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1,803.00067 | Constrained Classification and Ranking via Quantiles | In most machine learning applications, classification accuracy is not the
primary metric of interest. Binary classifiers which face class imbalance are
often evaluated by the $F_\beta$ score, area under the precision-recall curve,
Precision at K, and more. The maximization of many of these metrics can be
expressed as a constrained optimization problem, where the constraint is a
function of the classifier's predictions.
In this paper we propose a novel framework for learning with constraints that
can be expressed as a predicted positive rate (or negative rate) on a subset of
the training data. We explicitly model the threshold at which a classifier must
operate to satisfy the constraint, yielding a surrogate loss function which
avoids the complexity of constrained optimization. The method is model-agnostic
and only marginally more expensive than minimization of the unconstrained loss.
Experiments on a variety of benchmarks show competitive performance relative to
existing baselines.
| cs.LG stat.ML | in most machine learning applications classification accuracy is not the primary metric of interest binary classifiers which face class imbalance are often evaluated by the f_beta score area under the precisionrecall curve precision at k and more the maximization of many of these metrics can be expressed as a constrained optimization problem where the constraint is a function of the classifiers predictions in this paper we propose a novel framework for learning with constraints that can be expressed as a predicted positive rate or negative rate on a subset of the training data we explicitly model the threshold at which a classifier must operate to satisfy the constraint yielding a surrogate loss function which avoids the complexity of constrained optimization the method is modelagnostic and only marginally more expensive than minimization of the unconstrained loss experiments on a variety of benchmarks show competitive performance relative to existing baselines | [['in', 'most', 'machine', 'learning', 'applications', 'classification', 'accuracy', 'is', 'not', 'the', 'primary', 'metric', 'of', 'interest', 'binary', 'classifiers', 'which', 'face', 'class', 'imbalance', 'are', 'often', 'evaluated', 'by', 'the', 'f_beta', 'score', 'area', 'under', 'the', 'precisionrecall', 'curve', 'precision', 'at', 'k', 'and', 'more', 'the', 'maximization', 'of', 'many', 'of', 'these', 'metrics', 'can', 'be', 'expressed', 'as', 'a', 'constrained', 'optimization', 'problem', 'where', 'the', 'constraint', 'is', 'a', 'function', 'of', 'the', 'classifiers', 'predictions', 'in', 'this', 'paper', 'we', 'propose', 'a', 'novel', 'framework', 'for', 'learning', 'with', 'constraints', 'that', 'can', 'be', 'expressed', 'as', 'a', 'predicted', 'positive', 'rate', 'or', 'negative', 'rate', 'on', 'a', 'subset', 'of', 'the', 'training', 'data', 'we', 'explicitly', 'model', 'the', 'threshold', 'at', 'which', 'a', 'classifier', 'must', 'operate', 'to', 'satisfy', 'the', 'constraint', 'yielding', 'a', 'surrogate', 'loss', 'function', 'which', 'avoids', 'the', 'complexity', 'of', 'constrained', 'optimization', 'the', 'method', 'is', 'modelagnostic', 'and', 'only', 'marginally', 'more', 'expensive', 'than', 'minimization', 'of', 'the', 'unconstrained', 'loss', 'experiments', 'on', 'a', 'variety', 'of', 'benchmarks', 'show', 'competitive', 'performance', 'relative', 'to', 'existing', 'baselines']] | [-0.09211474774575264, 0.0019831464522065143, -0.06256002315785736, 0.07954991117553986, -0.08178883147539219, -0.18873893983058027, 0.06327054107866904, 0.3933206915956091, -0.2798591558923756, -0.34138770552503095, 0.11863364064373781, -0.2659212896550024, -0.14908954818777084, 0.22956936988093485, -0.11913581986005443, 0.10301103327514578, 0.11518449706616937, 0.08780154849031337, -0.12217956557838447, -0.3162905923405511, 0.299488153780584, 0.06114984306859205, 0.2928258557457829, 0.05218919927082843, 0.12870269255300454, -0.026202437433263136, 0.03199886838351754, 0.06592867192749337, -0.06512069333203309, 0.16472689405152877, 0.28338428423110695, 0.23009702168103005, 0.3255239804936069, -0.3165999942371068, -0.2302603802598094, 0.16548227933801812, 0.11181894974229303, 0.05376288273475586, -0.009894885213430531, -0.25167101440161493, 0.09216241101967171, -0.16270986330582182, 0.006148158261124548, -0.09889311814756208, -0.04570178233552724, 0.0024618414432011747, -0.34849136599931063, 0.06360070336754811, 0.04146364562370149, 0.04048269997803947, -0.06087427538020436, -0.14200212798911976, 0.014284996334715066, 0.06905707118032198, 0.064091298862115, 0.08970345943659619, 0.14702990696752896, -0.18269957966018607, -0.11575729044262836, 0.3904493570617224, -0.08261688647532561, -0.2624589303080496, 0.17332520385939829, -0.05190826512910929, -0.12413346014658895, 0.12485096967351195, 0.2578818234300392, 0.1743054128805729, -0.16361202457856788, 0.024240478671885824, -0.04058466356474202, 0.17583956417464022, 0.04780201322707423, -0.004668978584074491, 0.19247190239430229, 0.2176597895497154, 0.09015510721262733, 0.13455907214980423, -0.08940104125508082, -0.06081268679338029, -0.2726927951381013, -0.09828255445117483, -0.19354612961870846, 0.0064975336891940376, -0.09899108762820598, -0.13930688934979607, 0.35895492142771146, 0.1323272975514064, 0.2325766195442427, 0.1359662317121842, 0.3185234738897683, 0.12105695610532437, 0.09755071289082234, 0.07840184569736389, 0.22844234669920863, 0.015471240604686475, 0.03747812094091362, -0.18941569218814774, 0.11986490627561973, 0.04057751705467298] |
1,803.00068 | Gotta Adapt 'Em All: Joint Pixel and Feature-Level Domain Adaptation for
Recognition in the Wild | Recent developments in deep domain adaptation have allowed knowledge transfer
from a labeled source domain to an unlabeled target domain at the level of
intermediate features or input pixels. We propose that advantages may be
derived by combining them, in the form of different insights that lead to a
novel design and complementary properties that result in better performance. At
the feature level, inspired by insights from semi-supervised learning, we
propose a classification-aware domain adversarial neural network that brings
target examples into more classifiable regions of source domain. Next, we posit
that computer vision insights are more amenable to injection at the pixel
level. In particular, we use 3D geometry and image synthesis based on a
generalized appearance flow to preserve identity across pose transformations,
while using an attribute-conditioned CycleGAN to translate a single source into
multiple target images that differ in lower-level properties such as lighting.
Besides standard UDA benchmark, we validate on a novel and apt problem of car
recognition in unlabeled surveillance images using labeled images from the web,
handling explicitly specified, nameable factors of variation through
pixel-level and implicit, unspecified factors through feature-level adaptation.
| cs.CV | recent developments in deep domain adaptation have allowed knowledge transfer from a labeled source domain to an unlabeled target domain at the level of intermediate features or input pixels we propose that advantages may be derived by combining them in the form of different insights that lead to a novel design and complementary properties that result in better performance at the feature level inspired by insights from semisupervised learning we propose a classificationaware domain adversarial neural network that brings target examples into more classifiable regions of source domain next we posit that computer vision insights are more amenable to injection at the pixel level in particular we use 3d geometry and image synthesis based on a generalized appearance flow to preserve identity across pose transformations while using an attributeconditioned cyclegan to translate a single source into multiple target images that differ in lowerlevel properties such as lighting besides standard uda benchmark we validate on a novel and apt problem of car recognition in unlabeled surveillance images using labeled images from the web handling explicitly specified nameable factors of variation through pixellevel and implicit unspecified factors through featurelevel adaptation | [['recent', 'developments', 'in', 'deep', 'domain', 'adaptation', 'have', 'allowed', 'knowledge', 'transfer', 'from', 'a', 'labeled', 'source', 'domain', 'to', 'an', 'unlabeled', 'target', 'domain', 'at', 'the', 'level', 'of', 'intermediate', 'features', 'or', 'input', 'pixels', 'we', 'propose', 'that', 'advantages', 'may', 'be', 'derived', 'by', 'combining', 'them', 'in', 'the', 'form', 'of', 'different', 'insights', 'that', 'lead', 'to', 'a', 'novel', 'design', 'and', 'complementary', 'properties', 'that', 'result', 'in', 'better', 'performance', 'at', 'the', 'feature', 'level', 'inspired', 'by', 'insights', 'from', 'semisupervised', 'learning', 'we', 'propose', 'a', 'classificationaware', 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1,803.00069 | Critical properties of scalar field theory with Lorentz violation: Exact
treatment of Lorentz-violating mechanism | In this work, we compute analytically the infrared divergences of massless
O($N$) self-interacting scalar field theories with Lorentz violation, which are
exact in the Lorentz-violating $K_{\mu\nu}$ coefficients, for evaluating the
corresponding next-to-leading order critical exponents. For that, we apply
three distinct and independent field-theoretic renormalization group methods.
We find that the outcomes for the critical exponents are the same in the three
methods and, furthermore, are identical to their Lorentz invariant
counterparts. We generalize the results for all loop levels by employing a
general theorem arising from the exact procedure and give the corresponding
physical interpretation.
| hep-th cond-mat.stat-mech math-ph math.MP | in this work we compute analytically the infrared divergences of massless on selfinteracting scalar field theories with lorentz violation which are exact in the lorentzviolating k_munu coefficients for evaluating the corresponding nexttoleading order critical exponents for that we apply three distinct and independent fieldtheoretic renormalization group methods we find that the outcomes for the critical exponents are the same in the three methods and furthermore are identical to their lorentz invariant counterparts we generalize the results for all loop levels by employing a general theorem arising from the exact procedure and give the corresponding physical interpretation | [['in', 'this', 'work', 'we', 'compute', 'analytically', 'the', 'infrared', 'divergences', 'of', 'massless', 'on', 'selfinteracting', 'scalar', 'field', 'theories', 'with', 'lorentz', 'violation', 'which', 'are', 'exact', 'in', 'the', 'lorentzviolating', 'k_munu', 'coefficients', 'for', 'evaluating', 'the', 'corresponding', 'nexttoleading', 'order', 'critical', 'exponents', 'for', 'that', 'we', 'apply', 'three', 'distinct', 'and', 'independent', 'fieldtheoretic', 'renormalization', 'group', 'methods', 'we', 'find', 'that', 'the', 'outcomes', 'for', 'the', 'critical', 'exponents', 'are', 'the', 'same', 'in', 'the', 'three', 'methods', 'and', 'furthermore', 'are', 'identical', 'to', 'their', 'lorentz', 'invariant', 'counterparts', 'we', 'generalize', 'the', 'results', 'for', 'all', 'loop', 'levels', 'by', 'employing', 'a', 'general', 'theorem', 'arising', 'from', 'the', 'exact', 'procedure', 'and', 'give', 'the', 'corresponding', 'physical', 'interpretation']] | [-0.12109655057429336, 0.15990910375694511, -0.10316191171295941, 0.1092290572548033, -0.05498405204464992, -0.13225368524338896, 0.015203288368259868, 0.3371451808998245, -0.16209698621241841, -0.25715665391665726, 0.06340851208976044, -0.2887504092977906, -0.17987833851172277, 0.197116179864679, 0.015379339126714816, 0.08635995697113685, -0.03291212595649995, 0.05149463511770591, -0.1323905654377692, -0.25142531082383357, 0.37888620703597553, -0.006088275436923141, 0.2573714205645956, 0.05198983814504269, 0.0736402324885906, 0.017141396994702518, -0.06072811737734204, 0.0339119356503943, -0.16839719723990734, 0.11389429328240415, 0.22247755301456587, 0.043448249860906195, 0.17593230086557318, -0.38429394278985757, -0.1816481802331206, 0.10226828422067531, 0.13058946628249637, 0.14060209543943833, -0.0020316156890961188, -0.2659056592383422, 0.09453818582308789, -0.15968426358692037, -0.15892801575440293, -0.13087867094630687, -0.01777164719896973, -0.03139378214109456, -0.2749684829323087, 0.1283205170208627, -0.013676488558606556, 0.0513455493201036, -0.08285231780124984, -0.11000236162484119, 0.04198305630901208, 0.15151177881731806, 0.10685952100914164, -0.021937682244849082, 0.11015115547343157, -0.14694524701189948, -0.17594457592955828, 0.38211255755353096, -0.0638974761592787, -0.22582168978018066, 0.17646596245079613, -0.15802638071181718, -0.1874957684352315, 0.1046151733971783, 0.13685208375682123, 0.12799459121500453, -0.16491195626925523, 0.1517527386246608, -0.032605614115406446, 0.06439047372259665, 0.07904314759556048, 0.04912893276014074, 0.15889301977586, 0.004638627540164937, 0.020495070377364755, 0.11588460857213552, -0.009361626684646277, -0.13174966168541383, -0.40498901567965123, -0.14104274354758672, -0.12458176595949529, 0.06002046115221068, -0.1495521408743722, -0.14880054331054757, 0.39259402129876736, 0.20076602213763786, 0.16173288156278431, 0.11240830887618358, 0.2657513836068877, 0.17722350845967108, 0.062278120936146784, 0.09050396990884717, 0.27424476771072176, 0.11707469257574606, 0.03336866983348349, -0.23842724986267663, -0.049576940834716275, 0.14650390638659397] |
1,803.0007 | Lecture Notes in Cosmology | These lecture notes are based on the hand-written notes which I prepared for
the cosmology course taught to graduate students of PPGFis and PPGCosmo at the
Federal University of Esp\'irito Santo (UFES), starting from 2014. This course
covers topics ranging from the evidence of the expanding universe to Cosmic
Microwave Background anisotropies. They can be found also on my personal
webpage http://ofp.cosmo-ufes.org/ and shall be published by Springer in 2018.
| astro-ph.CO gr-qc hep-th | these lecture notes are based on the handwritten notes which i prepared for the cosmology course taught to graduate students of ppgfis and ppgcosmo at the federal university of espirito santo ufes starting from 2014 this course covers topics ranging from the evidence of the expanding universe to cosmic microwave background anisotropies they can be found also on my personal webpage httpofpcosmoufesorg and shall be published by springer in 2018 | [['these', 'lecture', 'notes', 'are', 'based', 'on', 'the', 'handwritten', 'notes', 'which', 'i', 'prepared', 'for', 'the', 'cosmology', 'course', 'taught', 'to', 'graduate', 'students', 'of', 'ppgfis', 'and', 'ppgcosmo', 'at', 'the', 'federal', 'university', 'of', 'espirito', 'santo', 'ufes', 'starting', 'from', '2014', 'this', 'course', 'covers', 'topics', 'ranging', 'from', 'the', 'evidence', 'of', 'the', 'expanding', 'universe', 'to', 'cosmic', 'microwave', 'background', 'anisotropies', 'they', 'can', 'be', 'found', 'also', 'on', 'my', 'personal', 'webpage', 'httpofpcosmoufesorg', 'and', 'shall', 'be', 'published', 'by', 'springer', 'in', '2018']] | [0.02260510419996885, 0.14390537108366305, -0.1685248419570808, 0.04731923449748697, -0.14279671107920316, -0.11157969143958046, -0.01629553334071086, 0.29552363989373237, -0.19347216304964743, -0.3863618331459852, 0.1205006139836489, -0.3321949733564487, -0.07526972081798773, 0.2455482795691261, -0.143231381676518, -0.08900658074909677, 0.08386059601146441, 0.006090715928719594, -0.026959598302626266, -0.4373509626513204, 0.3038414362531442, 0.1769956903388867, 0.2841504807942189, 0.04701223525290306, 0.10581068774828543, -0.049719466003947534, -0.1415255170602065, -0.03781064849776717, -0.14901079644377416, 0.12475033140598009, 0.426919564518791, 0.16237841662592611, 0.28541634953939, -0.4468487600867565, -0.0896940920358667, 0.0204980085675533, 0.07159000347153499, 0.16717775119580508, 0.002254425845323847, -0.3966419876194917, 0.002339207911147521, -0.14910771474958612, -0.04578283306641074, 0.028400772698939994, 0.03480088254436851, 0.016912343388853165, -0.10915279801075276, 0.04277391719873636, 0.010110661301475305, 0.22433532843222984, -0.03235513219574036, -0.19416076217133266, 0.01521121422545268, 0.1314461716522391, 0.018109131890993852, 0.046159383432509804, 0.15502294113572973, -0.10241185876254279, -0.1470117952244786, 0.39828788340091703, -0.048691705353737166, -0.01372290664853958, 0.1621421776747761, -0.161972520306993, -0.19786965970952924, 0.06663691367452534, 0.2562009791771953, 0.05389229159515638, -0.18096349611878396, 0.07354788322252437, 0.0228670096611425, 0.16711737232712598, 0.1187634497236174, -0.07019193017712007, 0.3066095036150028, 0.05448736349264016, -0.10116795624892871, 0.028401951115721694, 0.0070633407825460804, -0.042204961230835086, -0.3745963954581664, -0.13021344150631473, -0.18901621740884506, 0.09504384567531256, 0.04773589548198148, -0.10187836836736935, 0.3988166201716432, 0.1692090507525091, 0.0597782166244892, -0.03697160343961933, 0.1429689342299333, 0.004443612809364612, -0.042208343314436764, 0.09314577979250596, 0.19889581283697716, 0.05890503876412717, 0.2684579137282876, -0.04004995755564708, -0.005064988415688277, 0.05732790204481437] |
1,803.00071 | Casimir force in dense confined electrolytes | Understanding the force between charged surfaces immersed in an electrolyte
solution is a classic problem in soft matter and liquid-state theory. Recent
experiments showed that the force decays exponentially but the characteristic
decay length in a concentrated electrolyte is significantly larger than what
liquid-state theories predict based on analysing correlation functions in the
bulk electrolyte. Inspired by the classical Casimir effect, we consider an
alternative mechanism for force generation, namely the confinement of density
fluctuations in the electrolyte by the walls. We show analytically within the
random phase approximation, which assumes the ions to be point charges, that
this fluctuation-induced force is attractive and also decays exponentially,
albeit with a decay length that is half of the bulk correlation length. These
predictions change dramatically when excluded volume effects are accounted for
within the mean spherical approximation. At high ion concentrations the Casimir
force is found to be exponentially damped oscillatory as a function of the
distance between the confining surfaces. Our analysis does not resolve the
riddle of the anomalously long screening length observed in experiments, but
suggests that the Casimir force due to mode restriction in density fluctuations
could be an hitherto under-appreciated source of surface-surface interaction.
| cond-mat.soft cond-mat.stat-mech physics.chem-ph | understanding the force between charged surfaces immersed in an electrolyte solution is a classic problem in soft matter and liquidstate theory recent experiments showed that the force decays exponentially but the characteristic decay length in a concentrated electrolyte is significantly larger than what liquidstate theories predict based on analysing correlation functions in the bulk electrolyte inspired by the classical casimir effect we consider an alternative mechanism for force generation namely the confinement of density fluctuations in the electrolyte by the walls we show analytically within the random phase approximation which assumes the ions to be point charges that this fluctuationinduced force is attractive and also decays exponentially albeit with a decay length that is half of the bulk correlation length these predictions change dramatically when excluded volume effects are accounted for within the mean spherical approximation at high ion concentrations the casimir force is found to be exponentially damped oscillatory as a function of the distance between the confining surfaces our analysis does not resolve the riddle of the anomalously long screening length observed in experiments but suggests that the casimir force due to mode restriction in density fluctuations could be an hitherto underappreciated source of surfacesurface interaction | [['understanding', 'the', 'force', 'between', 'charged', 'surfaces', 'immersed', 'in', 'an', 'electrolyte', 'solution', 'is', 'a', 'classic', 'problem', 'in', 'soft', 'matter', 'and', 'liquidstate', 'theory', 'recent', 'experiments', 'showed', 'that', 'the', 'force', 'decays', 'exponentially', 'but', 'the', 'characteristic', 'decay', 'length', 'in', 'a', 'concentrated', 'electrolyte', 'is', 'significantly', 'larger', 'than', 'what', 'liquidstate', 'theories', 'predict', 'based', 'on', 'analysing', 'correlation', 'functions', 'in', 'the', 'bulk', 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1,803.00072 | Analysis of the Herschel DEBRIS Sun-like star sample | This paper presents a study of circumstellar debris around Sun-like stars
using data from the Herschel DEBRIS Key Programme. DEBRIS is an unbiased survey
comprising the nearest ~90 stars of each spectral type A-M. Analysis of the 275
F-K stars shows that excess emission from a debris disc was detected around 47
stars, giving a detection rate of 17.1+2.6-2.3 per cent, with lower rates for
later spectral types. For each target a blackbody spectrum was fitted to the
dust emission to determine its fractional luminosity and temperature. The
derived under- lying distribution of fractional luminosity versus blackbody
radius in the population showed that most detected discs are concentrated at f
~ 10^-5 and at temperatures corresponding to blackbody radii 7-40 AU, which
scales to ~40 AU for realistic dust properties (similar to the current Kuiper
belt). Two outlying populations are also evident; five stars have exceptionally
bright emission ( f > 5x10^-5), and one has unusually hot dust < 4 AU. The
excess emission distributions at all wavelengths were fitted with a
steady-state evolution model, showing these are compatible with all stars being
born with a narrow belt that then undergoes collisional grinding. However, the
model cannot explain the hot dust systems - likely originating in transient
events - and bright emission systems - arising potentially from atypically
massive discs or recent stirring. The emission from the present-day Kuiper belt
is predicted to be close to the median of the population, suggesting that half
of stars have either depleted their Kuiper belts (similar to the Solar System),
or had a lower planetesimal formation efficiency.
| astro-ph.EP astro-ph.SR | this paper presents a study of circumstellar debris around sunlike stars using data from the herschel debris key programme debris is an unbiased survey comprising the nearest 90 stars of each spectral type am analysis of the 275 fk stars shows that excess emission from a debris disc was detected around 47 stars giving a detection rate of 1712623 per cent with lower rates for later spectral types for each target a blackbody spectrum was fitted to the dust emission to determine its fractional luminosity and temperature the derived under lying distribution of fractional luminosity versus blackbody radius in the population showed that most detected discs are concentrated at f 105 and at temperatures corresponding to blackbody radii 740 au which scales to 40 au for realistic dust properties similar to the current kuiper belt two outlying populations are also evident five stars have exceptionally bright emission f 5x105 and one has unusually hot dust 4 au the excess emission distributions at all wavelengths were fitted with a steadystate evolution model showing these are compatible with all stars being born with a narrow belt that then undergoes collisional grinding however the model cannot explain the hot dust systems likely originating in transient events and bright emission systems arising potentially from atypically massive discs or recent stirring the emission from the presentday kuiper belt is predicted to be close to the median of the population suggesting that half of stars have either depleted their kuiper belts similar to the solar system or had a lower planetesimal formation efficiency | [['this', 'paper', 'presents', 'a', 'study', 'of', 'circumstellar', 'debris', 'around', 'sunlike', 'stars', 'using', 'data', 'from', 'the', 'herschel', 'debris', 'key', 'programme', 'debris', 'is', 'an', 'unbiased', 'survey', 'comprising', 'the', 'nearest', '90', 'stars', 'of', 'each', 'spectral', 'type', 'am', 'analysis', 'of', 'the', '275', 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1,803.00073 | Resolution Improvement of the Common Method for Presentating Arbitrary
Space Curves Voxel | The task of voxel resolution for a space curve in video memory of 3D display
is set. Furthermore, an approach solution of voxel resolution of arbitrary
space curve, given in parametric form, is studied. Numerous numbers of
intensive experiments are conducted and interesting results with significant
recommendations are presented.
| cs.GR | the task of voxel resolution for a space curve in video memory of 3d display is set furthermore an approach solution of voxel resolution of arbitrary space curve given in parametric form is studied numerous numbers of intensive experiments are conducted and interesting results with significant recommendations are presented | [['the', 'task', 'of', 'voxel', 'resolution', 'for', 'a', 'space', 'curve', 'in', 'video', 'memory', 'of', '3d', 'display', 'is', 'set', 'furthermore', 'an', 'approach', 'solution', 'of', 'voxel', 'resolution', 'of', 'arbitrary', 'space', 'curve', 'given', 'in', 'parametric', 'form', 'is', 'studied', 'numerous', 'numbers', 'of', 'intensive', 'experiments', 'are', 'conducted', 'and', 'interesting', 'results', 'with', 'significant', 'recommendations', 'are', 'presented']] | [-0.13546905219935032, 0.015657649131085513, -0.06801947237619636, 0.027296605553211912, -0.08296744946428404, -0.13868348926723917, -0.0024355473947160096, 0.48813915039811817, -0.19508057901260387, -0.3476154769637755, 0.11273571598457591, -0.2693088293379667, -0.1417407636644737, 0.2637471160946452, -0.10819948167446287, 0.12090188994699595, 0.15383840767115506, 0.06608872915789181, -0.03994328382291964, -0.2836854060522604, 0.2958978718640853, 0.02740558949109091, 0.29360530823849296, -0.014091812758421411, 0.1425985406932174, -0.050015789247593104, -0.07969181587425422, 0.041956252930448294, -0.10325347599858532, 0.1676370262035302, 0.3023019763736092, 0.1943743311971122, 0.24069758665234764, -0.41981578952803905, -0.22437716444612157, 0.06064152324154061, 0.12135532450843223, 0.04077761080496165, -0.07492491369116672, -0.26264605196002794, 0.0972018521944327, -0.12044506491523008, -0.0768588841702713, -0.1507836431454943, 0.05729768288378813, 0.01636853110881484, -0.27474021111919106, 0.04971593450659848, 0.013222822515598061, 0.1479192080394346, -0.07420083717443049, -0.09237728198534068, 0.011361921838085567, 0.1259083060110558, 0.010391517532799316, 0.06172273128426501, 0.0649226660441075, -0.18450402175741537, -0.10020922808622827, 0.4245083433354977, 0.0029541782616656653, -0.2103746836406312, 0.13599994465024495, -0.17421171565217022, -0.09178731868005528, 0.21528429806004373, 0.20254526006019846, 0.14382248729163286, -0.10179354077471155, 0.07884383366837604, -0.07240694742269661, 0.17292432460401738, 0.07164905636039164, 0.01932759333891338, 0.16854279336272454, 0.24260602115976568, 0.002507174767705859, 0.13445692422457647, -0.14129043756318943, -0.08856460775191687, -0.2802576198936345, -0.16257410946929332, -0.21276173318679237, -0.018610584245500515, -0.12809479745949515, -0.13352678763699166, 0.37219662021142336, 0.11040214158366529, 0.22204882469104262, 0.022070846121226038, 0.2793319892442348, 0.09742640211438161, 0.030899869734231308, -0.0020203846233554793, 0.10458390217046348, 0.009942293352428444, 0.10460014656490209, -0.15887765690912398, 0.008243184251596734, 0.04961063306094432] |
1,803.00074 | Ultrafast heat flow in heterostructures of Au nanoclusters on
thin-films: atomic-disorder induced by hot electrons | We study the ultrafast structural dynamics, in response to electronic
excitations, in heterostructures composed of Au$_{923}$ nanoclusters on
thin-film substrates with the use of femtosecond electron diffraction. Various
forms of atomic motion, such as thermal vibrations, thermal expansion and
lattice disordering, manifest as distinct and quantifiable reciprocal-space
observables. In photo-excited, supported nanoclusters thermal equilibration
proceeds through intrinsic heat flow, between their electrons and their
lattice, and extrinsic heat flow between the nanoclusters and their substrate.
For an in-depth understanding of this process, we have extended the
two-temperature model to the case of 0D/2D heterostructures and used it to
describe energy flow among the various subsystems, to quantify interfacial
coupling constants, and to elucidate the role of the optical and thermal
substrate properties. When lattice heating of Au nanoclusters is dominated by
intrinsic heat flow, a reversible disordering of atomic positions occurs, which
is absent when heat is injected as hot substrate-phonons. The present analysis
indicates that hot electrons can distort the lattice of nanoclusters, even if
the lattice temperature is below the equilibrium threshold for surface
pre-melting. Based on simple considerations, the effect is interpreted as
activation of surface diffusion due to modifications of the potential energy
surface at high electronic temperatures. We discuss the implications of such a
process in structural changes during surface chemical reactions.
| cond-mat.mes-hall | we study the ultrafast structural dynamics in response to electronic excitations in heterostructures composed of au_923 nanoclusters on thinfilm substrates with the use of femtosecond electron diffraction various forms of atomic motion such as thermal vibrations thermal expansion and lattice disordering manifest as distinct and quantifiable reciprocalspace observables in photoexcited supported nanoclusters thermal equilibration proceeds through intrinsic heat flow between their electrons and their lattice and extrinsic heat flow between the nanoclusters and their substrate for an indepth understanding of this process we have extended the twotemperature model to the case of 0d2d heterostructures and used it to describe energy flow among the various subsystems to quantify interfacial coupling constants and to elucidate the role of the optical and thermal substrate properties when lattice heating of au nanoclusters is dominated by intrinsic heat flow a reversible disordering of atomic positions occurs which is absent when heat is injected as hot substratephonons the present analysis indicates that hot electrons can distort the lattice of nanoclusters even if the lattice temperature is below the equilibrium threshold for surface premelting based on simple considerations the effect is interpreted as activation of surface diffusion due to modifications of the potential energy surface at high electronic temperatures we discuss the implications of such a process in structural changes during surface chemical reactions | [['we', 'study', 'the', 'ultrafast', 'structural', 'dynamics', 'in', 'response', 'to', 'electronic', 'excitations', 'in', 'heterostructures', 'composed', 'of', 'au_923', 'nanoclusters', 'on', 'thinfilm', 'substrates', 'with', 'the', 'use', 'of', 'femtosecond', 'electron', 'diffraction', 'various', 'forms', 'of', 'atomic', 'motion', 'such', 'as', 'thermal', 'vibrations', 'thermal', 'expansion', 'and', 'lattice', 'disordering', 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1,803.00075 | The global well-posedness and scattering for the $5$D defocusing
conformal invariant NLW with radial initial data in a critical Besov space | In this paper, we obtain the global well-posedness and scattering for the
radial solution to the defocusing conformal invariant nonlinear wave equation
with initial data in the critical Besov space
$\dot{B}^3_{1,1}\times\dot{B}^2_{1,1}(\mathbb{R}^5)$. This is the five
dimensional analogue of \cite{dodson-2016}, which is the first result on the
global well-posedness and scattering of the energy subcritical nonlinear wave
equation without the uniform boundedness assumption on the critical Sobolev
norms employed as a substitute of the missing conservation law with respect to
the scaling invariance of the equation. The proof is based on exploiting the
structure of the radial solution, developing the Strichartz-type estimates and
incorporation of the strategy in \cite{dodson-2016}, where we also avoid a
logarithm-type loss by employing the inhomogeneous Strichartz estimates.
| math.AP | in this paper we obtain the global wellposedness and scattering for the radial solution to the defocusing conformal invariant nonlinear wave equation with initial data in the critical besov space dotb3_11timesdotb2_11mathbbr5 this is the five dimensional analogue of citedodson2016 which is the first result on the global wellposedness and scattering of the energy subcritical nonlinear wave equation without the uniform boundedness assumption on the critical sobolev norms employed as a substitute of the missing conservation law with respect to the scaling invariance of the equation the proof is based on exploiting the structure of the radial solution developing the strichartztype estimates and incorporation of the strategy in citedodson2016 where we also avoid a logarithmtype loss by employing the inhomogeneous strichartz estimates | [['in', 'this', 'paper', 'we', 'obtain', 'the', 'global', 'wellposedness', 'and', 'scattering', 'for', 'the', 'radial', 'solution', 'to', 'the', 'defocusing', 'conformal', 'invariant', 'nonlinear', 'wave', 'equation', 'with', 'initial', 'data', 'in', 'the', 'critical', 'besov', 'space', 'dotb3_11timesdotb2_11mathbbr5', 'this', 'is', 'the', 'five', 'dimensional', 'analogue', 'of', 'citedodson2016', 'which', 'is', 'the', 'first', 'result', 'on', 'the', 'global', 'wellposedness', 'and', 'scattering', 'of', 'the', 'energy', 'subcritical', 'nonlinear', 'wave', 'equation', 'without', 'the', 'uniform', 'boundedness', 'assumption', 'on', 'the', 'critical', 'sobolev', 'norms', 'employed', 'as', 'a', 'substitute', 'of', 'the', 'missing', 'conservation', 'law', 'with', 'respect', 'to', 'the', 'scaling', 'invariance', 'of', 'the', 'equation', 'the', 'proof', 'is', 'based', 'on', 'exploiting', 'the', 'structure', 'of', 'the', 'radial', 'solution', 'developing', 'the', 'strichartztype', 'estimates', 'and', 'incorporation', 'of', 'the', 'strategy', 'in', 'citedodson2016', 'where', 'we', 'also', 'avoid', 'a', 'logarithmtype', 'loss', 'by', 'employing', 'the', 'inhomogeneous', 'strichartz', 'estimates']] | [-0.11921752418659753, 0.05069438397284534, -0.10273964002223337, 0.08581360402789315, -0.08614803921091922, -0.05602563078657298, -0.008287822029561246, 0.2682865861550731, -0.2776267355923557, -0.23707079038199558, 0.16418762678000287, -0.27842899864949916, -0.11404377424110801, 0.15402220650102502, -0.007323038385454881, 0.13987342434367006, 0.014291930079491715, 0.0006040758255995431, -0.10447252603342473, -0.2160479204445067, 0.43942814062239005, 0.022853570429892358, 0.29128199732878196, 0.03715151660460032, 0.1024548894404118, 0.04567810333377319, -0.008551430635911933, -0.03590679911416127, -0.1966984382686521, 0.14880554716623687, 0.1701615888031468, 0.04823112244110049, 0.2810863934208686, -0.41989276648136015, -0.2327833444854813, 0.11307430417687332, 0.1331153431023329, 0.11321083740114186, -0.07317996890998398, -0.3117999635731517, 0.057801572175822775, -0.07978015290810016, -0.23150573778597605, -0.06389870391606148, -0.01228402603922759, 0.07699736615769186, -0.31885234044985133, 0.14961569847601222, 0.10271934763986175, 0.017720275187580768, -0.20651961243474634, -0.03640089139058176, -0.03297708292444378, 0.05412677409900826, 0.07385431649459337, 0.04323079064093783, 0.027103117858533258, -0.1442645167726831, -0.036445109449895256, 0.3650573972549479, -0.09464788013848208, -0.2631528636724767, 0.1319594701813234, -0.14270133743362534, -0.10195755020525875, 0.09375551220629427, 0.18595579003725768, 0.1104519318612771, -0.13930012892696897, 0.13233421879309482, -0.04950622290790168, 0.1453586246707808, 0.10221495409890757, 0.031462006223530084, 0.04694223010091711, 0.1718052796658397, 0.17036913677959276, 0.1072607171619153, -0.07454153023151112, -0.08943660966499503, -0.36697667253093197, -0.16282787260015386, -0.19437030096285804, 0.10319357026852785, -0.1445494135956416, -0.16537687011552438, 0.36640604976683033, 0.1377279228180345, 0.16752519346457923, 0.0665449898589355, 0.2661269476274186, 0.20168804022102713, 0.0515051138097957, 0.08330786773556118, 0.24884522838865297, 0.15265159406580853, 0.18214175599169427, -0.23437474626590007, 0.044131622697084635, 0.16859992683206101] |
1,803.00076 | Left-orderablity for surgeries on $(-2,3,2s+1)$-pretzel knots | In this paper, we prove that the fundamental group of the manifold obtained
by Dehn surgery along a $(-2,3,2s+1)$-pretzel knot ($s\ge 3$) with slope
$\frac{p}{q}$ is not left orderable if $\frac{p}{q}\ge 2s+3$, and that it is
left orderable if $\frac{p}{q}$ is in a neighborhood of zero depending on $s$.
| math.GT | in this paper we prove that the fundamental group of the manifold obtained by dehn surgery along a 232s1pretzel knot sge 3 with slope fracpq is not left orderable if fracpqge 2s3 and that it is left orderable if fracpq is in a neighborhood of zero depending on s | [['in', 'this', 'paper', 'we', 'prove', 'that', 'the', 'fundamental', 'group', 'of', 'the', 'manifold', 'obtained', 'by', 'dehn', 'surgery', 'along', 'a', '232s1pretzel', 'knot', 'sge', '3', 'with', 'slope', 'fracpq', 'is', 'not', 'left', 'orderable', 'if', 'fracpqge', '2s3', 'and', 'that', 'it', 'is', 'left', 'orderable', 'if', 'fracpq', 'is', 'in', 'a', 'neighborhood', 'of', 'zero', 'depending', 'on', 's']] | [-0.1966081514478998, 0.167152041985475, -0.1085963469553501, 0.006506219451771455, -0.09953724267951986, -0.22177791090166948, 0.013880844864538534, 0.409612386784655, -0.337583434391529, -0.20023816875479322, 0.14713983606804718, -0.29201968608701484, -0.1579372777722459, 0.1931206646692404, -0.1560032559915426, -0.10216841396895494, 0.06265112408932219, 0.15297336820909319, -0.05880495058768924, -0.25637854049180414, 0.40242499041747537, -0.10248956754011043, 0.11160597320091217, 0.08100443393824265, 0.1216274192804431, 0.0027951944936463174, 0.04157419272876801, 0.01584901051853407, -0.1874668384283568, 0.03350049611973636, 0.20632254375897824, 0.06769511845220436, 0.22802169101827957, -0.3186494416775221, -0.13502878955307793, 0.15473698242072095, 0.17752647078893286, -0.08924525835491876, -0.057590993723158346, -0.24715642759853856, 0.1942084551234036, -0.10424840244206976, -0.13394183821064679, 0.025031886158877626, 0.13845132754996736, 0.0016254929706771323, -0.19144498213055602, 0.030748372382306037, 0.10144759500597386, 0.0766377751220097, -0.016511222327801774, -0.09002191006661729, -0.07646204111106852, 0.1079111494906643, 0.0813773007954451, 0.1409068387596214, 0.04560454075838974, -0.10219644531706705, -0.0629809074143463, 0.3486370644631221, -0.08282492948359156, -0.23617941557251393, 0.0772182616910481, -0.21774770978323005, -0.19647545876734435, 0.12784028921692453, -0.01794021025775595, 0.13861938954350797, -0.039402830503643196, 0.18433243590976767, -0.1626501130831844, 0.168571560306752, 0.08926515059268221, -0.09680182825615431, 0.10761997924047581, 0.12296314491949817, 0.15715704281675688, 0.06277191191119083, -0.008936719587469038, 0.06737044832094553, -0.3259223856507464, -0.22224243336971453, -0.15199847289539398, 0.15007939481484814, -0.03882725321795504, -0.14625894595333871, 0.38210404394788944, 0.04673980642110109, 0.16093908664473194, 0.11997132936611454, 0.2322741740561546, 0.06398660844152278, 0.008353740293928918, 0.15713835726591183, 0.13244813347750523, 0.13786670760291211, -0.03782058835148494, -0.14556584399233155, 0.013022292781859002, 0.14545593787222466] |
1,803.00077 | Distributed Synthesis Using Accelerated ADMM | We propose a convex distributed optimization algorithm for synthesizing
robust controllers for large-scale continuous time systems subject to exogenous
disturbances. Given a large scale system, instead of solving the larger
centralized synthesis task, we decompose the problem into a set of smaller
synthesis problems for the local subsystems with a given interconnection
topology. Hence, the synthesis problem is constrained to the sparsity pattern
dictated by the interconnection topology. To this end, for each subsystem, we
solve a local dissipation inequality and then check a small-gain like condition
for the overall system. To minimize the effect of disturbances, we consider the
$\mathrm{H}_\infty$ synthesis problems. We instantiate the distributed
synthesis method using accelerated alternating direction method of multipliers
(ADMM) with convergence rate $O(\frac{1}{k^2})$ with $k$ being the number of
iterations.
| math.OC | we propose a convex distributed optimization algorithm for synthesizing robust controllers for largescale continuous time systems subject to exogenous disturbances given a large scale system instead of solving the larger centralized synthesis task we decompose the problem into a set of smaller synthesis problems for the local subsystems with a given interconnection topology hence the synthesis problem is constrained to the sparsity pattern dictated by the interconnection topology to this end for each subsystem we solve a local dissipation inequality and then check a smallgain like condition for the overall system to minimize the effect of disturbances we consider the mathrmh_infty synthesis problems we instantiate the distributed synthesis method using accelerated alternating direction method of multipliers admm with convergence rate ofrac1k2 with k being the number of iterations | [['we', 'propose', 'a', 'convex', 'distributed', 'optimization', 'algorithm', 'for', 'synthesizing', 'robust', 'controllers', 'for', 'largescale', 'continuous', 'time', 'systems', 'subject', 'to', 'exogenous', 'disturbances', 'given', 'a', 'large', 'scale', 'system', 'instead', 'of', 'solving', 'the', 'larger', 'centralized', 'synthesis', 'task', 'we', 'decompose', 'the', 'problem', 'into', 'a', 'set', 'of', 'smaller', 'synthesis', 'problems', 'for', 'the', 'local', 'subsystems', 'with', 'a', 'given', 'interconnection', 'topology', 'hence', 'the', 'synthesis', 'problem', 'is', 'constrained', 'to', 'the', 'sparsity', 'pattern', 'dictated', 'by', 'the', 'interconnection', 'topology', 'to', 'this', 'end', 'for', 'each', 'subsystem', 'we', 'solve', 'a', 'local', 'dissipation', 'inequality', 'and', 'then', 'check', 'a', 'smallgain', 'like', 'condition', 'for', 'the', 'overall', 'system', 'to', 'minimize', 'the', 'effect', 'of', 'disturbances', 'we', 'consider', 'the', 'mathrmh_infty', 'synthesis', 'problems', 'we', 'instantiate', 'the', 'distributed', 'synthesis', 'method', 'using', 'accelerated', 'alternating', 'direction', 'method', 'of', 'multipliers', 'admm', 'with', 'convergence', 'rate', 'ofrac1k2', 'with', 'k', 'being', 'the', 'number', 'of', 'iterations']] | [-0.1697981537092389, 0.041348226859754936, -0.033262181609106344, 0.03392567431304868, -0.08920946684995974, -0.17129130068411097, 0.04857959601952278, 0.374450154998523, -0.3843981730568362, -0.324287774105461, 0.14828979177033808, -0.18830628277446093, -0.11959413164771918, 0.17140250645878571, -0.09397146530728054, 0.1269575677863432, 0.06495991937346815, -0.02380940007544526, -0.0904185433531048, -0.2335394874204126, 0.2779380026911422, 0.03164476768194339, 0.2602995543880022, -0.04953154805666349, 0.14015985206942858, 0.004521677213698978, 0.024585559725391084, 0.059247487352970314, -0.05914319839372527, 0.15029216602657314, 0.24859607726790187, 0.186252730762894, 0.3571917494120971, -0.446550336219077, -0.17804127903636838, 0.13530379412035773, 0.12610272208859366, 0.095621791719569, -0.07776412769682764, -0.2573410059200732, 0.13152357233196907, -0.12261192489591405, -0.08850417795451725, -0.02631791983914422, -0.016778164622043883, 0.030289111182269617, -0.3435399728115853, 0.03479171091393663, 0.035229063968680155, 0.03632077858893303, -0.14160283490480108, -0.0833914824266456, 0.04313412991051716, 0.09851406402079256, 0.01579236961978187, 0.014687635411515715, 0.16159074432693246, -0.06149546184034387, -0.11315586383722165, 0.35894588812247036, -0.0003696784916091857, -0.2476240918736934, 0.1453626079147491, -0.03351573324872283, -0.18171427166089416, 0.11317304400433822, 0.24673457321982215, 0.14875479094905497, -0.15636484105667964, 0.07269926089747611, -0.04176796870819462, 0.20460997427370048, -0.0009496562828229168, 0.0008869193765650115, 0.12428154552348075, 0.21201142716017785, 0.22187881980927324, 0.20513549882540016, -0.020822109841543625, -0.09384480951808569, -0.24850965149729976, -0.10770908375014002, -0.17335286552790463, -0.01084412791670894, -0.08393743281849883, -0.16686115176306934, 0.4108378811350723, 0.15152592741055634, 0.1691305585293554, 0.13300320065917637, 0.3376354424678904, 0.14670836926434705, 0.05268266455932176, 0.12103073784346348, 0.17411987782697977, 0.13068432502602556, 0.11134655951104296, -0.2792873856277887, 0.09118626517515013, 0.10779656296757263] |
1,803.00078 | Chaos and the Flow Capture Problem: Polluting is Easy, Cleaning is Hard | Cleaning pollution from a heterogeneous flow environment is far from simple.
