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1,802.0626 | A Collaborative Computer Aided Diagnosis (C-CAD) System with
Eye-Tracking, Sparse Attentional Model, and Deep Learning | There are at least two categories of errors in radiology screening that can
lead to suboptimal diagnostic decisions and interventions:(i)human fallibility
and (ii)complexity of visual search. Computer aided diagnostic (CAD) tools are
developed to help radiologists to compensate for some of these errors. However,
despite their significant improvements over conventional screening strategies,
most CAD systems do not go beyond their use as second opinion tools due to
producing a high number of false positives, which human interpreters need to
correct. In parallel with efforts in computerized analysis of radiology scans,
several researchers have examined behaviors of radiologists while screening
medical images to better understand how and why they miss tumors, how they
interact with the information in an image, and how they search for unknown
pathology in the images. Eye-tracking tools have been instrumental in exploring
answers to these fundamental questions. In this paper, we aim to develop a
paradigm shift CAD system, called collaborative CAD (C-CAD), that unifies both
of the above mentioned research lines: CAD and eye-tracking. We design an
eye-tracking interface providing radiologists with a real radiology reading
room experience. Then, we propose a novel algorithm that unifies eye-tracking
data and a CAD system. Specifically, we present a new graph based clustering
and sparsification algorithm to transform eye-tracking data (gaze) into a
signal model to interpret gaze patterns quantitatively and qualitatively. The
proposed C-CAD collaborates with radiologists via eye-tracking technology and
helps them to improve diagnostic decisions. The C-CAD learns radiologists'
search efficiency by processing their gaze patterns. To do this, the C-CAD uses
a deep learning algorithm in a newly designed multi-task learning platform to
segment and diagnose cancers simultaneously.
| cs.CV cs.AI cs.LG | there are at least two categories of errors in radiology screening that can lead to suboptimal diagnostic decisions and interventionsihuman fallibility and iicomplexity of visual search computer aided diagnostic cad tools are developed to help radiologists to compensate for some of these errors however despite their significant improvements over conventional screening strategies most cad systems do not go beyond their use as second opinion tools due to producing a high number of false positives which human interpreters need to correct in parallel with efforts in computerized analysis of radiology scans several researchers have examined behaviors of radiologists while screening medical images to better understand how and why they miss tumors how they interact with the information in an image and how they search for unknown pathology in the images eyetracking tools have been instrumental in exploring answers to these fundamental questions in this paper we aim to develop a paradigm shift cad system called collaborative cad ccad that unifies both of the above mentioned research lines cad and eyetracking we design an eyetracking interface providing radiologists with a real radiology reading room experience then we propose a novel algorithm that unifies eyetracking data and a cad system specifically we present a new graph based clustering and sparsification algorithm to transform eyetracking data gaze into a signal model to interpret gaze patterns quantitatively and qualitatively the proposed ccad collaborates with radiologists via eyetracking technology and helps them to improve diagnostic decisions the ccad learns radiologists search efficiency by processing their gaze patterns to do this the ccad uses a deep learning algorithm in a newly designed multitask learning platform to segment and diagnose cancers simultaneously | [['there', 'are', 'at', 'least', 'two', 'categories', 'of', 'errors', 'in', 'radiology', 'screening', 'that', 'can', 'lead', 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1,802.06261 | Non-degeneracy of cohomological traces for general Landau-Ginzburg
models | We prove non-degeneracy of the cohomological bulk and boundary traces for
general open-closed Landau-Ginzburg models associated to a pair $(X,W)$, where
$X$ is a non-compact complex manifold with trivial canonical line bundle and
$W$ is a complex-valued holomorphic function defined on $X$, assuming only that
the critical locus of $W$ is compact (but may not consist of isolated points).
These results can be viewed as certain "deformed" versions of Serre duality.
The first amounts to a duality property for the hypercohomology of the sheaf
Koszul complex of $W$, while the second is equivalent with the statement that a
certain power of the shift functor is a Serre functor on the even subcategory
of the $\mathbb{Z}_2$-graded category of topological D-branes of such models.
| math.AG hep-th math.CV | we prove nondegeneracy of the cohomological bulk and boundary traces for general openclosed landauginzburg models associated to a pair xw where x is a noncompact complex manifold with trivial canonical line bundle and w is a complexvalued holomorphic function defined on x assuming only that the critical locus of w is compact but may not consist of isolated points these results can be viewed as certain deformed versions of serre duality the first amounts to a duality property for the hypercohomology of the sheaf koszul complex of w while the second is equivalent with the statement that a certain power of the shift functor is a serre functor on the even subcategory of the mathbbz_2graded category of topological dbranes of such models | [['we', 'prove', 'nondegeneracy', 'of', 'the', 'cohomological', 'bulk', 'and', 'boundary', 'traces', 'for', 'general', 'openclosed', 'landauginzburg', 'models', 'associated', 'to', 'a', 'pair', 'xw', 'where', 'x', 'is', 'a', 'noncompact', 'complex', 'manifold', 'with', 'trivial', 'canonical', 'line', 'bundle', 'and', 'w', 'is', 'a', 'complexvalued', 'holomorphic', 'function', 'defined', 'on', 'x', 'assuming', 'only', 'that', 'the', 'critical', 'locus', 'of', 'w', 'is', 'compact', 'but', 'may', 'not', 'consist', 'of', 'isolated', 'points', 'these', 'results', 'can', 'be', 'viewed', 'as', 'certain', 'deformed', 'versions', 'of', 'serre', 'duality', 'the', 'first', 'amounts', 'to', 'a', 'duality', 'property', 'for', 'the', 'hypercohomology', 'of', 'the', 'sheaf', 'koszul', 'complex', 'of', 'w', 'while', 'the', 'second', 'is', 'equivalent', 'with', 'the', 'statement', 'that', 'a', 'certain', 'power', 'of', 'the', 'shift', 'functor', 'is', 'a', 'serre', 'functor', 'on', 'the', 'even', 'subcategory', 'of', 'the', 'mathbbz_2graded', 'category', 'of', 'topological', 'dbranes', 'of', 'such', 'models']] | [-0.20117179962585024, 0.0609195903974173, -0.08401263136699123, 0.09690525832769202, -0.12180740630338122, -0.17412656012800384, -0.008055209495188271, 0.3378962237372628, -0.34419229840401744, -0.1787847783511932, 0.0762743979585586, -0.2068159391303913, -0.1302005847988528, 0.17198281683272026, -0.1677797317054489, -0.004937695299244684, 0.0544015356973119, 0.10814139470229017, -0.12477439836423355, -0.24029402250183562, 0.4856887578231389, -0.05593542107304589, 0.24725123944112146, 0.04861872650102758, 0.11930990009950321, -0.030193332563627693, 0.03102861098242832, -0.006639199709275462, -0.07725597046218816, 0.13106855919554097, 0.28225580793997457, 0.047009833231492, 0.17108077257509022, -0.3685297827221087, -0.1498209963071725, 0.1990257701493006, 0.10212488742268522, 0.006719681163333723, 0.014163528954549158, -0.2885499728431345, 0.14259955687550963, -0.1483479767794946, -0.1494417446436452, -0.054310499503445187, 0.06249566391476842, 0.004427086620603032, -0.26574004285770364, -0.009354449197894237, 0.10819264934264643, 0.09074609586969018, -0.07821531587883403, -0.05579833349224455, -0.176399254446208, 0.05033071637229963, -0.02122075002083219, 0.09931266810277431, 0.13139117755316443, -0.14849514567262692, -0.08888043380117228, 0.37318470139728216, -0.08153914514982492, -0.2324078879700821, 0.14103215745818176, -0.14495393176005816, -0.16376666425055533, 0.1482071486897156, 0.015978725436219914, 0.17147017880266563, -0.00823546627719627, 0.19392860723281524, -0.14697713055934178, 0.08827688196887735, 0.07832076263873548, 0.02077107478961822, 0.185872555282883, 0.10907685640649717, 0.05324530900746095, 0.11985175550598498, -0.005150459289310317, -0.05614736920497457, -0.4095618458021982, -0.22727515287010275, -0.11564379003753916, 0.20637898955727474, -0.0856217492186201, -0.1859549536697994, 0.3731356641491417, 0.0394681548266996, 0.23327567756030954, 0.11243132622454499, 0.20449388348573788, 0.1028761666880722, 0.05756533392428299, -0.004066185380683326, 0.15718427291030224, 0.21989902579340107, 0.007052393095781569, -0.10520801348368386, -0.024344852837336967, 0.2087548532004117] |
1,802.06262 | Probing Patchy Saturation of Fluids in Nanoporous Media by Ultrasound | Nanoporous materials provide high surface area per unit mass and are capable
of fluids adsorption. While the measurements of overall amount of fluid
adsorbed by a nanopororus sample are straightforward, probing the fluid spacial
distribution is non-trivial. We consider published data on adsorption and
desorption of fluids in nanoporous glasses reported along with the measurements
of ultrasonic waves propagation. We analyse these using Biot's theory of
dynamic poroelasticity, approximating the patches as spherical shells. Our
calculations show that on adsorption the patch diameter is on the order of
10-20 pore diameters, while on desorption the patch size is comparable to the
sample size. Our analysis suggests that one can employ ultrasound to probe the
uniformity of fluid spatial distribution in nanoporous materials.
| physics.geo-ph physics.app-ph | nanoporous materials provide high surface area per unit mass and are capable of fluids adsorption while the measurements of overall amount of fluid adsorbed by a nanopororus sample are straightforward probing the fluid spacial distribution is nontrivial we consider published data on adsorption and desorption of fluids in nanoporous glasses reported along with the measurements of ultrasonic waves propagation we analyse these using biots theory of dynamic poroelasticity approximating the patches as spherical shells our calculations show that on adsorption the patch diameter is on the order of 1020 pore diameters while on desorption the patch size is comparable to the sample size our analysis suggests that one can employ ultrasound to probe the uniformity of fluid spatial distribution in nanoporous materials | [['nanoporous', 'materials', 'provide', 'high', 'surface', 'area', 'per', 'unit', 'mass', 'and', 'are', 'capable', 'of', 'fluids', 'adsorption', 'while', 'the', 'measurements', 'of', 'overall', 'amount', 'of', 'fluid', 'adsorbed', 'by', 'a', 'nanopororus', 'sample', 'are', 'straightforward', 'probing', 'the', 'fluid', 'spacial', 'distribution', 'is', 'nontrivial', 'we', 'consider', 'published', 'data', 'on', 'adsorption', 'and', 'desorption', 'of', 'fluids', 'in', 'nanoporous', 'glasses', 'reported', 'along', 'with', 'the', 'measurements', 'of', 'ultrasonic', 'waves', 'propagation', 'we', 'analyse', 'these', 'using', 'biots', 'theory', 'of', 'dynamic', 'poroelasticity', 'approximating', 'the', 'patches', 'as', 'spherical', 'shells', 'our', 'calculations', 'show', 'that', 'on', 'adsorption', 'the', 'patch', 'diameter', 'is', 'on', 'the', 'order', 'of', '1020', 'pore', 'diameters', 'while', 'on', 'desorption', 'the', 'patch', 'size', 'is', 'comparable', 'to', 'the', 'sample', 'size', 'our', 'analysis', 'suggests', 'that', 'one', 'can', 'employ', 'ultrasound', 'to', 'probe', 'the', 'uniformity', 'of', 'fluid', 'spatial', 'distribution', 'in', 'nanoporous', 'materials']] | [-0.07205246506674477, 0.14258463567792445, -0.05920284575482538, -0.03300634065877708, -0.020208941926517763, -0.09535831764478944, 0.010425070802919751, 0.4007221256067191, -0.2022574332442658, -0.3389859085687059, 0.052371172616373717, -0.3316335702114854, -0.07547238930936687, 0.1478985667003565, -0.012602340680650966, 0.06896936499623366, 0.042199539164974056, -0.058716059342740594, -0.026896903485209827, -0.20027956367501112, 0.24551616248112818, 0.09045453642087042, 0.3284827992473998, 0.08847958234731447, 0.09800104360184671, -0.02139302304645708, -0.009336501887175908, 0.06772258113838794, -0.2195797920240761, 0.12683419356014858, 0.21408099391471694, -0.00941541718900173, 0.1943972653185964, -0.5126999511631458, -0.3100942311238898, 0.007133970999212797, 0.12477006025088966, 0.12308202560390692, -0.06117057787593116, -0.21316848158420734, 0.04007587948436887, -0.06816976093340757, -0.12502937835895203, -0.04899891886536863, 0.02183708583759067, 0.0648208309998274, -0.2103359573703521, 0.11870549243590063, 0.0015370502456068253, 0.07433492355648773, -0.11529103435445115, -0.12874511099412791, -0.04704106592357713, 0.053216366087709945, 0.034883223867528755, -0.016668096142397684, 0.2595792269980735, -0.10930799968417518, -0.012722230420163102, 0.4167557251986024, -0.05560558116205868, -0.19066423430176807, 0.22520065059007938, -0.1908808995992203, -0.0531413959924224, 0.19718218804144663, 0.21171939252945018, 0.1378527675170359, -0.11894717254899134, 0.025715439764143946, -0.05423722877782916, 0.2213307631794702, 0.12738315657951121, -0.02437815820790581, 0.20818517929758906, 0.24666161930634956, 0.041798895236474176, 0.16027764038544548, -0.1700775195490415, -0.05525303789614584, -0.24489061307731616, -0.20808255000901985, -0.2424980335491748, 0.02580723496661945, -0.1667415832702363, -0.19061633844451964, 0.34905201653865253, 0.11793857232921502, 0.1521989984845758, 0.055101305677378476, 0.2631879637002452, 0.0016350028507242148, 0.06418639840943015, 0.030754604519244808, 0.24859642539335, 0.14827815076328568, 0.1058993353893257, -0.23496514056570644, 0.11492447700151358, 0.04027639486449809] |
1,802.06263 | Stochastic multiscale flux basis for Stokes-Darcy flows | Three algorithms are developed for uncertainty quantification in modeling
coupled Stokes and Darcy flows. The porous media may consist of multiple
regions with different properties. The permeability is modeled as a
non-stationary stochastic variable, with its log represented as a sum of local
Karhunen-Lo\`eve (KL) expansions. The problem is approximated by stochastic
collocation on either tensor-product or sparse grids, coupled with a multiscale
mortar mixed finite element method for the spatial discretization. A
non-overlapping domain decomposition algorithm reduces the global problem to a
coarse scale mortar interface problem, which is solved by an iterative solver,
for each stochastic realization. In the traditional implementation, each
subdomain solves a local Dirichlet or Neumann problem in every interface
iteration. To reduce this cost, two additional algorithms based on
deterministic or stochastic multiscale flux basis are introduced. The basis
consists of the local flux (or velocity trace) responses from each mortar
degree of freedom. It is computed by each subdomain independently before the
interface iteration begins. The use of the multiscale flux basis avoids the
need for subdomain solves on each iteration. The deterministic basis is
computed at each stochastic collocation and used only at this realization. The
stochastic basis is formed by further looping over all local realizations of a
subdomain's KL region before the stochastic collocation begins. It is reused
over multiple realizations. Numerical tests are presented to illustrate the
performance of the three algorithms, with the stochastic multiscale flux basis
showing significant savings in computational cost.
| math.NA | three algorithms are developed for uncertainty quantification in modeling coupled stokes and darcy flows the porous media may consist of multiple regions with different properties the permeability is modeled as a nonstationary stochastic variable with its log represented as a sum of local karhunenloeve kl expansions the problem is approximated by stochastic collocation on either tensorproduct or sparse grids coupled with a multiscale mortar mixed finite element method for the spatial discretization a nonoverlapping domain decomposition algorithm reduces the global problem to a coarse scale mortar interface problem which is solved by an iterative solver for each stochastic realization in the traditional implementation each subdomain solves a local dirichlet or neumann problem in every interface iteration to reduce this cost two additional algorithms based on deterministic or stochastic multiscale flux basis are introduced the basis consists of the local flux or velocity trace responses from each mortar degree of freedom it is computed by each subdomain independently before the interface iteration begins the use of the multiscale flux basis avoids the need for subdomain solves on each iteration the deterministic basis is computed at each stochastic collocation and used only at this realization the stochastic basis is formed by further looping over all local realizations of a subdomains kl region before the stochastic collocation begins it is reused over multiple realizations numerical tests are presented to illustrate the performance of the three algorithms with the stochastic multiscale flux basis showing significant savings in computational cost | [['three', 'algorithms', 'are', 'developed', 'for', 'uncertainty', 'quantification', 'in', 'modeling', 'coupled', 'stokes', 'and', 'darcy', 'flows', 'the', 'porous', 'media', 'may', 'consist', 'of', 'multiple', 'regions', 'with', 'different', 'properties', 'the', 'permeability', 'is', 'modeled', 'as', 'a', 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1,802.06264 | Monotonicity in inverse medium scattering on unbounded domains | We discuss a time-harmonic inverse scattering problem for the Helmholtz
equation with compactly supported penetrable and possibly inhomogeneous
scattering objects in an unbounded homogeneous background medium, and we
develop a monotonicity relation for the far field operator that maps
superpositions of incident plane waves to the far field patterns of the
corresponding scattered waves. We utilize this monotonicity relation to
establish novel characterizations of the support of the scattering objects in
terms of the far field operator. These are related to and extend corresponding
results known from factorization and linear sampling methods to determine the
support of unknown scattering objects from far field observations of scattered
fields. An attraction of the new characterizations is that they only require
the refractive index of the scattering objects to be above or below the
refractive index of the background medium locally and near the boundary of the
scatterers. An important tool to prove these results are so-called localized
wave functions that have arbitrarily large norm in some prescribed region while
at the same time having arbitrarily small norm in some other prescribed region.
We present numerical examples to illustrate our theoretical findings.
| math.AP | we discuss a timeharmonic inverse scattering problem for the helmholtz equation with compactly supported penetrable and possibly inhomogeneous scattering objects in an unbounded homogeneous background medium and we develop a monotonicity relation for the far field operator that maps superpositions of incident plane waves to the far field patterns of the corresponding scattered waves we utilize this monotonicity relation to establish novel characterizations of the support of the scattering objects in terms of the far field operator these are related to and extend corresponding results known from factorization and linear sampling methods to determine the support of unknown scattering objects from far field observations of scattered fields an attraction of the new characterizations is that they only require the refractive index of the scattering objects to be above or below the refractive index of the background medium locally and near the boundary of the scatterers an important tool to prove these results are socalled localized wave functions that have arbitrarily large norm in some prescribed region while at the same time having arbitrarily small norm in some other prescribed region we present numerical examples to illustrate our theoretical findings | [['we', 'discuss', 'a', 'timeharmonic', 'inverse', 'scattering', 'problem', 'for', 'the', 'helmholtz', 'equation', 'with', 'compactly', 'supported', 'penetrable', 'and', 'possibly', 'inhomogeneous', 'scattering', 'objects', 'in', 'an', 'unbounded', 'homogeneous', 'background', 'medium', 'and', 'we', 'develop', 'a', 'monotonicity', 'relation', 'for', 'the', 'far', 'field', 'operator', 'that', 'maps', 'superpositions', 'of', 'incident', 'plane', 'waves', 'to', 'the', 'far', 'field', 'patterns', 'of', 'the', 'corresponding', 'scattered', 'waves', 'we', 'utilize', 'this', 'monotonicity', 'relation', 'to', 'establish', 'novel', 'characterizations', 'of', 'the', 'support', 'of', 'the', 'scattering', 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1,802.06265 | Statistical Link Label Modeling for Sign Prediction: Smoothing Sparsity
by Joining Local and Global Information | One of the major issues in signed networks is to use network structure to
predict the missing sign of an edge. In this paper, we introduce a novel
probabilistic approach for the sign prediction problem. The main characteristic
of the proposed models is their ability to adapt to the sparsity level of an
input network. The sparsity of networks is one of the major reasons for the
poor performance of many link prediction algorithms, in general, and sign
prediction algorithms, in particular. Building a model that has an ability to
adapt to the sparsity of the data has not yet been considered in the previous
related works. We suggest that there exists a dilemma between local and global
structures and attempt to build sparsity adaptive models by resolving this
dilemma. To this end, we propose probabilistic prediction models based on local
and global structures and integrate them based on the concept of smoothing. The
model relies more on the global structures when the sparsity increases, whereas
it gives more weights to the information obtained from local structures for low
levels of the sparsity. The proposed model is assessed on three real-world
signed networks, and the experiments reveal its consistent superiority over the
state of the art methods. As compared to the previous methods, the proposed
model not only better handles the sparsity problem, but also has lower
computational complexity and can be updated using real-time data streams.
| cs.SI physics.soc-ph | one of the major issues in signed networks is to use network structure to predict the missing sign of an edge in this paper we introduce a novel probabilistic approach for the sign prediction problem the main characteristic of the proposed models is their ability to adapt to the sparsity level of an input network the sparsity of networks is one of the major reasons for the poor performance of many link prediction algorithms in general and sign prediction algorithms in particular building a model that has an ability to adapt to the sparsity of the data has not yet been considered in the previous related works we suggest that there exists a dilemma between local and global structures and attempt to build sparsity adaptive models by resolving this dilemma to this end we propose probabilistic prediction models based on local and global structures and integrate them based on the concept of smoothing the model relies more on the global structures when the sparsity increases whereas it gives more weights to the information obtained from local structures for low levels of the sparsity the proposed model is assessed on three realworld signed networks and the experiments reveal its consistent superiority over the state of the art methods as compared to the previous methods the proposed model not only better handles the sparsity problem but also has lower computational complexity and can be updated using realtime data streams | [['one', 'of', 'the', 'major', 'issues', 'in', 'signed', 'networks', 'is', 'to', 'use', 'network', 'structure', 'to', 'predict', 'the', 'missing', 'sign', 'of', 'an', 'edge', 'in', 'this', 'paper', 'we', 'introduce', 'a', 'novel', 'probabilistic', 'approach', 'for', 'the', 'sign', 'prediction', 'problem', 'the', 'main', 'characteristic', 'of', 'the', 'proposed', 'models', 'is', 'their', 'ability', 'to', 'adapt', 'to', 'the', 'sparsity', 'level', 'of', 'an', 'input', 'network', 'the', 'sparsity', 'of', 'networks', 'is', 'one', 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1,802.06266 | An analysis of training and generalization errors in shallow and deep
networks | This paper is motivated by an open problem around deep networks, namely, the
apparent absence of over-fitting despite large over-parametrization which
allows perfect fitting of the training data. In this paper, we analyze this
phenomenon in the case of regression problems when each unit evaluates a
periodic activation function. We argue that the minimal expected value of the
square loss is inappropriate to measure the generalization error in
approximation of compositional functions in order to take full advantage of the
compositional structure. Instead, we measure the generalization error in the
sense of maximum loss, and sometimes, as a pointwise error. We give estimates
on exactly how many parameters ensure both zero training error as well as a
good generalization error. We prove that a solution of a regularization problem
is guaranteed to yield a good training error as well as a good generalization
error and estimate how much error to expect at which test data.
| cs.LG cs.NA math.NA | this paper is motivated by an open problem around deep networks namely the apparent absence of overfitting despite large overparametrization which allows perfect fitting of the training data in this paper we analyze this phenomenon in the case of regression problems when each unit evaluates a periodic activation function we argue that the minimal expected value of the square loss is inappropriate to measure the generalization error in approximation of compositional functions in order to take full advantage of the compositional structure instead we measure the generalization error in the sense of maximum loss and sometimes as a pointwise error we give estimates on exactly how many parameters ensure both zero training error as well as a good generalization error we prove that a solution of a regularization problem is guaranteed to yield a good training error as well as a good generalization error and estimate how much error to expect at which test data | [['this', 'paper', 'is', 'motivated', 'by', 'an', 'open', 'problem', 'around', 'deep', 'networks', 'namely', 'the', 'apparent', 'absence', 'of', 'overfitting', 'despite', 'large', 'overparametrization', 'which', 'allows', 'perfect', 'fitting', 'of', 'the', 'training', 'data', 'in', 'this', 'paper', 'we', 'analyze', 'this', 'phenomenon', 'in', 'the', 'case', 'of', 'regression', 'problems', 'when', 'each', 'unit', 'evaluates', 'a', 'periodic', 'activation', 'function', 'we', 'argue', 'that', 'the', 'minimal', 'expected', 'value', 'of', 'the', 'square', 'loss', 'is', 'inappropriate', 'to', 'measure', 'the', 'generalization', 'error', 'in', 'approximation', 'of', 'compositional', 'functions', 'in', 'order', 'to', 'take', 'full', 'advantage', 'of', 'the', 'compositional', 'structure', 'instead', 'we', 'measure', 'the', 'generalization', 'error', 'in', 'the', 'sense', 'of', 'maximum', 'loss', 'and', 'sometimes', 'as', 'a', 'pointwise', 'error', 'we', 'give', 'estimates', 'on', 'exactly', 'how', 'many', 'parameters', 'ensure', 'both', 'zero', 'training', 'error', 'as', 'well', 'as', 'a', 'good', 'generalization', 'error', 'we', 'prove', 'that', 'a', 'solution', 'of', 'a', 'regularization', 'problem', 'is', 'guaranteed', 'to', 'yield', 'a', 'good', 'training', 'error', 'as', 'well', 'as', 'a', 'good', 'generalization', 'error', 'and', 'estimate', 'how', 'much', 'error', 'to', 'expect', 'at', 'which', 'test', 'data']] | [-0.07282179672150843, -0.011253797835876956, -0.0649690045133954, 0.15727527018095697, -0.06550176843039451, -0.11330917074134754, 0.09772589054078826, 0.36181139865769973, -0.3000423147072715, -0.3172301633824264, 0.12995036679034633, -0.2475892742495841, -0.1863356714876699, 0.18003137609545863, -0.1759488963792401, 0.10886858774108753, 0.11441224863932979, 0.05993122244253755, -0.11310129652040139, -0.27174451525863863, 0.289451797186367, 0.08746301412296992, 0.2773222369581251, 0.04506937296490275, 0.13237617330535525, -0.011574241128419677, 0.035408507867325696, 0.027301042235546535, -0.1250445880541285, 0.12328477197086378, 0.23892429022478961, 0.11762266221095717, 0.36482774716381344, -0.3535011671303261, -0.21130086175830765, 0.1502809172796626, 0.16180028048298892, 0.11887404131477759, -0.012738814871127326, -0.22138937052429444, 0.10024305326683867, -0.16025634611986816, -0.0950950387054153, -0.09017334346689525, -0.02982197725063851, 0.004986409646939607, -0.3338823731676976, 0.09737487750263103, 0.0874606609059077, 0.06448187502589257, -0.04999142431901888, -0.11282966075645338, 0.040955808428266356, 0.1302124286966113, 0.08230716107983983, 0.06967575829476118, 0.05645265077811576, -0.12346326505254594, -0.08933489697219263, 0.36336287686661367, -0.09584555953230349, -0.2478103384528602, 0.1214825285658721, -0.0987861609116437, -0.09339998328021817, 0.09383220604110148, 0.2119122193976035, 0.07639596732934156, -0.14659944194032914, 0.03510270741723117, -0.05045255182401067, 0.1710225701835307, 0.048932684028160665, 0.05339623264397585, 0.13012706310518327, 0.19252114504605772, 0.12153474494567021, 0.16863241752669697, -0.09337104741453885, -0.06773357678865713, -0.33674790279999856, -0.155742030848764, -0.21593650699660724, 0.03656278984065378, -0.09974332092491339, -0.21748552027969592, 0.37099822146757955, 0.1894206002084238, 0.24834701670934597, 0.13134551525506521, 0.3023850729598874, 0.10915161475404016, 0.07029998417973758, 0.08184192654331245, 0.22665492989753763, 0.09274042781682745, 0.04676762736071983, -0.1901999555888676, 0.09929095376883784, 0.052916790728246975] |
1,802.06267 | Gelation of patchy gold nanoparticles decorated by liquid-crystalline
ligands: computer simulation study | We consider patchy gold nanoparticles decorated by liquid crystalline
ligands. The cases of two, three, four and six symmetrically arranged patches
of ligands are discussed, as well as the cases of their equatorial and uniform
arrangement. A solution of decorated nanoparticles is considered within a flat
pore with the solid walls and the interior filled by a polar solvent. The
ligands form physical crosslinks between the nanoparticles due to strong liquid
crystalline interaction, turning the solution into a gel-like structure.
Gelation is done repeatedly starting each time from freshly equilibrated
dispersed state of nanoparticles. The gelation dynamics and the range of
network characteristics of gel are examined, depending on the type of patchy
decoration and the solution density. The emphasis is given to the suitability
of a gel for catalytic applications
| cond-mat.soft | we consider patchy gold nanoparticles decorated by liquid crystalline ligands the cases of two three four and six symmetrically arranged patches of ligands are discussed as well as the cases of their equatorial and uniform arrangement a solution of decorated nanoparticles is considered within a flat pore with the solid walls and the interior filled by a polar solvent the ligands form physical crosslinks between the nanoparticles due to strong liquid crystalline interaction turning the solution into a gellike structure gelation is done repeatedly starting each time from freshly equilibrated dispersed state of nanoparticles the gelation dynamics and the range of network characteristics of gel are examined depending on the type of patchy decoration and the solution density the emphasis is given to the suitability of a gel for catalytic applications | [['we', 'consider', 'patchy', 'gold', 'nanoparticles', 'decorated', 'by', 'liquid', 'crystalline', 'ligands', 'the', 'cases', 'of', 'two', 'three', 'four', 'and', 'six', 'symmetrically', 'arranged', 'patches', 'of', 'ligands', 'are', 'discussed', 'as', 'well', 'as', 'the', 'cases', 'of', 'their', 'equatorial', 'and', 'uniform', 'arrangement', 'a', 'solution', 'of', 'decorated', 'nanoparticles', 'is', 'considered', 'within', 'a', 'flat', 'pore', 'with', 'the', 'solid', 'walls', 'and', 'the', 'interior', 'filled', 'by', 'a', 'polar', 'solvent', 'the', 'ligands', 'form', 'physical', 'crosslinks', 'between', 'the', 'nanoparticles', 'due', 'to', 'strong', 'liquid', 'crystalline', 'interaction', 'turning', 'the', 'solution', 'into', 'a', 'gellike', 'structure', 'gelation', 'is', 'done', 'repeatedly', 'starting', 'each', 'time', 'from', 'freshly', 'equilibrated', 'dispersed', 'state', 'of', 'nanoparticles', 'the', 'gelation', 'dynamics', 'and', 'the', 'range', 'of', 'network', 'characteristics', 'of', 'gel', 'are', 'examined', 'depending', 'on', 'the', 'type', 'of', 'patchy', 'decoration', 'and', 'the', 'solution', 'density', 'the', 'emphasis', 'is', 'given', 'to', 'the', 'suitability', 'of', 'a', 'gel', 'for', 'catalytic', 'applications']] | [-0.11027200938067364, 0.18794648631525632, -0.029754127446874862, -0.013786475793529615, 0.0016122228067851703, -0.14699712639873605, 0.05425937481594222, 0.41984684409872264, -0.2532657343069561, -0.2761656246352443, 0.06890508706702758, -0.2914136996251026, -0.12187880916407204, 0.08344211759230563, 0.024298577990653065, -0.014093935233718565, 0.009372312403177374, -0.026909125901275237, -0.03946731410536716, -0.20672717105547492, 0.2605581401299884, 0.023278639410117655, 0.28050937309491497, 0.06570811359565491, 0.0990964799394248, -0.03817123113171631, 0.0732927638148704, 0.07410322975845299, -0.17058012537117906, 0.10325985220758571, 0.2114489239979116, -0.033981288683721585, 0.20118853926324493, -0.5252708674813727, -0.2070536842384866, 0.033666276628905124, 0.142261607810599, 0.10120655334632815, -0.06933006706189022, -0.28534917476523014, 0.02209618910685011, -0.11254050971527818, -0.16313692014522224, 0.005252313039457525, 0.0026840903291492973, 0.10242160320066925, -0.19411939208729917, 0.06056214953578994, 0.07146109849879276, 0.06208413112766415, -0.12417058434820812, -0.11772767756176927, -0.08709397713141871, 0.12316031508656033, 0.056017808279356714, -0.034632364874492394, 0.26833266036883563, -0.12612859676201038, -0.026863706035127166, 0.41698835764208253, -0.02435021795899401, -0.1921290681050479, 0.22815373960797114, -0.11175148685299258, -0.04652904601810315, 0.2202111191759668, 0.12916565303982214, 0.14451658865436912, -0.15464862763840806, 0.05428004789989058, -0.03475353689882051, 0.20017941251311822, 0.14333321406676625, -0.03578741534236278, 0.2708898800858901, 0.24268995189882872, 0.002582571961568626, 0.22384862974997013, -0.09629357705703219, -0.11869895986800316, -0.21108055447240695, -0.2059365204137302, -0.22468917736541907, 0.017801729269069796, -0.14600398375433674, -0.23883567075778284, 0.34724118576282, -0.018993301850295703, 0.18403998011979097, -0.027709182398038285, 0.20212822384734191, -0.05562381414547278, 0.03232380773921902, -0.033781958587524544, 0.21060522581016972, 0.1553642466897145, 0.09539483519820212, -0.20346712333839814, 0.10855662582783412, 0.07090070587049686] |
1,802.06268 | McKean-Vlasov diffusion and the well-posedness of the Hookean
bead-spring-chain model for dilute polymeric fluids: small-mass limit and
equilibration in momentum space | We reformulate a general class of classical bead-spring-chain models for
dilute polymeric fluids, with Hookean spring potentials, as McKean-Vlasov
diffusion. This results in a coupled system of partial differential equations
involving the unsteady incompressible linearized Navier-Stokes equations,
referred to as the Oseen system, for the velocity and the pressure of the
fluid, with a source term which is a nonlinear function of the probability
density function, and a second-order degenerate parabolic Fokker-Planck
equation, whose transport terms depend on the velocity field, for the
probability density function. We show that this coupled Oseen-Fokker-Planck
system has a large-data global weak solution. We then perform a rigorous
passage to the limit as the masses of the beads in the bead-spring-chain
converge to zero, which is shown in particular to result in equilibration in
momentum space. The limiting problem is then used to perform a rigorous
derivation of the Hookean bead-spring-chain model for dilute polymeric fluids,
which has the interesting feature that, if the flow domain is bounded, then so
is the associated configuration space domain and the associated Kramers stress
tensor is defined by integration over this bounded configuration domain.
| math.AP | we reformulate a general class of classical beadspringchain models for dilute polymeric fluids with hookean spring potentials as mckeanvlasov diffusion this results in a coupled system of partial differential equations involving the unsteady incompressible linearized navierstokes equations referred to as the oseen system for the velocity and the pressure of the fluid with a source term which is a nonlinear function of the probability density function and a secondorder degenerate parabolic fokkerplanck equation whose transport terms depend on the velocity field for the probability density function we show that this coupled oseenfokkerplanck system has a largedata global weak solution we then perform a rigorous passage to the limit as the masses of the beads in the beadspringchain converge to zero which is shown in particular to result in equilibration in momentum space the limiting problem is then used to perform a rigorous derivation of the hookean beadspringchain model for dilute polymeric fluids which has the interesting feature that if the flow domain is bounded then so is the associated configuration space domain and the associated kramers stress tensor is defined by integration over this bounded configuration domain | [['we', 'reformulate', 'a', 'general', 'class', 'of', 'classical', 'beadspringchain', 'models', 'for', 'dilute', 'polymeric', 'fluids', 'with', 'hookean', 'spring', 'potentials', 'as', 'mckeanvlasov', 'diffusion', 'this', 'results', 'in', 'a', 'coupled', 'system', 'of', 'partial', 'differential', 'equations', 'involving', 'the', 'unsteady', 'incompressible', 'linearized', 'navierstokes', 'equations', 'referred', 'to', 'as', 'the', 'oseen', 'system', 'for', 'the', 'velocity', 'and', 'the', 'pressure', 'of', 'the', 'fluid', 'with', 'a', 'source', 'term', 'which', 'is', 'a', 'nonlinear', 'function', 'of', 'the', 'probability', 'density', 'function', 'and', 'a', 'secondorder', 'degenerate', 'parabolic', 'fokkerplanck', 'equation', 'whose', 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'integration', 'over', 'this', 'bounded', 'configuration', 'domain']] | [-0.12279913035379122, 0.10186990997153184, -0.09235099222891593, 0.006824892854833755, -0.06760492237905662, -0.1147864112650515, -0.03756577904606538, 0.2864107551273479, -0.3146417934137086, -0.22323290383197888, 0.09568274801381693, -0.2572158432255189, -0.1345396539827268, 0.14395311207295225, -0.018998298330873412, 0.10554759995761498, 0.04696829359407627, 0.04923831992306977, -0.059407104397573136, -0.183567079308603, 0.33460151416660155, -0.007621144457790319, 0.2583084366689887, 0.02807712583472171, 0.16769595417164027, -0.02209322989582815, 0.04350591650212644, 0.058883831460436964, -0.17111356032335373, 0.060693125350035286, 0.21261585895210314, 0.010487465310581428, 0.27409699285072425, -0.42673960079749423, -0.2467243853275494, 0.08529348171476314, 0.14624765733637476, 0.11535400921861912, -0.00022456209896312606, -0.26985215031672066, 0.04286165757264982, -0.16478555172967213, -0.18441038938759957, 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1,802.06269 | Initial-boundary value problems for multi-term time-fractional diffusion
equations with x-dependent coefficients | In this paper, we discuss an initial-boundary value problem (IBVP) for the
multi-term time-fractional diffusion equation with x-dependent coefficients. By
means of the Mittag-Leffler functions and the eigenfunction expansion, we
reduce the IBVP to an equivalent integral equation to show the unique existence
and the analyticity of the solution for the equation. Especially, in the case
where all the coefficients of the time-fractional derivatives are non-negative,
by the Laplace and inversion Laplace transforms, it turns out that the decay
rate of the solution for long time is dominated by the lowest order of the
time-fractional derivatives. Finally, as an application of the analyticity of
the solution, the uniqueness of an inverse problem in determining the
fractional orders in the multi-term time-fractional diffusion equations from
one interior point observation is established.
| math.AP | in this paper we discuss an initialboundary value problem ibvp for the multiterm timefractional diffusion equation with xdependent coefficients by means of the mittagleffler functions and the eigenfunction expansion we reduce the ibvp to an equivalent integral equation to show the unique existence and the analyticity of the solution for the equation especially in the case where all the coefficients of the timefractional derivatives are nonnegative by the laplace and inversion laplace transforms it turns out that the decay rate of the solution for long time is dominated by the lowest order of the timefractional derivatives finally as an application of the analyticity of the solution the uniqueness of an inverse problem in determining the fractional orders in the multiterm timefractional diffusion equations from one interior point observation is established | [['in', 'this', 'paper', 'we', 'discuss', 'an', 'initialboundary', 'value', 'problem', 'ibvp', 'for', 'the', 'multiterm', 'timefractional', 'diffusion', 'equation', 'with', 'xdependent', 'coefficients', 'by', 'means', 'of', 'the', 'mittagleffler', 'functions', 'and', 'the', 'eigenfunction', 'expansion', 'we', 'reduce', 'the', 'ibvp', 'to', 'an', 'equivalent', 'integral', 'equation', 'to', 'show', 'the', 'unique', 'existence', 'and', 'the', 'analyticity', 'of', 'the', 'solution', 'for', 'the', 'equation', 'especially', 'in', 'the', 'case', 'where', 'all', 'the', 'coefficients', 'of', 'the', 'timefractional', 'derivatives', 'are', 'nonnegative', 'by', 'the', 'laplace', 'and', 'inversion', 'laplace', 'transforms', 'it', 'turns', 'out', 'that', 'the', 'decay', 'rate', 'of', 'the', 'solution', 'for', 'long', 'time', 'is', 'dominated', 'by', 'the', 'lowest', 'order', 'of', 'the', 'timefractional', 'derivatives', 'finally', 'as', 'an', 'application', 'of', 'the', 'analyticity', 'of', 'the', 'solution', 'the', 'uniqueness', 'of', 'an', 'inverse', 'problem', 'in', 'determining', 'the', 'fractional', 'orders', 'in', 'the', 'multiterm', 'timefractional', 'diffusion', 'equations', 'from', 'one', 'interior', 'point', 'observation', 'is', 'established']] | [-0.11625243538493613, 0.024717556628344867, -0.04826964981710682, 0.06082076689747807, -0.0976167011522473, -0.04613197546165723, -0.03530068667515969, 0.29040254066483334, -0.3744753502452603, -0.20326224520014455, 0.19740312205260405, -0.31901870088365214, -0.1233897533124456, 0.16767481197244846, 0.028813694698664433, 0.12346074975704631, 0.005055156241975895, 0.02601272360994839, -0.10066801834170921, -0.21172408863740902, 0.36977272116704485, -0.008979143632146028, 0.21804242332179385, 0.058004803142438714, 0.1454724318562792, -0.018803140778954213, -0.0309142879031312, -0.061093248423332205, -0.1843823014098654, 0.06798072019233727, 0.22796755676480154, 0.02701774780334045, 0.30485762847014347, -0.4143244793208746, -0.1872874669945584, 0.09887021241996151, 0.14921099468707466, 0.04926990113006188, -0.01910282314638607, -0.2960989368058598, 0.0598945987285473, -0.09268559109455404, -0.22564235476870986, -0.047302233134038175, 0.03963405600963877, 0.056654318644163704, -0.3089060738682747, 0.15706722202191417, 0.0552579931413325, -0.03136361477753291, -0.18179661647389786, -0.13068563645669762, 0.032475636530524266, 0.043876023196543636, 0.10487704670164161, -0.03683779990802018, -0.01428350954531477, -0.16264901446142735, -0.08536769475859518, 0.3817129318888944, -0.10416076944809621, -0.28763746600598095, 0.07024719883293773, -0.16370245033803468, -0.07964001116701043, 0.16336231090916464, 0.1053667906492662, 0.18827737970994068, -0.1802500324180493, 0.14164744022956047, -0.02159826963996658, 0.12057368780526584, 0.11106108591461984, -0.031441204615223865, 0.0677241544615334, 0.12911299356044484, 0.13466963950687877, 0.15319470403572688, -0.030347507805205308, -0.15157670899509237, -0.347998585122136, -0.23037868579894377, -0.19399817153327883, 0.0867019451653155, -0.1565764389279558, -0.1811839556357322, 0.36550058668676333, 0.14637371399798071, 0.14712947430268217, 0.04391255953277533, 0.2488213831343903, 0.3171936776551705, -0.024007880308020573, 0.06666158313074937, 0.17556809332723228, 0.1714960438091881, 0.15450943948414464, -0.2960826143532848, 0.07358420214687403, 0.15997206017446633] |
1,802.0627 | MAVIS: Managing Datacenters using Smartphones | Distributed monitoring plays a crucial role in managing the activities of
cloud-based datacenters. System administrators have long relied on monitoring
systems such as Nagios and Ganglia to obtain status alerts on their
desktop-class machines. However, the popularity of mobile devices is pushing
the community to develop datacenter monitoring solutions for smartphone-class
devices. Here we lay out desirable characteristics of such smartphone-based
monitoring and identify quantitatively the shortcomings from directly applying
existing solutions to this domain. Then we introduce a possible design that
addresses some of these shortcomings and provide results from an early
prototype, called MAVIS, using one month of monitoring data from approximately
3,000 machines hosted by Purdue's central IT organization.
| cs.DC | distributed monitoring plays a crucial role in managing the activities of cloudbased datacenters system administrators have long relied on monitoring systems such as nagios and ganglia to obtain status alerts on their desktopclass machines however the popularity of mobile devices is pushing the community to develop datacenter monitoring solutions for smartphoneclass devices here we lay out desirable characteristics of such smartphonebased monitoring and identify quantitatively the shortcomings from directly applying existing solutions to this domain then we introduce a possible design that addresses some of these shortcomings and provide results from an early prototype called mavis using one month of monitoring data from approximately 3000 machines hosted by purdues central it organization | [['distributed', 'monitoring', 'plays', 'a', 'crucial', 'role', 'in', 'managing', 'the', 'activities', 'of', 'cloudbased', 'datacenters', 'system', 'administrators', 'have', 'long', 'relied', 'on', 'monitoring', 'systems', 'such', 'as', 'nagios', 'and', 'ganglia', 'to', 'obtain', 'status', 'alerts', 'on', 'their', 'desktopclass', 'machines', 'however', 'the', 'popularity', 'of', 'mobile', 'devices', 'is', 'pushing', 'the', 'community', 'to', 'develop', 'datacenter', 'monitoring', 'solutions', 'for', 'smartphoneclass', 'devices', 'here', 'we', 'lay', 'out', 'desirable', 'characteristics', 'of', 'such', 'smartphonebased', 'monitoring', 'and', 'identify', 'quantitatively', 'the', 'shortcomings', 'from', 'directly', 'applying', 'existing', 'solutions', 'to', 'this', 'domain', 'then', 'we', 'introduce', 'a', 'possible', 'design', 'that', 'addresses', 'some', 'of', 'these', 'shortcomings', 'and', 'provide', 'results', 'from', 'an', 'early', 'prototype', 'called', 'mavis', 'using', 'one', 'month', 'of', 'monitoring', 'data', 'from', 'approximately', '3000', 'machines', 'hosted', 'by', 'purdues', 'central', 'it', 'organization']] | [-0.1336536054759226, 0.019198839631523395, -0.045772421324660105, 0.018235676102685155, -0.1017635840172362, -0.16057383912805193, 0.09294163420175512, 0.38596408487043204, -0.255826283470486, -0.3259678678673313, 0.20310911067429482, -0.301281845473029, -0.14216106618685578, 0.23342565991225894, -0.09422455985461259, 0.0702975860460558, 0.08250564533820445, -0.016714245723118505, 0.019135083039010944, -0.22403799396448043, 0.2935301005609627, 0.07740033376548025, 0.3275292495705394, 0.06030362861498294, 0.07361352990847081, 0.01747349457151291, -0.07450721471535939, -0.025946048635954934, -0.06339541216052398, 0.13498761617854513, 0.3473321658416858, 0.2272489866828201, 0.36068986605042247, -0.48435742095871653, -0.1897646464942092, 0.07927435414900107, 0.1615088180745564, 0.03964161553152802, -0.0746881569923264, -0.3063813718553219, 0.11408659438516393, -0.21482766357129784, -0.15029341370676844, -0.1167825599787412, 0.032363028843300765, 0.059071687336890374, -0.1680521807957794, -0.00492682441728431, -0.017873692257260834, 0.10698645608257114, -0.0861886676211725, -0.0778410451053383, 0.04285771802878352, 0.22393898810346233, 0.041207848149093076, -0.0019759649143519776, 0.19332404135019277, -0.14553893158450523, -0.14519781158930897, 0.3812287076510041, 0.019587697722104, -0.11199560584672899, 0.22113042315080142, -0.006864001603659104, -0.1857080177050429, 0.04677133748514785, 0.24327749372632415, 0.087472230144259, -0.22039086705384156, -0.006149003508013535, 0.029316173107535752, 0.17323000832133134, 0.01005077035450894, 0.06535405974783418, 0.24861276315318215, 0.2713038211136504, 0.06357706462559118, 0.11222103871706943, -0.08069687014169714, -0.06072536838743232, -0.21108672905851295, -0.1488333249559488, -0.14567823117788606, 0.05891864321677497, -0.009160797999491613, -0.12025973607173741, 0.40911413285195725, 0.22835982901354632, 0.13411276177416934, 0.026316293258720112, 0.31092196865938604, 0.03502396630291413, 0.15132425820532566, 0.13040455252889338, 0.19746338255424267, 0.013610997200615841, 0.2084885092055494, -0.16215481686069527, 0.10156215243990084, -0.02022013296286955] |
1,802.06271 | Lower Bounds on Sparse Spanners, Emulators, and Diameter-reducing
shortcuts | We prove better lower bounds on additive spanners and emulators, which are
lossy compression schemes for undirected graphs, as well as lower bounds on
shortcut sets, which reduce the diameter of directed graphs. We show that any
$O(n)$-size shortcut set cannot bring the diameter below $\Omega(n^{1/6})$, and
that any $O(m)$-size shortcut set cannot bring it below $\Omega(n^{1/11})$.
These improve Hesse's [Hesse03] lower bound of $\Omega(n^{1/17})$. By combining
these constructions with Abboud and Bodwin's [AbboudB17] edge-splitting
technique, we get additive stretch lower bounds of $+\Omega(n^{1/11})$ for
$O(n)$-size spanners and $+\Omega(n^{1/18})$ for $O(n)$-size emulators. These
improve Abboud and Bodwin's $+\Omega(n^{1/22})$ lower bounds.
| cs.DS | we prove better lower bounds on additive spanners and emulators which are lossy compression schemes for undirected graphs as well as lower bounds on shortcut sets which reduce the diameter of directed graphs we show that any onsize shortcut set cannot bring the diameter below omegan16 and that any omsize shortcut set cannot bring it below omegan111 these improve hesses hesse03 lower bound of omegan117 by combining these constructions with abboud and bodwins abboudb17 edgesplitting technique we get additive stretch lower bounds of omegan111 for onsize spanners and omegan118 for onsize emulators these improve abboud and bodwins omegan122 lower bounds | [['we', 'prove', 'better', 'lower', 'bounds', 'on', 'additive', 'spanners', 'and', 'emulators', 'which', 'are', 'lossy', 'compression', 'schemes', 'for', 'undirected', 'graphs', 'as', 'well', 'as', 'lower', 'bounds', 'on', 'shortcut', 'sets', 'which', 'reduce', 'the', 'diameter', 'of', 'directed', 'graphs', 'we', 'show', 'that', 'any', 'onsize', 'shortcut', 'set', 'can', 'not', 'bring', 'the', 'diameter', 'below', 'omegan16', 'and', 'that', 'any', 'omsize', 'shortcut', 'set', 'can', 'not', 'bring', 'it', 'below', 'omegan111', 'these', 'improve', 'hesses', 'hesse03', 'lower', 'bound', 'of', 'omegan117', 'by', 'combining', 'these', 'constructions', 'with', 'abboud', 'and', 'bodwins', 'abboudb17', 'edgesplitting', 'technique', 'we', 'get', 'additive', 'stretch', 'lower', 'bounds', 'of', 'omegan111', 'for', 'onsize', 'spanners', 'and', 'omegan118', 'for', 'onsize', 'emulators', 'these', 'improve', 'abboud', 'and', 'bodwins', 'omegan122', 'lower', 'bounds']] | [-0.06755335248269878, 0.14808751923124214, -0.044239818005935176, 0.08505427716606921, -0.09583053166804077, -0.20969137423518266, 0.144715187869004, 0.38305503342832836, -0.25051802662866457, -0.411910404763338, 0.10619921844636837, -0.24140524182591463, -0.17734952310898475, 0.287204808054062, -0.13169974238089807, 0.0841866355532637, 0.05228721552431215, 0.0036529750245940552, -0.05089845994978652, -0.33600275804401264, 0.2352949590678071, 0.060633966527817836, 0.2329446372709104, 0.1346619225127602, 0.036139787041715214, -0.06401500662092816, 0.008091025397304323, 0.0017618052866105196, -0.238777630507804, 0.16700459889821953, 0.2159066973561114, 0.15271955400776976, 0.17527372950872222, -0.4424350653867145, -0.2187442290238463, 0.2017201941235216, 0.1600617515383759, 0.12406177869690889, 0.03389925165306572, -0.2649760250370581, 0.14828897280628783, -0.09462621771208532, -0.0012890004020716464, -0.10234309222221702, -0.0016486186708149675, 0.051273408047803726, -0.2604160470642395, -0.007654084424887385, 0.17657185418679697, -0.029055369429413107, 0.004015909482284889, -0.2298110877419566, 0.05538848148541177, 0.11497020669240546, -0.10794672658564625, 0.043683582167192314, 0.07316778800296751, -0.0856516202502362, -0.17376740086455267, 0.27694825975961246, -0.11438651669995634, -0.18935569459163062, 0.13882166390808728, -0.05273150784157953, -0.14791215844145353, 0.09549404918164997, 0.2191087023692799, 0.07437775928813678, -0.04693638100442323, 0.09833221077844807, -0.06158142508222507, 0.13963001016732102, 0.19169770032269778, 0.10183469642076519, 0.041111218221076246, 0.10286309989680956, 0.2430887882812665, 0.16540045435489206, 0.004059275801569878, -0.005186198545353753, -0.2564291297202254, -0.08236226082106561, -0.2076785409884466, 0.02188512563716958, -0.21011904628028208, -0.1685889901767311, 0.28449202358599873, 0.1345915522856208, 0.1961935404074061, 0.23082548536738934, 0.2863038620082559, 0.09344757494681699, 0.06767721393063747, 0.2358741766803867, 0.22019020547824247, 0.1464652759713827, -0.045698829025930754, -0.09119938465428877, 0.09264278444103323, 0.11871629189904574] |
1,802.06272 | Superconductivity induced by flexural modes in non $\sigma_{\rm
h}$-symmetric Dirac-like two-dimensional materials: A theoretical study for
silicene and germanene | In two-dimensional crystals that lack symmetry under reflections on the
horizontal plane of the lattice (non-$\sigma_{\rm h}$-symmetric), electrons can
couple to flexural modes (ZA phonons) at first order. We show that in materials
of this type that also exhibit a Dirac-like electron dispersion, the strong
coupling can result in electron pairing mediated by these phonons, as long as
the flexural modes are not damped or suppressed by additional interactions with
a supporting substrate or gate insulator. We consider several models: The
weak-coupling limit, which is applicable only in the case of gapped and
parabolic materials, like stanene and HfSe$_{2}$, thanks to the weak coupling;
the full gap-equation, solved using the constant-gap approximation and
considering statically screened interactions; its extensions to
energy-dependent gap and to dynamic screening. We argue that in the case of
silicene and germanene superconductivity mediated by this process can exhibit a
critical temperature of a few degrees K, or even a few tens of degrees K when
accounting for the effect of a high-dielectric-constant environment. We
conclude that the electron/flexural-modes coupling should be included in
studies of possible superconductivity in non-$\sigma_{\rm h}$-symmetric
two-dimensional crystals, even if alternative forms of coupling are considered.
| cond-mat.supr-con | in twodimensional crystals that lack symmetry under reflections on the horizontal plane of the lattice nonsigma_rm hsymmetric electrons can couple to flexural modes za phonons at first order we show that in materials of this type that also exhibit a diraclike electron dispersion the strong coupling can result in electron pairing mediated by these phonons as long as the flexural modes are not damped or suppressed by additional interactions with a supporting substrate or gate insulator we consider several models the weakcoupling limit which is applicable only in the case of gapped and parabolic materials like stanene and hfse_2 thanks to the weak coupling the full gapequation solved using the constantgap approximation and considering statically screened interactions its extensions to energydependent gap and to dynamic screening we argue that in the case of silicene and germanene superconductivity mediated by this process can exhibit a critical temperature of a few degrees k or even a few tens of degrees k when accounting for the effect of a highdielectricconstant environment we conclude that the electronflexuralmodes coupling should be included in studies of possible superconductivity in nonsigma_rm hsymmetric twodimensional crystals even if alternative forms of coupling are considered | [['in', 'twodimensional', 'crystals', 'that', 'lack', 'symmetry', 'under', 'reflections', 'on', 'the', 'horizontal', 'plane', 'of', 'the', 'lattice', 'nonsigma_rm', 'hsymmetric', 'electrons', 'can', 'couple', 'to', 'flexural', 'modes', 'za', 'phonons', 'at', 'first', 'order', 'we', 'show', 'that', 'in', 'materials', 'of', 'this', 'type', 'that', 'also', 'exhibit', 'a', 'diraclike', 'electron', 'dispersion', 'the', 'strong', 'coupling', 'can', 'result', 'in', 'electron', 'pairing', 'mediated', 'by', 'these', 'phonons', 'as', 'long', 'as', 'the', 'flexural', 'modes', 'are', 'not', 'damped', 'or', 'suppressed', 'by', 'additional', 'interactions', 'with', 'a', 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1,802.06273 | Derivatives of Eisenstein series of weight 2 and intersections of
modular correspondences | We give a formula for certain values and derivatives of Siegel series and use
them to compute Fourier coefficients of derivatives of the Siegel Eisenstein
series of weight g/2 and genus g. When g=4, the Fourier coefficient is
approximated by a certain Fourier coefficient of the central derivative of the
Siegel Eisenstein series of weight 2 and genus 3, which is related to the
intersection of 3 arithmetic modular correspondences. Applications include a
relation between weighted averages of representation numbers of symmetric
matrices.
| math.NT | we give a formula for certain values and derivatives of siegel series and use them to compute fourier coefficients of derivatives of the siegel eisenstein series of weight g2 and genus g when g4 the fourier coefficient is approximated by a certain fourier coefficient of the central derivative of the siegel eisenstein series of weight 2 and genus 3 which is related to the intersection of 3 arithmetic modular correspondences applications include a relation between weighted averages of representation numbers of symmetric matrices | [['we', 'give', 'a', 'formula', 'for', 'certain', 'values', 'and', 'derivatives', 'of', 'siegel', 'series', 'and', 'use', 'them', 'to', 'compute', 'fourier', 'coefficients', 'of', 'derivatives', 'of', 'the', 'siegel', 'eisenstein', 'series', 'of', 'weight', 'g2', 'and', 'genus', 'g', 'when', 'g4', 'the', 'fourier', 'coefficient', 'is', 'approximated', 'by', 'a', 'certain', 'fourier', 'coefficient', 'of', 'the', 'central', 'derivative', 'of', 'the', 'siegel', 'eisenstein', 'series', 'of', 'weight', '2', 'and', 'genus', '3', 'which', 'is', 'related', 'to', 'the', 'intersection', 'of', '3', 'arithmetic', 'modular', 'correspondences', 'applications', 'include', 'a', 'relation', 'between', 'weighted', 'averages', 'of', 'representation', 'numbers', 'of', 'symmetric', 'matrices']] | [-0.20996562976681862, 0.05520070876634444, -0.1286128458485613, 0.05719499127569059, -0.10619578801140368, -0.0909170028416956, 0.004315523340939039, 0.2913809450545225, -0.3163592477878892, -0.2420126676211605, 0.11827536905184388, -0.2707795863634492, -0.1835888600852116, 0.24104448752365557, -0.07354526571278651, 0.08467219678943848, -0.006160370034939912, 0.09914365705910576, -0.14450640051838864, -0.3433752603245428, 0.3782599357192416, -0.04767163505732833, 0.16246166975652598, 0.01981503564684865, 0.10218675905300012, 0.01098552158842963, -0.062056318497711635, -0.14473344540470337, -0.11710565345339387, 0.22343391270926558, 0.30024649113057605, 0.015444974190589175, 0.21363825208496257, -0.3965686963624265, -0.10262117690283311, 0.13972372098276056, 0.08053231052493295, -0.09385508975064584, 0.06704392640974867, -0.17344915166676764, 0.037831597680966535, -0.16517282411994705, -0.16222466536831245, -0.1519150910927095, 0.08834631342710142, 0.05398406043469188, -0.3067527739892164, 0.11034407437476049, 0.046118328030815864, 0.15599328830434256, -0.038480158250060785, -0.16403153717113905, -0.0025522517242345466, 0.08839820811128053, 0.030672165772776646, 0.027147768845175762, 0.04057615964550031, -0.17127450524144863, -0.0726668049121298, 0.33003428874604673, -0.092193847752165, -0.19277294684784957, 0.06560309658792005, -0.2515884275458394, -0.15308811627777227, 0.11119684683136113, 0.11349732002400491, 0.15466818358771592, -0.00397515151995453, 0.14895716048945415, -0.05384121004895991, 0.10082723410009037, 0.21242817666336714, -0.05951356094510081, 0.13041327596384, -0.04480281697182232, 0.04166968403586063, 0.15467525366693735, -0.038295748304499944, -0.014457541088018208, -0.34514990422021913, -0.23338486706411624, -0.2106736961034048, 0.09922320712694382, -0.1931132398628495, -0.20901612470386527, 0.46299542776724123, 0.023356740374162972, 0.2205931850574372, 0.17185041311909502, 0.21460952626608581, 0.2061382008447166, 0.10300870612824717, 0.034112395180097545, 0.05622460521443422, 0.24933155361529305, -0.031505555991666864, -0.13374609105760255, -0.02883316583704226, 0.2340435917343361] |
1,802.06274 | Automatic Classification of Roof Shapes for Multicopter Emergency
Landing Site Selection | Geographic information systems (GIS) now provide accurate maps of terrain,
roads, waterways, and building footprints and heights. Aircraft, particularly
small unmanned aircraft systems, can exploit additional information such as
building roof structure to improve navigation accuracy and safety particularly
in urban regions. This paper proposes a method to automatically label building
roof shape types. Satellite imagery and LIDAR data from Witten, Germany are fed
to convolutional neural networks (CNN) to extract salient feature vectors.
