dataset compared to 228 db and 330 db provided by the former stateoftheart methods respectively | [['researchers', 'have', 'applied', 'deep', 'neural', 'networks', 'to', 'image', 'restoration', 'tasks', 'in', 'which', 'they', 'proposed', 'various', 'network', 'architectures', 'loss', 'functions', 'and', 'training', 'methods', 'in', 'particular', 'adversarial', 'training', 'which', 'is', 'employed', 'in', 'recent', 'studies', 'seems', 'to', 'be', 'a', 'key', 'ingredient', 'to', 'success', 'in', 'this', 'paper', 'we', 'show', 'that', 'simple', 'convolutional', 'autoencoders', 'caes', 'built', 'upon', 'only', 'standard', 'network', 'components', 'ie', 'convolutional', 'layers', 'and', 'skip', 'connections', 'can', 'outperform', 'the', 'stateoftheart', 'methods', 'which', 'employ', 'adversarial', 'training', 'and', 'sophisticated', 'loss', 'functions', 'the', 'secret', 'is', 'to', 'employ', 'an', 'evolutionary', 'algorithm', 'to', 'automatically', 'search', 'for', 'good', 'architectures', 'training', 'optimized', 'caes', 'by', 'minimizing', 'the', 'ell_2', 'loss', 'between', 'reconstructed', 'images', 'and', 'their', 'ground', 'truths', 'using', 'the', 'adam', 'optimizer', 'is', 'all', 'we', 'need', 'our', 'experimental', 'results', 'show', 'that', 'this', 'approach', 'achieves', '278', 'db', 'peak', 'signal', 'to', 'noise', 'ratio', 'psnr', 'on', 'the', 'celeba', 'dataset', 'and', '404', 'db', 'on', 'the', 'svhn', 'dataset', 'compared', 'to', '228', 'db', 'and', '330', 'db', 'provided', 'by', 'the', 'former', 'stateoftheart', 'methods', 'respectively']] | [-0.0232384666694736, -0.031358601415383, -0.04740780809273323, 0.09636661952157548, -0.05607619962740227, -0.20067684510966238, 0.06644110577683165, 0.5058997366699128, -0.24199549853687594, -0.366696943754172, 0.052107575026894705, -0.27677934244275093, -0.21459272662725518, 0.19019972383093356, -0.17125828828878725, 0.1246827148757814, 0.1770906565692641, 0.002808805191402439, -0.10605893153895082, -0.3639012870494447, 0.2676413432402792, 0.07521146980667444, 0.3835245889240231, 0.008157216204173203, 0.13586849004663284, -0.07590600354536102, 0.02016485897302772, -0.07005026269539752, -0.053158889832511526, 0.16153282808821673, 0.2947484770218318, 0.19426115320423265, 0.30827203723815044, -0.37959759838246054, -0.22422817806040662, 0.11273143073434339, 0.1263324497008903, 0.09057821044957058, -0.017213854115468517, -0.34482265513365956, 0.14092939514423314, -0.16546517319283358, 0.08118794374266834, -0.16908145898624377, -0.06863444208645329, -0.008797892259762567, -0.332822184851454, 0.05216726401419032, 0.07219497544776185, 0.04674164652276565, -0.037212341953848115, -0.20442248660206697, 0.012290912221514565, 0.1425624423100327, 0.0015967551548813099, 0.13930708313896473, 0.13923875481389414, -0.17054017306361774, -0.1576677726922569, 0.3090621584819423, -0.09609200928993279, -0.18573488187032686, 0.18727549941361168, 0.015482088853757268, -0.07368097437570005, 0.11274530678938806, 0.22789736276529, 0.095635780054175, -0.1495751688025551, -0.03767724429771572, -0.009154405746931587, 0.20380701315802296, 0.07470046850895277, 0.013387053784932577, 0.09605756742283215, 0.23729083994312175, 0.01658403169126151, 0.12651767746737116, -0.17408604568077457, -0.06336509032871915, -0.17994249354000963, -0.049171718491813524, -0.19987630312410154, -0.01482016133643638, -0.10373414557650107, -0.10157990842156746, 0.3843343559244733, 0.23764941353484795, 0.21189235533691114, 0.1222941669159161, 0.3932381198784105, 0.010091832781886207, 0.17099597999442587, 0.14105873830917784, 0.23952339723815813, 0.06162673237374405, 0.12528462442942268, -0.14309084179865963, 0.04619516997242018, 0.03952342183998743] |
1,803.00371 | Possible three-dimensional nematic odd-parity superconductivity in
Sr$_2$RuO$_4$ | The superconducting pairing in Sr$_2$RuO$_4$ is widely considered to be
chiral $p$-wave with $\vec{d}_{\boldsymbol k} \sim (k_x + ik_y)\hat{z}$, which
belongs to the $E_u$ representation of the crystalline $D_{4h}$ group. However,
this superconducting order appears hard to reconcile with a number of key
experiments. In this paper, based on symmetry analysis we discuss the
possibility of odd-parity pairing with inherent three-dimensional (3D)
character enforced by the inter-orbital interlayer coupling and the sizable
spin-orbit coupling (SOC) in the material. We focus on a yet unexplored $E_u$
pairing, which contains finite $(k_z \hat x$, $k_z \hat y)$-component in the
gap function. Under appropriate circumstances a novel time-reversal invariant
nematic pairing can be realized. This nematic superconducting state could make
contact with some puzzling observations on Sr$_2$RuO$_4$, such as the absence
of spontaneous edge current and no evidences of split transitions under
uniaxial strains.
| cond-mat.supr-con | the superconducting pairing in sr_2ruo_4 is widely considered to be chiral pwave with vecd_boldsymbol k sim k_x ik_yhatz which belongs to the e_u representation of the crystalline d_4h group however this superconducting order appears hard to reconcile with a number of key experiments in this paper based on symmetry analysis we discuss the possibility of oddparity pairing with inherent threedimensional 3d character enforced by the interorbital interlayer coupling and the sizable spinorbit coupling soc in the material we focus on a yet unexplored e_u pairing which contains finite k_z hat x k_z hat ycomponent in the gap function under appropriate circumstances a novel timereversal invariant nematic pairing can be realized this nematic superconducting state could make contact with some puzzling observations on sr_2ruo_4 such as the absence of spontaneous edge current and no evidences of split transitions under uniaxial strains | [['the', 'superconducting', 'pairing', 'in', 'sr_2ruo_4', 'is', 'widely', 'considered', 'to', 'be', 'chiral', 'pwave', 'with', 'vecd_boldsymbol', 'k', 'sim', 'k_x', 'ik_yhatz', 'which', 'belongs', 'to', 'the', 'e_u', 'representation', 'of', 'the', 'crystalline', 'd_4h', 'group', 'however', 'this', 'superconducting', 'order', 'appears', 'hard', 'to', 'reconcile', 'with', 'a', 'number', 'of', 'key', 'experiments', 'in', 'this', 'paper', 'based', 'on', 'symmetry', 'analysis', 'we', 'discuss', 'the', 'possibility', 'of', 'oddparity', 'pairing', 'with', 'inherent', 'threedimensional', '3d', 'character', 'enforced', 'by', 'the', 'interorbital', 'interlayer', 'coupling', 'and', 'the', 'sizable', 'spinorbit', 'coupling', 'soc', 'in', 'the', 'material', 'we', 'focus', 'on', 'a', 'yet', 'unexplored', 'e_u', 'pairing', 'which', 'contains', 'finite', 'k_z', 'hat', 'x', 'k_z', 'hat', 'ycomponent', 'in', 'the', 'gap', 'function', 'under', 'appropriate', 'circumstances', 'a', 'novel', 'timereversal', 'invariant', 'nematic', 'pairing', 'can', 'be', 'realized', 'this', 'nematic', 'superconducting', 'state', 'could', 'make', 'contact', 'with', 'some', 'puzzling', 'observations', 'on', 'sr_2ruo_4', 'such', 'as', 'the', 'absence', 'of', 'spontaneous', 'edge', 'current', 'and', 'no', 'evidences', 'of', 'split', 'transitions', 'under', 'uniaxial', 'strains']] | [-0.2674781907235097, 0.20748590879974063, -0.043373636283871274, 0.032480121365802334, -0.1313505542828985, -0.19259714878137238, 0.0690895942996418, 0.3941641868483545, -0.24637957335706206, -0.23823387495687473, -0.023647335611040824, -0.26176209933380934, -0.12815809891343224, 0.0956091053608427, 0.024409423102253535, 0.004526311207724654, -0.07120206362679196, -0.01007914323143769, -0.15469888251855213, -0.1989949099091894, 0.3507976823358162, -0.052458075966780496, 0.34809924349310284, 0.09459381110449928, 0.0030124862813323302, -0.0006340741875001054, 0.1378191348698422, -0.006691996588547161, -0.16134034806484546, 0.03908743401584418, 0.2925036345453312, -0.11340131438202292, 0.19183244729630541, -0.42636346695539745, -0.18767196310859552, 0.08846096818308359, 0.13147082476728206, 0.1160419992512038, -0.03258012942429902, -0.3330162818403577, 0.06743715135583087, -0.18421685367323243, -0.13637100860301027, -0.1396687320140199, -0.019134951384225184, -0.07197892247864544, -0.24492978381222466, 0.08456412427282582, 0.08193407470589854, 0.10443209945578533, -0.05952836649721839, -0.1248039903011227, -0.06421424445453221, -0.013704792167856425, 0.13424243651620665, 0.09684890629538079, 0.07864919036333247, -0.13308654028894482, -0.08560580178501381, 0.40395760841235734, -0.042823678634190204, -0.12586600013563168, 0.13368403198420192, -0.11550171909291986, -0.13307580656196544, 0.1427129970094108, 0.08354193119305199, 0.029740736133122034, -0.0678610256333174, 0.10808785886594864, -0.07015142442010668, 0.18352833623235262, 0.0064865942737357555, 0.07944217075988569, 0.2528709201424794, 0.1774243044977387, 0.06611866328824798, 0.11298487950082653, -0.12015399287633625, -0.03668840343227097, -0.3095717543611249, -0.13830743987194222, -0.22544602367797514, 0.07213927734140442, -0.03197706885027625, -0.17137659899890423, 0.38818803574700933, 0.14375469659158177, 0.21082813480561657, -0.11380978542602742, 0.21870861826734483, 0.0761331467631111, 0.1060578876134494, 0.03890410065313504, 0.2497672601006132, 0.1553183063507026, 0.03255257582413438, -0.3061266364209165, 0.08279362601393402, 0.0161735559206294] |
1,803.00372 | Interface induced Zeeman-protected superconductivity in ultrathin
crystalline lead films | Two dimensional (2D) superconducting systems are of great importance to
exploring exotic quantum physics. Recent development of fabrication techniques
stimulates the studies of high quality single crystalline 2D superconductors,
where intrinsic properties give rise to unprecedented physical phenomena. Here
we report the observation of Zeeman-type spin-orbit interaction protected
superconductivity (Zeeman-protected superconductivity) in 4 monolayer (ML) to 6
ML crystalline Pb films grown on striped incommensurate (SIC) Pb layers on
Si(111) substrates by molecular beam epitaxy (MBE). Anomalous large in-plane
critical field far beyond the Pauli limit is detected, which can be attributed
to the Zeeman-protected superconductivity due to the in-plane inversion
symmetry breaking at the interface. Our work demonstrates that in
superconducting heterostructures the interface can induce Zeeman-type
spin-orbit interaction (SOI) and modulate the superconductivity.
| cond-mat.supr-con | two dimensional 2d superconducting systems are of great importance to exploring exotic quantum physics recent development of fabrication techniques stimulates the studies of high quality single crystalline 2d superconductors where intrinsic properties give rise to unprecedented physical phenomena here we report the observation of zeemantype spinorbit interaction protected superconductivity zeemanprotected superconductivity in 4 monolayer ml to 6 ml crystalline pb films grown on striped incommensurate sic pb layers on si111 substrates by molecular beam epitaxy mbe anomalous large inplane critical field far beyond the pauli limit is detected which can be attributed to the zeemanprotected superconductivity due to the inplane inversion symmetry breaking at the interface our work demonstrates that in superconducting heterostructures the interface can induce zeemantype spinorbit interaction soi and modulate the superconductivity | [['two', 'dimensional', '2d', 'superconducting', 'systems', 'are', 'of', 'great', 'importance', 'to', 'exploring', 'exotic', 'quantum', 'physics', 'recent', 'development', 'of', 'fabrication', 'techniques', 'stimulates', 'the', 'studies', 'of', 'high', 'quality', 'single', 'crystalline', '2d', 'superconductors', 'where', 'intrinsic', 'properties', 'give', 'rise', 'to', 'unprecedented', 'physical', 'phenomena', 'here', 'we', 'report', 'the', 'observation', 'of', 'zeemantype', 'spinorbit', 'interaction', 'protected', 'superconductivity', 'zeemanprotected', 'superconductivity', 'in', '4', 'monolayer', 'ml', 'to', '6', 'ml', 'crystalline', 'pb', 'films', 'grown', 'on', 'striped', 'incommensurate', 'sic', 'pb', 'layers', 'on', 'si111', 'substrates', 'by', 'molecular', 'beam', 'epitaxy', 'mbe', 'anomalous', 'large', 'inplane', 'critical', 'field', 'far', 'beyond', 'the', 'pauli', 'limit', 'is', 'detected', 'which', 'can', 'be', 'attributed', 'to', 'the', 'zeemanprotected', 'superconductivity', 'due', 'to', 'the', 'inplane', 'inversion', 'symmetry', 'breaking', 'at', 'the', 'interface', 'our', 'work', 'demonstrates', 'that', 'in', 'superconducting', 'heterostructures', 'the', 'interface', 'can', 'induce', 'zeemantype', 'spinorbit', 'interaction', 'soi', 'and', 'modulate', 'the', 'superconductivity']] | [-0.21782600770890712, 0.21633681458979845, -0.014964989308267831, -0.0007136862976476551, -0.0721548078097403, -0.2192831545844674, 0.0832073974851519, 0.39773971870541575, -0.25150375701487065, -0.3181694464087486, -0.038579018785618244, -0.28674733060970903, -0.1414655581600964, 0.17416480416804553, 0.04355555289238691, 0.041430384581908584, -0.06682572051137686, -0.203554868305102, -0.15913161605782808, -0.2526824827007949, 0.26601948068942877, 0.0153367017833516, 0.42482125594373793, 0.1379968901090324, 0.012664905812591314, -0.02016050085797906, 0.19819132403470577, -0.03490251075103879, -0.18907795459992485, 0.08070892130583525, 0.29114777701813727, -0.1783387460494414, 0.14522262294590474, -0.5175871597081423, -0.23060206757858395, -0.05518911404907703, 0.15810120733082295, 0.1820266279084608, -0.13177854655869306, -0.318164419291541, 0.07180823120102287, -0.10698748160153627, -0.0979201693534851, -0.08117168727703393, -0.05477785047423094, -0.055968455273658034, -0.19930960615724325, 0.054626390766352416, 0.07438341211155057, 0.15345741811953484, -0.03487068485096097, -0.13141626144200563, -0.10997093426622451, -0.017606835544109346, 0.04418581354105845, 0.08269950108602643, 0.2086453144485131, -0.13405237732530803, -0.13716015516966582, 0.34578319754451514, -0.003398258848115802, -0.04724528793245554, 0.21305167574062944, -0.18086828682944178, -0.08679561261460185, 0.17540838388353586, 0.17781443617818876, 0.025565475296229125, -0.12101866344641894, 0.10543651628447696, 0.0396327094361186, 0.20621094574406743, 0.05638210934773087, 0.1333465622998774, 0.28927547539770604, 0.257705479292199, 0.014270998615771533, 0.1002546742549166, -0.13070832494832577, 0.0007574350461363792, -0.16734685261175036, -0.2087350060311146, -0.23189114736020566, 0.10973674598510842, -0.054311783291748725, -0.1842141556069255, 0.36056787867844103, 0.19379063924541698, 0.12467299855686724, -0.16338771652709694, 0.21486942495778202, 0.05249973242171109, 0.11586417662655003, -0.058946811530739066, 0.30380760664213446, 0.1943198091406375, 0.10512931754998862, -0.2706111152721569, 0.11238155752141028, -0.014954037362942473] |
1,803.00373 | Entanglement conditions involving intensity correlations of optical
fields: the case of multi-port interferometry | Normalized quantum Stokes operators introduced in [Phys. Rev. A {\bf 95},
042113 (2017)] enable one to better observe non-classical correlations of
entangled states of optical fields with undefined photon numbers. For a given
run of an experiment the new quantum Stokes operators are defined by the
differences of the measured intensities (or photon numbers) at the exits of a
polarizer divided by their sum. It is this ratio that is to be averaged, and
not the numerator and the denominator separately, as it is in the conventional
approach. The new approach allows to construct more robust entanglement
indicators against photon-loss noise, which can detect entangled optical states
in situations in which witnesses using standard Stokes operators fail. Here we
show an extension of this approach beyond phenomena linked with polarization.
We discuss EPR-like experiments involving correlations produced by optical
beams in a multi-mode bright squeezed vacuum state. EPR-inspired entanglement
conditions for all prime numbers of modes are presented. The conditions are
much more resistant to noise due to photon loss than similar ones which employ
standard Glauber-like intensity, correlations.
| quant-ph | normalized quantum stokes operators introduced in phys rev a bf 95 042113 2017 enable one to better observe nonclassical correlations of entangled states of optical fields with undefined photon numbers for a given run of an experiment the new quantum stokes operators are defined by the differences of the measured intensities or photon numbers at the exits of a polarizer divided by their sum it is this ratio that is to be averaged and not the numerator and the denominator separately as it is in the conventional approach the new approach allows to construct more robust entanglement indicators against photonloss noise which can detect entangled optical states in situations in which witnesses using standard stokes operators fail here we show an extension of this approach beyond phenomena linked with polarization we discuss eprlike experiments involving correlations produced by optical beams in a multimode bright squeezed vacuum state eprinspired entanglement conditions for all prime numbers of modes are presented the conditions are much more resistant to noise due to photon loss than similar ones which employ standard glauberlike intensity correlations | [['normalized', 'quantum', 'stokes', 'operators', 'introduced', 'in', 'phys', 'rev', 'a', 'bf', '95', '042113', '2017', 'enable', 'one', 'to', 'better', 'observe', 'nonclassical', 'correlations', 'of', 'entangled', 'states', 'of', 'optical', 'fields', 'with', 'undefined', 'photon', 'numbers', 'for', 'a', 'given', 'run', 'of', 'an', 'experiment', 'the', 'new', 'quantum', 'stokes', 'operators', 'are', 'defined', 'by', 'the', 'differences', 'of', 'the', 'measured', 'intensities', 'or', 'photon', 'numbers', 'at', 'the', 'exits', 'of', 'a', 'polarizer', 'divided', 'by', 'their', 'sum', 'it', 'is', 'this', 'ratio', 'that', 'is', 'to', 'be', 'averaged', 'and', 'not', 'the', 'numerator', 'and', 'the', 'denominator', 'separately', 'as', 'it', 'is', 'in', 'the', 'conventional', 'approach', 'the', 'new', 'approach', 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1,803.00374 | A bootstrap test to detect prominent Granger-causalities across
frequencies | Granger-causality in the frequency domain is an emerging tool to analyze the
causal relationship between two time series. We propose a bootstrap test on
unconditional and conditional Granger-causality spectra, as well as on their
difference, to catch particularly prominent causality cycles in relative terms.
In particular, we consider a stochastic process derived applying independently
the stationary bootstrap to the original series. Our null hypothesis is that
each causality or causality difference is equal to the median across
frequencies computed on that process. In this way, we are able to disambiguate
causalities which depart significantly from the median one obtained ignoring
the causality structure. Our test shows power one as the process tends to
non-stationarity, thus being more conservative than parametric alternatives. As
an example, we infer about the relationship between money stock and GDP in the
Euro Area via our approach, considering inflation, unemployment and interest
rates as conditioning variables. We point out that during the period 1999-2017
the money stock aggregate M1 had a significant impact on economic output at all
frequencies, while the opposite relationship is significant only at high
frequencies.
| q-fin.ST stat.AP | grangercausality in the frequency domain is an emerging tool to analyze the causal relationship between two time series we propose a bootstrap test on unconditional and conditional grangercausality spectra as well as on their difference to catch particularly prominent causality cycles in relative terms in particular we consider a stochastic process derived applying independently the stationary bootstrap to the original series our null hypothesis is that each causality or causality difference is equal to the median across frequencies computed on that process in this way we are able to disambiguate causalities which depart significantly from the median one obtained ignoring the causality structure our test shows power one as the process tends to nonstationarity thus being more conservative than parametric alternatives as an example we infer about the relationship between money stock and gdp in the euro area via our approach considering inflation unemployment and interest rates as conditioning variables we point out that during the period 19992017 the money stock aggregate m1 had a significant impact on economic output at all frequencies while the opposite relationship is significant only at high frequencies | [['grangercausality', 'in', 'the', 'frequency', 'domain', 'is', 'an', 'emerging', 'tool', 'to', 'analyze', 'the', 'causal', 'relationship', 'between', 'two', 'time', 'series', 'we', 'propose', 'a', 'bootstrap', 'test', 'on', 'unconditional', 'and', 'conditional', 'grangercausality', 'spectra', 'as', 'well', 'as', 'on', 'their', 'difference', 'to', 'catch', 'particularly', 'prominent', 'causality', 'cycles', 'in', 'relative', 'terms', 'in', 'particular', 'we', 'consider', 'a', 'stochastic', 'process', 'derived', 'applying', 'independently', 'the', 'stationary', 'bootstrap', 'to', 'the', 'original', 'series', 'our', 'null', 'hypothesis', 'is', 'that', 'each', 'causality', 'or', 'causality', 'difference', 'is', 'equal', 'to', 'the', 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1,803.00375 | Field-induced ordering in a random-bond quantum spin ladder compound
with weak anisotropy | The field induced quantum phase transitions in the disorder-free and
disordered samples of the spin ladder material (CH_3)_2CHNH_3Cu(Cl_{1-x}Br_x)_3
are studied using magnetic calorimetry and magnetic neutron diffraction on
single crystal samples. Drastically different critical indexes and correlation
lengths in the high field phase are found for two different orientations of the
applied field. It is argued that for a field applied along the crystallographic
c axis, as in previous studies [1], the transition is best described as an
Ising transition in random field and anisotropy, rather than as a magnetic Bose
Glass to Bose Condensate transition.
[1] Tao Hong, A. Zheludev, H. Manaka, and L.- P. Regnault, Phys. Rev. B 81,
060410 (2010).
| cond-mat.str-el | the field induced quantum phase transitions in the disorderfree and disordered samples of the spin ladder material ch_3_2chnh_3cucl_1xbr_x_3 are studied using magnetic calorimetry and magnetic neutron diffraction on single crystal samples drastically different critical indexes and correlation lengths in the high field phase are found for two different orientations of the applied field it is argued that for a field applied along the crystallographic c axis as in previous studies 1 the transition is best described as an ising transition in random field and anisotropy rather than as a magnetic bose glass to bose condensate transition 1 tao hong a zheludev h manaka and l p regnault phys rev b 81 060410 2010 | [['the', 'field', 'induced', 'quantum', 'phase', 'transitions', 'in', 'the', 'disorderfree', 'and', 'disordered', 'samples', 'of', 'the', 'spin', 'ladder', 'material', 'ch_3_2chnh_3cucl_1xbr_x_3', 'are', 'studied', 'using', 'magnetic', 'calorimetry', 'and', 'magnetic', 'neutron', 'diffraction', 'on', 'single', 'crystal', 'samples', 'drastically', 'different', 'critical', 'indexes', 'and', 'correlation', 'lengths', 'in', 'the', 'high', 'field', 'phase', 'are', 'found', 'for', 'two', 'different', 'orientations', 'of', 'the', 'applied', 'field', 'it', 'is', 'argued', 'that', 'for', 'a', 'field', 'applied', 'along', 'the', 'crystallographic', 'c', 'axis', 'as', 'in', 'previous', 'studies', '1', 'the', 'transition', 'is', 'best', 'described', 'as', 'an', 'ising', 'transition', 'in', 'random', 'field', 'and', 'anisotropy', 'rather', 'than', 'as', 'a', 'magnetic', 'bose', 'glass', 'to', 'bose', 'condensate', 'transition', '1', 'tao', 'hong', 'a', 'zheludev', 'h', 'manaka', 'and', 'l', 'p', 'regnault', 'phys', 'rev', 'b', '81', '060410', '2010']] | [-0.15314409850643726, 0.2539903189883612, -0.027315311971197433, -0.00034004757223531193, -0.018400660997957265, -0.13926622097021601, 0.032197601228115075, 0.4071588547763611, -0.18848265724581317, -0.30758520914668885, 0.006897530761983143, -0.2916809199121567, -0.07911934891561849, 0.16621722752621415, 0.05801233799632536, 0.0400079393870445, -0.06480548248002561, 0.010027169489190666, -0.09620553633377571, -0.23144154431866568, 0.25486903541524886, 0.008444445501423415, 0.34418443333220866, 0.027387227922883055, 0.01667556760046597, 0.06167635463237967, 0.08377576521982294, 0.06750518157480097, -0.17444657491459437, -0.04934705548210565, 0.22207005954894382, -0.030693542746263087, 0.1525761772314548, -0.3905033844548765, -0.2105013163431351, 0.03781843216611295, 0.12619601721542145, 0.1127436433773522, -0.04642032995636344, -0.30525964220831975, 0.019740818907857078, -0.12344803193097062, -0.11467630820297593, -0.08264576465130673, 0.08027891609753661, 0.017526190232383002, -0.2815410144148617, 0.09889378998676367, 0.09050449407702192, 0.14648671324873225, -0.056403774479745865, -0.13528459794913342, -0.026387406085882713, 0.04283122447054457, 0.01546865400490821, 0.1797737271499333, 0.16488368244021448, -0.11632550623131181, -0.13531830770213482, 0.3626058062692301, -0.05920163756345907, -0.08165075051290659, 0.18139988818385322, -0.20729157524268835, -0.09947653580456972, 0.17645803684317465, 0.11044469815753656, 0.11532688281849163, -0.12909969630642631, 0.09012226549084267, -0.008551351674789683, 0.14275267243026457, 0.07689540261197664, -0.02037633362080936, 0.20935940494709607, 0.11765336013695978, -0.02451159959710246, 0.15164153086482932, -0.13018521833780367, -0.09454358117056823, -0.21714663383798724, -0.18075194075028625, -0.26303811970277935, 0.058386721990045604, -0.0808335242512873, -0.1790382511691626, 0.36628101792596623, 0.14260684421986614, 0.21227902114186264, -0.08229471348362778, 0.20046571129078575, 0.07222768170182345, 0.030035540140597065, 0.05418721492479154, 0.2605372732921734, 0.24002597947528057, 0.14694891196308196, -0.22047470116970735, 0.01027547461706974, 0.02377116303771324] |
1,803.00376 | Orbital, spin and valley contributions to Zeeman splitting of excitonic
resonances in MoSe$_2$, WSe$_2$ and WS$_2$ monolayers | We present a comprehensive optical study of the excitonic Zeeman effects in
transition metal dichalcogenide monolayers, which are discussed comparatively
for selected materials: MoSe$_2$, WSe$_2$ and WS$_2$. We introduce a simple
semi-phenomenological description of the magnetic field evolution of individual
electronic states in fundamental sub-bands by considering three additive
components: valley, spin and orbital terms. We corroborate the validity of the
proposed description by inspecting the Zeeman-like splitting of neutral and
charged excitonic resonances in absorption-type spectra. The values of all
three terms are estimated based on the experimental data, demonstrating the
significance of the valley term for a consistent description of magnetic field
evolution of optical resonances, particularly those corresponding to charged
states. The established model is further exploited for discussion of
magneto-luminescence data. We propose an interpretation of the observed large
g-factor values of low energy emission lines, due to so-called bound/localized
excitons in tungsten based compounds, based on the brightening mechanisms of
dark excitonic states.
| cond-mat.mes-hall | we present a comprehensive optical study of the excitonic zeeman effects in transition metal dichalcogenide monolayers which are discussed comparatively for selected materials mose_2 wse_2 and ws_2 we introduce a simple semiphenomenological description of the magnetic field evolution of individual electronic states in fundamental subbands by considering three additive components valley spin and orbital terms we corroborate the validity of the proposed description by inspecting the zeemanlike splitting of neutral and charged excitonic resonances in absorptiontype spectra the values of all three terms are estimated based on the experimental data demonstrating the significance of the valley term for a consistent description of magnetic field evolution of optical resonances particularly those corresponding to charged states the established model is further exploited for discussion of magnetoluminescence data we propose an interpretation of the observed large gfactor values of low energy emission lines due to socalled boundlocalized excitons in tungsten based compounds based on the brightening mechanisms of dark excitonic states | [['we', 'present', 'a', 'comprehensive', 'optical', 'study', 'of', 'the', 'excitonic', 'zeeman', 'effects', 'in', 'transition', 'metal', 'dichalcogenide', 'monolayers', 'which', 'are', 'discussed', 'comparatively', 'for', 'selected', 'materials', 'mose_2', 'wse_2', 'and', 'ws_2', 'we', 'introduce', 'a', 'simple', 'semiphenomenological', 'description', 'of', 'the', 'magnetic', 'field', 'evolution', 'of', 'individual', 'electronic', 'states', 'in', 'fundamental', 'subbands', 'by', 'considering', 'three', 'additive', 'components', 'valley', 'spin', 'and', 'orbital', 'terms', 'we', 'corroborate', 'the', 'validity', 'of', 'the', 'proposed', 'description', 'by', 'inspecting', 'the', 'zeemanlike', 'splitting', 'of', 'neutral', 'and', 'charged', 'excitonic', 'resonances', 'in', 'absorptiontype', 'spectra', 'the', 'values', 'of', 'all', 'three', 'terms', 'are', 'estimated', 'based', 'on', 'the', 'experimental', 'data', 'demonstrating', 'the', 'significance', 'of', 'the', 'valley', 'term', 'for', 'a', 'consistent', 'description', 'of', 'magnetic', 'field', 'evolution', 'of', 'optical', 'resonances', 'particularly', 'those', 'corresponding', 'to', 'charged', 'states', 'the', 'established', 'model', 'is', 'further', 'exploited', 'for', 'discussion', 'of', 'magnetoluminescence', 'data', 'we', 'propose', 'an', 'interpretation', 'of', 'the', 'observed', 'large', 'gfactor', 'values', 'of', 'low', 'energy', 'emission', 'lines', 'due', 'to', 'socalled', 'boundlocalized', 'excitons', 'in', 'tungsten', 'based', 'compounds', 'based', 'on', 'the', 'brightening', 'mechanisms', 'of', 'dark', 'excitonic', 'states']] | [-0.15638497010374644, 0.14849336057685236, -0.011783001403128551, 0.08171200447724716, -0.021161401954295625, -0.11871615177091614, 0.07239046046112896, 0.396900813498647, -0.1705022756585697, -0.31060508600655634, -0.019613949024373557, -0.28286390066751915, -0.12400231223292411, 0.17444137911976665, 0.05433241238188782, 0.010122844749345343, 0.019359796262749585, -0.08506118700465863, -0.051565785207162804, -0.16721738108573184, 0.32622544134071296, 0.023647684331057937, 0.2991256377048743, 0.11398383214547755, 0.059514700587480006, 0.002205030789456455, 0.03335114193273483, -0.008813308026926344, -0.14576756288030535, 0.11842981091245214, 0.23599213607324537, -0.048859499356335705, 0.21326292778222947, -0.4172680489125715, -0.18065218140436395, 0.018035465701643355, 0.14352961414001264, 0.1560172980219707, -0.09786370950810326, -0.3127784877540959, 0.03341881245534843, -0.14460642733106946, -0.09620783065599953, -0.1052933072161143, 0.0006569239450322025, -0.002498049302335093, -0.23829676641352812, 0.09685891661140475, 0.021895029413762982, 0.09540128025039438, -0.14717852306747986, -0.17036354817022945, -0.0781115048897162, 0.05226452177936199, 0.052057081846828765, -0.03697468425520951, 0.1656537857885431, -0.13613997936948755, -0.1553454192467983, 0.3962021163089355, -0.10248015952225371, -0.09382667614348755, 0.14798429493412707, -0.1780950724959943, -0.07472403905692564, 0.15310735000700804, 0.14951517556313496, 0.11476709787692328, -0.14557392181306603, 0.04955691919913426, -0.01768432227170961, 0.13983921621849013, 0.012012668504530956, 0.1631767561549475, 0.2607701815973232, 0.17344450830203142, -0.035227625733539894, 0.13133532391560032, -0.13592684358821078, -0.06282589139689686, -0.2509255209733038, -0.159674266069997, -0.19776341589360505, 0.06495317862410645, -0.04524558148716479, -0.16802913821035415, 0.487422616031187, 0.1168804742947553, 0.21211680883221376, -0.02932460675576619, 0.26681498315031077, 0.12560193289878072, 0.06253246150341384, -0.00276087081374209, 0.29660539050841594, 0.1886107013323552, 0.06647054192010954, -0.27663597384331284, 0.03943571169819137, 0.01480524221607217] |
1,803.00377 | Measures that define a compact Cauchy transform | The aim of this work is to provide a geometric characterization of the
positive Radon measures $\mu$ with compact support on the plane such that the
associated Cauchy transform defines a compact operator from $L^2(\mu)$ to
$L^2(\mu).$ It turns out that a crucial role is played by the density of the
measure and by its Menger curvature.
| math.CA | the aim of this work is to provide a geometric characterization of the positive radon measures mu with compact support on the plane such that the associated cauchy transform defines a compact operator from l2mu to l2mu it turns out that a crucial role is played by the density of the measure and by its menger curvature | [['the', 'aim', 'of', 'this', 'work', 'is', 'to', 'provide', 'a', 'geometric', 'characterization', 'of', 'the', 'positive', 'radon', 'measures', 'mu', 'with', 'compact', 'support', 'on', 'the', 'plane', 'such', 'that', 'the', 'associated', 'cauchy', 'transform', 'defines', 'a', 'compact', 'operator', 'from', 'l2mu', 'to', 'l2mu', 'it', 'turns', 'out', 'that', 'a', 'crucial', 'role', 'is', 'played', 'by', 'the', 'density', 'of', 'the', 'measure', 'and', 'by', 'its', 'menger', 'curvature']] | [-0.1247758879081199, 0.08697720334391322, -0.15711090478457904, 0.0800296707638425, -0.08725444760108203, -0.043629481898326626, 0.029114268801844957, 0.3459708588687997, -0.2660033313083675, -0.16003581982824885, 0.1533361575974707, -0.2899537236805548, -0.17720263600022645, 0.1857512814519731, -0.07571679442761499, 0.055967186364190034, 0.025469240585440082, 0.08520253195086282, -0.052043954482334745, -0.1791846949766439, 0.44902249778571884, 0.07626498386002424, 0.2422163051577579, 0.08302824705095734, 0.11231794356973025, -0.019980701397320156, -0.09691771183555063, 0.02025851453736163, -0.16024098376331958, 0.14547174655946724, 0.20689412333855503, 0.14639919578800337, 0.3015310074807259, -0.3441038126812169, -0.1956256553694083, 0.18791446730233075, 0.05904765902577262, -0.09846140035803903, -0.07575705911547534, -0.3130085715991363, 0.0974298428188552, -0.05978776529235275, -0.1834385386206569, -0.09547265687663305, 0.06869575587150298, -0.01553582723595594, -0.2526066938278331, 0.029616167295005238, 0.15680955469673663, 0.0067659074342564535, -0.06527991633118833, -0.07298088831042773, -0.037741877073258684, 0.11358023527192704, 0.014948053636767884, 0.13093819710009388, 0.10289113029118693, -0.03312940293874003, -0.045507648763687986, 0.38359237011325986, -0.09315093038113494, -0.27504681821977883, 0.14090976013246467, -0.18420362469266383, -0.11165266775673158, 0.14738918468356133, 0.13026805751417814, 0.11892162585271555, -0.12133957921086173, 0.15588678336159015, -0.06677258451134294, 0.12058907551200766, 0.07541683674054711, 0.05043918971103011, 0.21738541639295586, 0.136999409286338, 0.1807192796677874, 0.18048542999384695, -0.06314779260592013, -0.06323029760990226, -0.34024673628441077, -0.23767540353889527, -0.24199456767497682, 0.13357660983149944, -0.06499840122246257, -0.17414435254115807, 0.36965150778230865, 0.07605969793114223, 0.20906463593015806, 0.050544369585910125, 0.23831348846617498, 0.1506173244448738, 0.054094218812360054, 0.029127301909683042, 0.14599963992000803, 0.19675306977883897, 0.06731861808517, -0.1999507684938675, 0.025100326696574166, 0.11024142636737802] |
1,803.00378 | An Arbitrary-Order Discontinuous Galerkin Method with One Unknown Per
Element | We propose an arbitrary-order discontinuous Galerkin method for second-order
elliptic problem on general polygonal mesh with only one degree of freedom per
element. This is achieved by locally solving a discrete least-squares over a
neighboring element patch. Under a geometrical condition on the element patch,
we prove an optimal a priori error estimates for the energy norm and for the
L$^2$ norm. The accuracy and the efficiency of the method up to order six on
several polygonal meshes are illustrated by a set of benchmark problems.
| math.NA | we propose an arbitraryorder discontinuous galerkin method for secondorder elliptic problem on general polygonal mesh with only one degree of freedom per element this is achieved by locally solving a discrete leastsquares over a neighboring element patch under a geometrical condition on the element patch we prove an optimal a priori error estimates for the energy norm and for the l2 norm the accuracy and the efficiency of the method up to order six on several polygonal meshes are illustrated by a set of benchmark problems | [['we', 'propose', 'an', 'arbitraryorder', 'discontinuous', 'galerkin', 'method', 'for', 'secondorder', 'elliptic', 'problem', 'on', 'general', 'polygonal', 'mesh', 'with', 'only', 'one', 'degree', 'of', 'freedom', 'per', 'element', 'this', 'is', 'achieved', 'by', 'locally', 'solving', 'a', 'discrete', 'leastsquares', 'over', 'a', 'neighboring', 'element', 'patch', 'under', 'a', 'geometrical', 'condition', 'on', 'the', 'element', 'patch', 'we', 'prove', 'an', 'optimal', 'a', 'priori', 'error', 'estimates', 'for', 'the', 'energy', 'norm', 'and', 'for', 'the', 'l2', 'norm', 'the', 'accuracy', 'and', 'the', 'efficiency', 'of', 'the', 'method', 'up', 'to', 'order', 'six', 'on', 'several', 'polygonal', 'meshes', 'are', 'illustrated', 'by', 'a', 'set', 'of', 'benchmark', 'problems']] | [-0.10618100755003303, -0.011701763241733209, -0.05150244108111554, 0.02231525180341546, -0.03752727202291405, -0.11696614710029302, 0.02846837954238317, 0.40140967117622495, -0.30873844835387415, -0.28349218567356815, 0.15038120238175398, -0.2541827816721918, -0.07226677529167297, 0.20296537663874237, -0.10735779883729857, 0.12551289575910846, 0.11134278749354011, 0.05733844648700121, -0.11716294553412428, -0.2568367492938198, 0.33101297668073065, -0.002367095236644842, 0.255787095899672, 0.04066817444778957, 0.19007127984091207, -0.0535187640980016, 0.008651601748952513, 0.04286216921714503, -0.109465922115284, 0.1816101932971262, 0.22135056998190838, 0.04758121720027872, 0.3340742288395589, -0.41214729339794015, -0.225603774579805, 0.1082093004180595, 0.10182383075508095, 0.08670968147601153, -0.07000666470293952, -0.2569735190657855, 0.11345881657393346, -0.10545334673563132, -0.15561397638453475, -0.0656522803501291, -0.049188682029760164, 0.03031581231977704, -0.389653769072665, 0.08315031173054216, 0.015663945343518674, 0.09583472854696042, -0.047059003261004595, -0.12332093506526245, 0.03516194170481677, 0.05513955886085886, -0.033898410970767484, 0.017870847449832878, 0.017010111356327354, -0.043853328051587, -0.08392793030023228, 0.38303480173872656, -0.03225898325340389, -0.344053813018078, 0.11069724547979963, -0.07164447686396712, -0.06451981363623122, 0.15211727386232204, 0.2116575303465821, 0.238182466520473, -0.10301123882189046, 0.09498197479081492, -0.04066836229677117, 0.1939751025029393, 0.07541568896213416, -0.03944440060606508, 0.08000370040860806, 0.17088901219066494, 0.19628205594368453, 0.10437391033439442, -0.09087421428710006, -0.10448924396836827, -0.3660191259628465, -0.14679284482046442, -0.2110118269676689, -0.032454990359499704, -0.20393484565503842, -0.20715556502190613, 0.4073202881832109, 0.08376185603434393, 0.1310956156704315, 0.07007523937432399, 0.2973558104618691, 0.12372826204278511, 0.02245158902477733, 0.10486398512215982, 0.19672204290115988, 0.10449511628749586, 0.043846880571732604, -0.24649068126238363, 0.031508056701399216, 0.259743396972501] |
1,803.00379 | Global analytic solutions of the semiconductor Boltzmann-Dirac-Benney
equation with relaxation time approximation | The global existence of a solution of the semiconductor
Boltzmann-Dirac-Benney equation \[ \partial_t f +
\nabla\epsilon(p)\cdot\nabla_x f - \nabla \rho_f(x,t)\cdot\nabla_p f =
\frac{\mathcal F_\lambda(p)-f}\tau, \quad x\in\mathbb{R}^d,\ p\in B, \ t>0 \]
is shown for small $\tau>0$ assuming that the initial data are analytic and
sufficiently close to $\mathcal F_\lambda$. This system contains an interaction
potential $\rho_f(x,t):=\int_{B}f(x,p,t)dp$ being significantly more singular
than the Coulomb potential, which causes major structural difficulties in the
analysis. The semiconductor Boltzmann-Dirac-Benney equation is a model for
ultracold atoms trapped in an optical lattice. Hence, the dispersion relation
is given by $\epsilon(p) = -\sum_{i=1}^d$ $\cos(2\pi p_i)$, $p\in
B=\mathbb{T}^d$ due to the optical lattice and the Fermi-Dirac distribution
$\mathcal F_\lambda(p)=1/(1+\exp(-\lambda_0-\lambda_1\epsilon(p)))$ describes
the equilibrium of ultracold fermionic clouds.
This equation is closely related to the Vlasov-Dirac-Benney equation with
$\epsilon(p)=\frac{p^2}2$, $p\in B=\mathbb R^d$ and r.h.s$.=0$, where the
existence of a global solution is still an open problem. So far, only local
existence and ill-posedness results were found for theses systems.
The key technique is based of the ideas of Mouhot and Villani by using
Gevrey-type norms which vary over time. The global existence result for small
initial data is also shown for a far more general setting, namely \[\partial_t
f + Lf=Q(f),\] where $L$ is a generator of an $C^0$-group with $\|e^{tL}\|\leq
Ce^{\omega t}$ for all $t\in\mathbb R$ and $\omega>0$ and, where further
additional analytic properties of $L$ and $Q$ are assumed.
| math.AP | the global existence of a solution of the semiconductor boltzmanndiracbenney equation partial_t f nablaepsilonpcdotnabla_x f nabla rho_fxtcdotnabla_p f fracmathcal f_lambdapftau quad xinmathbbrd pin b t0 is shown for small tau0 assuming that the initial data are analytic and sufficiently close to mathcal f_lambda this system contains an interaction potential rho_fxtint_bfxptdp being significantly more singular than the coulomb potential which causes major structural difficulties in the analysis the semiconductor boltzmanndiracbenney equation is a model for ultracold atoms trapped in an optical lattice hence the dispersion relation is given by epsilonp sum_i1d cos2pi p_i pin bmathbbtd due to the optical lattice and the fermidirac distribution mathcal f_lambdap11explambda_0lambda_1epsilonp describes the equilibrium of ultracold fermionic clouds this equation is closely related to the vlasovdiracbenney equation with epsilonpfracp22 pin bmathbb rd and rhs0 where the existence of a global solution is still an open problem so far only local existence and illposedness results were found for theses systems the key technique is based of the ideas of mouhot and villani by using gevreytype norms which vary over time the global existence result for small initial data is also shown for a far more general setting namely partial_t f lfqf where l is a generator of an c0group with etlleq ceomega t for all tinmathbb r and omega0 and where further additional analytic properties of l and q are assumed | [['the', 'global', 'existence', 'of', 'a', 'solution', 'of', 'the', 'semiconductor', 'boltzmanndiracbenney', 'equation', 'partial_t', 'f', 'nablaepsilonpcdotnabla_x', 'f', 'nabla', 'rho_fxtcdotnabla_p', 'f', 'fracmathcal', 'f_lambdapftau', 'quad', 'xinmathbbrd', 'pin', 'b', 't0', 'is', 'shown', 'for', 'small', 'tau0', 'assuming', 'that', 'the', 'initial', 'data', 'are', 'analytic', 'and', 'sufficiently', 'close', 'to', 'mathcal', 'f_lambda', 'this', 'system', 'contains', 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1,803.0038 | Detecting Volcano Deformation in InSAR using Deep learning | Globally 800 million people live within 100 km of a volcano and currently
1500 volcanoes are considered active, but half of these have no ground-based
monitoring. Alternatively, satellite radar (InSAR) can be employed to observe
volcanic ground deformation, which has shown a significant statistical link to
eruptions. Modern satellites provide large coverage with high resolution
signals, leading to huge amounts of data. The explosion in data has brought
major challenges associated with timely dissemination of information and
distinguishing volcano deformation patterns from noise, which currently relies
on manual inspection. Moreover, volcano observatories still lack expertise to
exploit satellite datasets, particularly in developing countries. This paper
presents a novel approach to detect volcanic ground deformation automatically
from wrapped-phase InSAR images. Convolutional neural networks (CNN) are
employed to detect unusual patterns within the radar data.
| cs.CV | globally 800 million people live within 100 km of a volcano and currently 1500 volcanoes are considered active but half of these have no groundbased monitoring alternatively satellite radar insar can be employed to observe volcanic ground deformation which has shown a significant statistical link to eruptions modern satellites provide large coverage with high resolution signals leading to huge amounts of data the explosion in data has brought major challenges associated with timely dissemination of information and distinguishing volcano deformation patterns from noise which currently relies on manual inspection moreover volcano observatories still lack expertise to exploit satellite datasets particularly in developing countries this paper presents a novel approach to detect volcanic ground deformation automatically from wrappedphase insar images convolutional neural networks cnn are employed to detect unusual patterns within the radar data | [['globally', '800', 'million', 'people', 'live', 'within', '100', 'km', 'of', 'a', 'volcano', 'and', 'currently', '1500', 'volcanoes', 'are', 'considered', 'active', 'but', 'half', 'of', 'these', 'have', 'no', 'groundbased', 'monitoring', 'alternatively', 'satellite', 'radar', 'insar', 'can', 'be', 'employed', 'to', 'observe', 'volcanic', 'ground', 'deformation', 'which', 'has', 'shown', 'a', 'significant', 'statistical', 'link', 'to', 'eruptions', 'modern', 'satellites', 'provide', 'large', 'coverage', 'with', 'high', 'resolution', 'signals', 'leading', 'to', 'huge', 'amounts', 'of', 'data', 'the', 'explosion', 'in', 'data', 'has', 'brought', 'major', 'challenges', 'associated', 'with', 'timely', 'dissemination', 'of', 'information', 'and', 'distinguishing', 'volcano', 'deformation', 'patterns', 'from', 'noise', 'which', 'currently', 'relies', 'on', 'manual', 'inspection', 'moreover', 'volcano', 'observatories', 'still', 'lack', 'expertise', 'to', 'exploit', 'satellite', 'datasets', 'particularly', 'in', 'developing', 'countries', 'this', 'paper', 'presents', 'a', 'novel', 'approach', 'to', 'detect', 'volcanic', 'ground', 'deformation', 'automatically', 'from', 'wrappedphase', 'insar', 'images', 'convolutional', 'neural', 'networks', 'cnn', 'are', 'employed', 'to', 'detect', 'unusual', 'patterns', 'within', 'the', 'radar', 'data']] | [-0.08694426280654673, 0.08893127506248709, -0.05662699477551015, 0.08714802326420952, -0.12479733795809902, -0.12907479488802023, 0.023708402987086402, 0.39780630538926315, -0.2250971645624482, -0.39156996276705786, 0.16068772054137312, -0.32249610808404877, -0.1640255650071273, 0.20391195586399363, -0.16541216181787222, 0.04251524353140783, 0.15524530394661024, -0.0026996849344157496, -0.018140815070627214, -0.21624425829465227, 0.21947078857416832, 0.0983372941430259, 0.32033747008868624, -0.008826036779980239, 0.10674305415088206, -0.07462802887158959, -0.08286207451317039, -0.06908881270739817, -0.06985557300941948, 0.1383842027529941, 0.3919044042678368, 0.18717709179491476, 0.26428489628619045, -0.46489188145089866, -0.24460708665052303, 0.11412102472093097, 0.12801788750417964, 0.058103718547280914, -0.05016272001518147, -0.3586796045555432, 0.06487991635274507, -0.19045123638921327, -0.1051711362503868, -0.08286566095850326, 0.024677799717887285, -0.01942716052237534, -0.21342812279323325, 0.04358929071463365, -0.049007702503297335, 0.16668103864815617, -0.04506907071673164, -0.07964985487156344, -0.022934071800174347, 0.17353397212516433, 0.06746683260723528, 0.05817744226100385, 0.15014396469402863, -0.1178956345472961, -0.06034153302837359, 0.3702886402379385, -0.014336353024341782, -0.06812423096770155, 0.2309896095088662, -0.12464493629697682, -0.1425541794711822, 0.22051825208232312, 0.21941722598744387, 0.0252844690782004, -0.21043376693207966, -0.0479508154032948, 0.004119455324191796, 0.19722067850774952, 0.09141418614067641, 0.007905168313519062, 0.2576226816421613, 0.23241547839996174, 0.08472718667813149, 0.07097972905581915, -0.23951269549018303, -0.05597534679975781, -0.1511677951509084, -0.06545120221912805, -0.15462160104521572, 0.025845578932938606, -0.04473441923925604, -0.14619838700282276, 0.35931982449343414, 0.18251135088223264, 0.1635210885468492, -0.007983688588947394, 0.3213630972808241, -0.041282730029993935, 0.15939742482890537, 0.07103405101574901, 0.24665748062578582, 0.04199802680151131, 0.17085639208457187, -0.12033929780678809, 0.08240283670471071, -0.02817397301358388] |
1,803.00381 | Dynamics of vortices in chiral media: the chiral propulsion effect | We study the motion of vortex filaments in chiral media, and find a
semi-classical analog of the anomaly-induced chiral magnetic effect. The
helical solitonic excitations on vortices in a parity-breaking medium are found
to carry an additional energy flow along the vortex in the direction dictated
by the sign of chirality imbalance; we call this new transport phenomenon the
Chiral Propulsion Effect (CPE). The dynamics of the filament is described by a
modified version of the localized induction equation in the parity-breaking
background. We analyze the linear stability of simple vortex configurations,
and study the effects of chiral media on the excitation spectrum and the growth
rate of the unstable modes. It is also shown that, if the equation of motion of
the filament is symmetric under the simultaneous reversal of parity and time,
the resulting planar solution cannot transport energy.