We consider the flow capture problem, which has flows and sinks in a
heterogeneous environment, and investigate the problem of positioning pollutant
capture units. We show that arrays of capture units carry a high risk of
failure without accounting for environmental heterogeneity and chaos in their
placement, design, and operation. Our idealized 2-dimensional models reveal
salient features of the problem. Maximum capture efficiency depends on the
required capture rate: long term efficiency decreases as the number of capture
units increases, whereas short term efficiency increases. If efficiency is
important, the capture process should begin as early as feasible. Knowledge of
transport controlling flow structures offers predictability for unit placement.
We demonstrate two heuristic approaches to near-optimally position capture
units.
| physics.soc-ph | cleaning pollution from a heterogeneous flow environment is far from simple we consider the flow capture problem which has flows and sinks in a heterogeneous environment and investigate the problem of positioning pollutant capture units we show that arrays of capture units carry a high risk of failure without accounting for environmental heterogeneity and chaos in their placement design and operation our idealized 2dimensional models reveal salient features of the problem maximum capture efficiency depends on the required capture rate long term efficiency decreases as the number of capture units increases whereas short term efficiency increases if efficiency is important the capture process should begin as early as feasible knowledge of transport controlling flow structures offers predictability for unit placement we demonstrate two heuristic approaches to nearoptimally position capture units | [['cleaning', 'pollution', 'from', 'a', 'heterogeneous', 'flow', 'environment', 'is', 'far', 'from', 'simple', 'we', 'consider', 'the', 'flow', 'capture', 'problem', 'which', 'has', 'flows', 'and', 'sinks', 'in', 'a', 'heterogeneous', 'environment', 'and', 'investigate', 'the', 'problem', 'of', 'positioning', 'pollutant', 'capture', 'units', 'we', 'show', 'that', 'arrays', 'of', 'capture', 'units', 'carry', 'a', 'high', 'risk', 'of', 'failure', 'without', 'accounting', 'for', 'environmental', 'heterogeneity', 'and', 'chaos', 'in', 'their', 'placement', 'design', 'and', 'operation', 'our', 'idealized', '2dimensional', 'models', 'reveal', 'salient', 'features', 'of', 'the', 'problem', 'maximum', 'capture', 'efficiency', 'depends', 'on', 'the', 'required', 'capture', 'rate', 'long', 'term', 'efficiency', 'decreases', 'as', 'the', 'number', 'of', 'capture', 'units', 'increases', 'whereas', 'short', 'term', 'efficiency', 'increases', 'if', 'efficiency', 'is', 'important', 'the', 'capture', 'process', 'should', 'begin', 'as', 'early', 'as', 'feasible', 'knowledge', 'of', 'transport', 'controlling', 'flow', 'structures', 'offers', 'predictability', 'for', 'unit', 'placement', 'we', 'demonstrate', 'two', 'heuristic', 'approaches', 'to', 'nearoptimally', 'position', 'capture', 'units']] | [-0.13640827018624316, 0.0708190362327374, -0.01803382011130452, 0.08007342756526878, -0.059242251221663676, -0.13164155839655836, 0.049027493578166915, 0.3801987648010254, -0.3191249559705074, -0.3390006463759794, 0.10145743698902572, -0.29140836778096857, -0.14705551566484457, 0.16818663160531566, -0.10035380111434139, 0.02818495445804169, 0.08221263144380198, 0.03799384988378733, -0.01904737180313812, -0.19139541312800318, 0.25299960849090264, 0.1114355537467278, 0.344367008887303, 0.0631285655957002, 0.1373587697565269, 0.01892210549603288, -0.04807552413728375, 0.01761936306451949, -0.08175288739713814, 0.10038782762309822, 0.24080130444034203, 0.1491273697071637, 0.29521169688982457, -0.4880930638370606, -0.31164844394303287, 0.08598187061904285, 0.13926139998366124, 0.06473418235904178, -0.024515924571404377, -0.18086464716694675, 0.08828046356793492, -0.18629649120490424, -0.07908747141034557, -0.07337817309710842, 0.019544739401541077, 0.06056846236774268, -0.2672840237796593, 0.08772262005010047, 0.06445615062346825, 0.043109653342085386, -0.05915828197836303, -0.059462495226986135, 0.0018176281412776846, 0.22458751862152265, 0.023316543920708893, -0.05974756769764309, 0.20942883823210232, -0.14622755946519855, -0.08580204686149955, 0.4082834910816298, -0.015913977860831298, -0.20261527992641695, 0.18918491922892056, -0.09792547917996462, -0.09837187116186565, 0.13964155100715847, 0.2520019276354175, 0.1277838988516193, -0.1271908011287451, 0.031050220208887296, 0.018564548867288977, 0.20071143311305115, 0.06269802055500734, 0.0568293650330116, 0.22238168311305345, 0.28162984427136295, 0.10977948510732788, 0.13522833979043822, -0.12385239428124176, -0.11410711499719092, -0.21230469861139473, -0.14108535176716172, -0.1346249036849118, 0.02213565805891099, -0.09518132096541544, -0.12815066049448573, 0.3459385682972005, 0.18484731426892373, 0.21182642912205596, 0.05321615335191242, 0.30838749764057305, 0.09258950712481658, 0.0666244766728666, 0.08537167933268043, 0.18194375357662257, 0.057794571162286534, 0.1022591478119676, -0.2635545386144748, 0.15693184836015392, 0.01601853098612852] |
1,803.00079 | Torsion points on elliptic curves and tame semistable coverings | In this paper, we study tame Galois coverings of semistable models that arise
from torsion points on elliptic curves. These coverings induce Galois morphisms
of intersection graphs and we express the decomposition groups of the edges in
terms of the reduction type of the elliptic curve. To that end, we first define
the reduction type of an elliptic curve $E/K(C)$ on a subgraph of the
intersection graph $\Sigma(\mathcal{C})$ of a strongly semistable model
$\mathcal{C}$. In particular, we define the notions of good and multiplicative
reduction on subgraphs of the intersection graph of $\Sigma(\mathcal{C})$.
After this, we show that if an elliptic curve has good reduction on an edge and
$\mathrm{char}(k)\nmid{N}$, then the $N$-torsion extension is unramified above
that edge, as in the codimension one case. Furthermore, we prove a
combinatorial version of a theorem by Serre on transvections for elliptic
curves with a non-integral $j$-invariant. Our version states that the Galois
representation $\rho_{\ell}:G\rightarrow{\mathrm{SL}_{2}(\mathbb{F}_{\ell})}$
of an elliptic curve with multiplicative reduction at an edge $e$ contains a
transvection $\sigma\in{I_{e'/e}}$ for prime numbers $\ell$ that do not divide
the slope of the normalized Laplacian of the $j$-invariant.
| math.AG | in this paper we study tame galois coverings of semistable models that arise from torsion points on elliptic curves these coverings induce galois morphisms of intersection graphs and we express the decomposition groups of the edges in terms of the reduction type of the elliptic curve to that end we first define the reduction type of an elliptic curve ekc on a subgraph of the intersection graph sigmamathcalc of a strongly semistable model mathcalc in particular we define the notions of good and multiplicative reduction on subgraphs of the intersection graph of sigmamathcalc after this we show that if an elliptic curve has good reduction on an edge and mathrmcharknmidn then the ntorsion extension is unramified above that edge as in the codimension one case furthermore we prove a combinatorial version of a theorem by serre on transvections for elliptic curves with a nonintegral jinvariant our version states that the galois representation rho_ellgrightarrowmathrmsl_2mathbbf_ell of an elliptic curve with multiplicative reduction at an edge e contains a transvection sigmaini_ee for prime numbers ell that do not divide the slope of the normalized laplacian of the jinvariant | [['in', 'this', 'paper', 'we', 'study', 'tame', 'galois', 'coverings', 'of', 'semistable', 'models', 'that', 'arise', 'from', 'torsion', 'points', 'on', 'elliptic', 'curves', 'these', 'coverings', 'induce', 'galois', 'morphisms', 'of', 'intersection', 'graphs', 'and', 'we', 'express', 'the', 'decomposition', 'groups', 'of', 'the', 'edges', 'in', 'terms', 'of', 'the', 'reduction', 'type', 'of', 'the', 'elliptic', 'curve', 'to', 'that', 'end', 'we', 'first', 'define', 'the', 'reduction', 'type', 'of', 'an', 'elliptic', 'curve', 'ekc', 'on', 'a', 'subgraph', 'of', 'the', 'intersection', 'graph', 'sigmamathcalc', 'of', 'a', 'strongly', 'semistable', 'model', 'mathcalc', 'in', 'particular', 'we', 'define', 'the', 'notions', 'of', 'good', 'and', 'multiplicative', 'reduction', 'on', 'subgraphs', 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1,803.0008 | On the relation of Lie algebroids to constrained systems and their
BV/BFV formulation | We observe that a system of irreducible, fiber-linear, first class
constraints on T*M is equivalent to the definition of a foliation Lie algebroid
over M. The BFV formulation of the constrained system is given by the
Hamiltonian lift of the Vaintrob description (E[1],Q) of the Lie algebroid to
its cotangent bundle T*E[1]. Affine deformations of the constraints are
parametrized by the first Lie algebroid cohomology H^1_Q and lead to
irreducible constraints also for much more general Lie algebroids such as Dirac
structures; the modified BFV function follows by the addition of a
representative of the deformation charge.
Adding a Hamiltonian to the system corresponds to a metric g on M. Evolution
invariance of the constraint surface introduces a connection nabla on E and one
reobtains the compatibility of g with (E,rho,nabla) found previously in the
literature. The covariantization of the Hamiltonian to a function on T*E[1]
serves as a BFV-Hamiltonian, iff, in addition, this connection is compatible
with the Lie algebroid structure, turning (E, rho, [ , ], nabla) into a
Cartan-Lie algebroid. The BV formulation of the system is obtained from BFV by
a (time-dependent) AKSZ procedure.
| math-ph hep-th math.DG math.MP | we observe that a system of irreducible fiberlinear first class constraints on tm is equivalent to the definition of a foliation lie algebroid over m the bfv formulation of the constrained system is given by the hamiltonian lift of the vaintrob description e1q of the lie algebroid to its cotangent bundle te1 affine deformations of the constraints are parametrized by the first lie algebroid cohomology h1_q and lead to irreducible constraints also for much more general lie algebroids such as dirac structures the modified bfv function follows by the addition of a representative of the deformation charge adding a hamiltonian to the system corresponds to a metric g on m evolution invariance of the constraint surface introduces a connection nabla on e and one reobtains the compatibility of g with erhonabla found previously in the literature the covariantization of the hamiltonian to a function on te1 serves as a bfvhamiltonian iff in addition this connection is compatible with the lie algebroid structure turning e rho nabla into a cartanlie algebroid the bv formulation of the system is obtained from bfv by a timedependent aksz procedure | [['we', 'observe', 'that', 'a', 'system', 'of', 'irreducible', 'fiberlinear', 'first', 'class', 'constraints', 'on', 'tm', 'is', 'equivalent', 'to', 'the', 'definition', 'of', 'a', 'foliation', 'lie', 'algebroid', 'over', 'm', 'the', 'bfv', 'formulation', 'of', 'the', 'constrained', 'system', 'is', 'given', 'by', 'the', 'hamiltonian', 'lift', 'of', 'the', 'vaintrob', 'description', 'e1q', 'of', 'the', 'lie', 'algebroid', 'to', 'its', 'cotangent', 'bundle', 'te1', 'affine', 'deformations', 'of', 'the', 'constraints', 'are', 'parametrized', 'by', 'the', 'first', 'lie', 'algebroid', 'cohomology', 'h1_q', 'and', 'lead', 'to', 'irreducible', 'constraints', 'also', 'for', 'much', 'more', 'general', 'lie', 'algebroids', 'such', 'as', 'dirac', 'structures', 'the', 'modified', 'bfv', 'function', 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1,803.00081 | Network Utility Maximization with Heterogeneous Traffic Flows | We consider the Network Utility Maximization (NUM) problem for wireless
networks in the presence of arbitrary types of flows, including unicast,
broadcast, multicast, and anycast traffic. Building upon the recent framework
of a universal control policy (UMW), we design a utility optimal cross-layer
admission control, routing and scheduling policy, called UMW+. The UMW+ policy
takes packet level actions based on a precedence-relaxed virtual network. Using
Lyapunov optimization techniques, we show that UMW+ maximizes network utility,
while simultaneously keeping the physical queues in the network stable.
Extensive simulation results validate the performance of UMW+; demonstrating
both optimal utility performance and bounded average queue occupancy. Moreover,
we establish a precise one-to-one correspondence between the dynamics of the
virtual queues under the UMW+ policy, and the dynamics of the dual variables of
an associated offline NUM program, under a subgradient algorithm. This
correspondence sheds further insight into our understanding of UMW+.
| math.OC | we consider the network utility maximization num problem for wireless networks in the presence of arbitrary types of flows including unicast broadcast multicast and anycast traffic building upon the recent framework of a universal control policy umw we design a utility optimal crosslayer admission control routing and scheduling policy called umw the umw policy takes packet level actions based on a precedencerelaxed virtual network using lyapunov optimization techniques we show that umw maximizes network utility while simultaneously keeping the physical queues in the network stable extensive simulation results validate the performance of umw demonstrating both optimal utility performance and bounded average queue occupancy moreover we establish a precise onetoone correspondence between the dynamics of the virtual queues under the umw policy and the dynamics of the dual variables of an associated offline num program under a subgradient algorithm this correspondence sheds further insight into our understanding of umw | [['we', 'consider', 'the', 'network', 'utility', 'maximization', 'num', 'problem', 'for', 'wireless', 'networks', 'in', 'the', 'presence', 'of', 'arbitrary', 'types', 'of', 'flows', 'including', 'unicast', 'broadcast', 'multicast', 'and', 'anycast', 'traffic', 'building', 'upon', 'the', 'recent', 'framework', 'of', 'a', 'universal', 'control', 'policy', 'umw', 'we', 'design', 'a', 'utility', 'optimal', 'crosslayer', 'admission', 'control', 'routing', 'and', 'scheduling', 'policy', 'called', 'umw', 'the', 'umw', 'policy', 'takes', 'packet', 'level', 'actions', 'based', 'on', 'a', 'precedencerelaxed', 'virtual', 'network', 'using', 'lyapunov', 'optimization', 'techniques', 'we', 'show', 'that', 'umw', 'maximizes', 'network', 'utility', 'while', 'simultaneously', 'keeping', 'the', 'physical', 'queues', 'in', 'the', 'network', 'stable', 'extensive', 'simulation', 'results', 'validate', 'the', 'performance', 'of', 'umw', 'demonstrating', 'both', 'optimal', 'utility', 'performance', 'and', 'bounded', 'average', 'queue', 'occupancy', 'moreover', 'we', 'establish', 'a', 'precise', 'onetoone', 'correspondence', 'between', 'the', 'dynamics', 'of', 'the', 'virtual', 'queues', 'under', 'the', 'umw', 'policy', 'and', 'the', 'dynamics', 'of', 'the', 'dual', 'variables', 'of', 'an', 'associated', 'offline', 'num', 'program', 'under', 'a', 'subgradient', 'algorithm', 'this', 'correspondence', 'sheds', 'further', 'insight', 'into', 'our', 'understanding', 'of', 'umw']] | [-0.23596509577085575, -0.03670288024849946, -0.08857537509848168, 0.03217092449982733, -0.07914518741700723, -0.19900293960901244, 0.1923557163856062, 0.41938065083659426, -0.3003824697332267, -0.25731437097043813, 0.08606953238023027, -0.2080037155929877, -0.23520638177260048, 0.16021433733657103, -0.14097813621391447, 0.13407908751055927, 0.0796956703293321, 0.02034311057749477, -0.004520376752998654, -0.26175082337070055, 0.2917644095188957, 0.077935868018896, 0.406464674560746, 0.06771298003981688, 0.1633555579449044, 0.08142873602697537, -0.00080465938544319, -0.001940531329866372, -0.11340330656544427, 0.1313330157442956, 0.2896486425573928, 0.24080860198215664, 0.35103780799070183, -0.43053463656379254, -0.23088500473084447, 0.06495770925123777, 0.11279956966030355, 0.01994909330106461, -0.03158418253795909, -0.2900687461326114, 0.09696769202603217, -0.20443353100622796, 0.003196757375484421, -0.0319988916811793, -0.09091257808261177, 0.05024775503429768, -0.348204265285258, -0.041973268067571935, -0.002972954475529948, -0.010814158195236913, -0.11939474263348851, -0.08323021374364085, -0.014618849538608777, 0.18995640233323788, 0.04045410576628103, -0.011489945332967948, 0.1576501792771932, -0.11866963249124383, -0.2081433832930515, 0.35118538955365924, 0.007352334358507679, -0.1946705045039151, 0.10876133111083791, 0.030597478588472823, -0.15070228813434666, 0.1333922503395703, 0.2794898996481254, 0.11834978412513678, -0.1682382483719563, 0.03665849565667715, -0.1090285060737206, 0.13282772150448188, 0.04538061498778359, 0.053253242381702676, 0.09645973683689676, 0.26139435334904054, 0.19131873628492055, 0.17450637229382485, -0.015740783489546835, -0.21735860910710125, -0.2402095191503818, -0.13337503446481463, -0.14350560211836277, 0.030105727919548447, -0.15850042803553535, -0.07548088136883009, 0.39859399802628015, 0.15337472984909403, 0.1256327228398905, 0.21089352353723073, 0.36386993735217726, 0.08740640697497114, -0.018989789515074823, 0.19451233695837714, 0.1564162266797063, 0.04842007885604691, 0.14773203360532935, -0.2815620622393314, 0.11378690701050899, 0.06841343542159817] |
1,803.00082 | GraphVar 2.0: A user-friendly toolbox for machine learning on functional
connectivity measures | Background: We previously presented GraphVar as a user-friendly MATLAB
toolbox for comprehensive graph analyses of functional brain connectivity. Here
we introduce a comprehensive extension of the toolbox allowing users to
seamlessly explore easily customizable decoding models across functional
connectivity measures as well as additional features.
New Method: GraphVar 2.0 provides machine learning (ML) model construction,
validation and exploration. Machine learning can be performed across any
combination of network measures and additional variables, allowing for a
flexibility in neuroimaging applications.
Results: In addition to previously integrated functionalities, such as
network construction and graph-theoretical analyses of brain connectivity with
a high-speed general linear model (GLM), users can now perform customizable ML
across connectivity matrices, network metrics and additionally imported
variables. The new extension also provides parametric and nonparametric testing
of classifier and regressor performance, data export, figure generation and
high quality export.
Comparison with existing methods: Compared to other existing toolboxes,
GraphVar 2.0 offers (1) comprehensive customization, (2) an all-in-one user
friendly interface, (3) customizable model design and manual hyperparameter
entry, (4) interactive results exploration and data export, (5) automated
cueing for modelling multiple outcome variables within the same session, (6) an
easy to follow introductory review.
Conclusions: GraphVar 2.0 allows comprehensive, user-friendly exploration of
encoding (GLM) and decoding (ML) modelling approaches on functional
connectivity measures making big data neuroscience readily accessible to a
broader audience of neuroimaging investigators.
| stat.AP | background we previously presented graphvar as a userfriendly matlab toolbox for comprehensive graph analyses of functional brain connectivity here we introduce a comprehensive extension of the toolbox allowing users to seamlessly explore easily customizable decoding models across functional connectivity measures as well as additional features new method graphvar 20 provides machine learning ml model construction validation and exploration machine learning can be performed across any combination of network measures and additional variables allowing for a flexibility in neuroimaging applications results in addition to previously integrated functionalities such as network construction and graphtheoretical analyses of brain connectivity with a highspeed general linear model glm users can now perform customizable ml across connectivity matrices network metrics and additionally imported variables the new extension also provides parametric and nonparametric testing of classifier and regressor performance data export figure generation and high quality export comparison with existing methods compared to other existing toolboxes graphvar 20 offers 1 comprehensive customization 2 an allinone user friendly interface 3 customizable model design and manual hyperparameter entry 4 interactive results exploration and data export 5 automated cueing for modelling multiple outcome variables within the same session 6 an easy to follow introductory review conclusions graphvar 20 allows comprehensive userfriendly exploration of encoding glm and decoding ml modelling approaches on functional connectivity measures making big data neuroscience readily accessible to a broader audience of neuroimaging investigators | [['background', 'we', 'previously', 'presented', 'graphvar', 'as', 'a', 'userfriendly', 'matlab', 'toolbox', 'for', 'comprehensive', 'graph', 'analyses', 'of', 'functional', 'brain', 'connectivity', 'here', 'we', 'introduce', 'a', 'comprehensive', 'extension', 'of', 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1,803.00083 | The [OIII] profiles of infrared-selected active galactic nuclei: More
powerful outflows in the obscured population | We explore the kinematics of ionized gas via the [O III] $\lambda$5007
emission lines in active galactic nuclei (AGN) selected on the basis of their
mid-infrared (IR) emission, and split into obscured and unobscured populations
based on their optical-IR colors. After correcting for differences in redshift
distributions, we provide composite spectra of spectroscopically and
photometrically defined obscured/Type 2 and unobscured/Type 1 AGN from 3500 to
7000 \AA. The IR-selected obscured sources contain a mixture of narrow-lined
Type 2 AGN and intermediate sources that have broad H$\alpha$ emission and
significantly narrower H$\beta$. Using both [OIII] luminosities and AGN
luminosities derived from optical-IR spectral energy distribution fitting, we
find evidence for enhanced large-scale obscuration in the obscured sources. In
matched bins of luminosity we find that the obscured population typically has
broader, more blueshifted \OIII\ emission than in the unobscured sample,
suggestive of more powerful AGN-driven outflows. This trend is not seen in
spectroscopically classified samples, and is unlikely to be entirely explained
by orientation effects. In addition, outflow velocities increase from small to
moderate AGN $E(B-V)$ values, before flattening out (as traced by FWHM) and
even decreasing (as traced by blueshift). While difficult to fully interpret in
a single physical model, due to both the averaging over populations and the
spatially-averaged spectra, these results agree with previous findings that
simple geometric unification models are insufficient for the IR-selected AGN
population, and may fit into an evolutionary model for obscured and unobscured
AGN.
| astro-ph.GA | we explore the kinematics of ionized gas via the o iii lambda5007 emission lines in active galactic nuclei agn selected on the basis of their midinfrared ir emission and split into obscured and unobscured populations based on their opticalir colors after correcting for differences in redshift distributions we provide composite spectra of spectroscopically and photometrically defined obscuredtype 2 and unobscuredtype 1 agn from 3500 to 7000 aa the irselected obscured sources contain a mixture of narrowlined type 2 agn and intermediate sources that have broad halpha emission and significantly narrower hbeta using both oiii luminosities and agn luminosities derived from opticalir spectral energy distribution fitting we find evidence for enhanced largescale obscuration in the obscured sources in matched bins of luminosity we find that the obscured population typically has broader more blueshifted oiii emission than in the unobscured sample suggestive of more powerful agndriven outflows this trend is not seen in spectroscopically classified samples and is unlikely to be entirely explained by orientation effects in addition outflow velocities increase from small to moderate agn ebv values before flattening out as traced by fwhm and even decreasing as traced by blueshift while difficult to fully interpret in a single physical model due to both the averaging over populations and the spatiallyaveraged spectra these results agree with previous findings that simple geometric unification models are insufficient for the irselected agn population and may fit into an evolutionary model for obscured and unobscured agn | [['we', 'explore', 'the', 'kinematics', 'of', 'ionized', 'gas', 'via', 'the', 'o', 'iii', 'lambda5007', 'emission', 'lines', 'in', 'active', 'galactic', 'nuclei', 'agn', 'selected', 'on', 'the', 'basis', 'of', 'their', 'midinfrared', 'ir', 'emission', 'and', 'split', 'into', 'obscured', 'and', 'unobscured', 'populations', 'based', 'on', 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1,803.00084 | The Geometry of the Secant Caustic of a Planar Curve | The secant caustic of a planar curve $M$ is the image of the singular set of
the secant map of $M$. We analyse the geometrical properties of the secant
caustic of a planar curve, i.e. the number of branches of the secant caustic,
the parity of the number of cusps and the number of inflexion points in each
branch of this set. In particular, we investigate in detail some of the
geometrical properties of the secant caustic of a rosette, i.e. a smooth
regular oriented closed curve with non-vanishing curvature.
| math.DG | the secant caustic of a planar curve m is the image of the singular set of the secant map of m we analyse the geometrical properties of the secant caustic of a planar curve ie the number of branches of the secant caustic the parity of the number of cusps and the number of inflexion points in each branch of this set in particular we investigate in detail some of the geometrical properties of the secant caustic of a rosette ie a smooth regular oriented closed curve with nonvanishing curvature | [['the', 'secant', 'caustic', 'of', 'a', 'planar', 'curve', 'm', 'is', 'the', 'image', 'of', 'the', 'singular', 'set', 'of', 'the', 'secant', 'map', 'of', 'm', 'we', 'analyse', 'the', 'geometrical', 'properties', 'of', 'the', 'secant', 'caustic', 'of', 'a', 'planar', 'curve', 'ie', 'the', 'number', 'of', 'branches', 'of', 'the', 'secant', 'caustic', 'the', 'parity', 'of', 'the', 'number', 'of', 'cusps', 'and', 'the', 'number', 'of', 'inflexion', 'points', 'in', 'each', 'branch', 'of', 'this', 'set', 'in', 'particular', 'we', 'investigate', 'in', 'detail', 'some', 'of', 'the', 'geometrical', 'properties', 'of', 'the', 'secant', 'caustic', 'of', 'a', 'rosette', 'ie', 'a', 'smooth', 'regular', 'oriented', 'closed', 'curve', 'with', 'nonvanishing', 'curvature']] | [-0.25104457422470056, -0.00572260282933712, -0.10379052945774876, 0.0370063043716881, -0.07417253542484509, -0.06496446361351345, 0.0199091422268086, 0.2712530759473642, -0.2910434411030211, -0.23915121394933925, 0.07070212960372575, -0.271575666550133, -0.15023003100003635, 0.1963065696000639, -0.08706280683012059, 0.07321240995079278, 0.002098078942961163, 0.08817419475979275, -0.10345871132384572, -0.2838419994339347, 0.4408629760146141, -0.07409723628726271, 0.2122967597304119, 0.01802134323451254, 0.11829536732079254, -0.006291125989001658, 0.03472270208100478, 0.050575363294531904, -0.16249375318487486, 0.16723717746790498, 0.1970086831961655, 0.14224889725446702, 0.14191958697823187, -0.3655319546452827, -0.15783884871958032, 0.17649016676263676, 0.11116437608272665, 0.04079784728172753, -0.01011396734457877, -0.18991832939080067, 0.10710683893412351, -0.058969763707783486, -0.2632113785069022, 0.05899200684701403, 0.05114693930889997, 0.10696491563382247, -0.1453272398147318, 0.0075805646988252795, 0.08454531437406937, 0.13646790109988716, 0.023637941223569214, -0.09351954714705547, -0.12234757487506916, 0.05764287502194444, 0.0016925204519389404, 0.015716246882867484, 0.07323145445229279, -0.16881488842837927, -0.0551197016508215, 0.4155474943962569, -0.024344271742221382, -0.16365965054266982, 0.0987224598104755, -0.2328759759819756, -0.10325084804660745, 0.20738048057796227, 0.1681690792967048, 0.17028289284143183, -0.02698228931468394, 0.13231982655965516, -0.08237720931776696, 0.06008433782844804, 0.16194444608150257, -0.029526191711839702, 0.2521089925120274, 0.11316072771894849, 0.07939612671939864, 0.17261757179059917, -0.18674708232542292, -0.03665430743971632, -0.35794283986712494, -0.22198271584800547, -0.1614761259406805, 0.062130630378507905, -0.18649813058356005, -0.2619358500983152, 0.49605166655447747, 0.030126789139790666, 0.26792453217009704, 0.04544374380622887, 0.2832207563954095, 0.07961554065534276, -0.0017392194105519188, 0.05003460420088635, 0.2225352749503347, 0.15224545338294573, -0.02799138854865709, -0.2151125049089185, -0.018590471919419038, 0.10637383743499716] |
1,803.00085 | Chinese Text in the Wild | We introduce Chinese Text in the Wild, a very large dataset of Chinese text
in street view images. While optical character recognition (OCR) in document
images is well studied and many commercial tools are available, detection and
recognition of text in natural images is still a challenging problem,
especially for more complicated character sets such as Chinese text. Lack of
training data has always been a problem, especially for deep learning methods
which require massive training data.
In this paper we provide details of a newly created dataset of Chinese text
with about 1 million Chinese characters annotated by experts in over 30
thousand street view images. This is a challenging dataset with good diversity.
It contains planar text, raised text, text in cities, text in rural areas, text
under poor illumination, distant text, partially occluded text, etc. For each
character in the dataset, the annotation includes its underlying character, its
bounding box, and 6 attributes. The attributes indicate whether it has complex
background, whether it is raised, whether it is handwritten or printed, etc.