Supervised training sets are automatically generated from pre-labeled buildings
contained in the OpenStreetMap database. Multiple CNN architectures are trained
and tested, with the best performing networks providing a condensed feature set
for support vector machine and decision tree classifiers. Satellite and LIDAR
data fusion is shown to provide greater classification accuracy than through
use of either data type individually.
| cs.RO | geographic information systems gis now provide accurate maps of terrain roads waterways and building footprints and heights aircraft particularly small unmanned aircraft systems can exploit additional information such as building roof structure to improve navigation accuracy and safety particularly in urban regions this paper proposes a method to automatically label building roof shape types satellite imagery and lidar data from witten germany are fed to convolutional neural networks cnn to extract salient feature vectors supervised training sets are automatically generated from prelabeled buildings contained in the openstreetmap database multiple cnn architectures are trained and tested with the best performing networks providing a condensed feature set for support vector machine and decision tree classifiers satellite and lidar data fusion is shown to provide greater classification accuracy than through use of either data type individually | [['geographic', 'information', 'systems', 'gis', 'now', 'provide', 'accurate', 'maps', 'of', 'terrain', 'roads', 'waterways', 'and', 'building', 'footprints', 'and', 'heights', 'aircraft', 'particularly', 'small', 'unmanned', 'aircraft', 'systems', 'can', 'exploit', 'additional', 'information', 'such', 'as', 'building', 'roof', 'structure', 'to', 'improve', 'navigation', 'accuracy', 'and', 'safety', 'particularly', 'in', 'urban', 'regions', 'this', 'paper', 'proposes', 'a', 'method', 'to', 'automatically', 'label', 'building', 'roof', 'shape', 'types', 'satellite', 'imagery', 'and', 'lidar', 'data', 'from', 'witten', 'germany', 'are', 'fed', 'to', 'convolutional', 'neural', 'networks', 'cnn', 'to', 'extract', 'salient', 'feature', 'vectors', 'supervised', 'training', 'sets', 'are', 'automatically', 'generated', 'from', 'prelabeled', 'buildings', 'contained', 'in', 'the', 'openstreetmap', 'database', 'multiple', 'cnn', 'architectures', 'are', 'trained', 'and', 'tested', 'with', 'the', 'best', 'performing', 'networks', 'providing', 'a', 'condensed', 'feature', 'set', 'for', 'support', 'vector', 'machine', 'and', 'decision', 'tree', 'classifiers', 'satellite', 'and', 'lidar', 'data', 'fusion', 'is', 'shown', 'to', 'provide', 'greater', 'classification', 'accuracy', 'than', 'through', 'use', 'of', 'either', 'data', 'type', 'individually']] | [-0.0747146451752983, 0.01988779075436742, -0.044521548622900524, 0.08547228992622542, -0.13851067683610477, -0.20083557891955292, 0.03828190909996208, 0.43681693593866394, -0.2553071707445092, -0.3396105287295479, 0.12978142490295372, -0.3038844351019514, -0.1745427537786501, 0.23263063156781227, -0.16207617085385032, 0.13078854333161188, 0.15900437450701638, 0.0286381763683569, 0.015389046467196448, -0.2594222387787361, 0.29002042513172654, 0.06776256261295394, 0.36446052120256245, -0.03587498195833506, 0.13383762409150796, -0.025462016617880147, -0.07123385406190291, -0.03638724373877609, -0.021907228672757913, 0.20953186827053533, 0.4199046897736931, 0.24173418932447308, 0.22821711978693038, -0.4390545460234459, -0.257512892421363, 0.11236768754031089, 0.13319880438835494, 0.07466916808564412, 0.006227454366652589, -0.409400824737504, 0.0680858090089256, -0.17393930627407744, -0.03460934193878386, -0.1502200274665217, -0.028037280501034968, 0.003991395690219295, -0.2806268720559363, -0.027028239626241356, 0.008857648280498228, 0.16442894079561407, -0.056955798205226324, -0.1116966305097523, -0.07274595145101106, 0.22604409987597088, -0.04722347859244205, 0.06931878837644774, 0.21501198897656418, -0.1600712764101397, -0.11349201207338042, 0.35362630911675613, 0.010155501395014912, -0.1981955299318481, 0.20488994923959436, -0.010532822803054986, -0.14502800229754775, 0.10130041659130413, 0.2853133631134896, 0.03466487266700995, -0.1987354729006844, -0.03554870778847052, -0.016507384517839306, 0.15529268152514628, 0.11155656640832767, -0.018850390904379965, 0.23792374863453852, 0.28039123365809593, 0.08168089499914165, 0.11086442883483562, -0.1866238026291524, -0.04962981716971284, -0.16079726003248707, -0.06630364851402451, -0.17459612007119826, -0.06710221863673874, -0.09282284166545282, -0.13767631725680357, 0.3418522653611083, 0.22960612939887756, 0.20909321393565575, 0.10266500550735377, 0.38038770308283937, -0.056088305305916276, 0.1482991127899491, 0.14225011140710317, 0.12174527805746722, -0.00012101971061158001, 0.1466748653315848, -0.06789458022081762, 0.058130930465842154, 0.09599088373264872] |
1,802.06275 | Protein motion in the nucleus: from anomalous diffusion to weak
interactions | Understanding how transcription factors (TFs) regulate mammalian gene
expression in space and time is a central topic in biology. To activate a gene,
a TF has first to diffuse in the available space of the nucleus until it
reaches a target DNA sequence or protein (target site). This eventually results
in the recruitment of the whole transcriptional machinery.
All these processes take place in the mammalian nucleoplasm, a highly
organized and dynamic environment, in which some complexes transiently assemble
and break apart, whereas others appear more stable. This diversity of dynamic
behaviors arises from the number of biomolecules that make up the nucleoplasm
and their pairwise interactions. Indeed, interactions energies that span
several orders of magnitude, from covalent bounds to transient and dynamic
interactions can shape nuclear landscapes. Thus, the nuclear environment
determines how frequently and how fast a TF contacts its target site, and
indirectly gene expression. How exactly transient interactions are involved in
the regulation of TF diffusion is unclear, but are reflected by live cell
imaging techniques such as fluorescence correlation spectroscopy, fluorescence
recovery after photobleaching or single-particle tracking. Overall, the
macroscopic result of these microscopic interactions is almost always anomalous
diffusion, a phenomenon widely studied and modeled.
Here, we review the connections between the anomalous diffusion of a TF and
the microscopic organization of the nucleus, including recently described
topologically associated domains and dynamic phase-separated compartments. We
propose that anomalous diffusion found in single particle tracking (SPT) data
result from weak and transient interactions with dynamic nuclear substructures,
and that SPT data analysis would benefit form a better description of such
structures.
| q-bio.SC physics.bio-ph | understanding how transcription factors tfs regulate mammalian gene expression in space and time is a central topic in biology to activate a gene a tf has first to diffuse in the available space of the nucleus until it reaches a target dna sequence or protein target site this eventually results in the recruitment of the whole transcriptional machinery all these processes take place in the mammalian nucleoplasm a highly organized and dynamic environment in which some complexes transiently assemble and break apart whereas others appear more stable this diversity of dynamic behaviors arises from the number of biomolecules that make up the nucleoplasm and their pairwise interactions indeed interactions energies that span several orders of magnitude from covalent bounds to transient and dynamic interactions can shape nuclear landscapes thus the nuclear environment determines how frequently and how fast a tf contacts its target site and indirectly gene expression how exactly transient interactions are involved in the regulation of tf diffusion is unclear but are reflected by live cell imaging techniques such as fluorescence correlation spectroscopy fluorescence recovery after photobleaching or singleparticle tracking overall the macroscopic result of these microscopic interactions is almost always anomalous diffusion a phenomenon widely studied and modeled here we review the connections between the anomalous diffusion of a tf and the microscopic organization of the nucleus including recently described topologically associated domains and dynamic phaseseparated compartments we propose that anomalous diffusion found in single particle tracking spt data result from weak and transient interactions with dynamic nuclear substructures and that spt data analysis would benefit form a better description of such structures | [['understanding', 'how', 'transcription', 'factors', 'tfs', 'regulate', 'mammalian', 'gene', 'expression', 'in', 'space', 'and', 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1,802.06276 | Interactive Estimation of the Fractal Properties of Carbonate Rocks | Scale invariance of intrinsic patterns is an important concept in geology
that can be observed in numerous geological objects and phenomena. These
geological objects and phenomena are described as containing statistically
selfsimilar patterns often modeled with fractal geometry. Fractal geometry has
been used extensively to characterize pore space and fracture distribution of
both carbonate and clastic rocks as well as the transport properties of porous
media and fluid flow in reservoirs. The fractal properties are usually
estimated from thin-section photomicrograph images or scanning electron
microscope images. For complex rock such as carbonate rocks, automatic feature
detection methods are often inaccurate. In addition, the rocks may be have been
subjected to facies selective diagenesis which preferentially affect some of
the rock fabric, thus increasing the difficulty in automatic detection of
certain features. We present an interactive program, GeoBoxCount, for analyzing
thin-section images and calculating the fractal dimension interactively. The
program relies on the geologists insight in interpreting the features of
interest; this significantly improves the accuracy of feature selection. The
program provides two options for calculating the fractal dimension: the
Hausdorff and the Minkowsi-Bouligand box-counting methods.
| physics.geo-ph | scale invariance of intrinsic patterns is an important concept in geology that can be observed in numerous geological objects and phenomena these geological objects and phenomena are described as containing statistically selfsimilar patterns often modeled with fractal geometry fractal geometry has been used extensively to characterize pore space and fracture distribution of both carbonate and clastic rocks as well as the transport properties of porous media and fluid flow in reservoirs the fractal properties are usually estimated from thinsection photomicrograph images or scanning electron microscope images for complex rock such as carbonate rocks automatic feature detection methods are often inaccurate in addition the rocks may be have been subjected to facies selective diagenesis which preferentially affect some of the rock fabric thus increasing the difficulty in automatic detection of certain features we present an interactive program geoboxcount for analyzing thinsection images and calculating the fractal dimension interactively the program relies on the geologists insight in interpreting the features of interest this significantly improves the accuracy of feature selection the program provides two options for calculating the fractal dimension the hausdorff and the minkowsibouligand boxcounting methods | [['scale', 'invariance', 'of', 'intrinsic', 'patterns', 'is', 'an', 'important', 'concept', 'in', 'geology', 'that', 'can', 'be', 'observed', 'in', 'numerous', 'geological', 'objects', 'and', 'phenomena', 'these', 'geological', 'objects', 'and', 'phenomena', 'are', 'described', 'as', 'containing', 'statistically', 'selfsimilar', 'patterns', 'often', 'modeled', 'with', 'fractal', 'geometry', 'fractal', 'geometry', 'has', 'been', 'used', 'extensively', 'to', 'characterize', 'pore', 'space', 'and', 'fracture', 'distribution', 'of', 'both', 'carbonate', 'and', 'clastic', 'rocks', 'as', 'well', 'as', 'the', 'transport', 'properties', 'of', 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1,802.06277 | Neutron lifetime, dark matter and search for sterile neutrino | A review is focused on experimental measurements on neutron lifetime. The
latest measurements with a gravitational trap (PNPI NRC KI) and a magnetic trap
(LANL, USA) confirmed PNPI result of 2005. The results of measurements with
storage of ultra cold neutrons are in agreement, yet, there is discrepancy with
a beam experiment by $3.5{\sigma}$ (1% of decay probability), which is
discussed in literature as "neutron anomaly" along with the ideas of explaining
it by decay into dark matter partially. The second part of the paper is devoted
to so called "reactor antineutrino anomaly", which refers to deficiency of the
measured flux of antineutrino from reactor in respect to the calculated flux by
$3{\sigma}$(deviation by 6.6%). Specific feature of the proposal in this paper
lies in the fact that both anomalies can be accounted for by one and the same
phenomenon of oscillation in baryon sector between a neutron and a neutron of
dark matter $n{\rightarrow}n'$ with mass $m_{n'}$, somewhat less than mass
$m_n$ of an ordinary neutron. Calculations of the proposed model require one
free parameter: mass difference $m_n-m_{n'}$ if one normalizes probability of
oscillations for a free neutron on "neutron anomaly" 1%, then, having succeeded
to interpret 6.6% of neutron anomaly in calculations, one can determine mass
difference. According to preliminary estimations, the mass difference is
$m_n-m_{n'}{\approx}$ 3 MeV. However, the analysis of cumulative yields of
isotopes occurs in fission fragments was performed and it does not confirm
possibility of existence of additional decay channel with emission of dark
matter neutron with mass difference $m_n-m_{n'}{\approx}$ 3 MeV. The result of
the analysis is the conclusion that for mirror neutrons the region of the mass
difference $m_n-m_{n'} {\geq}$ 3 MeV is closed. The region of the mass
difference $m_n-m_{n'}{\leq}$ 2 MeV turned out to be not closed.
| nucl-ex | a review is focused on experimental measurements on neutron lifetime the latest measurements with a gravitational trap pnpi nrc ki and a magnetic trap lanl usa confirmed pnpi result of 2005 the results of measurements with storage of ultra cold neutrons are in agreement yet there is discrepancy with a beam experiment by 35sigma 1 of decay probability which is discussed in literature as neutron anomaly along with the ideas of explaining it by decay into dark matter partially the second part of the paper is devoted to so called reactor antineutrino anomaly which refers to deficiency of the measured flux of antineutrino from reactor in respect to the calculated flux by 3sigmadeviation by 66 specific feature of the proposal in this paper lies in the fact that both anomalies can be accounted for by one and the same phenomenon of oscillation in baryon sector between a neutron and a neutron of dark matter nrightarrown with mass m_n somewhat less than mass m_n of an ordinary neutron calculations of the proposed model require one free parameter mass difference m_nm_n if one normalizes probability of oscillations for a free neutron on neutron anomaly 1 then having succeeded to interpret 66 of neutron anomaly in calculations one can determine mass difference according to preliminary estimations the mass difference is m_nm_napprox 3 mev however the analysis of cumulative yields of isotopes occurs in fission fragments was performed and it does not confirm possibility of existence of additional decay channel with emission of dark matter neutron with mass difference m_nm_napprox 3 mev the result of the analysis is the conclusion that for mirror neutrons the region of the mass difference m_nm_n geq 3 mev is closed the region of the mass difference m_nm_nleq 2 mev turned out to be not closed | [['a', 'review', 'is', 'focused', 'on', 'experimental', 'measurements', 'on', 'neutron', 'lifetime', 'the', 'latest', 'measurements', 'with', 'a', 'gravitational', 'trap', 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1,802.06278 | A discrete extrinsic and intrinsic Dirac operator | In differential geometry of surfaces the Dirac operator appears intrinsically
as a tool to address the immersion problem as well as in an extrinsic flavour
(that comes with spin transformations to comformally transfrom immersions) and
the two are naturally related.
In this paper we consider a corresponding pair of discrete Dirac operators,
the latter on discrete surfaces with polygonal faces and normals defined on
each face, and show that many key properties of the smooth theory are
preserved. In particular, the corresponding spin transformations, conformal
invariants for them, and the relation between this operator and its intrinsic
counterpart are discussed.
| math.DG | in differential geometry of surfaces the dirac operator appears intrinsically as a tool to address the immersion problem as well as in an extrinsic flavour that comes with spin transformations to comformally transfrom immersions and the two are naturally related in this paper we consider a corresponding pair of discrete dirac operators the latter on discrete surfaces with polygonal faces and normals defined on each face and show that many key properties of the smooth theory are preserved in particular the corresponding spin transformations conformal invariants for them and the relation between this operator and its intrinsic counterpart are discussed | [['in', 'differential', 'geometry', 'of', 'surfaces', 'the', 'dirac', 'operator', 'appears', 'intrinsically', 'as', 'a', 'tool', 'to', 'address', 'the', 'immersion', 'problem', 'as', 'well', 'as', 'in', 'an', 'extrinsic', 'flavour', 'that', 'comes', 'with', 'spin', 'transformations', 'to', 'comformally', 'transfrom', 'immersions', 'and', 'the', 'two', 'are', 'naturally', 'related', 'in', 'this', 'paper', 'we', 'consider', 'a', 'corresponding', 'pair', 'of', 'discrete', 'dirac', 'operators', 'the', 'latter', 'on', 'discrete', 'surfaces', 'with', 'polygonal', 'faces', 'and', 'normals', 'defined', 'on', 'each', 'face', 'and', 'show', 'that', 'many', 'key', 'properties', 'of', 'the', 'smooth', 'theory', 'are', 'preserved', 'in', 'particular', 'the', 'corresponding', 'spin', 'transformations', 'conformal', 'invariants', 'for', 'them', 'and', 'the', 'relation', 'between', 'this', 'operator', 'and', 'its', 'intrinsic', 'counterpart', 'are', 'discussed']] | [-0.11922517794716571, 0.10398176870661033, -0.03485767298130965, 0.09537532695388536, -0.0629318994707523, -0.1308051461059296, -0.04581960644668006, 0.38953169862198056, -0.32522124793304474, -0.26930617900299175, 0.11844331950272377, -0.30535225843421504, -0.20087481753862635, 0.16982119654932495, -0.1149381295473752, 0.06892521608067315, 0.004074307709780275, 0.036306471770572266, -0.13991437786334784, -0.19277897581923753, 0.39991869401585844, -0.04779876130918155, 0.24908115256017035, 0.07374137789201067, 0.11935267175015594, 0.025921274600217918, -0.010194362492813748, 0.018444813041928774, -0.0814114491990114, 0.15258690971781366, 0.24614020138598827, 0.042777572397370726, 0.15610150419346683, -0.41469158570529246, -0.1757855703270215, 0.09499608721032891, 0.1060364453777747, 0.05734295395858662, -0.04877807012621351, -0.28464891074572174, 0.05783193358172643, -0.0846144857683352, -0.15314032868197075, -0.06605956430635439, -0.0031415664267783264, -0.016780280574624027, -0.191141577035412, 0.05358266359318656, 0.10863173147782265, 0.054690633681887875, -0.06224069970170492, -0.07231020389542896, -0.09738677180590755, 0.12582079911775582, 0.07548534136849969, 0.02820159550471117, 0.08112259350279916, -0.12371346388221244, -0.1489506549182899, 0.43465717836302153, -0.05986769677006772, -0.29013135932309897, 0.18551285247489505, -0.13030739278741638, -0.1289737419633917, 0.05304356455821924, 0.1566330688264297, 0.1430134796941349, -0.14912956413261744, 0.14485834267795827, -0.04961754623515417, 0.07052013277057179, 0.05927704991677738, 0.0543827557062008, 0.21227124612778425, 0.08827067119763138, 0.1043596171425912, 0.1434589992372357, -0.035765116473918834, -0.10671927484835746, -0.35396464638488023, -0.20590364418885843, -0.15020054267073163, 0.0584668443652288, -0.08788468789387072, -0.18894055098703855, 0.40360108833303865, 0.07358345622360131, 0.2645104869021749, 0.041145077716483146, 0.24207382939983996, 0.12900033170875275, 0.060697832083025455, 0.03687603671901992, 0.17704431806353624, 0.16455402810658729, 0.047322834389312764, -0.20022401061830852, -0.006729131906616444, 0.11476411680662435] |
1,802.06279 | Statistical Reasoning: Choosing and Checking the Ingredients, Inferences
Based on a Measure of Statistical Evidence with Some Applications | The features of a logically sound approach to a theory of statistical
reasoning are discussed. A particular approach that satisfies these criteria is
reviewed. This is seen to involve selection of a model, model checking,
elicitation of a prior, checking the prior for bias, checking for prior-data
conflict and estimation and hypothesis assessment inferences based on a measure
of evidence. A long-standing anomalous example is resolved by this approach to
inference and an application is made to a practical problem of considerable
importance which, among other novel aspects of the analysis, involves the
development of a relevant elicitation algorithm.
| math.ST stat.TH | the features of a logically sound approach to a theory of statistical reasoning are discussed a particular approach that satisfies these criteria is reviewed this is seen to involve selection of a model model checking elicitation of a prior checking the prior for bias checking for priordata conflict and estimation and hypothesis assessment inferences based on a measure of evidence a longstanding anomalous example is resolved by this approach to inference and an application is made to a practical problem of considerable importance which among other novel aspects of the analysis involves the development of a relevant elicitation algorithm | [['the', 'features', 'of', 'a', 'logically', 'sound', 'approach', 'to', 'a', 'theory', 'of', 'statistical', 'reasoning', 'are', 'discussed', 'a', 'particular', 'approach', 'that', 'satisfies', 'these', 'criteria', 'is', 'reviewed', 'this', 'is', 'seen', 'to', 'involve', 'selection', 'of', 'a', 'model', 'model', 'checking', 'elicitation', 'of', 'a', 'prior', 'checking', 'the', 'prior', 'for', 'bias', 'checking', 'for', 'priordata', 'conflict', 'and', 'estimation', 'and', 'hypothesis', 'assessment', 'inferences', 'based', 'on', 'a', 'measure', 'of', 'evidence', 'a', 'longstanding', 'anomalous', 'example', 'is', 'resolved', 'by', 'this', 'approach', 'to', 'inference', 'and', 'an', 'application', 'is', 'made', 'to', 'a', 'practical', 'problem', 'of', 'considerable', 'importance', 'which', 'among', 'other', 'novel', 'aspects', 'of', 'the', 'analysis', 'involves', 'the', 'development', 'of', 'a', 'relevant', 'elicitation', 'algorithm']] | [-0.08881664200394292, 0.0009375694691441069, -0.13176553508247993, 0.11166973895689641, -0.14162603657542153, -0.17066989964664434, 0.09821520847705842, 0.36558344784061036, -0.23359476544452135, -0.3503269162781612, 0.1293686467335757, -0.21285329144824333, -0.1651582657312504, 0.18753563727762532, -0.08654563723959857, 0.08026335059872104, 0.08062684376761693, 0.03066123572338109, -0.04653466530285324, -0.19859812410096778, 0.30148419662322284, 0.04691661434304534, 0.3222063486793577, 0.0678007698693134, 0.12455614586831118, 0.028294318654117905, -0.0860411976487611, 0.05323669419746206, -0.12026690674813129, 0.18797707741943687, 0.27113041193271525, 0.24735788297792427, 0.3584965103202396, -0.3521853176632313, -0.2500975075921025, 0.08937057621325507, 0.09573417835671341, 0.11401795436783384, -0.04936670901070377, -0.2923204537970249, 0.07014655269155598, -0.1739613980274163, -0.09162685513552843, -0.09610457844430148, 0.016789634853149906, -0.045128628037719415, -0.30619050771223777, 0.05546595793066904, 0.1274830780716406, 0.07306904849982021, -0.03816387245450357, -0.07891032266703368, 0.06211885357877672, 0.08087317291835341, 0.0848597581898135, 0.0035358852556332796, 0.1088892489982148, -0.1428657892733963, -0.17344241422798598, 0.383494645799275, 0.018191475637558133, -0.19909512314409245, 0.17944132272539792, -0.028739116789603777, -0.2083616617718956, 0.0755864802061232, 0.1275418716097119, 0.11025474145726273, -0.18409527860572467, 0.048113532099200204, -0.040636066417915354, 0.19976567714992496, -0.013063377383249726, -0.03133147538667827, 0.2266314109585088, 0.2580165319083578, 0.034837635826658116, 0.10963734965642086, -0.047748978755370985, -0.09945461943726798, -0.3254895755207411, -0.16663815335116602, -0.1484557443317214, -0.01672506352975927, -0.08066576155272402, -0.20409066166089038, 0.3682778332608216, 0.23173529585127275, 0.16404553750680403, 0.03948719356552406, 0.3450242874748779, 0.1122220223910655, 0.02460624684894668, -0.004799415355059083, 0.1890153829102414, 0.13431052664398319, 0.05358386609051847, -0.1784547198942458, 0.16733853481333666, 0.027643392925536393] |
1,802.0628 | Spatial field reconstruction with INLA: Application to IFU galaxy data | Astronomical observations of extended sources, such as cubes of integral
field spectroscopy (IFS), encode auto-correlated spatial structures that cannot
be optimally exploited by standard methodologies. This work introduces a novel
technique to model IFS datasets, which treats the observed galaxy properties as
realizations of an unobserved Gaussian Markov random field. The method is
computationally efficient, resilient to the presence of low-signal-to-noise
regions, and uses an alternative to Markov Chain Monte Carlo for fast Bayesian
inference, the Integrated Nested Laplace Approximation (INLA). As a case study,
we analyse 721 IFS data cubes of nearby galaxies from the CALIFA and PISCO
surveys, for which we retrieve the maps of the following physical properties:
age, metallicity, mass and extinction. The proposed Bayesian approach, built on
a generative representation of the galaxy properties, enables the creation of
synthetic images, recovery of areas with bad pixels, and an increased power to
detect structures in datasets subject to substantial noise and/or sparsity of
sampling. A snippet code to reproduce the analysis of this paper is available
in the COIN toolbox, together with the field reconstructions of the CALIFA and
PISCO samples.
| astro-ph.IM astro-ph.GA | astronomical observations of extended sources such as cubes of integral field spectroscopy ifs encode autocorrelated spatial structures that cannot be optimally exploited by standard methodologies this work introduces a novel technique to model ifs datasets which treats the observed galaxy properties as realizations of an unobserved gaussian markov random field the method is computationally efficient resilient to the presence of lowsignaltonoise regions and uses an alternative to markov chain monte carlo for fast bayesian inference the integrated nested laplace approximation inla as a case study we analyse 721 ifs data cubes of nearby galaxies from the califa and pisco surveys for which we retrieve the maps of the following physical properties age metallicity mass and extinction the proposed bayesian approach built on a generative representation of the galaxy properties enables the creation of synthetic images recovery of areas with bad pixels and an increased power to detect structures in datasets subject to substantial noise andor sparsity of sampling a snippet code to reproduce the analysis of this paper is available in the coin toolbox together with the field reconstructions of the califa and pisco samples | [['astronomical', 'observations', 'of', 'extended', 'sources', 'such', 'as', 'cubes', 'of', 'integral', 'field', 'spectroscopy', 'ifs', 'encode', 'autocorrelated', 'spatial', 'structures', 'that', 'can', 'not', 'be', 'optimally', 'exploited', 'by', 'standard', 'methodologies', 'this', 'work', 'introduces', 'a', 'novel', 'technique', 'to', 'model', 'ifs', 'datasets', 'which', 'treats', 'the', 'observed', 'galaxy', 'properties', 'as', 'realizations', 'of', 'an', 'unobserved', 'gaussian', 'markov', 'random', 'field', 'the', 'method', 'is', 'computationally', 'efficient', 'resilient', 'to', 'the', 'presence', 'of', 'lowsignaltonoise', 'regions', 'and', 'uses', 'an', 'alternative', 'to', 'markov', 'chain', 'monte', 'carlo', 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1,802.06281 | Representations of the inverse hull of a 0-left cancellative semigroup | A semigroup S containing a zero element is said to be 0-left cancellative if
st = sr \neq 0 implies that t = r. Given such an S we build an inverse
semigroup H(S), called the inverse hull of S. Motivated by the study of certain
C*-algebras associated to H(S) (a task that we will address in a subsequent
article) we carry out a detailed analysis of the spectrum of the idempotent
semilattice E(S) of H(S) with a special interest in identifying the
ultra-characters. In order to produce examples of characters on E(S), we
introduce the notion of "strings" in a semigroup, attempting to make sense of
the "infinite paths" which are fundamental in the study of graph C*-algebras.
Our strongest results are obtained under the assumption that S admits "least
common multiples", but we also touch upon the notion of "finite alignment",
motivated by the corresponding notion from the theory of higher rank graphs,
and which has also appeared in recent papers by Spielberg and collaborators.
| math.OA math.DS | a semigroup s containing a zero element is said to be 0left cancellative if st sr neq 0 implies that t r given such an s we build an inverse semigroup hs called the inverse hull of s motivated by the study of certain calgebras associated to hs a task that we will address in a subsequent article we carry out a detailed analysis of the spectrum of the idempotent semilattice es of hs with a special interest in identifying the ultracharacters in order to produce examples of characters on es we introduce the notion of strings in a semigroup attempting to make sense of the infinite paths which are fundamental in the study of graph calgebras our strongest results are obtained under the assumption that s admits least common multiples but we also touch upon the notion of finite alignment motivated by the corresponding notion from the theory of higher rank graphs and which has also appeared in recent papers by spielberg and collaborators | [['a', 'semigroup', 's', 'containing', 'a', 'zero', 'element', 'is', 'said', 'to', 'be', '0left', 'cancellative', 'if', 'st', 'sr', 'neq', '0', 'implies', 'that', 't', 'r', 'given', 'such', 'an', 's', 'we', 'build', 'an', 'inverse', 'semigroup', 'hs', 'called', 'the', 'inverse', 'hull', 'of', 's', 'motivated', 'by', 'the', 'study', 'of', 'certain', 'calgebras', 'associated', 'to', 'hs', 'a', 'task', 'that', 'we', 'will', 'address', 'in', 'a', 'subsequent', 'article', 'we', 'carry', 'out', 'a', 'detailed', 'analysis', 'of', 'the', 'spectrum', 'of', 'the', 'idempotent', 'semilattice', 'es', 'of', 'hs', 'with', 'a', 'special', 'interest', 'in', 'identifying', 'the', 'ultracharacters', 'in', 'order', 'to', 'produce', 'examples', 'of', 'characters', 'on', 'es', 'we', 'introduce', 'the', 'notion', 'of', 'strings', 'in', 'a', 'semigroup', 'attempting', 'to', 'make', 'sense', 'of', 'the', 'infinite', 'paths', 'which', 'are', 'fundamental', 'in', 'the', 'study', 'of', 'graph', 'calgebras', 'our', 'strongest', 'results', 'are', 'obtained', 'under', 'the', 'assumption', 'that', 's', 'admits', 'least', 'common', 'multiples', 'but', 'we', 'also', 'touch', 'upon', 'the', 'notion', 'of', 'finite', 'alignment', 'motivated', 'by', 'the', 'corresponding', 'notion', 'from', 'the', 'theory', 'of', 'higher', 'rank', 'graphs', 'and', 'which', 'has', 'also', 'appeared', 'in', 'recent', 'papers', 'by', 'spielberg', 'and', 'collaborators']] | [-0.13048568858045148, 0.11849552904588864, -0.06482393632391525, 0.0449654843945319, -0.07401750789972399, -0.11980232458984467, 0.0386761369639322, 0.3432515457478221, -0.3072908576092243, -0.20987016247139753, 0.11292213018543652, -0.31373097305186093, -0.13369233863988172, 0.16245820818388937, -0.11185964620015099, -0.0009632043353356192, 0.06105081965872572, 0.1175278107538545, -0.09528575295530992, -0.22826670738386326, 0.3475914241430836, 0.034554307731839776, 0.20242826855010096, 0.037389861026627405, 0.08664051382202495, 0.006793739434125964, -0.07071479169549735, 0.046263759232988935, -0.16547743074000104, 0.1275305631741927, 0.2816530474021665, 0.1382746750008932, 0.28492784732952714, -0.3900478161597179, -0.15531610427252904, 0.17808934446549946, 0.09124447173079007, 0.02307219911320992, -0.021981453318063147, -0.250741139374262, 0.15779797316591707, -0.17805406355093364, -0.10745320738396076, -0.059918950794641775, 0.09510444624969404, -0.006275070702622822, -0.26202299751832714, 0.011997578725250199, 0.15219666134134688, 0.07056070917908765, -0.02934509046101497, -0.09342977919107291, -0.0363830990055176, 0.05357209871883041, 0.03836862721331875, 0.02982597334161858, 0.049978169736143864, -0.0700880583758506, -0.13144138253199655, 0.35484066544432036, -0.049393386715034626, -0.16817137246602928, 0.16597913245190551, -0.15071498126682878, -0.16319136806346612, 0.10838300963660896, 0.08966789828853373, 0.15582884129074664, -0.09126469533479104, 0.17258693824845017, -0.11425816321722661, 0.1329671545954067, 0.06733139083246512, 0.017578274385855058, 0.16308070722670276, 0.11765959990307963, 0.0837388052033729, 0.1635504329464244, -0.002798095678673497, -0.03031583485747773, -0.3252909666304391, -0.1554313507673839, -0.15827998994032572, 0.10036095291836061, -0.03523265113579138, -0.18205145743041318, 0.3946012366231691, 0.14709684865812034, 0.20134644767418047, 0.03810734236384371, 0.18038366588855312, 0.09656618713426969, 0.031332443482556774, 0.07133423341288192, 0.14248954425880148, 0.1621902830130278, 0.02562588890100275, -0.16757300537739497, 0.018220237580533684, 0.15242166718913733] |
1,802.06282 | Large Rank-Based Models with Common Noise | For large systems of Brownian particles interacting through their ranks
introduced in (Banner, Fernholz, Karatzas, 2005), the empirical cumulative
distribution function satisfies a porous medium PDE. However, when we introduce
a common noise, the limit is no longer deterministic. Instead, we show that
this limit is a solution of a stochastic PDE related to this porous medium PDE.
This stochastic PDE is somewhat similar to the equations developed for
conservation laws with rough stochastic fluxes (Lions, Perthame, Souganidis,
2013).
| math.PR | for large systems of brownian particles interacting through their ranks introduced in banner fernholz karatzas 2005 the empirical cumulative distribution function satisfies a porous medium pde however when we introduce a common noise the limit is no longer deterministic instead we show that this limit is a solution of a stochastic pde related to this porous medium pde this stochastic pde is somewhat similar to the equations developed for conservation laws with rough stochastic fluxes lions perthame souganidis 2013 | [['for', 'large', 'systems', 'of', 'brownian', 'particles', 'interacting', 'through', 'their', 'ranks', 'introduced', 'in', 'banner', 'fernholz', 'karatzas', '2005', 'the', 'empirical', 'cumulative', 'distribution', 'function', 'satisfies', 'a', 'porous', 'medium', 'pde', 'however', 'when', 'we', 'introduce', 'a', 'common', 'noise', 'the', 'limit', 'is', 'no', 'longer', 'deterministic', 'instead', 'we', 'show', 'that', 'this', 'limit', 'is', 'a', 'solution', 'of', 'a', 'stochastic', 'pde', 'related', 'to', 'this', 'porous', 'medium', 'pde', 'this', 'stochastic', 'pde', 'is', 'somewhat', 'similar', 'to', 'the', 'equations', 'developed', 'for', 'conservation', 'laws', 'with', 'rough', 'stochastic', 'fluxes', 'lions', 'perthame', 'souganidis', '2013']] | [-0.09316566110223154, 0.08434274637057812, -0.12069421094146711, 0.09501878206346985, -0.09764310450564269, -0.12765693948663112, 0.022563873636948912, 0.2890463086122152, -0.28979656943179005, -0.2788239414744739, 0.09016309201546438, -0.3214077465236187, -0.16646686620137927, 0.11696645054565388, -0.1783892727232998, 0.1093411808479813, 0.04291573915441957, -0.10496937563713593, 0.022012062963169023, -0.2366401505202668, 0.27545536402612925, 0.016543039294619914, 0.22877620391641992, -0.021935037337243557, 0.19542965876775545, 0.016325853517444075, -0.04847138351466082, 0.03929283176666668, -0.177822471635075, 0.07032314789351783, 0.22076649890764605, 0.031640099420483354, 0.32534670006908195, -0.4218134552098905, -0.24909580159413663, 0.10908612503988456, 0.09956292268750848, 0.07503337611420717, -0.04066085068013849, -0.27642712587066276, 0.021849350019393466, -0.13543184874932976, -0.17293543931644975, -0.013316466378731818, 0.08505643692009057, 0.09284297407964315, -0.29984356368644327, 0.134590516491687, 0.10736296313248953, 0.0301856594012838, -0.052250197033480376, -0.0817121447217238, -0.020462801008001914, 0.005184307587229871, 0.04138729217922008, -0.03854735206836198, 0.11760277314040857, -0.09842315258729402, -0.08755566802205919, 0.3469997868531301, -0.10694832733354849, -0.2661700595547504, 0.2109523546422207, -0.10052463715848854, -0.14563267956977075, 0.16237564693161588, 0.16729244936988513, 0.1422687122963746, -0.2706177050368993, 0.10614531387020892, -0.11696985165906858, 0.15637599613482156, 0.07342919931287252, -0.06828558006577336, 0.06018440770810541, 0.19926467137941736, 0.13199336924649116, 0.1051985231652573, 0.02440699292464724, -0.16564270896436292, -0.27792308493694173, -0.16963315506383211, -0.15571469263633406, 0.13727107005123096, -0.1156167803167165, -0.17970458817184914, 0.2857798751844447, 0.14030502733621228, 0.12238865993516165, 0.12788257688354654, 0.21942339117544454, 0.2298065185888753, -0.06115020478816255, 0.14234440888667219, 0.23269623492054547, 0.18300729549100883, 0.24486010266420774, -0.16992755479450466, 0.06685219958566035, 0.12110073005049667] |
1,802.06283 | Almost Sure Productivity | We define Almost Sure Productivity (ASP), a probabilistic generalization of
the productivity condition for coinductively defined structures. Intuitively, a
probabilistic coinductive stream or tree is ASP if it produces infinitely many
outputs with probability 1. Formally, we define almost sure productivity using
a final coalgebra semantics of programs inspired from Kerstan and K\"onig.
Then, we introduce a core language for probabilistic streams and trees, and
provide two approaches to verify ASP: a sufficient syntactic criterion, and a
reduction to model-checking pCTL* formulas on probabilistic pushdown automata.
The reduction shows that ASP is decidable for our core language.
| cs.PL cs.LO | we define almost sure productivity asp a probabilistic generalization of the productivity condition for coinductively defined structures intuitively a probabilistic coinductive stream or tree is asp if it produces infinitely many outputs with probability 1 formally we define almost sure productivity using a final coalgebra semantics of programs inspired from kerstan and konig then we introduce a core language for probabilistic streams and trees and provide two approaches to verify asp a sufficient syntactic criterion and a reduction to modelchecking pctl formulas on probabilistic pushdown automata the reduction shows that asp is decidable for our core language | [['we', 'define', 'almost', 'sure', 'productivity', 'asp', 'a', 'probabilistic', 'generalization', 'of', 'the', 'productivity', 'condition', 'for', 'coinductively', 'defined', 'structures', 'intuitively', 'a', 'probabilistic', 'coinductive', 'stream', 'or', 'tree', 'is', 'asp', 'if', 'it', 'produces', 'infinitely', 'many', 'outputs', 'with', 'probability', '1', 'formally', 'we', 'define', 'almost', 'sure', 'productivity', 'using', 'a', 'final', 'coalgebra', 'semantics', 'of', 'programs', 'inspired', 'from', 'kerstan', 'and', 'konig', 'then', 'we', 'introduce', 'a', 'core', 'language', 'for', 'probabilistic', 'streams', 'and', 'trees', 'and', 'provide', 'two', 'approaches', 'to', 'verify', 'asp', 'a', 'sufficient', 'syntactic', 'criterion', 'and', 'a', 'reduction', 'to', 'modelchecking', 'pctl', 'formulas', 'on', 'probabilistic', 'pushdown', 'automata', 'the', 'reduction', 'shows', 'that', 'asp', 'is', 'decidable', 'for', 'our', 'core', 'language']] | [-0.1047097398841288, 0.039100796417187667, -0.09830659690972728, 0.19559742646379163, -0.18405505473492667, -0.1693265104064873, 0.12904591019711611, 0.3738489353951688, -0.3375577427796088, -0.2735566473226451, 0.0684605664088546, -0.2353709105955204, -0.12258631042641355, 0.16955676051778332, -0.15736904495300527, 0.10892417070863303, 0.1409399245555202, 0.06256310019216471, 0.016793438469903776, -0.26689518052201794, 0.2860926103215509, -0.015103667889585873, 0.2268836857159234, 0.04652339057308078, 0.14084073436962777, 0.038631144401733764, -0.0026548111127340235, 0.07016412867233157, -0.10275360208220263, 0.1211764594821337, 0.3868505194817165, 0.3409232054206465, 0.3015279697040872, -0.3949229898765528, -0.07669833156978711, 0.06276501696750832, 0.10103501829386612, 0.07834842493321048, 0.024881576676610468, -0.2704276550648501, 0.13197936848155223, -0.23119579891984662, -0.006029448649011708, -0.11082733085883471, 0.05556613678345457, -0.018274456834963832, -0.28678696335555287, -0.07820150531673183, 0.2379949772633457, 0.12305576652170203, -0.02093813595032164, -0.0879595454025548, -0.04678226707377083, 0.05058363419326876, -0.039272710336566284, 0.007290808023147595, 0.10034323401729732, -0.05292620714317309, -0.26464423346624244, 0.3320006967566466, -0.08844000974689455, -0.21974186671044058, 0.23066976667420627, -0.0314858686348695, -0.21538672639386883, 0.06934906794049311, 0.08680426914361306, 0.08712782072446619, -0.16063537128987568, 0.11732678181579104, -0.0934811830908681, 0.2435993229058416, 0.17031673011175977, 0.008900686295722457, 0.17507096362533048, 0.22897286919275453, 0.10548942014126321, 0.1983416692043344, 0.09037081515270984, -0.07531797407379297, -0.25633577511568245, -0.1982555153372232, -0.048272951799541865, -0.019826728113306064, -0.09366959591898194, -0.28667495045616914, 0.3398760903122214, 0.1983179382320183, 0.11023768568217444, 0.2874438701837789, 0.3195489715387036, 0.1351690203622032, 0.06951344835882385, 0.09885181154459133, 0.06708811173787883, 0.14917018183041364, 0.1125041984487325, -0.11379702689373516, 0.17342044661684972, 0.15228612596304933] |
1,802.06284 | Similarities on Graphs: Kernels versus Proximity Measures | We analytically study proximity and distance properties of various kernels
and similarity measures on graphs. This helps to understand the mathematical
nature of such measures and can potentially be useful for recommending the
adoption of specific similarity measures in data analysis.
| math.CO math.MG | we analytically study proximity and distance properties of various kernels and similarity measures on graphs this helps to understand the mathematical nature of such measures and can potentially be useful for recommending the adoption of specific similarity measures in data analysis | [['we', 'analytically', 'study', 'proximity', 'and', 'distance', 'properties', 'of', 'various', 'kernels', 'and', 'similarity', 'measures', 'on', 'graphs', 'this', 'helps', 'to', 'understand', 'the', 'mathematical', 'nature', 'of', 'such', 'measures', 'and', 'can', 'potentially', 'be', 'useful', 'for', 'recommending', 'the', 'adoption', 'of', 'specific', 'similarity', 'measures', 'in', 'data', 'analysis']] | [-0.09192181349073242, 0.008866708180526407, -0.1237627697381668, 0.18996628797215567, -0.1275111184932473, -0.0829102612759282, 0.08867415281512388, 0.42920311367729813, -0.303878613088916, -0.3245433500581762, 0.09813870530200713, -0.3297481826228294, -0.1872566601193351, 0.20118818550211628, -0.08134514996327641, 0.08402439861035929, 0.054907401751090844, 0.04552955053201536, -0.09368046929650917, -0.16194012315898407, 0.38192859994497425, 0.09118764565848722, 0.3316630511203917, 0.1447081618840102, -0.008289135421807991, -0.014408969115920184, -0.09928370878191256, 0.0679248191597985, -0.1674690884424419, 0.1851338221714264, 0.28360039286497163, 0.22843214750244487, 0.2807169921196452, -0.3529544824325457, -0.2121843241564021, 0.15595588073270714, 0.10430172417119782, 0.02358621124905057, 0.00706040685167309, -0.3524766403545694, 0.07468169164366839, -0.10934754592798106, -0.09141552848059957, -0.17764452081627963, 0.04174326046756128, 0.06526055944538335, -0.2193662416099048, 0.03134881389304632, 0.025411011084698412, 0.060701504281563005, -0.014500351009390703, -0.06119567178553197, 0.037459713222299955, 0.21796836431433514, 0.0549363793989625, -0.04105407359055811, 0.11005151150293829, -0.12333755889657612, -0.1389587712814895, 0.3911152045901229, -0.025059842794179554, -0.24550505291397978, 0.28513163700699806, -0.11105327321807058, -0.14360183727268766, 0.01964711965765895, 0.2795872591981074, 0.09584634237718291, -0.23674153316220858, -0.00963625325906522, 0.00929543921133367, 0.11135751831277114, 0.05019159258411425, 0.09280759382356958, 0.1894582835730256, 0.1635668226404161, 0.033382327477561265, 0.14798665552956575, -0.044315017867697086, -0.0982970373659599, -0.20630373769417043, -0.18715943582355976, -0.16404067974810194, 0.012654630515565415, -0.18945182581104214, -0.15717175866409047, 0.44345531439999253, 0.2048559089864905, 0.20374754416506466, 0.008878279954376744, 0.22303702118920116, 0.08693031287502224, 0.07389797488363778, 0.04199813199570266, 0.15907329902416323, 0.12502802830053175, 0.06551704502369209, -0.18984837724441073, 0.16585252989401542, 0.04782536918888005] |
1,802.06285 | Greening Geographical Power Allocation for Cellular Networks | Harvesting energy from nature (solar, wind etc.) is envisioned as a key
enabler for realizing green wireless networks. However, green energy sources
are geographically distributed and the power amount is random which may not
enough to power a base station by a single energy site. Burning brown energy
sources such as coal and crude oil, though companied with carbon dioxide
emission, provides stable power. In this paper, without sacrificing
communication quality, we investigate how to perform green energy allocation to
abate the dependence on brown energy with hybrid brown and green energy
injected in power networks. We present a comprehensive framework to
characterize the performance of hybrid green and brown energy empowered
cellular network. Novel performance metric "bits/ton\ce{CO2}/Hz" is proposed to
evaluate the greenness of the communication network. As green energy is usually
generated from distributed geographical locations and is time varying, online
geographical power allocation algorithm is proposed to maximize the greenness
of communication network considering electricity transmission's physical laws
i.e., Ohm's law and Kirchhoff's circuit laws. Simulations show that
geographically distributed green energy sources complement each other by
improving the communication capacity while saving brown energy consumption.
Besides, the penetration of green energy can also help reduce power loss on the
transmission breaches.
| cs.IT math.IT | harvesting energy from nature solar wind etc is envisioned as a key enabler for realizing green wireless networks however green energy sources are geographically distributed and the power amount is random which may not enough to power a base station by a single energy site burning brown energy sources such as coal and crude oil though companied with carbon dioxide emission provides stable power in this paper without sacrificing communication quality we investigate how to perform green energy allocation to abate the dependence on brown energy with hybrid brown and green energy injected in power networks we present a comprehensive framework to characterize the performance of hybrid green and brown energy empowered cellular network novel performance metric bitstonceco2hz is proposed to evaluate the greenness of the communication network as green energy is usually generated from distributed geographical locations and is time varying online geographical power allocation algorithm is proposed to maximize the greenness of communication network considering electricity transmissions physical laws ie ohms law and kirchhoffs circuit laws simulations show that geographically distributed green energy sources complement each other by improving the communication capacity while saving brown energy consumption besides the penetration of green energy can also help reduce power loss on the transmission breaches | [['harvesting', 'energy', 'from', 'nature', 'solar', 'wind', 'etc', 'is', 'envisioned', 'as', 'a', 'key', 'enabler', 'for', 'realizing', 'green', 'wireless', 'networks', 'however', 'green', 'energy', 'sources', 'are', 'geographically', 'distributed', 'and', 'the', 'power', 'amount', 'is', 'random', 'which', 'may', 'not', 'enough', 'to', 'power', 'a', 'base', 'station', 'by', 'a', 'single', 'energy', 'site', 'burning', 'brown', 'energy', 'sources', 'such', 'as', 'coal', 'and', 'crude', 'oil', 'though', 'companied', 'with', 'carbon', 'dioxide', 'emission', 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1,802.06286 | Nonconvex Matrix Factorization from Rank-One Measurements | We consider the problem of recovering low-rank matrices from random rank-one
measurements, which spans numerous applications including covariance sketching,
phase retrieval, quantum state tomography, and learning shallow polynomial
neural networks, among others. Our approach is to directly estimate the
low-rank factor by minimizing a nonconvex quadratic loss function via vanilla
gradient descent, following a tailored spectral initialization. When the true
rank is small, this algorithm is guaranteed to converge to the ground truth (up
to global ambiguity) with near-optimal sample complexity and computational
complexity. To the best of our knowledge, this is the first guarantee that
achieves near-optimality in both metrics. In particular, the key enabler of
near-optimal computational guarantees is an implicit regularization phenomenon:
without explicit regularization, both spectral initialization and the gradient
descent iterates automatically stay within a region incoherent with the
measurement vectors. This feature allows one to employ much more aggressive
step sizes compared with the ones suggested in prior literature, without the
need of sample splitting.
| cs.IT cs.LG math.IT stat.ML | we consider the problem of recovering lowrank matrices from random rankone measurements which spans numerous applications including covariance sketching phase retrieval quantum state tomography and learning shallow polynomial neural networks among others our approach is to directly estimate the lowrank factor by minimizing a nonconvex quadratic loss function via vanilla gradient descent following a tailored spectral initialization when the true rank is small this algorithm is guaranteed to converge to the ground truth up to global ambiguity with nearoptimal sample complexity and computational complexity to the best of our knowledge this is the first guarantee that achieves nearoptimality in both metrics in particular the key enabler of nearoptimal computational guarantees is an implicit regularization phenomenon without explicit regularization both spectral initialization and the gradient descent iterates automatically stay within a region incoherent with the measurement vectors this feature allows one to employ much more aggressive step sizes compared with the ones suggested in prior literature without the need of sample splitting | [['we', 'consider', 'the', 'problem', 'of', 'recovering', 'lowrank', 'matrices', 'from', 'random', 'rankone', 'measurements', 'which', 'spans', 'numerous', 'applications', 'including', 'covariance', 'sketching', 'phase', 'retrieval', 'quantum', 'state', 'tomography', 'and', 'learning', 'shallow', 'polynomial', 'neural', 'networks', 'among', 'others', 'our', 'approach', 'is', 'to', 'directly', 'estimate', 'the', 'lowrank', 'factor', 'by', 'minimizing', 'a', 'nonconvex', 'quadratic', 'loss', 'function', 'via', 'vanilla', 'gradient', 'descent', 'following', 'a', 'tailored', 'spectral', 'initialization', 'when', 'the', 'true', 'rank', 'is', 'small', 'this', 'algorithm', 'is', 'guaranteed', 'to', 'converge', 'to', 'the', 'ground', 'truth', 'up', 'to', 'global', 'ambiguity', 'with', 'nearoptimal', 'sample', 'complexity', 'and', 'computational', 'complexity', 'to', 'the', 'best', 'of', 'our', 'knowledge', 'this', 'is', 'the', 'first', 'guarantee', 'that', 'achieves', 'nearoptimality', 'in', 'both', 'metrics', 'in', 'particular', 'the', 'key', 'enabler', 'of', 'nearoptimal', 'computational', 'guarantees', 'is', 'an', 'implicit', 'regularization', 'phenomenon', 'without', 'explicit', 'regularization', 'both', 'spectral', 'initialization', 'and', 'the', 'gradient', 'descent', 'iterates', 'automatically', 'stay', 'within', 'a', 'region', 'incoherent', 'with', 'the', 'measurement', 'vectors', 'this', 'feature', 'allows', 'one', 'to', 'employ', 'much', 'more', 'aggressive', 'step', 'sizes', 'compared', 'with', 'the', 'ones', 'suggested', 'in', 'prior', 'literature', 'without', 'the', 'need', 'of', 'sample', 'splitting']] | [-0.05828010175964269, -0.001527344322165399, -0.06652214638570682, 0.0743784556217399, -0.10667404663237101, -0.1695337734399913, 0.06791182589624656, 0.4262574762105942, -0.3212473156185161, -0.31251451800004665, 0.11157199686974495, -0.22971153845122988, -0.15841818998693574, 0.13885388222540387, -0.12254508915924907, 0.13042687369013053, 0.08600597355879196, 0.009384276970110324, -0.13794343155426964, -0.30420682988251035, 0.2860067626345694, 0.07640123784687422, 0.31742548303944723, -0.03527771660867176, 0.12938836112514324, -0.004366265313399041, -0.0043620307501752555, -0.024280138551300935, -0.03246759687810131, 0.15011395414154513, 0.30862551955694206, 0.18896172297192934, 0.36211121042970545, -0.3811403302621691, -0.20250820472940617, 0.17194469979680752, 0.14824969929018236, 0.14950019614814394, -0.05148487491722968, -0.2613268598381962, 0.09767076495083292, -0.11597765593135857, -0.0553797639137053, -0.1411998896593494, -0.06835068609805559, -0.010699733106205414, -0.3338437297074876, 0.06816671582471047, 0.06181460860624956, 0.006955434239949591, -0.05749226804216745, -0.1578442375146732, 0.06052804523963273, 0.12184017891970302, 0.05665265644469547, 0.07641642764847008, 0.12366367534514494, -0.13083274505406284, -0.10453960927122313, 0.337944558098542, -0.05202079979957353, -0.2143665972263921, 0.15491597805394913, -0.06961546002696298, -0.12230935056696744, 0.16983747448749054, 0.1730155613734705, 0.13743241775440349, -0.13142006682068968, 0.06849413399724869, -0.036395796395329215, 0.17914719860248776, 0.0345054251244523, 0.026380605439970745, 0.07450381205677524, 0.1683458914627431, 0.18171162560606746, 0.14024758902494072, -0.06655641699459994, -0.11683601220568601, -0.253503313565754, -0.0999526856336974, -0.23882443145472812, 0.021398791455467064, -0.1821415301565817, -0.15047588829393643, 0.3859418664627957, 0.1553769539578916, 0.22438254814467723, 0.13850163474790347, 0.36539647962481525, 0.08367360401276777, 0.0690659384173775, 0.1339164228830776, 0.21676461528176846, 0.11255150745038568, 0.09603794973060888, -0.2299254734455232, 0.11948390897965024, 0.09741243596553155] |
1,802.06287 | Unsupervised vehicle recognition using incremental reseeding of acoustic
signatures | Vehicle recognition and classification have broad applications, ranging from
traffic flow management to military target identification. We demonstrate an
unsupervised method for automated identification of moving vehicles from
roadside audio sensors. Using a short-time Fourier transform to decompose audio
signals, we treat the frequency signature in each time window as an individual
data point. We then use a spectral embedding for dimensionality reduction.
