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1,802.0876 | Sensitivity and Generalization in Neural Networks: an Empirical Study | In practice it is often found that large over-parameterized neural networks
generalize better than their smaller counterparts, an observation that appears
to conflict with classical notions of function complexity, which typically
favor smaller models. In this work, we investigate this tension between
complexity and generalization through an extensive empirical exploration of two
natural metrics of complexity related to sensitivity to input perturbations.
Our experiments survey thousands of models with various fully-connected
architectures, optimizers, and other hyper-parameters, as well as four
different image classification datasets.
We find that trained neural networks are more robust to input perturbations
in the vicinity of the training data manifold, as measured by the norm of the
input-output Jacobian of the network, and that it correlates well with
generalization. We further establish that factors associated with poor
generalization $-$ such as full-batch training or using random labels $-$
correspond to lower robustness, while factors associated with good
generalization $-$ such as data augmentation and ReLU non-linearities $-$ give
rise to more robust functions. Finally, we demonstrate how the input-output
Jacobian norm can be predictive of generalization at the level of individual
test points.
| stat.ML cs.AI cs.LG cs.NE | in practice it is often found that large overparameterized neural networks generalize better than their smaller counterparts an observation that appears to conflict with classical notions of function complexity which typically favor smaller models in this work we investigate this tension between complexity and generalization through an extensive empirical exploration of two natural metrics of complexity related to sensitivity to input perturbations our experiments survey thousands of models with various fullyconnected architectures optimizers and other hyperparameters as well as four different image classification datasets we find that trained neural networks are more robust to input perturbations in the vicinity of the training data manifold as measured by the norm of the inputoutput jacobian of the network and that it correlates well with generalization we further establish that factors associated with poor generalization such as fullbatch training or using random labels correspond to lower robustness while factors associated with good generalization such as data augmentation and relu nonlinearities give rise to more robust functions finally we demonstrate how the inputoutput jacobian norm can be predictive of generalization at the level of individual test points | [['in', 'practice', 'it', 'is', 'often', 'found', 'that', 'large', 'overparameterized', 'neural', 'networks', 'generalize', 'better', 'than', 'their', 'smaller', 'counterparts', 'an', 'observation', 'that', 'appears', 'to', 'conflict', 'with', 'classical', 'notions', 'of', 'function', 'complexity', 'which', 'typically', 'favor', 'smaller', 'models', 'in', 'this', 'work', 'we', 'investigate', 'this', 'tension', 'between', 'complexity', 'and', 'generalization', 'through', 'an', 'extensive', 'empirical', 'exploration', 'of', 'two', 'natural', 'metrics', 'of', 'complexity', 'related', 'to', 'sensitivity', 'to', 'input', 'perturbations', 'our', 'experiments', 'survey', 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1,802.08761 | Behavioral-clinical phenotyping with type 2 diabetes self-monitoring
data | Objective: To evaluate unsupervised clustering methods for identifying
individual-level behavioral-clinical phenotypes that relate personal biomarkers
and behavioral traits in type 2 diabetes (T2DM) self-monitoring data. Materials
and Methods: We used hierarchical clustering (HC) to identify groups of meals
with similar nutrition and glycemic impact for 6 individuals with T2DM who
collected self-monitoring data. We evaluated clusters on: 1) correspondence to
gold standards generated by certified diabetes educators (CDEs) for 3
participants; 2) face validity, rated by CDEs, and 3) impact on CDEs' ability
to identify patterns for another 3 participants. Results: Gold standard (GS)
included 9 patterns across 3 participants. Of these, all 9 were re-discovered
using HC: 4 GS patterns were consistent with patterns identified by HC (over
50% of meals in a cluster followed the pattern); another 5 were included as
sub-groups in broader clusers. 50% (9/18) of clusters were rated over 3 on
5-point Likert scale for validity, significance, and being actionable. After
reviewing clusters, CDEs identified patterns that were more consistent with
data (70% reduction in contradictions between patterns and participants'
records). Discussion: Hierarchical clustering of blood glucose and
macronutrient consumption appears suitable for discovering behavioral-clinical
phenotypes in T2DM. Most clusters corresponded to gold standard and were rated
positively by CDEs for face validity. Cluster visualizations helped CDEs
identify more robust patterns in nutrition and glycemic impact, creating new
possibilities for visual analytic solutions. Conclusion: Machine learning
methods can use diabetes self-monitoring data to create personalized
behavioral-clinical phenotypes, which may prove useful for delivering
personalized medicine.
| stat.AP stat.ML stat.OT | objective to evaluate unsupervised clustering methods for identifying individuallevel behavioralclinical phenotypes that relate personal biomarkers and behavioral traits in type 2 diabetes t2dm selfmonitoring data materials and methods we used hierarchical clustering hc to identify groups of meals with similar nutrition and glycemic impact for 6 individuals with t2dm who collected selfmonitoring data we evaluated clusters on 1 correspondence to gold standards generated by certified diabetes educators cdes for 3 participants 2 face validity rated by cdes and 3 impact on cdes ability to identify patterns for another 3 participants results gold standard gs included 9 patterns across 3 participants of these all 9 were rediscovered using hc 4 gs patterns were consistent with patterns identified by hc over 50 of meals in a cluster followed the pattern another 5 were included as subgroups in broader clusers 50 918 of clusters were rated over 3 on 5point likert scale for validity significance and being actionable after reviewing clusters cdes identified patterns that were more consistent with data 70 reduction in contradictions between patterns and participants records discussion hierarchical clustering of blood glucose and macronutrient consumption appears suitable for discovering behavioralclinical phenotypes in t2dm most clusters corresponded to gold standard and were rated positively by cdes for face validity cluster visualizations helped cdes identify more robust patterns in nutrition and glycemic impact creating new possibilities for visual analytic solutions conclusion machine learning methods can use diabetes selfmonitoring data to create personalized behavioralclinical phenotypes which may prove useful for delivering personalized medicine | [['objective', 'to', 'evaluate', 'unsupervised', 'clustering', 'methods', 'for', 'identifying', 'individuallevel', 'behavioralclinical', 'phenotypes', 'that', 'relate', 'personal', 'biomarkers', 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1,802.08762 | Diffusion Maps meet Nystr\"om | Diffusion maps are an emerging data-driven technique for non-linear
dimensionality reduction, which are especially useful for the analysis of
coherent structures and nonlinear embeddings of dynamical systems. However, the
computational complexity of the diffusion maps algorithm scales with the number
of observations. Thus, long time-series data presents a significant challenge
for fast and efficient embedding. We propose integrating the Nystr\"om method
with diffusion maps in order to ease the computational demand. We achieve a
speedup of roughly two to four times when approximating the dominant diffusion
map components.
| stat.ML cs.LG | diffusion maps are an emerging datadriven technique for nonlinear dimensionality reduction which are especially useful for the analysis of coherent structures and nonlinear embeddings of dynamical systems however the computational complexity of the diffusion maps algorithm scales with the number of observations thus long timeseries data presents a significant challenge for fast and efficient embedding we propose integrating the nystrom method with diffusion maps in order to ease the computational demand we achieve a speedup of roughly two to four times when approximating the dominant diffusion map components | [['diffusion', 'maps', 'are', 'an', 'emerging', 'datadriven', 'technique', 'for', 'nonlinear', 'dimensionality', 'reduction', 'which', 'are', 'especially', 'useful', 'for', 'the', 'analysis', 'of', 'coherent', 'structures', 'and', 'nonlinear', 'embeddings', 'of', 'dynamical', 'systems', 'however', 'the', 'computational', 'complexity', 'of', 'the', 'diffusion', 'maps', 'algorithm', 'scales', 'with', 'the', 'number', 'of', 'observations', 'thus', 'long', 'timeseries', 'data', 'presents', 'a', 'significant', 'challenge', 'for', 'fast', 'and', 'efficient', 'embedding', 'we', 'propose', 'integrating', 'the', 'nystrom', 'method', 'with', 'diffusion', 'maps', 'in', 'order', 'to', 'ease', 'the', 'computational', 'demand', 'we', 'achieve', 'a', 'speedup', 'of', 'roughly', 'two', 'to', 'four', 'times', 'when', 'approximating', 'the', 'dominant', 'diffusion', 'map', 'components']] | [-0.10994579746726561, 0.045728720466782266, -0.05163092691641809, 0.05828098964411765, -0.06655633970513009, -0.1217305856473765, -0.010652343890714374, 0.4018857075240124, -0.33946558651091024, -0.3195767893921584, 0.1389476767389252, -0.2693824728061868, -0.1639404699089937, 0.24246844693764366, -0.0336006348000162, 0.11121546156937256, 0.08833782180789224, -0.05832357474983754, -0.05295557086355984, -0.24377464621449524, 0.2869765775235878, 0.06164969193791463, 0.3054341636809774, -0.01209114310702055, 0.15467841918077532, -0.04445030412171036, -0.08388939501442523, 0.020982221933081746, -0.0701004479196854, 0.22127227557145737, 0.2705893696878444, 0.12740489950572903, 0.29105792729057034, -0.4184784633530812, -0.24321750334506345, 0.10001961534991013, 0.1676568346873947, 0.1429082077586579, -0.04772640927959318, -0.22825043760663408, 0.050991682372775606, -0.10712791697534901, -0.06809746331716517, -0.1564923330337148, 0.033952995102894915, 0.004683617845082402, -0.29599204307041044, 0.09545344453892374, 0.06070585003610838, 0.06240332801826298, -0.044828925620425834, -0.09747701336164027, 0.04541529208389958, 0.1371863203811782, 0.0301495772913437, 0.022693314600143243, 0.07887035645862026, -0.15617055327377535, -0.12148566456215287, 0.36921977232718334, -0.09682852973822843, -0.20772579285205045, 0.21601240255404264, -0.09139926329424436, -0.15091703574977477, 0.20112530537351797, 0.2294325715629384, 0.07899412129137834, -0.14283558536689353, 0.050280218180639415, 0.041844591572457415, 0.1843254064336758, 0.0039226502703968436, 0.03460036514496261, 0.12426734187597917, 0.26043843775088055, 0.11788360330741522, 0.10731114364567805, -0.13378956500673667, -0.09422658688907343, -0.2118186573497951, -0.17154916645218196, -0.14528778155694122, 0.026835720746682702, -0.17349408975959787, -0.17304859011942011, 0.38538870916024526, 0.17350671898086809, 0.22050259611569345, 0.09112399808972524, 0.32974961681545456, 0.12702746587720784, 0.061857969436625186, 0.10438121965324337, 0.14908887570411686, 0.10787831568053331, 0.11117568547011945, -0.20913444931978697, 0.028252758834489876, 0.0597010844133117] |
1,802.08763 | Berezinskii-Kosterlitz-Thouless transition of two-component Bose
mixtures with inter-component Josephson coupling | We study the Berezinskii-Kosterlitz-Thouless (BKT) transition of
two-component Bose mixtures in two spatial dimensions. When phases of both
components are decoupled, half-quantized vortex-antivortex pairs of each
component induce two-step BKT transitions. On the other hand, when phases of
the both components are synchronized through the inter-component Josephson
coupling, two species of vortices of each component are bind to form a
molecule, and in this case, we find that there is only one BKT transition by
molecule-antimolecule pairs. Our results can be tested by two weakly-connected
Bose systems such as two-component ultracold dilute Bose mixtures with the Rabi
oscillation, and multiband superconductors.
| cond-mat.stat-mech cond-mat.supr-con hep-th | we study the berezinskiikosterlitzthouless bkt transition of twocomponent bose mixtures in two spatial dimensions when phases of both components are decoupled halfquantized vortexantivortex pairs of each component induce twostep bkt transitions on the other hand when phases of the both components are synchronized through the intercomponent josephson coupling two species of vortices of each component are bind to form a molecule and in this case we find that there is only one bkt transition by moleculeantimolecule pairs our results can be tested by two weaklyconnected bose systems such as twocomponent ultracold dilute bose mixtures with the rabi oscillation and multiband superconductors | [['we', 'study', 'the', 'berezinskiikosterlitzthouless', 'bkt', 'transition', 'of', 'twocomponent', 'bose', 'mixtures', 'in', 'two', 'spatial', 'dimensions', 'when', 'phases', 'of', 'both', 'components', 'are', 'decoupled', 'halfquantized', 'vortexantivortex', 'pairs', 'of', 'each', 'component', 'induce', 'twostep', 'bkt', 'transitions', 'on', 'the', 'other', 'hand', 'when', 'phases', 'of', 'the', 'both', 'components', 'are', 'synchronized', 'through', 'the', 'intercomponent', 'josephson', 'coupling', 'two', 'species', 'of', 'vortices', 'of', 'each', 'component', 'are', 'bind', 'to', 'form', 'a', 'molecule', 'and', 'in', 'this', 'case', 'we', 'find', 'that', 'there', 'is', 'only', 'one', 'bkt', 'transition', 'by', 'moleculeantimolecule', 'pairs', 'our', 'results', 'can', 'be', 'tested', 'by', 'two', 'weaklyconnected', 'bose', 'systems', 'such', 'as', 'twocomponent', 'ultracold', 'dilute', 'bose', 'mixtures', 'with', 'the', 'rabi', 'oscillation', 'and', 'multiband', 'superconductors']] | [-0.2121334354672581, 0.29066432325169445, -0.02919650999829173, 0.01763394074048847, 0.011648710146546363, -0.2529196663154289, 0.038760316921398044, 0.3616740503339679, -0.1843479363527149, -0.19400913305929862, 0.012390682806726545, -0.32052565049380066, -0.10647167416289449, 0.13491902684792875, 0.10405015841126442, -0.0045744913787348195, -0.00908281147654634, -0.032163458224385974, -0.05820807141950354, -0.26621542290318756, 0.38563356779050084, -0.12484081778209656, 0.33112655512988565, 0.027779768756590783, 0.03592244689818472, -0.08169197217561304, 0.1345805534813553, -0.0018241319339722395, -0.11964819393389917, 0.01116963361389935, 0.28170200053195005, -0.039533351958962154, 0.12381840167567133, -0.4221644883044064, -0.2432197386212647, 0.1427317441161722, 0.2257009391626343, 0.167499777097255, 0.0017931796320772265, -0.3398013828857802, -0.05512361044995487, -0.1796530205430463, -0.08860176073852927, -0.1252028678858187, -0.0036705594742670656, 0.07503379402449355, -0.24977713370230048, 0.13809284649789333, 0.08461494023213163, 0.08510531412204728, -0.04254152273293585, -0.07214146718848496, -0.018055253971833735, 0.06995146737725008, 0.010355322656687349, -0.008140059940051287, 0.13736700621433556, -0.17173351281089708, -0.10621322287712247, 0.3905158132035285, -0.11516105477436213, -0.12562225360423326, 0.27446344658732413, -0.14438531022547976, -0.11094446135219187, 0.1393331037648022, 0.09770985077135265, 0.05353817326016724, -0.12743478584714465, -0.0028692563634831456, -0.06977497693151236, 0.18686319679603913, 0.05144950326532125, 0.018773892293684185, 0.31643959434702995, 0.1818479370092973, 0.00031790909692062995, 0.17941347532148938, -0.11564477464416996, -0.14575550869689324, -0.23844442319939843, -0.1592985075339675, -0.22499135039746762, -0.046159069452114634, -0.026499209981120656, -0.16874538513831794, 0.3700132899149321, 0.12063378693361301, 0.24278503065928816, -0.05111316935392097, 0.26928261369932444, 0.12320343839470298, 0.025425142850726844, 0.0040916256373748185, 0.2290323330857791, 0.1480482557741925, 0.03892913901247084, -0.2877972879074514, -0.0024431555066257715, 0.10130250622984022] |
1,802.08764 | Almost-primes in horospherical flows on the space of lattices | We study the asymptotic distribution of almost-prime entries of abelian
horospherical flows on {\Gamma}\SL(n,R), where {\Gamma} is either SL(n,Z) or a
cocompact lattice. In the cocompact case, we obtain a result that implies
density of almost-primes of a sufficient order, and in the space of lattices we
show the density of almost-primes in the orbits of points satisfying a certain
Diophantine condition. Along the way we give an effective equidistribution
result for arbitrary horospherical flows on the space of lattices, as well as
an effective rate for the equidistribution of arithmetic sequences of times in
abelian horospherical flows.
| math.DS math.NT | we study the asymptotic distribution of almostprime entries of abelian horospherical flows on gammaslnr where gamma is either slnz or a cocompact lattice in the cocompact case we obtain a result that implies density of almostprimes of a sufficient order and in the space of lattices we show the density of almostprimes in the orbits of points satisfying a certain diophantine condition along the way we give an effective equidistribution result for arbitrary horospherical flows on the space of lattices as well as an effective rate for the equidistribution of arithmetic sequences of times in abelian horospherical flows | [['we', 'study', 'the', 'asymptotic', 'distribution', 'of', 'almostprime', 'entries', 'of', 'abelian', 'horospherical', 'flows', 'on', 'gammaslnr', 'where', 'gamma', 'is', 'either', 'slnz', 'or', 'a', 'cocompact', 'lattice', 'in', 'the', 'cocompact', 'case', 'we', 'obtain', 'a', 'result', 'that', 'implies', 'density', 'of', 'almostprimes', 'of', 'a', 'sufficient', 'order', 'and', 'in', 'the', 'space', 'of', 'lattices', 'we', 'show', 'the', 'density', 'of', 'almostprimes', 'in', 'the', 'orbits', 'of', 'points', 'satisfying', 'a', 'certain', 'diophantine', 'condition', 'along', 'the', 'way', 'we', 'give', 'an', 'effective', 'equidistribution', 'result', 'for', 'arbitrary', 'horospherical', 'flows', 'on', 'the', 'space', 'of', 'lattices', 'as', 'well', 'as', 'an', 'effective', 'rate', 'for', 'the', 'equidistribution', 'of', 'arithmetic', 'sequences', 'of', 'times', 'in', 'abelian', 'horospherical', 'flows']] | [-0.24170039386786135, 0.12161983856145225, -0.1087624545871597, 0.06828936238019467, -0.01901345039938682, -0.04772728671483004, 0.06031204110549129, 0.3386803545518634, -0.2428074976640571, -0.20978754651300685, 0.14699325102054966, -0.23764214236481288, -0.11361602851976976, 0.228125381108239, -0.13453258028657167, 0.04776206224695923, 0.03126626491988289, 0.10024893175949794, -0.0866056597364356, -0.2916617829775073, 0.3743830806738937, -0.04064999302349908, 0.23199926361864068, 0.06139023278926298, 0.10827298181073874, 0.04118226108995756, 0.013082252702550944, -0.020623996316757744, -0.18855157225987138, 0.13487573304050363, 0.2254401173481007, 0.02445311528467333, 0.1909518935525617, -0.3787400255354179, -0.2003133778757963, 0.17032976498942554, 0.15246595016959094, 0.047998411936166975, -0.03846729244105518, -0.2635617169057086, 0.09161178937623489, -0.14358598443189846, -0.20178146521109588, -0.0865895497641459, 0.052987350582999665, 0.08211007551770158, -0.3096131330509622, 0.05986207153962579, 0.1523497299850467, 0.14681013066744067, -0.07534603956971586, -0.0760158658574921, -0.009317426464626008, 0.08047896403022417, 0.0664067163981843, 0.05389437830008413, 0.06395574249135158, -0.10859759186260096, -0.07501098097868494, 0.44908621784338015, -0.10349615231833238, -0.2214921643170192, 0.13463710401610463, -0.17270221521828286, -0.14412312430435234, 0.11466574113451175, 0.1891210656853143, 0.16365522353612272, -0.005313590010532092, 0.1588173633358518, -0.16229964832333638, 0.08724391784941413, 0.0964109342660486, 0.011550261240606149, 0.1245301505070679, 0.09348661416047013, 0.16468336149454885, 0.1502615104803873, -0.00873018828095849, -0.012052740671278275, -0.3630693455601169, -0.18529481729780584, -0.16105920394172066, 0.13767341353460072, -0.15007458517274497, -0.1964794157994623, 0.3502157352115844, 0.037138009717515144, 0.2259421078968294, 0.1144212244352153, 0.2006760149541282, 0.08277757781180889, -0.004511763911799902, 0.08595168986117717, 0.09194016482527416, 0.1865127362291684, -0.056646374188670794, -0.16075907548236631, -0.04214977524147283, 0.19027353487140738] |
1,802.08765 | Model Trees for Identifying Exceptional Players in the NHL Draft | Drafting strong players is crucial for the team success. We describe a new
data-driven interpretable approach for assessing draft prospects in the
National Hockey League. Successful previous approaches have built a predictive
model based on player features, or derived performance predictions from the
observed performance of comparable players in a cohort. This paper develops
model tree learning, which incorporates strengths of both model-based and
cohort-based approaches. A model tree partitions the feature space according to
the values of discrete features, or learned thresholds for continuous features.
Each leaf node in the tree defines a group of players, easily described to
hockey experts, with its own group regression model. Compared to a single
model, the model tree forms an ensemble that increases predictive power.
Compared to cohort-based approaches, the groups of comparables are discovered
from the data, without requiring a similarity metric. The performance
predictions of the model tree are competitive with the state-of-the-art
methods, which validates our model empirically. We show in case studies that
the model tree player ranking can be used to highlight strong and weak points
of players.
| cs.LG cs.SI | drafting strong players is crucial for the team success we describe a new datadriven interpretable approach for assessing draft prospects in the national hockey league successful previous approaches have built a predictive model based on player features or derived performance predictions from the observed performance of comparable players in a cohort this paper develops model tree learning which incorporates strengths of both modelbased and cohortbased approaches a model tree partitions the feature space according to the values of discrete features or learned thresholds for continuous features each leaf node in the tree defines a group of players easily described to hockey experts with its own group regression model compared to a single model the model tree forms an ensemble that increases predictive power compared to cohortbased approaches the groups of comparables are discovered from the data without requiring a similarity metric the performance predictions of the model tree are competitive with the stateoftheart methods which validates our model empirically we show in case studies that the model tree player ranking can be used to highlight strong and weak points of players | [['drafting', 'strong', 'players', 'is', 'crucial', 'for', 'the', 'team', 'success', 'we', 'describe', 'a', 'new', 'datadriven', 'interpretable', 'approach', 'for', 'assessing', 'draft', 'prospects', 'in', 'the', 'national', 'hockey', 'league', 'successful', 'previous', 'approaches', 'have', 'built', 'a', 'predictive', 'model', 'based', 'on', 'player', 'features', 'or', 'derived', 'performance', 'predictions', 'from', 'the', 'observed', 'performance', 'of', 'comparable', 'players', 'in', 'a', 'cohort', 'this', 'paper', 'develops', 'model', 'tree', 'learning', 'which', 'incorporates', 'strengths', 'of', 'both', 'modelbased', 'and', 'cohortbased', 'approaches', 'a', 'model', 'tree', 'partitions', 'the', 'feature', 'space', 'according', 'to', 'the', 'values', 'of', 'discrete', 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0.033157553741173275, 0.043259089812636375, -0.15893539935813505, 0.13997958003780006, 0.07138910011686796] |
1,802.08766 | Global well-posedness of the velocity-vorticity-Voigt model of the 3D
Navier-Stokes equations | The velocity-vorticity formulation of the 3D Navier-Stokes equations was
recently found to give excellent numerical results for flows with strong
rotation. In this work, we propose a new regularization of the 3D Navier-Stokes
equations, which we call the 3D velocity-vorticity-Voigt (VVV) model, with a
Voigt regularization term added to momentum equation in velocity-vorticity
form, but with no regularizing term in the vorticity equation. We prove global
well-posedness and regularity of this model under periodic boundary conditions.
We prove convergence of the model's velocity and vorticity to their
counterparts in the 3D Navier-Stokes equations as the Voigt modeling parameter
tends to zero. We prove that the curl of the model's velocity converges to the
model vorticity (which is solved for directly), as the Voigt modeling parameter
tends to zero. Finally, we provide a criterion for finite-time blow-up of the
3D Navier-Stokes equations based on this inviscid regularization.
| math.AP | the velocityvorticity formulation of the 3d navierstokes equations was recently found to give excellent numerical results for flows with strong rotation in this work we propose a new regularization of the 3d navierstokes equations which we call the 3d velocityvorticityvoigt vvv model with a voigt regularization term added to momentum equation in velocityvorticity form but with no regularizing term in the vorticity equation we prove global wellposedness and regularity of this model under periodic boundary conditions we prove convergence of the models velocity and vorticity to their counterparts in the 3d navierstokes equations as the voigt modeling parameter tends to zero we prove that the curl of the models velocity converges to the model vorticity which is solved for directly as the voigt modeling parameter tends to zero finally we provide a criterion for finitetime blowup of the 3d navierstokes equations based on this inviscid regularization | [['the', 'velocityvorticity', 'formulation', 'of', 'the', '3d', 'navierstokes', 'equations', 'was', 'recently', 'found', 'to', 'give', 'excellent', 'numerical', 'results', 'for', 'flows', 'with', 'strong', 'rotation', 'in', 'this', 'work', 'we', 'propose', 'a', 'new', 'regularization', 'of', 'the', '3d', 'navierstokes', 'equations', 'which', 'we', 'call', 'the', '3d', 'velocityvorticityvoigt', 'vvv', 'model', 'with', 'a', 'voigt', 'regularization', 'term', 'added', 'to', 'momentum', 'equation', 'in', 'velocityvorticity', 'form', 'but', 'with', 'no', 'regularizing', 'term', 'in', 'the', 'vorticity', 'equation', 'we', 'prove', 'global', 'wellposedness', 'and', 'regularity', 'of', 'this', 'model', 'under', 'periodic', 'boundary', 'conditions', 'we', 'prove', 'convergence', 'of', 'the', 'models', 'velocity', 'and', 'vorticity', 'to', 'their', 'counterparts', 'in', 'the', '3d', 'navierstokes', 'equations', 'as', 'the', 'voigt', 'modeling', 'parameter', 'tends', 'to', 'zero', 'we', 'prove', 'that', 'the', 'curl', 'of', 'the', 'models', 'velocity', 'converges', 'to', 'the', 'model', 'vorticity', 'which', 'is', 'solved', 'for', 'directly', 'as', 'the', 'voigt', 'modeling', 'parameter', 'tends', 'to', 'zero', 'finally', 'we', 'provide', 'a', 'criterion', 'for', 'finitetime', 'blowup', 'of', 'the', '3d', 'navierstokes', 'equations', 'based', 'on', 'this', 'inviscid', 'regularization']] | [-0.12394076287746429, 0.02039392843264444, -0.11230441523908541, 0.06003302107565105, -0.10019206147929975, -0.14111669046547393, -0.06296894760834502, 0.29102848405339593, -0.2923632790013377, -0.23569937046488812, 0.09330623448537341, -0.24427912178096073, -0.12917829626721555, 0.1400790760293603, -0.07238317065679564, 0.14023783789498026, 0.0729425611086832, -0.028062238160255847, -0.10179345230893071, -0.23270552728228786, 0.3367041092526553, 0.00024810034203632126, 0.2644393850117922, 0.017607537746541845, 0.158019682716785, -0.07301685624788033, -0.014138492717054384, 0.050328011460730744, -0.22444674183382726, 0.06021629915781448, 0.17251556846057264, 0.026129265372031207, 0.2882868283396137, -0.4519071939947276, -0.2643354528198211, 0.06132052977537287, 0.17949652133413174, 0.14908455810509622, -0.031894822103164064, -0.2864056770079609, 0.08690315722969585, -0.11222669900041715, -0.1975032170592197, -0.11566199873541963, 0.0010971520080006328, 0.04691655828440883, -0.3264743344472914, 0.1503027502302851, 0.09442031195483588, 0.060067385088267, -0.19399305928081018, -0.06139123197805522, -0.07831902399273782, 0.049049181646139535, 0.10232813894218798, 0.059287509113421726, 0.015332710010738208, -0.17778832942151046, -0.028963661508570458, 0.3827675824149929, -0.12123087602386508, -0.3444670352185595, 0.16641468858217884, -0.11496783301940766, -0.08936661420197323, 0.08408597685910504, 0.18849987557864395, 0.1337016790462979, -0.12254025312461729, 0.09918626196409479, -0.09326815909529426, 0.14109524565202922, 0.04173694121458664, -0.050283556946167915, 0.0994451345522599, 0.12078987718078082, 0.12413359215033465, 0.13786001168352005, -0.12872631850788885, -0.11752260901547712, -0.33566774853087705, -0.1690934897239866, -0.14135532411907253, 0.08567173650070768, -0.11805017638995698, -0.21829392042107365, 0.35704026925377547, 0.22180758835747838, 0.16686322099432863, 0.0837739428874619, 0.31221915254497834, 0.20253575236921936, 0.027787569250333412, 0.1128671713205504, 0.24122348820854878, 0.17194717714466667, 0.18403789004439425, -0.23905497078045174, 0.01089115334225112, 0.19910080920069895] |
1,802.08767 | Imaging Strain and Electric Fields in NV Ensembles using Stark Shift
Measurements | We report measurements of optically detected magnetic resonance spectra of
ensembles of negatively charged nitrogen-vacancy (NV) centers in diamonds in
the presence of strain and DC external electric fields. The Stark shift of the
spectral lines is stronger when the axial magnetic field along the NV centers
quantization axis is minimized. The shift is also enhanced at avoided crossings
between the hyperfine levels at an axial field of 77 uT. Since the intrinsic
strain in the diamond also induces a Stark shift, we are able to calculate the
magnitude and direction of the strain within the crystal. We also use the Stark
effect to map the electric field in the diamond volume between patterned
electrodes.
| physics.atom-ph cond-mat.mes-hall | we report measurements of optically detected magnetic resonance spectra of ensembles of negatively charged nitrogenvacancy nv centers in diamonds in the presence of strain and dc external electric fields the stark shift of the spectral lines is stronger when the axial magnetic field along the nv centers quantization axis is minimized the shift is also enhanced at avoided crossings between the hyperfine levels at an axial field of 77 ut since the intrinsic strain in the diamond also induces a stark shift we are able to calculate the magnitude and direction of the strain within the crystal we also use the stark effect to map the electric field in the diamond volume between patterned electrodes | [['we', 'report', 'measurements', 'of', 'optically', 'detected', 'magnetic', 'resonance', 'spectra', 'of', 'ensembles', 'of', 'negatively', 'charged', 'nitrogenvacancy', 'nv', 'centers', 'in', 'diamonds', 'in', 'the', 'presence', 'of', 'strain', 'and', 'dc', 'external', 'electric', 'fields', 'the', 'stark', 'shift', 'of', 'the', 'spectral', 'lines', 'is', 'stronger', 'when', 'the', 'axial', 'magnetic', 'field', 'along', 'the', 'nv', 'centers', 'quantization', 'axis', 'is', 'minimized', 'the', 'shift', 'is', 'also', 'enhanced', 'at', 'avoided', 'crossings', 'between', 'the', 'hyperfine', 'levels', 'at', 'an', 'axial', 'field', 'of', '77', 'ut', 'since', 'the', 'intrinsic', 'strain', 'in', 'the', 'diamond', 'also', 'induces', 'a', 'stark', 'shift', 'we', 'are', 'able', 'to', 'calculate', 'the', 'magnitude', 'and', 'direction', 'of', 'the', 'strain', 'within', 'the', 'crystal', 'we', 'also', 'use', 'the', 'stark', 'effect', 'to', 'map', 'the', 'electric', 'field', 'in', 'the', 'diamond', 'volume', 'between', 'patterned', 'electrodes']] | [-0.15626205601729454, 0.18806920790065423, 0.044589298510033154, -0.013276190756131774, 0.01776051476759755, -0.08818796210436393, 0.056296859381963375, 0.5137010956264061, -0.24794796571216504, -0.3147240448419167, -0.00802436083069314, -0.28095544137303596, -0.04335779997965564, 0.1435269541795487, -0.01410024001101113, -0.06322608113430603, -0.0026438230618267603, 0.03342244944170765, -0.06374060462188462, -0.1601591417039542, 0.2878413466979628, 0.0463883150530898, 0.36050421354239404, 0.09698030953497991, 0.061976176192579065, 0.02939009915065506, 0.1015217861039159, 0.05822393050336319, -0.08338396683254319, 0.12261137760689725, 0.18238195338003013, -0.06752271616183546, 0.20367411558070908, -0.4197231568234122, -0.12820245271710598, 0.10696932528086979, 0.1259624735209281, 0.21143444479726578, -0.06702476333745795, -0.28551208268365136, 0.05430831755713924, -0.0548641662882722, -0.1407151753852225, -0.020519857489220476, 0.006227624362699039, 0.011549923107351945, -0.23401178314310053, 0.10974743046074012, 0.03740082344789382, 0.12363165367556656, -0.12247881069779396, -0.07814452971341898, -0.05304749788432989, 0.06464198766119333, 0.035220737034535925, 0.09270798980015452, 0.27901336600518095, -0.09176690531894564, -0.13098635906556055, 0.33428803195609996, -0.09947723740529593, -0.08516224651880887, 0.0820070103155044, -0.2375555068861855, -0.07389308664056918, 0.14964751704373275, 0.14604312711061262, 0.11865598133097038, -0.11891811175497106, 0.07701438848313916, 0.05262178193838538, 0.15313103986175164, 0.11772894628305475, 0.023805677904949887, 0.24070843066210332, 0.06598187319569937, 0.09826334858232218, 0.17715758852377209, -0.1901394807531134, -0.016490907716038436, -0.24406634124967716, -0.14237094900530317, -0.1677068883796101, 0.06638479452053814, -0.08481921193205104, -0.16814513111729984, 0.39408491290700826, 0.14156701890346796, 0.1937831221287564, -0.12353200645226499, 0.29785235362208407, 0.13106596652803051, 0.13835941234846477, 0.01921782813075444, 0.3096776880323887, 0.27849825563962044, 0.11195374169265447, -0.355947324504023, -0.031850550671958404, -0.04107196442782879] |
1,802.08768 | Is Generator Conditioning Causally Related to GAN Performance? | Recent work (Pennington et al, 2017) suggests that controlling the entire
distribution of Jacobian singular values is an important design consideration
in deep learning. Motivated by this, we study the distribution of singular
values of the Jacobian of the generator in Generative Adversarial Networks
(GANs). We find that this Jacobian generally becomes ill-conditioned at the
beginning of training. Moreover, we find that the average (with z from p(z))
conditioning of the generator is highly predictive of two other ad-hoc metrics
for measuring the 'quality' of trained GANs: the Inception Score and the
Frechet Inception Distance (FID). We test the hypothesis that this relationship
is causal by proposing a 'regularization' technique (called Jacobian Clamping)
that softly penalizes the condition number of the generator Jacobian. Jacobian
Clamping improves the mean Inception Score and the mean FID for GANs trained on
several datasets. It also greatly reduces inter-run variance of the
aforementioned scores, addressing (at least partially) one of the main
criticisms of GANs.
| stat.ML cs.LG | recent work pennington et al 2017 suggests that controlling the entire distribution of jacobian singular values is an important design consideration in deep learning motivated by this we study the distribution of singular values of the jacobian of the generator in generative adversarial networks gans we find that this jacobian generally becomes illconditioned at the beginning of training moreover we find that the average with z from pz conditioning of the generator is highly predictive of two other adhoc metrics for measuring the quality of trained gans the inception score and the frechet inception distance fid we test the hypothesis that this relationship is causal by proposing a regularization technique called jacobian clamping that softly penalizes the condition number of the generator jacobian jacobian clamping improves the mean inception score and the mean fid for gans trained on several datasets it also greatly reduces interrun variance of the aforementioned scores addressing at least partially one of the main criticisms of gans | [['recent', 'work', 'pennington', 'et', 'al', '2017', 'suggests', 'that', 'controlling', 'the', 'entire', 'distribution', 'of', 'jacobian', 'singular', 'values', 'is', 'an', 'important', 'design', 'consideration', 'in', 'deep', 'learning', 'motivated', 'by', 'this', 'we', 'study', 'the', 'distribution', 'of', 'singular', 'values', 'of', 'the', 'jacobian', 'of', 'the', 'generator', 'in', 'generative', 'adversarial', 'networks', 'gans', 'we', 'find', 'that', 'this', 'jacobian', 'generally', 'becomes', 'illconditioned', 'at', 'the', 'beginning', 'of', 'training', 'moreover', 'we', 'find', 'that', 'the', 'average', 'with', 'z', 'from', 'pz', 'conditioning', 'of', 'the', 'generator', 'is', 'highly', 'predictive', 'of', 'two', 'other', 'adhoc', 'metrics', 'for', 'measuring', 'the', 'quality', 'of', 'trained', 'gans', 'the', 'inception', 'score', 'and', 'the', 'frechet', 'inception', 'distance', 'fid', 'we', 'test', 'the', 'hypothesis', 'that', 'this', 'relationship', 'is', 'causal', 'by', 'proposing', 'a', 'regularization', 'technique', 'called', 'jacobian', 'clamping', 'that', 'softly', 'penalizes', 'the', 'condition', 'number', 'of', 'the', 'generator', 'jacobian', 'jacobian', 'clamping', 'improves', 'the', 'mean', 'inception', 'score', 'and', 'the', 'mean', 'fid', 'for', 'gans', 'trained', 'on', 'several', 'datasets', 'it', 'also', 'greatly', 'reduces', 'interrun', 'variance', 'of', 'the', 'aforementioned', 'scores', 'addressing', 'at', 'least', 'partially', 'one', 'of', 'the', 'main', 'criticisms', 'of', 'gans']] | [-0.05904719299433055, 0.033113998379212715, -0.08183761629479705, 0.06624846584163606, -0.06919753903930541, -0.14809278678440024, -0.008331008544337238, 0.40013439117756205, -0.2766490767389769, -0.27957834099361206, 0.04924128232169096, -0.2590493285066259, -0.20932759321149205, 0.15956912776455284, -0.15603162032202816, 0.09492319018336275, 0.11652651475742459, 0.025633789993298704, -0.10787315667694201, -0.3258035092949285, 0.3405994371176348, 0.09702841722009907, 0.3369856656936463, 0.037109368336678016, 0.1590324503951706, -0.02265353620168753, -0.007424724638985936, -0.017901816888479517, -0.0774845387550613, 0.15831484539085067, 0.21687846550485118, 0.18797247991606128, 0.3585086896899156, -0.35490149082615974, -0.18516478626188473, 0.15837004266504665, 0.07856449157116004, 0.06692318059585886, -0.0036414425245311576, -0.2983508489909582, 0.09656658517633332, -0.1573863488098141, -0.048732048063538966, -0.06756822644965724, -0.009536118898540735, 0.0073691529389179776, -0.2867585213622078, 0.07348904929222044, 0.09542724349885248, 0.07847349381190724, -0.02770260286924895, -0.15612705088569784, -0.04328821945236996, 0.09410762486513705, 0.08883042293746257, 0.06062644254270708, 0.11979304285778199, -0.1910539215688914, -0.07761447754746768, 0.26851627584837845, -0.04441025757114403, -0.1665771731059067, 0.11559281171648764, -0.06656808198313229, -0.1400114065996604, 0.10247280549083371, 0.17232677231659183, 0.11118693181197159, -0.13121694758883679, 0.05829334115187521, -0.046146771960775365, 0.12462420521478634, 0.07135361425571318, -0.06597805151541251, 0.12794748038359102, 0.17877890794188717, 0.0393036108056549, 0.11399984786439746, -0.1303836273174966, -0.07886297075492621, -0.29856561619671995, -0.12515190544654614, -0.2562941192300059, 0.06311072953831172, -0.14331572022620093, -0.17290825431409756, 0.4451620324398391, 0.22292292020283638, 0.23414524706895462, 0.12994387715880293, 0.2991940106905531, 0.07051077393571177, 0.08601218774565496, 0.11274399866088061, 0.2573965888208477, 0.08817238355768495, 0.05821251905072131, -0.18936264456860954, 0.14206814489516545, 0.10685541005223058] |
1,802.08769 | Rule Primality, Minimal Generating Sets, Turing-Universality and Causal
Decomposition in Elementary Cellular Automata | We introduce several concepts such as prime and composite rule, tools and
methods for causal composition and decomposition. We discover and prove new
universality results in ECA, namely, that the Boolean composition of ECA rules
51 and 118, and 170, 15 and 118 can emulate ECA rule 110 and are thus
Turing-universal coupled systems. We construct the 4-colour Turing-universal
cellular automaton that carries the Boolean composition of the 2 and 3 ECA
rules emulating ECA rule 110 under multi-scale coarse-graining. We find that
rules generating the ECA rulespace by Boolean composition are of low complexity
and comprise prime rules implementing basic operations that when composed
enable complex behaviour. We also found a candidate minimal set with only 38
ECA prime rules---and several other small sets---capable of generating all
other (non-trivially symmetric) 88 ECA rules under Boolean composition.
| nlin.CG math.DS | we introduce several concepts such as prime and composite rule tools and methods for causal composition and decomposition we discover and prove new universality results in eca namely that the boolean composition of eca rules 51 and 118 and 170 15 and 118 can emulate eca rule 110 and are thus turinguniversal coupled systems we construct the 4colour turinguniversal cellular automaton that carries the boolean composition of the 2 and 3 eca rules emulating eca rule 110 under multiscale coarsegraining we find that rules generating the eca rulespace by boolean composition are of low complexity and comprise prime rules implementing basic operations that when composed enable complex behaviour we also found a candidate minimal set with only 38 eca prime rulesand several other small setscapable of generating all other nontrivially symmetric 88 eca rules under boolean composition | [['we', 'introduce', 'several', 'concepts', 'such', 'as', 'prime', 'and', 'composite', 'rule', 'tools', 'and', 'methods', 'for', 'causal', 'composition', 'and', 'decomposition', 'we', 'discover', 'and', 'prove', 'new', 'universality', 'results', 'in', 'eca', 'namely', 'that', 'the', 'boolean', 'composition', 'of', 'eca', 'rules', '51', 'and', '118', 'and', '170', '15', 'and', '118', 'can', 'emulate', 'eca', 'rule', '110', 'and', 'are', 'thus', 'turinguniversal', 'coupled', 'systems', 'we', 'construct', 'the', '4colour', 'turinguniversal', 'cellular', 'automaton', 'that', 'carries', 'the', 'boolean', 'composition', 'of', 'the', '2', 'and', '3', 'eca', 'rules', 'emulating', 'eca', 'rule', '110', 'under', 'multiscale', 'coarsegraining', 'we', 'find', 'that', 'rules', 'generating', 'the', 'eca', 'rulespace', 'by', 'boolean', 'composition', 'are', 'of', 'low', 'complexity', 'and', 'comprise', 'prime', 'rules', 'implementing', 'basic', 'operations', 'that', 'when', 'composed', 'enable', 'complex', 'behaviour', 'we', 'also', 'found', 'a', 'candidate', 'minimal', 'set', 'with', 'only', '38', 'eca', 'prime', 'rulesand', 'several', 'other', 'small', 'setscapable', 'of', 'generating', 'all', 'other', 'nontrivially', 'symmetric', '88', 'eca', 'rules', 'under', 'boolean', 'composition']] | [-0.10723695951820622, 0.15714705051200936, -0.013884903179349572, 0.07036806300713389, -0.06320174426055833, -0.191707534128244, 0.15759510162925877, 0.3648025334096833, -0.29170564567642426, -0.28861374550137614, 0.08093150908304657, -0.22733223269373845, -0.1762136538763211, 0.17992140300599718, 0.01915201873330872, 0.04591946102810829, 0.01952659410646936, 0.02026834964418589, -0.04158208428520654, -0.2552055021098106, 0.2856979189690814, 0.003967490132355979, 0.276461945193361, -0.013750436622823185, 0.09678688426432547, 0.029685964727072297, -0.05518852682686897, 0.0048240429944178065, -0.0577552986147539, 0.09936331201921593, 0.2514081758224586, 0.21757750950906593, 0.17650841712367846, -0.3867800602300176, -0.12572362170373994, 0.13887665657076373, 0.08831564494610222, 0.03563280942143504, 0.00023122268916566426, -0.19974415436553866, 0.18286134795991205, -0.1965638499154445, -0.08941395765121804, -0.14304557513904326, 0.061163912567226635, 0.06438300906638823, -0.27656718803597474, 0.02865564489694583, 0.1296667099683615, 0.13982796516797658, -0.04960346034269279, -0.19749007448415035, -0.0006242722686407949, 0.07496533424653454, -0.09058791863968346, -0.05396050775298543, 0.15254423094899464, -0.08040109185550584, -0.2200971587401217, 0.39639246071786133, -0.006101363300304137, -0.19094072699324408, 0.2250170239052777, -0.07499125189801205, -0.20117957331240177, 0.11294966320326524, 0.08398744559137901, 0.0877253051617864, -0.17326461192128795, 0.057900929192138434, -0.08717348178198783, 0.20045644983409472, 0.09330582866834393, 0.04112630944921455, 0.19068998634926418, 0.16712608496804457, 0.0024933540957298743, 0.13868862842192262, -0.07908713486420908, -0.1095493881665748, -0.2743523244280368, -0.12615530110840031, -0.09770748343443804, 0.06821554520996218, -0.09686568718461785, -0.14518992012755863, 0.3807268165635751, 0.12333804935411509, 0.14471432052091382, 0.15105043219423284, 0.16182318382979885, 0.048659053096072505, 0.1272558507982141, 0.06925537639450449, 0.13774077980014593, 0.13507380903830557, 0.03508090051481806, -0.1560965214939371, 0.01745869666720226, 0.08609595313779454] |
1,802.0877 | A Walk with SGD | We present novel empirical observations regarding how stochastic gradient
descent (SGD) navigates the loss landscape of over-parametrized deep neural
networks (DNNs). These observations expose the qualitatively different roles of
learning rate and batch-size in DNN optimization and generalization.
Specifically we study the DNN loss surface along the trajectory of SGD by
interpolating the loss surface between parameters from consecutive
\textit{iterations} and tracking various metrics during training. We find that
the loss interpolation between parameters before and after each training
iteration's update is roughly convex with a minimum (\textit{valley floor}) in
between for most of the training. Based on this and other metrics, we deduce
that for most of the training update steps, SGD moves in valley like regions of
the loss surface by jumping from one valley wall to another at a height above
the valley floor. This 'bouncing between walls at a height' mechanism helps SGD
traverse larger distance for small batch sizes and large learning rates which
we find play qualitatively different roles in the dynamics. While a large
learning rate maintains a large height from the valley floor, a small batch
size injects noise facilitating exploration. We find this mechanism is crucial
for generalization because the valley floor has barriers and this exploration
above the valley floor allows SGD to quickly travel far away from the
initialization point (without being affected by barriers) and find flatter
regions, corresponding to better generalization.
| stat.ML cs.LG | we present novel empirical observations regarding how stochastic gradient descent sgd navigates the loss landscape of overparametrized deep neural networks dnns these observations expose the qualitatively different roles of learning rate and batchsize in dnn optimization and generalization specifically we study the dnn loss surface along the trajectory of sgd by interpolating the loss surface between parameters from consecutive textititerations and tracking various metrics during training we find that the loss interpolation between parameters before and after each training iterations update is roughly convex with a minimum textitvalley floor in between for most of the training based on this and other metrics we deduce that for most of the training update steps sgd moves in valley like regions of the loss surface by jumping from one valley wall to another at a height above the valley floor this bouncing between walls at a height mechanism helps sgd traverse larger distance for small batch sizes and large learning rates which we find play qualitatively different roles in the dynamics while a large learning rate maintains a large height from the valley floor a small batch size injects noise facilitating exploration we find this mechanism is crucial for generalization because the valley floor has barriers and this exploration above the valley floor allows sgd to quickly travel far away from the initialization point without being affected by barriers and find flatter regions corresponding to better generalization | [['we', 'present', 'novel', 'empirical', 'observations', 'regarding', 'how', 'stochastic', 'gradient', 'descent', 'sgd', 'navigates', 'the', 'loss', 'landscape', 'of', 'overparametrized', 'deep', 'neural', 'networks', 'dnns', 'these', 'observations', 'expose', 'the', 'qualitatively', 'different', 'roles', 'of', 'learning', 'rate', 'and', 'batchsize', 'in', 'dnn', 'optimization', 'and', 'generalization', 'specifically', 'we', 'study', 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1,802.08771 | Atomic charges for modeling metal-organic frameworks: Why and how | Atomic partial charges are parameters of key importance in the simulation of
Metal-Organic Frameworks (MOFs), since Coulombic interactions decrease with the
distance more slowly than van der Waals interactions. But despite its
relevance, there is no method to unambiguously assign charges to each atom,
since atomic charges are not quantum observables. There are several methods
that allow the calculation of atomic charges, most of them starting from the
wavefunction or the electronic density or the system, as obtained with quantum
mechanical calculations. In this work, we describe the most common methods
employed to calculate atomic charges in MOFs. In order to show the influence
that even small variations of structure have on atomic charges, we present the
results that we obtained for DMOF-1. We also discuss the effect that small
variations of atomic charges have on the predicted structural properties
IRMOF-1.
| cond-mat.mtrl-sci physics.chem-ph | atomic partial charges are parameters of key importance in the simulation of metalorganic frameworks mofs since coulombic interactions decrease with the distance more slowly than van der waals interactions but despite its relevance there is no method to unambiguously assign charges to each atom since atomic charges are not quantum observables there are several methods that allow the calculation of atomic charges most of them starting from the wavefunction or the electronic density or the system as obtained with quantum mechanical calculations in this work we describe the most common methods employed to calculate atomic charges in mofs in order to show the influence that even small variations of structure have on atomic charges we present the results that we obtained for dmof1 we also discuss the effect that small variations of atomic charges have on the predicted structural properties irmof1 | [['atomic', 'partial', 'charges', 'are', 'parameters', 'of', 'key', 'importance', 'in', 'the', 'simulation', 'of', 'metalorganic', 'frameworks', 'mofs', 'since', 'coulombic', 'interactions', 'decrease', 'with', 'the', 'distance', 'more', 'slowly', 'than', 'van', 'der', 'waals', 'interactions', 'but', 'despite', 'its', 'relevance', 'there', 'is', 'no', 'method', 'to', 'unambiguously', 'assign', 'charges', 'to', 'each', 'atom', 'since', 'atomic', 'charges', 'are', 'not', 'quantum', 'observables', 'there', 'are', 'several', 'methods', 'that', 'allow', 'the', 'calculation', 'of', 'atomic', 'charges', 'most', 'of', 'them', 'starting', 'from', 'the', 'wavefunction', 'or', 'the', 'electronic', 'density', 'or', 'the', 'system', 'as', 'obtained', 'with', 'quantum', 'mechanical', 'calculations', 'in', 'this', 'work', 'we', 'describe', 'the', 'most', 'common', 'methods', 'employed', 'to', 'calculate', 'atomic', 'charges', 'in', 'mofs', 'in', 'order', 'to', 'show', 'the', 'influence', 'that', 'even', 'small', 'variations', 'of', 'structure', 'have', 'on', 'atomic', 'charges', 'we', 'present', 'the', 'results', 'that', 'we', 'obtained', 'for', 'dmof1', 'we', 'also', 'discuss', 'the', 'effect', 'that', 'small', 'variations', 'of', 'atomic', 'charges', 'have', 'on', 'the', 'predicted', 'structural', 'properties', 'irmof1']] | [-0.09138025173317749, 0.14586874562911958, -0.06082309795654104, 0.045376797055946196, 0.005430761438278521, -0.1172391510394355, 0.04153272096342374, 0.40625070781185685, -0.21573365567982358, -0.28702428916857825, -0.00034970730181327825, -0.3220766899544451, -0.13954296508777164, 0.1611551600223635, 0.014408461226941012, 0.035892351632251025, 0.031645487035832386, 0.010427154917202943, -0.09949442754137805, -0.2249179549042886, 0.30682913454504107, 0.035245829576747024, 0.24330399956105264, 0.10673208466375904, 0.048717677328574786, -0.008626097520116529, 0.02260773330960396, 0.06792282870561957, -0.15824865531147014, 0.15167285203799713, 0.2419556272785655, 0.022612755967424715, 0.24446898102659811, -0.5141616864732915, -0.2017822491464443, 0.06782024694780867, 0.0952943972271713, 0.18775068089473163, -0.08353991913427046, -0.24984936687505718, 0.08608706745741179, -0.17354840065667526, -0.12090990318508028, -0.14665396094804617, 0.03832156354521247, 0.07156563996464657, -0.1911787187044924, 0.06764552351434902, 0.028505070325951995, 0.048246104681808684, -0.08800838791041656, -0.14708591267556997, -0.036057366291061044, 0.140507366943756, 0.05691448086370185, -0.014617594082926889, 0.19676275135239038, -0.1205472380422029, -0.11024521288408382, 0.4413681260658522, -0.03621094240675087, -0.19706311596142087, 0.23277171102198338, -0.14094698139325643, -0.1447595752065452, 0.1034209125058846, 0.09384551207977179, 0.09738816529606208, -0.1379215033702291, 0.07388901750711228, 0.010106221969547621, 0.19764425947578798, 0.08039508902521389, 0.1083997334278249, 0.2092186039105969, 0.09553043676955665, 0.041747143244196615, 0.09750884942368691, -0.07772818839252028, -0.12815499052793966, -0.2506184780889814, -0.1484522289331004, -0.2088625455030622, 0.03219980808578368, -0.05514548372803088, -0.1949835691513936, 0.35404231868309083, 0.1900190803617668, 0.18501928252697314, -0.051857484209773354, 0.2614251113833404, 0.08441567938463267, 0.11889085734075458, 0.011394813217946309, 0.2870848477958775, 0.12711128464518048, 0.06646394892124603, -0.24399507842562665, 0.07584967547458121, 0.07027036846923582] |
1,802.08772 | Improving the Accuracy of Magnetic Field Tracing by Velocity Gradients:
Principal Component Analysis | Tracing of the magnetic field with Velocity Gradient Technique (VGT) allows
observers to probe magnetic field directions with spectroscopic data. In this
paper, we employ the method of Principal Component Analysis (PCA) to extract
the spectroscopic information most valuable for VGT. By using synthetic
observation data from numerical simulations, we show that PCA acts in a way
similar to spatial filtering along the velocity axis. We study both subsonic
and supersonic simulations and show that with the PCA filtering the tracing of
magnetic fields by the VGT is significantly improved. Using 21 cm GALFA data,
we demonstrate that the PCA filtering improves the alignment of the velocity
gradients and the Planck dust polarization.
| astro-ph.GA | tracing of the magnetic field with velocity gradient technique vgt allows observers to probe magnetic field directions with spectroscopic data in this paper we employ the method of principal component analysis pca to extract the spectroscopic information most valuable for vgt by using synthetic observation data from numerical simulations we show that pca acts in a way similar to spatial filtering along the velocity axis we study both subsonic and supersonic simulations and show that with the pca filtering the tracing of magnetic fields by the vgt is significantly improved using 21 cm galfa data we demonstrate that the pca filtering improves the alignment of the velocity gradients and the planck dust polarization | [['tracing', 'of', 'the', 'magnetic', 'field', 'with', 'velocity', 'gradient', 'technique', 'vgt', 'allows', 'observers', 'to', 'probe', 'magnetic', 'field', 'directions', 'with', 'spectroscopic', 'data', 'in', 'this', 'paper', 'we', 'employ', 'the', 'method', 'of', 'principal', 'component', 'analysis', 'pca', 'to', 'extract', 'the', 'spectroscopic', 'information', 'most', 'valuable', 'for', 'vgt', 'by', 'using', 'synthetic', 'observation', 'data', 'from', 'numerical', 'simulations', 'we', 'show', 'that', 'pca', 'acts', 'in', 'a', 'way', 'similar', 'to', 'spatial', 'filtering', 'along', 'the', 'velocity', 'axis', 'we', 'study', 'both', 'subsonic', 'and', 'supersonic', 'simulations', 'and', 'show', 'that', 'with', 'the', 'pca', 'filtering', 'the', 'tracing', 'of', 'magnetic', 'fields', 'by', 'the', 'vgt', 'is', 'significantly', 'improved', 'using', '21', 'cm', 'galfa', 'data', 'we', 'demonstrate', 'that', 'the', 'pca', 'filtering', 'improves', 'the', 'alignment', 'of', 'the', 'velocity', 'gradients', 'and', 'the', 'planck', 'dust', 'polarization']] | [-0.09569822138059983, 0.04018457576146234, -0.14088115817249086, -0.004995887475273679, -0.08744000219039422, -0.06773213767528996, -0.03741151518785947, 0.4379915539999451, -0.27638272877341646, -0.315890745515317, 0.10139013568260831, -0.2645585228364752, -0.10378037951474564, 0.22396924725807874, 0.03172277202582465, 0.022154344837397616, 0.07986244484493756, -0.048886226407722035, -0.04784965141989554, -0.18734459389429703, 0.3035575844526736, 0.1043925338911008, 0.2925457517262054, -0.03564596050872212, 0.10694701648090628, 0.013014516554179444, -0.13363259246127795, 0.07416371825620928, -0.09722801629937845, 0.12305218873100471, 0.19736862425337984, 0.14320962423194958, 0.22307644735823953, -0.3864298164123356, -0.23903198248039173, 0.0535585047570016, 0.18277996444695555, 0.1472513217940531, -0.05881655596575774, -0.29498672836451933, 0.058134362538369885, -0.08131511277824113, -0.14848429162829982, -0.12488109868151688, -0.046710440561389635, 0.04225229659628749, -0.27557159457520575, 0.13392806803988935, 0.021895113146649593, 0.11503736153080137, -0.09758353404612156, -0.08562920063701088, -0.04052410175842521, 0.07741767168045044, 0.07849873476908996, 0.07243494524319648, 0.17956384556137223, -0.09025594739004376, -0.08455764189098788, 0.35500239133340333, -0.1574768080990102, -0.1421721418577749, 0.15996164747975372, -0.16251945116483005, -0.14941706857437978, 0.1389020161653778, 0.1752219923763676, 0.09227581698946094, -0.12022164397238366, 0.00814903228990816, -0.048345518920702484, 0.20480305356987283, 0.015653679534726964, -0.03186599232371798, 0.19592349274100457, 0.11756459018627627, 0.07211951269413781, 0.11752001621834604, -0.24442331555538474, -0.012248126245969165, -0.22683005549623508, -0.1449028078030869, -0.1717900593962883, 0.009166947748322468, -0.1601891369535433, -0.11827915200584493, 0.3811934089898008, 0.2628008413449985, 0.20061046333027494, 0.016914598338833426, 0.39248191693847156, 0.06224346423389769, 0.08125328280822894, 0.13681180440960863, 0.2596737742712474, 0.20778232549556194, 0.12734739549161322, -0.2618415642012554, -0.020550627397389803, 0.006396560235344599] |
1,802.08773 | GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models | Modeling and generating graphs is fundamental for studying networks in
biology, engineering, and social sciences. However, modeling complex
distributions over graphs and then efficiently sampling from these
distributions is challenging due to the non-unique, high-dimensional nature of
graphs and the complex, non-local dependencies that exist between edges in a
given graph. Here we propose GraphRNN, a deep autoregressive model that
addresses the above challenges and approximates any distribution of graphs with
minimal assumptions about their structure. GraphRNN learns to generate graphs
by training on a representative set of graphs and decomposes the graph
generation process into a sequence of node and edge formations, conditioned on
the graph structure generated so far.