The large size and diversity of this dataset make it suitable for training
robust neural networks for various tasks, particularly detection and
recognition. We give baseline results using several state-of-the-art networks,
including AlexNet, OverFeat, Google Inception and ResNet for character
recognition, and YOLOv2 for character detection in images. Overall Google
Inception has the best performance on recognition with 80.5% top-1 accuracy,
while YOLOv2 achieves an mAP of 71.0% on detection. Dataset, source code and
trained models will all be publicly available on the website.
| cs.CV | we introduce chinese text in the wild a very large dataset of chinese text in street view images while optical character recognition ocr in document images is well studied and many commercial tools are available detection and recognition of text in natural images is still a challenging problem especially for more complicated character sets such as chinese text lack of training data has always been a problem especially for deep learning methods which require massive training data in this paper we provide details of a newly created dataset of chinese text with about 1 million chinese characters annotated by experts in over 30 thousand street view images this is a challenging dataset with good diversity it contains planar text raised text text in cities text in rural areas text under poor illumination distant text partially occluded text etc for each character in the dataset the annotation includes its underlying character its bounding box and 6 attributes the attributes indicate whether it has complex background whether it is raised whether it is handwritten or printed etc the large size and diversity of this dataset make it suitable for training robust neural networks for various tasks particularly detection and recognition we give baseline results using several stateoftheart networks including alexnet overfeat google inception and resnet for character recognition and yolov2 for character detection in images overall google inception has the best performance on recognition with 805 top1 accuracy while yolov2 achieves an map of 710 on detection dataset source code and trained models will all be publicly available on the website | [['we', 'introduce', 'chinese', 'text', 'in', 'the', 'wild', 'a', 'very', 'large', 'dataset', 'of', 'chinese', 'text', 'in', 'street', 'view', 'images', 'while', 'optical', 'character', 'recognition', 'ocr', 'in', 'document', 'images', 'is', 'well', 'studied', 'and', 'many', 'commercial', 'tools', 'are', 'available', 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1,803.00086 | From CCR to Levy Processes: An Excursion in Quantum Probability | This is an expositary article telling a short story made from the leaves of
quantum probability with the following ingredients: (i) A special projective,
unitary, irreducible and factorizable representation of the euclidean group of
a Hilbert space known as the Weyl representation. \item The infinitesimal
version of the Weyl representation includes the Heisenberg canonical
commutation relations (CCR) of quantum theory. It also yields the three
fundamental operator fields known as the creation, conservation and
annihilation fields. (ii) The three fundamental fields, with the inclusion of
time, lead to quantum stochastic integration and a calculus with an Ito's
formula for products of differentials. (iii) Appropriate linear combinations of
the fundamental operator processes yield all the L{\'e}vy processes of
classical probability theory along with the bonus of Ito's formula for products
of their differentials.
| quant-ph math-ph math.MP | this is an expositary article telling a short story made from the leaves of quantum probability with the following ingredients i a special projective unitary irreducible and factorizable representation of the euclidean group of a hilbert space known as the weyl representation item the infinitesimal version of the weyl representation includes the heisenberg canonical commutation relations ccr of quantum theory it also yields the three fundamental operator fields known as the creation conservation and annihilation fields ii the three fundamental fields with the inclusion of time lead to quantum stochastic integration and a calculus with an itos formula for products of differentials iii appropriate linear combinations of the fundamental operator processes yield all the levy processes of classical probability theory along with the bonus of itos formula for products of their differentials | [['this', 'is', 'an', 'expositary', 'article', 'telling', 'a', 'short', 'story', 'made', 'from', 'the', 'leaves', 'of', 'quantum', 'probability', 'with', 'the', 'following', 'ingredients', 'i', 'a', 'special', 'projective', 'unitary', 'irreducible', 'and', 'factorizable', 'representation', 'of', 'the', 'euclidean', 'group', 'of', 'a', 'hilbert', 'space', 'known', 'as', 'the', 'weyl', 'representation', 'item', 'the', 'infinitesimal', 'version', 'of', 'the', 'weyl', 'representation', 'includes', 'the', 'heisenberg', 'canonical', 'commutation', 'relations', 'ccr', 'of', 'quantum', 'theory', 'it', 'also', 'yields', 'the', 'three', 'fundamental', 'operator', 'fields', 'known', 'as', 'the', 'creation', 'conservation', 'and', 'annihilation', 'fields', 'ii', 'the', 'three', 'fundamental', 'fields', 'with', 'the', 'inclusion', 'of', 'time', 'lead', 'to', 'quantum', 'stochastic', 'integration', 'and', 'a', 'calculus', 'with', 'an', 'itos', 'formula', 'for', 'products', 'of', 'differentials', 'iii', 'appropriate', 'linear', 'combinations', 'of', 'the', 'fundamental', 'operator', 'processes', 'yield', 'all', 'the', 'levy', 'processes', 'of', 'classical', 'probability', 'theory', 'along', 'with', 'the', 'bonus', 'of', 'itos', 'formula', 'for', 'products', 'of', 'their', 'differentials']] | [-0.12132081950531264, 0.1394781268342622, -0.10743657614900695, 0.08507684117026684, -0.11739405168163526, -0.11210939022250303, 0.01915068913407567, 0.29178077929461277, -0.3283845958307044, -0.2060574814392865, 0.06755903684228098, -0.2557258588912406, -0.14626139805844154, 0.20272574384647016, -0.07142532397175336, 0.02977880043553487, 0.048836084656801275, 0.1150780224191551, -0.09443885654758463, -0.25262185237334883, 0.393252071419745, 0.024551425627717137, 0.24855122425884918, -0.005589796702967572, 0.16657087981402305, 0.07492534457954741, -0.08832215092265765, -0.06352460140541004, -0.0898122092040668, 0.14753984943572573, 0.24672925179355018, 0.09601685555850845, 0.20895620117689134, -0.4103887202715828, -0.1481564809987677, 0.1287375458405986, 0.09943505659506599, 0.06760098746771576, -0.02253335320483182, -0.3043266047318359, -0.016790938650845293, -0.19315822469781488, -0.16217137530502926, -0.07869750822588574, 0.03977672875510486, 0.00430383120898072, -0.2305220476643161, 0.04715159168721973, 0.12329690481752925, 0.05764007905341742, -0.07702199268005504, -0.10338626560250311, -0.030744945594884512, 0.12233995134366378, 0.03328326797176807, 0.0010504818002458748, 0.08890157416889458, -0.1351750000568377, -0.18828573679588806, 0.38648679622854215, -0.05846712219072079, -0.22153314081454323, 0.13399816804942283, -0.14767147015908177, -0.14569302909040668, 0.09574796089223579, 0.08111707835266267, 0.11711327015499598, -0.13117301328648023, 0.1758752067396524, -0.04760003767424185, 0.0539567161365661, 0.05988090888433557, 0.0701974997375132, 0.1585274511532332, 0.07473538469039757, 0.054178325267409094, 0.09346014951206459, -0.010782617190497295, -0.16695184066166519, -0.4125774099370678, -0.2535770804290695, -0.12138765828794167, 0.13068530347397309, -0.144566928427191, -0.22439379074418817, 0.3895242487521174, 0.058504321005902755, 0.18016541710715273, 0.0965556692090312, 0.23456291940404486, 0.17300178421810553, 0.06730436366086982, 0.0206956573022629, 0.13557694889320673, 0.23492360111141136, 0.05998503703282762, -0.16248188504615074, 0.004610397225558644, 0.17178653330072932] |
1,803.00087 | A Literature Survey on Ontology of Different Computing Platforms in
Smart Environments | Smart environments integrates various types of technologies, including cloud
computing, fog computing, and the IoT paradigm. In such environments, it is
essential to organize and manage efficiently the broad and complex set of
heterogeneous resources. For this reason, resources classification and
categorization becomes a vital issue in the control system. In this paper we
make an exhaustive literature survey about the various computing systems and
architectures which defines any type of ontology in the context of smart
environments, considering both, authors that explicitly propose resources
categorization and authors that implicitly propose some resources
classification as part of their system architecture. As part of this research
survey, we have built a table that summarizes all research works considered,
and which provides a compact and graphical snapshot of the current
classification trends. The goal and primary motivation of this literature
survey has been to understand the current state of the art and identify the
gaps between the different computing paradigms involved in smart environment
scenarios. As a result, we have found that it is essential to consider together
several computing paradigms and technologies, and that there is not, yet, any
research work that integrates a merged resources classification, taxonomy or
ontology required in such heterogeneous scenarios.
| cs.DC | smart environments integrates various types of technologies including cloud computing fog computing and the iot paradigm in such environments it is essential to organize and manage efficiently the broad and complex set of heterogeneous resources for this reason resources classification and categorization becomes a vital issue in the control system in this paper we make an exhaustive literature survey about the various computing systems and architectures which defines any type of ontology in the context of smart environments considering both authors that explicitly propose resources categorization and authors that implicitly propose some resources classification as part of their system architecture as part of this research survey we have built a table that summarizes all research works considered and which provides a compact and graphical snapshot of the current classification trends the goal and primary motivation of this literature survey has been to understand the current state of the art and identify the gaps between the different computing paradigms involved in smart environment scenarios as a result we have found that it is essential to consider together several computing paradigms and technologies and that there is not yet any research work that integrates a merged resources classification taxonomy or ontology required in such heterogeneous scenarios | [['smart', 'environments', 'integrates', 'various', 'types', 'of', 'technologies', 'including', 'cloud', 'computing', 'fog', 'computing', 'and', 'the', 'iot', 'paradigm', 'in', 'such', 'environments', 'it', 'is', 'essential', 'to', 'organize', 'and', 'manage', 'efficiently', 'the', 'broad', 'and', 'complex', 'set', 'of', 'heterogeneous', 'resources', 'for', 'this', 'reason', 'resources', 'classification', 'and', 'categorization', 'becomes', 'a', 'vital', 'issue', 'in', 'the', 'control', 'system', 'in', 'this', 'paper', 'we', 'make', 'an', 'exhaustive', 'literature', 'survey', 'about', 'the', 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1,803.00088 | The accuracy of semi-numerical reionization models in comparison with
radiative transfer simulations | We have developed a modular semi-numerical code that computes the time and
spatially dependent ionization of neutral hydrogen (HI), neutral (HeI) and
singly ionized helium (HeII) in the intergalactic medium (IGM). The model
accounts for recombinations and provides different descriptions for the
photoionization rate that are used to calculate the residual HI fraction in
ionized regions. We compare different semi-numerical reionization schemes to a
radiative transfer (RT) simulation. We use the RT simulation as a benchmark,
and find that the semi-numerical approaches produce similar HII and HeII
morphologies and power spectra of the HI 21cm signal throughout reionization.
As we do not track partial ionization of HeII, the extent of the double ionized
helium (HeIII) regions is consistently smaller. In contrast to previous
comparison projects, the ionizing emissivity in our semi-numerical scheme is
not adjusted to reproduce the redshift evolution of the RT simulation, but
directly derived from the RT simulation spectra. Among schemes that identify
the ionized regions by the ratio of the number of ionization and absorption
events on different spatial smoothing scales, we find those that mark the
entire sphere as ionized when the ionization criterion is fulfilled to result
in significantly accelerated reionization compared to the RT simulation.
Conversely, those that flag only the central cell as ionized yield very similar
but slightly delayed redshift evolution of reionization, with up to 20%
ionizing photons lost. Despite the overall agreement with the RT simulation,
our results suggests that constraining ionizing emissivity sensitive parameters
from semi-numerical galaxy formation-reionization models are subject to photon
nonconservation.
| astro-ph.CO | we have developed a modular seminumerical code that computes the time and spatially dependent ionization of neutral hydrogen hi neutral hei and singly ionized helium heii in the intergalactic medium igm the model accounts for recombinations and provides different descriptions for the photoionization rate that are used to calculate the residual hi fraction in ionized regions we compare different seminumerical reionization schemes to a radiative transfer rt simulation we use the rt simulation as a benchmark and find that the seminumerical approaches produce similar hii and heii morphologies and power spectra of the hi 21cm signal throughout reionization as we do not track partial ionization of heii the extent of the double ionized helium heiii regions is consistently smaller in contrast to previous comparison projects the ionizing emissivity in our seminumerical scheme is not adjusted to reproduce the redshift evolution of the rt simulation but directly derived from the rt simulation spectra among schemes that identify the ionized regions by the ratio of the number of ionization and absorption events on different spatial smoothing scales we find those that mark the entire sphere as ionized when the ionization criterion is fulfilled to result in significantly accelerated reionization compared to the rt simulation conversely those that flag only the central cell as ionized yield very similar but slightly delayed redshift evolution of reionization with up to 20 ionizing photons lost despite the overall agreement with the rt simulation our results suggests that constraining ionizing emissivity sensitive parameters from seminumerical galaxy formationreionization models are subject to photon nonconservation | [['we', 'have', 'developed', 'a', 'modular', 'seminumerical', 'code', 'that', 'computes', 'the', 'time', 'and', 'spatially', 'dependent', 'ionization', 'of', 'neutral', 'hydrogen', 'hi', 'neutral', 'hei', 'and', 'singly', 'ionized', 'helium', 'heii', 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1,803.00089 | Coarse-grained dynamics of operator and state entanglement | We give a detailed theory for the leading coarse-grained dynamics of
entanglement entropy of states and of operators in generic short-range
interacting quantum many-body systems. This includes operators spreading under
Heisenberg time evolution, which we find are much less entangled than "typical"
operators of the same spatial support. Extending previous conjectures based on
random circuit dynamics, we provide evidence that the leading-order
entanglement dynamics of a given chaotic system are determined by a function
$\mathcal{E}(v)$, which is model-dependent, but which we argue satisfies
certain general constraints. In a minimal membrane picture, $\mathcal{E}(v)$ is
the "surface tension" of the membrane and is a function of the membrane's
orientation $v$ in spacetime. For one-dimensional (1D) systems this surface
tension is related by a Legendre transformation to an entanglement entropy
growth rate $\Gamma(\partial S/\partial x)$ which depends on the spatial
"gradient" of the entanglement entropy $S(x,t)$ across the cut at position $x$.
We show how to extract the entanglement growth functions numerically in 1D at
infinite temperature using the concept of the operator entanglement of the time
evolution operator, and we discuss possible universality of $\mathcal{E}$ at
low temperatures. Our theoretical ideas are tested against and informed by
numerical results for a quantum-chaotic 1D spin Hamiltonian. These results are
relevant to the broad class of chaotic many-particle systems or field theories
with spatially local interactions, both in 1D and above.
| cond-mat.stat-mech cond-mat.str-el hep-th nlin.CD quant-ph | we give a detailed theory for the leading coarsegrained dynamics of entanglement entropy of states and of operators in generic shortrange interacting quantum manybody systems this includes operators spreading under heisenberg time evolution which we find are much less entangled than typical operators of the same spatial support extending previous conjectures based on random circuit dynamics we provide evidence that the leadingorder entanglement dynamics of a given chaotic system are determined by a function mathcalev which is modeldependent but which we argue satisfies certain general constraints in a minimal membrane picture mathcalev is the surface tension of the membrane and is a function of the membranes orientation v in spacetime for onedimensional 1d systems this surface tension is related by a legendre transformation to an entanglement entropy growth rate gammapartial spartial x which depends on the spatial gradient of the entanglement entropy sxt across the cut at position x we show how to extract the entanglement growth functions numerically in 1d at infinite temperature using the concept of the operator entanglement of the time evolution operator and we discuss possible universality of mathcale at low temperatures our theoretical ideas are tested against and informed by numerical results for a quantumchaotic 1d spin hamiltonian these results are relevant to the broad class of chaotic manyparticle systems or field theories with spatially local interactions both in 1d and above | [['we', 'give', 'a', 'detailed', 'theory', 'for', 'the', 'leading', 'coarsegrained', 'dynamics', 'of', 'entanglement', 'entropy', 'of', 'states', 'and', 'of', 'operators', 'in', 'generic', 'shortrange', 'interacting', 'quantum', 'manybody', 'systems', 'this', 'includes', 'operators', 'spreading', 'under', 'heisenberg', 'time', 'evolution', 'which', 'we', 'find', 'are', 'much', 'less', 'entangled', 'than', 'typical', 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1,803.0009 | Intermediate-line Emission in AGNs: The Effect of Prescription of the
Gas Density | The requirement of intermediate line component in the recently observed
spectra of several AGNs points to possibility of the existence of a physically
separate region between broad line region (BLR) and narrow line region (NLR).
In this paper we explore the emission from intermediate line region (ILR) by
using the photoionization simulations of the gas clouds distributed radially
from the AGN center. The gas clouds span distances typical for BLR, ILR and
NLR, and the appearance of dust at the sublimation radius is fully taken into
account in our model. Single cloud structure is calculated under the assumption
of the constant pressure. We show that the slope of the power law cloud density
radial profile does not affect the existence of ILR in major types of AGN. We
found that the low ionization iron line, Fe~II, appears to be highly sensitive
for the presence of dust and therefore becomes potential tracer of dust content
in line emitting regions. We show that the use of disk-like cloud density
profile computed at the upper part of the accretion disc atmosphere reproduces
the observed properties of the line emissivities. In particular, the distance
of H${\beta}$ line inferred from our model agrees with that obtained from the
reverberation mapping studies in Sy1 galaxy NGC 5548.
| astro-ph.GA | the requirement of intermediate line component in the recently observed spectra of several agns points to possibility of the existence of a physically separate region between broad line region blr and narrow line region nlr in this paper we explore the emission from intermediate line region ilr by using the photoionization simulations of the gas clouds distributed radially from the agn center the gas clouds span distances typical for blr ilr and nlr and the appearance of dust at the sublimation radius is fully taken into account in our model single cloud structure is calculated under the assumption of the constant pressure we show that the slope of the power law cloud density radial profile does not affect the existence of ilr in major types of agn we found that the low ionization iron line feii appears to be highly sensitive for the presence of dust and therefore becomes potential tracer of dust content in line emitting regions we show that the use of disklike cloud density profile computed at the upper part of the accretion disc atmosphere reproduces the observed properties of the line emissivities in particular the distance of hbeta line inferred from our model agrees with that obtained from the reverberation mapping studies in sy1 galaxy ngc 5548 | [['the', 'requirement', 'of', 'intermediate', 'line', 'component', 'in', 'the', 'recently', 'observed', 'spectra', 'of', 'several', 'agns', 'points', 'to', 'possibility', 'of', 'the', 'existence', 'of', 'a', 'physically', 'separate', 'region', 'between', 'broad', 'line', 'region', 'blr', 'and', 'narrow', 'line', 'region', 'nlr', 'in', 'this', 'paper', 'we', 'explore', 'the', 'emission', 'from', 'intermediate', 'line', 'region', 'ilr', 'by', 'using', 'the', 'photoionization', 'simulations', 'of', 'the', 'gas', 'clouds', 'distributed', 'radially', 'from', 'the', 'agn', 'center', 'the', 'gas', 'clouds', 'span', 'distances', 'typical', 'for', 'blr', 'ilr', 'and', 'nlr', 'and', 'the', 'appearance', 'of', 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1,803.00091 | Verification of Markov Decision Processes with Risk-Sensitive Measures | We develop a method for computing policies in Markov decision processes with
risk-sensitive measures subject to temporal logic constraints. Specifically, we
use a particular risk-sensitive measure from cumulative prospect theory, which
has been previously adopted in psychology and economics. The nonlinear
transformation of the probabilities and utility functions yields a nonlinear
programming problem, which makes computation of optimal policies typically
challenging. We show that this nonlinear weighting function can be accurately
approximated by the difference of two convex functions. This observation
enables efficient policy computation using convex-concave programming. We
demonstrate the effectiveness of the approach on several scenarios.
| cs.AI cs.LO | we develop a method for computing policies in markov decision processes with risksensitive measures subject to temporal logic constraints specifically we use a particular risksensitive measure from cumulative prospect theory which has been previously adopted in psychology and economics the nonlinear transformation of the probabilities and utility functions yields a nonlinear programming problem which makes computation of optimal policies typically challenging we show that this nonlinear weighting function can be accurately approximated by the difference of two convex functions this observation enables efficient policy computation using convexconcave programming we demonstrate the effectiveness of the approach on several scenarios | [['we', 'develop', 'a', 'method', 'for', 'computing', 'policies', 'in', 'markov', 'decision', 'processes', 'with', 'risksensitive', 'measures', 'subject', 'to', 'temporal', 'logic', 'constraints', 'specifically', 'we', 'use', 'a', 'particular', 'risksensitive', 'measure', 'from', 'cumulative', 'prospect', 'theory', 'which', 'has', 'been', 'previously', 'adopted', 'in', 'psychology', 'and', 'economics', 'the', 'nonlinear', 'transformation', 'of', 'the', 'probabilities', 'and', 'utility', 'functions', 'yields', 'a', 'nonlinear', 'programming', 'problem', 'which', 'makes', 'computation', 'of', 'optimal', 'policies', 'typically', 'challenging', 'we', 'show', 'that', 'this', 'nonlinear', 'weighting', 'function', 'can', 'be', 'accurately', 'approximated', 'by', 'the', 'difference', 'of', 'two', 'convex', 'functions', 'this', 'observation', 'enables', 'efficient', 'policy', 'computation', 'using', 'convexconcave', 'programming', 'we', 'demonstrate', 'the', 'effectiveness', 'of', 'the', 'approach', 'on', 'several', 'scenarios']] | [-0.06231399927031705, -0.01619978122916833, -0.12797435702832075, 0.12264823033569419, -0.1337138550565103, -0.13267701724069003, 0.09966291359754051, 0.4378493784983851, -0.32476168980213754, -0.30246469332855574, 0.1089601872883243, -0.22113493802406045, -0.24812783106059141, 0.21422925869915255, -0.1030936936024643, 0.15716424506699325, 0.020502747941705188, -0.017177202909880757, -0.07564645477009899, -0.2302276440801061, 0.3145800704856365, 0.01779791557382108, 0.30514973649108895, 0.05065457323478174, 0.15801307207866744, 0.04413164046840096, -0.004238354683644614, 0.04531604598029232, -0.11557741233716988, 0.1555416157115156, 0.3346565358450028, 0.2485309792219243, 0.40904574814651695, -0.3962752241641283, -0.20176398507271875, 0.11253097376368028, 0.0877942392146405, 0.095019116083027, -0.028514700900164565, -0.2666645413264632, 0.020029216657905857, -0.1932769388967783, -0.03720446926427587, -0.10780649998092226, -0.01651487270446152, 0.01784870042750726, -0.35175048563705413, 0.030429298830406005, -0.0009433676925848941, 0.01152135693582193, -0.0606790915908463, -0.10376809655964299, 0.0495922692471697, 0.07997134768603635, 0.034998555487135846, -0.002414537313375242, 0.1561067354181135, -0.0845132108435643, -0.24234454302540126, 0.3334347670042545, -0.04952650818004444, -0.25664783211909614, 0.17012839535327287, -0.07139892488889092, -0.16567603180812177, 0.10415391387341887, 0.25394595695222366, 0.17438316824655903, -0.20493560975265443, 0.10050486127385033, -0.03580336677081579, 0.14825146913775528, 0.025444416010904372, 0.01702744635867373, 0.152155122292057, 0.1994514491347292, 0.12000788805284063, 0.1875717465525816, -0.013412629185263448, -0.17810386150353114, -0.24179334296103644, -0.0956410346962559, -0.15319118951922472, -0.0018098264545373314, -0.09939610946190552, -0.13044907363625813, 0.3655626899752842, 0.189481835265891, 0.1137463542091071, 0.17397512023441722, 0.31278625271302096, 0.2191204025353096, 0.03914630269674508, 0.0813383665648574, 0.19246286391375625, 0.0800898796451107, 0.09540002738429727, -0.2347848699190559, 0.12843930777589962, 0.0448967763436578] |
1,803.00092 | NETT: Solving Inverse Problems with Deep Neural Networks | Recovering a function or high-dimensional parameter vector from indirect
measurements is a central task in various scientific areas. Several methods for
solving such inverse problems are well developed and well understood. Recently,
novel algorithms using deep learning and neural networks for inverse problems
appeared. While still in their infancy, these techniques show astonishing
performance for applications like low-dose CT or various sparse data problems.
However, there are few theoretical results for deep learning in inverse
problems. In this paper, we establish a complete convergence analysis for the
proposed NETT (Network Tikhonov) approach to inverse problems. NETT considers
data consistent solutions having small value of a regularizer defined by a
trained neural network. We derive well-posedness results and quantitative error
estimates, and propose a possible strategy for training the regularizer. Our
theoretical results and framework are different from any previous work using
neural networks for solving inverse problems. A possible data driven
regularizer is proposed. Numerical results are presented for a tomographic
sparse data problem, which demonstrate good performance of NETT even for
unknowns of different type from the training data. To derive the convergence
and convergence rates results we introduce a new framework based on the
absolute Bregman distance generalizing the standard Bregman distance from the
convex to the non-convex case.
| math.NA cs.LG cs.NA | recovering a function or highdimensional parameter vector from indirect measurements is a central task in various scientific areas several methods for solving such inverse problems are well developed and well understood recently novel algorithms using deep learning and neural networks for inverse problems appeared while still in their infancy these techniques show astonishing performance for applications like lowdose ct or various sparse data problems however there are few theoretical results for deep learning in inverse problems in this paper we establish a complete convergence analysis for the proposed nett network tikhonov approach to inverse problems nett considers data consistent solutions having small value of a regularizer defined by a trained neural network we derive wellposedness results and quantitative error estimates and propose a possible strategy for training the regularizer our theoretical results and framework are different from any previous work using neural networks for solving inverse problems a possible data driven regularizer is proposed numerical results are presented for a tomographic sparse data problem which demonstrate good performance of nett even for unknowns of different type from the training data to derive the convergence and convergence rates results we introduce a new framework based on the absolute bregman distance generalizing the standard bregman distance from the convex to the nonconvex case | [['recovering', 'a', 'function', 'or', 'highdimensional', 'parameter', 'vector', 'from', 'indirect', 'measurements', 'is', 'a', 'central', 'task', 'in', 'various', 'scientific', 'areas', 'several', 'methods', 'for', 'solving', 'such', 'inverse', 'problems', 'are', 'well', 'developed', 'and', 'well', 'understood', 'recently', 'novel', 'algorithms', 'using', 'deep', 'learning', 'and', 'neural', 'networks', 'for', 'inverse', 'problems', 'appeared', 'while', 'still', 'in', 'their', 'infancy', 'these', 'techniques', 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1,803.00093 | Divergent on average directions of Teichmuller geodesic flow | The set of directions from a quadratic differential that diverge on average
under Teichmuller geodesic flow has Hausdorff dimension exactly equal to
one-half.
| math.DS math.GT | the set of directions from a quadratic differential that diverge on average under teichmuller geodesic flow has hausdorff dimension exactly equal to onehalf | [['the', 'set', 'of', 'directions', 'from', 'a', 'quadratic', 'differential', 'that', 'diverge', 'on', 'average', 'under', 'teichmuller', 'geodesic', 'flow', 'has', 'hausdorff', 'dimension', 'exactly', 'equal', 'to', 'onehalf']] | [-0.1746144714238851, 0.13845234611274107, -0.1303425520334555, 0.046969452063026634, -0.0716726200695595, -0.16073023720199, -0.024200127662523933, 0.283321599273578, -0.29060289684845053, -0.1511595527762952, 0.07474394626510532, -0.43272260414517444, -0.08549361538303935, 0.23426410029439823, -0.11563748096966225, 0.12665259271212245, 0.015230959888709627, 0.21307512032596962, -0.10159108066000044, -0.34103752277872484, 0.39607774968380516, -0.035701701822488205, 0.2762067293667275, -0.0034701670444084575, 0.21937641242276068, 0.001293586830244116, 0.049440355202102146, 0.11075876612702142, -0.18474718569126233, 0.09612963251445604, 0.1928601857153294, 0.03570197286772663, 0.20113388675710428, -0.35748751445308974, -0.1932084368460852, 0.2061558584480182, 0.14610751835710328, -0.01482221876959438, 0.09263593255562465, -0.21646491247836663, 0.09315184356503027, -0.06802054041105768, -0.18483631970846784, -0.033260261660198805, 0.13026585867223534, -0.06262337813234847, -0.13698579366926267, 0.07145260217721047, 0.06521911546587944, 0.1651343936505525, -0.06737307479362124, -0.149674780221413, -0.0971269087926687, 0.09366744823004726, 0.1259228110961292, 0.12201902033437205, 0.12787206114634223, 0.008299064053141552, -0.1351249782854448, 0.3766209963182716, -0.11789635925189308, -0.3300173590769586, 0.12796042806914318, -0.24197781304626362, -0.07094713152669695, 0.22210165761087253, 0.2182959618208849, 0.10829388431232909, -0.08212090639964394, 0.1811940569592559, -0.09645584869481948, 0.07819596856184628, 0.15720027308587148, -0.033713092926241785, 0.1527495632029098, 0.016133118582808453, 0.27769722368406213, 0.15485587335475112, -0.05440447887446245, -0.14935027872738632, -0.36091469357842987, -0.13917259489307585, -0.17598838391511337, 0.18557209871284178, -0.17906764348078033, -0.1582727379611005, 0.3568316152238328, 0.10567031642827003, 0.1760247134320114, 0.16462867480257284, 0.26926730897115625, 0.12638273446013362, 0.08188817370682955, 0.18568698705538458, 0.17497634563757025, 0.11710135351218608, -0.0014555879220690417, -0.19339120091425013, 0.002004710775192665, 0.218395352444571] |
1,803.00094 | Neural Networks Should Be Wide Enough to Learn Disconnected Decision
Regions | In the recent literature the important role of depth in deep learning has
been emphasized. In this paper we argue that sufficient width of a feedforward
network is equally important by answering the simple question under which
conditions the decision regions of a neural network are connected. It turns out
that for a class of activation functions including leaky ReLU, neural networks
having a pyramidal structure, that is no layer has more hidden units than the
input dimension, produce necessarily connected decision regions. This implies
that a sufficiently wide hidden layer is necessary to guarantee that the
network can produce disconnected decision regions. We discuss the implications
of this result for the construction of neural networks, in particular the
relation to the problem of adversarial manipulation of classifiers.
| cs.LG cs.AI cs.CV stat.ML | in the recent literature the important role of depth in deep learning has been emphasized in this paper we argue that sufficient width of a feedforward network is equally important by answering the simple question under which conditions the decision regions of a neural network are connected it turns out that for a class of activation functions including leaky relu neural networks having a pyramidal structure that is no layer has more hidden units than the input dimension produce necessarily connected decision regions this implies that a sufficiently wide hidden layer is necessary to guarantee that the network can produce disconnected decision regions we discuss the implications of this result for the construction of neural networks in particular the relation to the problem of adversarial manipulation of classifiers | [['in', 'the', 'recent', 'literature', 'the', 'important', 'role', 'of', 'depth', 'in', 'deep', 'learning', 'has', 'been', 'emphasized', 'in', 'this', 'paper', 'we', 'argue', 'that', 'sufficient', 'width', 'of', 'a', 'feedforward', 'network', 'is', 'equally', 'important', 'by', 'answering', 'the', 'simple', 'question', 'under', 'which', 'conditions', 'the', 'decision', 'regions', 'of', 'a', 'neural', 'network', 'are', 'connected', 'it', 'turns', 'out', 'that', 'for', 'a', 'class', 'of', 'activation', 'functions', 'including', 'leaky', 'relu', 'neural', 'networks', 'having', 'a', 'pyramidal', 'structure', 'that', 'is', 'no', 'layer', 'has', 'more', 'hidden', 'units', 'than', 'the', 'input', 'dimension', 'produce', 'necessarily', 'connected', 'decision', 'regions', 'this', 'implies', 'that', 'a', 'sufficiently', 'wide', 'hidden', 'layer', 'is', 'necessary', 'to', 'guarantee', 'that', 'the', 'network', 'can', 'produce', 'disconnected', 'decision', 'regions', 'we', 'discuss', 'the', 'implications', 'of', 'this', 'result', 'for', 'the', 'construction', 'of', 'neural', 'networks', 'in', 'particular', 'the', 'relation', 'to', 'the', 'problem', 'of', 'adversarial', 'manipulation', 'of', 'classifiers']] | [-0.11175200125489937, 0.08379669545320212, -0.03196241067053052, 0.07870434499636758, -0.07995155327989778, -0.17167084387983778, 0.036750545094946574, 0.4459963824483566, -0.2602582202744088, -0.24486092584265862, 0.08055973676709982, -0.21669625522190472, -0.2688913251140548, 0.16974553621639643, -0.09048997354329913, 0.05809163086814806, 0.09707375464495271, 0.06614106732558866, -0.009122710237079446, -0.2749590551966321, 0.3577181590044347, 0.045971342351322164, 0.29660895148845157, 0.04677302354821222, 0.11254715676477645, -0.043138616643773275, 0.010456725634867325, 0.02269298095825434, -0.06960004388162133, 0.15648635809247935, 0.30679391389276134, 0.1581188888158067, 0.35303259240936313, -0.4359263968653977, -0.2778523288923225, 0.1576341392546965, 0.14645443177505513, 0.08976006684588356, -0.004694738710554702, -0.24528357480448904, 0.1311361604311969, -0.14190125071036164, -0.048343500500777736, -0.0875999487034278, 0.021436942497530254, -0.04557897103950381, -0.28353806963286843, 0.008874489173649636, 0.1462254075850069, 0.017576418998942245, -0.026714711351814913, -0.07404936953207653, -0.03174466611017124, 0.14383105174329103, -0.007654379382074694, 0.061475332308873476, 0.11803432119813806, -0.21929753410404373, -0.11393293590663234, 0.3027613712438324, 0.0022300832073369747, -0.19711135965553694, 0.1780091811924649, -0.05034001374951913, -0.1847579653513094, 0.10121853933378588, 0.2062636827249662, 0.09897659187481622, -0.1599051653834067, 0.048942269262624905, -0.12434818852489116, 0.17443952698158682, 0.06856320910083014, 0.03457700033959554, 0.1883801063449937, 0.2543374002325436, 0.08700440855682245, 0.18485422830781317, -0.08711245982340188, -0.09254537835658994, -0.2819421128151589, -0.10316897847428663, -0.1369483673297509, 0.028952233980817255, -0.09134846015149378, -0.18421717967430595, 0.39124792341317516, 0.16642986819715588, 0.2425206648913445, 0.09804230096870015, 0.26903470563411247, 0.08546939522921093, 0.1547071249151486, 0.10550977741877432, 0.2342696203631931, 0.14002615481695102, 0.09178842974597501, -0.12149760534794041, 0.14991715323867538, 0.04742726149561349] |
1,803.00095 | A computationally universal phase of quantum matter | We provide the first example of a symmetry protected quantum phase that has
universal computational power. Throughout this phase, which lives in spatial
dimension two, the ground state is a universal resource for measurement based
quantum computation.
| quant-ph | we provide the first example of a symmetry protected quantum phase that has universal computational power throughout this phase which lives in spatial dimension two the ground state is a universal resource for measurement based quantum computation | [['we', 'provide', 'the', 'first', 'example', 'of', 'a', 'symmetry', 'protected', 'quantum', 'phase', 'that', 'has', 'universal', 'computational', 'power', 'throughout', 'this', 'phase', 'which', 'lives', 'in', 'spatial', 'dimension', 'two', 'the', 'ground', 'state', 'is', 'a', 'universal', 'resource', 'for', 'measurement', 'based', 'quantum', 'computation']] | [-0.19011555591950546, 0.19707742741180434, -0.13930920667543606, 0.009692944888327573, -0.03776705429914433, -0.1780194324265058, 0.08813610558423239, 0.3239259653397509, -0.2330152742766045, -0.24506057990161148, 0.1226122736194599, -0.20217474685931527, -0.15044362827934124, 0.18414461522086248, -0.04305218443246181, 0.10152998310671113, -0.020072391810449394, 0.08590782473471041, -0.09868575193969584, -0.2206260817679199, 0.32056758834703547, 0.020314189599122148, 0.3551659303138385, 0.046859547181205975, 0.0873744679312851, -0.04886120273354086, 0.05600063009439288, -0.0226523469932176, -0.10845623694685118, 0.11014384178545426, 0.2884226735756808, 0.11654369480517768, 0.23975687760960412, -0.4210748525990828, -0.22224923240876682, 0.12941658088459154, 0.13944676148428306, 0.18489169461237318, -0.11010917291246555, -0.2776779383924362, 0.04717083235044737, -0.2126052944946128, -0.1159017077974371, -0.12900669691530434, 0.029184412261521495, -0.13975331358410217, -0.18451635655317758, 0.0696511512018136, 0.0390850140247494, 0.074356072682988, -0.023125480835300846, -0.02507194466385487, -0.004798163471089021, 0.1771328863404641, -0.0644576152144755, -0.000430354370257339, 0.08747273157791209, -0.129733977090517, -0.17879005030703707, 0.37112948840886756, 0.021683603776870546, -0.16270219899613309, 0.12637418239830514, -0.11055500702177351, -0.18293192287957347, 0.09509826252690039, 0.13686463215413527, 0.04827532207442296, -0.0793768883355566, 0.11694590455374203, -0.003817706645743267, 0.2499350623001118, -0.03514374627706569, 0.13853839468966062, 0.17893839723153696, 0.1894020027405507, 0.08478574336481255, 0.16899580548743945, -0.08272384799312095, -0.2213287107721978, -0.3412955061507386, -0.25520257951339353, -0.2801066627332664, 0.08437965389945216, -0.07298825810839599, -0.15447729423239426, 0.45621977806896774, 0.10469270681307928, 0.17258065208994053, 0.03521493754689456, 0.3539066915777889, 0.13752132714600176, 0.025325539899436204, 0.09191651541324383, 0.22329857661011251, 0.0812863838556852, 0.09549075151114045, -0.22115911219923481, 0.038747103167446076, 0.08796016164627429] |
1,803.00096 | Synthetic Control Methods and Big Data | Many macroeconomic policy questions may be assessed in a case study
framework, where the time series of a treated unit is compared to a
counterfactual constructed from a large pool of control units. I provide a
general framework for this setting, tailored to predict the counterfactual by
minimizing a tradeoff between underfitting (bias) and overfitting (variance).
The framework nests recently proposed structural and reduced form machine
learning approaches as special cases. Furthermore, difference-in-differences
with matching and the original synthetic control are restrictive cases of the
framework, in general not minimizing the bias-variance objective. Using
simulation studies I find that machine learning methods outperform traditional
methods when the number of potential controls is large or the treated unit is
substantially different from the controls. Equipped with a toolbox of
approaches, I revisit a study on the effect of economic liberalisation on
economic growth. I find effects for several countries where no effect was found
in the original study. Furthermore, I inspect how a systematically important
bank respond to increasing capital requirements by using a large pool of banks
to estimate the counterfactual. Finally, I assess the effect of a changing
product price on product sales using a novel scanner dataset.
| econ.EM | many macroeconomic policy questions may be assessed in a case study framework where the time series of a treated unit is compared to a counterfactual constructed from a large pool of control units i provide a general framework for this setting tailored to predict the counterfactual by minimizing a tradeoff between underfitting bias and overfitting variance the framework nests recently proposed structural and reduced form machine learning approaches as special cases furthermore differenceindifferences with matching and the original synthetic control are restrictive cases of the framework in general not minimizing the biasvariance objective using simulation studies i find that machine learning methods outperform traditional methods when the number of potential controls is large or the treated unit is substantially different from the controls equipped with a toolbox of approaches i revisit a study on the effect of economic liberalisation on economic growth i find effects for several countries where no effect was found in the original study furthermore i inspect how a systematically important bank respond to increasing capital requirements by using a large pool of banks to estimate the counterfactual finally i assess the effect of a changing product price on product sales using a novel scanner dataset | [['many', 'macroeconomic', 'policy', 'questions', 'may', 'be', 'assessed', 'in', 'a', 'case', 'study', 'framework', 'where', 'the', 'time', 'series', 'of', 'a', 'treated', 'unit', 'is', 'compared', 'to', 'a', 'counterfactual', 'constructed', 'from', 'a', 'large', 'pool', 'of', 'control', 'units', 'i', 'provide', 'a', 'general', 'framework', 'for', 'this', 'setting', 'tailored', 'to', 'predict', 'the', 'counterfactual', 'by', 'minimizing', 'a', 'tradeoff', 'between', 'underfitting', 'bias', 'and', 'overfitting', 'variance', 'the', 'framework', 'nests', 'recently', 'proposed', 'structural', 'and', 'reduced', 'form', 'machine', 'learning', 'approaches', 'as', 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1,803.00097 | Intelligent Irrigation System Based on Arduino | This paper explains how to build an intelligent irrigation system using
Arduino (a micro- controller) and many devices (humidity, temperature, pressure
and water flow sensors). Our irrigation system combines a precise method to
determine water balance of soils with an automatic response to water content
oscillations. Thus, it is an example of how we can perform better irrigation
systems by increasing the precision of measurements but also by automating
decisions.
| cs.CY | this paper explains how to build an intelligent irrigation system using arduino a micro controller and many devices humidity temperature pressure and water flow sensors our irrigation system combines a precise method to determine water balance of soils with an automatic response to water content oscillations thus it is an example of how we can perform better irrigation systems by increasing the precision of measurements but also by automating decisions | [['this', 'paper', 'explains', 'how', 'to', 'build', 'an', 'intelligent', 'irrigation', 'system', 'using', 'arduino', 'a', 'micro', 'controller', 'and', 'many', 'devices', 'humidity', 'temperature', 'pressure', 'and', 'water', 'flow', 'sensors', 'our', 'irrigation', 'system', 'combines', 'a', 'precise', 'method', 'to', 'determine', 'water', 'balance', 'of', 'soils', 'with', 'an', 'automatic', 'response', 'to', 'water', 'content', 'oscillations', 'thus', 'it', 'is', 'an', 'example', 'of', 'how', 'we', 'can', 'perform', 'better', 'irrigation', 'systems', 'by', 'increasing', 'the', 'precision', 'of', 'measurements', 'but', 'also', 'by', 'automating', 'decisions']] | [-0.11076658919919283, 0.11836827662669488, -0.08866479101457767, -0.03798722491067435, -0.07400354089747582, -0.16375713626454982, 0.08361014754544678, 0.380853330503617, -0.27025677297663475, -0.3780418585719807, 0.15723990576474794, -0.30415900551847047, -0.19819667361942786, 0.23079343725528037, -0.15816439882452998, 0.08975664214896305, 0.05539552416386349, -0.008371864332418356, 0.009624004177749158, -0.19937582151032984, 0.23843844238934772, 0.1418208204376112, 0.3041270824481866, 0.07826490852582668, 0.14504283739786064, -0.01772039522682982, 0.01974204861825066, 0.02442623486775639, -0.10278094076139886, 0.1307962188530447, 0.30646198481013665, 0.12294630551976816, 0.2662253196750368, -0.47247423908806274, -0.23673465043705488, 0.05664102137754006, 0.09830992917663285, 0.07356183847212898, -0.046426038833201996, -0.24921717001125215, 0.04703877689316869, -0.1980068591257025, -0.162808153674996, -0.14191107182164808, 0.001132004987448454, 0.04304599942074024, -0.24656823785709484, -0.013404483045867112, 0.014946953173992888, 0.12867323640467865, -0.08911940720136044, -0.04416381359832095, -0.030172406714076973, 0.21655086209066213, 0.008499716125827814, -0.00405156331669007, 0.27756402035509903, -0.11056173989615803, -0.07864382924245937, 0.3895232018083334, -0.037641406225572736, -0.18667606501174824, 0.20729018641037067, -0.03630593039893678, -0.08152107118761966, 0.13106243864500097, 0.22767446774690012, 0.07016588370315731, -0.25269749255052637, -0.08127959206467494, 0.011202112199472529, 0.27230279528136764, 0.05618299890110003, -0.0598901826322877, 0.22079578495717475, 0.299532405366855, 0.12975343502974804, 0.08845937554059284, -0.034537388843351176, 0.0037074063339137607, -0.17880167008510658, -0.195068399887532, -0.10816225022343652, -0.00023462121108812944, -0.03644371169045501, -0.14649353865534068, 0.34828547309818014, 0.22822673759822334, 0.12378444152751139, 0.029990167074304606, 0.38911061749926634, 0.0723030033188739, 0.05285701039912445, 0.04643944362178445, 0.1920154068818582, 0.07803067678718695, 0.22737470315104083, -0.25153318495223564, 0.10162252467125654, 0.016815561224939303] |
1,803.00098 | A general measure of the impact of priors in Bayesian statistics via
Stein's Method | We propose a measure of the impact of any two choices of prior distributions
by quantifying the Wasserstein distance between the respective resulting
posterior distributions at any fixed sample size. We illustrate this measure on
the normal, Binomial and Poisson models.