Based on the leading eigenvectors, we relate the performance of an incremental
reseeding algorithm to that of spectral clustering. We find that incremental
reseeding accurately identifies individual vehicles using their acoustic
signatures.
| stat.ML cs.LG physics.data-an | vehicle recognition and classification have broad applications ranging from traffic flow management to military target identification we demonstrate an unsupervised method for automated identification of moving vehicles from roadside audio sensors using a shorttime fourier transform to decompose audio signals we treat the frequency signature in each time window as an individual data point we then use a spectral embedding for dimensionality reduction based on the leading eigenvectors we relate the performance of an incremental reseeding algorithm to that of spectral clustering we find that incremental reseeding accurately identifies individual vehicles using their acoustic signatures | [['vehicle', 'recognition', 'and', 'classification', 'have', 'broad', 'applications', 'ranging', 'from', 'traffic', 'flow', 'management', 'to', 'military', 'target', 'identification', 'we', 'demonstrate', 'an', 'unsupervised', 'method', 'for', 'automated', 'identification', 'of', 'moving', 'vehicles', 'from', 'roadside', 'audio', 'sensors', 'using', 'a', 'shorttime', 'fourier', 'transform', 'to', 'decompose', 'audio', 'signals', 'we', 'treat', 'the', 'frequency', 'signature', 'in', 'each', 'time', 'window', 'as', 'an', 'individual', 'data', 'point', 'we', 'then', 'use', 'a', 'spectral', 'embedding', 'for', 'dimensionality', 'reduction', 'based', 'on', 'the', 'leading', 'eigenvectors', 'we', 'relate', 'the', 'performance', 'of', 'an', 'incremental', 'reseeding', 'algorithm', 'to', 'that', 'of', 'spectral', 'clustering', 'we', 'find', 'that', 'incremental', 'reseeding', 'accurately', 'identifies', 'individual', 'vehicles', 'using', 'their', 'acoustic', 'signatures']] | [-0.07831383685728437, 0.000329376381399159, -0.12205702514926854, 0.012672266236653454, -0.12085315552295038, -0.1389013595781044, 0.04558928658983572, 0.45467898610881285, -0.2908671822398901, -0.2884968708122247, 0.10961400717129245, -0.2874206733938895, -0.16191769365809466, 0.21430600891379933, -0.12887969198391624, 0.0792321919120456, 0.08211115995225937, 0.07623617110504327, -0.043959931985132004, -0.1634315979765042, 0.24083892486891464, -0.003244575015024135, 0.3624595917957394, -0.004240959221007009, 0.1384622815616527, 0.058598243986795605, -0.09323002631825052, -0.041530532795494714, -0.05221170265990383, 0.14176393366957965, 0.35745859951957276, 0.20122234233606018, 0.27017463429978017, -0.39446093265555404, -0.23111894426769333, 0.11627876231642931, 0.18364131404332032, 0.10535029745415637, -0.05990372762745737, -0.35498507094422455, 0.10740546270665761, -0.18025757995875258, -0.04525373225639525, -0.1342825393339521, 0.008148426459612031, 0.0368437455182797, -0.2605745720431993, 0.042488694161568814, 0.023603223876929598, 0.0968590657863962, -0.1189178250288885, -0.0627521200007514, 0.04713009238537205, 0.22451282402892647, 0.029581150638037606, -0.032906753981584, 0.17974129236352288, -0.12390943056854763, -0.14659670464890567, 0.3728544911859851, -0.06357208789375267, -0.16779031023187072, 0.19626780830960916, 0.011118264123797416, -0.1564196684691859, 0.1498395157095633, 0.28994147315327273, 0.10294033336440886, -0.17611043635927337, -0.01598123844382108, 0.011041715486269248, 0.20442242911728833, 0.09226753081403379, 0.006793340834739961, 0.15744587326679674, 0.2220572010270859, 0.13298070780981922, 0.12565597374310816, -0.21527919862174283, 0.007238916768447349, -0.20599435463143317, -0.13299608260001006, -0.21414900104466236, -0.005796255092871816, -0.12668293532655012, -0.16666279205758322, 0.42004366851362746, 0.22691197795303245, 0.20330262235120722, 0.06665642186999321, 0.3666574644415002, 0.08755204982344846, 0.05609641278554735, 0.09596258713618705, 0.1365754556180419, 0.02196718703367208, 0.14223523396802576, -0.2300376332806129, 0.0005885623468968429, 0.08812332740543705] |
1,802.06288 | Implementation of Neural Network and feature extraction to classify ECG
signals | This paper presents a suitable and efficient implementation of a feature
extraction algorithm (Pan Tompkins algorithm) on electrocardiography (ECG)
signals, for detection and classification of four cardiac diseases: Sleep
Apnea, Arrhythmia, Supraventricular Arrhythmia and Long Term Atrial
Fibrillation (AF) and differentiating them from the normal heart beat by using
pan Tompkins RR detection followed by feature extraction for classification
purpose .The paper also presents a new approach towards signal classification
using the existing neural networks classifiers.
| cs.NE | this paper presents a suitable and efficient implementation of a feature extraction algorithm pan tompkins algorithm on electrocardiography ecg signals for detection and classification of four cardiac diseases sleep apnea arrhythmia supraventricular arrhythmia and long term atrial fibrillation af and differentiating them from the normal heart beat by using pan tompkins rr detection followed by feature extraction for classification purpose the paper also presents a new approach towards signal classification using the existing neural networks classifiers | [['this', 'paper', 'presents', 'a', 'suitable', 'and', 'efficient', 'implementation', 'of', 'a', 'feature', 'extraction', 'algorithm', 'pan', 'tompkins', 'algorithm', 'on', 'electrocardiography', 'ecg', 'signals', 'for', 'detection', 'and', 'classification', 'of', 'four', 'cardiac', 'diseases', 'sleep', 'apnea', 'arrhythmia', 'supraventricular', 'arrhythmia', 'and', 'long', 'term', 'atrial', 'fibrillation', 'af', 'and', 'differentiating', 'them', 'from', 'the', 'normal', 'heart', 'beat', 'by', 'using', 'pan', 'tompkins', 'rr', 'detection', 'followed', 'by', 'feature', 'extraction', 'for', 'classification', 'purpose', 'the', 'paper', 'also', 'presents', 'a', 'new', 'approach', 'towards', 'signal', 'classification', 'using', 'the', 'existing', 'neural', 'networks', 'classifiers']] | [-0.08734784518678983, -0.04671656809747219, -0.05981904437144597, 0.041270023508307836, -0.06842766071359317, -0.2366332927159965, 0.0450310144542406, 0.3621199210298558, -0.21292655603339275, -0.21979164448877175, 0.15438273866971333, -0.2619518357266982, -0.27854621067643165, 0.26271422366301217, -0.13941591028039207, 0.05825442076350252, 0.1654765444334286, 0.07198831274484595, 0.0035966851065556207, -0.20968621634567777, 0.23669919579600293, 0.0367733702622354, 0.40377354635546603, -0.03178097486495972, 0.1290023397685339, 0.03308176686987281, -0.09926552722851435, -0.1030834512040019, -0.040454566764334836, 0.15693524613976478, 0.3738296324200928, 0.20864091509953142, 0.3040575327724218, -0.35021426290273666, -0.22399646198997894, 0.0946961144854625, 0.14721006218343974, 0.1332206207793206, -0.0406593312897409, -0.39952941636244454, 0.14053399473428727, -0.18364235249658425, 0.05192741617560387, -0.12527855422658224, 0.009048126731067896, -0.04375453668336073, -0.2892667518556118, 0.1694720397280374, 0.08700778976082801, 0.17434234581887723, -0.10735901462535063, -0.09774416483317812, 0.09660028462608655, 0.14277038098002473, 0.02425423510527859, 0.07913529841850202, 0.13109977844598084, -0.17878057674815256, -0.1750311202338586, 0.28647698638339836, -0.04450713058312734, -0.10242974083560208, 0.20588566948970158, 0.04070146080416938, -0.13976590593655905, 0.1353824465100964, 0.1941382384300232, 0.11044336058199405, -0.21019604411907494, -0.11635857007466256, 0.10984726780404647, 0.13622292606160044, 0.1175752536393702, -0.08367821517090003, 0.15346091359853745, 0.31219931301971277, -0.030179922506213187, 0.11683798112596075, -0.24365037386033994, 0.06203786974151929, -0.18454474135224397, -0.11540331487854322, -0.12094790869702896, -0.03204812145947168, -0.07376162063252802, -0.1649508750795697, 0.5222185582915941, 0.1619981960952282, 0.1289287254648904, 0.10161982030297319, 0.37345456873377164, -0.031605255901813505, 0.08370948477337757, 0.050724418560663856, 0.15241263488928478, 0.03802899039040009, 0.12231341312949856, -0.21908482120372355, 0.12318356571098169, 0.12928794174765548] |
1,802.06289 | Faster Algorithms for Integer Programs with Block Structure | We consider integer programming problems $\max \{ c^T x : \mathcal{A} x = b,
l \leq x \leq u, x \in \mathbb{Z}^{nt}\}$ where $\mathcal{A}$ has a (recursive)
block-structure generalizing "$n$-fold integer programs" which recently
received considerable attention in the literature. An $n$-fold IP is an integer
program where $\mathcal{A}$ consists of $n$ repetitions of submatrices $A \in
\mathbb{Z}^{r \times t}$ on the top horizontal part and $n$ repetitions of a
matrix $B \in \mathbb{Z}^{s \times t}$ on the diagonal below the top part.
Instead of allowing only two types of block matrices, one for the horizontal
line and one for the diagonal, we generalize the $n$-fold setting to allow for
arbitrary matrices in every block. We show that such an integer program can be
solved in time $n^2 t^2 {\phi} \cdot (rs{\Delta})^{\mathcal{O}(rs^2+ sr^2)}$
(ignoring logarithmic factors). Here ${\Delta}$ is an upper bound on the
largest absolute value of an entry of $\mathcal{A}$ and ${\phi}$ is the largest
binary encoding length of a coefficient of $c$. This improves upon the
previously best algorithm of Hemmecke, Onn and Romanchuk that runs in time
$n^3t^3 {\phi} \cdot {\Delta}^{\mathcal{O}(t^2s)}$. In particular, our
algorithm is not exponential in the number $t$ of columns of $A$ and $B$.
Our algorithm is based on a new upper bound on the $l_1$-norm of an element
of the "Graver basis" of an integer matrix and on a proximity bound between the
LP and IP optimal solutions tailored for IPs with block structure. These new
bounds rely on the "Steinitz Lemma".
Furthermore, we extend our techniques to the recently introduced "tree-fold
IPs", where we again present a more efficient algorithm in a generalized
setting.
| cs.DM cs.DS | we consider integer programming problems max ct x mathcala x b l leq x leq u x in mathbbznt where mathcala has a recursive blockstructure generalizing nfold integer programs which recently received considerable attention in the literature an nfold ip is an integer program where mathcala consists of n repetitions of submatrices a in mathbbzr times t on the top horizontal part and n repetitions of a matrix b in mathbbzs times t on the diagonal below the top part instead of allowing only two types of block matrices one for the horizontal line and one for the diagonal we generalize the nfold setting to allow for arbitrary matrices in every block we show that such an integer program can be solved in time n2 t2 phi cdot rsdeltamathcalors2 sr2 ignoring logarithmic factors here delta is an upper bound on the largest absolute value of an entry of mathcala and phi is the largest binary encoding length of a coefficient of c this improves upon the previously best algorithm of hemmecke onn and romanchuk that runs in time n3t3 phi cdot deltamathcalot2s in particular our algorithm is not exponential in the number t of columns of a and b our algorithm is based on a new upper bound on the l_1norm of an element of the graver basis of an integer matrix and on a proximity bound between the lp and ip optimal solutions tailored for ips with block structure these new bounds rely on the steinitz lemma furthermore we extend our techniques to the recently introduced treefold ips where we again present a more efficient algorithm in a generalized setting | [['we', 'consider', 'integer', 'programming', 'problems', 'max', 'ct', 'x', 'mathcala', 'x', 'b', 'l', 'leq', 'x', 'leq', 'u', 'x', 'in', 'mathbbznt', 'where', 'mathcala', 'has', 'a', 'recursive', 'blockstructure', 'generalizing', 'nfold', 'integer', 'programs', 'which', 'recently', 'received', 'considerable', 'attention', 'in', 'the', 'literature', 'an', 'nfold', 'ip', 'is', 'an', 'integer', 'program', 'where', 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1,802.0629 | TabVec: Table Vectors for Classification of Web Tables | There are hundreds of millions of tables in Web pages that contain useful
information for many applications. Leveraging data within these tables is
difficult because of the wide variety of structures, formats and data encoded
in these tables. TabVec is an unsupervised method to embed tables into a vector
space to support classification of tables into categories (entity, relational,
matrix, list, and non-data) with minimal user intervention. TabVec deploys
syntax and semantics of table cells, and embeds the structure of tables in a
table vector space. This enables superior classification of tables even in the
absence of domain annotations. Our evaluations in four real world domains show
that TabVec improves classification accuracy by more than 20% compared to three
state of the art systems, and that those systems require significant in domain
training to achieve good results.
| cs.IR | there are hundreds of millions of tables in web pages that contain useful information for many applications leveraging data within these tables is difficult because of the wide variety of structures formats and data encoded in these tables tabvec is an unsupervised method to embed tables into a vector space to support classification of tables into categories entity relational matrix list and nondata with minimal user intervention tabvec deploys syntax and semantics of table cells and embeds the structure of tables in a table vector space this enables superior classification of tables even in the absence of domain annotations our evaluations in four real world domains show that tabvec improves classification accuracy by more than 20 compared to three state of the art systems and that those systems require significant in domain training to achieve good results | [['there', 'are', 'hundreds', 'of', 'millions', 'of', 'tables', 'in', 'web', 'pages', 'that', 'contain', 'useful', 'information', 'for', 'many', 'applications', 'leveraging', 'data', 'within', 'these', 'tables', 'is', 'difficult', 'because', 'of', 'the', 'wide', 'variety', 'of', 'structures', 'formats', 'and', 'data', 'encoded', 'in', 'these', 'tables', 'tabvec', 'is', 'an', 'unsupervised', 'method', 'to', 'embed', 'tables', 'into', 'a', 'vector', 'space', 'to', 'support', 'classification', 'of', 'tables', 'into', 'categories', 'entity', 'relational', 'matrix', 'list', 'and', 'nondata', 'with', 'minimal', 'user', 'intervention', 'tabvec', 'deploys', 'syntax', 'and', 'semantics', 'of', 'table', 'cells', 'and', 'embeds', 'the', 'structure', 'of', 'tables', 'in', 'a', 'table', 'vector', 'space', 'this', 'enables', 'superior', 'classification', 'of', 'tables', 'even', 'in', 'the', 'absence', 'of', 'domain', 'annotations', 'our', 'evaluations', 'in', 'four', 'real', 'world', 'domains', 'show', 'that', 'tabvec', 'improves', 'classification', 'accuracy', 'by', 'more', 'than', '20', 'compared', 'to', 'three', 'state', 'of', 'the', 'art', 'systems', 'and', 'that', 'those', 'systems', 'require', 'significant', 'in', 'domain', 'training', 'to', 'achieve', 'good', 'results']] | [-0.09147380311768603, 0.006759663915578582, 0.00039053004044685923, 0.07253404249637693, -0.1301031083167687, -0.11833167381339917, 0.08108789169222769, 0.4265417893226836, -0.24887363930117257, -0.3704250600502527, 0.107857472557289, -0.3009817420103907, -0.11059223419313666, 0.25573287042279313, -0.11879859087193372, 0.032543898590697645, 0.17893859099284032, 0.07450849504590741, -0.08334499789677206, -0.31514507435418554, 0.32429950397496865, -0.012102470483399765, 0.3043334267210949, -0.0003166460996344142, 0.08467198112880961, 0.011645246708398536, -0.061799042153743225, -0.006923061229707503, -0.03693776851919111, 0.19740076778612922, 0.34771630408609433, 0.22780283539467594, 0.22094002008662444, -0.4071854655053059, -0.20506898420149067, 0.10019810626922297, 0.1378569860099713, 0.10713645122795754, -0.008583063654534444, -0.30920361122456347, 0.14762011619393517, -0.21029584392239034, -0.00020983555170632628, -0.19319570859397905, 0.027515945911924116, 0.0038226283406906755, -0.22817934915146376, -0.00038067476456835324, 0.06830681811289073, 0.1043048028861356, -0.0793549800346891, -0.14218745284172515, 0.0008342727002463419, 0.17212495874497957, -0.018372196082134536, 0.02945695669444645, 0.10362288216468844, -0.15675119256063696, -0.15703824202846192, 0.4222039544451846, -0.016142412493822002, -0.2148583451911372, 0.18261078342006806, -0.060570880587137964, -0.13211408605200858, 0.16773565810166952, 0.2039796469306546, 0.0581178229274976, -0.1170627706939946, 0.04124775642901319, -0.06451934097117207, 0.22870769953341596, 0.07634369489043462, 0.025526681015893383, 0.16632088970088732, 0.19794995392788284, 0.010668246785474492, 0.10167101848646183, -0.03935825775112331, -0.09823011210014922, -0.24728498344792285, -0.16688865774997722, -0.15900387184313722, -0.06322486917522267, -0.1354590210392111, -0.19425088875539545, 0.3758869527849321, 0.17602384155684145, 0.20838776433731626, 0.0414131619749985, 0.2875134376605062, -0.05252073723805837, 0.12129627622984329, 0.10556454852862406, 0.07934842776017685, 0.03851525458833543, 0.13241182585471195, -0.058187356193203234, 0.07783389895352243, 0.009462759921192974] |
1,802.06291 | Domination of Sample Maxima and Related Extremal Dependence Measures | For a given $d$-dimensional distribution function (df) $H$ we introduce the
class of dependence measures $ \mu(H,Q) = - \mathbb{E}\{ \ln H(Z_1, \ldots,
Z_d)\},$ where the random vector $(Z_1, \ldots, Z_d)$ has df $Q$ which has the
same marginal df's as $H$. If both $H$ and $Q$ are max-stable df's, we show
that for a df $F$ in the max-domain of attraction of $H$, this dependence
measure explains the extremal dependence exhibited by $F$. Moreover we prove
that $\mu(H,Q)$ is the limit of the probability that the maxima of a random
sample from $F$ is marginally dominated by some random vector with df in the
max-domain of attraction of $Q$. We show a similar result for the complete
domination of the sample maxima which leads to another measure of dependence
denoted by $\lambda(Q,H)$. In the literature $\lambda(H,H)$ with $H$ a
max-stable df has been studied in the context of records, multiple maxima,
concomitants of order statistics and concurrence probabilities. It turns out
that both $\mu(H,Q)$ and $\lambda(Q,H)$ are closely related. If $H$ is
max-stable we derive useful representations for both $\mu(H,Q)$ and
$\lambda(Q,H)$. Our applications include equivalent conditions for $H$ to be a
product df and $F$ to have asymptotically independent components.
| math.PR stat.ME | for a given ddimensional distribution function df h we introduce the class of dependence measures muhq mathbbe ln hz_1 ldots z_d where the random vector z_1 ldots z_d has df q which has the same marginal dfs as h if both h and q are maxstable dfs we show that for a df f in the maxdomain of attraction of h this dependence measure explains the extremal dependence exhibited by f moreover we prove that muhq is the limit of the probability that the maxima of a random sample from f is marginally dominated by some random vector with df in the maxdomain of attraction of q we show a similar result for the complete domination of the sample maxima which leads to another measure of dependence denoted by lambdaqh in the literature lambdahh with h a maxstable df has been studied in the context of records multiple maxima concomitants of order statistics and concurrence probabilities it turns out that both muhq and lambdaqh are closely related if h is maxstable we derive useful representations for both muhq and lambdaqh our applications include equivalent conditions for h to be a product df and f to have asymptotically independent components | [['for', 'a', 'given', 'ddimensional', 'distribution', 'function', 'df', 'h', 'we', 'introduce', 'the', 'class', 'of', 'dependence', 'measures', 'muhq', 'mathbbe', 'ln', 'hz_1', 'ldots', 'z_d', 'where', 'the', 'random', 'vector', 'z_1', 'ldots', 'z_d', 'has', 'df', 'q', 'which', 'has', 'the', 'same', 'marginal', 'dfs', 'as', 'h', 'if', 'both', 'h', 'and', 'q', 'are', 'maxstable', 'dfs', 'we', 'show', 'that', 'for', 'a', 'df', 'f', 'in', 'the', 'maxdomain', 'of', 'attraction', 'of', 'h', 'this', 'dependence', 'measure', 'explains', 'the', 'extremal', 'dependence', 'exhibited', 'by', 'f', 'moreover', 'we', 'prove', 'that', 'muhq', 'is', 'the', 'limit', 'of', 'the', 'probability', 'that', 'the', 'maxima', 'of', 'a', 'random', 'sample', 'from', 'f', 'is', 'marginally', 'dominated', 'by', 'some', 'random', 'vector', 'with', 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1,802.06292 | Nonparametric Estimation of Low Rank Matrix Valued Function | Let $A:[0,1]\rightarrow\mathbb{H}_m$ (the space of Hermitian matrices) be a
matrix valued function which is low rank with entries in H\"{o}lder class
$\Sigma(\beta,L)$. The goal of this paper is to study statistical estimation of
$A$ based on the regression model $\mathbb{E}(Y_j|\tau_j,X_j) = \langle
A(\tau_j), X_j \rangle,$ where $\tau_j$ are i.i.d. uniformly distributed in
$[0,1]$, $X_j$ are i.i.d. matrix completion sampling matrices, $Y_j$ are
independent bounded responses. We propose an innovative nuclear norm penalized
local polynomial estimator and establish an upper bound on its point-wise risk
measured by Frobenius norm. Then we extend this estimator globally and prove an
upper bound on its integrated risk measured by $L_2$-norm. We also propose
another new estimator based on bias-reducing kernels to study the case when $A$
is not necessarily low rank and establish an upper bound on its risk measured
by $L_{\infty}$-norm. We show that the obtained rates are all optimal up to
some logarithmic factor in minimax sense. Finally, we propose an adaptive
estimation procedure based on Lepskii's method and model selection with data
splitting which is computationally efficient and can be easily implemented and
parallelized.
| stat.ML cs.LG math.ST stat.TH | let a01rightarrowmathbbh_m the space of hermitian matrices be a matrix valued function which is low rank with entries in holder class sigmabetal the goal of this paper is to study statistical estimation of a based on the regression model mathbbey_jtau_jx_j langle atau_j x_j rangle where tau_j are iid uniformly distributed in 01 x_j are iid matrix completion sampling matrices y_j are independent bounded responses we propose an innovative nuclear norm penalized local polynomial estimator and establish an upper bound on its pointwise risk measured by frobenius norm then we extend this estimator globally and prove an upper bound on its integrated risk measured by l_2norm we also propose another new estimator based on biasreducing kernels to study the case when a is not necessarily low rank and establish an upper bound on its risk measured by l_inftynorm we show that the obtained rates are all optimal up to some logarithmic factor in minimax sense finally we propose an adaptive estimation procedure based on lepskiis method and model selection with data splitting which is computationally efficient and can be easily implemented and parallelized | [['let', 'a01rightarrowmathbbh_m', 'the', 'space', 'of', 'hermitian', 'matrices', 'be', 'a', 'matrix', 'valued', 'function', 'which', 'is', 'low', 'rank', 'with', 'entries', 'in', 'holder', 'class', 'sigmabetal', 'the', 'goal', 'of', 'this', 'paper', 'is', 'to', 'study', 'statistical', 'estimation', 'of', 'a', 'based', 'on', 'the', 'regression', 'model', 'mathbbey_jtau_jx_j', 'langle', 'atau_j', 'x_j', 'rangle', 'where', 'tau_j', 'are', 'iid', 'uniformly', 'distributed', 'in', '01', 'x_j', 'are', 'iid', 'matrix', 'completion', 'sampling', 'matrices', 'y_j', 'are', 'independent', 'bounded', 'responses', 'we', 'propose', 'an', 'innovative', 'nuclear', 'norm', 'penalized', 'local', 'polynomial', 'estimator', 'and', 'establish', 'an', 'upper', 'bound', 'on', 'its', 'pointwise', 'risk', 'measured', 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1,802.06293 | Black-Box Reductions for Parameter-free Online Learning in Banach Spaces | We introduce several new black-box reductions that significantly improve the
design of adaptive and parameter-free online learning algorithms by simplifying
analysis, improving regret guarantees, and sometimes even improving runtime. We
reduce parameter-free online learning to online exp-concave optimization, we
reduce optimization in a Banach space to one-dimensional optimization, and we
reduce optimization over a constrained domain to unconstrained optimization.
All of our reductions run as fast as online gradient descent. We use our new
techniques to improve upon the previously best regret bounds for parameter-free
learning, and do so for arbitrary norms.
| cs.LG math.OC stat.ML | we introduce several new blackbox reductions that significantly improve the design of adaptive and parameterfree online learning algorithms by simplifying analysis improving regret guarantees and sometimes even improving runtime we reduce parameterfree online learning to online expconcave optimization we reduce optimization in a banach space to onedimensional optimization and we reduce optimization over a constrained domain to unconstrained optimization all of our reductions run as fast as online gradient descent we use our new techniques to improve upon the previously best regret bounds for parameterfree learning and do so for arbitrary norms | [['we', 'introduce', 'several', 'new', 'blackbox', 'reductions', 'that', 'significantly', 'improve', 'the', 'design', 'of', 'adaptive', 'and', 'parameterfree', 'online', 'learning', 'algorithms', 'by', 'simplifying', 'analysis', 'improving', 'regret', 'guarantees', 'and', 'sometimes', 'even', 'improving', 'runtime', 'we', 'reduce', 'parameterfree', 'online', 'learning', 'to', 'online', 'expconcave', 'optimization', 'we', 'reduce', 'optimization', 'in', 'a', 'banach', 'space', 'to', 'onedimensional', 'optimization', 'and', 'we', 'reduce', 'optimization', 'over', 'a', 'constrained', 'domain', 'to', 'unconstrained', 'optimization', 'all', 'of', 'our', 'reductions', 'run', 'as', 'fast', 'as', 'online', 'gradient', 'descent', 'we', 'use', 'our', 'new', 'techniques', 'to', 'improve', 'upon', 'the', 'previously', 'best', 'regret', 'bounds', 'for', 'parameterfree', 'learning', 'and', 'do', 'so', 'for', 'arbitrary', 'norms']] | [-0.022637173584054992, -0.038580957734349955, -0.08998792821986844, 0.12097712631499313, -0.19248648441862315, -0.18560315930770468, 0.11832284253171604, 0.46028407537580834, -0.2996382066656066, -0.361046545005039, 0.1384451447608982, -0.19177860291877194, -0.18876834391520117, 0.22210488787411875, -0.16751849742950467, 0.15856978032894878, 0.09231545006775338, -0.0537295372063375, -0.14808367125173708, -0.38305760270905803, 0.22449727173712428, 0.09780932215811766, 0.2603868923716895, 0.023424457915572693, 0.11810950986514597, 0.08575788990824747, -0.016881023852519043, 0.039381318315344804, -0.13193765054142126, 0.19323561759665608, 0.3457265267675014, 0.2512959735063107, 0.3649773259100545, -0.3999389172734126, -0.18289853204370718, 0.16648956395321243, 0.21660695720018577, 0.11974285334176586, -0.06013157105442825, -0.2632873481463479, 0.06132562517735135, -0.1458928774151465, 0.011358129009908147, -0.20824466738849878, -0.09680125512101728, 0.028430045967030783, -0.35205600416267535, -0.019230245076038915, 0.06493282498305906, 0.021184272041706288, -0.08423305889996498, -0.17755094727339304, 0.14122299639933297, 0.08834900279812839, 0.052299034050362105, 0.06396662967239061, 0.17203137305889116, -0.10755102385503604, -0.22208879595627481, 0.3035882229147398, -0.08123821150713964, -0.2029898661244458, 0.1839469545697996, 0.04080924415535739, -0.19073869415518382, 0.14360680904887294, 0.3249665355567982, 0.15182830059253002, -0.13719561241526643, 0.09641436201206449, -0.023087389858277595, 0.15022308169579407, 0.026568788607382095, 0.019252748071701953, -0.022711132306342377, 0.220545921019157, 0.2186106206449356, 0.1360187696789027, 0.03560091542970875, -0.11219115548832949, -0.2240481786733574, -0.14468988067591967, -0.1566727150588438, -0.049796523433937895, -0.14875502345271147, -0.14584425173107657, 0.3453848614792704, 0.19239840520869778, 0.18976070147777058, 0.19915666074350313, 0.35064280573683587, 0.0689964254953615, 0.07287865828560747, 0.20614919706713408, 0.23172494059342527, 0.015178169413601332, 0.12106794677295155, -0.2443844403289562, 0.0829631655132803, 0.07777959065835761] |
1,802.06294 | Dynamics of strongly interacting unstable two-solitons for generalized
Korteweg-de Vries equations | We consider the generalized Korteweg-de Vries equation $\partial_t u =
-\partial_x(\partial_x^2 u + f(u))$, where $f(u)$ is an odd function of class
$C^3$. Under some assumptions on $f$, this equation admits \emph{solitary
waves}, that is solutions of the form $u(t, x) = Q_v(x - vt - x_0)$, for $v$ in
some range $(0, v_*)$. We study pure two-solitons in the case of the same limit
speed, in other words global solutions $u(t)$ such that \begin{equation}
\label{eq:abstract} \tag{$\ast$} \lim_{t\to\infty}\|u(t) - (Q_v(\cdot - x_1(t))
\pm Q_v(\cdot - x_2(t)))\|_{H^1} = 0, \qquad \text{with}\quad\lim_{t \to
\infty}x_2(t) - x_1(t) = \infty. \end{equation} Existence of such solutions is
known for $f(u) = |u|^{p-1}u$ with $p \in \mathbb{Z} \setminus \{5\}$ and $p >
2$. We describe the~dynamical behavior of any solution satisfying
\eqref{eq:abstract} under the assumption that $Q_v$ is linearly unstable (which
corresponds to $p > 5$ for power nonlinearities). We prove that in this case
the sign in \eqref{eq:abstract} is necessarily "$+$", which corresponds to an
attractive interaction. We also prove that the~distance $x_2(t) - x_1(t)$
between the solitons equals $\frac{2}{\sqrt v}\log(\kappa t) + o(1)$ for some
$\kappa = \kappa(v) > 0$.
| math.AP | we consider the generalized kortewegde vries equation partial_t u partial_xpartial_x2 u fu where fu is an odd function of class c3 under some assumptions on f this equation admits emphsolitary waves that is solutions of the form ut x q_vx vt x_0 for v in some range 0 v_ we study pure twosolitons in the case of the same limit speed in other words global solutions ut such that beginequation labeleqabstract tagast lim_ttoinftyut q_vcdot x_1t pm q_vcdot x_2t_h1 0 qquad textwithquadlim_t to inftyx_2t x_1t infty endequation existence of such solutions is known for fu up1u with p in mathbbz setminus 5 and p 2 we describe thedynamical behavior of any solution satisfying eqrefeqabstract under the assumption that q_v is linearly unstable which corresponds to p 5 for power nonlinearities we prove that in this case the sign in eqrefeqabstract is necessarily which corresponds to an attractive interaction we also prove that thedistance x_2t x_1t between the solitons equals frac2sqrt vlogkappa t o1 for some kappa kappav 0 | [['we', 'consider', 'the', 'generalized', 'kortewegde', 'vries', 'equation', 'partial_t', 'u', 'partial_xpartial_x2', 'u', 'fu', 'where', 'fu', 'is', 'an', 'odd', 'function', 'of', 'class', 'c3', 'under', 'some', 'assumptions', 'on', 'f', 'this', 'equation', 'admits', 'emphsolitary', 'waves', 'that', 'is', 'solutions', 'of', 'the', 'form', 'ut', 'x', 'q_vx', 'vt', 'x_0', 'for', 'v', 'in', 'some', 'range', '0', 'v_', 'we', 'study', 'pure', 'twosolitons', 'in', 'the', 'case', 'of', 'the', 'same', 'limit', 'speed', 'in', 'other', 'words', 'global', 'solutions', 'ut', 'such', 'that', 'beginequation', 'labeleqabstract', 'tagast', 'lim_ttoinftyut', 'q_vcdot', 'x_1t', 'pm', 'q_vcdot', 'x_2t_h1', '0', 'qquad', 'textwithquadlim_t', 'to', 'inftyx_2t', 'x_1t', 'infty', 'endequation', 'existence', 'of', 'such', 'solutions', 'is', 'known', 'for', 'fu', 'up1u', 'with', 'p', 'in', 'mathbbz', 'setminus', '5', 'and', 'p', '2', 'we', 'describe', 'thedynamical', 'behavior', 'of', 'any', 'solution', 'satisfying', 'eqrefeqabstract', 'under', 'the', 'assumption', 'that', 'q_v', 'is', 'linearly', 'unstable', 'which', 'corresponds', 'to', 'p', '5', 'for', 'power', 'nonlinearities', 'we', 'prove', 'that', 'in', 'this', 'case', 'the', 'sign', 'in', 'eqrefeqabstract', 'is', 'necessarily', 'which', 'corresponds', 'to', 'an', 'attractive', 'interaction', 'we', 'also', 'prove', 'that', 'thedistance', 'x_2t', 'x_1t', 'between', 'the', 'solitons', 'equals', 'frac2sqrt', 'vlogkappa', 't', 'o1', 'for', 'some', 'kappa', 'kappav', '0']] | [-0.22305564213997853, 0.10048065856207335, -0.012084551818239002, 0.0071747583517721165, -0.04547723075162279, -0.21391853747459558, -0.03691832114679691, 0.31482724287809855, -0.3044148031383371, -0.15639759948811469, 0.08798129167459989, -0.3796602361716139, -0.09917166711201365, 0.1484085632184407, -0.0578269339845671, 0.018076407233587444, -0.007032637120200655, 0.10011730364296967, -0.04939697280515714, -0.19285822144443862, 0.305759726458256, -0.16254968627561958, 0.14598569197532457, 0.024563298449487403, 0.09562150797193261, -0.0235328536050824, 0.12657683211205623, -0.07343659732335557, -0.27100034584616833, -0.07005984539715335, 0.22950117135433873, 0.08808179796697238, 0.28372070688317014, -0.3413095706118605, -0.15886050002136004, 0.213045850552123, 0.15509161499973673, -0.04044872960702588, 0.01792037574173614, -0.2669702100967511, 0.15298030415448385, -0.09596364040715763, -0.2010554017391629, -0.007842930638267158, 0.16385196286958092, 0.0828412433172791, -0.36327613695996264, 0.11800856913200383, 0.13377319110366395, -0.014098607396217397, -0.09755521607794201, -0.15731583556267792, -0.053031445967439465, 0.013373676350173086, 0.055980934821911014, 0.15261972210972977, -0.0161289050637816, -0.12159885055958651, -0.009522490413077414, 0.37807093948746723, -0.13670327400308077, -0.24816069081843567, 0.10040023401887037, -0.1721196378617046, -0.16789006024527436, 0.10217670635141146, 0.08322020137133315, 0.16431672611715606, -0.07432336316373832, 0.24596497791189437, -0.08839623090058851, 0.17939042903256053, 0.14824562726799065, 0.001246325753760548, 0.03513058763779461, 0.08259681205769691, 0.12000223308617326, 0.0807401316232221, -0.03624875469391461, -0.014200842811148435, -0.37811763933859766, -0.13080198702235252, -0.10958665121310893, 0.1874087987533317, -0.10215433256822647, -0.12318452063780756, 0.28352457637606093, 0.10888761012122417, 0.18329754512374982, 0.06934143001518141, 0.12728501428873876, 0.1729872509619245, -0.056588065858261705, 0.12338037325109606, 0.12645597659325292, 0.10707353820534757, 0.0973359886288213, -0.2194424292150264, 0.007749395547971989, 0.11850987248516713] |
1,802.06295 | Resonance line broadened quasilinear (RBQ) model for fast ion
distribution relaxation due to Alfv\'enic eigenmodes | The burning plasma performance is limited by the confinement of the
superalfvenic fusion products such as alpha particles and the auxiliary heating
ions capable of exciting the Alfv\'enic eigenmodes (AEs). In this work the
effect of AEs on fast ions is formulated within the quasi-linear (QL) theory
generalized for this problem recently. The generalization involves the
resonance line broadened interaction of energetic particles (EP) with AEs
supplemented by the diffusion coefficients depending on EP position in the
velocity space. A new resonance broadened QL code (or RBQ1D) based on this
formulation allowing for EP diffusion in radial direction is built and
presented in details. We reduce the wave particle interaction (WPI) dynamics to
1D case when the particle kinetic energy is nearly constant. The diffusion
equation for EP distribution evolution is then one dimensional and is solved
simultaneously for all particles with the equation for the evolution of the
wave angular momentum. The evolution of fast ion constants of motion is
governed by the QL diffusion equations which are adapted to find the fast ion
distribution function. We make initial applications of the RBQ1D to DIII-D
plasma with elevated q-profile where the beam ions show stiff transport
properties. AE driven fast ion profile relaxation is studied for validations of
the QL approach in realistic conditions of beam ion driven instabilities in
DIII-D.
| physics.plasm-ph | the burning plasma performance is limited by the confinement of the superalfvenic fusion products such as alpha particles and the auxiliary heating ions capable of exciting the alfvenic eigenmodes aes in this work the effect of aes on fast ions is formulated within the quasilinear ql theory generalized for this problem recently the generalization involves the resonance line broadened interaction of energetic particles ep with aes supplemented by the diffusion coefficients depending on ep position in the velocity space a new resonance broadened ql code or rbq1d based on this formulation allowing for ep diffusion in radial direction is built and presented in details we reduce the wave particle interaction wpi dynamics to 1d case when the particle kinetic energy is nearly constant the diffusion equation for ep distribution evolution is then one dimensional and is solved simultaneously for all particles with the equation for the evolution of the wave angular momentum the evolution of fast ion constants of motion is governed by the ql diffusion equations which are adapted to find the fast ion distribution function we make initial applications of the rbq1d to diiid plasma with elevated qprofile where the beam ions show stiff transport properties ae driven fast ion profile relaxation is studied for validations of the ql approach in realistic conditions of beam ion driven instabilities in diiid | [['the', 'burning', 'plasma', 'performance', 'is', 'limited', 'by', 'the', 'confinement', 'of', 'the', 'superalfvenic', 'fusion', 'products', 'such', 'as', 'alpha', 'particles', 'and', 'the', 'auxiliary', 'heating', 'ions', 'capable', 'of', 'exciting', 'the', 'alfvenic', 'eigenmodes', 'aes', 'in', 'this', 'work', 'the', 'effect', 'of', 'aes', 'on', 'fast', 'ions', 'is', 'formulated', 'within', 'the', 'quasilinear', 'ql', 'theory', 'generalized', 'for', 'this', 'problem', 'recently', 'the', 'generalization', 'involves', 'the', 'resonance', 'line', 'broadened', 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1,802.06296 | Collaborative model based design of automated and robotic agricultural
vehicles in the Crescendo Tool | This paper describes a collaborative modelling approach to automated and
robotic agricultural vehicle design. The Cresendo technology allows engineers
from different disciplines to collaborate and produce system models. The
combined models are called co-models and their execution co-simulation. To
support future development efforts a template library of different vehicle and
controllers types are provided. This paper describes a methodology to
developing co-models from initial problem definition to deployment of the
actual system. We illustrate the development methodology with an example
development case from the agricultural domain. The case relates to an
encountered speed controller problem on a differential driven vehicle, where we
iterate through different candidate solutions and end up with an adaptive
controller solution based on a combination of classical control and learning
feedforward. The second case is an example of combining human control interface
and co-simulation of agricultural robotic operation to illustrate collaborative
development
| cs.RO | this paper describes a collaborative modelling approach to automated and robotic agricultural vehicle design the cresendo technology allows engineers from different disciplines to collaborate and produce system models the combined models are called comodels and their execution cosimulation to support future development efforts a template library of different vehicle and controllers types are provided this paper describes a methodology to developing comodels from initial problem definition to deployment of the actual system we illustrate the development methodology with an example development case from the agricultural domain the case relates to an encountered speed controller problem on a differential driven vehicle where we iterate through different candidate solutions and end up with an adaptive controller solution based on a combination of classical control and learning feedforward the second case is an example of combining human control interface and cosimulation of agricultural robotic operation to illustrate collaborative development | [['this', 'paper', 'describes', 'a', 'collaborative', 'modelling', 'approach', 'to', 'automated', 'and', 'robotic', 'agricultural', 'vehicle', 'design', 'the', 'cresendo', 'technology', 'allows', 'engineers', 'from', 'different', 'disciplines', 'to', 'collaborate', 'and', 'produce', 'system', 'models', 'the', 'combined', 'models', 'are', 'called', 'comodels', 'and', 'their', 'execution', 'cosimulation', 'to', 'support', 'future', 'development', 'efforts', 'a', 'template', 'library', 'of', 'different', 'vehicle', 'and', 'controllers', 'types', 'are', 'provided', 'this', 'paper', 'describes', 'a', 'methodology', 'to', 'developing', 'comodels', 'from', 'initial', 'problem', 'definition', 'to', 'deployment', 'of', 'the', 'actual', 'system', 'we', 'illustrate', 'the', 'development', 'methodology', 'with', 'an', 'example', 'development', 'case', 'from', 'the', 'agricultural', 'domain', 'the', 'case', 'relates', 'to', 'an', 'encountered', 'speed', 'controller', 'problem', 'on', 'a', 'differential', 'driven', 'vehicle', 'where', 'we', 'iterate', 'through', 'different', 'candidate', 'solutions', 'and', 'end', 'up', 'with', 'an', 'adaptive', 'controller', 'solution', 'based', 'on', 'a', 'combination', 'of', 'classical', 'control', 'and', 'learning', 'feedforward', 'the', 'second', 'case', 'is', 'an', 'example', 'of', 'combining', 'human', 'control', 'interface', 'and', 'cosimulation', 'of', 'agricultural', 'robotic', 'operation', 'to', 'illustrate', 'collaborative', 'development']] | [-0.09844822368879275, 0.002846748788329818, -0.06535702793683101, -0.03093228785571857, -0.14416385557619738, -0.17117022743474666, 0.020039377195135384, 0.39190368129630815, -0.27249857892080415, -0.34611750493425275, 0.13896577595977333, -0.23935071464110624, -0.20924269071929938, 0.26257760338224734, -0.14896421351020675, 0.10716521996236243, 0.09502925902240343, -0.021948307645148955, 0.0016453883157215908, -0.20882707953101376, 0.2795400241735046, 0.03641929766944759, 0.32884596525919396, -0.003901035713774341, 0.14328733431761498, 0.007983485090227185, -0.05330521466171408, -0.040331152834362914, -0.09687069164433555, 0.1848187157314375, 0.33130623763851486, 0.20805562913769504, 0.3617839017961096, -0.44689996239657587, -0.159257081825729, 0.06889544715665255, 0.12714418551682696, 0.09902920307578532, -0.06478425847623706, -0.31094860945831515, 0.06404204798131283, -0.2561027567273566, -0.13989179216689998, -0.06917264298177682, -0.04679033364709857, 0.015378281181039927, -0.30421271479291306, -0.08397887563137534, 0.0024092253094044076, 0.10239980808765321, -0.10357814677217585, -0.07040262535064631, 0.013993287364581039, 0.18696469227438206, 0.034706806926011156, 0.02031130514635177, 0.18593715936013894, -0.14429947947497135, -0.16156570496154832, 0.35935594494148465, 0.000384621527306475, -0.19227919278184433, 0.22492675325455522, -0.014850466466554395, -0.12170380248095501, 0.05241570196446631, 0.2707811030363859, 0.09262906678242387, -0.20743590865570766, 0.03741989143793569, 0.06544155674055219, 0.16088123301897275, -0.000630561599403724, -0.0807776940348265, 0.19033802053207285, 0.28317239956109674, 0.12378383623240712, 0.1474099498847825, -0.032244603569664944, -0.14892013956160915, -0.2713938240821545, -0.1545839061166023, -0.11563424272397599, -0.026340825026903717, -0.040578796641540495, -0.13011884663931347, 0.39293838099583045, 0.21267474006488593, 0.09852090849821175, 0.061624135989760916, 0.3556473749735347, 0.07259379300594955, 0.03719646710628917, 0.07304766898408167, 0.15073550519634402, 0.04909162581377252, 0.20070110084619242, -0.21345652713154611, 0.08001288040248039, 0.027321646356356] |
1,802.06297 | Vortex interaction with a rough wall formed by a hexagonal lattice of
posts | An experimental study is reported which investigates the head-on collision of
a laminar vortex ring of diameter D (Re{\Gamma}= 3000) on a fakir-like surface
composed of circular posts of height h*=0.068 located on a planar bed. Lattices
of the posts in hexagonal and random distribution (average porosity of e = 0.94
in the layer) are compared to each other with respect to the plain wall. Prior
to impact, the vortex ring develops the early state of natural azimuthal
instabilities of different mode numbers N=5-7 competing with each other. While
impacting with the rough wall, a secondary ring is observed which is pushed
outwards and is not wrapped around the primary ring as in flat wall impact.
Between both rings of opposite sign vorticity, a strong fluid rebound is
induced. The hexagonal lattice causes the rapid growth of further secondary
vortex structures in a regular mode number N=6 arrangement at the outer edge of
the primary ring in form of six lobes which are aligned with the orientations
of preferential pathways in the layer. At the outer tip of the lobes radial
wall-jets are generated. Rotating the fakir geometry around the centre of
impact also rotates the jets location and direction accordingly. A surface with
random lattice of the posts at the same average number density is not able to
repeat this observation and no regular secondary flow pattern is visible until
full breakdown of the ring. The results show that a tailored arrangement of
such posts can be used for near-wall flow control when patterns of preferred
pathways in the posts layer lock-on with existing instability modes such as in
impacting jet flows or in turbulent boundary layer flows.
| physics.flu-dyn | an experimental study is reported which investigates the headon collision of a laminar vortex ring of diameter d regamma 3000 on a fakirlike surface composed of circular posts of height h0068 located on a planar bed lattices of the posts in hexagonal and random distribution average porosity of e 094 in the layer are compared to each other with respect to the plain wall prior to impact the vortex ring develops the early state of natural azimuthal instabilities of different mode numbers n57 competing with each other while impacting with the rough wall a secondary ring is observed which is pushed outwards and is not wrapped around the primary ring as in flat wall impact between both rings of opposite sign vorticity a strong fluid rebound is induced the hexagonal lattice causes the rapid growth of further secondary vortex structures in a regular mode number n6 arrangement at the outer edge of the primary ring in form of six lobes which are aligned with the orientations of preferential pathways in the layer at the outer tip of the lobes radial walljets are generated rotating the fakir geometry around the centre of impact also rotates the jets location and direction accordingly a surface with random lattice of the posts at the same average number density is not able to repeat this observation and no regular secondary flow pattern is visible until full breakdown of the ring the results show that a tailored arrangement of such posts can be used for nearwall flow control when patterns of preferred pathways in the posts layer lockon with existing instability modes such as in impacting jet flows or in turbulent boundary layer flows | [['an', 'experimental', 'study', 'is', 'reported', 'which', 'investigates', 'the', 'headon', 'collision', 'of', 'a', 'laminar', 'vortex', 'ring', 'of', 'diameter', 'd', 'regamma', '3000', 'on', 'a', 'fakirlike', 'surface', 'composed', 'of', 'circular', 'posts', 'of', 'height', 'h0068', 'located', 'on', 'a', 'planar', 'bed', 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1,802.06298 | Unimodality of the independence polynomials of non-regular caterpillars | The independence polynomial $I(G, x)$ of a graph $G$ is the polynomial in
variable $x$ in which the coefficient $a_n$ on $x^n$ gives the number of
independent subsets $S \subseteq V(G)$ of vertices of $G$ such that $|S| = n$.