In order to quantitatively evaluate the performance of GraphRNN, we introduce
a benchmark suite of datasets, baselines and novel evaluation metrics based on
Maximum Mean Discrepancy, which measure distances between sets of graphs. Our
experiments show that GraphRNN significantly outperforms all baselines,
learning to generate diverse graphs that match the structural characteristics
of a target set, while also scaling to graphs 50 times larger than previous
deep models.
| cs.LG cs.AI cs.SI | modeling and generating graphs is fundamental for studying networks in biology engineering and social sciences however modeling complex distributions over graphs and then efficiently sampling from these distributions is challenging due to the nonunique highdimensional nature of graphs and the complex nonlocal dependencies that exist between edges in a given graph here we propose graphrnn a deep autoregressive model that addresses the above challenges and approximates any distribution of graphs with minimal assumptions about their structure graphrnn learns to generate graphs by training on a representative set of graphs and decomposes the graph generation process into a sequence of node and edge formations conditioned on the graph structure generated so far in order to quantitatively evaluate the performance of graphrnn we introduce a benchmark suite of datasets baselines and novel evaluation metrics based on maximum mean discrepancy which measure distances between sets of graphs our experiments show that graphrnn significantly outperforms all baselines learning to generate diverse graphs that match the structural characteristics of a target set while also scaling to graphs 50 times larger than previous deep models | [['modeling', 'and', 'generating', 'graphs', 'is', 'fundamental', 'for', 'studying', 'networks', 'in', 'biology', 'engineering', 'and', 'social', 'sciences', 'however', 'modeling', 'complex', 'distributions', 'over', 'graphs', 'and', 'then', 'efficiently', 'sampling', 'from', 'these', 'distributions', 'is', 'challenging', 'due', 'to', 'the', 'nonunique', 'highdimensional', 'nature', 'of', 'graphs', 'and', 'the', 'complex', 'nonlocal', 'dependencies', 'that', 'exist', 'between', 'edges', 'in', 'a', 'given', 'graph', 'here', 'we', 'propose', 'graphrnn', 'a', 'deep', 'autoregressive', 'model', 'that', 'addresses', 'the', 'above', 'challenges', 'and', 'approximates', 'any', 'distribution', 'of', 'graphs', 'with', 'minimal', 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1,802.08774 | Tool Detection and Operative Skill Assessment in Surgical Videos Using
Region-Based Convolutional Neural Networks | Five billion people in the world lack access to quality surgical care.
Surgeon skill varies dramatically, and many surgical patients suffer
complications and avoidable harm. Improving surgical training and feedback
would help to reduce the rate of complications, half of which have been shown
to be preventable. To do this, it is essential to assess operative skill, a
process that currently requires experts and is manual, time consuming, and
subjective. In this work, we introduce an approach to automatically assess
surgeon performance by tracking and analyzing tool movements in surgical
videos, leveraging region-based convolutional neural networks. In order to
study this problem, we also introduce a new dataset, m2cai16-tool-locations,
which extends the m2cai16-tool dataset with spatial bounds of tools. While
previous methods have addressed tool presence detection, ours is the first to
not only detect presence but also spatially localize surgical tools in
real-world laparoscopic surgical videos. We show that our method both
effectively detects the spatial bounds of tools as well as significantly
outperforms existing methods on tool presence detection. We further demonstrate
the ability of our method to assess surgical quality through analysis of tool
usage patterns, movement range, and economy of motion.
| cs.CV | five billion people in the world lack access to quality surgical care surgeon skill varies dramatically and many surgical patients suffer complications and avoidable harm improving surgical training and feedback would help to reduce the rate of complications half of which have been shown to be preventable to do this it is essential to assess operative skill a process that currently requires experts and is manual time consuming and subjective in this work we introduce an approach to automatically assess surgeon performance by tracking and analyzing tool movements in surgical videos leveraging regionbased convolutional neural networks in order to study this problem we also introduce a new dataset m2cai16toollocations which extends the m2cai16tool dataset with spatial bounds of tools while previous methods have addressed tool presence detection ours is the first to not only detect presence but also spatially localize surgical tools in realworld laparoscopic surgical videos we show that our method both effectively detects the spatial bounds of tools as well as significantly outperforms existing methods on tool presence detection we further demonstrate the ability of our method to assess surgical quality through analysis of tool usage patterns movement range and economy of motion | [['five', 'billion', 'people', 'in', 'the', 'world', 'lack', 'access', 'to', 'quality', 'surgical', 'care', 'surgeon', 'skill', 'varies', 'dramatically', 'and', 'many', 'surgical', 'patients', 'suffer', 'complications', 'and', 'avoidable', 'harm', 'improving', 'surgical', 'training', 'and', 'feedback', 'would', 'help', 'to', 'reduce', 'the', 'rate', 'of', 'complications', 'half', 'of', 'which', 'have', 'been', 'shown', 'to', 'be', 'preventable', 'to', 'do', 'this', 'it', 'is', 'essential', 'to', 'assess', 'operative', 'skill', 'a', 'process', 'that', 'currently', 'requires', 'experts', 'and', 'is', 'manual', 'time', 'consuming', 'and', 'subjective', 'in', 'this', 'work', 'we', 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1,802.08775 | Revealing the formation and electrochemical properties of
bis(trifluoromethanesulfonyl) imide intercalated graphite with
first-principles calculations | Graphite has been reported to have anion as well as cation intercalation
capacities as both cathode and anode host materials for the dual ion battery.
In this work, we study the intercalation of bis(trifluoromethanesulfonyl) imide
(TFSI) anion from ionic liquid electrolyte into graphite with first-principles
calculations. We build models for TFSI-C$_n$ compounds with systematically
increasing unit cell sizes of graphene sheet and investigate their stabilities
by calculating the formation energy, resulting in the linear decrease and
arriving at the limit of stability. With identified unit cell sizes for stable
compound formation, we reveal that the interlayer distance and relative volume
expansion ratio of TFSI-C$_n$ increase as increasing the concentration of TFSI
intercalate during the charge process. The electrode voltage is determined to
be ranged from 3.8 V to 3.0 V at the specific capacity ranging from 30 mAh
g$^{-1}$ to 54 mAh g$^{-1}$ in agreement with experiment. Moreover, a very low
activation barrier of under 50 meV for TFSI migration and good electronic
conductivity give a proof of using these compounds as a promising cathode.
Through the analysis of charge transfer, we clarify the mechanism of TFSI-C$_n$
formation, and reveal new prospects for developing graphite based cathode.
| cond-mat.mtrl-sci | graphite has been reported to have anion as well as cation intercalation capacities as both cathode and anode host materials for the dual ion battery in this work we study the intercalation of bistrifluoromethanesulfonyl imide tfsi anion from ionic liquid electrolyte into graphite with firstprinciples calculations we build models for tfsic_n compounds with systematically increasing unit cell sizes of graphene sheet and investigate their stabilities by calculating the formation energy resulting in the linear decrease and arriving at the limit of stability with identified unit cell sizes for stable compound formation we reveal that the interlayer distance and relative volume expansion ratio of tfsic_n increase as increasing the concentration of tfsi intercalate during the charge process the electrode voltage is determined to be ranged from 38 v to 30 v at the specific capacity ranging from 30 mah g1 to 54 mah g1 in agreement with experiment moreover a very low activation barrier of under 50 mev for tfsi migration and good electronic conductivity give a proof of using these compounds as a promising cathode through the analysis of charge transfer we clarify the mechanism of tfsic_n formation and reveal new prospects for developing graphite based cathode | [['graphite', 'has', 'been', 'reported', 'to', 'have', 'anion', 'as', 'well', 'as', 'cation', 'intercalation', 'capacities', 'as', 'both', 'cathode', 'and', 'anode', 'host', 'materials', 'for', 'the', 'dual', 'ion', 'battery', 'in', 'this', 'work', 'we', 'study', 'the', 'intercalation', 'of', 'bistrifluoromethanesulfonyl', 'imide', 'tfsi', 'anion', 'from', 'ionic', 'liquid', 'electrolyte', 'into', 'graphite', 'with', 'firstprinciples', 'calculations', 'we', 'build', 'models', 'for', 'tfsic_n', 'compounds', 'with', 'systematically', 'increasing', 'unit', 'cell', 'sizes', 'of', 'graphene', 'sheet', 'and', 'investigate', 'their', 'stabilities', 'by', 'calculating', 'the', 'formation', 'energy', 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1,802.08776 | Bandwidth Partitioning and Downlink Analysis in Millimeter Wave
Integrated Access and Backhaul for 5G | With the increasing network densification, it has become exceedingly
difficult to provide traditional fiber backhaul access to each cell site, which
is especially true for small cell base stations (SBSs). The increasing maturity
of millimeter wave (mm-wave) communication has opened up the possibility of
providing high-speed wireless backhaul to such cell sites. Since mm-wave is
also suitable for access links, the third generation partnership project (3GPP)
is envisioning an integrated access and backhaul (IAB) architecture for the
fifth generation (5G) cellular networks in which the same infrastructure and
spectral resources will be used for both access and backhaul. In this paper, we
develop an analytical framework for IAB-enabled cellular network using which
its downlink rate coverage probability is accurately characterized. Using this
framework, we study the performance of three backhaul bandwidth (BW) partition
strategies: 1) equal partition: when all SBSs obtain equal share of the
backhaul BW; 2) instantaneous load-based partition: when the backhaul BW share
of an SBS is proportional to its instantaneous load; and 3) average load-based
partition: when the backhaul BW share of an SBS is proportional to its average
load. Our analysis shows that depending on the choice of the partition
strategy, there exists an optimal split of access and backhaul BW for which the
rate coverage is maximized. Further, there exists a critical volume of
cell-load (total number of users) beyond which the gains provided by the
IAB-enabled network disappear and its performance converges to that of the
traditional macro-only network with no SBSs.
| cs.IT cs.NI math.IT | with the increasing network densification it has become exceedingly difficult to provide traditional fiber backhaul access to each cell site which is especially true for small cell base stations sbss the increasing maturity of millimeter wave mmwave communication has opened up the possibility of providing highspeed wireless backhaul to such cell sites since mmwave is also suitable for access links the third generation partnership project 3gpp is envisioning an integrated access and backhaul iab architecture for the fifth generation 5g cellular networks in which the same infrastructure and spectral resources will be used for both access and backhaul in this paper we develop an analytical framework for iabenabled cellular network using which its downlink rate coverage probability is accurately characterized using this framework we study the performance of three backhaul bandwidth bw partition strategies 1 equal partition when all sbss obtain equal share of the backhaul bw 2 instantaneous loadbased partition when the backhaul bw share of an sbs is proportional to its instantaneous load and 3 average loadbased partition when the backhaul bw share of an sbs is proportional to its average load our analysis shows that depending on the choice of the partition strategy there exists an optimal split of access and backhaul bw for which the rate coverage is maximized further there exists a critical volume of cellload total number of users beyond which the gains provided by the iabenabled network disappear and its performance converges to that of the traditional macroonly network with no sbss | [['with', 'the', 'increasing', 'network', 'densification', 'it', 'has', 'become', 'exceedingly', 'difficult', 'to', 'provide', 'traditional', 'fiber', 'backhaul', 'access', 'to', 'each', 'cell', 'site', 'which', 'is', 'especially', 'true', 'for', 'small', 'cell', 'base', 'stations', 'sbss', 'the', 'increasing', 'maturity', 'of', 'millimeter', 'wave', 'mmwave', 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1,802.08777 | The sharp Poincar\'e--Sobolev type inequalities in the hyperbolic spaces
$\mathbb H^n$ | In this note, we establish a $L^p-$version of the Poincar\'e--Sobolev
inequalities in the hyperbolic spaces $\mathbb H^n$. The interest of this
result is that it relates both the Poincar\'e (or Hardy) inequality and the
Sobolev inequality with the sharp constant in $\mathbb H^n$. Our approach is
based on the comparison of the $L^p-$norm of gradient of the symmetric
decreasing rearrangement of a function in both the hyperbolic space and the
Euclidean space, and the sharp Sobolev inequalities in Euclidean spaces. This
approach also gives the proof of the Poincar\'e--Gagliardo--Nirenberg and
Poincar\'e--Morrey--Sobolev inequalities in the hyperbolic spaces $\mathbb
H^n$. Finally, we discuss several other Sobolev inequalities in the hyperbolic
spaces $\mathbb H^n$ which generalize the inequalities due to Mugelli and
Talenti in $\mathbb H^2$.
| math.FA math.AP | in this note we establish a lpversion of the poincaresobolev inequalities in the hyperbolic spaces mathbb hn the interest of this result is that it relates both the poincare or hardy inequality and the sobolev inequality with the sharp constant in mathbb hn our approach is based on the comparison of the lpnorm of gradient of the symmetric decreasing rearrangement of a function in both the hyperbolic space and the euclidean space and the sharp sobolev inequalities in euclidean spaces this approach also gives the proof of the poincaregagliardonirenberg and poincaremorreysobolev inequalities in the hyperbolic spaces mathbb hn finally we discuss several other sobolev inequalities in the hyperbolic spaces mathbb hn which generalize the inequalities due to mugelli and talenti in mathbb h2 | [['in', 'this', 'note', 'we', 'establish', 'a', 'lpversion', 'of', 'the', 'poincaresobolev', 'inequalities', 'in', 'the', 'hyperbolic', 'spaces', 'mathbb', 'hn', 'the', 'interest', 'of', 'this', 'result', 'is', 'that', 'it', 'relates', 'both', 'the', 'poincare', 'or', 'hardy', 'inequality', 'and', 'the', 'sobolev', 'inequality', 'with', 'the', 'sharp', 'constant', 'in', 'mathbb', 'hn', 'our', 'approach', 'is', 'based', 'on', 'the', 'comparison', 'of', 'the', 'lpnorm', 'of', 'gradient', 'of', 'the', 'symmetric', 'decreasing', 'rearrangement', 'of', 'a', 'function', 'in', 'both', 'the', 'hyperbolic', 'space', 'and', 'the', 'euclidean', 'space', 'and', 'the', 'sharp', 'sobolev', 'inequalities', 'in', 'euclidean', 'spaces', 'this', 'approach', 'also', 'gives', 'the', 'proof', 'of', 'the', 'poincaregagliardonirenberg', 'and', 'poincaremorreysobolev', 'inequalities', 'in', 'the', 'hyperbolic', 'spaces', 'mathbb', 'hn', 'finally', 'we', 'discuss', 'several', 'other', 'sobolev', 'inequalities', 'in', 'the', 'hyperbolic', 'spaces', 'mathbb', 'hn', 'which', 'generalize', 'the', 'inequalities', 'due', 'to', 'mugelli', 'and', 'talenti', 'in', 'mathbb', 'h2']] | [-0.11235532054755216, 0.05989839017080764, -0.05600723089107002, 0.1015949755392891, -0.04563524983047197, -0.09970802288347234, -0.017849701891342797, 0.31128482477118574, -0.3143296655267477, -0.17439686181023717, 0.15093501416655877, -0.2981508321206396, -0.14330042945415092, 0.2229818396444898, -0.15438447038371425, 0.04424121775664389, -0.033618187780181566, 0.055250045269106825, -0.16205545404809527, -0.29300227416291214, 0.38676240493853886, -0.09395429173794885, 0.21217516144970433, 0.115294032045252, 0.04671712272102013, -0.012086326494651682, -0.020170190425900122, -0.03596199981451112, -0.2666619415495006, 0.22007992287787298, 0.20884797474912678, 0.07812092926663657, 0.302683610150901, -0.37244250302513443, -0.19327205328736455, 0.21302422353764996, 0.1403256894166892, -0.04462485522672068, -0.06308445842490376, -0.3544336709659547, -0.04106384669333541, -0.050323562184348705, -0.18403277618732924, -0.04703739980856578, 0.02408284742462759, 0.06544439679322143, -0.29076338111578176, 0.10814491582204937, 0.1571580525642882, 0.020760359292034992, -0.1432026348076761, -0.05340725567463475, -0.008356200362322852, 0.036044556932756676, 0.03561620146889861, 0.13930151816263484, 0.02672373247720922, 0.013690209534252063, -0.10164021052963411, 0.3616022803510229, -0.07076430051674834, -0.2824941295121486, 0.10258286895502049, -0.23533244309170792, -0.17430573338254665, 0.0525818308427309, 0.15956803785714632, 0.1756399123192144, -0.020750489140239855, 0.22003347161371495, -0.10781126476358623, 0.05009716193405135, 0.07906359920743852, 0.06940689188195392, -0.022529034751156966, 0.06466874373533453, 0.16370221677934751, 0.17631053660734325, -0.03767964405888051, -0.09335725818333837, -0.3890177447348833, -0.2682685458023722, -0.20330821856271844, 0.09908555916044862, -0.2390471047754545, -0.16496910034523657, 0.2985825037195658, 0.020973274569648006, 0.16691376053204293, 0.13318786050755685, 0.1936789616709575, 0.0638678339649535, 0.05599020901718177, 0.07130140901426785, 0.21275293506332674, 0.19080784701315376, 0.10671608467819169, -0.14362623634903382, 0.01625273048800106, 0.22171465480544914] |
1,802.08778 | Measuring the Demand Effects of Formal and Informal Communication :
Evidence from Online Markets for Illicit Drugs | I present evidence that communication between marketplace participants is an
important influence on market demand. I find that consumer demand is
approximately equally influenced by communication on both formal and informal
networks- namely, product reviews and community forums. In addition, I find
empirical evidence of a vendor's ability to commit to disclosure dampening the
effect of communication on demand. I also find that product demand is more
responsive to average customer sentiment as the number of messages grows, as
may be expected in a Bayesian updating framework.
| stat.AP econ.EM | i present evidence that communication between marketplace participants is an important influence on market demand i find that consumer demand is approximately equally influenced by communication on both formal and informal networks namely product reviews and community forums in addition i find empirical evidence of a vendors ability to commit to disclosure dampening the effect of communication on demand i also find that product demand is more responsive to average customer sentiment as the number of messages grows as may be expected in a bayesian updating framework | [['i', 'present', 'evidence', 'that', 'communication', 'between', 'marketplace', 'participants', 'is', 'an', 'important', 'influence', 'on', 'market', 'demand', 'i', 'find', 'that', 'consumer', 'demand', 'is', 'approximately', 'equally', 'influenced', 'by', 'communication', 'on', 'both', 'formal', 'and', 'informal', 'networks', 'namely', 'product', 'reviews', 'and', 'community', 'forums', 'in', 'addition', 'i', 'find', 'empirical', 'evidence', 'of', 'a', 'vendors', 'ability', 'to', 'commit', 'to', 'disclosure', 'dampening', 'the', 'effect', 'of', 'communication', 'on', 'demand', 'i', 'also', 'find', 'that', 'product', 'demand', 'is', 'more', 'responsive', 'to', 'average', 'customer', 'sentiment', 'as', 'the', 'number', 'of', 'messages', 'grows', 'as', 'may', 'be', 'expected', 'in', 'a', 'bayesian', 'updating', 'framework']] | [-0.14684119028067377, 0.06552140597383165, -0.044810115927081, 0.07562641682633167, -0.12585191632588877, -0.2034499687303243, 0.12873394040915387, 0.44246517100382127, -0.2412059418214806, -0.3203055467456579, 0.09217006142315423, -0.35607773557990446, -0.16537248786811815, 0.15263095370161978, -0.1322263072394691, -0.032786469050179956, 0.04069847729720775, 0.07016690295649929, 0.056385510512788235, -0.3087890237013156, 0.2907961741218279, 0.06713029477830934, 0.3411430584309601, 0.09878066577145765, 0.013578231505321703, 0.0009867334979233042, -0.08888277892644207, 0.012069237033097908, -0.082198458957809, 0.14286616569834537, 0.32693005460260927, 0.24361817189826812, 0.38544423274438955, -0.4417762844789848, -0.13360740918794584, 0.09615787068360496, 0.14156334303405094, 0.027473025238034368, -0.04053908693768089, -0.24970332763275538, 0.03148205696080608, -0.2953146734628184, -0.006521238094388411, -0.08487689852628899, 0.08761052786233439, 0.03975934696908312, -0.2898951268316, 0.018831134363995524, 0.02055597221264723, 0.10115993502763924, -0.024485482256217248, -0.09797461456137485, -0.03681031959372219, 0.1741203413473378, 0.12744762299143464, -0.03256338570352601, 0.17726701168322015, -0.16174538769684305, -0.1542604291020764, 0.37831066351854936, -0.03090903849137583, -0.1304772550390027, 0.13319143104292025, -0.04105687743566673, -0.14729829036213202, 0.05281280792029253, 0.2590876865661007, 0.017332218448235386, -0.15767162829509068, -0.027568271583021116, -0.045923610613949, 0.23528701972601743, 0.021454466185693084, 0.025017738615259015, 0.2089173673898324, 0.18740734359484978, 0.14167369665556598, 0.084133911671133, -0.0009481126426494327, -0.12452051777863639, -0.2341651719320437, -0.1671760158249359, -0.18162131503267193, 0.07810032252093842, -0.1063465544276191, -0.1368223000853055, 0.33380471491094293, 0.19909949980317562, 0.1001009508410747, 0.09634234197437763, 0.312462846737826, 0.04242456758170035, 0.04357199009036881, 0.1528126740735827, 0.16939732074154135, -0.02057491569116112, 0.1700821409333797, -0.14670481410241504, 0.20386499234999733, -0.052737698836744513] |
1,802.08779 | Shortcut loading atoms into an optical lattice | We present an effective and fast (few microseconds) procedure for
transferring ultra-cold atoms from the ground state in a harmonic trap into the
desired bands of an optical lattice. Our shortcut method is a designed pulse
sequence where the time duration and the interval in each step are fully
optimized in order to maximize robustness and fidelity of the final state with
respect to the target state. The atoms can be prepared in a single band with
even or odd parity, and superposition states of different bands can be prepared
and manipulated. Furthermore, we extend this idea to the case of
two-dimensional or three-dimensional optical lattices where the energies of
excited states are degenerate. We experimentally demonstrate various examples
and show very good agreement with the theoretical model. Efficient shortcut
methods will find applications in the preparation of quantum systems, in
quantum information processing, in precise measurement and as a starting point
to investigate dynamics in excited bands.
| physics.atom-ph cond-mat.quant-gas | we present an effective and fast few microseconds procedure for transferring ultracold atoms from the ground state in a harmonic trap into the desired bands of an optical lattice our shortcut method is a designed pulse sequence where the time duration and the interval in each step are fully optimized in order to maximize robustness and fidelity of the final state with respect to the target state the atoms can be prepared in a single band with even or odd parity and superposition states of different bands can be prepared and manipulated furthermore we extend this idea to the case of twodimensional or threedimensional optical lattices where the energies of excited states are degenerate we experimentally demonstrate various examples and show very good agreement with the theoretical model efficient shortcut methods will find applications in the preparation of quantum systems in quantum information processing in precise measurement and as a starting point to investigate dynamics in excited bands | [['we', 'present', 'an', 'effective', 'and', 'fast', 'few', 'microseconds', 'procedure', 'for', 'transferring', 'ultracold', 'atoms', 'from', 'the', 'ground', 'state', 'in', 'a', 'harmonic', 'trap', 'into', 'the', 'desired', 'bands', 'of', 'an', 'optical', 'lattice', 'our', 'shortcut', 'method', 'is', 'a', 'designed', 'pulse', 'sequence', 'where', 'the', 'time', 'duration', 'and', 'the', 'interval', 'in', 'each', 'step', 'are', 'fully', 'optimized', 'in', 'order', 'to', 'maximize', 'robustness', 'and', 'fidelity', 'of', 'the', 'final', 'state', 'with', 'respect', 'to', 'the', 'target', 'state', 'the', 'atoms', 'can', 'be', 'prepared', 'in', 'a', 'single', 'band', 'with', 'even', 'or', 'odd', 'parity', 'and', 'superposition', 'states', 'of', 'different', 'bands', 'can', 'be', 'prepared', 'and', 'manipulated', 'furthermore', 'we', 'extend', 'this', 'idea', 'to', 'the', 'case', 'of', 'twodimensional', 'or', 'threedimensional', 'optical', 'lattices', 'where', 'the', 'energies', 'of', 'excited', 'states', 'are', 'degenerate', 'we', 'experimentally', 'demonstrate', 'various', 'examples', 'and', 'show', 'very', 'good', 'agreement', 'with', 'the', 'theoretical', 'model', 'efficient', 'shortcut', 'methods', 'will', 'find', 'applications', 'in', 'the', 'preparation', 'of', 'quantum', 'systems', 'in', 'quantum', 'information', 'processing', 'in', 'precise', 'measurement', 'and', 'as', 'a', 'starting', 'point', 'to', 'investigate', 'dynamics', 'in', 'excited', 'bands']] | [-0.09569113689793061, 0.1837561531915238, -0.04992630133623422, 0.026017347274088783, 0.030774166627751686, -0.17226842379999124, 0.07110123455014575, 0.45155771687343904, -0.2728125804304322, -0.30332960383973934, 0.060947233949540346, -0.26225613688466526, -0.06577713771198605, 0.18485771762826067, -0.01261190901793351, 0.09973615734275029, 0.10675147167536654, 0.020362939986199896, -0.0736085580900618, -0.22666556618170505, 0.2739725438111662, 0.041706455448536274, 0.29001288918801876, 0.026893994467380115, 0.0756717972734448, 0.001614853039312418, 0.06896693093843664, -0.0280938393854878, -0.09758700587774945, 0.12169320355508406, 0.2681336628633796, 0.032354959587366144, 0.24520384999510797, -0.46975641972326404, -0.19735637652139545, 0.08010263721269992, 0.13792824999081513, 0.21584291565173033, -0.040319317153100914, -0.31678911198749904, 0.03246229114836153, -0.14446849943524745, -0.11928393358741946, -0.12308325943510531, -0.002999373360926026, 0.017343397523406186, -0.2725724531455508, 0.03534386749581959, 0.023128289958871784, 0.041183848093157706, -0.07375808809648114, -0.0798308396110712, -0.011412668833951171, 0.13180780403398543, -0.05276816414854831, 0.030845070905435264, 0.09687933120346051, -0.11610517658358085, -0.13263080515486153, 0.3949980250665847, -0.08643737187847801, -0.16865065452772415, 0.17939020631410466, -0.12588898979769902, -0.0861766186080095, 0.11663696641703666, 0.1646157609139675, 0.1256152755415798, -0.10085935104650127, 0.015364726705114132, 0.013142281312118226, 0.20468005140130024, 0.05287471869547816, 0.08662668968615515, 0.2167838704050224, 0.15486053152516768, 0.06532919253377374, 0.19861507569174458, -0.10799362223383181, -0.09479459842867395, -0.2687320154737917, -0.168047178219827, -0.24319319472105913, 0.03548172066176805, -0.00994785032995838, -0.10115659312062154, 0.4433062705506064, 0.11627051506274014, 0.2102520850124545, -0.006731690390955044, 0.2776602383797305, 0.11741857626475394, 0.007875302838916076, 0.054564333384152196, 0.24851964737724844, 0.10012646653811753, 0.06351793687562222, -0.2183024496255631, 0.004154315088651603, 0.004727947012863204] |
1,802.0878 | Extremely Fast Decision Tree | We introduce a novel incremental decision tree learning algorithm, Hoeffding
Anytime Tree, that is statistically more efficient than the current
state-of-the-art, Hoeffding Tree. We demonstrate that an implementation of
Hoeffding Anytime Tree---"Extremely Fast Decision Tree", a minor modification
to the MOA implementation of Hoeffding Tree---obtains significantly superior
prequential accuracy on most of the largest classification datasets from the
UCI repository. Hoeffding Anytime Tree produces the asymptotic batch tree in
the limit, is naturally resilient to concept drift, and can be used as a higher
accuracy replacement for Hoeffding Tree in most scenarios, at a small
additional computational cost.
| cs.LG stat.ML | we introduce a novel incremental decision tree learning algorithm hoeffding anytime tree that is statistically more efficient than the current stateoftheart hoeffding tree we demonstrate that an implementation of hoeffding anytime treeextremely fast decision tree a minor modification to the moa implementation of hoeffding treeobtains significantly superior prequential accuracy on most of the largest classification datasets from the uci repository hoeffding anytime tree produces the asymptotic batch tree in the limit is naturally resilient to concept drift and can be used as a higher accuracy replacement for hoeffding tree in most scenarios at a small additional computational cost | [['we', 'introduce', 'a', 'novel', 'incremental', 'decision', 'tree', 'learning', 'algorithm', 'hoeffding', 'anytime', 'tree', 'that', 'is', 'statistically', 'more', 'efficient', 'than', 'the', 'current', 'stateoftheart', 'hoeffding', 'tree', 'we', 'demonstrate', 'that', 'an', 'implementation', 'of', 'hoeffding', 'anytime', 'treeextremely', 'fast', 'decision', 'tree', 'a', 'minor', 'modification', 'to', 'the', 'moa', 'implementation', 'of', 'hoeffding', 'treeobtains', 'significantly', 'superior', 'prequential', 'accuracy', 'on', 'most', 'of', 'the', 'largest', 'classification', 'datasets', 'from', 'the', 'uci', 'repository', 'hoeffding', 'anytime', 'tree', 'produces', 'the', 'asymptotic', 'batch', 'tree', 'in', 'the', 'limit', 'is', 'naturally', 'resilient', 'to', 'concept', 'drift', 'and', 'can', 'be', 'used', 'as', 'a', 'higher', 'accuracy', 'replacement', 'for', 'hoeffding', 'tree', 'in', 'most', 'scenarios', 'at', 'a', 'small', 'additional', 'computational', 'cost']] | [-0.07881069618330609, 0.04657242529856376, -0.09610760216310155, 0.14384697330388008, -0.13162455109704752, -0.14935246588720474, 0.16349143782766382, 0.39673809264058946, -0.2641884795181492, -0.29274142806934833, 0.11595312405309717, -0.26183270414670307, -0.14427216743448903, 0.21809511695755646, -0.15693917044942887, 0.07796851686922916, 0.21748585816142926, 0.06880508874322307, -0.008202250118605056, -0.3263200560589515, 0.2079633197366396, 0.14846677932170374, 0.3377609022233325, -0.009596751396505473, 0.11874737513911289, 0.030876812743372284, -0.015677103966784973, 0.03588628376989315, -0.033380497116771345, 0.13290855326340534, 0.29224873323497985, 0.2675982791358062, 0.31394766641703126, -0.32094680692534894, -0.11972890096755388, 0.13788723584487647, 0.1717401333662565, 0.1578269776741763, -0.05054783483941113, -0.2724294709720804, 0.13470379387338957, -0.19801376222555214, -0.0362290605577679, -0.09155710764146836, -0.049054334386407085, -0.08185298283933662, -0.29713995622781414, 0.0433904869739005, 0.06552909549403314, 0.032986448699375615, 0.05152163300954271, -0.259582415332261, 0.03288989175537912, 0.058701297829732844, -0.03972732265416804, 0.07612762049636028, 0.1574427546778073, -0.12153574648982612, -0.24570360189924637, 0.30861232973014313, -0.12648144808508732, -0.1700673923381449, 0.23132037928250307, -0.010323323241512602, -0.21137974257483924, 0.13040533161256462, 0.26636650289098424, 0.12490332252734031, -0.19511912122834474, 0.07893683254890978, -0.003064023699456205, 0.11703551635825231, 0.10985843282348166, -0.032997567778996505, 0.09569240344474868, 0.27715981618772884, 0.14746029692469165, 0.16826299534053155, -0.07172397641018809, -0.0882234027958475, -0.21806389361154288, -0.13807404405088164, -0.1946695088687799, -0.02727568879648364, -0.25367225951034317, -0.23385094969610995, 0.31128818369082484, 0.23713212964260796, 0.12589275825303048, 0.19675341037024432, 0.3376587849731247, 0.04692884866259798, 0.09632922261759329, 0.18775418345224656, 0.15739759106266624, 0.045263465493917465, 0.05903998201150292, -0.16260113353443253, 0.17794313138195625, 0.11208213329352172] |
1,802.08781 | Superpixel based Class-Semantic Texton Occurrences for Natural Roadside
Vegetation Segmentation | Vegetation segmentation from roadside data is a field that has received
relatively little attention in present studies, but can be of great potentials
in a wide range of real-world applications, such as road safety assessment and
vegetation condition monitoring. In this paper, we present a novel approach
that generates class-semantic color-texture textons and aggregates superpixel
based texton occurrences for vegetation segmentation in natural roadside
images. Pixel-level class-semantic textons are first learnt by generating two
individual sets of bag-of-word visual dictionaries from color and filter-bank
texture features separately for each object class using manually cropped
training data. For a testing image, it is first oversegmented into a set of
homogeneous superpixels. The color and texture features of all pixels in each
superpixel are extracted and further mapped to one of the learnt textons using
the nearest distance metric, resulting in a color and a texture texton
occurrence matrix. The color and texture texton occurrences are aggregated
using a linear mixing method over each superpixel and the segmentation is
finally achieved using a simple yet effective majority voting strategy.
Evaluations on two public image datasets from videos collected by the
Department of Transport and Main Roads (DTMR), Queensland, Australia, and a
public roadside grass dataset show high accuracy of the proposed approach. We
also demonstrate the effectiveness of the approach for vegetation segmentation
in real-world scenarios.
| cs.CV | vegetation segmentation from roadside data is a field that has received relatively little attention in present studies but can be of great potentials in a wide range of realworld applications such as road safety assessment and vegetation condition monitoring in this paper we present a novel approach that generates classsemantic colortexture textons and aggregates superpixel based texton occurrences for vegetation segmentation in natural roadside images pixellevel classsemantic textons are first learnt by generating two individual sets of bagofword visual dictionaries from color and filterbank texture features separately for each object class using manually cropped training data for a testing image it is first oversegmented into a set of homogeneous superpixels the color and texture features of all pixels in each superpixel are extracted and further mapped to one of the learnt textons using the nearest distance metric resulting in a color and a texture texton occurrence matrix the color and texture texton occurrences are aggregated using a linear mixing method over each superpixel and the segmentation is finally achieved using a simple yet effective majority voting strategy evaluations on two public image datasets from videos collected by the department of transport and main roads dtmr queensland australia and a public roadside grass dataset show high accuracy of the proposed approach we also demonstrate the effectiveness of the approach for vegetation segmentation in realworld scenarios | [['vegetation', 'segmentation', 'from', 'roadside', 'data', 'is', 'a', 'field', 'that', 'has', 'received', 'relatively', 'little', 'attention', 'in', 'present', 'studies', 'but', 'can', 'be', 'of', 'great', 'potentials', 'in', 'a', 'wide', 'range', 'of', 'realworld', 'applications', 'such', 'as', 'road', 'safety', 'assessment', 'and', 'vegetation', 'condition', 'monitoring', 'in', 'this', 'paper', 'we', 'present', 'a', 'novel', 'approach', 'that', 'generates', 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1,802.08782 | BLEBeacon: A Real-Subject Trial Dataset from Mobile Bluetooth Low Energy
Beacons | The BLEBeacon dataset is a collection of Bluetooth Low Energy (BLE)
advertisement packets/traces generated from BLE beacons carried by people
following their daily routine inside a university building. A network of
Raspberry Pi 3 (RPi)-based edge devices were deployed inside a multi-floor
facility continuously gathering BLE advertisement packets and storing them in a
cloud-based environment. The data were collected during an IRB (Institutional
Review Board forhe Protection of Human Subjects in Research) approved one-month
trial. Each facility occupant/participant was handed a BLE beacon to carry with
him at all times. The focus is on presenting a real-life realization of a
location-aware sensing infrastructure, that can provide insights for smart
sensing platforms, crowd-based applications, building management, and
user-localization frameworks. This work describes and documents the published
BLEBeacon dataset.
| cs.NI | the blebeacon dataset is a collection of bluetooth low energy ble advertisement packetstraces generated from ble beacons carried by people following their daily routine inside a university building a network of raspberry pi 3 rpibased edge devices were deployed inside a multifloor facility continuously gathering ble advertisement packets and storing them in a cloudbased environment the data were collected during an irb institutional review board forhe protection of human subjects in research approved onemonth trial each facility occupantparticipant was handed a ble beacon to carry with him at all times the focus is on presenting a reallife realization of a locationaware sensing infrastructure that can provide insights for smart sensing platforms crowdbased applications building management and userlocalization frameworks this work describes and documents the published blebeacon dataset | [['the', 'blebeacon', 'dataset', 'is', 'a', 'collection', 'of', 'bluetooth', 'low', 'energy', 'ble', 'advertisement', 'packetstraces', 'generated', 'from', 'ble', 'beacons', 'carried', 'by', 'people', 'following', 'their', 'daily', 'routine', 'inside', 'a', 'university', 'building', 'a', 'network', 'of', 'raspberry', 'pi', '3', 'rpibased', 'edge', 'devices', 'were', 'deployed', 'inside', 'a', 'multifloor', 'facility', 'continuously', 'gathering', 'ble', 'advertisement', 'packets', 'and', 'storing', 'them', 'in', 'a', 'cloudbased', 'environment', 'the', 'data', 'were', 'collected', 'during', 'an', 'irb', 'institutional', 'review', 'board', 'forhe', 'protection', 'of', 'human', 'subjects', 'in', 'research', 'approved', 'onemonth', 'trial', 'each', 'facility', 'occupantparticipant', 'was', 'handed', 'a', 'ble', 'beacon', 'to', 'carry', 'with', 'him', 'at', 'all', 'times', 'the', 'focus', 'is', 'on', 'presenting', 'a', 'reallife', 'realization', 'of', 'a', 'locationaware', 'sensing', 'infrastructure', 'that', 'can', 'provide', 'insights', 'for', 'smart', 'sensing', 'platforms', 'crowdbased', 'applications', 'building', 'management', 'and', 'userlocalization', 'frameworks', 'this', 'work', 'describes', 'and', 'documents', 'the', 'published', 'blebeacon', 'dataset']] | [-0.17103832760585647, 0.0984922428936746, -0.0564679555730739, 0.00030134878664587935, -0.1331097527698148, -0.23170557564978178, 0.10191504417841012, 0.407110948767513, -0.17596149797318503, -0.3532527328391249, 0.13947313408716583, -0.3820864419452846, -0.10966996584708492, 0.22437188241165132, -0.12224159426211069, 0.031917094509117304, 0.10449913564370945, 0.05008779666216772, 0.06410130725513834, -0.2661670692730695, 0.19282456170767545, 0.11003780865576118, 0.36218336515594274, 0.04488444038482461, 0.07699089956101185, 0.025620463210119244, -0.0626909515626418, -0.09366334386577364, -0.07264848980151631, 0.14628237439707542, 0.37552380628573395, 0.24512982349454734, 0.3642926354087346, -0.471933596363912, -0.1778667380179589, 0.05432095971967404, 0.10251186088426038, 0.02546772112546023, -0.1053460097842617, -0.37693164418451486, 0.09338670262756447, -0.26043615882129717, -0.09416644572617466, -0.009760309134920438, 0.030592534349610408, 0.025957062762851518, -0.23210491645537937, -0.09565043528564274, -0.10658873577291766, 0.140753219317412, -0.06578402100676613, -0.11617745730327442, 0.01697004458401352, 0.23091399538485954, 0.006562365401381006, 0.006854370048192019, 0.2398022480503035, -0.13936650363611988, -0.13899606312819135, 0.37226288549766956, 0.05956572936847806, -0.07873021002548436, 0.13046488275867887, -0.01964536969317123, -0.11990703752962872, 0.05670275424296657, 0.256517577561317, 0.057773866180408126, -0.223158857311743, 0.06499622610936058, -0.024992489667298893, 0.17130253241824298, 0.09447905554746588, -0.01216719241735215, 0.18567742591646189, 0.2512691277622556, 0.08406793329243858, 0.07363331832457334, -0.08347108881401558, -0.018120600706121575, -0.22621622624186177, -0.17254710733735312, -0.1987627402462143, 0.04474156658592013, 0.005789914371446988, -0.08783677976268033, 0.3859553679397019, 0.1453536408371292, 0.11986111259708802, -0.0001961194541460524, 0.3699649918358773, -0.05213352467787142, 0.10968725036558075, 0.1111962093040347, 0.09763327821080263, -0.08097966201166855, 0.25834961706229176, -0.08549894295429113, 0.03003678431462807, -0.014959011418977753] |
1,802.08783 | Making the PT symmetry unbreakable | It is well known that typical PT-symmetric systems suffer symmetry breaking
when the strength of the gain-loss terms exceeds a certain critical value. We
present a summary of recently published and newly produced results which
demonstrate various possibilities of extending the PT symmetry to arbitrarily
large values of the gain-loss coefficient. First, we recapitulate the analysis
which demonstrates a possibility of the restoration of the PT symmetry and,
moreover, complete avoidance of the breaking in a photonic waveguide of a
subwavelength width. The analysis is based on the Maxwell's equations, instead
of the usual paraxial approximation. Next, we review a recently proposed
possibility to construct stable one-dimensional (1D) PT-symmetric solitons in a
paraxial model with arbitrarily large values of the gain-loss coefficient,
provided that the self-trapping of the solitons is induced by self-defocusing
cubic nonlinearity, whose local strength grows sufficiently fast from the
center to periphery. The model admits a particular analytical solution for the
fundamental soliton, and provides full stability for families of fundamental
and dipole solitons. Finally, we report new results for unbreakable
PT-symmetric solitons in 2D extensions of the 1D model: one with a quasi-1D
modulation profile of the local gain-loss coefficient, and another with the
fully-2D modulation. These settings admit particular analytical solutions for
2D solitons, while generic soliton families are found in a numerical form. The
quasi-1D modulation profile gives rise to a stable family of single-peak 2D
solitons. The soliton stability in the full 2D model is possible if the local
gain-loss term is subject to spatial confinement.
| physics.optics nlin.PS | it is well known that typical ptsymmetric systems suffer symmetry breaking when the strength of the gainloss terms exceeds a certain critical value we present a summary of recently published and newly produced results which demonstrate various possibilities of extending the pt symmetry to arbitrarily large values of the gainloss coefficient first we recapitulate the analysis which demonstrates a possibility of the restoration of the pt symmetry and moreover complete avoidance of the breaking in a photonic waveguide of a subwavelength width the analysis is based on the maxwells equations instead of the usual paraxial approximation next we review a recently proposed possibility to construct stable onedimensional 1d ptsymmetric solitons in a paraxial model with arbitrarily large values of the gainloss coefficient provided that the selftrapping of the solitons is induced by selfdefocusing cubic nonlinearity whose local strength grows sufficiently fast from the center to periphery the model admits a particular analytical solution for the fundamental soliton and provides full stability for families of fundamental and dipole solitons finally we report new results for unbreakable ptsymmetric solitons in 2d extensions of the 1d model one with a quasi1d modulation profile of the local gainloss coefficient and another with the fully2d modulation these settings admit particular analytical solutions for 2d solitons while generic soliton families are found in a numerical form the quasi1d modulation profile gives rise to a stable family of singlepeak 2d solitons the soliton stability in the full 2d model is possible if the local gainloss term is subject to spatial confinement | [['it', 'is', 'well', 'known', 'that', 'typical', 'ptsymmetric', 'systems', 'suffer', 'symmetry', 'breaking', 'when', 'the', 'strength', 'of', 'the', 'gainloss', 'terms', 'exceeds', 'a', 'certain', 'critical', 'value', 'we', 'present', 'a', 'summary', 'of', 'recently', 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1,802.08784 | Facial Expression Analysis under Partial Occlusion: A Survey | Automatic machine-based Facial Expression Analysis (FEA) has made substantial
progress in the past few decades driven by its importance for applications in
psychology, security, health, entertainment and human computer interaction. The
vast majority of completed FEA studies are based on non-occluded faces
collected in a controlled laboratory environment. Automatic expression
recognition tolerant to partial occlusion remains less understood, particularly
in real-world scenarios. In recent years, efforts investigating techniques to
handle partial occlusion for FEA have seen an increase. The context is right
for a comprehensive perspective of these developments and the state of the art
from this perspective. This survey provides such a comprehensive review of
recent advances in dataset creation, algorithm development, and investigations
of the effects of occlusion critical for robust performance in FEA systems. It
outlines existing challenges in overcoming partial occlusion and discusses
possible opportunities in advancing the technology. To the best of our
knowledge, it is the first FEA survey dedicated to occlusion and aimed at
promoting better informed and benchmarked future work.
| cs.CV | automatic machinebased facial expression analysis fea has made substantial progress in the past few decades driven by its importance for applications in psychology security health entertainment and human computer interaction the vast majority of completed fea studies are based on nonoccluded faces collected in a controlled laboratory environment automatic expression recognition tolerant to partial occlusion remains less understood particularly in realworld scenarios in recent years efforts investigating techniques to handle partial occlusion for fea have seen an increase the context is right for a comprehensive perspective of these developments and the state of the art from this perspective this survey provides such a comprehensive review of recent advances in dataset creation algorithm development and investigations of the effects of occlusion critical for robust performance in fea systems it outlines existing challenges in overcoming partial occlusion and discusses possible opportunities in advancing the technology to the best of our knowledge it is the first fea survey dedicated to occlusion and aimed at promoting better informed and benchmarked future work | [['automatic', 'machinebased', 'facial', 'expression', 'analysis', 'fea', 'has', 'made', 'substantial', 'progress', 'in', 'the', 'past', 'few', 'decades', 'driven', 'by', 'its', 'importance', 'for', 'applications', 'in', 'psychology', 'security', 'health', 'entertainment', 'and', 'human', 'computer', 'interaction', 'the', 'vast', 'majority', 'of', 'completed', 'fea', 'studies', 'are', 'based', 'on', 'nonoccluded', 'faces', 'collected', 'in', 'a', 'controlled', 'laboratory', 'environment', 'automatic', 'expression', 'recognition', 'tolerant', 'to', 'partial', 'occlusion', 'remains', 'less', 'understood', 'particularly', 'in', 'realworld', 'scenarios', 'in', 'recent', 'years', 'efforts', 'investigating', 'techniques', 'to', 'handle', 'partial', 'occlusion', 'for', 'fea', 'have', 'seen', 'an', 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1,802.08785 | Steady and Stable: Numerical Investigations of Nonlinear Partial
Differential Equations | A nonlinear partial differential equation is a nonlinear relationship between
an unknown function and how it changes due to two or more input variables. A
numerical method reduces such an equation to arithmetic for quick
visualization, but this can be misleading if the method is not developed and
operated carefully due to numerical oscillations, instabilities, and other
artifacts of the programmed solution. This chapter provides a friendly
introduction to error analysis of numerical methods for nonlinear partial
differential equations along with modern approaches to visually demonstrate
analytical results with confidence. These techniques are illustrated through
several detailed examples with a relevant governing equation, and lead to open
questions suggested for undergraduate-accessible research.
| math.HO | a nonlinear partial differential equation is a nonlinear relationship between an unknown function and how it changes due to two or more input variables a numerical method reduces such an equation to arithmetic for quick visualization but this can be misleading if the method is not developed and operated carefully due to numerical oscillations instabilities and other artifacts of the programmed solution this chapter provides a friendly introduction to error analysis of numerical methods for nonlinear partial differential equations along with modern approaches to visually demonstrate analytical results with confidence these techniques are illustrated through several detailed examples with a relevant governing equation and lead to open questions suggested for undergraduateaccessible research | [['a', 'nonlinear', 'partial', 'differential', 'equation', 'is', 'a', 'nonlinear', 'relationship', 'between', 'an', 'unknown', 'function', 'and', 'how', 'it', 'changes', 'due', 'to', 'two', 'or', 'more', 'input', 'variables', 'a', 'numerical', 'method', 'reduces', 'such', 'an', 'equation', 'to', 'arithmetic', 'for', 'quick', 'visualization', 'but', 'this', 'can', 'be', 'misleading', 'if', 'the', 'method', 'is', 'not', 'developed', 'and', 'operated', 'carefully', 'due', 'to', 'numerical', 'oscillations', 'instabilities', 'and', 'other', 'artifacts', 'of', 'the', 'programmed', 'solution', 'this', 'chapter', 'provides', 'a', 'friendly', 'introduction', 'to', 'error', 'analysis', 'of', 'numerical', 'methods', 'for', 'nonlinear', 'partial', 'differential', 'equations', 'along', 'with', 'modern', 'approaches', 'to', 'visually', 'demonstrate', 'analytical', 'results', 'with', 'confidence', 'these', 'techniques', 'are', 'illustrated', 'through', 'several', 'detailed', 'examples', 'with', 'a', 'relevant', 'governing', 'equation', 'and', 'lead', 'to', 'open', 'questions', 'suggested', 'for', 'undergraduateaccessible', 'research']] | [-0.06865681679856435, 0.03334801880299818, -0.1051629083105237, 0.08643522162854604, -0.1449829808662872, -0.170509116510181, 0.02754799014603434, 0.34998813573573084, -0.3025442559590882, -0.3217834254206569, 0.13691526520493869, -0.2935128306281996, -0.22098570076438287, 0.2810563209386928, -0.0740354093890805, 0.09880482586654457, 0.08456938626110419, -0.02354901787397024, -0.09404267999340296, -0.25561681468467656, 0.2876814989087818, 0.04303717260827889, 0.22819410866076076, 0.05649131906915221, 0.1337986400438502, -0.05356478331576999, -0.08791969578528593, 0.032232517489811055, -0.13402567662391496, 0.10293501458160405, 0.3015086908598204, 0.11860846058960568, 0.32448685702842633, -0.4611158003057311, -0.21497042276602876, 0.028423783153312298, 0.16689255349086346, 0.15559291060980376, -0.07216162664822601, -0.2965926546393684, 0.07593530442918071, -0.14825745135847782, -0.15385882480086777, -0.14427537114048997, 0.010430825605720014, 0.030901393827957077, -0.29123405713235606, 0.07737686232504276, 0.039036280897169096, 0.05931012045910901, -0.03814200469977558, -0.07851351665853111, 0.013868150154447503, 0.08405712116216016, 0.05491935714517158, 0.007817840497303117, 0.08226849943785383, -0.10250768783871454, -0.10200652453276488, 0.35448453526708995, -0.02072355503568778, -0.30229112693855353, 0.20132700775473103, -0.05685677230022512, -0.10128107862672894, 0.14727279573242674, 0.17377266902392646, 0.10978405169202937, -0.17856339554864545, 0.013747116401875476, 0.036130601112302894, 0.20083851976894881, 0.03339240620718212, -0.025076475892182405, 0.1589456094546361, 0.16634804884450058, 0.05126774994874591, 0.11090678759579975, 0.006863725235316533, -0.09723576126402025, -0.2958999413090783, -0.12831409164779894, -0.0786283375561103, 0.022592908701873048, -0.043740264058264124, -0.18314416486736354, 0.37386234237199967, 0.18590419850656176, 0.16275575805690373, -0.009288268079897305, 0.3379925149283162, 0.17093479218011773, 0.00970001757587516, 0.041770093851966096, 0.1641211753914569, 0.15666994497784087, 0.1061808689631656, -0.20958373045678846, 0.05496942408609431, 0.04156518396124378] |
1,802.08786 | Syntax-Directed Variational Autoencoder for Structured Data | Deep generative models have been enjoying success in modeling continuous
data. However it remains challenging to capture the representations for
discrete structures with formal grammars and semantics, e.g., computer programs
and molecular structures. How to generate both syntactically and semantically
correct data still remains largely an open problem. Inspired by the theory of
compiler where the syntax and semantics check is done via syntax-directed
translation (SDT), we propose a novel syntax-directed variational autoencoder
(SD-VAE) by introducing stochastic lazy attributes. This approach converts the
offline SDT check into on-the-fly generated guidance for constraining the
decoder. Comparing to the state-of-the-art methods, our approach enforces
constraints on the output space so that the output will be not only
syntactically valid, but also semantically reasonable. We evaluate the proposed
model with applications in programming language and molecules, including
reconstruction and program/molecule optimization. The results demonstrate the
effectiveness in incorporating syntactic and semantic constraints in discrete
generative models, which is significantly better than current state-of-the-art
approaches.
| cs.LG cs.CL | deep generative models have been enjoying success in modeling continuous data however it remains challenging to capture the representations for discrete structures with formal grammars and semantics eg computer programs and molecular structures how to generate both syntactically and semantically correct data still remains largely an open problem inspired by the theory of compiler where the syntax and semantics check is done via syntaxdirected translation sdt we propose a novel syntaxdirected variational autoencoder sdvae by introducing stochastic lazy attributes this approach converts the offline sdt check into onthefly generated guidance for constraining the decoder comparing to the stateoftheart methods our approach enforces constraints on the output space so that the output will be not only syntactically valid but also semantically reasonable we evaluate the proposed model with applications in programming language and molecules including reconstruction and programmolecule optimization the results demonstrate the effectiveness in incorporating syntactic and semantic constraints in discrete generative models which is significantly better than current stateoftheart approaches | [['deep', 'generative', 'models', 'have', 'been', 'enjoying', 'success', 'in', 'modeling', 'continuous', 'data', 'however', 'it', 'remains', 'challenging', 'to', 'capture', 'the', 'representations', 'for', 'discrete', 'structures', 'with', 'formal', 'grammars', 'and', 'semantics', 'eg', 'computer', 'programs', 'and', 'molecular', 'structures', 'how', 'to', 'generate', 'both', 'syntactically', 'and', 'semantically', 'correct', 'data', 'still', 'remains', 'largely', 'an', 'open', 'problem', 'inspired', 'by', 'the', 'theory', 'of', 'compiler', 'where', 'the', 'syntax', 'and', 'semantics', 'check', 'is', 'done', 'via', 'syntaxdirected', 'translation', 'sdt', 'we', 'propose', 'a', 'novel', 'syntaxdirected', 'variational', 'autoencoder', 'sdvae', 'by', 'introducing', 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1,802.08787 | Magnetic properties of Type I and II Weyl Superconductors | Superconductivity was observed in certain range of pressure and chemical
composition in Weyl semi-metals of both the type I and type II (when the Dirac
cone tilt parameter $\kappa >1$). Magnetic properties of these superconductors
are studied on the basis of microscopic phonon mediated pairing model. The
Ginzburg - Landau effective theory for the order parameter is derived using
Gorkov approach and used to determine anisotropic coherence length, the
penetration depth determining the Abrikosov parameter for a layered material
and applied to recent extensive experiments on $% MoTe_{2}$. It is found that
superconductivity is of second kind near the topological transition at $\kappa
=1$, but becomes first kind away from it. For the superconductors of the second
kind the dependence of critical fields $H_{c2}$ and $H_{c1}$ on the tilt
parameter $\kappa $ (governed by pressure) is compared with the experiments.