| hep-th cond-mat.str-el hep-ph nucl-th | we study the motion of vortex filaments in chiral media and find a semiclassical analog of the anomalyinduced chiral magnetic effect the helical solitonic excitations on vortices in a paritybreaking medium are found to carry an additional energy flow along the vortex in the direction dictated by the sign of chirality imbalance we call this new transport phenomenon the chiral propulsion effect cpe the dynamics of the filament is described by a modified version of the localized induction equation in the paritybreaking background we analyze the linear stability of simple vortex configurations and study the effects of chiral media on the excitation spectrum and the growth rate of the unstable modes it is also shown that if the equation of motion of the filament is symmetric under the simultaneous reversal of parity and time the resulting planar solution cannot transport energy | [['we', 'study', 'the', 'motion', 'of', 'vortex', 'filaments', 'in', 'chiral', 'media', 'and', 'find', 'a', 'semiclassical', 'analog', 'of', 'the', 'anomalyinduced', 'chiral', 'magnetic', 'effect', 'the', 'helical', 'solitonic', 'excitations', 'on', 'vortices', 'in', 'a', 'paritybreaking', 'medium', 'are', 'found', 'to', 'carry', 'an', 'additional', 'energy', 'flow', 'along', 'the', 'vortex', 'in', 'the', 'direction', 'dictated', 'by', 'the', 'sign', 'of', 'chirality', 'imbalance', 'we', 'call', 'this', 'new', 'transport', 'phenomenon', 'the', 'chiral', 'propulsion', 'effect', 'cpe', 'the', 'dynamics', 'of', 'the', 'filament', 'is', 'described', 'by', 'a', 'modified', 'version', 'of', 'the', 'localized', 'induction', 'equation', 'in', 'the', 'paritybreaking', 'background', 'we', 'analyze', 'the', 'linear', 'stability', 'of', 'simple', 'vortex', 'configurations', 'and', 'study', 'the', 'effects', 'of', 'chiral', 'media', 'on', 'the', 'excitation', 'spectrum', 'and', 'the', 'growth', 'rate', 'of', 'the', 'unstable', 'modes', 'it', 'is', 'also', 'shown', 'that', 'if', 'the', 'equation', 'of', 'motion', 'of', 'the', 'filament', 'is', 'symmetric', 'under', 'the', 'simultaneous', 'reversal', 'of', 'parity', 'and', 'time', 'the', 'resulting', 'planar', 'solution', 'can', 'not', 'transport', 'energy']] | [-0.23479974373410994, 0.19541714008019098, -0.08723857054408167, 0.04385973563955598, -0.0794463167360551, -0.05098127195624713, -0.01132070136741853, 0.3353994399469844, -0.2879223943176404, -0.23538144130233518, 0.06271743250709079, -0.24233140820033952, -0.14722777604991177, 0.12442991224734086, 0.03446895393713469, 0.02648623554613179, -0.005304450416793181, 0.04571530004491059, -0.024269822743904948, -0.15759868796875465, 0.31959476258973, 0.033591837710151645, 0.33770233582259274, 0.06722133780926348, 0.09348117013816053, 0.003218973153861056, 0.019174508855376447, 0.04431154824872042, -0.15474722718248762, 0.0374732075232855, 0.11161631088323472, -0.03755819302996699, 0.18247691338347324, -0.47822639009010204, -0.21571313608831294, 0.049717393707038024, 0.16679735095109002, 0.17433398414049034, -0.07278654265195347, -0.28719261032201243, 0.060123495278801294, -0.13924579921139169, -0.1990678609073372, -0.05138303612811412, 0.01595606105434905, 0.02118481043603232, -0.23300219352134097, 0.12896341388585197, 0.09378655563894836, 0.0334894148637803, -0.09630117348959329, -0.029048450222291366, -0.08266463110380819, 0.06391978803300627, 0.09565435163840703, -0.010191096869317002, 0.14928331988220903, -0.19342405942406402, -0.13277427274996126, 0.3987935943673299, -0.06493073413853573, -0.2143720185229822, 0.12547800265593637, -0.14691356259306343, -0.0789921041750218, 0.16517781393445083, 0.1580159943735286, 0.11558052036963717, -0.1177634111467034, 0.04657139953290002, -0.0743558440016399, 0.1288967508598047, 0.07486233230955688, 0.018783087104322835, 0.24669954156450613, 0.14878291123002296, 0.07326765353201141, 0.1659340320063204, -0.11188942300849332, -0.12054207338295667, -0.3046648623290616, -0.13769650688185028, -0.16500040414434872, 0.04781769744202931, -0.04695198631765072, -0.1791458876914604, 0.43234614473701993, 0.11668638133568505, 0.16973925981950178, -0.04097540220278512, 0.26720543758300935, 0.14479351751375155, 0.06634156623671592, 0.07952944343765332, 0.2801108526616869, 0.1719999942472729, 0.14592021206126574, -0.3548030270702979, -0.001408154594147919, 0.0833492953572947] |
1,803.00382 | Early-warning signals for bifurcations in random dynamical systems with
bounded noise | We consider discrete-time one-dimensional random dynamical systems with
bounded noise, which generate an associated set-valued dynamical system. We
provide necessary and sufficient conditions for a discontinuous bifurcation of
a minimal invariant set of the set-valued dynamical system in terms of the
derivatives of the so-called extremal maps. We propose an algorithm for
reconstructing the derivatives of the extremal maps from a time series that is
generated by iterations of the original random dynamical system. We demonstrate
that the derivative reconstructed for different parameters can be used as an
early-warning signal to detect an upcoming bifurcation, and apply the algorithm
to the bifurcation analysis of the stochastic return map of the Koper model,
which is a three-dimensional multiple time scale ordinary differential equation
used as prototypical model for the formation of mixed-mode oscillation
patterns. We apply our algorithm to data generated by this map to detect an
upcoming transition.
| math.DS | we consider discretetime onedimensional random dynamical systems with bounded noise which generate an associated setvalued dynamical system we provide necessary and sufficient conditions for a discontinuous bifurcation of a minimal invariant set of the setvalued dynamical system in terms of the derivatives of the socalled extremal maps we propose an algorithm for reconstructing the derivatives of the extremal maps from a time series that is generated by iterations of the original random dynamical system we demonstrate that the derivative reconstructed for different parameters can be used as an earlywarning signal to detect an upcoming bifurcation and apply the algorithm to the bifurcation analysis of the stochastic return map of the koper model which is a threedimensional multiple time scale ordinary differential equation used as prototypical model for the formation of mixedmode oscillation patterns we apply our algorithm to data generated by this map to detect an upcoming transition | [['we', 'consider', 'discretetime', 'onedimensional', 'random', 'dynamical', 'systems', 'with', 'bounded', 'noise', 'which', 'generate', 'an', 'associated', 'setvalued', 'dynamical', 'system', 'we', 'provide', 'necessary', 'and', 'sufficient', 'conditions', 'for', 'a', 'discontinuous', 'bifurcation', 'of', 'a', 'minimal', 'invariant', 'set', 'of', 'the', 'setvalued', 'dynamical', 'system', 'in', 'terms', 'of', 'the', 'derivatives', 'of', 'the', 'socalled', 'extremal', 'maps', 'we', 'propose', 'an', 'algorithm', 'for', 'reconstructing', 'the', 'derivatives', 'of', 'the', 'extremal', 'maps', 'from', 'a', 'time', 'series', 'that', 'is', 'generated', 'by', 'iterations', 'of', 'the', 'original', 'random', 'dynamical', 'system', 'we', 'demonstrate', 'that', 'the', 'derivative', 'reconstructed', 'for', 'different', 'parameters', 'can', 'be', 'used', 'as', 'an', 'earlywarning', 'signal', 'to', 'detect', 'an', 'upcoming', 'bifurcation', 'and', 'apply', 'the', 'algorithm', 'to', 'the', 'bifurcation', 'analysis', 'of', 'the', 'stochastic', 'return', 'map', 'of', 'the', 'koper', 'model', 'which', 'is', 'a', 'threedimensional', 'multiple', 'time', 'scale', 'ordinary', 'differential', 'equation', 'used', 'as', 'prototypical', 'model', 'for', 'the', 'formation', 'of', 'mixedmode', 'oscillation', 'patterns', 'we', 'apply', 'our', 'algorithm', 'to', 'data', 'generated', 'by', 'this', 'map', 'to', 'detect', 'an', 'upcoming', 'transition']] | [-0.1387717403519452, 0.06636860782433555, -0.09211710492712194, 0.08745680799539342, -0.042945717558944344, -0.10581142850994207, 0.013767244723763922, 0.31097230598695474, -0.3316212815006037, -0.25466367994699424, 0.15756983956797174, -0.24549813046296304, -0.2134549122530262, 0.19653090987373395, -0.05750512347770603, 0.10763986055688882, 0.03935215583486432, 0.040730377225200586, -0.07004701419783807, -0.21267634934456264, 0.33365974609261834, 0.018519771683369636, 0.20022527326049433, -0.04153162277479832, 0.15041731461973837, -0.022685539819035882, -0.016613200890542183, 0.00721500107917834, -0.14542188920205337, 0.07295984350069351, 0.21937571981657497, 0.143596356927348, 0.24065893852605005, -0.39601280829693014, -0.2060233172520089, 0.16500999328422336, 0.12382669865159236, 0.11919242397699556, -0.04262181388440769, -0.3283388366973078, 0.08968526654958574, -0.14542871463502682, -0.14232022257056087, -0.11653061808288298, -0.008919089277451104, 0.050426825726439396, -0.335160348954535, 0.07133553295106804, 0.049017505362359305, 0.06306794660191077, -0.07890988884153902, -0.015245142547772391, -0.0564135553919383, 0.13630537807664558, -0.013178221411795381, 0.030597863706281862, 0.09935899468365351, -0.07718988339301841, -0.14863800061583468, 0.3621890688805866, -0.11794278922969022, -0.20713643551760078, 0.17505838730800394, -0.12596479471697397, -0.1425208002297409, 0.1447587003088154, 0.2205377969186048, 0.1258865193891767, -0.17882858878127425, 0.03854507940264444, -0.036312602347115405, 0.1718209404984058, 0.030991705408244318, -0.017228333562662877, 0.17213662541345567, 0.16866444590979537, 0.11738231514715841, 0.19123119790330403, -0.09557106363236262, -0.10140735731535666, -0.3107265266939853, -0.12272815737634192, -0.15534479621281796, 0.030877641522533236, -0.11399293156490957, -0.21333907420646298, 0.43133651997541655, 0.19363461739723445, 0.22208421286887717, 0.07935383834095823, 0.2679192925507605, 0.18860451539483228, -0.0022653924805470867, 0.02092815949141073, 0.17241050721762852, 0.11418185322985959, 0.10039175408294525, -0.22389189348826688, 0.049430151029515104, 0.10309617337957575] |
1,803.00383 | BRST quantization of Yang-Mills theory: A purely Hamiltonian approach on
Fock space | We develop the basic ideas and equations for the BRST quantization of
Yang-Mills theories in an explicit Hamiltonian approach, without any reference
to the Lagrangian approach at any stage of the development. We present a new
representation of ghost fields that combines desirable self-adjointness
properties with canonical anticommutation relations for ghost creation and
annihilation operators, thus enabling us to characterize the physical states on
a well-defined Fock space. The Hamiltonian is constructed by piecing together
simple BRST invariant operators to obtain a minimal invariant extension of the
free theory. It is verified that the evolution equations implied by the
resulting minimal Hamiltonian provide a quantum version of the classical
Yang-Mills equations. The modifications and requirements for the inclusion of
matter are discussed in detail.
| hep-th | we develop the basic ideas and equations for the brst quantization of yangmills theories in an explicit hamiltonian approach without any reference to the lagrangian approach at any stage of the development we present a new representation of ghost fields that combines desirable selfadjointness properties with canonical anticommutation relations for ghost creation and annihilation operators thus enabling us to characterize the physical states on a welldefined fock space the hamiltonian is constructed by piecing together simple brst invariant operators to obtain a minimal invariant extension of the free theory it is verified that the evolution equations implied by the resulting minimal hamiltonian provide a quantum version of the classical yangmills equations the modifications and requirements for the inclusion of matter are discussed in detail | [['we', 'develop', 'the', 'basic', 'ideas', 'and', 'equations', 'for', 'the', 'brst', 'quantization', 'of', 'yangmills', 'theories', 'in', 'an', 'explicit', 'hamiltonian', 'approach', 'without', 'any', 'reference', 'to', 'the', 'lagrangian', 'approach', 'at', 'any', 'stage', 'of', 'the', 'development', 'we', 'present', 'a', 'new', 'representation', 'of', 'ghost', 'fields', 'that', 'combines', 'desirable', 'selfadjointness', 'properties', 'with', 'canonical', 'anticommutation', 'relations', 'for', 'ghost', 'creation', 'and', 'annihilation', 'operators', 'thus', 'enabling', 'us', 'to', 'characterize', 'the', 'physical', 'states', 'on', 'a', 'welldefined', 'fock', 'space', 'the', 'hamiltonian', 'is', 'constructed', 'by', 'piecing', 'together', 'simple', 'brst', 'invariant', 'operators', 'to', 'obtain', 'a', 'minimal', 'invariant', 'extension', 'of', 'the', 'free', 'theory', 'it', 'is', 'verified', 'that', 'the', 'evolution', 'equations', 'implied', 'by', 'the', 'resulting', 'minimal', 'hamiltonian', 'provide', 'a', 'quantum', 'version', 'of', 'the', 'classical', 'yangmills', 'equations', 'the', 'modifications', 'and', 'requirements', 'for', 'the', 'inclusion', 'of', 'matter', 'are', 'discussed', 'in', 'detail']] | [-0.11856302972545006, 0.15183156322147565, -0.1306135800306595, 0.11433994736815352, -0.1096543651412151, -0.1009281111069985, -0.017552651473388615, 0.296842806123858, -0.23644549832979758, -0.2715856624286502, 0.05209924338381707, -0.2240715942893838, -0.15865201258190698, 0.13407983643288213, -0.06165019947577328, 0.06051159736460015, 0.07071633109702699, 0.04430969523462174, -0.14330036032779683, -0.21865257799753077, 0.366183910252256, 0.026348190910686107, 0.24495730629461188, 0.021399650692699418, 0.17228355128017644, 0.05564320242903646, -0.02879354999881358, -0.024387017294488503, -0.15002738316451864, 0.14728132126648205, 0.2107923102129491, 0.11621925447346462, 0.19373283551765547, -0.4403605498598828, -0.21987414682535605, 0.057732115604824594, 0.10733676422011829, 0.13326273294566612, -0.03128868482928092, -0.2999075537889204, 0.06447283399346258, -0.18088069802428025, -0.20444154465240577, -0.16304855961202375, -0.016103912670645985, -0.059587163092087836, -0.25498126418660244, 0.034517073810416, 0.05501359341489602, 0.044163664320348614, -0.08880413471225408, -0.04877701390444512, -0.04500832596057726, 0.07332155490744739, -0.01632165921393842, 0.0051609727163468636, 0.10961939201449915, -0.15537286912759526, -0.1443900909845627, 0.39046953932472295, -0.06810744011196337, -0.2708874276591345, 0.14853436601979117, -0.06906810073903011, -0.1645847693284703, 0.07956301929211364, 0.06270213740138006, 0.11942731291656533, -0.191954834574473, 0.20056985909984854, -0.009364091141539957, 0.10766907934580118, 0.05455137875246545, 0.08375641910148965, 0.16669076142622338, 0.07720718098120884, 0.08510827438365068, 0.12725648819093383, 0.043296531160106705, -0.15724315740836545, -0.41511621206788524, -0.18067135799285625, -0.12416746184763108, 0.05906147815685178, -0.0850782172409151, -0.16732074666558014, 0.40732569754078624, 0.14827036258957965, 0.13622176432944533, 0.062366196479659616, 0.2634826585905807, 0.17009919152535016, 0.10305006844517324, 0.05155803300928505, 0.19756945382781146, 0.21087805618486938, 0.07112894780678494, -0.24563064043937746, -0.0542967154873505, 0.1740864701587857] |
1,803.00384 | Fibres of Failure: Classifying errors in predictive processes | We describe Fibres of Failure (FiFa), a method to classify failure modes of
predictive processes using the Mapper algorithm from Topological Data Analysis.
Our method uses Mapper to build a graph model of input data stratified by
prediction error.
Groupings found in high-error regions of the Mapper model then provide
distinct failure modes of the predictive process.
We demonstrate FiFa on misclassifications of MNIST images with added noise,
and demonstrate two ways to use the failure mode classification: either to
produce a correction layer that adjusts predictions by similarity to the
failure modes; or to inspect members of the failure modes to illustrate and
investigate what characterizes each failure mode.
| cs.CV cs.DM math.AT | we describe fibres of failure fifa a method to classify failure modes of predictive processes using the mapper algorithm from topological data analysis our method uses mapper to build a graph model of input data stratified by prediction error groupings found in higherror regions of the mapper model then provide distinct failure modes of the predictive process we demonstrate fifa on misclassifications of mnist images with added noise and demonstrate two ways to use the failure mode classification either to produce a correction layer that adjusts predictions by similarity to the failure modes or to inspect members of the failure modes to illustrate and investigate what characterizes each failure mode | [['we', 'describe', 'fibres', 'of', 'failure', 'fifa', 'a', 'method', 'to', 'classify', 'failure', 'modes', 'of', 'predictive', 'processes', 'using', 'the', 'mapper', 'algorithm', 'from', 'topological', 'data', 'analysis', 'our', 'method', 'uses', 'mapper', 'to', 'build', 'a', 'graph', 'model', 'of', 'input', 'data', 'stratified', 'by', 'prediction', 'error', 'groupings', 'found', 'in', 'higherror', 'regions', 'of', 'the', 'mapper', 'model', 'then', 'provide', 'distinct', 'failure', 'modes', 'of', 'the', 'predictive', 'process', 'we', 'demonstrate', 'fifa', 'on', 'misclassifications', 'of', 'mnist', 'images', 'with', 'added', 'noise', 'and', 'demonstrate', 'two', 'ways', 'to', 'use', 'the', 'failure', 'mode', 'classification', 'either', 'to', 'produce', 'a', 'correction', 'layer', 'that', 'adjusts', 'predictions', 'by', 'similarity', 'to', 'the', 'failure', 'modes', 'or', 'to', 'inspect', 'members', 'of', 'the', 'failure', 'modes', 'to', 'illustrate', 'and', 'investigate', 'what', 'characterizes', 'each', 'failure', 'mode']] | [-0.0651753058149056, 0.01925741019235416, -0.11000879435503685, 0.067433241909897, -0.07367416061460971, -0.16182067923840474, 0.11079013194804165, 0.37071981904181567, -0.2616044900634072, -0.31298099809580227, 0.08584577071676243, -0.2926455762745305, -0.19290348452896897, 0.15012987408236686, -0.11953226955268871, 0.06103052972307937, 0.10702720116823912, 0.02376958161237946, 0.013826334474354305, -0.23202282466041982, 0.29366407616233287, 0.039755200408399104, 0.37101854386485433, -0.06429608579809692, 0.08444021937479688, -0.02967439105480232, -0.0654837127436291, -0.007328507172959772, -0.12708694486573222, 0.11112066037140639, 0.24364475231744687, 0.17780150583183224, 0.28207509031688627, -0.4134787818501619, -0.19053130728954618, 0.06310415709475901, 0.12326481186530806, 0.10046404959125953, 0.03805017828073522, -0.29286696693267333, 0.1314549526125616, -0.16777975808151743, -0.08127179025799375, -0.11034361845602027, -0.02900961471023038, -0.012442471368492327, -0.29865649682046336, 0.032761588129638274, 0.03312352808531035, 0.03724886758083647, -0.02991636340421709, -0.04532924702137031, -0.03771166393575682, 0.14359603402077814, 0.06013824908163356, -0.0026125786487351766, 0.18286632169884715, -0.11216832745386372, -0.17645015439272604, 0.36432925933464005, -0.05615005927499045, -0.18712898994033986, 0.18573577058586208, -0.08722313489680263, -0.10706115508748387, 0.1409869113767689, 0.2417792982548814, 0.037480877593837, -0.09461895481852645, -0.0612187500376339, 0.04622433847663077, 0.19488217719745907, 0.054956672033718366, -0.031526610005477615, 0.16168763185851276, 0.18561331397345798, 0.01820744640850039, 0.18911043678156353, -0.14840182250323283, -0.009162784181535244, -0.2739328918047249, -0.15130642750300466, -0.16551040737752126, -0.036056143131149423, -0.08622211483704054, -0.19331133022735064, 0.4632382483856583, 0.18867598545991562, 0.23860819544643164, 0.08745110772380775, 0.3227702820758251, 0.021924896107520908, 0.08805353817614642, 0.0745260995855047, 0.21824563223529947, 0.09841912543739785, 0.0061675927390090445, -0.18421732440209862, 0.07886564088350331, 0.050779551039026545] |
1,803.00385 | MAGAN: Aligning Biological Manifolds | It is increasingly common in many types of natural and physical systems
(especially biological systems) to have different types of measurements
performed on the same underlying system. In such settings, it is important to
align the manifolds arising from each measurement in order to integrate such
data and gain an improved picture of the system. We tackle this problem using
generative adversarial networks (GANs). Recently, GANs have been utilized to
try to find correspondences between sets of samples. However, these GANs are
not explicitly designed for proper alignment of manifolds. We present a new GAN
called the Manifold-Aligning GAN (MAGAN) that aligns two manifolds such that
related points in each measurement space are aligned together. We demonstrate
applications of MAGAN in single-cell biology in integrating two different
measurement types together. In our demonstrated examples, cells from the same
tissue are measured with both genomic (single-cell RNA-sequencing) and
proteomic (mass cytometry) technologies. We show that the MAGAN successfully
aligns them such that known correlations between measured markers are improved
compared to other recently proposed models.
| cs.CV | it is increasingly common in many types of natural and physical systems especially biological systems to have different types of measurements performed on the same underlying system in such settings it is important to align the manifolds arising from each measurement in order to integrate such data and gain an improved picture of the system we tackle this problem using generative adversarial networks gans recently gans have been utilized to try to find correspondences between sets of samples however these gans are not explicitly designed for proper alignment of manifolds we present a new gan called the manifoldaligning gan magan that aligns two manifolds such that related points in each measurement space are aligned together we demonstrate applications of magan in singlecell biology in integrating two different measurement types together in our demonstrated examples cells from the same tissue are measured with both genomic singlecell rnasequencing and proteomic mass cytometry technologies we show that the magan successfully aligns them such that known correlations between measured markers are improved compared to other recently proposed models | [['it', 'is', 'increasingly', 'common', 'in', 'many', 'types', 'of', 'natural', 'and', 'physical', 'systems', 'especially', 'biological', 'systems', 'to', 'have', 'different', 'types', 'of', 'measurements', 'performed', 'on', 'the', 'same', 'underlying', 'system', 'in', 'such', 'settings', 'it', 'is', 'important', 'to', 'align', 'the', 'manifolds', 'arising', 'from', 'each', 'measurement', 'in', 'order', 'to', 'integrate', 'such', 'data', 'and', 'gain', 'an', 'improved', 'picture', 'of', 'the', 'system', 'we', 'tackle', 'this', 'problem', 'using', 'generative', 'adversarial', 'networks', 'gans', 'recently', 'gans', 'have', 'been', 'utilized', 'to', 'try', 'to', 'find', 'correspondences', 'between', 'sets', 'of', 'samples', 'however', 'these', 'gans', 'are', 'not', 'explicitly', 'designed', 'for', 'proper', 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1,803.00386 | Context-Aware Learning using Transferable Features for Classification of
Breast Cancer Histology Images | Convolutional neural networks (CNNs) have been recently used for a variety of
histology image analysis. However, availability of a large dataset is a major
prerequisite for training a CNN which limits its use by the computational
pathology community. In previous studies, CNNs have demonstrated their
potential in terms of feature generalizability and transferability accompanied
with better performance. Considering these traits of CNN, we propose a simple
yet effective method which leverages the strengths of CNN combined with the
advantages of including contextual information, particularly designed for a
small dataset. Our method consists of two main steps: first it uses the
activation features of CNN trained for a patch-based classification and then it
trains a separate classifier using features of overlapping patches to perform
image-based classification using the contextual information. The proposed
framework outperformed the state-of-the-art method for breast cancer
classification.
| cs.CV | convolutional neural networks cnns have been recently used for a variety of histology image analysis however availability of a large dataset is a major prerequisite for training a cnn which limits its use by the computational pathology community in previous studies cnns have demonstrated their potential in terms of feature generalizability and transferability accompanied with better performance considering these traits of cnn we propose a simple yet effective method which leverages the strengths of cnn combined with the advantages of including contextual information particularly designed for a small dataset our method consists of two main steps first it uses the activation features of cnn trained for a patchbased classification and then it trains a separate classifier using features of overlapping patches to perform imagebased classification using the contextual information the proposed framework outperformed the stateoftheart method for breast cancer classification | [['convolutional', 'neural', 'networks', 'cnns', 'have', 'been', 'recently', 'used', 'for', 'a', 'variety', 'of', 'histology', 'image', 'analysis', 'however', 'availability', 'of', 'a', 'large', 'dataset', 'is', 'a', 'major', 'prerequisite', 'for', 'training', 'a', 'cnn', 'which', 'limits', 'its', 'use', 'by', 'the', 'computational', 'pathology', 'community', 'in', 'previous', 'studies', 'cnns', 'have', 'demonstrated', 'their', 'potential', 'in', 'terms', 'of', 'feature', 'generalizability', 'and', 'transferability', 'accompanied', 'with', 'better', 'performance', 'considering', 'these', 'traits', 'of', 'cnn', 'we', 'propose', 'a', 'simple', 'yet', 'effective', 'method', 'which', 'leverages', 'the', 'strengths', 'of', 'cnn', 'combined', 'with', 'the', 'advantages', 'of', 'including', 'contextual', 'information', 'particularly', 'designed', 'for', 'a', 'small', 'dataset', 'our', 'method', 'consists', 'of', 'two', 'main', 'steps', 'first', 'it', 'uses', 'the', 'activation', 'features', 'of', 'cnn', 'trained', 'for', 'a', 'patchbased', 'classification', 'and', 'then', 'it', 'trains', 'a', 'separate', 'classifier', 'using', 'features', 'of', 'overlapping', 'patches', 'to', 'perform', 'imagebased', 'classification', 'using', 'the', 'contextual', 'information', 'the', 'proposed', 'framework', 'outperformed', 'the', 'stateoftheart', 'method', 'for', 'breast', 'cancer', 'classification']] | [-0.005533168416669858, -0.08247704645618796, -0.06299027020244725, 0.04664078445057385, -0.08638736650879894, -0.19907587887386657, 0.0015796483527602894, 0.4462028972704762, -0.21275210440424935, -0.32523939819075165, 0.05558642370160669, -0.25138823939819954, -0.23550334276016135, 0.2080981430584091, -0.11124947165870773, 0.12756905174839112, 0.17836275999434292, 0.05519156833179295, -0.041403047178339744, -0.32075873561552726, 0.3061575291794725, 0.04298428600387914, 0.39473732096043285, 0.024840871184798222, 0.16674843750162316, -0.06079743671164449, -0.06064743966063751, 0.015421991136723332, -0.01199946347457756, 0.22041235305036286, 0.33378495562355964, 0.2021787884295918, 0.3602518257917836, -0.4013014472289277, -0.28192678024061024, 0.08930002818316488, 0.16758400023682044, 0.12245543212463547, -0.041218148677476814, -0.3682553873031533, 0.09663916201596813, -0.1713491128757596, 0.04151844521984458, -0.18347534907848706, -0.029477915284223853, -0.006273055069946817, -0.28530665431171653, 0.06644876699423809, 0.07857425934967718, 0.10719528749718198, -0.046275493187463976, -0.13035719582278812, 0.01682530201006947, 0.18283356167376041, 0.010061194366841976, 0.048738037016508834, 0.13716846088063903, -0.21580724389225778, -0.14179277094081044, 0.33072780944806124, -0.045665750868751534, -0.19935976291474486, 0.20409705378115178, 0.023310588200443558, -0.16813630243497235, 0.1370492497459054, 0.2187645816111139, 0.1096376762159967, -0.17532261615851893, -0.024544860594739606, -0.02286250846726554, 0.1749609338186149, 0.04336391005532018, -0.015259823873306492, 0.1668268929493414, 0.3581799152972443, -0.02382632527525337, 0.14135599103257326, -0.21504533339424858, -0.012909097809876714, -0.19022022782425796, -0.09246257676776233, -0.20244406686230962, -0.051851402215626355, -0.10192072302015731, -0.15854738594997408, 0.4770720262213477, 0.21157140812304404, 0.21912560606974044, 0.11223283410072327, 0.3473986120628459, -0.020092980491296788, 0.20648214806902354, 0.04611543828754553, 0.19376329741706805, 0.042437734157179614, 0.11324476660229266, -0.15223425834306648, 0.080303500689167, 0.08384034970076755] |
1,803.00387 | A General Pipeline for 3D Detection of Vehicles | Autonomous driving requires 3D perception of vehicles and other objects in
the in environment. Much of the current methods support 2D vehicle detection.
This paper proposes a flexible pipeline to adopt any 2D detection network and
fuse it with a 3D point cloud to generate 3D information with minimum changes
of the 2D detection networks. To identify the 3D box, an effective model
fitting algorithm is developed based on generalised car models and score maps.
A two-stage convolutional neural network (CNN) is proposed to refine the
detected 3D box. This pipeline is tested on the KITTI dataset using two
different 2D detection networks. The 3D detection results based on these two
networks are similar, demonstrating the flexibility of the proposed pipeline.
The results rank second among the 3D detection algorithms, indicating its
competencies in 3D detection.
| cs.CV eess.IV stat.ML | autonomous driving requires 3d perception of vehicles and other objects in the in environment much of the current methods support 2d vehicle detection this paper proposes a flexible pipeline to adopt any 2d detection network and fuse it with a 3d point cloud to generate 3d information with minimum changes of the 2d detection networks to identify the 3d box an effective model fitting algorithm is developed based on generalised car models and score maps a twostage convolutional neural network cnn is proposed to refine the detected 3d box this pipeline is tested on the kitti dataset using two different 2d detection networks the 3d detection results based on these two networks are similar demonstrating the flexibility of the proposed pipeline the results rank second among the 3d detection algorithms indicating its competencies in 3d detection | [['autonomous', 'driving', 'requires', '3d', 'perception', 'of', 'vehicles', 'and', 'other', 'objects', 'in', 'the', 'in', 'environment', 'much', 'of', 'the', 'current', 'methods', 'support', '2d', 'vehicle', 'detection', 'this', 'paper', 'proposes', 'a', 'flexible', 'pipeline', 'to', 'adopt', 'any', '2d', 'detection', 'network', 'and', 'fuse', 'it', 'with', 'a', '3d', 'point', 'cloud', 'to', 'generate', '3d', 'information', 'with', 'minimum', 'changes', 'of', 'the', '2d', 'detection', 'networks', 'to', 'identify', 'the', '3d', 'box', 'an', 'effective', 'model', 'fitting', 'algorithm', 'is', 'developed', 'based', 'on', 'generalised', 'car', 'models', 'and', 'score', 'maps', 'a', 'twostage', 'convolutional', 'neural', 'network', 'cnn', 'is', 'proposed', 'to', 'refine', 'the', 'detected', '3d', 'box', 'this', 'pipeline', 'is', 'tested', 'on', 'the', 'kitti', 'dataset', 'using', 'two', 'different', '2d', 'detection', 'networks', 'the', '3d', 'detection', 'results', 'based', 'on', 'these', 'two', 'networks', 'are', 'similar', 'demonstrating', 'the', 'flexibility', 'of', 'the', 'proposed', 'pipeline', 'the', 'results', 'rank', 'second', 'among', 'the', '3d', 'detection', 'algorithms', 'indicating', 'its', 'competencies', 'in', '3d', 'detection']] | [-0.05617055109948577, -0.0515223325268487, -0.021343826857285902, 0.0029697980053777643, -0.07001537242424949, -0.201959030216505, -0.01974316236908849, 0.42387485506718375, -0.20642216171494082, -0.3515145981424105, 0.09050726204141564, -0.26917065868003515, -0.21363707365954349, 0.1885261649988345, -0.115095065711477, 0.12024011581353775, 0.14057238329106064, 0.02958202058353516, -0.07025961822022081, -0.2295224149069434, 0.3042085270007031, 0.03951132780639455, 0.3723977580135825, 0.011778094528434688, 0.13220569906372795, -0.04057787852498757, -0.07461243436452658, 0.005780411615406218, -0.060467429578934306, 0.1893699857452221, 0.23182155187396347, 0.17276222737821037, 0.2470736012020258, -0.4445673819795689, -0.2690712641582222, 0.07866915826932253, 0.1316502684391761, 0.11975522308442604, -0.03293920721358411, -0.39824294595575127, 0.09097898905391798, -0.1813094050226295, -0.02566217600285788, -0.09340438032599838, -0.015597819740137579, -0.027184204387166948, -0.28306048509993537, -6.696188874604345e-05, 0.03648404877543833, 0.06390621964408852, -0.08955998345952727, -0.05959543274901294, -0.017644520749694064, 0.19464981751619181, -0.04935619740332614, 0.06812644059862256, 0.16328223217586876, -0.21708018349625705, -0.15095019080292651, 0.3938990563619882, -0.029402576145537963, -0.22286932148477612, 0.24447761023047326, -0.04516681985628298, -0.15519093308830634, 0.12507463628700113, 0.24172579529519733, 0.13006828448701793, -0.15488503233063966, -0.04357892497886068, -0.046518197943976915, 0.1671359198219708, -0.0149307447958135, -0.07391336509574424, 0.2233796800719574, 0.2802071791580495, 0.05235798088559771, 0.12814690185383798, -0.2603160076901344, -0.052654833369374765, -0.16772474889790723, -0.13871415985939914, -0.2059505307500812, -0.06632686974069871, -0.09941861120034232, -0.17235435872263385, 0.4416844763669788, 0.27461552307667103, 0.19341591707437628, 0.10944455628872246, 0.4068953425157815, 0.05116472128943047, 0.11738793974612183, 0.09132452648448046, 0.21388813047686947, -9.91527717012693e-05, 0.11964447219746516, -0.15731461810291342, 0.04685965965858058, 0.11877006063792471] |
1,803.00388 | Learning Filter Scale and Orientation In CNNs | Convolutional neural networks have many hyperparameters such as the filter
size, number of filters, and pooling size, which require manual tuning. Though
deep stacked structures are able to create multi-scale and hierarchical
representations, manually fixed filter sizes limit the scale of representations
that can be learned in a single convolutional layer.
This paper introduces a new adaptive filter model that allows variable scale
and orientation. The scale and orientation parameters of filters can be learned
using back propagation. Therefore, in a single convolution layer, we can create
filters of different scale and orientation that can adapt to small or large
features and objects. The proposed model uses a relatively large base size
(grid) for filters. In the grid, a differentiable function acts as an envelope
for the filters. The envelope function guides effective filter scale and
shape/orientation by masking the filter weights before the convolution.
Therefore, only the weights in the envelope are updated during training.
In this work, we employed a multivariate (2D) Gaussian as the envelope
function and showed that it can grow, shrink, or rotate by updating its
covariance matrix during back propagation training . We tested the new filter
model on MNIST, MNIST-cluttered, and CIFAR-10 and compared the results with the
networks that used conventional convolution layers. The results demonstrate
that the new model can effectively learn and produce filters of different
scales and orientations in a single layer. Moreover, the experiments show that
the adaptive convolution layers perform equally; or better, especially when
data includes objects of varying scale and noisy backgrounds.
| cs.CV | convolutional neural networks have many hyperparameters such as the filter size number of filters and pooling size which require manual tuning though deep stacked structures are able to create multiscale and hierarchical representations manually fixed filter sizes limit the scale of representations that can be learned in a single convolutional layer this paper introduces a new adaptive filter model that allows variable scale and orientation the scale and orientation parameters of filters can be learned using back propagation therefore in a single convolution layer we can create filters of different scale and orientation that can adapt to small or large features and objects the proposed model uses a relatively large base size grid for filters in the grid a differentiable function acts as an envelope for the filters the envelope function guides effective filter scale and shapeorientation by masking the filter weights before the convolution therefore only the weights in the envelope are updated during training in this work we employed a multivariate 2d gaussian as the envelope function and showed that it can grow shrink or rotate by updating its covariance matrix during back propagation training we tested the new filter model on mnist mnistcluttered and cifar10 and compared the results with the networks that used conventional convolution layers the results demonstrate that the new model can effectively learn and produce filters of different scales and orientations in a single layer moreover the experiments show that the adaptive convolution layers perform equally or better especially when data includes objects of varying scale and noisy backgrounds | [['convolutional', 'neural', 'networks', 'have', 'many', 'hyperparameters', 'such', 'as', 'the', 'filter', 'size', 'number', 'of', 'filters', 'and', 'pooling', 'size', 'which', 'require', 'manual', 'tuning', 'though', 'deep', 'stacked', 'structures', 'are', 'able', 'to', 'create', 'multiscale', 'and', 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1,803.00389 | Poisson Image Denoising Using Best Linear Prediction: A Post-processing
Framework | In this paper, we address the problem of denoising images degraded by Poisson
noise. We propose a new patch-based approach based on best linear prediction to
estimate the underlying clean image. A simplified prediction formula is derived
for Poisson observations, which requires the covariance matrix of the
underlying clean patch. We use the assumption that similar patches in a
neighborhood share the same covariance matrix, and we use off-the-shelf Poisson
denoising methods in order to obtain an initial estimate of the covariance
matrices. Our method can be seen as a post-processing step for Poisson
denoising methods and the results show that it improves upon several Poisson
denoising methods by relevant margins.
| cs.CV | in this paper we address the problem of denoising images degraded by poisson noise we propose a new patchbased approach based on best linear prediction to estimate the underlying clean image a simplified prediction formula is derived for poisson observations which requires the covariance matrix of the underlying clean patch we use the assumption that similar patches in a neighborhood share the same covariance matrix and we use offtheshelf poisson denoising methods in order to obtain an initial estimate of the covariance matrices our method can be seen as a postprocessing step for poisson denoising methods and the results show that it improves upon several poisson denoising methods by relevant margins | [['in', 'this', 'paper', 'we', 'address', 'the', 'problem', 'of', 'denoising', 'images', 'degraded', 'by', 'poisson', 'noise', 'we', 'propose', 'a', 'new', 'patchbased', 'approach', 'based', 'on', 'best', 'linear', 'prediction', 'to', 'estimate', 'the', 'underlying', 'clean', 'image', 'a', 'simplified', 'prediction', 'formula', 'is', 'derived', 'for', 'poisson', 'observations', 'which', 'requires', 'the', 'covariance', 'matrix', 'of', 'the', 'underlying', 'clean', 'patch', 'we', 'use', 'the', 'assumption', 'that', 'similar', 'patches', 'in', 'a', 'neighborhood', 'share', 'the', 'same', 'covariance', 'matrix', 'and', 'we', 'use', 'offtheshelf', 'poisson', 'denoising', 'methods', 'in', 'order', 'to', 'obtain', 'an', 'initial', 'estimate', 'of', 'the', 'covariance', 'matrices', 'our', 'method', 'can', 'be', 'seen', 'as', 'a', 'postprocessing', 'step', 'for', 'poisson', 'denoising', 'methods', 'and', 'the', 'results', 'show', 'that', 'it', 'improves', 'upon', 'several', 'poisson', 'denoising', 'methods', 'by', 'relevant', 'margins']] | [0.002572304334256563, -0.06513253997196967, -0.09303519137514134, 0.09952331459787381, -0.07129185737387554, -0.14224022895366223, 0.022086367951831908, 0.42911248900801746, -0.2980242169856488, -0.28415261446036866, 0.11461427227828887, -0.2211079394260237, -0.22470080147619015, 0.15986607608024603, -0.14990778414213712, 0.08977969552008519, 0.12189234318715092, 0.025709441896561568, -0.14785978505555708, -0.27809096594248806, 0.32135219300199336, 0.09171424556087267, 0.32815254234650115, -0.05335472142827269, 0.13952689329875415, 0.011576839592643417, -0.10457565963136133, -0.00952210101961821, -0.08713284202262461, 0.1381961718979837, 0.2635711285679995, 0.12145076938359453, 0.26999867859774745, -0.412490443989366, -0.22884457204323094, 0.1048359602001739, 0.1374903788857229, 0.1340078992636975, -0.03633238788513752, -0.3092702013746676, 0.11603664222534175, -0.12587196600564696, -0.04833880089525435, -0.09061484285242234, -0.08783458979057802, -0.024837512233470742, -0.3689479143568524, 0.10940918620247897, 0.11305496032005763, -0.017823043810035865, -0.06187714502081141, -0.15941747705460535, 0.06838690637199728, 0.11973124045694063, 0.007783467703087775, -0.0008116546842093403, 0.1514239373432288, -0.11874242573095536, -0.09721547873850139, 0.35098890787376474, -0.10682738895070748, -0.27834110834367304, 0.13283860169116174, -0.07307022319150132, -0.16994469407484405, 0.11446151180771759, 0.18034670059778937, 0.09359882875169451, -0.17567251000956102, 0.07856549078762229, -0.07897714896731682, 0.1515931642639476, 0.04556192300777446, -0.03518519354648553, 0.10878453529985235, 0.19048275598870204, 0.12402909350589858, 0.12471280335595512, -0.15970953304094035, -0.02079735712257323, -0.26707098271219515, -0.12676551944349665, -0.26069243338697395, 0.029433953956499493, -0.15889335601555965, -0.18317136423544841, 0.39905214831568636, 0.2273868748725199, 0.2523840226564422, 0.08261084650655755, 0.33702299088608845, 0.13221393692567274, 0.04107314099868139, 0.05663730261578887, 0.1406432023347431, 0.13454914331721426, 0.09015658386638975, -0.13141269753645132, 0.07908344690411075, 0.09775094526425541] |
1,803.0039 | On the relationship between the diffuse reflection and bounce-back
boundary condition in the continuum limit | In this work, we show that the widely used bounce-back boundary condition is
an incomplete form of the diffuse reflection boundary condition at the
continuum limit for lattice Boltzmann simulations. By utilizing this fact, we
can force the diffuse reflection scheme to work at its continuum limit so that
the no-slip boundary condition can be implemented without any non-physical slip
velocity error being induced by the standard bounce-back scheme. The revised
boundary formulation is validated numerically by solving a pressure-driven
channel flow, a lid-driven cavity flow and channel flow around a square
cylinder.
| physics.flu-dyn | in this work we show that the widely used bounceback boundary condition is an incomplete form of the diffuse reflection boundary condition at the continuum limit for lattice boltzmann simulations by utilizing this fact we can force the diffuse reflection scheme to work at its continuum limit so that the noslip boundary condition can be implemented without any nonphysical slip velocity error being induced by the standard bounceback scheme the revised boundary formulation is validated numerically by solving a pressuredriven channel flow a liddriven cavity flow and channel flow around a square cylinder | [['in', 'this', 'work', 'we', 'show', 'that', 'the', 'widely', 'used', 'bounceback', 'boundary', 'condition', 'is', 'an', 'incomplete', 'form', 'of', 'the', 'diffuse', 'reflection', 'boundary', 'condition', 'at', 'the', 'continuum', 'limit', 'for', 'lattice', 'boltzmann', 'simulations', 'by', 'utilizing', 'this', 'fact', 'we', 'can', 'force', 'the', 'diffuse', 'reflection', 'scheme', 'to', 'work', 'at', 'its', 'continuum', 'limit', 'so', 'that', 'the', 'noslip', 'boundary', 'condition', 'can', 'be', 'implemented', 'without', 'any', 'nonphysical', 'slip', 'velocity', 'error', 'being', 'induced', 'by', 'the', 'standard', 'bounceback', 'scheme', 'the', 'revised', 'boundary', 'formulation', 'is', 'validated', 'numerically', 'by', 'solving', 'a', 'pressuredriven', 'channel', 'flow', 'a', 'liddriven', 'cavity', 'flow', 'and', 'channel', 'flow', 'around', 'a', 'square', 'cylinder']] | [-0.14620836542779078, 0.11334872655010336, -0.12721544800273654, -0.002150406500184408, -0.09900930573943481, -0.16044101931695495, 0.02175467893085693, 0.3954614424397067, -0.29743345960053386, -0.23021701478990175, 0.15001551029197271, -0.19886096121783378, -0.08697902423239523, 0.171617854837208, -0.060377029852280696, 0.11920071445262781, 0.10605982477245952, -0.022848667957449473, -0.030118263676144202, -0.16588238186343143, 0.31086519729065637, 0.03203469496320493, 0.29403906419963366, 0.11823759067513209, 0.08680096042833181, -0.04047548140509315, 0.03491836217900498, 0.08523925115299281, -0.1889501888467993, 0.03848273516882972, 0.1490004796144222, -0.028923506858528302, 0.2204093900258823, -0.4636780255003482, -0.28795147320676234, 0.040519009113952675, 0.15876926718935652, 0.12493898646445364, -0.0015820208386147535, -0.2758166440512224, 0.06867498531937599, -0.141624783215824, -0.19456310954285405, 0.028667323681856354, -0.05693622594398837, -0.0672073412626501, -0.30982316693952006, 0.13174258468933003, 0.057428306639595056, 0.08067240632109103, -0.06322666249358125, -0.035089053203582125, -0.045074508270068515, 0.08033529844921163, 0.01471006827208624, 0.029420620962096158, 0.14008697973043527, -0.11717852878494449, -0.03689648735747542, 0.40016979441767736, -0.06535797261743136, -0.27067406471538286, 0.15794583227640638, -0.10515742508133732, -0.02115711790098939, 0.2011899729659881, 0.1302554608593064, 0.08746543770996473, -0.2038863683019274, 0.060530341595226037, -0.11729556109420755, 0.15463962973976728, 0.11793243581609379, -0.08672022986756538, 0.1662382297217846, 0.12003172896001288, 0.07935627137300788, 0.16996200784655308, -0.1364871783492466, -0.09646497088013797, -0.36223132217363, -0.11452810877373301, -0.20517627042918515, 0.02491187683797331, -0.08598623418243558, -0.15396732006782807, 0.2470384201648118, 0.1293735653312216, 0.13473825341951784, 0.03710382173879333, 0.35860870922765425, 0.187712728055895, 0.05133756067383514, 0.1582291905557917, 0.2425799529995739, 0.1591395092702481, 0.1102439578502409, -0.27865585815962607, 0.014352921561728562, 0.13265442256365093] |
1,803.00391 | Image Dataset for Visual Objects Classification in 3D Printing | The rapid development in additive manufacturing (AM), also known as 3D
printing, has brought about potential risk and security issues along with
significant benefits. In order to enhance the security level of the 3D printing
process, the present research aims to detect and recognize illegal components
using deep learning. In this work, we collected a dataset of 61,340 2D images
(28x28 for each image) of 10 classes including guns and other non-gun objects,
corresponding to the projection results of the original 3D models. To validate
the dataset, we train a convolutional neural network (CNN) model for gun
classification which can achieve 98.16% classification accuracy.
| cs.CV | the rapid development in additive manufacturing am also known as 3d printing has brought about potential risk and security issues along with significant benefits in order to enhance the security level of the 3d printing process the present research aims to detect and recognize illegal components using deep learning in this work we collected a dataset of 61340 2d images 28x28 for each image of 10 classes including guns and other nongun objects corresponding to the projection results of the original 3d models to validate the dataset we train a convolutional neural network cnn model for gun classification which can achieve 9816 classification accuracy | [['the', 'rapid', 'development', 'in', 'additive', 'manufacturing', 'am', 'also', 'known', 'as', '3d', 'printing', 'has', 'brought', 'about', 'potential', 'risk', 'and', 'security', 'issues', 'along', 'with', 'significant', 'benefits', 'in', 'order', 'to', 'enhance', 'the', 'security', 'level', 'of', 'the', '3d', 'printing', 'process', 'the', 'present', 'research', 'aims', 'to', 'detect', 'and', 'recognize', 'illegal', 'components', 'using', 'deep', 'learning', 'in', 'this', 'work', 'we', 'collected', 'a', 'dataset', 'of', '61340', '2d', 'images', '28x28', 'for', 'each', 'image', 'of', '10', 'classes', 'including', 'guns', 'and', 'other', 'nongun', 'objects', 'corresponding', 'to', 'the', 'projection', 'results', 'of', 'the', 'original', '3d', 'models', 'to', 'validate', 'the', 'dataset', 'we', 'train', 'a', 'convolutional', 'neural', 'network', 'cnn', 'model', 'for', 'gun', 'classification', 'which', 'can', 'achieve', '9816', 'classification', 'accuracy']] | [-0.016448802280408046, -0.0306234199952537, -0.024136211165217775, 0.02980633307485579, -0.09063398628726159, -0.19534276494073985, 0.0037521201780637598, 0.4237155837472528, -0.25714299913108496, -0.37214995072324675, 0.10768492907022892, -0.2927772320143067, -0.1723191605484353, 0.1897863411127894, -0.18057102970963362, 0.10587798496984904, 0.10952812019680248, 0.00963367820523753, -0.0695823512851528, -0.2937351499077873, 0.2886613582325454, 0.0611128220308715, 0.3482337343866135, 0.041316887861726305, 0.125129540614544, -0.05449910843191613, -0.04750379511514808, -0.03399794395565802, -0.059465058512137375, 0.19024755319322248, 0.31515740179434437, 0.1702364128414947, 0.3062425286158698, -0.42056148233685164, -0.22596852661866584, 0.08746802140290083, 0.13838926323754067, 0.10917982010811678, -0.06661295003285354, -0.3578490949152867, 0.10925137321695243, -0.20561182628026103, -0.0688593365288902, -0.12514117089017193, -0.014411138461690655, -0.01834543538049306, -0.2534231819987002, -0.009185176446750336, 0.07554074190787528, 0.07629800758481321, -0.0639457367406697, -0.08433453799087075, -0.011364336466730232, 0.20821335517091327, 0.01589101204264789, 0.07299060897819817, 0.14694116276684263, -0.2501373672189357, -0.16877202036626876, 0.3570361148178725, -0.049563247554861745, -0.16574475895797852, 0.21495442653056418, -0.053818716515445765, -0.11892867752230994, 0.12204225393159703, 0.2902133059641807, 0.044165867058062316, -0.1736849452631714, -0.03669783601820893, -0.002106575452086359, 0.18913343309489364, 0.06064788219841695, -0.03545333994644703, 0.18977089578488676, 0.26971349564765923, -0.013003357845813262, 0.1553171569012373, -0.2068128083869446, -0.023544253006872563, -0.1888305087398627, -0.1565209755969077, -0.1296537976463021, 0.003995087797670524, -0.04828265983807868, -0.14004512227112703, 0.4459146274216842, 0.2559335596241647, 0.17296852891582368, 0.05933565871518032, 0.3500095965746458, -0.010929518278992486, 0.1368606405368416, 0.05279443635245656, 0.18920602527271846, 0.029419432961298984, 0.14200252138359454, -0.11576183566288782, 0.05237163378313036, 0.0436649879320792] |
1,803.00392 | On the spectral unfolding of chaotic and mixed systems | Random matrix theory (RMT) provides a framework to study the spectral
fluctuations in physical systems. RMT is capable of making predictions for the
fluctuations only after the removal of the secular properties of the spectrum.