$I(G, x)$ is unimodal if there is an index $\mu$ such that that $a_0 \leq a_1
\leq$...$\leq a_{\mu-1} \leq a_{\mu} \geq a_{\mu +1} \geq$...$\geq a_{d-1} \geq
a_d$ While the independence polynomials of many families of graphs with highly
regular structure are known to be unimodal, little is known about less
regularly structured graphs. We analyze the independence polynomials of a large
infinite family of trees without regular structure and show that these
polynomials are unimodal through a combinatorial analysis of the polynomials
coefficients.
| math.CO | the independence polynomial ig x of a graph g is the polynomial in variable x in which the coefficient a_n on xn gives the number of independent subsets s subseteq vg of vertices of g such that s n ig x is unimodal if there is an index mu such that that a_0 leq a_1 leqleq a_mu1 leq a_mu geq a_mu 1 geqgeq a_d1 geq a_d while the independence polynomials of many families of graphs with highly regular structure are known to be unimodal little is known about less regularly structured graphs we analyze the independence polynomials of a large infinite family of trees without regular structure and show that these polynomials are unimodal through a combinatorial analysis of the polynomials coefficients | [['the', 'independence', 'polynomial', 'ig', 'x', 'of', 'a', 'graph', 'g', 'is', 'the', 'polynomial', 'in', 'variable', 'x', 'in', 'which', 'the', 'coefficient', 'a_n', 'on', 'xn', 'gives', 'the', 'number', 'of', 'independent', 'subsets', 's', 'subseteq', 'vg', 'of', 'vertices', 'of', 'g', 'such', 'that', 's', 'n', 'ig', 'x', 'is', 'unimodal', 'if', 'there', 'is', 'an', 'index', 'mu', 'such', 'that', 'that', 'a_0', 'leq', 'a_1', 'leqleq', 'a_mu1', 'leq', 'a_mu', 'geq', 'a_mu', '1', 'geqgeq', 'a_d1', 'geq', 'a_d', 'while', 'the', 'independence', 'polynomials', 'of', 'many', 'families', 'of', 'graphs', 'with', 'highly', 'regular', 'structure', 'are', 'known', 'to', 'be', 'unimodal', 'little', 'is', 'known', 'about', 'less', 'regularly', 'structured', 'graphs', 'we', 'analyze', 'the', 'independence', 'polynomials', 'of', 'a', 'large', 'infinite', 'family', 'of', 'trees', 'without', 'regular', 'structure', 'and', 'show', 'that', 'these', 'polynomials', 'are', 'unimodal', 'through', 'a', 'combinatorial', 'analysis', 'of', 'the', 'polynomials', 'coefficients']] | [-0.20253664096235297, 0.16289640181462345, -0.030075188506937897, 0.030345145213262488, -0.07402672298873464, -0.18152883201449488, -0.017575206053152215, 0.3819053920606772, -0.3375669892101238, -0.21087503443316866, 0.06766179445936965, -0.3160396964677299, -0.1133620641756958, 0.1573733161902055, -0.05974627633889516, -0.007349563126141826, 0.027826208260376007, 0.11771380759309977, -0.0344989374327876, -0.29808050312106693, 0.2916779269153873, -0.0744497435943534, 0.15854851620582242, -0.0025296584314977127, 0.1045967918743069, -0.0008701995830051601, 0.011074262027007838, -0.0032129838208978375, -0.17163452962534695, 0.0665301890966172, 0.2572623314335942, 0.17111378492403068, 0.22267016432015224, -0.30106793947246235, -0.1254316838691011, 0.26238823924795723, 0.15052886230647952, -0.07453799083111032, 0.030636996390724865, -0.20153724889969454, 0.16490970630159912, -0.11731329046500226, -0.14174251040288557, -0.04395839599892497, 0.17526967828938117, 0.04262431693059625, -0.35357218675490004, 0.0387790021894034, 0.12479915466004361, 0.08329793475568295, 0.10757130295193444, -0.2512371168442769, -0.06934252242014433, 0.02712461435779308, -0.013659038542148968, 0.09238034836016595, 0.011351700600547096, -0.11172482712815206, -0.10638398480756829, 0.3238790761150691, -0.018336075637489558, -0.2159022682462819, 0.10208919101084273, -0.23581810814794152, -0.21680628270842134, 0.130777221165287, 0.09241300584593166, 0.17356961459542314, -0.052846680993873935, 0.21340860023968464, -0.16418390264346575, 0.18307480843310867, 0.12384111362043768, 0.012841708801473336, 0.12500654333271086, 0.06566167228156701, 0.08267666507308605, 0.11898194799335518, 0.03705254368833266, 0.0412219622793297, -0.33319112826138736, -0.09413906596819288, -0.20693026977824047, 0.15082920206186828, -0.26007912604630595, -0.18553586004515335, 0.3463331893164044, 0.058621992609308414, 0.22673104518714052, 0.09532204173447098, 0.16570808096633602, 0.06498949090358414, 0.020152302039787174, 0.0907051252045979, 0.032829701035128285, 0.20593573647396018, -0.05173592393402941, -0.12660073421381338, 0.08999327265143317, 0.11136805230441192] |
1,802.06299 | Robotic design choice overview using co-simulation | Rapid robotic system development sets a demand for multi-disciplinary methods
and tools to explore and compare design alternatives. In this paper, we present
collaborative modeling that combines discrete-event models of controller
software with continuous-time models of physical robot components. The
presented co-modeling method utilized VDM for discrete-event and 20-sim for
continuous-time modeling. The collaborative modeling method is illustrated with
a concrete example of collaborative model development of a mobile robot animal
feeding system. Simulations are used to evaluate the robot model output
response in relation to operational demands. The result of the simulations
provides the developers with an overview of the impacts of each solution
instance in the chosen design space. Based on the solution overview the
developers can select candidates that are deemed viable to be deployed and
tested on an actual physical robot.
| cs.RO | rapid robotic system development sets a demand for multidisciplinary methods and tools to explore and compare design alternatives in this paper we present collaborative modeling that combines discreteevent models of controller software with continuoustime models of physical robot components the presented comodeling method utilized vdm for discreteevent and 20sim for continuoustime modeling the collaborative modeling method is illustrated with a concrete example of collaborative model development of a mobile robot animal feeding system simulations are used to evaluate the robot model output response in relation to operational demands the result of the simulations provides the developers with an overview of the impacts of each solution instance in the chosen design space based on the solution overview the developers can select candidates that are deemed viable to be deployed and tested on an actual physical robot | [['rapid', 'robotic', 'system', 'development', 'sets', 'a', 'demand', 'for', 'multidisciplinary', 'methods', 'and', 'tools', 'to', 'explore', 'and', 'compare', 'design', 'alternatives', 'in', 'this', 'paper', 'we', 'present', 'collaborative', 'modeling', 'that', 'combines', 'discreteevent', 'models', 'of', 'controller', 'software', 'with', 'continuoustime', 'models', 'of', 'physical', 'robot', 'components', 'the', 'presented', 'comodeling', 'method', 'utilized', 'vdm', 'for', 'discreteevent', 'and', '20sim', 'for', 'continuoustime', 'modeling', 'the', 'collaborative', 'modeling', 'method', 'is', 'illustrated', 'with', 'a', 'concrete', 'example', 'of', 'collaborative', 'model', 'development', 'of', 'a', 'mobile', 'robot', 'animal', 'feeding', 'system', 'simulations', 'are', 'used', 'to', 'evaluate', 'the', 'robot', 'model', 'output', 'response', 'in', 'relation', 'to', 'operational', 'demands', 'the', 'result', 'of', 'the', 'simulations', 'provides', 'the', 'developers', 'with', 'an', 'overview', 'of', 'the', 'impacts', 'of', 'each', 'solution', 'instance', 'in', 'the', 'chosen', 'design', 'space', 'based', 'on', 'the', 'solution', 'overview', 'the', 'developers', 'can', 'select', 'candidates', 'that', 'are', 'deemed', 'viable', 'to', 'be', 'deployed', 'and', 'tested', 'on', 'an', 'actual', 'physical', 'robot']] | [-0.10285453963499601, -0.00537660283616856, -0.08797256015521243, 0.018771238316942874, -0.12354129471613053, -0.1907489959087531, 0.001232904027552253, 0.4137218136032273, -0.2095022701581282, -0.34642019030056836, 0.10213468833108734, -0.23448780379293108, -0.19312184308900646, 0.2382912130333147, -0.10370401473701897, 0.10842605764956627, 0.11614809706509534, -0.0066632443681863465, 0.009839392724075824, -0.21662959372764803, 0.25801189745867387, 0.07599502895727642, 0.29132204403036405, -0.005361715982899182, 0.12988723460864976, 0.004014076213387395, -0.06574963872439991, -0.02368339862262207, -0.12213078888250896, 0.1538515673054004, 0.3383356963827111, 0.227577042100685, 0.3276928553394483, -0.4528009340979327, -0.19268669037470468, 0.057521986932654805, 0.1223705979659313, 0.04954647175141407, -0.06890915943610769, -0.3354384146168604, 0.07700167720283109, -0.2417976768631348, -0.12853258298850037, -0.08590348173699629, -0.019765584470522134, 0.02651014657607092, -0.30264494801290276, -0.08864120178316769, -0.014815332777658128, 0.08034096745760146, -0.09757737742729933, -0.08758167598459561, 0.01491238348382084, 0.17695026637643183, 0.01856376471250017, -0.01571890955293705, 0.19227792797750212, -0.11252717648520567, -0.18161202553673683, 0.3978725001776129, 7.419886165543606e-05, -0.20967205692978955, 0.22891489158496542, -0.0077356405843301375, -0.1365763195965411, 0.05107747502204843, 0.2675765754709272, 0.08583026314924534, -0.21446610889852719, 0.03331557780511229, 0.018848635009454943, 0.19109567877997582, -0.060751190722422826, -0.041161793220221045, 0.21253834661320412, 0.31330843529965086, 0.05264830549156312, 0.131760805805872, -0.020138157750226038, -0.1281414212189839, -0.266769980121565, -0.14629837015601702, -0.13589743257997403, -0.040169749850113144, -0.07787219966241837, -0.14025561666270173, 0.38667430665301983, 0.27000914383469554, 0.11912815828800649, 0.06158067876319016, 0.3568688433396777, 0.06800565173975508, 0.04275284167659238, 0.06826392115230315, 0.17744539423208489, 0.031178675434391078, 0.14900206853250475, -0.2064361698624391, 0.0989540329210012, 0.02963001657053268] |
1,802.063 | Exact and Robust Conformal Inference Methods for Predictive Machine
Learning With Dependent Data | We extend conformal inference to general settings that allow for time series
data. Our proposal is developed as a randomization method and accounts for
potential serial dependence by including block structures in the permutation
scheme. As a result, the proposed method retains the exact, model-free validity
when the data are i.i.d. or more generally exchangeable, similar to usual
conformal inference methods. When exchangeability fails, as is the case for
common time series data, the proposed approach is approximately valid under
weak assumptions on the conformity score.
| stat.ML cs.LG | we extend conformal inference to general settings that allow for time series data our proposal is developed as a randomization method and accounts for potential serial dependence by including block structures in the permutation scheme as a result the proposed method retains the exact modelfree validity when the data are iid or more generally exchangeable similar to usual conformal inference methods when exchangeability fails as is the case for common time series data the proposed approach is approximately valid under weak assumptions on the conformity score | [['we', 'extend', 'conformal', 'inference', 'to', 'general', 'settings', 'that', 'allow', 'for', 'time', 'series', 'data', 'our', 'proposal', 'is', 'developed', 'as', 'a', 'randomization', 'method', 'and', 'accounts', 'for', 'potential', 'serial', 'dependence', 'by', 'including', 'block', 'structures', 'in', 'the', 'permutation', 'scheme', 'as', 'a', 'result', 'the', 'proposed', 'method', 'retains', 'the', 'exact', 'modelfree', 'validity', 'when', 'the', 'data', 'are', 'iid', 'or', 'more', 'generally', 'exchangeable', 'similar', 'to', 'usual', 'conformal', 'inference', 'methods', 'when', 'exchangeability', 'fails', 'as', 'is', 'the', 'case', 'for', 'common', 'time', 'series', 'data', 'the', 'proposed', 'approach', 'is', 'approximately', 'valid', 'under', 'weak', 'assumptions', 'on', 'the', 'conformity', 'score']] | [-0.04214630132814501, -0.00433689409996881, -0.10534909724929305, 0.1271918905008216, -0.10483054646973086, -0.18281039243650643, 0.04082967098369155, 0.39786871807476465, -0.21704144827848257, -0.2665457746208927, 0.11142422294455342, -0.21403497462377472, -0.1386227612045247, 0.218679609619146, -0.07834734112500798, 0.10992618209444159, 0.055823966270572574, 0.045140186830986896, -0.06730610327957588, -0.3034879239939348, 0.2888375444900851, 0.06625887658447027, 0.38065186665978196, -0.024041084906351644, 0.101148875439702, 0.07345698793895196, -0.015210374128506628, 0.028283518110943394, -0.07035481859159962, 0.06797078282973014, 0.2548877851994232, 0.15291616426196036, 0.31927369556627994, -0.3985160517094787, -0.2164326490466158, 0.08879068316783496, 0.10135186743532676, 0.1323648532392284, -0.02028446583849578, -0.28087852472645164, 0.10046213486253522, -0.14322509879766163, -0.09892823409998443, -0.15550237221556695, -0.0069788333846169505, -0.012436951445632203, -0.36996289243011976, 0.14500477347728732, 0.1148219657980642, 0.017317413969701805, -0.008509267273164072, -0.09052502769884806, 0.05394658337206428, 0.08452092901719625, 0.09831156266777408, 0.003615013962654873, 0.11712319016738167, -0.04225827553201207, -0.14311620387855137, 0.37911702255003676, -0.06709338771450052, -0.22864977597410596, 0.17579816718217592, -0.11149160492472178, -0.18936541338104668, 0.08253294234380645, 0.13066909818498548, 0.1341655837564166, -0.15274636124343297, 0.10100399494796536, -0.05398330929537499, 0.11997968626620117, 0.040095746614645385, -0.026172433564910492, 0.11131291952374023, 0.15539088405582102, 0.07772451311046648, 0.11576371810528947, -0.08147827394815638, -0.13521260614940073, -0.30924482116215796, -0.11783721623265422, -0.2002761843181107, -0.006849720424344373, -0.15392658471784332, -0.15066523825598144, 0.34936849635274947, 0.20021443810079756, 0.15287474238010512, 0.16378420796570217, 0.3163465082342195, 0.08704140222750517, 0.09810144032979774, 0.08412707735091275, 0.1312921900995249, 0.13153456697554522, 0.0739799183874586, -0.1434561429778114, 0.1276119520649487, 0.054715202287445926] |
1,802.06301 | Structural Properties of Bichromatic Non-crossing Matchings | Given a set of $n$ red and $n$ blue points in the plane, we are interested in
matching red points with blue points by straight line segments so that the
segments do not cross. We develop a range of tools for dealing with the
non-crossing matchings of points in convex position. It turns out that the
points naturally partition into groups that we refer to as orbits, with a
number of properties that prove useful for studying and efficiently processing
the non-crossing matchings.
Bottleneck matching is such a matching that minimizes the length of the
longest segment. Illustrating the use of the developed tools, we solve the
problem of finding bottleneck matchings of points in convex position in
$O(n^2)$ time. Subsequently, combining our tools with a geometric analysis we
design an $O(n)$-time algorithm for the case where the given points lie on a
circle. Previously best known results were $O(n^3)$ for points in convex
position, and $O(n \log n$) for points on a circle.
| cs.CG | given a set of n red and n blue points in the plane we are interested in matching red points with blue points by straight line segments so that the segments do not cross we develop a range of tools for dealing with the noncrossing matchings of points in convex position it turns out that the points naturally partition into groups that we refer to as orbits with a number of properties that prove useful for studying and efficiently processing the noncrossing matchings bottleneck matching is such a matching that minimizes the length of the longest segment illustrating the use of the developed tools we solve the problem of finding bottleneck matchings of points in convex position in on2 time subsequently combining our tools with a geometric analysis we design an ontime algorithm for the case where the given points lie on a circle previously best known results were on3 for points in convex position and on log n for points on a circle | [['given', 'a', 'set', 'of', 'n', 'red', 'and', 'n', 'blue', 'points', 'in', 'the', 'plane', 'we', 'are', 'interested', 'in', 'matching', 'red', 'points', 'with', 'blue', 'points', 'by', 'straight', 'line', 'segments', 'so', 'that', 'the', 'segments', 'do', 'not', 'cross', 'we', 'develop', 'a', 'range', 'of', 'tools', 'for', 'dealing', 'with', 'the', 'noncrossing', 'matchings', 'of', 'points', 'in', 'convex', 'position', 'it', 'turns', 'out', 'that', 'the', 'points', 'naturally', 'partition', 'into', 'groups', 'that', 'we', 'refer', 'to', 'as', 'orbits', 'with', 'a', 'number', 'of', 'properties', 'that', 'prove', 'useful', 'for', 'studying', 'and', 'efficiently', 'processing', 'the', 'noncrossing', 'matchings', 'bottleneck', 'matching', 'is', 'such', 'a', 'matching', 'that', 'minimizes', 'the', 'length', 'of', 'the', 'longest', 'segment', 'illustrating', 'the', 'use', 'of', 'the', 'developed', 'tools', 'we', 'solve', 'the', 'problem', 'of', 'finding', 'bottleneck', 'matchings', 'of', 'points', 'in', 'convex', 'position', 'in', 'on2', 'time', 'subsequently', 'combining', 'our', 'tools', 'with', 'a', 'geometric', 'analysis', 'we', 'design', 'an', 'ontime', 'algorithm', 'for', 'the', 'case', 'where', 'the', 'given', 'points', 'lie', 'on', 'a', 'circle', 'previously', 'best', 'known', 'results', 'were', 'on3', 'for', 'points', 'in', 'convex', 'position', 'and', 'on', 'log', 'n', 'for', 'points', 'on', 'a', 'circle']] | [-0.14861165207992422, 0.04119098400237484, -0.0696560113986091, 0.045908086476762346, -0.07043970926386509, -0.10464272687421748, 0.12871009472575856, 0.39628175508213886, -0.25207664087278453, -0.2893330839508009, 0.08355598949838611, -0.3199924167995227, -0.15020033045211897, 0.19483634681943462, -0.1110094375482465, 0.059773505790295396, 0.10224103728002619, 0.042513626431705566, -0.02478599889209585, -0.266901926066522, 0.2832074645060546, -0.03430620351985165, 0.22080783459795184, 0.00045934636443966954, 0.0696095928731488, 0.057268769261654376, -0.014151492286700665, 0.05971113470008188, -0.13676118501572956, 0.11171498061576574, 0.28872813811389414, 0.14440191332279229, 0.249820340859204, -0.40321662693806903, -0.13657074729151025, 0.15850160441932087, 0.18623155769721672, 0.07124062956356239, -0.024382662193469202, -0.22456627826223421, 0.10152736079322583, -0.01966070208312353, -0.13832919169700045, -0.0005959520690006818, 0.05490550011674669, 0.03904875731322824, -0.25053029323831516, -0.0443084355863947, 0.04889656654126068, 0.052067389010406306, 0.0012039371649734676, -0.14076042615967552, -0.008113418404785236, 0.12295179668723083, -0.010726003180734994, 0.05411713041092565, 0.06639511445423633, -0.07972850586946417, -0.1400600776371614, 0.4120487914003273, -0.0031311990630744795, -0.1983768046171995, 0.1274596941998092, -0.10810867744134511, -0.17047933052526787, 0.13802509945419775, 0.15311391426182194, 0.17616558274276917, -0.09221832838659061, 0.10365912038016575, -0.10013038550096098, 0.1409585879485282, 0.12430859394172751, -0.0094755279644188, 0.20773596852654364, 0.1281112466772689, 0.15637992304556708, 0.15857588046474563, -0.08602428053102525, -0.09875493518235844, -0.30359384864455136, -0.1397822074103737, -0.21168859878898508, -0.010645090884059844, -0.13964823057575837, -0.2164963176601748, 0.37583859415906595, 0.11685722104199912, 0.29107426020612076, 0.11460346634437261, 0.2826982263430226, 0.09551675465157065, 0.04593743815792373, 0.14225996091178186, 0.14571337296809184, 0.08367351996329069, 0.0018163353414842633, -0.17734667167873916, 0.014447047960058582, 0.14157272558432163] |
1,802.06302 | Improving the accuracy of the fast inverse square root algorithm | We present improved algorithms for fast calculation of the inverse square
root for single-precision floating-point numbers. The algorithms are much more
accurate than the famous fast inverse square root algorithm and have the same
or similar computational cost. The main idea of our work consists in modifying
the Newton-Raphson method and demanding that the maximal error is as small as
possible. Such modification is possible when the distribution of Newton-Raphson
corrections is not symmetric (e.g., if they are non-positive functions).
| cs.NA | we present improved algorithms for fast calculation of the inverse square root for singleprecision floatingpoint numbers the algorithms are much more accurate than the famous fast inverse square root algorithm and have the same or similar computational cost the main idea of our work consists in modifying the newtonraphson method and demanding that the maximal error is as small as possible such modification is possible when the distribution of newtonraphson corrections is not symmetric eg if they are nonpositive functions | [['we', 'present', 'improved', 'algorithms', 'for', 'fast', 'calculation', 'of', 'the', 'inverse', 'square', 'root', 'for', 'singleprecision', 'floatingpoint', 'numbers', 'the', 'algorithms', 'are', 'much', 'more', 'accurate', 'than', 'the', 'famous', 'fast', 'inverse', 'square', 'root', 'algorithm', 'and', 'have', 'the', 'same', 'or', 'similar', 'computational', 'cost', 'the', 'main', 'idea', 'of', 'our', 'work', 'consists', 'in', 'modifying', 'the', 'newtonraphson', 'method', 'and', 'demanding', 'that', 'the', 'maximal', 'error', 'is', 'as', 'small', 'as', 'possible', 'such', 'modification', 'is', 'possible', 'when', 'the', 'distribution', 'of', 'newtonraphson', 'corrections', 'is', 'not', 'symmetric', 'eg', 'if', 'they', 'are', 'nonpositive', 'functions']] | [-0.08363046917802422, 0.07627223146264442, -0.053332568053156136, 0.13009577511547832, -0.09056861815042794, -0.15593791399151086, -0.011485777047346346, 0.36998118152841925, -0.2783479394449387, -0.31592758384067565, 0.1521069512455142, -0.2736244583502412, -0.17301128978142516, 0.24508969776507, -0.05982496974174865, 0.11258947956375778, 0.07788521817419677, 0.05795672808308154, -0.12066834263168857, -0.28364118873287225, 0.25386100025643826, 0.11241657801438124, 0.2319038611720316, 0.022679644089657815, 0.08080266561009922, 0.0006462461897172034, -0.03824029414681718, 0.010886882641352713, -0.05426611260381833, 0.1119166243908694, 0.18806510546128266, 0.13671386275673286, 0.32125202943570913, -0.39645742435241116, -0.11207814855733886, 0.15432784113509115, 0.1785476770184687, 0.11598693322157487, -0.019076389980364185, -0.19227652978734114, 0.1497747851535678, -0.1467801367456559, -0.14171411454444752, -0.08574019265652169, 0.02769650808768347, 0.057233629620168355, -0.26437839659047313, 0.10216668928042054, 0.05725140757858753, 0.042378294479567556, 0.01929686008952558, -0.21924948544183281, 0.04807002581364941, 0.09896386090731539, 0.028334365287446418, 0.029014976613689215, 0.10171463055885396, -0.10271349332761019, -0.10475097234302666, 0.4091620058519766, -0.0017413928095265873, -0.2366532194428146, 0.13854404367739334, -0.10183206364163197, -0.09787603976437823, 0.1594490936404327, 0.16272069976839704, 0.14386352126311977, -0.07474476399747801, 0.0702737034051097, -0.03048871085047722, 0.1456372853004723, 0.04560266996268183, 0.005762556270929054, 0.08803898119367659, 0.15904760594421533, 0.1222372241463745, 0.1261880716541782, -0.06961917200824246, -0.115979564616282, -0.297647704440169, -0.168371212151942, -0.2675641459936742, 0.02842237208114966, -0.12111403638227784, -0.19708618824370205, 0.36706084803445266, 0.12990821998100727, 0.12863698676228524, 0.11600998917128891, 0.36462423474295064, 0.15478191682923353, 0.12416603489546105, 0.11855821950593963, 0.2033027625933755, 0.08509713268722408, 0.04241868962417357, -0.21491788555285893, 0.07342773379641584, 0.12434584554284811] |
1,802.06303 | Links between functions and subdifferentials | A function in a class $\mathcal{F}(X)$ is said to be subdifferentially
determined in $\mathcal{F}(X)$ if it is equal up to an additive constant to any
function in $\mathcal{F}(X)$ with the same subdifferential. A function is said
to be subdifferentially representable if it can be recovered from a
subdifferential. We identify large classes of lower semicontinuous functions
that possess these properties.
| math.OC | a function in a class mathcalfx is said to be subdifferentially determined in mathcalfx if it is equal up to an additive constant to any function in mathcalfx with the same subdifferential a function is said to be subdifferentially representable if it can be recovered from a subdifferential we identify large classes of lower semicontinuous functions that possess these properties | [['a', 'function', 'in', 'a', 'class', 'mathcalfx', 'is', 'said', 'to', 'be', 'subdifferentially', 'determined', 'in', 'mathcalfx', 'if', 'it', 'is', 'equal', 'up', 'to', 'an', 'additive', 'constant', 'to', 'any', 'function', 'in', 'mathcalfx', 'with', 'the', 'same', 'subdifferential', 'a', 'function', 'is', 'said', 'to', 'be', 'subdifferentially', 'representable', 'if', 'it', 'can', 'be', 'recovered', 'from', 'a', 'subdifferential', 'we', 'identify', 'large', 'classes', 'of', 'lower', 'semicontinuous', 'functions', 'that', 'possess', 'these', 'properties']] | [-0.06879711129392187, 0.12151248695081449, -0.11423965447271864, 0.08415441030131963, -0.10780031884399553, -0.1338640435754011, 0.005875623261090368, 0.39548129551112654, -0.38387311743572355, -0.20335731838519375, 0.09302109809553562, -0.26410440120768425, -0.1530695809982717, 0.18776150314758222, -0.12551604613351325, -0.008002822939306497, -0.005608461960218847, 0.06826623880770058, -0.11019204887173449, -0.26187580097466706, 0.3100756388778488, -0.06258155268927415, 0.1514446794676284, 0.06992612993344664, 0.10653020590543746, -0.05122115634536992, 0.08094593450271835, 0.08965216030483134, -0.15196161034655234, 0.05086202343615393, 0.3029142182087526, 0.14352101261417072, 0.2863920047879219, -0.28914026155446965, -0.1390107838436961, 0.21922997891282042, 0.06171625383819143, -0.009474729630164802, 0.0441512633037443, -0.23327596532180905, 0.20592443329902987, -0.15907502456878622, -0.13225951463294525, -0.04872946998414894, 0.05963886011547099, 0.05093661270414789, -0.34151962475540737, -0.00791995992573599, 0.03928721866880854, -0.005481663920606176, -0.04897075775079429, -0.10073147874015073, -0.07844767121908565, 0.10923675366987785, 0.007837134246559192, 0.16676457918365487, 0.06634508898326506, -0.08164515923708678, -0.03916138596832752, 0.3534639330736051, -0.11013952324477334, -0.3144620433697128, 0.14900480792857707, -0.1793903284240514, -0.10479861839363973, 0.1617397052119486, 0.10663548552741607, 0.1724116973268489, -0.17233670024822156, 0.1341856495477259, -0.11101013240404427, 0.17139900997281074, 0.08359429260405402, 0.04373929169184218, 0.17103161555714905, 0.07901115939797212, 0.14847379573620856, 0.16087320739752614, 0.07035141636927923, 0.025169078058873615, -0.3116193059210976, -0.1462459719196583, -0.23618078422732652, 0.15164476252781847, -0.042845749435946344, -0.2188802320199708, 0.32947508444388707, 0.059390395414084196, 0.19465739062676826, 0.10119867610434691, 0.205826744188865, 0.23551579921816787, 0.15371771324813988, 0.13744432894357791, 0.20005268572519222, 0.14547604075244938, -0.06964268713879089, -0.09345276773674413, 0.12180063717532903, 0.0981658768064032] |
1,802.06304 | Lagrangian mean curvature flow of Whitney spheres | It is shown that an equivariant Lagrangian sphere with a positivity condition
on its Ricci curvature develops a type-II singularity under the Lagrangian mean
curvature flow that rescales to the product of a grim reaper with a flat
Lagrangian subspace. In particular this result applies to the Whitney spheres.
| math.DG math.AP | it is shown that an equivariant lagrangian sphere with a positivity condition on its ricci curvature develops a typeii singularity under the lagrangian mean curvature flow that rescales to the product of a grim reaper with a flat lagrangian subspace in particular this result applies to the whitney spheres | [['it', 'is', 'shown', 'that', 'an', 'equivariant', 'lagrangian', 'sphere', 'with', 'a', 'positivity', 'condition', 'on', 'its', 'ricci', 'curvature', 'develops', 'a', 'typeii', 'singularity', 'under', 'the', 'lagrangian', 'mean', 'curvature', 'flow', 'that', 'rescales', 'to', 'the', 'product', 'of', 'a', 'grim', 'reaper', 'with', 'a', 'flat', 'lagrangian', 'subspace', 'in', 'particular', 'this', 'result', 'applies', 'to', 'the', 'whitney', 'spheres']] | [-0.23020040192308702, 0.06630016003565492, -0.1583608253383819, 0.0680730045221898, -0.09762658969480165, -0.16283396531694702, -0.10193331872246095, 0.32797717485500844, -0.2844153879286379, -0.15139601650239176, 0.10365020353299546, -0.23011772182522988, -0.1576376061848536, 0.09629169565492443, -0.14370816657129598, 0.036231124202236155, 0.048800413078647485, 0.11875059168633757, -0.09596883344977182, -0.24018027049629492, 0.4086449338902472, 0.0329890904415931, 0.244840062342166, 0.11778574289245607, 0.1625897205643812, -0.02523690421248273, 0.07164676557770189, 0.07036079348977275, -0.20851488607342838, 0.10041351063766193, 0.16766180458469127, 0.05021514164816056, 0.23134084327184423, -0.35675137638285453, -0.23204638717734083, 0.1396079478994468, 0.09833614988138481, 0.02758064647490273, -0.02781701127152738, -0.2531028175536467, 0.09663756831302023, -0.12531486544187884, -0.2021538850706432, -0.07305064274720391, -0.034737642574105033, -0.07049824599632804, -0.23322366121015986, 0.05919745322126819, 0.12618142446236952, 0.03828819814537253, -0.06622838975899682, -0.0037624908335582943, -0.10075953042096629, -0.0013973200883792372, 0.0982421807403087, 0.09676260726374326, 0.1294592962866383, -0.08303700611280392, -0.02519774927320529, 0.38743890732602804, -0.1419321537892125, -0.32494331219670725, 0.10095538337695964, -0.07557711143958934, -0.09590080363333833, 0.16723391960127926, 0.09703120123595, 0.18524086794682912, -0.053274105098668714, 0.15513599441595832, -0.10491192575582133, 0.06949026886449784, 0.09110866762621671, -0.08202913173531391, 0.1451084473911597, 0.0605665459250081, 0.21992769183552996, 0.1361288186257743, -0.053337813122197986, -0.16005354744325184, -0.37386144728076703, -0.21877439810457278, -0.17207702637022856, 0.13771345475878644, -0.14728932921140342, -0.25854249200866825, 0.38484225456355786, -0.017102550373089557, 0.2181686296564888, 0.16014312021434307, 0.30719509709398357, 0.044278823694556345, 0.06887413959532064, 0.13778753121554546, 0.2583729629499876, 0.21178308505640955, 0.08130320233924847, -0.1473541645694296, -0.08783376082892017, 0.2086883069263125] |
1,802.06305 | Machine learning for Internet of Things data analysis: A survey | Rapid developments in hardware, software, and communication technologies have
allowed the emergence of Internet-connected sensory devices that provide
observation and data measurement from the physical world. By 2020, it is
estimated that the total number of Internet-connected devices being used will
be between 25 and 50 billion. As the numbers grow and technologies become more
mature, the volume of data published will increase. Internet-connected devices
technology, referred to as Internet of Things (IoT), continues to extend the
current Internet by providing connectivity and interaction between the physical
and cyber worlds. In addition to increased volume, the IoT generates Big Data
characterized by velocity in terms of time and location dependency, with a
variety of multiple modalities and varying data quality. Intelligent processing
and analysis of this Big Data is the key to developing smart IoT applications.
This article assesses the different machine learning methods that deal with the
challenges in IoT data by considering smart cities as the main use case. The
key contribution of this study is presentation of a taxonomy of machine
learning algorithms explaining how different techniques are applied to the data
in order to extract higher level information. The potential and challenges of
machine learning for IoT data analytics will also be discussed. A use case of
applying Support Vector Machine (SVM) on Aarhus Smart City traffic data is
presented for a more detailed exploration.
| cs.LG cs.CY cs.DC | rapid developments in hardware software and communication technologies have allowed the emergence of internetconnected sensory devices that provide observation and data measurement from the physical world by 2020 it is estimated that the total number of internetconnected devices being used will be between 25 and 50 billion as the numbers grow and technologies become more mature the volume of data published will increase internetconnected devices technology referred to as internet of things iot continues to extend the current internet by providing connectivity and interaction between the physical and cyber worlds in addition to increased volume the iot generates big data characterized by velocity in terms of time and location dependency with a variety of multiple modalities and varying data quality intelligent processing and analysis of this big data is the key to developing smart iot applications this article assesses the different machine learning methods that deal with the challenges in iot data by considering smart cities as the main use case the key contribution of this study is presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information the potential and challenges of machine learning for iot data analytics will also be discussed a use case of applying support vector machine svm on aarhus smart city traffic data is presented for a more detailed exploration | [['rapid', 'developments', 'in', 'hardware', 'software', 'and', 'communication', 'technologies', 'have', 'allowed', 'the', 'emergence', 'of', 'internetconnected', 'sensory', 'devices', 'that', 'provide', 'observation', 'and', 'data', 'measurement', 'from', 'the', 'physical', 'world', 'by', '2020', 'it', 'is', 'estimated', 'that', 'the', 'total', 'number', 'of', 'internetconnected', 'devices', 'being', 'used', 'will', 'be', 'between', '25', 'and', '50', 'billion', 'as', 'the', 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1,802.06306 | Learning Data-Driven Objectives to Optimize Interactive Systems | Effective optimization is essential for interactive systems to provide a
satisfactory user experience. However, it is often challenging to find an
objective to optimize for. Generally, such objectives are manually crafted and
rarely capture complex user needs in an accurate manner. We propose an approach
that infers the objective directly from observed user interactions. These
inferences can be made regardless of prior knowledge and across different types
of user behavior. We introduce interactive system optimization, a novel
algorithm that uses these inferred objectives for optimization. Our main
contribution is a new general principled approach to optimizing interactive
systems using data-driven objectives. We demonstrate the high effectiveness of
interactive system optimization over several simulations.
| cs.AI cs.HC cs.IR cs.LG | effective optimization is essential for interactive systems to provide a satisfactory user experience however it is often challenging to find an objective to optimize for generally such objectives are manually crafted and rarely capture complex user needs in an accurate manner we propose an approach that infers the objective directly from observed user interactions these inferences can be made regardless of prior knowledge and across different types of user behavior we introduce interactive system optimization a novel algorithm that uses these inferred objectives for optimization our main contribution is a new general principled approach to optimizing interactive systems using datadriven objectives we demonstrate the high effectiveness of interactive system optimization over several simulations | [['effective', 'optimization', 'is', 'essential', 'for', 'interactive', 'systems', 'to', 'provide', 'a', 'satisfactory', 'user', 'experience', 'however', 'it', 'is', 'often', 'challenging', 'to', 'find', 'an', 'objective', 'to', 'optimize', 'for', 'generally', 'such', 'objectives', 'are', 'manually', 'crafted', 'and', 'rarely', 'capture', 'complex', 'user', 'needs', 'in', 'an', 'accurate', 'manner', 'we', 'propose', 'an', 'approach', 'that', 'infers', 'the', 'objective', 'directly', 'from', 'observed', 'user', 'interactions', 'these', 'inferences', 'can', 'be', 'made', 'regardless', 'of', 'prior', 'knowledge', 'and', 'across', 'different', 'types', 'of', 'user', 'behavior', 'we', 'introduce', 'interactive', 'system', 'optimization', 'a', 'novel', 'algorithm', 'that', 'uses', 'these', 'inferred', 'objectives', 'for', 'optimization', 'our', 'main', 'contribution', 'is', 'a', 'new', 'general', 'principled', 'approach', 'to', 'optimizing', 'interactive', 'systems', 'using', 'datadriven', 'objectives', 'we', 'demonstrate', 'the', 'high', 'effectiveness', 'of', 'interactive', 'system', 'optimization', 'over', 'several', 'simulations']] | [-0.08115197064569302, -0.03192318854835482, -0.0979962791135922, 0.11691201731162887, -0.1637188723162476, -0.1906757041259743, 0.02146128830458738, 0.4647888856387771, -0.27181098015105304, -0.4141749194205071, 0.03627809449778952, -0.232628002801943, -0.2173921520104714, 0.20398979961245725, -0.13393250304331952, 0.09856626318058873, 0.09312904779428402, -0.0013927094681101925, -0.04197900185147218, -0.26686601613368016, 0.3184053718269003, 0.04293244149107321, 0.2972487576668505, 0.029934241129415094, 0.10410500752360485, 0.043339518137869584, -0.00612311582086171, 0.018924522217758727, -0.0891333849287017, 0.15668345136831688, 0.3503928026328671, 0.2618089020004974, 0.3606575764221164, -0.4088054741938821, -0.20643753880960455, 0.050091598466357044, 0.17073053398078916, 0.09258053424784633, -0.05917419165175045, -0.28019387293730624, 0.0906737431290403, -0.16755420002113797, -0.008584762373279813, -0.16397144506990383, -0.0704939216375351, 0.009873987534042217, -0.36119311593835596, -0.02165406009113309, 0.0297399202477088, 0.06498850505403449, -0.06770908045458847, -0.08661509685288389, 0.06864130818820764, 0.20059611467516825, 0.015903132298005235, 0.022880605953555982, 0.15392774928245792, -0.08933539694356443, -0.13255582334457244, 0.3778969936929973, -0.0025378348412964723, -0.22284150676620482, 0.25265614385098484, -0.010327099928574685, -0.1551168308782894, 0.16419899046155254, 0.26466168393174894, 0.14808146225337962, -0.21885218861949654, -0.022115334580998573, 0.00048086188107201484, 0.21723006683838578, -0.0454019693543401, -0.008758308136716188, 0.22644511901204828, 0.23829042376637194, 0.10120340738346618, 0.13263939214618958, 0.006453825119652052, -0.09105149703100324, -0.23701542061217853, -0.13523820462207561, -0.17932656008865586, -0.017101257351284798, -0.06673841431826576, -0.12056701541136522, 0.36915452153405454, 0.24843804564211616, 0.1555424739773575, 0.0865045537864604, 0.3976297157330323, 0.0751365849431975, 0.04035172682354408, 0.07217275872991412, 0.1890512297539848, -0.024193365201024355, 0.1375361623158019, -0.17147568080252845, 0.14626018055384996, -0.010605117248420694] |
1,802.06307 | Out-of-sample extension of graph adjacency spectral embedding | Many popular dimensionality reduction procedures have out-of-sample
extensions, which allow a practitioner to apply a learned embedding to
observations not seen in the initial training sample. In this work, we consider
the problem of obtaining an out-of-sample extension for the adjacency spectral
embedding, a procedure for embedding the vertices of a graph into Euclidean
space. We present two different approaches to this problem, one based on a
least-squares objective and the other based on a maximum-likelihood
formulation. We show that if the graph of interest is drawn according to a
certain latent position model called a random dot product graph, then both of
these out-of-sample extensions estimate the true latent position of the
out-of-sample vertex with the same error rate. Further, we prove a central
limit theorem for the least-squares-based extension, showing that the estimate
is asymptotically normal about the truth in the large-graph limit.
| stat.ML | many popular dimensionality reduction procedures have outofsample extensions which allow a practitioner to apply a learned embedding to observations not seen in the initial training sample in this work we consider the problem of obtaining an outofsample extension for the adjacency spectral embedding a procedure for embedding the vertices of a graph into euclidean space we present two different approaches to this problem one based on a leastsquares objective and the other based on a maximumlikelihood formulation we show that if the graph of interest is drawn according to a certain latent position model called a random dot product graph then both of these outofsample extensions estimate the true latent position of the outofsample vertex with the same error rate further we prove a central limit theorem for the leastsquaresbased extension showing that the estimate is asymptotically normal about the truth in the largegraph limit | [['many', 'popular', 'dimensionality', 'reduction', 'procedures', 'have', 'outofsample', 'extensions', 'which', 'allow', 'a', 'practitioner', 'to', 'apply', 'a', 'learned', 'embedding', 'to', 'observations', 'not', 'seen', 'in', 'the', 'initial', 'training', 'sample', 'in', 'this', 'work', 'we', 'consider', 'the', 'problem', 'of', 'obtaining', 'an', 'outofsample', 'extension', 'for', 'the', 'adjacency', 'spectral', 'embedding', 'a', 'procedure', 'for', 'embedding', 'the', 'vertices', 'of', 'a', 'graph', 'into', 'euclidean', 'space', 'we', 'present', 'two', 'different', 'approaches', 'to', 'this', 'problem', 'one', 'based', 'on', 'a', 'leastsquares', 'objective', 'and', 'the', 'other', 'based', 'on', 'a', 'maximumlikelihood', 'formulation', 'we', 'show', 'that', 'if', 'the', 'graph', 'of', 'interest', 'is', 'drawn', 'according', 'to', 'a', 'certain', 'latent', 'position', 'model', 'called', 'a', 'random', 'dot', 'product', 'graph', 'then', 'both', 'of', 'these', 'outofsample', 'extensions', 'estimate', 'the', 'true', 'latent', 'position', 'of', 'the', 'outofsample', 'vertex', 'with', 'the', 'same', 'error', 'rate', 'further', 'we', 'prove', 'a', 'central', 'limit', 'theorem', 'for', 'the', 'leastsquaresbased', 'extension', 'showing', 'that', 'the', 'estimate', 'is', 'asymptotically', 'normal', 'about', 'the', 'truth', 'in', 'the', 'largegraph', 'limit']] | [-0.03607716634227284, -0.0012913273606280763, -0.11365082616885674, 0.1094139671145842, -0.10874188374085673, -0.14082646664424703, 0.08041866910001584, 0.4011629196847307, -0.27586208428737935, -0.2752556789248925, 0.12977761130153748, -0.2591788843916408, -0.13934318687333244, 0.15885786588046827, -0.1294101303411198, 0.05915385912782673, 0.11295501986390044, 0.10091353049341203, -0.07606450516393344, -0.27506155334232407, 0.293860917387466, -0.001325356504269715, 0.2825952147872284, -0.010523057144520611, 0.12644506523241128, 0.053229203373836034, -0.018202489527777352, 0.04753265807115079, -0.12275994738678303, 0.1738312019757798, 0.25573184551863837, 0.17428833556939557, 0.3247272825029133, -0.3563454553097033, -0.2151497399305989, 0.17824261634578478, 0.12570554259361635, 0.0875916345325973, -0.005381313247349241, -0.2788713401055028, 0.0799616805968228, -0.15866241561698502, -0.0721250412953568, -0.049446539285367935, -0.020485709664930695, -0.03186363468010878, -0.31425153739943074, 0.015134513509813053, 0.1078544816580312, 0.014055887776716002, -0.059113120759740984, -0.1311619580659116, 0.004899740418226555, 0.10044168486291992, 0.05196281891187717, 0.04850934373600216, 0.07841733129562585, -0.08943697753972535, -0.13834605690027618, 0.33640043226809335, -0.059392486000433564, -0.231627233670061, 0.13005898464792248, -0.11109003667430631, -0.16874845598198088, 0.07458037080018427, 0.19033245959415518, 0.10229665805938942, -0.1423823416971698, 0.09149843345131276, -0.0842549630408657, 0.14848374056083888, 0.03886598917768048, -0.006455180857828337, 0.14620017442402655, 0.1791917269214474, 0.12724000328694118, 0.1636753246947874, -0.10364141357613021, -0.07102611773358337, -0.30461379404436667, -0.14181841248967525, -0.21704969630287638, 0.03461564626265719, -0.17906911630687256, -0.21327552102056557, 0.42025655448629423, 0.19118036654424564, 0.24340707197913836, 0.12612546017666443, 0.27996483082915175, 0.09273526051306519, 0.031304090097546575, 0.10715963334358972, 0.17675698681830845, 0.12404751307491599, 0.01822941972199699, -0.15671926268922357, 0.07110827507226375, 0.11917194687472335] |
1,802.06308 | Nonparametric Testing under Random Projection | A common challenge in nonparametric inference is its high computational
complexity when data volume is large. In this paper, we develop computationally
efficient nonparametric testing by employing a random projection strategy. In
the specific kernel ridge regression setup, a simple distance-based test
statistic is proposed. Notably, we derive the minimum number of random
projections that is sufficient for achieving testing optimality in terms of the
minimax rate. An adaptive testing procedure is further established without
prior knowledge of regularity. One technical contribution is to establish upper
bounds for a range of tail sums of empirical kernel eigenvalues. Simulations
and real data analysis are conducted to support our theory.
| math.ST stat.ME stat.ML stat.TH | a common challenge in nonparametric inference is its high computational complexity when data volume is large in this paper we develop computationally efficient nonparametric testing by employing a random projection strategy in the specific kernel ridge regression setup a simple distancebased test statistic is proposed notably we derive the minimum number of random projections that is sufficient for achieving testing optimality in terms of the minimax rate an adaptive testing procedure is further established without prior knowledge of regularity one technical contribution is to establish upper bounds for a range of tail sums of empirical kernel eigenvalues simulations and real data analysis are conducted to support our theory | [['a', 'common', 'challenge', 'in', 'nonparametric', 'inference', 'is', 'its', 'high', 'computational', 'complexity', 'when', 'data', 'volume', 'is', 'large', 'in', 'this', 'paper', 'we', 'develop', 'computationally', 'efficient', 'nonparametric', 'testing', 'by', 'employing', 'a', 'random', 'projection', 'strategy', 'in', 'the', 'specific', 'kernel', 'ridge', 'regression', 'setup', 'a', 'simple', 'distancebased', 'test', 'statistic', 'is', 'proposed', 'notably', 'we', 'derive', 'the', 'minimum', 'number', 'of', 'random', 'projections', 'that', 'is', 'sufficient', 'for', 'achieving', 'testing', 'optimality', 'in', 'terms', 'of', 'the', 'minimax', 'rate', 'an', 'adaptive', 'testing', 'procedure', 'is', 'further', 'established', 'without', 'prior', 'knowledge', 'of', 'regularity', 'one', 'technical', 'contribution', 'is', 'to', 'establish', 'upper', 'bounds', 'for', 'a', 'range', 'of', 'tail', 'sums', 'of', 'empirical', 'kernel', 'eigenvalues', 'simulations', 'and', 'real', 'data', 'analysis', 'are', 'conducted', 'to', 'support', 'our', 'theory']] | [-0.08616567880380899, -0.002462770780025662, -0.11635900405666756, 0.11023285312478913, -0.10266917395077783, -0.15182822911258512, 0.0947530133071942, 0.38515967899648884, -0.2338257969197541, -0.2996033046015159, 0.1605757573981666, -0.20763103039590297, -0.14483679148058096, 0.1922505159573457, -0.12088128130589784, 0.14002615907520521, 0.09197715326660762, 0.002097851989135422, -0.06135194251287936, -0.2978881282270541, 0.27621212307174037, 0.10348458883042137, 0.33926370673536976, 0.02290566623883529, 0.09382756106357125, 0.01422413403633982, -0.0634295805767006, -0.02228203219042539, -0.1390232990097165, 0.15071642778261943, 0.29538663012768934, 0.20560429715206502, 0.37489151012980276, -0.3722767263949478, -0.18789821143124114, 0.13598772906051534, 0.1038509506119967, 0.06546884315536806, -0.05730360997323361, -0.21232783681437098, 0.1027240695612919, -0.13410392903757315, -0.11560379501477452, -0.12977015836750744, -0.03399630403146148, -0.017228205998738606, -0.38412558406384456, 0.10729467219897304, 0.06798600704253961, 0.09823798782949948, -0.003424055349400819, -0.13420941010634932, 0.10244912330569976, 0.07179715406366288, 0.042061823907446255, 0.0036950566098783856, 0.09783981324559406, -0.10219356710634504, -0.10985954964740409, 0.29861326068553523, -0.05163092372822575, -0.22736120759509504, 0.18562345604274283, -0.1121296073715375, -0.17295072947335602, 0.11290475946885568, 0.21593686684759128, 0.10900375280201573, -0.1893599543347793, 0.10415814654347573, -0.06504275211719451, 0.1260889681159622, 0.03388891404891318, -0.01836850640312251, 0.10536947196211528, 0.22423887738087042, 0.11629577989138111, 0.16976905589507185, -0.12108763271114892, -0.06983890697894687, -0.31436597401517685, -0.15045313126649018, -0.27214964663747837, -0.029195498333407427, -0.1868718491253579, -0.19390621457865495, 0.35011266760907517, 0.16275938710047436, 0.1701951692380977, 0.15280840096615808, 0.3329103677619801, 0.12159959814312903, 0.012820513595619963, 0.0985499906776197, 0.184483444470162, 0.14600610753081533, 0.011601903014471409, -0.16232330421054805, 0.1252437241082252, 0.06805547804752779] |
1,802.06309 | Learning Adversarially Fair and Transferable Representations | In this paper, we advocate for representation learning as the key to
mitigating unfair prediction outcomes downstream. Motivated by a scenario where
learned representations are used by third parties with unknown objectives, we
propose and explore adversarial representation learning as a natural method of
ensuring those parties act fairly. We connect group fairness (demographic
parity, equalized odds, and equal opportunity) to different adversarial
objectives. Through worst-case theoretical guarantees and experimental
validation, we show that the choice of this objective is crucial to fair
prediction. Furthermore, we present the first in-depth experimental
demonstration of fair transfer learning and demonstrate empirically that our
learned representations admit fair predictions on new tasks while maintaining
utility, an essential goal of fair representation learning.
| cs.LG stat.ML | in this paper we advocate for representation learning as the key to mitigating unfair prediction outcomes downstream motivated by a scenario where learned representations are used by third parties with unknown objectives we propose and explore adversarial representation learning as a natural method of ensuring those parties act fairly we connect group fairness demographic parity equalized odds and equal opportunity to different adversarial objectives through worstcase theoretical guarantees and experimental validation we show that the choice of this objective is crucial to fair prediction furthermore we present the first indepth experimental demonstration of fair transfer learning and demonstrate empirically that our learned representations admit fair predictions on new tasks while maintaining utility an essential goal of fair representation learning | [['in', 'this', 'paper', 'we', 'advocate', 'for', 'representation', 'learning', 'as', 'the', 'key', 'to', 'mitigating', 'unfair', 'prediction', 'outcomes', 'downstream', 'motivated', 'by', 'a', 'scenario', 'where', 'learned', 'representations', 'are', 'used', 'by', 'third', 'parties', 'with', 'unknown', 'objectives', 'we', 'propose', 'and', 'explore', 'adversarial', 'representation', 'learning', 'as', 'a', 'natural', 'method', 'of', 'ensuring', 'those', 'parties', 'act', 'fairly', 'we', 'connect', 'group', 'fairness', 'demographic', 'parity', 'equalized', 'odds', 'and', 'equal', 'opportunity', 'to', 'different', 'adversarial', 'objectives', 'through', 'worstcase', 'theoretical', 'guarantees', 'and', 'experimental', 'validation', 'we', 'show', 'that', 'the', 'choice', 'of', 'this', 'objective', 'is', 'crucial', 'to', 'fair', 'prediction', 'furthermore', 'we', 'present', 'the', 'first', 'indepth', 'experimental', 'demonstration', 'of', 'fair', 'transfer', 'learning', 'and', 'demonstrate', 'empirically', 'that', 'our', 'learned', 'representations', 'admit', 'fair', 'predictions', 'on', 'new', 'tasks', 'while', 'maintaining', 'utility', 'an', 'essential', 'goal', 'of', 'fair', 'representation', 'learning']] | [-0.07311869510684256, 0.021938885770617797, -0.10285693212356536, 0.09089226778420549, -0.1300490027363999, -0.2054521988729062, 0.10028258609260675, 0.45243184781875934, -0.24990190513932906, -0.32765372794027586, 0.010276339532408332, -0.24410885096598073, -0.18505854947402478, 0.134165711186341, -0.17115351280682728, 0.12050728043619574, 0.10017010035040248, 0.030400212584141672, -0.01336616715181525, -0.2920498644166133, 0.33074227600757566, 0.1029711309507364, 0.3531856600179517, 0.03292470585413966, 0.11942776204810768, 0.021252544739620148, -0.04011153600749975, -0.024605208663504664, -0.09522492389701906, 0.17104747976291879, 0.3823079792078303, 0.23368298401664786, 0.3904944646657452, -0.4161229868802954, -0.15433757465531828, 0.13939940510317683, 0.11956680481059931, 0.07523736254969503, -0.09376603460913728, -0.2966094760678145, 0.06471651993269305, -0.21313443560223683, -0.013472814793784578, -0.1803137274740749, -0.08208893252411435, -0.02690252347921702, -0.33655347028295235, 0.01064977077219416, 0.05790141224861145, 0.06964520593917295, -0.07839815587024479, -0.13097555896251642, 0.05595975118579671, 0.2007232253134501, 0.07962792893253393, 0.01705968036793736, 0.10410310681752798, -0.13900770788558392, -0.2068065115634133, 0.3864177715956789, -0.05205110113817353, -0.17865507572717151, 0.17932942855179684, -0.0618037973822118, -0.16419620763123066, 0.0038295710897620987, 0.22128767576538214, 0.08859158268182718, -0.13259253982736283, -0.04292395570268463, -0.07663378718518456, 0.1568628788823966, 0.018269086037488544, 0.027601228523099434, 0.1673874439389891, 0.2290283110689278, 0.0969015120943429, 0.13367960354735872, 0.010206222246565363, -0.11440636780300811, -0.3032920547299275, -0.11549785398651197, -0.16663129784033767, -0.004669312518794371, -0.08242851373213225, -0.04858317524277871, 0.38804606088444965, 0.232514562578324, 0.20899683794793242, 0.14816841154414065, 0.36504409083814804, 0.016012280861487097, 0.029060335164623603, 0.10237407070357635, 0.20272360399694128, 0.023295935145540146, 0.053933419479436764, -0.20004030999907552, 0.15921387424058586, -0.00011076935676529127] |
1,802.0631 | Characterizing and Learning Equivalence Classes of Causal DAGs under
Interventions | We consider the problem of learning causal DAGs in the setting where both
observational and interventional data is available. This setting is common in
biology, where gene regulatory networks can be intervened on using chemical
reagents or gene deletions. Hauser and B\"uhlmann (2012) previously
characterized the identifiability of causal DAGs under perfect interventions,
which eliminate dependencies between targeted variables and their direct
causes. In this paper, we extend these identifiability results to general
interventions, which may modify the dependencies between targeted variables and
their causes without eliminating them. We define and characterize the
interventional Markov equivalence class that can be identified from general
(not necessarily perfect) intervention experiments. We also propose the first
provably consistent algorithm for learning DAGs in this setting and evaluate
our algorithm on simulated and biological datasets.
| stat.ME math.ST stat.AP stat.TH | we consider the problem of learning causal dags in the setting where both observational and interventional data is available this setting is common in biology where gene regulatory networks can be intervened on using chemical reagents or gene deletions hauser and buhlmann 2012 previously characterized the identifiability of causal dags under perfect interventions which eliminate dependencies between targeted variables and their direct causes in this paper we extend these identifiability results to general interventions which may modify the dependencies between targeted variables and their causes without eliminating them we define and characterize the interventional markov equivalence class that can be identified from general not necessarily perfect intervention experiments we also propose the first provably consistent algorithm for learning dags in this setting and evaluate our algorithm on simulated and biological datasets | [['we', 'consider', 'the', 'problem', 'of', 'learning', 'causal', 'dags', 'in', 'the', 'setting', 'where', 'both', 'observational', 'and', 'interventional', 'data', 'is', 'available', 'this', 'setting', 'is', 'common', 'in', 'biology', 'where', 'gene', 'regulatory', 'networks', 'can', 'be', 'intervened', 'on', 'using', 'chemical', 'reagents', 'or', 'gene', 'deletions', 'hauser', 'and', 'buhlmann', '2012', 'previously', 'characterized', 'the', 'identifiability', 'of', 'causal', 'dags', 'under', 'perfect', 'interventions', 'which', 'eliminate', 'dependencies', 'between', 'targeted', 'variables', 'and', 'their', 'direct', 'causes', 'in', 'this', 'paper', 'we', 'extend', 'these', 'identifiability', 'results', 'to', 'general', 'interventions', 'which', 'may', 'modify', 'the', 'dependencies', 'between', 'targeted', 'variables', 'and', 'their', 'causes', 'without', 'eliminating', 'them', 'we', 'define', 'and', 'characterize', 'the', 'interventional', 'markov', 'equivalence', 'class', 'that', 'can', 'be', 'identified', 'from', 'general', 'not', 'necessarily', 'perfect', 'intervention', 'experiments', 'we', 'also', 'propose', 'the', 'first', 'provably', 'consistent', 'algorithm', 'for', 'learning', 'dags', 'in', 'this', 'setting', 'and', 'evaluate', 'our', 'algorithm', 'on', 'simulated', 'and', 'biological', 'datasets']] | [-0.06806886361783925, 0.08356525566613743, -0.017544021055045475, 0.10985725777461869, -0.13312770629955725, -0.16991827084562022, 0.11292956376942372, 0.42314976888872285, -0.2757798559423394, -0.32977651769849864, 0.08064824575380572, -0.21379921077004144, -0.212460846087419, 0.15681715037865665, -0.15017917888765117, 0.051710998696339036, 0.10589939237124364, 0.005133114271719037, 0.0037519832493609372, -0.28438611724236196, 0.31436911592814476, 0.029624287058592182, 0.26655165900570477, 0.019561403332800667, 0.08924362748480963, 0.03435716485846361, -0.07258274948725633, 0.0726131622342727, -0.15572021109633413, 0.09617601106394764, 0.3625509663261047, 0.24270294375987794, 0.2984353561480423, -0.44030899827489417, -0.22718180804726676, 0.190169733540219, 0.09133322494289348, 0.14885855287605387, 0.0010505585677082871, -0.3061976715182985, 0.06769585103429285, -0.11299937094014337, -0.0014130963183200086, -0.12413547219680125, -0.05185673636817978, 0.00015747963045617097, -0.33101694380423724, 0.09113987012324634, 0.07138930134944448, 0.09450197910875764, -0.05722004805428495, -0.0917109030828534, -0.003731935052201152, 0.1446426650455918, 0.026917736301063526, -0.005439197257132703, 0.1120446654079297, -0.09496987973403617, -0.18931148453200267, 0.3080696950716372, 0.015458981499417138, -0.21151761393212065, 0.21495729055653773, -0.05724346565210637, -0.20927388394137716, 0.05360407768286594, 0.23347025871888258, 0.1357122281964385, -0.22201767969407313, 0.0568095330075176, -0.029879852382906975, 0.1385444437956992, 0.09330897000333906, -0.022598164229838617, 0.12871063907474337, 0.14831075464368207, 0.037991957198686276, 0.15706075683372978, -0.0732394961965935, -0.1057916909370941, -0.26455356331385727, -0.11460373374875209, -0.12960454380807987, -0.0012971524748807233, -0.13381103625801438, -0.16033697490160928, 0.3448996492981456, 0.21914134070522487, 0.1590452184865095, 0.11573722453979875, 0.27041087324290275, 0.02947025102268165, 0.05285705289383995, 0.10318596742314975, 0.1683517145241774, 0.08468333092155575, 0.06792747669583116, -0.201661144481005, 0.20541863790748796, -0.024171773731736963] |
1,802.06311 | Virtual Element Method: an equilibrium-based stress recovery procedure | Within the framework of the displacement-based Virtual Element Method (VEM)
for plane elasticity a significant problem is represented by an accurate
evaluation of the stress field. In particular, in the classical VEM
formulation, a suitable operator which maps to the strain field is introduced
in order to allow the calculation of the stiffness matrix. The stress field is
then computed using that strian field, by using the constitutive law.
Considering for example a first-order formulation for a homogeneous material,
strains are locally mapped onto constant functions, and stresses are
accordingly piecewise constant. However, the virtual displacements might
engender more complex strain fields for polygons which are not triangles. In
this paper, Recovery by Compatibility in Patches is used in order to mitigate
such an effect and, thus, enhance the accuracy of the recovered stress field.