Strength of thermal fluctuations is estimated and its is found that they are
strong enough to cause Abrikosov vortex lattice melting near $H_{c2}$. The
melting line is calculated and is consistent with experiments provided the
fluctuations are three dimensional in the type I phase (large pressure) and two
dimensional in the type II phase (small pressure).
| cond-mat.supr-con | superconductivity was observed in certain range of pressure and chemical composition in weyl semimetals of both the type i and type ii when the dirac cone tilt parameter kappa 1 magnetic properties of these superconductors are studied on the basis of microscopic phonon mediated pairing model the ginzburg landau effective theory for the order parameter is derived using gorkov approach and used to determine anisotropic coherence length the penetration depth determining the abrikosov parameter for a layered material and applied to recent extensive experiments on mote_2 it is found that superconductivity is of second kind near the topological transition at kappa 1 but becomes first kind away from it for the superconductors of the second kind the dependence of critical fields h_c2 and h_c1 on the tilt parameter kappa governed by pressure is compared with the experiments strength of thermal fluctuations is estimated and its is found that they are strong enough to cause abrikosov vortex lattice melting near h_c2 the melting line is calculated and is consistent with experiments provided the fluctuations are three dimensional in the type i phase large pressure and two dimensional in the type ii phase small pressure | [['superconductivity', 'was', 'observed', 'in', 'certain', 'range', 'of', 'pressure', 'and', 'chemical', 'composition', 'in', 'weyl', 'semimetals', 'of', 'both', 'the', 'type', 'i', 'and', 'type', 'ii', 'when', 'the', 'dirac', 'cone', 'tilt', 'parameter', 'kappa', '1', 'magnetic', 'properties', 'of', 'these', 'superconductors', 'are', 'studied', 'on', 'the', 'basis', 'of', 'microscopic', 'phonon', 'mediated', 'pairing', 'model', 'the', 'ginzburg', 'landau', 'effective', 'theory', 'for', 'the', 'order', 'parameter', 'is', 'derived', 'using', 'gorkov', 'approach', 'and', 'used', 'to', 'determine', 'anisotropic', 'coherence', 'length', 'the', 'penetration', 'depth', 'determining', 'the', 'abrikosov', 'parameter', 'for', 'a', 'layered', 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1,802.08788 | Improved Regularity Model-based EDA for Many-objective Optimization | The performance of multi-objective evolutionary algorithms deteriorates
appreciably in solving many-objective optimization problems which encompass
more than three objectives. One of the known rationales is the loss of
selection pressure which leads to the selected parents not generating promising
offspring towards Pareto-optimal front with diversity. Estimation of
distribution algorithms sample new solutions with a probabilistic model built
from the statistics extracting over the existing solutions so as to mitigate
the adverse impact of genetic operators. In this paper, an improved
regularity-based estimation of distribution algorithm is proposed to
effectively tackle unconstrained many-objective optimization problems. In the
proposed algorithm, \emph{diversity repairing mechanism} is utilized to mend
the areas where need non-dominated solutions with a closer proximity to the
Pareto-optimal front. Then \emph{favorable solutions} are generated by the
model built from the regularity of the solutions surrounding a group of
representatives. These two steps collectively enhance the selection pressure
which gives rise to the superior convergence of the proposed algorithm. In
addition, dimension reduction technique is employed in the decision space to
speed up the estimation search of the proposed algorithm. Finally, by assigning
the Pareto-optimal solutions to the uniformly distributed reference vectors, a
set of solutions with excellent diversity and convergence is obtained. To
measure the performance, NSGA-III, GrEA, MOEA/D, HypE, MBN-EDA, and RM-MEDA are
selected to perform comparison experiments over DTLZ and DTLZ$^-$ test suites
with $3$-, $5$-, $8$-, $10$-, and $15$-objective. Experimental results
quantified by the selected performance metrics reveal that the proposed
algorithm shows considerable competitiveness in addressing unconstrained
many-objective optimization problems.
| cs.NE | the performance of multiobjective evolutionary algorithms deteriorates appreciably in solving manyobjective optimization problems which encompass more than three objectives one of the known rationales is the loss of selection pressure which leads to the selected parents not generating promising offspring towards paretooptimal front with diversity estimation of distribution algorithms sample new solutions with a probabilistic model built from the statistics extracting over the existing solutions so as to mitigate the adverse impact of genetic operators in this paper an improved regularitybased estimation of distribution algorithm is proposed to effectively tackle unconstrained manyobjective optimization problems in the proposed algorithm emphdiversity repairing mechanism is utilized to mend the areas where need nondominated solutions with a closer proximity to the paretooptimal front then emphfavorable solutions are generated by the model built from the regularity of the solutions surrounding a group of representatives these two steps collectively enhance the selection pressure which gives rise to the superior convergence of the proposed algorithm in addition dimension reduction technique is employed in the decision space to speed up the estimation search of the proposed algorithm finally by assigning the paretooptimal solutions to the uniformly distributed reference vectors a set of solutions with excellent diversity and convergence is obtained to measure the performance nsgaiii grea moead hype mbneda and rmmeda are selected to perform comparison experiments over dtlz and dtlz test suites with 3 5 8 10 and 15objective experimental results quantified by the selected performance metrics reveal that the proposed algorithm shows considerable competitiveness in addressing unconstrained manyobjective optimization problems | [['the', 'performance', 'of', 'multiobjective', 'evolutionary', 'algorithms', 'deteriorates', 'appreciably', 'in', 'solving', 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1,802.08789 | Misclassified B stars in the Kepler field | Stellar fundamental parameters are important in the asteroseismic study of
Kepler light curves. However, the most used estimates in the Kepler Input
Catalog (KIC) are not accurate enough for hot stars. Using a sample of B stars
from the Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST)
spectral survey, we confirmed the systematic underestimation in KIC effective
temperature and overestimation in KIC surface gravity. The good agreement
between LAMOST and other follow-up observations proved the accuracy of
effective temperature and surface gravity of B stars derived from LAMOST
low-resolution spectra. By searching through LAMOST data, we found four
misclassified main-sequence B stars in the Kepler field, which had been
previously classified as A-type variables. We present spectroscopic and
detailed frequency analysis of these four stars based on LAMOST spectra and
Kepler photometry.
| astro-ph.SR | stellar fundamental parameters are important in the asteroseismic study of kepler light curves however the most used estimates in the kepler input catalog kic are not accurate enough for hot stars using a sample of b stars from the large sky area multiobject fiber spectroscopic telescope lamost spectral survey we confirmed the systematic underestimation in kic effective temperature and overestimation in kic surface gravity the good agreement between lamost and other followup observations proved the accuracy of effective temperature and surface gravity of b stars derived from lamost lowresolution spectra by searching through lamost data we found four misclassified mainsequence b stars in the kepler field which had been previously classified as atype variables we present spectroscopic and detailed frequency analysis of these four stars based on lamost spectra and kepler photometry | [['stellar', 'fundamental', 'parameters', 'are', 'important', 'in', 'the', 'asteroseismic', 'study', 'of', 'kepler', 'light', 'curves', 'however', 'the', 'most', 'used', 'estimates', 'in', 'the', 'kepler', 'input', 'catalog', 'kic', 'are', 'not', 'accurate', 'enough', 'for', 'hot', 'stars', 'using', 'a', 'sample', 'of', 'b', 'stars', 'from', 'the', 'large', 'sky', 'area', 'multiobject', 'fiber', 'spectroscopic', 'telescope', 'lamost', 'spectral', 'survey', 'we', 'confirmed', 'the', 'systematic', 'underestimation', 'in', 'kic', 'effective', 'temperature', 'and', 'overestimation', 'in', 'kic', 'surface', 'gravity', 'the', 'good', 'agreement', 'between', 'lamost', 'and', 'other', 'followup', 'observations', 'proved', 'the', 'accuracy', 'of', 'effective', 'temperature', 'and', 'surface', 'gravity', 'of', 'b', 'stars', 'derived', 'from', 'lamost', 'lowresolution', 'spectra', 'by', 'searching', 'through', 'lamost', 'data', 'we', 'found', 'four', 'misclassified', 'mainsequence', 'b', 'stars', 'in', 'the', 'kepler', 'field', 'which', 'had', 'been', 'previously', 'classified', 'as', 'atype', 'variables', 'we', 'present', 'spectroscopic', 'and', 'detailed', 'frequency', 'analysis', 'of', 'these', 'four', 'stars', 'based', 'on', 'lamost', 'spectra', 'and', 'kepler', 'photometry']] | [-0.06094342259915941, 0.10851137308319184, -0.11259548538928908, 0.0888151716560859, -0.18499393474705744, -0.07881132970832175, 0.11864389858568426, 0.37575966348363593, -0.09722916658872484, -0.41636755636357, 0.05401373005941312, -0.3592442108007769, -0.07487203866582025, 0.315067093695909, -0.14289797041000743, 0.056776960067288725, 0.21206558851885016, -0.06109026001738102, 0.007534463036184509, -0.33616949598107376, 0.27386589550720103, 0.037176139525730505, 0.14600469508546998, -0.16575997671338194, 0.00676874476784812, -0.09117199734177892, -0.14577695459240314, -0.011853669566776123, -0.20245698185030822, 0.03872312056465131, 0.2980768071526351, 0.12821282807801088, 0.17714040847071869, -0.24059519214020067, -0.23342125833909394, 0.03465727691990182, 0.21401415934440482, 0.040040788873077625, -0.08050921483399262, -0.2871563505420151, 0.06342746083100792, -0.15424210203557528, -0.14521300883356933, -0.059722309101330626, 0.06754726555046739, 0.0662118450546228, -0.20379466272424906, 0.05004506086565512, -0.027756736067010825, 0.27106248604303057, -0.20497313024878333, -0.21993302096344644, -0.11116651908554503, 0.13188183019784364, 0.00822393126277761, 0.07091122941346839, 0.06142692997430762, -0.0984572944598216, 0.08353116923491612, 0.3860647405046179, -0.1536248765857608, 0.047291407089990876, 0.12667436690323733, -0.18078815475614232, -0.19279448737065788, 0.08797956409839433, 0.1829374770168215, 0.17510124268345395, -0.23195468791263094, -0.010984330006370632, 0.038229410204979955, 0.21343498818301174, 0.06648666418693734, 0.0779993305697479, 0.34426865511517407, 0.11048067168501968, -0.009912556917477174, 0.052893700947275975, -0.33310257766755635, 0.004552413159134713, -0.25063880147751083, -0.058060782027468755, -0.15313481219874864, 0.04734892883811192, -0.12200113011567737, -0.11725957288801896, 0.36300346810568235, 0.1020425276153467, 0.16086083596438225, -0.010823270328039529, 0.3261927095926226, 0.029555983025630034, 0.12703072949917635, 0.08301552237103213, 0.37406699180913466, 0.22297755813854042, 0.09416174645549759, -0.24479161647755202, -0.004108195655245447, 0.004835123491692216] |
1,802.0879 | Spatially Constrained Location Prior for Scene Parsing | Semantic context is an important and useful cue for scene parsing in
complicated natural images with a substantial amount of variations in objects
and the environment. This paper proposes Spatially Constrained Location Prior
(SCLP) for effective modelling of global and local semantic context in the
scene in terms of inter-class spatial relationships. Unlike existing studies
focusing on either relative or absolute location prior of objects, the SCLP
effectively incorporates both relative and absolute location priors by
calculating object co-occurrence frequencies in spatially constrained image
blocks. The SCLP is general and can be used in conjunction with various visual
feature-based prediction models, such as Artificial Neural Networks and Support
Vector Machine (SVM), to enforce spatial contextual constraints on class
labels. Using SVM classifiers and a linear regression model, we demonstrate
that the incorporation of SCLP achieves superior performance compared to the
state-of-the-art methods on the Stanford background and SIFT Flow datasets.
| cs.CV | semantic context is an important and useful cue for scene parsing in complicated natural images with a substantial amount of variations in objects and the environment this paper proposes spatially constrained location prior sclp for effective modelling of global and local semantic context in the scene in terms of interclass spatial relationships unlike existing studies focusing on either relative or absolute location prior of objects the sclp effectively incorporates both relative and absolute location priors by calculating object cooccurrence frequencies in spatially constrained image blocks the sclp is general and can be used in conjunction with various visual featurebased prediction models such as artificial neural networks and support vector machine svm to enforce spatial contextual constraints on class labels using svm classifiers and a linear regression model we demonstrate that the incorporation of sclp achieves superior performance compared to the stateoftheart methods on the stanford background and sift flow datasets | [['semantic', 'context', 'is', 'an', 'important', 'and', 'useful', 'cue', 'for', 'scene', 'parsing', 'in', 'complicated', 'natural', 'images', 'with', 'a', 'substantial', 'amount', 'of', 'variations', 'in', 'objects', 'and', 'the', 'environment', 'this', 'paper', 'proposes', 'spatially', 'constrained', 'location', 'prior', 'sclp', 'for', 'effective', 'modelling', 'of', 'global', 'and', 'local', 'semantic', 'context', 'in', 'the', 'scene', 'in', 'terms', 'of', 'interclass', 'spatial', 'relationships', 'unlike', 'existing', 'studies', 'focusing', 'on', 'either', 'relative', 'or', 'absolute', 'location', 'prior', 'of', 'objects', 'the', 'sclp', 'effectively', 'incorporates', 'both', 'relative', 'and', 'absolute', 'location', 'priors', 'by', 'calculating', 'object', 'cooccurrence', 'frequencies', 'in', 'spatially', 'constrained', 'image', 'blocks', 'the', 'sclp', 'is', 'general', 'and', 'can', 'be', 'used', 'in', 'conjunction', 'with', 'various', 'visual', 'featurebased', 'prediction', 'models', 'such', 'as', 'artificial', 'neural', 'networks', 'and', 'support', 'vector', 'machine', 'svm', 'to', 'enforce', 'spatial', 'contextual', 'constraints', 'on', 'class', 'labels', 'using', 'svm', 'classifiers', 'and', 'a', 'linear', 'regression', 'model', 'we', 'demonstrate', 'that', 'the', 'incorporation', 'of', 'sclp', 'achieves', 'superior', 'performance', 'compared', 'to', 'the', 'stateoftheart', 'methods', 'on', 'the', 'stanford', 'background', 'and', 'sift', 'flow', 'datasets']] | [-0.025879458487033845, -0.03325781843692918, -0.048751030069154995, 0.09608365425917631, -0.11768590354981522, -0.15482619006186724, 0.010003956996370108, 0.4705451410512129, -0.2667992560317119, -0.35753696496171566, 0.07969213304885973, -0.2607956041147311, -0.1492486086813733, 0.1532255183874319, -0.1622015670593828, 0.09510447590999926, 0.10740752048790454, 0.07330387230962515, -0.08597413801743338, -0.23524107956987184, 0.31096916041492173, 0.08179343480616807, 0.3625575240639349, -0.002927628591035803, 0.13389885605623325, 0.019493997609242798, -0.09533650464999179, 0.044465790273000794, -0.025589246714419762, 0.20498540633464776, 0.3010336030859859, 0.1793325865563626, 0.25518859062033394, -0.3916783905029297, -0.2757450585750242, 0.10458621734132369, 0.1309934405128782, 0.09030571835513304, 0.0025465087895281614, -0.37734810218214987, 0.05118813138299932, -0.12107376168792447, 0.030876934016123413, -0.12813776335834215, -0.003053016916383058, 0.000936684833917146, -0.2934623347346981, 0.10691606494501078, 0.09514078295993386, 0.10308752046742788, -0.1038513895465682, -0.13076309183534857, 0.004695315324546148, 0.14681894752041746, 0.017386415603104978, 0.047198633967588347, 0.14918741540362437, -0.22091562527387093, -0.13044709880215427, 0.38690800363818806, -0.1025143566293021, -0.2435642099302883, 0.24031358249795934, -0.026888488507829607, -0.12566553067260733, 0.0680595259865125, 0.25541312446507314, 0.12759756980483264, -0.1567574558686465, 0.03521018598228693, -0.03842099453943471, 0.19650260190169017, 0.07167003143578768, 0.02602623862408412, 0.22653476198669523, 0.23249901339411735, 0.031275654928758743, 0.10306842167008047, -0.17170637509048295, -0.051902773330609005, -0.20376175318844617, -0.07975335190693537, -0.14859683433858056, -0.10302770781796426, -0.163834568435171, -0.13394970523018856, 0.38831605868414043, 0.23049939440873762, 0.219141114714245, 0.07962466545558224, 0.3376877606163422, 0.03301353098164934, 0.11512741020570198, 0.08182037910136084, 0.18368104007095098, 0.04903855549016346, 0.10363480970923168, -0.17097897811637572, 0.11730567396307985, 0.07258929745604595] |
1,802.08791 | Erd\H{o}s-Burgess constant of commutative semigroups | Let $\mathcal{S}$ be a nonempty commutative semigroup written additively. An
element $e$ of $\mathcal{S}$ is said to be idempotent if $e+e=e$. The
Erd\H{o}s-Burgess constant of the semigroup $\mathcal{S}$ is defined as the
smallest positive integer $\ell$ such that any $\mathcal{S}$-valued sequence
$T$ of length $\ell$ contain a nonempty subsequence the sum of whose terms is
an idempotent of $\mathcal{S}$. We make a study of ${\rm I}(\mathcal{S})$ when
$\mathcal{S}$ is a direct product of arbitrarily many of cyclic semigroups. We
give the necessary and sufficient conditions such that ${\rm I}(\mathcal{S})$
is finite, and in particular, we obtain sharp bounds of ${\rm I}(\mathcal{S})$
in case ${\rm I}(\mathcal{S})$ is finite, and determine the precise values of
${\rm I}(\mathcal{S})$ in some cases which unifies some well known results on
the precise values of Davenport constant in the setting of commutative
semigroups.
| math.CO math.NT | let mathcals be a nonempty commutative semigroup written additively an element e of mathcals is said to be idempotent if eee the erdhosburgess constant of the semigroup mathcals is defined as the smallest positive integer ell such that any mathcalsvalued sequence t of length ell contain a nonempty subsequence the sum of whose terms is an idempotent of mathcals we make a study of rm imathcals when mathcals is a direct product of arbitrarily many of cyclic semigroups we give the necessary and sufficient conditions such that rm imathcals is finite and in particular we obtain sharp bounds of rm imathcals in case rm imathcals is finite and determine the precise values of rm imathcals in some cases which unifies some well known results on the precise values of davenport constant in the setting of commutative semigroups | [['let', 'mathcals', 'be', 'a', 'nonempty', 'commutative', 'semigroup', 'written', 'additively', 'an', 'element', 'e', 'of', 'mathcals', 'is', 'said', 'to', 'be', 'idempotent', 'if', 'eee', 'the', 'erdhosburgess', 'constant', 'of', 'the', 'semigroup', 'mathcals', 'is', 'defined', 'as', 'the', 'smallest', 'positive', 'integer', 'ell', 'such', 'that', 'any', 'mathcalsvalued', 'sequence', 't', 'of', 'length', 'ell', 'contain', 'a', 'nonempty', 'subsequence', 'the', 'sum', 'of', 'whose', 'terms', 'is', 'an', 'idempotent', 'of', 'mathcals', 'we', 'make', 'a', 'study', 'of', 'rm', 'imathcals', 'when', 'mathcals', 'is', 'a', 'direct', 'product', 'of', 'arbitrarily', 'many', 'of', 'cyclic', 'semigroups', 'we', 'give', 'the', 'necessary', 'and', 'sufficient', 'conditions', 'such', 'that', 'rm', 'imathcals', 'is', 'finite', 'and', 'in', 'particular', 'we', 'obtain', 'sharp', 'bounds', 'of', 'rm', 'imathcals', 'in', 'case', 'rm', 'imathcals', 'is', 'finite', 'and', 'determine', 'the', 'precise', 'values', 'of', 'rm', 'imathcals', 'in', 'some', 'cases', 'which', 'unifies', 'some', 'well', 'known', 'results', 'on', 'the', 'precise', 'values', 'of', 'davenport', 'constant', 'in', 'the', 'setting', 'of', 'commutative', 'semigroups']] | [-0.18592586968374877, 0.1590663247850042, -0.018812524990233427, 0.05727956638935104, -0.05293203011559158, -0.13660637236556367, -0.01763043093481375, 0.31308026343364925, -0.3413231981034829, -0.1676369783715071, 0.1302054555477191, -0.29514017482014265, -0.056539359372383094, 0.21836589620900615, -0.09222938391929561, 0.006848178710831288, 0.04480883341816747, 0.16047628687845325, -0.08758741502157029, -0.23421600650181063, 0.31046515593633933, -0.03676319606465233, 0.1598129663205541, 0.07837181241826757, 0.08510277435775873, -0.036304354013683385, -0.026228568809421954, 0.02521717400206987, -0.23021229754104913, 0.04414833970847266, 0.31578158638191833, 0.13966949215254693, 0.2483087486573769, -0.354483229923062, -0.10040767002946642, 0.2575119893239496, 0.12450615844949532, -0.034375686625614486, 0.014698720207567984, -0.2237968918069622, 0.1785279800533317, -0.16659971817845123, -0.0963917466784444, -0.04802968099658542, 0.13455136740402154, 0.0016099578026435612, -0.3615307319114971, 0.011392725973992664, 0.12422028863709463, 0.08570943170514725, -0.014252826939542395, -0.15938134567284792, -0.03724982420509845, 0.09722563689031043, 0.0007849706042393603, 0.058112554445602545, 0.04168162147706265, -0.022028799034374328, -0.09256684605497867, 0.3742907567716697, -0.10476688914315994, -0.227924844933167, 0.082366252084338, -0.1941957532784299, -0.11491676369298469, 0.11910590623943683, 0.06419361436136943, 0.21243839683216614, -0.04790207947596226, 0.23520163871794733, -0.18138858876751757, 0.12295020882533315, 0.07796909545740004, 0.0724879202350755, 0.12394280804266386, 0.06748764139517922, 0.12539856072715552, 0.1311802850858144, 0.04095190262716428, 0.034426715057206285, -0.408192147158415, -0.18293996662011042, -0.15172829022904968, 0.17103801050346673, -0.11690080943309808, -0.2011463719082873, 0.32535575215896484, 0.06894801780649953, 0.2067114153144169, 0.09044916266044739, 0.18587425822282538, 0.1115529733707423, 0.03245425851577345, 0.0838175176142934, 0.05697752738770385, 0.20149722345847884, -0.07294583531192449, -0.18755843525286764, 0.04457196974373587, 0.16107882986783378] |
1,802.08792 | IGD Indicator-based Evolutionary Algorithm for Many-objective
Optimization Problems | Inverted Generational Distance (IGD) has been widely considered as a reliable
performance indicator to concurrently quantify the convergence and diversity of
multi- and many-objective evolutionary algorithms. In this paper, an IGD
indicator-based evolutionary algorithm for solving many-objective optimization
problems (MaOPs) has been proposed. Specifically, the IGD indicator is employed
in each generation to select the solutions with favorable convergence and
diversity. In addition, a computationally efficient dominance comparison method
is designed to assign the rank values of solutions along with three newly
proposed proximity distance assignments. Based on these two designs, the
solutions are selected from a global view by linear assignment mechanism to
concern the convergence and diversity simultaneously. In order to facilitate
the accuracy of the sampled reference points for the calculation of IGD
indicator, we also propose an efficient decomposition-based nadir point
estimation method for constructing the Utopian Pareto front which is regarded
as the best approximate Pareto front for real-world MaOPs at the early stage of
the evolution. To evaluate the performance, a series of experiments is
performed on the proposed algorithm against a group of selected
state-of-the-art many-objective optimization algorithms over optimization
problems with $8$-, $15$-, and $20$-objective. Experimental results measured by
the chosen performance metrics indicate that the proposed algorithm is very
competitive in addressing MaOPs.
| cs.NE | inverted generational distance igd has been widely considered as a reliable performance indicator to concurrently quantify the convergence and diversity of multi and manyobjective evolutionary algorithms in this paper an igd indicatorbased evolutionary algorithm for solving manyobjective optimization problems maops has been proposed specifically the igd indicator is employed in each generation to select the solutions with favorable convergence and diversity in addition a computationally efficient dominance comparison method is designed to assign the rank values of solutions along with three newly proposed proximity distance assignments based on these two designs the solutions are selected from a global view by linear assignment mechanism to concern the convergence and diversity simultaneously in order to facilitate the accuracy of the sampled reference points for the calculation of igd indicator we also propose an efficient decompositionbased nadir point estimation method for constructing the utopian pareto front which is regarded as the best approximate pareto front for realworld maops at the early stage of the evolution to evaluate the performance a series of experiments is performed on the proposed algorithm against a group of selected stateoftheart manyobjective optimization algorithms over optimization problems with 8 15 and 20objective experimental results measured by the chosen performance metrics indicate that the proposed algorithm is very competitive in addressing maops | [['inverted', 'generational', 'distance', 'igd', 'has', 'been', 'widely', 'considered', 'as', 'a', 'reliable', 'performance', 'indicator', 'to', 'concurrently', 'quantify', 'the', 'convergence', 'and', 'diversity', 'of', 'multi', 'and', 'manyobjective', 'evolutionary', 'algorithms', 'in', 'this', 'paper', 'an', 'igd', 'indicatorbased', 'evolutionary', 'algorithm', 'for', 'solving', 'manyobjective', 'optimization', 'problems', 'maops', 'has', 'been', 'proposed', 'specifically', 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1,802.08793 | Multispectral Image Intrinsic Decomposition via Low Rank Constraint | Multispectral images contain many clues of surface characteristics of the
objects, thus can be widely used in many computer vision tasks, e.g.,
recolorization and segmentation. However, due to the complex illumination and
the geometry structure of natural scenes, the spectra curves of a same surface
can look very different. In this paper, a Low Rank Multispectral Image
Intrinsic Decomposition model (LRIID) is presented to decompose the shading and
reflectance from a single multispectral image. We extend the Retinex model,
which is proposed for RGB image intrinsic decomposition, for multispectral
domain. Based on this, a low rank constraint is proposed to reduce the
ill-posedness of the problem and make the algorithm solvable. A dataset of 12
images is given with the ground truth of shadings and reflectance, so that the
objective evaluations can be conducted. The experiments demonstrate the
effectiveness of proposed method.
| cs.CV | multispectral images contain many clues of surface characteristics of the objects thus can be widely used in many computer vision tasks eg recolorization and segmentation however due to the complex illumination and the geometry structure of natural scenes the spectra curves of a same surface can look very different in this paper a low rank multispectral image intrinsic decomposition model lriid is presented to decompose the shading and reflectance from a single multispectral image we extend the retinex model which is proposed for rgb image intrinsic decomposition for multispectral domain based on this a low rank constraint is proposed to reduce the illposedness of the problem and make the algorithm solvable a dataset of 12 images is given with the ground truth of shadings and reflectance so that the objective evaluations can be conducted the experiments demonstrate the effectiveness of proposed method | [['multispectral', 'images', 'contain', 'many', 'clues', 'of', 'surface', 'characteristics', 'of', 'the', 'objects', 'thus', 'can', 'be', 'widely', 'used', 'in', 'many', 'computer', 'vision', 'tasks', 'eg', 'recolorization', 'and', 'segmentation', 'however', 'due', 'to', 'the', 'complex', 'illumination', 'and', 'the', 'geometry', 'structure', 'of', 'natural', 'scenes', 'the', 'spectra', 'curves', 'of', 'a', 'same', 'surface', 'can', 'look', 'very', 'different', 'in', 'this', 'paper', 'a', 'low', 'rank', 'multispectral', 'image', 'intrinsic', 'decomposition', 'model', 'lriid', 'is', 'presented', 'to', 'decompose', 'the', 'shading', 'and', 'reflectance', 'from', 'a', 'single', 'multispectral', 'image', 'we', 'extend', 'the', 'retinex', 'model', 'which', 'is', 'proposed', 'for', 'rgb', 'image', 'intrinsic', 'decomposition', 'for', 'multispectral', 'domain', 'based', 'on', 'this', 'a', 'low', 'rank', 'constraint', 'is', 'proposed', 'to', 'reduce', 'the', 'illposedness', 'of', 'the', 'problem', 'and', 'make', 'the', 'algorithm', 'solvable', 'a', 'dataset', 'of', '12', 'images', 'is', 'given', 'with', 'the', 'ground', 'truth', 'of', 'shadings', 'and', 'reflectance', 'so', 'that', 'the', 'objective', 'evaluations', 'can', 'be', 'conducted', 'the', 'experiments', 'demonstrate', 'the', 'effectiveness', 'of', 'proposed', 'method']] | [-0.030123588285600463, -0.011846617022727398, -0.13423399498408778, 0.06757729255316898, -0.10478142429314924, -0.11539631108059845, -0.04016423073402401, 0.4149393375883711, -0.27573524529101157, -0.3671920365085891, 0.14242515166237263, -0.2387650292366743, -0.19190980982801592, 0.2055177109923663, -0.17640904100704277, 0.08669769310257035, 0.12007520292020331, 0.026480372225228328, -0.055255515981425946, -0.2609920639995525, 0.2838009942910284, -0.0021936956343921363, 0.3277078542580947, 0.05843097059236153, 0.13989250299500974, -0.019793198174790392, -0.017635685147334497, 0.007376318865481282, -0.032824905127122196, 0.17380585583630315, 0.2959758205048687, 0.16527544833547997, 0.2223577424140766, -0.4002325011166275, -0.23935235434390129, 0.08990584248478742, 0.1126889647763835, 0.07954122821539508, -0.054485925431990774, -0.32100638222768374, 0.11530059914547183, -0.09017284401256512, -0.02049685150701949, -0.08204557007205085, -0.016363924542703693, -0.030226992024604153, -0.27297133425166437, 0.03965508525180531, 0.052343967145289036, 0.07886687371142828, -0.11774035782143738, -0.11946199953073887, -0.021677792290265254, 0.1667085235892194, 0.011356121594588278, 0.03133863970927947, 0.11620552396956593, -0.20430111456728467, -0.07926677720759936, 0.4293103870136518, -0.04033060291953755, -0.20506975954358883, 0.19900854067391457, -0.1052712224991926, -0.09293523049378331, 0.15597481012892586, 0.19790254516778052, 0.1587355999025873, -0.12913022559600162, 0.04675811350656053, -0.08589368527887244, 0.21333831567845657, 0.08037810542633929, 0.00787716801154447, 0.17667589068465622, 0.19000558146929486, 0.04783520449311241, 0.16710495847980789, -0.2087227948792675, 0.022409408160069203, -0.19356789285026438, -0.14862888369788516, -0.2225785944709911, -0.019297964284724908, -0.0822750627659121, -0.14502652243096778, 0.4430216624936525, 0.21579130241277475, 0.24082063665573902, 0.021994539996417543, 0.37630452943556275, 0.06066885111516619, 0.09713100017451332, 0.01718561080901996, 0.15834479756547626, 0.07533176525633002, 0.09353903257981577, -0.18240269111528834, 0.03811969940636492, 0.05687518505188009] |
1,802.08794 | Quantum entanglement in de Sitter space with a wall, and the decoherence
of bubble universes | We study the effect of a bubble wall on the entanglement entropy of a free
massive scalar field between two causally disconnected open charts in de Sitter
space. We assume there is a delta-functional wall between the open charts. This
can be thought of as a model of pair creation of bubble universes in de Sitter
space. We first derive the Euclidean vacuum mode functions of the scalar field
in the presence of the wall in the coordinates that respect the open charts. We
then derive the Bogoliubov transformation between the Euclidean vacuum and the
open chart vacua that makes the reduced density matrix diagonal. We find that
larger walls lead to less entanglement. Our result may be regarded as evidence
of decoherence of bubble universes from each other. We also note an interesting
relationship between our results and discussions of the black hole firewall
problem.
| hep-th gr-qc quant-ph | we study the effect of a bubble wall on the entanglement entropy of a free massive scalar field between two causally disconnected open charts in de sitter space we assume there is a deltafunctional wall between the open charts this can be thought of as a model of pair creation of bubble universes in de sitter space we first derive the euclidean vacuum mode functions of the scalar field in the presence of the wall in the coordinates that respect the open charts we then derive the bogoliubov transformation between the euclidean vacuum and the open chart vacua that makes the reduced density matrix diagonal we find that larger walls lead to less entanglement our result may be regarded as evidence of decoherence of bubble universes from each other we also note an interesting relationship between our results and discussions of the black hole firewall problem | [['we', 'study', 'the', 'effect', 'of', 'a', 'bubble', 'wall', 'on', 'the', 'entanglement', 'entropy', 'of', 'a', 'free', 'massive', 'scalar', 'field', 'between', 'two', 'causally', 'disconnected', 'open', 'charts', 'in', 'de', 'sitter', 'space', 'we', 'assume', 'there', 'is', 'a', 'deltafunctional', 'wall', 'between', 'the', 'open', 'charts', 'this', 'can', 'be', 'thought', 'of', 'as', 'a', 'model', 'of', 'pair', 'creation', 'of', 'bubble', 'universes', 'in', 'de', 'sitter', 'space', 'we', 'first', 'derive', 'the', 'euclidean', 'vacuum', 'mode', 'functions', 'of', 'the', 'scalar', 'field', 'in', 'the', 'presence', 'of', 'the', 'wall', 'in', 'the', 'coordinates', 'that', 'respect', 'the', 'open', 'charts', 'we', 'then', 'derive', 'the', 'bogoliubov', 'transformation', 'between', 'the', 'euclidean', 'vacuum', 'and', 'the', 'open', 'chart', 'vacua', 'that', 'makes', 'the', 'reduced', 'density', 'matrix', 'diagonal', 'we', 'find', 'that', 'larger', 'walls', 'lead', 'to', 'less', 'entanglement', 'our', 'result', 'may', 'be', 'regarded', 'as', 'evidence', 'of', 'decoherence', 'of', 'bubble', 'universes', 'from', 'each', 'other', 'we', 'also', 'note', 'an', 'interesting', 'relationship', 'between', 'our', 'results', 'and', 'discussions', 'of', 'the', 'black', 'hole', 'firewall', 'problem']] | [-0.15906265705237038, 0.16070399268728455, -0.13635111824698645, 0.1133972538941323, -0.05587030096060903, -0.10451190665440813, 0.036450043445044794, 0.34390818863494754, -0.2170591903372017, -0.24197932926512145, 0.09348967317045245, -0.25968601997329355, -0.1217766887941302, 0.17128259804871648, -0.051053037018553446, -0.02786148753820931, 0.016998231591184763, 0.05605111242756758, -0.0735263206935424, -0.2256496290609317, 0.38937480242489136, 0.023700110359142903, 0.27802119916304946, 0.053497208971275044, 0.07501329118481595, -0.021940812576970416, -0.028631738725131098, 0.03610371454413151, -0.17493332598803124, 0.0838473652089206, 0.2219582944964888, 0.1538043046746505, 0.2209406240224481, -0.43024057766090923, -0.17426020826838196, 0.13306706295466714, 0.17306239575096596, 0.1676798001847115, -0.05168779980955798, -0.3135023763122624, 0.014014211610878168, -0.1667976423600459, -0.16340798124900624, -0.0014657235197238115, 0.010581957139648905, -0.07227031589479326, -0.18433472245918867, 0.11083120475781819, 0.019194460789711303, -0.017655767513158387, -0.10215812979572236, -0.03246158556948888, -0.04708196157079241, 0.10933571744810675, 0.10026142175057393, 0.07684291529386265, 0.13999125681026545, -0.10434774425333647, -0.12015863930749107, 0.327920942395058, -0.09677358851321552, -0.2235423788182718, 0.15509601219314828, -0.17514304817520235, -0.06670881923741011, 0.0815249484651148, 0.11999161721666483, 0.11863882698386602, -0.11618446119206205, 0.13933217584088523, -0.038085004430398195, 0.13754448930857294, 0.1354202700010182, 0.04984122092759058, 0.2655412440047893, 0.0953783235873083, 0.07346542400933087, 0.21058629261216544, -0.027222060038682636, -0.14428916326181784, -0.38951436667511724, -0.21658504818572522, -0.15403223330890983, 0.04633744106283539, -0.15143083222190473, -0.23125943205357619, 0.33998484528712825, 0.0929114022172592, 0.18684417885817486, -0.03018853183172337, 0.22689368455887657, 0.04279081768369022, 0.045003160745007535, 0.12214734627265636, 0.2444000927386933, 0.10149474342254769, 0.08742002571920214, -0.2551873621717414, -0.04484889767298551, 0.06425375918608975] |
1,802.08795 | Constrained Image Generation Using Binarized Neural Networks with
Decision Procedures | We consider the problem of binary image generation with given properties.
This problem arises in a number of practical applications, including generation
of artificial porous medium for an electrode of lithium-ion batteries, for
composed materials, etc. A generated image represents a porous medium and, as
such, it is subject to two sets of constraints: topological constraints on the
structure and process constraints on the physical process over this structure.
To perform image generation we need to define a mapping from a porous medium to
its physical process parameters. For a given geometry of a porous medium, this
mapping can be done by solving a partial differential equation (PDE). However,
embedding a PDE solver into the search procedure is computationally expensive.
We use a binarized neural network to approximate a PDE solver. This allows us
to encode the entire problem as a logical formula. Our main contribution is
that, for the first time, we show that this problem can be tackled using
decision procedures. Our experiments show that our model is able to produce
random constrained images that satisfy both topological and process
constraints.
| cs.CV | we consider the problem of binary image generation with given properties this problem arises in a number of practical applications including generation of artificial porous medium for an electrode of lithiumion batteries for composed materials etc a generated image represents a porous medium and as such it is subject to two sets of constraints topological constraints on the structure and process constraints on the physical process over this structure to perform image generation we need to define a mapping from a porous medium to its physical process parameters for a given geometry of a porous medium this mapping can be done by solving a partial differential equation pde however embedding a pde solver into the search procedure is computationally expensive we use a binarized neural network to approximate a pde solver this allows us to encode the entire problem as a logical formula our main contribution is that for the first time we show that this problem can be tackled using decision procedures our experiments show that our model is able to produce random constrained images that satisfy both topological and process constraints | [['we', 'consider', 'the', 'problem', 'of', 'binary', 'image', 'generation', 'with', 'given', 'properties', 'this', 'problem', 'arises', 'in', 'a', 'number', 'of', 'practical', 'applications', 'including', 'generation', 'of', 'artificial', 'porous', 'medium', 'for', 'an', 'electrode', 'of', 'lithiumion', 'batteries', 'for', 'composed', 'materials', 'etc', 'a', 'generated', 'image', 'represents', 'a', 'porous', 'medium', 'and', 'as', 'such', 'it', 'is', 'subject', 'to', 'two', 'sets', 'of', 'constraints', 'topological', 'constraints', 'on', 'the', 'structure', 'and', 'process', 'constraints', 'on', 'the', 'physical', 'process', 'over', 'this', 'structure', 'to', 'perform', 'image', 'generation', 'we', 'need', 'to', 'define', 'a', 'mapping', 'from', 'a', 'porous', 'medium', 'to', 'its', 'physical', 'process', 'parameters', 'for', 'a', 'given', 'geometry', 'of', 'a', 'porous', 'medium', 'this', 'mapping', 'can', 'be', 'done', 'by', 'solving', 'a', 'partial', 'differential', 'equation', 'pde', 'however', 'embedding', 'a', 'pde', 'solver', 'into', 'the', 'search', 'procedure', 'is', 'computationally', 'expensive', 'we', 'use', 'a', 'binarized', 'neural', 'network', 'to', 'approximate', 'a', 'pde', 'solver', 'this', 'allows', 'us', 'to', 'encode', 'the', 'entire', 'problem', 'as', 'a', 'logical', 'formula', 'our', 'main', 'contribution', 'is', 'that', 'for', 'the', 'first', 'time', 'we', 'show', 'that', 'this', 'problem', 'can', 'be', 'tackled', 'using', 'decision', 'procedures', 'our', 'experiments', 'show', 'that', 'our', 'model', 'is', 'able', 'to', 'produce', 'random', 'constrained', 'images', 'that', 'satisfy', 'both', 'topological', 'and', 'process', 'constraints']] | [-0.08576810174962618, 0.027329853372702433, -0.07575458409442805, 0.03795567839718939, -0.12429844506614195, -0.12149413922150197, 0.044981971841122285, 0.39386355106927495, -0.34121430899866456, -0.3108234686110311, 0.12411127757863778, -0.2580191465566231, -0.17317712709022676, 0.20485396061570998, -0.060413021759684785, 0.09348869601823136, 0.08743208997483401, -0.031836833377353484, -0.04901327426631838, -0.22822210683724004, 0.31775106390419067, 0.002298332814412202, 0.2441086507481243, 0.03394111777691405, 0.15614381657984575, -0.017007718673179465, -0.011241823367397886, 0.04483840045776177, -0.09073868723884555, 0.14744366450194535, 0.26885706611115057, 0.1547035768353304, 0.26677995121686676, -0.46476389578660654, -0.2758024629164752, 0.0911353292539105, 0.10743834021821862, 0.11544736341415533, -0.07313806239561346, -0.2529045208261852, 0.0922924817362902, -0.1435923812010127, -0.0781979763249312, -0.06895000514029805, -0.015956046672339443, -0.015074262503964592, -0.328954211103506, 0.017613551382209516, 0.061338377953634234, -0.026902180280831585, -0.0628159631109196, -0.05139122218850369, 0.009028466071047988, 0.12108934278214412, -0.01529671515330489, 0.023309821400363914, 0.1344478282739683, -0.1457303813238796, -0.09156338562947174, 0.4425575554872166, -0.04788934290307304, -0.26746310187564765, 0.16482406145386663, -0.038814003612398285, -0.14505654100145474, 0.12086837308460979, 0.23558388468733088, 0.14634072913182644, -0.18296855697637332, 0.057699954429039566, -0.07098286509096541, 0.19063569108136202, 0.0200663315974522, -0.010390082017971519, 0.175150323643914, 0.2528352211051381, 0.08516424696363224, 0.18625146843101117, -0.046737815398478606, -0.05453784501210588, -0.2677006391564109, -0.16915019421426, -0.19059138215920515, 0.06354982878687114, -0.07504784025923321, -0.1860244847640721, 0.37329142474533755, 0.1939360669259328, 0.20027971247852336, 0.03874382096765146, 0.31662365191621206, 0.13381065786313623, 0.06248860549985792, 0.03293014636843419, 0.16433657920802308, 0.1284020114041193, 0.1298953461635074, -0.16722305373879037, 0.08256547305417436, 0.08272738467591505] |
1,802.08796 | Lexicographic and reverse lexicographic quadratic Gr\"obner bases of cut
ideals | Hibi conjectured that if a toric ideal has a quadratic Gr\"obner basis, then
the toric ideal has either a lexicographic or a reverse lexicographic quadratic
Gr\"obner basis. In this paper, we present a cut ideal of a graph that serves
as a counterexample to this conjecture. We also discuss the existence of a
quadratic Gr\"obner basis of a cut ideal of a cycle. Nagel and Petrovi\'c
claimed that a cut ideal of a cycle has a lexicographic quadratic Gr\"obner
basis using the results of Chifman and Petrovi\'c. However, we point out that
the results of Chifman and Petrovi\'c used by Nagel and Petrovi\'c are
incorrect for cycles of length greater than or equal to 6. Hence the existence
of a quadratic Gr\"obner basis for the cut ideal of a cycle (a ring graph) is
an open question. We also provide a lexicographic quadratic Gr\"obner basis of
a cut ideal of a cycle of length less than or equal to 7.
| math.AC | hibi conjectured that if a toric ideal has a quadratic grobner basis then the toric ideal has either a lexicographic or a reverse lexicographic quadratic grobner basis in this paper we present a cut ideal of a graph that serves as a counterexample to this conjecture we also discuss the existence of a quadratic grobner basis of a cut ideal of a cycle nagel and petrovic claimed that a cut ideal of a cycle has a lexicographic quadratic grobner basis using the results of chifman and petrovic however we point out that the results of chifman and petrovic used by nagel and petrovic are incorrect for cycles of length greater than or equal to 6 hence the existence of a quadratic grobner basis for the cut ideal of a cycle a ring graph is an open question we also provide a lexicographic quadratic grobner basis of a cut ideal of a cycle of length less than or equal to 7 | [['hibi', 'conjectured', 'that', 'if', 'a', 'toric', 'ideal', 'has', 'a', 'quadratic', 'grobner', 'basis', 'then', 'the', 'toric', 'ideal', 'has', 'either', 'a', 'lexicographic', 'or', 'a', 'reverse', 'lexicographic', 'quadratic', 'grobner', 'basis', 'in', 'this', 'paper', 'we', 'present', 'a', 'cut', 'ideal', 'of', 'a', 'graph', 'that', 'serves', 'as', 'a', 'counterexample', 'to', 'this', 'conjecture', 'we', 'also', 'discuss', 'the', 'existence', 'of', 'a', 'quadratic', 'grobner', 'basis', 'of', 'a', 'cut', 'ideal', 'of', 'a', 'cycle', 'nagel', 'and', 'petrovic', 'claimed', 'that', 'a', 'cut', 'ideal', 'of', 'a', 'cycle', 'has', 'a', 'lexicographic', 'quadratic', 'grobner', 'basis', 'using', 'the', 'results', 'of', 'chifman', 'and', 'petrovic', 'however', 'we', 'point', 'out', 'that', 'the', 'results', 'of', 'chifman', 'and', 'petrovic', 'used', 'by', 'nagel', 'and', 'petrovic', 'are', 'incorrect', 'for', 'cycles', 'of', 'length', 'greater', 'than', 'or', 'equal', 'to', '6', 'hence', 'the', 'existence', 'of', 'a', 'quadratic', 'grobner', 'basis', 'for', 'the', 'cut', 'ideal', 'of', 'a', 'cycle', 'a', 'ring', 'graph', 'is', 'an', 'open', 'question', 'we', 'also', 'provide', 'a', 'lexicographic', 'quadratic', 'grobner', 'basis', 'of', 'a', 'cut', 'ideal', 'of', 'a', 'cycle', 'of', 'length', 'less', 'than', 'or', 'equal', 'to', '7']] | [-0.1790056734578684, 0.0355362782874181, -0.10334907962242142, 0.06691799670443288, -0.10432390214409679, -0.1263584646192612, 0.07551668786545633, 0.29735160518903286, -0.3218060029903427, -0.19176277182414198, 0.13424751628408557, -0.23758467068400932, -0.126189697101654, 0.1791197784114047, -0.06003119687666185, 0.010275435200310313, 0.0916290235822089, 0.027253634641419923, -0.08176686645601876, -0.3212615010987065, 0.3113643753575161, 0.09329320655378978, 0.17720020431734157, 0.02578733102127444, 0.0931216169934487, 0.013185107312165201, 0.0030346604180522263, 0.08254754397930811, -0.18764798683496337, 0.1409098132266081, 0.2730297501475434, 0.17046338147483767, 0.2835055949748494, -0.4081667874590494, -0.07930161628755741, 0.18291007018415256, 0.10503966723626945, 0.07890837546437979, -0.005804262778838165, -0.09529896013555117, 0.12996028829511488, -0.19940435838652776, -0.1757032069581328, -0.05041434820741415, 0.1006553300539963, -0.011383444959210464, -0.3039821397745982, -0.04238876188501308, 0.1735470498795621, 0.18371498954948037, 0.012927750914968783, -0.14302061789203435, -0.0681994459591806, -0.01014890790975187, -0.07651419253234053, 0.10448723861773032, 0.04064878768404014, -0.10872010327875614, -0.22743819242168684, 0.38625635952921583, -0.058763958226518295, -0.1868937174032908, 0.1241919761596364, -0.09373835808655713, -0.09033200102567207, 0.11516542098834179, 0.06091987486579455, 0.11620157042634674, -0.07818324234685861, 0.11781079264037544, -0.1617471488971205, 0.09845478678926156, 0.14293488312105182, -0.02323358572612051, 0.167934367666021, 0.11062567681074142, 0.10887942340359587, 0.1853505034960108, 0.009095049800816924, 0.0028450345213059335, -0.29559836851549337, -0.23064861875027418, -0.2000016371777747, 0.15282718203525292, -0.10650990950052801, -0.21434214916662314, 0.4411417588591576, 0.0870144760934636, 0.13537044198601506, 0.060761376371374354, 0.2499634129111655, 0.07751825502418797, 0.04010121465835255, 0.10391605038603302, 0.1874824695987627, 0.14587521184876096, -0.03039196764875669, -0.15178200264927, 0.03330597396998201, 0.18077232356299647] |
1,802.08797 | Residual Dense Network for Image Super-Resolution | A very deep convolutional neural network (CNN) has recently achieved great
success for image super-resolution (SR) and offered hierarchical features as
well. However, most deep CNN based SR models do not make full use of the
hierarchical features from the original low-resolution (LR) images, thereby
achieving relatively-low performance. In this paper, we propose a novel
residual dense network (RDN) to address this problem in image SR. We fully
exploit the hierarchical features from all the convolutional layers.