Spectral unfolding procedure is used to separate the local level fluctuations
from overall energy dependence of the level separation. The unfolding procedure
is not unique. Several studies showed that statistics of long-term correlation
in the spectrum are very sensitive to the choice of the unfolding function in
polynomial unfolding. This can give misleading results regarding the chaoticity
of quantum systems. In this letter, we consider the spectra of ordered
eigenvalues of large random matrices. We show that the main cause behind the
reported sensitivity to the unfolding polynomial degree is the inclusion of
specific extreme eigenvalue(s) in the unfolding process.
| cond-mat.stat-mech nlin.CD | random matrix theory rmt provides a framework to study the spectral fluctuations in physical systems rmt is capable of making predictions for the fluctuations only after the removal of the secular properties of the spectrum spectral unfolding procedure is used to separate the local level fluctuations from overall energy dependence of the level separation the unfolding procedure is not unique several studies showed that statistics of longterm correlation in the spectrum are very sensitive to the choice of the unfolding function in polynomial unfolding this can give misleading results regarding the chaoticity of quantum systems in this letter we consider the spectra of ordered eigenvalues of large random matrices we show that the main cause behind the reported sensitivity to the unfolding polynomial degree is the inclusion of specific extreme eigenvalues in the unfolding process | [['random', 'matrix', 'theory', 'rmt', 'provides', 'a', 'framework', 'to', 'study', 'the', 'spectral', 'fluctuations', 'in', 'physical', 'systems', 'rmt', 'is', 'capable', 'of', 'making', 'predictions', 'for', 'the', 'fluctuations', 'only', 'after', 'the', 'removal', 'of', 'the', 'secular', 'properties', 'of', 'the', 'spectrum', 'spectral', 'unfolding', 'procedure', 'is', 'used', 'to', 'separate', 'the', 'local', 'level', 'fluctuations', 'from', 'overall', 'energy', 'dependence', 'of', 'the', 'level', 'separation', 'the', 'unfolding', 'procedure', 'is', 'not', 'unique', 'several', 'studies', 'showed', 'that', 'statistics', 'of', 'longterm', 'correlation', 'in', 'the', 'spectrum', 'are', 'very', 'sensitive', 'to', 'the', 'choice', 'of', 'the', 'unfolding', 'function', 'in', 'polynomial', 'unfolding', 'this', 'can', 'give', 'misleading', 'results', 'regarding', 'the', 'chaoticity', 'of', 'quantum', 'systems', 'in', 'this', 'letter', 'we', 'consider', 'the', 'spectra', 'of', 'ordered', 'eigenvalues', 'of', 'large', 'random', 'matrices', 'we', 'show', 'that', 'the', 'main', 'cause', 'behind', 'the', 'reported', 'sensitivity', 'to', 'the', 'unfolding', 'polynomial', 'degree', 'is', 'the', 'inclusion', 'of', 'specific', 'extreme', 'eigenvalues', 'in', 'the', 'unfolding', 'process']] | [-0.1262085337433274, 0.10946470665297023, -0.14674172387975787, 0.09909517297709061, -0.012543248288609364, -0.05352113073415778, 0.026600718028257014, 0.34001475634674233, -0.2844439077432509, -0.28641787460763696, 0.06794972848362738, -0.2715399421337578, -0.18788440591414218, 0.15015251393326454, -0.04493501765860452, 0.06947925705117759, 0.09003249229518352, 0.04722655743222546, -0.06556315195350046, -0.20104033613956912, 0.3159996198662729, 0.12279685508304586, 0.26933186003696863, 0.05809395197365019, 0.04577047737073843, 0.048463432647770754, -0.041431441071822686, -0.012603484063961164, -0.11608779548755761, 0.1180199939964546, 0.24164617412240694, 0.11490828112733585, 0.2552199325627751, -0.36740439821172644, -0.19332118966375236, 0.12132952581332238, 0.11959739945552968, 0.12316773046084024, 0.004468333064061072, -0.22021835389529804, 0.10789095331500802, -0.12523094024778672, -0.16541667426677628, -0.09969173056694368, 0.0002462065468231837, 0.019813948336781725, -0.2592759315588477, 0.11411764941488703, 0.08944995367416629, 0.06713039339002635, -0.0196111382537142, -0.10945133458547018, 0.005619135476579821, 0.12764785046585733, 0.04417349521514822, -0.05733907271512888, 0.15823634913260187, -0.1061373650993186, -0.09681565683567897, 0.3492668461744432, -0.055528826701144375, -0.16638807399301894, 0.17596347810079654, -0.18751302645714193, -0.17943576689533614, 0.15754245638295455, 0.13669365674119305, 0.07052529054797357, -0.15788288536929973, 0.07799117540319761, 0.020164256557149606, 0.1900507630366418, 0.03610587474476132, 0.03216010119972958, 0.18447518287985412, 0.13637585651595147, 0.05386145470569048, 0.13632601014752355, -0.07608851445148941, -0.11683327126558181, -0.30889266734873805, -0.09424756201786093, -0.21221652871894617, 0.07151623435018808, -0.11099722587882921, -0.20072296443912718, 0.4638813796625645, 0.1688365970446116, 0.2324726944565083, 0.0317815117675949, 0.2529398604813549, 0.14690898706343164, 0.039174307206714595, 0.017715589855625123, 0.24016095831024425, 0.14684236479267754, 0.08206293527036905, -0.2459427650000348, 0.09718938411247952, 0.045885047840851324] |
1,803.00393 | Lifespan of Solution to MHD Boundary Layer Equations with Analytic
Perturbation of General Shear Flow | In this paper, we consider the lifespan of solution to the MHD boundary layer
system as an analytic perturbation of general shear flow. By using the
cancellation mechanism in the system observed in \cite{LXY1}, the lifespan of
solution is shown to have a lower bound in the order of $\varepsilon^{-2+}$ if
the strength of the perturbation is of the order of $\varepsilon$. Since there
is no restriction on the strength of the shear flow and the lifespan estimate
is larger than the one obtained for the classical Prandtl system in this
setting, it reveals the stabilizing effect of the magnetic field on the
electrically conducting fluid near the boundary.
| math.AP | in this paper we consider the lifespan of solution to the mhd boundary layer system as an analytic perturbation of general shear flow by using the cancellation mechanism in the system observed in citelxy1 the lifespan of solution is shown to have a lower bound in the order of varepsilon2 if the strength of the perturbation is of the order of varepsilon since there is no restriction on the strength of the shear flow and the lifespan estimate is larger than the one obtained for the classical prandtl system in this setting it reveals the stabilizing effect of the magnetic field on the electrically conducting fluid near the boundary | [['in', 'this', 'paper', 'we', 'consider', 'the', 'lifespan', 'of', 'solution', 'to', 'the', 'mhd', 'boundary', 'layer', 'system', 'as', 'an', 'analytic', 'perturbation', 'of', 'general', 'shear', 'flow', 'by', 'using', 'the', 'cancellation', 'mechanism', 'in', 'the', 'system', 'observed', 'in', 'citelxy1', 'the', 'lifespan', 'of', 'solution', 'is', 'shown', 'to', 'have', 'a', 'lower', 'bound', 'in', 'the', 'order', 'of', 'varepsilon2', 'if', 'the', 'strength', 'of', 'the', 'perturbation', 'is', 'of', 'the', 'order', 'of', 'varepsilon', 'since', 'there', 'is', 'no', 'restriction', 'on', 'the', 'strength', 'of', 'the', 'shear', 'flow', 'and', 'the', 'lifespan', 'estimate', 'is', 'larger', 'than', 'the', 'one', 'obtained', 'for', 'the', 'classical', 'prandtl', 'system', 'in', 'this', 'setting', 'it', 'reveals', 'the', 'stabilizing', 'effect', 'of', 'the', 'magnetic', 'field', 'on', 'the', 'electrically', 'conducting', 'fluid', 'near', 'the', 'boundary']] | [-0.197905965976167, 0.09784870473703011, -0.05984267335454071, 0.022491897087699424, -0.02198669449986752, -0.06624131839015279, 0.01520091992117361, 0.2633279232239282, -0.26122807119800534, -0.29599889019435205, 0.14720668705801168, -0.25285012145630187, -0.12138236923567967, 0.19503592004723572, -0.028806294073109275, 0.039135738034491184, 0.010326927573057927, 0.10447950254375529, -0.004532376105931622, -0.21589391724491078, 0.3661423649733748, 0.029160661814751587, 0.26207815927315364, 0.06282783378588243, 0.062146801949927104, -0.049443335537540004, 0.042109457258549005, 0.039909822245438896, -0.1767497828695923, 0.07343774789047462, 0.1666142763958002, 0.02117929031158349, 0.2842917475607936, -0.44091208544732247, -0.22568785337972697, 0.07484456694995363, 0.14642351826100988, 0.11617292162824285, -0.026109363997993233, -0.2421423001214862, 0.10860849988293247, -0.1344034411164372, -0.13400574180694227, -0.006146513098091991, 0.02863718191252297, -0.019200231802339356, -0.300052383457552, 0.11131418302883739, 0.08761600041934461, 0.047528628803168736, -0.1374720451982554, -0.06756668943152935, -0.04095121386840388, 0.11702081772791981, 0.11772926275389856, 0.06363138642067227, 0.0880853797317724, -0.17402033720820867, -0.007863678702118772, 0.3600425756605411, -0.08536523609937932, -0.22429726681568557, 0.1765664283324171, -0.191795818619775, -0.04714320527596606, 0.13209792561139222, 0.17543357824561773, 0.13933297907243725, -0.11101377135384138, 0.0944940092088439, -0.05246487581731613, 0.18515487410089312, 0.0553952620399219, -0.01992142297752219, 0.13512593690743582, 0.19630733561374386, 0.1456410109897627, 0.16320785371310734, -0.09288612803599487, -0.07454914762638509, -0.304834411716675, -0.1706613249698421, -0.1881007433726659, 0.0377986772649887, -0.10683728844189856, -0.189717035008437, 0.36918361553560114, 0.16126236941447672, 0.1647990188212134, 0.0305668685080794, 0.301468743942678, 0.17310010597079614, 0.04927509942057508, 0.10568267025519162, 0.337298687685419, 0.1544288075593714, 0.131557475046335, -0.25684793129847905, 0.09184114224087724, 0.10333424722948284] |
1,803.00394 | General Non-Commutative Locally Compact Locally Hausdorff Stone Duality | We extend the classical Stone duality between zero dimensional compact
Hausdorff spaces and Boolean algebras. Specifically, we simultaneously remove
the zero dimensionality restriction and extend to \'etale groupoids, obtaining
a duality with an elementary class of inverse semigroups.
| math.LO math.GN math.OA | we extend the classical stone duality between zero dimensional compact hausdorff spaces and boolean algebras specifically we simultaneously remove the zero dimensionality restriction and extend to etale groupoids obtaining a duality with an elementary class of inverse semigroups | [['we', 'extend', 'the', 'classical', 'stone', 'duality', 'between', 'zero', 'dimensional', 'compact', 'hausdorff', 'spaces', 'and', 'boolean', 'algebras', 'specifically', 'we', 'simultaneously', 'remove', 'the', 'zero', 'dimensionality', 'restriction', 'and', 'extend', 'to', 'etale', 'groupoids', 'obtaining', 'a', 'duality', 'with', 'an', 'elementary', 'class', 'of', 'inverse', 'semigroups']] | [-0.06699428023574383, 0.12312384306699785, -0.07011511220939849, 0.210858333635291, -0.19090647872929511, -0.1603365142364055, 0.06313679332991964, 0.3595915495565063, -0.4069769670696635, -0.13077981274967132, 0.10682863488143898, -0.2378614795345225, -0.11031630900106393, 0.166475685362361, -0.1977329410759634, 0.01668324970983361, -0.015032425424770304, 0.03569212732346434, -0.2137674656574075, -0.258793741247102, 0.4632370738116534, -0.032127252908570596, 0.2718174656313893, 0.02209571606822704, 0.13332649263994475, 0.06553142476140668, -0.0564432790208804, 0.016372132262116985, -0.16463543173219813, 0.15769426652695984, 0.3696660714616117, 0.027915693905302567, 0.22322934406417372, -0.3755591321540506, -0.1536012526699587, 0.2510844815865551, 0.06626898613390758, 0.023675310229392427, -0.010773057657244959, -0.3262987656910953, 0.064968698351693, -0.17641581414806606, -0.1645989937364663, -0.12246432834255852, 0.08004700755210299, -0.03137043929707847, -0.24533902667462826, -0.021135442620633466, 0.19703604691465826, 0.12827217515165867, -0.10231735175291665, -0.04412487815869482, -0.0048652235786185456, 0.10511304427085347, -0.06363548009386777, 0.01576076661187567, 0.10224112478624049, 0.0011670327671469607, -0.21807299460296667, 0.259686250607238, 0.01172521999595981, -0.23769853822886944, 0.2641473929397762, -0.17894099937065652, -0.1764348597559882, 0.08536764018629726, 0.06115060951560736, 0.1290717723926431, -0.019252005523364795, 0.2503119937456703, -0.11974919768736551, 0.07244826490549665, 0.12978524851955867, 0.05565740192603124, 0.09940401279661608, 0.13176689146233625, 0.15054405279653638, 0.20875141978582465, 0.034304048571931686, -0.05915340320911797, -0.33856065571308136, -0.1986522473958566, -0.0941683215726363, 0.1606311785351289, -0.11239406260536175, -0.17191905734178267, 0.2937107115591827, 0.13794264893084274, 0.1703968393542853, 0.23384338362436546, 0.22025570802782712, 0.07714924823468257, 0.07885007079886763, 0.03993019089102745, 0.09269372534349953, 0.3287636431816377, -0.01616224813226022, -0.13426311282244952, -0.16537313291950054, 0.31407236925473336] |
1,803.00395 | Fast and robust misalignment correction of Fourier ptychographic
microscopy | Fourier ptychographi cmicroscopy(FPM) is a newly developed computational
imaging technique that can provide gigapixel images with both high resolution
(HR) and wide field of view (FOV). However, the positional misalignment of the
LED array induces a degradation of the reconstruction, especially in the
regions away from the optical axis. In this paper, we propose a robust and fast
method to correct the LED misalignment of FPM, termed as misalignment
correction for FPM (mcFPM). Although different regions in the FOV have
different sensitivity to the LED misalignment, the experimental results show
that mcFPM is robust to eliminate the degradation in each region. Compared with
the state-of-the-art methods, mcFPM is much faster.
| cs.CV physics.optics | fourier ptychographi cmicroscopyfpm is a newly developed computational imaging technique that can provide gigapixel images with both high resolution hr and wide field of view fov however the positional misalignment of the led array induces a degradation of the reconstruction especially in the regions away from the optical axis in this paper we propose a robust and fast method to correct the led misalignment of fpm termed as misalignment correction for fpm mcfpm although different regions in the fov have different sensitivity to the led misalignment the experimental results show that mcfpm is robust to eliminate the degradation in each region compared with the stateoftheart methods mcfpm is much faster | [['fourier', 'ptychographi', 'cmicroscopyfpm', 'is', 'a', 'newly', 'developed', 'computational', 'imaging', 'technique', 'that', 'can', 'provide', 'gigapixel', 'images', 'with', 'both', 'high', 'resolution', 'hr', 'and', 'wide', 'field', 'of', 'view', 'fov', 'however', 'the', 'positional', 'misalignment', 'of', 'the', 'led', 'array', 'induces', 'a', 'degradation', 'of', 'the', 'reconstruction', 'especially', 'in', 'the', 'regions', 'away', 'from', 'the', 'optical', 'axis', 'in', 'this', 'paper', 'we', 'propose', 'a', 'robust', 'and', 'fast', 'method', 'to', 'correct', 'the', 'led', 'misalignment', 'of', 'fpm', 'termed', 'as', 'misalignment', 'correction', 'for', 'fpm', 'mcfpm', 'although', 'different', 'regions', 'in', 'the', 'fov', 'have', 'different', 'sensitivity', 'to', 'the', 'led', 'misalignment', 'the', 'experimental', 'results', 'show', 'that', 'mcfpm', 'is', 'robust', 'to', 'eliminate', 'the', 'degradation', 'in', 'each', 'region', 'compared', 'with', 'the', 'stateoftheart', 'methods', 'mcfpm', 'is', 'much', 'faster']] | [-0.0926129146332473, 0.05306394829595876, -0.07781688731456934, 0.026224374066158716, -0.059934734524210435, -0.10691860731027124, -0.010441151360282674, 0.4173447722741575, -0.25054110468503227, -0.35693805376757626, 0.11073294994248836, -0.21735980347902686, -0.1452109199340662, 0.21975297092770538, -0.16077920382964225, 0.03115793514168925, 0.10712021269568207, -0.024027443354835094, -0.10421068802544917, -0.1791273524210771, 0.22680359475176642, 0.09873460895485348, 0.32189102931362057, 0.019169894331652258, 0.12855038058693968, -0.02448545315268415, -0.03336810486184226, 0.051755423071207826, -0.07404846701778027, 0.11359776189999173, 0.22630871547368803, 0.11184362075464041, 0.2754479003552761, -0.38557597153164724, -0.20265732030384243, 0.06653076665329367, 0.16258831891334719, 0.10128184651128119, -0.10315956990234554, -0.2765534159865368, 0.11909385122082851, -0.13307136165288588, -0.09483227625417014, -0.06753234520416569, -0.0182936110089671, -0.006506007377066891, -0.29809188531470243, 0.0600565556626491, -0.001012612119127341, 0.07120298852505921, -0.04155819775985071, -0.11025566214488612, 0.03709094184537039, 0.1300523188266972, 0.043859481149680773, 0.11933123187002449, 0.09997786894112963, -0.1582434882940207, -0.07153490853185455, 0.3613348881783033, -0.02974673943525111, -0.16060498060175665, 0.19846946889258646, -0.169565058744478, -0.09285726127025017, 0.2227836330421269, 0.15787995590276463, 0.11420345785855143, -0.09990655938249633, 0.03160408135240518, 0.01374674380618941, 0.21037839391027335, 0.05815462211008977, 0.08531235368637782, 0.20357052417885926, 0.17002503348178127, 0.07728812344484376, 0.1669056757570986, -0.26681636923199725, -0.048789532389491796, -0.20602368727257406, -0.12725305571048348, -0.1346589750006657, -0.015185064110146076, -0.09440467805850655, -0.13231118972806952, 0.40668370598858156, 0.24983145425492828, 0.19922919141956502, 0.027972206981050678, 0.36335719749331474, 0.03933010446403555, 0.1353300251411619, -0.00646265446536105, 0.2979350543184275, 0.08319930835730499, 0.1233230730501452, -0.21223209324266967, 0.04384579951012576, -0.020131546684920235] |
1,803.00396 | Speech Enhancement in Adverse Environments Based on Non-stationary
Noise-driven Spectral Subtraction and SNR-dependent Phase Compensation | A two-step enhancement method based on spectral subtraction and phase
spectrum compensation is presented in this paper for noisy speeches in adverse
environments involving non-stationary noise and medium to low levels of SNR.
The magnitude of the noisy speech spectrum is modified in the first step of the
proposed method by a spectral subtraction approach, where a new noise
estimation method based on the low frequency information of the noisy speech is
introduced. We argue that this method of noise estimation is capable of
estimating the non-stationary noise accurately. The phase spectrum of the noisy
speech is modified in the second step consisting of phase spectrum
compensation, where an SNR-dependent approach is incorporated to determine the
amount of compensation to be imposed on the phase spectrum. A modified complex
spectrum is obtained by aggregating the magnitude from the spectral subtraction
step and modified phase spectrum from the phase compensation step, which is
found to be a better representation of enhanced speech spectrum. Speech files
available in the NOIZEUS database are used to carry extensive simulations for
evaluation of the proposed method.
| eess.AS cs.SD | a twostep enhancement method based on spectral subtraction and phase spectrum compensation is presented in this paper for noisy speeches in adverse environments involving nonstationary noise and medium to low levels of snr the magnitude of the noisy speech spectrum is modified in the first step of the proposed method by a spectral subtraction approach where a new noise estimation method based on the low frequency information of the noisy speech is introduced we argue that this method of noise estimation is capable of estimating the nonstationary noise accurately the phase spectrum of the noisy speech is modified in the second step consisting of phase spectrum compensation where an snrdependent approach is incorporated to determine the amount of compensation to be imposed on the phase spectrum a modified complex spectrum is obtained by aggregating the magnitude from the spectral subtraction step and modified phase spectrum from the phase compensation step which is found to be a better representation of enhanced speech spectrum speech files available in the noizeus database are used to carry extensive simulations for evaluation of the proposed method | [['a', 'twostep', 'enhancement', 'method', 'based', 'on', 'spectral', 'subtraction', 'and', 'phase', 'spectrum', 'compensation', 'is', 'presented', 'in', 'this', 'paper', 'for', 'noisy', 'speeches', 'in', 'adverse', 'environments', 'involving', 'nonstationary', 'noise', 'and', 'medium', 'to', 'low', 'levels', 'of', 'snr', 'the', 'magnitude', 'of', 'the', 'noisy', 'speech', 'spectrum', 'is', 'modified', 'in', 'the', 'first', 'step', 'of', 'the', 'proposed', 'method', 'by', 'a', 'spectral', 'subtraction', 'approach', 'where', 'a', 'new', 'noise', 'estimation', 'method', 'based', 'on', 'the', 'low', 'frequency', 'information', 'of', 'the', 'noisy', 'speech', 'is', 'introduced', 'we', 'argue', 'that', 'this', 'method', 'of', 'noise', 'estimation', 'is', 'capable', 'of', 'estimating', 'the', 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1,803.00397 | Satellite imagery analysis for operational damage assessment in
Emergency situations | When major disaster occurs the questions are raised how to estimate the
damage in time to support the decision making process and relief efforts by
local authorities or humanitarian teams. In this paper we consider the use of
Machine Learning and Computer Vision on remote sensing imagery to improve time
efficiency of assessment of damaged buildings in disaster affected area. We
propose a general workflow that can be useful in various disaster management
applications, and demonstrate the use of the proposed workflow for the
assessment of the damage caused by the wildfires in California in 2017.
| cs.CV cs.LG | when major disaster occurs the questions are raised how to estimate the damage in time to support the decision making process and relief efforts by local authorities or humanitarian teams in this paper we consider the use of machine learning and computer vision on remote sensing imagery to improve time efficiency of assessment of damaged buildings in disaster affected area we propose a general workflow that can be useful in various disaster management applications and demonstrate the use of the proposed workflow for the assessment of the damage caused by the wildfires in california in 2017 | [['when', 'major', 'disaster', 'occurs', 'the', 'questions', 'are', 'raised', 'how', 'to', 'estimate', 'the', 'damage', 'in', 'time', 'to', 'support', 'the', 'decision', 'making', 'process', 'and', 'relief', 'efforts', 'by', 'local', 'authorities', 'or', 'humanitarian', 'teams', 'in', 'this', 'paper', 'we', 'consider', 'the', 'use', 'of', 'machine', 'learning', 'and', 'computer', 'vision', 'on', 'remote', 'sensing', 'imagery', 'to', 'improve', 'time', 'efficiency', 'of', 'assessment', 'of', 'damaged', 'buildings', 'in', 'disaster', 'affected', 'area', 'we', 'propose', 'a', 'general', 'workflow', 'that', 'can', 'be', 'useful', 'in', 'various', 'disaster', 'management', 'applications', 'and', 'demonstrate', 'the', 'use', 'of', 'the', 'proposed', 'workflow', 'for', 'the', 'assessment', 'of', 'the', 'damage', 'caused', 'by', 'the', 'wildfires', 'in', 'california', 'in', '2017']] | [-0.06069830154592637, 0.06275713671736109, -0.02705545204419953, 0.03350413507915088, -0.07086801858774076, -0.08745581196368828, 0.08450496487906396, 0.3708586786718418, -0.24205129651818424, -0.3508116042163844, 0.18353467679359406, -0.24590165354311466, -0.2073964547895836, 0.20102828769934908, -0.2329022923076991, 0.09049334357647847, 0.07753309494485923, 0.0032524124156528464, 0.02685592117146977, -0.28725232444170007, 0.2714811007269115, 0.10611705653718673, 0.3553029489897502, 0.11481371583795408, 0.009155043939244933, 0.06989032123889653, -0.07859080584118298, -0.018945959551880758, -0.089656519413514, 0.16358763805571166, 0.3995129214405703, 0.25346023111584753, 0.37957347665602964, -0.5103816177579574, -0.2425046886104004, 0.12834226011667246, 0.08671527332808182, 0.027069833391578868, -0.0442160367544299, -0.3353196653188206, 0.059454664389098376, -0.2040659686596579, -0.1099202517264833, -0.06936982327412504, 0.011717339177266695, -0.006947458993333082, -0.24025278784877932, 0.04242241541699817, 0.03934312900370666, 0.11977124882105272, -0.07258137469276942, -0.03633260116597133, 0.02765593334697769, 0.20949079159527173, 0.04930128275100287, 0.0007461475664361691, 0.22936758717454117, -0.1884305252096965, -0.15213313427132866, 0.4212656798772514, 0.03218304917390924, -0.10706422883716489, 0.1775892176895771, -0.07863528367791635, -0.16133553276207144, 0.04860773981878689, 0.29824197278746095, 0.05909176916369082, -0.1805229533815691, 0.025426452803003485, 0.061277775583827555, 0.10873393121679935, 0.09480303939684138, -0.06610254619045008, 0.18674485164228827, 0.24085879501217278, 0.09499279562199565, 0.16460192695255196, -0.10607480812905123, -0.05347186321159825, -0.2033642540106181, -0.1600752375937494, -0.16032172723983726, -0.021109649399780512, -0.032706700070927276, -0.11521586740855128, 0.3814138677941325, 0.25748616165462107, 0.1132232417682341, -0.018637411805684678, 0.34285437521369505, 0.04401365736460624, 0.06911751739971805, 0.0815104791157258, 0.19103136860455075, -0.023039505733322585, 0.16642229649975585, -0.19442943104028623, 0.1309851265978068, -0.017338276171358302] |
1,803.00398 | Robust positioning of drones for land use monitoring in strong terrain
relief using vision-based navigation | For land use monitoring, the main problems are robust positioning in urban
canyons and strong terrain reliefs with the use of GPS system only. Indeed,
satellite signal reflection and shielding in urban canyons and strong terrain
relief results in problems with correct positioning. Using GNSS-RTK does not
solve the problem completely because in some complex situations the whole
satellite's system works incorrectly. We transform the weakness (urban canyons
and strong terrain relief) to an advantage. It is a vision-based navigation
using a map of the terrain relief. We investigate and demonstrate the
effectiveness of this technology in Chinese region Xiaoshan. The accuracy of
the vision-based navigation system corresponds to the expected for these
conditions. . It was concluded that the maximum position error based on
vision-based navigation is 20 m and the maximum angle Euler error based on
vision-based navigation is 0.83 degree. In case of camera movement, the maximum
position error based on vision-based navigation is 30m and the maximum Euler
angle error based on vision-based navigation is 2.2 degrees.
| cs.CV | for land use monitoring the main problems are robust positioning in urban canyons and strong terrain reliefs with the use of gps system only indeed satellite signal reflection and shielding in urban canyons and strong terrain relief results in problems with correct positioning using gnssrtk does not solve the problem completely because in some complex situations the whole satellites system works incorrectly we transform the weakness urban canyons and strong terrain relief to an advantage it is a visionbased navigation using a map of the terrain relief we investigate and demonstrate the effectiveness of this technology in chinese region xiaoshan the accuracy of the visionbased navigation system corresponds to the expected for these conditions it was concluded that the maximum position error based on visionbased navigation is 20 m and the maximum angle euler error based on visionbased navigation is 083 degree in case of camera movement the maximum position error based on visionbased navigation is 30m and the maximum euler angle error based on visionbased navigation is 22 degrees | [['for', 'land', 'use', 'monitoring', 'the', 'main', 'problems', 'are', 'robust', 'positioning', 'in', 'urban', 'canyons', 'and', 'strong', 'terrain', 'reliefs', 'with', 'the', 'use', 'of', 'gps', 'system', 'only', 'indeed', 'satellite', 'signal', 'reflection', 'and', 'shielding', 'in', 'urban', 'canyons', 'and', 'strong', 'terrain', 'relief', 'results', 'in', 'problems', 'with', 'correct', 'positioning', 'using', 'gnssrtk', 'does', 'not', 'solve', 'the', 'problem', 'completely', 'because', 'in', 'some', 'complex', 'situations', 'the', 'whole', 'satellites', 'system', 'works', 'incorrectly', 'we', 'transform', 'the', 'weakness', 'urban', 'canyons', 'and', 'strong', 'terrain', 'relief', 'to', 'an', 'advantage', 'it', 'is', 'a', 'visionbased', 'navigation', 'using', 'a', 'map', 'of', 'the', 'terrain', 'relief', 'we', 'investigate', 'and', 'demonstrate', 'the', 'effectiveness', 'of', 'this', 'technology', 'in', 'chinese', 'region', 'xiaoshan', 'the', 'accuracy', 'of', 'the', 'visionbased', 'navigation', 'system', 'corresponds', 'to', 'the', 'expected', 'for', 'these', 'conditions', 'it', 'was', 'concluded', 'that', 'the', 'maximum', 'position', 'error', 'based', 'on', 'visionbased', 'navigation', 'is', '20', 'm', 'and', 'the', 'maximum', 'angle', 'euler', 'error', 'based', 'on', 'visionbased', 'navigation', 'is', '083', 'degree', 'in', 'case', 'of', 'camera', 'movement', 'the', 'maximum', 'position', 'error', 'based', 'on', 'visionbased', 'navigation', 'is', '30m', 'and', 'the', 'maximum', 'euler', 'angle', 'error', 'based', 'on', 'visionbased', 'navigation', 'is', '22', 'degrees']] | [-0.14687375067677794, 0.022670183158316787, -0.06477185827264163, 0.05040642076554442, -0.10369479802708763, -0.1124031289059314, 0.017465217038989067, 0.401454929218051, -0.2178237982803867, -0.3379011554310897, 0.15926658139436595, -0.2502394688753633, -0.2039207385636733, 0.2538836027781058, -0.20600699305889844, 0.0862203349326072, 0.07637327719878938, 0.007228927691661132, -0.037138483251120694, -0.19269647568026335, 0.23982740590526236, 0.055595951509617623, 0.32474900611365837, 0.05459765540546782, 0.1607337266628054, 0.045910653710875306, 0.02362506192487975, 0.005508682273405914, -0.08196157179177785, 0.09189629138910234, 0.27459529360168655, 0.14657057979465685, 0.2550793772208549, -0.39496253731860115, -0.21759453400348625, 0.07658493895516065, 0.10410949422276601, 0.05145640484157151, -0.027249129243760502, -0.37217111643708667, 0.052621873817683774, -0.12232179448524437, -0.1288596565580173, 0.025084618729167795, 0.028464972166278, 0.04222334459856419, -0.22872008151274972, 0.026223018316418996, 0.013426146963389502, 0.1571032508758695, -0.07408670103177428, -0.0848611135214534, -0.006966170164689954, 0.21140048308630607, 0.01095720076418296, 0.04900269442060519, 0.1816916574690757, -0.15620191170906073, -0.07901694282953117, 0.42778631724234273, -0.010885594181239694, -0.22208868317344846, 0.1930216399930595, -0.10696264771589388, -0.11301901574534852, 0.13234295592909412, 0.22361061724604087, 0.1109549741293969, -0.14158100526713366, 0.036995174703146665, -0.03808648710782152, 0.20056909693050243, 0.06555866409603152, -0.04861725268918755, 0.1771500471181103, 0.21439352788175234, 0.21570456602854565, 0.0633398122251189, -0.22990210158639543, -0.08720522771597773, -0.20838994783948042, -0.14384670784957485, -0.1782487580231169, -0.031221377286263823, -0.08753787423769868, -0.1023640601474437, 0.35115656204288825, 0.22379311382593142, 0.13269374877702267, 0.06973499204087559, 0.36479651071602437, 0.05297065364997945, 0.029614693980311996, 0.09155474460601995, 0.25690921595949995, 0.014247358401050968, 0.12624916873340095, -0.214306212300601, 0.09746556915065628, 0.08203899130291704] |
1,803.00399 | Calcium Removal From Cardiac CT Images Using Deep Convolutional Neural
Network | Coronary calcium causes beam hardening and blooming artifacts on cardiac
computed tomography angiography (CTA) images, which lead to overestimation of
lumen stenosis and reduction of diagnostic specificity. To properly remove
coronary calcification and restore arterial lumen precisely, we propose a
machine learning-based method with a multi-step inpainting process. We
developed a new network configuration, Dense-Unet, to achieve optimal
performance with low computational cost. Results after the calcium removal
process were validated by comparing with gold-standard X-ray angiography. Our
results demonstrated that removing coronary calcification from images with the
proposed approach was feasible, and may potentially improve the diagnostic
accuracy of CTA.
| cs.CV cs.LG | coronary calcium causes beam hardening and blooming artifacts on cardiac computed tomography angiography cta images which lead to overestimation of lumen stenosis and reduction of diagnostic specificity to properly remove coronary calcification and restore arterial lumen precisely we propose a machine learningbased method with a multistep inpainting process we developed a new network configuration denseunet to achieve optimal performance with low computational cost results after the calcium removal process were validated by comparing with goldstandard xray angiography our results demonstrated that removing coronary calcification from images with the proposed approach was feasible and may potentially improve the diagnostic accuracy of cta | [['coronary', 'calcium', 'causes', 'beam', 'hardening', 'and', 'blooming', 'artifacts', 'on', 'cardiac', 'computed', 'tomography', 'angiography', 'cta', 'images', 'which', 'lead', 'to', 'overestimation', 'of', 'lumen', 'stenosis', 'and', 'reduction', 'of', 'diagnostic', 'specificity', 'to', 'properly', 'remove', 'coronary', 'calcification', 'and', 'restore', 'arterial', 'lumen', 'precisely', 'we', 'propose', 'a', 'machine', 'learningbased', 'method', 'with', 'a', 'multistep', 'inpainting', 'process', 'we', 'developed', 'a', 'new', 'network', 'configuration', 'denseunet', 'to', 'achieve', 'optimal', 'performance', 'with', 'low', 'computational', 'cost', 'results', 'after', 'the', 'calcium', 'removal', 'process', 'were', 'validated', 'by', 'comparing', 'with', 'goldstandard', 'xray', 'angiography', 'our', 'results', 'demonstrated', 'that', 'removing', 'coronary', 'calcification', 'from', 'images', 'with', 'the', 'proposed', 'approach', 'was', 'feasible', 'and', 'may', 'potentially', 'improve', 'the', 'diagnostic', 'accuracy', 'of', 'cta']] | [0.056015820503234864, 0.019942685179412365, -0.046695364578044976, 0.04500747568807128, -0.07011266173096374, -0.16920451956335456, 0.027467492006253452, 0.45087363514117895, -0.20799831903743324, -0.33030908985063434, 0.1447993357665837, -0.2767734743794426, -0.1746542955841869, 0.21793335606344044, -0.19876632290892304, 0.09395204751286655, 0.19453811002895235, -0.06257599449716508, -0.008777726954431273, -0.23170843239990063, 0.1874642783321906, 0.11458925569429994, 0.3643364787474275, 0.07716240771114827, 0.15161446169018744, -0.0322250901395455, -0.037112650852650406, 0.025546454953728245, -0.09560488875293231, 0.13013023876119406, 0.3064900009054691, 0.18976473351824097, 0.2716230339743197, -0.4272885678318562, -0.257934046369046, 0.07812627839157357, 0.14736937986686827, 0.07606903193169273, -0.03208522103726864, -0.34927113050594927, 0.1229932488896884, -0.1283836909662932, -0.07285595024353825, -0.12081208349438384, -0.11507022705627605, -0.057615962747950104, -0.32367089395527726, 0.13242437616921962, -0.01606812616577372, 0.122661875737831, -0.1076887678494677, -0.11974722071550786, -0.011538801554124803, 0.1699307722458616, 0.016842435914441012, 0.10075437476858497, 0.2168092155409977, -0.15812734671868384, -0.12765167561359703, 0.30587038142606615, 0.002070453576743603, -0.15641553136112635, 0.1566880761180073, -0.0468072825204581, -0.09657430274412036, 0.22732853831723332, 0.17635282207280398, 0.07706588329747319, -0.18217791021801533, -0.10619107163249282, 0.1044074201688636, 0.2059875904233195, 0.12418931103311479, -0.09890470830956474, 0.09893873001681641, 0.2576701116573531, -0.024188365805894136, 0.17490054050926118, -0.2579361509368755, 0.06496735604479908, -0.18349660983076319, -0.13795498481485993, -0.08519759105052799, -0.009500650318805128, -0.06978390655422118, -0.16970644073560834, 0.38373360607307405, 0.21679849388659933, 0.1278316797222942, 0.01458510805037804, 0.3535612576454878, -0.003242210153257474, 0.10268544516293332, -0.022969822134182322, 0.18399986896663903, 0.03566341457888484, 0.09244761016685515, -0.2861980801238678, 0.12310348229948431, 0.07340643086936324] |
1,803.004 | Countable ordinal spaces and compact countable subsets of a metric space | We show in detail that every compact countable subset of a metric space is
homeomorphic to a countable ordinal number, which extends a result given by
Mazurkiewicz and Sierpinski for finite-dimensional Euclidean spaces. In order
to achieve this goal, we use Transfinite Induction to construct a specific
homeomorphism. In addition, we prove that for all metric space $(E,d)$, the
cardinality of the set of all the equivalence classes $\mathscr{K}_E$, up to
homeomorphisms, of compact countable subsets of $E$ is less than or equal to
$\aleph_1$, i.e. $|\mathscr{K}_E| \le \aleph_1$. We also show that for all
cardinal number $\kappa$ smaller than or equal to $\aleph_1$, there exists a
metric space $(E_{\kappa}, d_{\kappa})$ such that $|\mathscr{K}_{E_{\kappa}}|=
\kappa$.
| math.GN | we show in detail that every compact countable subset of a metric space is homeomorphic to a countable ordinal number which extends a result given by mazurkiewicz and sierpinski for finitedimensional euclidean spaces in order to achieve this goal we use transfinite induction to construct a specific homeomorphism in addition we prove that for all metric space ed the cardinality of the set of all the equivalence classes mathscrk_e up to homeomorphisms of compact countable subsets of e is less than or equal to aleph_1 ie mathscrk_e le aleph_1 we also show that for all cardinal number kappa smaller than or equal to aleph_1 there exists a metric space e_kappa d_kappa such that mathscrk_e_kappa kappa | [['we', 'show', 'in', 'detail', 'that', 'every', 'compact', 'countable', 'subset', 'of', 'a', 'metric', 'space', 'is', 'homeomorphic', 'to', 'a', 'countable', 'ordinal', 'number', 'which', 'extends', 'a', 'result', 'given', 'by', 'mazurkiewicz', 'and', 'sierpinski', 'for', 'finitedimensional', 'euclidean', 'spaces', 'in', 'order', 'to', 'achieve', 'this', 'goal', 'we', 'use', 'transfinite', 'induction', 'to', 'construct', 'a', 'specific', 'homeomorphism', 'in', 'addition', 'we', 'prove', 'that', 'for', 'all', 'metric', 'space', 'ed', 'the', 'cardinality', 'of', 'the', 'set', 'of', 'all', 'the', 'equivalence', 'classes', 'mathscrk_e', 'up', 'to', 'homeomorphisms', 'of', 'compact', 'countable', 'subsets', 'of', 'e', 'is', 'less', 'than', 'or', 'equal', 'to', 'aleph_1', 'ie', 'mathscrk_e', 'le', 'aleph_1', 'we', 'also', 'show', 'that', 'for', 'all', 'cardinal', 'number', 'kappa', 'smaller', 'than', 'or', 'equal', 'to', 'aleph_1', 'there', 'exists', 'a', 'metric', 'space', 'e_kappa', 'd_kappa', 'such', 'that', 'mathscrk_e_kappa', 'kappa']] | [-0.12744272523769387, 0.15212642656731512, -0.023838706715200265, 0.11335787039170428, -0.12578230172915905, -0.10773597877086685, 0.06328871546350923, 0.37209751030329513, -0.2724789244433244, -0.18556993442109307, 0.07169258541339454, -0.3279147773796508, -0.07764002202951291, 0.19550953710793625, -0.13039046011944846, 0.012056266788292575, 0.02994041331951414, 0.1245924315104882, -0.05363245757279901, -0.27086960356514733, 0.41760195295855, -0.10226877579079555, 0.20658077033622577, 0.003674300379111423, 0.15483106508314073, -0.03461724272635472, 0.02806893308219072, 0.11236470055410865, -0.19566308134911908, 0.11356462652350331, 0.29077545018984124, 0.1736013061841027, 0.2898584299685165, -0.3090457020205265, -0.1912425780608025, 0.27399134450499807, 0.09071379897356671, -0.04445447509600739, 0.05843443207669299, -0.25570578287515017, 0.1831541908443444, -0.16769348259025194, -0.11556299272421244, -0.11905233618207611, 0.1394634373855215, -0.04620058447340721, -0.2768121730234172, -0.06186016624072894, 0.16437687950527613, 0.06727009580831404, -0.07585739895181211, -0.08547435096267879, -0.04352291390459213, 0.0639438303145538, -0.015514841617865337, 0.14407234637670824, 0.03877952419665038, 0.0031344851894253814, -0.11114801041077117, 0.39706546766263945, -0.08336536204342747, -0.2708568737531702, 0.1904370059922069, -0.21964129025687104, -0.18448438179922533, 0.12261751797553655, 0.07287118149300416, 0.13152907048729626, -0.01661935655886794, 0.1681556465803763, -0.14596557732318058, 0.1915958197535695, 0.1312714919934536, 0.05436482916477027, 0.08293100618883162, 0.1487388015045105, 0.1899569004733191, 0.16291082399553275, 0.03405781432583525, 0.005492528353561857, -0.33810573683732803, -0.15895294137131255, -0.17577487965605246, 0.11871707303919368, -0.14550829354056413, -0.2023917245804458, 0.28721458425007024, 0.13436485126392753, 0.16474989189992886, 0.17827303273087317, 0.20952982010282073, 0.013297542049848425, 0.04248566411620191, 0.15390786510128696, 0.1136168976490562, 0.09939897832511044, -0.10847851936076139, -0.0954248925485084, -0.006942525812265303, 0.1711953131708543] |
1,803.00401 | Unravelling Robustness of Deep Learning based Face Recognition Against
Adversarial Attacks | Deep neural network (DNN) architecture based models have high expressive
power and learning capacity. However, they are essentially a black box method
since it is not easy to mathematically formulate the functions that are learned
within its many layers of representation. Realizing this, many researchers have
started to design methods to exploit the drawbacks of deep learning based
algorithms questioning their robustness and exposing their singularities. In
this paper, we attempt to unravel three aspects related to the robustness of
DNNs for face recognition: (i) assessing the impact of deep architectures for
face recognition in terms of vulnerabilities to attacks inspired by commonly
observed distortions in the real world that are well handled by shallow
learning methods along with learning based adversaries; (ii) detecting the
singularities by characterizing abnormal filter response behavior in the hidden
layers of deep networks; and (iii) making corrections to the processing
pipeline to alleviate the problem. Our experimental evaluation using multiple
open-source DNN-based face recognition networks, including OpenFace and
VGG-Face, and two publicly available databases (MEDS and PaSC) demonstrates
that the performance of deep learning based face recognition algorithms can
suffer greatly in the presence of such distortions. The proposed method is also
compared with existing detection algorithms and the results show that it is
able to detect the attacks with very high accuracy by suitably designing a
classifier using the response of the hidden layers in the network. Finally, we
present several effective countermeasures to mitigate the impact of adversarial
attacks and improve the overall robustness of DNN-based face recognition.
| cs.CV | deep neural network dnn architecture based models have high expressive power and learning capacity however they are essentially a black box method since it is not easy to mathematically formulate the functions that are learned within its many layers of representation realizing this many researchers have started to design methods to exploit the drawbacks of deep learning based algorithms questioning their robustness and exposing their singularities in this paper we attempt to unravel three aspects related to the robustness of dnns for face recognition i assessing the impact of deep architectures for face recognition in terms of vulnerabilities to attacks inspired by commonly observed distortions in the real world that are well handled by shallow learning methods along with learning based adversaries ii detecting the singularities by characterizing abnormal filter response behavior in the hidden layers of deep networks and iii making corrections to the processing pipeline to alleviate the problem our experimental evaluation using multiple opensource dnnbased face recognition networks including openface and vggface and two publicly available databases meds and pasc demonstrates that the performance of deep learning based face recognition algorithms can suffer greatly in the presence of such distortions the proposed method is also compared with existing detection algorithms and the results show that it is able to detect the attacks with very high accuracy by suitably designing a classifier using the response of the hidden layers in the network finally we present several effective countermeasures to mitigate the impact of adversarial attacks and improve the overall robustness of dnnbased face recognition | [['deep', 'neural', 'network', 'dnn', 'architecture', 'based', 'models', 'have', 'high', 'expressive', 'power', 'and', 'learning', 'capacity', 'however', 'they', 'are', 'essentially', 'a', 'black', 'box', 'method', 'since', 'it', 'is', 'not', 'easy', 'to', 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1,803.00402 | On Periodic Solutions to Lagrangian System With Singularities | A Lagrangian system with singularities is considered. The configuration space
is a non-compact manifold that depends on time. A set of periodic solutions has
been found.
| math.DS | a lagrangian system with singularities is considered the configuration space is a noncompact manifold that depends on time a set of periodic solutions has been found | [['a', 'lagrangian', 'system', 'with', 'singularities', 'is', 'considered', 'the', 'configuration', 'space', 'is', 'a', 'noncompact', 'manifold', 'that', 'depends', 'on', 'time', 'a', 'set', 'of', 'periodic', 'solutions', 'has', 'been', 'found']] | [-0.2853450020058797, 0.06218160491866561, -0.1424846571798508, 0.04289274753178828, -0.11506464353834207, -0.1351099899934175, -0.05692617468034419, 0.34771748145039266, -0.143746982418144, -0.20068639158629453, 0.17563290240314716, -0.28397297898594004, -0.15021551792653134, 0.17871870745259982, -0.08988054924143049, 0.05535263747263413, 0.10716506874618623, 0.1264800825424922, -0.09127563244412439, -0.24767606399613074, 0.44764272359987867, -0.028396797975381978, 0.24109243615888631, 0.0351365887058469, 0.1951561295785583, -0.07439041695593354, 0.056730476673692465, 0.08490598561631767, -0.10356863488833859, 0.006132377096666739, 0.1723209714087156, 0.08017417357768863, 0.2596312262690984, -0.37453744431527763, -0.27794251526491, 0.17120747497448555, 0.16727836079035813, 0.09389227424533321, -0.03151026678432782, -0.2699824096634984, 0.0877508708060934, -0.0899053244636609, -0.20617142464750662, -0.0732578202867164, 0.09683409383377203, 0.01205332326487853, -0.23867610498116568, -0.04733763323523677, 0.011022091198426027, 0.03793752322403284, -0.10071389445175345, -0.028012822968706202, -0.08371779783807981, 0.05853619077242911, 0.02858555601694836, 0.142140644763668, 0.0933118317132959, -0.0077234812510701325, -0.09680577213517748, 0.41265472081991345, -0.0668540225059797, -0.3415183446441705, 0.16980379357790717, -0.12255707325843665, -0.10367787055348834, 0.19254922393995982, 0.12132474613519242, 0.17935788015333506, -0.12392601334991363, 0.24145839306024405, -0.09359499554221447, 0.18903956977793804, 0.03718642578818477, -0.010185290575743867, 0.1558604478262938, 0.24826672959786195, 0.16078274159763867, 0.09841927069310959, -0.050655522697175354, -0.13375550590885374, -0.2890816961343472, -0.13464631487687045, -0.20762827451555774, 0.08894325173101746, -0.06161971644570048, -0.2225780100740779, 0.4183136425518359, -0.04071770364848467, 0.19093288853764534, 0.011034233388132773, 0.21254614394946167, 0.14739895747726012, 0.058534688889407195, 0.07135094007333884, 0.2323867094864209, 0.07885260234336154, 0.07312680259705163, -0.1611335416449807, 0.0374837104911701, 0.14549736357114923] |
1,803.00403 | A general formal memory framework in Coq for verifying the properties of
programs based on higher-order logic theorem proving with increased
automation, consistency, and reusability | In recent years, a number of lightweight programs have been deployed in
critical domains, such as in smart contracts based on blockchain technology.
Therefore, the security and reliability of such programs should be guaranteed
by the most credible technology. Higher-order logic theorem proving is one of
the most reliable technologies for verifying the properties of programs.
However, programs may be developed by different high-level programming
languages, and a general, extensible, and reusable formal memory (GERM)
framework that can simultaneously support different formal verification
specifications, particularly at the code level, is presently unavailable for
verifying the properties of programs. Therefore, the present work proposes a
GERM framework to fill this gap. The framework simulates physical memory
hardware structure, including a low-level formal memory space, and provides a
set of simple, nonintrusive application programming interfaces and assistant
tools using Coq that can support different formal verification specifications
simultaneously. The proposed GERM framework is independent and customizable,
and was verified entirely in Coq. We also present an extension of Curry-Howard
isomorphism, denoted as execution-verification isomorphism (EVI), which
combines symbolic execution and theorem proving for increasing the degree of
automation in higher-order logic theorem proving assistant tools. We also
implement a toy functional programming language in a generalized algebraic
datatypes style and a formal interpreter in Coq based on the GERM framework.
These implementations are then employed to demonstrate the application of EVI
to a simple code segment.
| cs.PL | in recent years a number of lightweight programs have been deployed in critical domains such as in smart contracts based on blockchain technology therefore the security and reliability of such programs should be guaranteed by the most credible technology higherorder logic theorem proving is one of the most reliable technologies for verifying the properties of programs however programs may be developed by different highlevel programming languages and a general extensible and reusable formal memory germ framework that can simultaneously support different formal verification specifications particularly at the code level is presently unavailable for verifying the properties of programs therefore the present work proposes a germ framework to fill this gap the framework simulates physical memory hardware structure including a lowlevel formal memory space and provides a set of simple nonintrusive application programming interfaces and assistant tools using coq that can support different formal verification specifications simultaneously the proposed germ framework is independent and customizable and was verified entirely in coq we also present an extension of curryhoward isomorphism denoted as executionverification isomorphism evi which combines symbolic execution and theorem proving for increasing the degree of automation in higherorder logic theorem proving assistant tools we also implement a toy functional programming language in a generalized algebraic datatypes style and a formal interpreter in coq based on the germ framework these implementations are then employed to demonstrate the application of evi to a simple code segment | [['in', 'recent', 'years', 'a', 'number', 'of', 'lightweight', 'programs', 'have', 'been', 'deployed', 'in', 'critical', 'domains', 'such', 'as', 'in', 'smart', 'contracts', 'based', 'on', 'blockchain', 'technology', 'therefore', 'the', 'security', 'and', 'reliability', 'of', 'such', 'programs', 'should', 'be', 'guaranteed', 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1,803.00404 | Deep Defense: Training DNNs with Improved Adversarial Robustness | Despite the efficacy on a variety of computer vision tasks, deep neural
networks (DNNs) are vulnerable to adversarial attacks, limiting their
applications in security-critical systems. Recent works have shown the
possibility of generating imperceptibly perturbed image inputs (a.k.a.,
adversarial examples) to fool well-trained DNN classifiers into making
arbitrary predictions. To address this problem, we propose a training recipe
named "deep defense". Our core idea is to integrate an adversarial
perturbation-based regularizer into the classification objective, such that the
obtained models learn to resist potential attacks, directly and precisely. The
whole optimization problem is solved just like training a recursive network.