The procedure is simple, efficient and can be readily implemented in existing
codes. Numerical tests confirm the soundness of the proposed approach.
| math.NA | within the framework of the displacementbased virtual element method vem for plane elasticity a significant problem is represented by an accurate evaluation of the stress field in particular in the classical vem formulation a suitable operator which maps to the strain field is introduced in order to allow the calculation of the stiffness matrix the stress field is then computed using that strian field by using the constitutive law considering for example a firstorder formulation for a homogeneous material strains are locally mapped onto constant functions and stresses are accordingly piecewise constant however the virtual displacements might engender more complex strain fields for polygons which are not triangles in this paper recovery by compatibility in patches is used in order to mitigate such an effect and thus enhance the accuracy of the recovered stress field the procedure is simple efficient and can be readily implemented in existing codes numerical tests confirm the soundness of the proposed approach | [['within', 'the', 'framework', 'of', 'the', 'displacementbased', 'virtual', 'element', 'method', 'vem', 'for', 'plane', 'elasticity', 'a', 'significant', 'problem', 'is', 'represented', 'by', 'an', 'accurate', 'evaluation', 'of', 'the', 'stress', 'field', 'in', 'particular', 'in', 'the', 'classical', 'vem', 'formulation', 'a', 'suitable', 'operator', 'which', 'maps', 'to', 'the', 'strain', 'field', 'is', 'introduced', 'in', 'order', 'to', 'allow', 'the', 'calculation', 'of', 'the', 'stiffness', 'matrix', 'the', 'stress', 'field', 'is', 'then', 'computed', 'using', 'that', 'strian', 'field', 'by', 'using', 'the', 'constitutive', 'law', 'considering', 'for', 'example', 'a', 'firstorder', 'formulation', 'for', 'a', 'homogeneous', 'material', 'strains', 'are', 'locally', 'mapped', 'onto', 'constant', 'functions', 'and', 'stresses', 'are', 'accordingly', 'piecewise', 'constant', 'however', 'the', 'virtual', 'displacements', 'might', 'engender', 'more', 'complex', 'strain', 'fields', 'for', 'polygons', 'which', 'are', 'not', 'triangles', 'in', 'this', 'paper', 'recovery', 'by', 'compatibility', 'in', 'patches', 'is', 'used', 'in', 'order', 'to', 'mitigate', 'such', 'an', 'effect', 'and', 'thus', 'enhance', 'the', 'accuracy', 'of', 'the', 'recovered', 'stress', 'field', 'the', 'procedure', 'is', 'simple', 'efficient', 'and', 'can', 'be', 'readily', 'implemented', 'in', 'existing', 'codes', 'numerical', 'tests', 'confirm', 'the', 'soundness', 'of', 'the', 'proposed', 'approach']] | [-0.10722530185086772, 0.09225639978066259, -0.06842793763065949, 0.020477677934253827, -0.053396209029671855, -0.10523448156145139, -0.009507281937821506, 0.382185392607099, -0.30716711410488456, -0.2697948103066152, 0.10660101926306453, -0.2019344144500792, -0.17378103459486738, 0.20296372589058218, -0.06523296521761669, 0.05710261530774192, 0.034098715359458506, 0.006379054135416085, -0.0640657721186215, -0.2255681671436506, 0.31065523826803726, 0.05871122154311683, 0.30034836700472695, 0.04585430902751306, 0.08642852746803935, 0.00507788493931189, -0.011811921411814788, 0.11959152218463341, -0.10059069610934575, 0.12524047527441348, 0.2551896129287063, 0.07502475932228546, 0.25778041396719903, -0.4451898195577833, -0.21710123494267464, 0.08497942375759475, 0.10871053249754298, 0.13400927794207698, -0.033518929940887926, -0.25468863416701937, 0.09059966179446723, -0.1482634792695395, -0.14114204756258866, -0.11541925026969506, -0.016867949459433127, -0.0005357024530008531, -0.3108981842479597, 0.08129520127728868, 0.02449284544145951, 0.05901537448442422, -0.09272921508930337, -0.08171956956637307, -0.0007969241869898561, 0.1016066780922791, 0.03842181762387881, 0.06633508329739413, 0.1343069166267434, -0.10840052174469934, -0.07743842251828681, 0.41641778661272466, -0.05749858356004832, -0.27853764704643535, 0.12650675937019956, -0.08033683164140736, -0.07509109816657236, 0.15870336247369266, 0.183642938588328, 0.11889670008163077, -0.16618981820125228, 0.08451398949397024, 0.011469169740732282, 0.15499902667794138, 0.05067477287360443, -0.06288119234467068, 0.16173922709034136, 0.12017921319392581, 0.044192154318667375, 0.14038458277205698, -0.03897663816594733, -0.08348871678119907, -0.31319066777061194, -0.190542133925658, -0.20084436286990054, -0.025754842538448522, -0.14309651714136587, -0.19201192693510213, 0.3629693445057059, 0.1465438943970441, 0.12387093708504182, 0.058725138094861254, 0.2968400807227366, 0.13373390971444166, 0.11282530995026135, 0.06615759995586884, 0.27083994718626714, 0.16109493443330464, 0.06386104938335335, -0.19336019535190785, 0.07924149251984766, 0.10815175665088762] |
1,802.06312 | Counting linear extensions of restricted posets | The classical 1991 result by Brightwell and Winkler states that the number of
linear extensions of a poset is #P-complete. We extend this result to posets
with certain restrictions. First, we prove that the number of linear extension
for posets of height two is #P-complete. Furthermore, we prove that this holds
for incidence posets of graphs. Finally, we prove that the number of linear
extensions for posets of dimension two is #P-complete.
| math.CO | the classical 1991 result by brightwell and winkler states that the number of linear extensions of a poset is pcomplete we extend this result to posets with certain restrictions first we prove that the number of linear extension for posets of height two is pcomplete furthermore we prove that this holds for incidence posets of graphs finally we prove that the number of linear extensions for posets of dimension two is pcomplete | [['the', 'classical', '1991', 'result', 'by', 'brightwell', 'and', 'winkler', 'states', 'that', 'the', 'number', 'of', 'linear', 'extensions', 'of', 'a', 'poset', 'is', 'pcomplete', 'we', 'extend', 'this', 'result', 'to', 'posets', 'with', 'certain', 'restrictions', 'first', 'we', 'prove', 'that', 'the', 'number', 'of', 'linear', 'extension', 'for', 'posets', 'of', 'height', 'two', 'is', 'pcomplete', 'furthermore', 'we', 'prove', 'that', 'this', 'holds', 'for', 'incidence', 'posets', 'of', 'graphs', 'finally', 'we', 'prove', 'that', 'the', 'number', 'of', 'linear', 'extensions', 'for', 'posets', 'of', 'dimension', 'two', 'is', 'pcomplete']] | [-0.14563407779981694, 0.12596654421132472, -0.005898698168392811, 0.11429986749135423, -0.1223979209898971, -0.1268036718182783, 0.03423615679558781, 0.33738295594230294, -0.34864631601117757, -0.2601903665862564, 0.09246470331143225, -0.22381595793801048, -0.17048745209144223, 0.19573516033900282, -0.16038922680955794, 0.03722542030219403, 0.052000957222036154, 0.02975425560079101, -0.006950182180541257, -0.3415608659076194, 0.3782940939482715, -0.06150450711397247, 0.18283065129071474, 0.098057090692843, 0.06305480846721265, 0.07020405147664456, 0.026494191713734634, 0.09950199130819076, -0.1820431026430419, 0.13040445257987207, 0.3010502719941239, 0.16100067522428516, 0.25867511548696914, -0.34297402328552884, -0.11174157115682545, 0.20317880711000827, 0.08229486885506453, 0.09714967377173404, 0.006838281836179603, -0.17916427713094485, 0.1553597725643259, -0.1792718328651972, -0.16103128848286966, -0.02331220349555628, 0.09475510468150282, 0.06015778551550789, -0.2844368771960338, -0.009670098169913722, 0.21655351612975615, 0.09019214403815567, -0.018045158526446257, -0.14628301250761272, -0.0298341119543895, 0.02616749117765317, -0.018674547884276964, -0.0635252500910105, -0.012014401953719143, -0.06216401731621267, -0.23491934644213566, 0.3228945617318257, -0.01944785385745086, -0.20323512317716247, 0.12830407491936865, -0.18841801971171257, -0.2343773273395426, 0.07281286061819021, 0.05388919859089785, 0.126814087620005, -0.0032627047168918783, 0.17910609671040825, -0.24362362610150334, 0.10316835173256954, 0.18909581332829678, 0.004971254041366693, 0.05008953394523511, 0.14042360968111703, 0.1502414134900189, 0.23284241002208242, 0.02012113104056981, -0.020713629032899108, -0.2968780818498797, -0.17623178499181652, -0.160816673680933, 0.033047508487167455, -0.1220794207181623, -0.17958073292134535, 0.3759034404841562, 0.17927866574852183, 0.17267122652588618, 0.2620857532019727, 0.2022602022237455, 0.07842015055939555, 0.014457971413826777, 0.07187346989263056, 0.14153890922251675, 0.2472193060190572, -0.05145800206810236, -0.14087236744429296, 0.044838304120478116, 0.20631079900906318] |
1,802.06313 | Deformation of a Half-Space from Anelastic Strain Confined in a
Tetrahedral Volume | Deformation in the lithosphere-asthenosphere system can be accommodated by
faulting and plastic flow. However, incorporating structural data in models of
distributed deformation still represents a challenge. Here, I present solutions
for the displacements and stress in a half-space caused by distributed
anelastic strain confined in a tetrahedral volume. These solutions form the
basis of curvilinear meshes that can adapt to realistic structural settings,
such as a mantle wedge corner, a spherical shell around a magma chamber, or an
aquifer. I provide computer programs to evaluate them in the cases of
anti-plane strain, in-plane strain, and three-dimensional deformation. These
tools may prove useful in the modeling of deformation data in tectonics,
volcanology, and hydrology.
| physics.geo-ph | deformation in the lithosphereasthenosphere system can be accommodated by faulting and plastic flow however incorporating structural data in models of distributed deformation still represents a challenge here i present solutions for the displacements and stress in a halfspace caused by distributed anelastic strain confined in a tetrahedral volume these solutions form the basis of curvilinear meshes that can adapt to realistic structural settings such as a mantle wedge corner a spherical shell around a magma chamber or an aquifer i provide computer programs to evaluate them in the cases of antiplane strain inplane strain and threedimensional deformation these tools may prove useful in the modeling of deformation data in tectonics volcanology and hydrology | [['deformation', 'in', 'the', 'lithosphereasthenosphere', 'system', 'can', 'be', 'accommodated', 'by', 'faulting', 'and', 'plastic', 'flow', 'however', 'incorporating', 'structural', 'data', 'in', 'models', 'of', 'distributed', 'deformation', 'still', 'represents', 'a', 'challenge', 'here', 'i', 'present', 'solutions', 'for', 'the', 'displacements', 'and', 'stress', 'in', 'a', 'halfspace', 'caused', 'by', 'distributed', 'anelastic', 'strain', 'confined', 'in', 'a', 'tetrahedral', 'volume', 'these', 'solutions', 'form', 'the', 'basis', 'of', 'curvilinear', 'meshes', 'that', 'can', 'adapt', 'to', 'realistic', 'structural', 'settings', 'such', 'as', 'a', 'mantle', 'wedge', 'corner', 'a', 'spherical', 'shell', 'around', 'a', 'magma', 'chamber', 'or', 'an', 'aquifer', 'i', 'provide', 'computer', 'programs', 'to', 'evaluate', 'them', 'in', 'the', 'cases', 'of', 'antiplane', 'strain', 'inplane', 'strain', 'and', 'threedimensional', 'deformation', 'these', 'tools', 'may', 'prove', 'useful', 'in', 'the', 'modeling', 'of', 'deformation', 'data', 'in', 'tectonics', 'volcanology', 'and', 'hydrology']] | [-0.11271641400964123, 0.11107317618399846, -0.06329153406442004, 0.0014141702579449763, -0.04528209918170376, -0.09998690514141216, -0.03271449172861083, 0.37347438521023874, -0.3400140280291372, -0.2494236031728508, 0.14160701736037215, -0.21792330513749503, -0.16842660929315387, 0.17659369637196537, -0.10236022876528199, 0.07440506228435356, 0.06741695475879958, -0.05905775320113521, -0.0716651881884256, -0.1716265077867537, 0.24918519085133037, 0.02625996274131853, 0.27651349697014793, 0.03383014640828545, 0.04530940393389788, -0.046569370191268195, 0.023506344024058993, 0.08976983662141398, -0.16847874871816998, 0.11804963384839434, 0.2919954575109978, 0.05854258043701818, 0.23971657427769583, -0.5571894799977277, -0.24005561110983908, 0.021033186205885315, 0.12041435445930841, 0.12115505717661793, -0.04518875572212304, -0.2420417237491141, 0.02057873337752128, -0.16531577629276215, -0.14373933524717536, -0.10911543859350206, 0.029369659301519923, 0.00784417252577594, -0.2703395669834804, 0.13004595345397174, 0.07661158902172939, 0.12016981451945231, -0.13209612548054583, -0.0893556192564671, -0.04542435465825606, 0.07556690249913496, 0.05420037868342041, 0.003514618392357916, 0.1927818275032054, -0.14328288012466225, -0.029978445830004932, 0.43617939949035645, 0.00927399990841153, -0.23861829877163457, 0.15789623573768588, -0.11391472222234032, -0.09547090025173616, 0.15061351262956066, 0.24009500418385837, 0.08285422789567666, -0.15789004597417286, 0.06668164368216113, -0.002525036447821597, 0.14347220761124538, 0.08365188087377928, -0.10562914092736565, 0.22713572598697074, 0.2080405136494098, 0.04244863449707133, 0.14357017876376374, -0.11735213760712052, -0.051895919709210904, -0.3062728290091706, -0.19147289432461612, -0.170636385201221, 0.016991704905331466, -0.12566959311870396, -0.22892963073002093, 0.3227452969949165, 0.08312740233475424, 0.1168368797073454, -0.012322613631531728, 0.23444227410884227, 0.001662039143379481, 0.09658104644478423, 0.06556551211999845, 0.2484247898435698, 0.11617810109556051, 0.11954151767520316, -0.1888160896405116, 0.08091109152059112, 0.04800944825617877] |
1,802.06314 | Autonomous Vehicle Speed Control for Safe Navigation of Occluded
Pedestrian Crosswalk | Both humans and the sensors on an autonomous vehicle have limited sensing
capabilities. When these limitations coincide with scenarios involving
vulnerable road users, it becomes important to account for these limitations in
the motion planner. For the scenario of an occluded pedestrian crosswalk, the
speed of the approaching vehicle should be a function of the amount of
uncertainty on the roadway. In this work, the longitudinal controller is
formulated as a partially observable Markov decision process and dynamic
programming is used to compute the control policy. The control policy scales
the speed profile to be used by a model predictive steering controller.
| cs.RO cs.AI cs.SY | both humans and the sensors on an autonomous vehicle have limited sensing capabilities when these limitations coincide with scenarios involving vulnerable road users it becomes important to account for these limitations in the motion planner for the scenario of an occluded pedestrian crosswalk the speed of the approaching vehicle should be a function of the amount of uncertainty on the roadway in this work the longitudinal controller is formulated as a partially observable markov decision process and dynamic programming is used to compute the control policy the control policy scales the speed profile to be used by a model predictive steering controller | [['both', 'humans', 'and', 'the', 'sensors', 'on', 'an', 'autonomous', 'vehicle', 'have', 'limited', 'sensing', 'capabilities', 'when', 'these', 'limitations', 'coincide', 'with', 'scenarios', 'involving', 'vulnerable', 'road', 'users', 'it', 'becomes', 'important', 'to', 'account', 'for', 'these', 'limitations', 'in', 'the', 'motion', 'planner', 'for', 'the', 'scenario', 'of', 'an', 'occluded', 'pedestrian', 'crosswalk', 'the', 'speed', 'of', 'the', 'approaching', 'vehicle', 'should', 'be', 'a', 'function', 'of', 'the', 'amount', 'of', 'uncertainty', 'on', 'the', 'roadway', 'in', 'this', 'work', 'the', 'longitudinal', 'controller', 'is', 'formulated', 'as', 'a', 'partially', 'observable', 'markov', 'decision', 'process', 'and', 'dynamic', 'programming', 'is', 'used', 'to', 'compute', 'the', 'control', 'policy', 'the', 'control', 'policy', 'scales', 'the', 'speed', 'profile', 'to', 'be', 'used', 'by', 'a', 'model', 'predictive', 'steering', 'controller']] | [-0.1136472775354324, 0.0797701351662445, -0.06531232069520389, 0.05214241143497858, -0.10976038840781971, -0.18250099661778293, 0.07105975217350266, 0.41233520984065297, -0.3039504190788184, -0.3202354286742561, 0.1540788782347626, -0.2360334996882753, -0.12415954045147873, 0.21824726860230678, -0.16818466031120397, 0.13782604376319796, 0.05714091338564222, 0.05984070558356596, 0.05720084606710931, -0.20902829284108626, 0.2506073872015501, 0.05845964235235371, 0.28965613033220755, 0.04198131883316034, 0.1627306639645979, 0.018199432896924952, 0.029884893173242316, 0.03048403742814473, -0.060902123859919166, 0.11702526226872578, 0.3130414172203517, 0.1797742512551885, 0.32030208587792575, -0.44040808492504496, -0.23527429944963432, 0.0879886230070363, 0.11346327066969346, 0.08400013503006787, 0.023753358208683923, -0.3678969798573092, 0.06313814596254744, -0.20399376071587788, -0.09433597862131565, -0.04136787257020307, -0.007008441226264718, -0.0039965408516353836, -0.2806801942488699, -0.005559582957157902, 0.0008517476620481295, 0.04901438961992515, -0.08601154312354457, -0.04394512182768142, 0.009848444622156083, 0.21992373998264333, 0.05629299526124526, 0.007634527091046467, 0.21998981696293307, -0.17039532325111842, -0.1557499730192563, 0.39511165895736683, 0.01791750894307031, -0.25837914674433277, 0.15833510717778815, -0.050801220555406285, -0.10980345134842484, 0.11952758562413282, 0.26094645068594985, 0.11000537762518826, -0.1691458762023317, 0.00875036877593683, -0.009327604617996543, 0.15083816548005916, 0.019564680517742447, -0.00860984590561951, 0.20108562446308925, 0.23352543781191915, 0.16314400945488802, 0.09879892636501394, -0.0899878468720571, -0.1304218163554027, -0.25219326188751295, -0.12034086826914812, -0.1350566544706988, -0.0430533301938946, -0.10239221048786752, -0.10540659219830059, 0.35149581656407786, 0.2360938165263945, 0.16358624624234497, 0.08820777722433501, 0.3858748679505844, 0.14320726489406263, 0.05914573116666254, 0.09686272791312898, 0.22718921451436758, 0.04703031171241081, 0.1590850886514447, -0.25947960423232586, 0.17302923726246638, -0.012319230301049994] |
1,802.06315 | Center of mass and K\"ahler structures | There is a sequence of positive numbers $\delta_{2n}$, such that for any
connected $2n$-dimensional Riemannian manifold $M$, there are two mutually
exclusive possibilities: $1)$ There is a complex structure on $M$ making it
into a K\"ahler manifold, or $2)$ For any almost complex structure $J$
compatible with the metric, at every point $p\in M$, there is a smooth loop
$\gamma$ at $p$ such that $dist(J_p, hol_\gamma^{-1}J_phol_\gamma)>
\delta_{2n}$.
| math.DG | there is a sequence of positive numbers delta_2n such that for any connected 2ndimensional riemannian manifold m there are two mutually exclusive possibilities 1 there is a complex structure on m making it into a kahler manifold or 2 for any almost complex structure j compatible with the metric at every point pin m there is a smooth loop gamma at p such that distj_p hol_gamma1j_phol_gamma delta_2n | [['there', 'is', 'a', 'sequence', 'of', 'positive', 'numbers', 'delta_2n', 'such', 'that', 'for', 'any', 'connected', '2ndimensional', 'riemannian', 'manifold', 'm', 'there', 'are', 'two', 'mutually', 'exclusive', 'possibilities', '1', 'there', 'is', 'a', 'complex', 'structure', 'on', 'm', 'making', 'it', 'into', 'a', 'kahler', 'manifold', 'or', '2', 'for', 'any', 'almost', 'complex', 'structure', 'j', 'compatible', 'with', 'the', 'metric', 'at', 'every', 'point', 'pin', 'm', 'there', 'is', 'a', 'smooth', 'loop', 'gamma', 'at', 'p', 'such', 'that', 'distj_p', 'hol_gamma1j_phol_gamma', 'delta_2n']] | [-0.23927153767301487, 0.15939580847939047, -0.04425898152761734, 0.049431698824852134, -0.07144877304537939, -0.23503484591268575, 0.0006434581618612775, 0.43329661482801807, -0.24167421898589683, -0.20516168383451608, 0.07549272236915736, -0.3050464533269405, -0.16507746229449716, 0.19728867191678057, -0.016209756382382833, -0.028846572588484446, 0.07925606801962623, 0.12774720017869884, -0.04032562069165019, -0.1952411441490627, 0.4603498310996936, -0.1004845706316141, 0.13919080434089454, 0.008947645142101324, 0.17202188971165855, -0.007122639975009056, 0.034997416703173745, 0.048212151573254515, -0.11299543636604749, 0.07335972359332328, 0.2916732738654201, 0.11466579645728836, 0.22546653839974448, -0.3240834544175484, -0.18728288968022053, 0.23132345223656067, 0.07319626020840728, -0.0648462489533883, -0.05360916931934368, -0.20711400053965356, 0.20283112126904038, -0.06532931890016279, -0.14878733626638468, -0.06584497450206142, 0.1296564445615961, -0.08615240716876892, -0.28506799299592295, -0.06637304701770727, 0.1177812984965455, 0.04612630604264828, -0.010400525498418854, -0.14838618833858233, -0.061658967085755786, 0.11934837895588805, -0.03504045181907713, 0.17270724882120983, 0.067558463385257, -0.028637351740438206, -0.10945549389490715, 0.36136803139144413, -0.027028415008233143, -0.289843034858887, 0.16158668949053837, -0.1648913433202184, -0.1815726422991317, 0.1739395329191421, 0.03987074798832719, 0.11822287103591057, -0.030306718099074294, 0.20467942730308725, -0.06526995623627534, 0.15230830721781016, 0.10932168647503623, -0.058861629106104374, 0.20137068142111486, 0.15483406835164015, 0.16873602927304232, -0.02256019012286113, -0.002746135093128452, -0.026686860613811474, -0.355917046391047, -0.18753459999481073, -0.16888499113802727, 0.24204894976260571, -0.13784181914077356, -0.16632176018140923, 0.28826108016073704, -0.06359095437308916, 0.25633042393108973, 0.04753801427566661, 0.24245920218527317, 0.03339374092622445, 0.058001278347980516, 0.1625559604440171, 0.12536224628297182, 0.12853689715266228, -0.0008298179564567713, -0.0750489956448571, -0.030795179386265, 0.027024037505571658] |
1,802.06316 | Projective dimension and regularity of edge ideal of some weighted
oriented graphs | In this paper we provide some exact formulas for the projective dimension and
the regularity of edge ideals associated to vertex weighted rooted forests and
oriented cycles. As some consequences, we give some exact formulas for the
depth of these ideals.
| math.AC | in this paper we provide some exact formulas for the projective dimension and the regularity of edge ideals associated to vertex weighted rooted forests and oriented cycles as some consequences we give some exact formulas for the depth of these ideals | [['in', 'this', 'paper', 'we', 'provide', 'some', 'exact', 'formulas', 'for', 'the', 'projective', 'dimension', 'and', 'the', 'regularity', 'of', 'edge', 'ideals', 'associated', 'to', 'vertex', 'weighted', 'rooted', 'forests', 'and', 'oriented', 'cycles', 'as', 'some', 'consequences', 'we', 'give', 'some', 'exact', 'formulas', 'for', 'the', 'depth', 'of', 'these', 'ideals']] | [-0.15667534410590078, 0.0798590429944963, -0.010683150598552169, 0.11926077763697632, -0.10960613003727503, -0.11124716051740617, 0.0402592824028078, 0.38566398756896575, -0.3039318129602002, -0.18076410644301555, 0.16263404591633687, -0.27504028400360814, -0.1658626622861115, 0.17604281587480772, -0.13030340413707764, 0.057370560493592807, 0.08813705530994367, 0.10418368749414207, -0.04579202603639626, -0.2953525765276537, 0.3817462976567629, 0.006496141948623628, 0.15804236114206838, 0.12486533442421294, 0.09598903710067999, 0.02534587371231216, -0.03052835568513085, 0.08863609075182821, -0.2827781994895237, 0.19427942336968532, 0.3277857265003571, 0.14708236925780954, 0.17150122545114377, -0.4867170497593356, -0.09058729237763256, 0.20740197131001367, 0.13123851379652213, 0.08965898923030714, -0.005991430385265409, -0.18039803097887738, 0.10193682062189753, -0.14327853722724973, -0.2044522751849599, -0.09776694325889211, 0.07031112155172883, 0.06201518108931983, -0.24674367632080868, -0.013437110430947164, 0.16606054250605223, 0.17471581143213483, -0.05167643592988209, -0.18064515954792137, -0.018297554118695054, 0.10043709135398542, -0.030728919885870887, -0.03121678184763324, -0.014180812598546831, -0.16271067299970948, -0.20048851501650927, 0.29752981302714565, 0.03111483186210801, -0.20999085235341294, 0.1327599083050722, -0.14052014373151994, -0.1947960896735511, 0.05904591737723932, 0.09360510092682955, 0.13793299201785064, -0.0544116091800899, 0.11252619530267377, -0.11481919821079184, -0.047957476001323725, 0.1467026845529312, 0.09783600534244281, 0.11906386466651428, 0.03156311582864785, 0.12378268984214562, 0.22132502625719075, -0.0394023903773348, 0.0033260881900787354, -0.36586811204963343, -0.17601515767845985, -0.09144886040196913, 0.09270473153943695, -0.19857006433644195, -0.2471937111510736, 0.4568499891859729, 0.2022350466832882, 0.24334885779677368, 0.18368215018474474, 0.25204996074118263, 0.12529816362359447, -0.011117300790984457, 0.1014693209038275, 0.09467108200074219, 0.24329344564821662, -0.011406763388616284, -0.10663533385661317, 0.06350823407765568, 0.2542433375946996] |
1,802.06317 | Spontaneous emission from radiative chiral nematic liquid crystals at
the photonic band gap edge: an investigation into the role of the density of
photon states near resonance | In this article, we investigate the spontaneous emission properties of
radiating molecules embedded in a chiral nematic liquid crystal, under the
assumption that the electronic transition frequency is close to the photonic
edge mode of the structure, i.e. at resonance. We take into account the
transition broadening and the decay of electromagnetic field modes supported by
the so-called `mirror-less' cavity. We employ the Jaynes-Cummings Hamiltonian
to describe the electron interaction with the electromagnetic field, focusing
on the mode with the diffracting polarization in the chiral nematic layer. As
known in these structures, the density of photon states, calculated via the
Wigner method, has distinct peaks on either side of the photonic band gap,
which manifests itself as a considerable modification of the emission spectrum.
We demonstrate that, near resonance, there are notable differences between the
behavior of the density of states and the spontaneous emission profile of these
structures. In addition, we examine in some detail the case of the logarithmic
peak exhibited in the density of states in 2D photonic structures and obtain
analytic relations for the Lamb shift and the broadening of the atomic
transition in the emission spectrum. The dynamical behavior of the atom-field
system is described by a system of two first order differential equations,
solved using the Green's function method and the Fourier transform. The
emission spectra are then calculated and compared with experimental data.
| physics.optics | in this article we investigate the spontaneous emission properties of radiating molecules embedded in a chiral nematic liquid crystal under the assumption that the electronic transition frequency is close to the photonic edge mode of the structure ie at resonance we take into account the transition broadening and the decay of electromagnetic field modes supported by the socalled mirrorless cavity we employ the jaynescummings hamiltonian to describe the electron interaction with the electromagnetic field focusing on the mode with the diffracting polarization in the chiral nematic layer as known in these structures the density of photon states calculated via the wigner method has distinct peaks on either side of the photonic band gap which manifests itself as a considerable modification of the emission spectrum we demonstrate that near resonance there are notable differences between the behavior of the density of states and the spontaneous emission profile of these structures in addition we examine in some detail the case of the logarithmic peak exhibited in the density of states in 2d photonic structures and obtain analytic relations for the lamb shift and the broadening of the atomic transition in the emission spectrum the dynamical behavior of the atomfield system is described by a system of two first order differential equations solved using the greens function method and the fourier transform the emission spectra are then calculated and compared with experimental data | [['in', 'this', 'article', 'we', 'investigate', 'the', 'spontaneous', 'emission', 'properties', 'of', 'radiating', 'molecules', 'embedded', 'in', 'a', 'chiral', 'nematic', 'liquid', 'crystal', 'under', 'the', 'assumption', 'that', 'the', 'electronic', 'transition', 'frequency', 'is', 'close', 'to', 'the', 'photonic', 'edge', 'mode', 'of', 'the', 'structure', 'ie', 'at', 'resonance', 'we', 'take', 'into', 'account', 'the', 'transition', 'broadening', 'and', 'the', 'decay', 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1,802.06318 | Large Neighborhood-Based Metaheuristic and Branch-and-Price for the
Pickup and Delivery Problem with Split Loads | We consider the multi-vehicle one-to-one pickup and delivery problem with
split loads, a NP-hard problem linked with a variety of applications for bulk
product transportation, bike-sharing systems and inventory re-balancing. This
problem is notoriously difficult due to the interaction of two challenging
vehicle routing attributes, "pickups and deliveries" and "split deliveries".
This possibly leads to optimal solutions of a size that grows exponentially
with the instance size, containing multiple visits per customer pair, even in
the same route. To solve this problem, we propose an iterated local search
metaheuristic as well as a branch-and-price algorithm. The core of the
metaheuristic consists of a new large neighborhood search, which reduces the
problem of finding the best insertion combination of a pickup and delivery pair
into a route (with possible splits) to a resource-constrained shortest path and
knapsack problem. Similarly, the branch-and-price algorithm uses sophisticated
labeling techniques, route relaxations, pre-processing and branching rules for
an efficient resolution. Our computational experiments on classical
single-vehicle instances demonstrate the excellent performance of the
metaheuristic, which produces new best known solutions for 92 out of 93 test
instances, and outperforms all previous algorithms. Experimental results on new
multi-vehicle instances with distance constraints are also reported. The
branch-and-price algorithm produces optimal solutions for instances with up to
20 pickup-and-delivery pairs, and very accurate solutions are found by the
metaheuristic.
| cs.AI | we consider the multivehicle onetoone pickup and delivery problem with split loads a nphard problem linked with a variety of applications for bulk product transportation bikesharing systems and inventory rebalancing this problem is notoriously difficult due to the interaction of two challenging vehicle routing attributes pickups and deliveries and split deliveries this possibly leads to optimal solutions of a size that grows exponentially with the instance size containing multiple visits per customer pair even in the same route to solve this problem we propose an iterated local search metaheuristic as well as a branchandprice algorithm the core of the metaheuristic consists of a new large neighborhood search which reduces the problem of finding the best insertion combination of a pickup and delivery pair into a route with possible splits to a resourceconstrained shortest path and knapsack problem similarly the branchandprice algorithm uses sophisticated labeling techniques route relaxations preprocessing and branching rules for an efficient resolution our computational experiments on classical singlevehicle instances demonstrate the excellent performance of the metaheuristic which produces new best known solutions for 92 out of 93 test instances and outperforms all previous algorithms experimental results on new multivehicle instances with distance constraints are also reported the branchandprice algorithm produces optimal solutions for instances with up to 20 pickupanddelivery pairs and very accurate solutions are found by the metaheuristic | [['we', 'consider', 'the', 'multivehicle', 'onetoone', 'pickup', 'and', 'delivery', 'problem', 'with', 'split', 'loads', 'a', 'nphard', 'problem', 'linked', 'with', 'a', 'variety', 'of', 'applications', 'for', 'bulk', 'product', 'transportation', 'bikesharing', 'systems', 'and', 'inventory', 'rebalancing', 'this', 'problem', 'is', 'notoriously', 'difficult', 'due', 'to', 'the', 'interaction', 'of', 'two', 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1,802.06319 | Consensus in Software Engineering: A Cognitive Mapping Study | Background: Philosophers of science including Collins, Feyerabend, Kuhn and
Latour have all emphasized the importance of consensus within scientific
communities of practice. Consensus is important for maintaining legitimacy with
outsiders, orchestrating future research, developing educational curricula and
agreeing industry standards. Low consensus contrastingly undermines a field's
reputation and hinders peer review. Aim: This paper aims to investigate the
degree of consensus within the software engineering academic community
concerning members' implicit theories of software engineering. Method: A
convenience sample of 60 software engineering researchers produced diagrams
describing their personal understanding of causal relationships between core
software engineering constructs. The diagrams were then analyzed for patterns
and clusters. Results: At least three schools of thought may be forming;
however, their interpretation is unclear since they do not correspond to known
divisions within the community (e.g. Agile vs. Plan-Driven methods).
Furthermore, over one third of participants do not belong to any cluster.
Conclusion: Although low consensus is common in social sciences, the rapid pace
of innovation observed in software engineering suggests that high consensus is
achievable given renewed commitment to empiricism and evidence-based practice.
| cs.SE | background philosophers of science including collins feyerabend kuhn and latour have all emphasized the importance of consensus within scientific communities of practice consensus is important for maintaining legitimacy with outsiders orchestrating future research developing educational curricula and agreeing industry standards low consensus contrastingly undermines a fields reputation and hinders peer review aim this paper aims to investigate the degree of consensus within the software engineering academic community concerning members implicit theories of software engineering method a convenience sample of 60 software engineering researchers produced diagrams describing their personal understanding of causal relationships between core software engineering constructs the diagrams were then analyzed for patterns and clusters results at least three schools of thought may be forming however their interpretation is unclear since they do not correspond to known divisions within the community eg agile vs plandriven methods furthermore over one third of participants do not belong to any cluster conclusion although low consensus is common in social sciences the rapid pace of innovation observed in software engineering suggests that high consensus is achievable given renewed commitment to empiricism and evidencebased practice | [['background', 'philosophers', 'of', 'science', 'including', 'collins', 'feyerabend', 'kuhn', 'and', 'latour', 'have', 'all', 'emphasized', 'the', 'importance', 'of', 'consensus', 'within', 'scientific', 'communities', 'of', 'practice', 'consensus', 'is', 'important', 'for', 'maintaining', 'legitimacy', 'with', 'outsiders', 'orchestrating', 'future', 'research', 'developing', 'educational', 'curricula', 'and', 'agreeing', 'industry', 'standards', 'low', 'consensus', 'contrastingly', 'undermines', 'a', 'fields', 'reputation', 'and', 'hinders', 'peer', 'review', 'aim', 'this', 'paper', 'aims', 'to', 'investigate', 'the', 'degree', 'of', 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1,802.0632 | Mass problem in the Standard Model | We propose a new $\mathrm{SU}(3)_C \otimes \mathrm{SU}(2)_{L}\otimes
\mathrm{U}(1)_{Y}\otimes \mathrm{U}(1)_{X}$ gauge model which is non universal
respect to the three fermion families of the Standard Model. We introduce
additional one top-like quark, two bottom-like quarks and three right handed
neutrinos in order to have an anomaly free theory. We also consider additional
three right handed neutrinos which are singlets respect to the gauge symmetry
of the model to implement see saw mechanism and give masses to the light
neutrinos according to the neutrino oscillation phenomenology. In the context
of this horizontal gauge symmetry we find mass ansatz for leptons and quarks.
In particular, from the analysis of solar, atmospheric, reactor and accelerator
neutrino oscillation experiments, we get the allow region for the Yukawa
couplings for the charge and neutral lepton sectors according with the mass
squared differences and mixing angles for the two neutrino hierarchy schemes,
normal and inverted.
| hep-ph | we propose a new mathrmsu3_c otimes mathrmsu2_lotimes mathrmu1_yotimes mathrmu1_x gauge model which is non universal respect to the three fermion families of the standard model we introduce additional one toplike quark two bottomlike quarks and three right handed neutrinos in order to have an anomaly free theory we also consider additional three right handed neutrinos which are singlets respect to the gauge symmetry of the model to implement see saw mechanism and give masses to the light neutrinos according to the neutrino oscillation phenomenology in the context of this horizontal gauge symmetry we find mass ansatz for leptons and quarks in particular from the analysis of solar atmospheric reactor and accelerator neutrino oscillation experiments we get the allow region for the yukawa couplings for the charge and neutral lepton sectors according with the mass squared differences and mixing angles for the two neutrino hierarchy schemes normal and inverted | [['we', 'propose', 'a', 'new', 'mathrmsu3_c', 'otimes', 'mathrmsu2_lotimes', 'mathrmu1_yotimes', 'mathrmu1_x', 'gauge', 'model', 'which', 'is', 'non', 'universal', 'respect', 'to', 'the', 'three', 'fermion', 'families', 'of', 'the', 'standard', 'model', 'we', 'introduce', 'additional', 'one', 'toplike', 'quark', 'two', 'bottomlike', 'quarks', 'and', 'three', 'right', 'handed', 'neutrinos', 'in', 'order', 'to', 'have', 'an', 'anomaly', 'free', 'theory', 'we', 'also', 'consider', 'additional', 'three', 'right', 'handed', 'neutrinos', 'which', 'are', 'singlets', 'respect', 'to', 'the', 'gauge', 'symmetry', 'of', 'the', 'model', 'to', 'implement', 'see', 'saw', 'mechanism', 'and', 'give', 'masses', 'to', 'the', 'light', 'neutrinos', 'according', 'to', 'the', 'neutrino', 'oscillation', 'phenomenology', 'in', 'the', 'context', 'of', 'this', 'horizontal', 'gauge', 'symmetry', 'we', 'find', 'mass', 'ansatz', 'for', 'leptons', 'and', 'quarks', 'in', 'particular', 'from', 'the', 'analysis', 'of', 'solar', 'atmospheric', 'reactor', 'and', 'accelerator', 'neutrino', 'oscillation', 'experiments', 'we', 'get', 'the', 'allow', 'region', 'for', 'the', 'yukawa', 'couplings', 'for', 'the', 'charge', 'and', 'neutral', 'lepton', 'sectors', 'according', 'with', 'the', 'mass', 'squared', 'differences', 'and', 'mixing', 'angles', 'for', 'the', 'two', 'neutrino', 'hierarchy', 'schemes', 'normal', 'and', 'inverted']] | [-0.0868719646743856, 0.3149354809805255, -0.009589026123524504, 0.1709721482132421, -0.08514867490157485, -0.18937199988581035, 0.04368897799437559, 0.3491446849035278, -0.21307221067549456, -0.291960948437918, 0.033130314957816154, -0.29977486752075694, -0.031289498863920365, 0.10346883432206072, 0.04408332406784797, 0.012041339331804074, 0.005038432780117409, 0.0182724503101781, -0.09802531539015982, -0.22181704766850613, 0.3324787704009333, -0.008204548388052962, 0.24519435554258015, 0.06584492775496759, 0.12011910547830854, -0.05909950259996399, -0.03560285385353618, -0.14719727369153326, -0.08659780988380415, 0.049155034456555155, 0.12371141711458858, 0.06025390565867395, 0.07597366087967912, -0.39911498744295887, -0.13072334328341004, 0.16182538314901088, 0.1492042821123941, 0.08615192390136318, -0.08414801817438373, -0.29356374536730245, 0.04971436812854946, -0.2400208189326007, -0.14806692437858843, -0.0721981363013795, -0.09410519557303354, -0.10173441044833154, -0.34561251177873514, 0.07229424107876409, -0.03259578122987016, 0.0041130964464048715, -0.0074846741060841165, -0.17494929584113192, -0.04029021168778306, 0.05609848782458432, 0.19811655912222978, -0.039510904948469504, 0.07354082079179272, -0.15780316423210125, -0.12017887980001021, 0.445288897045467, -0.08376657514121665, -0.21713052102211505, 0.14357293091797307, -0.16091471291960485, -0.13585682503547367, 0.05494383145889191, 0.1757180597872333, 0.04687266190510805, -0.1865995386328344, 0.12597759195311595, -0.11901577835111585, 0.1258626665779683, 0.06861916327472674, 0.0140180497506812, 0.27769805651081547, 0.15728250385600154, 0.10399006631333789, 0.003152393225631485, -0.08957954734397976, -0.06837645648621431, -0.3795162567179905, -0.14470072769821454, -0.014619840647106709, 0.03618689422987637, -0.07094511437026085, -0.1240757416027968, 0.49576121818733543, 0.15939337378750518, 0.19947723736858938, 0.01805523621741274, 0.25957140380242355, 0.05479361305064331, 0.08931443675051201, 0.02519391758737397, 0.2722859350910206, 0.16998396305150468, 0.11926758997716418, -0.25910930761793144, -0.07136221974330623, 0.13247203862367116] |
1,802.06321 | The Dangerous Dogmas of Software Engineering | To legitimize itself as a scientific discipline, the software engineering
academic community must let go of its non-empirical dogmas. A dogma is belief
held regardless of evidence. This paper analyzes the nature and detrimental
effects of four software engineering dogmas - 1) the belief that software has
"requirements"; 2) the division of software engineering tasks into analysis,
design, coding and testing; 3) the belief that software engineering is
predominantly concerned with designing "software" systems; 4) the belief that
software engineering follows methods effectively. Deconstructing these dogmas
reveals that they each oversimplify and over-rationalize aspects of software
engineering practice, which obscures underlying phenomena and misleads
researchers and practitioners. Evidenced-based practice is analyzed as a means
to expose and repudiate non-empirical dogmas. This analysis results in several
novel recommendations for overcoming the practical challenges of evidence-based
practice.
| cs.SE | to legitimize itself as a scientific discipline the software engineering academic community must let go of its nonempirical dogmas a dogma is belief held regardless of evidence this paper analyzes the nature and detrimental effects of four software engineering dogmas 1 the belief that software has requirements 2 the division of software engineering tasks into analysis design coding and testing 3 the belief that software engineering is predominantly concerned with designing software systems 4 the belief that software engineering follows methods effectively deconstructing these dogmas reveals that they each oversimplify and overrationalize aspects of software engineering practice which obscures underlying phenomena and misleads researchers and practitioners evidencedbased practice is analyzed as a means to expose and repudiate nonempirical dogmas this analysis results in several novel recommendations for overcoming the practical challenges of evidencebased practice | [['to', 'legitimize', 'itself', 'as', 'a', 'scientific', 'discipline', 'the', 'software', 'engineering', 'academic', 'community', 'must', 'let', 'go', 'of', 'its', 'nonempirical', 'dogmas', 'a', 'dogma', 'is', 'belief', 'held', 'regardless', 'of', 'evidence', 'this', 'paper', 'analyzes', 'the', 'nature', 'and', 'detrimental', 'effects', 'of', 'four', 'software', 'engineering', 'dogmas', '1', 'the', 'belief', 'that', 'software', 'has', 'requirements', '2', 'the', 'division', 'of', 'software', 'engineering', 'tasks', 'into', 'analysis', 'design', 'coding', 'and', 'testing', '3', 'the', 'belief', 'that', 'software', 'engineering', 'is', 'predominantly', 'concerned', 'with', 'designing', 'software', 'systems', '4', 'the', 'belief', 'that', 'software', 'engineering', 'follows', 'methods', 'effectively', 'deconstructing', 'these', 'dogmas', 'reveals', 'that', 'they', 'each', 'oversimplify', 'and', 'overrationalize', 'aspects', 'of', 'software', 'engineering', 'practice', 'which', 'obscures', 'underlying', 'phenomena', 'and', 'misleads', 'researchers', 'and', 'practitioners', 'evidencedbased', 'practice', 'is', 'analyzed', 'as', 'a', 'means', 'to', 'expose', 'and', 'repudiate', 'nonempirical', 'dogmas', 'this', 'analysis', 'results', 'in', 'several', 'novel', 'recommendations', 'for', 'overcoming', 'the', 'practical', 'challenges', 'of', 'evidencebased', 'practice']] | [-0.10687167703982789, 0.0376734018897299, -0.10412380852821199, 0.07505331221336471, -0.1315073512642729, -0.18800266988039244, 0.053386462874407174, 0.3806521014141088, -0.24755473665991853, -0.3238784037812641, 0.12380836556125856, -0.23502293132795868, -0.21733347105680767, 0.19200428160591843, -0.10283998593292905, 0.04422747721025988, 0.09241954935009056, -0.06422831878686945, -0.06202087178385365, -0.25091056029001874, 0.25986239964946767, 0.061037119294544966, 0.3841777315978878, 0.10503768175311218, 0.013575011984247601, 0.03799156464678659, -0.05197574144844883, -0.03320433951695591, -0.08552272638740815, 0.17416075979020784, 0.4413921586731731, 0.29249607859122934, 0.4548605421388691, -0.3949649282448897, -0.2094398518569175, 0.014347374271671993, 0.16116492412225908, 0.06619165941744788, -0.013578327700749716, -0.2693143790115066, 0.04774287433335275, -0.18384147843585885, -0.13200486920400042, -0.05821342463935831, -0.022194725651801986, -0.009606405457651073, -0.1785060459127029, -0.06292603493167201, 0.09295798759117271, 0.12840385221396433, 0.006685751577606425, -0.15950102500165952, 0.010676388639364053, 0.14938900135386962, 0.06678768796193639, 0.032474386993933214, 0.18399787436338197, -0.13421756173501667, -0.18093179072390989, 0.3940547417327197, 0.06228895869395329, -0.14688979839256994, 0.2005186496641148, -0.00045571364744594604, -0.2130301671144976, 0.04991351910443469, 0.18462081319497278, -0.0011365754162949143, -0.18530051877006012, 0.08302649784177034, 0.07217528468886898, 0.20930641426733046, 0.022249683757157374, -0.008460160582961345, 0.20118526054129965, 0.18893463463720048, -0.003413243811916221, 0.06468144039337692, 0.03436243851584467, -0.10631179424694204, -0.25177598242281063, -0.1535986174765805, -0.1447398814859546, 0.009197763684135156, -0.04001094060522243, -0.20202412269656744, 0.38080930645634053, 0.21001810238301527, 0.00016406890371757927, -0.0027711727016139776, 0.31720340552485804, 0.020906939464056806, 0.07031429684932598, 0.12456984837384273, 0.19412905741553527, 0.06024989949638081, 0.15113051038828085, -0.134287130987187, 0.15739470372103492, -0.03871496101474446] |
1,802.06322 | Spin Solid versus Magnetic Charge Ordered State in Artificial Honeycomb
Lattice of Connected Elements | The nature of magnetic correlation at low temperature in two-dimensional
artificial magnetic honeycomb lattice is a strongly debated issue. While
theoretical researches suggest that the system will develop a novel zero
entropy spin solid state as T --> 0 K, a confirmation to this effect in
artificial honeycomb lattice of connected elements is lacking. We report on the
investigation of magnetic correlation in newly designed artificial permalloy
honeycomb lattice of ultra-small elements, with a typical length of ~ 12 nm,
using neutron scattering measurements and temperature dependent micromagnetic
simulations. Numerical modeling of the polarized neutron reflectometry data
elucidates the temperature dependent evolution of spin correlation in this
system. As temperature reduces to ~ 7 K, the system tends to develop novel spin
solid state, manifested by the alternating distribution of magnetic vortex
loops of opposite chiralities. Experimental results are complemented by
temperature dependent micromagnetic simulations that confirm the dominance of
spin solid state over local magnetic charge ordered state in the artificial
honeycomb lattice with connected elements. Our results enable a direct
investigation of novel spin solid correlation in the connected honeycomb
geometry of two-dimensional artificial structure.
| cond-mat.mes-hall | the nature of magnetic correlation at low temperature in twodimensional artificial magnetic honeycomb lattice is a strongly debated issue while theoretical researches suggest that the system will develop a novel zero entropy spin solid state as t 0 k a confirmation to this effect in artificial honeycomb lattice of connected elements is lacking we report on the investigation of magnetic correlation in newly designed artificial permalloy honeycomb lattice of ultrasmall elements with a typical length of 12 nm using neutron scattering measurements and temperature dependent micromagnetic simulations numerical modeling of the polarized neutron reflectometry data elucidates the temperature dependent evolution of spin correlation in this system as temperature reduces to 7 k the system tends to develop novel spin solid state manifested by the alternating distribution of magnetic vortex loops of opposite chiralities experimental results are complemented by temperature dependent micromagnetic simulations that confirm the dominance of spin solid state over local magnetic charge ordered state in the artificial honeycomb lattice with connected elements our results enable a direct investigation of novel spin solid correlation in the connected honeycomb geometry of twodimensional artificial structure | [['the', 'nature', 'of', 'magnetic', 'correlation', 'at', 'low', 'temperature', 'in', 'twodimensional', 'artificial', 'magnetic', 'honeycomb', 'lattice', 'is', 'a', 'strongly', 'debated', 'issue', 'while', 'theoretical', 'researches', 'suggest', 'that', 'the', 'system', 'will', 'develop', 'a', 'novel', 'zero', 'entropy', 'spin', 'solid', 'state', 'as', 't', '0', 'k', 'a', 'confirmation', 'to', 'this', 'effect', 'in', 'artificial', 'honeycomb', 'lattice', 'of', 'connected', 'elements', 'is', 'lacking', 'we', 'report', 'on', 'the', 'investigation', 'of', 'magnetic', 'correlation', 'in', 'newly', 'designed', 'artificial', 'permalloy', 'honeycomb', 'lattice', 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1,802.06323 | New Description of Evolution of Magnetic Phases in Artificial Honeycomb
Lattice | Artificial magnetic honeycomb lattice provides a two-dimensional archetypal
system to explore novel phenomena of geometrically frustrated magnets.
According to theoretical reports, an artificial magnetic honeycomb lattice is
expected to exhibit several phase transitions to unique magnetic states as a
function of reducing temperature. Experimental investigations of permalloy
artificial honeycomb lattice of connected ultra- small elements, ~ 12 nm,
reveal a more complicated behavior. First, upon cooling the sample to
intermediate temperature, T ~ 175 K, the system manifests a non-unique state
where the long range order co-exists with short-range magnetic charge order and
weak spin ice state. Second, at much lower temperature, T ~ 6 K, the long-range
spin solid state exhibits a re-entrant behavior. Both observations are in
direct contrast to the present understanding of this system. New theoretical
approaches are needed to develop a comprehensive formulation of this two
dimensional magnet.
| cond-mat.mes-hall | artificial magnetic honeycomb lattice provides a twodimensional archetypal system to explore novel phenomena of geometrically frustrated magnets according to theoretical reports an artificial magnetic honeycomb lattice is expected to exhibit several phase transitions to unique magnetic states as a function of reducing temperature experimental investigations of permalloy artificial honeycomb lattice of connected ultra small elements 12 nm reveal a more complicated behavior first upon cooling the sample to intermediate temperature t 175 k the system manifests a nonunique state where the long range order coexists with shortrange magnetic charge order and weak spin ice state second at much lower temperature t 6 k the longrange spin solid state exhibits a reentrant behavior both observations are in direct contrast to the present understanding of this system new theoretical approaches are needed to develop a comprehensive formulation of this two dimensional magnet | [['artificial', 'magnetic', 'honeycomb', 'lattice', 'provides', 'a', 'twodimensional', 'archetypal', 'system', 'to', 'explore', 'novel', 'phenomena', 'of', 'geometrically', 'frustrated', 'magnets', 'according', 'to', 'theoretical', 'reports', 'an', 'artificial', 'magnetic', 'honeycomb', 'lattice', 'is', 'expected', 'to', 'exhibit', 'several', 'phase', 'transitions', 'to', 'unique', 'magnetic', 'states', 'as', 'a', 'function', 'of', 'reducing', 'temperature', 'experimental', 'investigations', 'of', 'permalloy', 'artificial', 'honeycomb', 'lattice', 'of', 'connected', 'ultra', 'small', 'elements', '12', 'nm', 'reveal', 'a', 'more', 'complicated', 'behavior', 'first', 'upon', 'cooling', 'the', 'sample', 'to', 'intermediate', 'temperature', 't', '175', 'k', 'the', 'system', 'manifests', 'a', 'nonunique', 'state', 'where', 'the', 'long', 'range', 'order', 'coexists', 'with', 'shortrange', 'magnetic', 'charge', 'order', 'and', 'weak', 'spin', 'ice', 'state', 'second', 'at', 'much', 'lower', 'temperature', 't', '6', 'k', 'the', 'longrange', 'spin', 'solid', 'state', 'exhibits', 'a', 'reentrant', 'behavior', 'both', 'observations', 'are', 'in', 'direct', 'contrast', 'to', 'the', 'present', 'understanding', 'of', 'this', 'system', 'new', 'theoretical', 'approaches', 'are', 'needed', 'to', 'develop', 'a', 'comprehensive', 'formulation', 'of', 'this', 'two', 'dimensional', 'magnet']] | [-0.20548856720644315, 0.22975333095954348, -0.026598549057130834, 0.0022335453474495027, -0.08627244672139309, -0.15446327411835747, 0.06889225105655246, 0.38512574325847837, -0.24492499898853046, -0.3135920423881284, 0.0423195852137204, -0.3072131034286161, -0.09707150498371837, 0.143408892736105, 0.06803521903763925, 0.02920971751196443, -0.03714895426029605, 0.012551934850801314, -0.10525236321180793, -0.19981685352743495, 0.22305061811514731, 0.026009329671173222, 0.2994019654280107, 0.052452400110528936, 0.05515640042202514, -0.03824868145831195, 0.12447241740550713, 0.021977855196486675, -0.16437767846509815, 0.03620304602812082, 0.2363502354078394, -0.07887769856357149, 0.21214754154706109, -0.46711651021588685, -0.23906042467403624, 0.04676102725456336, 0.12138315429412094, 0.1476384624772306, -0.04337921564552484, -0.2589663156773895, 0.03397982316756887, -0.13927007535073374, -0.16378850706719927, -0.13772733375164015, -0.008651078984673535, -0.03403819696686696, -0.27369984701092887, 0.0721641963006862, 0.10771314088487997, 0.14294774681329728, -0.07833185699502272, -0.1602469363723815, -0.020977578320473964, 0.05978073374634343, 0.006529242636835468, 0.10020266550792647, 0.12220994748308191, -0.11295652745492199, -0.14124993079208903, 0.36848992199437425, -0.043735844485594755, -0.054871829000434705, 0.22573941018365856, -0.18293712157589784, -0.09393634742258915, 0.19947036372364632, 0.16141074859936322, 0.09619969376362861, -0.13425046347297862, 0.027205588538449125, -0.029846903488838245, 0.21589240432118198, -0.04368398477755753, 0.08131735415580417, 0.27715296486525665, 0.25780049581745906, 0.054772937540630146, 0.1992855878546834, -0.10518902894701958, -0.11857209747124996, -0.2165495111607015, -0.13747890200465918, -0.21575954619994653, 0.08728400112928024, -0.07039622626252821, -0.19692463922713485, 0.3537244333619518, 0.1639554357833861, 0.21185485765204898, -0.048273592988854426, 0.24446724601356046, 0.04639326407757056, 0.02593731201569816, 0.006515897996723652, 0.21180391396296078, 0.1866645139824998, 0.14653656180016697, -0.25700773773901997, 0.016962974227499216, 0.03458029012794473] |
1,802.06324 | Fractional electrical dimensionality in the spin solid phase of
artificial honeycomb lattice | Two-dimensional artificial magnetic honeycomb lattice is at the forefront of
research on unconventional magnetic materials. Among the many emergent magnetic
phases that are predicted to arise as a function of temperature, the low
temperature spin solid phase with zero magnetization and entropy is of special
importance. Here, we report an interesting perspective to the consequence of
spin solid order in an artificial honeycomb lattice of ultra-small connected
elements using electrical dimensionality analysis. At low temperature, $T \leq$
30 K, the system exhibits a very strong insulating characteristic. The
electrical dimensionality analysis of the experimental data reveals a
fractional dimensionality of $d$ = 0.6(0.04) in the spin solid phase of
honeycomb lattice at low temperature. The much smaller electrical dimension in
the spin solid phase, perhaps, underscores the strong insulating behavior in
this system. Also, the fractional dimensionality in an otherwise
two-dimensional system suggests a non-surface-like electrical transport at low
temperature in an artificial honeycomb lattice.