Specifically, we propose residual dense block (RDB) to extract abundant local
features via dense connected convolutional layers. RDB further allows direct
connections from the state of preceding RDB to all the layers of current RDB,
leading to a contiguous memory (CM) mechanism. Local feature fusion in RDB is
then used to adaptively learn more effective features from preceding and
current local features and stabilizes the training of wider network. After
fully obtaining dense local features, we use global feature fusion to jointly
and adaptively learn global hierarchical features in a holistic way. Extensive
experiments on benchmark datasets with different degradation models show that
our RDN achieves favorable performance against state-of-the-art methods.
| cs.CV | a very deep convolutional neural network cnn has recently achieved great success for image superresolution sr and offered hierarchical features as well however most deep cnn based sr models do not make full use of the hierarchical features from the original lowresolution lr images thereby achieving relativelylow performance in this paper we propose a novel residual dense network rdn to address this problem in image sr we fully exploit the hierarchical features from all the convolutional layers specifically we propose residual dense block rdb to extract abundant local features via dense connected convolutional layers rdb further allows direct connections from the state of preceding rdb to all the layers of current rdb leading to a contiguous memory cm mechanism local feature fusion in rdb is then used to adaptively learn more effective features from preceding and current local features and stabilizes the training of wider network after fully obtaining dense local features we use global feature fusion to jointly and adaptively learn global hierarchical features in a holistic way extensive experiments on benchmark datasets with different degradation models show that our rdn achieves favorable performance against stateoftheart methods | [['a', 'very', 'deep', 'convolutional', 'neural', 'network', 'cnn', 'has', 'recently', 'achieved', 'great', 'success', 'for', 'image', 'superresolution', 'sr', 'and', 'offered', 'hierarchical', 'features', 'as', 'well', 'however', 'most', 'deep', 'cnn', 'based', 'sr', 'models', 'do', 'not', 'make', 'full', 'use', 'of', 'the', 'hierarchical', 'features', 'from', 'the', 'original', 'lowresolution', 'lr', 'images', 'thereby', 'achieving', 'relativelylow', 'performance', 'in', 'this', 'paper', 'we', 'propose', 'a', 'novel', 'residual', 'dense', 'network', 'rdn', 'to', 'address', 'this', 'problem', 'in', 'image', 'sr', 'we', 'fully', 'exploit', 'the', 'hierarchical', 'features', 'from', 'all', 'the', 'convolutional', 'layers', 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1,802.08798 | Automatic adaptation of MCMC algorithms | Markov chain Monte Carlo (MCMC) methods are ubiquitous tools for
simulation-based inference in many fields but designing and identifying good
MCMC samplers is still an open question. This paper introduces a novel MCMC
algorithm, namely, Auto Adapt MCMC. For sampling variables or blocks of
variables, we use two levels of adaptation where the inner adaptation optimizes
the MCMC performance within each sampler, while the outer adaptation explores
the valid space of kernels to find the optimal samplers. We provide a
theoretical foundation for our approach. To show the generality and usefulness
of the approach, we describe a framework using only standard MCMC samplers as
candidate samplers and some adaptation schemes for both inner and outer
iterations. In several benchmark problems, we show that our proposed approach
substantially outperforms other approaches, including an automatic blocking
algorithm, in terms of MCMC efficiency and computational time.
| stat.ME stat.CO | markov chain monte carlo mcmc methods are ubiquitous tools for simulationbased inference in many fields but designing and identifying good mcmc samplers is still an open question this paper introduces a novel mcmc algorithm namely auto adapt mcmc for sampling variables or blocks of variables we use two levels of adaptation where the inner adaptation optimizes the mcmc performance within each sampler while the outer adaptation explores the valid space of kernels to find the optimal samplers we provide a theoretical foundation for our approach to show the generality and usefulness of the approach we describe a framework using only standard mcmc samplers as candidate samplers and some adaptation schemes for both inner and outer iterations in several benchmark problems we show that our proposed approach substantially outperforms other approaches including an automatic blocking algorithm in terms of mcmc efficiency and computational time | [['markov', 'chain', 'monte', 'carlo', 'mcmc', 'methods', 'are', 'ubiquitous', 'tools', 'for', 'simulationbased', 'inference', 'in', 'many', 'fields', 'but', 'designing', 'and', 'identifying', 'good', 'mcmc', 'samplers', 'is', 'still', 'an', 'open', 'question', 'this', 'paper', 'introduces', 'a', 'novel', 'mcmc', 'algorithm', 'namely', 'auto', 'adapt', 'mcmc', 'for', 'sampling', 'variables', 'or', 'blocks', 'of', 'variables', 'we', 'use', 'two', 'levels', 'of', 'adaptation', 'where', 'the', 'inner', 'adaptation', 'optimizes', 'the', 'mcmc', 'performance', 'within', 'each', 'sampler', 'while', 'the', 'outer', 'adaptation', 'explores', 'the', 'valid', 'space', 'of', 'kernels', 'to', 'find', 'the', 'optimal', 'samplers', 'we', 'provide', 'a', 'theoretical', 'foundation', 'for', 'our', 'approach', 'to', 'show', 'the', 'generality', 'and', 'usefulness', 'of', 'the', 'approach', 'we', 'describe', 'a', 'framework', 'using', 'only', 'standard', 'mcmc', 'samplers', 'as', 'candidate', 'samplers', 'and', 'some', 'adaptation', 'schemes', 'for', 'both', 'inner', 'and', 'outer', 'iterations', 'in', 'several', 'benchmark', 'problems', 'we', 'show', 'that', 'our', 'proposed', 'approach', 'substantially', 'outperforms', 'other', 'approaches', 'including', 'an', 'automatic', 'blocking', 'algorithm', 'in', 'terms', 'of', 'mcmc', 'efficiency', 'and', 'computational', 'time']] | [-0.040771898883557714, 0.024650061153196175, -0.08943769884608373, 0.11078141646954667, -0.0683661218361369, -0.17296785575018583, 0.08930631175432094, 0.4680910857876281, -0.28593160949282714, -0.351076070662145, 0.1045455931257652, -0.17382987682101922, -0.12547040060716128, 0.26573810135698195, -0.09129293980500237, 0.08462476841714624, 0.1375157617722879, -0.06484573361951487, -0.0864001239138701, -0.28587807016854233, 0.21252916300656288, 0.07912148669414171, 0.29162936414741764, -0.0482586253323703, 0.16386338648991985, 0.002008037853308699, -0.04631344210177373, -0.023230269603582647, -0.15594781879230826, 0.15675337743087367, 0.2914005432691477, 0.21442872350319073, 0.35844218385751014, -0.35682301851757964, -0.22017610277589802, 0.10994757900594154, 0.22067616046959912, 0.12521228476075807, -0.05964418508802782, -0.25958816250590805, 0.032150938703151014, -0.17858501497682158, -0.04054341187791808, -0.1723967603134265, -0.11802315484029013, 0.037680117829810254, -0.31123334559175625, 0.031943280093528176, 0.09115713405654069, 0.04990798608771154, 0.003099191837807323, -0.18513118613420146, 0.0663610951896835, 0.08152340532124198, 0.05948115131570274, 0.0016299761747094717, 0.12756666147729734, -0.09050544557630547, -0.19895803205786156, 0.30218107060861443, -0.0315522444076263, -0.2395651116413618, 0.2136524383896975, -0.02203417783016925, -0.2198781013404104, 0.10523512039880653, 0.1744750737883411, 0.1649009308719135, -0.1630607088962635, 0.13370042003591878, 0.02505678852199466, 0.1279956811438217, -0.016780428533032014, -0.027272669253284698, 0.11821322095046354, 0.23268905393709063, 0.10018446467697972, 0.16158143806774364, -0.11415934968427137, -0.19865196232254115, -0.24631578424844527, -0.19410214840330287, -0.18410027518353939, -0.08798876942185867, -0.14788265908541975, -0.19454868811417136, 0.3794056733840608, 0.28605036638165693, 0.15715164641061655, 0.13951132239177955, 0.36065743408178896, 0.040005883736815954, 0.026206046388438948, 0.17296927955027644, 0.1284369335852163, 0.07758149778848136, 0.04009473099295128, -0.2012033011842441, 0.09774727501150976, 0.06695043326231774] |
1,802.08799 | On Pseudo-disk Hypergraphs | Let $F$ be a family of pseudo-disks in the plane, and $P$ be a finite subset
of $F$. Consider the hypergraph $H(P,F)$ whose vertices are the pseudo-disks in
$P$ and the edges are all subsets of $P$ of the form $\{D \in P \mid D \cap S
\neq \emptyset\}$, where $S$ is a pseudo-disk in $F$. We give an upper bound of
$O(nk^3)$ for the number of edges in $H(P,F)$ of cardinality at most $k$. This
generalizes a result of Buzaglo et al. (2013).
As an application of our bound, we obtain an algorithm that computes a
constant-factor approximation to the smallest _weighted_ dominating set in a
collection of pseudo-disks in the plane, in expected polynomial time.
| cs.CG math.CO | let f be a family of pseudodisks in the plane and p be a finite subset of f consider the hypergraph hpf whose vertices are the pseudodisks in p and the edges are all subsets of p of the form d in p mid d cap s neq emptyset where s is a pseudodisk in f we give an upper bound of onk3 for the number of edges in hpf of cardinality at most k this generalizes a result of buzaglo et al 2013 as an application of our bound we obtain an algorithm that computes a constantfactor approximation to the smallest _weighted_ dominating set in a collection of pseudodisks in the plane in expected polynomial time | [['let', 'f', 'be', 'a', 'family', 'of', 'pseudodisks', 'in', 'the', 'plane', 'and', 'p', 'be', 'a', 'finite', 'subset', 'of', 'f', 'consider', 'the', 'hypergraph', 'hpf', 'whose', 'vertices', 'are', 'the', 'pseudodisks', 'in', 'p', 'and', 'the', 'edges', 'are', 'all', 'subsets', 'of', 'p', 'of', 'the', 'form', 'd', 'in', 'p', 'mid', 'd', 'cap', 's', 'neq', 'emptyset', 'where', 's', 'is', 'a', 'pseudodisk', 'in', 'f', 'we', 'give', 'an', 'upper', 'bound', 'of', 'onk3', 'for', 'the', 'number', 'of', 'edges', 'in', 'hpf', 'of', 'cardinality', 'at', 'most', 'k', 'this', 'generalizes', 'a', 'result', 'of', 'buzaglo', 'et', 'al', '2013', 'as', 'an', 'application', 'of', 'our', 'bound', 'we', 'obtain', 'an', 'algorithm', 'that', 'computes', 'a', 'constantfactor', 'approximation', 'to', 'the', 'smallest', '_weighted_', 'dominating', 'set', 'in', 'a', 'collection', 'of', 'pseudodisks', 'in', 'the', 'plane', 'in', 'expected', 'polynomial', 'time']] | [-0.21283229625095493, 0.09854643287316835, -0.06083643571676119, -0.04687646410387495, -0.04472281592898071, -0.10099557741828587, 0.06964789707233886, 0.2937737227822452, -0.28031733100930145, -0.28981914714221724, 0.01074986887042937, -0.3129256302571815, -0.0881043952687279, 0.15849265433523965, -0.07343948946496391, -0.007716226240660509, 0.020108635729664693, 0.0848631438880187, 0.020653717954764547, -0.2926926576188239, 0.2482099760647701, -0.0553837984557385, 0.12506362990192746, 0.030378379316433616, 0.014831234417531801, 0.010707356019512468, 0.03136913813650608, 0.0358209900294795, -0.19841613125938, 0.06661391840518817, 0.2878133047372103, 0.17865517332178096, 0.27346597399724565, -0.36393219564595947, -0.11651160037145018, 0.20834912918912976, 0.12213441529918624, -0.0013328793377656003, 0.02514221013888069, -0.19092455675582523, 0.15491738558706383, -0.10301071645125098, -0.13255226153313465, 0.028925069745468057, 0.17690255742358124, 0.008137875034109406, -0.36835668806148614, -0.021647679004008356, 0.14633346395162136, 0.0610855652199066, -0.00749922849683334, -0.20454992320307572, -0.04131418484384599, 0.020127934399668288, -0.07792840692234913, 0.16371136576585146, -0.0012334895401221256, -0.08048057368470599, -0.11033993637108284, 0.36458415130872035, -0.08458998924850122, -0.17418367033014479, 0.10627982282768125, -0.17315121778165515, -0.13677599994546694, 0.1473638383631149, 0.18093970180691585, 0.21188250069061051, -0.06123245293350445, 0.2286004003694119, -0.17546094315855398, 0.08763070965752653, 0.10761321151386137, 0.01830172284060846, 0.12116643758490682, 0.1068330413358205, 0.16345114928629736, 0.15125028437367924, -0.06910563751690738, 0.043576989656455976, -0.3653253696615929, -0.1444926977613131, -0.23473177103062526, 0.07240801720637018, -0.11506171013689195, -0.1923781331466592, 0.35058908677740913, 0.07248265509615126, 0.2773636710870525, 0.056715230497977005, 0.2052191209371971, 0.08203876595450667, -0.012431399495867284, 0.1886249800455635, 0.13054813502923301, 0.11497589654248694, -0.07757945020237694, -0.16371095330737856, 0.06839022368354641, 0.12218342716603176] |
1,802.088 | Stochastic Gradient Descent on Highly-Parallel Architectures | There is an increased interest in building data analytics frameworks with
advanced algebraic capabilities both in industry and academia. Many of these
frameworks, e.g., TensorFlow and BIDMach, implement their compute-intensive
primitives in two flavors---as multi-thread routines for multi-core CPUs and as
highly-parallel kernels executed on GPU. Stochastic gradient descent (SGD) is
the most popular optimization method for model training implemented extensively
on modern data analytics platforms. While the data-intensive properties of SGD
are well-known, there is an intense debate on which of the many SGD variants is
better in practice. In this paper, we perform a comprehensive study of parallel
SGD for training generalized linear models. We consider the impact of three
factors -- computing architecture (multi-core CPU or GPU), synchronous or
asynchronous model updates, and data sparsity -- on three measures---hardware
efficiency, statistical efficiency, and time to convergence. In the process, we
design an optimized asynchronous SGD algorithm for GPU that leverages warp
shuffling and cache coalescing for data and model access. We draw several
interesting findings from our extensive experiments with logistic regression
(LR) and support vector machines (SVM) on five real datasets. For synchronous
SGD, GPU always outperforms parallel CPU---they both outperform a sequential
CPU solution by more than 400X. For asynchronous SGD, parallel CPU is the
safest choice while GPU with data replication is better in certain situations.
The choice between synchronous GPU and asynchronous CPU depends on the task and
the characteristics of the data. As a reference, our best implementation
outperforms TensorFlow and BIDMach consistently. We hope that our insights
provide a useful guide for applying parallel SGD to generalized linear models.
| cs.DB cs.DC cs.LG cs.PF | there is an increased interest in building data analytics frameworks with advanced algebraic capabilities both in industry and academia many of these frameworks eg tensorflow and bidmach implement their computeintensive primitives in two flavorsas multithread routines for multicore cpus and as highlyparallel kernels executed on gpu stochastic gradient descent sgd is the most popular optimization method for model training implemented extensively on modern data analytics platforms while the dataintensive properties of sgd are wellknown there is an intense debate on which of the many sgd variants is better in practice in this paper we perform a comprehensive study of parallel sgd for training generalized linear models we consider the impact of three factors computing architecture multicore cpu or gpu synchronous or asynchronous model updates and data sparsity on three measureshardware efficiency statistical efficiency and time to convergence in the process we design an optimized asynchronous sgd algorithm for gpu that leverages warp shuffling and cache coalescing for data and model access we draw several interesting findings from our extensive experiments with logistic regression lr and support vector machines svm on five real datasets for synchronous sgd gpu always outperforms parallel cputhey both outperform a sequential cpu solution by more than 400x for asynchronous sgd parallel cpu is the safest choice while gpu with data replication is better in certain situations the choice between synchronous gpu and asynchronous cpu depends on the task and the characteristics of the data as a reference our best implementation outperforms tensorflow and bidmach consistently we hope that our insights provide a useful guide for applying parallel sgd to generalized linear models | [['there', 'is', 'an', 'increased', 'interest', 'in', 'building', 'data', 'analytics', 'frameworks', 'with', 'advanced', 'algebraic', 'capabilities', 'both', 'in', 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1,802.08801 | Non-saturating Quantum Magnetization in Weyl semimetal TaAs | Detecting the spectroscopic signatures of Dirac-like quasiparticles in
emergent topological materials is crucial for searching their potential
applications. Magnetometry is a powerful tool for fathoming electrons in
solids, yet its ability for discerning Dirac-like quasiparticles has not been
recognized. Adopting the probes of magnetic torque and parallel magnetization
for the archetype Weyl semimetal TaAs in strong magnetic field, we observed a
quasi-linear field dependent effective transverse magnetization and a strongly
enhanced parallel magnetization when the system is in the quantum limit.
Distinct from the saturating magnetic responses for massive carriers, the
non-saturating signals of TaAs in strong field is consistent with our newly
developed magnetization calculation for a Weyl fermion system in an arbitrary
angle. Our results for the first time establish a thermodynamic criterion for
detecting the unique magnetic response of 3D massless Weyl fermions in the
quantum limit.
| cond-mat.mes-hall cond-mat.mtrl-sci | detecting the spectroscopic signatures of diraclike quasiparticles in emergent topological materials is crucial for searching their potential applications magnetometry is a powerful tool for fathoming electrons in solids yet its ability for discerning diraclike quasiparticles has not been recognized adopting the probes of magnetic torque and parallel magnetization for the archetype weyl semimetal taas in strong magnetic field we observed a quasilinear field dependent effective transverse magnetization and a strongly enhanced parallel magnetization when the system is in the quantum limit distinct from the saturating magnetic responses for massive carriers the nonsaturating signals of taas in strong field is consistent with our newly developed magnetization calculation for a weyl fermion system in an arbitrary angle our results for the first time establish a thermodynamic criterion for detecting the unique magnetic response of 3d massless weyl fermions in the quantum limit | [['detecting', 'the', 'spectroscopic', 'signatures', 'of', 'diraclike', 'quasiparticles', 'in', 'emergent', 'topological', 'materials', 'is', 'crucial', 'for', 'searching', 'their', 'potential', 'applications', 'magnetometry', 'is', 'a', 'powerful', 'tool', 'for', 'fathoming', 'electrons', 'in', 'solids', 'yet', 'its', 'ability', 'for', 'discerning', 'diraclike', 'quasiparticles', 'has', 'not', 'been', 'recognized', 'adopting', 'the', 'probes', 'of', 'magnetic', 'torque', 'and', 'parallel', 'magnetization', 'for', 'the', 'archetype', 'weyl', 'semimetal', 'taas', 'in', 'strong', 'magnetic', 'field', 'we', 'observed', 'a', 'quasilinear', 'field', 'dependent', 'effective', 'transverse', 'magnetization', 'and', 'a', 'strongly', 'enhanced', 'parallel', 'magnetization', 'when', 'the', 'system', 'is', 'in', 'the', 'quantum', 'limit', 'distinct', 'from', 'the', 'saturating', 'magnetic', 'responses', 'for', 'massive', 'carriers', 'the', 'nonsaturating', 'signals', 'of', 'taas', 'in', 'strong', 'field', 'is', 'consistent', 'with', 'our', 'newly', 'developed', 'magnetization', 'calculation', 'for', 'a', 'weyl', 'fermion', 'system', 'in', 'an', 'arbitrary', 'angle', 'our', 'results', 'for', 'the', 'first', 'time', 'establish', 'a', 'thermodynamic', 'criterion', 'for', 'detecting', 'the', 'unique', 'magnetic', 'response', 'of', '3d', 'massless', 'weyl', 'fermions', 'in', 'the', 'quantum', 'limit']] | [-0.22092748525485928, 0.2190352336712489, -0.06087265320654426, 0.058429409023873245, -0.10598860705337887, -0.17778370059095322, 0.05562760551193995, 0.3257384016045502, -0.2226662827655673, -0.29908384015517575, -0.023993966718054642, -0.3038342007735212, -0.1445312841861908, 0.231231518761654, 0.06046335302027209, 0.07211015902950228, -0.027739000633092863, 0.01804256485442498, -0.06238103473692068, -0.17729717083607932, 0.271786262477482, 0.028991348091845535, 0.32349287760610296, 0.060894660006410306, 0.07343223062837621, 0.027386759810282716, 0.08995425304996647, 0.06836144360048431, -0.07424999108022479, 0.02281448674787368, 0.24911624602474539, -0.08269374620535277, 0.19008040245888488, -0.4276854564568826, -0.22329046763438132, 0.0341835983529953, 0.15933401964471808, 0.14223639569245278, -0.14347157585268308, -0.30689697564147145, 0.06906202040013991, -0.10428835002073486, -0.1651450929364988, -0.14203327028746052, 0.009911309620864422, -0.047942620261372734, -0.249807849699781, 0.1010584685176062, 0.056859418330714104, 0.12757632858119905, -0.11528295666461677, -0.06390258889878168, -0.035168396438738064, 0.08785909329370563, 0.060111066718984925, 0.05229011255360092, 0.11233388583308884, -0.18073157659798328, -0.1573008383657517, 0.37616780772805214, -0.06710049328768426, -0.11847314063925296, 0.17363225604806629, -0.17740737454233957, -0.12292690457709665, 0.1411091615645481, 0.1416938590479341, 0.13437978814888213, -0.18379587181729481, 0.1180889721535745, -0.037184863288088565, 0.10520545037330262, -0.029815051305091142, 0.10297084500281406, 0.3391307135179107, 0.17900532843195832, 0.04366666398377025, 0.113525303321824, -0.12890056975385442, -0.022283084980491755, -0.2373208211680841, -0.2535745233629963, -0.26558251266999705, 0.08183995443240356, -0.07575919314133768, -0.20139509906792746, 0.43630858555303087, 0.14820450537704996, 0.11623926520344087, -0.046546257907591225, 0.2738772888285374, 0.13802561453443818, 0.09627957967542378, 0.08147479983578836, 0.28060757858079993, 0.21211771530964013, 0.15842689516305525, -0.2847614217399886, 0.038194505012195026, 0.018333257034620538] |
1,802.08802 | Reinforcement Learning on Web Interfaces Using Workflow-Guided
Exploration | Reinforcement learning (RL) agents improve through trial-and-error, but when
reward is sparse and the agent cannot discover successful action sequences,
learning stagnates. This has been a notable problem in training deep RL agents
to perform web-based tasks, such as booking flights or replying to emails,
where a single mistake can ruin the entire sequence of actions. A common remedy
is to "warm-start" the agent by pre-training it to mimic expert demonstrations,
but this is prone to overfitting. Instead, we propose to constrain exploration
using demonstrations. From each demonstration, we induce high-level "workflows"
which constrain the allowable actions at each time step to be similar to those
in the demonstration (e.g., "Step 1: click on a textbox; Step 2: enter some
text"). Our exploration policy then learns to identify successful workflows and
samples actions that satisfy these workflows. Workflows prune out bad
exploration directions and accelerate the agent's ability to discover rewards.
We use our approach to train a novel neural policy designed to handle the
semi-structured nature of websites, and evaluate on a suite of web tasks,
including the recent World of Bits benchmark. We achieve new state-of-the-art
results, and show that workflow-guided exploration improves sample efficiency
over behavioral cloning by more than 100x.
| cs.AI | reinforcement learning rl agents improve through trialanderror but when reward is sparse and the agent cannot discover successful action sequences learning stagnates this has been a notable problem in training deep rl agents to perform webbased tasks such as booking flights or replying to emails where a single mistake can ruin the entire sequence of actions a common remedy is to warmstart the agent by pretraining it to mimic expert demonstrations but this is prone to overfitting instead we propose to constrain exploration using demonstrations from each demonstration we induce highlevel workflows which constrain the allowable actions at each time step to be similar to those in the demonstration eg step 1 click on a textbox step 2 enter some text our exploration policy then learns to identify successful workflows and samples actions that satisfy these workflows workflows prune out bad exploration directions and accelerate the agents ability to discover rewards we use our approach to train a novel neural policy designed to handle the semistructured nature of websites and evaluate on a suite of web tasks including the recent world of bits benchmark we achieve new stateoftheart results and show that workflowguided exploration improves sample efficiency over behavioral cloning by more than 100x | [['reinforcement', 'learning', 'rl', 'agents', 'improve', 'through', 'trialanderror', 'but', 'when', 'reward', 'is', 'sparse', 'and', 'the', 'agent', 'can', 'not', 'discover', 'successful', 'action', 'sequences', 'learning', 'stagnates', 'this', 'has', 'been', 'a', 'notable', 'problem', 'in', 'training', 'deep', 'rl', 'agents', 'to', 'perform', 'webbased', 'tasks', 'such', 'as', 'booking', 'flights', 'or', 'replying', 'to', 'emails', 'where', 'a', 'single', 'mistake', 'can', 'ruin', 'the', 'entire', 'sequence', 'of', 'actions', 'a', 'common', 'remedy', 'is', 'to', 'warmstart', 'the', 'agent', 'by', 'pretraining', 'it', 'to', 'mimic', 'expert', 'demonstrations', 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1,802.08803 | Toric vector bundles on Bott tower | In this paper, using Klyachko's classification theorem we study positivity
and semi-stability of toric vector bundles on a class of nonsingular projective
toric varieties, known as Bott towers. In particular, we give a criterion of
$s$-jet ampleness of line bundles and characterize nef and big line bundles on
Bott towers using Bott numbers. We obtain a criterion for the ampleness of
discriminant zero semi-stable toric vector bundles on nonsingular projective
varieties. We also describe toric subbundles of toric vector bundles on
nonsingular toric varieties.
| math.AG | in this paper using klyachkos classification theorem we study positivity and semistability of toric vector bundles on a class of nonsingular projective toric varieties known as bott towers in particular we give a criterion of sjet ampleness of line bundles and characterize nef and big line bundles on bott towers using bott numbers we obtain a criterion for the ampleness of discriminant zero semistable toric vector bundles on nonsingular projective varieties we also describe toric subbundles of toric vector bundles on nonsingular toric varieties | [['in', 'this', 'paper', 'using', 'klyachkos', 'classification', 'theorem', 'we', 'study', 'positivity', 'and', 'semistability', 'of', 'toric', 'vector', 'bundles', 'on', 'a', 'class', 'of', 'nonsingular', 'projective', 'toric', 'varieties', 'known', 'as', 'bott', 'towers', 'in', 'particular', 'we', 'give', 'a', 'criterion', 'of', 'sjet', 'ampleness', 'of', 'line', 'bundles', 'and', 'characterize', 'nef', 'and', 'big', 'line', 'bundles', 'on', 'bott', 'towers', 'using', 'bott', 'numbers', 'we', 'obtain', 'a', 'criterion', 'for', 'the', 'ampleness', 'of', 'discriminant', 'zero', 'semistable', 'toric', 'vector', 'bundles', 'on', 'nonsingular', 'projective', 'varieties', 'we', 'also', 'describe', 'toric', 'subbundles', 'of', 'toric', 'vector', 'bundles', 'on', 'nonsingular', 'toric', 'varieties']] | [-0.2763445564007095, -0.04488747385793354, -0.08457664493471384, 0.1626589311194236, -0.11664591170847416, -0.25665955389681533, -0.04223018678056682, 0.32142750013350363, -0.2700324865529336, -0.09576625719733806, 0.13077628669744157, -0.16120050993400165, -0.20007994775492025, 0.1853640902596694, -0.25832724235727483, -0.007560488711818155, 0.04256741886576974, 0.05660310513283833, -0.14539376795516315, -0.44137706807309607, 0.6242228727563318, -0.06112111227990812, 0.32052710501993276, 0.16486136292118625, 0.13549097466927038, 0.04085318165752722, 0.035359853844955026, -0.06965538522744753, -0.16917143481592817, 0.13448603445641608, 0.3794940816510626, 0.12465904347867851, 0.03922602879893349, -0.3322495534814086, -0.1053899799092078, 0.33055885669253543, 0.09056101789899978, -0.0034427612133503677, 0.048083099115819455, -0.2521004254918501, 0.09646759297490451, -0.09265832753335855, -0.24603655373296104, -0.2053626445178167, 0.012008308794865587, 0.054352194449516486, -0.1594709947884801, -0.12960008764394043, 0.10133287083941052, 0.2964862965563514, -0.08048598997923265, -0.07128085144402752, -0.18196827971168894, -0.09478999512180326, -0.018508076174072473, 0.007530600012066853, 0.107333044914237, -0.06011054250243378, -0.18199409514732928, 0.3332703389698662, -0.11810796927795353, -0.2671024042899799, 0.03562869003648499, -0.06939845474391997, -0.16047055016170783, 0.21073896501841674, 0.15671774804317223, 0.2505759838617867, 0.1728524130219257, 0.132917822577783, -0.22188497118719192, -0.026441170239304923, 0.11687717182808612, -0.06352384510753026, 0.18164879069046444, 0.06379120567173932, 0.033929478309779285, 0.10902776416525783, -0.048317782328385545, -0.07998475546286588, -0.43464428461325094, -0.3103229107267885, -0.04394651913218739, 0.3515547039772732, -0.13373340407042086, -0.21291671030459006, 0.4547513955418604, 0.007024270757269788, 0.2731018754687295, 0.18731015274323612, 0.2514403612768076, -0.09852437090538217, -0.0012256704068866122, -0.0031629068873744145, 0.1302925291168223, 0.38230154820414913, -0.029424892087657768, -0.020989879819912363, -0.13722914916179874, 0.34139006335782] |
1,802.08804 | Implementing Majorana fermions in a cold-atom honeycomb lattice with
textured pairings | Recent studies in the realization of Majorana fermion (MF) quasiparticles
have focused on engineering topological superconductivity by combining
conventional superconductors and spin-textured electronic materials. We propose
an effective model to create unpaired MFs at a honeycomb lattice edge by
generalizing a 2-dimensional topologically nontrivial Haldane model and
introducing textured pairings. The core idea is to add both the spin-singlet
and textured spin-triplet pairings to a pseudospin-state dependent,
time-reversal symmetry (TRS) noninvariant honeycomb lattice, and to satisfy
generalized "sweet spot" conditions as in the Kitaev chain model. Our model has
a gapped superconducting phase and a gapless phase; either phase may have zero
or nonzero topological winding numbers. The discriminant that distinguishes
those two phases gives a measure of TRS breaking and may have more general
implications. Effective Majorana zero modes arise at edges in distinct phases
with different degrees of degeneracy. Our theoretical model motivates concepts,
such as "textured pairings" and the "strength" of TRS breaking, that may play
important roles in future implementation of MFs with cold atoms in optical
lattices.
| cond-mat.mes-hall cond-mat.quant-gas quant-ph | recent studies in the realization of majorana fermion mf quasiparticles have focused on engineering topological superconductivity by combining conventional superconductors and spintextured electronic materials we propose an effective model to create unpaired mfs at a honeycomb lattice edge by generalizing a 2dimensional topologically nontrivial haldane model and introducing textured pairings the core idea is to add both the spinsinglet and textured spintriplet pairings to a pseudospinstate dependent timereversal symmetry trs noninvariant honeycomb lattice and to satisfy generalized sweet spot conditions as in the kitaev chain model our model has a gapped superconducting phase and a gapless phase either phase may have zero or nonzero topological winding numbers the discriminant that distinguishes those two phases gives a measure of trs breaking and may have more general implications effective majorana zero modes arise at edges in distinct phases with different degrees of degeneracy our theoretical model motivates concepts such as textured pairings and the strength of trs breaking that may play important roles in future implementation of mfs with cold atoms in optical lattices | [['recent', 'studies', 'in', 'the', 'realization', 'of', 'majorana', 'fermion', 'mf', 'quasiparticles', 'have', 'focused', 'on', 'engineering', 'topological', 'superconductivity', 'by', 'combining', 'conventional', 'superconductors', 'and', 'spintextured', 'electronic', 'materials', 'we', 'propose', 'an', 'effective', 'model', 'to', 'create', 'unpaired', 'mfs', 'at', 'a', 'honeycomb', 'lattice', 'edge', 'by', 'generalizing', 'a', '2dimensional', 'topologically', 'nontrivial', 'haldane', 'model', 'and', 'introducing', 'textured', 'pairings', 'the', 'core', 'idea', 'is', 'to', 'add', 'both', 'the', 'spinsinglet', 'and', 'textured', 'spintriplet', 'pairings', 'to', 'a', 'pseudospinstate', 'dependent', 'timereversal', 'symmetry', 'trs', 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1,802.08805 | Multispectral Focal Stack Acquisition Using A Chromatic Aberration
Enlarged Camera | Capturing more information, e.g. geometry and material, using optical cameras
can greatly help the perception and understanding of complex scenes. This paper
proposes a novel method to capture the spectral and light field information
simultaneously. By using a delicately designed chromatic aberration enlarged
camera, the spectral-varying slices at different depths of the scene can be
easily captured. Afterwards, the multispectral focal stack, which is composed
of a stack of multispectral slice images focusing on different depths, can be
recovered from the spectral-varying slices by using a Local Linear
Transformation (LLT) based algorithm. The experiments verify the effectiveness
of the proposed method.
| eess.IV | capturing more information eg geometry and material using optical cameras can greatly help the perception and understanding of complex scenes this paper proposes a novel method to capture the spectral and light field information simultaneously by using a delicately designed chromatic aberration enlarged camera the spectralvarying slices at different depths of the scene can be easily captured afterwards the multispectral focal stack which is composed of a stack of multispectral slice images focusing on different depths can be recovered from the spectralvarying slices by using a local linear transformation llt based algorithm the experiments verify the effectiveness of the proposed method | [['capturing', 'more', 'information', 'eg', 'geometry', 'and', 'material', 'using', 'optical', 'cameras', 'can', 'greatly', 'help', 'the', 'perception', 'and', 'understanding', 'of', 'complex', 'scenes', 'this', 'paper', 'proposes', 'a', 'novel', 'method', 'to', 'capture', 'the', 'spectral', 'and', 'light', 'field', 'information', 'simultaneously', 'by', 'using', 'a', 'delicately', 'designed', 'chromatic', 'aberration', 'enlarged', 'camera', 'the', 'spectralvarying', 'slices', 'at', 'different', 'depths', 'of', 'the', 'scene', 'can', 'be', 'easily', 'captured', 'afterwards', 'the', 'multispectral', 'focal', 'stack', 'which', 'is', 'composed', 'of', 'a', 'stack', 'of', 'multispectral', 'slice', 'images', 'focusing', 'on', 'different', 'depths', 'can', 'be', 'recovered', 'from', 'the', 'spectralvarying', 'slices', 'by', 'using', 'a', 'local', 'linear', 'transformation', 'llt', 'based', 'algorithm', 'the', 'experiments', 'verify', 'the', 'effectiveness', 'of', 'the', 'proposed', 'method']] | [-0.05482036395661646, 0.05211750304825002, -0.13954848997915784, 0.0224274893178407, -0.10060026697964981, -0.14893540406994749, -0.02875533963276092, 0.42108593700980446, -0.28259574783721353, -0.3645647175309032, 0.13183210445850185, -0.20924255390642116, -0.19932135472996065, 0.22160080248358274, -0.14511491433773754, 0.047121484059548815, 0.0723290611575875, -0.01193978710833824, -0.07059396967066056, -0.24866458643792252, 0.3158742971224428, 0.041786394053787895, 0.3213750075906365, 0.0174442873185858, 0.16140915470661107, 0.07175763652688175, -0.06719356102661954, 0.08735152043289307, -0.051528242322606874, 0.1844897680901781, 0.25553393183567685, 0.15334834961803875, 0.19977911714125762, -0.4405240147666197, -0.24411076819994534, 0.045003296819167454, 0.15375253327959335, 0.06511499892068895, -0.030715869743914364, -0.3742580773553463, 0.07221958285663277, -0.09343171435775179, -0.06381420907799644, -0.06709227033636787, -0.09002331288935936, 0.017780701639224784, -0.24564992516969492, -0.00248144999502322, 0.01406696755815335, 0.09081562020171509, -0.04519404702575294, -0.033026953848699726, -0.029391875354140395, 0.15706055490488205, -0.04940528767753741, -0.00026768429741037613, 0.15161502843892033, -0.16121429624271136, -0.06914491298363892, 0.3966558320127954, -0.05565371923618997, -0.19362728346863556, 0.1492541568245588, -0.10994695556923897, -0.026966767383250174, 0.19109661826355892, 0.21213337951669037, 0.15791086680161756, -0.16202585848819728, 0.020430693684517397, -0.02922299478410019, 0.20874004384897876, 0.11577379091812129, 0.017996197947385637, 0.2541023933639129, 0.19239736591098885, 0.00914484948317304, 0.15528726433356754, -0.20760527574645374, 0.022530650743518513, -0.21457027493872577, -0.13876249454445158, -0.19364047704871293, -0.029079558572383844, -0.11933515883580607, -0.07814805813585267, 0.46410322467787096, 0.20073148206398456, 0.20033971381119706, 0.04018856993420144, 0.3898452008989724, 0.06307548588386389, 0.14067020673173095, 0.019406284389057844, 0.16620227814924837, 0.058620423894620154, 0.11672044768835409, -0.17199035374696997, 0.041098249554069655, 0.08853379282818148] |
1,802.08806 | Latest R\&D news and beam test performance of the highly granular
SiW-ECAL technological prototype for the ILC | High precision physics at future colliders as the International Linear
Collider (ILC) require unprecedented high precision in the determination of the
energy of final state particles. The needed precision will be achieved thanks
to the Particle Flow algorithms (PF) which require highly granular and hermetic
calorimeters systems.The physical proof of concept of the PF was performed in
the previous campaign of beam tests of physic prototypes within the CALICE
collaboration. One of these prototypes was the physics prototype of the
Silicon-Tungsten Electromagnetic Calorimeter (SiW-ECAL) for the ILC. In this
document we present the latest news on R\&D of the next generation prototype,
the technological prototype with fully embedded very front-end (VFE)
electronics, of the SiW-ECAL. Special emphasis is given to the presentation and
discussion of the first results from the beam test done at DESY in June 2017.
The physics program for such beam test consisted in the calibration and
commissioning of the current set of available SiW ECAL modules; the test of
performance of individual slabs under 1T magnetic fields; and the study of
electromagnetic showers events.
| physics.ins-det | high precision physics at future colliders as the international linear collider ilc require unprecedented high precision in the determination of the energy of final state particles the needed precision will be achieved thanks to the particle flow algorithms pf which require highly granular and hermetic calorimeters systemsthe physical proof of concept of the pf was performed in the previous campaign of beam tests of physic prototypes within the calice collaboration one of these prototypes was the physics prototype of the silicontungsten electromagnetic calorimeter siwecal for the ilc in this document we present the latest news on rd of the next generation prototype the technological prototype with fully embedded very frontend vfe electronics of the siwecal special emphasis is given to the presentation and discussion of the first results from the beam test done at desy in june 2017 the physics program for such beam test consisted in the calibration and commissioning of the current set of available siw ecal modules the test of performance of individual slabs under 1t magnetic fields and the study of electromagnetic showers events | [['high', 'precision', 'physics', 'at', 'future', 'colliders', 'as', 'the', 'international', 'linear', 'collider', 'ilc', 'require', 'unprecedented', 'high', 'precision', 'in', 'the', 'determination', 'of', 'the', 'energy', 'of', 'final', 'state', 'particles', 'the', 'needed', 'precision', 'will', 'be', 'achieved', 'thanks', 'to', 'the', 'particle', 'flow', 'algorithms', 'pf', 'which', 'require', 'highly', 'granular', 'and', 'hermetic', 'calorimeters', 'systemsthe', 'physical', 'proof', 'of', 'concept', 'of', 'the', 'pf', 'was', 'performed', 'in', 'the', 'previous', 'campaign', 'of', 'beam', 'tests', 'of', 'physic', 'prototypes', 'within', 'the', 'calice', 'collaboration', 'one', 'of', 'these', 'prototypes', 'was', 'the', 'physics', 'prototype', 'of', 'the', 'silicontungsten', 'electromagnetic', 'calorimeter', 'siwecal', 'for', 'the', 'ilc', 'in', 'this', 'document', 'we', 'present', 'the', 'latest', 'news', 'on', 'rd', 'of', 'the', 'next', 'generation', 'prototype', 'the', 'technological', 'prototype', 'with', 'fully', 'embedded', 'very', 'frontend', 'vfe', 'electronics', 'of', 'the', 'siwecal', 'special', 'emphasis', 'is', 'given', 'to', 'the', 'presentation', 'and', 'discussion', 'of', 'the', 'first', 'results', 'from', 'the', 'beam', 'test', 'done', 'at', 'desy', 'in', 'june', '2017', 'the', 'physics', 'program', 'for', 'such', 'beam', 'test', 'consisted', 'in', 'the', 'calibration', 'and', 'commissioning', 'of', 'the', 'current', 'set', 'of', 'available', 'siw', 'ecal', 'modules', 'the', 'test', 'of', 'performance', 'of', 'individual', 'slabs', 'under', '1t', 'magnetic', 'fields', 'and', 'the', 'study', 'of', 'electromagnetic', 'showers', 'events']] | [-0.08378765472535336, 0.13716580572517148, -0.05567526485603512, 0.0012119131612727482, -0.05250982063811984, -0.1132507249294372, -0.033603538548458746, 0.35369828441494217, -0.1860929199159564, -0.37093371702515093, 0.109468608052114, -0.28783339617925624, 0.008310565468593595, 0.22645886948795235, 0.02940458293874706, 0.1557167162830952, 0.12933338172419856, 0.002241106342859148, -0.05398295687974609, -0.2368608920892512, 0.2390748548290033, 0.22474772642690982, 0.3091148509528865, 0.046838554774418874, 0.14749172818062178, 0.018569481092128443, -0.08292152979568149, -0.04167152109315221, -0.09122348853810089, 0.071390703066744, 0.2938056591024434, 0.12928547446479957, 0.22500612601779107, -0.432868100681834, -0.09487144312536176, 0.03436156095193929, 0.044754929857569216, 0.014129569368787486, -0.0895481993937394, -0.31409736394128773, 0.06294260150066242, -0.20961523835620519, -0.1420214894421273, -0.0033145273566047213, -0.04415008645986071, 0.06786533682921043, -0.22791848001018, -0.03591480167723899, 0.009211048165341483, 0.08920774886677607, -0.008495406556372227, -0.1709411454104473, 0.04067381342553816, 0.08763646416750663, 0.007775427944947746, 0.04299120833767641, 0.14922681003200738, -0.17427146183640768, -0.14207602934022381, 0.39066013192569604, -0.009656906432375375, -0.12922277346546396, 0.18007294955209494, -0.2227762912483781, -0.13495868196438873, 0.12910446054396987, 0.27862515123665665, 0.050747616054343705, -0.18706527840034784, 0.09475723359854159, 0.01360894163437397, 0.16830401763508326, 0.04093390965985992, 0.03243870869817819, 0.23083464097718218, 0.31789344609348796, 0.021900795269992956, 0.12578155013818942, -0.11562380135802322, 0.004859670353111591, -0.4029106772870997, -0.17668478346293728, -0.1451626655101525, -0.011781499268528953, 0.006997047055459037, -0.11619166245035241, 0.42631398607699433, 0.13264724453178683, 0.09525204627523513, -0.04852760424955194, 0.2963669735394167, -0.006729902152748376, 0.06491525592678096, -0.008680048267624545, 0.32688075603441114, 0.10822787193000694, 0.19578151026311633, -0.18117048262403965, 0.020354783193867527, 0.040450668642611314] |
1,802.08807 | Global weak solutions to a 3-dimensional degenerate and singular
chemotaxis-Navier--Stokes system with logistic source | This paper considers the degenerate and singular chemotaxis-Navier--Stokes
system with logistic term
$n_t + u\cdot\nabla n =\Delta n^m - \chi\nabla\cdot(n\nabla c) + \kappa n
-\mu n^2$, $x \in \Omega,\ t>0$,
$c_t + u\cdot\nabla c = \Delta c - nc$, $x \in \Omega,\ t>0$,
$u_t + (u\cdot\nabla)u = \Delta u + \nabla P + n\nabla\Phi, \quad \nabla\cdot
u = 0$, $x \in \Omega,\ t>0$, where $\Omega\subset \mathbb{R}^3$ is a bounded
domain and $\chi,\kappa \ge 0$ and $m, \mu >0$. In the above system without
fluid environment Jin (J. Differential Equations, 2017) showed existence and
boundedness of global weak solutions. On the other hand, in the above system
with $m=1$, Lankeit (Math.\ Models Methods Appl. Sci., 2016) established global
existence of weak solutions. However, the above system with $m>0$ has not been
studied yet. The purpose of this talk is to establish global existence of weak
solutions in the chemotaxis-Navier--Stokes system with degenerate diffusion and
logistic term.
| math.AP | this paper considers the degenerate and singular chemotaxisnavierstokes system with logistic term n_t ucdotnabla n delta nm chinablacdotnnabla c kappa n mu n2 x in omega t0 c_t ucdotnabla c delta c nc x in omega t0 u_t ucdotnablau delta u nabla p nnablaphi quad nablacdot u 0 x in omega t0 where omegasubset mathbbr3 is a bounded domain and chikappa ge 0 and m mu 0 in the above system without fluid environment jin j differential equations 2017 showed existence and boundedness of global weak solutions on the other hand in the above system with m1 lankeit math models methods appl sci 2016 established global existence of weak solutions however the above system with m0 has not been studied yet the purpose of this talk is to establish global existence of weak solutions in the chemotaxisnavierstokes system with degenerate diffusion and logistic term | [['this', 'paper', 'considers', 'the', 'degenerate', 'and', 'singular', 'chemotaxisnavierstokes', 'system', 'with', 'logistic', 'term', 'n_t', 'ucdotnabla', 'n', 'delta', 'nm', 'chinablacdotnnabla', 'c', 'kappa', 'n', 'mu', 'n2', 'x', 'in', 'omega', 't0', 'c_t', 'ucdotnabla', 'c', 'delta', 'c', 'nc', 'x', 'in', 'omega', 't0', 'u_t', 'ucdotnablau', 'delta', 'u', 'nabla', 'p', 'nnablaphi', 'quad', 'nablacdot', 'u', '0', 'x', 'in', 'omega', 't0', 'where', 'omegasubset', 'mathbbr3', 'is', 'a', 'bounded', 'domain', 'and', 'chikappa', 'ge', '0', 'and', 'm', 'mu', '0', 'in', 'the', 'above', 'system', 'without', 'fluid', 'environment', 'jin', 'j', 'differential', 'equations', '2017', 'showed', 'existence', 'and', 'boundedness', 'of', 'global', 'weak', 'solutions', 'on', 'the', 'other', 'hand', 'in', 'the', 'above', 'system', 'with', 'm1', 'lankeit', 'math', 'models', 'methods', 'appl', 'sci', '2016', 'established', 'global', 'existence', 'of', 'weak', 'solutions', 'however', 'the', 'above', 'system', 'with', 'm0', 'has', 'not', 'been', 'studied', 'yet', 'the', 'purpose', 'of', 'this', 'talk', 'is', 'to', 'establish', 'global', 'existence', 'of', 'weak', 'solutions', 'in', 'the', 'chemotaxisnavierstokes', 'system', 'with', 'degenerate', 'diffusion', 'and', 'logistic', 'term']] | [-0.2289529359367277, 0.07735061703050243, 0.03872961853630841, -0.039736219414875706, -0.021404389741032252, -0.2529511363212285, 0.006635250012290531, 0.2984258700960449, -0.2909758347818362, -0.20335243104158768, 0.09952312433243996, -0.36428709738621756, -0.0571303569279345, 0.08935960138665645, -0.028915360383689404, 0.059468008152076174, 0.02097413756086358, 0.04960569868562743, -0.07030663469673268, -0.20621758122994963, 0.31133626074131043, -0.12783217933028937, 0.18073601072059578, 0.03776664817705751, 0.08811411561743755, -0.046298356202896684, 0.06636021807124572, -0.056255025562963315, -0.27472945704524004, -0.015227778771493053, 0.2235803509941044, 0.039301967276592874, 0.27923632361385636, -0.33942089259092295, -0.19636528091920938, 0.17636816101148725, 0.126800401462242, -0.05809337587478305, 0.06198338293969365, -0.3116799189376512, 0.14320569330136226, -0.07807466234080493, -0.18784380481312318, -0.02858960227375584, 0.18710130816325546, 0.07236744012001768, -0.36053044792331224, 0.1657764367958797, 0.15969101035069408, 0.044042007897847464, -0.070379671768751, -0.22897698483346696, -0.09206712242947625, 0.021004683344757982, 0.013627335329407029, 0.1753504867299593, -0.00766286803492611, -0.09860365017915942, -0.007136280335752028, 0.32992607360605947, -0.16618594179827986, -0.23707767407010708, 0.15661452547979673, -0.21781273069292573, -0.14073951028819595, 0.07933775389101357, 0.11662099044104772, 0.1749182547442615, -0.1199306902342609, 0.3193652859934705, -0.05154635332458254, 0.19717366030838873, 0.13766165621179555, -0.046167841813127906, 0.04379191560936826, 0.14111536901577243, 0.12425946383071797, 0.012086103810413208, -0.02860784190997947, -0.02349725544066002, -0.366140573764486, -0.11619438039405006, -0.1291809128231502, 0.17474688279087006, -0.05178245475929413, -0.10984338223268943, 0.22854691735202712, 0.0706919889697539, 0.15963000214126494, 0.030505996088530603, 0.15073169722953544, 0.13565660897376283, -0.09367671972057516, 0.1287208010525709, 0.13243000035706376, 0.1514185709978587, 0.22405892888283624, -0.24505141267020789, -0.03200479826357748, 0.12859709344037193] |
1,802.08808 | Single Image Super-Resolution via Cascaded Multi-Scale Cross Network | The deep convolutional neural networks have achieved significant improvements
in accuracy and speed for single image super-resolution. However, as the depth
of network grows, the information flow is weakened and the training becomes
harder and harder. On the other hand, most of the models adopt a single-stream
structure with which integrating complementary contextual information under
different receptive fields is difficult. To improve information flow and to
capture sufficient knowledge for reconstructing the high-frequency details, we
propose a cascaded multi-scale cross network (CMSC) in which a sequence of
subnetworks is cascaded to infer high resolution features in a coarse-to-fine
manner. In each cascaded subnetwork, we stack multiple multi-scale cross (MSC)
modules to fuse complementary multi-scale information in an efficient way as
well as to improve information flow across the layers. Meanwhile, by
introducing residual-features learning in each stage, the relative information
between high-resolution and low-resolution features is fully utilized to
further boost reconstruction performance. We train the proposed network with
cascaded-supervision and then assemble the intermediate predictions of the
cascade to achieve high quality image reconstruction. Extensive quantitative
and qualitative evaluations on benchmark datasets illustrate the superiority of
our proposed method over state-of-the-art super-resolution methods.
| cs.CV | the deep convolutional neural networks have achieved significant improvements in accuracy and speed for single image superresolution however as the depth of network grows the information flow is weakened and the training becomes harder and harder on the other hand most of the models adopt a singlestream structure with which integrating complementary contextual information under different receptive fields is difficult to improve information flow and to capture sufficient knowledge for reconstructing the highfrequency details we propose a cascaded multiscale cross network cmsc in which a sequence of subnetworks is cascaded to infer high resolution features in a coarsetofine manner in each cascaded subnetwork we stack multiple multiscale cross msc modules to fuse complementary multiscale information in an efficient way as well as to improve information flow across the layers meanwhile by introducing residualfeatures learning in each stage the relative information between highresolution and lowresolution features is fully utilized to further boost reconstruction performance we train the proposed network with cascadedsupervision and then assemble the intermediate predictions of the cascade to achieve high quality image reconstruction extensive quantitative and qualitative evaluations on benchmark datasets illustrate the superiority of our proposed method over stateoftheart superresolution methods | [['the', 'deep', 'convolutional', 'neural', 'networks', 'have', 'achieved', 'significant', 'improvements', 'in', 'accuracy', 'and', 'speed', 'for', 'single', 'image', 'superresolution', 'however', 'as', 'the', 'depth', 'of', 'network', 'grows', 'the', 'information', 'flow', 'is', 'weakened', 'and', 'the', 'training', 'becomes', 'harder', 'and', 'harder', 'on', 'the', 'other', 'hand', 'most', 'of', 'the', 'models', 'adopt', 'a', 'singlestream', 'structure', 'with', 'which', 'integrating', 'complementary', 'contextual', 'information', 'under', 'different', 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1,802.08809 | Uniform semimodular lattices and valuated matroids | In this paper, we present a lattice-theoretic characterization for valuated
matroids, which is an extension of the well-known cryptomorphic equivalence
between matroids and geometric lattices ($=$ atomistic semimodular lattices).
We introduce a class of semimodular lattices, called uniform semimodular
lattices, and establish a cryptomorphic equivalence between integer-valued
valuated matroids and uniform semimodular lattices. Our result includes a
coordinate-free lattice-theoretic characterization of integer points in
tropical linear spaces, incorporates the Dress-Terhalle completion process of
valuated matroids, and establishes a smooth connection with Euclidean buildings
of type A.
| math.CO | in this paper we present a latticetheoretic characterization for valuated matroids which is an extension of the wellknown cryptomorphic equivalence between matroids and geometric lattices atomistic semimodular lattices we introduce a class of semimodular lattices called uniform semimodular lattices and establish a cryptomorphic equivalence between integervalued valuated matroids and uniform semimodular lattices our result includes a coordinatefree latticetheoretic characterization of integer points in tropical linear spaces incorporates the dressterhalle completion process of valuated matroids and establishes a smooth connection with euclidean buildings of type a | [['in', 'this', 'paper', 'we', 'present', 'a', 'latticetheoretic', 'characterization', 'for', 'valuated', 'matroids', 'which', 'is', 'an', 'extension', 'of', 'the', 'wellknown', 'cryptomorphic', 'equivalence', 'between', 'matroids', 'and', 'geometric', 'lattices', 'atomistic', 'semimodular', 'lattices', 'we', 'introduce', 'a', 'class', 'of', 'semimodular', 'lattices', 'called', 'uniform', 'semimodular', 'lattices', 'and', 'establish', 'a', 'cryptomorphic', 'equivalence', 'between', 'integervalued', 'valuated', 'matroids', 'and', 'uniform', 'semimodular', 'lattices', 'our', 'result', 'includes', 'a', 'coordinatefree', 'latticetheoretic', 'characterization', 'of', 'integer', 'points', 'in', 'tropical', 'linear', 'spaces', 'incorporates', 'the', 'dressterhalle', 'completion', 'process', 'of', 'valuated', 'matroids', 'and', 'establishes', 'a', 'smooth', 'connection', 'with', 'euclidean', 'buildings', 'of', 'type', 'a']] | [-0.21374455165295375, 0.10546915053956105, -0.05813553636627538, 0.08431071505706964, -0.14761772874321433, -0.1376428548357494, 0.057153356964201, 0.40250550481002956, -0.34967790073936894, -0.15710422696567894, 0.09703792938740835, -0.19020785151847772, -0.20490293229176176, 0.16401280649006367, -0.1618468170026539, 0.008007007362764506, 0.025214547091828927, -0.06448653181793079, -0.12866127495709362, -0.28886173131676124, 0.320369914628654, -0.036934785245518596, 0.2403439694594237, 0.05375077805088256, 0.15014532279954956, 0.10584034092382699, -0.0017154946302374203, 0.06900317705280724, -0.23200430048607468, 0.2246143007373792, 0.3176977987445536, 0.05616665290047725, 0.180343568214171, -0.3664826830250344, -0.12893580736237623, 0.15765101542984622, 0.026628070161677897, 0.04463099906847458, -0.009403604548424482, -0.20678547896178706, 0.029432315800693772, -0.15419184440647118, -0.1407372215901324, -0.0935747177960972, 0.05829210214904465, 0.06557983375664446, -0.33018497846621486, -0.07008603898741837, 0.27172729186713696, 0.2380138239268923, -0.04396821846187647, -0.10184590899318989, 0.04473408133379139, 0.015942411929635063, -0.14653253426998467, 0.03945773603793766, -0.025865835886049484, -0.022640851961027476, -0.22369077709680868, 0.3955933546558732, -0.011061731781367035, -0.19859075752486074, 0.18585054337468354, -0.08970811565051831, -0.15685845405650548, 0.06800101901448909, 0.1442807961831845, 0.1299248769036716, -0.08237195604791243, 0.1790966057867211, -0.2574602453525932, 0.06867005427678426, 0.14032841464948087, 0.03897306590806693, 0.18097212114593103, 0.13616767189177198, 0.14239760901823284, 0.2868748615008025, 0.10471029617335825, -0.06880969335422076, -0.33731911201002296, -0.17654716761206232, -0.14637596725619265, 0.09465363996181016, -0.17093221199562927, -0.2793683153577149, 0.3375370646161692, 0.0359277504917589, 0.1335617223798874, 0.21546514607117778, 0.1628984115086496, -0.025715437028945114, 0.052158354027640255, 0.08120862917608715, 0.12703170856860066, 0.30134947986031574, -0.012990071172160762, -0.04783265146016631, -0.04248259893003186, 0.30870928399131764] |
1,802.0881 | Approaches for Modeling of the Complex Heat and Fluid Flows and Another
Physical Processes of Fractal Nature | The paper deals with the fundamental problem of a modeling of the physical,
in particular, thermal hydraulic processes, in various media of fractal
structure of the natural, technological and technical systems and devices. The
examples of a few complex systems (mainly, the physical processes) are shown as
the different fractal structures, e.g. the ones obtained by cooling and
solidification of the multiphase mixtures. Some materials reveal the structural
features having a significant influence on the behaviors of these materials.