Experimental results demonstrate that our method outperforms training with
adversarial/Parseval regularizations by large margins on various datasets
(including MNIST, CIFAR-10 and ImageNet) and different DNN architectures. Code
and models for reproducing our results are available at
https://github.com/ZiangYan/deepdefense.pytorch
| cs.CV cs.LG cs.NE | despite the efficacy on a variety of computer vision tasks deep neural networks dnns are vulnerable to adversarial attacks limiting their applications in securitycritical systems recent works have shown the possibility of generating imperceptibly perturbed image inputs aka adversarial examples to fool welltrained dnn classifiers into making arbitrary predictions to address this problem we propose a training recipe named deep defense our core idea is to integrate an adversarial perturbationbased regularizer into the classification objective such that the obtained models learn to resist potential attacks directly and precisely the whole optimization problem is solved just like training a recursive network experimental results demonstrate that our method outperforms training with adversarialparseval regularizations by large margins on various datasets including mnist cifar10 and imagenet and different dnn architectures code and models for reproducing our results are available at httpsgithubcomziangyandeepdefensepytorch | [['despite', 'the', 'efficacy', 'on', 'a', 'variety', 'of', 'computer', 'vision', 'tasks', 'deep', 'neural', 'networks', 'dnns', 'are', 'vulnerable', 'to', 'adversarial', 'attacks', 'limiting', 'their', 'applications', 'in', 'securitycritical', 'systems', 'recent', 'works', 'have', 'shown', 'the', 'possibility', 'of', 'generating', 'imperceptibly', 'perturbed', 'image', 'inputs', 'aka', 'adversarial', 'examples', 'to', 'fool', 'welltrained', 'dnn', 'classifiers', 'into', 'making', 'arbitrary', 'predictions', 'to', 'address', 'this', 'problem', 'we', 'propose', 'a', 'training', 'recipe', 'named', 'deep', 'defense', 'our', 'core', 'idea', 'is', 'to', 'integrate', 'an', 'adversarial', 'perturbationbased', 'regularizer', 'into', 'the', 'classification', 'objective', 'such', 'that', 'the', 'obtained', 'models', 'learn', 'to', 'resist', 'potential', 'attacks', 'directly', 'and', 'precisely', 'the', 'whole', 'optimization', 'problem', 'is', 'solved', 'just', 'like', 'training', 'a', 'recursive', 'network', 'experimental', 'results', 'demonstrate', 'that', 'our', 'method', 'outperforms', 'training', 'with', 'adversarialparseval', 'regularizations', 'by', 'large', 'margins', 'on', 'various', 'datasets', 'including', 'mnist', 'cifar10', 'and', 'imagenet', 'and', 'different', 'dnn', 'architectures', 'code', 'and', 'models', 'for', 'reproducing', 'our', 'results', 'are', 'available', 'at', 'httpsgithubcomziangyandeepdefensepytorch']] | [-0.025711815889614322, -0.045715216494540474, -0.02271597292619171, 0.11194064127091595, -0.11085133300404305, -0.24349665850469912, -0.0003964311132828395, 0.4526995444946267, -0.2545539991585193, -0.34009854357551644, 0.08426510268667092, -0.28160243090932013, -0.2602629813865793, 0.25082723238953836, -0.18437271046762665, 0.16503329313601608, 0.1730881026238893, -0.010362525079054413, -0.06938304873528305, -0.3824255060551136, 0.3364452774050059, 0.02294100457414364, 0.343560242718215, 0.015255459864986026, 0.13272171990872636, -0.08596082254209453, 0.02304619159463241, -0.04008498698628197, -0.019990315378527156, 0.14164125309325754, 0.3326894621078585, 0.2432530137123885, 0.3724184508435428, -0.43726709258777124, -0.26635832667902665, 0.107147526078754, 0.11520003228862252, 0.15724817660251306, -0.04109936620246757, -0.38945895935650227, 0.1177686372668379, -0.18998135606654817, 0.0235700229631254, -0.2164143066473857, -0.042273820698675184, 0.0026132869430714185, -0.31245601511111964, -0.029600699718489693, 0.1020400147954071, 0.037814643970449216, -0.06242776964273718, -0.13630720809515978, 0.014779432015321045, 0.1244929236497868, 0.028261985033060665, 0.04778318023826513, 0.18268802662065287, -0.21389055405398485, -0.16042759503686319, 0.30683820244890675, -0.023423544058783188, -0.20916847870857627, 0.20181597012129646, 0.08550810722151288, -0.16126072375724712, 0.06179911334550491, 0.29314755522818475, 0.12333959747899186, -0.13061206683309542, 0.012499775442605218, -0.03073799353161896, 0.14804962884496758, 0.05210015764459967, -0.03874093059933296, 0.13703113357122573, 0.2738181038587182, -0.006498576579960408, 0.18533037785371698, -0.12283064962744161, -0.09054379838829239, -0.17937374773383555, -0.0099572964560206, -0.21973319894599694, -0.009919621661753843, -0.09480306845529143, -0.14031242889349987, 0.3832740304094774, 0.26232888529905013, 0.23397593833506108, 0.17811064034941848, 0.3929012810328492, -0.027097793538295837, 0.1764735014229599, 0.11939110374078155, 0.18166530433766268, 0.02192878300772497, 0.10243690557846868, -0.13710754604113323, 0.0996316584939551, 0.024106251158648066] |
1,803.00405 | Symplectic realisation of electric charge in fields of monopole
distributions | We construct a symplectic realisation of the twisted Poisson structure on the
phase space of an electric charge in the background of an arbitrary smooth
magnetic monopole density in three dimensions. We use the extended phase space
variables to study the classical and quantum dynamics of charged particles in
arbitrary magnetic fields by constructing a suitable Hamiltonian that
reproduces the Lorentz force law for the physical degrees of freedom. In the
source-free case the auxiliary variables can be eliminated via Hamiltonian
reduction, while for non-zero monopole densities they are necessary for a
consistent formulation and are related to the extra degrees of freedom usually
required in the Hamiltonian description of dissipative systems. We obtain new
perspectives on the dynamics of dyons and motion in the field of a Dirac
monopole, which can be formulated without Dirac strings. We compare our
associative phase space formalism with the approach based on nonassociative
quantum mechanics, reproducing extended versions of the characteristic
translation group three-cocycles and minimal momentum space volumes, and prove
that the two approaches are formally equivalent. We also comment on the
implications of our symplectic realisation in the dual framework of
non-geometric string theory and double field theory.
| hep-th math-ph math.MP math.QA math.SG quant-ph | we construct a symplectic realisation of the twisted poisson structure on the phase space of an electric charge in the background of an arbitrary smooth magnetic monopole density in three dimensions we use the extended phase space variables to study the classical and quantum dynamics of charged particles in arbitrary magnetic fields by constructing a suitable hamiltonian that reproduces the lorentz force law for the physical degrees of freedom in the sourcefree case the auxiliary variables can be eliminated via hamiltonian reduction while for nonzero monopole densities they are necessary for a consistent formulation and are related to the extra degrees of freedom usually required in the hamiltonian description of dissipative systems we obtain new perspectives on the dynamics of dyons and motion in the field of a dirac monopole which can be formulated without dirac strings we compare our associative phase space formalism with the approach based on nonassociative quantum mechanics reproducing extended versions of the characteristic translation group threecocycles and minimal momentum space volumes and prove that the two approaches are formally equivalent we also comment on the implications of our symplectic realisation in the dual framework of nongeometric string theory and double field theory | [['we', 'construct', 'a', 'symplectic', 'realisation', 'of', 'the', 'twisted', 'poisson', 'structure', 'on', 'the', 'phase', 'space', 'of', 'an', 'electric', 'charge', 'in', 'the', 'background', 'of', 'an', 'arbitrary', 'smooth', 'magnetic', 'monopole', 'density', 'in', 'three', 'dimensions', 'we', 'use', 'the', 'extended', 'phase', 'space', 'variables', 'to', 'study', 'the', 'classical', 'and', 'quantum', 'dynamics', 'of', 'charged', 'particles', 'in', 'arbitrary', 'magnetic', 'fields', 'by', 'constructing', 'a', 'suitable', 'hamiltonian', 'that', 'reproduces', 'the', 'lorentz', 'force', 'law', 'for', 'the', 'physical', 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1,803.00406 | Left ventricle segmentation By modelling uncertainty in prediction of
deep convolutional neural networks and adaptive thresholding inference | Deep neural networks have shown great achievements in solving complex
problems. However, there are fundamental problems that limit their real world
applications. Lack of measurable criteria for estimating uncertainty in the
network outputs is one of these problems. In this paper, we address this
limitation by introducing deformation to the network input and measuring the
level of stability in the network's output. We calculate simple random
transformations to estimate the prediction uncertainty of deep convolutional
neural networks. For a real use-case, we apply this method to left ventricle
segmentation in MRI cardiac images. We also propose an adaptive thresholding
method to consider the deep neural network uncertainty. Experimental results
demonstrate state-of-the-art performance and highlight the capabilities of
simple methods in conjunction with deep neural networks.
| cs.CV | deep neural networks have shown great achievements in solving complex problems however there are fundamental problems that limit their real world applications lack of measurable criteria for estimating uncertainty in the network outputs is one of these problems in this paper we address this limitation by introducing deformation to the network input and measuring the level of stability in the networks output we calculate simple random transformations to estimate the prediction uncertainty of deep convolutional neural networks for a real usecase we apply this method to left ventricle segmentation in mri cardiac images we also propose an adaptive thresholding method to consider the deep neural network uncertainty experimental results demonstrate stateoftheart performance and highlight the capabilities of simple methods in conjunction with deep neural networks | [['deep', 'neural', 'networks', 'have', 'shown', 'great', 'achievements', 'in', 'solving', 'complex', 'problems', 'however', 'there', 'are', 'fundamental', 'problems', 'that', 'limit', 'their', 'real', 'world', 'applications', 'lack', 'of', 'measurable', 'criteria', 'for', 'estimating', 'uncertainty', 'in', 'the', 'network', 'outputs', 'is', 'one', 'of', 'these', 'problems', 'in', 'this', 'paper', 'we', 'address', 'this', 'limitation', 'by', 'introducing', 'deformation', 'to', 'the', 'network', 'input', 'and', 'measuring', 'the', 'level', 'of', 'stability', 'in', 'the', 'networks', 'output', 'we', 'calculate', 'simple', 'random', 'transformations', 'to', 'estimate', 'the', 'prediction', 'uncertainty', 'of', 'deep', 'convolutional', 'neural', 'networks', 'for', 'a', 'real', 'usecase', 'we', 'apply', 'this', 'method', 'to', 'left', 'ventricle', 'segmentation', 'in', 'mri', 'cardiac', 'images', 'we', 'also', 'propose', 'an', 'adaptive', 'thresholding', 'method', 'to', 'consider', 'the', 'deep', 'neural', 'network', 'uncertainty', 'experimental', 'results', 'demonstrate', 'stateoftheart', 'performance', 'and', 'highlight', 'the', 'capabilities', 'of', 'simple', 'methods', 'in', 'conjunction', 'with', 'deep', 'neural', 'networks']] | [-0.06189998555462808, -0.02610054921626579, -0.013284714924171567, 0.07321732410602272, -0.07280549500882626, -0.14875825689174235, -0.006217084317468107, 0.4700723929479718, -0.310159705132246, -0.2841198560334742, 0.09861487213242799, -0.2379906287714839, -0.2804088796437718, 0.2084196291230619, -0.17277652417495848, 0.13854192616045474, 0.17075209386646747, 0.012555411741137504, -0.0567401035213843, -0.2733065316285938, 0.309625059729442, 0.0109870774326846, 0.348675298050046, 0.0454677459448576, 0.1615156306400895, -0.04525551277026534, -0.020705576526932418, 0.0025089317159727215, -0.08644702916807728, 0.21534026828035713, 0.3194018663465977, 0.18395440164208413, 0.3661558516116347, -0.4551031130235642, -0.28179191329889, 0.14508999375253917, 0.1323396547548473, 0.11234157629683614, -0.0017972728610038758, -0.32164217947423457, 0.101650335079059, -0.14728390136361122, -0.025589630346745253, -0.15712347538396715, -0.02813843612652272, -0.008633780051954091, -0.2711754944324493, 0.05329707511304878, 0.041594443291425705, 0.06998362720012664, -0.06488325950875878, -0.09690314791165293, 0.08197212934307754, 0.1655096586532891, 0.006242351182736456, 0.033340079390676694, 0.12714230574667454, -0.2260568188447505, -0.17276414288952946, 0.3267848511338234, -0.034080935802310704, -0.22653221645951271, 0.1459133784621954, -0.04551686206832528, -0.19716983181610703, 0.054772999376058576, 0.2659470265135169, 0.09180267476290464, -0.17395113153755665, 0.021997207052074374, -0.026500708438456057, 0.13736323197931052, 0.031530242826789616, -0.017864606741815805, 0.15238994250632823, 0.3049599638879299, 0.01602726797340438, 0.13623265119083225, -0.1443379120954778, -0.060841143595986066, -0.21274051173776387, -0.07668281216174364, -0.1761871137134731, -0.015177566466853023, -0.1076386617308017, -0.16814987137238496, 0.40329655023664235, 0.2629988893792033, 0.20747143483906985, 0.12236260775849223, 0.3632415899336338, 0.08698237111046911, 0.10628919889844837, 0.056659135926514864, 0.23888593384250997, 0.11256165563501418, 0.1300750910412753, -0.1703834619373083, 0.06347829661890865, 0.05384946716949344] |
1,803.00407 | Yedrouj-Net: An efficient CNN for spatial steganalysis | For about 10 years, detecting the presence of a secret message hidden in an
image was performed with an Ensemble Classifier trained with Rich features. In
recent years, studies such as Xu et al. have indicated that well-designed
convolutional Neural Networks (CNN) can achieve comparable performance to the
two-step machine learning approaches.
In this paper, we propose a CNN that outperforms the state-ofthe-art in terms
of error probability. The proposition is in the continuity of what has been
recently proposed and it is a clever fusion of important bricks used in various
papers. Among the essential parts of the CNN, one can cite the use of a
pre-processing filterbank and a Truncation activation function, five
convolutional layers with a Batch Normalization associated with a Scale Layer,
as well as the use of a sufficiently sized fully connected section. An
augmented database has also been used to improve the training of the CNN.
Our CNN was experimentally evaluated against S-UNIWARD and WOW embedding
algorithms and its performances were compared with those of three other
methods: an Ensemble Classifier plus a Rich Model, and two other CNN
steganalyzers.
| cs.CV cs.CR | for about 10 years detecting the presence of a secret message hidden in an image was performed with an ensemble classifier trained with rich features in recent years studies such as xu et al have indicated that welldesigned convolutional neural networks cnn can achieve comparable performance to the twostep machine learning approaches in this paper we propose a cnn that outperforms the stateoftheart in terms of error probability the proposition is in the continuity of what has been recently proposed and it is a clever fusion of important bricks used in various papers among the essential parts of the cnn one can cite the use of a preprocessing filterbank and a truncation activation function five convolutional layers with a batch normalization associated with a scale layer as well as the use of a sufficiently sized fully connected section an augmented database has also been used to improve the training of the cnn our cnn was experimentally evaluated against suniward and wow embedding algorithms and its performances were compared with those of three other methods an ensemble classifier plus a rich model and two other cnn steganalyzers | [['for', 'about', '10', 'years', 'detecting', 'the', 'presence', 'of', 'a', 'secret', 'message', 'hidden', 'in', 'an', 'image', 'was', 'performed', 'with', 'an', 'ensemble', 'classifier', 'trained', 'with', 'rich', 'features', 'in', 'recent', 'years', 'studies', 'such', 'as', 'xu', 'et', 'al', 'have', 'indicated', 'that', 'welldesigned', 'convolutional', 'neural', 'networks', 'cnn', 'can', 'achieve', 'comparable', 'performance', 'to', 'the', 'twostep', 'machine', 'learning', 'approaches', 'in', 'this', 'paper', 'we', 'propose', 'a', 'cnn', 'that', 'outperforms', 'the', 'stateoftheart', 'in', 'terms', 'of', 'error', 'probability', 'the', 'proposition', 'is', 'in', 'the', 'continuity', 'of', 'what', 'has', 'been', 'recently', 'proposed', 'and', 'it', 'is', 'a', 'clever', 'fusion', 'of', 'important', 'bricks', 'used', 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-0.116837839981521, 0.10540352595071258, 0.07735323998326014] |
1,803.00408 | Quantum models based on finite groups | We consider a constructive modification of quantum-mechanical formalism.
Replacement of a general unitary group by unitary representations of finite
groups makes it possible to reproduce quantum formalism without loss of its
empirical content. Since any linear representation of a finite group can be
implemented as a subrepresentation of a permutation representation,
quantum-mechanical problems can be formulated in terms of permutation groups.
Reproducing quantum behavior in the framework of permutation representations of
finite groups makes it possible to clarify the meaning of a number of physical
concepts.
| physics.gen-ph | we consider a constructive modification of quantummechanical formalism replacement of a general unitary group by unitary representations of finite groups makes it possible to reproduce quantum formalism without loss of its empirical content since any linear representation of a finite group can be implemented as a subrepresentation of a permutation representation quantummechanical problems can be formulated in terms of permutation groups reproducing quantum behavior in the framework of permutation representations of finite groups makes it possible to clarify the meaning of a number of physical concepts | [['we', 'consider', 'a', 'constructive', 'modification', 'of', 'quantummechanical', 'formalism', 'replacement', 'of', 'a', 'general', 'unitary', 'group', 'by', 'unitary', 'representations', 'of', 'finite', 'groups', 'makes', 'it', 'possible', 'to', 'reproduce', 'quantum', 'formalism', 'without', 'loss', 'of', 'its', 'empirical', 'content', 'since', 'any', 'linear', 'representation', 'of', 'a', 'finite', 'group', 'can', 'be', 'implemented', 'as', 'a', 'subrepresentation', 'of', 'a', 'permutation', 'representation', 'quantummechanical', 'problems', 'can', 'be', 'formulated', 'in', 'terms', 'of', 'permutation', 'groups', 'reproducing', 'quantum', 'behavior', 'in', 'the', 'framework', 'of', 'permutation', 'representations', 'of', 'finite', 'groups', 'makes', 'it', 'possible', 'to', 'clarify', 'the', 'meaning', 'of', 'a', 'number', 'of', 'physical', 'concepts']] | [-0.11388599223912109, 0.14389534339005516, -0.17113396317459817, 0.08932916861200749, -0.11728157206481798, -0.12925057582622176, 0.05628301160475022, 0.3643163759694543, -0.3055496968892078, -0.2564322734901378, 0.03188117397245193, -0.20805084827724238, -0.17770321569163527, 0.15734669815229121, -0.10804513988063433, 0.04777256627824309, 0.03730686082028199, 0.1236909270221584, -0.16091995996586858, -0.25187300385495776, 0.3086181390207521, 0.014088398915022438, 0.2570106526430653, 0.02228068162925368, 0.1295067345896779, 0.042841075242735276, -0.048881450778977986, 0.001293190301471758, 0.0016993828967950026, 0.10901073954707055, 0.3281099541156098, 0.14246951101001265, 0.25701108528842587, -0.41082442226971305, -0.23838861537880676, 0.14974025325026624, 0.12747328692700627, 0.13292470488827243, -0.018501369899875202, -0.27367043502758753, 0.10683808578882194, -0.24459082514134256, -0.14454479148438157, -0.10369734625808548, 0.02725977307662021, -0.06481392300676901, -0.23251163765658125, 0.022252265228565004, 0.07745273091267188, 0.08522081868939621, -0.02094781092438457, -0.05215065986822294, 0.034802406268237635, 0.14311509367165176, -0.035832671206282064, -0.027123250151161363, 0.11324295388273407, -0.10969286490513315, -0.15439333028138377, 0.46823649758169816, -0.024260276625322742, -0.25137423452661306, 0.14713590292714882, -0.11085596768900233, -0.13606422745113717, 0.06403259147326787, 0.13530856516038955, 0.1233013307920733, -0.1373776285800823, 0.1541248829818742, -0.10930767348902516, 0.10966563365303067, 0.06157760273361968, 0.042899130998574585, 0.15854540547940793, 0.11216596582211381, 0.03650887939689118, 0.13897814557936347, 0.06969528379416916, -0.10185698385290845, -0.3663875723560882, -0.1994055284527814, -0.18877716719302848, 0.08494643936323565, -0.09777417031744885, -0.18989777762908489, 0.40012389288262223, 0.10984720131598932, 0.1850095188331812, 0.08229296510432695, 0.21436109220565752, 0.12247776177164904, 0.09733372399865022, 0.00800247860227733, 0.09668450114902022, 0.24062760985662165, -0.059207038970144336, -0.2262715400889689, 0.03942074629932989, 0.14342973147367322] |
1,803.00409 | A simple proof of the theorem of Sklar and its extension to distribution
functions | In this note we provide a quick proof of the Sklar's Theorem on the existence
of copulas by using the generalized inverse functions as in the one dimensional
case, but a little more sophisticated.
| math.PR | in this note we provide a quick proof of the sklars theorem on the existence of copulas by using the generalized inverse functions as in the one dimensional case but a little more sophisticated | [['in', 'this', 'note', 'we', 'provide', 'a', 'quick', 'proof', 'of', 'the', 'sklars', 'theorem', 'on', 'the', 'existence', 'of', 'copulas', 'by', 'using', 'the', 'generalized', 'inverse', 'functions', 'as', 'in', 'the', 'one', 'dimensional', 'case', 'but', 'a', 'little', 'more', 'sophisticated']] | [-0.05781228320933331, 0.01846900226219612, -0.14487030718694716, 0.16720237219637699, -0.07554894307737842, -0.10607992898782387, 0.05785592007176841, 0.31945614587953863, -0.2221612710495452, -0.22131998289157362, 0.15309752042487482, -0.20713509309708195, -0.1682015089865993, 0.2568560111172059, -0.07699226445573218, -0.006791380206670831, 0.027748598141924423, 0.02553817092334879, -0.09627320573610418, -0.23356774750658693, 0.36267299774815054, 0.05361447886988411, 0.2251725784393356, 0.08324331253328744, 0.09369002871544045, 0.10793588370742167, -0.04669204766533392, -0.024409039055599886, -0.14690579688998268, 0.16380394337808385, 0.24483885957777282, 0.10356522755533018, 0.3227110923651387, -0.4120914493413532, -0.17600060662473827, 0.13797657232841148, 0.09567612847861122, 0.11173578192863394, -0.07989834783032113, -0.23850808165940074, 0.060044532477417416, -0.18353415373712778, -0.1846198507666807, -0.0852642339270781, -0.005840320235994809, 0.006429856533513349, -0.23115554616293488, 0.09966284183182698, 0.18603724093340776, 0.09402350015828714, -0.008293091723531047, -0.09993325113592778, 0.04726975185193998, 0.022461858701289576, 0.062110904220710784, 0.003492475377724451, 0.01920360152828781, -0.10160813411044621, -0.1196795939193929, 0.3297148021759794, -0.04963220880531213, -0.27031446881044435, 0.21428565231754498, -0.12631272868362858, -0.18023842830649195, 0.052185349522487205, 0.15618389254600248, 0.18054696402567275, -0.13623183697242947, 0.10013359234503963, -0.1313530157374985, 0.14183446408852057, 0.0923685373409706, 0.006050147855763926, 0.1402279667227584, 0.157234275021919, 0.12469279122374513, 0.19957108992864103, 0.011045634342521867, -0.061651217093800795, -0.324793225263848, -0.17640190905727007, -0.17357168790391264, 0.10711756986839806, -0.11078629198977176, -0.22412653754958334, 0.41709210241542144, 0.11642080466502674, 0.20223881994538448, 0.07677538460120559, 0.3061966474770623, 0.1459666462306974, 0.06509866539443679, 0.028014523963279584, 0.19604744570439353, 0.16006700306281665, 0.12499579509227153, -0.07357510429745376, 0.04815467153949773, 0.1448517518994563] |
1,803.0041 | Thermodynamics of Radiation Pressure and Photon Momentum (Part 2) | We derive some of the properties of blackbody radiation using thermodynamic
identities. A few of the results reported earlier (in Part 1 of the present
paper) will be re-derived here from a different perspective. We argue that the
fluctuations of thermal radiation can be expressed as the sum of two
contributions: one resulting from classical considerations in the limit when
Planck's constant h_bar goes to zero, and a second one that is rooted in the
discrete nature of electromagnetic particles (photons). Johnson noise and the
Nyquist theorem will be another topic of discussion, where the role played by
blackbody radiation in generating noise within an electronic circuit will be
emphasized. In the context of thermal fluctuations, we also analyze the various
sources of noise in photodetection, relating the statistics of photo-electron
counts to energy-density fluctuations associated with blackbody radiation. The
remaining sections of the paper are devoted to an analysis of the thermodynamic
properties of a monatomic gas under conditions of thermal equilibrium. This
latter part of the paper aims to provide a basis for comparisons between a
photon gas and a rarefied gas of identical rigid particles of matter.
| physics.optics physics.class-ph | we derive some of the properties of blackbody radiation using thermodynamic identities a few of the results reported earlier in part 1 of the present paper will be rederived here from a different perspective we argue that the fluctuations of thermal radiation can be expressed as the sum of two contributions one resulting from classical considerations in the limit when plancks constant h_bar goes to zero and a second one that is rooted in the discrete nature of electromagnetic particles photons johnson noise and the nyquist theorem will be another topic of discussion where the role played by blackbody radiation in generating noise within an electronic circuit will be emphasized in the context of thermal fluctuations we also analyze the various sources of noise in photodetection relating the statistics of photoelectron counts to energydensity fluctuations associated with blackbody radiation the remaining sections of the paper are devoted to an analysis of the thermodynamic properties of a monatomic gas under conditions of thermal equilibrium this latter part of the paper aims to provide a basis for comparisons between a photon gas and a rarefied gas of identical rigid particles of matter | [['we', 'derive', 'some', 'of', 'the', 'properties', 'of', 'blackbody', 'radiation', 'using', 'thermodynamic', 'identities', 'a', 'few', 'of', 'the', 'results', 'reported', 'earlier', 'in', 'part', '1', 'of', 'the', 'present', 'paper', 'will', 'be', 'rederived', 'here', 'from', 'a', 'different', 'perspective', 'we', 'argue', 'that', 'the', 'fluctuations', 'of', 'thermal', 'radiation', 'can', 'be', 'expressed', 'as', 'the', 'sum', 'of', 'two', 'contributions', 'one', 'resulting', 'from', 'classical', 'considerations', 'in', 'the', 'limit', 'when', 'plancks', 'constant', 'h_bar', 'goes', 'to', 'zero', 'and', 'a', 'second', 'one', 'that', 'is', 'rooted', 'in', 'the', 'discrete', 'nature', 'of', 'electromagnetic', 'particles', 'photons', 'johnson', 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1,803.00411 | Generalised Sierpinski Triangles | The family of Generalised Sierpinski triangles consist of the classical
Sierpinski triangle, the previously well investigated Pedal triangle and two
new triangular shaped fractal objects denoted by $\triangle FNN$ and $\triangle
FFN$. All of the generalised Sierpinski triangles are defined in terms of
iterated functions systems (IFS's) found by generalising the classic IFS used
for the Sierpinski triangle. In this paper the new IFSs for the two new types
of fractal triangles are defined, the dimensions of the triangles are analysed,
and applications for pedagogical use and tiling theory discussed.
| math.DS | the family of generalised sierpinski triangles consist of the classical sierpinski triangle the previously well investigated pedal triangle and two new triangular shaped fractal objects denoted by triangle fnn and triangle ffn all of the generalised sierpinski triangles are defined in terms of iterated functions systems ifss found by generalising the classic ifs used for the sierpinski triangle in this paper the new ifss for the two new types of fractal triangles are defined the dimensions of the triangles are analysed and applications for pedagogical use and tiling theory discussed | [['the', 'family', 'of', 'generalised', 'sierpinski', 'triangles', 'consist', 'of', 'the', 'classical', 'sierpinski', 'triangle', 'the', 'previously', 'well', 'investigated', 'pedal', 'triangle', 'and', 'two', 'new', 'triangular', 'shaped', 'fractal', 'objects', 'denoted', 'by', 'triangle', 'fnn', 'and', 'triangle', 'ffn', 'all', 'of', 'the', 'generalised', 'sierpinski', 'triangles', 'are', 'defined', 'in', 'terms', 'of', 'iterated', 'functions', 'systems', 'ifss', 'found', 'by', 'generalising', 'the', 'classic', 'ifs', 'used', 'for', 'the', 'sierpinski', 'triangle', 'in', 'this', 'paper', 'the', 'new', 'ifss', 'for', 'the', 'two', 'new', 'types', 'of', 'fractal', 'triangles', 'are', 'defined', 'the', 'dimensions', 'of', 'the', 'triangles', 'are', 'analysed', 'and', 'applications', 'for', 'pedagogical', 'use', 'and', 'tiling', 'theory', 'discussed']] | [-0.11555576579671147, 0.10069519360032346, -0.0058291645306679934, 0.13035512424151724, -0.042601526316462295, -0.1337224145497506, -0.05075026360443897, 0.3143163379902641, -0.29773632805380557, -0.24933982053771614, 0.08600001465917255, -0.3463876653773089, -0.21313886737657917, 0.19652660977509287, -0.09074386946029134, 0.12181951627652678, 0.008138460241672066, 0.030204795404440828, -0.021932338585611433, -0.2679455756727192, 0.37075400134910724, -0.05839551424400674, 0.23418262728955597, 0.03217789269983769, 0.04931782913497752, 0.01782398444807364, -0.06381290232659215, 0.11204235201908483, -0.20995662870506446, 0.14980982683547256, 0.13956096267534626, 0.07922634228339626, 0.12769007918735345, -0.39720656532380316, -0.13840111824166443, 0.13207350040061605, 0.17609305151531265, -0.05508711507750882, -0.006288667747543918, -0.31240911775579056, 0.03937714057974517, -0.096969428283046, -0.21380026204925445, -0.03891758890305128, 0.030258061987761823, 0.1047655746444232, -0.1822783484020167, 0.019474894403376512, 0.1368418303434737, 0.12059415057301522, 0.035488290106877686, -0.18979306107179986, -0.0035296377596548863, 0.11217022254261085, -0.09568167340217365, -0.016284449512345922, 0.052960042241546844, -0.05144274996127933, -0.221689262924095, 0.4074995949055948, 0.043044303000594177, -0.2523356185010117, 0.11906869726954028, -0.14266998734739092, -0.2137695431140148, 0.056372884247038096, 0.09326442107040848, 0.11354840129820837, -0.17150563009393713, 0.14106527694966645, -0.12801456538856856, 0.046720176521274775, 0.22866513451703618, -0.013306265148437685, 0.15040187074078454, 0.12206455839590893, 0.08896154875142706, 0.24385263029899862, -0.0194880492778288, -0.15307005903580123, -0.2743070242305597, -0.13001235086056923, -0.19925262858806592, -0.001964134174502558, -0.18246088229934684, -0.26253805118612944, 0.3856562993592686, 0.010236878953744762, 0.1709537493944582, 0.060183470196918484, 0.2034246601578262, 0.0631809057499696, 0.10923562952213817, 0.04554452979161094, 0.16883007380674825, 0.12334113686552478, 0.028779744261151388, -0.08211076596958769, -0.031769203425695496, 0.23362523887772113] |
1,803.00412 | A theory of sequence indexing and working memory in recurrent neural
networks | To accommodate structured approaches of neural computation, we propose a
class of recurrent neural networks for indexing and storing sequences of
symbols or analog data vectors. These networks with randomized input weights
and orthogonal recurrent weights implement coding principles previously
described in vector symbolic architectures (VSA), and leverage properties of
reservoir computing. In general, the storage in reservoir computing is lossy
and crosstalk noise limits the retrieval accuracy and information capacity. A
novel theory to optimize memory performance in such networks is presented and
compared with simulation experiments. The theory describes linear readout of
analog data, and readout with winner-take-all error correction of symbolic data
as proposed in VSA models. We find that diverse VSA models from the literature
have universal performance properties, which are superior to what previous
analyses predicted. Further, we propose novel VSA models with the statistically
optimal Wiener filter in the readout that exhibit much higher information
capacity, in particular for storing analog data.
The presented theory also applies to memory buffers, networks with gradual
forgetting, which can operate on infinite data streams without memory overflow.
Interestingly, we find that different forgetting mechanisms, such as
attenuating recurrent weights or neural nonlinearities, produce very similar
behavior if the forgetting time constants are aligned. Such models exhibit
extensive capacity when their forgetting time constant is optimized for given
noise conditions and network size. These results enable the design of new types
of VSA models for the online processing of data streams.
| cs.NE | to accommodate structured approaches of neural computation we propose a class of recurrent neural networks for indexing and storing sequences of symbols or analog data vectors these networks with randomized input weights and orthogonal recurrent weights implement coding principles previously described in vector symbolic architectures vsa and leverage properties of reservoir computing in general the storage in reservoir computing is lossy and crosstalk noise limits the retrieval accuracy and information capacity a novel theory to optimize memory performance in such networks is presented and compared with simulation experiments the theory describes linear readout of analog data and readout with winnertakeall error correction of symbolic data as proposed in vsa models we find that diverse vsa models from the literature have universal performance properties which are superior to what previous analyses predicted further we propose novel vsa models with the statistically optimal wiener filter in the readout that exhibit much higher information capacity in particular for storing analog data the presented theory also applies to memory buffers networks with gradual forgetting which can operate on infinite data streams without memory overflow interestingly we find that different forgetting mechanisms such as attenuating recurrent weights or neural nonlinearities produce very similar behavior if the forgetting time constants are aligned such models exhibit extensive capacity when their forgetting time constant is optimized for given noise conditions and network size these results enable the design of new types of vsa models for the online processing of data streams | [['to', 'accommodate', 'structured', 'approaches', 'of', 'neural', 'computation', 'we', 'propose', 'a', 'class', 'of', 'recurrent', 'neural', 'networks', 'for', 'indexing', 'and', 'storing', 'sequences', 'of', 'symbols', 'or', 'analog', 'data', 'vectors', 'these', 'networks', 'with', 'randomized', 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1,803.00413 | Unknot Recognition Through Quantifier Elimination | Unknot recognition is one of the fundamental questions in low dimensional
topology. In this work, we show that this problem can be encoded as a validity
problem in the existential fragment of the first-order theory of real closed
fields. This encoding is derived using a well-known result on SU(2)
representations of knot groups by Kronheimer-Mrowka. We further show that
applying existential quantifier elimination to the encoding enables an UnKnot
Recogntion algorithm with a complexity of the order $2^{\mathcal{O}(n)}$, where
$n$ is the number of crossings in the given knot diagram. Our algorithm is
simple to describe and has the same runtime as the currently best known unknot
recognition algorithms.
| math.GT | unknot recognition is one of the fundamental questions in low dimensional topology in this work we show that this problem can be encoded as a validity problem in the existential fragment of the firstorder theory of real closed fields this encoding is derived using a wellknown result on su2 representations of knot groups by kronheimermrowka we further show that applying existential quantifier elimination to the encoding enables an unknot recogntion algorithm with a complexity of the order 2mathcalon where n is the number of crossings in the given knot diagram our algorithm is simple to describe and has the same runtime as the currently best known unknot recognition algorithms | [['unknot', 'recognition', 'is', 'one', 'of', 'the', 'fundamental', 'questions', 'in', 'low', 'dimensional', 'topology', 'in', 'this', 'work', 'we', 'show', 'that', 'this', 'problem', 'can', 'be', 'encoded', 'as', 'a', 'validity', 'problem', 'in', 'the', 'existential', 'fragment', 'of', 'the', 'firstorder', 'theory', 'of', 'real', 'closed', 'fields', 'this', 'encoding', 'is', 'derived', 'using', 'a', 'wellknown', 'result', 'on', 'su2', 'representations', 'of', 'knot', 'groups', 'by', 'kronheimermrowka', 'we', 'further', 'show', 'that', 'applying', 'existential', 'quantifier', 'elimination', 'to', 'the', 'encoding', 'enables', 'an', 'unknot', 'recogntion', 'algorithm', 'with', 'a', 'complexity', 'of', 'the', 'order', '2mathcalon', 'where', 'n', 'is', 'the', 'number', 'of', 'crossings', 'in', 'the', 'given', 'knot', 'diagram', 'our', 'algorithm', 'is', 'simple', 'to', 'describe', 'and', 'has', 'the', 'same', 'runtime', 'as', 'the', 'currently', 'best', 'known', 'unknot', 'recognition', 'algorithms']] | [-0.1622470524604001, 0.06006567096200241, -0.09848416422028095, 0.0720739172463288, -0.0992426570328125, -0.14590877814097675, 0.048521134039792406, 0.3430105665853868, -0.30544561692254824, -0.3165463912990634, 0.07930371141561342, -0.19856307574513335, -0.20124077857946288, 0.18868679033281902, -0.14186505239491384, 0.03285501060007071, 0.03427421385896633, 0.13100211916888063, -0.04547166953691178, -0.31358267663529626, 0.29791938723927297, -0.025582813968261082, 0.20536892949086097, 0.06643943916316386, 0.08741177589184156, 0.0020561224686119844, 0.03375605559321465, 0.03686651784098811, -0.10138329524286543, 0.11789045689618904, 0.3032908245761603, 0.1904805471659293, 0.2042360175551881, -0.3671988381969708, -0.15849364933954482, 0.09973795486601172, 0.19828283866108568, 0.11930321523040119, -0.0024729174253513555, -0.2748835557226644, 0.10073289010863475, -0.14685012606895287, -0.07181907534444083, -0.07623446974644645, 0.010303919980759491, -0.024802854797733878, -0.21262394754147088, -0.001420846116228926, 0.10870292632844886, 0.08402941703658413, -0.012407675480332088, -0.0721617593948462, 0.050914143136685235, 0.13804611006182516, 0.022786585312267696, 0.0907550545029894, 0.07477220213385644, -0.14918318095368266, -0.21064651847161628, 0.3881547383584634, -0.041910141616999345, -0.19845959252496767, 0.16000495291815173, -0.0968697152523048, -0.2003501129500499, 0.166180564433388, 0.08886009941515685, 0.14343025058904593, -0.07045564486610668, 0.14207617891278695, -0.12612981889914307, 0.21123926402651705, 0.10444850179461625, -0.021584843613069365, 0.12603982327574934, 0.17489963133077793, 0.060453889374103814, 0.22645788595696945, -0.030228665079145383, -0.06621731600844888, -0.29160513730905085, -0.1890412051536798, -0.17323993331390536, 0.014539904760938414, -0.10672858592833681, -0.1745454581579435, 0.39030169901714007, 0.13980461896075955, 0.15904531514065134, 0.11915689184226924, 0.3545247458904568, 0.1219473210868374, 0.0755944721814659, 0.0882497392853515, 0.14202363903772225, 0.14121564248516397, -0.009185034044397375, -0.20320432414128273, 0.058191198553821, 0.15920769518103312] |
1,803.00414 | Global Regularity of Three-Dimensional Ricci Limit Spaces | We construct a global homeomorphism from any 3D Ricci limit space to a smooth
manifold, that is locally bi-Holder. This extends the recent work of Miles
Simon and the second author, and we build upon their techniques.
A key step in our proof is the construction of local "pyramid Ricci flows",
existing on uniform regions of spacetime, that are inspired by Hochard's
partial Ricci flows.
| math.DG math.AP | we construct a global homeomorphism from any 3d ricci limit space to a smooth manifold that is locally biholder this extends the recent work of miles simon and the second author and we build upon their techniques a key step in our proof is the construction of local pyramid ricci flows existing on uniform regions of spacetime that are inspired by hochards partial ricci flows | [['we', 'construct', 'a', 'global', 'homeomorphism', 'from', 'any', '3d', 'ricci', 'limit', 'space', 'to', 'a', 'smooth', 'manifold', 'that', 'is', 'locally', 'biholder', 'this', 'extends', 'the', 'recent', 'work', 'of', 'miles', 'simon', 'and', 'the', 'second', 'author', 'and', 'we', 'build', 'upon', 'their', 'techniques', 'a', 'key', 'step', 'in', 'our', 'proof', 'is', 'the', 'construction', 'of', 'local', 'pyramid', 'ricci', 'flows', 'existing', 'on', 'uniform', 'regions', 'of', 'spacetime', 'that', 'are', 'inspired', 'by', 'hochards', 'partial', 'ricci', 'flows']] | [-0.1406266372796381, 0.05347001692280173, -0.12805837020277977, 0.01532635338116961, -0.1377253658065456, -0.09806867945735576, -0.032655279497703305, 0.3311663527128985, -0.23931327912578126, -0.22548611981983413, 0.1468427955533116, -0.25323618850961793, -0.16620067816620576, 0.16392960540360946, -0.1644555860766559, 0.040140219671229715, 0.03199193815817125, -0.00044960633385926485, -0.056245382787892595, -0.2710660722659668, 0.4595270211575553, 0.03285161918029189, 0.2690017877466744, 0.06913456943584606, 0.13913550740471692, -0.06765854843979469, -0.07109039511124138, 0.02167677639909016, -0.20676248239146844, 0.1885049955053546, 0.1975707321689697, 0.10566038000251865, 0.24351217088405974, -0.4038538702297956, -0.28546461787072985, 0.10628950480167987, 0.1277769799125963, 0.11877968606131617, -0.051177142491724226, -0.3090659726585727, 0.11659770269034198, -0.11657024784653913, -0.12537640177924914, -0.100329151355254, -0.0007449248660122976, 0.008007759512111079, -0.2387490416294895, 0.043162274265341694, 0.16822443205819582, 0.054339042831998086, -0.04748318180645583, -0.033257651411986444, -0.05787103388502146, 0.11526265729480656, 0.013008727204578463, 0.13460804055648623, 0.12102409244107548, -0.0562274436269945, -0.09932831757760141, 0.29800569714279845, -0.1130832809722051, -0.2177428953036724, 0.1855987933522556, -0.07665653705043951, -0.13944100624939892, 0.07936484301171731, 0.15584211479290389, 0.1993482290272368, -0.08250858720202814, 0.14770920535193, -0.09808878196008664, 0.13159461104805814, 0.09110792788123945, -0.06542683296720497, 0.1215399541251827, 0.14406377612613142, 0.19955785355705302, 0.09299875169381266, -0.0048511232789678616, -0.09454120482405415, -0.3308870871733234, -0.17988403261233543, -0.1883632359931653, 0.0834622268230305, -0.11304350242244254, -0.13741184715763666, 0.40202592644345714, 0.07748245930997655, 0.23861329736246262, 0.1494201810201048, 0.30497865879442543, 0.01376890796564112, 0.04532597246361547, 0.15323958145745564, 0.19843944497006305, 0.15591015368772787, 0.13750712175533408, -0.10030778333248236, -0.0352762645616167, 0.2342628007827443] |
1,803.00415 | A survey on the unconditional convergence and the invertibility of
multipliers with implementation | The paper presents a survey over frame multipliers and related concepts. In
particular, it includes a short motivation of why multipliers are of interest
to consider, a review as well as extension of recent results, devoted to the
unconditional convergence of multipliers, sufficient and/or necessary
conditions for the invertibility of multipliers, and representation of the
inverse via Neumann-like series and via multipliers with particular parameters.
Multipliers for frames with specific structure, namely, Gabor and wavelet
multipliers, are also considered. Some of the results for the representation of
the inverse multiplier are implemented in Matlab codes and the algorithms are
described.
| math.FA | the paper presents a survey over frame multipliers and related concepts in particular it includes a short motivation of why multipliers are of interest to consider a review as well as extension of recent results devoted to the unconditional convergence of multipliers sufficient andor necessary conditions for the invertibility of multipliers and representation of the inverse via neumannlike series and via multipliers with particular parameters multipliers for frames with specific structure namely gabor and wavelet multipliers are also considered some of the results for the representation of the inverse multiplier are implemented in matlab codes and the algorithms are described | [['the', 'paper', 'presents', 'a', 'survey', 'over', 'frame', 'multipliers', 'and', 'related', 'concepts', 'in', 'particular', 'it', 'includes', 'a', 'short', 'motivation', 'of', 'why', 'multipliers', 'are', 'of', 'interest', 'to', 'consider', 'a', 'review', 'as', 'well', 'as', 'extension', 'of', 'recent', 'results', 'devoted', 'to', 'the', 'unconditional', 'convergence', 'of', 'multipliers', 'sufficient', 'andor', 'necessary', 'conditions', 'for', 'the', 'invertibility', 'of', 'multipliers', 'and', 'representation', 'of', 'the', 'inverse', 'via', 'neumannlike', 'series', 'and', 'via', 'multipliers', 'with', 'particular', 'parameters', 'multipliers', 'for', 'frames', 'with', 'specific', 'structure', 'namely', 'gabor', 'and', 'wavelet', 'multipliers', 'are', 'also', 'considered', 'some', 'of', 'the', 'results', 'for', 'the', 'representation', 'of', 'the', 'inverse', 'multiplier', 'are', 'implemented', 'in', 'matlab', 'codes', 'and', 'the', 'algorithms', 'are', 'described']] | [-0.08486323940567672, 0.0385360328713432, -0.05074913707736414, 0.06470333794131874, -0.08485175915760919, -0.10508858969900757, -0.04976730205700733, 0.4109304642677307, -0.299915293334052, -0.23182971626520157, 0.20982227283297106, -0.2052371532469988, -0.1502991956891492, 0.2598585097119212, -0.09154419771628454, 0.10144421592820435, 0.047257468937896195, 0.033349111755378545, -0.1528620082946145, -0.2488686814904213, 0.31055793892592193, 0.08262173045426607, 0.23542434360831976, 0.018693277263082564, 0.10298392493277789, 0.04023916400503367, -0.10420175320236012, -0.037711419095285234, -0.11693543949397281, 0.14579323246493003, 0.2658611712604761, 0.14133194011636077, 0.29543779640691353, -0.4195052057318389, -0.1588032336626202, 0.06283812582027166, 0.17098841020837427, 0.005756394509226084, -0.1167464766651392, -0.27047960692085327, 0.09515515761217103, -0.15274032176472246, -0.07807061871513725, -0.10264109896495938, -0.006270640343427658, 0.14948697946965694, -0.3198651697096648, 0.05523037578648655, 0.12165237280656584, 0.07880011324770748, -0.13442881602793932, -0.13260839235037566, 0.08453892258228735, 0.08026581374928356, 0.07170048132888042, -0.022339980781544, 0.07731850834563375, -0.06298732672818005, -0.11319383695721626, 0.3807705109938979, -0.03577893236652017, -0.2747895869088825, 0.19987224620010238, -0.09392481653019785, -0.12116870192810893, 0.047538600676925856, 0.16144679945893586, 0.14812252206727863, -0.15668771505879703, 0.11815580029688136, -0.06430984759703279, 0.08309019472450017, 0.05641478664241731, 0.07633010882884264, 0.11900673465803266, 0.10835861548315734, 0.1364267007727176, 0.16911171001236652, -0.04502893232740462, -0.054508362542837856, -0.36354085441678763, -0.199905136449961, -0.17169205603189766, -0.05686681028455496, -0.021789079975424102, -0.1402928792696912, 0.4955240271240473, 0.152382278933801, 0.19436567462282256, 0.04102501515299082, 0.25234123662114144, 0.11066501432971564, 0.07420800824649632, 0.06018393035046756, 0.17621181067923317, 0.2129433854110539, 0.11206365465535782, -0.14451321098720654, 0.007814126933226362, 0.1838802905799821] |
1,803.00416 | Embeddability and quasi-isometric classification of partially
commutative groups | The main goal of this note is to suggest an algebraic approach to the
quasi-isometric classification of partially commutative groups (alias
right-angled Artin groups). More precisely, we conjecture that if the partially
commutative groups $\mathbb{G}(\Delta)$ and $\mathbb{G}(\Gamma)$ are
quasi-isometric, then $\mathbb{G}(\Delta)$ is a (nice) subgroup of
$\mathbb{G}(\Gamma)$ and vice-versa. We show that the conjecture holds for all
known cases of quasi-isometric classification of partially commutative groups,
namely for the classes of $n$-tress and atomic graphs.
As in the classical Mostow rigidity theory for irreducible lattices, we
relate the quasi-isometric rigidity of the class of atomic partially
commutative groups with the algebraic rigidity, that is with the co-Hopfian
property of their $\mathbb{Q}$-completions.
| math.GR | the main goal of this note is to suggest an algebraic approach to the quasiisometric classification of partially commutative groups alias rightangled artin groups more precisely we conjecture that if the partially commutative groups mathbbgdelta and mathbbggamma are quasiisometric then mathbbgdelta is a nice subgroup of mathbbggamma and viceversa we show that the conjecture holds for all known cases of quasiisometric classification of partially commutative groups namely for the classes of ntress and atomic graphs as in the classical mostow rigidity theory for irreducible lattices we relate the quasiisometric rigidity of the class of atomic partially commutative groups with the algebraic rigidity that is with the cohopfian property of their mathbbqcompletions | [['the', 'main', 'goal', 'of', 'this', 'note', 'is', 'to', 'suggest', 'an', 'algebraic', 'approach', 'to', 'the', 'quasiisometric', 'classification', 'of', 'partially', 'commutative', 'groups', 'alias', 'rightangled', 'artin', 'groups', 'more', 'precisely', 'we', 'conjecture', 'that', 'if', 'the', 'partially', 'commutative', 'groups', 'mathbbgdelta', 'and', 'mathbbggamma', 'are', 'quasiisometric', 'then', 'mathbbgdelta', 'is', 'a', 'nice', 'subgroup', 'of', 'mathbbggamma', 'and', 'viceversa', 'we', 'show', 'that', 'the', 'conjecture', 'holds', 'for', 'all', 'known', 'cases', 'of', 'quasiisometric', 'classification', 'of', 'partially', 'commutative', 'groups', 'namely', 'for', 'the', 'classes', 'of', 'ntress', 'and', 'atomic', 'graphs', 'as', 'in', 'the', 'classical', 'mostow', 'rigidity', 'theory', 'for', 'irreducible', 'lattices', 'we', 'relate', 'the', 'quasiisometric', 'rigidity', 'of', 'the', 'class', 'of', 'atomic', 'partially', 'commutative', 'groups', 'with', 'the', 'algebraic', 'rigidity', 'that', 'is', 'with', 'the', 'cohopfian', 'property', 'of', 'their', 'mathbbqcompletions']] | [-0.13403398806029665, 0.12501223061269792, -0.09953410249380838, 0.09260097450044538, -0.09528971413327825, -0.1466112148327132, -0.012026622214554144, 0.3994537500753289, -0.3683861913469931, -0.2018324347745095, 0.11002158932609572, -0.24344860032821694, -0.15820564966027936, 0.20757192571957905, -0.19437800276847111, -0.04595359645650855, 0.049379582578937216, 0.09774737631281218, -0.06098646441962392, -0.3018509313464165, 0.4388568004726299, -0.05121833933960824, 0.21984575504348391, 0.09133558317664124, 0.047854525835386345, 0.049461478447275505, -0.0077423158855665294, 0.0370989374540451, -0.13384429678380477, 0.1383567274176693, 0.33180086729781966, 0.06395223886218099, 0.18741043490313347, -0.3327131499669382, -0.15466467613642593, 0.21546795744825864, 0.11750172153447888, 0.05652180418948687, -0.04522530515747522, -0.3038063144311309, 0.14697193023249774, -0.16096855730616622, -0.16529348252252454, -0.06997344456613064, 0.055109500304041874, -0.020119424527954486, -0.1604195593922798, 0.038749927956433526, 0.20377786663316544, 0.10918207413383893, -0.09675806983861895, -0.05930156924407042, -0.05364636148193053, 0.17010382376167746, -0.008659453847489897, -0.024369307944462414, 0.06285048013198234, -0.049787389404983035, -0.14141347567173873, 0.44175444770426975, -0.0029945218846911476, -0.17977459176133076, 0.18916629644642982, -0.20174284823061456, -0.2660980280355683, 0.11741307337901422, 0.06218998515978456, 0.13380373252467032, -0.014548896359545843, 0.16877208687198747, -0.2014900447645535, 0.09722884483191939, 0.07292332733049989, -0.015654040584784177, 0.09735520130821637, 0.07826515830875862, 0.128345936611073, 0.1657183596886517, 0.08711934298099507, 0.05274830295127772, -0.3153218002457704, -0.177644389344468, -0.08038306679864902, 0.1279079987090968, -0.12564259508937886, -0.19303315405379093, 0.3849928874167658, 0.07948647299726005, 0.11431980192483891, 0.1896719931957445, 0.21364946880244784, -0.03859935501435151, 0.044376666291749904, 0.06024406424368776, 0.1443980593322998, 0.2891063425135577, -0.10019981420288483, -0.09884534730088143, -0.026512762074846596, 0.2037733568588183] |
1,803.00417 | Highly parallelisable simulations of time-dependent viscoplastic fluid
flow simulations with structured adaptive mesh refinement | We present the extension of an efficient and highly parallelisable framework
for incompressible fluid flow simulations to viscoplastic fluids. The system is
governed by incompressible conservation of mass, the Cauchy momentum equation
and a generalised Newtonian constitutive law. In order to simulate a wide range
of viscoplastic fluids, we employ the Herschel-Bulkley model for yield-stress
fluids with nonlinear stress-strain dependency above the yield limit. We
utilise Papanastasiou regularisation in our algorithm to deal with the
singularity in apparent viscosity. The resulting system of partial differential
equations is solved using the IAMR code (Incompressible Adaptive Mesh
Refinement), which uses second-order Godunov methodology for the advective
terms and semi-implicit diffusion in the context of an approximate projection
method to solve on adaptively refined meshes. By augmenting the IAMR code with
the ability to simulate regularised Herschel-Bulkley fluids, we obtain
efficient numerical software for time-dependent viscoplastic flow in three
dimensions, which can be used to investigate systems not considered previously
due to computational expense. We validate results from simulations using this
new capability against previously published data for Bingham plastics and
power-law fluids in the two-dimensional lid-driven cavity. In doing so, we
expand the range of Bingham and Reynolds numbers which have been considered in
the benchmark tests. Moreover, extensions to time-dependent flow of
Herschel-Bulkley fluids and three spatial dimensions offer new insights into
the flow of viscoplastic fluids in this test case, and we provide missing
benchmark results for these extensions.
| physics.flu-dyn physics.comp-ph | we present the extension of an efficient and highly parallelisable framework for incompressible fluid flow simulations to viscoplastic fluids the system is governed by incompressible conservation of mass the cauchy momentum equation and a generalised newtonian constitutive law in order to simulate a wide range of viscoplastic fluids we employ the herschelbulkley model for yieldstress fluids with nonlinear stressstrain dependency above the yield limit we utilise papanastasiou regularisation in our algorithm to deal with the singularity in apparent viscosity the resulting system of partial differential equations is solved using the iamr code incompressible adaptive mesh refinement which uses secondorder godunov methodology for the advective terms and semiimplicit diffusion in the context of an approximate projection method to solve on adaptively refined meshes by augmenting the iamr code with the ability to simulate regularised herschelbulkley fluids we obtain efficient numerical software for timedependent viscoplastic flow in three dimensions which can be used to investigate systems not considered previously due to computational expense we validate results from simulations using this new capability against previously published data for bingham plastics and powerlaw fluids in the twodimensional liddriven cavity in doing so we expand the range of bingham and reynolds numbers which have been considered in the benchmark tests moreover extensions to timedependent flow of herschelbulkley fluids and three spatial dimensions offer new insights into the flow of viscoplastic fluids in this test case and we provide missing benchmark results for these extensions | [['we', 'present', 'the', 'extension', 'of', 'an', 'efficient', 'and', 'highly', 'parallelisable', 'framework', 'for', 'incompressible', 'fluid', 'flow', 'simulations', 'to', 'viscoplastic', 'fluids', 'the', 'system', 'is', 'governed', 'by', 'incompressible', 'conservation', 'of', 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1,803.00418 | An explicit staggered-grid method for numerical simulation of
large-scale natural gas pipeline networks | We present an explicit second order staggered finite difference (FD)
discretization scheme for forward simulation of natural gas transport in
pipeline networks. By construction, this discretization approach guarantees
that the conservation of mass condition is satisfied exactly. The mathematical
model is formulated in terms of density, pressure, and mass flux variables, and
as a result permits the use of a general equation of state to define the
relation between the gas density and pressure for a given temperature. In a
single pipe, the model represents the dynamics of the density by propagation of
a non-linear wave according to a variable wave speed. We derive compatibility
conditions for linking domain boundary values to enable efficient, explicit
simulation of gas flows propagating through a network with pressure changes
created by gas compressors. We compare Kiuchi's implicit method and an explicit
operator splitting method with our staggered grid method, and perform numerical
experiments to validate the convergence order of the new method. In addition,
we perform several computations to investigate the influence of non-ideal
equation of state models and temperature effects into pipeline simulations with
boundary conditions over various time and space scales.
| eess.SP | we present an explicit second order staggered finite difference fd discretization scheme for forward simulation of natural gas transport in pipeline networks by construction this discretization approach guarantees that the conservation of mass condition is satisfied exactly the mathematical model is formulated in terms of density pressure and mass flux variables and as a result permits the use of a general equation of state to define the relation between the gas density and pressure for a given temperature in a single pipe the model represents the dynamics of the density by propagation of a nonlinear wave according to a variable wave speed we derive compatibility conditions for linking domain boundary values to enable efficient explicit simulation of gas flows propagating through a network with pressure changes created by gas compressors we compare kiuchis implicit method and an explicit operator splitting method with our staggered grid method and perform numerical experiments to validate the convergence order of the new method in addition we perform several computations to investigate the influence of nonideal equation of state models and temperature effects into pipeline simulations with boundary conditions over various time and space scales | [['we', 'present', 'an', 'explicit', 'second', 'order', 'staggered', 'finite', 'difference', 'fd', 'discretization', 'scheme', 'for', 'forward', 'simulation', 'of', 'natural', 'gas', 'transport', 'in', 'pipeline', 'networks', 'by', 'construction', 'this', 'discretization', 'approach', 'guarantees', 'that', 'the', 'conservation', 'of', 'mass', 'condition', 'is', 'satisfied', 'exactly', 'the', 'mathematical', 'model', 'is', 'formulated', 'in', 'terms', 'of', 'density', 'pressure', 'and', 'mass', 'flux', 'variables', 'and', 'as', 'a', 'result', 'permits', 'the', 'use', 'of', 'a', 'general', 'equation', 'of', 'state', 'to', 'define', 'the', 'relation', 'between', 'the', 'gas', 'density', 'and', 'pressure', 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1,803.00419 | Technical Report about Tiramisu: a Three-Layered Abstraction for Hiding
Hardware Complexity from DSL Compilers | High-performance DSL developers work hard to take advantage of modern
hardware. The DSL compilers have to build their own complex middle-ends before
they can target a common back-end such as LLVM, which only handles single
instruction streams with SIMD instructions. We introduce Tiramisu, a common
middle-end that can generate efficient code for modern processors and
accelerators such as multicores, GPUs, FPGAs and distributed clusters. Tiramisu
introduces a novel three-level IR that separates the algorithm, how that
algorithm is executed, and where intermediate data are stored. This separation
simplifies optimization and makes targeting multiple hardware architectures
from the same algorithm easier. As a result, DSL compilers can be made
considerably less complex with no loss of performance while immediately
targeting multiple hardware or hardware combinations such as distributed nodes
with both CPUs and GPUs. We evaluated Tiramisu by creating a new middle-end for
the Halide and Julia compilers. We show that Tiramisu extends Halide and Julia
with many new capabilities including the ability to: express new algorithms
(such as recurrent filters and non-rectangular iteration spaces), perform new
complex loop nest transformations (such as wavefront parallelization, loop
shifting and loop fusion) and generate efficient code for more architectures
(such as combinations of distributed clusters, multicores, GPUs and FPGAs).