| cond-mat.mes-hall | twodimensional artificial magnetic honeycomb lattice is at the forefront of research on unconventional magnetic materials among the many emergent magnetic phases that are predicted to arise as a function of temperature the low temperature spin solid phase with zero magnetization and entropy is of special importance here we report an interesting perspective to the consequence of spin solid order in an artificial honeycomb lattice of ultrasmall connected elements using electrical dimensionality analysis at low temperature t leq 30 k the system exhibits a very strong insulating characteristic the electrical dimensionality analysis of the experimental data reveals a fractional dimensionality of d 06004 in the spin solid phase of honeycomb lattice at low temperature the much smaller electrical dimension in the spin solid phase perhaps underscores the strong insulating behavior in this system also the fractional dimensionality in an otherwise twodimensional system suggests a nonsurfacelike electrical transport at low temperature in an artificial honeycomb lattice | [['twodimensional', 'artificial', 'magnetic', 'honeycomb', 'lattice', 'is', 'at', 'the', 'forefront', 'of', 'research', 'on', 'unconventional', 'magnetic', 'materials', 'among', 'the', 'many', 'emergent', 'magnetic', 'phases', 'that', 'are', 'predicted', 'to', 'arise', 'as', 'a', 'function', 'of', 'temperature', 'the', 'low', 'temperature', 'spin', 'solid', 'phase', 'with', 'zero', 'magnetization', 'and', 'entropy', 'is', 'of', 'special', 'importance', 'here', 'we', 'report', 'an', 'interesting', 'perspective', 'to', 'the', 'consequence', 'of', 'spin', 'solid', 'order', 'in', 'an', 'artificial', 'honeycomb', 'lattice', 'of', 'ultrasmall', 'connected', 'elements', 'using', 'electrical', 'dimensionality', 'analysis', 'at', 'low', 'temperature', 't', 'leq', '30', 'k', 'the', 'system', 'exhibits', 'a', 'very', 'strong', 'insulating', 'characteristic', 'the', 'electrical', 'dimensionality', 'analysis', 'of', 'the', 'experimental', 'data', 'reveals', 'a', 'fractional', 'dimensionality', 'of', 'd', '06004', 'in', 'the', 'spin', 'solid', 'phase', 'of', 'honeycomb', 'lattice', 'at', 'low', 'temperature', 'the', 'much', 'smaller', 'electrical', 'dimension', 'in', 'the', 'spin', 'solid', 'phase', 'perhaps', 'underscores', 'the', 'strong', 'insulating', 'behavior', 'in', 'this', 'system', 'also', 'the', 'fractional', 'dimensionality', 'in', 'an', 'otherwise', 'twodimensional', 'system', 'suggests', 'a', 'nonsurfacelike', 'electrical', 'transport', 'at', 'low', 'temperature', 'in', 'an', 'artificial', 'honeycomb', 'lattice']] | [-0.19811291459145172, 0.24737487426893112, -0.0260994203793081, -0.03004956294975464, -0.0465181008437771, -0.15249651389833735, 0.07369355625767064, 0.363125738222152, -0.29442794660204336, -0.2760321236350329, 0.060049198808356824, -0.3177371404002004, -0.13915759107974163, 0.18127328248106336, 0.03201642241267683, 0.03072152704637694, -0.0688661550061385, 0.010636013609320415, -0.11977280138250392, -0.2108851762176001, 0.24365433795671715, 0.045153908112919645, 0.33607203760613247, 0.08733161811802634, 0.06835209936176206, -0.015809950741976008, 0.11880205606116521, 0.04755962791999704, -0.13574144985752928, 0.03759232259012366, 0.24537780927647346, -0.09915624032146297, 0.19924653596306025, -0.4538584363732585, -0.23006973520923088, 0.04555583004472091, 0.10978681756195759, 0.15485291852076588, -0.06556468521854472, -0.22096830918943802, 0.018484250311494657, -0.12182586302821476, -0.1735132085710862, -0.11430626699247544, 0.031146778081739813, -0.06086540081741987, -0.20796129429149196, 0.09992620190065568, 0.08670999488401178, 0.17860721165197901, -0.0872958594118245, -0.18333525689223806, -0.07639907132589112, 0.04546881985381907, 0.02694136575538371, 0.07116408577789307, 0.12241446619277428, -0.1582892199742338, -0.10622109777882303, 0.3639538482101144, -0.03472361390403852, -0.08113382589439616, 0.22182731987069068, -0.20076857768713366, -0.12163642409416896, 0.17628282732651937, 0.17369578039812805, 0.04563415672869029, -0.10876369307822499, 0.08008079754692612, -0.032357225791967814, 0.2029918962415602, -0.01704484855566223, 0.09801644904502227, 0.290451623726097, 0.2636518375212817, 0.06422539106470619, 0.18166545315302515, -0.11267660194119487, -0.05597204459512508, -0.2053724901427478, -0.1774064352135419, -0.2785190944100338, 0.10024353346479505, -0.15411313308552627, -0.19803022122696826, 0.3611576335112515, 0.15748022961365918, 0.18717125589147313, -0.09207393989383586, 0.252819117014664, 0.09939377947854441, 0.02899564416025226, 0.02780280588347906, 0.21262898896269403, 0.1759365220358105, 0.15232777570092462, -0.30508266573796344, 0.02606188043443437, 0.033229627885463596] |
1,802.06325 | Scaling of nonlinear susceptibilities in artificial permalloy honeycomb
lattice | Two-dimensional artificial magnetic honeycomb lattice is predicted to
manifest thermodynamic phase transition to the spin solid order ground state at
low temperature. Nonlinear susceptibilities are very sensitive to thermodynamic
phase transition. We have performed the analysis of nonlinear susceptibility to
explore the thermodynamic nature of spin solid phase transition in artificial
honeycomb lattice of ultra-small connected permalloy (Ni$_{0.81}$Fe$_{0.19}$)
elements, typical length of $\simeq$ 12 nm. The nonlinear susceptibility,
$\chi_{n1}$, is found to exhibit an unusual cross-over character in both
temperature and magnetic field. The higher order susceptibility $\chi_3$
changes from positive to negative as the system traverses through the spin
solid phase transition at $T_s$ = 29 K. Additionally, the static critical
exponents, used to test the scaling of $\chi_{n1}$, do not follow the
conventional scaling relation. We conclude that the transition to the ground
state is not truly thermodynamic, thus raises doubt about the validity of
predicted zero entropy state in the spin solid phase.
| cond-mat.mes-hall | twodimensional artificial magnetic honeycomb lattice is predicted to manifest thermodynamic phase transition to the spin solid order ground state at low temperature nonlinear susceptibilities are very sensitive to thermodynamic phase transition we have performed the analysis of nonlinear susceptibility to explore the thermodynamic nature of spin solid phase transition in artificial honeycomb lattice of ultrasmall connected permalloy ni_081fe_019 elements typical length of simeq 12 nm the nonlinear susceptibility chi_n1 is found to exhibit an unusual crossover character in both temperature and magnetic field the higher order susceptibility chi_3 changes from positive to negative as the system traverses through the spin solid phase transition at t_s 29 k additionally the static critical exponents used to test the scaling of chi_n1 do not follow the conventional scaling relation we conclude that the transition to the ground state is not truly thermodynamic thus raises doubt about the validity of predicted zero entropy state in the spin solid phase | [['twodimensional', 'artificial', 'magnetic', 'honeycomb', 'lattice', 'is', 'predicted', 'to', 'manifest', 'thermodynamic', 'phase', 'transition', 'to', 'the', 'spin', 'solid', 'order', 'ground', 'state', 'at', 'low', 'temperature', 'nonlinear', 'susceptibilities', 'are', 'very', 'sensitive', 'to', 'thermodynamic', 'phase', 'transition', 'we', 'have', 'performed', 'the', 'analysis', 'of', 'nonlinear', 'susceptibility', 'to', 'explore', 'the', 'thermodynamic', 'nature', 'of', 'spin', 'solid', 'phase', 'transition', 'in', 'artificial', 'honeycomb', 'lattice', 'of', 'ultrasmall', 'connected', 'permalloy', 'ni_081fe_019', 'elements', 'typical', 'length', 'of', 'simeq', '12', 'nm', 'the', 'nonlinear', 'susceptibility', 'chi_n1', 'is', 'found', 'to', 'exhibit', 'an', 'unusual', 'crossover', 'character', 'in', 'both', 'temperature', 'and', 'magnetic', 'field', 'the', 'higher', 'order', 'susceptibility', 'chi_3', 'changes', 'from', 'positive', 'to', 'negative', 'as', 'the', 'system', 'traverses', 'through', 'the', 'spin', 'solid', 'phase', 'transition', 'at', 't_s', '29', 'k', 'additionally', 'the', 'static', 'critical', 'exponents', 'used', 'to', 'test', 'the', 'scaling', 'of', 'chi_n1', 'do', 'not', 'follow', 'the', 'conventional', 'scaling', 'relation', 'we', 'conclude', 'that', 'the', 'transition', 'to', 'the', 'ground', 'state', 'is', 'not', 'truly', 'thermodynamic', 'thus', 'raises', 'doubt', 'about', 'the', 'validity', 'of', 'predicted', 'zero', 'entropy', 'state', 'in', 'the', 'spin', 'solid', 'phase']] | [-0.17133073675905786, 0.2527841930103593, -0.04395799062464362, 0.015398029975282649, -0.06260489083954371, -0.14669238578138594, 0.1001560838095649, 0.3607666965831205, -0.24747709659068218, -0.2788113740035424, 0.05746477873755461, -0.3443576258795908, -0.10399252166241116, 0.12365331525193053, 0.09183931609713077, 0.07805488066248645, -0.0892684397141577, 0.0497959153285384, -0.1528953218549563, -0.16567055928198243, 0.27630656640946233, 0.013020837171234532, 0.3369451695140086, 0.07555674151760934, 0.03308560614614121, -0.05075184743260977, 0.14600627392749577, 0.04521775499193205, -0.16780261712699857, -0.030066362323779294, 0.23272517314972052, -0.043369141206437464, 0.14243587801914687, -0.41992814867495515, -0.21579977632591538, 0.06864493516855112, 0.12670402060296337, 0.12436702265860695, 0.004050042405663655, -0.25741053599669456, 0.03700833997135264, -0.12801823176571406, -0.17428799769902006, -0.15011084237854222, 0.006207627722890013, -0.05309381687730624, -0.2135535158929141, 0.14305785788661418, 0.07701738522801962, 0.11027485934493977, -0.08352787536416763, -0.1258101921667059, -0.07605954610097398, 0.10981123317093923, 0.02271698843581249, 0.07832474523494812, 0.15682891897516313, -0.11926513505198695, -0.08666204080423888, 0.3703724720965754, -0.057387720161951546, -0.06543386320954835, 0.18095353472630912, -0.22797377872603392, -0.08226308210150284, 0.19397401000263498, 0.136956065110984, 0.061130950857392126, -0.09815415174523795, 0.011395643183503458, 0.03802513948708675, 0.2484193730816318, 0.013074990322362949, 0.043497583443098065, 0.2741804571450974, 0.17042914558250724, 0.002895761786479186, 0.17832448499070275, -0.09717109113980353, -0.15495901237534104, -0.22390169163350188, -0.14678885701288974, -0.23749330703449112, 0.09165633381652537, -0.108878511542761, -0.21702836500785125, 0.3419828863899692, 0.20051569137965736, 0.17275247187810203, -0.012865112863140168, 0.21129720164021612, 0.13010368567778807, 0.041430833842301, 0.028398904578115038, 0.28088231664150953, 0.19501663184819099, 0.15847223718632378, -0.30587664321582353, 0.05650672227223373, 0.037386407494374545] |
1,802.06326 | Sensitivity and Bifurcation Analysis of a DAE Model for a Microbial
Electrolysis Cell | Microbial electrolysis cells (MECs) are a promising new technology for
producing hydrogen cheaply, efficiently, and sustainably. However, to scale up
this technology, we need a better understanding of the processes in the
devices. In this effort, we present a differential-algebraic equation (DAE)
model of a microbial electrolysis cell with an algebraic constraint on current.
We then perform sensitivity and bifurcation analysis for the DAE system. The
model can be applied either to batch-cycle MECs or to continuous-flow MECs. We
conduct differential-algebraic sensitivity analysis after fitting simulations
to current density data for a batch-cycle MEC. The sensitivity analysis
suggests which parameters have the greatest influence on the current density at
particular times during the experiment. In particular, growth and consumption
parameters for exoelectrogenic bacteria have a strong effect prior to the peak
current density. An alternative strategy to maximizing peak current density is
maintaining a long term stable equilibrium with non-zero current density in a
continuous-flow MEC. We characterize the minimum dilution rate required for a
stable nonzero current equilibrium and demonstrate transcritical bifurcations
in the dilution rate parameter that exchange stability between several curves
of equilibria. Specifically, increasing the dilution rate transitions the
system through three regimes where the stable equilibrium exhibits (i)
competitive exclusion by methanogens, (ii) coexistence, and (iii) competitive
exclusion by exolectrogens. Positive long term current production is only
feasible in the final two regimes. These results suggest how to modify system
parameters to increase peak current density in a batch-cycle MEC or to increase
the long term current density equilibrium value in a continuous-flow MEC.
| math.DS q-bio.OT | microbial electrolysis cells mecs are a promising new technology for producing hydrogen cheaply efficiently and sustainably however to scale up this technology we need a better understanding of the processes in the devices in this effort we present a differentialalgebraic equation dae model of a microbial electrolysis cell with an algebraic constraint on current we then perform sensitivity and bifurcation analysis for the dae system the model can be applied either to batchcycle mecs or to continuousflow mecs we conduct differentialalgebraic sensitivity analysis after fitting simulations to current density data for a batchcycle mec the sensitivity analysis suggests which parameters have the greatest influence on the current density at particular times during the experiment in particular growth and consumption parameters for exoelectrogenic bacteria have a strong effect prior to the peak current density an alternative strategy to maximizing peak current density is maintaining a long term stable equilibrium with nonzero current density in a continuousflow mec we characterize the minimum dilution rate required for a stable nonzero current equilibrium and demonstrate transcritical bifurcations in the dilution rate parameter that exchange stability between several curves of equilibria specifically increasing the dilution rate transitions the system through three regimes where the stable equilibrium exhibits i competitive exclusion by methanogens ii coexistence and iii competitive exclusion by exolectrogens positive long term current production is only feasible in the final two regimes these results suggest how to modify system parameters to increase peak current density in a batchcycle mec or to increase the long term current density equilibrium value in a continuousflow mec | [['microbial', 'electrolysis', 'cells', 'mecs', 'are', 'a', 'promising', 'new', 'technology', 'for', 'producing', 'hydrogen', 'cheaply', 'efficiently', 'and', 'sustainably', 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1,802.06327 | Directional and Causal Information Flow in EEG for Assessing Perceived
Audio Quality | In this paper, electroencephalography (EEG) measurements are used to infer
change in cortical functional connectivity in response to change in audio
stimulus. Experiments are conducted wherein the EEG activity of human subjects
is recorded as they listen to audio sequences whose quality varies with time. A
causal information theoretic framework is then proposed to measure the
information flow between EEG sensors appropriately grouped into different
regions of interest (ROI) over the cortex. A new causal bidirectional
information (CBI) measure is defined as an improvement over standard directed
information measures for the purposes of identifying connectivity between ROIs
in a generalized cortical network setting. CBI can be intuitively interpreted
as a causal bidirectional modification of directed information, and inherently
calculates the divergence of the observed data from a multiple access channel
with feedback. Further, we determine the analytical relationship between the
different causal measures and compare how well they are able to distinguish
between the perceived audio quality. The connectivity results inferred indicate
a significant change in the rate of information flow between ROIs as the
subjects listen to different audio qualities, with CBI being the best in
discriminating between the perceived audio quality, compared to using standard
directed information measures.
| eess.SP eess.AS q-bio.NC | in this paper electroencephalography eeg measurements are used to infer change in cortical functional connectivity in response to change in audio stimulus experiments are conducted wherein the eeg activity of human subjects is recorded as they listen to audio sequences whose quality varies with time a causal information theoretic framework is then proposed to measure the information flow between eeg sensors appropriately grouped into different regions of interest roi over the cortex a new causal bidirectional information cbi measure is defined as an improvement over standard directed information measures for the purposes of identifying connectivity between rois in a generalized cortical network setting cbi can be intuitively interpreted as a causal bidirectional modification of directed information and inherently calculates the divergence of the observed data from a multiple access channel with feedback further we determine the analytical relationship between the different causal measures and compare how well they are able to distinguish between the perceived audio quality the connectivity results inferred indicate a significant change in the rate of information flow between rois as the subjects listen to different audio qualities with cbi being the best in discriminating between the perceived audio quality compared to using standard directed information measures | [['in', 'this', 'paper', 'electroencephalography', 'eeg', 'measurements', 'are', 'used', 'to', 'infer', 'change', 'in', 'cortical', 'functional', 'connectivity', 'in', 'response', 'to', 'change', 'in', 'audio', 'stimulus', 'experiments', 'are', 'conducted', 'wherein', 'the', 'eeg', 'activity', 'of', 'human', 'subjects', 'is', 'recorded', 'as', 'they', 'listen', 'to', 'audio', 'sequences', 'whose', 'quality', 'varies', 'with', 'time', 'a', 'causal', 'information', 'theoretic', 'framework', 'is', 'then', 'proposed', 'to', 'measure', 'the', 'information', 'flow', 'between', 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1,802.06328 | Minimum length RNA folding trajectories | The Kinfold and KFOLD programs for RNA folding kinetics implement the
Gillespie algorithm to generate stochastic folding trajectories from an initial
structure s to a target structure t, in which each intermediate secondary
structure is obtained from its predecessor by the addition, removal or shift of
a single base pair. Define MS2 distance between secondary structures s and t to
be the minimum path length to refold s to t, where a move from MS2 is applied
in each step. We describe algorithms to compute the shortest MS2 folding
trajectory between any two given RNA secondary structures. These algorithms
include an optimal integer programming (IP) algorithm, an accurate and
efficient near-optimal algorithm, a greedy algorithm, a branch-and-bound
algorithm, and an optimal algorithm if one allows intermediate structures to
contain pseudoknots. Our optimal IP [resp. near-optimal IP] algorithm maximizes
[resp. approximately maximizes] the number of shifts and minimizes [resp.
approximately minimizes] the number of base pair additions and removals by
applying integer programming to (essentially) solve the minimum feedback vertex
set (FVS) problem for the RNA conflict digraph, then applies topological sort
to tether subtrajectories into the final optimal folding trajectory. We prove
NP-hardness of the problem to determine the minimum barrier energy over all
possible MS2 folding pathways, and conjecture that computing the MS2 distance
between arbitrary secondary structures is NP-hard. Since our optimal IP
algorithm relies on the FVS, known to be NP-complete for arbitrary digraphs, we
compare the family of RNA conflict digraphs with the following classes of
digraphs (planar, reducible flow graph, Eulerian, and tournament) for which FVS
is known to be either polynomial time computable or NP-hard. Source code
available at http://bioinformatics.bc.edu/clotelab/MS2distance/.
| cs.DS q-bio.BM | the kinfold and kfold programs for rna folding kinetics implement the gillespie algorithm to generate stochastic folding trajectories from an initial structure s to a target structure t in which each intermediate secondary structure is obtained from its predecessor by the addition removal or shift of a single base pair define ms2 distance between secondary structures s and t to be the minimum path length to refold s to t where a move from ms2 is applied in each step we describe algorithms to compute the shortest ms2 folding trajectory between any two given rna secondary structures these algorithms include an optimal integer programming ip algorithm an accurate and efficient nearoptimal algorithm a greedy algorithm a branchandbound algorithm and an optimal algorithm if one allows intermediate structures to contain pseudoknots our optimal ip resp nearoptimal ip algorithm maximizes resp approximately maximizes the number of shifts and minimizes resp approximately minimizes the number of base pair additions and removals by applying integer programming to essentially solve the minimum feedback vertex set fvs problem for the rna conflict digraph then applies topological sort to tether subtrajectories into the final optimal folding trajectory we prove nphardness of the problem to determine the minimum barrier energy over all possible ms2 folding pathways and conjecture that computing the ms2 distance between arbitrary secondary structures is nphard since our optimal ip algorithm relies on the fvs known to be npcomplete for arbitrary digraphs we compare the family of rna conflict digraphs with the following classes of digraphs planar reducible flow graph eulerian and tournament for which fvs is known to be either polynomial time computable or nphard source code available at httpbioinformaticsbceduclotelabms2distance | [['the', 'kinfold', 'and', 'kfold', 'programs', 'for', 'rna', 'folding', 'kinetics', 'implement', 'the', 'gillespie', 'algorithm', 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1,802.06329 | Metrics in projective differential geometry: the geometry of solutions
to the metrizability equation | Pseudo-Riemannian metrics with Levi-Civita connection in the projective class
of a given torsion free affine connection can be obtained from (and are
equivalent to) the maximal rank solutions of a certain overdetermined
projectively invariant differential equation often called the metrizability
equation. Dropping this rank assumption we study the solutions to this equation
given less restrictive generic conditions on its prolonged system. In this
setting we find that the solution stratifies the manifold according to the
strict signature (pointwise) of the solution and does this in way that locally
generalizes the stratification of a model, where the model is, in each case, a
corresponding Lie group orbit decomposition of the sphere. Thus the solutions
give curved generalizations of such embedded orbit structures. We describe the
smooth nature of the strata and determine the geometries of each of the
different strata types; this includes a metric on the open strata that becomes
singular at the strata boundary, with the latter a type of projective infinity
for the given metric. The approach reveals and exploits interesting highly
non-linear relationships between different linear geometric partial
differential equations. Apart from their direct significance, the results show
that, for the metrizability equation, strong results arising for so-called
normal BGG solutions, and the corresponding projective holonomy reduction,
extend to a far wider class of solutions. The work also provides new results
for the projective compactification of scalar-flat metrics.
| math.DG | pseudoriemannian metrics with levicivita connection in the projective class of a given torsion free affine connection can be obtained from and are equivalent to the maximal rank solutions of a certain overdetermined projectively invariant differential equation often called the metrizability equation dropping this rank assumption we study the solutions to this equation given less restrictive generic conditions on its prolonged system in this setting we find that the solution stratifies the manifold according to the strict signature pointwise of the solution and does this in way that locally generalizes the stratification of a model where the model is in each case a corresponding lie group orbit decomposition of the sphere thus the solutions give curved generalizations of such embedded orbit structures we describe the smooth nature of the strata and determine the geometries of each of the different strata types this includes a metric on the open strata that becomes singular at the strata boundary with the latter a type of projective infinity for the given metric the approach reveals and exploits interesting highly nonlinear relationships between different linear geometric partial differential equations apart from their direct significance the results show that for the metrizability equation strong results arising for socalled normal bgg solutions and the corresponding projective holonomy reduction extend to a far wider class of solutions the work also provides new results for the projective compactification of scalarflat metrics | [['pseudoriemannian', 'metrics', 'with', 'levicivita', 'connection', 'in', 'the', 'projective', 'class', 'of', 'a', 'given', 'torsion', 'free', 'affine', 'connection', 'can', 'be', 'obtained', 'from', 'and', 'are', 'equivalent', 'to', 'the', 'maximal', 'rank', 'solutions', 'of', 'a', 'certain', 'overdetermined', 'projectively', 'invariant', 'differential', 'equation', 'often', 'called', 'the', 'metrizability', 'equation', 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0.1373347915825938] |
1,802.0633 | The Category of Factorization | We introduce and investigate the category of factorization of a
multiplicative, commutative, cancellative, pre-ordered monoid $A$, which we
denote $\mathcal{F}(A)$. The objects of $\mathcal{F}(A)$ are factorizations of
elements of $A$, and the morphisms in $\mathcal{F}(A)$ encode combinatorial
similarities and differences between the factorizations. We pay particular
attention to the divisibility pre-order and to the monoid $A=D\setminus\{0\}$
where $D$ is an integral domain.
Among other results, we show that $\mathcal{F}(A)$ is a symmetric and strict
monoidal category with weak equivalences and compute the associated category of
fractions obtained by inverting the weak equivalences. Also, we use this
construction to characterize various factorization properties of integral
domains: atomicity, unique factorization, and so on.
| math.AC math.CT | we introduce and investigate the category of factorization of a multiplicative commutative cancellative preordered monoid a which we denote mathcalfa the objects of mathcalfa are factorizations of elements of a and the morphisms in mathcalfa encode combinatorial similarities and differences between the factorizations we pay particular attention to the divisibility preorder and to the monoid adsetminus0 where d is an integral domain among other results we show that mathcalfa is a symmetric and strict monoidal category with weak equivalences and compute the associated category of fractions obtained by inverting the weak equivalences also we use this construction to characterize various factorization properties of integral domains atomicity unique factorization and so on | [['we', 'introduce', 'and', 'investigate', 'the', 'category', 'of', 'factorization', 'of', 'a', 'multiplicative', 'commutative', 'cancellative', 'preordered', 'monoid', 'a', 'which', 'we', 'denote', 'mathcalfa', 'the', 'objects', 'of', 'mathcalfa', 'are', 'factorizations', 'of', 'elements', 'of', 'a', 'and', 'the', 'morphisms', 'in', 'mathcalfa', 'encode', 'combinatorial', 'similarities', 'and', 'differences', 'between', 'the', 'factorizations', 'we', 'pay', 'particular', 'attention', 'to', 'the', 'divisibility', 'preorder', 'and', 'to', 'the', 'monoid', 'adsetminus0', 'where', 'd', 'is', 'an', 'integral', 'domain', 'among', 'other', 'results', 'we', 'show', 'that', 'mathcalfa', 'is', 'a', 'symmetric', 'and', 'strict', 'monoidal', 'category', 'with', 'weak', 'equivalences', 'and', 'compute', 'the', 'associated', 'category', 'of', 'fractions', 'obtained', 'by', 'inverting', 'the', 'weak', 'equivalences', 'also', 'we', 'use', 'this', 'construction', 'to', 'characterize', 'various', 'factorization', 'properties', 'of', 'integral', 'domains', 'atomicity', 'unique', 'factorization', 'and', 'so', 'on']] | [-0.14676118450747294, 0.04490751362609444, -0.07136477322571656, 0.11694348650582304, -0.13329948373562234, -0.10302443996241147, 0.001457774012603543, 0.4184597050601786, -0.43522810143503277, -0.17656596546823328, 0.08614765280614269, -0.25734749217941, -0.13607728359374133, 0.12267434160140428, -0.1389247558841651, -0.06057707862081853, 0.05883661369285123, 0.06574192507700487, -0.13480676556401622, -0.20578732770571315, 0.4514631269668991, -0.04237666084346446, 0.2279935591600158, 0.03585711064833132, 0.1213222460472025, -0.005640305756506594, -0.06953638553280722, 0.032282692797228014, -0.1548683336505333, 0.15646907255392184, 0.2907598754539239, 0.1100158864589916, 0.18599472040077672, -0.3923694334924221, -0.04599320702439978, 0.19718310971829026, 0.0662416938374835, 0.008601794943255796, -0.03731653124348006, -0.3042098801583052, 0.15372573901391165, -0.2298088382855481, -0.03436956254723059, -0.11120782817121257, 0.09730882612480359, 0.061056183879687025, -0.29246490188789637, -0.07305189145289742, 0.1187839267902415, 0.08752767650241201, -0.04043681523550979, -0.092268044158647, -0.015579063467554409, 0.11915619788183407, -0.0036823679312047632, -0.035200341807847674, 0.08676798827522858, -0.11996133202178912, -0.13512176906012677, 0.384648981470276, -0.027206100248308343, -0.21057069379920987, 0.1667477580570531, -0.15367988458072598, -0.17431893040087412, 0.09618330717679452, 0.06067308173938231, 0.15388166245750406, -0.06301207090304657, 0.1882059183104006, -0.16436422658999536, 0.10210234579439699, 0.11096473592333496, 0.0909700912985665, 0.11891407612711191, 0.07864619544072246, 0.02087873151017861, 0.18006446736028672, 0.023521898506442086, -0.05809970308857208, -0.3098941800216298, -0.17968219838089267, -0.0760862005747516, 0.0984263984052549, -0.08595517003731484, -0.1883086045357314, 0.3755444861863824, 0.1343012006276033, 0.1977237587455999, 0.14822942599844696, 0.21255405259276317, 0.052834573599763894, 0.07077480478381569, 0.0049817017829892314, 0.11479646819220347, 0.26351793457711625, -0.005701256217435002, -0.14609388615089383, 0.03315088539384305, 0.20891525584527038] |
1,802.06331 | On the general dual Orlicz-Minkowski problem | For $K\subseteq \mathbb{R}^n$ a convex body with the origin $o$ in its
interior, and $\phi:\mathbb{R}^n\setminus\{o\}\rightarrow(0, \infty)$ a
continuous function, define the general dual ($L_{\phi})$ Orlicz
quermassintegral of $K$ by $$\mathcal{V}_\phi(K)=\int_{\mathbb{R}^n \setminus
K} \phi(x)\,dx.$$ Under certain conditions on $\phi$, we prove a variational
formula for the general dual ($L_{\phi})$ Orlicz quermassintegral, which
motivates the definition of $\widetilde{C}_{\phi,\mathcal{V}}(K, \cdot)$, the
general dual ($L_{\phi})$ Orlicz curvature measure of $K$.
We pose the following general dual Orlicz-Minkowski problem: {\it Given a
nonzero finite Borel measure $\mu$ defined on $S^{n-1}$ and a continuous
function $\phi: \mathbb{R}^n\setminus\{o\}\rightarrow (0, \infty)$, can one
find a constant $\tau>0$ and a convex body $K$ (ideally, containing $o$ in its
interior), such that,} $$\mu=\tau\widetilde{C}_{\phi,\mathcal{V}}(K,\cdot)? $$
Based on the method of Lagrange multipliers and the established variational
formula for the general dual ($L_{\phi})$ Orlicz quermassintegral, a solution
to the general dual Orlicz-Minkowski problem is provided. In some special
cases, the uniqueness of solutions is proved and the solution for $\mu$ being a
discrete measure is characterized.
| math.MG math.AP | for ksubseteq mathbbrn a convex body with the origin o in its interior and phimathbbrnsetminusorightarrow0 infty a continuous function define the general dual l_phi orlicz quermassintegral of k by mathcalv_phikint_mathbbrn setminus k phixdx under certain conditions on phi we prove a variational formula for the general dual l_phi orlicz quermassintegral which motivates the definition of widetildec_phimathcalvk cdot the general dual l_phi orlicz curvature measure of k we pose the following general dual orliczminkowski problem it given a nonzero finite borel measure mu defined on sn1 and a continuous function phi mathbbrnsetminusorightarrow 0 infty can one find a constant tau0 and a convex body k ideally containing o in its interior such that mutauwidetildec_phimathcalvkcdot based on the method of lagrange multipliers and the established variational formula for the general dual l_phi orlicz quermassintegral a solution to the general dual orliczminkowski problem is provided in some special cases the uniqueness of solutions is proved and the solution for mu being a discrete measure is characterized | [['for', 'ksubseteq', 'mathbbrn', 'a', 'convex', 'body', 'with', 'the', 'origin', 'o', 'in', 'its', 'interior', 'and', 'phimathbbrnsetminusorightarrow0', 'infty', 'a', 'continuous', 'function', 'define', 'the', 'general', 'dual', 'l_phi', 'orlicz', 'quermassintegral', 'of', 'k', 'by', 'mathcalv_phikint_mathbbrn', 'setminus', 'k', 'phixdx', 'under', 'certain', 'conditions', 'on', 'phi', 'we', 'prove', 'a', 'variational', 'formula', 'for', 'the', 'general', 'dual', 'l_phi', 'orlicz', 'quermassintegral', 'which', 'motivates', 'the', 'definition', 'of', 'widetildec_phimathcalvk', 'cdot', 'the', 'general', 'dual', 'l_phi', 'orlicz', 'curvature', 'measure', 'of', 'k', 'we', 'pose', 'the', 'following', 'general', 'dual', 'orliczminkowski', 'problem', 'it', 'given', 'a', 'nonzero', 'finite', 'borel', 'measure', 'mu', 'defined', 'on', 'sn1', 'and', 'a', 'continuous', 'function', 'phi', 'mathbbrnsetminusorightarrow', '0', 'infty', 'can', 'one', 'find', 'a', 'constant', 'tau0', 'and', 'a', 'convex', 'body', 'k', 'ideally', 'containing', 'o', 'in', 'its', 'interior', 'such', 'that', 'mutauwidetildec_phimathcalvkcdot', 'based', 'on', 'the', 'method', 'of', 'lagrange', 'multipliers', 'and', 'the', 'established', 'variational', 'formula', 'for', 'the', 'general', 'dual', 'l_phi', 'orlicz', 'quermassintegral', 'a', 'solution', 'to', 'the', 'general', 'dual', 'orliczminkowski', 'problem', 'is', 'provided', 'in', 'some', 'special', 'cases', 'the', 'uniqueness', 'of', 'solutions', 'is', 'proved', 'and', 'the', 'solution', 'for', 'mu', 'being', 'a', 'discrete', 'measure', 'is', 'characterized']] | [-0.14333760623885378, 0.09098016179409946, -0.09347413412953733, 0.07993947330100024, -0.07042966532123507, -0.17159344504178045, 0.026948684890262167, 0.31055368751192547, -0.3386654818377155, -0.12506199854716754, 0.11131481926926785, -0.2691255110526564, -0.1169139471225393, 0.1514807594524827, -0.13122277812747535, 0.07859276933817988, -0.02141365612068089, 0.09233043433157216, -0.1332572204487956, -0.19480972642755243, 0.31857337947389125, -0.07541637249575679, 0.21395792352676296, 0.09570941282028428, 0.12961194741059165, 0.01477456156010176, 0.04391601023517822, 0.042580257838073905, -0.2430331369754536, 0.1068964196283038, 0.24763303057581282, 0.16203498085931087, 0.30354810120526016, -0.3480007611333754, -0.15803266135133376, 0.1622469967415046, 0.06443845902889919, -0.07288955048595668, -0.04208064823230479, -0.28693697249196515, 0.09665218978871111, -0.06669993977845427, -0.177960250962668, -0.03481044688196794, 0.09920420602652108, 0.01181299104147656, -0.366118546456668, 0.04687825921970378, 0.10696211250374318, 0.033319957239944846, -0.15120480963100152, -0.1466729098228014, 0.010935330346736844, 0.02201602292680152, 0.036053331101692884, 0.17870786752563658, 0.07488272376773748, -0.040743829937544974, -0.043241726697250536, 0.34948196743798865, -0.08717854022030617, -0.2921723647837996, 0.1231664527922418, -0.19406715905958205, -0.11735798715714627, 0.07949650467145049, 0.11009085070857909, 0.24697775449188555, -0.10145319060416548, 0.24451012615890058, -0.12118737218059172, 0.08137781684105967, 0.09539363614081814, 0.059217158066565585, 0.12350002782727787, 0.07729660905897617, 0.1927654392317317, 0.1779237237255086, -0.010487321911789003, -0.07213060028708665, -0.36592930517378885, -0.21412018577236042, -0.20497749478906202, 0.1289207960840813, -0.15889113894943668, -0.17663181977714323, 0.3027023119054094, 0.004560267941278826, 0.17320260857854775, 0.12013862805313484, 0.21221350952478682, 0.14855766736540446, 0.023994472902564772, 0.07286986966979257, 0.1429605415737135, 0.18415038076609042, 0.04750616482168341, -0.22239519090385765, 0.01628006595204685, 0.18768372209327425] |
1,802.06332 | A rank-based Cram\'er-von-Mises-type test for two samples | We study a rank based univariate two-sample distribution-free test. The test
statistic is the difference between the average of between-group rank distances
and the average of within-group rank distances. This test statistic is closely
related to the two-sample Cram\'er-von Mises criterion. They are different
empirical versions of a same quantity for testing the equality of two
population distributions. Although they may be different for finite samples,
they share the same expected value, variance and asymptotic properties. The
advantage of the new rank based test over the classical one is its ease to
generalize to the multivariate case. Rather than using the empirical process
approach, we provide a different easier proof, bringing in a different
perspective and insight. In particular, we apply the H\'ajek projection and
orthogonal decomposition technique in deriving the asymptotics of the proposed
rank based statistic. A numerical study compares power performance of the rank
formulation test with other commonly-used nonparametric tests and
recommendations on those tests are provided. Lastly, we propose a multivariate
extension of the test based on the spatial rank.
| stat.ME | we study a rank based univariate twosample distributionfree test the test statistic is the difference between the average of betweengroup rank distances and the average of withingroup rank distances this test statistic is closely related to the twosample cramervon mises criterion they are different empirical versions of a same quantity for testing the equality of two population distributions although they may be different for finite samples they share the same expected value variance and asymptotic properties the advantage of the new rank based test over the classical one is its ease to generalize to the multivariate case rather than using the empirical process approach we provide a different easier proof bringing in a different perspective and insight in particular we apply the hajek projection and orthogonal decomposition technique in deriving the asymptotics of the proposed rank based statistic a numerical study compares power performance of the rank formulation test with other commonlyused nonparametric tests and recommendations on those tests are provided lastly we propose a multivariate extension of the test based on the spatial rank | [['we', 'study', 'a', 'rank', 'based', 'univariate', 'twosample', 'distributionfree', 'test', 'the', 'test', 'statistic', 'is', 'the', 'difference', 'between', 'the', 'average', 'of', 'betweengroup', 'rank', 'distances', 'and', 'the', 'average', 'of', 'withingroup', 'rank', 'distances', 'this', 'test', 'statistic', 'is', 'closely', 'related', 'to', 'the', 'twosample', 'cramervon', 'mises', 'criterion', 'they', 'are', 'different', 'empirical', 'versions', 'of', 'a', 'same', 'quantity', 'for', 'testing', 'the', 'equality', 'of', 'two', 'population', 'distributions', 'although', 'they', 'may', 'be', 'different', 'for', 'finite', 'samples', 'they', 'share', 'the', 'same', 'expected', 'value', 'variance', 'and', 'asymptotic', 'properties', 'the', 'advantage', 'of', 'the', 'new', 'rank', 'based', 'test', 'over', 'the', 'classical', 'one', 'is', 'its', 'ease', 'to', 'generalize', 'to', 'the', 'multivariate', 'case', 'rather', 'than', 'using', 'the', 'empirical', 'process', 'approach', 'we', 'provide', 'a', 'different', 'easier', 'proof', 'bringing', 'in', 'a', 'different', 'perspective', 'and', 'insight', 'in', 'particular', 'we', 'apply', 'the', 'hajek', 'projection', 'and', 'orthogonal', 'decomposition', 'technique', 'in', 'deriving', 'the', 'asymptotics', 'of', 'the', 'proposed', 'rank', 'based', 'statistic', 'a', 'numerical', 'study', 'compares', 'power', 'performance', 'of', 'the', 'rank', 'formulation', 'test', 'with', 'other', 'commonlyused', 'nonparametric', 'tests', 'and', 'recommendations', 'on', 'those', 'tests', 'are', 'provided', 'lastly', 'we', 'propose', 'a', 'multivariate', 'extension', 'of', 'the', 'test', 'based', 'on', 'the', 'spatial', 'rank']] | [-0.038770866413999884, 0.009108089762074606, -0.18094801804375102, 0.14878280202763564, -0.07535203803796321, -0.1652199608007712, 0.05400565015085574, 0.3504439082848174, -0.2174526300520769, -0.28200594980535765, 0.11628644432872534, -0.24383126092276403, -0.12286755135548966, 0.212319930855717, -0.09372279191522725, 0.06627361290289888, 0.024246758203953504, 0.0377338886648483, -0.12403963085729629, -0.31652882715154973, 0.3049588985954012, 0.08449093765446118, 0.37918153953472417, -0.02365248360937195, 0.080220527316269, 0.012529641091823578, -0.09519612521199243, 0.04840210243394332, -0.11200500001332589, 0.14704504170321991, 0.224097570943247, 0.20181538625154644, 0.32947233142863425, -0.3480187636028443, -0.12786008812221034, 0.15178402705267738, 0.07337951642161768, 0.04828080377940621, -0.005036985872845565, -0.24052385260351003, 0.07628617605593588, -0.16288591813429126, -0.1111432381396714, -0.06409510368215186, -0.04600285158692194, 0.05711071257346443, -0.3279912187478372, 0.11301556143111416, 0.05194291696085462, 0.08729181179776788, -0.023882828491207743, -0.1890472417511046, 0.0639662219557379, 0.09415662119431155, 0.09421250439365395, -0.05164561209934098, 0.0957569382605808, -0.0596676759807659, -0.11855609716049262, 0.34512083164017116, -0.024964607073010743, -0.25068486519157884, 0.2278163730619209, -0.14598700434202327, -0.1399749358956303, 0.025645013230719736, 0.2071395084847297, 0.13143300746018732, -0.13255355031229554, 0.033940513204184494, -0.06882817217015795, 0.13732414658580508, 0.0765675342535334, -0.009221277327409813, 0.15634776496195368, 0.14381126995065383, 0.05956351867078671, 0.16030606701437916, -0.11797261213883757, -0.08433232062363198, -0.29262367095798253, -0.18377683127831135, -0.21921448702524815, 0.012680713136547377, -0.17545147441552086, -0.16893808291626297, 0.43016399342034545, 0.18104976719012483, 0.1699006949099047, 0.1431184516527823, 0.2912330881931952, 0.09227886996897204, 0.024869986761893545, 0.06310037335913096, 0.19312170604509968, 0.15292262483920369, -0.003472080707683095, -0.1581590306572616, 0.10137531376310757, 0.10373674217345459] |
1,802.06333 | Explicit equations of a fake projective plane | Fake projective planes are smooth complex surfaces of general type with Betti
numbers equal to those of the usual projective plane. They come in complex
conjugate pairs and have been classified as quotients of the two-dimensional
ball by explicitly written arithmetic subgroups. In this paper we find
equations of a projective model of a conjugate pair of fake projective planes
by studying the geometry of the quotient of such surface by an order seven
automorphism.
| math.AG | fake projective planes are smooth complex surfaces of general type with betti numbers equal to those of the usual projective plane they come in complex conjugate pairs and have been classified as quotients of the twodimensional ball by explicitly written arithmetic subgroups in this paper we find equations of a projective model of a conjugate pair of fake projective planes by studying the geometry of the quotient of such surface by an order seven automorphism | [['fake', 'projective', 'planes', 'are', 'smooth', 'complex', 'surfaces', 'of', 'general', 'type', 'with', 'betti', 'numbers', 'equal', 'to', 'those', 'of', 'the', 'usual', 'projective', 'plane', 'they', 'come', 'in', 'complex', 'conjugate', 'pairs', 'and', 'have', 'been', 'classified', 'as', 'quotients', 'of', 'the', 'twodimensional', 'ball', 'by', 'explicitly', 'written', 'arithmetic', 'subgroups', 'in', 'this', 'paper', 'we', 'find', 'equations', 'of', 'a', 'projective', 'model', 'of', 'a', 'conjugate', 'pair', 'of', 'fake', 'projective', 'planes', 'by', 'studying', 'the', 'geometry', 'of', 'the', 'quotient', 'of', 'such', 'surface', 'by', 'an', 'order', 'seven', 'automorphism']] | [-0.20126308903098106, 0.07797802297573071, -0.03175571334858735, 0.07349803225913396, -0.08159495695494115, -0.11555304575711489, -0.03697865269922962, 0.32951858078440033, -0.3063396969437599, -0.26387077011168003, 0.08085693922204276, -0.2892548603564501, -0.13236648411800464, 0.21644114354935784, -0.13467874870325128, 0.02573356964935859, -0.008345597914109628, 0.07056222920926909, -0.13745968374889345, -0.34643255109588306, 0.4383233775570989, -0.08381882717212041, 0.2293734778712193, -0.03108572941273451, 0.11108510601023833, 0.008840332225275537, -0.0310637194228669, 0.043573620282113555, -0.11982799853508672, 0.1215616129975145, 0.30839785671482484, 0.059666983637337884, 0.1367742016973595, -0.413560813292861, -0.1759352117838959, 0.2301774211662511, 0.16591523425653576, 0.025519397836178542, -0.029089505101243654, -0.26502837834258874, 0.07087726330384612, -0.14155996964002648, -0.18011233074590563, -0.07988358178486427, 0.009994863122701645, 0.056402172694603604, -0.12697395256099603, 0.006243761889636517, 0.08480816598981619, 0.18634385275964935, 0.027332976056883732, -0.06676338545822849, -0.13068272888660432, 0.06975235290204486, -0.0030388914343590537, 0.0370070591631035, 0.07946754664803544, -0.07032890826153258, -0.1339231430995278, 0.40474029337366424, 0.002057293571221332, -0.2928832198989888, 0.12784885110954444, -0.16601184816410144, -0.08259943616266052, 0.18361206211149692, 0.1693927067021529, 0.20004781362911064, -0.054903410859406, 0.13937385724158957, -0.10323383678371707, 0.06034501062395672, 0.10956524771327773, -0.046608198188090075, 0.18513546420882146, 0.03880932341019312, 0.030590234079087775, 0.12182736215492089, -0.012686537615954875, -0.05105927700487276, -0.3518361877898375, -0.25109712979445853, -0.10533457021694631, 0.14209214810281992, -0.08885792560235131, -0.17595426061501104, 0.4080508306622505, -0.01669997611393531, 0.23747979710499445, 0.017025815354039273, 0.2670198969791333, 0.01681089917663485, 0.051979470948378244, 0.014990277097870906, 0.1673210808262229, 0.1866770014265785, -0.06990357546756665, -0.10264494178506235, -0.015042827501893043, 0.18705876884632744] |
1,802.06334 | New Physics Searches Using Precision Spectroscopy | The exceptional precision attainable using modern spectroscopic techniques
provides a promising avenue to search for signatures of physics beyond the
Standard Model in tiny shifts of the energy levels of atoms and molecules. We
briefly review three categories of new-physics searches based in precision
measurements: tests of QED using measurements of the anomalous magnetic moment
of the electron and the value of the fine-structure constant, searches for time
variation of the fundamental constants, and searches for a permanent electric
dipole moment of an electron or atomic nucleus.
| physics.atom-ph | the exceptional precision attainable using modern spectroscopic techniques provides a promising avenue to search for signatures of physics beyond the standard model in tiny shifts of the energy levels of atoms and molecules we briefly review three categories of newphysics searches based in precision measurements tests of qed using measurements of the anomalous magnetic moment of the electron and the value of the finestructure constant searches for time variation of the fundamental constants and searches for a permanent electric dipole moment of an electron or atomic nucleus | [['the', 'exceptional', 'precision', 'attainable', 'using', 'modern', 'spectroscopic', 'techniques', 'provides', 'a', 'promising', 'avenue', 'to', 'search', 'for', 'signatures', 'of', 'physics', 'beyond', 'the', 'standard', 'model', 'in', 'tiny', 'shifts', 'of', 'the', 'energy', 'levels', 'of', 'atoms', 'and', 'molecules', 'we', 'briefly', 'review', 'three', 'categories', 'of', 'newphysics', 'searches', 'based', 'in', 'precision', 'measurements', 'tests', 'of', 'qed', 'using', 'measurements', 'of', 'the', 'anomalous', 'magnetic', 'moment', 'of', 'the', 'electron', 'and', 'the', 'value', 'of', 'the', 'finestructure', 'constant', 'searches', 'for', 'time', 'variation', 'of', 'the', 'fundamental', 'constants', 'and', 'searches', 'for', 'a', 'permanent', 'electric', 'dipole', 'moment', 'of', 'an', 'electron', 'or', 'atomic', 'nucleus']] | [-0.11192771141556487, 0.1801004386629077, -0.0011183626403839424, 0.05979028806804904, -0.030356659404077065, -0.12410459679219572, 0.0983556553510007, 0.3184875513095109, -0.2206890496543084, -0.3585866737452432, 0.04948503297389667, -0.28446629714092303, -0.024929397528049076, 0.22893268824019736, 0.09341822932848985, 0.06684334337946841, 0.02774036393083375, 0.02258274472992996, -0.08885189001020258, -0.16242136159676246, 0.27086359428391715, 0.12187584434989197, 0.24297479380607262, 0.11195139846125224, 0.08582734896623444, -0.006249129715985779, -0.023495448967334867, -0.0014168449581183236, -0.13611180990420538, 0.1787022009984344, 0.19233076703659763, 0.08959508490288394, 0.18380793644350835, -0.42618262005605917, -0.12674805277894283, 0.10170941580429502, 0.10511928173745501, 0.14547120050216714, -0.12894882092081095, -0.2974757399549173, -0.02370620336404991, -0.10943539253564487, -0.1650637055736506, -0.12087583999502761, 0.017115094795309264, 0.043155080585301604, -0.3044151597624195, 0.085528832897758, 0.002331143047061117, 0.10256014133792841, -0.09818794850618633, -0.19437587855319524, 0.10167816127168722, 0.09744007490179234, 0.07332590614223382, 0.007938409577412852, 0.19133371573433966, -0.1851584324131495, -0.2087000790962029, 0.4393456195877201, -0.14318287971860397, -0.09988004420402236, 0.1492440256520292, -0.21134889960802836, -0.16827569275173818, 0.12829300020446724, 0.1755978817537684, 0.11583980699551516, -0.14110796811894097, 0.12107921068773381, 0.02222384345428697, 0.17611835333893353, 0.03674561301267695, 0.10924213564547913, 0.25903454782633945, 0.20429391629362328, 0.032934680581092834, 0.046043786220252514, -0.19299225207824988, -0.026880106998436237, -0.31266255359882594, -0.164198388388359, -0.14532974139712324, 0.048379569111027934, -0.056830080359372655, -0.14242771936944504, 0.41427298578508626, 0.15254159123990044, 0.14803852500586673, -0.056682771810426795, 0.33149866858261756, 0.07604501214167424, 0.07938523231952012, -0.03293214249157015, 0.32409709418626054, 0.16142860042123958, 0.06961020931665754, -0.24352148454502526, 0.019955704208120876, 0.04524160387401266] |
1,802.06335 | A Pieri-type formula and a factorization formula for sums of
$K$-$k$-Schur functions | We give a Pieri-type formula for the sum of $K$-$k$-Schur functions
$\sum_{\mu\le\lambda} g^{(k)}_{\mu}$ over a principal order ideal of the poset
of $k$-bounded partitions under the strong Bruhat order, which sum we denote by
$\widetilde{g}^{(k)}_{\lambda}$. As an application of this, we also give a
$k$-rectangle factorization formula
$\widetilde{g}^{(k)}_{R_t\cup\lambda}=\widetilde{g}^{(k)}_{R_t}
\widetilde{g}^{(k)}_{\lambda}$ where $R_t=(t^{k+1-t})$, analogous to that of
$k$-Schur functions $s^{(k)}_{R_t\cup\lambda}=s^{(k)}_{R_t}s^{(k)}_{\lambda}$.
| math.CO | we give a pieritype formula for the sum of kkschur functions sum_mulelambda gk_mu over a principal order ideal of the poset of kbounded partitions under the strong bruhat order which sum we denote by widetildegk_lambda as an application of this we also give a krectangle factorization formula widetildegk_r_tcuplambdawidetildegk_r_t widetildegk_lambda where r_ttk1t analogous to that of kschur functions sk_r_tcuplambdask_r_tsk_lambda | [['we', 'give', 'a', 'pieritype', 'formula', 'for', 'the', 'sum', 'of', 'kkschur', 'functions', 'sum_mulelambda', 'gk_mu', 'over', 'a', 'principal', 'order', 'ideal', 'of', 'the', 'poset', 'of', 'kbounded', 'partitions', 'under', 'the', 'strong', 'bruhat', 'order', 'which', 'sum', 'we', 'denote', 'by', 'widetildegk_lambda', 'as', 'an', 'application', 'of', 'this', 'we', 'also', 'give', 'a', 'krectangle', 'factorization', 'formula', 'widetildegk_r_tcuplambdawidetildegk_r_t', 'widetildegk_lambda', 'where', 'r_ttk1t', 'analogous', 'to', 'that', 'of', 'kschur', 'functions', 'sk_r_tcuplambdask_r_tsk_lambda']] | [-0.16367158891633152, 0.045270961581554726, -0.11310897499322892, 0.042140022951643916, -0.08107471558265388, -0.03693198493681848, 0.05950227078050375, 0.29739612044766545, -0.3173369850218296, -0.1768122972920537, 0.08545754791237414, -0.22714853316545486, -0.1721469149366021, 0.16799759875983, -0.03862850086763501, -0.012990489862859248, -0.02125384198501706, 0.0839976001251489, -0.12338898114394396, -0.26813455448951573, 0.3569498661160469, -0.017636272348463534, 0.2114510068297386, 0.09962628444074653, 0.12066721421666443, 0.08651317125186324, 0.023385404273867608, 0.02279257507296279, -0.1995267857106228, 0.14834453570190817, 0.27626757220365106, 0.12564401103183628, 0.22841368321795016, -0.371989844404161, -0.045990302888676525, 0.19972430126741528, 0.15623005878180266, -0.010481194027233869, 0.025198851134628057, -0.20084667783230542, 0.13685175502672792, -0.253554068878293, -0.1269797599222511, -0.10539248086512089, 0.0543498376570642, 0.07388830196112395, -0.37586773552000524, 0.006638924641883932, 0.12717403775081038, 0.07234711164608598, -0.04303874119184911, -0.17283586064353584, 0.037977492790669205, 0.08866857972228899, 0.0075009303167462345, 0.04485132654197514, -0.017101513855159282, -0.07999254941474647, -0.1710316230170429, 0.39187774922698737, -0.06141793963033706, -0.23025283068418503, 0.028220709031447768, -0.1737375153694302, -0.13088433213531972, 0.10007385225966572, 0.10949997555464507, 0.16660369381308557, -0.05333054265007377, 0.10270165270194412, -0.18908654479309916, 0.049345902353525165, 0.12015302894636988, 0.04883672366850078, 0.13111148972529918, 0.06515580199658871, 0.11044449932873249, 0.2600318210851401, 0.0074460278451442714, -0.035552510023117066, -0.40107603937387465, -0.21701881505548953, -0.16970064742490648, 0.1278412402421236, -0.1559803796047345, -0.24415886394679545, 0.4076478168275207, 0.10252513896673918, 0.2152519009448588, 0.15198738042498008, 0.26421968473121527, 0.16999334500869737, 0.07764308206737042, -0.019567479509860278, 0.056971845673397185, 0.22037402023095637, -0.04961746440501884, -0.14076347792521118, 0.04802686051465571, 0.24394369270652533] |
1,802.06336 | Uniqueness of power of a meromorphic function with its differential
polynomial | In this paper, taking the question of Zhang and L\"{u} into the background,
we present one theorem which will improve and extend some recent results
related to the Br\"{u}ck Conjecture.
| math.CV | in this paper taking the question of zhang and lu into the background we present one theorem which will improve and extend some recent results related to the bruck conjecture | [['in', 'this', 'paper', 'taking', 'the', 'question', 'of', 'zhang', 'and', 'lu', 'into', 'the', 'background', 'we', 'present', 'one', 'theorem', 'which', 'will', 'improve', 'and', 'extend', 'some', 'recent', 'results', 'related', 'to', 'the', 'bruck', 'conjecture']] | [-0.05749794367390374, 0.04475020087944965, -0.07638223643104235, 0.015600444977947821, -0.07166225990901391, -0.08644154760986567, 0.05444832878420129, 0.2650085193105042, -0.26107152352730434, -0.2862787805187205, 0.11960027888029193, -0.24488219853956253, -0.1733930978924036, 0.20921341540912788, -0.17671190382291874, 0.0031740374552706877, 0.03287920284395417, -0.0482985479136308, -0.05864871412826081, -0.40255418717861174, 0.34753639126817387, 0.051623011582220595, 0.21518929060548544, 0.18855898045003414, 0.021593385220815737, 0.045372009044513106, -0.1344159665517509, 0.02080315984785557, -0.1868990105887254, 0.1800243752077222, 0.22180984796335299, 0.1574652929790318, 0.26298060760212444, -0.4171182692050934, -0.20840155777987093, 0.11151747615076602, 0.13208040840302904, 0.11291375583581005, -0.04132393008718888, -0.31048475528756775, 0.15009780880063772, -0.14896839127565423, -0.14334385972470046, -0.05588047308071206, -0.0119626106073459, -0.024874460697174073, -0.22850191922237476, 0.035323999791095656, 0.23293768853570024, -0.04427059981971979, -0.07044170772035917, -0.12785781069348257, 0.06527963842575749, 0.12607254567556084, 0.07408331527064244, 0.08583195760535697, 0.007824166289841136, -0.06746527371772876, -0.14519188326473037, 0.32380755208432677, -0.0432842655107379, -0.13913700549552838, 0.15269486357768378, -0.1447282428542773, -0.26975071243941784, 0.02508410606533289, 0.14043043743198116, 0.09640551342939337, -0.0880559425180157, 0.12537459316275393, -0.11918521684904894, 0.059042612090706825, 0.11937550147995353, 0.014276154587666194, 0.10900036171078682, 0.11207233493526776, 0.018578610041489203, 0.15623704832202445, -0.006630262732505799, -0.011870528012514115, -0.24293626074989635, -0.18543380402649443, -0.08473961289661626, 0.0849745012819767, -0.004826512776586848, -0.08846906802306573, 0.412779018531243, 0.2462573555763811, 0.2437441346856455, 0.06014732517845308, 0.2546722364301483, 0.09116247712324063, -0.040464806153128544, 0.07431495686372121, 0.2257738831656752, 0.22079990282654763, 0.13407584950327872, -0.1390101034194231, -0.034625301754567774, 0.15574180328597625] |
1,802.06337 | Large thermoelectric response in a diluted ferroelectric system:
Ba0.7Eu0.3Ti1-xNbxO3 | We investigated the electrical conductivity, thermal conductivity and
thermopower as a function of Nb content (x) in Ba0.7Eu0.3Ti1-xNbxO3 (x = 0.001-
0.10) in the temperature range T = 400-2 K. The substitution of Nb destabilizes
the ferroelectric insulating ground state of Ba0.7Eu0.3TiO3 and transforms into
a paramagnetic metal for x = 0.1. Thermopower is negative in the entire
composition range (S = -613 microVolt/K at 400 K for x = 0.001) and its
magnitude decreases with increasing Nb content which suggests doping of
electrons into empty Ti-3d(t2g) conduction band. In this series, the
dimensionless figure of merit (ZT) increases with temperature for all the
compositions and the x = 0.03 composition exhibits the maximum ZT (= 0.12 at
400 K). The enhanced value of ZT is primarily due to the low thermal
conductivity of samples in this series (~ 0.7 to 1 W/(m.K) at 400 K) compared
to other potential high temperature n-type thermoelectric oxides such as
carrier doped SrTiO3 and CaMnO3. The low thermal conductivity in our compounds
most likely arises from heavy Eu2+ ion and lattice disorder introduced by Nb5+
which scatter phonons effectively.