The thermal hydraulic processes which are going during a melts cooling, with
the further water and steam flow through a porous or a channeled media, are
presenting the characteristic examples. The best models of the thermal
hydraulic and other physical processes in such fractal media should be the ones
based on the fractional differential equations. An order of the fractional
derivatives in time and space may change in the process. For example, by a
cooling of a melt, with a change of the fractal properties of the system, it is
going as follows: first, the vapor, water, melt mixture is interacting, then a
formation of the solid structure is performing due to solidification of a melt,
and a vapor flows through the permeable structure developed in a process. The
dynamically changing fractal systems (dynamic fractals) must be modeled by the
corresponding equations changing according to the evolving process. Therefore,
it is not surprising that so far some of such tasks have not been properly
resolved even in a simplified formulation. There are many similar problems in
the modern physics, technology, etc., which are discussed in the paper.
| physics.comp-ph | the paper deals with the fundamental problem of a modeling of the physical in particular thermal hydraulic processes in various media of fractal structure of the natural technological and technical systems and devices the examples of a few complex systems mainly the physical processes are shown as the different fractal structures eg the ones obtained by cooling and solidification of the multiphase mixtures some materials reveal the structural features having a significant influence on the behaviors of these materials the thermal hydraulic processes which are going during a melts cooling with the further water and steam flow through a porous or a channeled media are presenting the characteristic examples the best models of the thermal hydraulic and other physical processes in such fractal media should be the ones based on the fractional differential equations an order of the fractional derivatives in time and space may change in the process for example by a cooling of a melt with a change of the fractal properties of the system it is going as follows first the vapor water melt mixture is interacting then a formation of the solid structure is performing due to solidification of a melt and a vapor flows through the permeable structure developed in a process the dynamically changing fractal systems dynamic fractals must be modeled by the corresponding equations changing according to the evolving process therefore it is not surprising that so far some of such tasks have not been properly resolved even in a simplified formulation there are many similar problems in the modern physics technology etc which are discussed in the paper | [['the', 'paper', 'deals', 'with', 'the', 'fundamental', 'problem', 'of', 'a', 'modeling', 'of', 'the', 'physical', 'in', 'particular', 'thermal', 'hydraulic', 'processes', 'in', 'various', 'media', 'of', 'fractal', 'structure', 'of', 'the', 'natural', 'technological', 'and', 'technical', 'systems', 'and', 'devices', 'the', 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1,802.08811 | Bound on the diameter of split metacyclic groups | Let $G_{m,n,k} = \mathbb{Z}_m \ltimes_k \mathbb{Z}_n$ be the split metacyclic
group, where $k$ is a unit modulo $n$. We derive an upper bound for the
diameter of $G_{m,n,k}$ using an arithmetic parameter called the
\textit{weight}, which depends on $n$, $k$, and the order of $k$. As an
application, we show how this would determine a bound on the diameter of an
arbitrary metacyclic group.
| math.CO math.GR | let g_mnk mathbbz_m ltimes_k mathbbz_n be the split metacyclic group where k is a unit modulo n we derive an upper bound for the diameter of g_mnk using an arithmetic parameter called the textitweight which depends on n k and the order of k as an application we show how this would determine a bound on the diameter of an arbitrary metacyclic group | [['let', 'g_mnk', 'mathbbz_m', 'ltimes_k', 'mathbbz_n', 'be', 'the', 'split', 'metacyclic', 'group', 'where', 'k', 'is', 'a', 'unit', 'modulo', 'n', 'we', 'derive', 'an', 'upper', 'bound', 'for', 'the', 'diameter', 'of', 'g_mnk', 'using', 'an', 'arithmetic', 'parameter', 'called', 'the', 'textitweight', 'which', 'depends', 'on', 'n', 'k', 'and', 'the', 'order', 'of', 'k', 'as', 'an', 'application', 'we', 'show', 'how', 'this', 'would', 'determine', 'a', 'bound', 'on', 'the', 'diameter', 'of', 'an', 'arbitrary', 'metacyclic', 'group']] | [-0.2227843111801532, 0.16474912522244267, -0.08668311508071999, -0.04254812466716694, -0.06633599520841192, -0.1274545973377122, 0.04428154362305518, 0.3050164060757285, -0.2683766332507554, -0.3174148045720593, 0.11248417596535516, -0.23851143718967516, -0.09545379795599729, 0.17890317915683432, -0.06306554267423288, -0.08024957962334156, -0.049871370887323734, 0.20783862086283345, -0.042878170377544815, -0.3109101098211062, 0.27418667907195704, 0.002185287224429269, 0.1688908757942338, 0.06915099421260698, 0.06897981182461785, 0.040907977351678476, 0.063620044579429, -0.008453800812393667, -0.22802257903611228, 0.09356815467077878, 0.23567414912770712, 0.07307441347849465, 0.22351368429559854, -0.3713940941097756, -0.11316279570511993, 0.1945488584948884, 0.1998164820665073, -0.00992938758046817, 0.00937868349419366, -0.23556944595709925, 0.15597628680388292, -0.14563280072123294, -0.13578933602078788, -0.017192687764162978, 0.1112302147873467, -0.03387696412016427, -0.3186992278262492, -0.07228014947125508, 0.10560403261033277, 0.1048480046179799, -0.01857260007020687, -0.17872480627509854, 0.03224358940497041, 0.11758440151630391, -0.07489186229800145, 0.051840704415113695, 0.061417484486986314, -0.054592392613901, -0.10027500201437262, 0.3632418606430292, -0.098612833927731, -0.17752138672456627, 0.061842853398693186, -0.10434880971367802, -0.15081152572266518, 0.09672979520814072, 0.1741955944021503, 0.19848109523375188, -0.02040453213116815, 0.20415593109810876, -0.17620767004066898, 0.23450679496313714, 0.06929724119723804, -0.01809446244395428, 0.10424266460881147, 0.0998701133479875, 0.15815203621863358, 0.1785986156544588, -0.05857904747714319, 0.06715268746859604, -0.3369736855308856, -0.18908356970754422, -0.19543125504447567, 0.1435235270013612, -0.15408083831026753, -0.1547712984855377, 0.30570029948026906, 0.07281590972636495, 0.22615794801423628, 0.11031070320243616, 0.19822643389324507, 0.11118618946995647, 0.05975344990410151, 0.12435052059440603, 0.07010038958622082, 0.19688952990597294, -0.143000460590326, -0.22833099637571122, 0.005862845475935648, 0.14254197161719803] |
1,802.08812 | Kernel-smoothed proper orthogonal decomposition (KSPOD)-based emulation
for prediction of spatiotemporally evolving flow dynamics | This interdisciplinary study, which combines machine learning, statistical
methodologies, high-fidelity simulations, and flow physics, demonstrates a new
process for building an efficient surrogate model for predicting
spatiotemporally evolving flow dynamics. In our previous work, a common-grid
proper-orthogonal-decomposition (CPOD) technique was developed to establish a
physics-based surrogate (emulation) model for prediction of mean flowfields and
design exploration over a wide parameter space. The CPOD technique is
substantially improved upon here using a kernel-smoothed POD (KSPOD) technique,
which leverages kriging-based weighted functions from the design matrix. The
resultant emulation model is then trained using a dataset obtained through
high-fidelity simulations. As an example, the flow evolution in a swirl
injector is considered for a wide range of design parameters and operating
conditions. The KSPOD-based emulation model performs well, and can faithfully
capture the spatiotemporal flow dynamics. The model enables effective design
surveys utilizing high-fidelity simulation data, achieving a turnaround time
for evaluating new design points that is 42,000 times faster than the original
simulation.
| cs.CE | this interdisciplinary study which combines machine learning statistical methodologies highfidelity simulations and flow physics demonstrates a new process for building an efficient surrogate model for predicting spatiotemporally evolving flow dynamics in our previous work a commongrid properorthogonaldecomposition cpod technique was developed to establish a physicsbased surrogate emulation model for prediction of mean flowfields and design exploration over a wide parameter space the cpod technique is substantially improved upon here using a kernelsmoothed pod kspod technique which leverages krigingbased weighted functions from the design matrix the resultant emulation model is then trained using a dataset obtained through highfidelity simulations as an example the flow evolution in a swirl injector is considered for a wide range of design parameters and operating conditions the kspodbased emulation model performs well and can faithfully capture the spatiotemporal flow dynamics the model enables effective design surveys utilizing highfidelity simulation data achieving a turnaround time for evaluating new design points that is 42000 times faster than the original simulation | [['this', 'interdisciplinary', 'study', 'which', 'combines', 'machine', 'learning', 'statistical', 'methodologies', 'highfidelity', 'simulations', 'and', 'flow', 'physics', 'demonstrates', 'a', 'new', 'process', 'for', 'building', 'an', 'efficient', 'surrogate', 'model', 'for', 'predicting', 'spatiotemporally', 'evolving', 'flow', 'dynamics', 'in', 'our', 'previous', 'work', 'a', 'commongrid', 'properorthogonaldecomposition', 'cpod', 'technique', 'was', 'developed', 'to', 'establish', 'a', 'physicsbased', 'surrogate', 'emulation', 'model', 'for', 'prediction', 'of', 'mean', 'flowfields', 'and', 'design', 'exploration', 'over', 'a', 'wide', 'parameter', 'space', 'the', 'cpod', 'technique', 'is', 'substantially', 'improved', 'upon', 'here', 'using', 'a', 'kernelsmoothed', 'pod', 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-0.19111676749516612, 0.08521198876237926, 0.05735719547523425] |
1,802.08813 | Observer-Based Controllers for Incrementally Quadratic Nonlinear Systems
with Disturbances | Robust global stabilization of nonlinear systems by observer-based feedback
controllers is a challenging task. This article investigates the problem of
designing observer-based stabilizing controllers for incrementally quadratic
nonlinear systems with external disturbances. The nonlinearities considered in
the system model satisfy the incremental quadratic constraints, which are
characterized by incremental multiplier matrices and encompass many common
nonlinearities. The simultaneous search for the observer and the controller
gain matrices is formulated as a feasibility problem of linear matrix
inequalities, for two parameterizations (i.e., the block diagonal
parameterization and the block anti-triangular parameterization) of the
incremental multiplier matrices, respectively. The closed-loop system
implementing the observer-based feedback controller is proven to be
input-to-state stable with respect to external disturbances. Using the proposed
continuous-time observer-based controllers, event-triggered controllers with
time regularization are constructed for globally Lipschitz systems, such that
the closed-loop system is Zeno-free and input-to-state practically stable.
| math.OC | robust global stabilization of nonlinear systems by observerbased feedback controllers is a challenging task this article investigates the problem of designing observerbased stabilizing controllers for incrementally quadratic nonlinear systems with external disturbances the nonlinearities considered in the system model satisfy the incremental quadratic constraints which are characterized by incremental multiplier matrices and encompass many common nonlinearities the simultaneous search for the observer and the controller gain matrices is formulated as a feasibility problem of linear matrix inequalities for two parameterizations ie the block diagonal parameterization and the block antitriangular parameterization of the incremental multiplier matrices respectively the closedloop system implementing the observerbased feedback controller is proven to be inputtostate stable with respect to external disturbances using the proposed continuoustime observerbased controllers eventtriggered controllers with time regularization are constructed for globally lipschitz systems such that the closedloop system is zenofree and inputtostate practically stable | [['robust', 'global', 'stabilization', 'of', 'nonlinear', 'systems', 'by', 'observerbased', 'feedback', 'controllers', 'is', 'a', 'challenging', 'task', 'this', 'article', 'investigates', 'the', 'problem', 'of', 'designing', 'observerbased', 'stabilizing', 'controllers', 'for', 'incrementally', 'quadratic', 'nonlinear', 'systems', 'with', 'external', 'disturbances', 'the', 'nonlinearities', 'considered', 'in', 'the', 'system', 'model', 'satisfy', 'the', 'incremental', 'quadratic', 'constraints', 'which', 'are', 'characterized', 'by', 'incremental', 'multiplier', 'matrices', 'and', 'encompass', 'many', 'common', 'nonlinearities', 'the', 'simultaneous', 'search', 'for', 'the', 'observer', 'and', 'the', 'controller', 'gain', 'matrices', 'is', 'formulated', 'as', 'a', 'feasibility', 'problem', 'of', 'linear', 'matrix', 'inequalities', 'for', 'two', 'parameterizations', 'ie', 'the', 'block', 'diagonal', 'parameterization', 'and', 'the', 'block', 'antitriangular', 'parameterization', 'of', 'the', 'incremental', 'multiplier', 'matrices', 'respectively', 'the', 'closedloop', 'system', 'implementing', 'the', 'observerbased', 'feedback', 'controller', 'is', 'proven', 'to', 'be', 'inputtostate', 'stable', 'with', 'respect', 'to', 'external', 'disturbances', 'using', 'the', 'proposed', 'continuoustime', 'observerbased', 'controllers', 'eventtriggered', 'controllers', 'with', 'time', 'regularization', 'are', 'constructed', 'for', 'globally', 'lipschitz', 'systems', 'such', 'that', 'the', 'closedloop', 'system', 'is', 'zenofree', 'and', 'inputtostate', 'practically', 'stable']] | [-0.23862174129882058, 0.02482877310912509, -0.02781839768599289, 0.011007946175913866, -0.06387136771402263, -0.25769594282732455, -0.021914552586231303, 0.3639767436974949, -0.3480999618508182, -0.24857757956488058, 0.21303236557714564, -0.18513275546484165, -0.19325915858852905, 0.1975485331141162, -0.1236109191468767, 0.20859215671678524, 0.044228268741675604, -0.024642362232056243, -0.046772383523036865, -0.23476126842148654, 0.3220124143937772, 0.05616250215301785, 0.21661764483937954, -0.10875793425952429, 0.1950158590216424, 0.0022773240250046852, 0.022890813502163945, 0.008070577185119053, -0.034940041781012766, 0.07114881426226217, 0.3077354026238688, 0.13217077451478038, 0.33269341633870053, -0.40048724033876315, -0.1905146271683979, 0.14490580961264513, 0.09652832462419789, 0.08385011935981082, -0.05820109511536735, -0.31559267813678493, 0.12878329277696363, -0.17010929303934108, -0.1117515911094167, -0.10348571170206097, -0.03032932563040119, 0.039805834923576366, -0.3987255252415796, 0.01488291575749229, 0.10023292997453877, 0.04163927138030816, -0.16000076310246714, -0.07007418973517923, -0.0021352966322557075, 0.0970151970078322, -0.0289614231893923, -0.029159813275938477, 0.171031675028771, -0.0633891832435152, -0.13409340678277862, 0.32180154300131997, 0.0070518140528038455, -0.2854320632352435, 0.1451561360626475, -0.005021769781369013, -0.09680656549338433, 0.11979896570002282, 0.22407051244216575, 0.10299758848219694, -0.2096022283332748, 0.10045167970979078, 0.005634134674405719, 0.1990625212177426, 0.0007596147227120566, 0.04014954426801288, 0.1116076363869324, 0.17719137388235934, 0.23497829513609655, 0.15154764577469598, 0.07321116804280416, -0.1491820685943211, -0.28894450191173937, -0.05301644068563005, -0.11035086576488208, -0.05184639624476276, -0.10715461373809478, -0.15770891176575755, 0.38123088244713477, 0.11259628204674414, 0.099810368052547, 0.1283116080575147, 0.3507042460182643, 0.17229961694814133, 0.0445244224095665, 0.09886660127879375, 0.28507165037994303, 0.15901691963166728, 0.11630373469625528, -0.30516479873152164, 0.11140763168744222, 0.11503965192689346] |
1,802.08814 | Monte Carlo Ray-Trace Diffraction Based On the Huygens-Fresnel Principle | The goal of this effort is to establish the conditions and limits under which
the Huygens-Fresnel principle accurately describes diffraction in the Monte
Carlo ray-trace environment. This goal is achieved by systematic
intercomparison of dedicated experimental, theoretical, and numerical results.
We evaluate the success of the Huygens-Fresnel principle by predicting and
carefully measuring the diffraction fringes produced by both single slit and
circular apertures. We then compare the results from the analytical and
numerical approaches with each other and with dedicated experimental results.
We conclude that use of the MCRT method to accurately describe diffraction
requires that careful attention be paid to the interplay among the number of
aperture points, the number of rays traced per aperture point, and the number
of bins on the screen. This conclusion is supported by standard statistical
analysis, including the adjusted coefficient of determination, the
root-mean-square deviation, and the reduced chi-square statistics.
| physics.optics | the goal of this effort is to establish the conditions and limits under which the huygensfresnel principle accurately describes diffraction in the monte carlo raytrace environment this goal is achieved by systematic intercomparison of dedicated experimental theoretical and numerical results we evaluate the success of the huygensfresnel principle by predicting and carefully measuring the diffraction fringes produced by both single slit and circular apertures we then compare the results from the analytical and numerical approaches with each other and with dedicated experimental results we conclude that use of the mcrt method to accurately describe diffraction requires that careful attention be paid to the interplay among the number of aperture points the number of rays traced per aperture point and the number of bins on the screen this conclusion is supported by standard statistical analysis including the adjusted coefficient of determination the rootmeansquare deviation and the reduced chisquare statistics | [['the', 'goal', 'of', 'this', 'effort', 'is', 'to', 'establish', 'the', 'conditions', 'and', 'limits', 'under', 'which', 'the', 'huygensfresnel', 'principle', 'accurately', 'describes', 'diffraction', 'in', 'the', 'monte', 'carlo', 'raytrace', 'environment', 'this', 'goal', 'is', 'achieved', 'by', 'systematic', 'intercomparison', 'of', 'dedicated', 'experimental', 'theoretical', 'and', 'numerical', 'results', 'we', 'evaluate', 'the', 'success', 'of', 'the', 'huygensfresnel', 'principle', 'by', 'predicting', 'and', 'carefully', 'measuring', 'the', 'diffraction', 'fringes', 'produced', 'by', 'both', 'single', 'slit', 'and', 'circular', 'apertures', 'we', 'then', 'compare', 'the', 'results', 'from', 'the', 'analytical', 'and', 'numerical', 'approaches', 'with', 'each', 'other', 'and', 'with', 'dedicated', 'experimental', 'results', 'we', 'conclude', 'that', 'use', 'of', 'the', 'mcrt', 'method', 'to', 'accurately', 'describe', 'diffraction', 'requires', 'that', 'careful', 'attention', 'be', 'paid', 'to', 'the', 'interplay', 'among', 'the', 'number', 'of', 'aperture', 'points', 'the', 'number', 'of', 'rays', 'traced', 'per', 'aperture', 'point', 'and', 'the', 'number', 'of', 'bins', 'on', 'the', 'screen', 'this', 'conclusion', 'is', 'supported', 'by', 'standard', 'statistical', 'analysis', 'including', 'the', 'adjusted', 'coefficient', 'of', 'determination', 'the', 'rootmeansquare', 'deviation', 'and', 'the', 'reduced', 'chisquare', 'statistics']] | [-0.04520261651841055, 0.0707906199103171, -0.09592780026582044, 0.05897304643735264, -0.04372098448377958, -0.08046357934300259, 0.10394667829009327, 0.391990383671647, -0.23618586967082544, -0.35976474935415426, 0.07201998692078239, -0.3047846830324144, -0.10249848560920942, 0.23996621588043668, -0.04526587999960709, 0.11324437380755774, 0.09961443383724906, -0.023059635177707753, -0.05998820255860443, -0.22218990413475204, 0.2853026112975754, 0.11592569035427594, 0.3111746649736086, 0.05476265768260959, 0.09153798701103173, 0.05495407744122015, -0.10736160417011864, 0.05293026872724924, -0.15167829792404222, 0.13511065681578238, 0.20914896989140558, 0.11366225646594791, 0.22817983818429247, -0.42028115353723233, -0.19754235164224598, 0.06529049443181704, 0.09045298258314614, 0.08015447153593414, -0.047440181502826965, -0.28196454066405624, 0.0804792831437914, -0.12934078434419288, -0.15955991256428328, -0.06410166221305828, -0.061106023082912374, 0.05240673525888171, -0.2526080322332328, 0.04603719327526444, -0.0014267396539720278, 0.07844964042935218, -0.004577820431566923, -0.11361058422590832, 0.0213665625637372, 0.12449335285880905, 0.0827796261205426, 0.013763075993346, 0.11748157735486087, -0.08868848164269907, -0.0912305609467817, 0.39205204353139206, -0.031835128987409374, -0.1951448068469511, 0.14092182109645265, -0.1815094555951529, -0.08485997362200774, 0.14547267275377856, 0.13633492202309236, 0.10080920309268844, -0.14275819982700538, 0.03047069675616316, -0.024949862564303534, 0.190436467123446, 0.05705201669086425, -0.021110708985754567, 0.20475316801536325, 0.17985825592375085, 0.00295823353116174, 0.13947198560226998, -0.1647336667362676, -0.08213971086110719, -0.30735483029983135, -0.1109347070419431, -0.20996927308717844, 0.0030817174261461035, -0.0900305522144835, -0.0904382573231438, 0.3697292664815118, 0.18554497668456688, 0.16972107196783623, 0.058799917917428036, 0.33860785337290855, 0.09878427038957188, 0.03858441535565952, -0.004420846379542962, 0.2767331158440258, 0.13834245847347365, 0.07836687360651086, -0.26654044487293355, 0.05105987786019624, 0.04076360719517578] |
1,802.08815 | Entanglement entropy in (1+1)D CFTs with multiple local excitations | In this paper, we use the replica approach to study the R\'enyi entropy $S_L$
of generic locally excited states in (1+1)D CFTs, which are constructed from
the insertion of multiple product of local primary operators on vacuum.
Alternatively, one can calculate the R\'enyi entropy $S_R$ corresponding to the
same states using Schmidt decomposition and operator product expansion, which
reduces the multiple product of local primary operators to linear combination
of operators. The equivalence $S_L=S_R$ translates into an identity in terms of
the $F$ symbols and quantum dimensions for rational CFT, and the latter can be
proved algebraically. This, along with a series of papers, gives a complete
picture of how the quantum information quantities and the intrinsic structure
of (1+1)D CFTs are consistently related.
| hep-th quant-ph | in this paper we use the replica approach to study the renyi entropy s_l of generic locally excited states in 11d cfts which are constructed from the insertion of multiple product of local primary operators on vacuum alternatively one can calculate the renyi entropy s_r corresponding to the same states using schmidt decomposition and operator product expansion which reduces the multiple product of local primary operators to linear combination of operators the equivalence s_ls_r translates into an identity in terms of the f symbols and quantum dimensions for rational cft and the latter can be proved algebraically this along with a series of papers gives a complete picture of how the quantum information quantities and the intrinsic structure of 11d cfts are consistently related | [['in', 'this', 'paper', 'we', 'use', 'the', 'replica', 'approach', 'to', 'study', 'the', 'renyi', 'entropy', 's_l', 'of', 'generic', 'locally', 'excited', 'states', 'in', '11d', 'cfts', 'which', 'are', 'constructed', 'from', 'the', 'insertion', 'of', 'multiple', 'product', 'of', 'local', 'primary', 'operators', 'on', 'vacuum', 'alternatively', 'one', 'can', 'calculate', 'the', 'renyi', 'entropy', 's_r', 'corresponding', 'to', 'the', 'same', 'states', 'using', 'schmidt', 'decomposition', 'and', 'operator', 'product', 'expansion', 'which', 'reduces', 'the', 'multiple', 'product', 'of', 'local', 'primary', 'operators', 'to', 'linear', 'combination', 'of', 'operators', 'the', 'equivalence', 's_ls_r', 'translates', 'into', 'an', 'identity', 'in', 'terms', 'of', 'the', 'f', 'symbols', 'and', 'quantum', 'dimensions', 'for', 'rational', 'cft', 'and', 'the', 'latter', 'can', 'be', 'proved', 'algebraically', 'this', 'along', 'with', 'a', 'series', 'of', 'papers', 'gives', 'a', 'complete', 'picture', 'of', 'how', 'the', 'quantum', 'information', 'quantities', 'and', 'the', 'intrinsic', 'structure', 'of', '11d', 'cfts', 'are', 'consistently', 'related']] | [-0.12915329969482406, 0.15390115047010453, -0.09673770044634981, 0.05422963038639627, -0.005489441484773184, -0.12267764250376845, 0.00013469965708237596, 0.26406526454247353, -0.27194147791365175, -0.2190075426445744, 0.0939416350594275, -0.30613377150072435, -0.11021150232338142, 0.1365619838025754, -0.06725213794052844, 0.057114671499324525, 0.009971851172546545, 0.09533369183404053, -0.12815022265644577, -0.25072192533009846, 0.38840664061528396, 0.00743247527909291, 0.2813196202391774, 0.053584617663863895, 0.08047592627808331, 0.03336449464919364, -0.03702781287521669, 0.011676511799204883, -0.11824729764546321, 0.1789654313986061, 0.25531426741650737, 0.12197990085914488, 0.19784759848787473, -0.422008979429559, -0.17203760273542587, 0.10664518886223072, 0.1600997463943876, 0.07650558102868377, 0.055578892996602854, -0.2668048556197465, 0.04784960876128114, -0.21140931771568408, -0.11693151811576956, -0.0978884504827845, -0.006403030630776553, -0.04970661881834087, -0.28501679680766384, 0.09080793464374615, 0.05075878411819174, 0.03638791666002717, -0.07329036745174629, -0.06132011360685697, -0.07303190916375356, 0.13999524120453413, 0.032602915918923975, 0.03505504500154951, 0.1113605510246584, -0.08361132612557916, -0.14023308946213464, 0.30549370382374863, -0.06654070258496435, -0.23036317308092627, 0.14742743569933545, -0.1534539592685178, -0.10991961722701364, 0.07086156390993516, 0.12272974904399456, 0.1576316628029676, -0.12990604236209008, 0.1473706314450024, -0.04102519258281322, 0.12284547875529321, 0.08318234315308613, 0.08719346140382614, 0.20340584043027243, -0.016657096515523225, 0.058751453327514774, 0.22308071249077566, 0.016481890765253485, -0.1269008287141569, -0.3599201148173325, -0.21477201506798346, -0.1934336318361868, 0.09906213741931247, -0.12764210594255795, -0.19053155535877478, 0.43564613920644046, 0.0839367401076299, 0.19721676294864118, 0.048087529272098486, 0.2308330060989876, 0.1640621061243753, 0.09224160713696383, 0.061865249713049915, 0.15756898034151975, 0.17547590068420318, 0.038447579410593444, -0.21802008124607866, -0.007688180978856678, 0.19321193079638288] |
1,802.08816 | Lagrangian distributions on asymptotically Euclidean manifolds | We develop the notion of Lagrangian distribution on scattering manifolds,
meaning on the compactified cotangent bundle, which is a manifold with corners
equipped with a scattering symplectic structure. In particular, we study the
notion of principal symbol of the arising class of distributions.
| math.AP | we develop the notion of lagrangian distribution on scattering manifolds meaning on the compactified cotangent bundle which is a manifold with corners equipped with a scattering symplectic structure in particular we study the notion of principal symbol of the arising class of distributions | [['we', 'develop', 'the', 'notion', 'of', 'lagrangian', 'distribution', 'on', 'scattering', 'manifolds', 'meaning', 'on', 'the', 'compactified', 'cotangent', 'bundle', 'which', 'is', 'a', 'manifold', 'with', 'corners', 'equipped', 'with', 'a', 'scattering', 'symplectic', 'structure', 'in', 'particular', 'we', 'study', 'the', 'notion', 'of', 'principal', 'symbol', 'of', 'the', 'arising', 'class', 'of', 'distributions']] | [-0.22641274958935587, 0.06053728328714537, -0.10887095947251764, 0.061253317688635095, -0.13841032202160636, -0.09828320132611795, -0.05683730022851811, 0.35478129739829795, -0.29244318431199984, -0.20677316372910903, 0.07472606256961563, -0.27716285241551175, -0.20289668094280155, 0.09752087596110827, -0.1385884593175941, -0.002656899268067507, 0.0630622238765449, 0.1018135308851163, -0.15883448481733023, -0.16537521913550188, 0.5718644774237345, 0.025688009152492117, 0.26641407782255216, 0.03783166165961776, 0.16601357130377098, 0.05822014870381979, -0.005325659871274649, -0.018471072414391783, -0.13976736992684213, 0.21377554243473812, 0.2214021315300014, 0.04031190414761388, 0.14739510017466803, -0.3952517816580312, -0.1950067741428177, 0.13049061241191487, 0.11326822703487652, -0.0009415043480044534, 0.0278555063996464, -0.2890580122380756, 0.04738852588546484, -0.127990506892634, -0.18126089563376682, -0.061549840603283675, -0.036187230414429374, -0.02074760273856999, -0.18687581141657997, -0.06321672828824715, 0.08921285701352497, 0.11763631643424201, -0.07383435803227299, -0.050097838044166565, -0.048247362183796806, 0.013861713801012483, 0.05661200963198965, 0.03148016105622573, 0.13672282398396798, -0.04811441514987585, -0.11052956958409658, 0.38159324207104917, -0.10778475639431975, -0.3302639611065388, 0.08972588510707367, -0.1712646308831524, -0.17911032597036208, 0.1483900650122831, 0.1883428202465523, 0.1938805602880758, -0.027559540517422435, 0.1705926697874485, -0.09866135991936506, 0.04021096021630043, 0.05913742783284465, 0.03878963409468185, 0.14522350172317305, 0.18654740464245512, 0.10144321172129969, 0.11033529597659443, -0.13968775349907403, -0.16966016997778138, -0.3873577641193257, -0.20549392587570256, -0.12764358312584634, 0.17616496198312487, -0.0971381051011037, -0.22035961289521913, 0.3831378290819567, 0.020410738652572036, 0.27278145805521065, 0.14702201979288962, 0.22283880785107613, 0.07408351216568114, 0.06742616311841926, 0.030922084537789572, 0.12570213240592978, 0.2432241181648055, 0.009852826822722373, -0.15247042207480516, -0.03117954243667597, 0.15862162088498818] |
1,802.08817 | A Twofold Siamese Network for Real-Time Object Tracking | Observing that Semantic features learned in an image classification task and
Appearance features learned in a similarity matching task complement each
other, we build a twofold Siamese network, named SA-Siam, for real-time object
tracking. SA-Siam is composed of a semantic branch and an appearance branch.
Each branch is a similarity-learning Siamese network. An important design
choice in SA-Siam is to separately train the two branches to keep the
heterogeneity of the two types of features. In addition, we propose a channel
attention mechanism for the semantic branch. Channel-wise weights are computed
according to the channel activations around the target position. While the
inherited architecture from SiamFC \cite{SiamFC} allows our tracker to operate
beyond real-time, the twofold design and the attention mechanism significantly
improve the tracking performance. The proposed SA-Siam outperforms all other
real-time trackers by a large margin on OTB-2013/50/100 benchmarks.
| cs.CV | observing that semantic features learned in an image classification task and appearance features learned in a similarity matching task complement each other we build a twofold siamese network named sasiam for realtime object tracking sasiam is composed of a semantic branch and an appearance branch each branch is a similaritylearning siamese network an important design choice in sasiam is to separately train the two branches to keep the heterogeneity of the two types of features in addition we propose a channel attention mechanism for the semantic branch channelwise weights are computed according to the channel activations around the target position while the inherited architecture from siamfc citesiamfc allows our tracker to operate beyond realtime the twofold design and the attention mechanism significantly improve the tracking performance the proposed sasiam outperforms all other realtime trackers by a large margin on otb201350100 benchmarks | [['observing', 'that', 'semantic', 'features', 'learned', 'in', 'an', 'image', 'classification', 'task', 'and', 'appearance', 'features', 'learned', 'in', 'a', 'similarity', 'matching', 'task', 'complement', 'each', 'other', 'we', 'build', 'a', 'twofold', 'siamese', 'network', 'named', 'sasiam', 'for', 'realtime', 'object', 'tracking', 'sasiam', 'is', 'composed', 'of', 'a', 'semantic', 'branch', 'and', 'an', 'appearance', 'branch', 'each', 'branch', 'is', 'a', 'similaritylearning', 'siamese', 'network', 'an', 'important', 'design', 'choice', 'in', 'sasiam', 'is', 'to', 'separately', 'train', 'the', 'two', 'branches', 'to', 'keep', 'the', 'heterogeneity', 'of', 'the', 'two', 'types', 'of', 'features', 'in', 'addition', 'we', 'propose', 'a', 'channel', 'attention', 'mechanism', 'for', 'the', 'semantic', 'branch', 'channelwise', 'weights', 'are', 'computed', 'according', 'to', 'the', 'channel', 'activations', 'around', 'the', 'target', 'position', 'while', 'the', 'inherited', 'architecture', 'from', 'siamfc', 'citesiamfc', 'allows', 'our', 'tracker', 'to', 'operate', 'beyond', 'realtime', 'the', 'twofold', 'design', 'and', 'the', 'attention', 'mechanism', 'significantly', 'improve', 'the', 'tracking', 'performance', 'the', 'proposed', 'sasiam', 'outperforms', 'all', 'other', 'realtime', 'trackers', 'by', 'a', 'large', 'margin', 'on', 'otb201350100', 'benchmarks']] | [-0.0889499793101075, -0.006765828114941913, -0.07914349946061713, 0.03811089983413907, -0.1363809244659641, -0.19719446591020012, 0.033835476991740894, 0.4517025106525334, -0.2683302153116024, -0.31285363635605706, 0.043361677186577206, -0.27406200685168286, -0.17601774864741704, 0.1333844431567203, -0.15344502533493687, 0.04301522358109916, 0.14467790694975288, 0.09733170661803363, -0.060604526754808576, -0.23735409179688804, 0.31080465625638454, 0.046531686967198, 0.37469036958731006, -0.012330623843662958, 0.14780714424360156, -0.014764885445308946, -0.04051850182361166, -0.05724888650857016, -0.024282435368481823, 0.1601853974989486, 0.2902158822959466, 0.17089535734623867, 0.3041628186767717, -0.3727858448740992, -0.19971274983572918, 0.06834668932604963, 0.1446468223355384, 0.072960697418677, -0.028954309605938946, -0.3473823068894609, 0.08364182463626167, -0.17090271180835517, -0.002683356632036667, -0.08523909051530403, -0.034356407447289815, -0.040638241346289884, -0.2684503625058671, -0.039321992702673385, 0.08575721765389534, -0.003926440449703457, -0.05935763926661648, -0.11573582892408119, -0.03279051430570981, 0.21417421245961077, 0.0018576938947016904, 0.06933878454631262, 0.14806810782773652, -0.23617497433901485, -0.14581340887876104, 0.326900761234608, -0.05166871330775592, -0.2168301445547573, 0.19808888679156147, -0.012696815702221254, -0.15121948328990842, 0.11464816557556173, 0.217530878722994, 0.11612381359874985, -0.1771891221036985, -0.03882306560586771, -0.012871381246746538, 0.19391624358428275, 0.05623037199469379, 0.03230700118766323, 0.21562552163003515, 0.30053331493569985, 0.054456134619068924, 0.17640935439008024, -0.19490563192907856, -0.07109029010026614, -0.22497543248001242, -0.08358721528542194, -0.16519500700039721, -0.12641095689898968, -0.09534061570232479, -0.13122774784310456, 0.46018880883055013, 0.20087095215767078, 0.2527081418687301, 0.08997531324271521, 0.3365413775721932, 0.011899260526189894, 0.15017501966361582, 0.09656997950472292, 0.21587172667257978, -0.01139528841888328, 0.12628919366036753, -0.20465792370382288, 0.08638513035542011, 0.11381141872002479] |
1,802.08818 | Optimal QoS Constraint Service Composition in Mobile Ad Hoc Networks | In recent year's computational capability of the mobile nodes have been
greatly improved. The mobile nodes have the capability of running different
applications. Implementation of services in Mobile Ad Hoc Networks (MANETs)
increases the flexibility of using mobile devices for running a wide variety of
applications. Single service cannot satisfy the user needs. The complex needs
of the users can be satisfied by the service composition. Service composition
means, combining the atomic services into a complex service. In this paper we
propose QoS constraint service composition in MANETs. We considered both
service QoS parameters as well node parameters. Response time and throughput as
parameters for services and energy and hop count as node parameters. These four
QoS parameters are optimized using a mathematical model Hammerstein model to
generate a single output. Based on generated output, max valued (optimal)
services are considered in service composition path. The simulation results
shown that, our proposed method outperforms than the traditional AODV method of
service composition.
| cs.NI | in recent years computational capability of the mobile nodes have been greatly improved the mobile nodes have the capability of running different applications implementation of services in mobile ad hoc networks manets increases the flexibility of using mobile devices for running a wide variety of applications single service cannot satisfy the user needs the complex needs of the users can be satisfied by the service composition service composition means combining the atomic services into a complex service in this paper we propose qos constraint service composition in manets we considered both service qos parameters as well node parameters response time and throughput as parameters for services and energy and hop count as node parameters these four qos parameters are optimized using a mathematical model hammerstein model to generate a single output based on generated output max valued optimal services are considered in service composition path the simulation results shown that our proposed method outperforms than the traditional aodv method of service composition | [['in', 'recent', 'years', 'computational', 'capability', 'of', 'the', 'mobile', 'nodes', 'have', 'been', 'greatly', 'improved', 'the', 'mobile', 'nodes', 'have', 'the', 'capability', 'of', 'running', 'different', 'applications', 'implementation', 'of', 'services', 'in', 'mobile', 'ad', 'hoc', 'networks', 'manets', 'increases', 'the', 'flexibility', 'of', 'using', 'mobile', 'devices', 'for', 'running', 'a', 'wide', 'variety', 'of', 'applications', 'single', 'service', 'can', 'not', 'satisfy', 'the', 'user', 'needs', 'the', 'complex', 'needs', 'of', 'the', 'users', 'can', 'be', 'satisfied', 'by', 'the', 'service', 'composition', 'service', 'composition', 'means', 'combining', 'the', 'atomic', 'services', 'into', 'a', 'complex', 'service', 'in', 'this', 'paper', 'we', 'propose', 'qos', 'constraint', 'service', 'composition', 'in', 'manets', 'we', 'considered', 'both', 'service', 'qos', 'parameters', 'as', 'well', 'node', 'parameters', 'response', 'time', 'and', 'throughput', 'as', 'parameters', 'for', 'services', 'and', 'energy', 'and', 'hop', 'count', 'as', 'node', 'parameters', 'these', 'four', 'qos', 'parameters', 'are', 'optimized', 'using', 'a', 'mathematical', 'model', 'hammerstein', 'model', 'to', 'generate', 'a', 'single', 'output', 'based', 'on', 'generated', 'output', 'max', 'valued', 'optimal', 'services', 'are', 'considered', 'in', 'service', 'composition', 'path', 'the', 'simulation', 'results', 'shown', 'that', 'our', 'proposed', 'method', 'outperforms', 'than', 'the', 'traditional', 'aodv', 'method', 'of', 'service', 'composition']] | [-0.2092816296015349, 0.026369932610427308, 0.0108451831691607, 0.004400628278220495, -0.10096936807767745, -0.20714030153706578, 0.10803595079649012, 0.43803577969262114, -0.2604069946322339, -0.3695162498146478, 0.051091045089083166, -0.24180775749114303, -0.13061224935473476, 0.19040463341148628, -0.12697151098171808, 0.1290207067618798, 0.09219814162627091, 0.05874344314579949, -0.029447710588631545, -0.30585132828667944, 0.263174124756268, 0.08066634004478532, 0.3766055255842629, 0.07463457801042159, 0.03493069839360981, 0.012153387633923646, 0.0009575218306511549, -0.010775936390358978, -0.0854010371867788, 0.13272814383227494, 0.3033999684238178, 0.2208602818177636, 0.27920589330063367, -0.46196759807551563, -0.2671206033920035, 0.06322098352969195, 0.17126447853683693, -0.031142140213011597, -0.04103268629891307, -0.29040418681545765, 0.11879996218907138, -0.2913388243756411, -0.06578355015566546, -0.056480280530033344, -0.05724399240881753, 0.12459281385423433, -0.3156377263167199, -0.06622141135707954, -0.07898178191475592, 0.03954401338027299, -0.06931439476729402, -0.09279258579495678, -0.027669423305503794, 0.19345408756045934, 0.03495051592811257, -0.037673824991705185, 0.18417858234299575, -0.1196414792800945, -0.1504526007206299, 0.4276847453599373, -0.004071986507442266, -0.19699136319747365, 0.15688250818935456, 0.034231817761173275, -0.18319101101790977, 0.10454448289595018, 0.23070654008777502, 0.0742869743323125, -0.24136133397717965, 0.06473269428310778, 0.016334673732610567, 0.16068746492358074, 0.0781266405104311, 0.09451306117641414, 0.1529531107651319, 0.25107391189463846, 0.1257054439311607, 0.0661299132400598, -0.03406620800939383, -0.10325850435372648, -0.1755661590087679, -0.14300627571936309, -0.21366727075459538, 0.010886390077930795, -0.14675914923465122, -0.1144317990357122, 0.424983231939284, 0.16467030376104488, 0.13075335019845172, 0.08680283030255075, 0.3795756160687267, 0.10143124223172527, 0.10793858849832022, 0.1298759422362125, 0.13281543068359233, -0.023630348315599597, 0.2253218586785159, -0.12756218390105054, 0.17049873048551037, 0.024349100065011918] |
1,802.08819 | Well-posedness of the free boundary problem in incompressible
elastodynamics | In this paper, we prove the local well-posedness of the free boundary problem
in incompressible elastodynamics under a natural stability condition, which
ensures that the evolution equation describing the free boundary is strictly
hyperbolic. Our result gives a rigorous confirmation that the elasticity has a
stabilizing effect on the Rayleigh-Taylor instability.
| math.AP | in this paper we prove the local wellposedness of the free boundary problem in incompressible elastodynamics under a natural stability condition which ensures that the evolution equation describing the free boundary is strictly hyperbolic our result gives a rigorous confirmation that the elasticity has a stabilizing effect on the rayleightaylor instability | [['in', 'this', 'paper', 'we', 'prove', 'the', 'local', 'wellposedness', 'of', 'the', 'free', 'boundary', 'problem', 'in', 'incompressible', 'elastodynamics', 'under', 'a', 'natural', 'stability', 'condition', 'which', 'ensures', 'that', 'the', 'evolution', 'equation', 'describing', 'the', 'free', 'boundary', 'is', 'strictly', 'hyperbolic', 'our', 'result', 'gives', 'a', 'rigorous', 'confirmation', 'that', 'the', 'elasticity', 'has', 'a', 'stabilizing', 'effect', 'on', 'the', 'rayleightaylor', 'instability']] | [-0.21929795302304567, 0.10692058260753459, -0.15625968358168998, 0.026889895728113605, -0.10958027388608339, -0.08141090069879212, -0.04426232095141256, 0.22179177688325152, -0.2981351579667306, -0.19310902676307687, 0.14269134607257358, -0.20963388761761143, -0.17295541581423843, 0.14005661918325166, -0.0903182229501944, 0.08236558167446478, 0.0978994334262668, 0.001893184412562964, -0.04994380106126853, -0.20035242661833763, 0.37171290172081367, -0.0171940225158252, 0.3228282296628344, 0.10173978038730246, 0.12861698154615714, -0.01391591918746046, 0.04906598500469152, 0.047062269138062704, -0.24586511785581058, 0.06004495586168167, 0.18568809706644684, -7.608176355122352e-05, 0.35021613576157273, -0.4439076682297038, -0.2778404673524931, 0.06428816903601675, 0.1272064876907012, 0.12961237569677406, -0.10079919831945981, -0.2373294659305875, 0.0903208157339809, -0.09927219388020389, -0.21313328856565789, 0.011820024381592577, -0.008869213809934901, -0.03959893700027583, -0.31397968833791273, 0.13447308443559736, 0.1547416705711215, 0.06654605037229154, -0.22324715740065657, 0.011180955250108359, -0.04998729819450162, 0.04655229147779299, 0.0813104933009063, -0.004325220525703009, 0.040128672440700675, -0.10927260604540945, -0.013095857502490866, 0.4128449752050288, -0.06073830736910596, -0.30366148621154326, 0.19486206704202821, -0.1106289452599252, -0.11240287753296833, 0.12764026066653578, 0.10689031699781909, 0.1163793408914524, -0.12169698092575167, 0.1484025137352885, -0.1287635027421821, 0.1448124379659181, 0.08611097678030823, -0.042535371411883946, 0.11482279599808595, 0.20342671592701592, 0.1891312097911449, 0.17957560673319534, 0.017143509035710904, -0.10953687710285771, -0.39819956512427795, -0.18543441980785014, -0.12068509219177798, 0.09534500770763878, -0.08396420605005879, -0.2586885534372984, 0.35808281932829644, 0.14628589660951904, 0.0971909202452676, 0.09319784689703774, 0.28706239846388937, 0.1668809165244502, -0.009555349272547984, 0.092907049957955, 0.2682194390571585, 0.17374251751850048, 0.1090817078308878, -0.27694494535635206, 0.08391013453878901, 0.1832280135256987] |
1,802.0882 | AGN Neutrino flux estimates for a realistic hybrid model | Recent reports of possible correlations between high energy neutrinos
observed by IceCube and Active Galactic Nuclei (AGN) activity sparked a burst
of publications that attempt to predict the neutrino flux of these sources.