| math.ST stat.TH | we propose a measure of the impact of any two choices of prior distributions by quantifying the wasserstein distance between the respective resulting posterior distributions at any fixed sample size we illustrate this measure on the normal binomial and poisson models | [['we', 'propose', 'a', 'measure', 'of', 'the', 'impact', 'of', 'any', 'two', 'choices', 'of', 'prior', 'distributions', 'by', 'quantifying', 'the', 'wasserstein', 'distance', 'between', 'the', 'respective', 'resulting', 'posterior', 'distributions', 'at', 'any', 'fixed', 'sample', 'size', 'we', 'illustrate', 'this', 'measure', 'on', 'the', 'normal', 'binomial', 'and', 'poisson', 'models']] | [-0.04618336691907266, 0.07834847198753822, -0.1486090787504686, 0.10168662774887663, -0.014440897289981566, -0.0625587875000769, 0.10274938602999943, 0.405049213185543, -0.2962244941239677, -0.3170696896296449, 0.00909082893645618, -0.26826132652236195, -0.0833062892552556, 0.12272426329280545, -0.08648719768062597, 0.05101914994600343, 0.03061603468547507, 0.00993886626348263, -0.16359133207489077, -0.21152470974115337, 0.42148182819960867, 0.06075812580927116, 0.33884335095744306, -0.01624269081597648, 0.1591994427825983, 0.008910747131378186, -0.10631490943998825, -0.019471306580959295, -0.2253166535051494, 0.1431165441598107, 0.15370028241505213, 0.15192657444489802, 0.33361542240784664, -0.3260889559657321, -0.1929408480163391, 0.22268375081986916, 0.05422010231853985, 0.05900027593824922, -0.0028349525187300836, -0.27368214779260863, 0.0713669621135767, -0.15667849697354364, -0.10270546407370669, -0.029560531330544773, 0.015412251714889596, 0.1045054378010696, -0.2730899173993526, 0.05362513404702995, 0.06622530120175059, 0.05810017854247878, -0.02661090830295551, -0.18126477946232006, -0.019603218258608404, 0.13769587105503533, 0.04392118288612947, -0.03569190330212799, 0.1187895856614554, -0.08178797677136594, -0.12383515115191297, 0.2978173722853748, -0.061618852588098225, -0.3048448410884636, 0.1704590734912128, -0.2060365333994169, -0.08080400917224767, 0.032987876931523406, 0.22117773277639616, 0.12073460312151327, -0.1590372404628774, 0.07529217865539534, -0.016997284917501597, 0.09573917935897665, 0.07434645729003156, -0.012077325500729607, 0.18455257112296616, 0.13120276347461451, 0.08743825978485913, 0.14689232796294296, -0.18015609210834088, -0.09477981561567725, -0.34999438977187, -0.1535565152128295, -0.23307173512876034, 0.04261809042314204, -0.2591876897662055, -0.18162611831070447, 0.3719023160818147, 0.16352097981427682, 0.2969535519255371, 0.14284289578293882, 0.2806450273023873, 0.13975035047113168, -0.019296832377000188, 0.09548607086422058, 0.14390515785936903, 0.11381915471198537, -0.040267234384195834, -0.19345514374080955, 0.197363095778245, -0.0021568049744861884] |
1,803.00099 | The Difficulty of Monte Carlo Approximation of Multivariate Monotone
Functions | We study the $L_1$-approximation of $d$-variate monotone functions based on
information from $n$ function evaluations. It is known that this problem
suffers from the curse of dimensionality in the deterministic setting, that is,
the number $n(\varepsilon,d)$ of function evaluations needed in order to
approximate an unknown monotone function within a given error threshold
$\varepsilon$ grows at least exponentially in $d$. This is not the case in the
randomized setting (Monte Carlo setting) where the complexity
$n(\varepsilon,d)$ grows exponentially in $\sqrt{d}$ (modulo logarithmic terms)
only. An algorithm exhibiting this complexity is presented. Still, the problem
remains difficult as best known methods are deterministic if $\varepsilon$ is
comparably small, namely $\varepsilon \preceq 1/\sqrt{d}$. This inherent
difficulty is confirmed by lower complexity bounds which reveal a joint
$(\varepsilon,d)$-dependency and from which we deduce that the problem is not
weakly tractable.
| math.NA | we study the l_1approximation of dvariate monotone functions based on information from n function evaluations it is known that this problem suffers from the curse of dimensionality in the deterministic setting that is the number nvarepsilond of function evaluations needed in order to approximate an unknown monotone function within a given error threshold varepsilon grows at least exponentially in d this is not the case in the randomized setting monte carlo setting where the complexity nvarepsilond grows exponentially in sqrtd modulo logarithmic terms only an algorithm exhibiting this complexity is presented still the problem remains difficult as best known methods are deterministic if varepsilon is comparably small namely varepsilon preceq 1sqrtd this inherent difficulty is confirmed by lower complexity bounds which reveal a joint varepsilonddependency and from which we deduce that the problem is not weakly tractable | [['we', 'study', 'the', 'l_1approximation', 'of', 'dvariate', 'monotone', 'functions', 'based', 'on', 'information', 'from', 'n', 'function', 'evaluations', 'it', 'is', 'known', 'that', 'this', 'problem', 'suffers', 'from', 'the', 'curse', 'of', 'dimensionality', 'in', 'the', 'deterministic', 'setting', 'that', 'is', 'the', 'number', 'nvarepsilond', 'of', 'function', 'evaluations', 'needed', 'in', 'order', 'to', 'approximate', 'an', 'unknown', 'monotone', 'function', 'within', 'a', 'given', 'error', 'threshold', 'varepsilon', 'grows', 'at', 'least', 'exponentially', 'in', 'd', 'this', 'is', 'not', 'the', 'case', 'in', 'the', 'randomized', 'setting', 'monte', 'carlo', 'setting', 'where', 'the', 'complexity', 'nvarepsilond', 'grows', 'exponentially', 'in', 'sqrtd', 'modulo', 'logarithmic', 'terms', 'only', 'an', 'algorithm', 'exhibiting', 'this', 'complexity', 'is', 'presented', 'still', 'the', 'problem', 'remains', 'difficult', 'as', 'best', 'known', 'methods', 'are', 'deterministic', 'if', 'varepsilon', 'is', 'comparably', 'small', 'namely', 'varepsilon', 'preceq', '1sqrtd', 'this', 'inherent', 'difficulty', 'is', 'confirmed', 'by', 'lower', 'complexity', 'bounds', 'which', 'reveal', 'a', 'joint', 'varepsilonddependency', 'and', 'from', 'which', 'we', 'deduce', 'that', 'the', 'problem', 'is', 'not', 'weakly', 'tractable']] | [-0.12154731183203266, 0.08989679474603583, -0.024365597476443165, 0.10324746851641547, -0.05709519733414602, -0.15690779466154603, 0.07730194935613648, 0.3329380956094931, -0.31832380291512785, -0.2955138163695879, 0.10881507487176671, -0.2822157672524233, -0.1854652690304005, 0.18614702211155365, -0.10609685650321327, 0.10562567861737027, 0.030571570185095293, 0.05235242431209532, -0.06840065689860186, -0.3021188293258306, 0.28434252321010395, 0.027342899116988787, 0.22367477012962542, 0.021978801088955474, 0.10120117366450894, -0.0035976470406988963, 0.0045826573965742304, 0.023725754253331722, -0.14223227794499346, 0.05520394381421605, 0.26871450802804353, 0.15522234455120804, 0.35960403279921804, -0.3538490291205509, -0.1391076373949866, 0.16729539585717515, 0.19090679348686107, 0.10622412614312078, 0.004109258017706452, -0.19391077032196335, 0.10468188009005697, -0.11057224556784337, -0.10347992725334787, -0.051671237558099055, 0.0224352819564314, -0.014619625134133351, -0.3080432613941786, 0.09221454100984641, 0.07765613750362878, 0.009874319373493028, 0.011116053669384736, -0.14621846929452764, 0.060062734112280476, 0.07232427815838224, 0.06937541409399059, 0.11221230294819072, 0.05126960798824096, -0.12260432179798098, -0.08936588075148928, 0.3394004749339622, -0.03768798224336933, -0.256185810381259, 0.1669827034604489, -0.13815069587230133, -0.14582321362104267, 0.17277165717335746, 0.14281975191346752, 0.1749679091256386, -0.08476200303052525, 0.18925289938402678, -0.08218541194219142, 0.23277284895148648, 0.037596110280205985, 0.03686689769999836, 0.021615084452445015, 0.17437666142359376, 0.14390281914315625, 0.1725769902148065, -0.018510114050620946, -0.09628512669587508, -0.3011786010088947, -0.09742950258206452, -0.26652949086993055, 0.04746590395263732, -0.12171157763280943, -0.19102764101476435, 0.28256120839710896, 0.12332481947754893, 0.21330051251125576, 0.17052723754450733, 0.33480650097515213, 0.15425549279675846, 0.00946798364405849, 0.13883091325132066, 0.17933142294778542, 0.07856578075135236, 0.02137491259011714, -0.2098687060865546, 0.1637488856452846, 0.08667771918836105] |
1,803.001 | The TT* deformation at large central charge | We study Zamolodchikov's TT* deformation of two dimensional quantum field
theories in a 't Hooft-like limit, in which we scale the number of degrees of
freedom c to infinity and the deformation parameter t to zero, keeping their
product t*c fixed (more precisely, we keep energies and distances fixed in
units of t*c). In this limit the Hagedorn temperature remains fixed, but other
non-local aspects of the theory disappear. We show that in this limit
correlation functions may be computed exactly, and they are local in space and
polynomials in t. We compute explicitly the deformed three-point functions of
the energy-momentum tensor for a TT*-deformed conformal field theory.
| hep-th | we study zamolodchikovs tt deformation of two dimensional quantum field theories in a t hooftlike limit in which we scale the number of degrees of freedom c to infinity and the deformation parameter t to zero keeping their product tc fixed more precisely we keep energies and distances fixed in units of tc in this limit the hagedorn temperature remains fixed but other nonlocal aspects of the theory disappear we show that in this limit correlation functions may be computed exactly and they are local in space and polynomials in t we compute explicitly the deformed threepoint functions of the energymomentum tensor for a ttdeformed conformal field theory | [['we', 'study', 'zamolodchikovs', 'tt', 'deformation', 'of', 'two', 'dimensional', 'quantum', 'field', 'theories', 'in', 'a', 't', 'hooftlike', 'limit', 'in', 'which', 'we', 'scale', 'the', 'number', 'of', 'degrees', 'of', 'freedom', 'c', 'to', 'infinity', 'and', 'the', 'deformation', 'parameter', 't', 'to', 'zero', 'keeping', 'their', 'product', 'tc', 'fixed', 'more', 'precisely', 'we', 'keep', 'energies', 'and', 'distances', 'fixed', 'in', 'units', 'of', 'tc', 'in', 'this', 'limit', 'the', 'hagedorn', 'temperature', 'remains', 'fixed', 'but', 'other', 'nonlocal', 'aspects', 'of', 'the', 'theory', 'disappear', 'we', 'show', 'that', 'in', 'this', 'limit', 'correlation', 'functions', 'may', 'be', 'computed', 'exactly', 'and', 'they', 'are', 'local', 'in', 'space', 'and', 'polynomials', 'in', 't', 'we', 'compute', 'explicitly', 'the', 'deformed', 'threepoint', 'functions', 'of', 'the', 'energymomentum', 'tensor', 'for', 'a', 'ttdeformed', 'conformal', 'field', 'theory']] | [-0.1689791917359178, 0.22503098657476567, -0.088908590393785, 0.05547494016735701, -0.019679713234845027, -0.12589586506353986, 0.03467603410576758, 0.3408319299309471, -0.2771527468244258, -0.22735580672927758, 0.046521858174014386, -0.2860019616173389, -0.12104372817709624, 0.10072545510516545, -0.008432250839840864, 0.03806694080426454, -0.04109813454430376, 0.09074615369437733, -0.14430188023263293, -0.26603412219958605, 0.3130443973758336, 0.0312245276693481, 0.2338303972436028, 0.06500710040400519, 0.07511809149976802, 0.013155546184046038, 0.017161330591156102, 0.0811185840096773, -0.15915875981386945, 0.05932101613306658, 0.27670024789730524, 0.06551109570313558, 0.19579175161632978, -0.4011677222883451, -0.18940523441959278, 0.11524244905318891, 0.1323715676292429, 0.12644037712804937, 0.059036086171307324, -0.18949349457833253, 0.07130492790645668, -0.16015189850842146, -0.18721324944363854, -0.11620325278316702, 0.03769737049484643, -0.03217780081093938, -0.23415071996816686, 0.09788551208754635, 0.050912321775838314, 0.0787606129562047, -0.04250263451668216, -0.1091835548857523, -0.04106357070469411, 0.10637701458132295, 0.08188187139474343, 0.055145602205454884, 0.1345879192612021, -0.18161294171152795, -0.04960471414743322, 0.32614185785543975, -0.08657016168200524, -0.21213437723666081, 0.16562773406975598, -0.21053033153239253, -0.17554403065103236, 0.06775506801041557, 0.13203080669785283, 0.16594612880862344, -0.10449107294188481, 0.20055818369516354, -0.0018321947830024167, 0.1278296706297165, 0.11260315091305664, 0.057858984364701486, 0.20283654917936736, 0.053343632338986355, 0.05069933474612222, 0.13934886324487053, -0.021739265441929348, -0.11632832486582832, -0.379958626566124, -0.1580738748650058, -0.16895666560941489, 0.08973347587798174, -0.14545135706103904, -0.1784304765827745, 0.36819348880742614, 0.1467175488217427, 0.2335471130682321, 0.083440904136721, 0.19814280988039257, 0.15027478489607016, 0.10954667885774728, 0.10236705671647721, 0.2207967827148805, 0.16443551138530824, 0.08268750493227565, -0.21662517022950767, -0.07953046264549038, 0.12018183195772016] |
1,803.00101 | Model-Based Value Estimation for Efficient Model-Free Reinforcement
Learning | Recent model-free reinforcement learning algorithms have proposed
incorporating learned dynamics models as a source of additional data with the
intention of reducing sample complexity. Such methods hold the promise of
incorporating imagined data coupled with a notion of model uncertainty to
accelerate the learning of continuous control tasks. Unfortunately, they rely
on heuristics that limit usage of the dynamics model. We present model-based
value expansion, which controls for uncertainty in the model by only allowing
imagination to fixed depth. By enabling wider use of learned dynamics models
within a model-free reinforcement learning algorithm, we improve value
estimation, which, in turn, reduces the sample complexity of learning.
| cs.LG cs.AI stat.ML | recent modelfree reinforcement learning algorithms have proposed incorporating learned dynamics models as a source of additional data with the intention of reducing sample complexity such methods hold the promise of incorporating imagined data coupled with a notion of model uncertainty to accelerate the learning of continuous control tasks unfortunately they rely on heuristics that limit usage of the dynamics model we present modelbased value expansion which controls for uncertainty in the model by only allowing imagination to fixed depth by enabling wider use of learned dynamics models within a modelfree reinforcement learning algorithm we improve value estimation which in turn reduces the sample complexity of learning | [['recent', 'modelfree', 'reinforcement', 'learning', 'algorithms', 'have', 'proposed', 'incorporating', 'learned', 'dynamics', 'models', 'as', 'a', 'source', 'of', 'additional', 'data', 'with', 'the', 'intention', 'of', 'reducing', 'sample', 'complexity', 'such', 'methods', 'hold', 'the', 'promise', 'of', 'incorporating', 'imagined', 'data', 'coupled', 'with', 'a', 'notion', 'of', 'model', 'uncertainty', 'to', 'accelerate', 'the', 'learning', 'of', 'continuous', 'control', 'tasks', 'unfortunately', 'they', 'rely', 'on', 'heuristics', 'that', 'limit', 'usage', 'of', 'the', 'dynamics', 'model', 'we', 'present', 'modelbased', 'value', 'expansion', 'which', 'controls', 'for', 'uncertainty', 'in', 'the', 'model', 'by', 'only', 'allowing', 'imagination', 'to', 'fixed', 'depth', 'by', 'enabling', 'wider', 'use', 'of', 'learned', 'dynamics', 'models', 'within', 'a', 'modelfree', 'reinforcement', 'learning', 'algorithm', 'we', 'improve', 'value', 'estimation', 'which', 'in', 'turn', 'reduces', 'the', 'sample', 'complexity', 'of', 'learning']] | [0.0013937317373072904, 0.02924253113286675, -0.10368773628043819, 0.02828523280639377, -0.14438757983273562, -0.17576191019495283, 0.0945521094462887, 0.40987712096528345, -0.2826924187939544, -0.3799265415126563, 0.08195067460446637, -0.20709235991465524, -0.14305193415614512, 0.22734061527760033, -0.12627186284637745, 0.1157111783351591, 0.0919414959685385, 0.01574956812843118, -0.08080082445049588, -0.27333369642882693, 0.2815280574646269, 0.05771682496657068, 0.33302356820237244, -0.037149798371038344, 0.15443955412143315, 0.028356763261879952, -0.015912130505795468, 0.01999722849929108, -0.09243339749377381, 0.21102659056349746, 0.29489865198267134, 0.22817468059354656, 0.3864385388211681, -0.4092525441438687, -0.27831165921294465, 0.13532297131341864, 0.14726872763262605, 0.10633107479046679, -0.038201690351652016, -0.32182489228347, 0.01017770842859908, -0.18067256809774576, -0.051963487376441374, -0.13034284102097857, -0.047184011190009464, 0.0012585752828152112, -0.28281213292184304, 0.05492260960756608, 0.09504738347253429, 0.04508780987212821, -0.05829122899548274, -0.10521801637555912, 0.05145200737532369, 0.159100522291583, 0.04860297107809873, 0.029821047392445354, 0.17669255616811086, -0.2045336611160376, -0.19434593172462764, 0.3351880080013905, -0.0569654331753417, -0.19623918658262998, 0.16905035550172334, -0.038328896988323834, -0.15423943904527235, 0.11600145173505089, 0.2641614742867016, 0.1207656691208886, -0.14718898626061966, 0.0917906508789802, 0.0009180708735618951, 0.17108222056742547, -0.030769307612230616, 0.000571052653644726, 0.14177341488594153, 0.27717371177771744, 0.05497946380417935, 0.10929277334098686, -0.052960589824613394, -0.13924699825704764, -0.2157370320022686, -0.07893248900050684, -0.16787223448484856, -0.01946055014037861, -0.13832650979857822, -0.13174802162420918, 0.3377323910804852, 0.2700661514822025, 0.1874416743247014, 0.13885575121945157, 0.3281125270954843, 0.08575859411843648, 0.12425239248868991, 0.1006935763827367, 0.2251439404625551, 0.07108930342189335, 0.11128209803675143, -0.21544078361783353, 0.14211580927048828, 0.04554496745947959] |
1,803.00102 | Fault-tolerant detection of a quantum error | A critical component of any quantum error-correcting scheme is detection of
errors by using an ancilla system. However, errors occurring in the ancilla can
propagate onto the logical qubit, irreversibly corrupting the encoded
information. We demonstrate a fault-tolerant error-detection scheme that
suppresses spreading of ancilla errors by a factor of 5, while maintaining the
assignment fidelity. The same method is used to prevent propagation of ancilla
excitations, increasing the logical qubit dephasing time by an order of
magnitude. Our approach is hardware-efficient, as it uses a single multilevel
transmon ancilla and a cavity-encoded logical qubit, whose interaction is
engineered in situ by using an off-resonant sideband drive. The results
demonstrate that hardware-efficient approaches that exploit system-specific
error models can yield advances toward fault-tolerant quantum computation.
| quant-ph | a critical component of any quantum errorcorrecting scheme is detection of errors by using an ancilla system however errors occurring in the ancilla can propagate onto the logical qubit irreversibly corrupting the encoded information we demonstrate a faulttolerant errordetection scheme that suppresses spreading of ancilla errors by a factor of 5 while maintaining the assignment fidelity the same method is used to prevent propagation of ancilla excitations increasing the logical qubit dephasing time by an order of magnitude our approach is hardwareefficient as it uses a single multilevel transmon ancilla and a cavityencoded logical qubit whose interaction is engineered in situ by using an offresonant sideband drive the results demonstrate that hardwareefficient approaches that exploit systemspecific error models can yield advances toward faulttolerant quantum computation | [['a', 'critical', 'component', 'of', 'any', 'quantum', 'errorcorrecting', 'scheme', 'is', 'detection', 'of', 'errors', 'by', 'using', 'an', 'ancilla', 'system', 'however', 'errors', 'occurring', 'in', 'the', 'ancilla', 'can', 'propagate', 'onto', 'the', 'logical', 'qubit', 'irreversibly', 'corrupting', 'the', 'encoded', 'information', 'we', 'demonstrate', 'a', 'faulttolerant', 'errordetection', 'scheme', 'that', 'suppresses', 'spreading', 'of', 'ancilla', 'errors', 'by', 'a', 'factor', 'of', '5', 'while', 'maintaining', 'the', 'assignment', 'fidelity', 'the', 'same', 'method', 'is', 'used', 'to', 'prevent', 'propagation', 'of', 'ancilla', 'excitations', 'increasing', 'the', 'logical', 'qubit', 'dephasing', 'time', 'by', 'an', 'order', 'of', 'magnitude', 'our', 'approach', 'is', 'hardwareefficient', 'as', 'it', 'uses', 'a', 'single', 'multilevel', 'transmon', 'ancilla', 'and', 'a', 'cavityencoded', 'logical', 'qubit', 'whose', 'interaction', 'is', 'engineered', 'in', 'situ', 'by', 'using', 'an', 'offresonant', 'sideband', 'drive', 'the', 'results', 'demonstrate', 'that', 'hardwareefficient', 'approaches', 'that', 'exploit', 'systemspecific', 'error', 'models', 'can', 'yield', 'advances', 'toward', 'faulttolerant', 'quantum', 'computation']] | [-0.17997415208693354, 0.19213244812541938, -0.008271273527850186, -0.013192087578053976, 0.030768777641858303, -0.25949559374261766, 0.10730240363421129, 0.38718310464173555, -0.2536290297247169, -0.3006576825564186, 0.03313755024892969, -0.24431026429538766, -0.11110677470588276, 0.22518921651006227, -0.10298024995168933, 0.12767926252056513, 0.11400516663167265, 0.0046120547565571485, -0.0873657748658931, -0.2722355938786941, 0.27220471950638436, 0.07694300368518359, 0.30791195350954487, -0.010843523894436657, 0.14373721224406072, 0.0064935314735668085, 0.03052206178225817, -0.07618491808628483, -0.03593137506821574, 0.1078733007325941, 0.26961891825777135, 0.14071479204651569, 0.3087802739558561, -0.4683535036165267, -0.22456041094847023, 0.04615664019084145, 0.18776710355474102, 0.27225052351845547, -0.005019462832640256, -0.32262975395086313, 0.029291353787430714, -0.19697588666402285, -0.03249183787979306, -0.1320263689549099, -0.05382556965454451, -0.05893817561962116, -0.26991353406206586, 0.05807487614032242, 0.11365435280716948, 0.02450751508736322, 0.04948866573507867, 2.6918813443532393e-05, 0.02265411733476926, 0.13264645286084664, -0.10061512828894681, 0.08136942011075124, 0.2420714822139651, -0.10131461347872016, -0.21686887347529973, 0.3006304835097023, -0.03138139757474193, -0.20366342471642118, 0.10221638185753217, -0.018186591758773917, -0.05397153114280363, 0.13330130435098264, 0.12205036917387793, 0.04123739534970223, -0.12005219861725157, 0.04762797495265746, 0.06646563516058508, 0.3094377339166972, 0.04645883888306637, 0.13197296996593416, 0.149050434826002, 0.15750395009789855, 0.10481727268122468, 0.18023621333920728, -0.08661718016880895, -0.07768869381623282, -0.29559451073498255, -0.1601309773020236, -0.2293885939927291, 0.08928060070836856, -0.08049948841712208, -0.12191555247221503, 0.3758864758609331, 0.18208240939214104, 0.15778336211505736, 0.020693826616831845, 0.38635808908786146, 0.14334101020734036, 0.10839022913451997, 0.09179878840974022, 0.2215491471992385, 0.14822508661722344, 0.008063222854323085, -0.30804391900798483, 0.10810758414370339, 0.037823183114420726] |
1,803.00103 | Nested Algebraic Bethe Ansatz in integrable models: recent results | This short note summarizes the works done in collaboration between S.
Belliard (CEA, Saclay), L. Frappat (LAPTh, Annecy), S. Pakuliak (JINR, Dubna),
E. Ragoucy (LAPTh, Annecy), N. Slavnov (Steklov Math. Inst., Moscow) and more
recently A. Hutsalyuk (Wuppertal / Moscow) and A. Liashyk (Kiev / Moscow).
It presents the construction of Bethe vectors, their scalar products and the
form factors of local operator for integrable models based on the
(super)algebras $gl_n$, $gl_{m|p}$ or their quantum deformations.
It corresponds to two talks given by E.R. and N.S. at \textsl{Correlation
functions of quantum integrable systems and beyond}, in honor of Jean-Michel
Maillet for his 60's (ENS Lyon, October 2017).
| math-ph hep-th math.MP | this short note summarizes the works done in collaboration between s belliard cea saclay l frappat lapth annecy s pakuliak jinr dubna e ragoucy lapth annecy n slavnov steklov math inst moscow and more recently a hutsalyuk wuppertal moscow and a liashyk kiev moscow it presents the construction of bethe vectors their scalar products and the form factors of local operator for integrable models based on the superalgebras gl_n gl_mp or their quantum deformations it corresponds to two talks given by er and ns at textslcorrelation functions of quantum integrable systems and beyond in honor of jeanmichel maillet for his 60s ens lyon october 2017 | [['this', 'short', 'note', 'summarizes', 'the', 'works', 'done', 'in', 'collaboration', 'between', 's', 'belliard', 'cea', 'saclay', 'l', 'frappat', 'lapth', 'annecy', 's', 'pakuliak', 'jinr', 'dubna', 'e', 'ragoucy', 'lapth', 'annecy', 'n', 'slavnov', 'steklov', 'math', 'inst', 'moscow', 'and', 'more', 'recently', 'a', 'hutsalyuk', 'wuppertal', 'moscow', 'and', 'a', 'liashyk', 'kiev', 'moscow', 'it', 'presents', 'the', 'construction', 'of', 'bethe', 'vectors', 'their', 'scalar', 'products', 'and', 'the', 'form', 'factors', 'of', 'local', 'operator', 'for', 'integrable', 'models', 'based', 'on', 'the', 'superalgebras', 'gl_n', 'gl_mp', 'or', 'their', 'quantum', 'deformations', 'it', 'corresponds', 'to', 'two', 'talks', 'given', 'by', 'er', 'and', 'ns', 'at', 'textslcorrelation', 'functions', 'of', 'quantum', 'integrable', 'systems', 'and', 'beyond', 'in', 'honor', 'of', 'jeanmichel', 'maillet', 'for', 'his', '60s', 'ens', 'lyon', 'october', '2017']] | [-0.11680419950789656, 0.11879777991756175, -0.08712438826235787, 0.03545701134135015, -0.061895484701381065, -0.15543325912161285, 0.0016253270296147093, 0.24531949763453062, -0.16488730144677297, -0.28879943253317225, 0.08516313449581503, -0.33394674588150036, -0.1204612203631162, 0.17988683422784865, -0.10430969431763515, 0.011083880002843216, 0.06152845118049299, 0.042126873430485524, -0.07700168439381135, -0.2983106575460018, 0.25567216996569186, 0.13812140605053477, 0.24863739047820368, 0.05337024727001941, 0.12245750027553488, 0.08167001147618673, -0.06553569590808668, -0.14277081409818493, -0.15531719336286187, 0.13787693479874483, 0.31966538085229484, 0.11288916976263863, 0.22175700736503737, -0.4153564733375485, -0.07538407562424254, 0.08271066996773395, 0.019083528903138358, 0.013097902444618134, 0.07484998374805703, -0.3554958894189137, 0.027510619485231775, -0.22680144821060821, -0.12252549207187258, 0.024398292359061696, 0.13585958032732984, 0.004715848282406417, -0.18292712440597825, 0.043296771313180216, 0.08373006439069286, 0.1596230056893546, -0.01834774091548752, -0.22219295231237388, 0.010669381639066463, 0.0474081355278031, -0.04816369598665915, 0.0789214708978155, 0.050011038843270704, -0.0487417179744322, -0.14105467838938543, 0.3150407963451774, -0.004458872082371575, -0.07906958547998026, 0.12523123991498628, -0.13770634373698462, -0.17899777623946042, 0.053231565145930894, 0.14287706108007114, 0.10619995066857275, -0.13591225852542266, 0.23476180447505612, -0.04863037389410844, 0.058645523977853976, 0.18640391293835515, -0.057741640319970124, 0.13857096718857065, 0.09844020036568206, -0.03393242764771761, 0.042158674862245483, -0.007376319900989377, -0.07153946684168962, -0.31334317446453497, -0.16129946976707288, -0.1631978581523678, 0.10903838739492737, 0.049500627283881236, -0.09185998435229219, 0.3663491104064936, 0.028071020453353412, 0.1146629816260732, 0.01334309015025307, 0.12535689325401714, 0.028661125577248942, 0.013656251510838047, 0.14880699689153212, 0.16123920971100839, 0.20057619178745276, 0.21709939839881068, -0.13901034538866952, -0.06450520896275218, 0.17587017701589502] |
1,803.00104 | Active Matter Class with Second-Order Transition to Quasi-Long-Range
Polar Order | We introduce and study in two dimensions a new class of dry, aligning, active
matter that exhibits a direct transition to orientational order, without the
phase-separation phenomenology usually observed in this context. Characterized
by self-propelled particles with velocity reversals and ferromagnetic alignment
of polarities, systems in this class display quasi-long-range polar order with
continuously-varying scaling exponents and yet a numerical study of the
transition leads to conclude that it does not belong to the
Berezinskii-Kosterlitz-Thouless universality class, but is best described as a
standard critical point with algebraic divergence of correlations. We
rationalize these findings by showing that the interplay between order and
density changes the role of defects.
| cond-mat.soft cond-mat.stat-mech | we introduce and study in two dimensions a new class of dry aligning active matter that exhibits a direct transition to orientational order without the phaseseparation phenomenology usually observed in this context characterized by selfpropelled particles with velocity reversals and ferromagnetic alignment of polarities systems in this class display quasilongrange polar order with continuouslyvarying scaling exponents and yet a numerical study of the transition leads to conclude that it does not belong to the berezinskiikosterlitzthouless universality class but is best described as a standard critical point with algebraic divergence of correlations we rationalize these findings by showing that the interplay between order and density changes the role of defects | [['we', 'introduce', 'and', 'study', 'in', 'two', 'dimensions', 'a', 'new', 'class', 'of', 'dry', 'aligning', 'active', 'matter', 'that', 'exhibits', 'a', 'direct', 'transition', 'to', 'orientational', 'order', 'without', 'the', 'phaseseparation', 'phenomenology', 'usually', 'observed', 'in', 'this', 'context', 'characterized', 'by', 'selfpropelled', 'particles', 'with', 'velocity', 'reversals', 'and', 'ferromagnetic', 'alignment', 'of', 'polarities', 'systems', 'in', 'this', 'class', 'display', 'quasilongrange', 'polar', 'order', 'with', 'continuouslyvarying', 'scaling', 'exponents', 'and', 'yet', 'a', 'numerical', 'study', 'of', 'the', 'transition', 'leads', 'to', 'conclude', 'that', 'it', 'does', 'not', 'belong', 'to', 'the', 'berezinskiikosterlitzthouless', 'universality', 'class', 'but', 'is', 'best', 'described', 'as', 'a', 'standard', 'critical', 'point', 'with', 'algebraic', 'divergence', 'of', 'correlations', 'we', 'rationalize', 'these', 'findings', 'by', 'showing', 'that', 'the', 'interplay', 'between', 'order', 'and', 'density', 'changes', 'the', 'role', 'of', 'defects']] | [-0.1589435228860057, 0.1990736288727697, -0.06454053271771196, 0.050593235037597155, -0.06015230340182508, -0.1384651280273128, 0.03562623595951313, 0.3577109350525953, -0.24074502422638802, -0.3203696849149302, 0.024538382104973933, -0.2709026485724255, -0.20575067294129265, 0.12062538987657892, 0.008873807777807654, 0.015293979444840086, -0.05433324647217203, -0.015993708830087557, -0.1127699561216313, -0.1847235805380645, 0.33025996368421123, -0.016068857129013868, 0.282693126640462, 0.033313850932028315, 0.06015151563109061, -0.056842407694411116, 0.032563246252579156, 0.08557733307686557, -0.17347972954450114, 0.0688570010377155, 0.2522837031673247, -0.02797873412182025, 0.20863862870551578, -0.3749031495695951, -0.23320936963577335, 0.13061175079067844, 0.16170677014652196, 0.10473295949493458, -0.086097320665631, -0.2683130732370079, 0.06800852955207912, -0.15055542716055836, -0.1629108821806592, -0.10405006926130811, -0.0029283857161025387, 0.0435475631314757, -0.2606465521101934, 0.13333293285067066, 0.13035847126173045, 0.10018329975972755, -0.03361077200612822, -0.03405280028354513, -0.012075924088136045, 0.10120278074824755, 0.08291841298982569, 0.027305552849135953, 0.12738464123765947, -0.14957559383431568, -0.1501636283809678, 0.3876466316880163, -0.026449760711546817, -0.1503197538726237, 0.24347688905347925, -0.20318440410282906, -0.13399073632792868, 0.16904689092189074, 0.1523266264425437, 0.06103440100720169, -0.10929007034475377, 0.02076435236719815, -0.016935166668941463, 0.1876192025964708, 0.02258167213572343, 0.017960374196564113, 0.2569251705918017, 0.16805723064190306, 0.04015125347079214, 0.16757961963260665, -0.08523610755886643, -0.17681744814756403, -0.2976663896318423, -0.15956504156795936, -0.17353637644662223, 0.016060093637911492, -0.0761643485361048, -0.18624044044387586, 0.3748493640845932, 0.15801758928344065, 0.2214341914738366, 0.05031515561779021, 0.20464643305423882, 0.06606441206371394, 0.05937856997773393, 0.05002504091180109, 0.26401561414481167, 0.11354544458466523, 0.1093841606212797, -0.24954546638123623, 0.10812173363829561, 0.08601412759589698] |
1,803.00105 | Computational International Relations: What Can Programming, Coding and
Internet Research Do for the Discipline? | Computational Social Science emerged as a highly technical and popular
discipline in the last few years, owing to the substantial advances in
communication technology and daily production of vast quantities of personal
data. As per capita data production significantly increased in the last decade,
both in terms of its size, bytes, as well as its detail, heart rate monitors,
Internet connected appliances, smartphones, social scientists ability to
extract meaningful social, political and demographic information from digital
data also increased. A vast methodological gap exists in computational
international relations, or ComInt, which refers to the use of one or a
combination of tools such as data mining, natural language processing,
automated text analysis, web scraping, geospatial analysis and machine learning
to provide larger and better organized data to test more advanced theories of
IR. After providing an overview of the potentials of computational IR and how
an IR scholar can establish technical proficiency in computer science, such as
starting with Python, R, QGis, ArcGIS or Github, this paper will focus on some
of the author's works in providing an idea for IR students on how to think
about computational IR. The paper argues that computational methods transcend
the methodological schism between qualitative and quantitative approaches and
form a solid foundation for building truly multi method research design.
| cs.CY | computational social science emerged as a highly technical and popular discipline in the last few years owing to the substantial advances in communication technology and daily production of vast quantities of personal data as per capita data production significantly increased in the last decade both in terms of its size bytes as well as its detail heart rate monitors internet connected appliances smartphones social scientists ability to extract meaningful social political and demographic information from digital data also increased a vast methodological gap exists in computational international relations or comint which refers to the use of one or a combination of tools such as data mining natural language processing automated text analysis web scraping geospatial analysis and machine learning to provide larger and better organized data to test more advanced theories of ir after providing an overview of the potentials of computational ir and how an ir scholar can establish technical proficiency in computer science such as starting with python r qgis arcgis or github this paper will focus on some of the authors works in providing an idea for ir students on how to think about computational ir the paper argues that computational methods transcend the methodological schism between qualitative and quantitative approaches and form a solid foundation for building truly multi method research design | [['computational', 'social', 'science', 'emerged', 'as', 'a', 'highly', 'technical', 'and', 'popular', 'discipline', 'in', 'the', 'last', 'few', 'years', 'owing', 'to', 'the', 'substantial', 'advances', 'in', 'communication', 'technology', 'and', 'daily', 'production', 'of', 'vast', 'quantities', 'of', 'personal', 'data', 'as', 'per', 'capita', 'data', 'production', 'significantly', 'increased', 'in', 'the', 'last', 'decade', 'both', 'in', 'terms', 'of', 'its', 'size', 'bytes', 'as', 'well', 'as', 'its', 'detail', 'heart', 'rate', 'monitors', 'internet', 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1,803.00106 | RF Energy Harvesting Sensor Networks for Healthcare of Animals:
Opportunities and Challenges | In recent years, the radio frequency (RF) energy harvesting technique is
considered as a favorable alternative to supply power for the next-generation
wireless sensor networks. Due to the features of energy self-sustainability and
long lifetime, the energy harvesting sensor network (RF-EHSN) becomes a
promising solution to provide smart healthcare services. Nowadays, many energy
harvesting based applications have been developed for monitoring the health
status of human beings; how to benefit animals, however, has not yet drawn
people's attention. This article explores the potential of applying RF-EHSNs to
monitoring the health level of animals. The unique challenges and potential
solutions at different layers of an RF-EHSN for animals' healthcare service are
studied.
| cs.NI | in recent years the radio frequency rf energy harvesting technique is considered as a favorable alternative to supply power for the nextgeneration wireless sensor networks due to the features of energy selfsustainability and long lifetime the energy harvesting sensor network rfehsn becomes a promising solution to provide smart healthcare services nowadays many energy harvesting based applications have been developed for monitoring the health status of human beings how to benefit animals however has not yet drawn peoples attention this article explores the potential of applying rfehsns to monitoring the health level of animals the unique challenges and potential solutions at different layers of an rfehsn for animals healthcare service are studied | [['in', 'recent', 'years', 'the', 'radio', 'frequency', 'rf', 'energy', 'harvesting', 'technique', 'is', 'considered', 'as', 'a', 'favorable', 'alternative', 'to', 'supply', 'power', 'for', 'the', 'nextgeneration', 'wireless', 'sensor', 'networks', 'due', 'to', 'the', 'features', 'of', 'energy', 'selfsustainability', 'and', 'long', 'lifetime', 'the', 'energy', 'harvesting', 'sensor', 'network', 'rfehsn', 'becomes', 'a', 'promising', 'solution', 'to', 'provide', 'smart', 'healthcare', 'services', 'nowadays', 'many', 'energy', 'harvesting', 'based', 'applications', 'have', 'been', 'developed', 'for', 'monitoring', 'the', 'health', 'status', 'of', 'human', 'beings', 'how', 'to', 'benefit', 'animals', 'however', 'has', 'not', 'yet', 'drawn', 'peoples', 'attention', 'this', 'article', 'explores', 'the', 'potential', 'of', 'applying', 'rfehsns', 'to', 'monitoring', 'the', 'health', 'level', 'of', 'animals', 'the', 'unique', 'challenges', 'and', 'potential', 'solutions', 'at', 'different', 'layers', 'of', 'an', 'rfehsn', 'for', 'animals', 'healthcare', 'service', 'are', 'studied']] | [-0.14561105257167514, 0.06696010098689671, -0.014076358360600554, 0.027765690943515102, -0.12331591294733463, -0.18376376975707157, 0.04740372978375052, 0.42228197399526834, -0.26199820992985257, -0.311333661088375, 0.12206625200310049, -0.3057375730670713, -0.15841973657478337, 0.22605289244610402, -0.15199079262782578, 0.08235597259502996, 0.07850347064582079, 0.06409042558651115, 0.07677930156609768, -0.21754641276008138, 0.2198517989923453, 0.1346514740769096, 0.39999905902961336, 0.1176984559106468, 0.09879625114370827, -0.0082525044541668, -0.0019967884894598414, -0.055089430289593073, -0.09243679669237247, 0.18520479040496327, 0.39235072894478906, 0.1800335670187552, 0.3709153115266451, -0.4829550677462033, -0.3049400113501448, 0.1675156807113025, 0.14359603731793835, 0.05197263455354712, -0.11025682957498012, -0.28579571491314304, 0.0845518379916299, -0.2604749294222091, -0.15282371973929307, -0.05397844939660798, 0.04492796465378719, 0.06438485383275999, -0.21889400834873043, -0.009135664165821962, -0.06967702512931148, 0.09253232490435173, -0.10953643982281426, -0.11655425918892164, -0.014958976992164497, 0.2367701834041832, 0.10118365505736114, -0.022015273815432685, 0.1764454837905726, -0.1849662572710292, -0.11895519295810825, 0.3719554545694134, 0.0728728094017478, -0.11840805121162837, 0.20393971986939302, -0.012121610761689092, -0.16569082472576863, 0.08188248995726032, 0.23727677904362618, 0.04064168900044428, -0.2615924984662949, 0.029227975220203883, 0.0850345579545117, 0.1355589485804861, 0.05620280920131201, 0.10208789212629199, 0.2255886473899914, 0.2755158384916959, 0.1383419814535753, 0.07906078799041333, -0.08736337631664894, -0.07338123406602622, -0.11981827454400007, -0.12361825690432279, -0.2141233127374478, 0.08353329086646598, -0.029633094733139, -0.11649735986807004, 0.429897775563101, 0.17205463116126204, 0.10311357921886223, 0.011726949597855477, 0.3786502896497647, 0.06325053876692739, 0.10730385773004619, 0.03996743304499736, 0.22047688982328745, 0.025219681817193137, 0.2671838782262057, -0.1548358038934465, 0.08356975057891018, -0.05462370405654871] |
1,803.00107 | Pulling Out All the Tops with Computer Vision and Deep Learning | We apply computer vision with deep learning -- in the form of a convolutional
neural network (CNN) -- to build a highly effective boosted top tagger.