| cond-mat.mtrl-sci | we investigated the electrical conductivity thermal conductivity and thermopower as a function of nb content x in ba07eu03ti1xnbxo3 x 0001 010 in the temperature range t 4002 k the substitution of nb destabilizes the ferroelectric insulating ground state of ba07eu03tio3 and transforms into a paramagnetic metal for x 01 thermopower is negative in the entire composition range s 613 microvoltk at 400 k for x 0001 and its magnitude decreases with increasing nb content which suggests doping of electrons into empty ti3dt2g conduction band in this series the dimensionless figure of merit zt increases with temperature for all the compositions and the x 003 composition exhibits the maximum zt 012 at 400 k the enhanced value of zt is primarily due to the low thermal conductivity of samples in this series 07 to 1 wmk at 400 k compared to other potential high temperature ntype thermoelectric oxides such as carrier doped srtio3 and camno3 the low thermal conductivity in our compounds most likely arises from heavy eu2 ion and lattice disorder introduced by nb5 which scatter phonons effectively | [['we', 'investigated', 'the', 'electrical', 'conductivity', 'thermal', 'conductivity', 'and', 'thermopower', 'as', 'a', 'function', 'of', 'nb', 'content', 'x', 'in', 'ba07eu03ti1xnbxo3', 'x', '0001', '010', 'in', 'the', 'temperature', 'range', 't', '4002', 'k', 'the', 'substitution', 'of', 'nb', 'destabilizes', 'the', 'ferroelectric', 'insulating', 'ground', 'state', 'of', 'ba07eu03tio3', 'and', 'transforms', 'into', 'a', 'paramagnetic', 'metal', 'for', 'x', '01', 'thermopower', 'is', 'negative', 'in', 'the', 'entire', 'composition', 'range', 's', '613', 'microvoltk', 'at', '400', 'k', 'for', 'x', '0001', 'and', 'its', 'magnitude', 'decreases', 'with', 'increasing', 'nb', 'content', 'which', 'suggests', 'doping', 'of', 'electrons', 'into', 'empty', 'ti3dt2g', 'conduction', 'band', 'in', 'this', 'series', 'the', 'dimensionless', 'figure', 'of', 'merit', 'zt', 'increases', 'with', 'temperature', 'for', 'all', 'the', 'compositions', 'and', 'the', 'x', '003', 'composition', 'exhibits', 'the', 'maximum', 'zt', '012', 'at', '400', 'k', 'the', 'enhanced', 'value', 'of', 'zt', 'is', 'primarily', 'due', 'to', 'the', 'low', 'thermal', 'conductivity', 'of', 'samples', 'in', 'this', 'series', '07', 'to', '1', 'wmk', 'at', '400', 'k', 'compared', 'to', 'other', 'potential', 'high', 'temperature', 'ntype', 'thermoelectric', 'oxides', 'such', 'as', 'carrier', 'doped', 'srtio3', 'and', 'camno3', 'the', 'low', 'thermal', 'conductivity', 'in', 'our', 'compounds', 'most', 'likely', 'arises', 'from', 'heavy', 'eu2', 'ion', 'and', 'lattice', 'disorder', 'introduced', 'by', 'nb5', 'which', 'scatter', 'phonons', 'effectively']] | [-0.12196439385665687, 0.23795828466362523, 0.02478696986492295, -0.06489769116461534, 0.017439875617254012, -0.15523397008588688, 0.17324121388634828, 0.3927092570383316, -0.3120112826749308, -0.30946614369535924, -0.025496093579032725, -0.43366561861087877, 0.009244467447586786, 0.1753461803604003, 0.028830694719806486, -0.02029906074889807, -0.07829896820835895, -0.0492896091771023, -0.13848469344278178, -0.24497750101493265, 0.18009909188332057, 0.04427717418822021, 0.3133856517078098, 0.11686967985822294, 0.03886316736207354, -0.030107729190468103, 0.1540666910653784, 0.030780657157087804, -0.1653201095427851, 0.015595615692918414, 0.30569165112215213, -0.12909374884784694, 0.19033414087841694, -0.3178091518227653, -0.20201183237328096, -0.012881826890375593, 0.0989258862843191, 0.00971787460228621, -0.05876094813795824, -0.1732471046076777, 0.11181422590877561, -0.11983458163757306, -0.08708655507432232, -0.02159921851961863, 0.04984542023759849, -0.05662159607263988, -0.26104847040701784, 0.15691116694381044, 0.04396150590739502, 0.11377140136713955, -0.09270778161288913, -0.3073259733467438, -0.11891111100076474, -0.011220347419937795, 0.06487146263203487, 0.09974517702157128, 0.22893161534053413, -0.07982734259988727, 0.001677935120740627, 0.37553210871229914, -0.10672895018621509, -0.014393754865460354, 0.13444178291842684, -0.24775689588217387, -0.03923219050921034, 0.23862105989480797, 0.0992287918314774, 0.10611021651848937, -0.12178778652658408, 0.09077014848315586, 0.05817496582555274, 0.20586557640408815, 0.07325877849917174, 0.09080665982221307, 0.2549700313033643, 0.18977478193052263, 0.009715362438589505, 0.13053295713618207, -0.12242066535038938, 0.08104455233123367, -0.17783752326243396, -0.21621178668634644, -0.21095045580295846, 0.1463558245771404, -0.18474395974788776, -0.16302852595245718, 0.3578204194019581, 0.16303987933112704, 0.22165163789190695, -0.05784233761335798, 0.1914842719755446, 0.11405316826381624, 0.04533523427598157, 0.042747352430837805, 0.18137568814663924, 0.186149589728093, 0.17032992402519817, -0.27887072120712875, 0.09937466296163955, -0.032494165202008624] |
1,802.06338 | Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM
Encoder-Decoder Architecture | In this paper, we propose a deep learning based vehicle trajectory prediction
technique which can generate the future trajectory sequence of surrounding
vehicles in real time. We employ the encoder-decoder architecture which
analyzes the pattern underlying in the past trajectory using the long
short-term memory (LSTM) based encoder and generates the future trajectory
sequence using the LSTM based decoder. This structure produces the $K$ most
likely trajectory candidates over occupancy grid map by employing the beam
search technique which keeps the $K$ locally best candidates from the decoder
output. The experiments conducted on highway traffic scenarios show that the
prediction accuracy of the proposed method is significantly higher than the
conventional trajectory prediction techniques.
| cs.LG | in this paper we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time we employ the encoderdecoder architecture which analyzes the pattern underlying in the past trajectory using the long shortterm memory lstm based encoder and generates the future trajectory sequence using the lstm based decoder this structure produces the k most likely trajectory candidates over occupancy grid map by employing the beam search technique which keeps the k locally best candidates from the decoder output the experiments conducted on highway traffic scenarios show that the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques | [['in', 'this', 'paper', 'we', 'propose', 'a', 'deep', 'learning', 'based', 'vehicle', 'trajectory', 'prediction', 'technique', 'which', 'can', 'generate', 'the', 'future', 'trajectory', 'sequence', 'of', 'surrounding', 'vehicles', 'in', 'real', 'time', 'we', 'employ', 'the', 'encoderdecoder', 'architecture', 'which', 'analyzes', 'the', 'pattern', 'underlying', 'in', 'the', 'past', 'trajectory', 'using', 'the', 'long', 'shortterm', 'memory', 'lstm', 'based', 'encoder', 'and', 'generates', 'the', 'future', 'trajectory', 'sequence', 'using', 'the', 'lstm', 'based', 'decoder', 'this', 'structure', 'produces', 'the', 'k', 'most', 'likely', 'trajectory', 'candidates', 'over', 'occupancy', 'grid', 'map', 'by', 'employing', 'the', 'beam', 'search', 'technique', 'which', 'keeps', 'the', 'k', 'locally', 'best', 'candidates', 'from', 'the', 'decoder', 'output', 'the', 'experiments', 'conducted', 'on', 'highway', 'traffic', 'scenarios', 'show', 'that', 'the', 'prediction', 'accuracy', 'of', 'the', 'proposed', 'method', 'is', 'significantly', 'higher', 'than', 'the', 'conventional', 'trajectory', 'prediction', 'techniques']] | [-0.11414196577511336, 0.025005115228786803, -0.10348481139813534, 0.022381634985529735, -0.07025934625090215, -0.18409377695551435, 0.08326761078869663, 0.4565998341626765, -0.2910578815121481, -0.3140755089963868, 0.0749769650017761, -0.23564426628709362, -0.19940649569361357, 0.18922412047507814, -0.08605597369789489, 0.10893667739991654, 0.15478866088016188, 0.09152291358036005, -0.036069684745662176, -0.2618094919057432, 0.2399724407382963, 0.11723130924822231, 0.3546149507328345, -0.03822690341621637, 0.1491356023093032, 0.019516562879608387, -0.020928984891884682, -0.05916098978272394, -0.04660159212685437, 0.1537771708609765, 0.23204733360264646, 0.1824305982169646, 0.2781862568686177, -0.43597412294005616, -0.2761715469604129, 0.07615083152951117, 0.1308934571785166, 0.08940236059348344, -0.040493942491849066, -0.3330710092559457, 0.11557469170095333, -0.1691670485163565, -0.0015898065931539524, -0.054290823932541046, -0.06037592828241095, 0.01448094763480568, -0.2824490684918904, 0.009590967400040347, 0.038005341583732186, 0.009331432138487958, -0.06016851960387277, -0.0985332413825713, 0.013045184926963166, 0.16359413739114084, 0.03828853732710214, 0.11783920141476158, 0.1423657160832367, -0.10899951826043737, -0.16940616656058027, 0.35864639380260516, -0.10273100938425878, -0.1742499160372015, 0.11708272681694086, -0.08328000083157237, -0.08684943981975186, 0.12732357623692797, 0.2307400341684881, 0.14404273250859165, -0.15957093497446181, 0.004539966370334457, -0.028289514977746365, 0.1991179405196913, 0.03381154636174375, -0.019884990140174825, 0.21884756644354447, 0.3037526778174205, 0.07356460377770035, 0.13059374331823437, -0.21404053526904981, -0.13815371362030865, -0.22544382715172934, -0.07917726749407225, -0.15662284095959508, -0.0389115742199417, -0.08763763250917372, -0.10523684365315396, 0.43073677008546757, 0.25092573265806495, 0.21347163330044663, 0.11203810480615255, 0.3860967966417472, 0.055273592039093114, 0.11808056309574137, 0.15277252693889304, 0.15107726718282752, -0.007789029778602223, 0.13548156074523976, -0.21527845113591307, 0.1189909200592522, 0.10912088608254858] |
1,802.06339 | Level-zero van der Kallen modules and specialization of nonsymmetric
Macdonald polynomials at $t = \infty$ | Let $\lambda \in P^{+}$ be a level-zero dominant integral weight, and $w$ an
arbitrary coset representative of minimal length for the cosets in
$W/W_{\lambda}$, where $W_{\lambda}$ is the stabilizer of $\lambda$ in a finite
Weyl group $W$. In this paper, we give a module $\mathbb{K}_{w}(\lambda)$ over
the negative part of a quantum affine algebra whose graded character is
identical to the specialization at $t = \infty$ of the nonsymmetric Macdonald
polynomial $E_{w \lambda}(q,\,t)$ multiplied by a certain explicit finite
product of rational functions of $q$ of the form $(1 - q^{-r})^{-1}$ for a
positive integer $r$. This module $\mathbb{K}_{w}(\lambda)$ (called a
level-zero van der Kallen module) is defined to be the quotient module of the
level-zero Demazure module $V_{w}^{-}(\lambda)$ by the sum of the submodules
$V_{z}^{-}(\lambda)$ for all those coset representatives $z$ of minimal length
for the cosets in $W/W_{\lambda}$ such that $z > w$ in the Bruhat order $<$ on
$W$.
| math.QA math.RT | let lambda in p be a levelzero dominant integral weight and w an arbitrary coset representative of minimal length for the cosets in ww_lambda where w_lambda is the stabilizer of lambda in a finite weyl group w in this paper we give a module mathbbk_wlambda over the negative part of a quantum affine algebra whose graded character is identical to the specialization at t infty of the nonsymmetric macdonald polynomial e_w lambdaqt multiplied by a certain explicit finite product of rational functions of q of the form 1 qr1 for a positive integer r this module mathbbk_wlambda called a levelzero van der kallen module is defined to be the quotient module of the levelzero demazure module v_wlambda by the sum of the submodules v_zlambda for all those coset representatives z of minimal length for the cosets in ww_lambda such that z w in the bruhat order on w | [['let', 'lambda', 'in', 'p', 'be', 'a', 'levelzero', 'dominant', 'integral', 'weight', 'and', 'w', 'an', 'arbitrary', 'coset', 'representative', 'of', 'minimal', 'length', 'for', 'the', 'cosets', 'in', 'ww_lambda', 'where', 'w_lambda', 'is', 'the', 'stabilizer', 'of', 'lambda', 'in', 'a', 'finite', 'weyl', 'group', 'w', 'in', 'this', 'paper', 'we', 'give', 'a', 'module', 'mathbbk_wlambda', 'over', 'the', 'negative', 'part', 'of', 'a', 'quantum', 'affine', 'algebra', 'whose', 'graded', 'character', 'is', 'identical', 'to', 'the', 'specialization', 'at', 't', 'infty', 'of', 'the', 'nonsymmetric', 'macdonald', 'polynomial', 'e_w', 'lambdaqt', 'multiplied', 'by', 'a', 'certain', 'explicit', 'finite', 'product', 'of', 'rational', 'functions', 'of', 'q', 'of', 'the', 'form', '1', 'qr1', 'for', 'a', 'positive', 'integer', 'r', 'this', 'module', 'mathbbk_wlambda', 'called', 'a', 'levelzero', 'van', 'der', 'kallen', 'module', 'is', 'defined', 'to', 'be', 'the', 'quotient', 'module', 'of', 'the', 'levelzero', 'demazure', 'module', 'v_wlambda', 'by', 'the', 'sum', 'of', 'the', 'submodules', 'v_zlambda', 'for', 'all', 'those', 'coset', 'representatives', 'z', 'of', 'minimal', 'length', 'for', 'the', 'cosets', 'in', 'ww_lambda', 'such', 'that', 'z', 'w', 'in', 'the', 'bruhat', 'order', 'on', 'w']] | [-0.19078144006404682, 0.13713066765127443, -0.0401679283199528, -0.006393355210906172, -0.07017405693095627, -0.17746303264540456, -0.011981392173585317, 0.2956994433775016, -0.3825806776946741, -0.14700964382719184, 0.050213144205391726, -0.24839965526529767, -0.0953290445535239, 0.18177118887200105, -0.059213394005221465, -0.015884585044485457, 0.023376763077925047, 0.1441366653856392, -0.08921257962789783, -0.30388502707929477, 0.3756863693871819, 0.021834956114642746, 0.17908143448636782, 0.006700936810556033, 0.1367612454003212, 0.04600602865271957, -0.007215506502869704, -0.0505410964191637, -0.1703093827645652, 0.10044110400671576, 0.3217243936361996, 0.024243784559798157, 0.2462413267311739, -0.30297459835014867, -0.06306410939201501, 0.18604192551199003, 0.15676325787162632, -0.03406690947500104, 0.023397877841282978, -0.21383561007926202, 0.14845620267793325, -0.2578906487931121, -0.11529250047169626, 0.018464171149312182, 0.1064095509978315, 0.010001844373128727, -0.31410152850844336, -0.016011353195743993, 0.0847872219028625, 0.0991829276936961, -0.028027076868617787, -0.17623666098922597, -0.05047781675796401, 0.05537317182079407, -0.08911250578404688, 0.06927759786456797, 0.05826153331403191, -0.11615972780351731, -0.12408975418657064, 0.3629593643432496, -0.08683330630834037, -0.27068887436960604, 0.05870773333144632, -0.16361010846259164, -0.12247318047237524, 0.12199451021044284, 0.0611623451014624, 0.09629938335336269, 0.00047061325493433795, 0.2475061464079669, -0.12493852244075784, 0.05718988471281391, 0.11036693827903017, 0.016688280345885254, 0.14806267712265253, 0.06078126989569224, 0.026794149371934063, 0.14443744482399884, 0.03761955768906954, 0.014562248609008942, -0.41555966986409315, -0.25565204606886877, -0.1606980250250046, 0.13882293282010666, -0.14250407069052506, -0.21978070294973276, 0.4154156568040741, 0.002741137364258369, 0.19800148979952256, 0.10325677361973423, 0.1557233002654389, 0.10204093413202898, 0.11703150071441493, 0.017402708077974987, 0.08110075036612016, 0.19341631538683798, -0.05632871015786675, -0.1821952408661815, -0.005578401063177536, 0.22793837453417004] |
1,802.0634 | Graphical Models for Non-Negative Data Using Generalized Score Matching | A common challenge in estimating parameters of probability density functions
is the intractability of the normalizing constant. While in such cases maximum
likelihood estimation may be implemented using numerical integration, the
approach becomes computationally intensive. In contrast, the score matching
method of Hyv\"arinen (2005) avoids direct calculation of the normalizing
constant and yields closed-form estimates for exponential families of
continuous distributions over $\mathbb{R}^m$. Hyv\"arinen (2007) extended the
approach to distributions supported on the non-negative orthant
$\mathbb{R}_+^m$. In this paper, we give a generalized form of score matching
for non-negative data that improves estimation efficiency. We also generalize
the regularized score matching method of Lin et al. (2016) for non-negative
Gaussian graphical models, with improved theoretical guarantees.
| stat.ME | a common challenge in estimating parameters of probability density functions is the intractability of the normalizing constant while in such cases maximum likelihood estimation may be implemented using numerical integration the approach becomes computationally intensive in contrast the score matching method of hyvarinen 2005 avoids direct calculation of the normalizing constant and yields closedform estimates for exponential families of continuous distributions over mathbbrm hyvarinen 2007 extended the approach to distributions supported on the nonnegative orthant mathbbr_m in this paper we give a generalized form of score matching for nonnegative data that improves estimation efficiency we also generalize the regularized score matching method of lin et al 2016 for nonnegative gaussian graphical models with improved theoretical guarantees | [['a', 'common', 'challenge', 'in', 'estimating', 'parameters', 'of', 'probability', 'density', 'functions', 'is', 'the', 'intractability', 'of', 'the', 'normalizing', 'constant', 'while', 'in', 'such', 'cases', 'maximum', 'likelihood', 'estimation', 'may', 'be', 'implemented', 'using', 'numerical', 'integration', 'the', 'approach', 'becomes', 'computationally', 'intensive', 'in', 'contrast', 'the', 'score', 'matching', 'method', 'of', 'hyvarinen', '2005', 'avoids', 'direct', 'calculation', 'of', 'the', 'normalizing', 'constant', 'and', 'yields', 'closedform', 'estimates', 'for', 'exponential', 'families', 'of', 'continuous', 'distributions', 'over', 'mathbbrm', 'hyvarinen', '2007', 'extended', 'the', 'approach', 'to', 'distributions', 'supported', 'on', 'the', 'nonnegative', 'orthant', 'mathbbr_m', 'in', 'this', 'paper', 'we', 'give', 'a', 'generalized', 'form', 'of', 'score', 'matching', 'for', 'nonnegative', 'data', 'that', 'improves', 'estimation', 'efficiency', 'we', 'also', 'generalize', 'the', 'regularized', 'score', 'matching', 'method', 'of', 'lin', 'et', 'al', '2016', 'for', 'nonnegative', 'gaussian', 'graphical', 'models', 'with', 'improved', 'theoretical', 'guarantees']] | [-0.052198340812617025, -0.01912104957534567, -0.08801582290831467, 0.058083360891222305, -0.08181183563504854, -0.13810117149361126, 0.07137508813150065, 0.3947088129170563, -0.20922337611327352, -0.3159002740503005, 0.08399088833400088, -0.2520463771713169, -0.15911308675295796, 0.17369509368487027, -0.13266576908771757, 0.15498698611298334, 0.10155203405403249, -0.019884242117404936, -0.12048307807999663, -0.29577248579134113, 0.2572644773951691, 0.059229656728996855, 0.29606671215075514, 0.046456356943868425, 0.11652566482130762, 0.05003032954409718, -0.09925522392453706, 0.009799049607507819, -0.16575126667822074, 0.1600655804772902, 0.22593644529358994, 0.19914275213833088, 0.308118607217203, -0.32800539386782634, -0.22056196869310477, 0.14664984345436097, 0.13015435275560974, 0.046158835732216096, -0.014443217020522318, -0.2588179078600977, 0.06152742265359215, -0.1824139613820159, -0.08787427905785, -0.10865851232539052, 0.014456810202935468, 0.013082032496332312, -0.40060237490612527, 0.16197749235156073, 0.04607374160348073, 0.012676891939633566, -0.04940068486915982, -0.20329527205423167, 0.04371423870730012, 0.04696614454480131, 0.02772056783306534, 0.0451553448913214, 0.058515092771014444, -0.09487299552065846, -0.12094304908228957, 0.2764328162469294, -0.10354651130087998, -0.25969940204497266, 0.11714328478859819, -0.05548146325770927, -0.1833993091288468, 0.1303741928756885, 0.19464296265505254, 0.15554996530484894, -0.12411361749729384, 0.11577720110375515, -0.07836074747408375, 0.10852098267363465, 0.11080534016794484, -0.05222135534074725, 0.12650214151843733, 0.11695301993142651, 0.07588177172203912, 0.13341172266569312, -0.04979906527163542, -0.11450169099261984, -0.2693792872272594, -0.13536750758370467, -0.252657683643148, -0.01709610397886971, -0.13799457654379732, -0.20433984607579592, 0.38006330001896815, 0.15078535804059356, 0.1948800156581337, 0.16409048258286454, 0.2978674374885209, 0.16000260675892883, -0.03447800785222131, 0.13027622350611795, 0.18231628776892372, 0.13359401051117026, 0.023280577820158847, -0.154226804026605, 0.13018348716027306, 0.08943973293568452] |
1,802.06341 | The Mu2e Calorimeter Final Technical Design Report | Since the first version of the Mu2e TDR released at the beginning of 2015,
the Mu2e Calorimeter system has undergone a long list of changes to arrive to
its final design. These changes were primarily caused by two reasons: (i) the
technology choice between the TDR proposed solution of BaF2 crystals readout
with solar blind Avalanche Photodiodes (APDs) and the backup option of CsI
crystals readout with Silicon Photomultipliers (SiPM) has been completed and
(ii) the channels numbering, the mechanical system and the readout electronics
were substantially modified while proceeding with engineering towards the final
project. This document updates the description of the calorimeter system adding
the most recent engineering drawings and tecnical progresses.
| physics.ins-det hep-ex | since the first version of the mu2e tdr released at the beginning of 2015 the mu2e calorimeter system has undergone a long list of changes to arrive to its final design these changes were primarily caused by two reasons i the technology choice between the tdr proposed solution of baf2 crystals readout with solar blind avalanche photodiodes apds and the backup option of csi crystals readout with silicon photomultipliers sipm has been completed and ii the channels numbering the mechanical system and the readout electronics were substantially modified while proceeding with engineering towards the final project this document updates the description of the calorimeter system adding the most recent engineering drawings and tecnical progresses | [['since', 'the', 'first', 'version', 'of', 'the', 'mu2e', 'tdr', 'released', 'at', 'the', 'beginning', 'of', '2015', 'the', 'mu2e', 'calorimeter', 'system', 'has', 'undergone', 'a', 'long', 'list', 'of', 'changes', 'to', 'arrive', 'to', 'its', 'final', 'design', 'these', 'changes', 'were', 'primarily', 'caused', 'by', 'two', 'reasons', 'i', 'the', 'technology', 'choice', 'between', 'the', 'tdr', 'proposed', 'solution', 'of', 'baf2', 'crystals', 'readout', 'with', 'solar', 'blind', 'avalanche', 'photodiodes', 'apds', 'and', 'the', 'backup', 'option', 'of', 'csi', 'crystals', 'readout', 'with', 'silicon', 'photomultipliers', 'sipm', 'has', 'been', 'completed', 'and', 'ii', 'the', 'channels', 'numbering', 'the', 'mechanical', 'system', 'and', 'the', 'readout', 'electronics', 'were', 'substantially', 'modified', 'while', 'proceeding', 'with', 'engineering', 'towards', 'the', 'final', 'project', 'this', 'document', 'updates', 'the', 'description', 'of', 'the', 'calorimeter', 'system', 'adding', 'the', 'most', 'recent', 'engineering', 'drawings', 'and', 'tecnical', 'progresses']] | [-0.08670372976633035, 0.12603447535817008, -0.032761029884697195, -0.08499670416565892, -0.028885281174979377, -0.1996777064488393, 0.0068709574473931135, 0.38788994489056344, -0.20444377350303616, -0.3919723552025327, 0.1252449250623452, -0.33499074340344664, -0.053654683779088566, 0.1628800089088829, -0.06837023679209593, 0.1246222284611158, 0.11895788969900624, -0.05946199403688378, -0.07713100262217026, -0.26687864570227343, 0.16268333345981298, 0.20082587367995122, 0.32924865500432793, -0.010009274694376288, 0.17548026437088188, -0.02321057642636969, -0.07604751894734718, -0.07677919598114966, -0.10895818348281679, 0.06381170120849006, 0.2807843860865167, 0.11908252363142648, 0.22004090124263173, -0.47337196299196344, -0.12781535761031132, 0.05969450949996182, 0.08036978713409827, 0.07498632407071382, -0.07660545134274042, -0.3025084555445783, 0.06427897601718066, -0.21490373833731344, -0.09307111104006624, 0.0570022871107918, -0.01927770135215426, 0.02751580907590275, -0.15675426632231843, -0.08299103487732169, 0.03905839338667889, 0.02092170751595919, -0.024955887455633322, -0.17090850757059964, 0.0044912169234151336, 0.09324694746297545, 0.0064001376905470295, 0.04054636429723319, 0.19665121188205956, -0.12642439094009456, -0.10717081610912836, 0.3287265207783311, 0.02190442934047134, -0.08155831022072682, 0.15187879991346756, -0.1709911247995575, -0.11368252539314923, 0.19112202037224726, 0.15703973490476972, 0.01895256765200695, -0.22774046609091647, 0.01525679010016003, 0.0633540572232847, 0.21109078230492903, 0.07770761964323444, 0.05873822621523147, 0.20910708461187583, 0.2885545359067289, 0.04681263565687479, 0.13635193554898806, -0.12099031941652034, -0.018738824173610294, -0.3342777062866804, -0.17770154347264133, -0.147474409054256, 0.007247330306666549, 0.028185056984544096, -0.13933497732010694, 0.42601571133178945, 0.11477645198954681, 0.07765645390392932, -0.0581902923408599, 0.30185325179183115, 0.04662886649922394, 0.11036010679327347, -0.011740618805705974, 0.2942401599633483, 0.12655734476443808, 0.23404692902345112, -0.20357409612465222, 0.09995341617140127, 0.051540657713086205] |
1,802.06342 | Stability Theorems for Group Actions on Uniform Spaces | We extend the notions of topological stability, shadowing and persistence
from homeomorphisms to finitely generated group actions on uniform spaces and
prove that an expansive action with either shadowing or persistence is
topologically stable. Using the concept of null set of a Borel measure $\mu$,
we introduce the notions of $\mu$-expansivity, $\mu$-topological stability,
$\mu$-shadowing and $\mu$-persistence for finitely generated group actions on
uniform spaces and show that a $\mu$-expansive action with either
$\mu$-shadowing or $\mu$-persistence is $\mu$-topologically stable.
| math.DS | we extend the notions of topological stability shadowing and persistence from homeomorphisms to finitely generated group actions on uniform spaces and prove that an expansive action with either shadowing or persistence is topologically stable using the concept of null set of a borel measure mu we introduce the notions of muexpansivity mutopological stability mushadowing and mupersistence for finitely generated group actions on uniform spaces and show that a muexpansive action with either mushadowing or mupersistence is mutopologically stable | [['we', 'extend', 'the', 'notions', 'of', 'topological', 'stability', 'shadowing', 'and', 'persistence', 'from', 'homeomorphisms', 'to', 'finitely', 'generated', 'group', 'actions', 'on', 'uniform', 'spaces', 'and', 'prove', 'that', 'an', 'expansive', 'action', 'with', 'either', 'shadowing', 'or', 'persistence', 'is', 'topologically', 'stable', 'using', 'the', 'concept', 'of', 'null', 'set', 'of', 'a', 'borel', 'measure', 'mu', 'we', 'introduce', 'the', 'notions', 'of', 'muexpansivity', 'mutopological', 'stability', 'mushadowing', 'and', 'mupersistence', 'for', 'finitely', 'generated', 'group', 'actions', 'on', 'uniform', 'spaces', 'and', 'show', 'that', 'a', 'muexpansive', 'action', 'with', 'either', 'mushadowing', 'or', 'mupersistence', 'is', 'mutopologically', 'stable']] | [-0.15993799301130432, 0.2056707886398986, -0.13843058736196587, 0.07027087058938508, -0.035401705067072596, -0.08670972525807363, 0.0363222665419536, 0.4155455668057714, -0.33877960299806936, -0.134437182153176, 0.14978697329892643, -0.2834011856998716, -0.12910050846237156, 0.17388929978146084, -0.15451557920979603, 0.01963241265288421, 0.04957651599709477, 0.11132448815873691, -0.07223336279525289, -0.2347404406372724, 0.4415809987911156, -0.061700558542673076, 0.2590273646105613, 0.05223043147208435, 0.14718228653073312, 0.028293333914397017, -0.04850623094077621, 0.10383952223097108, -0.1625803379219308, 0.11942608268665415, 0.15320734759193977, 0.08981899741172258, 0.249832366460136, -0.3387390357575246, -0.21003524237977608, 0.18201956619907703, 0.053791003567831855, -0.03355661097448319, -0.06800955128856004, -0.3841429313910859, 0.20036811670288443, -0.18665796441159077, -0.13713507147705448, -0.1431854669553494, 0.040859195696456095, 0.02382327988743782, -0.2816583461926452, -0.04003340267975416, 0.11990738195766296, 0.15294347825859275, -0.09908557064565164, 0.01583915234410337, -0.08331416372675449, 0.13222898693888316, 0.057412361852558595, 0.00994586663852845, 0.14517599534509437, -0.05721029700445277, -0.16139250517423664, 0.3922739901979055, -0.10381750173068473, -0.29550659983818023, 0.25388847198337317, -0.1703056656622461, -0.13927221704673554, 0.11532821604715926, 0.11314132430457643, 0.1357233049680612, -0.03169217226760728, 0.1590215709093692, -0.0860171484627894, 0.1226776228419372, 0.08171545373541968, 0.05755327712478382, 0.12944093213549682, 0.11593404891235488, 0.21797658993995617, 0.13406398658241545, 0.031015769192682844, -0.05726037188266803, -0.3141329541802406, -0.12468215761972325, -0.09032539460541947, 0.11046821311382311, -0.059605782165557945, -0.24280646022941385, 0.375699168709772, 0.08969076671720748, 0.14990356722846626, 0.1594171845753278, 0.2572669654552426, 0.03474781985382184, 0.008085000822653196, 0.07187469843775034, 0.11618215502904994, 0.14843983172572084, -0.1083297295895006, -0.1412079799754013, 0.03322063906650458, 0.1883062135162098] |
1,802.06343 | On an infinite limit of BGG categories O | We study a version of the BGG category O for Dynkin Borel subalgebras of
root-reductive Lie algebras g, such as gl(\infty). We prove results about
extension fullness and compute the higher extensions of simple modules by Verma
modules. In addition, we show that our category O is Ringel self-dual and
initiate the study of Koszul duality. An important tool in obtaining these
results is an equivalence we establish between appropriate Serre subquotients
of category O for g and category O for finite dimensional reductive subalgebras
of g.
| math.RT | we study a version of the bgg category o for dynkin borel subalgebras of rootreductive lie algebras g such as glinfty we prove results about extension fullness and compute the higher extensions of simple modules by verma modules in addition we show that our category o is ringel selfdual and initiate the study of koszul duality an important tool in obtaining these results is an equivalence we establish between appropriate serre subquotients of category o for g and category o for finite dimensional reductive subalgebras of g | [['we', 'study', 'a', 'version', 'of', 'the', 'bgg', 'category', 'o', 'for', 'dynkin', 'borel', 'subalgebras', 'of', 'rootreductive', 'lie', 'algebras', 'g', 'such', 'as', 'glinfty', 'we', 'prove', 'results', 'about', 'extension', 'fullness', 'and', 'compute', 'the', 'higher', 'extensions', 'of', 'simple', 'modules', 'by', 'verma', 'modules', 'in', 'addition', 'we', 'show', 'that', 'our', 'category', 'o', 'is', 'ringel', 'selfdual', 'and', 'initiate', 'the', 'study', 'of', 'koszul', 'duality', 'an', 'important', 'tool', 'in', 'obtaining', 'these', 'results', 'is', 'an', 'equivalence', 'we', 'establish', 'between', 'appropriate', 'serre', 'subquotients', 'of', 'category', 'o', 'for', 'g', 'and', 'category', 'o', 'for', 'finite', 'dimensional', 'reductive', 'subalgebras', 'of', 'g']] | [-0.15097663111599355, -0.01913244396832172, -0.02946293322990338, 0.09983026242986236, -0.12063258918451852, -0.1479386939538707, -0.02648212503770302, 0.415878808618277, -0.3651813763868192, -0.1812469850109633, 0.08042382410374181, -0.1822232379278318, -0.12689997244026813, 0.17385426623447017, -0.21134090695754978, -0.13994670112285462, 0.12632446485068435, 0.11286555831160011, -0.07576625138767407, -0.2780818332778825, 0.44639670506291007, -0.021802301628343845, 0.2414313462059731, 0.029305606577331306, 0.07431090273626748, 0.05091650627189498, -0.021046090238170976, -0.020411968967160105, -0.1839869050340006, 0.14767570600941263, 0.3978709627007102, 0.06487038739871276, 0.1990195466259002, -0.3264531368903559, -0.020351024489079057, 0.18462264575278278, 0.15365250655797835, 0.027585462265615833, -0.027598155279718768, -0.3014051739511819, 0.13353355165742253, -0.27768319200767183, -0.12183639645876213, -0.06460338109975745, 0.15065160264855187, -0.02138512588544995, -0.2589324852665779, -0.025588028400536925, 0.11149863205079374, 0.1838090287029058, -0.12213711696944532, -0.10719714591001299, -0.09883027424468477, 0.06878082814958246, -0.09446498772782412, -0.022897538707602298, 0.08862643388110673, -0.09900874945433107, -0.18250715869596634, 0.350301721973623, -0.07427820948691204, -0.15588984704822645, 0.16210646014232402, -0.16353049428983665, -0.1856449718918951, 0.05547813777865349, -0.004084335789940823, 0.1748224413985836, -0.009102484104277074, 0.24370954206933257, -0.1876294118748314, -0.022826342939816672, 0.08684155908038561, 0.021776204179415757, 0.12275479752142196, 0.10473539368731194, 0.0339845283620659, 0.14792223870968338, 0.07143680190389183, 0.0674707470996969, -0.3989498866614939, -0.256031293250706, -0.0009969732812445226, 0.1391722359587492, -0.12270144408454332, -0.14660042975012255, 0.36921753331847573, 0.137822473835837, 0.15594018470153384, 0.1809174477112019, 0.10277393115042098, 0.05325226480109167, 0.079174038627581, 0.04884847461235249, 0.10958142968074648, 0.36881663006109494, -0.07582266737396519, -0.1498221081799303, -0.0841178864136274, 0.32928460130960435] |
1,802.06344 | CGC/saturation approach: an impact-parameter dependent model for
diffraction production in DIS | In the paper we discussed the evolution equations for diffractive production
in the framework of CGC/saturation approach, and found the analytical solutions
for several kinematic regions. The most impressive features of these solutions
are, that diffractive production does not exibit geometric scaling behaviour
i.e. being a function of one variable.
Based on these solutions, we suggest an impact parameter dependent saturation
model, which is suitable for describing diffraction production both deep in the
saturation region, and in the vicinity of the saturation scale. Using the model
we attempted to fit the combined data on diffraction production from H1 and
ZEUS collaborations. We found that we are able describe both
$x_\pom$ and $\beta$ dependence, as well as $Q$ behavior of the measured
cross sections. In spite of the sufficiently large $\chi^2/d.o.f.$ we believe
that our description provides an initial impetus to find a fit of the
experimental data, based on the solution of the CGC/saturation equation, rather
than on describing the diffraction system in simplistic manner, assuming that
only quark-antiquark pair and one extra gluons, are produced.
| hep-ph | in the paper we discussed the evolution equations for diffractive production in the framework of cgcsaturation approach and found the analytical solutions for several kinematic regions the most impressive features of these solutions are that diffractive production does not exibit geometric scaling behaviour ie being a function of one variable based on these solutions we suggest an impact parameter dependent saturation model which is suitable for describing diffraction production both deep in the saturation region and in the vicinity of the saturation scale using the model we attempted to fit the combined data on diffraction production from h1 and zeus collaborations we found that we are able describe both x_pom and beta dependence as well as q behavior of the measured cross sections in spite of the sufficiently large chi2dof we believe that our description provides an initial impetus to find a fit of the experimental data based on the solution of the cgcsaturation equation rather than on describing the diffraction system in simplistic manner assuming that only quarkantiquark pair and one extra gluons are produced | [['in', 'the', 'paper', 'we', 'discussed', 'the', 'evolution', 'equations', 'for', 'diffractive', 'production', 'in', 'the', 'framework', 'of', 'cgcsaturation', 'approach', 'and', 'found', 'the', 'analytical', 'solutions', 'for', 'several', 'kinematic', 'regions', 'the', 'most', 'impressive', 'features', 'of', 'these', 'solutions', 'are', 'that', 'diffractive', 'production', 'does', 'not', 'exibit', 'geometric', 'scaling', 'behaviour', 'ie', 'being', 'a', 'function', 'of', 'one', 'variable', 'based', 'on', 'these', 'solutions', 'we', 'suggest', 'an', 'impact', 'parameter', 'dependent', 'saturation', 'model', 'which', 'is', 'suitable', 'for', 'describing', 'diffraction', 'production', 'both', 'deep', 'in', 'the', 'saturation', 'region', 'and', 'in', 'the', 'vicinity', 'of', 'the', 'saturation', 'scale', 'using', 'the', 'model', 'we', 'attempted', 'to', 'fit', 'the', 'combined', 'data', 'on', 'diffraction', 'production', 'from', 'h1', 'and', 'zeus', 'collaborations', 'we', 'found', 'that', 'we', 'are', 'able', 'describe', 'both', 'x_pom', 'and', 'beta', 'dependence', 'as', 'well', 'as', 'q', 'behavior', 'of', 'the', 'measured', 'cross', 'sections', 'in', 'spite', 'of', 'the', 'sufficiently', 'large', 'chi2dof', 'we', 'believe', 'that', 'our', 'description', 'provides', 'an', 'initial', 'impetus', 'to', 'find', 'a', 'fit', 'of', 'the', 'experimental', 'data', 'based', 'on', 'the', 'solution', 'of', 'the', 'cgcsaturation', 'equation', 'rather', 'than', 'on', 'describing', 'the', 'diffraction', 'system', 'in', 'simplistic', 'manner', 'assuming', 'that', 'only', 'quarkantiquark', 'pair', 'and', 'one', 'extra', 'gluons', 'are', 'produced']] | [-0.06941445650061889, 0.0926321430612337, -0.13569238877707077, 0.10788953910245751, -0.04968036226901776, -0.08074924001994077, 0.009177751530263446, 0.35421481785703113, -0.21424930092987118, -0.2892323888478462, 0.06564832134344298, -0.33164689266017044, -0.09704663063166663, 0.20325444370180115, 0.01808131174419197, 0.09538120082156225, 0.05286349547399864, 0.025336488116987643, -0.02694792897223537, -0.1998660139930011, 0.34221439269921655, 0.03715088426790978, 0.28628086929463525, 0.10446320170872654, 0.10448752310714388, 0.027750312934089874, -0.02917618258702119, 0.01965846813188612, -0.13780867223001472, 0.09888747122816065, 0.20678157105959888, 0.09541325441519306, 0.1693343123717403, -0.4224855420642681, -0.17864624722278677, 0.06847638659978243, 0.18244511415303516, 0.09664510985352205, -0.07914495936446739, -0.22796704700555315, 0.0783113444191589, -0.17525206307817082, -0.12751806665024065, -0.09966287425115959, 0.0006937606747008183, 0.028528547814031215, -0.29832542837936094, 0.06884249521747486, 0.009914278862032172, 0.030711736742060482, -0.11543533445034303, -0.1349960571248878, -0.03295573604092086, 0.06424810436775177, 0.07509444691161414, 0.036327228275860594, 0.11661077482924818, -0.18041574812906963, -0.09113937125022692, 0.3566895076814531, -0.07218396266406754, -0.18319856656969272, 0.18002087987207976, -0.20483881510021587, -0.14063520908794916, 0.12256125936453993, 0.18486006861944174, 0.12027849890662103, -0.1738236335698854, 0.05991935658113851, -0.024217664227101275, 0.19853818151816746, 0.05811108479577921, 0.04175937609662386, 0.157703486117746, 0.2207074405576515, -0.02159020701550286, 0.08780946191439977, -0.1060974525224248, -0.12961184891173616, -0.3738674395082688, -0.08268580836011097, -0.1146042123635363, 0.021260838924635293, -0.08343069971657645, -0.13859615667818367, 0.3477408758428117, 0.1571698490199031, 0.2607172697687268, 0.035751624974099366, 0.2776140749338083, 0.12427426243547499, 0.07444260952682023, 0.06428878328270829, 0.2901156286984174, 0.09239710151450709, 0.11994925899621607, -0.21232653254595457, 0.09162605973489751, 0.026026512248790823] |
1,802.06345 | Resolving Two Conjectures on Staircase Encodings and Boundary Grids of
$132$ and $123$-avoiding permutations | This paper analyzes relations between pattern avoidance of certain
permutations and graphs on staircase grids and boundary grids, and proves two
conjectures posed by Bean, Tannock, and Ulfarsson (2015). More specifically,
this paper enumerates a certain family of staircase encodings and proves that
the downcore graph, a certain graph established on the boundary grid, is pure
if and only if the permutation corresponding to the boundary grid avoids the
classical patterns 123 and 2143.
| math.CO | this paper analyzes relations between pattern avoidance of certain permutations and graphs on staircase grids and boundary grids and proves two conjectures posed by bean tannock and ulfarsson 2015 more specifically this paper enumerates a certain family of staircase encodings and proves that the downcore graph a certain graph established on the boundary grid is pure if and only if the permutation corresponding to the boundary grid avoids the classical patterns 123 and 2143 | [['this', 'paper', 'analyzes', 'relations', 'between', 'pattern', 'avoidance', 'of', 'certain', 'permutations', 'and', 'graphs', 'on', 'staircase', 'grids', 'and', 'boundary', 'grids', 'and', 'proves', 'two', 'conjectures', 'posed', 'by', 'bean', 'tannock', 'and', 'ulfarsson', '2015', 'more', 'specifically', 'this', 'paper', 'enumerates', 'a', 'certain', 'family', 'of', 'staircase', 'encodings', 'and', 'proves', 'that', 'the', 'downcore', 'graph', 'a', 'certain', 'graph', 'established', 'on', 'the', 'boundary', 'grid', 'is', 'pure', 'if', 'and', 'only', 'if', 'the', 'permutation', 'corresponding', 'to', 'the', 'boundary', 'grid', 'avoids', 'the', 'classical', 'patterns', '123', 'and', '2143']] | [-0.1780401043280209, 0.12514606608925127, -0.0536313785899741, 0.05944316407193481, -0.08671122633515754, -0.13787694052896565, 0.0876706113502021, 0.33266894933250213, -0.2907687839017146, -0.3206603442473958, 0.15044650472474233, -0.24328512394115226, -0.1894012375592461, 0.14073070799834872, -0.17334752487173924, 0.08418262801003745, 0.09251685012714006, -0.020653232155988615, -0.039269967292461336, -0.2609649968322224, 0.3724933524305622, -0.026741463988501992, 0.26980716693732476, 0.05379601870663464, 0.07777981084978415, 0.0291094358286096, -0.06893202283794785, 0.03123591122049321, -0.15501604408735956, 0.1044026626671742, 0.19527672654173026, 0.13485940673854202, 0.19387604337599543, -0.41148788960547084, -0.1366584902603386, 0.1482790902906951, 0.1254338312656829, 0.05839308600924495, 0.0034895119208360687, -0.23178181489412156, 0.13842508810598197, -0.13088167229175773, -0.08033042335753432, 0.005769629678171542, 0.06253798321510355, 0.02293579765440275, -0.28313695835984415, -0.01206627001698103, 0.14842172955266303, 0.0951903262693021, 0.028968845595954917, -0.1219312321045436, -0.041664253499928035, 0.06916986022972399, -0.03571083499183361, 0.0032827317675886056, 0.001435537699661735, -0.08111483824258256, -0.13900598073248854, 0.36857921301836744, 0.08187040381340517, -0.1960791800585058, 0.15073922905139625, -0.14996434381464496, -0.15286138180332878, 0.04789956086056514, 0.09748566344690819, 0.10784755203510737, -0.12227421335410327, 0.09540704494591207, -0.14510652085138848, 0.1342327049674673, 0.210195871665039, -0.07298280956456438, 0.14845011740302047, 0.0802685175052223, 0.14668559757733923, 0.1766125047708758, -0.030701458890689537, -0.08403076556149042, -0.2562997102245895, -0.08747028626708521, -0.17073379446325512, -0.007700448668199695, -0.1433349832936478, -0.23506017267290089, 0.43763010058965945, 0.12739464510862794, 0.19135340272461893, 0.10707112916538285, 0.2223837849063178, 0.08468339321876152, 0.02047659965076794, 0.12197883634750421, 0.04878844072421392, 0.15551725486875512, 0.06292179852769347, -0.1840911477855924, 0.05318258757728876, 0.1586585143684513] |
1,802.06346 | Design and status of the Mu2e crystal calorimeter | The Mu2e experiment at Fermilab searches for the charged-lepton flavour
violating (CLFV) conversion of a negative muon into an electron in the field of
an aluminum nucleus, with a distinctive signature of a mono-energetic electron
of energy slightly below the muon rest mass (104.967 MeV). The Mu2e goal is to
improve by four orders of magnitude the search sensitivity with respect to the
previous experiments. Any observation of a CLFV signal will be a clear
indication of new physics. The Mu2e detector is composed of a tracker, an
electro- magnetic calorimeter and an external veto for cosmic rays surrounding
the solenoid. The calorimeter plays an important role in providing particle
identification capabilities, a fast online trigger filter, a seed for track
reconstruction while working in vacuum, in the presence of 1 T axial magnetic
field and in an harsh radiation environment. The calorimeter requirements are
to provide a large acceptance for 100 MeV electrons and reach at these
energies: (a) a time resolution better than 0.5 ns; (b) an energy resolution <
10% and (c) a position resolution of 1 cm. The calorimeter design consists of
two disks, each one made of 674 undoped CsI crystals read by two large area
arrays of UV-extended SiPMs. We report here the construction and test of the
Module-0 prototype. The Module-0 has been exposed to an electron beam in the
energy range around 100 MeV at the Beam Test Facility in Frascati. Preliminary
results of timing and energy resolution at normal incidence are shown. A
discussion of the technical aspects of the calorimeter engineering is also
reported in this paper.
| physics.ins-det hep-ex | the mu2e experiment at fermilab searches for the chargedlepton flavour violating clfv conversion of a negative muon into an electron in the field of an aluminum nucleus with a distinctive signature of a monoenergetic electron of energy slightly below the muon rest mass 104967 mev the mu2e goal is to improve by four orders of magnitude the search sensitivity with respect to the previous experiments any observation of a clfv signal will be a clear indication of new physics the mu2e detector is composed of a tracker an electro magnetic calorimeter and an external veto for cosmic rays surrounding the solenoid the calorimeter plays an important role in providing particle identification capabilities a fast online trigger filter a seed for track reconstruction while working in vacuum in the presence of 1 t axial magnetic field and in an harsh radiation environment the calorimeter requirements are to provide a large acceptance for 100 mev electrons and reach at these energies a a time resolution better than 05 ns b an energy resolution 10 and c a position resolution of 1 cm the calorimeter design consists of two disks each one made of 674 undoped csi crystals read by two large area arrays of uvextended sipms we report here the construction and test of the module0 prototype the module0 has been exposed to an electron beam in the energy range around 100 mev at the beam test facility in frascati preliminary results of timing and energy resolution at normal incidence are shown a discussion of the technical aspects of the calorimeter engineering is also reported in this paper | [['the', 'mu2e', 'experiment', 'at', 'fermilab', 'searches', 'for', 'the', 'chargedlepton', 'flavour', 'violating', 'clfv', 'conversion', 'of', 'a', 'negative', 'muon', 'into', 'an', 'electron', 'in', 'the', 'field', 'of', 'an', 'aluminum', 'nucleus', 'with', 'a', 'distinctive', 'signature', 'of', 'a', 'monoenergetic', 'electron', 'of', 'energy', 'slightly', 'below', 'the', 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1,802.06347 | Optimal stopping, randomized stopping and singular control with partial
information flow | The purpose of this paper is two-fold:
We extend the well-known relation between optimal stopping and randomized
stopping of a given stochastic process to a situation where the available
information flow is a filtration with no a priori assumed relation to the
filtration of the process. We call these problems optimal stopping and
randomized stopping with general information.
Following an idea of Krylov [K] we introduce a special singular stochastic
control problem with general information and show that this is also equivalent
to the partial information optimal stopping and randomized stopping problems.
Then we show that the solution of this singular control problem can be
expressed in terms of partial information variational inequalities.
| math.OC | the purpose of this paper is twofold we extend the wellknown relation between optimal stopping and randomized stopping of a given stochastic process to a situation where the available information flow is a filtration with no a priori assumed relation to the filtration of the process we call these problems optimal stopping and randomized stopping with general information following an idea of krylov k we introduce a special singular stochastic control problem with general information and show that this is also equivalent to the partial information optimal stopping and randomized stopping problems then we show that the solution of this singular control problem can be expressed in terms of partial information variational inequalities | [['the', 'purpose', 'of', 'this', 'paper', 'is', 'twofold', 'we', 'extend', 'the', 'wellknown', 'relation', 'between', 'optimal', 'stopping', 'and', 'randomized', 'stopping', 'of', 'a', 'given', 'stochastic', 'process', 'to', 'a', 'situation', 'where', 'the', 'available', 'information', 'flow', 'is', 'a', 'filtration', 'with', 'no', 'a', 'priori', 'assumed', 'relation', 'to', 'the', 'filtration', 'of', 'the', 'process', 'we', 'call', 'these', 'problems', 'optimal', 'stopping', 'and', 'randomized', 'stopping', 'with', 'general', 'information', 'following', 'an', 'idea', 'of', 'krylov', 'k', 'we', 'introduce', 'a', 'special', 'singular', 'stochastic', 'control', 'problem', 'with', 'general', 'information', 'and', 'show', 'that', 'this', 'is', 'also', 'equivalent', 'to', 'the', 'partial', 'information', 'optimal', 'stopping', 'and', 'randomized', 'stopping', 'problems', 'then', 'we', 'show', 'that', 'the', 'solution', 'of', 'this', 'singular', 'control', 'problem', 'can', 'be', 'expressed', 'in', 'terms', 'of', 'partial', 'information', 'variational', 'inequalities']] | [-0.090636768551395, 0.05072911539997399, -0.12000591996892364, 0.08661436117826615, -0.12661915739371846, -0.1539463152968844, 0.065548282863564, 0.35357333485663467, -0.39771658488738854, -0.2326149146929713, 0.17367508279188512, -0.2533877561944413, -0.1676884092044382, 0.13984350420007136, -0.15060269627390446, 0.09217248048089378, 0.04786149086018579, 0.04847137073252713, -0.0879178723954983, -0.2527782155130784, 0.34497068105760004, 0.03432561211757758, 0.2581156902056418, 0.04713895417486144, 0.16455254354720225, 0.014531141967075853, -0.03282477372466067, 0.0442298315795122, -0.18858880121984614, 0.13816505628308298, 0.29062625965663946, 0.1830193887143272, 0.34051530200790253, -0.4065994613564911, -0.1547071453826749, 0.15458457438126866, 0.1291282118779228, 0.12739585247508917, -0.039533983495712806, -0.21338710332272853, 0.07575847431852491, -0.12534517107275933, -0.08522960245485657, -0.020374914519983318, -0.011425534114900944, 0.023396095664825586, -0.38042944023566966, 0.060597648679029475, 0.07746780744498283, 0.016176513879378615, -0.058600933406403106, -0.07909919515837281, 0.06325958279657912, 0.07277102950976881, 0.039571440260985094, 0.0054054872979681975, 0.08381718669119662, -0.07290789688343957, -0.15350509622851305, 0.35646796189001306, -0.04112340367102452, -0.2525072354262909, 0.10128103797035538, -0.07674496209834068, -0.13547918835935074, 0.11518916274746469, 0.15845128188972912, 0.19645950351470867, -0.18617885626967898, 0.029851544374369283, -0.09337605573072107, 0.1252402063279899, 0.039123759526693454, 0.01242616094764223, 0.07733777845772889, 0.14789735467741844, 0.19946579820995705, 0.16933756822666599, -0.010817702554693791, -0.13789701617561875, -0.34055243615798686, -0.16111894569203125, -0.1420617270720216, 0.07920373823345367, -0.11457879137272527, -0.14014363539017802, 0.32871948782585364, 0.16498389538476424, 0.18750902604047967, 0.08476665767684446, 0.28153577198035423, 0.1689309391473313, -0.05606835773956459, 0.10104929548234934, 0.16225561047945403, 0.11473617978719583, 0.07815932454458907, -0.22445598155861737, 0.08955583043923948, 0.09709509729976411] |
1,802.06348 | Quantitative Predictions in Quantum Decision Theory | Quantum Decision Theory, advanced earlier by the authors, and illustrated for
lotteries with gains, is generalized to the games containing lotteries with
gains as well as losses. The mathematical structure of the approach is based on
the theory of quantum measurements, which makes this approach relevant both for
the description of decision making of humans and the creation of artificial
quantum intelligence. General rules are formulated allowing for the explicit
calculation of quantum probabilities representing the fraction of decision
makers preferring the considered prospects. This provides a method to
quantitatively predict decision-maker choices, including the cases of games
with high uncertainty for which the classical expected utility theory fails.