However, often rather crude estimates are used to derive the neutrino rate from
the observed photon spectra. In this work neutrino fluxes were computed in a
wide parameter space. The starting point of the model was a representation of
the full spectral energy density (SED) of \textit{3C 279}. The time-dependent
hybrid model that was used for this study takes into account the full $p\gamma$
reaction chain as well as proton synchrotron, electron-positron-pair cascades
and the full SSC scheme. We compare our results to estimates frequently used in
the literature. This allows to identify regions in the parameter space for
which such estimates are still valid and those in which they can produce
significant errors. Furthermore, if estimates for the Doppler factor, magnetic
field, proton and electron densities of a source exist, the expected IceCube
detection rate is readily available.
| astro-ph.HE | recent reports of possible correlations between high energy neutrinos observed by icecube and active galactic nuclei agn activity sparked a burst of publications that attempt to predict the neutrino flux of these sources however often rather crude estimates are used to derive the neutrino rate from the observed photon spectra in this work neutrino fluxes were computed in a wide parameter space the starting point of the model was a representation of the full spectral energy density sed of textit3c 279 the timedependent hybrid model that was used for this study takes into account the full pgamma reaction chain as well as proton synchrotron electronpositronpair cascades and the full ssc scheme we compare our results to estimates frequently used in the literature this allows to identify regions in the parameter space for which such estimates are still valid and those in which they can produce significant errors furthermore if estimates for the doppler factor magnetic field proton and electron densities of a source exist the expected icecube detection rate is readily available | [['recent', 'reports', 'of', 'possible', 'correlations', 'between', 'high', 'energy', 'neutrinos', 'observed', 'by', 'icecube', 'and', 'active', 'galactic', 'nuclei', 'agn', 'activity', 'sparked', 'a', 'burst', 'of', 'publications', 'that', 'attempt', 'to', 'predict', 'the', 'neutrino', 'flux', 'of', 'these', 'sources', 'however', 'often', 'rather', 'crude', 'estimates', 'are', 'used', 'to', 'derive', 'the', 'neutrino', 'rate', 'from', 'the', 'observed', 'photon', 'spectra', 'in', 'this', 'work', 'neutrino', 'fluxes', 'were', 'computed', 'in', 'a', 'wide', 'parameter', 'space', 'the', 'starting', 'point', 'of', 'the', 'model', 'was', 'a', 'representation', 'of', 'the', 'full', 'spectral', 'energy', 'density', 'sed', 'of', 'textit3c', '279', 'the', 'timedependent', 'hybrid', 'model', 'that', 'was', 'used', 'for', 'this', 'study', 'takes', 'into', 'account', 'the', 'full', 'pgamma', 'reaction', 'chain', 'as', 'well', 'as', 'proton', 'synchrotron', 'electronpositronpair', 'cascades', 'and', 'the', 'full', 'ssc', 'scheme', 'we', 'compare', 'our', 'results', 'to', 'estimates', 'frequently', 'used', 'in', 'the', 'literature', 'this', 'allows', 'to', 'identify', 'regions', 'in', 'the', 'parameter', 'space', 'for', 'which', 'such', 'estimates', 'are', 'still', 'valid', 'and', 'those', 'in', 'which', 'they', 'can', 'produce', 'significant', 'errors', 'furthermore', 'if', 'estimates', 'for', 'the', 'doppler', 'factor', 'magnetic', 'field', 'proton', 'and', 'electron', 'densities', 'of', 'a', 'source', 'exist', 'the', 'expected', 'icecube', 'detection', 'rate', 'is', 'readily', 'available']] | [-0.02921534903423741, 0.1515774932630055, -0.05004462416366162, 0.1901583168010584, -0.05359481765796044, -0.0702505101611816, 0.06127455947125516, 0.4024251733868443, -0.19744311408030837, -0.3542136757318693, 0.05049497882729363, -0.27005888709169745, -0.03768708569961682, 0.22593762239370474, -0.00479932723850099, 0.033293390385153, 0.056964095671013085, -0.008540837399182264, -0.053878740770728684, -0.18864016653152935, 0.28296261311274523, 0.14255937109347325, 0.27053312213862674, 0.039024905470466754, 0.0888467849292166, -0.04179723330157978, -0.07709971720581515, -0.017848896483920667, -0.13094814506276684, 0.09335974375179516, 0.27285448285309893, 0.1124068550760068, 0.18191057692516094, -0.41717616053657575, -0.2784104631146231, 0.14078235321362334, 0.1485816764601708, 0.09918712619664856, -0.04346295675379905, -0.2621218551995984, 0.036045261841180204, -0.20819468675753133, -0.1179426766330191, -0.03587102773190852, -0.00452626835519437, 0.0501978569507479, -0.28236071502092724, 0.09525321109481809, 0.005111366451162388, 0.0007508941185701508, -0.10440226640226864, -0.12431646415443644, -0.017584760518123705, 0.12875544531161934, 0.10474312784182921, 0.04813952572737025, 0.13446546928434258, -0.11807393805091188, -0.09403140645076613, 0.37845136699054327, -0.049952188891709964, -0.09828787354741529, 0.15495249537522332, -0.18687971332791256, -0.13734740035455914, 0.19863942349384053, 0.1738874987394097, 0.08736965071126732, -0.17781864522210164, 0.04221095999141839, -0.03503017443877628, 0.17429271817962333, -0.0006295543174907478, 0.02111947766575984, 0.2090160366452751, 0.13868852952026833, 0.03755377583772118, 0.06698077455113985, -0.19734858650692375, -0.03764197889379122, -0.3043689448359199, -0.09199833820100155, -0.1519880072752896, 0.06817409280981672, -0.06247044349469146, -0.12749690622634566, 0.39961912683884826, 0.13983634973475725, 0.20335071994814127, -0.005583535734161037, 0.29575713803414366, 0.11800109327305108, 0.06484856603024473, 0.08431087055227213, 0.305328643559451, 0.10906061751098094, 0.10737732612141217, -0.18872992192872948, 0.0707932594715623, 0.03748317197379139] |
1,802.08821 | Necessity for quantum coherence of nondegeneracy in energy flow | In this work, we show that the quantum coherence among non-degenerate energy
subspaces (CANES) is essential for the energy flow in any quantum system. CANES
satisfies almost all of the requirements as a coherence measure, except that
the coherence within degenerate subspaces is explicitly eliminated.We show that
the energy of a system becomes frozen if and only if the corresponding CANES
vanishes, which is true regardless of the form of interaction with the
environment. However, CANES can remain zero even if the entanglement changes
over time. Furthermore, we show how the power of energy flow is bounded by the
value of CANES. An explicit relation connecting the variation of energy and
CANES is also presented. These results allow us to bound the generation of
system-environment correlation through the local measurement of the system's
energy flow.
| quant-ph | in this work we show that the quantum coherence among nondegenerate energy subspaces canes is essential for the energy flow in any quantum system canes satisfies almost all of the requirements as a coherence measure except that the coherence within degenerate subspaces is explicitly eliminatedwe show that the energy of a system becomes frozen if and only if the corresponding canes vanishes which is true regardless of the form of interaction with the environment however canes can remain zero even if the entanglement changes over time furthermore we show how the power of energy flow is bounded by the value of canes an explicit relation connecting the variation of energy and canes is also presented these results allow us to bound the generation of systemenvironment correlation through the local measurement of the systems energy flow | [['in', 'this', 'work', 'we', 'show', 'that', 'the', 'quantum', 'coherence', 'among', 'nondegenerate', 'energy', 'subspaces', 'canes', 'is', 'essential', 'for', 'the', 'energy', 'flow', 'in', 'any', 'quantum', 'system', 'canes', 'satisfies', 'almost', 'all', 'of', 'the', 'requirements', 'as', 'a', 'coherence', 'measure', 'except', 'that', 'the', 'coherence', 'within', 'degenerate', 'subspaces', 'is', 'explicitly', 'eliminatedwe', 'show', 'that', 'the', 'energy', 'of', 'a', 'system', 'becomes', 'frozen', 'if', 'and', 'only', 'if', 'the', 'corresponding', 'canes', 'vanishes', 'which', 'is', 'true', 'regardless', 'of', 'the', 'form', 'of', 'interaction', 'with', 'the', 'environment', 'however', 'canes', 'can', 'remain', 'zero', 'even', 'if', 'the', 'entanglement', 'changes', 'over', 'time', 'furthermore', 'we', 'show', 'how', 'the', 'power', 'of', 'energy', 'flow', 'is', 'bounded', 'by', 'the', 'value', 'of', 'canes', 'an', 'explicit', 'relation', 'connecting', 'the', 'variation', 'of', 'energy', 'and', 'canes', 'is', 'also', 'presented', 'these', 'results', 'allow', 'us', 'to', 'bound', 'the', 'generation', 'of', 'systemenvironment', 'correlation', 'through', 'the', 'local', 'measurement', 'of', 'the', 'systems', 'energy', 'flow']] | [-0.1725206706297598, 0.15298467125657184, -0.08379316069324737, 0.057862280429264445, 0.0033889722787955804, -0.11877079923122899, 0.01512638833408536, 0.34548080330818837, -0.29008177575766486, -0.2635598332072094, 0.08617819908054185, -0.26414073088011747, -0.11688377280413771, 0.17592375116672979, -0.025779496789770897, -0.005886826980442031, 0.07867660982110329, 0.0936271370781252, -0.0702950046745254, -0.251910894052751, 0.3780647118005957, 0.02669941179919988, 0.26857940161100297, 0.08830912954021078, 0.11388339339268964, -0.002982478532349385, 0.034660367051890094, 0.01000981355336175, -0.12267923911718727, 0.09274584836020949, 0.2197398503919987, 0.12930533232222965, 0.2662889212960683, -0.36665212354316973, -0.17710479995871742, 0.1544636898715772, 0.14590625163278917, 0.08431930658181629, -0.006931250690788364, -0.22521126408602543, 0.08576851684353384, -0.16076819054997846, -0.1368354617395619, -0.06227579366749347, 0.061486989683680125, 0.036652059025833135, -0.23925230895349783, 0.12932166170633794, 0.110706585865996, 0.021495458878465552, -0.09574447871783553, -0.03484118546866603, -0.07174362199825805, 0.14894418779468693, 9.067728246715087e-05, -0.0008242216183623271, 0.10546438639884495, -0.13264129855565782, -0.03362265794968872, 0.38369698155977167, -0.07329989831943287, -0.17042253548870168, 0.1603970985873533, -0.1608815265779467, -0.11076427551260941, 0.09254475030353043, 0.09435219314894569, 0.06645249747740689, -0.1320666627013194, 0.1283524028232335, -0.054877153820177514, 0.19889826597750132, 0.032656727990295405, 0.10053026416589186, 0.18454106675405332, 0.09929609591308147, 0.14848005107895873, 0.11327877059863853, -0.09701387217935564, -0.1022983576456057, -0.34086245612533234, -0.20182421122401356, -0.2198249630138292, 0.06815252588964554, -0.07133670224536232, -0.12395361823780435, 0.39193098350434996, 0.14133235629050256, 0.1972899638514257, 0.05482117469863855, 0.28849423793144524, 0.1388203169613618, 0.05442951026862598, 0.14443858217103964, 0.2707515639433665, 0.119910687738126, 0.07139810328215444, -0.2620484827767446, 0.07280490199562663, 0.02669920749063586] |
1,802.08822 | PSO-based Fuzzy Markup Language for Student Learning Performance
Evaluation and Educational Application | This paper proposes an agent with particle swarm optimization (PSO) based on
a Fuzzy Markup Language (FML) for students learning performance evaluation and
educational applications, and the proposed agent is according to the response
data from a conventional test and an item response theory. First, we apply a
GS-based parameter estimation mechanism to estimate the items parameters
according to the response data, and then to compare its results with those of
an IRT-based Bayesian parameter estimation mechanism. In addition, we propose a
static-IRT test assembly mechanism to assemble a form for the conventional
test. The presented FML-based dynamic assessment mechanism infers the
probability of making a correct response to the item for a student with various
abilities. Moreover, this paper also proposes a novel PFML learning mechanism
for optimizing the parameters between items and students. Finally, we adopt a
K-fold cross validation mechanism to evaluate the performance of the proposed
agent. Experimental results show that the novel PFML learning mechanism for the
parameter estimation and learning optimization performs favorably. We believe
the proposed PFML will be a reference for education research and pedagogy and
an important co-learning mechanism for future human-machine educational
applications.
| cs.AI | this paper proposes an agent with particle swarm optimization pso based on a fuzzy markup language fml for students learning performance evaluation and educational applications and the proposed agent is according to the response data from a conventional test and an item response theory first we apply a gsbased parameter estimation mechanism to estimate the items parameters according to the response data and then to compare its results with those of an irtbased bayesian parameter estimation mechanism in addition we propose a staticirt test assembly mechanism to assemble a form for the conventional test the presented fmlbased dynamic assessment mechanism infers the probability of making a correct response to the item for a student with various abilities moreover this paper also proposes a novel pfml learning mechanism for optimizing the parameters between items and students finally we adopt a kfold cross validation mechanism to evaluate the performance of the proposed agent experimental results show that the novel pfml learning mechanism for the parameter estimation and learning optimization performs favorably we believe the proposed pfml will be a reference for education research and pedagogy and an important colearning mechanism for future humanmachine educational applications | [['this', 'paper', 'proposes', 'an', 'agent', 'with', 'particle', 'swarm', 'optimization', 'pso', 'based', 'on', 'a', 'fuzzy', 'markup', 'language', 'fml', 'for', 'students', 'learning', 'performance', 'evaluation', 'and', 'educational', 'applications', 'and', 'the', 'proposed', 'agent', 'is', 'according', 'to', 'the', 'response', 'data', 'from', 'a', 'conventional', 'test', 'and', 'an', 'item', 'response', 'theory', 'first', 'we', 'apply', 'a', 'gsbased', 'parameter', 'estimation', 'mechanism', 'to', 'estimate', 'the', 'items', 'parameters', 'according', 'to', 'the', 'response', 'data', 'and', 'then', 'to', 'compare', 'its', 'results', 'with', 'those', 'of', 'an', 'irtbased', 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1,802.08823 | How can $X^{\pm}(5568)$ escape detection? | Multi-quark states were predicted by Gell-Mann when the quark model was first
formulated. Recently, numerous exotic states that are considered to be
multi-quark states have been experimentally confirmed (four-quark mesons and
five-quark baryons). Theoretical research indicates that the four-quark state
might comprise molecular and/or tetraquark structures. We consider that the
meson containing four different flavors $su\bar b\bar d$ should exist and decay
via the $X(5568)\to B_s\pi$ channel. However, except for the D0 collaboration,
all other experimental collaborations have reported negative observations for
$X(5568)$ in this golden portal. This contradiction has stimulated the interest
of both theorists and experimentalists. To address this discrepancy, we propose
that the assumed $X(5568)$ is a mixture of a molecular state and tetraquark,
which contributes destructively to $X(5568)\to B_s\pi$. The cancellation may be
accidental and it should be incomplete. In this scenario, there should be two
physical states with the same flavor ingredients, with spectra of $5344\pm307$
and $6318\pm315$. $X(5568)$ lies in the error range of the first state. We
predict the width of the second state (designated as $S_2$) as
$\Gamma(X_{S_2}\to B_s\pi)=224\pm97$ MeV. We strongly suggest searching for it
in future experiments.
| hep-ph hep-ex | multiquark states were predicted by gellmann when the quark model was first formulated recently numerous exotic states that are considered to be multiquark states have been experimentally confirmed fourquark mesons and fivequark baryons theoretical research indicates that the fourquark state might comprise molecular andor tetraquark structures we consider that the meson containing four different flavors subar bbar d should exist and decay via the x5568to b_spi channel however except for the d0 collaboration all other experimental collaborations have reported negative observations for x5568 in this golden portal this contradiction has stimulated the interest of both theorists and experimentalists to address this discrepancy we propose that the assumed x5568 is a mixture of a molecular state and tetraquark which contributes destructively to x5568to b_spi the cancellation may be accidental and it should be incomplete in this scenario there should be two physical states with the same flavor ingredients with spectra of 5344pm307 and 6318pm315 x5568 lies in the error range of the first state we predict the width of the second state designated as s_2 as gammax_s_2to b_spi224pm97 mev we strongly suggest searching for it in future experiments | [['multiquark', 'states', 'were', 'predicted', 'by', 'gellmann', 'when', 'the', 'quark', 'model', 'was', 'first', 'formulated', 'recently', 'numerous', 'exotic', 'states', 'that', 'are', 'considered', 'to', 'be', 'multiquark', 'states', 'have', 'been', 'experimentally', 'confirmed', 'fourquark', 'mesons', 'and', 'fivequark', 'baryons', 'theoretical', 'research', 'indicates', 'that', 'the', 'fourquark', 'state', 'might', 'comprise', 'molecular', 'andor', 'tetraquark', 'structures', 'we', 'consider', 'that', 'the', 'meson', 'containing', 'four', 'different', 'flavors', 'subar', 'bbar', 'd', 'should', 'exist', 'and', 'decay', 'via', 'the', 'x5568to', 'b_spi', 'channel', 'however', 'except', 'for', 'the', 'd0', 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1,802.08824 | The AdobeIndoorNav Dataset: Towards Deep Reinforcement Learning based
Real-world Indoor Robot Visual Navigation | Deep reinforcement learning (DRL) demonstrates its potential in learning a
model-free navigation policy for robot visual navigation. However, the
data-demanding algorithm relies on a large number of navigation trajectories in
training. Existing datasets supporting training such robot navigation
algorithms consist of either 3D synthetic scenes or reconstructed scenes.
Synthetic data suffers from domain gap to the real-world scenes while visual
inputs rendered from 3D reconstructed scenes have undesired holes and
artifacts. In this paper, we present a new dataset collected in real-world to
facilitate the research in DRL based visual navigation. Our dataset includes 3D
reconstruction for real-world scenes as well as densely captured real 2D images
from the scenes. It provides high-quality visual inputs with real-world scene
complexity to the robot at dense grid locations. We further study and benchmark
one recent DRL based navigation algorithm and present our attempts and thoughts
on improving its generalizability to unseen test targets in the scenes.
| cs.RO | deep reinforcement learning drl demonstrates its potential in learning a modelfree navigation policy for robot visual navigation however the datademanding algorithm relies on a large number of navigation trajectories in training existing datasets supporting training such robot navigation algorithms consist of either 3d synthetic scenes or reconstructed scenes synthetic data suffers from domain gap to the realworld scenes while visual inputs rendered from 3d reconstructed scenes have undesired holes and artifacts in this paper we present a new dataset collected in realworld to facilitate the research in drl based visual navigation our dataset includes 3d reconstruction for realworld scenes as well as densely captured real 2d images from the scenes it provides highquality visual inputs with realworld scene complexity to the robot at dense grid locations we further study and benchmark one recent drl based navigation algorithm and present our attempts and thoughts on improving its generalizability to unseen test targets in the scenes | [['deep', 'reinforcement', 'learning', 'drl', 'demonstrates', 'its', 'potential', 'in', 'learning', 'a', 'modelfree', 'navigation', 'policy', 'for', 'robot', 'visual', 'navigation', 'however', 'the', 'datademanding', 'algorithm', 'relies', 'on', 'a', 'large', 'number', 'of', 'navigation', 'trajectories', 'in', 'training', 'existing', 'datasets', 'supporting', 'training', 'such', 'robot', 'navigation', 'algorithms', 'consist', 'of', 'either', '3d', 'synthetic', 'scenes', 'or', 'reconstructed', 'scenes', 'synthetic', 'data', 'suffers', 'from', 'domain', 'gap', 'to', 'the', 'realworld', 'scenes', 'while', 'visual', 'inputs', 'rendered', 'from', '3d', 'reconstructed', 'scenes', 'have', 'undesired', 'holes', 'and', 'artifacts', 'in', 'this', 'paper', 'we', 'present', 'a', 'new', 'dataset', 'collected', 'in', 'realworld', 'to', 'facilitate', 'the', 'research', 'in', 'drl', 'based', 'visual', 'navigation', 'our', 'dataset', 'includes', '3d', 'reconstruction', 'for', 'realworld', 'scenes', 'as', 'well', 'as', 'densely', 'captured', 'real', '2d', 'images', 'from', 'the', 'scenes', 'it', 'provides', 'highquality', 'visual', 'inputs', 'with', 'realworld', 'scene', 'complexity', 'to', 'the', 'robot', 'at', 'dense', 'grid', 'locations', 'we', 'further', 'study', 'and', 'benchmark', 'one', 'recent', 'drl', 'based', 'navigation', 'algorithm', 'and', 'present', 'our', 'attempts', 'and', 'thoughts', 'on', 'improving', 'its', 'generalizability', 'to', 'unseen', 'test', 'targets', 'in', 'the', 'scenes']] | [-0.05347343268743777, -0.04045844792959873, -0.06550396776111046, 0.0294936213531991, -0.15548287062942015, -0.15596854089541579, 0.009293199547826519, 0.49865628279406915, -0.21284794853395456, -0.44925135221670975, 0.053802980347415555, -0.28240429939303013, -0.21377785848121025, 0.23811836958017696, -0.2397086417465773, 0.14236318487401858, 0.20768149616801507, 0.016604812274529757, -0.017160063184168062, -0.2547883622396689, 0.27194512179484226, -0.021586615318778674, 0.3136595305710783, -0.018472253985993274, 0.16161877542277556, -0.02300531460673778, -0.026525542551240365, -0.016514086125225022, 0.009545622212062408, 0.15001941955793505, 0.3666407677447786, 0.2725823290098773, 0.26286329361065836, -0.43145727370442316, -0.25015119371887345, 0.07286872841151697, 0.14217957449689894, 0.09056024303799268, -0.11015347800332473, -0.4943844946585112, 0.05664668020840686, -0.09334681746258866, 0.001812191807072271, -0.15253669927924773, -0.007180887968778344, -0.03875198062810164, -0.2878088368278287, -0.007274315896589164, 0.028324585522119826, 0.1550104562223393, -0.08060042550424476, -0.07509237349444589, 0.017931913925250145, 0.2547476782127852, 0.03298489274226534, 0.056543529839496814, 0.1946068447107425, -0.23938490333513346, -0.1778252275340114, 0.4248019593296113, 0.01904540980694356, -0.1960188416981852, 0.27110627788578634, -0.06006860002790662, -0.12804802609668053, 0.11936341103940427, 0.3258347424720837, 0.1732130614018624, -0.16134754995295367, 0.020870633306913078, -0.08429567850430726, 0.14963355212315144, 0.03536931753752741, -0.05255045546460655, 0.1801913927684163, 0.3323949619813205, 0.05613429174462164, 0.1255098123628005, -0.16617066580605658, -0.07196803631634427, -0.1608145252843811, -0.052401834173587625, -0.22012185241857712, -0.06163599484829933, -0.12875343020060462, -0.1476769402816698, 0.3681642276948536, 0.33636779802270816, 0.2300080788055701, 0.11453387842138672, 0.43438603698292333, -0.08840756763268698, 0.08995968735377703, 0.09027475636609673, 0.15441999412232454, -0.08968171327036213, 0.22020552841970673, -0.1558819410592233, 0.04313770233571844, 0.017396301106707035] |
1,802.08825 | Kernel Estimation for Panel Data with Heterogeneous Dynamics | This paper proposes nonparametric kernel-smoothing estimation for panel data
to examine the degree of heterogeneity across cross-sectional units. We first
estimate the sample mean, autocovariances, and autocorrelations for each unit
and then apply kernel smoothing to compute their density functions. The
dependence of the kernel estimator on bandwidth makes asymptotic bias of very
high order affect the required condition on the relative magnitudes of the
cross-sectional sample size (N) and the time-series length (T). In particular,
it makes the condition on N and T stronger and more complicated than those
typically observed in the long-panel literature without kernel smoothing. We
also consider a split-panel jackknife method to correct bias and construction
of confidence intervals. An empirical application and Monte Carlo simulations
illustrate our procedure in finite samples.
| econ.EM | this paper proposes nonparametric kernelsmoothing estimation for panel data to examine the degree of heterogeneity across crosssectional units we first estimate the sample mean autocovariances and autocorrelations for each unit and then apply kernel smoothing to compute their density functions the dependence of the kernel estimator on bandwidth makes asymptotic bias of very high order affect the required condition on the relative magnitudes of the crosssectional sample size n and the timeseries length t in particular it makes the condition on n and t stronger and more complicated than those typically observed in the longpanel literature without kernel smoothing we also consider a splitpanel jackknife method to correct bias and construction of confidence intervals an empirical application and monte carlo simulations illustrate our procedure in finite samples | [['this', 'paper', 'proposes', 'nonparametric', 'kernelsmoothing', 'estimation', 'for', 'panel', 'data', 'to', 'examine', 'the', 'degree', 'of', 'heterogeneity', 'across', 'crosssectional', 'units', 'we', 'first', 'estimate', 'the', 'sample', 'mean', 'autocovariances', 'and', 'autocorrelations', 'for', 'each', 'unit', 'and', 'then', 'apply', 'kernel', 'smoothing', 'to', 'compute', 'their', 'density', 'functions', 'the', 'dependence', 'of', 'the', 'kernel', 'estimator', 'on', 'bandwidth', 'makes', 'asymptotic', 'bias', 'of', 'very', 'high', 'order', 'affect', 'the', 'required', 'condition', 'on', 'the', 'relative', 'magnitudes', 'of', 'the', 'crosssectional', 'sample', 'size', 'n', 'and', 'the', 'timeseries', 'length', 't', 'in', 'particular', 'it', 'makes', 'the', 'condition', 'on', 'n', 'and', 't', 'stronger', 'and', 'more', 'complicated', 'than', 'those', 'typically', 'observed', 'in', 'the', 'longpanel', 'literature', 'without', 'kernel', 'smoothing', 'we', 'also', 'consider', 'a', 'splitpanel', 'jackknife', 'method', 'to', 'correct', 'bias', 'and', 'construction', 'of', 'confidence', 'intervals', 'an', 'empirical', 'application', 'and', 'monte', 'carlo', 'simulations', 'illustrate', 'our', 'procedure', 'in', 'finite', 'samples']] | [-0.04553644335736328, 0.04742171675706419, -0.08893077252506619, 0.12686993899230387, -0.07095683778872684, -0.1019619123396715, 0.07782765843310496, 0.42677477392412366, -0.23652254255296337, -0.325736811447386, 0.1061130689026507, -0.2776031639271726, -0.10014717293661161, 0.2014902411841802, -0.07473394619892278, 0.07736831243401246, 0.060297862767049716, 0.011419604321764339, -0.09452634333773324, -0.2917964529164786, 0.2659999927975208, 0.09398908224252481, 0.3184354082784719, -0.008781282772635303, 0.11500817015933405, 0.056613863527124365, -0.10805670713399729, 0.021266574154622735, -0.17881330110073562, 0.09342696804494138, 0.20122850678181128, 0.07455908667503132, 0.3195196549807276, -0.3782912854483815, -0.16007515231294314, 0.12642073703722823, 0.14355710408251202, 0.07601427767307513, 0.0405363388167138, -0.20620594584819166, 0.09356000092774926, -0.14945820964688514, -0.1053650598808576, -0.07560795414379783, 0.016397184718193278, 0.019196971820386512, -0.3275802893756283, 0.1287343373930078, 0.03994549953385435, 0.09623356485399344, -0.022445186237168926, -0.14741121910305488, 0.012002318690989226, 0.08387611105406124, 0.05561303573712292, -0.024155138529807565, 0.12286938313720008, -0.09333146180743204, -0.027504405609169412, 0.2618007837676458, -0.05560046049642898, -0.19852469015556076, 0.1585090075442124, -0.1843007048222399, -0.1329456599843171, 0.08112592356545585, 0.20878044566756765, 0.13438420235696766, -0.1424386536698818, 0.08263651178530713, 0.01663128377792115, 0.17879034770977875, 0.0428069717590771, -0.005117508808210019, 0.09577527660174324, 0.13702176696628274, 0.1047788286574244, 0.1481557782214608, -0.16674011941104833, -0.06055679362607262, -0.3217642530392382, -0.13344847230034665, -0.21093446036268557, 0.0013632417493869388, -0.18807301098503457, -0.21943738012175476, 0.3730549745867768, 0.22236441052518785, 0.22828686518002383, 0.14347236643019612, 0.2932413582290922, 0.1197740553096602, 0.06102789661484181, 0.06646412168629467, 0.1157673422454132, 0.139011928091927, 0.03408126610456892, -0.22329656344409737, 0.12189504474638001, 0.035884957468610194] |
1,802.08826 | Role of management information system in time saving | This Case Study will be used in order to investigate and establish the
importance of role of Management Information System in time saving during the
payment of automobile tax in Sindh through e-filling methods. Moreover it will
also highlight the important factors which are involved as barriers and limit
the role of MIS in time saving techniques. The approach which is used in this
case study is descriptive research type along with the survey. The data used
was collected from the specimen of common people working in different
environments along with the officers working at Civic Centre (Automobile Tax
Collection Branch excise department). The audience included were all well
informed by the process and were eligible to give their opinions on the
following research. A system design is also proposed along with an Erd which
can be useful in the coming future. This research could likewise be expanded to
include different of respondents, for example, paid taxpayers and different
types of taxpayers. Paid tax payers are given the rights by their clients to
prepare their assessment matters. They use the e-filing system for different
types of clients and are more frequent users of the e-filing system than
taxpayers who file for themselves. It would be interesting to understand which
facets of hazard are larger to them. Different types of taxpayers, for example,
company authorized cars may deal with more complex exchanges than single car
taxpayers, consequently, they may emphasize different hazard facets when filing
in the government form frame electronically
| cs.CY | this case study will be used in order to investigate and establish the importance of role of management information system in time saving during the payment of automobile tax in sindh through efilling methods moreover it will also highlight the important factors which are involved as barriers and limit the role of mis in time saving techniques the approach which is used in this case study is descriptive research type along with the survey the data used was collected from the specimen of common people working in different environments along with the officers working at civic centre automobile tax collection branch excise department the audience included were all well informed by the process and were eligible to give their opinions on the following research a system design is also proposed along with an erd which can be useful in the coming future this research could likewise be expanded to include different of respondents for example paid taxpayers and different types of taxpayers paid tax payers are given the rights by their clients to prepare their assessment matters they use the efiling system for different types of clients and are more frequent users of the efiling system than taxpayers who file for themselves it would be interesting to understand which facets of hazard are larger to them different types of taxpayers for example company authorized cars may deal with more complex exchanges than single car taxpayers consequently they may emphasize different hazard facets when filing in the government form frame electronically | [['this', 'case', 'study', 'will', 'be', 'used', 'in', 'order', 'to', 'investigate', 'and', 'establish', 'the', 'importance', 'of', 'role', 'of', 'management', 'information', 'system', 'in', 'time', 'saving', 'during', 'the', 'payment', 'of', 'automobile', 'tax', 'in', 'sindh', 'through', 'efilling', 'methods', 'moreover', 'it', 'will', 'also', 'highlight', 'the', 'important', 'factors', 'which', 'are', 'involved', 'as', 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1,802.08827 | Integrable spin chains with random interactions | We study a Yang-Baxter integrable quantum spin-1/2 chain with random
interactions. The Hamiltonian is local and involves two and three-spin
interactions with random parameters. We show that the energy eigenstates of the
model are never localized and in fact exhibit perfect energy and spin transport
at both zero and infinite temperatures. By considering the vicinity of a free
fermion point in the model we demonstrate that this behavior persists under
deformations that break integrability but preserve the free fermion nature of
the Hamiltonian. In this case the ballistic behavior can be understood as
arising from the correlated nature of the disorder in the model. We conjecture
that the model belongs to a broad class of models avoiding localization in 1D.
| cond-mat.dis-nn | we study a yangbaxter integrable quantum spin12 chain with random interactions the hamiltonian is local and involves two and threespin interactions with random parameters we show that the energy eigenstates of the model are never localized and in fact exhibit perfect energy and spin transport at both zero and infinite temperatures by considering the vicinity of a free fermion point in the model we demonstrate that this behavior persists under deformations that break integrability but preserve the free fermion nature of the hamiltonian in this case the ballistic behavior can be understood as arising from the correlated nature of the disorder in the model we conjecture that the model belongs to a broad class of models avoiding localization in 1d | [['we', 'study', 'a', 'yangbaxter', 'integrable', 'quantum', 'spin12', 'chain', 'with', 'random', 'interactions', 'the', 'hamiltonian', 'is', 'local', 'and', 'involves', 'two', 'and', 'threespin', 'interactions', 'with', 'random', 'parameters', 'we', 'show', 'that', 'the', 'energy', 'eigenstates', 'of', 'the', 'model', 'are', 'never', 'localized', 'and', 'in', 'fact', 'exhibit', 'perfect', 'energy', 'and', 'spin', 'transport', 'at', 'both', 'zero', 'and', 'infinite', 'temperatures', 'by', 'considering', 'the', 'vicinity', 'of', 'a', 'free', 'fermion', 'point', 'in', 'the', 'model', 'we', 'demonstrate', 'that', 'this', 'behavior', 'persists', 'under', 'deformations', 'that', 'break', 'integrability', 'but', 'preserve', 'the', 'free', 'fermion', 'nature', 'of', 'the', 'hamiltonian', 'in', 'this', 'case', 'the', 'ballistic', 'behavior', 'can', 'be', 'understood', 'as', 'arising', 'from', 'the', 'correlated', 'nature', 'of', 'the', 'disorder', 'in', 'the', 'model', 'we', 'conjecture', 'that', 'the', 'model', 'belongs', 'to', 'a', 'broad', 'class', 'of', 'models', 'avoiding', 'localization', 'in', '1d']] | [-0.15766607467667199, 0.21524839165310064, -0.05272780092297277, 0.07020573121747778, 0.00227744293709596, -0.17208062077018743, 0.0032542855051967004, 0.3556375130855789, -0.2875436370416234, -0.25937619497029424, 0.04764873037444583, -0.29768611787003463, -0.15933110617139998, 0.1303599842392335, 0.01700232649842898, 0.017122753465082498, 0.044759081901671986, 0.041088957915296, -0.10440141854342073, -0.2056525654460226, 0.3209588093139852, 0.007263861909935562, 0.277115102019161, 0.07328338185470784, 0.07226722524501383, 0.04790339282481, 0.09409620230629419, 0.013608914337237366, -0.11726390038893442, 0.049636131548322734, 0.1971202519347874, -0.014417496928945183, 0.19628677135557762, -0.40240419590845705, -0.2464898672827985, 0.1283654101289964, 0.13396327894297427, 0.16945689485097926, -0.029281879567618792, -0.2746446282195393, 0.058450842695310715, -0.17453676376026123, -0.18928364622794713, -0.07198611557250842, -0.058301814808510245, 0.009543746581766755, -0.23451865621221563, 0.14448957156079512, 0.12041298059436183, 0.05403674489352852, -0.0810788902725714, -0.027496460809682807, -0.06050681705431392, 0.11258102817616115, 0.05121147059447442, -0.03655700550298206, 0.07581183568108826, -0.1597244592073063, -0.11599965799832716, 0.3973243324474121, -0.07056461597870414, -0.21624326949434666, 0.20693756700881447, -0.14281934721463282, -0.1319442543395174, 0.12074764983262867, 0.11679789278423414, 0.08598646254589161, -0.1527095361976535, 0.16602801909272483, -0.06319845035516967, 0.12144416700393776, -0.004507842671591789, 0.05769061389534424, 0.23001102151659628, 0.12829899172065778, 0.041936845045226316, 0.1765235868660966, -0.07054735019240373, -0.1859355311219891, -0.2961074457814296, -0.12108633167420825, -0.2143066396781554, 0.1035324891951556, -0.05703304893431778, -0.19486940382436538, 0.44392256503924726, 0.20817152898308297, 0.21260210057953371, 0.04380569797940552, 0.1919833712473822, 0.14503834395727608, 0.04294352597595814, 0.06408637852485602, 0.21431403474610608, 0.11887204785404416, 0.0452172198992533, -0.25445832240608673, 0.019513124700946113, 0.07357475191432362] |
1,802.08828 | Torus actions of complexity one and their local properties | We consider an effective action of a compact (n-1)-torus on a smooth
2n-manifold with isolated fixed points. We prove that under certain conditions
the orbit space is a closed topological manifold. In particular, this holds for
certain torus actions with disconnected stabilizers. There is a filtration of
the orbit manifold by orbit dimensions. The subset of orbits of dimensions less
than n-1 has a specific topology which we axiomatize in the notion of a sponge.
In many cases the original manifold can be recovered from its orbit manifold,
the sponge, and the weights of tangent representations at fixed points.
| math.AT | we consider an effective action of a compact n1torus on a smooth 2nmanifold with isolated fixed points we prove that under certain conditions the orbit space is a closed topological manifold in particular this holds for certain torus actions with disconnected stabilizers there is a filtration of the orbit manifold by orbit dimensions the subset of orbits of dimensions less than n1 has a specific topology which we axiomatize in the notion of a sponge in many cases the original manifold can be recovered from its orbit manifold the sponge and the weights of tangent representations at fixed points | [['we', 'consider', 'an', 'effective', 'action', 'of', 'a', 'compact', 'n1torus', 'on', 'a', 'smooth', '2nmanifold', 'with', 'isolated', 'fixed', 'points', 'we', 'prove', 'that', 'under', 'certain', 'conditions', 'the', 'orbit', 'space', 'is', 'a', 'closed', 'topological', 'manifold', 'in', 'particular', 'this', 'holds', 'for', 'certain', 'torus', 'actions', 'with', 'disconnected', 'stabilizers', 'there', 'is', 'a', 'filtration', 'of', 'the', 'orbit', 'manifold', 'by', 'orbit', 'dimensions', 'the', 'subset', 'of', 'orbits', 'of', 'dimensions', 'less', 'than', 'n1', 'has', 'a', 'specific', 'topology', 'which', 'we', 'axiomatize', 'in', 'the', 'notion', 'of', 'a', 'sponge', 'in', 'many', 'cases', 'the', 'original', 'manifold', 'can', 'be', 'recovered', 'from', 'its', 'orbit', 'manifold', 'the', 'sponge', 'and', 'the', 'weights', 'of', 'tangent', 'representations', 'at', 'fixed', 'points']] | [-0.23264247363414428, 0.11185339740092391, -0.11157378230262736, 0.03230536470514243, -0.05567792793876971, -0.13794829881270276, 0.04214069631063577, 0.3778444350217328, -0.24832271045600676, -0.1734620907691994, 0.12764514298201773, -0.23479369625618512, -0.1607720456368318, 0.18644164510146535, -0.16661281932634536, 0.00012892966259816528, 0.09610089353953648, 0.14120537183374504, -0.12396243023403893, -0.26615418102639266, 0.4369947047001033, -0.043122936947967365, 0.1772113773111028, -0.008129633053539866, 0.1572795089971006, -0.0010866736075982, 0.08542281122069166, 0.05114552495641222, -0.13169653065812204, 0.11957382559488208, 0.22294481662619445, 0.06595487059964186, 0.220003517572721, -0.36481064708545957, -0.21991371427371045, 0.15298360803945346, 0.0824880970246864, 0.04418904226475555, 0.0008986735953996428, -0.2964912872253494, 0.15158186560600168, -0.13151043041543375, -0.19574723690465995, -0.053662604398348114, 0.0692691867136293, -0.05836250467785643, -0.22734351466983707, -0.04759054774132727, 0.1091963512108031, 0.10688660678105673, -0.09944977030049859, -0.04318350289167479, -0.09308519376902794, 0.12024480912533372, 0.02146384017800705, 0.07490743019834462, 0.12005829700090066, -0.07293218519359902, -0.0978841367436366, 0.39392531316990803, -0.04811977550950056, -0.2880700857124545, 0.19731318731463016, -0.18281669185186425, -0.1380362739419621, 0.152339188711285, 0.12063070031052286, 0.1692536019089848, -0.06032760161906481, 0.20611927107933703, -0.10394049982418015, 0.09889530172604466, 0.10756776934357906, -0.00362392934979965, 0.18566461544215793, 0.14040129327843193, 0.18091194406257605, 0.12860051810040582, -0.049850349034643686, -0.05702910242094235, -0.31812590221413456, -0.15620373977310578, -0.1548725866237561, 0.13140625183028404, -0.14873740875683703, -0.1829712084927267, 0.38376150568570905, 0.037873065910646646, 0.24812084067415333, 0.06444871940531278, 0.24071634177946383, 0.08074706361741957, 0.0507536969315778, 0.13367681695392938, 0.14763027322807848, 0.09723846871415247, -0.045052166486093115, -0.1307259965370494, -0.02452241677809695, 0.15413185106496316] |
1,802.08829 | Three strongly hyperbolic metrics on ptolemy spaces | Recently, strongly hyperbolic space as certain analytic enhancements of
Gromov hyperbolic space was introduced by B. Nica and J. Spakula. In this note,
we prove that the log-metric log(1+d) on a Ptolemy space (X,d) is a strongly
hyperbolic metric. Using our result, we construct three metrics on a Ptolemy
metric space and prove they are strongly hyperbolic.
| math.MG | recently strongly hyperbolic space as certain analytic enhancements of gromov hyperbolic space was introduced by b nica and j spakula in this note we prove that the logmetric log1d on a ptolemy space xd is a strongly hyperbolic metric using our result we construct three metrics on a ptolemy metric space and prove they are strongly hyperbolic | [['recently', 'strongly', 'hyperbolic', 'space', 'as', 'certain', 'analytic', 'enhancements', 'of', 'gromov', 'hyperbolic', 'space', 'was', 'introduced', 'by', 'b', 'nica', 'and', 'j', 'spakula', 'in', 'this', 'note', 'we', 'prove', 'that', 'the', 'logmetric', 'log1d', 'on', 'a', 'ptolemy', 'space', 'xd', 'is', 'a', 'strongly', 'hyperbolic', 'metric', 'using', 'our', 'result', 'we', 'construct', 'three', 'metrics', 'on', 'a', 'ptolemy', 'metric', 'space', 'and', 'prove', 'they', 'are', 'strongly', 'hyperbolic']] | [-0.16020591154017233, 0.17538537361913106, -0.11355106322602793, 0.1161516628482125, -0.10418593021617695, -0.08673074027862061, -0.012308113412423567, 0.37840891792015596, -0.23373155627738346, -0.20381870886141604, 0.10343770434529605, -0.27419913892041553, -0.2354784558442506, 0.23833158619024536, -0.1694829117337411, 0.030720601925118402, 0.08072808647358959, 0.02679060697555542, -0.095615646393377, -0.29645794900980865, 0.45355194305831736, -0.025252374735745518, 0.21251566391279497, 0.08214921436526558, 0.13467591276646337, -0.013020423875952309, -0.08173374355855313, 0.08782555351922797, -0.21409557629019466, 0.14322912083430725, 0.2540243730075996, 0.11059946567666802, 0.17771361654793674, -0.24635552369396796, -0.23459658644754777, 0.14024792516773396, 0.0729484891095622, -0.0345271721990271, -0.04892526693557474, -0.3605022505264391, 0.06255003545771945, -0.14328737415542656, -0.16094785529950803, -0.10377078337425535, 0.032842658189210026, 0.006120702217925679, -0.19305403821847655, -0.0716140413318168, 0.11116702976551923, 0.015232417749410325, -0.03681017521091483, -0.023157476286657833, -0.04991127428260039, 0.012427198963070457, -0.027193729668347672, 0.13301758043129336, 0.059111018673601474, 0.06748741027073596, -0.08796779393133793, 0.38094443272460593, -0.11415122054009275, -0.2890581909228455, 0.1960495047440583, -0.21315600852566688, -0.16956695022738794, 0.08617568337781863, 0.15620406274260445, 0.17667538103732197, -0.0706660966981541, 0.23209413232662798, -0.1270160842856223, 0.06823866113343022, 0.11550427496602589, -0.01492261162705042, 0.08298579244451089, 0.1101048847491091, 0.15132439627566122, 0.10249400081282312, 0.020422405360097234, -0.03562678001313047, -0.3256351907483556, -0.25819998836483465, -0.15350203659724104, 0.10346507639624178, -0.08321166979942725, -0.19137634986969218, 0.30147470689632677, -0.02947783839296211, 0.20261187736283648, 0.08450873521241274, 0.18163610515608028, 0.04868320517495952, -0.014283410797361284, 0.11579948793250051, 0.2219312166710469, 0.11651465205729684, 0.042096406238322906, -0.1386682432229546, -0.02884183663197539, 0.23147997453131458] |
1,802.0883 | Field Theories for Loop-Erased Random Walks | Self-avoiding walks (SAWs) and loop-erased random walks (LERWs) are two
ensembles of random paths with numerous applications in mathematics,
statistical physics and quantum field theory. While SAWs are described by the
$n \to 0$ limit of $\phi^4$-theory with $O(n)$-symmetry, LERWs have no obvious
field-theoretic description. We analyse two candidates for a field theory of
LERWs, and discover a connection between the corresponding and a priori
unrelated theories. The first such candidate is the $O(n)$-symmetric $\phi^4$
theory at $n=-2$ whose link to LERWs was known in two dimensions due to
conformal field theory. Here it is established in arbitrary dimension via a
perturbation expansion in the coupling constant. The second candidate is a
field theory for charge-density waves pinned by quenched disorder, whose
relation to LERWs had been conjectured earlier using analogies with Abelian
sandpiles. We explicitly show that both theories yield identical results to
4-loop order and give both a perturbative and a non-perturbative proof of their
equivalence. This allows us to compute the fractal dimension of LERWs to order
$\epsilon^5$ where $\epsilon=4-d$. In particular, in $d=3$ our theory gives
$z_{\rm LERW}(d=3)= 1.6243 \pm 0.001$, in excellent agreement with the estimate
$z = 1.624 00 \pm 0.00005$ of numerical simulations.
| cond-mat.stat-mech hep-th math-ph math.MP | selfavoiding walks saws and looperased random walks lerws are two ensembles of random paths with numerous applications in mathematics statistical physics and quantum field theory while saws are described by the n to 0 limit of phi4theory with onsymmetry lerws have no obvious fieldtheoretic description we analyse two candidates for a field theory of lerws and discover a connection between the corresponding and a priori unrelated theories the first such candidate is the onsymmetric phi4 theory at n2 whose link to lerws was known in two dimensions due to conformal field theory here it is established in arbitrary dimension via a perturbation expansion in the coupling constant the second candidate is a field theory for chargedensity waves pinned by quenched disorder whose relation to lerws had been conjectured earlier using analogies with abelian sandpiles we explicitly show that both theories yield identical results to 4loop order and give both a perturbative and a nonperturbative proof of their equivalence this allows us to compute the fractal dimension of lerws to order epsilon5 where epsilon4d in particular in d3 our theory gives z_rm lerwd3 16243 pm 0001 in excellent agreement with the estimate z 1624 00 pm 000005 of numerical simulations | [['selfavoiding', 'walks', 'saws', 'and', 'looperased', 'random', 'walks', 'lerws', 'are', 'two', 'ensembles', 'of', 'random', 'paths', 'with', 'numerous', 'applications', 'in', 'mathematics', 'statistical', 'physics', 'and', 'quantum', 'field', 'theory', 'while', 'saws', 'are', 'described', 'by', 'the', 'n', 'to', '0', 'limit', 'of', 'phi4theory', 'with', 'onsymmetry', 'lerws', 'have', 'no', 'obvious', 'fieldtheoretic', 'description', 'we', 'analyse', 'two', 'candidates', 'for', 'a', 'field', 'theory', 'of', 'lerws', 'and', 'discover', 'a', 'connection', 'between', 'the', 'corresponding', 'and', 'a', 'priori', 'unrelated', 'theories', 'the', 'first', 'such', 'candidate', 'is', 'the', 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1,802.08831 | Convolutional Neural Networks combined with Runge-Kutta Methods | A convolutional neural network can be constructed using numerical methods for
solving dynamical systems, since the forward pass of the network can be
regarded as a trajectory of a dynamical system. However, existing models based
on numerical solvers cannot avoid the iterations of implicit methods, which
makes the models inefficient at inference time. In this paper, we reinterpret
the pre-activation Residual Networks (ResNets) and their variants from the
dynamical systems view. We consider that the iterations of implicit Runge-Kutta
methods are fused into the training of these models. Moreover, we propose a
novel approach to constructing network models based on high-order Runge-Kutta
methods in order to achieve higher efficiency. Our proposed models are referred
to as the Runge-Kutta Convolutional Neural Networks (RKCNNs). The RKCNNs are
evaluated on multiple benchmark datasets. The experimental results show that
RKCNNs are vastly superior to other dynamical system network models: they
achieve higher accuracy with much fewer resources. They also expand the family
of network models based on numerical methods for dynamical systems.
| cs.CV cs.LG | a convolutional neural network can be constructed using numerical methods for solving dynamical systems since the forward pass of the network can be regarded as a trajectory of a dynamical system however existing models based on numerical solvers cannot avoid the iterations of implicit methods which makes the models inefficient at inference time in this paper we reinterpret the preactivation residual networks resnets and their variants from the dynamical systems view we consider that the iterations of implicit rungekutta methods are fused into the training of these models moreover we propose a novel approach to constructing network models based on highorder rungekutta methods in order to achieve higher efficiency our proposed models are referred to as the rungekutta convolutional neural networks rkcnns the rkcnns are evaluated on multiple benchmark datasets the experimental results show that rkcnns are vastly superior to other dynamical system network models they achieve higher accuracy with much fewer resources they also expand the family of network models based on numerical methods for dynamical systems | [['a', 'convolutional', 'neural', 'network', 'can', 'be', 'constructed', 'using', 'numerical', 'methods', 'for', 'solving', 'dynamical', 'systems', 'since', 'the', 'forward', 'pass', 'of', 'the', 'network', 'can', 'be', 'regarded', 'as', 'a', 'trajectory', 'of', 'a', 'dynamical', 'system', 'however', 'existing', 'models', 'based', 'on', 'numerical', 'solvers', 'can', 'not', 'avoid', 'the', 'iterations', 'of', 'implicit', 'methods', 'which', 'makes', 'the', 'models', 'inefficient', 'at', 'inference', 'time', 'in', 'this', 'paper', 'we', 'reinterpret', 'the', 'preactivation', 'residual', 'networks', 'resnets', 'and', 'their', 'variants', 'from', 'the', 'dynamical', 'systems', 'view', 'we', 'consider', 'that', 'the', 'iterations', 'of', 'implicit', 'rungekutta', 'methods', 'are', 'fused', 'into', 'the', 'training', 'of', 'these', 'models', 'moreover', 'we', 'propose', 'a', 'novel', 'approach', 'to', 'constructing', 'network', 'models', 'based', 'on', 'highorder', 'rungekutta', 'methods', 'in', 'order', 'to', 'achieve', 'higher', 'efficiency', 'our', 'proposed', 'models', 'are', 'referred', 'to', 'as', 'the', 'rungekutta', 'convolutional', 'neural', 'networks', 'rkcnns', 'the', 'rkcnns', 'are', 'evaluated', 'on', 'multiple', 'benchmark', 'datasets', 'the', 'experimental', 'results', 'show', 'that', 'rkcnns', 'are', 'vastly', 'superior', 'to', 'other', 'dynamical', 'system', 'network', 'models', 'they', 'achieve', 'higher', 'accuracy', 'with', 'much', 'fewer', 'resources', 'they', 'also', 'expand', 'the', 'family', 'of', 'network', 'models', 'based', 'on', 'numerical', 'methods', 'for', 'dynamical', 'systems']] | [-0.08435737548021258, -0.026759029186975498, -0.10048879281143773, 0.07286612287138935, -0.0409461835911773, -0.1777141482339202, 0.009862680010877815, 0.4425758105267346, -0.27038520193774496, -0.3057261623442862, 0.11821136019776779, -0.233731427048085, -0.1976388248342183, 0.23987100396536776, -0.09565826664408723, 0.11057128804631135, 0.1417625438733324, 0.002371871047625704, -0.1288225495231685, -0.3082198261148324, 0.3020766610996081, 0.05297517529330575, 0.2871355092119888, -0.02160231055851651, 0.13334564042150887, -0.12386551560260929, -0.013183717775026325, 0.02422166801093576, -0.04188800828998768, 0.16126138265051784, 0.2501739617287946, 0.14462523593583845, 0.31997578600201115, -0.47860906903560346, -0.2927712836969622, 0.10060767309957862, 0.18353019661777073, 0.14989047396396626, 0.016993785107552388, -0.3115067115403469, 0.11301352991998063, -0.19987815412687215, -0.006530494296912258, -0.19277019090619843, -0.09881049571228186, 0.06548834959466628, -0.2726620481950203, 0.047200966969279834, 0.05123383014930616, 0.026068007076137076, -0.03149274415639643, -0.14304762281416833, -0.025759543354220732, 0.10834410276398966, 0.01258184424788862, -0.011981729926409189, 0.09446788591349266, -0.13870756886210622, -0.1846081043301514, 0.3613272273658841, -0.060857570393991894, -0.24629680793484418, 0.24216890104594446, 0.002230256299912577, -0.16106123186802193, 0.10413474023077615, 0.27232466900263796, 0.17614843733002536, -0.129739323068829, 0.00014522524527136303, -0.015304481581724165, 0.18658147003369394, -0.006743853578355009, 0.0008993827622141359, 0.15073349670170855, 0.2531749678543354, 0.03644305933817957, 0.09304236324494373, -0.08536064171524355, -0.12734662828493049, -0.21371251679219846, -0.08753001379653723, -0.16206238803470277, -0.033662319800471366, -0.09844573831711634, -0.14945573076473537, 0.37405654258430887, 0.24749518544881566, 0.17822885148216194, 0.15084149001907424, 0.35007074076858674, 0.10956349399301758, 0.14269501099103962, 0.10074318967451006, 0.20768761790230783, 0.06890146850233556, 0.10745796430911098, -0.171706418310135, 0.06621457245366195, 0.13540495711670825] |
1,802.08832 | The lattice of characteristic subspaces of an endomorphism with
Jordan-Chevalley decomposition | Given an endomorphism A over a finite dimensional vector space having
Jordan-Chevalley decomposition, the lattices of invariant and hyperinvariant
subspaces of A can be obtained from the nilpotent part of this decomposition.
We extend this result for lattices of characteristic subspaces. We also obtain
a generalization of Shoda's Theorem about the characterization of the existence
of characteristic non hyperinvariant subspaces.
| math.RA | given an endomorphism a over a finite dimensional vector space having jordanchevalley decomposition the lattices of invariant and hyperinvariant subspaces of a can be obtained from the nilpotent part of this decomposition we extend this result for lattices of characteristic subspaces we also obtain a generalization of shodas theorem about the characterization of the existence of characteristic non hyperinvariant subspaces | [['given', 'an', 'endomorphism', 'a', 'over', 'a', 'finite', 'dimensional', 'vector', 'space', 'having', 'jordanchevalley', 'decomposition', 'the', 'lattices', 'of', 'invariant', 'and', 'hyperinvariant', 'subspaces', 'of', 'a', 'can', 'be', 'obtained', 'from', 'the', 'nilpotent', 'part', 'of', 'this', 'decomposition', 'we', 'extend', 'this', 'result', 'for', 'lattices', 'of', 'characteristic', 'subspaces', 'we', 'also', 'obtain', 'a', 'generalization', 'of', 'shodas', 'theorem', 'about', 'the', 'characterization', 'of', 'the', 'existence', 'of', 'characteristic', 'non', 'hyperinvariant', 'subspaces']] | [-0.17579803902966282, 0.09956680635441444, -0.1391177551820874, 0.013209944261082759, -0.06556229538594684, -0.06661031634236375, 0.007880929578095674, 0.3055227719557782, -0.34511084817349913, -0.12968492666259407, 0.13092684567285082, -0.20162190386715034, -0.12424897042413553, 0.19587264653916162, -0.10325567536056042, -0.02374056262585024, 0.07092171296632538, 0.0877324259839952, -0.14940306758508087, -0.281143135484308, 0.3999297734815627, -0.03520565567208299, 0.20825559468163798, 0.008616741486669828, 0.15838134761434047, 0.04796004945722719, -0.026362596929538996, 0.019835634598954734, -0.15745551840712627, 0.16377030986671645, 0.2702947796322405, 0.11051147220035394, 0.22826638376961153, -0.2683748428399364, -0.17068532148065665, 0.22827094708724568, 0.1546065555109332, 0.0887401832267642, 0.006153090624138713, -0.2936027845367789, 0.13066787689458578, -0.16613156910364826, -0.18138182970384756, -0.1259396994796892, 0.022875258660254378, -0.0498356922219197, -0.2757268570130691, 0.016190292542160024, 0.192360956252863, 0.1630749307119307, -0.15522529189862933, -0.12093180939094357, 0.0001207053583736221, 0.05235051163472235, -0.04340238821847985, 0.007774299569427967, 0.03754711161988477, -0.03472193380354535, -0.1415947476401925, 0.3656460063531995, -0.08550127260386944, -0.2528014038068553, 0.16522848550230265, -0.15518741129587094, -0.10803378968654821, 0.14675286075410743, 0.15899996170774103, 0.13886710200458766, -0.03950005321142574, 0.17578344546782318, -0.19283176537137478, 0.06879819830258688, 0.1094587271567434, 0.08315050969831646, 0.14432386298819136, 0.10638111976440996, 0.16132001627702267, 0.17187094183755108, 0.019989910442382096, 0.012931380397640168, -0.3620633382350206, -0.2023942537062491, -0.18937216417398303, 0.15528940682609876, -0.12126313963259842, -0.20613067913800479, 0.45635122593957933, 0.052263781128567645, 0.24133697216166183, 0.0977989129566898, 0.21929327516506117, 0.0862311185648044, 0.05175251183100045, 0.06534171849804503, 0.1098262949536244, 0.252452288614586, -0.024243699776707216, -0.13636788311220396, -0.06523576219721387, 0.20050147003494204] |
1,802.08833 | Adaptive Deep Learning through Visual Domain Localization | A commercial robot, trained by its manufacturer to recognize a predefined
number and type of objects, might be used in many settings, that will in
general differ in their illumination conditions, background, type and degree of
clutter, and so on. Recent computer vision works tackle this generalization
issue through domain adaptation methods, assuming as source the visual domain
where the system is trained and as target the domain of deployment. All
approaches assume to have access to images from all classes of the target
during training, an unrealistic condition in robotics applications. We address
this issue proposing an algorithm that takes into account the specific needs of
robot vision. Our intuition is that the nature of the domain shift experienced
mostly in robotics is local. We exploit this through the learning of maps that
spatially ground the domain and quantify the degree of shift, embedded into an
end-to-end deep domain adaptation architecture. By explicitly localizing the
roots of the domain shift we significantly reduce the number of parameters of
the architecture to tune, we gain the flexibility necessary to deal with subset
of categories in the target domain at training time, and we provide a clear
feedback on the rationale behind any classification decision, which can be
exploited in human-robot interactions. Experiments on two different settings of
the iCub World database confirm the suitability of our method for robot vision.
| cs.CV cs.RO | a commercial robot trained by its manufacturer to recognize a predefined number and type of objects might be used in many settings that will in general differ in their illumination conditions background type and degree of clutter and so on recent computer vision works tackle this generalization issue through domain adaptation methods assuming as source the visual domain where the system is trained and as target the domain of deployment all approaches assume to have access to images from all classes of the target during training an unrealistic condition in robotics applications we address this issue proposing an algorithm that takes into account the specific needs of robot vision our intuition is that the nature of the domain shift experienced mostly in robotics is local we exploit this through the learning of maps that spatially ground the domain and quantify the degree of shift embedded into an endtoend deep domain adaptation architecture by explicitly localizing the roots of the domain shift we significantly reduce the number of parameters of the architecture to tune we gain the flexibility necessary to deal with subset of categories in the target domain at training time and we provide a clear feedback on the rationale behind any classification decision which can be exploited in humanrobot interactions experiments on two different settings of the icub world database confirm the suitability of our method for robot vision | [['a', 'commercial', 'robot', 'trained', 'by', 'its', 'manufacturer', 'to', 'recognize', 'a', 'predefined', 'number', 'and', 'type', 'of', 'objects', 'might', 'be', 'used', 'in', 'many', 'settings', 'that', 'will', 'in', 'general', 'differ', 'in', 'their', 'illumination', 'conditions', 'background', 'type', 'and', 'degree', 'of', 'clutter', 'and', 'so', 'on', 'recent', 'computer', 'vision', 'works', 'tackle', 'this', 'generalization', 'issue', 'through', 'domain', 'adaptation', 'methods', 'assuming', 'as', 'source', 'the', 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1,802.08834 | LAMOST CCD camera-control system based on RTS2 | The Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST) is the
largest existing spectroscopic survey telescope, having 32 scientific
charge-coupled-device (CCD) cameras for receiving spectra. Stability and
automation of the camera-control software are essential, but cannot be provided
by the existing system. The Remote Telescope System 2nd Version (RTS2) is an
open-source and automatic observatory-control system. However, all previous
RTS2 applications have concerned small telescopes. This paper focuses on
implementation of an RTS2-based camera-control system for the 32 CCDs of
LAMOST. A virtual camera module inherited from the RTS2 camera module is built
as a device component working on the RTS2 framework. To improve the
controllability and robustness, a virtualized layer is designed using the
master-slave software paradigm, and the virtual camera module is mapped to the
32 real cameras of LAMOST. The new system is deployed in the actual environment
and experimentally tested. Finally, multiple observations are conducted using
this new RTS2-framework-based control system. The new camera-control system is
found to satisfy the requirements for LAMOST automatic camera control. This is
the first time that RTS2 has been applied to a large telescope, and provides a
referential solution for full RTS2 introduction to the LAMOST observatory
control system.
| astro-ph.IM | the large sky area multiobject fiber spectroscopic telescope lamost is the largest existing spectroscopic survey telescope having 32 scientific chargecoupleddevice ccd cameras for receiving spectra stability and automation of the cameracontrol software are essential but cannot be provided by the existing system the remote telescope system 2nd version rts2 is an opensource and automatic observatorycontrol system however all previous rts2 applications have concerned small telescopes this paper focuses on implementation of an rts2based cameracontrol system for the 32 ccds of lamost a virtual camera module inherited from the rts2 camera module is built as a device component working on the rts2 framework to improve the controllability and robustness a virtualized layer is designed using the masterslave software paradigm and the virtual camera module is mapped to the 32 real cameras of lamost the new system is deployed in the actual environment and experimentally tested finally multiple observations are conducted using this new rts2frameworkbased control system the new cameracontrol system is found to satisfy the requirements for lamost automatic camera control this is the first time that rts2 has been applied to a large telescope and provides a referential solution for full rts2 introduction to the lamost observatory control system | [['the', 'large', 'sky', 'area', 'multiobject', 'fiber', 'spectroscopic', 'telescope', 'lamost', 'is', 'the', 'largest', 'existing', 'spectroscopic', 'survey', 'telescope', 'having', '32', 'scientific', 'chargecoupleddevice', 'ccd', 'cameras', 'for', 'receiving', 'spectra', 'stability', 'and', 'automation', 'of', 'the', 'cameracontrol', 'software', 'are', 'essential', 'but', 'can', 'not', 'be', 'provided', 'by', 'the', 'existing', 'system', 'the', 'remote', 'telescope', 'system', '2nd', 'version', 'rts2', 'is', 'an', 'opensource', 'and', 'automatic', 'observatorycontrol', 'system', 'however', 'all', 'previous', 'rts2', 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1,802.08835 | The Volume of Two-Qubit States by Information Geometry | Using the information geometry approach, we determine the volume of the set
of two-qubit states with maximally disordered subsystems. Particular attention
is devoted to the behavior of the volume of sub-manifolds of separable and
entangled states with fixed purity. We show that the usage of the classical
Fisher metric on phase space probability representation of quantum states gives
the same qualitative results with respect to different versions of the quantum
Fisher metric.
| math-ph math.MP quant-ph | using the information geometry approach we determine the volume of the set of twoqubit states with maximally disordered subsystems particular attention is devoted to the behavior of the volume of submanifolds of separable and entangled states with fixed purity we show that the usage of the classical fisher metric on phase space probability representation of quantum states gives the same qualitative results with respect to different versions of the quantum fisher metric | [['using', 'the', 'information', 'geometry', 'approach', 'we', 'determine', 'the', 'volume', 'of', 'the', 'set', 'of', 'twoqubit', 'states', 'with', 'maximally', 'disordered', 'subsystems', 'particular', 'attention', 'is', 'devoted', 'to', 'the', 'behavior', 'of', 'the', 'volume', 'of', 'submanifolds', 'of', 'separable', 'and', 'entangled', 'states', 'with', 'fixed', 'purity', 'we', 'show', 'that', 'the', 'usage', 'of', 'the', 'classical', 'fisher', 'metric', 'on', 'phase', 'space', 'probability', 'representation', 'of', 'quantum', 'states', 'gives', 'the', 'same', 'qualitative', 'results', 'with', 'respect', 'to', 'different', 'versions', 'of', 'the', 'quantum', 'fisher', 'metric']] | [-0.11135666175848907, 0.13720551082709184, -0.09722292508619527, 0.03407817876076377, 0.016011096766710073, -0.11477699247188866, 0.055075097512195095, 0.32769611523771247, -0.2271499512458427, -0.2359237327448985, 0.04521650909090466, -0.33025843403690186, -0.14462073902702993, 0.16756448356641662, -0.0970403445405989, 0.11650277723351286, 0.0773130366125972, 0.12029613493683024, -0.13879582002280383, -0.29413271227127147, 0.4214564842534148, 0.025964178749644715, 0.33348378342472845, 0.009075103191814074, 0.12218425751456784, 0.04231390229607415, 0.006516075641330745, 0.041565182428409266, -0.15048432436550582, 0.17426280145122697, 0.22801013080202714, 0.1557983647669769, 0.21392136237894496, -0.3779941577102161, -0.17653140804678616, 0.130341113350975, 0.03485326304265376, 0.1354469009319372, 0.016989782172863163, -0.372459422201953, 0.031085560673899535, -0.159748348197253, -0.11893932666862383, -0.1302730537459461, -0.01490019980378242, -0.031198558672460623, -0.19712784198863018, 0.07176490107344256, 0.04343956571796702, 0.020088181960292988, -0.04179661289607692, -0.06159333503455855, -0.033286476837626346, 0.14023480075411499, -0.022610983096658148, 0.004679510449123982, 0.11901024485834771, -0.11892192642618385, -0.13669638558995859, 0.3514559655838336, -0.04178307479337996, -0.24669081844492918, 0.1724793747998774, -0.1683321058970048, -0.08439519144788694, 0.0841469177167811, 0.15604444595778155, 0.0944219019729644, -0.07882215942825294, 0.10977428095955272, -0.030223648290201608, 0.18383829194095191, 0.029480017695782915, 0.148671458753395, 0.15482633157322803, 0.10864432009596688, 0.11047202294381957, 0.22923676857317332, -0.05945184778667883, -0.1847481221322798, -0.3232551513032781, -0.2133572791531656, -0.23323158542108205, 0.07052081152667394, -0.12977681323839838, -0.18033730594389555, 0.43142171612190494, 0.081844044903442, 0.18782153997906587, 0.05523386972749399, 0.21902520732126302, 0.06687373541697322, -0.011612232476990256, 0.07781574704373877, 0.22263571175022256, 0.19627256005898946, 0.049474699429184615, -0.2239346495609627, 0.028459815085322287, 0.08662116349053879] |
1,802.08836 | Some infinitely generated non projective modules over path algebras and
their extensions under Martin's Axiom | In this paper it is proved that, when $Q$ is a quiver that admits some
closure, for any algebraically closed field $K$ and any finite dimensional
$K$-linear representation $\mathcal{X}$ of $Q$, if ${\rm
Ext}^1_{KQ}(\mathcal{X},KQ)=0$ then $\mathcal{X}$ is projective (Theorem 1.10).