Finally, we demonstrate that Tiramisu can generate very efficient code that
matches the highly optimized Intel MKL gemm (generalized matrix multiplication)
implementation, we also show speedups reaching 4X in Halide and 16X in Julia
due to optimizations enabled by Tiramisu.
| cs.PL cs.PF | highperformance dsl developers work hard to take advantage of modern hardware the dsl compilers have to build their own complex middleends before they can target a common backend such as llvm which only handles single instruction streams with simd instructions we introduce tiramisu a common middleend that can generate efficient code for modern processors and accelerators such as multicores gpus fpgas and distributed clusters tiramisu introduces a novel threelevel ir that separates the algorithm how that algorithm is executed and where intermediate data are stored this separation simplifies optimization and makes targeting multiple hardware architectures from the same algorithm easier as a result dsl compilers can be made considerably less complex with no loss of performance while immediately targeting multiple hardware or hardware combinations such as distributed nodes with both cpus and gpus we evaluated tiramisu by creating a new middleend for the halide and julia compilers we show that tiramisu extends halide and julia with many new capabilities including the ability to express new algorithms such as recurrent filters and nonrectangular iteration spaces perform new complex loop nest transformations such as wavefront parallelization loop shifting and loop fusion and generate efficient code for more architectures such as combinations of distributed clusters multicores gpus and fpgas finally we demonstrate that tiramisu can generate very efficient code that matches the highly optimized intel mkl gemm generalized matrix multiplication implementation we also show speedups reaching 4x in halide and 16x in julia due to optimizations enabled by tiramisu | [['highperformance', 'dsl', 'developers', 'work', 'hard', 'to', 'take', 'advantage', 'of', 'modern', 'hardware', 'the', 'dsl', 'compilers', 'have', 'to', 'build', 'their', 'own', 'complex', 'middleends', 'before', 'they', 'can', 'target', 'a', 'common', 'backend', 'such', 'as', 'llvm', 'which', 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1,803.0042 | Tractable and Scalable Schatten Quasi-Norm Approximations for Rank
Minimization | The Schatten quasi-norm was introduced to bridge the gap between the trace
norm and rank function. However, existing algorithms are too slow or even
impractical for large-scale problems. Motivated by the equivalence relation
between the trace norm and its bilinear spectral penalty, we define two
tractable Schatten norms, i.e.\ the bi-trace and tri-trace norms, and prove
that they are in essence the Schatten-$1/2$ and $1/3$ quasi-norms,
respectively. By applying the two defined Schatten quasi-norms to various rank
minimization problems such as MC and RPCA, we only need to solve much smaller
factor matrices. We design two efficient linearized alternating minimization
algorithms to solve our problems and establish that each bounded sequence
generated by our algorithms converges to a critical point. We also provide the
restricted strong convexity (RSC) based and MC error bounds for our algorithms.
Our experimental results verified both the efficiency and effectiveness of our
algorithms compared with the state-of-the-art methods.
| cs.LG math.OC stat.ML | the schatten quasinorm was introduced to bridge the gap between the trace norm and rank function however existing algorithms are too slow or even impractical for largescale problems motivated by the equivalence relation between the trace norm and its bilinear spectral penalty we define two tractable schatten norms ie the bitrace and tritrace norms and prove that they are in essence the schatten12 and 13 quasinorms respectively by applying the two defined schatten quasinorms to various rank minimization problems such as mc and rpca we only need to solve much smaller factor matrices we design two efficient linearized alternating minimization algorithms to solve our problems and establish that each bounded sequence generated by our algorithms converges to a critical point we also provide the restricted strong convexity rsc based and mc error bounds for our algorithms our experimental results verified both the efficiency and effectiveness of our algorithms compared with the stateoftheart methods | [['the', 'schatten', 'quasinorm', 'was', 'introduced', 'to', 'bridge', 'the', 'gap', 'between', 'the', 'trace', 'norm', 'and', 'rank', 'function', 'however', 'existing', 'algorithms', 'are', 'too', 'slow', 'or', 'even', 'impractical', 'for', 'largescale', 'problems', 'motivated', 'by', 'the', 'equivalence', 'relation', 'between', 'the', 'trace', 'norm', 'and', 'its', 'bilinear', 'spectral', 'penalty', 'we', 'define', 'two', 'tractable', 'schatten', 'norms', 'ie', 'the', 'bitrace', 'and', 'tritrace', 'norms', 'and', 'prove', 'that', 'they', 'are', 'in', 'essence', 'the', 'schatten12', 'and', '13', 'quasinorms', 'respectively', 'by', 'applying', 'the', 'two', 'defined', 'schatten', 'quasinorms', 'to', 'various', 'rank', 'minimization', 'problems', 'such', 'as', 'mc', 'and', 'rpca', 'we', 'only', 'need', 'to', 'solve', 'much', 'smaller', 'factor', 'matrices', 'we', 'design', 'two', 'efficient', 'linearized', 'alternating', 'minimization', 'algorithms', 'to', 'solve', 'our', 'problems', 'and', 'establish', 'that', 'each', 'bounded', 'sequence', 'generated', 'by', 'our', 'algorithms', 'converges', 'to', 'a', 'critical', 'point', 'we', 'also', 'provide', 'the', 'restricted', 'strong', 'convexity', 'rsc', 'based', 'and', 'mc', 'error', 'bounds', 'for', 'our', 'algorithms', 'our', 'experimental', 'results', 'verified', 'both', 'the', 'efficiency', 'and', 'effectiveness', 'of', 'our', 'algorithms', 'compared', 'with', 'the', 'stateoftheart', 'methods']] | [-0.06677282329959174, 0.019696362860656035, -0.03150385584410591, 0.14917179221908253, -0.06915252177044749, -0.1595595767814666, 0.03315496033756062, 0.410225871304671, -0.3349129424119989, -0.3056674226249258, 0.16324262791235622, -0.2433651036284088, -0.1642227085803946, 0.2030757180834189, -0.08529856553534046, 0.1316794145825164, 0.08414060693234206, 0.0013020588350870336, -0.17074147400679066, -0.287212138789085, 0.325821838855433, 0.021504232790321112, 0.2569336985315507, 0.06856890528462828, 0.07445499906005959, -0.02885751231883963, -0.020802955686425168, 0.039247535041843855, -0.11304903819895117, 0.1859715690277517, 0.2734172249895831, 0.17389468356811752, 0.3450289542010675, -0.4136690882779658, -0.1500642940797843, 0.18657596247270702, 0.12176882865062605, 0.004407133931914965, -0.04000153131627788, -0.283299759067595, 0.13295692167244852, -0.11859051063579197, -0.04866880584663401, -0.11506190525988738, -0.04418285573367029, 0.0491693120659329, -0.3448739556626727, 0.08189189321109248, 0.06731849766957264, 0.02393237288420399, -0.09693716142093763, -0.19452591217122972, 0.08434193720808253, 0.09139280445097635, 0.07493362362030893, 0.019990937781209746, 0.09486829776316881, -0.06268714185804129, -0.13557255898291867, 0.3373704800506433, -0.03228442115476355, -0.2286888917783896, 0.2036960433772765, -0.05924493187728028, -0.11018956197736164, 0.07544745723173643, 0.1404598472919315, 0.1593860586488154, -0.09064450963710745, 0.08917880378935175, -0.04059626544515292, 0.13594566576279854, 0.04845715124470492, 0.011983150175462167, 0.030629278228540596, 0.0946547928204139, 0.15005972758556405, 0.14513354652406027, -0.0357734187777775, -0.09752918172938128, -0.24205585421994327, -0.12414745150211577, -0.21514631231004994, -0.038406765021694204, -0.1337636497344162, -0.14120289797506605, 0.3496531389436374, 0.15774137913559874, 0.18287315108037244, 0.14229390127118677, 0.3115056858273844, 0.10147062968928366, 0.06683122711954638, 0.15388725994465252, 0.24128452021473398, 0.17458856793741384, 0.05023508550909658, -0.21902899981476368, 0.04210530959069729, 0.13916229138490355] |
1,803.00421 | Structural correlations and dependent scattering mechanism on the
radiative properties of random media | The dependent scattering mechanism is known to have a significant impact on
the radiative properties of random media containing discrete scatterers. Here
we theoretically demonstrate the role of dependent scattering on the radiative
properties of disordered media composed of nonabsorbing, dual-dipolar
particles. Based on our theoretical formulas for the radiative properties for
such media, we investigate the dependent scattering effects, including the
effect of modification of the electric and magnetic dipole excitations and the
far-field interference effect, both induced and influenced by the structural
correlations. We study in detail how the structural correlations play a role in
the dependent scattering mechanism by using two types of particle system, i.e.,
the hard-sphere system and the sticky-hard-sphere system. We show that the
inverse stickiness parameter, which controls the interparticle adhesive force
and thus the particle correlations, can tune the radiative properties
significantly. Particularly, increasing the surface stickiness can result in a
higher scattering coefficient and a larger asymmetry factor. The results also
imply that in the present system, the far-field interference effect plays a
dominant role in the radiative properties while the effect of modification of
the electric and magnetic dipole excitations is more subtle. Our study is
promising in understanding and manipulating the radiative properties of
dual-dipolar random media.
| cond-mat.mes-hall physics.app-ph physics.optics | the dependent scattering mechanism is known to have a significant impact on the radiative properties of random media containing discrete scatterers here we theoretically demonstrate the role of dependent scattering on the radiative properties of disordered media composed of nonabsorbing dualdipolar particles based on our theoretical formulas for the radiative properties for such media we investigate the dependent scattering effects including the effect of modification of the electric and magnetic dipole excitations and the farfield interference effect both induced and influenced by the structural correlations we study in detail how the structural correlations play a role in the dependent scattering mechanism by using two types of particle system ie the hardsphere system and the stickyhardsphere system we show that the inverse stickiness parameter which controls the interparticle adhesive force and thus the particle correlations can tune the radiative properties significantly particularly increasing the surface stickiness can result in a higher scattering coefficient and a larger asymmetry factor the results also imply that in the present system the farfield interference effect plays a dominant role in the radiative properties while the effect of modification of the electric and magnetic dipole excitations is more subtle our study is promising in understanding and manipulating the radiative properties of dualdipolar random media | [['the', 'dependent', 'scattering', 'mechanism', 'is', 'known', 'to', 'have', 'a', 'significant', 'impact', 'on', 'the', 'radiative', 'properties', 'of', 'random', 'media', 'containing', 'discrete', 'scatterers', 'here', 'we', 'theoretically', 'demonstrate', 'the', 'role', 'of', 'dependent', 'scattering', 'on', 'the', 'radiative', 'properties', 'of', 'disordered', 'media', 'composed', 'of', 'nonabsorbing', 'dualdipolar', 'particles', 'based', 'on', 'our', 'theoretical', 'formulas', 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1,803.00422 | Distributed Multivariate Regression Modeling For Selecting Biomarkers
Under Data Protection Constraints | The discovery of clinical biomarkers requires large patient cohorts and is
aided by a pooled data approach across institutions. In many countries, data
protection constraints, especially in the clinical environment, forbid the
exchange of individual-level data between different research institutes,
impeding the conduct of a joint analyses. To circumvent this problem, only
non-disclosive aggregated data is exchanged, which is often done manually and
requires explicit permission before transfer, i.e., the number of data calls
and the amount of data should be limited. This does not allow for more complex
tasks such as variable selection, as only simple aggregated summary statistics
are typically transferred. Other methods have been proposed that require more
complex aggregated data or use input data perturbation, but these methods can
either not deal with a high number of biomarkers or lose information. Here, we
propose a multivariable regression approach for identifying biomarkers by
automatic variable selection based on aggregated data in iterative calls, which
can be implemented under data protection constraints. The approach can be used
to jointly analyze data distributed across several locations. To minimize the
amount of transferred data and the number of calls, we also provide a heuristic
variant of the approach. When performing global data standardization, the
proposed method yields the same results as pooled individual-level data
analysis. In a simulation study, the information loss introduced by local
standardization is seen to be minimal. In a typical scenario, the heuristic
decreases the number of data calls from more than 10 to 3, rendering manual
data releases feasible. To make our approach widely available for application,
we provide an implementation of the heuristic version incorporated in the
DataSHIELD framework.\
| stat.ML cs.CR cs.DC | the discovery of clinical biomarkers requires large patient cohorts and is aided by a pooled data approach across institutions in many countries data protection constraints especially in the clinical environment forbid the exchange of individuallevel data between different research institutes impeding the conduct of a joint analyses to circumvent this problem only nondisclosive aggregated data is exchanged which is often done manually and requires explicit permission before transfer ie the number of data calls and the amount of data should be limited this does not allow for more complex tasks such as variable selection as only simple aggregated summary statistics are typically transferred other methods have been proposed that require more complex aggregated data or use input data perturbation but these methods can either not deal with a high number of biomarkers or lose information here we propose a multivariable regression approach for identifying biomarkers by automatic variable selection based on aggregated data in iterative calls which can be implemented under data protection constraints the approach can be used to jointly analyze data distributed across several locations to minimize the amount of transferred data and the number of calls we also provide a heuristic variant of the approach when performing global data standardization the proposed method yields the same results as pooled individuallevel data analysis in a simulation study the information loss introduced by local standardization is seen to be minimal in a typical scenario the heuristic decreases the number of data calls from more than 10 to 3 rendering manual data releases feasible to make our approach widely available for application we provide an implementation of the heuristic version incorporated in the datashield framework | [['the', 'discovery', 'of', 'clinical', 'biomarkers', 'requires', 'large', 'patient', 'cohorts', 'and', 'is', 'aided', 'by', 'a', 'pooled', 'data', 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1,803.00423 | Strong Convergence of a Stochastic Rosenbrock-type Scheme for the Finite
Element Discretization of Semilinear SPDEs Driven by Multiplicative and
Additive Noise | This paper aims to investigate the numerical approximation of a general
second order parabolic stochastic partial differential equation(SPDE) driven by
multiplicative and additive noise. Our main interest is on such SPDEs where the
nonlinear part is stronger than the linear part, usually called stochastic
dominated transport equations. Most standard numerical schemes lose their good
stability properties on such equations, including the current linear implicit
Euler method. We discretise the SPDE in space by the finite element method and
propose a new scheme in time appropriate for such equations, called stochastic
Rosenbrock-Type scheme, which is based on the local linearisation of the
semi-discrete problem obtained after space discretisation. We provide a strong
convergence of the new fully discrete scheme toward the exact solution for
multiplicative and additive noise. Our convergence rates are in agreement with
results in the literature. Numerical experiments to sustain our theoretical
results are provided.
| math.NA cs.NA | this paper aims to investigate the numerical approximation of a general second order parabolic stochastic partial differential equationspde driven by multiplicative and additive noise our main interest is on such spdes where the nonlinear part is stronger than the linear part usually called stochastic dominated transport equations most standard numerical schemes lose their good stability properties on such equations including the current linear implicit euler method we discretise the spde in space by the finite element method and propose a new scheme in time appropriate for such equations called stochastic rosenbrocktype scheme which is based on the local linearisation of the semidiscrete problem obtained after space discretisation we provide a strong convergence of the new fully discrete scheme toward the exact solution for multiplicative and additive noise our convergence rates are in agreement with results in the literature numerical experiments to sustain our theoretical results are provided | [['this', 'paper', 'aims', 'to', 'investigate', 'the', 'numerical', 'approximation', 'of', 'a', 'general', 'second', 'order', 'parabolic', 'stochastic', 'partial', 'differential', 'equationspde', 'driven', 'by', 'multiplicative', 'and', 'additive', 'noise', 'our', 'main', 'interest', 'is', 'on', 'such', 'spdes', 'where', 'the', 'nonlinear', 'part', 'is', 'stronger', 'than', 'the', 'linear', 'part', 'usually', 'called', 'stochastic', 'dominated', 'transport', 'equations', 'most', 'standard', 'numerical', 'schemes', 'lose', 'their', 'good', 'stability', 'properties', 'on', 'such', 'equations', 'including', 'the', 'current', 'linear', 'implicit', 'euler', 'method', 'we', 'discretise', 'the', 'spde', 'in', 'space', 'by', 'the', 'finite', 'element', 'method', 'and', 'propose', 'a', 'new', 'scheme', 'in', 'time', 'appropriate', 'for', 'such', 'equations', 'called', 'stochastic', 'rosenbrocktype', 'scheme', 'which', 'is', 'based', 'on', 'the', 'local', 'linearisation', 'of', 'the', 'semidiscrete', 'problem', 'obtained', 'after', 'space', 'discretisation', 'we', 'provide', 'a', 'strong', 'convergence', 'of', 'the', 'new', 'fully', 'discrete', 'scheme', 'toward', 'the', 'exact', 'solution', 'for', 'multiplicative', 'and', 'additive', 'noise', 'our', 'convergence', 'rates', 'are', 'in', 'agreement', 'with', 'results', 'in', 'the', 'literature', 'numerical', 'experiments', 'to', 'sustain', 'our', 'theoretical', 'results', 'are', 'provided']] | [-0.10245550375076987, 0.014525215990435915, -0.06388443978946834, 0.06150022409159411, -0.09329733800865253, -0.10133387428093828, 0.01809487007890961, 0.33607293690117646, -0.3246060321273814, -0.22674682925353903, 0.138095705060377, -0.24219920784139967, -0.16819689421578735, 0.2290074731267634, -0.08214084166070433, 0.12010226892640016, 0.06510939850269178, -0.019990881082726973, -0.07526582656871705, -0.26780933341198304, 0.33906380268333314, 0.03348907827380664, 0.25131903954965323, -0.017565274984911592, 0.1802546692866401, -0.06221056655112679, -0.10631793666770364, 0.03331168614871179, -0.1425598952649985, 0.1037175455930497, 0.24037524557919526, 0.02093422187644304, 0.34457778364901437, -0.43829804404201556, -0.2226546071976626, 0.06535769207403064, 0.11720931898667097, 0.13514494754573597, -0.09876461045857722, -0.2880939679540263, 0.1279530736243948, -0.15603434073352287, -0.11579967149514325, -0.12044244255636166, -0.04933997174268778, 0.09443819865609715, -0.3144675795777644, 0.10925755885328312, 0.10031247098350479, 0.021705200523454822, -0.06391227239153671, -0.11476613769900104, 0.020067734094209918, 0.05885482620650611, 0.023341620211065017, -0.012444372912932213, 0.04381175782392118, -0.0854970884531559, -0.09474473805515235, 0.3789416083331112, -0.12833762216687813, -0.2797377307085181, 0.17024497362012106, -0.10183635130397925, -0.12021327840772514, 0.16563800004228543, 0.18863200614241515, 0.16298597366517398, -0.14259104237600298, 0.10597254908493194, -0.039706835060754196, 0.1438299451454156, 0.002066440555602819, 0.015772609687899317, 0.06379068002332838, 0.18904422668797172, 0.12680737855749166, 0.09328730413207126, -0.018879470968980114, -0.18655506172692593, -0.3423984762990758, -0.1255056098013242, -0.1291079699112495, 0.026322434952507922, -0.10689424060918248, -0.16864989435008795, 0.36844305613743505, 0.1519095482173824, 0.13068088639640807, 0.08506662972654742, 0.3352740323107664, 0.21161434704954196, -0.033524681175710495, 0.0812652661363344, 0.21427490052744924, 0.16375798122722934, 0.0986590914469415, -0.24845983801575117, 0.0790679363888644, 0.1648921142237223] |
1,803.00424 | Autonomic Vehicular Networks: Safety, Privacy, Cybersecurity and
Societal Issues | Safety, efficiency, privacy, and cybersecurity can be achieved jointly in
self-organizing networks of communicating vehicles of various automated driving
levels. The underlying approach, solutions and novel results are briefly
exposed. We explain why we are faced with a crucial choice regarding motorized
society and cyber surveillance.
| cs.CR cs.CY | safety efficiency privacy and cybersecurity can be achieved jointly in selforganizing networks of communicating vehicles of various automated driving levels the underlying approach solutions and novel results are briefly exposed we explain why we are faced with a crucial choice regarding motorized society and cyber surveillance | [['safety', 'efficiency', 'privacy', 'and', 'cybersecurity', 'can', 'be', 'achieved', 'jointly', 'in', 'selforganizing', 'networks', 'of', 'communicating', 'vehicles', 'of', 'various', 'automated', 'driving', 'levels', 'the', 'underlying', 'approach', 'solutions', 'and', 'novel', 'results', 'are', 'briefly', 'exposed', 'we', 'explain', 'why', 'we', 'are', 'faced', 'with', 'a', 'crucial', 'choice', 'regarding', 'motorized', 'society', 'and', 'cyber', 'surveillance']] | [-0.18491713469848037, 0.06495667373478088, -0.019018631610695436, 0.09454151049620756, -0.079280368052423, -0.2295760065620846, 0.04246060270816088, 0.41958065023240837, -0.22896896784558243, -0.36830768394081487, 0.1140751984937157, -0.25008449379516684, -0.23950522243166747, 0.18538604773904965, -0.25561349646633735, 0.09606326909978753, 0.05154077711251929, -0.027615295642096062, 0.09769618039464821, -0.27538179436131666, 0.3103383956477046, 0.03828191607380691, 0.37139899672373483, 0.09039144952902975, 0.09914883981337366, -0.0314475840828179, -0.06299395318669469, 0.02903849225612762, -0.05164376668551046, 0.18517612712710854, 0.39527717317737965, 0.25337267339067615, 0.3669598979632492, -0.4918326716060224, -0.19420935150802784, 0.10753440914635101, 0.1474570904544595, 0.04718751109043217, -0.07200462741615332, -0.3799559290156416, 0.08138367717923678, -0.2220289940216943, -0.16563113786928033, -0.15447242053873514, -0.07037844460295595, 0.0932142344991798, -0.2159405291275344, -0.042068626104003706, 0.02709134777440973, 0.10933968866162974, -0.09425646461718513, -0.06694456250609263, -0.0036649310723473522, 0.23817356225386585, 0.0736258531384089, -0.056603181874379516, 0.2035816544059502, -0.2000178790388062, -0.17865213617156056, 0.36906731274464855, 0.04366080685640159, -0.17825709408877985, 0.2030489240214988, 0.024938811993469364, -0.201095346685337, 0.043050702792875796, 0.24139293668913128, 0.048447914785988956, -0.2189490254867174, 0.010630748268854602, 0.07666443633649539, 0.17297427291455475, 0.06080303488947127, 0.0065442454171083545, 0.23492861648454613, 0.28969659676532383, 0.07946265533404506, 0.03943408742476174, -0.04291204950245826, -0.16226782219018787, -0.2226339685730636, -0.10745103711910221, -0.09394466120030974, 0.01573692725809372, -0.05193452695897102, -0.03041217060577448, 0.33911452252093865, 0.2602521635328784, 0.12642157390592215, 0.0041006442118922005, 0.36088102275465167, 0.07626299345460923, 0.010547419534186307, 0.08571781384070283, 0.18576249423558297, 0.012751238258636517, 0.19272995634895304, -0.16854873963767086, 0.14949842570754496, -0.01978497262335504] |
1,803.00425 | Graph Kernels based on High Order Graphlet Parsing and Hashing | Graph-based methods are known to be successful in many machine learning and
pattern classification tasks. These methods consider semi-structured data as
graphs where nodes correspond to primitives (parts, interest points, segments,
etc.) and edges characterize the relationships between these primitives.
However, these non-vectorial graph data cannot be straightforwardly plugged
into off-the-shelf machine learning algorithms without a preliminary step of --
explicit/implicit -- graph vectorization and embedding. This embedding process
should be resilient to intra-class graph variations while being highly
discriminant. In this paper, we propose a novel high-order stochastic graphlet
embedding (SGE) that maps graphs into vector spaces. Our main contribution
includes a new stochastic search procedure that efficiently parses a given
graph and extracts/samples unlimitedly high-order graphlets. We consider these
graphlets, with increasing orders, to model local primitives as well as their
increasingly complex interactions. In order to build our graph representation,
we measure the distribution of these graphlets into a given graph, using
particular hash functions that efficiently assign sampled graphlets into
isomorphic sets with a very low probability of collision. When combined with
maximum margin classifiers, these graphlet-based representations have positive
impact on the performance of pattern comparison and recognition as corroborated
through extensive experiments using standard benchmark databases.
| cs.CV | graphbased methods are known to be successful in many machine learning and pattern classification tasks these methods consider semistructured data as graphs where nodes correspond to primitives parts interest points segments etc and edges characterize the relationships between these primitives however these nonvectorial graph data cannot be straightforwardly plugged into offtheshelf machine learning algorithms without a preliminary step of explicitimplicit graph vectorization and embedding this embedding process should be resilient to intraclass graph variations while being highly discriminant in this paper we propose a novel highorder stochastic graphlet embedding sge that maps graphs into vector spaces our main contribution includes a new stochastic search procedure that efficiently parses a given graph and extractssamples unlimitedly highorder graphlets we consider these graphlets with increasing orders to model local primitives as well as their increasingly complex interactions in order to build our graph representation we measure the distribution of these graphlets into a given graph using particular hash functions that efficiently assign sampled graphlets into isomorphic sets with a very low probability of collision when combined with maximum margin classifiers these graphletbased representations have positive impact on the performance of pattern comparison and recognition as corroborated through extensive experiments using standard benchmark databases | [['graphbased', 'methods', 'are', 'known', 'to', 'be', 'successful', 'in', 'many', 'machine', 'learning', 'and', 'pattern', 'classification', 'tasks', 'these', 'methods', 'consider', 'semistructured', 'data', 'as', 'graphs', 'where', 'nodes', 'correspond', 'to', 'primitives', 'parts', 'interest', 'points', 'segments', 'etc', 'and', 'edges', 'characterize', 'the', 'relationships', 'between', 'these', 'primitives', 'however', 'these', 'nonvectorial', 'graph', 'data', 'can', 'not', 'be', 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1,803.00426 | Computing the Cumulative Distribution Function and Quantiles of the
limit of the Two-sided Kolmogorov-Smirnov Statistic | The cumulative distribution and quantile functions for the two-sided one
sample Kolmogorov-Smirnov probability distributions are used for
goodness-of-fit testing. The CDF is notoriously difficult to explicitly
describe and to compute, and for large sample size use of the limiting
distribution is an attractive alternative, with its lower computational
requirements. No closed form solution for the computation of the quantiles is
known. Computing the quantile function by a numeric root-finder for any
specific probability may require multiple evaluations of both the CDF and its
derivative. Approximations to both the CDF and its derivative can be used to
reduce the computational demands. We show that the approximations in use inside
the open source SciPy python software result in increased computation, not just
reduced accuracy, and cause convergence failures in the root-finding. Then we
provide alternate algorithms which restore accuracy and efficiency across the
whole domain.
| stat.CO | the cumulative distribution and quantile functions for the twosided one sample kolmogorovsmirnov probability distributions are used for goodnessoffit testing the cdf is notoriously difficult to explicitly describe and to compute and for large sample size use of the limiting distribution is an attractive alternative with its lower computational requirements no closed form solution for the computation of the quantiles is known computing the quantile function by a numeric rootfinder for any specific probability may require multiple evaluations of both the cdf and its derivative approximations to both the cdf and its derivative can be used to reduce the computational demands we show that the approximations in use inside the open source scipy python software result in increased computation not just reduced accuracy and cause convergence failures in the rootfinding then we provide alternate algorithms which restore accuracy and efficiency across the whole domain | [['the', 'cumulative', 'distribution', 'and', 'quantile', 'functions', 'for', 'the', 'twosided', 'one', 'sample', 'kolmogorovsmirnov', 'probability', 'distributions', 'are', 'used', 'for', 'goodnessoffit', 'testing', 'the', 'cdf', 'is', 'notoriously', 'difficult', 'to', 'explicitly', 'describe', 'and', 'to', 'compute', 'and', 'for', 'large', 'sample', 'size', 'use', 'of', 'the', 'limiting', 'distribution', 'is', 'an', 'attractive', 'alternative', 'with', 'its', 'lower', 'computational', 'requirements', 'no', 'closed', 'form', 'solution', 'for', 'the', 'computation', 'of', 'the', 'quantiles', 'is', 'known', 'computing', 'the', 'quantile', 'function', 'by', 'a', 'numeric', 'rootfinder', 'for', 'any', 'specific', 'probability', 'may', 'require', 'multiple', 'evaluations', 'of', 'both', 'the', 'cdf', 'and', 'its', 'derivative', 'approximations', 'to', 'both', 'the', 'cdf', 'and', 'its', 'derivative', 'can', 'be', 'used', 'to', 'reduce', 'the', 'computational', 'demands', 'we', 'show', 'that', 'the', 'approximations', 'in', 'use', 'inside', 'the', 'open', 'source', 'scipy', 'python', 'software', 'result', 'in', 'increased', 'computation', 'not', 'just', 'reduced', 'accuracy', 'and', 'cause', 'convergence', 'failures', 'in', 'the', 'rootfinding', 'then', 'we', 'provide', 'alternate', 'algorithms', 'which', 'restore', 'accuracy', 'and', 'efficiency', 'across', 'the', 'whole', 'domain']] | [-0.040093835775408294, -0.003802993031071297, -0.09279756855636419, 0.13611865727396982, -0.08865249552275542, -0.12959141918568762, 0.08563655705868521, 0.39163091810016365, -0.2530001981801809, -0.3239571821218083, 0.12220708648202017, -0.25421721135572223, -0.07844813887707212, 0.20435815296051177, -0.08351526451191273, 0.12921206739289895, 0.05050082315879685, 0.008065620455378016, -0.09779625410259656, -0.2841360709936052, 0.2219919301477749, 0.0665164691171368, 0.294163038047239, 0.052826576058629095, 0.07034995732421928, 0.00017362563314007504, -0.043010123998864544, -0.01637787779704, -0.10406319082779134, 0.12011616463346368, 0.26296431335158177, 0.21309045742842259, 0.30364659472557526, -0.41655724581566694, -0.13833968252125944, 0.13449403761325965, 0.14904960635984382, 0.0502128370042721, -0.021950220129895514, -0.24140578276441935, 0.08571471588790312, -0.19686898355977936, -0.14179453146611373, -0.10687450225093773, 0.004168198163901175, 0.049787772418866, -0.31269826849114957, 0.07371681503678014, 0.005726349201392044, 0.03477727614629727, -0.012770407723256393, -0.13882023827140252, 0.010609850979416625, 0.1379022101832593, 0.052206584794603134, 0.024880488920904748, 0.11282188239852553, -0.15194161848117876, -0.1091146033580441, 0.32189168601941603, -0.047573389283583296, -0.27942472213709896, 0.1932280944119748, -0.13127500483427534, -0.11830033004036421, 0.132030887745472, 0.18732876686861258, 0.09569905680389358, -0.1612079959245516, 0.1063121536692077, 0.038102963034037526, 0.14817684918208132, 0.06356053275277616, 0.012355055737348235, 0.14304753482810684, 0.12995269657210348, 0.08120093789681063, 0.14569335770744413, -0.10794717008964373, -0.09657836043262867, -0.31800903982712425, -0.18105599649537069, -0.2333886934642947, -0.01702270734152786, -0.12633068621139468, -0.20884489524666663, 0.36423741101932067, 0.17088339144742146, 0.17421957614043584, 0.14015476789770784, 0.323085154517443, 0.171931386531259, 0.05838442137595136, 0.13062660942192783, 0.171534808731957, 0.07526548237171858, 0.06644585690220127, -0.19876274170184677, 0.14332381341853864, 0.014678714412648779] |
1,803.00427 | The Dynamic Geometry of Interaction Machine: A Token-Guided Graph
Rewriter | In implementing evaluation strategies of the lambda-calculus, both
correctness and efficiency of implementation are valid concerns. While the
notion of correctness is determined by the evaluation strategy, regarding
efficiency there is a larger design space that can be explored, in particular
the trade-off between space versus time efficiency. Aiming at a unified
framework that would enable the study of this trade-off, we introduce an
abstract machine, inspired by Girard's Geometry of Interaction (GoI), a machine
combining token passing and graph rewriting. We show soundness and completeness
of our abstract machine, called the \emph{Dynamic GoI Machine} (DGoIM), with
respect to three evaluations: call-by-need, left-to-right call-by-value, and
right-to-left call-by-value. Analysing time cost of its execution classifies
the machine as ``efficient'' in Accattoli's taxonomy of abstract machines.
| cs.LO cs.PL | in implementing evaluation strategies of the lambdacalculus both correctness and efficiency of implementation are valid concerns while the notion of correctness is determined by the evaluation strategy regarding efficiency there is a larger design space that can be explored in particular the tradeoff between space versus time efficiency aiming at a unified framework that would enable the study of this tradeoff we introduce an abstract machine inspired by girards geometry of interaction goi a machine combining token passing and graph rewriting we show soundness and completeness of our abstract machine called the emphdynamic goi machine dgoim with respect to three evaluations callbyneed lefttoright callbyvalue and righttoleft callbyvalue analysing time cost of its execution classifies the machine as efficient in accattolis taxonomy of abstract machines | [['in', 'implementing', 'evaluation', 'strategies', 'of', 'the', 'lambdacalculus', 'both', 'correctness', 'and', 'efficiency', 'of', 'implementation', 'are', 'valid', 'concerns', 'while', 'the', 'notion', 'of', 'correctness', 'is', 'determined', 'by', 'the', 'evaluation', 'strategy', 'regarding', 'efficiency', 'there', 'is', 'a', 'larger', 'design', 'space', 'that', 'can', 'be', 'explored', 'in', 'particular', 'the', 'tradeoff', 'between', 'space', 'versus', 'time', 'efficiency', 'aiming', 'at', 'a', 'unified', 'framework', 'that', 'would', 'enable', 'the', 'study', 'of', 'this', 'tradeoff', 'we', 'introduce', 'an', 'abstract', 'machine', 'inspired', 'by', 'girards', 'geometry', 'of', 'interaction', 'goi', 'a', 'machine', 'combining', 'token', 'passing', 'and', 'graph', 'rewriting', 'we', 'show', 'soundness', 'and', 'completeness', 'of', 'our', 'abstract', 'machine', 'called', 'the', 'emphdynamic', 'goi', 'machine', 'dgoim', 'with', 'respect', 'to', 'three', 'evaluations', 'callbyneed', 'lefttoright', 'callbyvalue', 'and', 'righttoleft', 'callbyvalue', 'analysing', 'time', 'cost', 'of', 'its', 'execution', 'classifies', 'the', 'machine', 'as', 'efficient', 'in', 'accattolis', 'taxonomy', 'of', 'abstract', 'machines']] | [-0.13480228671593772, 0.028050804748354786, -0.0986325660712957, 0.08289917182241145, -0.13956815557133767, -0.17271358920862118, 0.10926291175119038, 0.3834775819202825, -0.30887373666008633, -0.3238733315848813, 0.054299657924979504, -0.20531190419569612, -0.10774557508862458, 0.19031326388264255, -0.14750364421296022, 0.07842645195506198, 0.07341372156741037, 0.00312806872065149, -0.04841201813119809, -0.25275062217547056, 0.3229776718031615, 0.04977320873689267, 0.29482081208956396, 0.040540370571937774, 0.1088228353178091, 0.06919250651563127, -0.04430047459168089, 0.024816588890966922, -0.08556134885821526, 0.17361197419344418, 0.3131227067098089, 0.27307599034881397, 0.34277804430213665, -0.39212620675924326, -0.11066139406794982, 0.08091679183886416, 0.12105291078783452, 0.051454294149974183, -0.01481382255142014, -0.2806683074863207, 0.10333104506923607, -0.18278769480868154, -0.03148996771591145, -0.14423579778503476, 0.005323865435897342, 0.016868939823677553, -0.20555279629244919, -0.034255403086483, 0.13844398979469383, 0.12773159175788262, -0.017528798019010274, -0.07316777815351323, 0.0065929743662221174, 0.07933514249030381, -0.007069694520454974, 0.05018411950966824, 0.1251998705237413, -0.10558512272039097, -0.2438440978707325, 0.37553220070643173, -0.05139726341792172, -0.1927201716394085, 0.17358844557161174, 0.03038244543506974, -0.15375848624089192, 0.05619317007761809, 0.17255781135416678, 0.08996464917436242, -0.13381421845406294, 0.1234883893818234, 0.0025016561698829455, 0.19213753581918294, 0.09054613767394555, 0.03724201215851691, 0.12564698490314186, 0.27564532576962525, 0.011561457796262638, 0.19187051712651737, 0.017777572573733427, -0.13992330458976568, -0.3203305241561705, -0.20482611023039826, -0.11612264829760065, -0.05859167133683279, -0.1097903616436299, -0.13453233578512747, 0.366864453456665, 0.19438589726304334, 0.12680489356599509, 0.18518855943224363, 0.34008024741835413, 0.08361137080411878, 0.05110336307834293, 0.097841824147655, 0.16681583682751103, 0.06607321021522605, 0.11316920138121914, -0.21942039002146152, 0.11567678582909022, 0.09079680305665298] |
1,803.00428 | Acceleration of Cosmic Rays in Supernova Shocks: elemental selectivity
of the injection mechanism | Precise measurements of galactic cosmic rays revealed a significant
difference between the rigidity spectral indices of protons and helium ions.
This finding is a notable contrast to the commonly accepted theoretical
prediction that supernova remnant (SNR) shocks accelerate protons and helium
ions with the same rigidity alike. Most of the earlier explanations for the
"paradox" appealed to SNR environmental factors, such as inhomogeneous $p$/He
mixes in the shock upstream medium, variable ionization states of He, or a
multi-SNR origin of the observed spectra. The newest observations, however, are
in tension with most of them. In this paper, we show by self-consistent hybrid
simulations that such special conditions are not vital for the explanation of
the cosmic ray rigidity spectra. In particular, our simulations prove that an
SNR shock can modify the chemical composition of accelerated cosmic rays by
preferentially extracting them from a homogeneous background plasma without
additional, largely untestable assumptions. Our results confirm the earlier
theoretical predictions of how the efficiency of injection depends on the shock
Mach number $M.$ Its increase with the charge-to-mass ratio saturates at a
level that grows with $M.$ We have convolved the time-dependent injection rates
of protons and helium ions, obtained from the simulations, with a decreasing
shock strength over the active life of SNRs. The integrated SNR rigidity
spectrum for $p$/He ratio compares well with the AMS-02 and PAMELA data.
| astro-ph.HE | precise measurements of galactic cosmic rays revealed a significant difference between the rigidity spectral indices of protons and helium ions this finding is a notable contrast to the commonly accepted theoretical prediction that supernova remnant snr shocks accelerate protons and helium ions with the same rigidity alike most of the earlier explanations for the paradox appealed to snr environmental factors such as inhomogeneous phe mixes in the shock upstream medium variable ionization states of he or a multisnr origin of the observed spectra the newest observations however are in tension with most of them in this paper we show by selfconsistent hybrid simulations that such special conditions are not vital for the explanation of the cosmic ray rigidity spectra in particular our simulations prove that an snr shock can modify the chemical composition of accelerated cosmic rays by preferentially extracting them from a homogeneous background plasma without additional largely untestable assumptions our results confirm the earlier theoretical predictions of how the efficiency of injection depends on the shock mach number m its increase with the chargetomass ratio saturates at a level that grows with m we have convolved the timedependent injection rates of protons and helium ions obtained from the simulations with a decreasing shock strength over the active life of snrs the integrated snr rigidity spectrum for phe ratio compares well with the ams02 and pamela data | [['precise', 'measurements', 'of', 'galactic', 'cosmic', 'rays', 'revealed', 'a', 'significant', 'difference', 'between', 'the', 'rigidity', 'spectral', 'indices', 'of', 'protons', 'and', 'helium', 'ions', 'this', 'finding', 'is', 'a', 'notable', 'contrast', 'to', 'the', 'commonly', 'accepted', 'theoretical', 'prediction', 'that', 'supernova', 'remnant', 'snr', 'shocks', 'accelerate', 'protons', 'and', 'helium', 'ions', 'with', 'the', 'same', 'rigidity', 'alike', 'most', 'of', 'the', 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1,803.00429 | Learning Human-Aware Path Planning with Fully Convolutional Networks | This work presents an approach to learn path planning for robot social
navigation by demonstration. We make use of Fully Convolutional Neural Networks
(FCNs) to learn from expert's path demonstrations a map that marks a feasible
path to the goal as a classification problem. The use of FCNs allows us to
overcome the problem of manually designing/identifying the cost-map and
relevant features for the task of robot navigation. The method makes use of
optimal Rapidly-exploring Random Tree planner (RRT*) to overcome eventual
errors in the path prediction; the FCNs prediction is used as cost-map and also
to partially bias the sampling of the configuration space, leading the planner
to behave similarly to the learned expert behavior. The approach is evaluated
in experiments with real trajectories and compared with Inverse Reinforcement
Learning algorithms that use RRT* as underlying planner.
| cs.RO | this work presents an approach to learn path planning for robot social navigation by demonstration we make use of fully convolutional neural networks fcns to learn from experts path demonstrations a map that marks a feasible path to the goal as a classification problem the use of fcns allows us to overcome the problem of manually designingidentifying the costmap and relevant features for the task of robot navigation the method makes use of optimal rapidlyexploring random tree planner rrt to overcome eventual errors in the path prediction the fcns prediction is used as costmap and also to partially bias the sampling of the configuration space leading the planner to behave similarly to the learned expert behavior the approach is evaluated in experiments with real trajectories and compared with inverse reinforcement learning algorithms that use rrt as underlying planner | [['this', 'work', 'presents', 'an', 'approach', 'to', 'learn', 'path', 'planning', 'for', 'robot', 'social', 'navigation', 'by', 'demonstration', 'we', 'make', 'use', 'of', 'fully', 'convolutional', 'neural', 'networks', 'fcns', 'to', 'learn', 'from', 'experts', 'path', 'demonstrations', 'a', 'map', 'that', 'marks', 'a', 'feasible', 'path', 'to', 'the', 'goal', 'as', 'a', 'classification', 'problem', 'the', 'use', 'of', 'fcns', 'allows', 'us', 'to', 'overcome', 'the', 'problem', 'of', 'manually', 'designingidentifying', 'the', 'costmap', 'and', 'relevant', 'features', 'for', 'the', 'task', 'of', 'robot', 'navigation', 'the', 'method', 'makes', 'use', 'of', 'optimal', 'rapidlyexploring', 'random', 'tree', 'planner', 'rrt', 'to', 'overcome', 'eventual', 'errors', 'in', 'the', 'path', 'prediction', 'the', 'fcns', 'prediction', 'is', 'used', 'as', 'costmap', 'and', 'also', 'to', 'partially', 'bias', 'the', 'sampling', 'of', 'the', 'configuration', 'space', 'leading', 'the', 'planner', 'to', 'behave', 'similarly', 'to', 'the', 'learned', 'expert', 'behavior', 'the', 'approach', 'is', 'evaluated', 'in', 'experiments', 'with', 'real', 'trajectories', 'and', 'compared', 'with', 'inverse', 'reinforcement', 'learning', 'algorithms', 'that', 'use', 'rrt', 'as', 'underlying', 'planner']] | [-0.02546384745705737, 0.03726181446629437, -0.09970381825713671, 0.055699699447000824, -0.18111125154108026, -0.15123282214218792, 0.05565402325753456, 0.5037983607663943, -0.315163876820546, -0.34457299190770535, 0.05838216354655795, -0.2431274556919608, -0.22438746233246404, 0.17859229305419175, -0.15496382837176975, 0.145896981019921, 0.1547485288218969, 0.05206324363698381, -0.011158584202401829, -0.2309529137343889, 0.267303315902224, 0.06454394845441527, 0.2863298522811519, -0.03004160219715079, 0.19453517319362637, 0.03413338502393152, 0.014839279611533793, 0.004380454516410488, -0.10401122573387969, 0.1733174951837133, 0.345784316600783, 0.1958895659441278, 0.3496475314388364, -0.40040996939922774, -0.18533555135457186, 0.10050167326890204, 0.1302770469679725, 0.12290021416520441, 0.03501173172717112, -0.35436481439311357, 0.09635199735835058, -0.16350900788322417, -0.07368599081982988, -0.11331602995093558, -0.05168904364448938, -0.04214461006557256, -0.29140284675153066, -0.03611908514536136, 0.05435972888852014, 0.009103545042121932, -0.05100138889613772, -0.036920052742327215, 0.036420640148168065, 0.22325539102533112, 0.024870369236671578, 0.10402271398649054, 0.14450395086535464, -0.1920829753605581, -0.24040253134784254, 0.3738940288958541, 0.007173040480672443, -0.2216195545650434, 0.16057614904206355, -0.010299526756615753, -0.11051681242121832, 0.12224137598825414, 0.23755875246745473, 0.14296537591781752, -0.18336177925259745, 0.008046215789708697, -0.009627600960997715, 0.13499945876304142, 0.04461459210397417, -0.06795438900453983, 0.11588821896580456, 0.2832749989087673, 0.1298680500922738, 0.13120266742134182, -0.09102830521245725, -0.11126233313069508, -0.2053487512561744, -0.11958217943073625, -0.1969388840349568, -0.02503250565135131, -0.09493093505932144, -0.1667507167552499, 0.34493234453478105, 0.29939993352622446, 0.20932357473448462, 0.16051788218183458, 0.33831958675988183, 0.0414884023675967, 0.09436462323324088, 0.08449159827666275, 0.1679535033674163, 0.04530796372793941, 0.1309267648998784, -0.19686668060114948, 0.12049207627691709, 0.08924131899949735] |
1,803.0043 | Interactive Sound Rendering on Mobile Devices using Ray-Parameterized
Reverberation Filters | We present a new sound rendering pipeline that is able to generate plausible
sound propagation effects for interactive dynamic scenes. Our approach combines
ray-tracing-based sound propagation with reverberation filters using robust
automatic reverb parameter estimation that is driven by impulse responses
computed at a low sampling rate.We propose a unified spherical harmonic
representation of directional sound in both the propagation and auralization
modules and use this formulation to perform a constant number of convolution
operations for any number of sound sources while rendering spatial audio. In
comparison to previous geometric acoustic methods, we achieve a speedup of over
an order of magnitude while delivering similar audio to high-quality
convolution rendering algorithms. As a result, our approach is the first
capable of rendering plausible dynamic sound propagation effects on commodity
smartphones.