Previous work (the "DeepTop" tagger of Kasieczka et al) has shown that a
CNN-based top tagger can achieve comparable performance to state-of-the-art
conventional top taggers based on high-level inputs. Here, we introduce a
number of improvements to the DeepTop tagger, including architecture, training,
image preprocessing, sample size and color pixels. Our final CNN top tagger
outperforms BDTs based on high-level inputs by a factor of $\sim 2$--3 or more
in background rejection, over a wide range of tagging efficiencies and fiducial
jet selections. As reference points, we achieve a QCD background rejection
factor of 500 (60) at 50\% top tagging efficiency for fully-merged (non-merged)
top jets with $p_T$ in the 800--900 GeV (350--450 GeV) range. Our CNN can also
be straightforwardly extended to the classification of other types of jets, and
the lessons learned here may be useful to others designing their own deep NNs
for LHC applications.
| hep-ph hep-ex | we apply computer vision with deep learning in the form of a convolutional neural network cnn to build a highly effective boosted top tagger previous work the deeptop tagger of kasieczka et al has shown that a cnnbased top tagger can achieve comparable performance to stateoftheart conventional top taggers based on highlevel inputs here we introduce a number of improvements to the deeptop tagger including architecture training image preprocessing sample size and color pixels our final cnn top tagger outperforms bdts based on highlevel inputs by a factor of sim 23 or more in background rejection over a wide range of tagging efficiencies and fiducial jet selections as reference points we achieve a qcd background rejection factor of 500 60 at 50 top tagging efficiency for fullymerged nonmerged top jets with p_t in the 800900 gev 350450 gev range our cnn can also be straightforwardly extended to the classification of other types of jets and the lessons learned here may be useful to others designing their own deep nns for lhc applications | [['we', 'apply', 'computer', 'vision', 'with', 'deep', 'learning', 'in', 'the', 'form', 'of', 'a', 'convolutional', 'neural', 'network', 'cnn', 'to', 'build', 'a', 'highly', 'effective', 'boosted', 'top', 'tagger', 'previous', 'work', 'the', 'deeptop', 'tagger', 'of', 'kasieczka', 'et', 'al', 'has', 'shown', 'that', 'a', 'cnnbased', 'top', 'tagger', 'can', 'achieve', 'comparable', 'performance', 'to', 'stateoftheart', 'conventional', 'top', 'taggers', 'based', 'on', 'highlevel', 'inputs', 'here', 'we', 'introduce', 'a', 'number', 'of', 'improvements', 'to', 'the', 'deeptop', 'tagger', 'including', 'architecture', 'training', 'image', 'preprocessing', 'sample', 'size', 'and', 'color', 'pixels', 'our', 'final', 'cnn', 'top', 'tagger', 'outperforms', 'bdts', 'based', 'on', 'highlevel', 'inputs', 'by', 'a', 'factor', 'of', 'sim', '23', 'or', 'more', 'in', 'background', 'rejection', 'over', 'a', 'wide', 'range', 'of', 'tagging', 'efficiencies', 'and', 'fiducial', 'jet', 'selections', 'as', 'reference', 'points', 'we', 'achieve', 'a', 'qcd', 'background', 'rejection', 'factor', 'of', '500', '60', 'at', '50', 'top', 'tagging', 'efficiency', 'for', 'fullymerged', 'nonmerged', 'top', 'jets', 'with', 'p_t', 'in', 'the', '800900', 'gev', '350450', 'gev', 'range', 'our', 'cnn', 'can', 'also', 'be', 'straightforwardly', 'extended', 'to', 'the', 'classification', 'of', 'other', 'types', 'of', 'jets', 'and', 'the', 'lessons', 'learned', 'here', 'may', 'be', 'useful', 'to', 'others', 'designing', 'their', 'own', 'deep', 'nns', 'for', 'lhc', 'applications']] | [0.033109515147647386, 0.0689492205973472, -0.07315155234705856, 0.0727316695237918, -0.11915054171726372, -0.18036607953791434, 0.028458928066641975, 0.45582905025965365, -0.21512947525669265, -0.3851619052821936, -0.009112033397152183, -0.28232809724341307, -0.06686095265842446, 0.21595766527175947, -0.07467385769419416, 0.10849883233764644, 0.16689203370819916, 0.011754969520681708, -0.08537311451482041, -0.2987317555995945, 0.2524470502328811, 0.1009536342080214, 0.3258973921000952, 0.03887621519712389, 0.11223620118452071, -0.02077330619691921, -0.04550283466474037, -0.024622276536266172, -0.027163118897110965, 0.11374401477490656, 0.291444615058645, 0.15075757156385208, 0.2392448005602301, -0.3182466096744081, -0.17628646934454567, 0.05331188336444589, 0.14409273067908085, 0.06589951305429743, -0.04585017694341854, -0.3196862939009498, 0.17379316578608162, -0.2334531049048473, 0.015966680609020432, -0.1088686333363049, -0.0467370810047806, -0.03062146501725859, -0.29772743834568377, 0.017094350786750156, 0.056601819234759654, 0.04047958907731891, 0.03084576392088901, -0.20007115818516036, -0.007941907365426156, 0.09645523150870301, -0.026580802123640945, 0.1418341589484022, 0.24129206344165657, -0.24805326690654872, -0.19924869664432204, 0.31868505034287126, -0.09827383633526648, -0.19732850605963603, 0.2367779628439812, -0.06585413634435401, -0.15686803263579027, 0.1089214948474391, 0.307870321435931, 0.11344383862806406, -0.16228015640111276, 0.005011290830171518, -0.014254494989015295, 0.22376281697269992, 0.09756695722180182, 0.02464961882311432, 0.16344161703769317, 0.30960717788462105, -0.014081800246291612, 0.1150748019121152, -0.17821712465819037, -0.015237233829174808, -0.2635604454281591, -0.08665816950923129, -0.09919716265967347, 0.020742357269717522, -0.10051968330546687, -0.10540844928580005, 0.4348419092901426, 0.19778658543780897, 0.2624754892419693, 0.0666771023213924, 0.3356341050370161, -0.0017798152916166468, 0.15325394567493458, 0.12523959056314457, 0.2621029837321512, 0.03355840655434987, 0.15215207741267914, -0.09419436252692483, 0.0250690520063219, 0.06367560048152268] |
1,803.00108 | Martingale decomposition of a $L^2$ space with nonlinear stochastic
integrals | This paper presents a generalization of the Kunita-Watanabe decomposition of
a $L^2$ space with nonlinear stochastic integrals where the integrator is a
family of continuous martingales bounded in $L^2$. To get the result, a useful
relation between the regularity of the martingale family respect to its
parameter and the regularity of the integrand in its martingale decomposition
is shown.The decomposition presented in the main result is also the solution of
an optimization problem in $L^2$. Finally, an example is given where the
optimization problem is solved explicitely.
| math.PR | this paper presents a generalization of the kunitawatanabe decomposition of a l2 space with nonlinear stochastic integrals where the integrator is a family of continuous martingales bounded in l2 to get the result a useful relation between the regularity of the martingale family respect to its parameter and the regularity of the integrand in its martingale decomposition is shownthe decomposition presented in the main result is also the solution of an optimization problem in l2 finally an example is given where the optimization problem is solved explicitely | [['this', 'paper', 'presents', 'a', 'generalization', 'of', 'the', 'kunitawatanabe', 'decomposition', 'of', 'a', 'l2', 'space', 'with', 'nonlinear', 'stochastic', 'integrals', 'where', 'the', 'integrator', 'is', 'a', 'family', 'of', 'continuous', 'martingales', 'bounded', 'in', 'l2', 'to', 'get', 'the', 'result', 'a', 'useful', 'relation', 'between', 'the', 'regularity', 'of', 'the', 'martingale', 'family', 'respect', 'to', 'its', 'parameter', 'and', 'the', 'regularity', 'of', 'the', 'integrand', 'in', 'its', 'martingale', 'decomposition', 'is', 'shownthe', 'decomposition', 'presented', 'in', 'the', 'main', 'result', 'is', 'also', 'the', 'solution', 'of', 'an', 'optimization', 'problem', 'in', 'l2', 'finally', 'an', 'example', 'is', 'given', 'where', 'the', 'optimization', 'problem', 'is', 'solved', 'explicitely']] | [-0.1250628018210274, 0.01335973201385905, -0.12475808969222356, 0.06616109936378983, -0.08766564835122852, -0.06129182633662293, -0.00397708642751325, 0.3183077472413695, -0.38045531688907813, -0.21385222380821117, 0.17916557247490558, -0.2325825752842045, -0.16642791821166528, 0.17915300446627444, -0.1489160041200257, 0.07937636782366479, 0.06742813924414127, 0.0361365066884562, -0.06442457374163665, -0.22151234278152154, 0.32755767587615653, 0.00017071220763894014, 0.2077812181601604, 0.02894598600822826, 0.14460576779945472, -0.011944121361688473, -0.019567388932892057, -0.037954231025989514, -0.14757057655667669, 0.15511747737425965, 0.24692618311924297, 0.11985426184147337, 0.34043204550479733, -0.34098621815183133, -0.1219528570857852, 0.15981303823050544, 0.139153174374748, 0.03865806028506784, -0.028082017743483532, -0.2512350587910691, 0.056691331101234914, -0.11539858960828116, -0.16538220072208448, -0.06485560509311251, 0.008434563008851783, 0.018727445281868756, -0.3407734456190536, 0.0781181624215394, 0.12574328653728264, 0.008199048451409232, -0.12203950494755232, -0.07610748194667055, -0.015911753051156222, 0.04052467653419563, 0.07545264956248968, 0.08861261191453006, 0.015335844353187916, -0.09328418400755889, -0.10386567217362828, 0.36529056220396616, -0.06890444585249413, -0.3199671266294561, 0.11450490412887099, -0.1276126659995075, -0.14151264782345227, 0.08566124226118244, 0.14998938763774064, 0.17751350929570753, -0.15298020088135503, 0.176438278312183, -0.07804333971445124, 0.10919538440540173, 0.052897720944223015, 0.005459388761326324, 0.04342343663082047, 0.19573846438813003, 0.21734521688005431, 0.19352022883330666, -0.017726381157719812, -0.12300178057752377, -0.35953542919353, -0.19342821111989228, -0.21039401879534125, 0.055789145456930235, -0.15989334698273058, -0.19608688553751902, 0.394814255295408, 0.08512101023522921, 0.18317378676214882, 0.08812058342994343, 0.26704083597504125, 0.20982025837157528, -0.01987741908145159, 0.056804042913257906, 0.18678009573933343, 0.17153366470741932, 0.09840420692032853, -0.20267127611441538, 0.05170301526654945, 0.17584633756328108] |
1,803.00109 | Asymptotics of quantum channels: conserved quantities, an adiabatic
limit, and matrix product states | This work derives an analytical formula for the asymptotic state---the
quantum state resulting from an infinite number of applications of a general
quantum channel on some initial state. For channels admitting multiple fixed or
rotating points, conserved quantities---the left fixed/rotating points of the
channel---determine the dependence of the asymptotic state on the initial
state. The formula stems from a Noether-like theorem stating that, for any
channel admitting a full-rank fixed point, conserved quantities commute with
that channel's Kraus operators up to a phase. The formula is applied to
adiabatic transport of the fixed-point space of channels, revealing cases where
the dissipative/spectral gap can close during any segment of the adiabatic
path. The formula is also applied to calculate expectation values of
noninjective matrix product states (MPS) in the thermodynamic limit, revealing
that those expectation values can also be calculated using an MPS with reduced
bond dimension and a modified boundary.
| quant-ph | this work derives an analytical formula for the asymptotic statethe quantum state resulting from an infinite number of applications of a general quantum channel on some initial state for channels admitting multiple fixed or rotating points conserved quantitiesthe left fixedrotating points of the channeldetermine the dependence of the asymptotic state on the initial state the formula stems from a noetherlike theorem stating that for any channel admitting a fullrank fixed point conserved quantities commute with that channels kraus operators up to a phase the formula is applied to adiabatic transport of the fixedpoint space of channels revealing cases where the dissipativespectral gap can close during any segment of the adiabatic path the formula is also applied to calculate expectation values of noninjective matrix product states mps in the thermodynamic limit revealing that those expectation values can also be calculated using an mps with reduced bond dimension and a modified boundary | [['this', 'work', 'derives', 'an', 'analytical', 'formula', 'for', 'the', 'asymptotic', 'statethe', 'quantum', 'state', 'resulting', 'from', 'an', 'infinite', 'number', 'of', 'applications', 'of', 'a', 'general', 'quantum', 'channel', 'on', 'some', 'initial', 'state', 'for', 'channels', 'admitting', 'multiple', 'fixed', 'or', 'rotating', 'points', 'conserved', 'quantitiesthe', 'left', 'fixedrotating', 'points', 'of', 'the', 'channeldetermine', 'the', 'dependence', 'of', 'the', 'asymptotic', 'state', 'on', 'the', 'initial', 'state', 'the', 'formula', 'stems', 'from', 'a', 'noetherlike', 'theorem', 'stating', 'that', 'for', 'any', 'channel', 'admitting', 'a', 'fullrank', 'fixed', 'point', 'conserved', 'quantities', 'commute', 'with', 'that', 'channels', 'kraus', 'operators', 'up', 'to', 'a', 'phase', 'the', 'formula', 'is', 'applied', 'to', 'adiabatic', 'transport', 'of', 'the', 'fixedpoint', 'space', 'of', 'channels', 'revealing', 'cases', 'where', 'the', 'dissipativespectral', 'gap', 'can', 'close', 'during', 'any', 'segment', 'of', 'the', 'adiabatic', 'path', 'the', 'formula', 'is', 'also', 'applied', 'to', 'calculate', 'expectation', 'values', 'of', 'noninjective', 'matrix', 'product', 'states', 'mps', 'in', 'the', 'thermodynamic', 'limit', 'revealing', 'that', 'those', 'expectation', 'values', 'can', 'also', 'be', 'calculated', 'using', 'an', 'mps', 'with', 'reduced', 'bond', 'dimension', 'and', 'a', 'modified', 'boundary']] | [-0.1663703019635714, 0.15972666301141686, -0.11722343010181675, 0.03649621037845532, -0.012007458089553074, -0.1494320923063372, 0.09692383625664983, 0.29333906241876334, -0.26365002630069395, -0.22371920964800987, 0.10223480655738869, -0.26038184189902885, -0.08753100636915691, 0.18400447342234352, -0.03616607965518195, 0.07028441606140949, 0.08414437263241024, 0.09123267553315885, -0.1416202792153396, -0.17959632581974172, 0.35324112501717647, 0.010496017483512865, 0.261787678381162, 0.034550150035924855, 0.1289090267477595, 0.028337241788743202, 0.03611382255790008, -0.016075943702100606, -0.1335782068012877, 0.06574747497556402, 0.24285941219850615, 0.07140675275333125, 0.19006366500643646, -0.4124506395183453, -0.18888949466628485, 0.1358073713942146, 0.13621235652557467, 0.14507001614971024, 0.00994269706492572, -0.28039866542684383, 0.06461409944169806, -0.1880060944351412, -0.17629165710409356, -0.07716134968087027, 0.044271109828434026, -0.03752660268258785, -0.30098047369432185, 0.07626893353564539, 0.05464515535376307, 0.02687093556294737, -0.09021246234956039, -0.1414420611207646, -0.044891779208048974, 0.14484612044140216, 0.02587723346477767, 0.018258455085099302, 0.11682495972471071, -0.12074318023867348, -0.1215770244661744, 0.3089823688342407, -0.07138914116943369, -0.23341822899801998, 0.12808480529117772, -0.11776074801324582, -0.08773021626786716, 0.12286473275954221, 0.08293393739581513, 0.12645495957283137, -0.13004071540123985, 0.12760509713671683, -0.06071540016956551, 0.13719092318225892, 0.08186627126165799, 0.05599488639375702, 0.16493167065316217, 0.029010022393282073, 0.09121131861731284, 0.17391082896774976, -0.04486059413060267, -0.16655707877401743, -0.33788287811667095, -0.18205176612526988, -0.2341968446754578, 0.1134412863868333, -0.14153948446510434, -0.19477778300642967, 0.3663118508720428, 0.11262975752410688, 0.19579348890554338, 0.0736009737121917, 0.24266340815443166, 0.19325720873616992, 0.03012793830779641, 0.11219926025452358, 0.1836196615205853, 0.18020181896380422, 0.051106274020256234, -0.21680223643082847, 0.059334823316862795, 0.10514587510478537] |
1,803.0011 | Maximizing Activity in Ising Networks via the TAP Approximation | A wide array of complex biological, social, and physical systems have
recently been shown to be quantitatively described by Ising models, which lie
at the intersection of statistical physics and machine learning. Here, we study
the fundamental question of how to optimize the state of a networked Ising
system given a budget of external influence. In the continuous setting where
one can tune the influence applied to each node, we propose a series of
approximate gradient ascent algorithms based on the Plefka expansion, which
generalizes the na\"{i}ve mean field and TAP approximations. In the discrete
setting where one chooses a small set of influential nodes, the problem is
equivalent to the famous influence maximization problem in social networks with
an additional stochastic noise term. In this case, we provide sufficient
conditions for when the objective is submodular, allowing a greedy algorithm to
achieve an approximation ratio of $1-1/e$. Additionally, we compare the
Ising-based algorithms with traditional influence maximization algorithms,
demonstrating the practical importance of accurately modeling stochastic
fluctuations in the system.
| physics.soc-ph cond-mat.stat-mech cs.SI | a wide array of complex biological social and physical systems have recently been shown to be quantitatively described by ising models which lie at the intersection of statistical physics and machine learning here we study the fundamental question of how to optimize the state of a networked ising system given a budget of external influence in the continuous setting where one can tune the influence applied to each node we propose a series of approximate gradient ascent algorithms based on the plefka expansion which generalizes the naive mean field and tap approximations in the discrete setting where one chooses a small set of influential nodes the problem is equivalent to the famous influence maximization problem in social networks with an additional stochastic noise term in this case we provide sufficient conditions for when the objective is submodular allowing a greedy algorithm to achieve an approximation ratio of 11e additionally we compare the isingbased algorithms with traditional influence maximization algorithms demonstrating the practical importance of accurately modeling stochastic fluctuations in the system | [['a', 'wide', 'array', 'of', 'complex', 'biological', 'social', 'and', 'physical', 'systems', 'have', 'recently', 'been', 'shown', 'to', 'be', 'quantitatively', 'described', 'by', 'ising', 'models', 'which', 'lie', 'at', 'the', 'intersection', 'of', 'statistical', 'physics', 'and', 'machine', 'learning', 'here', 'we', 'study', 'the', 'fundamental', 'question', 'of', 'how', 'to', 'optimize', 'the', 'state', 'of', 'a', 'networked', 'ising', 'system', 'given', 'a', 'budget', 'of', 'external', 'influence', 'in', 'the', 'continuous', 'setting', 'where', 'one', 'can', 'tune', 'the', 'influence', 'applied', 'to', 'each', 'node', 'we', 'propose', 'a', 'series', 'of', 'approximate', 'gradient', 'ascent', 'algorithms', 'based', 'on', 'the', 'plefka', 'expansion', 'which', 'generalizes', 'the', 'naive', 'mean', 'field', 'and', 'tap', 'approximations', 'in', 'the', 'discrete', 'setting', 'where', 'one', 'chooses', 'a', 'small', 'set', 'of', 'influential', 'nodes', 'the', 'problem', 'is', 'equivalent', 'to', 'the', 'famous', 'influence', 'maximization', 'problem', 'in', 'social', 'networks', 'with', 'an', 'additional', 'stochastic', 'noise', 'term', 'in', 'this', 'case', 'we', 'provide', 'sufficient', 'conditions', 'for', 'when', 'the', 'objective', 'is', 'submodular', 'allowing', 'a', 'greedy', 'algorithm', 'to', 'achieve', 'an', 'approximation', 'ratio', 'of', '11e', 'additionally', 'we', 'compare', 'the', 'isingbased', 'algorithms', 'with', 'traditional', 'influence', 'maximization', 'algorithms', 'demonstrating', 'the', 'practical', 'importance', 'of', 'accurately', 'modeling', 'stochastic', 'fluctuations', 'in', 'the', 'system']] | [-0.10597836982107542, 0.02346436453629857, -0.07318958651535866, 0.08350353513514808, -0.09374254928732956, -0.15833736177838859, 0.06386764804500411, 0.3816133753467373, -0.30373485477597656, -0.30897829237486624, 0.07578199452037068, -0.23112761858055553, -0.20256391198957088, 0.15307215139901603, -0.06579693835718851, 0.08055756611433643, 0.04127828402128833, 0.06825611285190643, -0.028652996415059948, -0.30143693777901387, 0.29459621702260963, 0.06811922885292368, 0.2993529838488072, 0.03439263409760655, 0.1365905077276991, 0.019234837705476418, 0.022893164896444357, 0.05833936421941334, -0.11498031371588988, 0.12082673010841143, 0.2637402410274523, 0.15125738862188456, 0.3697186362281529, -0.40829695047734427, -0.2231087970563717, 0.15401722427644107, 0.14028747279124815, 0.10261301289323434, 0.005142707031149395, -0.2515536184458073, 0.06955704345008391, -0.15833507007674166, -0.06863208346365685, -0.06574132057636628, -0.01945181760041054, 0.053548980511082284, -0.3180188792648763, 0.029583265831999002, 0.04010642845829001, 0.051783710391375054, -0.0568391536770944, -0.10969145132408462, 0.061222996075272734, 0.1287060314021822, 0.027184496408366537, 0.038133286996962425, 0.14499700995848366, -0.12839009063127751, -0.16213413671595833, 0.3905922666148484, -0.05132270425732373, -0.22552293177410873, 0.16370111227999523, -0.06924343457423716, -0.15706346801629192, 0.09784741805558643, 0.24072216237127259, 0.12504030432693705, -0.16142934791402339, 0.07923481474145981, -0.045501733702547544, 0.1252913085515039, 0.004932193796297437, -0.003085099656287341, 0.14592812917527487, 0.1926836965330577, 0.10977857335394368, 0.17983161544152235, -0.03870422036416981, -0.14704103783121583, -0.22008858305233264, -0.10693443868832107, -0.199793653262436, 0.040891593011717, -0.11450691069096765, -0.17557001408861475, 0.4050888209746693, 0.19481986615131472, 0.18153948233291312, 0.05912700379594115, 0.29458035333681176, 0.12295246718640981, 0.007401647721553406, 0.09994309208334058, 0.22244388634769235, 0.12441881880714226, 0.1043392134672412, -0.21996096629997966, 0.10146185100536075, 0.0660812239576545] |
1,803.00111 | Predicting Recall Probability to Adaptively Prioritize Study | Students have a limited time to study and are typically ineffective at
allocating study time. Machine-directed study strategies that identify which
items need reinforcement and dictate the spacing of repetition have been shown
to help students optimize mastery (Mozer & Lindsey 2017). The large volume of
research on this matter is typically conducted in constructed experimental
settings with fixed instruction, content, and scheduling; in contrast, we aim
to develop methods that can address any demographic, subject matter, or study
schedule. We show two methods that model item-specific recall probability for
use in a discrepancy-reduction instruction strategy. The first model predicts
item recall probability using a multiple logistic regression (MLR) model based
on previous answer correctness and temporal spacing of study. Prompted by
literature suggesting that forgetting is better modeled by the power law than
an exponential decay (Wickelgren 1974), we compare the MLR approach with a
Recurrent Power Law (RPL) model which adaptively fits a forgetting curve. We
then discuss the performance of these models against study datasets comprised
of millions of answers and show that the RPL approach is more accurate and
flexible than the MLR model. Finally, we give an overview of promising future
approaches to knowledge modeling.
| cs.CY cs.LG | students have a limited time to study and are typically ineffective at allocating study time machinedirected study strategies that identify which items need reinforcement and dictate the spacing of repetition have been shown to help students optimize mastery mozer lindsey 2017 the large volume of research on this matter is typically conducted in constructed experimental settings with fixed instruction content and scheduling in contrast we aim to develop methods that can address any demographic subject matter or study schedule we show two methods that model itemspecific recall probability for use in a discrepancyreduction instruction strategy the first model predicts item recall probability using a multiple logistic regression mlr model based on previous answer correctness and temporal spacing of study prompted by literature suggesting that forgetting is better modeled by the power law than an exponential decay wickelgren 1974 we compare the mlr approach with a recurrent power law rpl model which adaptively fits a forgetting curve we then discuss the performance of these models against study datasets comprised of millions of answers and show that the rpl approach is more accurate and flexible than the mlr model finally we give an overview of promising future approaches to knowledge modeling | [['students', 'have', 'a', 'limited', 'time', 'to', 'study', 'and', 'are', 'typically', 'ineffective', 'at', 'allocating', 'study', 'time', 'machinedirected', 'study', 'strategies', 'that', 'identify', 'which', 'items', 'need', 'reinforcement', 'and', 'dictate', 'the', 'spacing', 'of', 'repetition', 'have', 'been', 'shown', 'to', 'help', 'students', 'optimize', 'mastery', 'mozer', 'lindsey', '2017', 'the', 'large', 'volume', 'of', 'research', 'on', 'this', 'matter', 'is', 'typically', 'conducted', 'in', 'constructed', 'experimental', 'settings', 'with', 'fixed', 'instruction', 'content', 'and', 'scheduling', 'in', 'contrast', 'we', 'aim', 'to', 'develop', 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1,803.00112 | Cooperation dynamics of generalized reciprocity in state-based social
dilemmas | We introduce a framework for studying social dilemmas in networked societies
where individuals follow a simple state-based behavioral mechanism based on
generalized reciprocity, which is rooted in the principle "help anyone if
helped by someone". Within this general framework, which applies to a wide
range of social dilemmas including, among others, public goods, donation and
snowdrift games, we study the cooperation dynamics on a variety of complex
network examples. By interpreting the studied model through the lenses of
nonlinear dynamical systems, we show that cooperation through generalized
reciprocity always emerges as the unique attractor in which the overall level
of cooperation is maximized, while simultaneously exploitation of the
participating individuals is prevented. The analysis elucidates the role of the
network structure, here captured by a local centrality measure which uniquely
quantifies the propensity of the network structure to cooperation, by dictating
the degree of cooperation displayed both at microscopic and macroscopic level.
We demonstrate the applicability of the analysis on a practical example by
considering an interaction structure that couples a donation process with a
public goods game.
| q-bio.PE | we introduce a framework for studying social dilemmas in networked societies where individuals follow a simple statebased behavioral mechanism based on generalized reciprocity which is rooted in the principle help anyone if helped by someone within this general framework which applies to a wide range of social dilemmas including among others public goods donation and snowdrift games we study the cooperation dynamics on a variety of complex network examples by interpreting the studied model through the lenses of nonlinear dynamical systems we show that cooperation through generalized reciprocity always emerges as the unique attractor in which the overall level of cooperation is maximized while simultaneously exploitation of the participating individuals is prevented the analysis elucidates the role of the network structure here captured by a local centrality measure which uniquely quantifies the propensity of the network structure to cooperation by dictating the degree of cooperation displayed both at microscopic and macroscopic level we demonstrate the applicability of the analysis on a practical example by considering an interaction structure that couples a donation process with a public goods game | [['we', 'introduce', 'a', 'framework', 'for', 'studying', 'social', 'dilemmas', 'in', 'networked', 'societies', 'where', 'individuals', 'follow', 'a', 'simple', 'statebased', 'behavioral', 'mechanism', 'based', 'on', 'generalized', 'reciprocity', 'which', 'is', 'rooted', 'in', 'the', 'principle', 'help', 'anyone', 'if', 'helped', 'by', 'someone', 'within', 'this', 'general', 'framework', 'which', 'applies', 'to', 'a', 'wide', 'range', 'of', 'social', 'dilemmas', 'including', 'among', 'others', 'public', 'goods', 'donation', 'and', 'snowdrift', 'games', 'we', 'study', 'the', 'cooperation', 'dynamics', 'on', 'a', 'variety', 'of', 'complex', 'network', 'examples', 'by', 'interpreting', 'the', 'studied', 'model', 'through', 'the', 'lenses', 'of', 'nonlinear', 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1,803.00113 | Approximate Inference for Constructing Astronomical Catalogs from Images | We present a new, fully generative model for constructing astronomical
catalogs from optical telescope image sets. Each pixel intensity is treated as
a random variable with parameters that depend on the latent properties of stars
and galaxies. These latent properties are themselves modeled as random. We
compare two procedures for posterior inference. One procedure is based on
Markov chain Monte Carlo (MCMC) while the other is based on variational
inference (VI). The MCMC procedure excels at quantifying uncertainty, while the
VI procedure is 1000 times faster. On a supercomputer, the VI procedure
efficiently uses 665,000 CPU cores to construct an astronomical catalog from 50
terabytes of images in 14.6 minutes, demonstrating the scaling characteristics
necessary to construct catalogs for upcoming astronomical surveys.
| stat.AP astro-ph.IM cs.LG stat.ML | we present a new fully generative model for constructing astronomical catalogs from optical telescope image sets each pixel intensity is treated as a random variable with parameters that depend on the latent properties of stars and galaxies these latent properties are themselves modeled as random we compare two procedures for posterior inference one procedure is based on markov chain monte carlo mcmc while the other is based on variational inference vi the mcmc procedure excels at quantifying uncertainty while the vi procedure is 1000 times faster on a supercomputer the vi procedure efficiently uses 665000 cpu cores to construct an astronomical catalog from 50 terabytes of images in 146 minutes demonstrating the scaling characteristics necessary to construct catalogs for upcoming astronomical surveys | [['we', 'present', 'a', 'new', 'fully', 'generative', 'model', 'for', 'constructing', 'astronomical', 'catalogs', 'from', 'optical', 'telescope', 'image', 'sets', 'each', 'pixel', 'intensity', 'is', 'treated', 'as', 'a', 'random', 'variable', 'with', 'parameters', 'that', 'depend', 'on', 'the', 'latent', 'properties', 'of', 'stars', 'and', 'galaxies', 'these', 'latent', 'properties', 'are', 'themselves', 'modeled', 'as', 'random', 'we', 'compare', 'two', 'procedures', 'for', 'posterior', 'inference', 'one', 'procedure', 'is', 'based', 'on', 'markov', 'chain', 'monte', 'carlo', 'mcmc', 'while', 'the', 'other', 'is', 'based', 'on', 'variational', 'inference', 'vi', 'the', 'mcmc', 'procedure', 'excels', 'at', 'quantifying', 'uncertainty', 'while', 'the', 'vi', 'procedure', 'is', '1000', 'times', 'faster', 'on', 'a', 'supercomputer', 'the', 'vi', 'procedure', 'efficiently', 'uses', '665000', 'cpu', 'cores', 'to', 'construct', 'an', 'astronomical', 'catalog', 'from', '50', 'terabytes', 'of', 'images', 'in', '146', 'minutes', 'demonstrating', 'the', 'scaling', 'characteristics', 'necessary', 'to', 'construct', 'catalogs', 'for', 'upcoming', 'astronomical', 'surveys']] | [0.0011995766188733835, 0.09063211759691385, -0.06671316211673715, 0.10340417267693099, -0.13286948809015356, -0.1283561674500854, 0.06930013191462918, 0.4930962948513425, -0.22374909729812265, -0.3696608222705458, 0.11420985241516778, -0.28684455357214883, -0.04377688767680944, 0.25673933328544984, -0.026922363749405077, 0.07855267761167416, 0.1364380971120655, -0.04801637652399372, -0.05736744183900743, -0.2812951337392173, 0.2323238829858909, 0.08857923245811758, 0.28096424905229206, -0.13657487150712694, 0.15686488335505744, -0.019627759633354905, -0.11195571319207795, -0.032636981395896804, -0.11266366426462802, 0.10063580373563796, 0.2526751658369302, 0.20431589212053078, 0.2881416032621868, -0.3773981036930076, -0.20372679692592996, 0.06277811597293813, 0.1405152275173132, 0.10138430712313569, 0.01145595300188365, -0.3052523980871582, 0.03262002308167948, -0.14809202103421462, -0.07802323136124606, -0.08726582617291119, -0.031478010090106465, 0.06425770354738136, -0.26118193915467863, 0.08664611316466904, -0.03631992322154158, 0.10059143250330913, -0.026868781074881554, -0.13950093259480745, -0.010832677466394611, 0.07706401402838836, -0.03826831513823372, 0.03516200693201058, 0.15268146673078872, -0.10658908841654288, -0.10201013050126759, 0.3747053633959702, -0.05307120338759639, -0.12658750427471213, 0.15142641020053613, -0.06237378666439086, -0.23603333161050868, 0.12356351142218105, 0.18580972420221026, 0.1472332745329631, -0.21513047611941238, 0.04252271145180037, 0.017128788738142053, 0.20274063077378124, -0.009035672980922558, -0.01935421604247502, 0.19861299851678374, 0.17980363106447433, 0.054274829363350346, 0.15328208640004193, -0.23582678218949432, -0.1116892488765692, -0.27112369547397946, -0.11876528073209992, -0.20328134621978422, 0.01959482787084604, -0.17103705095299923, -0.19394210912287235, 0.35713798749022, 0.24167913983240974, 0.18359357631988515, 0.10785669769537597, 0.3112789906199621, 0.04529399930283982, 0.1019875538219844, 0.1054838507778727, 0.16439680394924377, 0.09854972340967044, 0.04490044325482377, -0.126834532625065, 0.10108888161961328, 0.08080556789751757] |
1,803.00114 | SQL-Rank: A Listwise Approach to Collaborative Ranking | In this paper, we propose a listwise approach for constructing user-specific
rankings in recommendation systems in a collaborative fashion. We contrast the
listwise approach to previous pointwise and pairwise approaches, which are
based on treating either each rating or each pairwise comparison as an
independent instance respectively. By extending the work of (Cao et al. 2007),
we cast listwise collaborative ranking as maximum likelihood under a
permutation model which applies probability mass to permutations based on a low
rank latent score matrix. We present a novel algorithm called SQL-Rank, which
can accommodate ties and missing data and can run in linear time. We develop a
theoretical framework for analyzing listwise ranking methods based on a novel
representation theory for the permutation model. Applying this framework to
collaborative ranking, we derive asymptotic statistical rates as the number of
users and items grow together. We conclude by demonstrating that our SQL-Rank
method often outperforms current state-of-the-art algorithms for implicit
feedback such as Weighted-MF and BPR and achieve favorable results when
compared to explicit feedback algorithms such as matrix factorization and
collaborative ranking.
| stat.ML cs.IR cs.LG | in this paper we propose a listwise approach for constructing userspecific rankings in recommendation systems in a collaborative fashion we contrast the listwise approach to previous pointwise and pairwise approaches which are based on treating either each rating or each pairwise comparison as an independent instance respectively by extending the work of cao et al 2007 we cast listwise collaborative ranking as maximum likelihood under a permutation model which applies probability mass to permutations based on a low rank latent score matrix we present a novel algorithm called sqlrank which can accommodate ties and missing data and can run in linear time we develop a theoretical framework for analyzing listwise ranking methods based on a novel representation theory for the permutation model applying this framework to collaborative ranking we derive asymptotic statistical rates as the number of users and items grow together we conclude by demonstrating that our sqlrank method often outperforms current stateoftheart algorithms for implicit feedback such as weightedmf and bpr and achieve favorable results when compared to explicit feedback algorithms such as matrix factorization and collaborative ranking | [['in', 'this', 'paper', 'we', 'propose', 'a', 'listwise', 'approach', 'for', 'constructing', 'userspecific', 'rankings', 'in', 'recommendation', 'systems', 'in', 'a', 'collaborative', 'fashion', 'we', 'contrast', 'the', 'listwise', 'approach', 'to', 'previous', 'pointwise', 'and', 'pairwise', 'approaches', 'which', 'are', 'based', 'on', 'treating', 'either', 'each', 'rating', 'or', 'each', 'pairwise', 'comparison', 'as', 'an', 'independent', 'instance', 'respectively', 'by', 'extending', 'the', 'work', 'of', 'cao', 'et', 'al', '2007', 'we', 'cast', 'listwise', 'collaborative', 'ranking', 'as', 'maximum', 'likelihood', 'under', 'a', 'permutation', 'model', 'which', 'applies', 'probability', 'mass', 'to', 'permutations', 'based', 'on', 'a', 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1,803.00115 | Holomorphic quadratic differentials on graphs and the chromatic
polynomial | We study "holomorphic quadratic differentials" on graphs. We relate them to
the reactive power in an LC circuit, and also to the chromatic polynomial of a
graph. Specifically, we show that the chromatic polynomial $\chi$ of a graph
$G$, at negative integer values, can be evaluated as the degree of a certain
rational mapping, arising from the defining equations for a holomorphic
quadratic differential. This allows us to give an explicit integral expression
for $\chi(-k)$.
| math.CO | we study holomorphic quadratic differentials on graphs we relate them to the reactive power in an lc circuit and also to the chromatic polynomial of a graph specifically we show that the chromatic polynomial chi of a graph g at negative integer values can be evaluated as the degree of a certain rational mapping arising from the defining equations for a holomorphic quadratic differential this allows us to give an explicit integral expression for chik | [['we', 'study', 'holomorphic', 'quadratic', 'differentials', 'on', 'graphs', 'we', 'relate', 'them', 'to', 'the', 'reactive', 'power', 'in', 'an', 'lc', 'circuit', 'and', 'also', 'to', 'the', 'chromatic', 'polynomial', 'of', 'a', 'graph', 'specifically', 'we', 'show', 'that', 'the', 'chromatic', 'polynomial', 'chi', 'of', 'a', 'graph', 'g', 'at', 'negative', 'integer', 'values', 'can', 'be', 'evaluated', 'as', 'the', 'degree', 'of', 'a', 'certain', 'rational', 'mapping', 'arising', 'from', 'the', 'defining', 'equations', 'for', 'a', 'holomorphic', 'quadratic', 'differential', 'this', 'allows', 'us', 'to', 'give', 'an', 'explicit', 'integral', 'expression', 'for', 'chik']] | [-0.20007810227572917, 0.03037892721942626, -0.09838314679140846, 0.058508932759674885, -0.16779800010224183, -0.13891878252228101, 0.003373406504591306, 0.32159564991792045, -0.3371726802488168, -0.28161009076982735, 0.04796168794079373, -0.25425806345747937, -0.21235195137560367, 0.2216164480894804, -0.0838014438127478, 0.06012501158305288, 0.005392364077270031, 0.10046727988868952, -0.05648279140082498, -0.2545615238362613, 0.3583825273187055, -0.013393056181569895, 0.11878149380907416, 0.09506566043943167, 0.16993699225441863, 0.006819008657087882, -0.014014138678709665, 0.04301133832583825, -0.1922542906049057, 0.13730563652391234, 0.291479277908802, 0.07045235695317388, 0.21751663868984905, -0.413463317155838, -0.1130624456393222, 0.2247566506639123, 0.09233434138198694, 0.04936902603755395, 0.01382967985390375, -0.19283934481441975, 0.09486641630840799, -0.12922822244950416, -0.13016008374591667, -0.0952446261793375, 0.06077505448212226, 0.03187901065684855, -0.326335764726003, -0.03373133515046599, 0.07260711919516324, 0.11711897770563762, -0.006482056133293857, -0.12209034982447822, -0.019900407760093608, 0.07583532740672429, -0.052680828350906574, 0.03495433227779965, 0.05240370926136772, -0.14343967072355251, -0.13354416707064956, 0.33289755910634994, -0.10377180966238181, -0.21353867329657078, 0.0873664832363526, -0.1481156198812338, -0.1806250999867916, 0.12849310121188562, 0.19949431558450062, 0.14392790047762294, -0.08151816130926212, 0.13392019662928456, -0.0987345751002431, 0.13677409320759276, 0.11269444645692905, -0.045619115500400466, 0.13841714991256596, 0.018230199376121164, 0.11161050704618294, 0.22980345780495554, 0.0009495161035253356, -0.029352338910102846, -0.3341135065630078, -0.1873637926702698, -0.15106735846648614, 0.11170133293606341, -0.17599649895584055, -0.19218626495450736, 0.4598780812074741, 0.07454067088042696, 0.1971828240342438, 0.17418335201839605, 0.2527942779411872, 0.2177674085693434, 0.06794480691819141, 0.04085833587994178, 0.1267444664457192, 0.17214567970484496, 0.01315145772571365, -0.22960175348756215, 0.026542793090144793, 0.13606908741096654] |
1,803.00116 | Separators and Adjustment Sets in Causal Graphs: Complete Criteria and
an Algorithmic Framework | Principled reasoning about the identifiability of causal effects from
non-experimental data is an important application of graphical causal models.