In contrast, we show that if $Q$ is a specific quiver of the type above, then
there is an infinitely generated non-projective $KQ$-module $M_{\omega_1}$ such
that, when $K$ is a countable field, $\operatorname{\sf MA}_{\aleph_1}$
(Martin's Axiom for $\aleph_1$ many dense sets, which is a combinatorial axiom
in set theory) implies that ${\rm Ext}^1_{KQ}(M_{\omega_1},KQ)=0$ (Theorem
2.11).
| math.RT math.LO | in this paper it is proved that when q is a quiver that admits some closure for any algebraically closed field k and any finite dimensional klinear representation mathcalx of q if rm ext1_kqmathcalxkq0 then mathcalx is projective theorem 110 in contrast we show that if q is a specific quiver of the type above then there is an infinitely generated nonprojective kqmodule m_omega_1 such that when k is a countable field operatornamesf ma_aleph_1 martins axiom for aleph_1 many dense sets which is a combinatorial axiom in set theory implies that rm ext1_kqm_omega_1kq0 theorem 211 | [['in', 'this', 'paper', 'it', 'is', 'proved', 'that', 'when', 'q', 'is', 'a', 'quiver', 'that', 'admits', 'some', 'closure', 'for', 'any', 'algebraically', 'closed', 'field', 'k', 'and', 'any', 'finite', 'dimensional', 'klinear', 'representation', 'mathcalx', 'of', 'q', 'if', 'rm', 'ext1_kqmathcalxkq0', 'then', 'mathcalx', 'is', 'projective', 'theorem', '110', 'in', 'contrast', 'we', 'show', 'that', 'if', 'q', 'is', 'a', 'specific', 'quiver', 'of', 'the', 'type', 'above', 'then', 'there', 'is', 'an', 'infinitely', 'generated', 'nonprojective', 'kqmodule', 'm_omega_1', 'such', 'that', 'when', 'k', 'is', 'a', 'countable', 'field', 'operatornamesf', 'ma_aleph_1', 'martins', 'axiom', 'for', 'aleph_1', 'many', 'dense', 'sets', 'which', 'is', 'a', 'combinatorial', 'axiom', 'in', 'set', 'theory', 'implies', 'that', 'rm', 'ext1_kqm_omega_1kq0', 'theorem', '211']] | [-0.18715871907253231, 0.17647120835301597, -0.09387017465713951, 0.05242725086864084, -0.09538877935459217, -0.1889548580058747, -0.009027262740871973, 0.35397481253991525, -0.33553652443612614, -0.12755857412962035, 0.06187589152121088, -0.2277483437779463, -0.11463002337453267, 0.19969211544181842, -0.13531592731984954, -0.0817197813755936, 0.0655224980864053, 0.17287282991326516, -0.0463816360970668, -0.28879649606015945, 0.3822803308618151, -0.1439963666515218, 0.2286726186853937, 0.06879124684880177, 0.14907765697894826, 0.022018227378268623, 0.08529480413223306, 0.09636776662535138, -0.15164014174960255, 0.04730857165995985, 0.3398129902366135, 0.17152441034300459, 0.25952669044749604, -0.2975815633311868, -0.1676173859482838, 0.2464845784784605, 0.12986493984030353, 0.025235788476291217, -0.006731611024588346, -0.19467877607999576, 0.21853925758558843, -0.16255131688796812, -0.13957191607914865, -0.057390465163108376, 0.15117245488800107, -0.0416074194583214, -0.34339296648589274, -0.026373826650281748, 0.19863213117027448, 0.1469534610501594, -0.04562779667062892, -0.07243420004637705, -0.08082924207103336, -0.02226674889615323, -0.03533885469401462, 0.1543986699440413, 0.04700752406109435, -0.08364109062410231, -0.09865233615144259, 0.3576922016425265, -0.06437066030792064, -0.22571561784586971, 0.1325032180796067, -0.16153173424924414, -0.19548096927917666, 0.1497954603801999, -0.008723051820157302, 0.1428793692133493, -0.029414030748173697, 0.28372250762186013, -0.2255524882218904, 0.1790999666425503, 0.1111312744518121, -0.00525744927322699, 0.12922107292753127, 0.07405603378493752, 0.09427975393094433, 0.10020646596741345, 0.0018826101027015184, 0.013832519099944167, -0.38216077159676287, -0.16052608287168874, -0.1439344908828692, 0.18803356158443624, -0.13119576371051758, -0.18543845194702346, 0.2595389970474773, 0.11159317193297384, 0.1412063602047662, 0.09459550194571623, 0.23841102998703717, 0.08933779505071773, 0.026521306671202182, 0.11184961570737263, 0.1014541946057256, 0.21284516067761514, -0.08866580142122175, -0.09887086001431776, -0.009054282646522754, 0.16138569374258319] |
1,802.08837 | A gradient enhanced $\ell_1$-minimization for sparse approximation of
polynomial chaos expansions | We investigate a gradient-enhanced $\ell_1$-minimization for constructing
sparse polynomial chaos expansions. In addition to function evaluations,
measurements of the function gradient is also included to accelerate the
identification of expansion coefficients. By designing appropriate
preconditioners to the measurement matrix, we show gradient-enhanced $\ell_1$
minimization leads to stable and accurate coefficient recovery. The framework
for designing preconditioners is quite general and it applies to recover of
functions whose domain is bounded or unbounded. Comparisons between the
gradient enhanced approach and the standard $\ell_1$-minimization are also
presented and numerical examples suggest that the inclusion of derivative
information can guarantee sparse recovery at a reduced computational cost.
| math.NA | we investigate a gradientenhanced ell_1minimization for constructing sparse polynomial chaos expansions in addition to function evaluations measurements of the function gradient is also included to accelerate the identification of expansion coefficients by designing appropriate preconditioners to the measurement matrix we show gradientenhanced ell_1 minimization leads to stable and accurate coefficient recovery the framework for designing preconditioners is quite general and it applies to recover of functions whose domain is bounded or unbounded comparisons between the gradient enhanced approach and the standard ell_1minimization are also presented and numerical examples suggest that the inclusion of derivative information can guarantee sparse recovery at a reduced computational cost | [['we', 'investigate', 'a', 'gradientenhanced', 'ell_1minimization', 'for', 'constructing', 'sparse', 'polynomial', 'chaos', 'expansions', 'in', 'addition', 'to', 'function', 'evaluations', 'measurements', 'of', 'the', 'function', 'gradient', 'is', 'also', 'included', 'to', 'accelerate', 'the', 'identification', 'of', 'expansion', 'coefficients', 'by', 'designing', 'appropriate', 'preconditioners', 'to', 'the', 'measurement', 'matrix', 'we', 'show', 'gradientenhanced', 'ell_1', 'minimization', 'leads', 'to', 'stable', 'and', 'accurate', 'coefficient', 'recovery', 'the', 'framework', 'for', 'designing', 'preconditioners', 'is', 'quite', 'general', 'and', 'it', 'applies', 'to', 'recover', 'of', 'functions', 'whose', 'domain', 'is', 'bounded', 'or', 'unbounded', 'comparisons', 'between', 'the', 'gradient', 'enhanced', 'approach', 'and', 'the', 'standard', 'ell_1minimization', 'are', 'also', 'presented', 'and', 'numerical', 'examples', 'suggest', 'that', 'the', 'inclusion', 'of', 'derivative', 'information', 'can', 'guarantee', 'sparse', 'recovery', 'at', 'a', 'reduced', 'computational', 'cost']] | [-0.0898494649445638, 0.033364507369697094, -0.07766810520619262, 0.0650401157808329, -0.08530657888891605, -0.14854780819195396, 0.021921294645066015, 0.39003195563474524, -0.33945516092129624, -0.2450362629069087, 0.15855526049563196, -0.2594681899063289, -0.19060531049707116, 0.181222872309333, -0.07064794927226523, 0.11172756106288244, 0.10598836766663365, -0.0065515278122172905, -0.16395287187109575, -0.2530181239305351, 0.2363546055295361, 0.08168522640167233, 0.2392360850420888, 0.04483961120982153, 0.10870948562380643, -0.00791859851988892, -0.07075797289144248, 0.015687918458262666, -0.10414755955382414, 0.1590508106992974, 0.3001581616162394, 0.13942618827478817, 0.2980946577691401, -0.39788958801028246, -0.20908036885353234, 0.1343062294562025, 0.13433854096085548, 0.10291363110389704, -0.06482490955050497, -0.22272583605864874, 0.12930410619222898, -0.13797120552939865, -0.11780102282780437, -0.1874632399323253, -0.05634275962354597, 0.021224024419028025, -0.42094912433710235, 0.11522287234178154, 0.05026896679415726, -0.0008959177200897381, -0.05656099408560504, -0.14526046414371543, 0.06365789659321308, 0.046514447579214066, 0.02856721016444051, 0.028146398906336308, 0.1264129296289936, -0.09897332297315678, -0.06477465117887522, 0.3449360204484457, -0.07609761926981334, -0.27981188880781144, 0.17350233344656701, -0.09155842367685042, -0.10011524088062848, 0.12405490090159568, 0.20002252830622289, 0.1255797979851755, -0.10383841803279491, 0.09020858079580088, 0.0007320475818302769, 0.14335303841373667, 0.07372042320023936, 0.007060294472187077, 0.051805767633665636, 0.11810551919580366, 0.140849232297534, 0.14871712098381698, -0.00755755458242045, -0.1063287346825326, -0.2986244587944104, -0.11743769137501658, -0.22641895326355901, -0.03307227025148817, -0.16141550845518046, -0.18774485827060955, 0.37962991289364606, 0.14338268223890246, 0.15989569666159625, 0.10218330526437897, 0.3196870995004876, 0.17172553589849626, 0.037063901957411036, 0.08053440586305581, 0.21373876173479053, 0.16297357803649412, 0.04903983893410231, -0.2372985117522498, 0.09892863750260752, 0.1227315744756477] |
1,802.08838 | Slurm: fluid particle-in-cell code for plasma modeling | With the approach of exascale computing era, particle-based models are
becoming the focus of research due to their excellent scalability. We present a
new code, Slurm, which implements the classic particle-in-cell algorithm for
modeling magnetized fluids and plasmas. It features particle volume evolution
which damps the numerical finite grid instability, and allows modeling of key
physical instabilities such as Kelvin-Helmholtz and Rayleigh-Taylor. The
magnetic field in Slurm is handled via the electromagnetic vector potential
carried by particles. Numerical diffusion of the magnetic flux is extremely
low, and the solenoidality of the magnetic field is preserved to machine
precision. A double-linked list is used to carry particles, thus implementation
of open boundary conditions is simple and efficient. The code is written in C++
with OpenMP multi-threading, and has no external dependencies except for Boost.
It is easy to install and use on multi-core desktop computers as well as on
large shared-memory machines. Slurm is an ideal tool for its primary goal,
modeling of space weather events in the heliosphere. This article walks the
reader through the physical model, the algorithm, and all important details of
implementation. Ideally, after finishing this paper, the reader should be able
to either use Slurm for solving the desired problem, or create a new fluid PIC
code.
| physics.comp-ph | with the approach of exascale computing era particlebased models are becoming the focus of research due to their excellent scalability we present a new code slurm which implements the classic particleincell algorithm for modeling magnetized fluids and plasmas it features particle volume evolution which damps the numerical finite grid instability and allows modeling of key physical instabilities such as kelvinhelmholtz and rayleightaylor the magnetic field in slurm is handled via the electromagnetic vector potential carried by particles numerical diffusion of the magnetic flux is extremely low and the solenoidality of the magnetic field is preserved to machine precision a doublelinked list is used to carry particles thus implementation of open boundary conditions is simple and efficient the code is written in c with openmp multithreading and has no external dependencies except for boost it is easy to install and use on multicore desktop computers as well as on large sharedmemory machines slurm is an ideal tool for its primary goal modeling of space weather events in the heliosphere this article walks the reader through the physical model the algorithm and all important details of implementation ideally after finishing this paper the reader should be able to either use slurm for solving the desired problem or create a new fluid pic code | [['with', 'the', 'approach', 'of', 'exascale', 'computing', 'era', 'particlebased', 'models', 'are', 'becoming', 'the', 'focus', 'of', 'research', 'due', 'to', 'their', 'excellent', 'scalability', 'we', 'present', 'a', 'new', 'code', 'slurm', 'which', 'implements', 'the', 'classic', 'particleincell', 'algorithm', 'for', 'modeling', 'magnetized', 'fluids', 'and', 'plasmas', 'it', 'features', 'particle', 'volume', 'evolution', 'which', 'damps', 'the', 'numerical', 'finite', 'grid', 'instability', 'and', 'allows', 'modeling', 'of', 'key', 'physical', 'instabilities', 'such', 'as', 'kelvinhelmholtz', 'and', 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1,802.08839 | New fractal dimensions and some applications to arithmetic patches | In this paper, we define new fractal dimensions of a metric space in order to
calculate the roughness of a set on a large scale. These fractal dimensions are
called upper zeta dimension and lower zeta dimension. The upper zeta dimension
is an extension of the zeta dimension introduced by Doty, Gu, Lutz, Elvira,
Mayordomo, and Moser. We show that the upper zeta dimension is always a lower
bound for the Assouad dimension. Moreover, we apply the upper zeta dimension to
the existence of weak arithmetic patches of a given set. Arithmetic patches are
higher dimensionalized arithmetic progressions. As a corollary, we get the
affirmative solution to a higher dimensional weak analogue of the
Erd\H{o}s-Tur\'an conjecture. Here the one dimensional case is proved by Fraser
and Yu. As examples, we prove the existence of weak arithmetic patches of the
set of all irreducible elements of $\mathbb{Z}[\alpha]$, and the set of all
prime numbers of the form $p(p(n))$, where $\alpha$ is an imaginary quadratic
integer and $p(n)$ denotes the $n$-th prime number.
| math.NT math.MG | in this paper we define new fractal dimensions of a metric space in order to calculate the roughness of a set on a large scale these fractal dimensions are called upper zeta dimension and lower zeta dimension the upper zeta dimension is an extension of the zeta dimension introduced by doty gu lutz elvira mayordomo and moser we show that the upper zeta dimension is always a lower bound for the assouad dimension moreover we apply the upper zeta dimension to the existence of weak arithmetic patches of a given set arithmetic patches are higher dimensionalized arithmetic progressions as a corollary we get the affirmative solution to a higher dimensional weak analogue of the erdhosturan conjecture here the one dimensional case is proved by fraser and yu as examples we prove the existence of weak arithmetic patches of the set of all irreducible elements of mathbbzalpha and the set of all prime numbers of the form ppn where alpha is an imaginary quadratic integer and pn denotes the nth prime number | [['in', 'this', 'paper', 'we', 'define', 'new', 'fractal', 'dimensions', 'of', 'a', 'metric', 'space', 'in', 'order', 'to', 'calculate', 'the', 'roughness', 'of', 'a', 'set', 'on', 'a', 'large', 'scale', 'these', 'fractal', 'dimensions', 'are', 'called', 'upper', 'zeta', 'dimension', 'and', 'lower', 'zeta', 'dimension', 'the', 'upper', 'zeta', 'dimension', 'is', 'an', 'extension', 'of', 'the', 'zeta', 'dimension', 'introduced', 'by', 'doty', 'gu', 'lutz', 'elvira', 'mayordomo', 'and', 'moser', 'we', 'show', 'that', 'the', 'upper', 'zeta', 'dimension', 'is', 'always', 'a', 'lower', 'bound', 'for', 'the', 'assouad', 'dimension', 'moreover', 'we', 'apply', 'the', 'upper', 'zeta', 'dimension', 'to', 'the', 'existence', 'of', 'weak', 'arithmetic', 'patches', 'of', 'a', 'given', 'set', 'arithmetic', 'patches', 'are', 'higher', 'dimensionalized', 'arithmetic', 'progressions', 'as', 'a', 'corollary', 'we', 'get', 'the', 'affirmative', 'solution', 'to', 'a', 'higher', 'dimensional', 'weak', 'analogue', 'of', 'the', 'erdhosturan', 'conjecture', 'here', 'the', 'one', 'dimensional', 'case', 'is', 'proved', 'by', 'fraser', 'and', 'yu', 'as', 'examples', 'we', 'prove', 'the', 'existence', 'of', 'weak', 'arithmetic', 'patches', 'of', 'the', 'set', 'of', 'all', 'irreducible', 'elements', 'of', 'mathbbzalpha', 'and', 'the', 'set', 'of', 'all', 'prime', 'numbers', 'of', 'the', 'form', 'ppn', 'where', 'alpha', 'is', 'an', 'imaginary', 'quadratic', 'integer', 'and', 'pn', 'denotes', 'the', 'nth', 'prime', 'number']] | [-0.2144206048632484, 0.09534896478029016, -0.07775708904799623, 0.10370322164367064, -0.056417899835477806, -0.11382859140229855, 0.03628861651980385, 0.24217615897456804, -0.26235777965658125, -0.24288868203681582, 0.08626896059667204, -0.2615455092094462, -0.16754692914574185, 0.2065365959417596, -0.06925022971422766, 0.03469222752443914, -0.03816177038935989, 0.09016007312623385, -0.02168593754079969, -0.3326197470250052, 0.3902966271465023, -0.030968972470700032, 0.16625533537340484, 0.06826390600153467, 0.08871930268027686, -0.027620454160829207, 0.014522938456918513, 0.025861937860186333, -0.19704044515349647, 0.1620966039010368, 0.21605003220472663, 0.07159651835848178, 0.2563965719947148, -0.3291163227709365, -0.16411417339778772, 0.16235001379668357, 0.14057652253125394, 0.021266087878584153, 0.03124413870336, -0.22994905919629327, 0.14137656295774614, -0.1220818676520139, -0.1972543605315011, -0.0697086160570117, 0.09681456900268261, -0.007527700816613755, -0.30817470815964043, 0.03500111967358472, 0.11192028737942954, 0.12491431510716211, -0.05671423675292837, -0.15707141412643805, -0.006365561759420892, 0.08458969850228944, 0.018937340688093433, 0.053611883793824484, 0.02103683874399091, -0.09098614340932511, -0.11531221348005125, 0.30459017062925603, -0.06415618337216854, -0.21676604264593197, 0.17064660880991833, -0.19894274313091523, -0.15556914771219627, 0.10691118057556673, 0.15069611623351062, 0.15161233241226346, -0.022967170405622926, 0.20558725560182384, -0.1424572216402296, 0.15651130151153275, 0.18547740660696513, 0.020203255396890676, 0.09533012407926089, 0.07851927263185471, 0.11175368400013429, 0.18075741485448643, -0.07793911730931584, 0.008949792458484549, -0.3439890001373853, -0.22165081404333856, -0.2141510445411162, 0.13345880439252747, -0.1609609094277749, -0.2027226049186928, 0.3401253297717111, 0.09420901039473913, 0.2058570494631394, 0.13597294579016134, 0.2568748088697681, 0.15423803299650207, 0.02575756002139921, 0.08305229860845775, 0.12635968633648667, 0.1804399265300682, -0.01723698029083954, -0.119491559863534, 0.004429015220098552, 0.26223847524185356] |
1,802.0884 | Broadband mixing of ${\cal PT}$-symmetric and ${\cal PT}$-broken phases
in photonic heterostructures with a one-dimensional loss/gain bilayer | Combining loss and gain components in one photonic heterostructure opens a
new route to efficient manipulation by radiation, transmission, absorption, and
scattering of electromagnetic waves. Therefore, loss/gain structures enabling
${\cal PT}$-symmetric and ${\cal PT}$-broken phases for eigenvalues have
extensively been studied in the last decade. In particular, translation from
one phase to another, which occurs at the critical point in the two-channel
structures with one-dimensional loss/gain components, is often associated with
one-way transmission. In this report, broadband mixing of the ${\cal
PT}$-symmetric and ${\cal PT}$-broken phases for eigenvalues is theoretically
demonstrated in heterostructures with four channels obtained by combining a
one-dimensional loss/gain bilayer and one or two thin polarization-converting
components (PCCs). The broadband phase mixing in the four-channel case is
expected to yield advanced transmission and absorption regimes. Various
configurations are analyzed, which are distinguished in symmetry properties and
polarization conversion regime of PCCs. The conditions necessary for phase
mixing are discussed. The simplest two-component configurations with broadband
mixing are found, as well as the more complex three-component configurations
wherein symmetric and broken sets are not yet mixed and appear in the
neighbouring frequency ranges. Peculiarities of eigenvalue behaviour are
considered for different permittivity ranges of loss/gain medium, i.e., from
epsilon-near-zero to high-epsilon regime.
| physics.optics physics.app-ph | combining loss and gain components in one photonic heterostructure opens a new route to efficient manipulation by radiation transmission absorption and scattering of electromagnetic waves therefore lossgain structures enabling cal ptsymmetric and cal ptbroken phases for eigenvalues have extensively been studied in the last decade in particular translation from one phase to another which occurs at the critical point in the twochannel structures with onedimensional lossgain components is often associated with oneway transmission in this report broadband mixing of the cal ptsymmetric and cal ptbroken phases for eigenvalues is theoretically demonstrated in heterostructures with four channels obtained by combining a onedimensional lossgain bilayer and one or two thin polarizationconverting components pccs the broadband phase mixing in the fourchannel case is expected to yield advanced transmission and absorption regimes various configurations are analyzed which are distinguished in symmetry properties and polarization conversion regime of pccs the conditions necessary for phase mixing are discussed the simplest twocomponent configurations with broadband mixing are found as well as the more complex threecomponent configurations wherein symmetric and broken sets are not yet mixed and appear in the neighbouring frequency ranges peculiarities of eigenvalue behaviour are considered for different permittivity ranges of lossgain medium ie from epsilonnearzero to highepsilon regime | [['combining', 'loss', 'and', 'gain', 'components', 'in', 'one', 'photonic', 'heterostructure', 'opens', 'a', 'new', 'route', 'to', 'efficient', 'manipulation', 'by', 'radiation', 'transmission', 'absorption', 'and', 'scattering', 'of', 'electromagnetic', 'waves', 'therefore', 'lossgain', 'structures', 'enabling', 'cal', 'ptsymmetric', 'and', 'cal', 'ptbroken', 'phases', 'for', 'eigenvalues', 'have', 'extensively', 'been', 'studied', 'in', 'the', 'last', 'decade', 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1,802.08841 | Fierce selection and interference in B-cell repertoire response to
chronic HIV-1 | During chronic infection, HIV-1 engages in a rapid coevolutionary arms race
with the host's adaptive immune system. While it is clear that HIV exerts
strong selection on the adaptive immune system, the characteristics of the
somatic evolution that shape the immune response are still unknown. Traditional
population genetics methods fail to distinguish chronic immune response from
healthy repertoire evolution. Here, we infer the evolutionary modes of B-cell
repertoires and identify complex dynamics with a constant production of better
B-cell receptor mutants that compete, maintaining large clonal diversity and
potentially slowing down adaptation. A substantial fraction of mutations that
rise to high frequencies in pathogen engaging CDRs of B-cell receptors (BCRs)
are beneficial, in contrast to many such changes in structurally relevant
frameworks that are deleterious and circulate by hitchhiking. We identify a
pattern where BCRs in patients who experience larger viral expansions undergo
stronger selection with a rapid turnover of beneficial mutations due to clonal
interference in their CDR3 regions. Using population genetics modeling, we show
that the extinction of these beneficial mutations can be attributed to the rise
of competing beneficial alleles and clonal interference. The picture is of a
dynamic repertoire, where better clones may be outcompeted by new mutants
before they fix.
| q-bio.PE | during chronic infection hiv1 engages in a rapid coevolutionary arms race with the hosts adaptive immune system while it is clear that hiv exerts strong selection on the adaptive immune system the characteristics of the somatic evolution that shape the immune response are still unknown traditional population genetics methods fail to distinguish chronic immune response from healthy repertoire evolution here we infer the evolutionary modes of bcell repertoires and identify complex dynamics with a constant production of better bcell receptor mutants that compete maintaining large clonal diversity and potentially slowing down adaptation a substantial fraction of mutations that rise to high frequencies in pathogen engaging cdrs of bcell receptors bcrs are beneficial in contrast to many such changes in structurally relevant frameworks that are deleterious and circulate by hitchhiking we identify a pattern where bcrs in patients who experience larger viral expansions undergo stronger selection with a rapid turnover of beneficial mutations due to clonal interference in their cdr3 regions using population genetics modeling we show that the extinction of these beneficial mutations can be attributed to the rise of competing beneficial alleles and clonal interference the picture is of a dynamic repertoire where better clones may be outcompeted by new mutants before they fix | [['during', 'chronic', 'infection', 'hiv1', 'engages', 'in', 'a', 'rapid', 'coevolutionary', 'arms', 'race', 'with', 'the', 'hosts', 'adaptive', 'immune', 'system', 'while', 'it', 'is', 'clear', 'that', 'hiv', 'exerts', 'strong', 'selection', 'on', 'the', 'adaptive', 'immune', 'system', 'the', 'characteristics', 'of', 'the', 'somatic', 'evolution', 'that', 'shape', 'the', 'immune', 'response', 'are', 'still', 'unknown', 'traditional', 'population', 'genetics', 'methods', 'fail', 'to', 'distinguish', 'chronic', 'immune', 'response', 'from', 'healthy', 'repertoire', 'evolution', 'here', 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1,802.08842 | Back to Basics: Benchmarking Canonical Evolution Strategies for Playing
Atari | Evolution Strategies (ES) have recently been demonstrated to be a viable
alternative to reinforcement learning (RL) algorithms on a set of challenging
deep RL problems, including Atari games and MuJoCo humanoid locomotion
benchmarks. While the ES algorithms in that work belonged to the specialized
class of natural evolution strategies (which resemble approximate gradient RL
algorithms, such as REINFORCE), we demonstrate that even a very basic canonical
ES algorithm can achieve the same or even better performance. This success of a
basic ES algorithm suggests that the state-of-the-art can be advanced further
by integrating the many advances made in the field of ES in the last decades.
We also demonstrate qualitatively that ES algorithms have very different
performance characteristics than traditional RL algorithms: on some games, they
learn to exploit the environment and perform much better while on others they
can get stuck in suboptimal local minima. Combining their strengths with those
of traditional RL algorithms is therefore likely to lead to new advances in the
state of the art.
| cs.NE | evolution strategies es have recently been demonstrated to be a viable alternative to reinforcement learning rl algorithms on a set of challenging deep rl problems including atari games and mujoco humanoid locomotion benchmarks while the es algorithms in that work belonged to the specialized class of natural evolution strategies which resemble approximate gradient rl algorithms such as reinforce we demonstrate that even a very basic canonical es algorithm can achieve the same or even better performance this success of a basic es algorithm suggests that the stateoftheart can be advanced further by integrating the many advances made in the field of es in the last decades we also demonstrate qualitatively that es algorithms have very different performance characteristics than traditional rl algorithms on some games they learn to exploit the environment and perform much better while on others they can get stuck in suboptimal local minima combining their strengths with those of traditional rl algorithms is therefore likely to lead to new advances in the state of the art | [['evolution', 'strategies', 'es', 'have', 'recently', 'been', 'demonstrated', 'to', 'be', 'a', 'viable', 'alternative', 'to', 'reinforcement', 'learning', 'rl', 'algorithms', 'on', 'a', 'set', 'of', 'challenging', 'deep', 'rl', 'problems', 'including', 'atari', 'games', 'and', 'mujoco', 'humanoid', 'locomotion', 'benchmarks', 'while', 'the', 'es', 'algorithms', 'in', 'that', 'work', 'belonged', 'to', 'the', 'specialized', 'class', 'of', 'natural', 'evolution', 'strategies', 'which', 'resemble', 'approximate', 'gradient', 'rl', 'algorithms', 'such', 'as', 'reinforce', 'we', 'demonstrate', 'that', 'even', 'a', 'very', 'basic', 'canonical', 'es', 'algorithm', 'can', 'achieve', 'the', 'same', 'or', 'even', 'better', 'performance', 'this', 'success', 'of', 'a', 'basic', 'es', 'algorithm', 'suggests', 'that', 'the', 'stateoftheart', 'can', 'be', 'advanced', 'further', 'by', 'integrating', 'the', 'many', 'advances', 'made', 'in', 'the', 'field', 'of', 'es', 'in', 'the', 'last', 'decades', 'we', 'also', 'demonstrate', 'qualitatively', 'that', 'es', 'algorithms', 'have', 'very', 'different', 'performance', 'characteristics', 'than', 'traditional', 'rl', 'algorithms', 'on', 'some', 'games', 'they', 'learn', 'to', 'exploit', 'the', 'environment', 'and', 'perform', 'much', 'better', 'while', 'on', 'others', 'they', 'can', 'get', 'stuck', 'in', 'suboptimal', 'local', 'minima', 'combining', 'their', 'strengths', 'with', 'those', 'of', 'traditional', 'rl', 'algorithms', 'is', 'therefore', 'likely', 'to', 'lead', 'to', 'new', 'advances', 'in', 'the', 'state', 'of', 'the', 'art']] | [-0.01719733735029145, -0.0007142615931648651, -0.13172727757128033, 0.1052245499349899, -0.09762757471703185, -0.18854103120561888, 0.022191680838304334, 0.4667439391869589, -0.24647777335933194, -0.3604865698860242, 0.09471812241878802, -0.20628889760046581, -0.20074940677291367, 0.24375144132005172, -0.1360379312402354, 0.06458625828395405, 0.09125936305778619, -0.0018887500906881144, -0.08861508146451907, -0.32232651701155696, 0.24648825883226105, 0.044216220083711226, 0.30483872728345135, -0.04250928785200038, 0.08808041838984165, -0.0696664008603999, 0.02591996244017101, 0.06619026760857266, -0.05567034410630331, 0.1309711644839297, 0.31388785144225995, 0.18912661871211547, 0.37017996741469794, -0.4458716260103961, -0.21679550296208167, 0.1543534267693758, 0.18815655079146726, 0.1053763293063946, -0.038549060785189576, -0.2967799116620386, 0.08595623297280337, -0.15679447362254534, -0.0308117347462213, -0.1417442523604314, -0.01338041276387187, 0.041670408826624455, -0.2530537117865025, -0.009315550463865153, 0.05985085101462019, 0.010749985676082457, -0.030148134390498408, -0.18658639989743703, 0.04113426384482995, 0.14341466249374552, 0.036630675016169456, 0.049300210043435266, 0.15337084070917154, -0.19812503105894658, -0.22024535851486954, 0.33836900848707946, -0.04580231498316903, -0.14765413817894485, 0.28083704445048197, -0.04016269060518571, -0.1643309351111922, 0.11106748742150617, 0.20643532727486813, 0.15002659404341726, -0.1034254386095253, 0.06511883473935808, -0.015154411788952686, 0.1385393076157473, 0.012716213219176382, 0.03863348242049739, 0.16161087788806777, 0.2133431853423413, 0.08218261620722789, 0.059428993963840354, -0.017017685553367586, -0.154298228465197, -0.13277667932096626, -0.12658087805343363, -0.13480752268971424, -0.010485976580365082, -0.06733952441990525, -0.11739158273659737, 0.3677193040424991, 0.21812383404563693, 0.17311016303217094, 0.08910525749339694, 0.33325050670940143, 0.058233637256722086, 0.09227167069085339, 0.13602725167609972, 0.2942018319452085, 0.014105563116750333, 0.13141204363021713, -0.20576211893600385, 0.09693986437415715, 0.010250524471867995] |
1,802.08843 | On the sizes of $k$-edge-maximal $r$-uniform hypergraphs | Let $H=(V,E)$ be a hypergraph, where $V$ is a set of vertices and $E$ is a
set of non-empty subsets of $V$ called edges. If all edges of $H$ have the same
cardinality $r$, then $H$ is a $r$-uniform hypergraph; if $E$ consists of all
$r$-subsets of $V$, then $H$ is a complete $r$-uniform hypergraph, denoted by
$K_n^r$, where $n=|V|$. A hypergraph $H'=(V',E')$ is called a subhypergraph of
$H=(V,E)$ if $V'\subseteq V$ and $E'\subseteq E$. A $r$-uniform hypergraph
$H=(V,E)$ is $k$-edge-maximal if every subhypergraph of $H$ has
edge-connectivity at most $k$, but for any edge $e\in E(K_n^r)\setminus E(H)$,
$H+e$ contains at least one subhypergraph with edge-connectivity at least
$k+1$.
Let $k$ and $r$ be integers with $k\geq2$ and $r\geq2$, and let $t=t(k,r)$ be
the largest integer such that $(^{t-1}_{r-1})\leq k$. That is, $t$ is the
integer satisfies $(^{t-1}_{r-1})\leq k<(^{t}_{r-1})$. We prove that if $H$ is
a $r$-uniform $k$-edge-maximal hypergraph such that $n=|V(H)|\geq t$, then
($i$) $|E(H)|\leq (^{t}_{r})+(n-t)k$, and this bound is best possible;
($ii$) $|E(H)|\geq (n-1)k -((t-1)k-(^{t}_{r}))\lfloor\frac{n}{t}\rfloor$, and
this bound is best possible.
This extends former results in [8] and [6].
| math.CO | let hve be a hypergraph where v is a set of vertices and e is a set of nonempty subsets of v called edges if all edges of h have the same cardinality r then h is a runiform hypergraph if e consists of all rsubsets of v then h is a complete runiform hypergraph denoted by k_nr where nv a hypergraph hve is called a subhypergraph of hve if vsubseteq v and esubseteq e a runiform hypergraph hve is kedgemaximal if every subhypergraph of h has edgeconnectivity at most k but for any edge ein ek_nrsetminus eh he contains at least one subhypergraph with edgeconnectivity at least k1 let k and r be integers with kgeq2 and rgeq2 and let ttkr be the largest integer such that t1_r1leq k that is t is the integer satisfies t1_r1leq kt_r1 we prove that if h is a runiform kedgemaximal hypergraph such that nvhgeq t then i ehleq t_rntk and this bound is best possible ii ehgeq n1k t1kt_rlfloorfracntrfloor and this bound is best possible this extends former results in 8 and 6 | [['let', 'hve', 'be', 'a', 'hypergraph', 'where', 'v', 'is', 'a', 'set', 'of', 'vertices', 'and', 'e', 'is', 'a', 'set', 'of', 'nonempty', 'subsets', 'of', 'v', 'called', 'edges', 'if', 'all', 'edges', 'of', 'h', 'have', 'the', 'same', 'cardinality', 'r', 'then', 'h', 'is', 'a', 'runiform', 'hypergraph', 'if', 'e', 'consists', 'of', 'all', 'rsubsets', 'of', 'v', 'then', 'h', 'is', 'a', 'complete', 'runiform', 'hypergraph', 'denoted', 'by', 'k_nr', 'where', 'nv', 'a', 'hypergraph', 'hve', 'is', 'called', 'a', 'subhypergraph', 'of', 'hve', 'if', 'vsubseteq', 'v', 'and', 'esubseteq', 'e', 'a', 'runiform', 'hypergraph', 'hve', 'is', 'kedgemaximal', 'if', 'every', 'subhypergraph', 'of', 'h', 'has', 'edgeconnectivity', 'at', 'most', 'k', 'but', 'for', 'any', 'edge', 'ein', 'ek_nrsetminus', 'eh', 'he', 'contains', 'at', 'least', 'one', 'subhypergraph', 'with', 'edgeconnectivity', 'at', 'least', 'k1', 'let', 'k', 'and', 'r', 'be', 'integers', 'with', 'kgeq2', 'and', 'rgeq2', 'and', 'let', 'ttkr', 'be', 'the', 'largest', 'integer', 'such', 'that', 't1_r1leq', 'k', 'that', 'is', 't', 'is', 'the', 'integer', 'satisfies', 't1_r1leq', 'kt_r1', 'we', 'prove', 'that', 'if', 'h', 'is', 'a', 'runiform', 'kedgemaximal', 'hypergraph', 'such', 'that', 'nvhgeq', 't', 'then', 'i', 'ehleq', 't_rntk', 'and', 'this', 'bound', 'is', 'best', 'possible', 'ii', 'ehgeq', 'n1k', 't1kt_rlfloorfracntrfloor', 'and', 'this', 'bound', 'is', 'best', 'possible', 'this', 'extends', 'former', 'results', 'in', '8', 'and', '6']] | [-0.20361231381445047, 0.16436009416511455, -0.02006585184199402, -0.06862639756388747, -0.0891421959557802, -0.2761284855592759, 0.02650980374673543, 0.3452011148830219, -0.25512809735773984, -0.268422632031358, 0.03510363304629556, -0.4038553601400012, -0.056870873576040905, 0.0339320212605307, -0.09074756060188603, -0.05659430162034689, 0.11494719141366154, 0.16425311562707487, 0.10698590719348326, -0.29038764555559016, 0.25819943141665935, -0.09513426608691326, 0.08886870778989413, 0.1084665760850114, 0.06856579302858583, 0.027078827577939182, 0.07642319311274787, 0.12729960008298258, -0.2017479685945719, 0.049160538984171914, 0.29346251745595237, 0.23298449929927428, 0.3124038518401067, -0.30204225219755887, -0.12635826142881648, 0.28389686195858127, 0.14020207787007954, -0.0481244837111562, 0.04859724135213461, -0.1752938982045461, 0.25153847471568624, -0.09429942505875591, -0.05297614302690749, 0.06478134873780729, 0.24401175004640066, -0.03741965522569728, -0.4067867887475544, -0.07834320986977203, 0.14386969498988522, 0.04276156830843646, 0.10095324048320252, -0.23073095464379112, -0.13523377848498394, -0.03375444893968814, -0.16869731861532725, 0.1685118387766839, 0.0032073236787234127, -0.043775171515056756, -0.1246490761438503, 0.36640428824920873, -0.10249193768404596, -0.10405860339006075, 0.06598800138941821, -0.1500018033268996, -0.15642262427028636, 0.11765987963706384, 0.03808517626492102, 0.19132982670044194, -0.054162817280416546, 0.25498518873964343, -0.1899344616936657, 0.10957474490315411, 0.11650544696762039, 0.03903993292350989, 0.09936593370673628, 0.10944199867848027, 0.21591744419171158, 0.12132302589776436, -0.007993885961958783, 0.1791235225946734, -0.3600392452726474, -0.14737440122094952, -0.28284971897942524, 0.14274039841929956, -0.13415284021663335, -0.130035096776774, 0.3722517441721321, 0.07551810746431092, 0.20938937550119927, 0.05887665309959409, 0.20804086322169427, 0.06982628919733984, 0.006667232369691823, 0.25690739501450705, 0.05639945442190749, 0.18621856965409925, -0.12849512388337556, -0.13611860823349198, 0.04899519348161758, 0.14514160989864455] |
1,802.08844 | Search for $thj$ production with $h\rightarrow \gamma\gamma$ at the LHC
in the littlest Higgs model with T-parity | In the littlest Higgs model with T-parity, we study associated production of
a Higgs and a single top quark at the 14 TeV LHC. We focus on the Higgs to two
photons decay and the semileptonic top decay channel. By performing a fast
detector simulation, we find that the $thj$ search in the selected channel can
excluded the top partner mass $m_{T_{+}}$ up to 805 (857) GeV for case A (case
B) at $2\sigma$ confidence level at 14 TeV LHC with the integrated luminosity
$L= 3 \rm ab^{-1}$.
| hep-ph | in the littlest higgs model with tparity we study associated production of a higgs and a single top quark at the 14 tev lhc we focus on the higgs to two photons decay and the semileptonic top decay channel by performing a fast detector simulation we find that the thj search in the selected channel can excluded the top partner mass m_t_ up to 805 857 gev for case a case b at 2sigma confidence level at 14 tev lhc with the integrated luminosity l 3 rm ab1 | [['in', 'the', 'littlest', 'higgs', 'model', 'with', 'tparity', 'we', 'study', 'associated', 'production', 'of', 'a', 'higgs', 'and', 'a', 'single', 'top', 'quark', 'at', 'the', '14', 'tev', 'lhc', 'we', 'focus', 'on', 'the', 'higgs', 'to', 'two', 'photons', 'decay', 'and', 'the', 'semileptonic', 'top', 'decay', 'channel', 'by', 'performing', 'a', 'fast', 'detector', 'simulation', 'we', 'find', 'that', 'the', 'thj', 'search', 'in', 'the', 'selected', 'channel', 'can', 'excluded', 'the', 'top', 'partner', 'mass', 'm_t_', 'up', 'to', '805', '857', 'gev', 'for', 'case', 'a', 'case', 'b', 'at', '2sigma', 'confidence', 'level', 'at', '14', 'tev', 'lhc', 'with', 'the', 'integrated', 'luminosity', 'l', '3', 'rm', 'ab1']] | [-0.06202143553210507, 0.22139480715321208, 0.0032116393876177344, 0.16916824885728685, -0.03483763958898966, -0.23208238201385195, 0.07489593977912921, 0.35900460577315907, -0.1748641780604058, -0.3279219559626654, 0.02478399765624834, -0.35367052553390915, 0.11462865097829225, 0.14329144595434296, 0.12547683246984062, 0.09636567330876873, 0.1343761079399635, 0.009599401721392165, -0.049850154093953526, -0.2674369101349095, 0.23873943018473007, 0.08516624706945466, 0.17766073394820772, 0.11716639188075946, 0.04888178281147371, 0.005799225780223919, -0.026962132200424094, -0.15270602179754694, -0.13372686658021848, 0.07935659513829953, 0.16886992322542937, 0.03857873124599626, 0.12198554442941466, -0.250220192252362, -0.03894022655599243, 0.16199129333571446, 0.13980664055138317, 0.026822961909188467, -0.05812401155840648, -0.3420941441436298, 0.19233794520947745, -0.27002747981301084, -0.0768450788402168, 0.07900356478586962, -0.04880145120048176, -0.16165336905396543, -0.3344287389931692, 0.0804228100999105, -0.10461716494649988, 0.06673374999875457, 0.028102928743464872, -0.215301943090956, -0.1221776570521549, -0.057116698886437174, 0.06345693521143403, 0.06365376640678733, 0.2094192326873202, -0.1934736805481159, -0.21776774963258172, 0.3318454469554126, -0.14114193929443983, -0.15108708768225784, 0.204925098971051, -0.22405614926670256, -0.16517740163412367, 0.14278054198207843, 0.3097456109667705, 0.03174821370471777, -0.19865462791428648, 0.1635578960294052, -0.006152336205890275, 0.21367263143871573, 0.06976332536115395, 0.01722101622197608, 0.25009504112478514, 0.25567579635985155, 0.018859138765203006, 0.08330337223808536, -0.17403784240873807, -0.0053047311640429225, -0.4899488934104077, -0.1032070814637171, -0.013517585093557665, 0.0644893476697193, -0.049552943827042, 0.007372326058843596, 0.4075663647487421, 0.09127329258163544, 0.3053518647158688, 0.05576886700212278, 0.2453113567230916, 0.07962254306237976, 0.08325505481347632, 0.09799741416513412, 0.3466014963077297, 0.10775811257190071, 0.15970802148909902, -0.19624498977579855, -0.05401513367012905, 0.02366917897862467] |
1,802.08845 | Mesoscopic real space structures in aging spin-glasses: the
Edwards-Anderson model | Isothermal simulational data for the 3D Edwards-Anderson spin glass are
collected at several temperatures below $T_{\rm c}$ and, in analogy with a
recent model of dense colloidal suspensions,interpreted in terms of clusters of
contiguous spins overturned by quakes, non-equilibrium events linked to record
sized energy fluctuations. We show numerically that, to a good approximation,
these quakes are statistically independent and constitute a Poisson process
whose average grows logarithmically in time. The overturned clusters are local
projections on one of the two ground states of the model, and grow likewise
logarithmically in time. Data collected at different temperatures $T$ can be
collapsed by scaling them with $T^{1.75}$, a hitherto unnoticed feature of the
E-A model, which we relate on the one hand to the geometry of configuration
space and on the other to experimental memory and rejuvenation effects. The
rate at which a cluster flips is shown to decrease exponentially with the size
of the cluster, as recently assumed in a coarse grained model of dense
colloidal dynamics. The evolving structure of clusters in real space is finally
sssociated to the decay of the thermo-remanent magnetization.
Our analysis provides an unconventional coarse-grained description of spin
glass aging as statistically subordinated to a Poisson quaking process and
highlights record dynamics as a viable common theoretical framework for aging
in different systems.
| cond-mat.stat-mech | isothermal simulational data for the 3d edwardsanderson spin glass are collected at several temperatures below t_rm c and in analogy with a recent model of dense colloidal suspensionsinterpreted in terms of clusters of contiguous spins overturned by quakes nonequilibrium events linked to record sized energy fluctuations we show numerically that to a good approximation these quakes are statistically independent and constitute a poisson process whose average grows logarithmically in time the overturned clusters are local projections on one of the two ground states of the model and grow likewise logarithmically in time data collected at different temperatures t can be collapsed by scaling them with t175 a hitherto unnoticed feature of the ea model which we relate on the one hand to the geometry of configuration space and on the other to experimental memory and rejuvenation effects the rate at which a cluster flips is shown to decrease exponentially with the size of the cluster as recently assumed in a coarse grained model of dense colloidal dynamics the evolving structure of clusters in real space is finally sssociated to the decay of the thermoremanent magnetization our analysis provides an unconventional coarsegrained description of spin glass aging as statistically subordinated to a poisson quaking process and highlights record dynamics as a viable common theoretical framework for aging in different systems | [['isothermal', 'simulational', 'data', 'for', 'the', '3d', 'edwardsanderson', 'spin', 'glass', 'are', 'collected', 'at', 'several', 'temperatures', 'below', 't_rm', 'c', 'and', 'in', 'analogy', 'with', 'a', 'recent', 'model', 'of', 'dense', 'colloidal', 'suspensionsinterpreted', 'in', 'terms', 'of', 'clusters', 'of', 'contiguous', 'spins', 'overturned', 'by', 'quakes', 'nonequilibrium', 'events', 'linked', 'to', 'record', 'sized', 'energy', 'fluctuations', 'we', 'show', 'numerically', 'that', 'to', 'a', 'good', 'approximation', 'these', 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1,802.08846 | To the problem of mathematical and physical modeling of the turbulent
jets of mutually immiscible liquids like the oil and water | Peculiarities of the turbulent two phase and multiphase flows of the mutually
immiscible liquids and averaged differential equations for their modeling are
considered based on the approach, which was first developed and proposed by
Prof. A.I. Nakorchevski as an alternative to a number of the well known
averaged multiphase dynamics equations. The main difference of the new method
was in an averaging of the Navier Stokes equations by phases and components in
time instead of the widely spread spatial averaging in multiphase mechanics.
What was more, introduction of the so called function indicator of the phases
in a flow allowed recognizing the phases in their movement in a multiphase
mixture, both theoretically and experimentally. For experimental study the
special micro sensor was invented and created, which was successfully applied.