| cs.GR | we present a new sound rendering pipeline that is able to generate plausible sound propagation effects for interactive dynamic scenes our approach combines raytracingbased sound propagation with reverberation filters using robust automatic reverb parameter estimation that is driven by impulse responses computed at a low sampling ratewe propose a unified spherical harmonic representation of directional sound in both the propagation and auralization modules and use this formulation to perform a constant number of convolution operations for any number of sound sources while rendering spatial audio in comparison to previous geometric acoustic methods we achieve a speedup of over an order of magnitude while delivering similar audio to highquality convolution rendering algorithms as a result our approach is the first capable of rendering plausible dynamic sound propagation effects on commodity smartphones | [['we', 'present', 'a', 'new', 'sound', 'rendering', 'pipeline', 'that', 'is', 'able', 'to', 'generate', 'plausible', 'sound', 'propagation', 'effects', 'for', 'interactive', 'dynamic', 'scenes', 'our', 'approach', 'combines', 'raytracingbased', 'sound', 'propagation', 'with', 'reverberation', 'filters', 'using', 'robust', 'automatic', 'reverb', 'parameter', 'estimation', 'that', 'is', 'driven', 'by', 'impulse', 'responses', 'computed', 'at', 'a', 'low', 'sampling', 'ratewe', 'propose', 'a', 'unified', 'spherical', 'harmonic', 'representation', 'of', 'directional', 'sound', 'in', 'both', 'the', 'propagation', 'and', 'auralization', 'modules', 'and', 'use', 'this', 'formulation', 'to', 'perform', 'a', 'constant', 'number', 'of', 'convolution', 'operations', 'for', 'any', 'number', 'of', 'sound', 'sources', 'while', 'rendering', 'spatial', 'audio', 'in', 'comparison', 'to', 'previous', 'geometric', 'acoustic', 'methods', 'we', 'achieve', 'a', 'speedup', 'of', 'over', 'an', 'order', 'of', 'magnitude', 'while', 'delivering', 'similar', 'audio', 'to', 'highquality', 'convolution', 'rendering', 'algorithms', 'as', 'a', 'result', 'our', 'approach', 'is', 'the', 'first', 'capable', 'of', 'rendering', 'plausible', 'dynamic', 'sound', 'propagation', 'effects', 'on', 'commodity', 'smartphones']] | [-0.08415935705774105, 0.08322030431691718, -0.09709530539380816, 0.03903688174905255, -0.15836801231086542, -0.12043792686353509, 0.013256370052337074, 0.4320312728675512, -0.2524482807646004, -0.3131788135004731, 0.07071415586934353, -0.2407136562280357, -0.1490763111398197, 0.2539797783829272, -0.08725775251424728, 0.09800576247731582, 0.06541950032383634, 0.01116815955700496, -0.06112079186579929, -0.186985225441794, 0.23162999913956112, 0.043735366651358513, 0.31096835102026277, -0.010807216419086147, 0.19028900360616927, 0.03457192446938, -0.06782005394880589, 0.016222988908143283, -0.04881416079286002, 0.15145765872528918, 0.2792468318590321, 0.14089524194222086, 0.28913351618326627, -0.41506156999474536, -0.26134984132189015, 0.04853976147177701, 0.14643425217184883, 0.1419756196654187, -0.07322051530578531, -0.3073828423825594, 0.0915039273722169, -0.17090490755553428, -0.05005083318728094, -0.14098951146096134, 0.0022965605764721448, -0.016508735825146476, -0.2983186965700812, 0.0793316812241056, 0.04931477145650066, 0.06709332026971074, -0.04376966054372203, -0.03272367097222461, 0.06924761968145433, 0.11630543585723409, -0.021840751183649094, 0.034881063994879904, 0.11294677256821439, -0.14391867184536888, -0.11180184829192093, 0.39839557412152105, -0.09468304292531685, -0.21391976195602463, 0.20183409636553665, -0.03498638003186968, -0.06376519910991192, 0.17477057844651148, 0.2304683953622141, 0.11674064960903846, -0.13316513657426604, -0.009610248968461887, 0.012291883630678058, 0.22263408242545735, 0.07932099441352945, 0.041676516329439785, 0.17879316962980038, 0.22058932380392574, 0.054862626591840616, 0.14513802653393493, -0.11825393884120365, -0.008370484895287798, -0.27801425938422863, -0.1116394076245622, -0.14977668499788985, -0.04831750656191546, -0.11841583134440813, -0.19204608508734963, 0.4073837204406468, 0.2532066343567119, 0.16113000487765441, 0.1589659504001387, 0.4300270140887453, 0.06934453946126339, 0.060670827474230186, 0.10684245608102244, 0.1844228568003298, 0.06508486643970872, 0.12169354420352298, -0.15300279632568933, 0.06561572739603715, 0.05621725853102712] |
1,803.00431 | Electron beam transfer line design for plasma driven Free Electron
Lasers | Plasma driven particle accelerators represent the future of compact
accelerating machines and Free Electron Lasers are going to benefit from these
new technologies. One of the main issue of this new approach to FEL machines is
the design of the transfer line needed to match of the electron-beam with the
magnetic undulators. Despite the reduction of the chromaticity of plasma beams
is one of the main goals, the target of this line is to be effective even in
cases of beams with a considerable value of chromaticity. The method here
explained is based on the code GIOTTO [1] that works using a homemade genetic
algorithm and that is capable of finding optimal matching line layouts directly
using a full 3D tracking code.
| physics.acc-ph | plasma driven particle accelerators represent the future of compact accelerating machines and free electron lasers are going to benefit from these new technologies one of the main issue of this new approach to fel machines is the design of the transfer line needed to match of the electronbeam with the magnetic undulators despite the reduction of the chromaticity of plasma beams is one of the main goals the target of this line is to be effective even in cases of beams with a considerable value of chromaticity the method here explained is based on the code giotto 1 that works using a homemade genetic algorithm and that is capable of finding optimal matching line layouts directly using a full 3d tracking code | [['plasma', 'driven', 'particle', 'accelerators', 'represent', 'the', 'future', 'of', 'compact', 'accelerating', 'machines', 'and', 'free', 'electron', 'lasers', 'are', 'going', 'to', 'benefit', 'from', 'these', 'new', 'technologies', 'one', 'of', 'the', 'main', 'issue', 'of', 'this', 'new', 'approach', 'to', 'fel', 'machines', 'is', 'the', 'design', 'of', 'the', 'transfer', 'line', 'needed', 'to', 'match', 'of', 'the', 'electronbeam', 'with', 'the', 'magnetic', 'undulators', 'despite', 'the', 'reduction', 'of', 'the', 'chromaticity', 'of', 'plasma', 'beams', 'is', 'one', 'of', 'the', 'main', 'goals', 'the', 'target', 'of', 'this', 'line', 'is', 'to', 'be', 'effective', 'even', 'in', 'cases', 'of', 'beams', 'with', 'a', 'considerable', 'value', 'of', 'chromaticity', 'the', 'method', 'here', 'explained', 'is', 'based', 'on', 'the', 'code', 'giotto', '1', 'that', 'works', 'using', 'a', 'homemade', 'genetic', 'algorithm', 'and', 'that', 'is', 'capable', 'of', 'finding', 'optimal', 'matching', 'line', 'layouts', 'directly', 'using', 'a', 'full', '3d', 'tracking', 'code']] | [-0.07622485626756106, 0.11793882373079169, -0.06268064512443713, 0.03488644835688021, -0.05550357972302276, -0.1584882849892296, 0.00302866738972819, 0.4256714354040193, -0.2363864934772299, -0.3200249079019442, 0.08900331073253584, -0.2420885298156836, -0.06986953276133195, 0.2629061674357575, -0.05342573424649509, 0.07873121443677877, 0.07821753592856351, -0.027102646776704025, -0.03137162287879857, -0.20926291211202863, 0.33876466951103973, 0.13457488890004451, 0.28770610714163325, 0.042231203455737505, 0.12542473465265308, -0.018284523180212644, -0.02694830025134028, 0.004304587924155787, -0.05629657908282185, 0.18584676518975224, 0.23429820083127526, 0.15832048917158706, 0.262259634410138, -0.41686921241525254, -0.21071093772217386, 0.06328255879372115, 0.12249072448381024, 0.13490713454141726, -0.06313180747789285, -0.20641379852657069, 0.07711512334100887, -0.14090270109161673, -0.14356462118887633, -0.01166190777439624, -0.037111901502567726, 0.08299500405784604, -0.26660179031524256, -0.03622784992497697, 0.05092627986631982, 0.037778645906536304, -0.0324565267159802, -0.1069863296257519, 0.02171279894203314, 0.11954958124666429, 0.016815976604254396, 0.08359886467701098, 0.14911365263225113, -0.14529545851372427, -0.14795930185317077, 0.4043336241643448, -0.015839171233266346, -0.14816617882489913, 0.16662466356752165, -0.1463843681234065, -0.06623370320436957, 0.18875439305499683, 0.1736285437001526, 0.12009694019607345, -0.13438548470683181, 0.030049422856030954, -0.016189223308055126, 0.16830874060387493, 0.0607812440873994, -0.007506217700658274, 0.22485386754279255, 0.22819769193922154, 0.0525976633147306, 0.12725448360395633, -0.11777172687074139, -0.034215907451742494, -0.2899836037941582, -0.15076700520470784, -0.19327787999598095, -0.0068157211290915745, -0.028629329488526832, -0.1611005438426815, 0.42209902491237294, 0.19663703851172792, 0.13528265361841119, -0.028252637319144656, 0.37216071040965004, 0.09269790851976722, 0.09769476284883673, 0.06558321570198922, 0.23071126077995927, 0.10519750599871527, 0.10969234173583081, -0.24227797074556412, 0.019484945397335487, 0.028698348459314373] |
1,803.00432 | Zeros of irreducible characters in factorised groups | An element $g$ of a finite group $G$ is said to be vanishing in $G$ if there
exists an irreducible character $\chi$ of $G$ such that $\chi(g)=0$; in this
case, $g$ is also called a zero of $G$. The aim of this paper is to obtain
structural properties of a factorised group $G=AB$ when we impose some
conditions on prime power order elements $g\in A\cup B$ which are
(non-)vanishing in $G$.
| math.GR | an element g of a finite group g is said to be vanishing in g if there exists an irreducible character chi of g such that chig0 in this case g is also called a zero of g the aim of this paper is to obtain structural properties of a factorised group gab when we impose some conditions on prime power order elements gin acup b which are nonvanishing in g | [['an', 'element', 'g', 'of', 'a', 'finite', 'group', 'g', 'is', 'said', 'to', 'be', 'vanishing', 'in', 'g', 'if', 'there', 'exists', 'an', 'irreducible', 'character', 'chi', 'of', 'g', 'such', 'that', 'chig0', 'in', 'this', 'case', 'g', 'is', 'also', 'called', 'a', 'zero', 'of', 'g', 'the', 'aim', 'of', 'this', 'paper', 'is', 'to', 'obtain', 'structural', 'properties', 'of', 'a', 'factorised', 'group', 'gab', 'when', 'we', 'impose', 'some', 'conditions', 'on', 'prime', 'power', 'order', 'elements', 'gin', 'acup', 'b', 'which', 'are', 'nonvanishing', 'in', 'g']] | [-0.24052265120138014, 0.1601406111020229, -0.13558097231600966, -0.03656178007534306, -0.14607287993588086, -0.1076949852186122, 0.001236463273276708, 0.39552316479384897, -0.3018518616046224, -0.21892267231430326, 0.06729879891666185, -0.27636582904628343, -0.12956312819650131, 0.14173306512779424, -0.10599258110326315, -0.06517127392747041, 0.06769575603705431, 0.20756749749582792, -0.04927660377475799, -0.2490487203401114, 0.3341580750017394, -0.08567110208262291, 0.17010490496509842, 0.062225456455988544, 0.0781839550472796, -0.04552407069131732, 0.056062229636258315, 0.03919753634503909, -0.17069522745493618, 0.032525046043364064, 0.3130691203621349, 0.0641398138965347, 0.28778734338868944, -0.3434821311251393, -0.15080554644976343, 0.2662458475146975, 0.07494104502589576, -0.06192941331204825, -0.043962634622585026, -0.19339790712443314, 0.2159129180945456, -0.2016978854446539, -0.12048126602811472, -0.039553973863699604, 0.16775341616677386, -0.04872362866132919, -0.32571312739912955, -0.02246446806779464, 0.11356412643300635, 0.09638037617717471, 0.059171506809070705, -0.13748715324327349, -0.048027954323749456, 0.12484986286352588, -0.010388146244388606, 0.08705502010748854, 0.0004303702020219394, -0.11674975886541818, -0.022418608656153083, 0.44896696868485636, -0.1286929474927352, -0.2055768041738442, 0.10499118309734123, -0.16810370602511934, -0.19080811434957598, 0.08491206920173551, 0.12246531933279974, 0.16981109168513545, -0.0722378601829405, 0.202104068760361, -0.128143084162314, 0.1315566994782005, 0.021808522041620954, -0.020300547314608203, 0.12852908931007342, 0.07443212563438074, 0.1401883498499436, 0.11839872372303424, 0.0436719861646582, 0.17540085113474302, -0.4006461241814707, -0.1517989833972284, -0.19858824449724385, 0.13263823859659687, -0.07720662915546979, -0.19473042893888695, 0.37312346792646817, 0.09907005590586258, 0.16346072953726565, 0.01932511586596125, 0.19128265681876136, 0.11509052536317281, 0.048683677682752856, 0.1163793590784605, 0.10201006857220948, 0.23978149946779012, -0.12746663468091615, -0.20701036880990223, 0.016566395034481374, 0.14043744995391794] |
1,803.00433 | A note on NSOP$_{1}$ in one variable | We prove that, in order to establish that a theory is NSOP$_{1}$, it suffices
to show that no formula in a single free variable has SOP$_{1}$.
| math.LO | we prove that in order to establish that a theory is nsop_1 it suffices to show that no formula in a single free variable has sop_1 | [['we', 'prove', 'that', 'in', 'order', 'to', 'establish', 'that', 'a', 'theory', 'is', 'nsop_1', 'it', 'suffices', 'to', 'show', 'that', 'no', 'formula', 'in', 'a', 'single', 'free', 'variable', 'has', 'sop_1']] | [-0.11933210013434291, 0.09535209950059652, -0.1877572495676577, 0.09547493355814368, -0.07438693154603243, -0.1501596437767148, 0.05563330487813801, 0.40576725371181965, -0.24945132851600646, -0.22659192331135272, 0.062433007517829535, -0.25882733732461927, -0.20934432931244373, 0.18370981615036727, -0.11123843833804131, -0.029082511067390442, 0.02578275273554027, 0.16020136892795564, -0.05587439191527665, -0.23372524216771126, 0.26640627197921274, -0.06750393892638386, 0.23721881955862045, 0.09552882993593811, 0.15476795874536037, 0.014945740159600974, 0.06382510103285313, 0.040490359999239445, -0.1618591564471717, 0.08348281104117632, 0.25402784094214437, 0.10357016089372337, 0.307734094792977, -0.3845391935575753, -0.24679397873580455, 0.16131658207625152, 0.10767613552510738, 0.1409558614715934, -0.05850822382606566, -0.09908447325229645, 0.23851347491145133, -0.1756499583274126, -0.1680589210614562, -0.13885158956050872, 0.058793924599885944, -0.04970434054732323, -0.3181808404251933, 0.026128754392266272, 0.10620626747608185, -0.04034826796501875, -0.03424539972096682, 0.012176366113126278, -0.022274018973112108, 0.07891221385449171, 0.05780844914726913, 0.08084728434681893, -0.011689047496765852, -0.0862022240459919, -0.11958695666864515, 0.3603644171357155, -0.1296622937405482, -0.21444469563663004, 0.1548251747712493, -0.21566671101376414, -0.19349916400387884, 0.08453183591365815, 0.05709738191217184, 0.13453713834285735, -0.13157835751771926, 0.140605777092278, -0.147154549844563, 0.25680142611265183, 0.0427701236307621, -0.012388303470797836, 0.07449480362236499, 0.08007312908768655, 0.15493676841259002, 0.18709839653223753, -0.048115453124046324, -0.07855843469500541, -0.35162462770938874, -0.22637157269753516, -0.1747603552043438, 0.08444088373333215, -0.0364909803494811, -0.19986820012331008, 0.279362725391984, 0.2229803304374218, 0.1684833148494363, 0.07967163611203432, 0.22617652460932733, 0.17748659888282417, 0.10899785459041596, 0.07785866482183337, 0.22331269536167384, 0.14552113210782408, 0.015745505169034005, -0.13513934951275586, 0.055858422517776486, 0.10234959676861763] |
1,803.00434 | Polynomials with Surjective Arboreal Galois Representations Exist in
Every Degree | Let~$E$ be a Hilbertian field of characteristic~$0$. R.W.K. Odoni conjectured
that for every positive integer~$n$ there exists a polynomial~$f\in E[X]$ of
degree~$n$ such that each iterate~$f^{\circ{k}}$ of~$f$ is irreducible and the
Galois group of the splitting field of~$f^{\circ k}$ is isomorphic to the
automorphism group of a regular,~$n$-branching tree of height~$k.$ We prove
this conjecture when~$E$ is a number field.
| math.NT | lete be a hilbertian field of characteristic0 rwk odoni conjectured that for every positive integern there exists a polynomialfin ex of degreen such that each iteratefcirck off is irreducible and the galois group of the splitting field offcirc k is isomorphic to the automorphism group of a regularnbranching tree of heightk we prove this conjecture whene is a number field | [['lete', 'be', 'a', 'hilbertian', 'field', 'of', 'characteristic0', 'rwk', 'odoni', 'conjectured', 'that', 'for', 'every', 'positive', 'integern', 'there', 'exists', 'a', 'polynomialfin', 'ex', 'of', 'degreen', 'such', 'that', 'each', 'iteratefcirck', 'off', 'is', 'irreducible', 'and', 'the', 'galois', 'group', 'of', 'the', 'splitting', 'field', 'offcirc', 'k', 'is', 'isomorphic', 'to', 'the', 'automorphism', 'group', 'of', 'a', 'regularnbranching', 'tree', 'of', 'heightk', 'we', 'prove', 'this', 'conjecture', 'whene', 'is', 'a', 'number', 'field']] | [-0.25737421977548647, 0.1434818595611992, -0.1735203371096689, 0.031530061361711256, -0.0912440549224042, -0.1308815424115612, 0.005402895263754404, 0.35871689981566024, -0.3228522539138794, -0.18630854769323307, 0.0873708458384499, -0.25348785902427223, -0.14350797745954388, 0.15952766264000764, -0.08335167514339376, -0.09232059807086793, 0.020192386663089004, 0.1897683164033179, -0.028523339661590468, -0.2791174949670676, 0.3647872769775299, -0.07749698543921113, 0.17354628955945373, 0.07138304479527645, 0.14264387202162582, 0.028332572654247858, 0.052471027709543705, 0.030849479521678474, -0.09853148275159093, 0.06010523015776506, 0.3113885253954392, 0.12212672436950155, 0.28666646270161994, -0.30223898894697554, -0.1322390150789243, 0.26112305489368737, 0.11887264258873004, 0.02056836512369605, -0.07606996331238546, -0.21210834276504242, 0.157996958712689, -0.14710730239032552, -0.14062889407460505, -0.030376832782237146, 0.11861702636591732, -0.045225204720806614, -0.28676746094312805, -0.033216176384415194, 0.1046255355199369, 0.15568307446889007, -0.034002290841621846, -0.11465713349529184, -0.06273652118845628, 0.09886712990163897, 0.021430862660054117, 0.1661487090414784, 0.06509159559880992, -0.10385505200918907, -0.11161750748466986, 0.3452680413253032, -0.09246616348480949, -0.14964557245660287, 0.07777531215777764, -0.17350045063246328, -0.17213418710385808, 0.16996345826191828, 0.04969454348946993, 0.1419197988624756, 0.013986466398749214, 0.206646334360882, -0.2120311800390482, 0.13411417734236097, 0.08916943163897556, -0.07822501731033508, 0.1253373860829295, 0.09770894365815017, 0.1115374002748957, 0.115313707588939, 0.03070722086928212, 0.05066393130423071, -0.32909914448212546, -0.2470014886572384, -0.14586982340551913, 0.14478834881447256, -0.09125774317894517, -0.18558573964625025, 0.3612396689848258, 0.09378985222876789, 0.15840988217566448, 0.1057878108157848, 0.19906159424751352, 0.11360089694454263, 0.09750635859269935, 0.12119978844510534, 0.12681174480195084, 0.25202786080682504, -0.10097687215042803, -0.17134785869767746, -0.015646557170279827, 0.1379484276000697] |
1,803.00435 | Partitions of the polytope of Doubly Substochastic Matrices | In this paper, we provide three different ways to partition the polytope of
doubly substochastic matrices into subpolytopes via the prescribed row and
column sums, the sum of all elements and the sub-defect respectively. Then we
characterize the extreme points of each type of convex subpolytopes. The
relations of the extreme points of the subpolytopes in the three partitions are
also given.
| math.CO | in this paper we provide three different ways to partition the polytope of doubly substochastic matrices into subpolytopes via the prescribed row and column sums the sum of all elements and the subdefect respectively then we characterize the extreme points of each type of convex subpolytopes the relations of the extreme points of the subpolytopes in the three partitions are also given | [['in', 'this', 'paper', 'we', 'provide', 'three', 'different', 'ways', 'to', 'partition', 'the', 'polytope', 'of', 'doubly', 'substochastic', 'matrices', 'into', 'subpolytopes', 'via', 'the', 'prescribed', 'row', 'and', 'column', 'sums', 'the', 'sum', 'of', 'all', 'elements', 'and', 'the', 'subdefect', 'respectively', 'then', 'we', 'characterize', 'the', 'extreme', 'points', 'of', 'each', 'type', 'of', 'convex', 'subpolytopes', 'the', 'relations', 'of', 'the', 'extreme', 'points', 'of', 'the', 'subpolytopes', 'in', 'the', 'three', 'partitions', 'are', 'also', 'given']] | [-0.11795539338691313, 0.11309543273747578, -0.02306922861435985, 0.02888933394188214, 0.005672552340404421, -0.058983738747898674, 0.09436214666576964, 0.32832499056077397, -0.31965713789228534, -0.2056729103072134, 0.12982282124566616, -0.3339461049095529, -0.1537648017716701, 0.09618811223839151, -0.06116348927932196, 0.05389609620677399, 0.014675384982809668, 0.03570836662270938, -0.13032617462707347, -0.3108389269499505, 0.39349900882263655, -0.07298090150121783, 0.20797632129282737, 0.019906382091709824, 0.11957855651643677, 0.01188356964467246, -0.03845263177864864, 0.04458959221259737, -0.12388120646603772, 0.17853560785335473, 0.23682738627597202, 0.1983659090321572, 0.23156220404828182, -0.4235042023212939, -0.06567147645916117, 0.18036255872518311, 0.1228641754352167, -0.005687264572890078, 0.018440882438702174, -0.20668911982755192, 0.1007597812787309, -0.13049618786842118, -0.11981351698031191, -0.012156630766990244, 0.00056234807096666, 0.09084790309921639, -0.29243839489387685, -0.014626129198777413, 0.06596981401204086, 0.029985091801671707, -0.06498529188350209, -0.2158401850916323, -0.009425002211307893, 0.1389537778163909, 0.022406951531951057, -0.059396516249255564, 0.025058534683384856, -0.06713885448292875, -0.12014383124187589, 0.38837118709429364, 0.022002530915922195, -0.2212252960479284, 0.13216616303400427, -0.21266027856007463, -0.16313483140843568, 0.1037401806307407, 0.1646378468569429, 0.14138053204925333, -0.13814750753465246, 0.06928975647124538, -0.1336975668053158, 0.04349508200634698, 0.11297563575666214, 0.03815090141762964, 0.2047429826935051, 0.05407992330547728, 0.08672724132135999, 0.2033850630042983, -0.07346126301305704, -0.06257823901250958, -0.3385090458008354, -0.18023741619707254, -0.1748654631016104, 0.01063989664687485, -0.1941257406967944, -0.17906742060526473, 0.44386392990585233, 0.08017354986828859, 0.25959081612680046, 0.08182069955424207, 0.24749896678401798, 0.12575657348163793, 0.02317959656480883, 0.055731541431341014, 0.14736145929066982, 0.15255707051208028, -0.036373609249464804, -0.16584947902984062, 0.019768669239443835, 0.17368582170456648] |
1,803.00436 | Optimal Accuracy-Privacy Trade-Off for Secure Multi-Party Computations | The purpose of Secure Multi-Party Computation is to enable protocol
participants to compute a public function of their private inputs while keeping
their inputs secret, without resorting to any trusted third party. However,
opening the public output of such computations inevitably reveals some
information about the private inputs. We propose a measure generalising both
Renyi entropy and g-entropy so as to quantify this information leakage. In
order to control and restrain such information flows, we introduce the notion
of function substitution which replaces the computation of a function that
reveals sensitive information with that of an approximate function. We exhibit
theoretical bounds for the privacy gains that this approach provides and
experimentally show that this enhances the confidentiality of the inputs while
controlling the distortion of computed output values. Finally, we investigate
the inherent compromise between accuracy of computation and privacy of inputs
and we demonstrate how to realise such optimal trade-offs.
| cs.CR | the purpose of secure multiparty computation is to enable protocol participants to compute a public function of their private inputs while keeping their inputs secret without resorting to any trusted third party however opening the public output of such computations inevitably reveals some information about the private inputs we propose a measure generalising both renyi entropy and gentropy so as to quantify this information leakage in order to control and restrain such information flows we introduce the notion of function substitution which replaces the computation of a function that reveals sensitive information with that of an approximate function we exhibit theoretical bounds for the privacy gains that this approach provides and experimentally show that this enhances the confidentiality of the inputs while controlling the distortion of computed output values finally we investigate the inherent compromise between accuracy of computation and privacy of inputs and we demonstrate how to realise such optimal tradeoffs | [['the', 'purpose', 'of', 'secure', 'multiparty', 'computation', 'is', 'to', 'enable', 'protocol', 'participants', 'to', 'compute', 'a', 'public', 'function', 'of', 'their', 'private', 'inputs', 'while', 'keeping', 'their', 'inputs', 'secret', 'without', 'resorting', 'to', 'any', 'trusted', 'third', 'party', 'however', 'opening', 'the', 'public', 'output', 'of', 'such', 'computations', 'inevitably', 'reveals', 'some', 'information', 'about', 'the', 'private', 'inputs', 'we', 'propose', 'a', 'measure', 'generalising', 'both', 'renyi', 'entropy', 'and', 'gentropy', 'so', 'as', 'to', 'quantify', 'this', 'information', 'leakage', 'in', 'order', 'to', 'control', 'and', 'restrain', 'such', 'information', 'flows', 'we', 'introduce', 'the', 'notion', 'of', 'function', 'substitution', 'which', 'replaces', 'the', 'computation', 'of', 'a', 'function', 'that', 'reveals', 'sensitive', 'information', 'with', 'that', 'of', 'an', 'approximate', 'function', 'we', 'exhibit', 'theoretical', 'bounds', 'for', 'the', 'privacy', 'gains', 'that', 'this', 'approach', 'provides', 'and', 'experimentally', 'show', 'that', 'this', 'enhances', 'the', 'confidentiality', 'of', 'the', 'inputs', 'while', 'controlling', 'the', 'distortion', 'of', 'computed', 'output', 'values', 'finally', 'we', 'investigate', 'the', 'inherent', 'compromise', 'between', 'accuracy', 'of', 'computation', 'and', 'privacy', 'of', 'inputs', 'and', 'we', 'demonstrate', 'how', 'to', 'realise', 'such', 'optimal', 'tradeoffs']] | [-0.15547440646351962, 0.019340738337367484, -0.08284499407406674, 0.08477112979147065, -0.06535038204604623, -0.1871176073481853, 0.13987961788168363, 0.3439891060123479, -0.3190429986682751, -0.3520065051386293, 0.08666481911158498, -0.2901879124935494, -0.14047556939775382, 0.17066828757586167, -0.12954701088613288, 0.10953024489466265, 0.007923643884945982, 0.024996999210203128, -0.06886939165515467, -0.2669773055857282, 0.3219425002489214, 0.05529807283487541, 0.2978442731313022, 0.09707899209241451, 0.12835325365762737, 0.035408952694373906, -0.06597196769018639, -0.03448986443413409, -0.13341862268823768, 0.16422904935967647, 0.27223569690095667, 0.2062475391500167, 0.318158149540375, -0.39704519798300714, -0.16310515856395, 0.13042635939990646, 0.10918372242853341, 0.15673260755655016, -0.05687966530784866, -0.25907841702886175, 0.10789865143103711, -0.19724565152917675, -0.05981097107642139, -0.15530029700341208, -0.05301093905763269, 0.02050678659730054, -0.30985910307912046, 0.03478850280582313, 0.044950638395049515, 0.059161709467356174, 0.006426696338696985, -0.026406888293398434, -0.04129283952939984, 0.22680655196586635, 0.030912284310612232, 0.010622757769942235, 0.15465145129309535, -0.1576319429668789, -0.14046377952007072, 0.3399711639083774, -0.025860383899632643, -0.22221323206175786, 0.1125964827770799, -0.10592166374326492, -0.10590079903528588, 0.05767903930279395, 0.19976235875576143, 0.06331970079475088, -0.1229609919230867, 0.02024453270363942, -0.005083942514480345, 0.24038590106107788, 0.051350261349946455, 0.15300779139919096, 0.14250167539429587, 0.10632847174878744, 0.09512011207862209, 0.1874381716776368, -0.04191627629858361, -0.13465913589380757, -0.2693278422465067, -0.2006517633370167, -0.16971127269481193, 0.02286779170621507, -0.10060356570889985, -0.12798240427960894, 0.36178178296405944, 0.23890930908232533, 0.20646121631074227, 0.09637342417837792, 0.3849465770289203, 0.0615489501960958, 0.075787406788664, 0.13439927485752975, 0.22259526193818072, 0.07223122756286765, 0.08104504093478453, -0.21563398783459076, 0.1746637684752585, -0.008268491833760643] |
1,803.00437 | Lattice Boltzmann model approximated with finite difference expressions | We show that the asymptotic properties of the link-wise artificial
compressibility method are not compatible with a correct approximation of fluid
properties. We propose to adapt the previous method through a framework
suggested by the Taylor expansion method and to replace first order terms in
the expansion by appropriate three or five points finite differences and to add
non linear terms. The "FD-LBM" scheme obtained by this method is tested in two
dimensions for shear wave, Stokes modes and Poiseuille flow. The results are
compared with the usual lattice Boltzmann method in the framework of multiple
relaxation times.
| math.NA physics.comp-ph physics.flu-dyn | we show that the asymptotic properties of the linkwise artificial compressibility method are not compatible with a correct approximation of fluid properties we propose to adapt the previous method through a framework suggested by the taylor expansion method and to replace first order terms in the expansion by appropriate three or five points finite differences and to add non linear terms the fdlbm scheme obtained by this method is tested in two dimensions for shear wave stokes modes and poiseuille flow the results are compared with the usual lattice boltzmann method in the framework of multiple relaxation times | [['we', 'show', 'that', 'the', 'asymptotic', 'properties', 'of', 'the', 'linkwise', 'artificial', 'compressibility', 'method', 'are', 'not', 'compatible', 'with', 'a', 'correct', 'approximation', 'of', 'fluid', 'properties', 'we', 'propose', 'to', 'adapt', 'the', 'previous', 'method', 'through', 'a', 'framework', 'suggested', 'by', 'the', 'taylor', 'expansion', 'method', 'and', 'to', 'replace', 'first', 'order', 'terms', 'in', 'the', 'expansion', 'by', 'appropriate', 'three', 'or', 'five', 'points', 'finite', 'differences', 'and', 'to', 'add', 'non', 'linear', 'terms', 'the', 'fdlbm', 'scheme', 'obtained', 'by', 'this', 'method', 'is', 'tested', 'in', 'two', 'dimensions', 'for', 'shear', 'wave', 'stokes', 'modes', 'and', 'poiseuille', 'flow', 'the', 'results', 'are', 'compared', 'with', 'the', 'usual', 'lattice', 'boltzmann', 'method', 'in', 'the', 'framework', 'of', 'multiple', 'relaxation', 'times']] | [-0.09798461816168015, 0.08704560700583167, -0.11448218396951243, 0.008045045606139088, -0.03682282918430481, -0.08636912511488826, 0.018036856152808544, 0.3484614231076437, -0.2624924006612645, -0.2694340056941374, 0.09668110388681561, -0.2448540531943754, -0.16522043342606077, 0.18507175424769906, 0.003171415605879936, 0.10446057954476666, 0.045473143123522325, 0.021244574875868474, -0.0721328690451253, -0.2647001096047461, 0.3125848861065568, 0.0019853643853380586, 0.31847664284683075, 0.006487435680458841, 0.10925603253627676, -0.035632300685092655, -0.056346308893629724, 0.09035252730755769, -0.13734985506631608, 0.1257793719462628, 0.19195885933604398, 0.040779616366879844, 0.261676654642083, -0.4383615997917566, -0.24256793987582026, 0.03491299984419776, 0.13061805072807006, 0.12573310947189703, 0.013615725192327783, -0.23209425583289764, 0.11687103312308948, -0.18369951400757029, -0.1585808402148181, -0.1398287846793219, -0.06598550017404649, 0.04004120229822632, -0.2892101355444294, 0.12634121541164278, 0.061742467354483827, 0.02070313684651916, -0.07638184637666594, -0.10415433733195988, 0.037898863430528604, 0.06089113582856953, 0.06836308501708831, 0.006384549556856917, 0.0626571420484136, -0.09347263357083592, -0.11743738230548262, 0.4122405708143392, -0.12060850872730203, -0.2533735223306516, 0.17697370120266587, -0.12240268280441613, -0.06251686267775589, 0.13260550948685593, 0.1509638154283934, 0.13966090090035163, -0.14972279845861738, 0.059278590113198214, -0.008500615282304651, 0.1041574763522973, 0.08671149974400849, -0.029222760627948745, 0.13621304158755032, 0.11515814181148391, 0.028576442530167473, 0.14322017236209483, -0.07206407206171413, -0.09597771401761926, -0.30240484661621897, -0.13731166636319256, -0.18268162118674247, -0.031072459924730865, -0.10706923996562201, -0.15264818077359693, 0.3814016788822197, 0.15887464280000207, 0.21270808957867593, 0.05850494217089156, 0.3259959954806824, 0.12734962740586592, 0.039078534840001275, 0.09993319162983716, 0.24257594902493232, 0.12995568140333078, 0.09486869893507245, -0.2585688738065973, 0.027842144832283873, 0.16467694360186758] |
1,803.00438 | Sequentialization and Procedural Complexity in Automata Networks | In this article we consider finite automata networks (ANs) with two kinds of
update schedules: the parallel one (all automata are updated all together) and
the sequential ones (the automata are updated periodically one at a time
according to a total order w). The cost of sequentialization of a given AN h is
the number of additional automata required to simulate h by a sequential AN
with the same alphabet. We construct, for any n and q, an AN h of size n and
alphabet size q whose cost of sequentialization is at least n/3. We also show
that, if q $\ge$ 4, we can find one whose cost is at least n/2 -- log q (n). We
prove that n/2 + log q (n/2 + 1) is an upper bound for the cost of
sequentialization of any AN h of size n and alphabet size q. Finally, we
exhibit the exact relation between the cost of sequentialization of h and its
procedural complexity with unlimited memory and prove that its cost of
sequentialization is less than or equal to the pathwidth of its interaction
graph.
| cs.DM | in this article we consider finite automata networks ans with two kinds of update schedules the parallel one all automata are updated all together and the sequential ones the automata are updated periodically one at a time according to a total order w the cost of sequentialization of a given an h is the number of additional automata required to simulate h by a sequential an with the same alphabet we construct for any n and q an an h of size n and alphabet size q whose cost of sequentialization is at least n3 we also show that if q ge 4 we can find one whose cost is at least n2 log q n we prove that n2 log q n2 1 is an upper bound for the cost of sequentialization of any an h of size n and alphabet size q finally we exhibit the exact relation between the cost of sequentialization of h and its procedural complexity with unlimited memory and prove that its cost of sequentialization is less than or equal to the pathwidth of its interaction graph | [['in', 'this', 'article', 'we', 'consider', 'finite', 'automata', 'networks', 'ans', 'with', 'two', 'kinds', 'of', 'update', 'schedules', 'the', 'parallel', 'one', 'all', 'automata', 'are', 'updated', 'all', 'together', 'and', 'the', 'sequential', 'ones', 'the', 'automata', 'are', 'updated', 'periodically', 'one', 'at', 'a', 'time', 'according', 'to', 'a', 'total', 'order', 'w', 'the', 'cost', 'of', 'sequentialization', 'of', 'a', 'given', 'an', 'h', 'is', 'the', 'number', 'of', 'additional', 'automata', 'required', 'to', 'simulate', 'h', 'by', 'a', 'sequential', 'an', 'with', 'the', 'same', 'alphabet', 'we', 'construct', 'for', 'any', 'n', 'and', 'q', 'an', 'an', 'h', 'of', 'size', 'n', 'and', 'alphabet', 'size', 'q', 'whose', 'cost', 'of', 'sequentialization', 'is', 'at', 'least', 'n3', 'we', 'also', 'show', 'that', 'if', 'q', 'ge', '4', 'we', 'can', 'find', 'one', 'whose', 'cost', 'is', 'at', 'least', 'n2', 'log', 'q', 'n', 'we', 'prove', 'that', 'n2', 'log', 'q', 'n2', '1', 'is', 'an', 'upper', 'bound', 'for', 'the', 'cost', 'of', 'sequentialization', 'of', 'any', 'an', 'h', 'of', 'size', 'n', 'and', 'alphabet', 'size', 'q', 'finally', 'we', 'exhibit', 'the', 'exact', 'relation', 'between', 'the', 'cost', 'of', 'sequentialization', 'of', 'h', 'and', 'its', 'procedural', 'complexity', 'with', 'unlimited', 'memory', 'and', 'prove', 'that', 'its', 'cost', 'of', 'sequentialization', 'is', 'less', 'than', 'or', 'equal', 'to', 'the', 'pathwidth', 'of', 'its', 'interaction', 'graph']] | [-0.17119843882249028, 0.16709128932062348, -0.014858829805403541, 0.03611841115113246, -0.01927740679025813, -0.19471409212787702, 0.09721696711287839, 0.3690697681464133, -0.27505611145329445, -0.3240110306934418, 0.08841446637401174, -0.2954141823727576, -0.0698299082225097, 0.13641193366095505, -0.06214227632344624, 0.02870747725297366, 0.019181904244959313, 0.13819063239084567, -0.03151088875089899, -0.3358155284682861, 0.27552510111317896, 0.011521102188796293, 0.1651449182886335, 0.004953718375251756, 0.07583446446500841, 0.004931865800095752, -0.0027336170845995837, 0.01474795007884669, -0.13441191584598164, 0.09905642131089024, 0.2112237543142477, 0.16583007523825627, 0.2819546248275237, -0.40271879432346325, -0.11981464557023973, 0.17604046397145248, 0.1319798951169547, 0.06814096400595672, 0.023417598989033877, -0.14486368181937134, 0.14111764982083172, -0.15293275949652077, -0.09686339118828376, 0.021724784904531117, 0.1272650665611212, 0.0089257968693992, -0.3021553343548719, -0.02839036817968406, 0.10806336515211587, 0.047075052306538225, 0.0038843255577853224, -0.17988501306333135, -0.06461966843624177, 0.09984232335035878, -0.026407047293830886, 0.05301207831241394, 0.042962536405990806, -0.11051174293811653, -0.1579405359435277, 0.34657337127222876, -0.055155119867667766, -0.18746879571651826, 0.1592878495200532, -0.1222187739573012, -0.14325995165142205, 0.15321991126348208, 0.1451157214835062, 0.12510533174347194, -0.05023275543757475, 0.17135043814333023, -0.06775337700920353, 0.2541041939412473, 0.09051240905813995, 0.03575714156184284, 0.07094702311369076, 0.1542048221810268, 0.1140372297634966, 0.16984924185789532, -0.028816040177814296, -0.012745059924861772, -0.3343434869903369, -0.1944187397472535, -0.19360523185020953, 0.0659417430819343, -0.1603548597549839, -0.13721217220595686, 0.3063023423602998, 0.11072153811879924, 0.22384006982475846, 0.18099136699164092, 0.2600425469480414, 0.12976340871161776, 0.034547700834221366, 0.16279683752453636, 0.08949804311246698, 0.08824474392636918, 0.0023783764577994908, -0.2383519700789427, 0.06029383085103186, 0.09678495156736484] |
1,803.00439 | Synchronization and Aggregation of Nonlinear Power Systems with
Consideration of Bus Network Structures | We study nonlinear power systems consisting of generators, generator buses,
and non-generator buses. First, looking at a generator and its bus' variables
jointly, we introduce a synchronization concept for a pair of such joint
generators and buses. We show that this concept is related to graph symmetry.
Next, we extend, in two ways, the synchronization from a pair to a partition of
all generators in the networks and show that they are related to either graph
symmetry or equitable partitions. Finally, we show how an exact reduced model
can be obtained by aggregating the generators and associated buses in the
network when the original system is synchronized with respect to a partition,
provided that the initial condition respects the partition. Additionally, the
aggregation-based reduced model is again a power system.
| cs.SY math.DS | we study nonlinear power systems consisting of generators generator buses and nongenerator buses first looking at a generator and its bus variables jointly we introduce a synchronization concept for a pair of such joint generators and buses we show that this concept is related to graph symmetry next we extend in two ways the synchronization from a pair to a partition of all generators in the networks and show that they are related to either graph symmetry or equitable partitions finally we show how an exact reduced model can be obtained by aggregating the generators and associated buses in the network when the original system is synchronized with respect to a partition provided that the initial condition respects the partition additionally the aggregationbased reduced model is again a power system | [['we', 'study', 'nonlinear', 'power', 'systems', 'consisting', 'of', 'generators', 'generator', 'buses', 'and', 'nongenerator', 'buses', 'first', 'looking', 'at', 'a', 'generator', 'and', 'its', 'bus', 'variables', 'jointly', 'we', 'introduce', 'a', 'synchronization', 'concept', 'for', 'a', 'pair', 'of', 'such', 'joint', 'generators', 'and', 'buses', 'we', 'show', 'that', 'this', 'concept', 'is', 'related', 'to', 'graph', 'symmetry', 'next', 'we', 'extend', 'in', 'two', 'ways', 'the', 'synchronization', 'from', 'a', 'pair', 'to', 'a', 'partition', 'of', 'all', 'generators', 'in', 'the', 'networks', 'and', 'show', 'that', 'they', 'are', 'related', 'to', 'either', 'graph', 'symmetry', 'or', 'equitable', 'partitions', 'finally', 'we', 'show', 'how', 'an', 'exact', 'reduced', 'model', 'can', 'be', 'obtained', 'by', 'aggregating', 'the', 'generators', 'and', 'associated', 'buses', 'in', 'the', 'network', 'when', 'the', 'original', 'system', 'is', 'synchronized', 'with', 'respect', 'to', 'a', 'partition', 'provided', 'that', 'the', 'initial', 'condition', 'respects', 'the', 'partition', 'additionally', 'the', 'aggregationbased', 'reduced', 'model', 'is', 'again', 'a', 'power', 'system']] | [-0.17177515690640885, 0.09809161216851284, -0.04737215106873665, 0.04450218883061478, -0.05148923973184685, -0.12897333520818358, 0.039269893545686274, 0.387270909755729, -0.3023618075188047, -0.271241638237744, 0.10394103858031535, -0.3021406031740728, -0.18588516711214081, 0.17272322432340173, -0.0857008251148572, 0.04245112208936493, 0.0964863710610788, 0.07816333564489271, -0.06000736087845826, -0.21692140245234393, 0.3045102295054253, 0.030350081774211207, 0.2806714635302652, -0.027089816370127043, 0.1368433300499595, 0.003472516280707232, 0.015640895384140024, 0.06621264905195914, -0.08431753921840301, 0.11116819210754808, 0.21162567647578295, 0.14922750747379984, 0.22998857366773048, -0.42960032097302203, -0.13724986657991198, 0.1497844665116349, 0.10707180348925235, 0.09939051000220889, -0.0036222506458907164, -0.25395598524618285, 0.13703109910206276, -0.21611110788459578, -0.0981174665160275, -0.04787793857146545, -0.01089312668434929, 0.06808760389505142, -0.320810058457387, -0.018248746633645174, 0.06601535969291085, 0.02545870237175173, 0.009971674007378056, -0.029549273286852265, -0.06950200148027881, 0.1367890773633903, 0.0022435882742437283, -0.005025071750734319, 0.07829359526720707, -0.11959696003928953, -0.162350941397062, 0.3675898302620811, 0.013647919663096338, -0.21350113604246646, 0.15925546890384581, -0.08657448769496508, -0.1558425691440817, 0.048205299530351575, 0.19819918497602723, 0.06197269807042655, -0.15985858033994635, 0.04882350802744645, -0.053115795707590015, 0.1799481777950775, 0.036837915539770394, -0.005738183043810518, 0.1650874501512956, 0.14100799840187309, 0.12117375686129396, 0.2046327234467838, 0.0036772135568443366, -0.07761019859968345, -0.3187821831684126, -0.15525338357556323, -0.1966724538311088, 0.03979952140285294, -0.09327300201842918, -0.10867577742121017, 0.46214021226867685, 0.1725533543237622, 0.20422124918983425, 0.0856472905201823, 0.28908714232605326, 0.15994382069027413, 0.07374491647035677, 0.11587534549234565, 0.13786960016329622, 0.0811142985927403, 0.06362348149508931, -0.20944147712421105, 0.013555186663487161, 0.0990567267258731] |
1,803.0044 | The Dunkl Weight Function for Rational Cherednik Algebras | In this paper we prove the existence of the Dunkl weight function $K_{c,
\lambda}$ for any irreducible representation $\lambda$ of any finite Coxeter
group $W$, generalizing previous results of Dunkl. In particular, $K_{c,
\lambda}$ is a family of tempered distributions on the real reflection
representation of $W$ taking values in $\text{End}_\mathbb{C}(\lambda)$, with
holomorphic dependence on the complex multi-parameter $c$. When the parameter
$c$ is real, the distribution $K_{c, \lambda}$ provides an integral formula for
Cherednik's Gaussian inner product $\gamma_{c, \lambda}$ on the Verma module
$\Delta_c(\lambda)$ for the rational Cherednik algebra $H_c(W, \mathfrak{h})$.
In this case, the restriction of $K_{c, \lambda}$ to the hyperplane arrangement
complement $\mathfrak{h}_{\mathbb{R}, reg}$ is given by integration against an
analytic function whose values can be interpreted as braid group invariant
Hermitian forms on $KZ(\Delta_c(\lambda))$, where $KZ$ denotes the
Knizhnik-Zamolodchikov functor introduced by Ginzburg-Guay-Opdam-Rouquier. This
provides a concrete connection between invariant Hermitian forms on
representations of rational Cherednik algebras and invariant Hermitian forms on
representations of Iwahori-Hecke algebras, and we exploit this connection to
show that the $KZ$ functor preserves signatures, and in particular
unitarizability, in an appropriate sense.
| math.RT | in this paper we prove the existence of the dunkl weight function k_c lambda for any irreducible representation lambda of any finite coxeter group w generalizing previous results of dunkl in particular k_c lambda is a family of tempered distributions on the real reflection representation of w taking values in textend_mathbbclambda with holomorphic dependence on the complex multiparameter c when the parameter c is real the distribution k_c lambda provides an integral formula for cheredniks gaussian inner product gamma_c lambda on the verma module delta_clambda for the rational cherednik algebra h_cw mathfrakh in this case the restriction of k_c lambda to the hyperplane arrangement complement mathfrakh_mathbbr reg is given by integration against an analytic function whose values can be interpreted as braid group invariant hermitian forms on kzdelta_clambda where kz denotes the knizhnikzamolodchikov functor introduced by ginzburgguayopdamrouquier this provides a concrete connection between invariant hermitian forms on representations of rational cherednik algebras and invariant hermitian forms on representations of iwahorihecke algebras and we exploit this connection to show that the kz functor preserves signatures and in particular unitarizability in an appropriate sense | [['in', 'this', 'paper', 'we', 'prove', 'the', 'existence', 'of', 'the', 'dunkl', 'weight', 'function', 'k_c', 'lambda', 'for', 'any', 'irreducible', 'representation', 'lambda', 'of', 'any', 'finite', 'coxeter', 'group', 'w', 'generalizing', 'previous', 'results', 'of', 'dunkl', 'in', 'particular', 'k_c', 'lambda', 'is', 'a', 'family', 'of', 'tempered', 'distributions', 'on', 'the', 'real', 'reflection', 'representation', 'of', 'w', 'taking', 'values', 'in', 'textend_mathbbclambda', 'with', 'holomorphic', 'dependence', 'on', 'the', 'complex', 'multiparameter', 'c', 'when', 'the', 'parameter', 'c', 'is', 'real', 'the', 'distribution', 'k_c', 'lambda', 'provides', 'an', 'integral', 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1,803.00441 | Quantum metrology with a quantum-chaotic sensor | Quantum metrology promises high-precision measurements of classical
parameters with far reaching implications for science and technology. So far,
research has concentrated almost exclusively on quantum-enhancements in
integrable systems, such as precessing spins or harmonic oscillators prepared
in non-classical states. Here we show that large benefits can be drawn from
rendering integrable quantum sensors chaotic, both in terms of achievable
sensitivity as well as robustness to noise, while avoiding the challenge of
preparing and protecting large-scale entanglement. We apply the method to
spin-precession magnetometry and show in particular that the sensitivity of
state-of-the-art magnetometers can be further enhanced by subjecting the
spin-precession to non-linear kicks that renders the dynamics chaotic.
| quant-ph | quantum metrology promises highprecision measurements of classical parameters with far reaching implications for science and technology so far research has concentrated almost exclusively on quantumenhancements in integrable systems such as precessing spins or harmonic oscillators prepared in nonclassical states here we show that large benefits can be drawn from rendering integrable quantum sensors chaotic both in terms of achievable sensitivity as well as robustness to noise while avoiding the challenge of preparing and protecting largescale entanglement we apply the method to spinprecession magnetometry and show in particular that the sensitivity of stateoftheart magnetometers can be further enhanced by subjecting the spinprecession to nonlinear kicks that renders the dynamics chaotic | [['quantum', 'metrology', 'promises', 'highprecision', 'measurements', 'of', 'classical', 'parameters', 'with', 'far', 'reaching', 'implications', 'for', 'science', 'and', 'technology', 'so', 'far', 'research', 'has', 'concentrated', 'almost', 'exclusively', 'on', 'quantumenhancements', 'in', 'integrable', 'systems', 'such', 'as', 'precessing', 'spins', 'or', 'harmonic', 'oscillators', 'prepared', 'in', 'nonclassical', 'states', 'here', 'we', 'show', 'that', 'large', 'benefits', 'can', 'be', 'drawn', 'from', 'rendering', 'integrable', 'quantum', 'sensors', 'chaotic', 'both', 'in', 'terms', 'of', 'achievable', 'sensitivity', 'as', 'well', 'as', 'robustness', 'to', 'noise', 'while', 'avoiding', 'the', 'challenge', 'of', 'preparing', 'and', 'protecting', 'largescale', 'entanglement', 'we', 'apply', 'the', 'method', 'to', 'spinprecession', 'magnetometry', 'and', 'show', 'in', 'particular', 'that', 'the', 'sensitivity', 'of', 'stateoftheart', 'magnetometers', 'can', 'be', 'further', 'enhanced', 'by', 'subjecting', 'the', 'spinprecession', 'to', 'nonlinear', 'kicks', 'that', 'renders', 'the', 'dynamics', 'chaotic']] | [-0.10743367616777066, 0.19947141099988516, -0.027600446133874357, -0.0008637297788583157, -0.033830304203244545, -0.1648800294691076, 0.006775832135678717, 0.3617975619725055, -0.2734443816373608, -0.30123227569533306, 0.11782470190815662, -0.29716766624079793, -0.14955287045773324, 0.2974382650982416, -0.0696442943945941, 0.12565293474472128, 0.09051157159222013, 0.001875443384051323, -0.0704388781730948, -0.21757490155006828, 0.2423725591958188, 0.06220072384328685, 0.2857446989984493, 0.03274426080234763, 0.11476016197250122, -0.014197258673246123, 0.04777937488958101, 0.02625213281027283, -0.0700155374996501, 0.0793654284158644, 0.2989563848650842, 0.09673937868878797, 0.2497165973332745, -0.4449840550509247, -0.23074004290349506, 0.09949241928586196, 0.19024105246515116, 0.18772264744836353, -0.07205028235330246, -0.3380125263636863, 0.02661824999032197, -0.15950096235394962, -0.1537833913807171, -0.17029373396050046, -0.004863648823495033, 0.03205661051766516, -0.23123198115542806, 0.07445096811797056, 0.06545215919376696, 0.07739618863410282, -0.0013746106374633706, -0.07607232792199486, -0.006601689661490834, 0.14093912932461267, -0.019812397210410348, 0.015501285918785638, 0.1809647662901423, -0.14196320372219715, -0.15985938242671113, 0.3633972504693601, -0.08678514235793112, -0.18520799019218734, 0.18739600335377166, -0.16766007672736627, -0.12291411316039523, 0.056976133291350886, 0.17135093324921197, 0.08579281830504813, -0.13386947261945656, 0.06636445120858736, 0.055196123555544076, 0.17683641505600126, 0.06313228944566583, 0.14600854423069567, 0.2420427115683668, 0.17130642397225732, 0.09876820446785402, 0.1632147410612864, -0.1037183054959988, -0.1479459581034327, -0.2021526718572541, -0.10590094646990851, -0.2279454817299093, 0.12182118243072182, -0.01831980269034156, -0.11641578924738699, 0.3569941862779497, 0.22646172904502168, 0.12221545731234881, -0.014492839477801075, 0.33395501183069964, 0.07963233361995124, 0.06775179010367503, 0.01748215216125741, 0.33396028309060194, 0.14341098459273646, 0.08032727489222048, -0.2313013392289307, 0.028329492766513593, -0.06030740675999335] |
1,803.00442 | Strange stars in Krori-Barua space-time under f(R; T) gravity | In the present work, we study about highly dense compact stars which are made
of quarks, specially strange quarks, adopting the Krori-Barua
(KB)~\cite{Krori1975} metric in the framework of $f(R,T)$ gravity. The equation
of state (EOS) of a strange star can be represented by the MIT bag model as
$p_r(r)=\frac{1}{3}[\rho(r)-4B_g]$ where $B_g$ is the bag constant, arises due
to the quark pressure. Main motive behind our study is to find out singularity
free and physically acceptable solutions for different features of strange
stars. Here we also investigate the effect of alternative gravity in the
formation of strange stars. We find that our model is consistent with various
energy conditions and also satisfies Herrera's cracking condition, TOV
equation, static stability criteria of Harrison-Zel$'$dovich-Novikov etc. The
value of the adiabatic indices as well as the EOS parameters re-establish the
acceptability of our model. Here in detail we have studied specifically three
different strange star candidates, viz. $PSRJ~1614~2230, Vela~X-1$ and
$Cen~X-3$. As a whole, present model fulfils all the criteria for stability.