This paper focuses on effects that are identifiable by covariate adjustment, a
commonly used estimation approach. We present an algorithmic framework for
efficiently testing, constructing, and enumerating $m$-separators in ancestral
graphs (AGs), a class of graphical causal models that can represent uncertainty
about the presence of latent confounders. Furthermore, we prove a reduction
from causal effect identification by covariate adjustment to $m$-separation in
a subgraph for directed acyclic graphs (DAGs) and maximal ancestral graphs
(MAGs). Jointly, these results yield constructive criteria that characterize
all adjustment sets as well as all minimal and minimum adjustment sets for
identification of a desired causal effect with multivariate exposures and
outcomes in the presence of latent confounding. Our results extend several
existing solutions for special cases of these problems. Our efficient
algorithms allowed us to empirically quantify the identifiability gap between
covariate adjustment and the do-calculus in random DAGs and MAGs, covering a
wide range of scenarios. Implementations of our algorithms are provided in the
R package dagitty.
| cs.AI cs.LG | principled reasoning about the identifiability of causal effects from nonexperimental data is an important application of graphical causal models this paper focuses on effects that are identifiable by covariate adjustment a commonly used estimation approach we present an algorithmic framework for efficiently testing constructing and enumerating mseparators in ancestral graphs ags a class of graphical causal models that can represent uncertainty about the presence of latent confounders furthermore we prove a reduction from causal effect identification by covariate adjustment to mseparation in a subgraph for directed acyclic graphs dags and maximal ancestral graphs mags jointly these results yield constructive criteria that characterize all adjustment sets as well as all minimal and minimum adjustment sets for identification of a desired causal effect with multivariate exposures and outcomes in the presence of latent confounding our results extend several existing solutions for special cases of these problems our efficient algorithms allowed us to empirically quantify the identifiability gap between covariate adjustment and the docalculus in random dags and mags covering a wide range of scenarios implementations of our algorithms are provided in the r package dagitty | [['principled', 'reasoning', 'about', 'the', 'identifiability', 'of', 'causal', 'effects', 'from', 'nonexperimental', 'data', 'is', 'an', 'important', 'application', 'of', 'graphical', 'causal', 'models', 'this', 'paper', 'focuses', 'on', 'effects', 'that', 'are', 'identifiable', 'by', 'covariate', 'adjustment', 'a', 'commonly', 'used', 'estimation', 'approach', 'we', 'present', 'an', 'algorithmic', 'framework', 'for', 'efficiently', 'testing', 'constructing', 'and', 'enumerating', 'mseparators', 'in', 'ancestral', 'graphs', 'ags', 'a', 'class', 'of', 'graphical', 'causal', 'models', 'that', 'can', 'represent', 'uncertainty', 'about', 'the', 'presence', 'of', 'latent', 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1,803.00117 | Redundancy allocation in finite-length nested codes for nonvolatile
memories | In this paper, we investigate the optimum way to allocate redundancy of
finite-length nested codes for modern nonvolatile memories suffering from both
permanent defects and transient errors (erasures or random errors). A nested
coding approach such as partitioned codes can handle both permanent defects and
transient errors by using two parts of redundancy: 1) redundancy to deal with
permanent defects and 2) redundancy for transient errors. We consider two
different channel models of the binary defect and erasure channel (BDEC) and
the binary defect and symmetric channel (BDSC). The transient errors of the
BDEC are erasures and the BDSC's transient errors are modeled by the binary
symmetric channel, respectively. Asymptotically, the probability of recovery
failure can converge to zero if the capacity region conditions of nested codes
are satisfied. However, the probability of recovery failure of finite-length
nested codes can be significantly variable for different redundancy allocations
even though they all satisfy the capacity region conditions. Hence, we
formulate the redundancy allocation problem of finite-length nested codes to
minimize the recovery failure probability. We derive the upper bounds on the
probability of recovery failure and use them to estimate the optimal redundancy
allocation. Numerical results show that our estimated redundancy allocation
matches well the optimal redundancy allocation.
| cs.IT math.IT | in this paper we investigate the optimum way to allocate redundancy of finitelength nested codes for modern nonvolatile memories suffering from both permanent defects and transient errors erasures or random errors a nested coding approach such as partitioned codes can handle both permanent defects and transient errors by using two parts of redundancy 1 redundancy to deal with permanent defects and 2 redundancy for transient errors we consider two different channel models of the binary defect and erasure channel bdec and the binary defect and symmetric channel bdsc the transient errors of the bdec are erasures and the bdscs transient errors are modeled by the binary symmetric channel respectively asymptotically the probability of recovery failure can converge to zero if the capacity region conditions of nested codes are satisfied however the probability of recovery failure of finitelength nested codes can be significantly variable for different redundancy allocations even though they all satisfy the capacity region conditions hence we formulate the redundancy allocation problem of finitelength nested codes to minimize the recovery failure probability we derive the upper bounds on the probability of recovery failure and use them to estimate the optimal redundancy allocation numerical results show that our estimated redundancy allocation matches well the optimal redundancy allocation | [['in', 'this', 'paper', 'we', 'investigate', 'the', 'optimum', 'way', 'to', 'allocate', 'redundancy', 'of', 'finitelength', 'nested', 'codes', 'for', 'modern', 'nonvolatile', 'memories', 'suffering', 'from', 'both', 'permanent', 'defects', 'and', 'transient', 'errors', 'erasures', 'or', 'random', 'errors', 'a', 'nested', 'coding', 'approach', 'such', 'as', 'partitioned', 'codes', 'can', 'handle', 'both', 'permanent', 'defects', 'and', 'transient', 'errors', 'by', 'using', 'two', 'parts', 'of', 'redundancy', '1', 'redundancy', 'to', 'deal', 'with', 'permanent', 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1,803.00118 | Surges of collective human activity emerge from simple pairwise
correlations | Human populations exhibit complex behaviors---characterized by long-range
correlations and surges in activity---across a range of social, political, and
technological contexts. Yet it remains unclear where these collective behaviors
come from, or if there even exists a set of unifying principles. Indeed,
existing explanations typically rely on context-specific mechanisms, such as
traffic jams driven by work schedules or spikes in online traffic induced by
significant events. However, analogies with statistical mechanics suggest a
more general mechanism: that collective patterns can emerge organically from
fine-scale interactions within a population. Here, across four different modes
of human activity, we show that the simplest correlations in a
population---those between pairs of individuals---can yield accurate
quantitative predictions for the large-scale behavior of the entire population.
To quantify the minimal consequences of pairwise correlations, we employ the
principle of maximum entropy, making our description equivalent to an Ising
model whose interactions and external fields are notably calculated from past
observations of population activity. In addition to providing accurate
quantitative predictions, we show that the topology of learned Ising
interactions resembles the network of inter-human communication within a
population. Together, these results demonstrate that fine-scale correlations
can be used to predict large-scale social behaviors, a perspective that has
critical implications for modeling and resource allocation in human
populations.
| physics.soc-ph cs.SI | human populations exhibit complex behaviorscharacterized by longrange correlations and surges in activityacross a range of social political and technological contexts yet it remains unclear where these collective behaviors come from or if there even exists a set of unifying principles indeed existing explanations typically rely on contextspecific mechanisms such as traffic jams driven by work schedules or spikes in online traffic induced by significant events however analogies with statistical mechanics suggest a more general mechanism that collective patterns can emerge organically from finescale interactions within a population here across four different modes of human activity we show that the simplest correlations in a populationthose between pairs of individualscan yield accurate quantitative predictions for the largescale behavior of the entire population to quantify the minimal consequences of pairwise correlations we employ the principle of maximum entropy making our description equivalent to an ising model whose interactions and external fields are notably calculated from past observations of population activity in addition to providing accurate quantitative predictions we show that the topology of learned ising interactions resembles the network of interhuman communication within a population together these results demonstrate that finescale correlations can be used to predict largescale social behaviors a perspective that has critical implications for modeling and resource allocation in human populations | [['human', 'populations', 'exhibit', 'complex', 'behaviorscharacterized', 'by', 'longrange', 'correlations', 'and', 'surges', 'in', 'activityacross', 'a', 'range', 'of', 'social', 'political', 'and', 'technological', 'contexts', 'yet', 'it', 'remains', 'unclear', 'where', 'these', 'collective', 'behaviors', 'come', 'from', 'or', 'if', 'there', 'even', 'exists', 'a', 'set', 'of', 'unifying', 'principles', 'indeed', 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1,803.00119 | Integrating Human-Provided Information Into Belief State Representation
Using Dynamic Factorization | In partially observed environments, it can be useful for a human to provide
the robot with declarative information that represents probabilistic relational
constraints on properties of objects in the world, augmenting the robot's
sensory observations. For instance, a robot tasked with a search-and-rescue
mission may be informed by the human that two victims are probably in the same
room. An important question arises: how should we represent the robot's
internal knowledge so that this information is correctly processed and combined
with raw sensory information? In this paper, we provide an efficient belief
state representation that dynamically selects an appropriate factoring,
combining aspects of the belief when they are correlated through information
and separating them when they are not. This strategy works in open domains, in
which the set of possible objects is not known in advance, and provides
significant improvements in inference time over a static factoring, leading to
more efficient planning for complex partially observed tasks. We validate our
approach experimentally in two open-domain planning problems: a 2D discrete
gridworld task and a 3D continuous cooking task. A supplementary video can be
found at http://tinyurl.com/chitnis-iros-18.
| cs.AI cs.RO | in partially observed environments it can be useful for a human to provide the robot with declarative information that represents probabilistic relational constraints on properties of objects in the world augmenting the robots sensory observations for instance a robot tasked with a searchandrescue mission may be informed by the human that two victims are probably in the same room an important question arises how should we represent the robots internal knowledge so that this information is correctly processed and combined with raw sensory information in this paper we provide an efficient belief state representation that dynamically selects an appropriate factoring combining aspects of the belief when they are correlated through information and separating them when they are not this strategy works in open domains in which the set of possible objects is not known in advance and provides significant improvements in inference time over a static factoring leading to more efficient planning for complex partially observed tasks we validate our approach experimentally in two opendomain planning problems a 2d discrete gridworld task and a 3d continuous cooking task a supplementary video can be found at httptinyurlcomchitnisiros18 | [['in', 'partially', 'observed', 'environments', 'it', 'can', 'be', 'useful', 'for', 'a', 'human', 'to', 'provide', 'the', 'robot', 'with', 'declarative', 'information', 'that', 'represents', 'probabilistic', 'relational', 'constraints', 'on', 'properties', 'of', 'objects', 'in', 'the', 'world', 'augmenting', 'the', 'robots', 'sensory', 'observations', 'for', 'instance', 'a', 'robot', 'tasked', 'with', 'a', 'searchandrescue', 'mission', 'may', 'be', 'informed', 'by', 'the', 'human', 'that', 'two', 'victims', 'are', 'probably', 'in', 'the', 'same', 'room', 'an', 'important', 'question', 'arises', 'how', 'should', 'we', 'represent', 'the', 'robots', 'internal', 'knowledge', 'so', 'that', 'this', 'information', 'is', 'correctly', 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1,803.0012 | Simplified Weak Galerkin and New Finite Difference Schemes for the
Stokes Equation | This article presents a simplified formulation for the weak Galerkin finite
element method for the Stokes equation without using the degrees of freedom
associated with the unknowns in the interior of each element as formulated in
the original weak Galerkin algorithm. The simplified formulation preserves the
important mass conservation property locally on each element and allows the use
of general polygonal partitions. A particular application of the simplified
weak Galerkin on rectangular partitions yields a new class of 5- and 7-point
finite difference schemes for the Stokes equation. An explicit formula is
presented for the computation of the element stiffness matrices on arbitrary
polygonal elements. Error estimates of optimal order are established for the
simplified weak Galerkin finite element method in the H^1 and L^2 norms.
Furthermore, a superconvergence of order O(h^{1.5}) is established on
rectangular partitions for the velocity approximation in the H^1 norm at cell
centers, and a similar superconvergence is derived for the pressure
approximation in the L^2 norm at cell centers. Some numerical results are
reported to confirm the convergence and superconvergence theory.
| math.NA | this article presents a simplified formulation for the weak galerkin finite element method for the stokes equation without using the degrees of freedom associated with the unknowns in the interior of each element as formulated in the original weak galerkin algorithm the simplified formulation preserves the important mass conservation property locally on each element and allows the use of general polygonal partitions a particular application of the simplified weak galerkin on rectangular partitions yields a new class of 5 and 7point finite difference schemes for the stokes equation an explicit formula is presented for the computation of the element stiffness matrices on arbitrary polygonal elements error estimates of optimal order are established for the simplified weak galerkin finite element method in the h1 and l2 norms furthermore a superconvergence of order oh15 is established on rectangular partitions for the velocity approximation in the h1 norm at cell centers and a similar superconvergence is derived for the pressure approximation in the l2 norm at cell centers some numerical results are reported to confirm the convergence and superconvergence theory | [['this', 'article', 'presents', 'a', 'simplified', 'formulation', 'for', 'the', 'weak', 'galerkin', 'finite', 'element', 'method', 'for', 'the', 'stokes', 'equation', 'without', 'using', 'the', 'degrees', 'of', 'freedom', 'associated', 'with', 'the', 'unknowns', 'in', 'the', 'interior', 'of', 'each', 'element', 'as', 'formulated', 'in', 'the', 'original', 'weak', 'galerkin', 'algorithm', 'the', 'simplified', 'formulation', 'preserves', 'the', 'important', 'mass', 'conservation', 'property', 'locally', 'on', 'each', 'element', 'and', 'allows', 'the', 'use', 'of', 'general', 'polygonal', 'partitions', 'a', 'particular', 'application', 'of', 'the', 'simplified', 'weak', 'galerkin', 'on', 'rectangular', 'partitions', 'yields', 'a', 'new', 'class', 'of', '5', 'and', '7point', 'finite', 'difference', 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1,803.00121 | Symmetry in optics and photonics: a group theory approach | Group theory (GT) provides a rigorous framework for studying symmetries in
various disciplines in physics ranging from quantum field theories and the
standard model to fluid mechanics and chaos theory. To date, the application of
such a powerful tool in optical physics remains limited. Over the past few
years however, several quantum-inspired symmetry principles (such as
parity-time invariance and supersymmetry) have been introduced in optics and
photonics for the first time. Despite the intense activities in these new
research directions, only few works utilized the power of group theory.
Motivated by this status quo, here we present a brief overview of the
application of GT in optics, deliberately choosing examples that illustrate the
power of this tool in both continuous and discrete setups. We hope that this
review will stimulate further research that exploits the full potential of GT
for investigating various symmetry paradigms in optics, eventually leading to
new photonic devices.
| physics.optics | group theory gt provides a rigorous framework for studying symmetries in various disciplines in physics ranging from quantum field theories and the standard model to fluid mechanics and chaos theory to date the application of such a powerful tool in optical physics remains limited over the past few years however several quantuminspired symmetry principles such as paritytime invariance and supersymmetry have been introduced in optics and photonics for the first time despite the intense activities in these new research directions only few works utilized the power of group theory motivated by this status quo here we present a brief overview of the application of gt in optics deliberately choosing examples that illustrate the power of this tool in both continuous and discrete setups we hope that this review will stimulate further research that exploits the full potential of gt for investigating various symmetry paradigms in optics eventually leading to new photonic devices | [['group', 'theory', 'gt', 'provides', 'a', 'rigorous', 'framework', 'for', 'studying', 'symmetries', 'in', 'various', 'disciplines', 'in', 'physics', 'ranging', 'from', 'quantum', 'field', 'theories', 'and', 'the', 'standard', 'model', 'to', 'fluid', 'mechanics', 'and', 'chaos', 'theory', 'to', 'date', 'the', 'application', 'of', 'such', 'a', 'powerful', 'tool', 'in', 'optical', 'physics', 'remains', 'limited', 'over', 'the', 'past', 'few', 'years', 'however', 'several', 'quantuminspired', 'symmetry', 'principles', 'such', 'as', 'paritytime', 'invariance', 'and', 'supersymmetry', 'have', 'been', 'introduced', 'in', 'optics', 'and', 'photonics', 'for', 'the', 'first', 'time', 'despite', 'the', 'intense', 'activities', 'in', 'these', 'new', 'research', 'directions', 'only', 'few', 'works', 'utilized', 'the', 'power', 'of', 'group', 'theory', 'motivated', 'by', 'this', 'status', 'quo', 'here', 'we', 'present', 'a', 'brief', 'overview', 'of', 'the', 'application', 'of', 'gt', 'in', 'optics', 'deliberately', 'choosing', 'examples', 'that', 'illustrate', 'the', 'power', 'of', 'this', 'tool', 'in', 'both', 'continuous', 'and', 'discrete', 'setups', 'we', 'hope', 'that', 'this', 'review', 'will', 'stimulate', 'further', 'research', 'that', 'exploits', 'the', 'full', 'potential', 'of', 'gt', 'for', 'investigating', 'various', 'symmetry', 'paradigms', 'in', 'optics', 'eventually', 'leading', 'to', 'new', 'photonic', 'devices']] | [-0.08551989628406438, 0.11447229741851946, -0.09529325466811363, 0.03375960448772132, -0.10020613290467545, -0.15615311261009132, 0.015570664178659698, 0.38798989185536475, -0.2698825187490959, -0.3063945721193082, 0.11341592224657927, -0.23360870179328086, -0.21610135897246532, 0.23436807589084646, -0.06447538437848714, 0.09664097893064057, -0.009397410389379059, -0.0031519901240244508, -0.07235630907387913, -0.20001705465715772, 0.25926495019475143, 0.07270780577872708, 0.3033450375553098, 0.07938235995396481, 0.06854931887018641, 0.01313095558495996, -0.0524115144232239, -0.004155605069459661, -0.13339501595591605, 0.13282577769438686, 0.29743526706864176, 0.13418867949267702, 0.3319440955345176, -0.4907038662288534, -0.26688265578674253, 0.06331266479346116, 0.11839105993184555, 0.16245091872406192, -0.14398112512686853, -0.2970462801091152, 0.03903289997986959, -0.20042490662614765, -0.14482257238542892, -0.11575906928460188, 0.025255114266704033, -0.014211950977608611, -0.17151012973351354, -0.004869685126897438, 0.03909271964680796, 0.12384389657037038, -0.013955889074886249, -0.0901373672899545, 0.06808504463314373, 0.11931011204203394, 0.06493638533605631, 0.020953312418178507, 0.09823026374062083, -0.16581853208481334, -0.1678466337662509, 0.4069036124151592, -0.011110321900464203, -0.11792964897559662, 0.19865451412701268, -0.1186167828518113, -0.20946252357373996, 0.054434414870277247, 0.20110013954437914, 0.10390594595159391, -0.1604993933517682, 0.12379179918322111, 0.00517461581866404, 0.11910374741043932, 0.016543947432900927, 0.09068141059986756, 0.22869408958113605, 0.19096336896702842, 0.03159559666681545, 0.08613007407831519, -0.014044885178200753, -0.13983871448305618, -0.3350530709844622, -0.15465857575940103, -0.14858491274399535, 0.04679670893988499, -0.021480207024643806, -0.08838021494882828, 0.4537257567403446, 0.1935662040303118, 0.11069991678538683, -0.013502849334863162, 0.2694967812690026, 0.049287090478556866, 0.06125565239620444, 0.005738409628209315, 0.27160725930068447, 0.15543559887967268, 0.1405771862107366, -0.15137778808466942, -0.029388637579119716, 0.021363216305249615] |
1,803.00122 | Geometric random graphs and Rado sets of continuous functions | We prove the existence of Rado sets in the Banach space of continuous
functions on [0,1]. A countable dense set S is Rado if with probability 1, the
infinite geometric random graph on S, formed by probabilistically making
adjacent elements of S that are within unit distance of each other, is unique
up to isomorphism. We show that for a suitable measure which we construct,
almost all countable dense sets in the subspaces of piecewise linear functions
and of polynomials are Rado. Moreover, all graphs arising from such sets are of
a unique isomorphism type. For the subspace of Brownian motion paths, almost
all countable subsets are Rado (for a suitable measure) and the resulting
graphs are of a unique isomorphism type. We show that the graph arising from
piecewise linear functions and polynomials is not isomorphic to the graph
arising from Brownian motion paths. Moreover, these graphs are non-isomorphic
to graphs arising from Rado sets in $\mathbb{R}^n$, or the sequence spaces $c$
and $c_0$.
| math.CO | we prove the existence of rado sets in the banach space of continuous functions on 01 a countable dense set s is rado if with probability 1 the infinite geometric random graph on s formed by probabilistically making adjacent elements of s that are within unit distance of each other is unique up to isomorphism we show that for a suitable measure which we construct almost all countable dense sets in the subspaces of piecewise linear functions and of polynomials are rado moreover all graphs arising from such sets are of a unique isomorphism type for the subspace of brownian motion paths almost all countable subsets are rado for a suitable measure and the resulting graphs are of a unique isomorphism type we show that the graph arising from piecewise linear functions and polynomials is not isomorphic to the graph arising from brownian motion paths moreover these graphs are nonisomorphic to graphs arising from rado sets in mathbbrn or the sequence spaces c and c_0 | [['we', 'prove', 'the', 'existence', 'of', 'rado', 'sets', 'in', 'the', 'banach', 'space', 'of', 'continuous', 'functions', 'on', '01', 'a', 'countable', 'dense', 'set', 's', 'is', 'rado', 'if', 'with', 'probability', '1', 'the', 'infinite', 'geometric', 'random', 'graph', 'on', 's', 'formed', 'by', 'probabilistically', 'making', 'adjacent', 'elements', 'of', 's', 'that', 'are', 'within', 'unit', 'distance', 'of', 'each', 'other', 'is', 'unique', 'up', 'to', 'isomorphism', 'we', 'show', 'that', 'for', 'a', 'suitable', 'measure', 'which', 'we', 'construct', 'almost', 'all', 'countable', 'dense', 'sets', 'in', 'the', 'subspaces', 'of', 'piecewise', 'linear', 'functions', 'and', 'of', 'polynomials', 'are', 'rado', 'moreover', 'all', 'graphs', 'arising', 'from', 'such', 'sets', 'are', 'of', 'a', 'unique', 'isomorphism', 'type', 'for', 'the', 'subspace', 'of', 'brownian', 'motion', 'paths', 'almost', 'all', 'countable', 'subsets', 'are', 'rado', 'for', 'a', 'suitable', 'measure', 'and', 'the', 'resulting', 'graphs', 'are', 'of', 'a', 'unique', 'isomorphism', 'type', 'we', 'show', 'that', 'the', 'graph', 'arising', 'from', 'piecewise', 'linear', 'functions', 'and', 'polynomials', 'is', 'not', 'isomorphic', 'to', 'the', 'graph', 'arising', 'from', 'brownian', 'motion', 'paths', 'moreover', 'these', 'graphs', 'are', 'nonisomorphic', 'to', 'graphs', 'arising', 'from', 'rado', 'sets', 'in', 'mathbbrn', 'or', 'the', 'sequence', 'spaces', 'c', 'and', 'c_0']] | [-0.15104976318437946, 0.15085081804374403, -0.023615553024027385, 0.04859075821533969, -0.06869191288327177, -0.09438966980881312, 0.04777392434083264, 0.409224384932807, -0.4032587957201582, -0.19556554275931734, 0.11824916556837375, -0.3379439908728907, -0.10628910064838375, 0.1867745551982697, -0.09881785286054241, 0.05665670828399898, 0.10490104129368609, 0.07268574125500339, -0.025773535929902485, -0.2514704177605877, 0.3932094056146029, -0.14035308019004084, 0.16661591564276904, -0.0009721386860209433, 0.13470815702369718, -0.011997841247780756, -0.017516731276092205, 0.06703222857516569, -0.15807502830156767, 0.1261506703117806, 0.26419277229967214, 0.17383714836006137, 0.255063306016911, -0.344590354586641, -0.15206677351485598, 0.2407851915524313, 0.08849830462061095, 0.020099148805478983, 0.008630995006498061, -0.27893614305296177, 0.1318373030277364, -0.07885239424790735, -0.08918246604840864, -0.07687636347995563, 0.10492227826493256, 0.09555496900681068, -0.3128471223214133, 0.005005574281412092, 0.12059760969837732, 0.054751793532208964, -0.033860787531981865, -0.112902871129865, -0.05585276869859433, 0.09565639561183299, -0.055080687727384045, 0.07337868971129259, 0.05203217223681735, -0.02738749153460517, -0.16814031142910774, 0.3960255919024348, -0.02832002082105839, -0.2138244767306429, 0.19999319285041456, -0.17864942078506857, -0.1691078057456197, 0.13324091585748124, 0.11250210546405111, 0.12698258584873243, -0.10154242581038764, 0.19105142154730856, -0.13029529682614588, 0.1025191104265325, 0.14005762426267293, 0.05257623007549255, 0.11996074328763467, 0.07499645162853553, 0.1490002291841489, 0.15659442152298816, 0.03554333670234138, -0.04431645127796483, -0.3438632330998327, -0.10080880371035274, -0.2074705330062319, 0.10506861328269058, -0.1760779493654175, -0.24382544273623463, 0.36102039331683156, 0.06855627566322008, 0.19000078676212015, 0.13236692574256184, 0.17846026321252187, 0.05537438570939809, 0.005336453694370434, 0.12416374914799676, 0.09826317872061874, 0.17979154035759468, -0.07282668459370281, -0.09689765639433806, 0.015487127202196103, 0.17623037618556708] |
1,803.00123 | On generalized Walsh bases | This paper continues the study of orthonormal bases (ONB) of $L^2[0,1]$
introduced in \cite{DPS14} by means of Cuntz algebra $\mathcal{O}_N$
representations on $L^2[0,1]$. For $N=2$, one obtains the classic Walsh system.
We show that the ONB property holds precisely because the $\mathcal{O}_N$
representations are irreducible. We prove an uncertainty principle related to
these bases. As an application to discrete signal processing we find a fast
generalized transform and compare this generalized transform with the classic
one with respect to compression and sparse signal recovery.
| math.FA | this paper continues the study of orthonormal bases onb of l201 introduced in citedps14 by means of cuntz algebra mathcalo_n representations on l201 for n2 one obtains the classic walsh system we show that the onb property holds precisely because the mathcalo_n representations are irreducible we prove an uncertainty principle related to these bases as an application to discrete signal processing we find a fast generalized transform and compare this generalized transform with the classic one with respect to compression and sparse signal recovery | [['this', 'paper', 'continues', 'the', 'study', 'of', 'orthonormal', 'bases', 'onb', 'of', 'l201', 'introduced', 'in', 'citedps14', 'by', 'means', 'of', 'cuntz', 'algebra', 'mathcalo_n', 'representations', 'on', 'l201', 'for', 'n2', 'one', 'obtains', 'the', 'classic', 'walsh', 'system', 'we', 'show', 'that', 'the', 'onb', 'property', 'holds', 'precisely', 'because', 'the', 'mathcalo_n', 'representations', 'are', 'irreducible', 'we', 'prove', 'an', 'uncertainty', 'principle', 'related', 'to', 'these', 'bases', 'as', 'an', 'application', 'to', 'discrete', 'signal', 'processing', 'we', 'find', 'a', 'fast', 'generalized', 'transform', 'and', 'compare', 'this', 'generalized', 'transform', 'with', 'the', 'classic', 'one', 'with', 'respect', 'to', 'compression', 'and', 'sparse', 'signal', 'recovery']] | [-0.09469390707546341, 0.06312496339971009, -0.06603999597480498, 0.05127495098354138, -0.07524318243545222, -0.10315454308594386, -0.02068611727999411, 0.3653333742306175, -0.37361951307000885, -0.18159691964525795, 0.13527359490153795, -0.2556673647409462, -0.18982847797942448, 0.17992275888212475, -0.14576400957261043, 0.07304337107946715, 0.0696597650804254, 0.09643242891081216, -0.12257982003755015, -0.2467672104262803, 0.30634473625525654, 0.03646526257619711, 0.2728787305461894, -0.06852310790444713, 0.12023578241961189, 0.056424673671935155, -0.04781202022659491, -0.08558652398501893, -0.1068625882390153, 0.1706114746553049, 0.26529969560565897, 0.168382751466482, 0.2473364796500698, -0.38940681891897355, -0.1444162001345217, 0.18146963503368826, 0.15241633464458837, 0.0379883101327816, -0.03377446104598467, -0.2610579775085291, 0.086719766163817, -0.1649685038522963, -0.1126983360701566, -0.12743666095950876, 0.02554458298956055, -0.010032902856031993, -0.33321339373353376, 0.050258964008416035, 0.12863928904330516, 0.040997701899025095, -0.07103660458092947, -0.11465282301020147, 0.03719377096360886, 0.056090504395304226, -0.002498590129326625, 0.06436076096864979, 0.060326237730816544, -0.032011542497314964, -0.141897983951438, 0.38139320757374706, -0.039183774769463275, -0.2548414484249899, 0.16397487056856774, -0.1392237885479243, -0.1950881218100079, 0.052555397138874756, 0.10618797359365341, 0.07661529469965811, -0.0929773808064231, 0.12781259666494352, -0.09372532006678541, 0.15415374017657196, 0.12533047032845487, 0.04494222514443549, 0.06455838498771639, 0.08375411907053855, 0.10475359604615404, 0.2102080887542611, 0.009874522075876144, -0.02409330228657787, -0.271487088346607, -0.19692114801202193, -0.18889787754447884, 0.06832351436816066, -0.0995509409922854, -0.1406452442597912, 0.3814257308055299, 0.140400748722345, 0.18397690304162273, 0.12139260378408145, 0.2544885107781738, 0.14866667843272305, 0.04211848462561527, 0.07461648517434137, 0.14854158734319262, 0.183526966297236, 0.025626376086105424, -0.16424654831503888, -0.012812125591942704, 0.18187142826661648] |
1,803.00124 | Improving Sentiment Analysis in Arabic Using Word Representation | The complexities of Arabic language in morphology, orthography and dialects
makes sentiment analysis for Arabic more challenging. Also, text feature
extraction from short messages like tweets, in order to gauge the sentiment,
makes this task even more difficult. In recent years, deep neural networks were
often employed and showed very good results in sentiment classification and
natural language processing applications. Word embedding, or word distributing
approach, is a current and powerful tool to capture together the closest words
from a contextual text. In this paper, we describe how we construct Word2Vec
models from a large Arabic corpus obtained from ten newspapers in different
Arab countries. By applying different machine learning algorithms and
convolutional neural networks with different text feature selections, we report
improved accuracy of sentiment classification (91%-95%) on our publicly
available Arabic language health sentiment dataset [1]
| cs.CL | the complexities of arabic language in morphology orthography and dialects makes sentiment analysis for arabic more challenging also text feature extraction from short messages like tweets in order to gauge the sentiment makes this task even more difficult in recent years deep neural networks were often employed and showed very good results in sentiment classification and natural language processing applications word embedding or word distributing approach is a current and powerful tool to capture together the closest words from a contextual text in this paper we describe how we construct word2vec models from a large arabic corpus obtained from ten newspapers in different arab countries by applying different machine learning algorithms and convolutional neural networks with different text feature selections we report improved accuracy of sentiment classification 9195 on our publicly available arabic language health sentiment dataset 1 | [['the', 'complexities', 'of', 'arabic', 'language', 'in', 'morphology', 'orthography', 'and', 'dialects', 'makes', 'sentiment', 'analysis', 'for', 'arabic', 'more', 'challenging', 'also', 'text', 'feature', 'extraction', 'from', 'short', 'messages', 'like', 'tweets', 'in', 'order', 'to', 'gauge', 'the', 'sentiment', 'makes', 'this', 'task', 'even', 'more', 'difficult', 'in', 'recent', 'years', 'deep', 'neural', 'networks', 'were', 'often', 'employed', 'and', 'showed', 'very', 'good', 'results', 'in', 'sentiment', 'classification', 'and', 'natural', 'language', 'processing', 'applications', 'word', 'embedding', 'or', 'word', 'distributing', 'approach', 'is', 'a', 'current', 'and', 'powerful', 'tool', 'to', 'capture', 'together', 'the', 'closest', 'words', 'from', 'a', 'contextual', 'text', 'in', 'this', 'paper', 'we', 'describe', 'how', 'we', 'construct', 'word2vec', 'models', 'from', 'a', 'large', 'arabic', 'corpus', 'obtained', 'from', 'ten', 'newspapers', 'in', 'different', 'arab', 'countries', 'by', 'applying', 'different', 'machine', 'learning', 'algorithms', 'and', 'convolutional', 'neural', 'networks', 'with', 'different', 'text', 'feature', 'selections', 'we', 'report', 'improved', 'accuracy', 'of', 'sentiment', 'classification', '9195', 'on', 'our', 'publicly', 'available', 'arabic', 'language', 'health', 'sentiment', 'dataset', '1']] | [0.0014339859553198092, -0.02767543477706448, -0.048094129985211974, 0.1588835147452493, -0.20508852436370387, -0.1929306339261795, 0.05427046401687918, 0.4937488275179028, -0.2678578740638429, -0.31670302199199796, 0.05130663432391619, -0.37648120892648823, -0.17374885904969797, 0.21764593512834438, -0.14767355328412168, 0.07233711065602129, 0.1840759550635941, 0.08688521765709521, -0.01737283372802891, -0.3252446638406628, 0.30182117334546615, 0.0009271035352840775, 0.4084339235453819, 0.0018333635211371592, 0.09040809813275064, -0.06203505681678109, -0.13806505993103785, -0.08353141550464134, -0.031993025977565585, 0.22882456011070876, 0.4651182776832, 0.2496399149047143, 0.3120172464349953, -0.3743222684728621, -0.1800875276901562, 0.03165099813507693, 0.16325969259886847, 0.1539338533930144, 0.010331305661400182, -0.39393386483722687, 0.06727547845191384, -0.2175490534643432, 0.1297019363719168, -0.16147716811188517, 0.08073132049740993, -0.045850499743854044, -0.17379898086519927, 0.04743994029904172, 0.09588947868056215, 0.1741215009854114, -0.00024290415322421677, -0.1522739178392302, 0.06747548955995726, 0.20402673147040942, 0.10799317895502776, 0.08890649964896975, 0.10701163687886003, -0.20239024674587877, -0.17647255696084377, 0.3918416626669847, -0.09606170688711624, -0.1798977901322944, 0.21451505033951931, -0.015779629524756412, -0.2427706035792175, 0.039694373964937066, 0.2690923103627606, 0.09436349866293153, -0.19514519507133396, -0.016332718867461883, -0.04655742992980093, 0.29786113638729944, 0.13502052079471522, -0.05386421772019163, 0.1768616298825419, 0.30597860947332894, -0.06805312287497477, 0.139026322002679, -0.08950057477064866, -0.016688559714188107, -0.12830216094280464, -0.08481916075531584, -0.1456031521211249, -0.014097064217708895, -0.14117104477828188, -0.13619075293387592, 0.41685787172491806, 0.23227611642643592, 0.14563044547409254, 0.10445583887299673, 0.2885659362536168, -0.07842807813945234, 0.12606521096270884, 0.10883102174525414, 0.07733901517540488, -0.035297475914967104, 0.23347832669714724, -0.08221037164126543, 0.08672612702667741, 0.057347841186951985] |
1,803.00125 | A social Network Analysis of the Operations Research/Industrial
Engineering Faculty Hiring Network | We study the U.S. Operations Research/Industrial-Systems Engineering (ORIE)
faculty hiring network, consisting of 1,179 faculty origin and destination data
together with attribute data from 83 ORIE departments. A social network
analysis of faculty hires can reveal important patterns in an academic field,
such as the existence of a hierarchy or sociological aspects such as the
presence of communities of departments. We first statistically test for the
existence of a linear hierarchy in the network and for its steepness. We find a
near linear hierarchical order of the departments, proposing a new index for
hiring networks, which we contrast with other indicators of hierarchy,
including published rankings. A single index is not capable to capture the full
structure of a complex network, however, so we next fit a latent exponential
random graph model (ERGM) to the network, which is able to reproduce its main
observed characteristics: high incidence of self-hiring, skewed out-degree
distribution, low density and clustering. Finally, we use the latent variables
in the ERGM to simplify the network to one where faculty hires take place among
three groups of departments. We contrast our findings with those reported for
other related disciplines, Computer Science and Business.
| stat.AP cs.SI | we study the us operations researchindustrialsystems engineering orie faculty hiring network consisting of 1179 faculty origin and destination data together with attribute data from 83 orie departments a social network analysis of faculty hires can reveal important patterns in an academic field such as the existence of a hierarchy or sociological aspects such as the presence of communities of departments we first statistically test for the existence of a linear hierarchy in the network and for its steepness we find a near linear hierarchical order of the departments proposing a new index for hiring networks which we contrast with other indicators of hierarchy including published rankings a single index is not capable to capture the full structure of a complex network however so we next fit a latent exponential random graph model ergm to the network which is able to reproduce its main observed characteristics high incidence of selfhiring skewed outdegree distribution low density and clustering finally we use the latent variables in the ergm to simplify the network to one where faculty hires take place among three groups of departments we contrast our findings with those reported for other related disciplines computer science and business | [['we', 'study', 'the', 'us', 'operations', 'researchindustrialsystems', 'engineering', 'orie', 'faculty', 'hiring', 'network', 'consisting', 'of', '1179', 'faculty', 'origin', 'and', 'destination', 'data', 'together', 'with', 'attribute', 'data', 'from', '83', 'orie', 'departments', 'a', 'social', 'network', 'analysis', 'of', 'faculty', 'hires', 'can', 'reveal', 'important', 'patterns', 'in', 'an', 'academic', 'field', 'such', 'as', 'the', 'existence', 'of', 'a', 'hierarchy', 'or', 'sociological', 'aspects', 'such', 'as', 'the', 'presence', 'of', 'communities', 'of', 'departments', 'we', 'first', 'statistically', 'test', 'for', 'the', 'existence', 'of', 'a', 'linear', 'hierarchy', 'in', 'the', 'network', 'and', 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1,803.00126 | Efficient sampling of reversible cross-linking polymers: Self-assembly
of single-chain polymeric nanoparticles | We present a new simulation technique to study systems of polymers
functionalized by reactive sites that bind/unbind forming reversible linkages.