In this paper, the method of Nakorchevski is described in application to some
multiphase tasks, and a discussion is presented as concern to advantages of the
method for study of turbulent two phase flows of water oil immiscible mixture,
as well as many other similar mixtures, where it is important to know the
peculiarities of the phases movement and their mixing in a multiphase flow.
| physics.flu-dyn | peculiarities of the turbulent two phase and multiphase flows of the mutually immiscible liquids and averaged differential equations for their modeling are considered based on the approach which was first developed and proposed by prof ai nakorchevski as an alternative to a number of the well known averaged multiphase dynamics equations the main difference of the new method was in an averaging of the navier stokes equations by phases and components in time instead of the widely spread spatial averaging in multiphase mechanics what was more introduction of the so called function indicator of the phases in a flow allowed recognizing the phases in their movement in a multiphase mixture both theoretically and experimentally for experimental study the special micro sensor was invented and created which was successfully applied in this paper the method of nakorchevski is described in application to some multiphase tasks and a discussion is presented as concern to advantages of the method for study of turbulent two phase flows of water oil immiscible mixture as well as many other similar mixtures where it is important to know the peculiarities of the phases movement and their mixing in a multiphase flow | [['peculiarities', 'of', 'the', 'turbulent', 'two', 'phase', 'and', 'multiphase', 'flows', 'of', 'the', 'mutually', 'immiscible', 'liquids', 'and', 'averaged', 'differential', 'equations', 'for', 'their', 'modeling', 'are', 'considered', 'based', 'on', 'the', 'approach', 'which', 'was', 'first', 'developed', 'and', 'proposed', 'by', 'prof', 'ai', 'nakorchevski', 'as', 'an', 'alternative', 'to', 'a', 'number', 'of', 'the', 'well', 'known', 'averaged', 'multiphase', 'dynamics', 'equations', 'the', 'main', 'difference', 'of', 'the', 'new', 'method', 'was', 'in', 'an', 'averaging', 'of', 'the', 'navier', 'stokes', 'equations', 'by', 'phases', 'and', 'components', 'in', 'time', 'instead', 'of', 'the', 'widely', 'spread', 'spatial', 'averaging', 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'their', 'mixing', 'in', 'a', 'multiphase', 'flow']] | [-0.08109695871523097, 0.13065498630885486, -0.1076482755112617, 0.03179122309302329, -0.029090467365070555, -0.10417919800238451, -0.02648496156022399, 0.3379203656707735, -0.28432710840327974, -0.31156749023284647, 0.11461070177210786, -0.23652956958903815, -0.15045112358347978, 0.17705137657079226, -0.05168767569314999, 0.09693334839691185, -0.014454805015702732, -0.009928138216006724, -0.02896311296596347, -0.22719046127895126, 0.29251365166298154, 0.01534031457170689, 0.2946951364186437, 0.047063306049191546, 0.12709502915822668, -0.034064722545736004, -0.05503782812108208, 0.046442393211085196, -0.11548497179561916, 0.07306033229785196, 0.2527315017214278, 0.06517929284927959, 0.25785676870145835, -0.4368078366969712, -0.2692328881433544, 0.06573643905297406, 0.15946034000323075, 0.08731247519972385, -0.04590484190437868, -0.26974306498110917, 0.036166586011556014, -0.16908031501225196, -0.12119034896507704, 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1,802.08847 | Coverage Control for Wire-Traversing Robots | In this paper we consider the coverage control problem for a team of
wire-traversing robots. The two-dimensional motion of robots moving in a planar
environment has to be projected to one-dimensional manifolds representing the
wires. Starting from Lloyd's descent algorithm for coverage control, a solution
that generates continuous motion of the robots on the wires is proposed. This
is realized by means of a Continuous Onto Wires (COW) map: the robots'
workspace is mapped onto the wires on which the motion of the robots is
constrained to be. A final projection step is introduced to ensure that the
configuration of the robots on the wires is a local minimizer of the
constrained locational cost. An algorithm for the continuous constrained
coverage control problem is proposed and it is tested both in simulation and on
a team of mobile robots.
| cs.RO | in this paper we consider the coverage control problem for a team of wiretraversing robots the twodimensional motion of robots moving in a planar environment has to be projected to onedimensional manifolds representing the wires starting from lloyds descent algorithm for coverage control a solution that generates continuous motion of the robots on the wires is proposed this is realized by means of a continuous onto wires cow map the robots workspace is mapped onto the wires on which the motion of the robots is constrained to be a final projection step is introduced to ensure that the configuration of the robots on the wires is a local minimizer of the constrained locational cost an algorithm for the continuous constrained coverage control problem is proposed and it is tested both in simulation and on a team of mobile robots | [['in', 'this', 'paper', 'we', 'consider', 'the', 'coverage', 'control', 'problem', 'for', 'a', 'team', 'of', 'wiretraversing', 'robots', 'the', 'twodimensional', 'motion', 'of', 'robots', 'moving', 'in', 'a', 'planar', 'environment', 'has', 'to', 'be', 'projected', 'to', 'onedimensional', 'manifolds', 'representing', 'the', 'wires', 'starting', 'from', 'lloyds', 'descent', 'algorithm', 'for', 'coverage', 'control', 'a', 'solution', 'that', 'generates', 'continuous', 'motion', 'of', 'the', 'robots', 'on', 'the', 'wires', 'is', 'proposed', 'this', 'is', 'realized', 'by', 'means', 'of', 'a', 'continuous', 'onto', 'wires', 'cow', 'map', 'the', 'robots', 'workspace', 'is', 'mapped', 'onto', 'the', 'wires', 'on', 'which', 'the', 'motion', 'of', 'the', 'robots', 'is', 'constrained', 'to', 'be', 'a', 'final', 'projection', 'step', 'is', 'introduced', 'to', 'ensure', 'that', 'the', 'configuration', 'of', 'the', 'robots', 'on', 'the', 'wires', 'is', 'a', 'local', 'minimizer', 'of', 'the', 'constrained', 'locational', 'cost', 'an', 'algorithm', 'for', 'the', 'continuous', 'constrained', 'coverage', 'control', 'problem', 'is', 'proposed', 'and', 'it', 'is', 'tested', 'both', 'in', 'simulation', 'and', 'on', 'a', 'team', 'of', 'mobile', 'robots']] | [-0.15779766895191616, 0.046848408349024546, -0.08309260644761009, -0.033484503298667194, -0.056967060557445104, -0.13789370336461867, 0.039611029222504694, 0.42353498125853745, -0.27453768576252396, -0.30079064448026643, 0.1212967654809936, -0.2439401396718956, -0.10600322273566617, 0.16514494461749774, -0.1275105232531475, 0.08336189498557993, 0.07708862724889448, 0.03248730061602765, -0.01247726861206963, -0.22573506597183185, 0.2666346735761339, 0.023526472579417885, 0.2531965308809194, -0.012384792577232356, 0.18110198239741873, 0.040325266978154264, 0.057951792177028845, 0.07773249315779548, -0.09868997561568614, 0.12595460000211725, 0.23308203141609934, 0.1114643316284038, 0.27585550600095937, -0.4231655638449002, -0.17562765605153813, 0.10312730363448677, 0.1284079636696834, 0.09094437066232786, -0.02924536329700841, -0.3556334510769533, 0.0750235427370079, -0.12271244612866608, -0.11695667346467034, -0.012820441527224646, 0.008718242018443087, 0.016437950577802847, -0.28738621210652415, -0.03845596146108447, 0.03827514218679373, 0.03522374335190524, -0.09288130238638732, -0.019854881220321724, -0.00959455398707718, 0.1471503297224695, -0.020606938166631575, 0.07826165897447777, 0.18461568065770512, -0.13688403111545072, -0.130313524671837, 0.4211758351817295, 0.023776490092817425, -0.2715526305263916, 0.1767126608106807, -0.06997414018521828, -0.09420622221828587, 0.1301136839851413, 0.22058702594992044, 0.14653355060685155, -0.1863853426775018, 0.10040246256529287, -0.06917953915705068, 0.14600359819018774, 0.032667841256150736, -0.07933927436217504, 0.19106119526955095, 0.2542268170138308, 0.20879085952470053, 0.13781052340746627, -0.0813670085651962, -0.1443921372579559, -0.24850900285536479, -0.1858421613637736, -0.26148407070803037, -0.015322911438356707, -0.038825911688036285, -0.1377240569614198, 0.3920866104679695, 0.12483289553041475, 0.17622802200038795, 0.09105975124596928, 0.3196269328776828, 0.118022670184829, 0.07018464832040279, 0.07095975364369435, 0.2185721026895487, 0.06887984110474371, 0.09679952606160626, -0.22802901946757312, 0.06772555871967874, 0.05530061450807135] |
1,802.08848 | Combining historical data and bookmakers'odds in modelling football
scores | Modelling football outcomes has gained increasing attention, in large part
due to the potential for making substantial profits. Despite the strong
connection existing between football models and the bookmakers' betting odds,
no authors have used the latter for improving the fit and the predictive
accuracy of these models. We have developed a hierarchical Bayesian Poisson
model in which the scoring rates of the teams are convex combinations of
parameters estimated from historical data and the additional source of the
betting odds. We apply our analysis to a nine-year dataset of the most popular
European leagues in order to predict match outcomes for their tenth seasons. In
this paper, we provide numerical and graphical checks for our model.
| stat.AP | modelling football outcomes has gained increasing attention in large part due to the potential for making substantial profits despite the strong connection existing between football models and the bookmakers betting odds no authors have used the latter for improving the fit and the predictive accuracy of these models we have developed a hierarchical bayesian poisson model in which the scoring rates of the teams are convex combinations of parameters estimated from historical data and the additional source of the betting odds we apply our analysis to a nineyear dataset of the most popular european leagues in order to predict match outcomes for their tenth seasons in this paper we provide numerical and graphical checks for our model | [['modelling', 'football', 'outcomes', 'has', 'gained', 'increasing', 'attention', 'in', 'large', 'part', 'due', 'to', 'the', 'potential', 'for', 'making', 'substantial', 'profits', 'despite', 'the', 'strong', 'connection', 'existing', 'between', 'football', 'models', 'and', 'the', 'bookmakers', 'betting', 'odds', 'no', 'authors', 'have', 'used', 'the', 'latter', 'for', 'improving', 'the', 'fit', 'and', 'the', 'predictive', 'accuracy', 'of', 'these', 'models', 'we', 'have', 'developed', 'a', 'hierarchical', 'bayesian', 'poisson', 'model', 'in', 'which', 'the', 'scoring', 'rates', 'of', 'the', 'teams', 'are', 'convex', 'combinations', 'of', 'parameters', 'estimated', 'from', 'historical', 'data', 'and', 'the', 'additional', 'source', 'of', 'the', 'betting', 'odds', 'we', 'apply', 'our', 'analysis', 'to', 'a', 'nineyear', 'dataset', 'of', 'the', 'most', 'popular', 'european', 'leagues', 'in', 'order', 'to', 'predict', 'match', 'outcomes', 'for', 'their', 'tenth', 'seasons', 'in', 'this', 'paper', 'we', 'provide', 'numerical', 'and', 'graphical', 'checks', 'for', 'our', 'model']] | [-0.03430965007481794, -0.022609827757422995, -0.10135003949841882, 0.10978340467290841, -0.1086316146593318, -0.12805866497831467, 0.09519113995966652, 0.39314728152229744, -0.19479881268906066, -0.3500450086525172, 0.12541192468381626, -0.3193831818544457, -0.11540295385843159, 0.18493001840801704, -0.10612969205547602, 0.05650063320043155, 0.1051888534417137, 0.019928905975847688, -0.007232820264931418, -0.30946846012002194, 0.2761185940187902, 0.11591163620901987, 0.305959411813185, 0.011940313332005698, 0.09631970586080073, -0.00011002059436888776, -0.10784482446491209, 0.00613406642825685, -0.11877016772508112, 0.16358116962230548, 0.282247325067986, 0.17018557594627395, 0.3182243564301449, -0.4061179387136402, -0.19354772184871966, 0.1300096249398895, 0.049213075230264254, 0.08851412984116687, -0.025938441769944295, -0.262297186620422, 0.030983266838563558, -0.22878872650142154, -0.07516880419391853, -0.09242569925024724, 0.008359840378547326, 0.022473687586637262, -0.3059274996193046, 0.06417439272229233, 0.009168691652564284, 0.0778385995226538, -0.07303884782844311, -0.19405926448719887, -0.018871381628112152, 0.18029760313817325, 0.12254794371368077, 0.009126569614069074, 0.044314625672996044, -0.15676106441503343, -0.17616295192438441, 0.3690621295593615, -0.05706140770413714, -0.16018110907708222, 0.20129777148811737, -0.13192259387757915, -0.1748190478194887, 0.06467291085710192, 0.23031238720823938, 0.06655399044020435, -0.1366540894795878, 0.01585660085010414, -0.04709142934467293, 0.1757770977460612, 0.04891521283863192, -0.05262578559271259, 0.2170071007612233, 0.20088013396080998, 0.00034693932622416405, 0.09546599780710843, -0.08615562189012192, -0.15890551778750542, -0.245342302796805, -0.08648713952294575, -0.1192340812781173, -0.021782607474945422, -0.1252614174542638, -0.14879192287723222, 0.4024674532433542, 0.22120698244095996, 0.16547800520132494, 0.10421475385112736, 0.2847605563031557, 0.06991877185745946, 0.0705071191649693, 0.0391919729180443, 0.27108226337621355, 0.04138948585097797, 0.1085232481137555, -0.16170566356900093, 0.13947304300605678, -0.014547053119565686] |
1,802.08849 | Epidemic extinction in networks: Insights from the 12,110 smallest
graphs | We investigate the expected time to extinction in the
susceptible-infectious-susceptible (SIS) model of disease spreading. Rather
than using stochastic simulations, or asymptotic calculations in network
models, we solve the extinction time exactly for all connected graphs with
three to eight vertices. This approach enables us to discover descriptive
relations that would be impossible with stochastic simulations. It also helps
us discovering graphs and configurations of S and I with anomalous behaviors
with respect to disease spreading. We find that for large transmission rates
the extinction time is independent of the configurations, just dependent on the
graph. In this limit, the number of vertices and edges determine the extinction
time very accurately (deviations primarily coming from the fluctuations in
degrees). We find that the rankings of configurations with respect to
extinction times at low and high transmission rates are correlated at low
prevalences and negatively correlated for high prevalences. The most important
structural factor determining this ranking is the degrees of the infectious
vertices.
| q-bio.PE | we investigate the expected time to extinction in the susceptibleinfectioussusceptible sis model of disease spreading rather than using stochastic simulations or asymptotic calculations in network models we solve the extinction time exactly for all connected graphs with three to eight vertices this approach enables us to discover descriptive relations that would be impossible with stochastic simulations it also helps us discovering graphs and configurations of s and i with anomalous behaviors with respect to disease spreading we find that for large transmission rates the extinction time is independent of the configurations just dependent on the graph in this limit the number of vertices and edges determine the extinction time very accurately deviations primarily coming from the fluctuations in degrees we find that the rankings of configurations with respect to extinction times at low and high transmission rates are correlated at low prevalences and negatively correlated for high prevalences the most important structural factor determining this ranking is the degrees of the infectious vertices | [['we', 'investigate', 'the', 'expected', 'time', 'to', 'extinction', 'in', 'the', 'susceptibleinfectioussusceptible', 'sis', 'model', 'of', 'disease', 'spreading', 'rather', 'than', 'using', 'stochastic', 'simulations', 'or', 'asymptotic', 'calculations', 'in', 'network', 'models', 'we', 'solve', 'the', 'extinction', 'time', 'exactly', 'for', 'all', 'connected', 'graphs', 'with', 'three', 'to', 'eight', 'vertices', 'this', 'approach', 'enables', 'us', 'to', 'discover', 'descriptive', 'relations', 'that', 'would', 'be', 'impossible', 'with', 'stochastic', 'simulations', 'it', 'also', 'helps', 'us', 'discovering', 'graphs', 'and', 'configurations', 'of', 's', 'and', 'i', 'with', 'anomalous', 'behaviors', 'with', 'respect', 'to', 'disease', 'spreading', 'we', 'find', 'that', 'for', 'large', 'transmission', 'rates', 'the', 'extinction', 'time', 'is', 'independent', 'of', 'the', 'configurations', 'just', 'dependent', 'on', 'the', 'graph', 'in', 'this', 'limit', 'the', 'number', 'of', 'vertices', 'and', 'edges', 'determine', 'the', 'extinction', 'time', 'very', 'accurately', 'deviations', 'primarily', 'coming', 'from', 'the', 'fluctuations', 'in', 'degrees', 'we', 'find', 'that', 'the', 'rankings', 'of', 'configurations', 'with', 'respect', 'to', 'extinction', 'times', 'at', 'low', 'and', 'high', 'transmission', 'rates', 'are', 'correlated', 'at', 'low', 'prevalences', 'and', 'negatively', 'correlated', 'for', 'high', 'prevalences', 'the', 'most', 'important', 'structural', 'factor', 'determining', 'this', 'ranking', 'is', 'the', 'degrees', 'of', 'the', 'infectious', 'vertices']] | [-0.10750068124616519, 0.11933655158525185, -0.056773785783115066, 0.08865208097553577, -0.027459994946932135, -0.15753436397384007, 0.08323224591472661, 0.3926006529731619, -0.2476926139917259, -0.3198311602206832, 0.08142974740751316, -0.3374887155883151, -0.1364063914776694, 0.12363551777467544, -0.04805847808455854, 0.0059497342337825635, 0.050037084283903376, 0.021856539415272717, -0.013314966904427971, -0.263327143815856, 0.3010880882263275, 0.07364265784404121, 0.24886572652959202, 0.0364616526750722, 0.0901358709299217, 0.009818794022948465, -0.05369987122234574, 0.046883822360898775, -0.1550230840870527, 0.1094916342511956, 0.26193102501701737, 0.10577064799318951, 0.20757405939826204, -0.40805928211407794, -0.20021480497292762, 0.15777579582570567, 0.13361793737022915, 0.13049153187826412, 0.04528089242407401, -0.21524228989518035, 0.07068592785040569, -0.11944785214206376, -0.15398131930869233, -0.03810735817632975, 0.06040999889973711, 0.03644848676067006, -0.2753808740983529, 0.07787031519631683, -0.029542803673996403, 0.04680359276811317, -0.004753017276696907, -0.10120222707524713, -0.045296933739074716, 0.16220832214386494, 0.05173573056393035, -0.012251387207420326, 0.10767499813267195, -0.15311456923058794, -0.11308723248175682, 0.364279680831095, -0.05106491804098999, -0.15434068135997178, 0.22324229332338447, -0.1985099220568417, -0.1522539510431648, 0.17488374535360038, 0.2063101968225206, 0.12559262176467095, -0.16171609516615518, -0.009484199131410454, 0.003439120448371567, 0.14927728272377416, 0.0659502983590348, 0.03107941959144696, 0.16876963690277533, 0.17618841458500528, 0.08172243630707812, 0.09521057522938547, -0.10171108629320251, -0.0987126768660943, -0.23872809765997566, -0.08499103170916712, -0.1251380333078431, 0.0752723759863, -0.1796415928986739, -0.1478743800847717, 0.408010384319293, 0.19673718503773488, 0.20924765348685848, 0.10617691321595589, 0.2399973982503706, 0.10402990066863459, 0.06018694786624869, 0.11517530586856183, 0.19314411683636576, 0.10824102165493144, 0.056518408632694396, -0.2294378855659918, 0.147454134924223, -0.0035414507364757083] |
1,802.0885 | Role of switching-on and -off effects in the vacuum instability | We find exact differential mean numbers of fermions and bosons created from
the vacuum due to a composite electric field of special configuration. This
configuration imitates a finite switching-on and -off regime and consists of
fields that switch-on exponentially from the infinitely remote past, remains
constant during a certain interval $T$ and switch-off exponentially to the
infinitely remote future. We show that calculations in the slowly varying field
approximation are completely predictable in the framework of a locally constant
field approximation. Beyond the slowly varying field approximation, we study
effects of fast switching-on and -off in a number of cases when the size of the
dimensionless parameter $\sqrt{eE}T$ is either close or exceeds the threshold
value that determines the transition from a regime sensitive to on-off
parameters to the slowly varying regime for which these effects are secondary.
| hep-th | we find exact differential mean numbers of fermions and bosons created from the vacuum due to a composite electric field of special configuration this configuration imitates a finite switchingon and off regime and consists of fields that switchon exponentially from the infinitely remote past remains constant during a certain interval t and switchoff exponentially to the infinitely remote future we show that calculations in the slowly varying field approximation are completely predictable in the framework of a locally constant field approximation beyond the slowly varying field approximation we study effects of fast switchingon and off in a number of cases when the size of the dimensionless parameter sqrteet is either close or exceeds the threshold value that determines the transition from a regime sensitive to onoff parameters to the slowly varying regime for which these effects are secondary | [['we', 'find', 'exact', 'differential', 'mean', 'numbers', 'of', 'fermions', 'and', 'bosons', 'created', 'from', 'the', 'vacuum', 'due', 'to', 'a', 'composite', 'electric', 'field', 'of', 'special', 'configuration', 'this', 'configuration', 'imitates', 'a', 'finite', 'switchingon', 'and', 'off', 'regime', 'and', 'consists', 'of', 'fields', 'that', 'switchon', 'exponentially', 'from', 'the', 'infinitely', 'remote', 'past', 'remains', 'constant', 'during', 'a', 'certain', 'interval', 't', 'and', 'switchoff', 'exponentially', 'to', 'the', 'infinitely', 'remote', 'future', 'we', 'show', 'that', 'calculations', 'in', 'the', 'slowly', 'varying', 'field', 'approximation', 'are', 'completely', 'predictable', 'in', 'the', 'framework', 'of', 'a', 'locally', 'constant', 'field', 'approximation', 'beyond', 'the', 'slowly', 'varying', 'field', 'approximation', 'we', 'study', 'effects', 'of', 'fast', 'switchingon', 'and', 'off', 'in', 'a', 'number', 'of', 'cases', 'when', 'the', 'size', 'of', 'the', 'dimensionless', 'parameter', 'sqrteet', 'is', 'either', 'close', 'or', 'exceeds', 'the', 'threshold', 'value', 'that', 'determines', 'the', 'transition', 'from', 'a', 'regime', 'sensitive', 'to', 'onoff', 'parameters', 'to', 'the', 'slowly', 'varying', 'regime', 'for', 'which', 'these', 'effects', 'are', 'secondary']] | [-0.17417176385294564, 0.24922092425480594, -0.05023990336288936, 0.041198504364702605, -0.013054946179155015, -0.16695713553659236, 0.10963561662462587, 0.3472042949258411, -0.27655742001672184, -0.2966348704963542, 0.08060767959691176, -0.24537720689108175, -0.09535156367652553, 0.18727254334252572, 0.005007403178725147, 0.010185999870572213, 0.029134786375084498, 0.05806560884399788, -0.062227965806726446, -0.19649958976180748, 0.304423417309707, 0.044142761520096475, 0.2650887161411726, 0.023875777119342374, 0.08780756408695377, 0.03707610700877696, 0.031976435887525335, 0.06703339811393183, -0.1061161308993656, 0.003046216755887888, 0.18829840299143136, 0.026807460301712046, 0.3000506012816064, -0.4312188901673377, -0.19051598721476148, 0.10444514816932815, 0.16286521135102006, 0.13790998875969054, -0.04021768993528111, -0.244614515601433, 0.06928492947031206, -0.15145082695810735, -0.177035270333562, -0.042042771616039705, 0.06430247335619953, 0.05827706356458094, -0.3192469940459641, 0.054968596035002794, 0.02490529439490234, 0.002452802281335156, -0.05045399758845812, -0.06741109199781162, 0.015779220650353244, 0.10942259653115366, 0.10874586933705425, 0.054781263702664604, 0.17576173325851016, -0.17401620835222195, -0.01017316604155476, 0.30756546747972713, -0.11752373391735184, -0.16234927262811766, 0.16512554624784112, -0.18429047319303898, -0.05717069140030411, 0.16398222696624126, 0.17847957724134308, 0.14824160054176502, -0.0852371285535458, 0.14394042627625916, 0.006918584601613727, 0.16244666379397177, 0.07742935465308871, 0.01844159778663005, 0.22032042107388486, 0.11470415901705405, 0.061858760571127665, 0.15704347879496694, -0.0806648995040121, -0.14080131145697222, -0.3053487964793632, -0.09008548765342995, -0.14982833963968403, 0.07721255185255903, -0.08724058255921611, -0.19997710663227053, 0.3889223619425384, 0.12833319304606122, 0.2170652864589254, 0.028790195779297093, 0.2889850273911916, 0.13342686243912708, 0.03705583270435242, 0.09707497769667611, 0.27209683325155265, 0.12752228368406804, 0.08273851795353158, -0.21941871483246014, 0.042235108490108794, 0.012066381593106525] |
1,802.08851 | Euler angles based loss function for camera relocalization with Deep
learning | Deep learning has been applied to camera relocalization, in particular,
PoseNet and its extended work are the convolutional neural networks which
regress the camera pose from a single image. However there are many problems,
one of them is expensive parameter selection. In this paper, we directly
explore the three Euler angles as the orientation representation in the camera
pose regressor. There is no need to select the parameter, which is not tolerant
in the previous works. Experimental results on the 7 Scenes datasets and the
King's College dataset demonstrate that it has competitive performances.
| cs.RO | deep learning has been applied to camera relocalization in particular posenet and its extended work are the convolutional neural networks which regress the camera pose from a single image however there are many problems one of them is expensive parameter selection in this paper we directly explore the three euler angles as the orientation representation in the camera pose regressor there is no need to select the parameter which is not tolerant in the previous works experimental results on the 7 scenes datasets and the kings college dataset demonstrate that it has competitive performances | [['deep', 'learning', 'has', 'been', 'applied', 'to', 'camera', 'relocalization', 'in', 'particular', 'posenet', 'and', 'its', 'extended', 'work', 'are', 'the', 'convolutional', 'neural', 'networks', 'which', 'regress', 'the', 'camera', 'pose', 'from', 'a', 'single', 'image', 'however', 'there', 'are', 'many', 'problems', 'one', 'of', 'them', 'is', 'expensive', 'parameter', 'selection', 'in', 'this', 'paper', 'we', 'directly', 'explore', 'the', 'three', 'euler', 'angles', 'as', 'the', 'orientation', 'representation', 'in', 'the', 'camera', 'pose', 'regressor', 'there', 'is', 'no', 'need', 'to', 'select', 'the', 'parameter', 'which', 'is', 'not', 'tolerant', 'in', 'the', 'previous', 'works', 'experimental', 'results', 'on', 'the', '7', 'scenes', 'datasets', 'and', 'the', 'kings', 'college', 'dataset', 'demonstrate', 'that', 'it', 'has', 'competitive', 'performances']] | [-0.06577904701995801, 0.008995592148300815, -0.08448822343563463, 0.03689249133582207, -0.12844718380910444, -0.20109021436284355, -0.040727841711177076, 0.47570619906516787, -0.22848398843284776, -0.3542317153212238, 0.11791323894198905, -0.2681941129044293, -0.19260170245344968, 0.1898615732590886, -0.18091218722389735, 0.08938201518910144, 0.14508774561282406, 0.03800214532802396, -0.029725891321852924, -0.3072503821805437, 0.29175197523820434, 0.0060641342731306645, 0.3192646049602116, 0.003877556449337367, 0.14639241733598185, -0.030318454807089206, -0.025521881841222182, 0.0044500984380299105, -0.06187587686446854, 0.11244365850096925, 0.3271186347813048, 0.1602686873285417, 0.293724324648328, -0.39349468577494645, -0.2345833523357485, 0.12434092476131751, 0.1083104670681852, 0.10075509003156108, -0.0300457438768601, -0.32511597440796014, 0.05733694625582467, -0.14092850030252482, -0.015320655156640297, -0.0817871622721407, 0.003650922952417998, -0.036820664667484765, -0.2396640676678099, 0.008194235371465379, 0.033056988915864456, 0.0448339456850861, -0.041659232297109716, -0.1153067732923367, 0.024536245953331284, 0.19208929837839875, 0.04323611436461277, 0.10436440515827308, 0.11214817514979934, -0.24171095882156032, -0.1262287939353747, 0.3711663857102394, -0.00197357242491017, -0.24073706853619598, 0.20714590530421467, -0.06859432737977106, -0.17266094990252973, 0.11428323208770537, 0.19496480156568455, 0.1278336793035665, -0.14844232115973818, 0.06777208118000999, -0.10280118176238016, 0.19141873476055868, 0.05522822816558975, -0.04645426613972225, 0.15112830048545878, 0.21810924297228376, 0.050042034601767926, 0.11116722448075071, -0.2203370529735347, -0.060147413151695375, -0.19665558205204123, -0.09376803772806368, -0.2046991236175985, -0.025625228720681216, -0.08788020611075595, -0.12983073708551757, 0.4095953031581767, 0.2706440639622668, 0.20370671038634758, 0.06826842227762446, 0.35578031432220436, 0.02784981779590685, 0.14651413079093586, 0.04680962304267636, 0.27075674593389193, 0.006520284171354898, 0.12439570217789646, -0.13300346086009782, 0.09289909249538199, 0.030584479528578356] |
1,802.08852 | Variations of completely bounded maps on operator spaces | We introduce weighted cb maps and $\Lambda_\mu$-cb maps on operator spaces
which are generalizations of completely bounded maps and a certain class of
bilinear maps on operator spaces which we call $\lambda_\mu$-cb bilinear maps.
Some basic properties of these maps and an operator space tensor product
associated to $\lambda_\mu$-cb bilinear maps have been studied.
| math.OA | we introduce weighted cb maps and lambda_mucb maps on operator spaces which are generalizations of completely bounded maps and a certain class of bilinear maps on operator spaces which we call lambda_mucb bilinear maps some basic properties of these maps and an operator space tensor product associated to lambda_mucb bilinear maps have been studied | [['we', 'introduce', 'weighted', 'cb', 'maps', 'and', 'lambda_mucb', 'maps', 'on', 'operator', 'spaces', 'which', 'are', 'generalizations', 'of', 'completely', 'bounded', 'maps', 'and', 'a', 'certain', 'class', 'of', 'bilinear', 'maps', 'on', 'operator', 'spaces', 'which', 'we', 'call', 'lambda_mucb', 'bilinear', 'maps', 'some', 'basic', 'properties', 'of', 'these', 'maps', 'and', 'an', 'operator', 'space', 'tensor', 'product', 'associated', 'to', 'lambda_mucb', 'bilinear', 'maps', 'have', 'been', 'studied']] | [-0.09894746986942159, 0.09160062594202356, -0.00047085786031352147, 0.1271599924226326, -0.07965595451080137, -0.09123756150991001, -0.04704165258617313, 0.4067364615491695, -0.37630489468574524, -0.10108040969956804, 0.20197796203730697, -0.3167955212260562, -0.22398489441170735, 0.19195408594829064, -0.09800966268543292, 0.0668241295584098, 0.011980647893829478, 0.0009731884735325972, -0.19910123659711745, -0.21646620197161273, 0.5310514862304209, -0.026840294478461146, 0.19467681930917832, 0.043751557978490986, 0.19734777152296845, -0.03739073212017064, -0.08044011051350497, 0.007369579962364191, -0.14643930455807735, 0.1793518641066772, 0.17600041996963597, 0.12961303838959862, 0.18841509220806915, -0.3525685536916609, -0.19501645550890653, 0.26234214373484804, 0.08508880642518678, -0.040603329203871975, -0.013162678180917821, -0.3262378281948191, 0.05336886853048647, -0.14530183230009344, -0.02674369083251804, -0.1978193210584491, 0.00013368626780532026, 0.04772354816776459, -0.3013295945970135, -0.022898809967079648, 0.10432278813311348, 0.07505826396798645, -0.16362458818454156, -0.12126618853114822, -0.052752628068750106, 0.10314936207227961, -0.11193561488417564, 0.06566758788432236, 0.08745693238624544, -0.057420945191686903, -0.09916764089009827, 0.29310128389409296, -0.10642181386895201, -0.30525470676797406, 0.15530799249945967, -0.13408550534707805, -0.21219392147570573, 0.02703314333188313, 0.16412883285536534, 0.12746827104301364, -0.1288120023112882, 0.16369523033521277, -0.14040456643259083, 0.05547472365476467, 0.07538470826801602, 0.0946725953232359, 0.12089970022120893, -0.022592564639462916, 0.18075519629874853, 0.1947088641528454, 0.007515613635337946, -0.10866616408263023, -0.26743290911394135, -0.13500840782567305, -0.08669576266159613, 0.02777121033243559, -0.12310822671026, -0.2204946036864486, 0.4305929630494642, 0.040476389419011494, 0.2554261051991489, 0.10000270772811577, 0.1847810963237727, 0.156254777856197, 0.12611239307766986, 0.03386581839165754, 0.10488213392899423, 0.22538646389902742, 0.07236419407719816, -0.04578792019660964, -0.027359004227306555, 0.25832691013514436] |
1,802.08853 | Transfer matrix spectrum for cyclic representations of the 6-vertex
reflection algebra II | This article is a direct continuation of [1] where we begun the study of the
transfer matrix spectral problem for the cyclic representations of the
trigonometric 6-vertex reflection algebra associated to the Bazhanov-Stroganov
Lax operator. There we addressed this problem for the case where one of the
K-matrices describing the boundary conditions is triangular. In the present
article we consider the most general integrable boundary conditions, namely the
most general boundary K-matrices satisfying the reflection equation. The
spectral analysis is developed by implementing the method of Separation of
Variables (SoV). We first design a suitable gauge transformation that enable us
to put into correspondence the spectral problem for the most general boundary
conditions with another one having one boundary K-matrix in a triangular form.
In these settings the SoV resolution can be obtained along an extension of the
method described in [1]. The transfer matrix spectrum is then completely
characterized in terms of the set of solutions to a discrete system of
polynomial equations in a given class of functions and equivalently as the set
of solutions to an analogue of Baxter's T-Q functional equation. We further
describe scalar product properties of the separate states including eigenstates
of the transfer matrix.
| math-ph cond-mat.stat-mech hep-th math.MP nlin.SI | this article is a direct continuation of 1 where we begun the study of the transfer matrix spectral problem for the cyclic representations of the trigonometric 6vertex reflection algebra associated to the bazhanovstroganov lax operator there we addressed this problem for the case where one of the kmatrices describing the boundary conditions is triangular in the present article we consider the most general integrable boundary conditions namely the most general boundary kmatrices satisfying the reflection equation the spectral analysis is developed by implementing the method of separation of variables sov we first design a suitable gauge transformation that enable us to put into correspondence the spectral problem for the most general boundary conditions with another one having one boundary kmatrix in a triangular form in these settings the sov resolution can be obtained along an extension of the method described in 1 the transfer matrix spectrum is then completely characterized in terms of the set of solutions to a discrete system of polynomial equations in a given class of functions and equivalently as the set of solutions to an analogue of baxters tq functional equation we further describe scalar product properties of the separate states including eigenstates of the transfer matrix | [['this', 'article', 'is', 'a', 'direct', 'continuation', 'of', '1', 'where', 'we', 'begun', 'the', 'study', 'of', 'the', 'transfer', 'matrix', 'spectral', 'problem', 'for', 'the', 'cyclic', 'representations', 'of', 'the', 'trigonometric', '6vertex', 'reflection', 'algebra', 'associated', 'to', 'the', 'bazhanovstroganov', 'lax', 'operator', 'there', 'we', 'addressed', 'this', 'problem', 'for', 'the', 'case', 'where', 'one', 'of', 'the', 'kmatrices', 'describing', 'the', 'boundary', 'conditions', 'is', 'triangular', 'in', 'the', 'present', 'article', 'we', 'consider', 'the', 'most', 'general', 'integrable', 'boundary', 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1,802.08854 | Factorization of Scalar Piecewise Continuous Almost Periodic Functions | This paper is to characterize piecewise continuous almost periodic functions
as the product of Bohr almost periodic functions and sequences. As an
application, the result is used to discuss piecewise continuous almost periodic
solutions of impulsive differential equations.
| math.CA | this paper is to characterize piecewise continuous almost periodic functions as the product of bohr almost periodic functions and sequences as an application the result is used to discuss piecewise continuous almost periodic solutions of impulsive differential equations | [['this', 'paper', 'is', 'to', 'characterize', 'piecewise', 'continuous', 'almost', 'periodic', 'functions', 'as', 'the', 'product', 'of', 'bohr', 'almost', 'periodic', 'functions', 'and', 'sequences', 'as', 'an', 'application', 'the', 'result', 'is', 'used', 'to', 'discuss', 'piecewise', 'continuous', 'almost', 'periodic', 'solutions', 'of', 'impulsive', 'differential', 'equations']] | [-0.17118998175781025, 0.07581402983027817, -0.016808372407563422, 0.07785427358650945, -0.0203429508071981, -0.07029507898627535, -0.033891719474922866, 0.41414195897155687, -0.37600344194001273, -0.16912541906104275, 0.18773559559332698, -0.23440846313085212, -0.21496186405420303, 0.25333128624448653, -0.11258322512730956, 0.15754197871214465, 0.01479975374317483, 0.023453002587254895, -0.12109023763945229, -0.2589961456527051, 0.3732317827249828, -0.032973609570609894, 0.1666673041487995, -0.02924211284047679, 0.14188770832199799, 0.03540914870896622, -0.04710636520758271, -0.050556651322710276, -0.15909762670727154, 0.0594640533488832, 0.24662191719797097, 0.06322850473225117, 0.25853810959348555, -0.37182614566317124, -0.17239579955410017, 0.1449126966273118, 0.13248699611837142, 0.055078100020948206, 0.011544969447545315, -0.2595175534094635, 0.07081243947246357, -0.1378574749258788, -0.25260899396074055, -0.09483968089089582, 0.06068873856412737, 0.14422778306087772, -0.312945779020849, 0.037672477186118304, 0.11918853635066434, 0.048064078817046, -0.1854071993576853, 0.0005080994880317073, -0.02389730319478794, 0.13499185888978996, 0.06730178212379351, 0.06847065964125489, 0.07385141775012016, 0.013318077673351294, -0.1423815177312415, 0.3722020900857292, -0.14992054772416227, -0.2526575623594813, 0.194185768891322, -0.11460327308343135, -0.08586497619552047, 0.1562549337548645, 0.16776923750723272, 0.1483810355788783, -0.18973805784787, 0.11563822526429583, -0.062325363390539824, 0.17375255233951306, 0.17913630722384705, 0.009762646668700893, 0.08738374298953108, 0.08426882928286336, 0.21369986859836468, 0.1771455044417005, 0.0989940327739245, -0.10576873913554377, -0.3227611193188319, -0.11855996772646904, -0.14987419457419923, 0.08739031238579437, -0.04678524024501186, -0.380207018240502, 0.4080681708200197, 0.02353636217058489, 0.14372862904871764, 0.06625407473429253, 0.24883087764886258, 0.21144882986003435, -0.041556607352839295, 0.011992171382237422, 0.16204693062402503, 0.18798100598475062, 0.11329568520580467, -0.09526589238329937, 0.03533214122351063, 0.10402799922188646] |
1,802.08855 | Minimax Distribution Estimation in Wasserstein Distance | The Wasserstein metric is an important measure of distance between
probability distributions, with applications in machine learning, statistics,
probability theory, and data analysis. This paper provides upper and lower
bounds on statistical minimax rates for the problem of estimating a probability
distribution under Wasserstein loss, using only metric properties, such as
covering and packing numbers, of the sample space, and weak moment assumptions
on the probability distributions.
| math.ST cs.IT cs.LG math.IT stat.ML stat.TH | the wasserstein metric is an important measure of distance between probability distributions with applications in machine learning statistics probability theory and data analysis this paper provides upper and lower bounds on statistical minimax rates for the problem of estimating a probability distribution under wasserstein loss using only metric properties such as covering and packing numbers of the sample space and weak moment assumptions on the probability distributions | [['the', 'wasserstein', 'metric', 'is', 'an', 'important', 'measure', 'of', 'distance', 'between', 'probability', 'distributions', 'with', 'applications', 'in', 'machine', 'learning', 'statistics', 'probability', 'theory', 'and', 'data', 'analysis', 'this', 'paper', 'provides', 'upper', 'and', 'lower', 'bounds', 'on', 'statistical', 'minimax', 'rates', 'for', 'the', 'problem', 'of', 'estimating', 'a', 'probability', 'distribution', 'under', 'wasserstein', 'loss', 'using', 'only', 'metric', 'properties', 'such', 'as', 'covering', 'and', 'packing', 'numbers', 'of', 'the', 'sample', 'space', 'and', 'weak', 'moment', 'assumptions', 'on', 'the', 'probability', 'distributions']] | [-0.06834482007534869, 0.06336386192007053, -0.13780531975954993, 0.1630141665714223, -0.011915145031829824, -0.0767395288349866, 0.12165044289544474, 0.3709160409061544, -0.2763935864082913, -0.3292950187295453, 0.07804131833662682, -0.2954219206611612, -0.10925935025313008, 0.21770985192382958, -0.1321072931707239, 0.1605417186032925, 0.05988107838167517, 0.0729749086243448, -0.10645098316677805, -0.21996874520694143, 0.3719401861568774, 0.09314919757976461, 0.36724992766420345, 0.043691749763570785, 0.13629202581003808, 0.025895217586475523, 0.004355738168832527, 0.01332697466340736, -0.2071871556126193, 0.18736559815475468, 0.21204952836912402, 0.2208372294596994, 0.3099530576658783, -0.31590809466944114, -0.20180124946550201, 0.16373194146678963, 0.04338204563220045, 0.01329819763551897, -0.055689601175514844, -0.28554340449523236, 0.05372887260780962, -0.12383001834265332, -0.05978891208990297, -0.06397682773783359, 0.05410494626875022, 0.06753103416969082, -0.3201363128957464, 0.09942662361453274, 0.0680590270837741, 0.0700134394039685, -0.06626067330727159, -0.15033288808789716, 0.04957770257354228, 0.11939970804934404, 0.10055877051121596, 0.0438965796006482, 0.1092922357258512, -0.14091281218579343, -0.09714158886431981, 0.32126610360539226, -0.09034641238568879, -0.24913161220168, 0.15830697978276814, -0.16150852367837928, -0.1402586100678613, 0.06937153700655743, 0.2865189812632639, 0.1464942269645798, -0.1544041030331334, 0.11622063610911258, -0.03505785044616283, 0.1302319423281657, 0.061556603050610025, 0.1028630653980063, 0.12742758629871392, 0.15428426772800843, 0.18008284379186026, 0.11882219605370244, -0.1616058652011205, -0.13271502064151772, -0.2968652829416652, -0.15010319088599575, -0.25657306594857526, 0.051472357034669326, -0.19331626299987617, -0.1627181675823739, 0.2887598520992741, 0.10856877375088299, 0.24354284789079605, 0.17981561088461928, 0.2815131727022244, 0.12647818214322593, -0.052808167536242574, 0.10472485779979225, 0.17502382403211808, 0.18963362309914916, -0.008140131316856663, -0.11253886417583076, 0.1602440960774782, 0.1061207984449036] |
1,802.08856 | Hypergeometry inspired by irrationality questions | We report new hypergeometric constructions of rational approximations to
Catalan's constant, $\log2$, and $\pi^2$, their connection with already known
ones, and underlying "permutation group" structures. Our principal arithmetic
achievement is a new partial irrationality result for the values of Riemann's
zeta function at odd integers.
| math.NT math.CA math.CO | we report new hypergeometric constructions of rational approximations to catalans constant log2 and pi2 their connection with already known ones and underlying permutation group structures our principal arithmetic achievement is a new partial irrationality result for the values of riemanns zeta function at odd integers | [['we', 'report', 'new', 'hypergeometric', 'constructions', 'of', 'rational', 'approximations', 'to', 'catalans', 'constant', 'log2', 'and', 'pi2', 'their', 'connection', 'with', 'already', 'known', 'ones', 'and', 'underlying', 'permutation', 'group', 'structures', 'our', 'principal', 'arithmetic', 'achievement', 'is', 'a', 'new', 'partial', 'irrationality', 'result', 'for', 'the', 'values', 'of', 'riemanns', 'zeta', 'function', 'at', 'odd', 'integers']] | [-0.26959296837449076, 0.09339158658662604, -0.1053037318194078, 0.0991112053885849, -0.15757337806539404, -0.18543904509602321, 0.09725786574288375, 0.3050568326479859, -0.31289749079280427, -0.27834609125016463, 0.05184299744044741, -0.26772463673518765, -0.21080549768068724, 0.2038597693045934, -0.060529645656545956, 0.06766276931577725, -0.019261779563708437, 0.03679637677139706, -0.12578074048118046, -0.3595866603569852, 0.33906687373916305, -0.025747518634630574, 0.1452973054514991, 0.04540547832018799, 0.04657101758445303, -0.017495981220983798, -0.026248568379216724, -0.13080635335710314, -0.1598535696251525, 0.08881673790100549, 0.29155165899751917, 0.062227886766939064, 0.24126878113796313, -0.34480877166820895, -0.06439386743845212, 0.13642211710086688, 0.13821177740270893, 0.0017277912547190985, -0.006614232684175173, -0.22243539753059546, 0.08221824535479148, -0.10651754140853882, -0.21371858538024954, -0.10556713866276873, 0.07828143132436606, 0.044804060510877104, -0.2757268623345428, 0.04212271574781173, 0.05075798767308394, 0.13165570421454806, 0.013360177234022153, -0.2927102786488831, 0.10087838315197992, 0.07719493597331974, 0.08675255528651178, 0.06814689886652761, 0.030018516433321766, -0.07816649339058333, -0.15538676818056654, 0.3116999014384217, -0.03303652037349012, -0.22053085408276982, 0.06178426877078083, -0.187035111275812, -0.2278329610514144, 0.130398546366228, 0.07066774964332581, 0.15296896410485109, 0.02667584092252784, 0.11118317030939377, -0.1100306985537625, 0.14671759658700062, 0.18104933918350272, 0.01526587117049429, 0.12089898830486669, 0.0008028448766304387, 0.0022566364664170476, 0.08560535638696617, 0.05717627143280374, -0.058460108946181005, -0.3667461762825648, -0.19425949895133574, -0.15174059291312006, 0.14421113785356282, -0.1699515689448971, -0.22197545362998627, 0.37125146090984346, 0.039812627972828016, 0.14043835343586075, 0.23549787561512656, 0.25187340242167316, 0.1074214128492814, 0.03460319663087527, 0.008158958197519597, 0.09421047828056746, 0.21917472746119732, -0.0028338642273512153, -0.11850161540011565, 0.07450383404890697, 0.18455251627084282] |
1,802.08857 | Visual Manipulation Relationship Network | Robotic grasping detection is one of the most important fields in robotics,
in which great progress has been made recent years with the help of
convolutional neural network (CNN). However, including multiple objects in one
scene can invalidate the existing CNN-based grasping detection algorithms,
because manipulation relationships among objects are not considered, which are
required to guide the robot to grasp things in the right order. This paper
presents a new CNN architecture called Visual Manipulation Relationship Network
(VMRN) to help robot detect targets and predict the manipulation relationships
in real time. To implement end-to-end training and meet real-time requirements
in robot tasks, we propose the Object Pairing Pooling Layer (OP2L) to help to
predict all manipulation relationships in one forward process. Moreover, in
order to train VMRN, we collect a dataset named Visual Manipulation
Relationship Dataset (VMRD) consisting of 5185 images with more than 17000
object instances and the manipulation relationships between all possible pairs
of objects in every image, which is labeled by the manipulation relationship
tree. The experimental results show that the new network architecture can
detect objects and predict manipulation relationships simultaneously and meet
the real-time requirements in robot tasks.
| cs.RO | robotic grasping detection is one of the most important fields in robotics in which great progress has been made recent years with the help of convolutional neural network cnn however including multiple objects in one scene can invalidate the existing cnnbased grasping detection algorithms because manipulation relationships among objects are not considered which are required to guide the robot to grasp things in the right order this paper presents a new cnn architecture called visual manipulation relationship network vmrn to help robot detect targets and predict the manipulation relationships in real time to implement endtoend training and meet realtime requirements in robot tasks we propose the object pairing pooling layer op2l to help to predict all manipulation relationships in one forward process moreover in order to train vmrn we collect a dataset named visual manipulation relationship dataset vmrd consisting of 5185 images with more than 17000 object instances and the manipulation relationships between all possible pairs of objects in every image which is labeled by the manipulation relationship tree the experimental results show that the new network architecture can detect objects and predict manipulation relationships simultaneously and meet the realtime requirements in robot tasks | [['robotic', 'grasping', 'detection', 'is', 'one', 'of', 'the', 'most', 'important', 'fields', 'in', 'robotics', 'in', 'which', 'great', 'progress', 'has', 'been', 'made', 'recent', 'years', 'with', 'the', 'help', 'of', 'convolutional', 'neural', 'network', 'cnn', 'however', 'including', 'multiple', 'objects', 'in', 'one', 'scene', 'can', 'invalidate', 'the', 'existing', 'cnnbased', 'grasping', 'detection', 'algorithms', 'because', 'manipulation', 'relationships', 'among', 'objects', 'are', 'not', 'considered', 'which', 'are', 'required', 'to', 'guide', 'the', 'robot', 'to', 'grasp', 'things', 'in', 'the', 'right', 'order', 'this', 'paper', 'presents', 'a', 'new', 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1,802.08858 | A Project Based Approach to Statistics and Data Science | In an increasingly data-driven world, facility with statistics is more
important than ever for our students. At institutions without a statistician,
it often falls to the mathematics faculty to teach statistics courses. This
paper presents a model that a mathematician asked to teach statistics can
follow. This model entails connecting with faculty from numerous departments on
campus to develop a list of topics, building a repository of real-world
datasets from these faculty, and creating projects where students interface
with these datasets to write lab reports aimed at consumers of statistics in
other disciplines. The end result is students who are well prepared for
interdisciplinary research, who are accustomed to coping with the
idiosyncrasies of real data, and who have sharpened their technical writing and
speaking skills.
| stat.OT | in an increasingly datadriven world facility with statistics is more important than ever for our students at institutions without a statistician it often falls to the mathematics faculty to teach statistics courses this paper presents a model that a mathematician asked to teach statistics can follow this model entails connecting with faculty from numerous departments on campus to develop a list of topics building a repository of realworld datasets from these faculty and creating projects where students interface with these datasets to write lab reports aimed at consumers of statistics in other disciplines the end result is students who are well prepared for interdisciplinary research who are accustomed to coping with the idiosyncrasies of real data and who have sharpened their technical writing and speaking skills | [['in', 'an', 'increasingly', 'datadriven', 'world', 'facility', 'with', 'statistics', 'is', 'more', 'important', 'than', 'ever', 'for', 'our', 'students', 'at', 'institutions', 'without', 'a', 'statistician', 'it', 'often', 'falls', 'to', 'the', 'mathematics', 'faculty', 'to', 'teach', 'statistics', 'courses', 'this', 'paper', 'presents', 'a', 'model', 'that', 'a', 'mathematician', 'asked', 'to', 'teach', 'statistics', 'can', 'follow', 'this', 'model', 'entails', 'connecting', 'with', 'faculty', 'from', 'numerous', 'departments', 'on', 'campus', 'to', 'develop', 'a', 'list', 'of', 'topics', 'building', 'a', 'repository', 'of', 'realworld', 'datasets', 'from', 'these', 'faculty', 'and', 'creating', 'projects', 'where', 'students', 'interface', 'with', 'these', 'datasets', 'to', 'write', 'lab', 'reports', 'aimed', 'at', 'consumers', 'of', 'statistics', 'in', 'other', 'disciplines', 'the', 'end', 'result', 'is', 'students', 'who', 'are', 'well', 'prepared', 'for', 'interdisciplinary', 'research', 'who', 'are', 'accustomed', 'to', 'coping', 'with', 'the', 'idiosyncrasies', 'of', 'real', 'data', 'and', 'who', 'have', 'sharpened', 'their', 'technical', 'writing', 'and', 'speaking', 'skills']] | [0.011720453668940103, 0.08921978467928028, -0.15361922107348663, 0.06854477598671875, -0.20203343671303065, -0.21654022931091724, 0.07191869181244531, 0.3812932294806362, -0.18701231810042546, -0.4396244827510109, 0.0672733510072003, -0.36062962701544166, -0.10784818004402849, 0.2256373520755756, -0.14659168146964577, -0.0008309772201178093, 0.10866394934857944, 0.026957089059232248, 0.029123624256398115, -0.36013957949924386, 0.2953354214865064, 0.10889365002032488, 0.31692437789151595, 0.027254886134335445, 0.05307988352825983, -0.029007521745774686, -0.07154680142411962, -0.05110438342117483, -0.06103001328099506, 0.1728004841281781, 0.48230694785218925, 0.22912117990753836, 0.4510486092093208, -0.40950914153030943, -0.11284527449225563, 0.04216946021372837, 0.08418784302378458, 0.08730052922559452, -0.022626396696963754, -0.3610804495623424, 0.024323890983526195, -0.1678165857022303, -0.0954616091339775, -0.05007638103727784, 0.019955301598187477, -0.009270399091913113, -0.21071787609068293, -0.009285126251005938, 0.0022747962308737137, 0.20902669111946745, 0.02567113410230608, -0.16283093683702488, 0.047963008693870275, 0.23362569869422015, 0.08839282018893826, 0.026419259737142257, 0.14731426592103192, -0.20631064865959897, -0.1598656775119404, 0.3994823856693175, 0.0776866639622197, -0.11400776409584704, 0.23437440131110923, -0.13032298834831824, -0.15868947954316223, 0.013221153507142194, 0.2531864213062421, 0.054139489444741416, -0.19757486126844823, 0.01353628081057058, -0.015398483287306532, 0.1515463114723504, 0.0709818169134595, -0.09151607166813125, 0.23313238347847282, 0.15564148237795702, 0.028593859400614978, 0.0752877044340869, 0.05637633110330041, -0.09323978901929444, -0.23581669812962885, -0.17324668073671914, -0.15872807473698186, 0.040590220829471946, 0.047201060713430505, -0.1291931055464028, 0.3709958795206769, 0.21093026967349623, 0.07058028204898749, 0.05701868394611492, 0.24253123886291944, 0.003706435765793902, 0.08898649790919283, 0.11719485225954226, 0.12467625011349955, 0.023423310679694016, 0.2595349833709262, -0.04255213660912381, 0.07704765371038645, -0.034661360735458044] |
1,802.08859 | Localisation Transition in the Driven Aubry-Andr\'e Model | A recent experiment by P. Bordia et al. (Periodically Driving a Many Body
Localized Quantum System, Nat Phys, Jan 2017) has demonstrated that
periodically modulating the potential of a localised many-body quantum system
described by the Aubry-Andr\'e Hamiltonian with on-site interactions can lead
to a many-body localisation-delocalisation transition, provided the modulation
amplitude is big enough. Here, we consider the noninteracting counterpart of
that model in order to explore its phase diagram as a function of the strength
of the disordered potential, the driving frequency and its amplitude. We will
first of all mimic the experimental procedure of the experiment and use the
even-odd sites imbalance as a parameter in order to discern between different
phases. Then we compute the Floquet eigenstates and relate the
localisation-delocalisation transition to their Inverse Participation Ratio.
Both these approaches show that the delocalisation transition occurs for
frequencies that are low compared to the bandwidth of the time independent
model. Moreover, in agreement with the results of the experiment there is an
amplitude threshold below which no delocalisation transition occurs. We
estimate both the critical values for the frequency and the amplitude.
| quant-ph cond-mat.dis-nn cond-mat.stat-mech cond-mat.str-el | a recent experiment by p bordia et al periodically driving a many body localized quantum system nat phys jan 2017 has demonstrated that periodically modulating the potential of a localised manybody quantum system described by the aubryandre hamiltonian with onsite interactions can lead to a manybody localisationdelocalisation transition provided the modulation amplitude is big enough here we consider the noninteracting counterpart of that model in order to explore its phase diagram as a function of the strength of the disordered potential the driving frequency and its amplitude we will first of all mimic the experimental procedure of the experiment and use the evenodd sites imbalance as a parameter in order to discern between different phases then we compute the floquet eigenstates and relate the localisationdelocalisation transition to their inverse participation ratio both these approaches show that the delocalisation transition occurs for frequencies that are low compared to the bandwidth of the time independent model moreover in agreement with the results of the experiment there is an amplitude threshold below which no delocalisation transition occurs we estimate both the critical values for the frequency and the amplitude | [['a', 'recent', 'experiment', 'by', 'p', 'bordia', 'et', 'al', 'periodically', 'driving', 'a', 'many', 'body', 'localized', 'quantum', 'system', 'nat', 'phys', 'jan', '2017', 'has', 'demonstrated', 'that', 'periodically', 'modulating', 'the', 'potential', 'of', 'a', 'localised', 'manybody', 'quantum', 'system', 'described', 'by', 'the', 'aubryandre', 'hamiltonian', 'with', 'onsite', 'interactions', 'can', 'lead', 'to', 'a', 'manybody', 'localisationdelocalisation', 'transition', 'provided', 'the', 'modulation', 'amplitude', 'is', 'big', 'enough', 'here', 'we', 'consider', 'the', 'noninteracting', 'counterpart', 'of', 'that', 'model', 'in', 'order', 'to', 'explore', 'its', 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