Another fascinating point we have discussed is the value of the bag constant
which lies in the range $(40-45)$~MeV/fm$^{3}$. This is quite smaller than the
predicted range, i.e., $(55-75)$~MeV/fm$^{3}$ ~\cite{Farhi1984,Alcock1986}. The
presence of the constant ($\chi$), arises due to the coupling between matter
and geometry, is responsible behind this reduction in $B_g$ value. For
$\chi=0$, we get the higher value for $B_g$ as the above mentioned predicted
range.
| gr-qc | in the present work we study about highly dense compact stars which are made of quarks specially strange quarks adopting the kroribarua kbcitekrori1975 metric in the framework of frt gravity the equation of state eos of a strange star can be represented by the mit bag model as p_rrfrac13rhor4b_g where b_g is the bag constant arises due to the quark pressure main motive behind our study is to find out singularity free and physically acceptable solutions for different features of strange stars here we also investigate the effect of alternative gravity in the formation of strange stars we find that our model is consistent with various energy conditions and also satisfies herreras cracking condition tov equation static stability criteria of harrisonzeldovichnovikov etc the value of the adiabatic indices as well as the eos parameters reestablish the acceptability of our model here in detail we have studied specifically three different strange star candidates viz psrj16142230 velax1 and cenx3 as a whole present model fulfils all the criteria for stability another fascinating point we have discussed is the value of the bag constant which lies in the range 4045mevfm3 this is quite smaller than the predicted range ie 5575mevfm3 citefarhi1984alcock1986 the presence of the constant chi arises due to the coupling between matter and geometry is responsible behind this reduction in b_g value for chi0 we get the higher value for b_g as the above mentioned predicted range | [['in', 'the', 'present', 'work', 'we', 'study', 'about', 'highly', 'dense', 'compact', 'stars', 'which', 'are', 'made', 'of', 'quarks', 'specially', 'strange', 'quarks', 'adopting', 'the', 'kroribarua', 'kbcitekrori1975', 'metric', 'in', 'the', 'framework', 'of', 'frt', 'gravity', 'the', 'equation', 'of', 'state', 'eos', 'of', 'a', 'strange', 'star', 'can', 'be', 'represented', 'by', 'the', 'mit', 'bag', 'model', 'as', 'p_rrfrac13rhor4b_g', 'where', 'b_g', 'is', 'the', 'bag', 'constant', 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1,803.00443 | Knowledge Transfer with Jacobian Matching | Classical distillation methods transfer representations from a "teacher"
neural network to a "student" network by matching their output activations.
Recent methods also match the Jacobians, or the gradient of output activations
with the input. However, this involves making some ad hoc decisions, in
particular, the choice of the loss function.
In this paper, we first establish an equivalence between Jacobian matching
and distillation with input noise, from which we derive appropriate loss
functions for Jacobian matching. We then rely on this analysis to apply
Jacobian matching to transfer learning by establishing equivalence of a recent
transfer learning procedure to distillation.
We then show experimentally on standard image datasets that Jacobian-based
penalties improve distillation, robustness to noisy inputs, and transfer
learning.
| cs.LG cs.CV | classical distillation methods transfer representations from a teacher neural network to a student network by matching their output activations recent methods also match the jacobians or the gradient of output activations with the input however this involves making some ad hoc decisions in particular the choice of the loss function in this paper we first establish an equivalence between jacobian matching and distillation with input noise from which we derive appropriate loss functions for jacobian matching we then rely on this analysis to apply jacobian matching to transfer learning by establishing equivalence of a recent transfer learning procedure to distillation we then show experimentally on standard image datasets that jacobianbased penalties improve distillation robustness to noisy inputs and transfer learning | [['classical', 'distillation', 'methods', 'transfer', 'representations', 'from', 'a', 'teacher', 'neural', 'network', 'to', 'a', 'student', 'network', 'by', 'matching', 'their', 'output', 'activations', 'recent', 'methods', 'also', 'match', 'the', 'jacobians', 'or', 'the', 'gradient', 'of', 'output', 'activations', 'with', 'the', 'input', 'however', 'this', 'involves', 'making', 'some', 'ad', 'hoc', 'decisions', 'in', 'particular', 'the', 'choice', 'of', 'the', 'loss', 'function', 'in', 'this', 'paper', 'we', 'first', 'establish', 'an', 'equivalence', 'between', 'jacobian', 'matching', 'and', 'distillation', 'with', 'input', 'noise', 'from', 'which', 'we', 'derive', 'appropriate', 'loss', 'functions', 'for', 'jacobian', 'matching', 'we', 'then', 'rely', 'on', 'this', 'analysis', 'to', 'apply', 'jacobian', 'matching', 'to', 'transfer', 'learning', 'by', 'establishing', 'equivalence', 'of', 'a', 'recent', 'transfer', 'learning', 'procedure', 'to', 'distillation', 'we', 'then', 'show', 'experimentally', 'on', 'standard', 'image', 'datasets', 'that', 'jacobianbased', 'penalties', 'improve', 'distillation', 'robustness', 'to', 'noisy', 'inputs', 'and', 'transfer', 'learning']] | [-0.012954083077299098, 0.004203910983172439, -0.07883214754983783, 0.07218375467637088, -0.09810256488077963, -0.18763233526842668, 0.09075628713568827, 0.45663510396455725, -0.3288238038289516, -0.3238182127359323, 0.08563320425552472, -0.2153958093491383, -0.22100350461162938, 0.12937594753845286, -0.17324725022384277, 0.139779148704838, 0.14870802014290044, 0.017730368262467284, -0.1377254055327891, -0.2883086781017482, 0.40079178659555814, 0.04698853689866762, 0.3275714635264497, 0.021732971879343192, 0.15896963300959518, 0.013031030613152932, -0.01547295530132639, -0.09695728422569422, -0.06512046796494057, 0.20843763318359076, 0.28327138383562367, 0.20402502417564392, 0.3102921101439279, -0.40103730534513793, -0.24227027900827428, 0.14745236511807888, 0.1000178535701707, 0.13958808442900286, -0.020734303132242834, -0.27154256151989103, 0.052768263620479657, -0.18978320769577597, 0.05686248255660757, -0.16121839695842938, -0.045697829868489255, 0.011762251366356698, -0.35578251009186107, 0.003952079838442538, 0.07835121402774045, 0.06970191777218133, -0.019159818611418206, -0.11420808741653067, 0.029054648145878065, 0.176585781025157, 0.020326347680141528, 0.04962511574073384, 0.13553413077024742, -0.20630487078839602, -0.13320319739480813, 0.267505992242756, -0.06368959185007649, -0.2097799691914891, 0.16395357625248533, -0.0173032372413824, -0.12901507157366723, 0.04459334942512214, 0.2069144977761122, 0.10168566526068995, -0.11369202963639206, -0.002374960040712419, -0.04202363590738969, 0.17888855482742655, 0.09580725471581293, -0.03819102473498788, 0.09607081201393157, 0.16049734169112828, 0.054910524367975694, 0.16907612301874905, -0.06527084483095677, -0.07213087888861386, -0.27741069338905316, -0.10421391280057529, -0.21753668543533422, 0.052754299855829835, -0.12188623765226415, -0.14030865826644004, 0.381887209136039, 0.21548921091161902, 0.25778617064934223, 0.1522137732177119, 0.3670384235990544, 0.07437573635252193, 0.09845315883285366, 0.13254081208724527, 0.21844476648960456, 0.1218307848146651, 0.0910875993152634, -0.19703434564580674, 0.10028958616118568, 0.11474210103042423] |
1,803.00444 | Inverse Reinforcement Learning via Nonparametric Spatio-Temporal Subgoal
Modeling | Advances in the field of inverse reinforcement learning (IRL) have led to
sophisticated inference frameworks that relax the original modeling assumption
of observing an agent behavior that reflects only a single intention. Instead
of learning a global behavioral model, recent IRL methods divide the
demonstration data into parts, to account for the fact that different
trajectories may correspond to different intentions, e.g., because they were
generated by different domain experts. In this work, we go one step further:
using the intuitive concept of subgoals, we build upon the premise that even a
single trajectory can be explained more efficiently locally within a certain
context than globally, enabling a more compact representation of the observed
behavior. Based on this assumption, we build an implicit intentional model of
the agent's goals to forecast its behavior in unobserved situations. The result
is an integrated Bayesian prediction framework that significantly outperforms
existing IRL solutions and provides smooth policy estimates consistent with the
expert's plan. Most notably, our framework naturally handles situations where
the intentions of the agent change over time and classical IRL algorithms fail.
In addition, due to its probabilistic nature, the model can be
straightforwardly applied in active learning scenarios to guide the
demonstration process of the expert.
| cs.LG cs.AI cs.RO cs.SY stat.ML | advances in the field of inverse reinforcement learning irl have led to sophisticated inference frameworks that relax the original modeling assumption of observing an agent behavior that reflects only a single intention instead of learning a global behavioral model recent irl methods divide the demonstration data into parts to account for the fact that different trajectories may correspond to different intentions eg because they were generated by different domain experts in this work we go one step further using the intuitive concept of subgoals we build upon the premise that even a single trajectory can be explained more efficiently locally within a certain context than globally enabling a more compact representation of the observed behavior based on this assumption we build an implicit intentional model of the agents goals to forecast its behavior in unobserved situations the result is an integrated bayesian prediction framework that significantly outperforms existing irl solutions and provides smooth policy estimates consistent with the experts plan most notably our framework naturally handles situations where the intentions of the agent change over time and classical irl algorithms fail in addition due to its probabilistic nature the model can be straightforwardly applied in active learning scenarios to guide the demonstration process of the expert | [['advances', 'in', 'the', 'field', 'of', 'inverse', 'reinforcement', 'learning', 'irl', 'have', 'led', 'to', 'sophisticated', 'inference', 'frameworks', 'that', 'relax', 'the', 'original', 'modeling', 'assumption', 'of', 'observing', 'an', 'agent', 'behavior', 'that', 'reflects', 'only', 'a', 'single', 'intention', 'instead', 'of', 'learning', 'a', 'global', 'behavioral', 'model', 'recent', 'irl', 'methods', 'divide', 'the', 'demonstration', 'data', 'into', 'parts', 'to', 'account', 'for', 'the', 'fact', 'that', 'different', 'trajectories', 'may', 'correspond', 'to', 'different', 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1,803.00445 | A Class of Finite-Dimensional Numerically Solvable McKean-Vlasov Control
Problems | We address a class of McKean-Vlasov (MKV) control problems with common noise,
called polynomial conditional MKV, and extending the known class of linear
quadratic stochastic MKV control problems. We show how this polynomial class
can be reduced by suitable Markov embedding to finite-dimensional stochastic
control problems, and provide a discussion and comparison of three
probabilistic numerical methods for solving the reduced control problem:
quantization, regression by control randomization, and regress later methods.
Our numerical results are illustrated on various examples from portfolio
selection and liquidation under drift uncertainty, and a model of interbank
systemic risk with partial observation.
| math.OC | we address a class of mckeanvlasov mkv control problems with common noise called polynomial conditional mkv and extending the known class of linear quadratic stochastic mkv control problems we show how this polynomial class can be reduced by suitable markov embedding to finitedimensional stochastic control problems and provide a discussion and comparison of three probabilistic numerical methods for solving the reduced control problem quantization regression by control randomization and regress later methods our numerical results are illustrated on various examples from portfolio selection and liquidation under drift uncertainty and a model of interbank systemic risk with partial observation | [['we', 'address', 'a', 'class', 'of', 'mckeanvlasov', 'mkv', 'control', 'problems', 'with', 'common', 'noise', 'called', 'polynomial', 'conditional', 'mkv', 'and', 'extending', 'the', 'known', 'class', 'of', 'linear', 'quadratic', 'stochastic', 'mkv', 'control', 'problems', 'we', 'show', 'how', 'this', 'polynomial', 'class', 'can', 'be', 'reduced', 'by', 'suitable', 'markov', 'embedding', 'to', 'finitedimensional', 'stochastic', 'control', 'problems', 'and', 'provide', 'a', 'discussion', 'and', 'comparison', 'of', 'three', 'probabilistic', 'numerical', 'methods', 'for', 'solving', 'the', 'reduced', 'control', 'problem', 'quantization', 'regression', 'by', 'control', 'randomization', 'and', 'regress', 'later', 'methods', 'our', 'numerical', 'results', 'are', 'illustrated', 'on', 'various', 'examples', 'from', 'portfolio', 'selection', 'and', 'liquidation', 'under', 'drift', 'uncertainty', 'and', 'a', 'model', 'of', 'interbank', 'systemic', 'risk', 'with', 'partial', 'observation']] | [-0.05776811532714233, 0.008074715225102037, -0.033689510742468495, 0.08977242283005153, -0.1345611412496287, -0.2035709944582183, 0.04951869501086066, 0.3661541299305248, -0.3667424844615922, -0.26618268896768593, 0.19266999573494326, -0.21772569709229378, -0.18618599212329304, 0.23091574816736488, -0.14613744671627574, 0.1479100385129604, 0.08372806023321666, -0.05679007981902663, -0.0721378559531758, -0.29113893290240395, 0.3163163105717252, 0.0010965814224767442, 0.24641275968478651, -0.03354759629143459, 0.18482599627673246, 0.02685788176224415, -0.06993526232675934, 0.07594488622924807, -0.12771064188207823, 0.1458638873918229, 0.31980191346029846, 0.14266102194990393, 0.4113300622497894, -0.38053813827585203, -0.24037423378274758, 0.12108656639656128, 0.0778089654507182, 0.10655094949262482, -0.025041014798299163, -0.3134035145179654, 0.04680705828620691, -0.1777191282091401, -0.0775168783848687, -0.10452131589646546, -0.06449128572867081, 0.053760477797869514, -0.3394644249002544, 0.06825259723522396, 0.06328846263813273, 0.07068525945853289, -0.08261331209760843, -0.12324066230152943, 0.03446140053933867, 0.033627169363542785, 0.037783278778375944, -0.042924367796572646, 0.14034607646302605, -0.08141962535992948, -0.22240940066605655, 0.3286093016421156, -0.030546914215902894, -0.27345175632484714, 0.1399151201179365, -0.04961712121944494, -0.1645676611176673, 0.1271828878694689, 0.22865998368634252, 0.11335606419253258, -0.18079985919579558, 0.08076841512110502, -0.0480901875191045, 0.1152052441611886, 0.022083173615249748, -0.031535761597167165, 0.08819984048082284, 0.1852132696895955, 0.15476727172998445, 0.1516546707669254, -0.030890590442363555, -0.18424960425864828, -0.3078997555587973, -0.07725648305435874, -0.1049559662897824, 0.04511607246715765, -0.14905062582811854, -0.14797096640555835, 0.3772654817819747, 0.16309599067578662, 0.13460635118737665, 0.15800016169550316, 0.24763874386494256, 0.17842695339967743, -0.04038231327122419, 0.06244485776595848, 0.1467074649295608, 0.1456923965051086, 0.04661570829624424, -0.24196810886140305, 0.11672872424657856, 0.07337130987256461] |
1,803.00446 | Inferring Missing Categorical Information in Noisy and Sparse Web Markup | Embedded markup of Web pages has seen widespread adoption throughout the past
years driven by standards such as RDFa and Microdata and initiatives such as
schema.org, where recent studies show an adoption by 39% of all Web pages
already in 2016. While this constitutes an important information source for
tasks such as Web search, Web page classification or knowledge graph
augmentation, individual markup nodes are usually sparsely described and often
lack essential information. For instance, from 26 million nodes describing
events within the Common Crawl in 2016, 59% of nodes provide less than six
statements and only 257,000 nodes (0.96%) are typed with more specific event
subtypes. Nevertheless, given the scale and diversity of Web markup data, nodes
that provide missing information can be obtained from the Web in large
quantities, in particular for categorical properties. Such data constitutes
potential training data for inferring missing information to significantly
augment sparsely described nodes. In this work, we introduce a supervised
approach for inferring missing categorical properties in Web markup. Our
experiments, conducted on properties of events and movies, show a performance
of 79% and 83% F1 score correspondingly, significantly outperforming existing
baselines.
| cs.LG stat.ML | embedded markup of web pages has seen widespread adoption throughout the past years driven by standards such as rdfa and microdata and initiatives such as schemaorg where recent studies show an adoption by 39 of all web pages already in 2016 while this constitutes an important information source for tasks such as web search web page classification or knowledge graph augmentation individual markup nodes are usually sparsely described and often lack essential information for instance from 26 million nodes describing events within the common crawl in 2016 59 of nodes provide less than six statements and only 257000 nodes 096 are typed with more specific event subtypes nevertheless given the scale and diversity of web markup data nodes that provide missing information can be obtained from the web in large quantities in particular for categorical properties such data constitutes potential training data for inferring missing information to significantly augment sparsely described nodes in this work we introduce a supervised approach for inferring missing categorical properties in web markup our experiments conducted on properties of events and movies show a performance of 79 and 83 f1 score correspondingly significantly outperforming existing baselines | [['embedded', 'markup', 'of', 'web', 'pages', 'has', 'seen', 'widespread', 'adoption', 'throughout', 'the', 'past', 'years', 'driven', 'by', 'standards', 'such', 'as', 'rdfa', 'and', 'microdata', 'and', 'initiatives', 'such', 'as', 'schemaorg', 'where', 'recent', 'studies', 'show', 'an', 'adoption', 'by', '39', 'of', 'all', 'web', 'pages', 'already', 'in', '2016', 'while', 'this', 'constitutes', 'an', 'important', 'information', 'source', 'for', 'tasks', 'such', 'as', 'web', 'search', 'web', 'page', 'classification', 'or', 'knowledge', 'graph', 'augmentation', 'individual', 'markup', 'nodes', 'are', 'usually', 'sparsely', 'described', 'and', 'often', 'lack', 'essential', 'information', 'for', 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1,803.00447 | Optimal localist and distributed coding of spatiotemporal spike patterns
through STDP and coincidence detection | Repeating spatiotemporal spike patterns exist and carry information. Here we
investigated how a single spiking neuron can optimally respond to one given
pattern (localist coding), or to either one of several patterns (distributed
coding, i.e. the neuron's response is ambiguous but the identity of the pattern
could be inferred from the response of multiple neurons), but not to random
inputs. To do so, we extended a theory developed in a previous paper
[Masquelier, 2017], which was limited to localist coding. More specifically, we
computed analytically the signal-to-noise ratio (SNR) of a
multi-pattern-detector neuron, using a threshold-free leaky integrate-and-fire
(LIF) neuron model with non-plastic unitary synapses and homogeneous Poisson
inputs. Surprisingly, when increasing the number of patterns, the SNR decreases
slowly, and remains acceptable for several tens of independent patterns.
In addition, we investigated whether spike-timing-dependent plasticity (STDP)
could enable a neuron to reach the theoretical optimal SNR. To this aim, we
simulated a LIF equipped with STDP, and repeatedly exposed it to multiple input
spike patterns, embedded in equally dense Poisson spike trains. The LIF
progressively became selective to every repeating pattern with no supervision,
and stopped discharging during the Poisson spike trains. Furthermore, using
certain STDP parameters, the resulting pattern detectors were optimal. Tens of
independent patterns could be learned by a single neuron using a low adaptive
threshold, in contrast with previous studies, in which higher thresholds led to
localist coding only.
Taken together these results suggest that coincidence detection and STDP are
powerful mechanisms, fully compatible with distributed coding. Yet we
acknowledge that our theory is limited to single neurons, and thus also applies
to feed-forward networks, but not to recurrent ones.
| cs.NE | repeating spatiotemporal spike patterns exist and carry information here we investigated how a single spiking neuron can optimally respond to one given pattern localist coding or to either one of several patterns distributed coding ie the neurons response is ambiguous but the identity of the pattern could be inferred from the response of multiple neurons but not to random inputs to do so we extended a theory developed in a previous paper masquelier 2017 which was limited to localist coding more specifically we computed analytically the signaltonoise ratio snr of a multipatterndetector neuron using a thresholdfree leaky integrateandfire lif neuron model with nonplastic unitary synapses and homogeneous poisson inputs surprisingly when increasing the number of patterns the snr decreases slowly and remains acceptable for several tens of independent patterns in addition we investigated whether spiketimingdependent plasticity stdp could enable a neuron to reach the theoretical optimal snr to this aim we simulated a lif equipped with stdp and repeatedly exposed it to multiple input spike patterns embedded in equally dense poisson spike trains the lif progressively became selective to every repeating pattern with no supervision and stopped discharging during the poisson spike trains furthermore using certain stdp parameters the resulting pattern detectors were optimal tens of independent patterns could be learned by a single neuron using a low adaptive threshold in contrast with previous studies in which higher thresholds led to localist coding only taken together these results suggest that coincidence detection and stdp are powerful mechanisms fully compatible with distributed coding yet we acknowledge that our theory is limited to single neurons and thus also applies to feedforward networks but not to recurrent ones | [['repeating', 'spatiotemporal', 'spike', 'patterns', 'exist', 'and', 'carry', 'information', 'here', 'we', 'investigated', 'how', 'a', 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1,803.00448 | Path integral approach to one-dimensional discrete-time quantum walk | Discrete-time quantum walk in one-dimension is studied from a path-integral
perspective. This enables derivation of a closed-form expression for amplitudes
corresponding to any coin-position basis of the state vector of the quantum
walker at an arbitrary step of the walk. This provides a new approach to the
foundations and applications of quantum walks.
| quant-ph | discretetime quantum walk in onedimension is studied from a pathintegral perspective this enables derivation of a closedform expression for amplitudes corresponding to any coinposition basis of the state vector of the quantum walker at an arbitrary step of the walk this provides a new approach to the foundations and applications of quantum walks | [['discretetime', 'quantum', 'walk', 'in', 'onedimension', 'is', 'studied', 'from', 'a', 'pathintegral', 'perspective', 'this', 'enables', 'derivation', 'of', 'a', 'closedform', 'expression', 'for', 'amplitudes', 'corresponding', 'to', 'any', 'coinposition', 'basis', 'of', 'the', 'state', 'vector', 'of', 'the', 'quantum', 'walker', 'at', 'an', 'arbitrary', 'step', 'of', 'the', 'walk', 'this', 'provides', 'a', 'new', 'approach', 'to', 'the', 'foundations', 'and', 'applications', 'of', 'quantum', 'walks']] | [-0.12166887173376414, 0.14362318792741718, -0.15577724074312257, 0.0007211101874418091, -0.05519301902485203, -0.1833401367964469, 0.09267435953205556, 0.33628989225429184, -0.22533134423758625, -0.17607203762064566, 0.03827766305547348, -0.204941822822153, -0.200255696894601, 0.20248193473045556, -0.04440933618835121, 0.11238562993986427, 0.03549456887193165, 0.07188676209804022, -0.06911131927198819, -0.19765879331543199, 0.2332019555153993, 0.05269559483562227, 0.3005534362504786, 0.041104371501308565, 0.18435765616595745, 0.07229792895696108, -0.004554517953744474, -0.028310398158248304, -0.16533918517096988, 0.16128878602933772, 0.21940873935818672, 0.0893376609486229, 0.2990183524506272, -0.42147894038484907, -0.2022016082345596, 0.0828980346087296, 0.14481646811835608, 0.2647350875941931, -0.0006400062263293086, -0.35706584738075453, 0.019866067424135388, -0.1603414780759024, -0.16734094787740483, -0.02701374782987361, 0.062181018152326906, -0.07496126027742647, -0.2634742813852598, 0.039922522934469976, 0.06197889225188432, 0.05512600172451645, 0.00844139547772565, -0.06707756828411289, 0.08963504485469663, 0.16417193206308303, -0.07366756879162255, 0.03778969440257774, 0.10393258250968636, -0.12011576471147391, -0.23993049354626322, 0.39486402652296676, -0.0414171236532055, -0.23833320182660278, 0.12075900477340736, -0.13162453646578318, -0.14911988708805643, 0.10363675309520848, 0.1679462180824353, 0.10720593345193367, -0.19920832257858426, 0.15406813827356464, -0.016512214645462216, 0.06607926382138482, 0.011262290724465307, 0.06313940842266914, 0.20010669394132663, 0.11620099905808016, 0.09769084220224956, 0.19460769124188512, -0.04680262976941073, -0.24196327959169756, -0.34710991292541743, -0.23333677381523094, -0.2443246776413805, 0.13901110199051644, -0.12553633264466935, -0.210196379273427, 0.4209250291141699, 0.14648186838922073, 0.18648460011858986, 0.12143109087779555, 0.2640390874782823, 0.2050239226938981, -0.05115500134679506, 0.051304978819317976, 0.12121642869457884, 0.181309099818738, 0.10405188384202291, -0.1838452689841671, 0.03472545856328787, 0.13805318324176488] |
1,803.00449 | On Courant's nodal domain property for linear combinations of
eigenfunctions, Part II | Generalizing Courant's nodal domain theorem, the "Extended Courant property"
is the statement that a linear combination of the first $n$ eigenfunctions has
at most $n$ nodal domains. In a previous paper (Documenta Mathematica, 2018,
Vol. 23, pp. 1561--1585), we gave simple counterexamples to this property,
including convex domains. In the present paper, using some input from numerical
computations, we pursue the investigation of the Extended Courant property with
two new examples, the equilateral rhombus and the regular hexagon.
| math.SP math.AP math.DG | generalizing courants nodal domain theorem the extended courant property is the statement that a linear combination of the first n eigenfunctions has at most n nodal domains in a previous paper documenta mathematica 2018 vol 23 pp 15611585 we gave simple counterexamples to this property including convex domains in the present paper using some input from numerical computations we pursue the investigation of the extended courant property with two new examples the equilateral rhombus and the regular hexagon | [['generalizing', 'courants', 'nodal', 'domain', 'theorem', 'the', 'extended', 'courant', 'property', 'is', 'the', 'statement', 'that', 'a', 'linear', 'combination', 'of', 'the', 'first', 'n', 'eigenfunctions', 'has', 'at', 'most', 'n', 'nodal', 'domains', 'in', 'a', 'previous', 'paper', 'documenta', 'mathematica', '2018', 'vol', '23', 'pp', '15611585', 'we', 'gave', 'simple', 'counterexamples', 'to', 'this', 'property', 'including', 'convex', 'domains', 'in', 'the', 'present', 'paper', 'using', 'some', 'input', 'from', 'numerical', 'computations', 'we', 'pursue', 'the', 'investigation', 'of', 'the', 'extended', 'courant', 'property', 'with', 'two', 'new', 'examples', 'the', 'equilateral', 'rhombus', 'and', 'the', 'regular', 'hexagon']] | [-0.14760137775794954, 0.0023037113987102913, -0.07506467562241408, 0.04167898468576468, -0.14387403821040476, -0.12963004304434766, 0.014650125999922877, 0.3164490172518538, -0.25071594603934216, -0.2600382651238666, 0.1023724742880841, -0.31550728292627767, -0.1587164319126395, 0.1696421247060326, -0.08958173903648729, 0.04383738001040485, 0.07165621479900626, -0.01347941898003027, -0.07190381040548688, -0.27060450838618705, 0.32509482708191134, -0.014902670051321967, 0.22058716296617473, 0.07005438326204849, 0.03777309721214818, 0.007087694529395599, -0.016332373385886095, 0.0011459363793784921, -0.1848198003533437, 0.16036196536227287, 0.2817557894340535, 0.0738604938091299, 0.24567577319050377, -0.4120043783460732, -0.11460268470857825, 0.09383029125437334, 0.13104842883208162, 0.08858587740400395, -0.04584389580443937, -0.27030672256338906, 0.09563380565083066, -0.148752177351868, -0.1751095126806335, -0.06787536310201342, 0.026707272408167265, -0.011811778312186142, -0.23056317181265973, 0.04438856602706886, 0.19475252616715122, 0.13995992741849902, -0.019489283005211067, -0.13404610124114272, -0.004289576039927734, 0.017809702270942463, 0.0045822121613234125, 0.04263475207708679, 0.0029117964002509395, -0.01481311711144041, -0.17934268411678297, 0.3406854604262036, -0.0026284693135553367, -0.20370246571573344, 0.15640712263933443, -0.1471680445632861, -0.20870384747501125, 0.10003923509850518, 0.1351537946732594, 0.13410884316696167, -0.10094676649367268, 0.1946344841324555, -0.14817736559099964, 0.11408934139876396, 0.17031196186730227, -0.026339545031834104, 0.10344229018097961, 0.12601912596154136, 0.09152121446397785, 0.18535066307703782, -0.038101223769126, -0.08196591225440626, -0.35339281443119436, -0.14300187620600419, -0.18533814698457718, 0.04368540298121123, -0.08505410321836221, -0.1794361337148524, 0.40555223600043877, 0.08809110128801534, 0.18564405154388447, 0.08963225414226582, 0.1976398831813947, 0.08004293094280285, 0.025713462092272647, 0.11168795905012469, 0.1615097259655867, 0.14713469476668867, 0.11279304961782771, -0.1216055643714529, -0.04279528075962195, 0.17674632326324846] |
1,803.0045 | Casimir Energies for Isorefractive or Diaphanous Balls | It is familiar that the Casimir self-energy of a homogeneous dielectric ball
is divergent, although a finite self-energy can be extracted through second
order in the deviation of the permittivity from the vacuum value. The exception
occurs when the speed of light inside the spherical boundary is the same as
that outside, so the self-energy of a perfectly conducting spherical shell is
finite, as is the energy of a dielectric-diamagnetic sphere with
$\varepsilon\mu=1$, a so-called isorefractive or diaphanous ball. Here we
re-examine that example, and attempt to extend it to an electromagnetic
$\delta$-function sphere, where the electric and magnetic couplings are equal
and opposite. Unfortunately, although the energy expression is superficially
ultraviolet finite, additional divergences appear that render it difficult to
extract a meaningful result in general, but some limited results are presented.
| hep-th | it is familiar that the casimir selfenergy of a homogeneous dielectric ball is divergent although a finite selfenergy can be extracted through second order in the deviation of the permittivity from the vacuum value the exception occurs when the speed of light inside the spherical boundary is the same as that outside so the selfenergy of a perfectly conducting spherical shell is finite as is the energy of a dielectricdiamagnetic sphere with varepsilonmu1 a socalled isorefractive or diaphanous ball here we reexamine that example and attempt to extend it to an electromagnetic deltafunction sphere where the electric and magnetic couplings are equal and opposite unfortunately although the energy expression is superficially ultraviolet finite additional divergences appear that render it difficult to extract a meaningful result in general but some limited results are presented | [['it', 'is', 'familiar', 'that', 'the', 'casimir', 'selfenergy', 'of', 'a', 'homogeneous', 'dielectric', 'ball', 'is', 'divergent', 'although', 'a', 'finite', 'selfenergy', 'can', 'be', 'extracted', 'through', 'second', 'order', 'in', 'the', 'deviation', 'of', 'the', 'permittivity', 'from', 'the', 'vacuum', 'value', 'the', 'exception', 'occurs', 'when', 'the', 'speed', 'of', 'light', 'inside', 'the', 'spherical', 'boundary', 'is', 'the', 'same', 'as', 'that', 'outside', 'so', 'the', 'selfenergy', 'of', 'a', 'perfectly', 'conducting', 'spherical', 'shell', 'is', 'finite', 'as', 'is', 'the', 'energy', 'of', 'a', 'dielectricdiamagnetic', 'sphere', 'with', 'varepsilonmu1', 'a', 'socalled', 'isorefractive', 'or', 'diaphanous', 'ball', 'here', 'we', 'reexamine', 'that', 'example', 'and', 'attempt', 'to', 'extend', 'it', 'to', 'an', 'electromagnetic', 'deltafunction', 'sphere', 'where', 'the', 'electric', 'and', 'magnetic', 'couplings', 'are', 'equal', 'and', 'opposite', 'unfortunately', 'although', 'the', 'energy', 'expression', 'is', 'superficially', 'ultraviolet', 'finite', 'additional', 'divergences', 'appear', 'that', 'render', 'it', 'difficult', 'to', 'extract', 'a', 'meaningful', 'result', 'in', 'general', 'but', 'some', 'limited', 'results', 'are', 'presented']] | [-0.10262729996421303, 0.16527588333533808, -0.10585255733906077, 0.08693695179312132, -0.12522372852366132, -0.124538192156559, 0.009451004239515616, 0.36917565235008415, -0.2189429153688252, -0.24827542840861358, 0.0927026566888134, -0.31020198442185154, -0.1299723129773226, 0.16405844861963906, -0.0362614307525711, 0.00707984930352093, 0.009827142291200849, 0.1013510535363681, -0.06613768679448045, -0.18171467188602455, 0.3272675089197806, 0.01839190024900465, 0.2453743897844106, 0.11046712886398802, 0.055385379991135915, -0.0073999213753268124, 0.03564928144288178, 0.07248245595882719, -0.06912572905712296, 0.06368504521960858, 0.22145654732146516, 0.023264073464088143, 0.24791074213213646, -0.4224982173683552, -0.18880868145766166, 0.09314266578294336, 0.1317800993243089, 0.11087505746489534, -0.03551389816611145, -0.21673523204830977, 0.05625182485136275, -0.14942306361089533, -0.18946217237971724, -0.05222190985312829, 0.009277607531000214, -0.01459665701438028, -0.26003504569570607, 0.0849109372195716, 0.05751000148548673, 0.0009035583943701707, -0.07874098589393096, -0.09701660954877017, -0.025082500099849244, 0.12497646467370661, 0.08815776532372603, 0.057865325504770644, 0.1374202651473192, -0.11854720398998604, -0.012323840349339522, 0.4217351155212292, -0.05817305491073057, -0.21759484322168507, 0.1617077679803165, -0.19901248679424707, -0.024941782015733993, 0.170199202429145, 0.1013999790557696, 0.12138311874497539, -0.15931027637528203, 0.13399972229918394, -0.05542438924939443, 0.15517546653595324, 0.11157808524305718, 0.0024897687840096366, 0.2117618986262152, 0.08956388692693928, 0.039298263550377806, 0.15905571015139755, -0.061849117578150564, -0.0900071137978767, -0.3681159299126683, -0.1539235130776293, -0.24257442557897704, 0.06371031572612432, -0.06694630513800523, -0.24259025622875643, 0.3199524447286072, 0.12031146271563976, 0.19570574073944813, 0.010562738127863179, 0.3350017702063689, 0.12814612748065535, 0.10733032340112214, 0.07148676161618474, 0.2919079155421619, 0.11716451880414612, 0.07317035265374355, -0.20880289334432187, -0.0035126930107183467, 0.035612781001971315] |
1,803.00451 | Developing a functional prototype master patient index (MPI) for
interoperability of e-health systems in Sri Lanka | Introduction: A Master Patient Index(MPI) is a centralized index of all
patients in a healthcare system. This index is composed of a unique identifier
for each patient link to his/her demographic data and clinical encounters. A
MPI is essential to ensure data interoperability in the different healthcare
institution. The The health ministry of Sri Lanka planning to develop MPI for
the country. This project focused on developing the prototype MPI for Sri Lanka
with the view to implementing and scaling up at the national level. Methods:
This project consisted of 3 phases. Phase 1: requirement analysis using focus
group discussions (FGD) with information system users. Phase 2: identification
of the suitable Application Programming interface (API) model. Phase 3:
development of the prototype MPI. Results: FGD were conducted in 6 hospitals.
There were 78 interviewers (Male -36, and female - 42). They highlighted the
key requirements for the MPI. Which were the unique identification method and
different searching criteria and merging records to avoid duplication. Using
this information, the requirements specification for MPI was developed. A
combination of monolithic and microservices architecture was selected to
develop the MPI. The API using the Personal Health Number (PHN) as the unique
patient identifier and HL7 standard was developed and implemented. Conclusions:
Development and implementation of a MPI has facilitated the long due need for
interoperability among health information systems in Sri Lankan. KEYWORDS MPI,
Interoperability, Unique Identifier, PHN, API
| cs.CY | introduction a master patient indexmpi is a centralized index of all patients in a healthcare system this index is composed of a unique identifier for each patient link to hisher demographic data and clinical encounters a mpi is essential to ensure data interoperability in the different healthcare institution the the health ministry of sri lanka planning to develop mpi for the country this project focused on developing the prototype mpi for sri lanka with the view to implementing and scaling up at the national level methods this project consisted of 3 phases phase 1 requirement analysis using focus group discussions fgd with information system users phase 2 identification of the suitable application programming interface api model phase 3 development of the prototype mpi results fgd were conducted in 6 hospitals there were 78 interviewers male 36 and female 42 they highlighted the key requirements for the mpi which were the unique identification method and different searching criteria and merging records to avoid duplication using this information the requirements specification for mpi was developed a combination of monolithic and microservices architecture was selected to develop the mpi the api using the personal health number phn as the unique patient identifier and hl7 standard was developed and implemented conclusions development and implementation of a mpi has facilitated the long due need for interoperability among health information systems in sri lankan keywords mpi interoperability unique identifier phn api | [['introduction', 'a', 'master', 'patient', 'indexmpi', 'is', 'a', 'centralized', 'index', 'of', 'all', 'patients', 'in', 'a', 'healthcare', 'system', 'this', 'index', 'is', 'composed', 'of', 'a', 'unique', 'identifier', 'for', 'each', 'patient', 'link', 'to', 'hisher', 'demographic', 'data', 'and', 'clinical', 'encounters', 'a', 'mpi', 'is', 'essential', 'to', 'ensure', 'data', 'interoperability', 'in', 'the', 'different', 'healthcare', 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1,803.00452 | A Cold/Ultracold Neutron Detector using Fine-grained Nuclear Emulsion
with Spatial Resolution less than 100 nm | A new type of cold/ultracold neutron detector that can realize a spatial
resolution of less than 100 nm was developed using nuclear emulsion. The
detector consists of a fine-grained nuclear emulsion coating and a 50-nm thick
$^{10}$B$_4$C layer for the neutron conversion. The detector was exposed to
cold and ultracold neutrons (UCNs) at the J-PARC. Detection efficiencies were
measured as (0.16$\pm$0.02)% and (12$\pm$2)% for cold and ultracold neutrons
consistently with the $^{10}$B content in the converter. Positions of
individual neutrons can be determined by observing secondary particle tracks
recorded in the nuclear emulsion. The spatial resolution of incident neutrons
were found to be in the range of 11-99 nm in the angle region of tan$\theta\leq
1.9$, where $\theta$ is the angle between a recorded track and the normal
direction of the converter layer. The achieved spatial resolution corresponds
to the improvement of one or two orders of magnitude compared with conventional
techniques and it is comparable with the wavelength of UCNs.
| physics.ins-det hep-ex nucl-ex | a new type of coldultracold neutron detector that can realize a spatial resolution of less than 100 nm was developed using nuclear emulsion the detector consists of a finegrained nuclear emulsion coating and a 50nm thick 10b_4c layer for the neutron conversion the detector was exposed to cold and ultracold neutrons ucns at the jparc detection efficiencies were measured as 016pm002 and 12pm2 for cold and ultracold neutrons consistently with the 10b content in the converter positions of individual neutrons can be determined by observing secondary particle tracks recorded in the nuclear emulsion the spatial resolution of incident neutrons were found to be in the range of 1199 nm in the angle region of tanthetaleq 19 where theta is the angle between a recorded track and the normal direction of the converter layer the achieved spatial resolution corresponds to the improvement of one or two orders of magnitude compared with conventional techniques and it is comparable with the wavelength of ucns | [['a', 'new', 'type', 'of', 'coldultracold', 'neutron', 'detector', 'that', 'can', 'realize', 'a', 'spatial', 'resolution', 'of', 'less', 'than', '100', 'nm', 'was', 'developed', 'using', 'nuclear', 'emulsion', 'the', 'detector', 'consists', 'of', 'a', 'finegrained', 'nuclear', 'emulsion', 'coating', 'and', 'a', '50nm', 'thick', '10b_4c', 'layer', 'for', 'the', 'neutron', 'conversion', 'the', 'detector', 'was', 'exposed', 'to', 'cold', 'and', 'ultracold', 'neutrons', 'ucns', 'at', 'the', 'jparc', 'detection', 'efficiencies', 'were', 'measured', 'as', '016pm002', 'and', '12pm2', 'for', 'cold', 'and', 'ultracold', 'neutrons', 'consistently', 'with', 'the', '10b', 'content', 'in', 'the', 'converter', 'positions', 'of', 'individual', 'neutrons', 'can', 'be', 'determined', 'by', 'observing', 'secondary', 'particle', 'tracks', 'recorded', 'in', 'the', 'nuclear', 'emulsion', 'the', 'spatial', 'resolution', 'of', 'incident', 'neutrons', 'were', 'found', 'to', 'be', 'in', 'the', 'range', 'of', '1199', 'nm', 'in', 'the', 'angle', 'region', 'of', 'tanthetaleq', '19', 'where', 'theta', 'is', 'the', 'angle', 'between', 'a', 'recorded', 'track', 'and', 'the', 'normal', 'direction', 'of', 'the', 'converter', 'layer', 'the', 'achieved', 'spatial', 'resolution', 'corresponds', 'to', 'the', 'improvement', 'of', 'one', 'or', 'two', 'orders', 'of', 'magnitude', 'compared', 'with', 'conventional', 'techniques', 'and', 'it', 'is', 'comparable', 'with', 'the', 'wavelength', 'of', 'ucns']] | [-0.024512627156144832, 0.22348427919293695, -0.06486492307821312, 0.002706445443379115, 0.01923723503522858, -0.12546839053695433, 0.003444719384719111, 0.39753097623492345, -0.2119400469067542, -0.41016377599414766, 0.031014050085899195, -0.3343712352157299, 0.06693582151149977, 0.19209016134588142, -0.009396568746961759, 0.05011280965099783, 0.03677457672414146, 0.03142035689759009, -0.07700137920011589, -0.16798365622083952, 0.24360020479918282, 0.13900150172559755, 0.28762844612208915, 0.04512572445305465, 0.15943752555471338, -0.02680664989342795, -0.016930640126943966, -0.03984610928529048, -0.08496009455751546, 0.08513049350880511, 0.27886860976084077, 0.02185050769240442, 0.12746589171075368, -0.44960919613183675, -0.18054170513973583, 0.06512675815721668, 0.12030050906505954, 0.04487203372070637, -0.055929019952876656, -0.2852431581668037, 0.0848190739030266, -0.19737272168792597, -0.1379177672036959, 0.012044306585117232, 0.00031807760360261683, 0.04029104235151759, -0.24504944156705036, 0.04333725117465269, 0.013993837086696036, 0.07220122251435643, -0.05135587172706529, -0.14919391120017705, 0.022038044257655362, 0.059173310557203485, -0.010950570864178524, 0.06838268585520785, 0.20126500959516225, -0.15242955079479026, -0.0629481821662829, 0.3430637914416251, -0.04035146297311154, -0.11170949425479793, 0.17603106549469447, -0.19811455480153142, 0.025527342644083916, 0.22656506663659895, 0.14405074260563036, 0.14292765699203208, -0.14672731419530097, -0.022985166241562206, -0.01270854077117718, 0.270761660726069, 0.1867405645478564, 0.03127706589625229, 0.2072408576437001, 0.2710332270937123, 0.0236715796827987, 0.12640112526847427, -0.2707726919104027, -0.018366759826423294, -0.23133192911060363, -0.15386689614056598, -0.12727407868465268, 0.008022677129771135, -0.044291922886880955, -0.046637918128148666, 0.3680564890191242, 0.06167401008814856, 0.16921671305612154, -0.06648848725105577, 0.2988841302274384, 0.04252273003427795, 0.1019087903425569, -0.008000220630032046, 0.3097429404829487, 0.14485114881668618, 0.1281950801811358, -0.24122876651464267, 0.06400258867571107, -0.004719661478000351] |
1,803.00453 | LOFAR observations of the quiet solar corona | The quiet solar corona emits meter-wave thermal bremsstrahlung. Coronal radio
emission can only propagate above that radius, $R_\omega$, where the local
plasma frequency eqals the observing frequency. The radio interferometer LOw
Frequency ARray (LOFAR) observes in its low band (10 -- 90 MHz) solar radio
emission originating from the middle and upper corona. We present the first
solar aperture synthesis imaging observations in the low band of LOFAR in 12
frequencies each separated by 5 MHz. From each of these radio maps we infer
$R_\omega$, and a scale height temperature, $T$. These results can be combined
into coronal density and temperature profiles. We derived radial intensity
profiles from the radio images. We focus on polar directions with simpler,
radial magnetic field structure. Intensity profiles were modeled by ray-tracing
simulations, following wave paths through the refractive solar corona, and
including free-free emission and absorption. We fitted model profiles to
observations with $R_\omega$ and $T$ as fitting parameters. In the low corona,
$R_\omega < 1.5$ solar radii, we find high scale height temperatures up to
2.2e6 K, much more than the brightness temperatures usually found there. But if
all $R_\omega$ values are combined into a density profile, this profile can be
fitted by a hydrostatic model with the same temperature, thereby confirming
this with two independent methods. The density profile deviates from the
hydrostatic model above 1.5 solar radii, indicating the transition into the
solar wind. These results demonstrate what information can be gleaned from
solar low-frequency radio images. The scale height temperatures we find are not
only higher than brightness temperatures, but also than temperatures derived
from coronograph or EUV data. Future observations will provide continuous
frequency coverage, eliminating the need for local hydrostatic density models.
| astro-ph.SR | the quiet solar corona emits meterwave thermal bremsstrahlung coronal radio emission can only propagate above that radius r_omega where the local plasma frequency eqals the observing frequency the radio interferometer low frequency array lofar observes in its low band 10 90 mhz solar radio emission originating from the middle and upper corona we present the first solar aperture synthesis imaging observations in the low band of lofar in 12 frequencies each separated by 5 mhz from each of these radio maps we infer r_omega and a scale height temperature t these results can be combined into coronal density and temperature profiles we derived radial intensity profiles from the radio images we focus on polar directions with simpler radial magnetic field structure intensity profiles were modeled by raytracing simulations following wave paths through the refractive solar corona and including freefree emission and absorption we fitted model profiles to observations with r_omega and t as fitting parameters in the low corona r_omega 15 solar radii we find high scale height temperatures up to 22e6 k much more than the brightness temperatures usually found there but if all r_omega values are combined into a density profile this profile can be fitted by a hydrostatic model with the same temperature thereby confirming this with two independent methods the density profile deviates from the hydrostatic model above 15 solar radii indicating the transition into the solar wind these results demonstrate what information can be gleaned from solar lowfrequency radio images the scale height temperatures we find are not only higher than brightness temperatures but also than temperatures derived from coronograph or euv data future observations will provide continuous frequency coverage eliminating the need for local hydrostatic density models | [['the', 'quiet', 'solar', 'corona', 'emits', 'meterwave', 'thermal', 'bremsstrahlung', 'coronal', 'radio', 'emission', 'can', 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1,803.00454 | Invasion of open space by two competitors: spreading properties of
monostable two-species competition--diffusion systems | This paper is concerned with some spreading properties of monostable
Lotka--Volterra two-species competition--diffusion systems when the initial
values are null or exponentially decaying in a right half-line. Thanks to a
careful construction of super-solutions and sub-solutions, we improve
previously known results and settle open questions. In particular, we show that
if the weaker competitor is also the faster one, then it is able to evade the
stronger and slower competitor by invading first into unoccupied territories.