The approach is applied to experimental results obtained on a set of lottery
gambles with gains and losses. Our predictions, involving no fitting
parameters, are in very good agreement with experimental data. The use of
quantum decision making in game theory is described. A principal scheme of
creating quantum artificial intelligence is suggested.
| physics.soc-ph quant-ph | quantum decision theory advanced earlier by the authors and illustrated for lotteries with gains is generalized to the games containing lotteries with gains as well as losses the mathematical structure of the approach is based on the theory of quantum measurements which makes this approach relevant both for the description of decision making of humans and the creation of artificial quantum intelligence general rules are formulated allowing for the explicit calculation of quantum probabilities representing the fraction of decision makers preferring the considered prospects this provides a method to quantitatively predict decisionmaker choices including the cases of games with high uncertainty for which the classical expected utility theory fails the approach is applied to experimental results obtained on a set of lottery gambles with gains and losses our predictions involving no fitting parameters are in very good agreement with experimental data the use of quantum decision making in game theory is described a principal scheme of creating quantum artificial intelligence is suggested | [['quantum', 'decision', 'theory', 'advanced', 'earlier', 'by', 'the', 'authors', 'and', 'illustrated', 'for', 'lotteries', 'with', 'gains', 'is', 'generalized', 'to', 'the', 'games', 'containing', 'lotteries', 'with', 'gains', 'as', 'well', 'as', 'losses', 'the', 'mathematical', 'structure', 'of', 'the', 'approach', 'is', 'based', 'on', 'the', 'theory', 'of', 'quantum', 'measurements', 'which', 'makes', 'this', 'approach', 'relevant', 'both', 'for', 'the', 'description', 'of', 'decision', 'making', 'of', 'humans', 'and', 'the', 'creation', 'of', 'artificial', 'quantum', 'intelligence', 'general', 'rules', 'are', 'formulated', 'allowing', 'for', 'the', 'explicit', 'calculation', 'of', 'quantum', 'probabilities', 'representing', 'the', 'fraction', 'of', 'decision', 'makers', 'preferring', 'the', 'considered', 'prospects', 'this', 'provides', 'a', 'method', 'to', 'quantitatively', 'predict', 'decisionmaker', 'choices', 'including', 'the', 'cases', 'of', 'games', 'with', 'high', 'uncertainty', 'for', 'which', 'the', 'classical', 'expected', 'utility', 'theory', 'fails', 'the', 'approach', 'is', 'applied', 'to', 'experimental', 'results', 'obtained', 'on', 'a', 'set', 'of', 'lottery', 'gambles', 'with', 'gains', 'and', 'losses', 'our', 'predictions', 'involving', 'no', 'fitting', 'parameters', 'are', 'in', 'very', 'good', 'agreement', 'with', 'experimental', 'data', 'the', 'use', 'of', 'quantum', 'decision', 'making', 'in', 'game', 'theory', 'is', 'described', 'a', 'principal', 'scheme', 'of', 'creating', 'quantum', 'artificial', 'intelligence', 'is', 'suggested']] | [-0.07022124625466487, 0.08154162226164323, -0.09456815999461177, 0.08699306875487421, -0.09408058151516512, -0.1690172785806849, 0.09716717888497645, 0.36055534823372226, -0.21079519507187752, -0.32833718587098254, 0.07521313771729093, -0.26680859214522773, -0.1748814680642322, 0.22063810958952448, -0.07496158245743977, 0.09345046540055378, 0.07806998956086184, 0.06727745842453046, -0.0044850014181761644, -0.2820731725444569, 0.28298118975398673, 0.09017267355186588, 0.2955046026955968, 0.04112350482516634, 0.11020387697571107, 0.05067845794694199, -0.04148863507492215, 0.03663947222184454, -0.09383317189618808, 0.1882594037660179, 0.32691534368298303, 0.16038905994184774, 0.3114548109394188, -0.40365150578917913, -0.21548883337527514, 0.06658567550856574, 0.05516315084295692, 0.13439022019048463, -0.019049704125162537, -0.2969242825552269, 0.053085835443602666, -0.18792954687011876, -0.11907017542003298, -0.10904535099743279, -0.02556877275267731, -0.011583293852091617, -0.3272728799906255, 0.02497988596363108, 0.012919596020551974, 0.05508352232016163, -0.05907131988629929, -0.13965293375579754, 0.03654225192027005, 0.13228880177700408, 0.01998632348769195, -0.017739987437739784, 0.12086077125665422, -0.16387014826299784, -0.2309062898958125, 0.4233624738537603, -0.02089097370956967, -0.19895682310150867, 0.16872298932364097, -0.05509105939159377, -0.10459922235689045, 0.07335693105612594, 0.12076010351948854, 0.09484324521416752, -0.11827063063816397, 0.0625435929177462, -0.024044224418941194, 0.1325750453857544, 0.03186141520186707, 0.029509564960060318, 0.19038579731972682, 0.1880658120089383, 0.024720222012996444, 0.11794933427935811, -0.021112195451824384, -0.2268191115114332, -0.30296082116295525, -0.1301994452563425, -0.14964780870273156, 0.047354322082052626, -0.0891412486819684, -0.16866448770829098, 0.32007258483660994, 0.17512049792545709, 0.14638260687592894, 0.08628916002401829, 0.3114516541913704, 0.13755512515517357, 0.03461545189543639, 0.01968672348726771, 0.23097417085649974, 0.12331559767883363, 0.07552537186298358, -0.19550847603197866, 0.12452026313734184, 0.015403995370883265] |
1,802.06349 | Isotropic LQC and LQC--inspired Models with a massless scalar field as
Generalised Brans--Dicke theories | We explore whether generalised Brans -- Dicke theories, which have a scalar
field $\Phi$ and a function $\omega(\Phi)$, can be the effective actions
leading to the effective equations of motion of the LQC and the LQC--inspired
models, which have a massless scalar field $\sigma$ and a function $f(m) \;$.
We find that this is possible for isotropic cosmology. We relate the pairs
$(\sigma, f)$ and $(\Phi, \omega)$ and, using examples, illustrate these
relations. We find that near the bounce of the LQC evolutions for which $f(m) =
sin \; m$, the corresponding field $\Phi \to 0$ and the function $\omega(\Phi)
\propto \Phi^2 \;$. We also find that the class of generalised Brans -- Dicke
theories, which we had found earlier to lead to non singular isotropic
evolutions, may be written as an LQC--inspired model. The relations found here
in the isotropic cases do not apply to the anisotropic cases, which perhaps
require more general effective actions.
| gr-qc hep-th | we explore whether generalised brans dicke theories which have a scalar field phi and a function omegaphi can be the effective actions leading to the effective equations of motion of the lqc and the lqcinspired models which have a massless scalar field sigma and a function fm we find that this is possible for isotropic cosmology we relate the pairs sigma f and phi omega and using examples illustrate these relations we find that near the bounce of the lqc evolutions for which fm sin m the corresponding field phi to 0 and the function omegaphi propto phi2 we also find that the class of generalised brans dicke theories which we had found earlier to lead to non singular isotropic evolutions may be written as an lqcinspired model the relations found here in the isotropic cases do not apply to the anisotropic cases which perhaps require more general effective actions | [['we', 'explore', 'whether', 'generalised', 'brans', 'dicke', 'theories', 'which', 'have', 'a', 'scalar', 'field', 'phi', 'and', 'a', 'function', 'omegaphi', 'can', 'be', 'the', 'effective', 'actions', 'leading', 'to', 'the', 'effective', 'equations', 'of', 'motion', 'of', 'the', 'lqc', 'and', 'the', 'lqcinspired', 'models', 'which', 'have', 'a', 'massless', 'scalar', 'field', 'sigma', 'and', 'a', 'function', 'fm', 'we', 'find', 'that', 'this', 'is', 'possible', 'for', 'isotropic', 'cosmology', 'we', 'relate', 'the', 'pairs', 'sigma', 'f', 'and', 'phi', 'omega', 'and', 'using', 'examples', 'illustrate', 'these', 'relations', 'we', 'find', 'that', 'near', 'the', 'bounce', 'of', 'the', 'lqc', 'evolutions', 'for', 'which', 'fm', 'sin', 'm', 'the', 'corresponding', 'field', 'phi', 'to', '0', 'and', 'the', 'function', 'omegaphi', 'propto', 'phi2', 'we', 'also', 'find', 'that', 'the', 'class', 'of', 'generalised', 'brans', 'dicke', 'theories', 'which', 'we', 'had', 'found', 'earlier', 'to', 'lead', 'to', 'non', 'singular', 'isotropic', 'evolutions', 'may', 'be', 'written', 'as', 'an', 'lqcinspired', 'model', 'the', 'relations', 'found', 'here', 'in', 'the', 'isotropic', 'cases', 'do', 'not', 'apply', 'to', 'the', 'anisotropic', 'cases', 'which', 'perhaps', 'require', 'more', 'general', 'effective', 'actions']] | [-0.12841399172179377, 0.17712490566909028, -0.11116496184085672, 0.11578891862410347, -0.08082715570737885, -0.18394609767238837, -0.023806981412014244, 0.3280845214235219, -0.21383183009645626, -0.26611593444957526, 0.03727365637314506, -0.2561869917097627, -0.1744319061210656, 0.1582029856886161, 0.009890007496987646, -0.0011347425233169987, -0.0031541483508221602, 0.07184301497339196, -0.1204429872580206, -0.24066906181365452, 0.3561965664408195, 0.001113910906447878, 0.20174986788864574, 0.018202939558447012, 0.07232808564928032, -0.05351651418715011, 0.038900081307400726, 0.04679483978403319, -0.19892737976619715, 0.04139310181159778, 0.19303970070937007, 0.08420994834395477, 0.20334182318451033, -0.35108162773571705, -0.22221304262308655, 0.16102818164005372, 0.14681403067692914, 0.11338458317756728, 0.021569117568130448, -0.29393450489796297, 0.0640857712856519, -0.1865084769606993, -0.16152410027319314, -0.09977429812125913, 0.06029151736780674, -0.03160306195590041, -0.3029123461692015, 0.08811511469822428, 0.0483993468460048, -0.015768715199334798, -0.04449557507964405, -0.12025804538830384, -0.019899515101585438, 0.07016896148136742, 0.06425829944625572, 0.07689427281021946, 0.10432566366334622, -0.13631382868530834, -0.07885022991418687, 0.3905244124628528, -0.1178177202913306, -0.2423822593094932, 0.14040777888274877, -0.15439378519894909, -0.12851019359722332, 0.04284420166466687, 0.13317343241824592, 0.11741383572547962, -0.12579360680742743, 0.16494302493535812, -0.028131467535638728, 0.15614752379730595, 0.06154985093146663, 0.010081355066299817, 0.24213261609563502, 0.03526765291541312, 0.011114424633765215, 0.11140371117633893, -0.016326256692598295, -0.09070061113271623, -0.3767981508973281, -0.1345349179417197, -0.08279033288095347, 0.10912151015971275, -0.11221977310019979, -0.18427837354672216, 0.3501597950486718, 0.13914603794172936, 0.16410256818415145, 0.062440522497274435, 0.1976125280473482, 0.14742251125563918, 0.06115007957418424, 0.09403590700664632, 0.27538648742955885, 0.14076112888319217, 0.08989267866441829, -0.23089599436639832, -0.05289999932377926, 0.018918594678177976] |
1,802.0635 | Spatial modelling with R-INLA: A review | Coming up with Bayesian models for spatial data is easy, but performing
inference with them can be challenging. Writing fast inference code for a
complex spatial model with realistically-sized datasets from scratch is
time-consuming, and if changes are made to the model, there is little guarantee
that the code performs well. The key advantages of R-INLA are the ease with
which complex models can be created and modified, without the need to write
complex code, and the speed at which inference can be done even for spatial
problems with hundreds of thousands of observations.
R-INLA handles latent Gaussian models, where fixed effects, structured and
unstructured Gaussian random effects are combined linearly in a linear
predictor, and the elements of the linear predictor are observed through one or
more likelihoods. The structured random effects can be both standard areal
model such as the Besag and the BYM models, and geostatistical models from a
subset of the Mat\'ern Gaussian random fields. In this review, we discuss the
large success of spatial modelling with R-INLA and the types of spatial models
that can be fitted, we give an overview of recent developments for areal
models, and we give an overview of the stochastic partial differential equation
(SPDE) approach and some of the ways it can be extended beyond the assumptions
of isotropy and separability. In particular, we describe how slight changes to
the SPDE approach leads to straight-forward approaches for non-stationary
spatial models and non-separable space-time models.
| stat.ME stat.CO | coming up with bayesian models for spatial data is easy but performing inference with them can be challenging writing fast inference code for a complex spatial model with realisticallysized datasets from scratch is timeconsuming and if changes are made to the model there is little guarantee that the code performs well the key advantages of rinla are the ease with which complex models can be created and modified without the need to write complex code and the speed at which inference can be done even for spatial problems with hundreds of thousands of observations rinla handles latent gaussian models where fixed effects structured and unstructured gaussian random effects are combined linearly in a linear predictor and the elements of the linear predictor are observed through one or more likelihoods the structured random effects can be both standard areal model such as the besag and the bym models and geostatistical models from a subset of the matern gaussian random fields in this review we discuss the large success of spatial modelling with rinla and the types of spatial models that can be fitted we give an overview of recent developments for areal models and we give an overview of the stochastic partial differential equation spde approach and some of the ways it can be extended beyond the assumptions of isotropy and separability in particular we describe how slight changes to the spde approach leads to straightforward approaches for nonstationary spatial models and nonseparable spacetime models | [['coming', 'up', 'with', 'bayesian', 'models', 'for', 'spatial', 'data', 'is', 'easy', 'but', 'performing', 'inference', 'with', 'them', 'can', 'be', 'challenging', 'writing', 'fast', 'inference', 'code', 'for', 'a', 'complex', 'spatial', 'model', 'with', 'realisticallysized', 'datasets', 'from', 'scratch', 'is', 'timeconsuming', 'and', 'if', 'changes', 'are', 'made', 'to', 'the', 'model', 'there', 'is', 'little', 'guarantee', 'that', 'the', 'code', 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1,802.06351 | Mixing angle and decay constants of $J^P=1^+$ heavy-light mesons | The mass spectra, mixing angle and decay constants of the $J^P=1^+$
heavy-light mesons are systematically studied within the framework of the
Bethe-Salpeter equation (BSE). The full $1^+$ Salpeter wave function is given
for the first time. The mixing between the $1^{+-}$ and $1^{++}$ in the $1^+$
heavy-light systems are automatically determined by the dynamics in the
equation without any man-made mixing. The results indicate that in a rigorous
study there exists the phenomenon of mixing angle inversion or mass inversion
within $1^{+}$ heavy-light doublet, which is sensitive to the $s$-quark mass
for the charmed mesons and $u$- or $d$-quark masses for the bottomed mesons.
This inversion phenomenon can answer the question of why we have confused
mixing angles in the literature and partly explain the lower mass of
$D_{s1}(2460)$ compared to that of $D_{s1}(2536)$. The decay constants are also
presented and can behave as a good quantity to distinguish the $1^+$ doublet in
heavy-light mesons. This study indicates that the light-quark mass may play an
important role in deciding the mass order, mixing angle, and decay constant
relation between the $\ket{j_l=\frac{3}{2}}$ and $\ket{j_l=\frac{1}{2}}$
heavy-light mesons.
| hep-ph hep-ex | the mass spectra mixing angle and decay constants of the jp1 heavylight mesons are systematically studied within the framework of the bethesalpeter equation bse the full 1 salpeter wave function is given for the first time the mixing between the 1 and 1 in the 1 heavylight systems are automatically determined by the dynamics in the equation without any manmade mixing the results indicate that in a rigorous study there exists the phenomenon of mixing angle inversion or mass inversion within 1 heavylight doublet which is sensitive to the squark mass for the charmed mesons and u or dquark masses for the bottomed mesons this inversion phenomenon can answer the question of why we have confused mixing angles in the literature and partly explain the lower mass of d_s12460 compared to that of d_s12536 the decay constants are also presented and can behave as a good quantity to distinguish the 1 doublet in heavylight mesons this study indicates that the lightquark mass may play an important role in deciding the mass order mixing angle and decay constant relation between the ketj_lfrac32 and ketj_lfrac12 heavylight mesons | [['the', 'mass', 'spectra', 'mixing', 'angle', 'and', 'decay', 'constants', 'of', 'the', 'jp1', 'heavylight', 'mesons', 'are', 'systematically', 'studied', 'within', 'the', 'framework', 'of', 'the', 'bethesalpeter', 'equation', 'bse', 'the', 'full', '1', 'salpeter', 'wave', 'function', 'is', 'given', 'for', 'the', 'first', 'time', 'the', 'mixing', 'between', 'the', '1', 'and', '1', 'in', 'the', '1', 'heavylight', 'systems', 'are', 'automatically', 'determined', 'by', 'the', 'dynamics', 'in', 'the', 'equation', 'without', 'any', 'manmade', 'mixing', 'the', 'results', 'indicate', 'that', 'in', 'a', 'rigorous', 'study', 'there', 'exists', 'the', 'phenomenon', 'of', 'mixing', 'angle', 'inversion', 'or', 'mass', 'inversion', 'within', '1', 'heavylight', 'doublet', 'which', 'is', 'sensitive', 'to', 'the', 'squark', 'mass', 'for', 'the', 'charmed', 'mesons', 'and', 'u', 'or', 'dquark', 'masses', 'for', 'the', 'bottomed', 'mesons', 'this', 'inversion', 'phenomenon', 'can', 'answer', 'the', 'question', 'of', 'why', 'we', 'have', 'confused', 'mixing', 'angles', 'in', 'the', 'literature', 'and', 'partly', 'explain', 'the', 'lower', 'mass', 'of', 'd_s12460', 'compared', 'to', 'that', 'of', 'd_s12536', 'the', 'decay', 'constants', 'are', 'also', 'presented', 'and', 'can', 'behave', 'as', 'a', 'good', 'quantity', 'to', 'distinguish', 'the', '1', 'doublet', 'in', 'heavylight', 'mesons', 'this', 'study', 'indicates', 'that', 'the', 'lightquark', 'mass', 'may', 'play', 'an', 'important', 'role', 'in', 'deciding', 'the', 'mass', 'order', 'mixing', 'angle', 'and', 'decay', 'constant', 'relation', 'between', 'the', 'ketj_lfrac32', 'and', 'ketj_lfrac12', 'heavylight', 'mesons']] | [-0.10350598972494322, 0.250193563094722, -0.028110754040013897, 0.17120866527363576, -0.0187368332026679, -0.13162499396741145, 0.050458690369135285, 0.34957173712999445, -0.21981666446790668, -0.27345327026327776, 0.010079288855195045, -0.28479446090574634, -0.0458373908141801, 0.08674528077753678, 0.06929808065977172, 0.07828045297602365, 0.04868144067920378, 0.004688667324295298, -0.09573959667983724, -0.13649758123194727, 0.35381580849529287, -0.029563710270029662, 0.17791008651052995, 0.1481909014366038, -0.0064127495451295966, -0.032315346341111946, 0.013355481285662925, -0.10131454156060281, -0.1452673984649117, 0.047838140577408664, 0.18506794012421385, 0.10113691610584412, 0.17054983042180538, -0.32739036719574305, -0.1319347143865348, 0.10257487222196056, 0.19690938177725295, 0.09780224300144862, -0.0636093720623236, -0.2928448548957787, 0.07369203294520496, -0.14062508604682655, -0.17100824015418606, -0.07315711944766058, 0.02625813923491155, -0.054526358641826554, -0.3173807783489996, 0.13856589508003078, -0.017782585419933286, 0.011051704235568317, -0.057315975341111255, -0.22103571713662865, -0.06703868333652331, 0.10560935068254669, 0.14038755937213823, 0.06371190298928833, 0.08901121971691078, -0.13998458015126852, -0.09081389942165728, 0.435086739631217, -0.09147703431640838, -0.20526437195815664, 0.11748379758493008, -0.17522437161832152, -0.12446274753415683, 0.10964771607493767, 0.18582694597359076, 0.08857105350213461, -0.15625809832053678, 0.07706818150774998, -0.10312696909676675, 0.20626339822997405, 0.09937885352232255, 0.07551113053611759, 0.19865322746132894, 0.14824569966806903, -0.01838677008178268, 0.01139943226230397, -0.04944067477131558, -0.09210799036985212, -0.31773052125395645, -0.14970220380737523, -0.12625777437936903, 0.06608145839772937, -0.08222600810340787, -0.0869565759225662, 0.4087746409047904, 0.09543483085418883, 0.24635400259078485, 0.02785238716942182, 0.2880282151033145, 0.12036623393282131, 0.07400026072524081, 0.08588829257174349, 0.3528031668412026, 0.22280362501099218, 0.12595616233957513, -0.30166684591358967, 0.04709066161275154, 0.06757641472991129] |
1,802.06352 | Dynamic spin injection into a quantum well coupled to a spin-split bound
state | We present a theoretical analysis of dynamic spin injection due to
spin-dependent tunneling between a quantum well (QW) and a bound state split in
spin projection due to an exchange interaction or external magnetic field. We
focus on the impact of Coulomb correlations at the bound state on spin
polarization and sheet density kinetics of the charge carriers in the QW. The
theoretical approach is based on kinetic equations for the electron occupation
numbers taking into account high order correlation functions for the bound
state electrons. It is shown that the on-site Coulomb repulsion leads to an
enhanced dynamic spin polarization of the electrons in the QW and a delay in
the carriers tunneling into the bound state. The interplay of these two effects
leads to non-trivial dependence of the spin polarization degree, which can be
probed experimentally using time-resolved photoluminescence experiments. It is
demonstrated that the influence of the Coulomb interactions can be controlled
by adjusting the relaxation rates. These findings open a new way of studying
the Hubbard-like electron interactions experimentally.
| cond-mat.mes-hall | we present a theoretical analysis of dynamic spin injection due to spindependent tunneling between a quantum well qw and a bound state split in spin projection due to an exchange interaction or external magnetic field we focus on the impact of coulomb correlations at the bound state on spin polarization and sheet density kinetics of the charge carriers in the qw the theoretical approach is based on kinetic equations for the electron occupation numbers taking into account high order correlation functions for the bound state electrons it is shown that the onsite coulomb repulsion leads to an enhanced dynamic spin polarization of the electrons in the qw and a delay in the carriers tunneling into the bound state the interplay of these two effects leads to nontrivial dependence of the spin polarization degree which can be probed experimentally using timeresolved photoluminescence experiments it is demonstrated that the influence of the coulomb interactions can be controlled by adjusting the relaxation rates these findings open a new way of studying the hubbardlike electron interactions experimentally | [['we', 'present', 'a', 'theoretical', 'analysis', 'of', 'dynamic', 'spin', 'injection', 'due', 'to', 'spindependent', 'tunneling', 'between', 'a', 'quantum', 'well', 'qw', 'and', 'a', 'bound', 'state', 'split', 'in', 'spin', 'projection', 'due', 'to', 'an', 'exchange', 'interaction', 'or', 'external', 'magnetic', 'field', 'we', 'focus', 'on', 'the', 'impact', 'of', 'coulomb', 'correlations', 'at', 'the', 'bound', 'state', 'on', 'spin', 'polarization', 'and', 'sheet', 'density', 'kinetics', 'of', 'the', 'charge', 'carriers', 'in', 'the', 'qw', 'the', 'theoretical', 'approach', 'is', 'based', 'on', 'kinetic', 'equations', 'for', 'the', 'electron', 'occupation', 'numbers', 'taking', 'into', 'account', 'high', 'order', 'correlation', 'functions', 'for', 'the', 'bound', 'state', 'electrons', 'it', 'is', 'shown', 'that', 'the', 'onsite', 'coulomb', 'repulsion', 'leads', 'to', 'an', 'enhanced', 'dynamic', 'spin', 'polarization', 'of', 'the', 'electrons', 'in', 'the', 'qw', 'and', 'a', 'delay', 'in', 'the', 'carriers', 'tunneling', 'into', 'the', 'bound', 'state', 'the', 'interplay', 'of', 'these', 'two', 'effects', 'leads', 'to', 'nontrivial', 'dependence', 'of', 'the', 'spin', 'polarization', 'degree', 'which', 'can', 'be', 'probed', 'experimentally', 'using', 'timeresolved', 'photoluminescence', 'experiments', 'it', 'is', 'demonstrated', 'that', 'the', 'influence', 'of', 'the', 'coulomb', 'interactions', 'can', 'be', 'controlled', 'by', 'adjusting', 'the', 'relaxation', 'rates', 'these', 'findings', 'open', 'a', 'new', 'way', 'of', 'studying', 'the', 'hubbardlike', 'electron', 'interactions', 'experimentally']] | [-0.15452734960143513, 0.22138698661388329, -0.080546448338976, 0.08447594087600514, -0.0037323371372643232, -0.1303375241120556, 0.06284112830961264, 0.3683226012296415, -0.2773727334099095, -0.3160021217927509, -0.027041334863429124, -0.2907554377354897, -0.09773333506481022, 0.18909431684821457, 0.08471981148811521, 0.0007417002111414953, -0.0018826056745527796, -0.03372668976642493, -0.056192997762276604, -0.21650886113050183, 0.2970109335873599, 0.04470030392851207, 0.3066604396091462, 0.1854052274229511, 0.08670810935749657, 0.06526095229550938, 0.07159283973217398, 0.02814014987061823, -0.10729463208936804, 0.07463472710136389, 0.19266810936676984, -0.0489962453875863, 0.21237527011931373, -0.4735370594254463, -0.19571750311213248, 0.02107979675130434, 0.15717755350138926, 0.18140338806131306, -0.08670788653655183, -0.32071471620334924, -0.026405934174410527, -0.1739624543890197, -0.09058108401950449, -0.08192341310386626, 0.006380891514204528, 0.01226659313502393, -0.28291717822518136, 0.10237260154755301, 0.06506691299812233, 0.003531497284080941, -0.09100182346509758, -0.07984642097403462, -0.05694324052300455, 0.10042142694832143, 0.037170263061116884, 0.027871645144536834, 0.1611559039716625, -0.13160774486430118, -0.14723873175000987, 0.32440988844626645, -0.07076231160704477, -0.19448673121114343, 0.1583461208643144, -0.1934374320977556, -0.03442849990281928, 0.13969594906834545, 0.16138714278450592, 0.09976853938730207, -0.1372551730244469, 0.09258064458239554, 0.015364051970782437, 0.16236972877072073, 0.014267751348145693, 0.09538645267680373, 0.23864539854788366, 0.1766560569922359, 0.07713886653476884, 0.12869604337675164, -0.12863064626355608, -0.11422441998830421, -0.23247266509650932, -0.136870391345233, -0.2309882888500444, 0.10838726145616394, -0.05490516566826539, -0.12055354816566566, 0.4419233817915026, 0.140365200316415, 0.19295943114298228, -0.04996785614033892, 0.28507490604146885, 0.17437765675171607, 0.045673532998243165, 0.005240940771501705, 0.2721958375425546, 0.1930162370261451, 0.04836460532125279, -0.3462320442440667, 0.08549722237512469, 0.016252425616348667] |
1,802.06353 | On the well-posedness of a multiscale mathematical model for Lithium-ion
batteries | We consider the mathematical treatment of a system of nonlinear partial
differential equations based on a model, proposed in 1972 by J. Newman, in
which the coupling between the Lithium concentration, the phase potentials and
temperature in the electrodes and the electrolyte of a Lithium battery cell is
considered. After introducing some functional spaces well-adapted to our
framework we obtain some rigorous results showing the well-posedness of the
system, first for some short time and then, by considering some hypothesis on
the nonlinearities, globally in time. As far as we know, this is the first
result in the literature proving existence in time of the full Newman model,
which follows previous results by the third author in 2016 regarding a
simplified case.
| math.AP | we consider the mathematical treatment of a system of nonlinear partial differential equations based on a model proposed in 1972 by j newman in which the coupling between the lithium concentration the phase potentials and temperature in the electrodes and the electrolyte of a lithium battery cell is considered after introducing some functional spaces welladapted to our framework we obtain some rigorous results showing the wellposedness of the system first for some short time and then by considering some hypothesis on the nonlinearities globally in time as far as we know this is the first result in the literature proving existence in time of the full newman model which follows previous results by the third author in 2016 regarding a simplified case | [['we', 'consider', 'the', 'mathematical', 'treatment', 'of', 'a', 'system', 'of', 'nonlinear', 'partial', 'differential', 'equations', 'based', 'on', 'a', 'model', 'proposed', 'in', '1972', 'by', 'j', 'newman', 'in', 'which', 'the', 'coupling', 'between', 'the', 'lithium', 'concentration', 'the', 'phase', 'potentials', 'and', 'temperature', 'in', 'the', 'electrodes', 'and', 'the', 'electrolyte', 'of', 'a', 'lithium', 'battery', 'cell', 'is', 'considered', 'after', 'introducing', 'some', 'functional', 'spaces', 'welladapted', 'to', 'our', 'framework', 'we', 'obtain', 'some', 'rigorous', 'results', 'showing', 'the', 'wellposedness', 'of', 'the', 'system', 'first', 'for', 'some', 'short', 'time', 'and', 'then', 'by', 'considering', 'some', 'hypothesis', 'on', 'the', 'nonlinearities', 'globally', 'in', 'time', 'as', 'far', 'as', 'we', 'know', 'this', 'is', 'the', 'first', 'result', 'in', 'the', 'literature', 'proving', 'existence', 'in', 'time', 'of', 'the', 'full', 'newman', 'model', 'which', 'follows', 'previous', 'results', 'by', 'the', 'third', 'author', 'in', '2016', 'regarding', 'a', 'simplified', 'case']] | [-0.11023988844979493, 0.04095594907278714, -0.05776867517804513, 0.026650557938589118, -0.029900657371846866, -0.10646845782786364, 0.07402826082266745, 0.31510019765739317, -0.23647449146498178, -0.2965814749801867, 0.10845336826903684, -0.24440912025614228, -0.16614892061601286, 0.1865487957457233, -0.07704304062406395, 0.05314615944812655, 0.014497241425160014, 0.026502021179977256, -0.027445222300736875, -0.2684803110658054, 0.3238345463435761, 0.043258556248295714, 0.2224258464738654, 0.07982185325722714, 0.08381619427513454, 0.0047341865338537785, -0.030029611329960286, 0.024266453944604662, -0.1584637137816944, 0.07445362052254256, 0.21981093572971763, 0.0815581371740545, 0.29613155390700846, -0.4486931778918036, -0.24582839115202182, 0.06118647335860573, 0.06642022079284318, 0.1500240828733143, -0.07222772497095961, -0.29212326254718557, 0.06537955957197691, -0.1382858646712953, -0.1402353320736438, -0.034911800786608556, 0.04369474935811013, 0.04904651557110616, -0.256222951066581, 0.1019554214292496, 0.12185788186663976, 0.03394834534646791, -0.11656137663283248, -0.10367192803752288, -0.001980805286725403, 0.0893049693063329, 0.0552782119036133, -0.009515764519999751, 0.03911798107285114, -0.08699088162154753, -0.09653622635304317, 0.33797327577151726, -0.08987610276070897, -0.2032363367587572, 0.15779051472639025, -0.13789945899997455, -0.14387932839635453, 0.05736240127684976, 0.1365434469475762, 0.15781055879015782, -0.18828932308118607, 0.10500096618894442, -0.0638738452403455, 0.15595296017642393, 0.0875600555255154, -0.029945206152824838, 0.11795220455750212, 0.20523194946944104, 0.04525003010468161, 0.1207770318724215, -0.022959177134192137, -0.11436577397923474, -0.3254345392075474, -0.19230550022215628, -0.17043556495508577, 0.03906738417620053, -0.06851343867704406, -0.1319503543180887, 0.40682577816616805, 0.13803694468968716, 0.18952309425737038, 0.061630310515537245, 0.266612701866104, 0.12461338699394532, -0.004056686455322827, 0.040201752417965135, 0.25926924788595374, 0.15619253740287156, 0.15850472722782707, -0.1999219921970984, 0.06937892845312714, 0.11585128402383235] |
1,802.06354 | K2SUPERSTAMP: The release of calibrated mosaics for the {\em Kepler/K2}
Mission | We describe the release of a new High Level Science Product (HLSP) available
at the MAST archive. The HLSP, called K2Superstamp, consists of a series of
FITS images for four open star clusters observed by the K2 Mission using
so-called "superstamp" pixel masks: M35, the $\sim$150 Myr old open cluster
observed during K2 Campaign 0, M67, the solar-age, solar-metallicity benchmark
cluster observed during Campaign 5, Ruprecht 147, the $\sim$3 Gyr-old open
cluster observed during Campaign 7, and the Lagoon Nebula (M8/NGC 6530), the
high-mass star-forming region observed during Campaign 9. While the data for
these regions have long been served on MAST, until now they were only available
as a disconnected set of smaller Target Pixel Files (TPFs) because the
spacecraft stored these observations in small chunks. As a result, these
regions have hitherto been ignored by many lightcurve and planet search
pipelines. With this new release, we have stitched these TPFs together into
spatially contiguous FITS images (one per cadence) to make their scientific
analysis easier. In addition, each image has been fit with an accurate WCS
solution so that you may locate any object of interest via its right ascension
and declination. We describe here the process of stitching and astrometric
calibration.
| astro-ph.IM | we describe the release of a new high level science product hlsp available at the mast archive the hlsp called k2superstamp consists of a series of fits images for four open star clusters observed by the k2 mission using socalled superstamp pixel masks m35 the sim150 myr old open cluster observed during k2 campaign 0 m67 the solarage solarmetallicity benchmark cluster observed during campaign 5 ruprecht 147 the sim3 gyrold open cluster observed during campaign 7 and the lagoon nebula m8ngc 6530 the highmass starforming region observed during campaign 9 while the data for these regions have long been served on mast until now they were only available as a disconnected set of smaller target pixel files tpfs because the spacecraft stored these observations in small chunks as a result these regions have hitherto been ignored by many lightcurve and planet search pipelines with this new release we have stitched these tpfs together into spatially contiguous fits images one per cadence to make their scientific analysis easier in addition each image has been fit with an accurate wcs solution so that you may locate any object of interest via its right ascension and declination we describe here the process of stitching and astrometric calibration | [['we', 'describe', 'the', 'release', 'of', 'a', 'new', 'high', 'level', 'science', 'product', 'hlsp', 'available', 'at', 'the', 'mast', 'archive', 'the', 'hlsp', 'called', 'k2superstamp', 'consists', 'of', 'a', 'series', 'of', 'fits', 'images', 'for', 'four', 'open', 'star', 'clusters', 'observed', 'by', 'the', 'k2', 'mission', 'using', 'socalled', 'superstamp', 'pixel', 'masks', 'm35', 'the', 'sim150', 'myr', 'old', 'open', 'cluster', 'observed', 'during', 'k2', 'campaign', '0', 'm67', 'the', 'solarage', 'solarmetallicity', 'benchmark', 'cluster', 'observed', 'during', 'campaign', '5', 'ruprecht', '147', 'the', 'sim3', 'gyrold', 'open', 'cluster', 'observed', 'during', 'campaign', '7', 'and', 'the', 'lagoon', 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1,802.06355 | Stochastic Chebyshev Gradient Descent for Spectral Optimization | A large class of machine learning techniques requires the solution of
optimization problems involving spectral functions of parametric matrices, e.g.
log-determinant and nuclear norm. Unfortunately, computing the gradient of a
spectral function is generally of cubic complexity, as such gradient descent
methods are rather expensive for optimizing objectives involving the spectral
function. Thus, one naturally turns to stochastic gradient methods in hope that
they will provide a way to reduce or altogether avoid the computation of full
gradients. However, here a new challenge appears: there is no straightforward
way to compute unbiased stochastic gradients for spectral functions. In this
paper, we develop unbiased stochastic gradients for spectral-sums, an important
subclass of spectral functions. Our unbiased stochastic gradients are based on
combining randomized trace estimators with stochastic truncation of the
Chebyshev expansions. A careful design of the truncation distribution allows us
to offer distributions that are variance-optimal, which is crucial for fast and
stable convergence of stochastic gradient methods. We further leverage our
proposed stochastic gradients to devise stochastic methods for objective
functions involving spectral-sums, and rigorously analyze their convergence
rate. The utility of our methods is demonstrated in numerical experiments.
| cs.LG cs.CC stat.ML | a large class of machine learning techniques requires the solution of optimization problems involving spectral functions of parametric matrices eg logdeterminant and nuclear norm unfortunately computing the gradient of a spectral function is generally of cubic complexity as such gradient descent methods are rather expensive for optimizing objectives involving the spectral function thus one naturally turns to stochastic gradient methods in hope that they will provide a way to reduce or altogether avoid the computation of full gradients however here a new challenge appears there is no straightforward way to compute unbiased stochastic gradients for spectral functions in this paper we develop unbiased stochastic gradients for spectralsums an important subclass of spectral functions our unbiased stochastic gradients are based on combining randomized trace estimators with stochastic truncation of the chebyshev expansions a careful design of the truncation distribution allows us to offer distributions that are varianceoptimal which is crucial for fast and stable convergence of stochastic gradient methods we further leverage our proposed stochastic gradients to devise stochastic methods for objective functions involving spectralsums and rigorously analyze their convergence rate the utility of our methods is demonstrated in numerical experiments | [['a', 'large', 'class', 'of', 'machine', 'learning', 'techniques', 'requires', 'the', 'solution', 'of', 'optimization', 'problems', 'involving', 'spectral', 'functions', 'of', 'parametric', 'matrices', 'eg', 'logdeterminant', 'and', 'nuclear', 'norm', 'unfortunately', 'computing', 'the', 'gradient', 'of', 'a', 'spectral', 'function', 'is', 'generally', 'of', 'cubic', 'complexity', 'as', 'such', 'gradient', 'descent', 'methods', 'are', 'rather', 'expensive', 'for', 'optimizing', 'objectives', 'involving', 'the', 'spectral', 'function', 'thus', 'one', 'naturally', 'turns', 'to', 'stochastic', 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1,802.06356 | Lamellar ordering, droplet formation and phase inversion in exotic
active emulsions | We study numerically the behaviour of a mixture of a passive isotropic fluid
and an active polar gel, in the presence of a surfactant favouring
emulsification. Focussing on parameters for which the underlying free energy
favours the lamellar phase in the passive limit, we show that the interplay
between nonequilibrium and thermodynamic forces creates a range of multifarious
exotic emulsions. When the active component is contractile (e.g., an actomyosin
solution), moderate activity enhances the efficiency of lamellar ordering,
whereas strong activity favours the creation of passive droplets within an
active matrix. For extensile activity (occurring, e.g., in microtubule-motor
suspensions), instead, we observe an emulsion of spontaneously rotating
droplets of different size. By tuning the overall composition, we can create
high internal phase emulsions, which undergo sudden phase inversion when
activity is switched off. Therefore, we find that activity provides a single
control parameter to design composite materials with a strikingly rich range of
morphologies.
| physics.bio-ph cond-mat.soft | we study numerically the behaviour of a mixture of a passive isotropic fluid and an active polar gel in the presence of a surfactant favouring emulsification focussing on parameters for which the underlying free energy favours the lamellar phase in the passive limit we show that the interplay between nonequilibrium and thermodynamic forces creates a range of multifarious exotic emulsions when the active component is contractile eg an actomyosin solution moderate activity enhances the efficiency of lamellar ordering whereas strong activity favours the creation of passive droplets within an active matrix for extensile activity occurring eg in microtubulemotor suspensions instead we observe an emulsion of spontaneously rotating droplets of different size by tuning the overall composition we can create high internal phase emulsions which undergo sudden phase inversion when activity is switched off therefore we find that activity provides a single control parameter to design composite materials with a strikingly rich range of morphologies | [['we', 'study', 'numerically', 'the', 'behaviour', 'of', 'a', 'mixture', 'of', 'a', 'passive', 'isotropic', 'fluid', 'and', 'an', 'active', 'polar', 'gel', 'in', 'the', 'presence', 'of', 'a', 'surfactant', 'favouring', 'emulsification', 'focussing', 'on', 'parameters', 'for', 'which', 'the', 'underlying', 'free', 'energy', 'favours', 'the', 'lamellar', 'phase', 'in', 'the', 'passive', 'limit', 'we', 'show', 'that', 'the', 'interplay', 'between', 'nonequilibrium', 'and', 'thermodynamic', 'forces', 'creates', 'a', 'range', 'of', 'multifarious', 'exotic', 'emulsions', 'when', 'the', 'active', 'component', 'is', 'contractile', 'eg', 'an', 'actomyosin', 'solution', 'moderate', 'activity', 'enhances', 'the', 'efficiency', 'of', 'lamellar', 'ordering', 'whereas', 'strong', 'activity', 'favours', 'the', 'creation', 'of', 'passive', 'droplets', 'within', 'an', 'active', 'matrix', 'for', 'extensile', 'activity', 'occurring', 'eg', 'in', 'microtubulemotor', 'suspensions', 'instead', 'we', 'observe', 'an', 'emulsion', 'of', 'spontaneously', 'rotating', 'droplets', 'of', 'different', 'size', 'by', 'tuning', 'the', 'overall', 'composition', 'we', 'can', 'create', 'high', 'internal', 'phase', 'emulsions', 'which', 'undergo', 'sudden', 'phase', 'inversion', 'when', 'activity', 'is', 'switched', 'off', 'therefore', 'we', 'find', 'that', 'activity', 'provides', 'a', 'single', 'control', 'parameter', 'to', 'design', 'composite', 'materials', 'with', 'a', 'strikingly', 'rich', 'range', 'of', 'morphologies']] | [-0.16176194727251475, 0.27787908025323294, -0.07742593016523819, 0.005661774065357621, -0.02932879776246362, -0.11913262611835615, 0.04969158464915712, 0.38756950712436206, -0.25175393916448796, -0.3096274264684467, 0.050108171586709264, -0.23215371106944785, -0.1631008576867836, 0.1309999313187338, -0.00929760610436803, -0.04366523256773347, -0.02861587388755439, -0.0344045140331478, -0.05443326021580921, -0.1166839983258336, 0.2557241683224875, 0.04349897244292033, 0.32821568182681676, 0.026722783443530077, 0.10098651818501726, 0.009689647525960169, 0.04942901358431713, 0.025396078335145462, -0.13877329740164177, 0.04235136763543471, 0.22102440278009547, -2.5107216864050207e-06, 0.21682603324989505, -0.4729093003098841, -0.2388401971767088, 0.1205888596036798, 0.15655594183545554, 0.1024263906096072, -0.13602274704449388, -0.23231829034604695, 0.04510434814305468, -0.18381369242734916, -0.15929078140035996, -0.06435189189165588, 0.02774588272350465, 0.05763926531351474, -0.27166913381426944, 0.11588051965968175, 0.07524738421772498, 0.08840916415845806, -0.119217413016474, -0.029781845015739754, -0.0662949194696236, 0.11342867030104466, 0.05427777511974821, -0.02984202221525809, 0.27722140323890787, -0.1911022247234057, -0.03418020965522397, 0.36471613364403027, -0.03374033855416437, -0.13354399470104414, 0.24840802878637303, -0.1139818802627118, -0.08466010970175387, 0.2094149587300981, 0.20625616515708434, 0.0989675605397478, -0.09397050537348138, -0.014885572775557347, -0.02182905649004335, 0.25410803590066633, 0.02097304250529813, -0.008429853115593652, 0.2598397901354285, 0.26002326536343084, 0.03932336244925663, 0.20558797426116593, -0.1042999133162273, -0.1428779087682533, -0.22341505354633193, -0.144169075969401, -0.11499125971277425, 0.012695113983150427, -0.13439603875313322, -0.20864799141871754, 0.3529355476713171, 0.04348117869964184, 0.1921230676726063, -0.015298406915533563, 0.23621859104529797, 0.0018729753429432968, 0.039029099074397965, 0.02772730803752149, 0.3109683317628193, 0.09289367124891852, 0.11503714951157401, -0.2789097828367217, 0.1077009481211592, 0.024478859071583522] |
1,802.06357 | Convergence of Online Mirror Descent | In this paper we consider online mirror descent (OMD) algorithms, a class of
scalable online learning algorithms exploiting data geometric structures
through mirror maps. Necessary and sufficient conditions are presented in terms
of the step size sequence $\{\eta_t\}_{t}$ for the convergence of an OMD
algorithm with respect to the expected Bregman distance induced by the mirror
map. The condition is $\lim_{t\to\infty}\eta_t=0,
\sum_{t=1}^{\infty}\eta_t=\infty$ in the case of positive variances. It is
reduced to $\sum_{t=1}^{\infty}\eta_t=\infty$ in the case of zero variances for
which the linear convergence may be achieved by taking a constant step size
sequence. A sufficient condition on the almost sure convergence is also given.
We establish tight error bounds under mild conditions on the mirror map, the
loss function, and the regularizer. Our results are achieved by some novel
analysis on the one-step progress of the OMD algorithm using smoothness and
strong convexity of the mirror map and the loss function.
| cs.LG cs.AI math.OC stat.ML | in this paper we consider online mirror descent omd algorithms a class of scalable online learning algorithms exploiting data geometric structures through mirror maps necessary and sufficient conditions are presented in terms of the step size sequence eta_t_t for the convergence of an omd algorithm with respect to the expected bregman distance induced by the mirror map the condition is lim_ttoinftyeta_t0 sum_t1inftyeta_tinfty in the case of positive variances it is reduced to sum_t1inftyeta_tinfty in the case of zero variances for which the linear convergence may be achieved by taking a constant step size sequence a sufficient condition on the almost sure convergence is also given we establish tight error bounds under mild conditions on the mirror map the loss function and the regularizer our results are achieved by some novel analysis on the onestep progress of the omd algorithm using smoothness and strong convexity of the mirror map and the loss function | [['in', 'this', 'paper', 'we', 'consider', 'online', 'mirror', 'descent', 'omd', 'algorithms', 'a', 'class', 'of', 'scalable', 'online', 'learning', 'algorithms', 'exploiting', 'data', 'geometric', 'structures', 'through', 'mirror', 'maps', 'necessary', 'and', 'sufficient', 'conditions', 'are', 'presented', 'in', 'terms', 'of', 'the', 'step', 'size', 'sequence', 'eta_t_t', 'for', 'the', 'convergence', 'of', 'an', 'omd', 'algorithm', 'with', 'respect', 'to', 'the', 'expected', 'bregman', 'distance', 'induced', 'by', 'the', 'mirror', 'map', 'the', 'condition', 'is', 'lim_ttoinftyeta_t0', 'sum_t1inftyeta_tinfty', 'in', 'the', 'case', 'of', 'positive', 'variances', 'it', 'is', 'reduced', 'to', 'sum_t1inftyeta_tinfty', 'in', 'the', 'case', 'of', 'zero', 'variances', 'for', 'which', 'the', 'linear', 'convergence', 'may', 'be', 'achieved', 'by', 'taking', 'a', 'constant', 'step', 'size', 'sequence', 'a', 'sufficient', 'condition', 'on', 'the', 'almost', 'sure', 'convergence', 'is', 'also', 'given', 'we', 'establish', 'tight', 'error', 'bounds', 'under', 'mild', 'conditions', 'on', 'the', 'mirror', 'map', 'the', 'loss', 'function', 'and', 'the', 'regularizer', 'our', 'results', 'are', 'achieved', 'by', 'some', 'novel', 'analysis', 'on', 'the', 'onestep', 'progress', 'of', 'the', 'omd', 'algorithm', 'using', 'smoothness', 'and', 'strong', 'convexity', 'of', 'the', 'mirror', 'map', 'and', 'the', 'loss', 'function']] | [-0.10247825095555871, 0.03243553856274859, -0.10141176668725707, 0.08125693382021996, -0.03129174086781269, -0.13602430085538308, 0.049324869971904856, 0.41978464351111167, -0.3023438549071953, -0.28875100249747077, 0.15280787010733793, -0.20211653820415204, -0.17056341397511535, 0.19484666994193922, -0.1401165379400088, 0.09915974142338224, 0.09613425621913897, 0.031572242132136344, -0.10938768801468148, -0.3198443834545607, 0.3219599569990375, 0.0814261159897354, 0.28763109083740496, 0.04490501764175369, 0.15366024782840237, 0.00589815791180307, 0.004787958091804506, 0.0013372820787874327, -0.14525141514447593, 0.13158559578634496, 0.16527138189632898, 0.15451242986474992, 0.31427962279475824, -0.37608504273009963, -0.1190467716471569, 0.1505126015698487, 0.10167186736204736, 0.08845544857133457, -0.0762914752806348, -0.2817994333942094, 0.1306161425671087, -0.06819388184639802, -0.10571296757552773, -0.08571198211378746, -0.07238988480782388, 0.0552252281608211, -0.35591629728976937, 0.04058325043828321, 0.11849553959532264, 0.06367542891058366, -0.034311693542435566, -0.10009488832469829, 0.01677314197877422, 0.10738291996027771, 0.07046908935346338, 0.04285537406748689, 0.09377368740466852, -0.11609086094619203, -0.07190907792490278, 0.3290849064344641, -0.0901947930719502, -0.23133528456123345, 0.12530831017883848, -0.07941217846677613, -0.12656541009011954, 0.13105658502821382, 0.1885158773448721, 0.14765186986030154, -0.13672749751020927, 0.08984387915336049, -0.04045325900881073, 0.12301282015962627, 0.06310156798871183, 0.0328146675884774, 0.09121810120681452, 0.14828658241596124, 0.20461170758259156, 0.1820527200431349, -0.04970363025220011, -0.06808060095676952, -0.34723601666455334, -0.13331336016464676, -0.19964224315009307, -0.003770136764442951, -0.1566900011664099, -0.11842331688495579, 0.35470960766542703, 0.08015981479941213, 0.22564912182153077, 0.137152865163296, 0.29943881625611635, 0.13140204820681262, 0.046234561767542384, 0.07502717748158486, 0.22913953405273985, 0.1516718715193329, 0.06147775029121722, -0.23596615544079827, 0.10957118617433652, 0.12374134044354856] |
1,802.06358 | Comments on $^"$Cosmic evolution in Brans-Dicke chameleon cosmology$^"$ | The authors of Ref. \cite{1-2}, investigated cosmic evolution in an external
interacting model of scalar tensor gravity namely Brans Dicke chameleon
scenario. The procedure of this work contains novelties but, it shall be
observed from this comment, their report faces three fundamental drawbacks. One
of them concerns the energy conservation equation, and the other two flaws are
about mathematical mistakes. In the scalar tensor gravity models, a well-known
method, in order to obtain conservation equation one must combine Friedmann
equations with modified Klein-Gordon equation. But in Ref. \cite{1-2}, by
virtue of the mentioned approach one would not be able to achieve a correct
result for conservation equation. In addition, one can readily realize that
their mathematical mistakes lead to different results compared to the present
report.
| gr-qc | the authors of ref cite12 investigated cosmic evolution in an external interacting model of scalar tensor gravity namely brans dicke chameleon scenario the procedure of this work contains novelties but it shall be observed from this comment their report faces three fundamental drawbacks one of them concerns the energy conservation equation and the other two flaws are about mathematical mistakes in the scalar tensor gravity models a wellknown method in order to obtain conservation equation one must combine friedmann equations with modified kleingordon equation but in ref cite12 by virtue of the mentioned approach one would not be able to achieve a correct result for conservation equation in addition one can readily realize that their mathematical mistakes lead to different results compared to the present report | [['the', 'authors', 'of', 'ref', 'cite12', 'investigated', 'cosmic', 'evolution', 'in', 'an', 'external', 'interacting', 'model', 'of', 'scalar', 'tensor', 'gravity', 'namely', 'brans', 'dicke', 'chameleon', 'scenario', 'the', 'procedure', 'of', 'this', 'work', 'contains', 'novelties', 'but', 'it', 'shall', 'be', 'observed', 'from', 'this', 'comment', 'their', 'report', 'faces', 'three', 'fundamental', 'drawbacks', 'one', 'of', 'them', 'concerns', 'the', 'energy', 'conservation', 'equation', 'and', 'the', 'other', 'two', 'flaws', 'are', 'about', 'mathematical', 'mistakes', 'in', 'the', 'scalar', 'tensor', 'gravity', 'models', 'a', 'wellknown', 'method', 'in', 'order', 'to', 'obtain', 'conservation', 'equation', 'one', 'must', 'combine', 'friedmann', 'equations', 'with', 'modified', 'kleingordon', 'equation', 'but', 'in', 'ref', 'cite12', 'by', 'virtue', 'of', 'the', 'mentioned', 'approach', 'one', 'would', 'not', 'be', 'able', 'to', 'achieve', 'a', 'correct', 'result', 'for', 'conservation', 'equation', 'in', 'addition', 'one', 'can', 'readily', 'realize', 'that', 'their', 'mathematical', 'mistakes', 'lead', 'to', 'different', 'results', 'compared', 'to', 'the', 'present', 'report']] | [-0.107071378918749, 0.08981760432594918, -0.10004311050741105, 0.07710476083097063, -0.13961960265915546, -0.19779720647437943, -0.040467288731301704, 0.27740593041692463, -0.22102379108158252, -0.35388762768476256, 0.04394470784159023, -0.28115154240309953, -0.14888802127883075, 0.1398139881609481, -0.07921612321672636, 0.028096095851016422, 0.04530527893849279, 0.025377480623622734, -0.04217707042929731, -0.306483578040368, 0.34362730760467314, 0.026216761619296102, 0.24634295801026007, 0.040778188001861546, 0.10771581961122888, -0.0735794193876375, -0.034405023698502825, -0.004569828850291078, -0.12753401402715964, 0.07608469571763028, 0.22394766370407956, 0.10358355329593733, 0.2520198411426492, -0.4445234454931721, -0.25767136456829215, 0.06808101485246822, 0.13602917932993955, 0.1831249051342999, -0.03628730482333118, -0.28033026011424167, 0.05314565447735645, -0.2055158386550223, -0.1976149662301713, -0.08999844741756244, -0.02560380209119074, -0.03631197838740985, -0.1800348431145447, 0.10910923371002787, 0.06735017167640821, -0.029580240811420872, -0.08463840192416683, -0.14144148016422395, 0.01602588595438098, 0.06256222659500227, 0.10255465591197005, -0.004546119453614607, 0.08671390355402042, -0.12315912582585403, -0.14265176358388826, 0.41820363726999077, -0.07270057325986849, -0.2541884757644896, 0.15470570124434455, -0.10431222150486613, -0.16822070187504684, 0.054040909261583156, 0.10753935407329765, 0.09564972712495734, -0.20763095352976096, 0.09583619329015843, 0.010757274198574975, 0.159923458689942, 0.09253096192084725, 0.009801243657305364, 0.19011224406061783, 0.08911520174135351, -0.009183558432148799, 0.11503737629807022, 0.0315621877622713, -0.10834806834510158, -0.3424410613665786, -0.1674209046621053, -0.14418170509845138, 0.059773470644791986, -0.06109221754510174, -0.12766490622039234, 0.3720282976818641, 0.23180383701424395, 0.11655074796761342, 0.007461951967949669, 0.2734493861447961, 0.1217588343554073, 0.041229316887135305, 0.05379430020767604, 0.30013361709961106, 0.14096544380114961, 0.15634063236223208, -0.19408924953590723, 0.0038268933006163153, 0.08117708804026719] |
1,802.06359 | Geostatistical methods for disease mapping and visualization using data
from spatio-temporally referenced prevalence surveys | In this paper we set out general principles and develop geostatistical
methods for the analysis of data from spatio-temporally referenced prevalence
surveys. Our objective is to provide a tutorial guide that can be used in order
to identify parsimonious geostatistical models for prevalence mapping. A
general variogram-based Monte Carlo procedure is proposed to check the validity
of the modelling assumptions. We describe and contrast likelihood-based and
Bayesian methods of inference, showing how to account for parameter uncertainty
under each of the two paradigms. We also describe extensions of the standard
model for disease prevalence that can be used when stationarity of the
spatio-temporal covariance function is not supported by the data. We discuss
how to define predictive targets and argue that exceedance probabilities
provide one of the most effective ways to convey uncertainty in prevalence
estimates. We describe statistical software for the visualization of
spatio-temporal predictive summaries of prevalence through interactive
animations. Finally, we illustrate an application to historical malaria
prevalence data from 1334 surveys conducted in Senegal between 1905 and 2014.
| stat.ME | in this paper we set out general principles and develop geostatistical methods for the analysis of data from spatiotemporally referenced prevalence surveys our objective is to provide a tutorial guide that can be used in order to identify parsimonious geostatistical models for prevalence mapping a general variogrambased monte carlo procedure is proposed to check the validity of the modelling assumptions we describe and contrast likelihoodbased and bayesian methods of inference showing how to account for parameter uncertainty under each of the two paradigms we also describe extensions of the standard model for disease prevalence that can be used when stationarity of the spatiotemporal covariance function is not supported by the data we discuss how to define predictive targets and argue that exceedance probabilities provide one of the most effective ways to convey uncertainty in prevalence estimates we describe statistical software for the visualization of spatiotemporal predictive summaries of prevalence through interactive animations finally we illustrate an application to historical malaria prevalence data from 1334 surveys conducted in senegal between 1905 and 2014 | [['in', 'this', 'paper', 'we', 'set', 'out', 'general', 'principles', 'and', 'develop', 'geostatistical', 'methods', 'for', 'the', 'analysis', 'of', 'data', 'from', 'spatiotemporally', 'referenced', 'prevalence', 'surveys', 'our', 'objective', 'is', 'to', 'provide', 'a', 'tutorial', 'guide', 'that', 'can', 'be', 'used', 'in', 'order', 'to', 'identify', 'parsimonious', 'geostatistical', 'models', 'for', 'prevalence', 'mapping', 'a', 'general', 'variogrambased', 'monte', 'carlo', 'procedure', 'is', 'proposed', 'to', 'check', 'the', 'validity', 'of', 'the', 'modelling', 'assumptions', 'we', 'describe', 'and', 'contrast', 'likelihoodbased', 'and', 'bayesian', 'methods', 'of', 'inference', 'showing', 'how', 'to', 'account', 'for', 'parameter', 'uncertainty', 'under', 'each', 'of', 'the', 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