Functionalized polymers feature self-assembly and responsive properties that
are unmatched by systems lacking selective interactions. The scales at which
the functional properties of these materials emerge are difficult to model,
especially in the reversible regime where such properties result from many
binding/unbinding events. This difficulty is related to large entropic barriers
associated with the formation of intra-molecular loops. In this work we present
a simulation scheme that sidesteps configurational costs by dedicated Monte
Carlo moves capable of binding/unbinding reactive sites in a single step.
Cross-linking reactions are implemented by trial moves that reconstruct chain
sections attempting, at the same time, a dimerization reaction between pairs of
reactive sites. The model is parametrized by the reaction equilibrium constant
of the reactive species free in solution. This quantity can be obtained by
means of experiments or atomistic/quantum simulations. We use the proposed
methodology to study self-assembly of single--chain polymeric nanoparticles,
starting from flexible precursors carrying regularly or randomly distributed
reactive sites. During a single run, almost all pairs of reactive monomers
interact at least once. We focus on understanding differences in the morphology
of chain nanoparticles when linkages are reversible as compared to the well
studied case of irreversible reactions. Intriguingly, we find that the size of
regularly functionalsized chains, in good solvent conditions, is non-monotonous
as a function of the degree of functionalization. We clarify how this result
follows from excluded volume interactions and is peculiar of reversible
linkages and regular functionalizations.
| cond-mat.soft cond-mat.stat-mech | we present a new simulation technique to study systems of polymers functionalized by reactive sites that bindunbind forming reversible linkages functionalized polymers feature selfassembly and responsive properties that are unmatched by systems lacking selective interactions the scales at which the functional properties of these materials emerge are difficult to model especially in the reversible regime where such properties result from many bindingunbinding events this difficulty is related to large entropic barriers associated with the formation of intramolecular loops in this work we present a simulation scheme that sidesteps configurational costs by dedicated monte carlo moves capable of bindingunbinding reactive sites in a single step crosslinking reactions are implemented by trial moves that reconstruct chain sections attempting at the same time a dimerization reaction between pairs of reactive sites the model is parametrized by the reaction equilibrium constant of the reactive species free in solution this quantity can be obtained by means of experiments or atomisticquantum simulations we use the proposed methodology to study selfassembly of singlechain polymeric nanoparticles starting from flexible precursors carrying regularly or randomly distributed reactive sites during a single run almost all pairs of reactive monomers interact at least once we focus on understanding differences in the morphology of chain nanoparticles when linkages are reversible as compared to the well studied case of irreversible reactions intriguingly we find that the size of regularly functionalsized chains in good solvent conditions is nonmonotonous as a function of the degree of functionalization we clarify how this result follows from excluded volume interactions and is peculiar of reversible linkages and regular functionalizations | [['we', 'present', 'a', 'new', 'simulation', 'technique', 'to', 'study', 'systems', 'of', 'polymers', 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1,803.00127 | SalientDSO: Bringing Attention to Direct Sparse Odometry | Although cluttered indoor scenes have a lot of useful high-level semantic
information which can be used for mapping and localization, most Visual
Odometry (VO) algorithms rely on the usage of geometric features such as
points, lines and planes. Lately, driven by this idea, the joint optimization
of semantic labels and obtaining odometry has gained popularity in the robotics
community. The joint optimization is good for accurate results but is generally
very slow. At the same time, in the vision community, direct and sparse
approaches for VO have stricken the right balance between speed and accuracy.
We merge the successes of these two communities and present a way to
incorporate semantic information in the form of visual saliency to Direct
Sparse Odometry - a highly successful direct sparse VO algorithm. We also
present a framework to filter the visual saliency based on scene parsing. Our
framework, SalientDSO, relies on the widely successful deep learning based
approaches for visual saliency and scene parsing which drives the feature
selection for obtaining highly-accurate and robust VO even in the presence of
as few as 40 point features per frame. We provide extensive quantitative
evaluation of SalientDSO on the ICL-NUIM and TUM monoVO datasets and show that
we outperform DSO and ORB-SLAM - two very popular state-of-the-art approaches
in the literature. We also collect and publicly release a CVL-UMD dataset which
contains two indoor cluttered sequences on which we show qualitative
evaluations. To our knowledge this is the first paper to use visual saliency
and scene parsing to drive the feature selection in direct VO.
| cs.CV cs.RO | although cluttered indoor scenes have a lot of useful highlevel semantic information which can be used for mapping and localization most visual odometry vo algorithms rely on the usage of geometric features such as points lines and planes lately driven by this idea the joint optimization of semantic labels and obtaining odometry has gained popularity in the robotics community the joint optimization is good for accurate results but is generally very slow at the same time in the vision community direct and sparse approaches for vo have stricken the right balance between speed and accuracy we merge the successes of these two communities and present a way to incorporate semantic information in the form of visual saliency to direct sparse odometry a highly successful direct sparse vo algorithm we also present a framework to filter the visual saliency based on scene parsing our framework salientdso relies on the widely successful deep learning based approaches for visual saliency and scene parsing which drives the feature selection for obtaining highlyaccurate and robust vo even in the presence of as few as 40 point features per frame we provide extensive quantitative evaluation of salientdso on the iclnuim and tum monovo datasets and show that we outperform dso and orbslam two very popular stateoftheart approaches in the literature we also collect and publicly release a cvlumd dataset which contains two indoor cluttered sequences on which we show qualitative evaluations to our knowledge this is the first paper to use visual saliency and scene parsing to drive the feature selection in direct vo | [['although', 'cluttered', 'indoor', 'scenes', 'have', 'a', 'lot', 'of', 'useful', 'highlevel', 'semantic', 'information', 'which', 'can', 'be', 'used', 'for', 'mapping', 'and', 'localization', 'most', 'visual', 'odometry', 'vo', 'algorithms', 'rely', 'on', 'the', 'usage', 'of', 'geometric', 'features', 'such', 'as', 'points', 'lines', 'and', 'planes', 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1,803.00128 | Real-Time Detection of Hybrid and Stealthy Cyber-Attacks in Smart Grid | For a safe and reliable operation of the smart grid, timely detection of
cyber-attacks is of critical importance. Moreover, considering smarter and more
capable attackers, robust detection mechanisms are needed against a diverse
range of cyber-attacks. With these purposes, we propose a robust online
detection algorithm for (possibly combined) false data injection (FDI) and
jamming attacks, that also provides online estimates of the unknown and
time-varying attack parameters and recovered state estimates. Further,
considering smarter attackers that are capable of designing stealthy attacks to
prevent the detection or to increase the detection delay of the proposed
algorithm, we propose additional countermeasures. Numerical studies illustrate
the quick and reliable response of the proposed detection mechanisms against
hybrid and stealthy cyber-attacks.
| cs.IT cs.CR math.IT | for a safe and reliable operation of the smart grid timely detection of cyberattacks is of critical importance moreover considering smarter and more capable attackers robust detection mechanisms are needed against a diverse range of cyberattacks with these purposes we propose a robust online detection algorithm for possibly combined false data injection fdi and jamming attacks that also provides online estimates of the unknown and timevarying attack parameters and recovered state estimates further considering smarter attackers that are capable of designing stealthy attacks to prevent the detection or to increase the detection delay of the proposed algorithm we propose additional countermeasures numerical studies illustrate the quick and reliable response of the proposed detection mechanisms against hybrid and stealthy cyberattacks | [['for', 'a', 'safe', 'and', 'reliable', 'operation', 'of', 'the', 'smart', 'grid', 'timely', 'detection', 'of', 'cyberattacks', 'is', 'of', 'critical', 'importance', 'moreover', 'considering', 'smarter', 'and', 'more', 'capable', 'attackers', 'robust', 'detection', 'mechanisms', 'are', 'needed', 'against', 'a', 'diverse', 'range', 'of', 'cyberattacks', 'with', 'these', 'purposes', 'we', 'propose', 'a', 'robust', 'online', 'detection', 'algorithm', 'for', 'possibly', 'combined', 'false', 'data', 'injection', 'fdi', 'and', 'jamming', 'attacks', 'that', 'also', 'provides', 'online', 'estimates', 'of', 'the', 'unknown', 'and', 'timevarying', 'attack', 'parameters', 'and', 'recovered', 'state', 'estimates', 'further', 'considering', 'smarter', 'attackers', 'that', 'are', 'capable', 'of', 'designing', 'stealthy', 'attacks', 'to', 'prevent', 'the', 'detection', 'or', 'to', 'increase', 'the', 'detection', 'delay', 'of', 'the', 'proposed', 'algorithm', 'we', 'propose', 'additional', 'countermeasures', 'numerical', 'studies', 'illustrate', 'the', 'quick', 'and', 'reliable', 'response', 'of', 'the', 'proposed', 'detection', 'mechanisms', 'against', 'hybrid', 'and', 'stealthy', 'cyberattacks']] | [-0.18431575946458967, 0.03614102022237137, -0.03860967124209685, 0.08901855441132177, -0.09015670103733153, -0.2698372931986609, 0.14363526716483993, 0.3534229412252287, -0.18978725096047955, -0.3238891161148663, 0.16717735236557593, -0.29032072483585414, -0.18563117674833648, 0.23056013617682183, -0.14736696306866506, 0.13666679235879614, 0.042984058736126964, -0.044317256033170126, 0.011018880558818453, -0.2531699373097229, 0.27238353883393673, 0.12420071166071321, 0.3185273326870774, 0.09045127097971174, 0.07255065764453136, 0.038644915837951065, -0.07572035978258658, -0.009775483775336076, -0.07963728137324591, 0.11268088835909837, 0.34565504434902, 0.2228136715310931, 0.33635014716629724, -0.4294211660363093, -0.24027402281855084, 0.13755198267284593, 0.10687827546650372, 0.13787306817209333, -0.12882927906889755, -0.3647666900817837, 0.16060135245201873, -0.25901198865329267, -0.11673295322586508, -0.16835348421566876, 0.010385549748551194, 0.03754450885594642, -0.2951932327446191, 0.0194796922128988, 0.021129775782577506, 0.0309763970557882, -0.060067761149413944, -0.04371207302921459, 0.00955795670053413, 0.16975143876419785, 0.04086000161409816, -0.07569703465609365, 0.2114148380424624, -0.15026558226780543, -0.14031670896821663, 0.33982929178834465, 0.0113887457145738, -0.15070471057153265, 0.1971938919604701, 0.04944767098592109, -0.11923990262468823, 0.17372214508501171, 0.2631424802410252, 0.13021799370239512, -0.13122396600557915, -0.08897662683176816, 0.07962497584365245, 0.17046978605613738, -0.01887360805285578, 0.05211179332510263, 0.18501972220055446, 0.21070221925600424, 0.16950972159510524, 0.12454460739713757, -0.15110459066379597, -0.0496361910075951, -0.2334486744327455, -0.08621591452446555, -0.1307853870406872, -0.014435310400005024, -0.08627762616049017, -0.12587902622939157, 0.41173399402564315, 0.2813819545798567, 0.1082777358540686, 0.07892129288794582, 0.4423867453421865, 0.02415877889779185, 0.03925511604096709, 0.1120784436743863, 0.22803941088877305, -0.008788818055216004, 0.07576732614523723, -0.20354131683746368, 0.1999827285038949, -0.04863951118083812] |
1,803.00129 | Modal approach to the controllability problem of distributed parameter
systems with damping | This paper is devoted to the controllability analysis of a class of linear
control systems in a Hilbert space. It is proposed to use the minimum energy
controls of a reduced lumped parameter system for solving the infinite
dimensional steering problem approximately. Sufficient conditions of the
approximate controllability are formulated for a modal representation of a
flexible structure with small damping.
| math.OC | this paper is devoted to the controllability analysis of a class of linear control systems in a hilbert space it is proposed to use the minimum energy controls of a reduced lumped parameter system for solving the infinite dimensional steering problem approximately sufficient conditions of the approximate controllability are formulated for a modal representation of a flexible structure with small damping | [['this', 'paper', 'is', 'devoted', 'to', 'the', 'controllability', 'analysis', 'of', 'a', 'class', 'of', 'linear', 'control', 'systems', 'in', 'a', 'hilbert', 'space', 'it', 'is', 'proposed', 'to', 'use', 'the', 'minimum', 'energy', 'controls', 'of', 'a', 'reduced', 'lumped', 'parameter', 'system', 'for', 'solving', 'the', 'infinite', 'dimensional', 'steering', 'problem', 'approximately', 'sufficient', 'conditions', 'of', 'the', 'approximate', 'controllability', 'are', 'formulated', 'for', 'a', 'modal', 'representation', 'of', 'a', 'flexible', 'structure', 'with', 'small', 'damping']] | [-0.17596353991739605, 0.05511703076543378, -0.040779035836152853, 0.03558022154258472, -0.09390495677448077, -0.14521775106884172, -0.0016889506015834994, 0.31095298739592925, -0.3037547728688013, -0.26912895321357444, 0.1464310977903775, -0.18202009016159373, -0.14000011667214166, 0.18846097286240976, -0.0631323808376662, 0.1512127060588205, 0.07835167944889332, 0.0435511093304233, -0.09799741701696252, -0.20359146484692933, 0.3152817556916996, 0.052509723231196404, 0.27198171872095983, -0.014839082872342379, 0.18959208641995173, 0.023232083584441513, 0.05304368730771859, 0.07307379464494812, -0.1486935104037345, 0.17798381894025342, 0.29479525291712066, 0.10532115274643311, 0.3587829107609501, -0.3651526184355626, -0.2205575006846033, 0.13970311503613092, 0.10802149982955002, 0.11095909664376838, -0.0060419543096643, -0.2492396531046414, 0.0721346068989913, -0.16153824610299752, -0.16260498511742372, -0.06519235656825734, 0.02499389969056747, -0.020516299413608722, -0.34187589799240997, 0.057796911267777445, 0.08209404337113022, 0.06566142413734657, -0.13000969343711852, -0.02466949489784473, 0.005237401753175454, 0.06586697527024223, -0.04384297934803562, -0.041196655638546485, 0.04933462118859724, -0.09161685668010756, -0.05664706668343212, 0.3730366145123224, -0.01979665902011921, -0.31207776594845976, 0.15942352152139436, -0.07632399698505636, -0.10098633346086765, 0.15284847874255453, 0.21740603217946702, 0.13262527588693823, -0.1753673036821324, 0.12472005301445234, -0.067015889299209, 0.18880000337958336, 0.03362918815377061, 0.04167997137047961, 0.12701764895168485, 0.2536715023815021, 0.16808749730775102, 0.18768700719219403, -0.0018504031220084576, -0.09311687997084295, -0.32061343322523306, -0.17624545310906395, -0.1812640644732069, 0.01761455530277835, -0.05929338792720657, -0.17722422470811938, 0.41813084983160015, 0.1233357288884228, 0.175656830372869, 0.07594306963556981, 0.2781858692159418, 0.2021461672666788, 0.0463357521831745, 0.05486758363234703, 0.2034046351604286, 0.2088063185820814, 0.09158244013755781, -0.25933447684787336, 0.0484426282002728, 0.09244584231866432] |
1,803.0013 | Ring loss: Convex Feature Normalization for Face Recognition | We motivate and present Ring loss, a simple and elegant feature normalization
approach for deep networks designed to augment standard loss functions such as
Softmax. We argue that deep feature normalization is an important aspect of
supervised classification problems where we require the model to represent each
class in a multi-class problem equally well. The direct approach to feature
normalization through the hard normalization operation results in a non-convex
formulation. Instead, Ring loss applies soft normalization, where it gradually
learns to constrain the norm to the scaled unit circle while preserving
convexity leading to more robust features. We apply Ring loss to large-scale
face recognition problems and present results on LFW, the challenging protocols
of IJB-A Janus, Janus CS3 (a superset of IJB-A Janus), Celebrity
Frontal-Profile (CFP) and MegaFace with 1 million distractors. Ring loss
outperforms strong baselines, matches state-of-the-art performance on IJB-A
Janus and outperforms all other results on the challenging Janus CS3 thereby
achieving state-of-the-art. We also outperform strong baselines in handling
extremely low resolution face matching.
| cs.CV | we motivate and present ring loss a simple and elegant feature normalization approach for deep networks designed to augment standard loss functions such as softmax we argue that deep feature normalization is an important aspect of supervised classification problems where we require the model to represent each class in a multiclass problem equally well the direct approach to feature normalization through the hard normalization operation results in a nonconvex formulation instead ring loss applies soft normalization where it gradually learns to constrain the norm to the scaled unit circle while preserving convexity leading to more robust features we apply ring loss to largescale face recognition problems and present results on lfw the challenging protocols of ijba janus janus cs3 a superset of ijba janus celebrity frontalprofile cfp and megaface with 1 million distractors ring loss outperforms strong baselines matches stateoftheart performance on ijba janus and outperforms all other results on the challenging janus cs3 thereby achieving stateoftheart we also outperform strong baselines in handling extremely low resolution face matching | [['we', 'motivate', 'and', 'present', 'ring', 'loss', 'a', 'simple', 'and', 'elegant', 'feature', 'normalization', 'approach', 'for', 'deep', 'networks', 'designed', 'to', 'augment', 'standard', 'loss', 'functions', 'such', 'as', 'softmax', 'we', 'argue', 'that', 'deep', 'feature', 'normalization', 'is', 'an', 'important', 'aspect', 'of', 'supervised', 'classification', 'problems', 'where', 'we', 'require', 'the', 'model', 'to', 'represent', 'each', 'class', 'in', 'a', 'multiclass', 'problem', 'equally', 'well', 'the', 'direct', 'approach', 'to', 'feature', 'normalization', 'through', 'the', 'hard', 'normalization', 'operation', 'results', 'in', 'a', 'nonconvex', 'formulation', 'instead', 'ring', 'loss', 'applies', 'soft', 'normalization', 'where', 'it', 'gradually', 'learns', 'to', 'constrain', 'the', 'norm', 'to', 'the', 'scaled', 'unit', 'circle', 'while', 'preserving', 'convexity', 'leading', 'to', 'more', 'robust', 'features', 'we', 'apply', 'ring', 'loss', 'to', 'largescale', 'face', 'recognition', 'problems', 'and', 'present', 'results', 'on', 'lfw', 'the', 'challenging', 'protocols', 'of', 'ijba', 'janus', 'janus', 'cs3', 'a', 'superset', 'of', 'ijba', 'janus', 'celebrity', 'frontalprofile', 'cfp', 'and', 'megaface', 'with', '1', 'million', 'distractors', 'ring', 'loss', 'outperforms', 'strong', 'baselines', 'matches', 'stateoftheart', 'performance', 'on', 'ijba', 'janus', 'and', 'outperforms', 'all', 'other', 'results', 'on', 'the', 'challenging', 'janus', 'cs3', 'thereby', 'achieving', 'stateoftheart', 'we', 'also', 'outperform', 'strong', 'baselines', 'in', 'handling', 'extremely', 'low', 'resolution', 'face', 'matching']] | [-0.055238554191489056, -0.0689698486090686, -0.0440486595876716, 0.06527068712073517, -0.0992515502247868, -0.21940316527271808, -0.017053200394885074, 0.4759351737334354, -0.24339059127505713, -0.31519145725746833, 0.07760583964994827, -0.3030689063784391, -0.160187000978446, 0.2129009532171836, -0.16841028485799858, 0.11045629924890646, 0.16056634001048095, 0.005202701340082306, -0.08528241839607043, -0.2872103933388224, 0.2555597472120319, 0.0451777478519513, 0.3402333004708738, 0.07436066147590266, 0.14173285036000627, 0.002208227882734858, 0.005621048405535034, -0.0038600120867034915, -0.04541473242113423, 0.16333326043426086, 0.24238599827182453, 0.1697748490697401, 0.2574639349580693, -0.36964241004192144, -0.16676842462899683, 0.08653121072311262, 0.13982309895352676, 0.06363643905748585, -0.04475808485947376, -0.27316359397762363, 0.0899217060108231, -0.19192305804897078, -0.018488356901453975, -0.162612815410279, -0.013129776944378776, -0.026091539043693435, -0.30846266238536885, 0.08355069486338221, 0.0889671877606568, 0.032002705659100114, -0.07145656684424574, -0.14592302593639528, 0.03877629798330014, 0.11823693534756112, 0.02899076012941353, 0.057592388836270166, 0.16847983226779376, -0.2201996846568837, -0.1446014941096879, 0.3948421775688522, -0.09834641605937997, -0.2220858181992094, 0.2506705734013585, -0.032840654945563104, -0.17279583395365075, 0.11747394625880045, 0.20570391824898812, 0.1673577712175778, -0.09373890321437248, 0.019670088882744036, -0.0959230297404898, 0.19683752759062195, 0.08728920568067294, 0.009943121554434697, 0.16027250261178252, 0.25544587337307234, 0.07709629363731685, 0.17874545074025303, -0.136340995390139, -0.08596865973606177, -0.1932511535809326, -0.0685440491893213, -0.1604891865322719, -0.02634609126645697, -0.10843370479441003, -0.16204891565873028, 0.3488437375604842, 0.20475789092786986, 0.23402315360692347, 0.12279695228359402, 0.33428316865909913, -0.01358281181003766, 0.1481792337630363, 0.08719347806140428, 0.20938107222745736, 0.0009715841234435697, 0.08312647239602661, -0.20016453991307925, 0.016488935102325637, 0.08210069618116204] |
1,803.00131 | Male pelvic synthetic CT generation from T1-weighted MRI using 2D and 3D
convolutional neural networks | To achieve magnetic resonance (MR)-only radiotherapy, a method needs to be
employed to estimate a synthetic CT (sCT) for generating electron density maps
and patient positioning reference images. We investigated 2D and 3D
convolutional neural network (CNN) methods to generate a male pelvic sCT using
a T1-weighted MR image. A retrospective study was performed using CTs and
T1-weighted MR images of 20 prostate cancer patients. The proposed 2D CNN
model, which contained 27 convolutional layers, was modified from the SegNet
for better performance. 3D version of the CNN model was also developed. Both
CNN models were trained from scratch to map intensities of T1-weighted MR
images to CT Hounsfield Unit (HU) values. Each sCT was generated in a
five-fold-cross-validation framework and compared with the corresponding CT
using voxel-wise mean absolute error (MAE), and dice similarity coefficient
(DSC), recall, and precision for bony structures. Wilcoxon signed-rank tests
were performed to evaluate the differences between the both models. The MAE
averaged across all patients were 40.5 $\pm$ 5.4 HU and 37.6 $\pm$ 5.1 HU for
the 2D and 3D CNN models, respectively. The DSC, recall, and precision of the
bony structures were 0.81 $\pm$ 0.04, 0.85 $\pm$ 0.04, and 0.77 $\pm$ 0.09 for
the 2D CNN model, and 0.82 $\pm$ 0.04, 0.84 $\pm$ 0.04, and 0.80 $\pm$ 0.08 for
the 3D CNN model, respectively. P values of the Wilcoxon signed-rank tests were
less than 0.05 except for recall, which was 0.6. The 2D and 3D CNN models
generated accurate pelvic sCTs for the 20 patients using T1-weighted MR images.
The evaluation metrics and statistical tests indicated that the 3D model was
able to generate sCTs with better MAE, bone DSC, and bone precision. The
accuracy of the dose calculation and patient positioning using generated sCTs
will be tested and compared for the two models in the future.
| physics.med-ph | to achieve magnetic resonance mronly radiotherapy a method needs to be employed to estimate a synthetic ct sct for generating electron density maps and patient positioning reference images we investigated 2d and 3d convolutional neural network cnn methods to generate a male pelvic sct using a t1weighted mr image a retrospective study was performed using cts and t1weighted mr images of 20 prostate cancer patients the proposed 2d cnn model which contained 27 convolutional layers was modified from the segnet for better performance 3d version of the cnn model was also developed both cnn models were trained from scratch to map intensities of t1weighted mr images to ct hounsfield unit hu values each sct was generated in a fivefoldcrossvalidation framework and compared with the corresponding ct using voxelwise mean absolute error mae and dice similarity coefficient dsc recall and precision for bony structures wilcoxon signedrank tests were performed to evaluate the differences between the both models the mae averaged across all patients were 405 pm 54 hu and 376 pm 51 hu for the 2d and 3d cnn models respectively the dsc recall and precision of the bony structures were 081 pm 004 085 pm 004 and 077 pm 009 for the 2d cnn model and 082 pm 004 084 pm 004 and 080 pm 008 for the 3d cnn model respectively p values of the wilcoxon signedrank tests were less than 005 except for recall which was 06 the 2d and 3d cnn models generated accurate pelvic scts for the 20 patients using t1weighted mr images the evaluation metrics and statistical tests indicated that the 3d model was able to generate scts with better mae bone dsc and bone precision the accuracy of the dose calculation and patient positioning using generated scts will be tested and compared for the two models in the future | [['to', 'achieve', 'magnetic', 'resonance', 'mronly', 'radiotherapy', 'a', 'method', 'needs', 'to', 'be', 'employed', 'to', 'estimate', 'a', 'synthetic', 'ct', 'sct', 'for', 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1,803.00132 | Development of GEM Detectors at Hampton University | Two GEM telescopes, each consisting of three 10x10 cm$^2$ triple-GEM chambers
were built, tested and operated by the Hampton University group. The GEMs are
read out with APV25 frontend chips and FPGA based digitizing electronics
developed by INFN Rome.
The telescopes were used for the luminosity monitoring system at the OLYMPUS
experiment at DESY in Germany, with positron and electron beams at 2 GeV. The
GEM elements have been recycled to serve in another two applications: Three GEM
elements are used to track beam particles in the MUSE experiment at PSI in
Switzerland. A set of four elements has been configured as a prototype tracker
for phase 1a of the DarkLight experiment at the Low-Energy Recirculator
Facility (LERF) at Jefferson Lab in Newport News, USA, in a first test run in
summer 2016.
The Hampton group is responsible for the DarkLight phase-I lepton tracker in
preparation. Further efforts are ongoing to optimize the data acquisition speed
for GEM operations in MUSE and DarkLight. An overview of the group's GEM
detector related activities will be given.
| physics.ins-det nucl-ex | two gem telescopes each consisting of three 10x10 cm2 triplegem chambers were built tested and operated by the hampton university group the gems are read out with apv25 frontend chips and fpga based digitizing electronics developed by infn rome the telescopes were used for the luminosity monitoring system at the olympus experiment at desy in germany with positron and electron beams at 2 gev the gem elements have been recycled to serve in another two applications three gem elements are used to track beam particles in the muse experiment at psi in switzerland a set of four elements has been configured as a prototype tracker for phase 1a of the darklight experiment at the lowenergy recirculator facility lerf at jefferson lab in newport news usa in a first test run in summer 2016 the hampton group is responsible for the darklight phasei lepton tracker in preparation further efforts are ongoing to optimize the data acquisition speed for gem operations in muse and darklight an overview of the groups gem detector related activities will be given | [['two', 'gem', 'telescopes', 'each', 'consisting', 'of', 'three', '10x10', 'cm2', 'triplegem', 'chambers', 'were', 'built', 'tested', 'and', 'operated', 'by', 'the', 'hampton', 'university', 'group', 'the', 'gems', 'are', 'read', 'out', 'with', 'apv25', 'frontend', 'chips', 'and', 'fpga', 'based', 'digitizing', 'electronics', 'developed', 'by', 'infn', 'rome', 'the', 'telescopes', 'were', 'used', 'for', 'the', 'luminosity', 'monitoring', 'system', 'at', 'the', 'olympus', 'experiment', 'at', 'desy', 'in', 'germany', 'with', 'positron', 'and', 'electron', 'beams', 'at', '2', 'gev', 'the', 'gem', 'elements', 'have', 'been', 'recycled', 'to', 'serve', 'in', 'another', 'two', 'applications', 'three', 'gem', 'elements', 'are', 'used', 'to', 'track', 'beam', 'particles', 'in', 'the', 'muse', 'experiment', 'at', 'psi', 'in', 'switzerland', 'a', 'set', 'of', 'four', 'elements', 'has', 'been', 'configured', 'as', 'a', 'prototype', 'tracker', 'for', 'phase', '1a', 'of', 'the', 'darklight', 'experiment', 'at', 'the', 'lowenergy', 'recirculator', 'facility', 'lerf', 'at', 'jefferson', 'lab', 'in', 'newport', 'news', 'usa', 'in', 'a', 'first', 'test', 'run', 'in', 'summer', '2016', 'the', 'hampton', 'group', 'is', 'responsible', 'for', 'the', 'darklight', 'phasei', 'lepton', 'tracker', 'in', 'preparation', 'further', 'efforts', 'are', 'ongoing', 'to', 'optimize', 'the', 'data', 'acquisition', 'speed', 'for', 'gem', 'operations', 'in', 'muse', 'and', 'darklight', 'an', 'overview', 'of', 'the', 'groups', 'gem', 'detector', 'related', 'activities', 'will', 'be', 'given']] | [-0.04506660052575171, 0.15523984539264346, -0.0812169220458184, -0.009307902712774064, -0.005355230202632291, -0.19724423226501261, -0.053473905907677755, 0.3931013482596193, -0.1369307294614347, -0.35456983646644014, 0.11466858541526433, -0.3563853050582111, 0.032991914530949934, 0.2581051851662674, -0.023065309237156596, 0.10169106821995229, 0.09883045190413084, -0.03152374705964966, -0.0637820504233241, -0.29442259435142787, 0.19363459132877844, 0.17717705042766674, 0.2957549634123487, 0.007578193428260939, 0.17641468480695038, -0.037570426720527135, -0.06658808425867131, -0.08135778807103634, -0.03384038308502308, 0.022336996048688888, 0.36973222275397605, 0.12042129084201796, 0.197715636542333, -0.46830263031380515, -0.04764770245046488, 0.024314715057345372, 0.060927783567125776, -0.049537045361607204, -0.08951653865498624, -0.3185812315664121, 0.0629896707367152, -0.25543223426810335, -0.15686157685039298, 0.051441801940756184, 0.003477538100310734, 0.05480724127697093, -0.24880373514390416, -0.09844961676879653, -0.08534546949634594, 0.09696333566680551, -0.02119544919596852, -0.17099824508053382, 0.07227554431584264, 0.10160031544310706, -0.0508333382822041, 0.08731521226731795, 0.18377050884068014, -0.08476820506155491, -0.14063676116189786, 0.311134727228699, -0.02955710154425885, -0.08087035636351045, 0.16932416196646435, -0.21805619609675236, -0.16552858476926174, 0.12028953010482447, 0.22032330614647694, 0.028681156327947974, -0.23957004799374512, 0.07101078438306493, -0.004154045054955142, 0.17277907073497772, 0.10348618362365024, -0.03193306140601635, 0.2330239402236683, 0.2601156801623957, 0.02777490111200937, 0.09708978112953316, -0.1948332823014685, 0.043336177190898786, -0.3319933225926278, -0.17772525924657073, -0.10946872994037611, -0.021493235647462177, 0.07543303364921095, -0.019825442094089728, 0.4004980221284287, 0.07784272510558367, 0.1012915593571961, -0.08177743776967483, 0.276353367750666, -0.017618792086224338, 0.15119728435895272, 0.017286322018397707, 0.25757449052163534, 0.08065197985885399, 0.21557024074452263, -0.17841735269874334, 0.013978218196591894, 0.07229922231818948] |
1,803.00133 | Materials data validation and imputation with an artificial neural
network | We apply an artificial neural network to model and verify material
properties. The neural network algorithm has a unique capability to handle
incomplete data sets in both training and predicting, so it can regard
properties as inputs allowing it to exploit both composition-property and
property-property correlations to enhance the quality of predictions, and can
also handle a graphical data as a single entity. The framework is tested with
different validation schemes, and then applied to materials case studies of
alloys and polymers. The algorithm found twenty errors in a commercial
materials database that were confirmed against primary data sources.
| physics.comp-ph cond-mat.mtrl-sci | we apply an artificial neural network to model and verify material properties the neural network algorithm has a unique capability to handle incomplete data sets in both training and predicting so it can regard properties as inputs allowing it to exploit both compositionproperty and propertyproperty correlations to enhance the quality of predictions and can also handle a graphical data as a single entity the framework is tested with different validation schemes and then applied to materials case studies of alloys and polymers the algorithm found twenty errors in a commercial materials database that were confirmed against primary data sources | [['we', 'apply', 'an', 'artificial', 'neural', 'network', 'to', 'model', 'and', 'verify', 'material', 'properties', 'the', 'neural', 'network', 'algorithm', 'has', 'a', 'unique', 'capability', 'to', 'handle', 'incomplete', 'data', 'sets', 'in', 'both', 'training', 'and', 'predicting', 'so', 'it', 'can', 'regard', 'properties', 'as', 'inputs', 'allowing', 'it', 'to', 'exploit', 'both', 'compositionproperty', 'and', 'propertyproperty', 'correlations', 'to', 'enhance', 'the', 'quality', 'of', 'predictions', 'and', 'can', 'also', 'handle', 'a', 'graphical', 'data', 'as', 'a', 'single', 'entity', 'the', 'framework', 'is', 'tested', 'with', 'different', 'validation', 'schemes', 'and', 'then', 'applied', 'to', 'materials', 'case', 'studies', 'of', 'alloys', 'and', 'polymers', 'the', 'algorithm', 'found', 'twenty', 'errors', 'in', 'a', 'commercial', 'materials', 'database', 'that', 'were', 'confirmed', 'against', 'primary', 'data', 'sources']] | [-0.044361913106260226, -0.00022967970439775358, -0.07983321860461429, 0.09231313524081267, -0.0911476614443374, -0.19725877644263745, 0.05007619435716536, 0.44828244867091327, -0.27457547882936667, -0.35041844298506214, 0.10344844739872136, -0.30519224462320205, -0.16306475634907477, 0.21320620309513486, -0.07737709293333986, 0.12321270503506986, 0.111624768973549, -0.003983246354559033, -0.034078799627394865, -0.2690909128304878, 0.2595097028227888, 0.08029250291742615, 0.34672729808749564, 0.028697336116594446, 0.09826844365974485, -0.04112961651162081, -0.01778960836926456, 0.06252985524215265, -0.06131500072728365, 0.1049706812860377, 0.28399991164627225, 0.15051689252410969, 0.2622774160015829, -0.4436671100364825, -0.25251110147723216, 0.09963290624742963, 0.12231882182477984, 0.1430283267838285, -0.022607708493842908, -0.28833528105697603, 0.11309073359427071, -0.1775048274844501, -0.07198535698494807, -0.17450304303471884, -0.00946260211970081, 0.02436665023410616, -0.3067833512070944, -0.014767245012971563, 0.0259807226776155, 0.04231773829207631, -0.07450268240573518, -0.10043199675292083, -0.03182827517121416, 0.1618528145547841, 0.04209407898669429, 0.026692213913060956, 0.13143512030544968, -0.12649496903009327, -0.1625840053515576, 0.40563192032277584, -0.03390833129626267, -0.20202975781615248, 0.2027445090430568, -0.04611928393277004, -0.14001209147216886, 0.09998360413520299, 0.2314604935668332, 0.0744685196778593, -0.20337762088831707, -0.004414665329116445, 0.0053953460427289155, 0.2095391205175943, -0.0033771969064753313, 0.01403288128419741, 0.1653896593056696, 0.21883248453287735, -0.030458845966255543, 0.17753237284739146, -0.12644778024981318, -0.027963225607347396, -0.170010807250684, -0.14026397603237523, -0.1919820818992466, -0.04605156562972775, -0.06967131657071036, -0.15435163970957463, 0.38053094784815594, 0.1997252122166605, 0.18136412434318325, 0.050131168586115556, 0.3036677976398124, -0.008907807004378782, 0.11678442852475594, 0.05637482167269612, 0.20032996372751816, 0.10606372481951318, 0.12494249437388379, -0.13484051546139508, 0.11657535159941186, -0.0312841017106452] |
1,803.00134 | A Characterization of Boundary Representations of Positive Matrices in
the Hardy Space via the Abel Product | Spectral measures give rise to a natural harmonic analysis on the unit disc
via a boundary representation of a positive matrix arising from a spectrum of
the measure. We consider in this paper the reverse: for a positive matrix in
the Hardy space of the unit disc we consider which measures, if any, yield a
boundary representation of the positive matrix. We prove a characterization of
those representing measures via a matrix identity by introducing a new operator
product called the Abel Product.
| math.FA math.CV | spectral measures give rise to a natural harmonic analysis on the unit disc via a boundary representation of a positive matrix arising from a spectrum of the measure we consider in this paper the reverse for a positive matrix in the hardy space of the unit disc we consider which measures if any yield a boundary representation of the positive matrix we prove a characterization of those representing measures via a matrix identity by introducing a new operator product called the abel product | [['spectral', 'measures', 'give', 'rise', 'to', 'a', 'natural', 'harmonic', 'analysis', 'on', 'the', 'unit', 'disc', 'via', 'a', 'boundary', 'representation', 'of', 'a', 'positive', 'matrix', 'arising', 'from', 'a', 'spectrum', 'of', 'the', 'measure', 'we', 'consider', 'in', 'this', 'paper', 'the', 'reverse', 'for', 'a', 'positive', 'matrix', 'in', 'the', 'hardy', 'space', 'of', 'the', 'unit', 'disc', 'we', 'consider', 'which', 'measures', 'if', 'any', 'yield', 'a', 'boundary', 'representation', 'of', 'the', 'positive', 'matrix', 'we', 'prove', 'a', 'characterization', 'of', 'those', 'representing', 'measures', 'via', 'a', 'matrix', 'identity', 'by', 'introducing', 'a', 'new', 'operator', 'product', 'called', 'the', 'abel', 'product']] | [-0.1129241903278842, 0.08027341588209551, -0.09470094091831202, 0.0548693266548553, -0.04591675843280482, -0.0927830967681864, 0.04278735262313745, 0.3197798676429743, -0.3199529630098357, -0.1678604428398322, 0.15301882566783173, -0.2658814384029752, -0.17866177008633155, 0.16075012315603265, -0.06757626596120107, 0.0046374186963202, 0.08098475195198174, 0.0821015055639198, -0.1390927129997367, -0.13723033225074052, 0.43369951386408634, -0.019171115616897504, 0.17977744144533414, 0.060166366342230736, 0.14309088406657958, 0.021922987936266576, -0.06906100767608507, -0.0029876843878174626, -0.15089718575578137, 0.1978539428813944, 0.18099957163703728, 0.1657358443291566, 0.300053033795848, -0.384755032742957, -0.19567405711298427, 0.18941018011151667, 0.08560769802453108, 0.011406332292279834, -0.06861884521561708, -0.26666558031217164, 0.06317507783624243, -0.18071301133338227, -0.10677150042772472, -0.04172449911304985, -0.0024601296039230853, -0.02566284048427121, -0.3418411865202059, 0.054766712379249106, 0.09502438238137458, 0.06534912413075926, -0.1040362282936652, -0.08277192214189703, 0.03949512638914657, 0.0964800352763654, -0.014784595887008381, 0.035190362883002375, 0.09451570393831794, -0.06877437116948507, -0.10242578252050352, 0.3605699743851122, -0.11837615286763084, -0.2871185684105359, 0.12023218447364957, -0.19940638929852342, -0.11101958825511027, 0.08321517301970217, 0.16473638436880456, 0.10661113307059529, -0.11755245523415057, 0.12937166091862573, -0.11548132234791586, 0.13485729351011386, 0.06562286606395101, 0.005159011138715299, 0.16048082300876998, 0.05904482006577842, 0.15516344949333782, 0.2165752483015678, -0.010656809380820795, -0.05037486821929195, -0.3078726641384952, -0.24709074962210942, -0.20862250864303794, 0.12233153005629913, -0.13432462930858854, -0.26284623534295215, 0.4420602541046599, 0.03952756380460348, 0.27839800417675714, 0.07651310662194773, 0.23893630334052696, 0.18587920207709793, 0.07070085125924817, 0.024393066382372236, 0.1161183590989515, 0.20518126196497266, 0.06575304535161479, -0.15701978202314532, 0.030411535256867666, 0.1655586676571114] |
1,803.00135 | Quantum annealing versus classical machine learning applied to a
simplified computational biology problem | Transcription factors regulate gene expression, but how these proteins
recognize and specifically bind to their DNA targets is still debated. Machine
learning models are effective means to reveal interaction mechanisms. Here we
studied the ability of a quantum machine learning approach to predict binding
specificity. Using simplified datasets of a small number of DNA sequences
derived from actual binding affinity experiments, we trained a commercially
available quantum annealer to classify and rank transcription factor binding.