The pair of speeds depends on the initial values. If these are null in a right
half-line, then the first speed is the KPP speed of the fastest competitor and
the second speed is given by an exact formula describing the possibility of
non-local pulling. Furthermore, the unbounded set of pairs of speeds achievable
with exponentially decaying initial values is characterized, up to a negligible
set.
| math.AP | this paper is concerned with some spreading properties of monostable lotkavolterra twospecies competitiondiffusion systems when the initial values are null or exponentially decaying in a right halfline thanks to a careful construction of supersolutions and subsolutions we improve previously known results and settle open questions in particular we show that if the weaker competitor is also the faster one then it is able to evade the stronger and slower competitor by invading first into unoccupied territories the pair of speeds depends on the initial values if these are null in a right halfline then the first speed is the kpp speed of the fastest competitor and the second speed is given by an exact formula describing the possibility of nonlocal pulling furthermore the unbounded set of pairs of speeds achievable with exponentially decaying initial values is characterized up to a negligible set | [['this', 'paper', 'is', 'concerned', 'with', 'some', 'spreading', 'properties', 'of', 'monostable', 'lotkavolterra', 'twospecies', 'competitiondiffusion', 'systems', 'when', 'the', 'initial', 'values', 'are', 'null', 'or', 'exponentially', 'decaying', 'in', 'a', 'right', 'halfline', 'thanks', 'to', 'a', 'careful', 'construction', 'of', 'supersolutions', 'and', 'subsolutions', 'we', 'improve', 'previously', 'known', 'results', 'and', 'settle', 'open', 'questions', 'in', 'particular', 'we', 'show', 'that', 'if', 'the', 'weaker', 'competitor', 'is', 'also', 'the', 'faster', 'one', 'then', 'it', 'is', 'able', 'to', 'evade', 'the', 'stronger', 'and', 'slower', 'competitor', 'by', 'invading', 'first', 'into', 'unoccupied', 'territories', 'the', 'pair', 'of', 'speeds', 'depends', 'on', 'the', 'initial', 'values', 'if', 'these', 'are', 'null', 'in', 'a', 'right', 'halfline', 'then', 'the', 'first', 'speed', 'is', 'the', 'kpp', 'speed', 'of', 'the', 'fastest', 'competitor', 'and', 'the', 'second', 'speed', 'is', 'given', 'by', 'an', 'exact', 'formula', 'describing', 'the', 'possibility', 'of', 'nonlocal', 'pulling', 'furthermore', 'the', 'unbounded', 'set', 'of', 'pairs', 'of', 'speeds', 'achievable', 'with', 'exponentially', 'decaying', 'initial', 'values', 'is', 'characterized', 'up', 'to', 'a', 'negligible', 'set']] | [-0.13022824706719585, 0.16272841820677392, -0.026840470127866303, 0.05929669119502929, -0.08828633909054677, -0.1880519232109294, 0.044463049206377704, 0.31527235872492615, -0.2863835665072695, -0.21081940165366714, 0.11083326013070124, -0.2932472824558458, -0.0954852372352165, 0.21509853819824598, -0.01714080158935886, 0.05229858331442532, 0.0843275683664743, 0.09005533158697161, -0.0387833347973663, -0.2924717774378582, 0.3364203933062373, 0.0014517545900110838, 0.23356367580094178, 0.05923216008830448, 0.08766663527693337, -0.06603687500882842, 0.017754161041754653, 0.008738928353151595, -0.1551833443041226, 0.08524463407721528, 0.1465673365000583, 0.12469181046217785, 0.3106286785119331, -0.41429864502393865, -0.14822711184777443, 0.12904483233791658, 0.18873553245630062, 0.12076457035334283, -0.007368088221508668, -0.30965168398319626, 0.1138594334749964, -0.1260963277038659, -0.19223700916643818, -0.012962811311680667, 0.06553861072195143, 0.07459574421657852, -0.2805270948447287, 0.09049043507773501, 0.08112746190515832, -0.011706133446251174, -0.0771546922492939, -0.08241317109663931, -0.04637582251198695, 0.1051805884600699, 0.09861979034440724, 0.030057850038029358, 0.07263692036848253, -0.1472244814979437, -0.07604514775981366, 0.3444986725160459, -0.08189439522364819, -0.23556614452710664, 0.22209932952856934, -0.161191784074976, -0.06842851755209267, 0.14218908820865334, 0.13859192487573854, 0.1316453907573202, -0.1273907274045714, 0.09027331842939225, -0.054225776324861905, 0.15371262590592713, 0.12304335629278926, 0.009557219602587357, 0.12239910229469951, 0.17776922513993168, 0.1378411477215221, 0.13913121885246216, -0.02908354760049252, -0.12838777294054524, -0.28634177733370114, -0.16416510489452008, -0.1502178569134115, 0.060179521821801626, -0.06417679166237653, -0.15948647926364776, 0.3888896413304856, 0.1450394030396407, 0.22130510223267802, 0.1020471527893752, 0.2722834670181635, 0.13092635661912067, 0.0033290240880631526, 0.10393852919837564, 0.23707354206917317, 0.09250792854098262, 0.08062526451515585, -0.1960315259821585, 0.11070179751283869, 0.07953668752923759] |
1,803.00455 | LCR-Net++: Multi-person 2D and 3D Pose Detection in Natural Images | We propose an end-to-end architecture for joint 2D and 3D human pose
estimation in natural images. Key to our approach is the generation and scoring
of a number of pose proposals per image, which allows us to predict 2D and 3D
poses of multiple people simultaneously. Hence, our approach does not require
an approximate localization of the humans for initialization. Our
Localization-Classification-Regression architecture, named LCR-Net, contains 3
main components: 1) the pose proposal generator that suggests candidate poses
at different locations in the image; 2) a classifier that scores the different
pose proposals; and 3) a regressor that refines pose proposals both in 2D and
3D. All three stages share the convolutional feature layers and are trained
jointly. The final pose estimation is obtained by integrating over neighboring
pose hypotheses, which is shown to improve over a standard non maximum
suppression algorithm. Our method recovers full-body 2D and 3D poses,
hallucinating plausible body parts when the persons are partially occluded or
truncated by the image boundary. Our approach significantly outperforms the
state of the art in 3D pose estimation on Human3.6M, a controlled environment.
Moreover, it shows promising results on real images for both single and
multi-person subsets of the MPII 2D pose benchmark and demonstrates satisfying
3D pose results even for multi-person images.
| cs.CV | we propose an endtoend architecture for joint 2d and 3d human pose estimation in natural images key to our approach is the generation and scoring of a number of pose proposals per image which allows us to predict 2d and 3d poses of multiple people simultaneously hence our approach does not require an approximate localization of the humans for initialization our localizationclassificationregression architecture named lcrnet contains 3 main components 1 the pose proposal generator that suggests candidate poses at different locations in the image 2 a classifier that scores the different pose proposals and 3 a regressor that refines pose proposals both in 2d and 3d all three stages share the convolutional feature layers and are trained jointly the final pose estimation is obtained by integrating over neighboring pose hypotheses which is shown to improve over a standard non maximum suppression algorithm our method recovers fullbody 2d and 3d poses hallucinating plausible body parts when the persons are partially occluded or truncated by the image boundary our approach significantly outperforms the state of the art in 3d pose estimation on human36m a controlled environment moreover it shows promising results on real images for both single and multiperson subsets of the mpii 2d pose benchmark and demonstrates satisfying 3d pose results even for multiperson images | [['we', 'propose', 'an', 'endtoend', 'architecture', 'for', 'joint', '2d', 'and', '3d', 'human', 'pose', 'estimation', 'in', 'natural', 'images', 'key', 'to', 'our', 'approach', 'is', 'the', 'generation', 'and', 'scoring', 'of', 'a', 'number', 'of', 'pose', 'proposals', 'per', 'image', 'which', 'allows', 'us', 'to', 'predict', '2d', 'and', '3d', 'poses', 'of', 'multiple', 'people', 'simultaneously', 'hence', 'our', 'approach', 'does', 'not', 'require', 'an', 'approximate', 'localization', 'of', 'the', 'humans', 'for', 'initialization', 'our', 'localizationclassificationregression', 'architecture', 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1,803.00456 | Love wave gas sensor based on DWNTs sensitive material | This work focuses on the application related to the detection of low moisture
and environmental pollutants. A novel gas sensor with inkjet printed Double
Walled Carbon Nano Tubes (DWNTs) on a Love wave sensor platform was developed
for Volatile Organic Compounds (VOCs) and humidity detection application. The
experiments were conducted in real-time at ambient conditions. Results
demonstrate the adsorption of vapor compounds on DWNTs sensitive material and
leads for example to frequency shifts of 1.97 kHz and 2.93 kHz with 120 ppm of
ethanol vapor and 6.22 % RH, respectively.
| physics.ins-det | this work focuses on the application related to the detection of low moisture and environmental pollutants a novel gas sensor with inkjet printed double walled carbon nano tubes dwnts on a love wave sensor platform was developed for volatile organic compounds vocs and humidity detection application the experiments were conducted in realtime at ambient conditions results demonstrate the adsorption of vapor compounds on dwnts sensitive material and leads for example to frequency shifts of 197 khz and 293 khz with 120 ppm of ethanol vapor and 622 rh respectively | [['this', 'work', 'focuses', 'on', 'the', 'application', 'related', 'to', 'the', 'detection', 'of', 'low', 'moisture', 'and', 'environmental', 'pollutants', 'a', 'novel', 'gas', 'sensor', 'with', 'inkjet', 'printed', 'double', 'walled', 'carbon', 'nano', 'tubes', 'dwnts', 'on', 'a', 'love', 'wave', 'sensor', 'platform', 'was', 'developed', 'for', 'volatile', 'organic', 'compounds', 'vocs', 'and', 'humidity', 'detection', 'application', 'the', 'experiments', 'were', 'conducted', 'in', 'realtime', 'at', 'ambient', 'conditions', 'results', 'demonstrate', 'the', 'adsorption', 'of', 'vapor', 'compounds', 'on', 'dwnts', 'sensitive', 'material', 'and', 'leads', 'for', 'example', 'to', 'frequency', 'shifts', 'of', '197', 'khz', 'and', '293', 'khz', 'with', '120', 'ppm', 'of', 'ethanol', 'vapor', 'and', '622', 'rh', 'respectively']] | [-0.09441261158358787, 0.1896221434247544, 0.05593670793191603, -0.08015765302966275, 0.0017533101811167899, -0.14070601893191256, 0.10172563069630833, 0.4178768987401148, -0.1611460763437862, -0.3522755280137062, 0.13180333702809313, -0.3267357299481048, -0.1225772756570427, 0.2390332335208574, -0.055950960018829016, 0.06404782716561569, 0.048608472978884584, -0.04037845256120971, -0.02596049585766839, -0.19321704489764957, 0.17689248670436694, 0.10736076536933693, 0.3577639278903436, 0.15126792970898278, 0.12907747579790818, -0.07759093045482014, 0.020129239908681158, -0.08174234576355875, -0.15554358067221186, 0.10123520685631908, 0.30025430782331847, -0.02716175320798929, 0.18664678074210211, -0.4639463815000955, -0.21052859044434985, -0.027206129467721735, 0.015068968052954905, 0.037578143870060365, -0.09630473883524328, -0.2845959665274687, 0.07700222437636237, -0.17686213863741482, -0.08339729058566724, -0.01358525235770961, 0.01555771655873971, 0.04246241283822763, -0.2412091028259209, 0.08715642203597791, -0.034077466044344765, 0.16436050022988882, -0.16646307556408593, -0.19833714640019148, -0.029605060133015674, 0.025244051763222794, -0.002440934781026974, -0.05728515019912398, 0.33272155964475, -0.04526064243805961, -0.032756490381748486, 0.42799231686284034, -0.1229266480037817, -0.06014909307482872, 0.2394056161970235, -0.11198898670106625, -0.07285197630135168, 0.1807770151958874, 0.19221923501345883, 0.12437991394002117, -0.16366246373397875, -0.03256361794512813, 0.011140521867050309, 0.22561563141748645, 0.1855201562814247, -0.01287841655083754, 0.2208334040995478, 0.24975556544854902, 0.003777795306438308, 0.17285392589584578, -0.19121886110927366, 0.03546995810871379, -0.12723933614455582, -0.243789995533906, -0.12587578762113377, 0.03871265399316195, -0.06499690696871133, -0.15431488845615093, 0.3641754761024305, 0.1411420987039972, 0.10260829730796513, -0.06659169092230248, 0.2852131756169073, 0.0020834729598646754, 0.06840939343687188, -0.04253419490761302, 0.2056898827026232, 0.16241593487571213, 0.19581471415487736, -0.2369412814314069, 0.10429925973307383, -0.01911768128603613] |
1,803.00457 | Optimization of Structural Flood Mitigation Strategies | The dynamics of flooding are primarily influenced by the shape, height, and
roughness (friction) of the underlying topography. For this reason, mechanisms
to mitigate floods frequently employ structural measures that either modify
topographic elevation, e.g., through the placement of levees and sandbags, or
increase roughness, e.g., through revegetation projects. However, the
configuration of these measures is typically decided in an ad hoc manner,
limiting their overall effectiveness. The advent of high-performance surface
water modeling software and improvements in black-box optimization suggest that
a more principled design methodology may be possible. This paper proposes a new
computational approach to the problem of designing structural mitigation
strategies under physical and budgetary constraints. It presents the
development of a problem discretization amenable to simulation-based,
derivative-free optimization. However, meta-heuristics alone are found to be
insufficient for obtaining quality solutions in a reasonable amount of time. As
a result, this paper proposes novel numerical and physics-based procedures to
improve convergence to a high-quality mitigation. The efficiency of the
approach is demonstrated on hypothetical dam break scenarios of varying
complexity under various mitigation budget constraints. In particular,
experimental results show that, on average, the final proposed algorithm
results in a 65% improvement in solution quality compared to a direct
implementation.
| math.OC | the dynamics of flooding are primarily influenced by the shape height and roughness friction of the underlying topography for this reason mechanisms to mitigate floods frequently employ structural measures that either modify topographic elevation eg through the placement of levees and sandbags or increase roughness eg through revegetation projects however the configuration of these measures is typically decided in an ad hoc manner limiting their overall effectiveness the advent of highperformance surface water modeling software and improvements in blackbox optimization suggest that a more principled design methodology may be possible this paper proposes a new computational approach to the problem of designing structural mitigation strategies under physical and budgetary constraints it presents the development of a problem discretization amenable to simulationbased derivativefree optimization however metaheuristics alone are found to be insufficient for obtaining quality solutions in a reasonable amount of time as a result this paper proposes novel numerical and physicsbased procedures to improve convergence to a highquality mitigation the efficiency of the approach is demonstrated on hypothetical dam break scenarios of varying complexity under various mitigation budget constraints in particular experimental results show that on average the final proposed algorithm results in a 65 improvement in solution quality compared to a direct implementation | [['the', 'dynamics', 'of', 'flooding', 'are', 'primarily', 'influenced', 'by', 'the', 'shape', 'height', 'and', 'roughness', 'friction', 'of', 'the', 'underlying', 'topography', 'for', 'this', 'reason', 'mechanisms', 'to', 'mitigate', 'floods', 'frequently', 'employ', 'structural', 'measures', 'that', 'either', 'modify', 'topographic', 'elevation', 'eg', 'through', 'the', 'placement', 'of', 'levees', 'and', 'sandbags', 'or', 'increase', 'roughness', 'eg', 'through', 'revegetation', 'projects', 'however', 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1,803.00458 | C-3PO: Click-sequence-aware DeeP Neural Network (DNN)-based Pop-uPs
RecOmmendation | With the emergence of mobile and wearable devices, push notification becomes
a powerful tool to connect and maintain the relationship with App users, but
sending inappropriate or too many messages at the wrong time may result in the
App being removed by the users. In order to maintain the retention rate and the
delivery rate of advertisement, we adopt Deep Neural Network (DNN) to develop a
pop-up recommendation system "Click sequence-aware deeP neural network
(DNN)-based Pop-uPs recOmmendation (C-3PO)" enabled by collaborative
filtering-based hybrid user behavioral analysis. We further verified the system
with real data collected from the product Security Master, Clean Master and CM
Browser, supported by Leopard Mobile Inc. (Cheetah Mobile Taiwan Agency). In
this way, we can know precisely about users' preference and frequency to click
on the push notification/pop-ups, decrease the troublesome to users
efficiently, and meanwhile increase the click through rate of push
notifications/pop-ups.
| cs.CY cs.HC cs.IR | with the emergence of mobile and wearable devices push notification becomes a powerful tool to connect and maintain the relationship with app users but sending inappropriate or too many messages at the wrong time may result in the app being removed by the users in order to maintain the retention rate and the delivery rate of advertisement we adopt deep neural network dnn to develop a popup recommendation system click sequenceaware deep neural network dnnbased popups recommendation c3po enabled by collaborative filteringbased hybrid user behavioral analysis we further verified the system with real data collected from the product security master clean master and cm browser supported by leopard mobile inc cheetah mobile taiwan agency in this way we can know precisely about users preference and frequency to click on the push notificationpopups decrease the troublesome to users efficiently and meanwhile increase the click through rate of push notificationspopups | [['with', 'the', 'emergence', 'of', 'mobile', 'and', 'wearable', 'devices', 'push', 'notification', 'becomes', 'a', 'powerful', 'tool', 'to', 'connect', 'and', 'maintain', 'the', 'relationship', 'with', 'app', 'users', 'but', 'sending', 'inappropriate', 'or', 'too', 'many', 'messages', 'at', 'the', 'wrong', 'time', 'may', 'result', 'in', 'the', 'app', 'being', 'removed', 'by', 'the', 'users', 'in', 'order', 'to', 'maintain', 'the', 'retention', 'rate', 'and', 'the', 'delivery', 'rate', 'of', 'advertisement', 'we', 'adopt', 'deep', 'neural', 'network', 'dnn', 'to', 'develop', 'a', 'popup', 'recommendation', 'system', 'click', 'sequenceaware', 'deep', 'neural', 'network', 'dnnbased', 'popups', 'recommendation', 'c3po', 'enabled', 'by', 'collaborative', 'filteringbased', 'hybrid', 'user', 'behavioral', 'analysis', 'we', 'further', 'verified', 'the', 'system', 'with', 'real', 'data', 'collected', 'from', 'the', 'product', 'security', 'master', 'clean', 'master', 'and', 'cm', 'browser', 'supported', 'by', 'leopard', 'mobile', 'inc', 'cheetah', 'mobile', 'taiwan', 'agency', 'in', 'this', 'way', 'we', 'can', 'know', 'precisely', 'about', 'users', 'preference', 'and', 'frequency', 'to', 'click', 'on', 'the', 'push', 'notificationpopups', 'decrease', 'the', 'troublesome', 'to', 'users', 'efficiently', 'and', 'meanwhile', 'increase', 'the', 'click', 'through', 'rate', 'of', 'push', 'notificationspopups']] | [-0.1166471264385682, 0.012210647666456867, -0.0328584245951489, 0.05716061105250783, -0.1550292087183015, -0.2511857197913405, 0.16089066578879033, 0.390720732497332, -0.26952812694210865, -0.3331340548824774, 0.07550724943835059, -0.33565363330983444, -0.17253242243298214, 0.17396118453455042, -0.11912258951733373, 0.03600459966859589, 0.07353805409017185, 0.08009920388855653, 0.014099727726018387, -0.30079283612965263, 0.278557118320238, 0.11547119918953921, 0.3343549423264212, 0.04182184203719235, 0.057938832251039296, 0.04882104247999824, -0.06696561524207538, -0.0666651304552893, -0.07347725774958883, 0.14152486457757346, 0.3389679795961942, 0.22660524270669494, 0.33349819056536645, -0.4681034437954834, -0.16607592733934112, 0.057661435786915356, 0.16481477849357698, 0.0575234270144305, -0.055144336699625346, -0.37284850399966724, 0.12785391850805242, -0.2966882593432212, -0.06056153458654354, -0.062052824056854035, -0.015545210099383576, 0.03501929997861998, -0.2764553093632096, -0.03299216997222847, -0.026006575137989162, 0.06868053297990652, 0.00021366887653728132, -0.018817959807825926, -0.011272990034752819, 0.23422389693338144, 0.06715004614152474, 0.021349205860145683, 0.21264110828673288, -0.15339042747724954, -0.09662109709421351, 0.33123761537956864, -0.032382584919304624, -0.13046425861491479, 0.1874824325003492, -0.05190304428903539, -0.08744072536810314, 0.1065226588653375, 0.2667512694478979, 0.04957083780446077, -0.18823613918447, -0.031220423813577588, 0.02135639100603453, 0.20561572774680179, 0.08106000996386148, -0.007242206257627639, 0.19869433380408238, 0.22485167355187338, 0.060153550959960875, 0.07937462439470325, -0.059732292962184, -0.06163554078235916, -0.15295872314552433, -0.14910897178805038, -0.1539251058926959, 0.0523527939971345, -0.07059354214448232, -0.0840657843302374, 0.35157829684313796, 0.20673310183298022, 0.14601988649021272, 0.08193524001957211, 0.3502320086787621, 0.06207710573105269, 0.12277790488936773, 0.10921189281132633, 0.1641188327586661, -0.039642538398517016, 0.2530415893902313, -0.15363405462934904, 0.13260499306967202, 0.008805327249407028] |
1,803.00459 | Challenges and opportunities in visual interpretation of Big Data | We live in a world where data generation is omnipresent. Innovations in
computer hardware in the last few decades coupled with increasingly reliable
connectivity among them have fueled this phenomenon. We are constantly creating
and consuming data across digital devices of varying form factors. Leveraging
huge quantities of data involves making interpretations from it. However,
interpreting data is still a difficult task. We need data analysts to help make
decisions. These experts apply their domain knowledge, understanding of the
problem space and numerical analysis to draw inferences from the data in order
to support decision making. Existing tools and techniques for interference
serve users making decisions with hard constraints. Consumer systems are often
built to support exploratory data analysis in mind rather than sense making.
| cs.HC | we live in a world where data generation is omnipresent innovations in computer hardware in the last few decades coupled with increasingly reliable connectivity among them have fueled this phenomenon we are constantly creating and consuming data across digital devices of varying form factors leveraging huge quantities of data involves making interpretations from it however interpreting data is still a difficult task we need data analysts to help make decisions these experts apply their domain knowledge understanding of the problem space and numerical analysis to draw inferences from the data in order to support decision making existing tools and techniques for interference serve users making decisions with hard constraints consumer systems are often built to support exploratory data analysis in mind rather than sense making | [['we', 'live', 'in', 'a', 'world', 'where', 'data', 'generation', 'is', 'omnipresent', 'innovations', 'in', 'computer', 'hardware', 'in', 'the', 'last', 'few', 'decades', 'coupled', 'with', 'increasingly', 'reliable', 'connectivity', 'among', 'them', 'have', 'fueled', 'this', 'phenomenon', 'we', 'are', 'constantly', 'creating', 'and', 'consuming', 'data', 'across', 'digital', 'devices', 'of', 'varying', 'form', 'factors', 'leveraging', 'huge', 'quantities', 'of', 'data', 'involves', 'making', 'interpretations', 'from', 'it', 'however', 'interpreting', 'data', 'is', 'still', 'a', 'difficult', 'task', 'we', 'need', 'data', 'analysts', 'to', 'help', 'make', 'decisions', 'these', 'experts', 'apply', 'their', 'domain', 'knowledge', 'understanding', 'of', 'the', 'problem', 'space', 'and', 'numerical', 'analysis', 'to', 'draw', 'inferences', 'from', 'the', 'data', 'in', 'order', 'to', 'support', 'decision', 'making', 'existing', 'tools', 'and', 'techniques', 'for', 'interference', 'serve', 'users', 'making', 'decisions', 'with', 'hard', 'constraints', 'consumer', 'systems', 'are', 'often', 'built', 'to', 'support', 'exploratory', 'data', 'analysis', 'in', 'mind', 'rather', 'than', 'sense', 'making']] | [-0.09959230872616172, 0.04510265224426985, -0.07267082183621824, 0.0976138959126547, -0.21522419314086438, -0.15669440421462058, 0.10039689818024636, 0.4363294045999646, -0.2648577476516366, -0.3832241881079972, 0.17957510312926026, -0.30495259968563915, -0.15161460888246076, 0.2367038197815418, -0.09359920081496238, 0.07752321054413915, 0.1134057571347803, 0.00555064120516181, -0.011827956832945347, -0.26951827824115754, 0.3168287591813132, 0.056040999750839544, 0.3285048090144992, 0.002349160823971033, 0.02032954251114279, 0.009866687430068851, -0.13849422690924257, -0.042064554918790235, -0.06683425529301167, 0.18976900166412816, 0.4034358606934547, 0.2222424656227231, 0.3428402701765299, -0.5188679889068007, -0.19867334607988596, 0.11547033460799139, 0.13893005136959255, 0.0950077449157834, -0.02939397456590086, -0.27549578296393157, 0.052050394426099954, -0.15075769644975662, -0.09093494261603337, -0.15811456349864603, 0.002215371245518327, -0.027455257651396097, -0.27318548791110514, 0.010863039258867502, -0.0061366743929684164, 0.09524856791645288, -0.022699109553941527, -0.07577910671383142, 0.03926765256002546, 0.2171049946025014, 0.10696711621806025, 0.0005461032725870609, 0.14772681670216845, -0.16834230517409743, -0.13053640878386796, 0.38895165560394523, 0.05686516825517174, -0.18228107930009718, 0.22565428735781462, -0.08691683302819729, -0.1794403795339167, 0.10565831931494177, 0.2042494278140366, 0.04446500293910503, -0.19686748648807406, 0.029160195883829145, 0.05524735167622566, 0.19751573011279105, 0.03212435725517571, 0.02762973739206791, 0.24436221033334732, 0.24970692399144173, 0.0028060912415385246, 0.06424881872534752, 0.0011497252041008323, -0.12434159753564746, -0.16942157547175885, -0.10308570518344641, -0.17209053375571967, 0.024904855554923416, -0.046701038060360585, -0.12836758401989937, 0.27845250349026174, 0.230614207662642, 0.17869706290401519, 0.028117509338771926, 0.36818583419173956, 0.030093499646987765, 0.12054775678925216, 0.08918792775971815, 0.16780117548722773, 0.052628064000979066, 0.18980430195853115, -0.06697168426215648, 0.11635581677779555, -0.08179455202817917] |
1,803.0046 | VOCs monitoring using microwave capacitive resonator and conductive
polymer -- MWCNTs nanocomposites for environmental applications | This paper presents a chemical microwave flexible sensor based on a resonant
electromagnetic transducer in micro-strip technology with poly
(3,4-ethylenedioxythiophene) polystyrene sulfonate -- multi wall carbon
nanotubes (PEDOT:PSS-MWCNTs) as sensitive material for Volatile Organic
Compounds (VOCs) detection. The results show a high sensitivity to ethanol and
toluene vapors on the S parameters of a passive resonator over a large
frequency range. It is equal to -1.59 kHz/ppm and -1.45 kHz/ppm for ethanol and
toluene vapors respectively. This kind of sensor can be integrated into
real-time multi-sensing platform adaptable for the Internet of Things (IoT).
| physics.ins-det | this paper presents a chemical microwave flexible sensor based on a resonant electromagnetic transducer in microstrip technology with poly 34ethylenedioxythiophene polystyrene sulfonate multi wall carbon nanotubes pedotpssmwcnts as sensitive material for volatile organic compounds vocs detection the results show a high sensitivity to ethanol and toluene vapors on the s parameters of a passive resonator over a large frequency range it is equal to 159 khzppm and 145 khzppm for ethanol and toluene vapors respectively this kind of sensor can be integrated into realtime multisensing platform adaptable for the internet of things iot | [['this', 'paper', 'presents', 'a', 'chemical', 'microwave', 'flexible', 'sensor', 'based', 'on', 'a', 'resonant', 'electromagnetic', 'transducer', 'in', 'microstrip', 'technology', 'with', 'poly', '34ethylenedioxythiophene', 'polystyrene', 'sulfonate', 'multi', 'wall', 'carbon', 'nanotubes', 'pedotpssmwcnts', 'as', 'sensitive', 'material', 'for', 'volatile', 'organic', 'compounds', 'vocs', 'detection', 'the', 'results', 'show', 'a', 'high', 'sensitivity', 'to', 'ethanol', 'and', 'toluene', 'vapors', 'on', 'the', 's', 'parameters', 'of', 'a', 'passive', 'resonator', 'over', 'a', 'large', 'frequency', 'range', 'it', 'is', 'equal', 'to', '159', 'khzppm', 'and', '145', 'khzppm', 'for', 'ethanol', 'and', 'toluene', 'vapors', 'respectively', 'this', 'kind', 'of', 'sensor', 'can', 'be', 'integrated', 'into', 'realtime', 'multisensing', 'platform', 'adaptable', 'for', 'the', 'internet', 'of', 'things', 'iot']] | [-0.12382611581225596, 0.1527827967429007, 0.03573703859001398, -0.11068816132382894, -0.03073766566661146, -0.19677329250186196, 0.056966710419383955, 0.4398325068228271, -0.1849133172629239, -0.27895755300541286, 0.07057223447127794, -0.3102061081900383, -0.09946746628164597, 0.23663490187660183, -0.04482108944870086, 0.05820653730315035, 0.02494985762598884, -0.07621295023304613, 0.04733042500953635, -0.13611080801448502, 0.14562362182439995, 0.04590192848168637, 0.34075319734604465, 0.1183582755342207, 0.13135999895419684, -0.043860774658083836, 0.051981865767511015, -0.05227883575919449, -0.13377351971829068, 0.13532030692785274, 0.336360948767675, -0.009312938992440215, 0.21704273130339774, -0.42811717690252094, -0.2087831225792837, 0.0696411942354525, 0.10670305056239317, 0.059077967019022806, -0.07940595617021798, -0.28498384733076976, 0.09462595165519894, -0.21153898393411352, -0.08085606271482032, -0.025688277263923184, 0.0569381125514274, 0.0658171389191209, -0.26964928019467904, 0.02109986090141794, -0.05102384857752401, 0.08189910693402888, -0.09764175856506209, -0.14905341240860845, -0.02591954387532061, 0.02083118102999161, -0.07193719937577439, -0.02584477562239677, 0.3165935489409806, -0.08084850335675899, -0.032372996292036514, 0.39061623978372867, -0.1201676793558442, -0.10662375624752199, 0.19263736617437605, -0.05799553620264582, -0.0844161570902266, 0.1806482702149483, 0.17929390978837467, 0.12713303024445297, -0.223010326243441, -0.01836401152452353, -0.003836356739275684, 0.2654807320251838, 0.13119899397513465, 0.06662214221432805, 0.2344197669552396, 0.2913695959698247, 0.03764390303631839, 0.16439457242908564, -0.10805464902912955, 0.03617398482342453, -0.13614580522129394, -0.26654002878248045, -0.17181283172786885, 0.06425858484378652, -0.09279228372400647, -0.173754101133217, 0.33305339094089426, 0.10246474240177675, 0.09173372922890374, -0.0067360330241448855, 0.33289802317386086, -0.025274680457938382, 0.1258053647597199, -0.028978849067757634, 0.21806037863311561, 0.1117755128475635, 0.21044764230812332, -0.20515073423840754, 0.09126368270264483, -0.039772481973910624] |
1,803.00461 | Modeling the evolution and propagation of the 2017 September 9th and
10th CMEs and SEPs arriving at Mars constrained by remote-sensing and in-situ
measurement | On 2017-09-10, solar energetic particles (SEPs) originating from the active
region 12673 were registered as a ground level enhancement (GLE) at Earth and
the biggest GLE on the surface of Mars as observed by the Radiation Assessment
Detector (RAD) since the landing of the Curiosity rover in August 2012. Based
on multi-point coronagraph images, we identify the initial 3D kinematics of an
extremely fast CME and its shock front as well as another 2 CMEs launched hours
earlier (with moderate speeds) using the Graduated Cylindrical Shell (GCS)
model. These three CMEs interacted as they propagated outwards into the
heliosphere and merged into a complex interplanetary CME (ICME). The arrival of
the shock and ICME at Mars caused a very significant Forbush Decrease (FD) seen
by RAD only a few hours later than that at Earth which is about 0.5 AU closer
to the Sun. We investigate the propagation of the three CMEs and the consequent
ICME together with the shock using the Drag Based Model (DBM) and the WSA-ENLIL
plus cone model constrained by the in-situ SEP and FD/shock onset timing. The
synergistic modeling of the ICME and SEP arrivals at Earth and Mars suggests
that in order to better predict potentially hazardous space weather impacts at
Earth and other heliospheric locations for human exploration missions, it is
essential to analyze 1) the CME kinematics, especially during their
interactions and 2) the spatially and temporally varying heliospheric
conditions, such as the evolution and propagation of the stream interaction
regions.
| physics.space-ph astro-ph.EP | on 20170910 solar energetic particles seps originating from the active region 12673 were registered as a ground level enhancement gle at earth and the biggest gle on the surface of mars as observed by the radiation assessment detector rad since the landing of the curiosity rover in august 2012 based on multipoint coronagraph images we identify the initial 3d kinematics of an extremely fast cme and its shock front as well as another 2 cmes launched hours earlier with moderate speeds using the graduated cylindrical shell gcs model these three cmes interacted as they propagated outwards into the heliosphere and merged into a complex interplanetary cme icme the arrival of the shock and icme at mars caused a very significant forbush decrease fd seen by rad only a few hours later than that at earth which is about 05 au closer to the sun we investigate the propagation of the three cmes and the consequent icme together with the shock using the drag based model dbm and the wsaenlil plus cone model constrained by the insitu sep and fdshock onset timing the synergistic modeling of the icme and sep arrivals at earth and mars suggests that in order to better predict potentially hazardous space weather impacts at earth and other heliospheric locations for human exploration missions it is essential to analyze 1 the cme kinematics especially during their interactions and 2 the spatially and temporally varying heliospheric conditions such as the evolution and propagation of the stream interaction regions | [['on', '20170910', 'solar', 'energetic', 'particles', 'seps', 'originating', 'from', 'the', 'active', 'region', '12673', 'were', 'registered', 'as', 'a', 'ground', 'level', 'enhancement', 'gle', 'at', 'earth', 'and', 'the', 'biggest', 'gle', 'on', 'the', 'surface', 'of', 'mars', 'as', 'observed', 'by', 'the', 'radiation', 'assessment', 'detector', 'rad', 'since', 'the', 'landing', 'of', 'the', 'curiosity', 'rover', 'in', 'august', '2012', 'based', 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1,803.00462 | SH-SAW VOCs sensor based on ink-jet printed MWNTs / polymer
nanocomposite films | This study presents Shear Horizontal Surface Acoustic Wave (SH-SAW) sensor
based on ink-jet printed poly (3,4-ethylenedioxythiophene) polystyrene
sulfonate -- multi wall carbon nanotubes (PEDOT:PSS-MWCNTs) and MWNTs-based
inks as sensitive gas material. Experiments show the validation of fabrication
process of acoustic platform and ink-jet printed sensitive layers. The
characterization of two devices under different concentrations of ethanol vapor
shows promising results in terms of reproducibility of the measurements. A
sensitivity of 12Hz/ppm was recorded with the sensor based on ink-jet printed
600nm thickness sensitive layer.
| physics.ins-det | this study presents shear horizontal surface acoustic wave shsaw sensor based on inkjet printed poly 34ethylenedioxythiophene polystyrene sulfonate multi wall carbon nanotubes pedotpssmwcnts and mwntsbased inks as sensitive gas material experiments show the validation of fabrication process of acoustic platform and inkjet printed sensitive layers the characterization of two devices under different concentrations of ethanol vapor shows promising results in terms of reproducibility of the measurements a sensitivity of 12hzppm was recorded with the sensor based on inkjet printed 600nm thickness sensitive layer | [['this', 'study', 'presents', 'shear', 'horizontal', 'surface', 'acoustic', 'wave', 'shsaw', 'sensor', 'based', 'on', 'inkjet', 'printed', 'poly', '34ethylenedioxythiophene', 'polystyrene', 'sulfonate', 'multi', 'wall', 'carbon', 'nanotubes', 'pedotpssmwcnts', 'and', 'mwntsbased', 'inks', 'as', 'sensitive', 'gas', 'material', 'experiments', 'show', 'the', 'validation', 'of', 'fabrication', 'process', 'of', 'acoustic', 'platform', 'and', 'inkjet', 'printed', 'sensitive', 'layers', 'the', 'characterization', 'of', 'two', 'devices', 'under', 'different', 'concentrations', 'of', 'ethanol', 'vapor', 'shows', 'promising', 'results', 'in', 'terms', 'of', 'reproducibility', 'of', 'the', 'measurements', 'a', 'sensitivity', 'of', '12hzppm', 'was', 'recorded', 'with', 'the', 'sensor', 'based', 'on', 'inkjet', 'printed', '600nm', 'thickness', 'sensitive', 'layer']] | [-0.10221881888102434, 0.13788248688760626, 0.012307388165706321, -0.15834611297213996, -0.0351813817779899, -0.17332264933664399, 0.004041872414630614, 0.43989186158663107, -0.18699427272039879, -0.3128243914652097, 0.10121396292278048, -0.30866850339630736, -0.1355349373402475, 0.24513534213050822, -0.0608747036467436, 0.10685540903713318, 0.10188144344771635, -0.09525314631247067, 0.0009593559028227118, -0.16811279516926483, 0.2000426113652655, 0.06622509119584213, 0.4703690266543174, 0.10594202505197682, 0.12624808320410172, -0.03244951991010693, -0.015505826525107215, -0.006905200511004917, -0.19687409605830908, 0.11225358559086895, 0.25413088290657426, -0.047698717528977724, 0.1682699813118464, -0.5424173289461981, -0.19852477962834925, -0.06595248248003706, 0.07694921452740702, 0.05187557159062428, -0.10320029434559372, -0.27723142490545405, 0.07387482724447228, -0.10869921136741774, -0.0646944968823765, -0.005704506913412221, -0.023507770539933366, 0.06581971122694638, -0.1912801004592565, 0.06185794897271391, -0.028995203449614818, 0.11158592832610718, -0.08414332444512114, -0.17445196762468806, -0.07480894338156839, 0.06463877938664224, -0.032635396494179424, -0.04408860104335354, 0.3408063450781039, -0.11835846192604285, -0.016423899303131467, 0.3673187001384323, -0.1164559460233284, -0.12635327064980387, 0.2011700957465351, -0.08111513110137061, -0.011844738386571407, 0.15788960125580243, 0.1992286809166021, 0.1383173208259329, -0.20268407169305072, -0.0513696838420804, 0.001905090682675673, 0.24745169781366527, 0.18135539434932738, 0.007741836889918092, 0.21772261305794685, 0.3563012081799628, -0.02678835346021607, 0.22649979295763129, -0.1841316975688538, 0.10363908036600185, -0.18033223542608792, -0.2509567707576612, -0.18734126075913635, 0.04415181343487163, -0.074182111793124, -0.2619452274321944, 0.3625038053274532, 0.1230335794271359, 0.11131938659627273, -0.056353941512635994, 0.32919706571611423, -0.08119341781468992, 0.11119381574135792, -0.078975011376499, 0.24736255108484928, 0.12750468914738938, 0.19703120821969042, -0.189123994429136, 0.13747944515397406, -0.002494733698100229] |
1,803.00463 | Chemical gas sensor based on a novel capacitive microwave flexible
transducer and composite polymer carbon nanomaterials | This study presents the results on the feasibility of a resonant planar
chemical capacitive sensor in the microwave frequency range suitable for gas
detection and for wireless communications applications. The objective is to
develop a low cost ultra-sensitive sensor that can be integrated into a real
time multi-sensing platform. The first demonstrators target the detection of
harmful gases such as volatile organic compounds (VOCs) to monitor
environmental pollution.
| physics.ins-det | this study presents the results on the feasibility of a resonant planar chemical capacitive sensor in the microwave frequency range suitable for gas detection and for wireless communications applications the objective is to develop a low cost ultrasensitive sensor that can be integrated into a real time multisensing platform the first demonstrators target the detection of harmful gases such as volatile organic compounds vocs to monitor environmental pollution | [['this', 'study', 'presents', 'the', 'results', 'on', 'the', 'feasibility', 'of', 'a', 'resonant', 'planar', 'chemical', 'capacitive', 'sensor', 'in', 'the', 'microwave', 'frequency', 'range', 'suitable', 'for', 'gas', 'detection', 'and', 'for', 'wireless', 'communications', 'applications', 'the', 'objective', 'is', 'to', 'develop', 'a', 'low', 'cost', 'ultrasensitive', 'sensor', 'that', 'can', 'be', 'integrated', 'into', 'a', 'real', 'time', 'multisensing', 'platform', 'the', 'first', 'demonstrators', 'target', 'the', 'detection', 'of', 'harmful', 'gases', 'such', 'as', 'volatile', 'organic', 'compounds', 'vocs', 'to', 'monitor', 'environmental', 'pollution']] | [-0.18125627693285318, 0.10446825157166184, 0.02260782229988014, -0.02492650135365479, -0.028964338813881007, -0.1554346879848334, 0.06652617129250704, 0.3961040996672476, -0.22964575912748628, -0.30300510622670546, 0.09679353308199685, -0.2795718012459795, -0.16245904456659713, 0.2511321918436271, -0.08291312898098327, 0.09459149399909246, 0.02782966138329357, -0.03484111071071204, 0.030960604123024763, -0.16448012589792008, 0.22341090360800187, 0.08324972063522129, 0.32232300572864275, 0.12154032035922523, 0.09730224387192934, -0.030939633656731424, 0.027847823543090594, -0.05808199686683057, -0.05139728375722959, 0.11179160064442412, 0.38273674720788703, 0.10412739209748585, 0.27470639879431796, -0.4491252851234201, -0.2786005589809707, 0.12355586450875682, 0.0742452288926442, 0.060569305739858574, -0.13952660818433607, -0.3022296387318741, 0.05395376785000896, -0.19355167592327824, -0.10699223766706008, -0.06261712657193558, 0.008391030365601182, 0.04653337062678903, -0.2863459622980479, 0.004766470492433976, -0.038372384630801046, 0.06338624816442676, -0.10512064131634201, -0.07065366574234384, 0.02642815390273052, 0.15255776355180012, -0.06358517089050592, -0.03287998061140945, 0.2627150723299779, -0.11112224267836769, -0.06322769313941107, 0.407041444999563, -0.10180539457017884, -0.1110046801263439, 0.23184801610734532, -0.077700261862072, -0.11474460166509208, 0.13072746842825675, 0.27693999111857814, 0.0939030847101308, -0.23241281544152811, 0.02771222878629273, 0.05022911985168535, 0.1680660214932526, 0.0561851595309289, 0.0843516141655581, 0.24365890530444792, 0.27435592822182703, 0.10687655210494995, 0.1505283178326756, -0.13636444283389104, 0.00827548116007272, -0.21671794920557125, -0.22208133860326865, -0.20070111066760385, 0.05174304750364493, -0.06527865574646378, -0.15097121091778665, 0.3774989355382893, 0.18537838915225996, 0.12602200546055375, -0.0156070980900789, 0.4169267435722491, 0.07779388935544856, 0.09242283580276896, -0.045021007809897554, 0.2235149279346361, 0.0690417612912664, 0.16305073239046203, -0.2560553527026273, 0.09331842978238403, -0.04876855768516714] |
1,803.00464 | Mortality data reliability in an internal model | In this paper, we discuss the impact of some mortality data anomalies on an
internal model capturing longevity risk in the Solvency 2 framework. In
particular, we are concerned with abnormal cohort effects such as those for
generations 1919 and 1920, for which the period tables provided by the Human
Mortality Database show particularly low and high mortality rates respectively.
To provide corrected tables for the three countries of interest here (France,
Italy and West Germany), we use the approach developed by Boumezoued (2016) for
countries for which the method applies (France and Italy), and provide an
extension of the method for West Germany as monthly fertility histories are not
sufficient to cover the generations of interest. These mortality tables are
crucial inputs to stochastic mortality models forecasting future scenarios,
from which the extreme 0,5% longevity improvement can be extracted, allowing
for the calculation of the Solvency Capital Requirement (SCR). More precisely,
to assess the impact of such anomalies in the Solvency II framework, we use a
simplified internal model based on three usual stochastic models to project
mortality rates in the future combined with a closure table methodology for
older ages. Correcting this bias obviously improves the data quality of the
mortality inputs, which is of paramount importance today, and slightly
decreases the capital requirement. Overall, the longevity risk assessment
remains stable, as well as the selection of the stochastic mortality model. As
a collateral gain of this data quality improvement, the more regular estimated
parameters allow for new insights and a refined assessment regarding longevity
risk.
| q-fin.RM math.PR stat.AP | in this paper we discuss the impact of some mortality data anomalies on an internal model capturing longevity risk in the solvency 2 framework in particular we are concerned with abnormal cohort effects such as those for generations 1919 and 1920 for which the period tables provided by the human mortality database show particularly low and high mortality rates respectively to provide corrected tables for the three countries of interest here france italy and west germany we use the approach developed by boumezoued 2016 for countries for which the method applies france and italy and provide an extension of the method for west germany as monthly fertility histories are not sufficient to cover the generations of interest these mortality tables are crucial inputs to stochastic mortality models forecasting future scenarios from which the extreme 05 longevity improvement can be extracted allowing for the calculation of the solvency capital requirement scr more precisely to assess the impact of such anomalies in the solvency ii framework we use a simplified internal model based on three usual stochastic models to project mortality rates in the future combined with a closure table methodology for older ages correcting this bias obviously improves the data quality of the mortality inputs which is of paramount importance today and slightly decreases the capital requirement overall the longevity risk assessment remains stable as well as the selection of the stochastic mortality model as a collateral gain of this data quality improvement the more regular estimated parameters allow for new insights and a refined assessment regarding longevity risk | [['in', 'this', 'paper', 'we', 'discuss', 'the', 'impact', 'of', 'some', 'mortality', 'data', 'anomalies', 'on', 'an', 'internal', 'model', 'capturing', 'longevity', 'risk', 'in', 'the', 'solvency', '2', 'framework', 'in', 'particular', 'we', 'are', 'concerned', 'with', 'abnormal', 'cohort', 'effects', 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'regular', 'estimated', 'parameters', 'allow', 'for', 'new', 'insights', 'and', 'a', 'refined', 'assessment', 'regarding', 'longevity', 'risk']] | [-0.026152032700182456, 0.06831809060724184, -0.0426460907120504, 0.1583041610868452, -0.060267751740204746, -0.10779359467066679, 0.1057607789658887, 0.3430572946407336, -0.20516680512968669, -0.3356917174619075, 0.17215693352747607, -0.26671659768330735, -0.1196142297986884, 0.22370887640591872, -0.16927378805325247, 0.02254196723555269, 0.07090486428548187, 0.011492412261246315, 0.012183245555914445, -0.29956282315783, 0.2643810508642273, 0.12592858369987647, 0.3245854765873906, 0.05059020043915167, 0.07436200689229071, -0.013503200206941842, -0.08234205464653559, -0.006278003286026737, -0.12135395862625056, 0.15141517447456282, 0.29493030860915564, 0.19864841181411613, 0.3412463759246068, -0.41443052108250994, -0.21055860842436783, 0.10831616915842149, 0.06652238161485653, 0.06860197892870958, -0.02661331558515361, -0.27353810899119085, 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1,803.00465 | The multistep homology of the simplex and representations of symmetric
groups | The symmetric group on a set acts transitively on its subsets of a given
size. We define homomorphisms between the corresponding permutation modules,
defined over a field of characteristic two, which generalize the boundary maps
from simplicial homology. The main results determine when these chain complexes
are exact and when they are split exact. As a corollary we obtain a new
explicit construction of the basic spin modules for the symmetric group.
| math.RT | the symmetric group on a set acts transitively on its subsets of a given size we define homomorphisms between the corresponding permutation modules defined over a field of characteristic two which generalize the boundary maps from simplicial homology the main results determine when these chain complexes are exact and when they are split exact as a corollary we obtain a new explicit construction of the basic spin modules for the symmetric group | [['the', 'symmetric', 'group', 'on', 'a', 'set', 'acts', 'transitively', 'on', 'its', 'subsets', 'of', 'a', 'given', 'size', 'we', 'define', 'homomorphisms', 'between', 'the', 'corresponding', 'permutation', 'modules', 'defined', 'over', 'a', 'field', 'of', 'characteristic', 'two', 'which', 'generalize', 'the', 'boundary', 'maps', 'from', 'simplicial', 'homology', 'the', 'main', 'results', 'determine', 'when', 'these', 'chain', 'complexes', 'are', 'exact', 'and', 'when', 'they', 'are', 'split', 'exact', 'as', 'a', 'corollary', 'we', 'obtain', 'a', 'new', 'explicit', 'construction', 'of', 'the', 'basic', 'spin', 'modules', 'for', 'the', 'symmetric', 'group']] | [-0.16151924687437713, 0.09496907878039768, -0.07149129719214721, 0.06732473601328416, -0.086538632626697, -0.11305227162989064, 0.0053956788938699495, 0.36986647789470023, -0.3403803632180724, -0.2214967508852068, 0.13663183150735372, -0.221244610076408, -0.13580144610669878, 0.20916979507698366, -0.085699610476796, -0.07702090009843232, 0.042356813656321414, 0.13722630898701027, -0.11896518519238776, -0.24567666504460955, 0.40224382008050774, -0.04411603642317156, 0.23358065994883268, 0.022324536302928917, 0.13638532716625681, 0.02135318125753353, -0.04365194325024883, 0.021455637871339504, -0.18150572308028737, 0.11435955707889257, 0.2611177951071618, 0.062348440535263054, 0.16996280466102892, -0.3795777317653928, -0.09056122172518775, 0.17324871803349298, 0.10478978080209345, 0.09564452355415495, -0.016842270105068263, -0.29209250880780424, 0.11538979651716848, -0.1815064240880828, -0.10381942616853242, -0.044311693035221346, 0.03505458987557278, 0.07704472954436722, -0.25121217103312826, -0.044293889872884996, 0.06566901911153561, 0.08886092744715926, -0.08063028748438228, -0.11919080303050578, -0.0663590405375645, 0.1879009456000252, -0.05234011543992286, 0.014980269039774107, 0.13173354477233565, -0.06013251807437175, -0.13496645980436975, 0.34479707137991983, -0.025857808262420196, -0.2531821625824604, 0.1752273432794027, -0.12156302662333474, -0.15637774422066286, 0.10149548306233352, 0.10472038461981963, 0.1460581617300502, -0.03680050148856632, 0.15319934875313063, -0.17130574211316546, 0.0816832441697544, 0.0825898555210895, 0.01146055398405426, 0.17281668504518974, 0.10074379189043409, 0.12632651891585234, 0.19550136167931165, 0.01113567424019695, -0.05889882717100489, -0.3647923089253406, -0.1838191251934202, -0.15417083513198626, 0.12280082006085043, -0.13391896775101486, -0.2156415906113883, 0.44530578177525765, 0.07380210554039321, 0.20762495890731872, 0.15601493595588384, 0.22103281017431678, 0.0508121662587655, 0.0900475613695259, 0.057962468682995275, 0.07622603556632788, 0.23425224967973513, -0.08690862243303046, -0.14478223814350916, -0.01151641522301361, 0.19497200313748586] |
1,803.00466 | Intelligent Virtual Assistant knows Your Life | In the IoT world, intelligent virtual assistant (IVA) is a popular service to
interact with users based on voice command. For optimal performance and
efficient data management, famous IVAs like Amazon Alexa and Google Assistant
usually operate based on the cloud computing architecture. In this process, a
large amount of behavioral traces that include user voice activity history with
detailed descriptions can be stored in the remote servers within an IVA
ecosystem. If those data (as also known as IVA cloud native data) are leaked by
attacks, malicious person may be able to not only harvest detailed usage
history of IVA services, but also reveals additional user related information
through various data analysis techniques. In this paper, we firstly show and
categorize types of IVA related data that can be collected from popular IVA,
Amazon Alexa. We then analyze an experimental dataset covering three months
with Alexa service, and characterize the properties of user lifestyle and life
patterns. Our results show that it is possible to uncover new insights on
personal information such as user interests, IVA usage patterns and sleeping,
wakeup patterns. The results presented in this paper provide important
implications for and privacy threats to IVA vendors and users as well.
| cs.CY | in the iot world intelligent virtual assistant iva is a popular service to interact with users based on voice command for optimal performance and efficient data management famous ivas like amazon alexa and google assistant usually operate based on the cloud computing architecture in this process a large amount of behavioral traces that include user voice activity history with detailed descriptions can be stored in the remote servers within an iva ecosystem if those data as also known as iva cloud native data are leaked by attacks malicious person may be able to not only harvest detailed usage history of iva services but also reveals additional user related information through various data analysis techniques in this paper we firstly show and categorize types of iva related data that can be collected from popular iva amazon alexa we then analyze an experimental dataset covering three months with alexa service and characterize the properties of user lifestyle and life patterns our results show that it is possible to uncover new insights on personal information such as user interests iva usage patterns and sleeping wakeup patterns the results presented in this paper provide important implications for and privacy threats to iva vendors and users as well | [['in', 'the', 'iot', 'world', 'intelligent', 'virtual', 'assistant', 'iva', 'is', 'a', 'popular', 'service', 'to', 'interact', 'with', 'users', 'based', 'on', 'voice', 'command', 'for', 'optimal', 'performance', 'and', 'efficient', 'data', 'management', 'famous', 'ivas', 'like', 'amazon', 'alexa', 'and', 'google', 'assistant', 'usually', 'operate', 'based', 'on', 'the', 'cloud', 'computing', 'architecture', 'in', 'this', 'process', 'a', 'large', 'amount', 'of', 'behavioral', 'traces', 'that', 'include', 'user', 'voice', 'activity', 'history', 'with', 'detailed', 'descriptions', 'can', 'be', 'stored', 'in', 'the', 'remote', 'servers', 'within', 'an', 'iva', 'ecosystem', 'if', 'those', 'data', 'as', 'also', 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