The results were compared to state-of-the-art classical approaches for the same
simplified datasets, including simulated annealing, simulated quantum
annealing, multiple linear regression, LASSO, and extreme gradient boosting.
Despite technological limitations, we find a slight advantage in classification
performance and nearly equal ranking performance using the quantum annealer for
these fairly small training data sets. Thus, we propose that quantum annealing
might be an effective method to implement machine learning for certain
computational biology problems.
| quant-ph q-bio.GN | transcription factors regulate gene expression but how these proteins recognize and specifically bind to their dna targets is still debated machine learning models are effective means to reveal interaction mechanisms here we studied the ability of a quantum machine learning approach to predict binding specificity using simplified datasets of a small number of dna sequences derived from actual binding affinity experiments we trained a commercially available quantum annealer to classify and rank transcription factor binding the results were compared to stateoftheart classical approaches for the same simplified datasets including simulated annealing simulated quantum annealing multiple linear regression lasso and extreme gradient boosting despite technological limitations we find a slight advantage in classification performance and nearly equal ranking performance using the quantum annealer for these fairly small training data sets thus we propose that quantum annealing might be an effective method to implement machine learning for certain computational biology problems | [['transcription', 'factors', 'regulate', 'gene', 'expression', 'but', 'how', 'these', 'proteins', 'recognize', 'and', 'specifically', 'bind', 'to', 'their', 'dna', 'targets', 'is', 'still', 'debated', 'machine', 'learning', 'models', 'are', 'effective', 'means', 'to', 'reveal', 'interaction', 'mechanisms', 'here', 'we', 'studied', 'the', 'ability', 'of', 'a', 'quantum', 'machine', 'learning', 'approach', 'to', 'predict', 'binding', 'specificity', 'using', 'simplified', 'datasets', 'of', 'a', 'small', 'number', 'of', 'dna', 'sequences', 'derived', 'from', 'actual', 'binding', 'affinity', 'experiments', 'we', 'trained', 'a', 'commercially', 'available', 'quantum', 'annealer', 'to', 'classify', 'and', 'rank', 'transcription', 'factor', 'binding', 'the', 'results', 'were', 'compared', 'to', 'stateoftheart', 'classical', 'approaches', 'for', 'the', 'same', 'simplified', 'datasets', 'including', 'simulated', 'annealing', 'simulated', 'quantum', 'annealing', 'multiple', 'linear', 'regression', 'lasso', 'and', 'extreme', 'gradient', 'boosting', 'despite', 'technological', 'limitations', 'we', 'find', 'a', 'slight', 'advantage', 'in', 'classification', 'performance', 'and', 'nearly', 'equal', 'ranking', 'performance', 'using', 'the', 'quantum', 'annealer', 'for', 'these', 'fairly', 'small', 'training', 'data', 'sets', 'thus', 'we', 'propose', 'that', 'quantum', 'annealing', 'might', 'be', 'an', 'effective', 'method', 'to', 'implement', 'machine', 'learning', 'for', 'certain', 'computational', 'biology', 'problems']] | [-0.0369965794330991, 0.07711413696233649, -0.047610421171224354, 0.13492698212163584, -0.08495940856835586, -0.25425374167252507, 0.07759424097267223, 0.4536643246276267, -0.27731443826720936, -0.34669118246763647, 0.00668166574690766, -0.2611550823504093, -0.1906343223675523, 0.23553215944201955, -0.09173754461722186, 0.10709784302435466, 0.1519187759903366, 0.0375383508644823, -0.038272336561345315, -0.34076358324975276, 0.22838612785406612, 0.071022223755436, 0.34995105161000317, 0.04643415150251365, 0.1153039250799436, -0.06315324621137557, 0.034631764141205175, 0.011142988234520859, -0.12001255515443462, 0.18025878804537387, 0.3381383706984353, 0.1895321037951582, 0.3113307889813445, -0.41519901953987626, -0.22708559439479664, 0.10126466654872385, 0.15465352847240865, 0.1840328339367383, -0.06678912325155915, -0.2555345532503104, 0.05644058258797748, -0.1468890179308044, -0.004280334169782648, -0.19349754053471147, -0.03712070411955625, 0.0031882884705926747, -0.24243011923456853, 0.07063402933804569, 0.007235222730485585, 0.0977613615628407, -0.07083506562400159, -0.1686572628603598, 0.0620900334340314, 0.14917672326750803, 0.02752760974471551, 0.020944618641855133, 0.2470476377799157, -0.16241348312305123, -0.19006212339845904, 0.36608576984613533, -0.03138181202163632, -0.1950268765923961, 0.24368099305275187, -0.004984931826541488, -0.16012646995903612, 0.09300882866427977, 0.21727035805620504, 0.08407130452586281, -0.18231474046264118, 0.024594493096244317, 0.040671562307182975, 0.20119335633021274, 0.08692825690768159, -0.03806513530083451, 0.13813864192909767, 0.21990757425559448, -0.03226191034828056, 0.13870164277828956, -0.09917535005299837, -0.13639923394345146, -0.16510322887877052, -0.10304723455900351, -0.19007310682545173, 0.05497585361957825, -0.09803609137974942, -0.17252697911732298, 0.33812994209912, 0.18323087864266333, 0.165732182790494, 0.1068800130496489, 0.29986968409620074, 0.009016820154584094, 0.12532495132150542, 0.05529479124589554, 0.1708586704979757, 0.07558649780084972, 0.04572943404032505, -0.26914682436965703, 0.08795165300725541, 0.001417640247186896] |
1,803.00136 | Structure of Anomalies of 4d SCFTs from M5-branes, and Anomaly Inflow | We study the 't Hooft anomalies of four-dimensional superconformal field
theories that arise from M5-branes wrapped on a punctured Riemann surface. In
general there are two independent contributions to the anomalies. There is a
bulk term obtained by integrating the anomaly polynomial of the world-volume
theory on the M5-branes over the Riemann surface; this contribution knows about
the punctures only through its dependence on the Euler characteristic of the
surface. The second set of contributions comes from local data at the
punctures; these terms are independent from the bulk data of the surface. Using
anomaly inflow in M-theory, we describe the general structure of the anomalies
for cases when the four-dimensional theories preserve N=2 supersymmetry. We
additionally discuss the anomalies corresponding to (p,q) punctures in N=1
theories.
| hep-th | we study the t hooft anomalies of fourdimensional superconformal field theories that arise from m5branes wrapped on a punctured riemann surface in general there are two independent contributions to the anomalies there is a bulk term obtained by integrating the anomaly polynomial of the worldvolume theory on the m5branes over the riemann surface this contribution knows about the punctures only through its dependence on the euler characteristic of the surface the second set of contributions comes from local data at the punctures these terms are independent from the bulk data of the surface using anomaly inflow in mtheory we describe the general structure of the anomalies for cases when the fourdimensional theories preserve n2 supersymmetry we additionally discuss the anomalies corresponding to pq punctures in n1 theories | [['we', 'study', 'the', 't', 'hooft', 'anomalies', 'of', 'fourdimensional', 'superconformal', 'field', 'theories', 'that', 'arise', 'from', 'm5branes', 'wrapped', 'on', 'a', 'punctured', 'riemann', 'surface', 'in', 'general', 'there', 'are', 'two', 'independent', 'contributions', 'to', 'the', 'anomalies', 'there', 'is', 'a', 'bulk', 'term', 'obtained', 'by', 'integrating', 'the', 'anomaly', 'polynomial', 'of', 'the', 'worldvolume', 'theory', 'on', 'the', 'm5branes', 'over', 'the', 'riemann', 'surface', 'this', 'contribution', 'knows', 'about', 'the', 'punctures', 'only', 'through', 'its', 'dependence', 'on', 'the', 'euler', 'characteristic', 'of', 'the', 'surface', 'the', 'second', 'set', 'of', 'contributions', 'comes', 'from', 'local', 'data', 'at', 'the', 'punctures', 'these', 'terms', 'are', 'independent', 'from', 'the', 'bulk', 'data', 'of', 'the', 'surface', 'using', 'anomaly', 'inflow', 'in', 'mtheory', 'we', 'describe', 'the', 'general', 'structure', 'of', 'the', 'anomalies', 'for', 'cases', 'when', 'the', 'fourdimensional', 'theories', 'preserve', 'n2', 'supersymmetry', 'we', 'additionally', 'discuss', 'the', 'anomalies', 'corresponding', 'to', 'pq', 'punctures', 'in', 'n1', 'theories']] | [-0.18435730833897557, 0.12289438716696913, -0.061874904401241035, 0.07078201841474988, -0.0706788653280498, -0.11887401097828126, -0.006051666811878581, 0.25106315827363823, -0.22691227742434134, -0.27092791208264044, 0.0983815740832443, -0.32748085922400255, -0.17846890123319029, 0.13128074683894322, -0.07987163999066578, -0.002487285411458345, -0.021474890156069727, 0.07046503924185366, -0.13352807938839917, -0.29694553182137706, 0.397264236015979, -0.031196765253763997, 0.22767253620660036, 0.08553973410102561, 0.10069632300131727, -0.02546566162156638, -0.015334017494121405, 0.013875070331520451, -0.12078999760809335, 0.11671799574575321, 0.2540157087253568, 0.06174137418817886, 0.06749867406072403, -0.4961005692612233, -0.24301372027743284, 0.06926486025909036, 0.12205103436470266, 0.09502292578438605, 0.022289278740099977, -0.25332885865474075, 0.07319499472317499, -0.08120498229111509, -0.1360766464673848, -0.018335400715719645, 0.00871047663308064, -0.10162157587355047, -0.19312749206735644, 0.06167632242936025, -0.0009530621734777774, 0.10038718006272954, -0.05833527445518155, -0.09803059414637781, -0.0819565033389798, 0.11410736691093945, 0.1910097143066475, 0.06486693846142491, 0.14100097688134966, -0.1791825618349221, -0.10829290033017439, 0.344356334599571, -0.102061092923212, -0.23572326095327442, 0.16162566552818644, -0.15378612503922712, -0.20056409432413422, 0.1596502502720187, 0.1053038478396305, 0.18369545429939493, -0.08034778073144523, 0.226109293861476, -0.06421908162402239, 0.08576369851160295, 0.10769266720795197, -0.01552983020582625, 0.2861779608991085, 0.07348169778899004, 0.03625685047251943, 0.11812550189362209, -0.08501985056259793, -0.05994122045657297, -0.45885278701078236, -0.1555100162073559, -0.13996342299542736, 0.105781743359774, -0.16105349344296718, -0.15295591841701153, 0.3888903271817551, 0.10796504483858901, 0.181678231688231, 0.08671761461804936, 0.21104967304483407, 0.08980886569655937, 0.1079424545975534, 0.06825810874716973, 0.2033975214805586, 0.14122195457342981, 0.0523280367854704, -0.25878855468376216, -0.08368000430678289, 0.15018290369661422] |
1,803.00137 | Low-mass eclipsing binaries in the WFCAM Transit Survey: the persistence
of the M-dwarf radius inflation problem | We present the characterization of 5 new short-period low-mass eclipsing
binaries from the WFCAM Transit Survey. The analysis was performed by using the
photometric WFCAM J-mag data and additional low- and intermediate-resolution
spectroscopic data to obtain both orbital and physical properties of the
studied sample. The light curves and the measured radial velocity curves were
modeled simultaneously with the JKTEBOP code, with Markov chain Monte Carlo
simulations for the error estimates. The best-model fit have revealed that the
investigated detached binaries are in very close orbits, with orbital
separations of $2.9 \leq a \leq 6.7$ $R_{\odot}$ and short periods of $0.59
\leq P_{\rm orb} \leq 1.72$ d, approximately. We have derived stellar masses
between $0.24$ and $0.72$ $M_{\odot}$ and radii ranging from $0.42$ to $0.67$
$R_{\odot}$. The great majority of the LMEBs in our sample has an estimated
radius far from the predicted values according to evolutionary models. The
components with derived masses of $M < 0.6$ $M_{\odot}$ present a radius
inflation of $\sim$$9\%$ or more. This general behavior follows the trend of
inflation for partially-radiative stars proposed previously. These systems add
to the increasing sample of low-mass stellar radii that are not well-reproduced
by stellar models. They further highlight the need to understand the magnetic
activity and physical state of small stars. Missions like TESS will provide
many such systems to perform high-precision radius measurements to tightly
constrain low-mass stellar evolution models.
| astro-ph.SR | we present the characterization of 5 new shortperiod lowmass eclipsing binaries from the wfcam transit survey the analysis was performed by using the photometric wfcam jmag data and additional low and intermediateresolution spectroscopic data to obtain both orbital and physical properties of the studied sample the light curves and the measured radial velocity curves were modeled simultaneously with the jktebop code with markov chain monte carlo simulations for the error estimates the bestmodel fit have revealed that the investigated detached binaries are in very close orbits with orbital separations of 29 leq a leq 67 r_odot and short periods of 059 leq p_rm orb leq 172 d approximately we have derived stellar masses between 024 and 072 m_odot and radii ranging from 042 to 067 r_odot the great majority of the lmebs in our sample has an estimated radius far from the predicted values according to evolutionary models the components with derived masses of m 06 m_odot present a radius inflation of sim9 or more this general behavior follows the trend of inflation for partiallyradiative stars proposed previously these systems add to the increasing sample of lowmass stellar radii that are not wellreproduced by stellar models they further highlight the need to understand the magnetic activity and physical state of small stars missions like tess will provide many such systems to perform highprecision radius measurements to tightly constrain lowmass stellar evolution models | [['we', 'present', 'the', 'characterization', 'of', '5', 'new', 'shortperiod', 'lowmass', 'eclipsing', 'binaries', 'from', 'the', 'wfcam', 'transit', 'survey', 'the', 'analysis', 'was', 'performed', 'by', 'using', 'the', 'photometric', 'wfcam', 'jmag', 'data', 'and', 'additional', 'low', 'and', 'intermediateresolution', 'spectroscopic', 'data', 'to', 'obtain', 'both', 'orbital', 'and', 'physical', 'properties', 'of', 'the', 'studied', 'sample', 'the', 'light', 'curves', 'and', 'the', 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1,803.00138 | A novel approach for fusion of heterogeneous sources of data | With advancements in sensor technology, a heterogeneous set of data,
containing samples of scalar, waveform signal, image, or even structured point
cloud are becoming increasingly popular. Developing a statistical model,
representing the behavior of the underlying system based upon such a
heterogeneous set of data can be used in monitoring, control, and optimization
of the system. Unfortunately, available methods only focus on the scalar and
curve data and do not provide a general framework that can integrate different
sources of data to construct a model. This paper poses the problem of
estimating a process output, measured by a scalar, curve, an image, or a point
cloud by a set of heterogeneous process variables such as scalar process
setting, sensor readings, and images. We introduce a general approach in which
each set of input data (predictor) as well as the output measurements are
represented by tensors. We formulate a linear regression model between the
input and output tensors and estimate the parameters by minimizing a least
square loss function. In order to avoid overfitting and to reduce the number of
parameters to be estimated, we decompose the model parameters using several
bases, spanning the input and output spaces. Next, we learn both the bases and
their spanning coefficients when minimizing the loss function using an
alternating least square (ALS) algorithm. We show that such a minimization has
a closed-form solution in each iteration and can be computed very efficiently.
Through several simulation and case studies, we evaluate the performance of the
proposed method. The results reveal the advantage of the proposed method over
some benchmarks in the literature in terms of the mean square prediction error.
| stat.ME | with advancements in sensor technology a heterogeneous set of data containing samples of scalar waveform signal image or even structured point cloud are becoming increasingly popular developing a statistical model representing the behavior of the underlying system based upon such a heterogeneous set of data can be used in monitoring control and optimization of the system unfortunately available methods only focus on the scalar and curve data and do not provide a general framework that can integrate different sources of data to construct a model this paper poses the problem of estimating a process output measured by a scalar curve an image or a point cloud by a set of heterogeneous process variables such as scalar process setting sensor readings and images we introduce a general approach in which each set of input data predictor as well as the output measurements are represented by tensors we formulate a linear regression model between the input and output tensors and estimate the parameters by minimizing a least square loss function in order to avoid overfitting and to reduce the number of parameters to be estimated we decompose the model parameters using several bases spanning the input and output spaces next we learn both the bases and their spanning coefficients when minimizing the loss function using an alternating least square als algorithm we show that such a minimization has a closedform solution in each iteration and can be computed very efficiently through several simulation and case studies we evaluate the performance of the proposed method the results reveal the advantage of the proposed method over some benchmarks in the literature in terms of the mean square prediction error | [['with', 'advancements', 'in', 'sensor', 'technology', 'a', 'heterogeneous', 'set', 'of', 'data', 'containing', 'samples', 'of', 'scalar', 'waveform', 'signal', 'image', 'or', 'even', 'structured', 'point', 'cloud', 'are', 'becoming', 'increasingly', 'popular', 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1,803.00139 | Stochastic multi-symplectic Runge-Kutta methods for stochastic
Hamiltonian PDEs | In this paper, we consider stochastic Runge-Kutta methods for stochastic
Hamiltonian partial differential equations and present some sufficient
conditions for multisymplecticity of stochastic Runge-Kutta methods of
stochastic Hamiltonian partial differential equations. Particularly, we apply
these ideas to stochastic Maxwell equations with multiplicative noise,
possessing the stochastic multi-symplectic conservation law and energy
conservation law. Theoretical analysis shows that the methods can preserve both
the discrete stochastic multi-symplectic conservation law and discrete energy
conservation law almost surely.
| math.SG | in this paper we consider stochastic rungekutta methods for stochastic hamiltonian partial differential equations and present some sufficient conditions for multisymplecticity of stochastic rungekutta methods of stochastic hamiltonian partial differential equations particularly we apply these ideas to stochastic maxwell equations with multiplicative noise possessing the stochastic multisymplectic conservation law and energy conservation law theoretical analysis shows that the methods can preserve both the discrete stochastic multisymplectic conservation law and discrete energy conservation law almost surely | [['in', 'this', 'paper', 'we', 'consider', 'stochastic', 'rungekutta', 'methods', 'for', 'stochastic', 'hamiltonian', 'partial', 'differential', 'equations', 'and', 'present', 'some', 'sufficient', 'conditions', 'for', 'multisymplecticity', 'of', 'stochastic', 'rungekutta', 'methods', 'of', 'stochastic', 'hamiltonian', 'partial', 'differential', 'equations', 'particularly', 'we', 'apply', 'these', 'ideas', 'to', 'stochastic', 'maxwell', 'equations', 'with', 'multiplicative', 'noise', 'possessing', 'the', 'stochastic', 'multisymplectic', 'conservation', 'law', 'and', 'energy', 'conservation', 'law', 'theoretical', 'analysis', 'shows', 'that', 'the', 'methods', 'can', 'preserve', 'both', 'the', 'discrete', 'stochastic', 'multisymplectic', 'conservation', 'law', 'and', 'discrete', 'energy', 'conservation', 'law', 'almost', 'surely']] | [-0.15931139789521695, 0.013220791518688203, -0.09463959952195486, 0.11567195475567132, -0.13079344011532765, -0.16828373265452684, -0.05970373607706279, 0.27434659615159035, -0.3399858028938373, -0.29728551316385465, 0.08387333446958413, -0.27520129903530083, -0.1739579748734832, 0.12446430622289578, -0.1281628489991029, 0.15476843759417533, 0.04275435743232568, -0.07605562719206015, -0.10186485760845244, -0.2138073599866281, 0.32463704695304235, -0.01002281571427981, 0.2506975034996867, -0.05233055797715982, 0.27426191807414096, -0.0796542837532858, -0.09719622902882596, 0.024631952742735545, -0.152283463543766, 0.042252112589776514, 0.24886424906551838, 0.04692269206047058, 0.29430525886515774, -0.511941291683664, -0.2110169306769967, 0.0871793877830108, 0.11816760211911363, 0.12742770137886206, -0.055001710231105484, -0.2692266360173623, 0.05697122523250679, -0.16423843532800675, -0.17758075382560493, -0.19456329744309186, -0.04003848733380437, 0.1612076925703635, -0.2570189839104811, 0.1807067323848605, 0.1801431334639589, 0.04411814270541072, -0.12016952811429898, -0.07735934571052591, -0.058214114786436164, -0.010509515907615423, 0.07449705863526712, -0.14500139609600107, 0.07607775305708249, -0.0342432544597735, -0.14670287008707722, 0.38511215365181367, -0.11525679213926196, -0.3407165290166934, 0.08803514542678992, -0.08712982562681039, -0.2478433761621515, 0.14680459530403217, 0.18503410705520462, 0.16594311631789121, -0.2585650666492681, 0.1351455841003917, 0.015118434984857838, 0.09797114509933938, -0.006608288077016671, -0.014598243609070779, 0.019754216546813648, 0.08815163479497036, 0.18607224111755688, 0.04652088387558857, 0.012458313644553224, -0.29839105317989983, -0.3802542910973231, -0.15817524923942983, -0.0779871865361929, 0.15218633593525738, -0.1448065845577124, -0.16885991635421913, 0.32473701213796935, 0.12311371545617779, 0.025924017826716106, 0.16722719252109527, 0.29780255074302353, 0.3193718137925801, -0.041125516171256704, 0.10492610660692056, 0.15163059669119927, 0.26253586923082667, 0.23590321543316045, -0.2524256973961989, -0.01091040148710211, 0.18341310593610008] |
1,803.0014 | Homological approximations for Profinite and Pro-$p$ limit groups | We study homological approximations of the profinite completion of a limit
group (see Thm.~A) and obtain the analogous of Bridson and Howie's Theorem for
the profinite completion of a non-abelian limit group (see Thm.~B).
| math.GR | we study homological approximations of the profinite completion of a limit group see thma and obtain the analogous of bridson and howies theorem for the profinite completion of a nonabelian limit group see thmb | [['we', 'study', 'homological', 'approximations', 'of', 'the', 'profinite', 'completion', 'of', 'a', 'limit', 'group', 'see', 'thma', 'and', 'obtain', 'the', 'analogous', 'of', 'bridson', 'and', 'howies', 'theorem', 'for', 'the', 'profinite', 'completion', 'of', 'a', 'nonabelian', 'limit', 'group', 'see', 'thmb']] | [-0.12836250007967465, 0.0388282775238622, -0.20992745808325708, 0.14079299056902528, -0.066238482540939, -0.08277065472793765, 0.10949778664507903, 0.298450589296408, -0.27272264566272497, -0.23521687230095267, 0.18289645049662795, -0.21234874450601637, -0.12423381255939603, 0.17844953655730933, -0.18481194143532775, -0.06641472366754897, 0.06558929820312187, 0.15504196903202683, -0.12048246810445562, -0.24284932087175548, 0.31885957907070406, -0.044119835569290444, 0.24299907378735952, 0.08948567404877394, 0.085729419690324, 0.07559690000198316, -0.12220133323717164, -0.042780808289535344, -0.1778415969456546, 0.09511266404297203, 0.30380299233365804, 0.004247285076417029, 0.18608376034535468, -0.35087695776019245, -0.14550118379702326, 0.19250981582626991, 0.08691235026344657, 0.03973126318305731, -0.04002621324616484, -0.3067877877037972, 0.11640718948910944, -0.24614018225111067, -0.12494378385599703, -0.06894454755820334, 0.04490096995141357, -0.020494185308052693, -0.21493826736696064, -0.00595900399639504, 0.13691972655942664, 0.11112024463363923, -0.04983639673446305, -0.034550115931779146, 0.024405252013821155, 0.13587759823712986, 0.0661352836759761, -0.0025830351587501355, 0.14066339745477308, -0.14912051480496302, -0.11568414850626141, 0.45467706822091714, -0.11410098636406474, -0.0689553854172118, 0.1241332411300391, -0.16371287842048332, -0.23485358915058896, 0.07389803673140705, 0.07372888940153643, 0.17603966512251645, 0.0221452117402805, 0.2688130980786809, -0.215522043639794, 0.05812743175192736, 0.098103660158813, 0.03669672796968371, 0.0644709297339432, 0.12955497112125158, 0.12063541941461153, 0.11676096444716677, 0.083156558292103, 0.05646858282852918, -0.3824221594259143, -0.23702056426554918, -0.11160991206998006, 0.16936693608295172, -0.11152915053298784, -0.20180826261639595, 0.3748927841661498, 0.1281322751456173, 0.10128375107888132, 0.20959279665839858, 0.15127243904862553, 0.0801971924083773, -0.01028854787000455, 0.07096108116093092, 0.1417037117498694, 0.30833969998639077, -0.04920901689911261, -0.1462929641129449, -0.1218931904877536, 0.2840574672445655] |
1,803.00141 | Entanglement concentration and purification of two-mode squeezed
microwave photons in circuit QED | We present a theoretical proposal for a physical implementation of
entanglement concentration and purification protocols for two-mode squeezed
microwave photons in circuit quantum electrodynamics (QED). First, we give the
description of the cross-Kerr effect induced between two resonators in circuit
QED. Then we use the cross-Kerr media to design the effective quantum
nondemolition (QND) measurement on microwave-photon number. By using the QND
measurement, the parties in quantum communication can accomplish the
entanglement concentration and purification of nonlocal two-mode squeezed
microwave photons. We discuss the feasibility of our schemes by giving the
detailed parameters which can be realized with current experimental technology.
Our work can improve some practical applications in continuous-variable
microwave-based quantum information processing.
| quant-ph | we present a theoretical proposal for a physical implementation of entanglement concentration and purification protocols for twomode squeezed microwave photons in circuit quantum electrodynamics qed first we give the description of the crosskerr effect induced between two resonators in circuit qed then we use the crosskerr media to design the effective quantum nondemolition qnd measurement on microwavephoton number by using the qnd measurement the parties in quantum communication can accomplish the entanglement concentration and purification of nonlocal twomode squeezed microwave photons we discuss the feasibility of our schemes by giving the detailed parameters which can be realized with current experimental technology our work can improve some practical applications in continuousvariable microwavebased quantum information processing | [['we', 'present', 'a', 'theoretical', 'proposal', 'for', 'a', 'physical', 'implementation', 'of', 'entanglement', 'concentration', 'and', 'purification', 'protocols', 'for', 'twomode', 'squeezed', 'microwave', 'photons', 'in', 'circuit', 'quantum', 'electrodynamics', 'qed', 'first', 'we', 'give', 'the', 'description', 'of', 'the', 'crosskerr', 'effect', 'induced', 'between', 'two', 'resonators', 'in', 'circuit', 'qed', 'then', 'we', 'use', 'the', 'crosskerr', 'media', 'to', 'design', 'the', 'effective', 'quantum', 'nondemolition', 'qnd', 'measurement', 'on', 'microwavephoton', 'number', 'by', 'using', 'the', 'qnd', 'measurement', 'the', 'parties', 'in', 'quantum', 'communication', 'can', 'accomplish', 'the', 'entanglement', 'concentration', 'and', 'purification', 'of', 'nonlocal', 'twomode', 'squeezed', 'microwave', 'photons', 'we', 'discuss', 'the', 'feasibility', 'of', 'our', 'schemes', 'by', 'giving', 'the', 'detailed', 'parameters', 'which', 'can', 'be', 'realized', 'with', 'current', 'experimental', 'technology', 'our', 'work', 'can', 'improve', 'some', 'practical', 'applications', 'in', 'continuousvariable', 'microwavebased', 'quantum', 'information', 'processing']] | [-0.15130796781005829, 0.20950208755267227, -0.045328654324341765, -0.03263404481970754, -0.0063239768670316325, -0.24601162735601528, 0.06498241337146937, 0.3741674370606217, -0.19364908955206997, -0.27950269473602246, -0.0027105679384253002, -0.26397529877558873, -0.10954268523410224, 0.28575276531148375, -0.06014952944232184, 0.16727763380479524, 0.04364978991808337, -0.046598976959980894, -0.02450519608202995, -0.2555312150558293, 0.28174748173669767, 0.04732371383681567, 0.33966204913725195, 0.07672393725564082, 0.10383042858978897, 0.02980661508627236, 0.019938584430380154, -0.05701768176074613, -0.11502016401617877, 0.11754315152045405, 0.2894958322765632, 0.13371736622056024, 0.24332552343063676, -0.47261505606665943, -0.21650246193779535, 0.11774464323912469, 0.10924687192840665, 0.25676681198028584, -0.0699979221987489, -0.3613963685929775, -0.05696156934279491, -0.22194406096445674, -0.0620892593114261, -0.1289120942942406, -0.08382534049042877, -0.0449657381896191, -0.2494227660109142, 0.03456425060504811, 0.015380163458502736, 0.036109847706954995, 0.08377686352060552, 0.021349752959060043, 0.10140070922493019, 0.08722999535313104, -0.15728218064300323, -0.06802239099603244, 0.2061148853359842, -0.1298641814164033, -0.22443527750785283, 0.31654887987921637, -0.08007782597397045, -0.17641567402078132, 0.10798874047682866, -0.08244886511748885, -0.09269213830345523, -0.011994085222556206, 0.14050740268685968, 0.03340472707222624, -0.14679959101557502, 0.053110790555365384, 0.019413753216596025, 0.2303511780286371, 0.08265488161582892, 0.2113507195874199, 0.17204305111316212, 0.1242351390527594, 0.043715046641106405, 0.23207679162876224, -0.022395119207845836, -0.09186643063858674, -0.3712327056155916, -0.2034970247921975, -0.24022248578094468, 0.13226055888164984, -0.1069910395356904, -0.08777042737908727, 0.409425188860807, 0.21224666820234506, 0.07734954123091149, -0.03791937209857758, 0.41708858383067865, 0.09935131047550066, 0.034001271460989586, 0.010103387415016951, 0.32465342103565936, 0.21785219249380916, 0.08680993320600114, -0.31178829171531425, 0.018280293003108614, 0.005557840779881205] |
1,803.00142 | Invariants of deformations of quotient surface singularities | We find all $P$-resolutions of quotient surface singularities (especially,
tetrahedral, octahedral, and icosahedral singularities) together with their
dual graphs, which reproduces Jan Steven's list [Manuscripta Math. 1993] of the
numbers of $P$-resolutions of each singularities. We then compute the
dimensions and Milnor numbers of the corresponding irreducible components of
the reduced base spaces of versal deformations of each singularities.
Furthermore we realize Milnor fibers as complements of certain divisors
(depending only on the singularities) in rational surfaces via the minimal
model program for 3-folds. Then we compare Milnor fibers with minimal
symplectic fillings, where the latter are classified by Bhupal and Ono [Nagoya
Math. J. 2012]. As an application, we show that there are 6 pairs of entries in
the list of Bhupal and Ono [Nagoya Math. J. 2012] such that two entries in each
pairs represent diffeomorphic minimal symplectic fillings.
| math.AG math.GT math.SG | we find all presolutions of quotient surface singularities especially tetrahedral octahedral and icosahedral singularities together with their dual graphs which reproduces jan stevens list manuscripta math 1993 of the numbers of presolutions of each singularities we then compute the dimensions and milnor numbers of the corresponding irreducible components of the reduced base spaces of versal deformations of each singularities furthermore we realize milnor fibers as complements of certain divisors depending only on the singularities in rational surfaces via the minimal model program for 3folds then we compare milnor fibers with minimal symplectic fillings where the latter are classified by bhupal and ono nagoya math j 2012 as an application we show that there are 6 pairs of entries in the list of bhupal and ono nagoya math j 2012 such that two entries in each pairs represent diffeomorphic minimal symplectic fillings | [['we', 'find', 'all', 'presolutions', 'of', 'quotient', 'surface', 'singularities', 'especially', 'tetrahedral', 'octahedral', 'and', 'icosahedral', 'singularities', 'together', 'with', 'their', 'dual', 'graphs', 'which', 'reproduces', 'jan', 'stevens', 'list', 'manuscripta', 'math', '1993', 'of', 'the', 'numbers', 'of', 'presolutions', 'of', 'each', 'singularities', 'we', 'then', 'compute', 'the', 'dimensions', 'and', 'milnor', 'numbers', 'of', 'the', 'corresponding', 'irreducible', 'components', 'of', 'the', 'reduced', 'base', 'spaces', 'of', 'versal', 'deformations', 'of', 'each', 'singularities', 'furthermore', 'we', 'realize', 'milnor', 'fibers', 'as', 'complements', 'of', 'certain', 'divisors', 'depending', 'only', 'on', 'the', 'singularities', 'in', 'rational', 'surfaces', 'via', 'the', 'minimal', 'model', 'program', 'for', '3folds', 'then', 'we', 'compare', 'milnor', 'fibers', 'with', 'minimal', 'symplectic', 'fillings', 'where', 'the', 'latter', 'are', 'classified', 'by', 'bhupal', 'and', 'ono', 'nagoya', 'math', 'j', '2012', 'as', 'an', 'application', 'we', 'show', 'that', 'there', 'are', '6', 'pairs', 'of', 'entries', 'in', 'the', 'list', 'of', 'bhupal', 'and', 'ono', 'nagoya', 'math', 'j', '2012', 'such', 'that', 'two', 'entries', 'in', 'each', 'pairs', 'represent', 'diffeomorphic', 'minimal', 'symplectic', 'fillings']] | [-0.19011506481849133, 0.050644189462908676, -0.039047629803630475, 0.05213131761535051, -0.07715962457814063, -0.154728677229467, 0.003787608908040198, 0.32208258587642447, -0.23745101557529352, -0.2550890305680586, 0.0765205773554663, -0.2937948699142282, -0.19970342591867274, 0.1484816610426741, -0.16853373311790273, -0.017067250954185393, 0.052655364162163106, -0.0005875547641082436, -0.09817308122424263, -0.35984477151766525, 0.3704117591499437, -0.017712077013619824, 0.1896687753441585, 0.058182502205067495, 0.11808364765829216, 0.030680096160012778, -0.0281139816205407, -0.0313532115686817, -0.18326157240697813, 0.11169798338640455, 0.31522218137318997, 0.021920005280643383, 0.12480386563556943, -0.37238576191853967, -0.09710782463166942, 0.17261916763935212, 0.09141710451110563, 0.020201057627473477, 0.06805567949075675, -0.2661776998086918, 0.09150829959903507, -0.1530176329101165, -0.16925779178656855, -0.09281233269787321, 0.07790066228175523, 0.09614050063364049, -0.17268029274749205, -0.009551094149335136, 0.08541357600467002, 0.16238998358157722, -0.027111122015812467, -0.15757262291624508, -0.14633673823484822, 0.0824841634760089, -0.0075102104443779336, 0.028249419690605175, 0.06274011376287444, -0.0961491994143642, -0.16078313918955994, 0.34660215737919015, -0.005780833717171393, -0.19073768609685574, 0.16686393057676494, -0.11464093892062281, -0.18424258374358057, 0.16609423540050414, 0.0776796313927423, 0.13507129909827353, -0.022752030600681372, 0.17783621716286643, -0.10684249414533958, 0.06272094206494801, 0.22503113601330324, -0.040529096439639305, 0.12300295954576779, 0.027796473562162607, 0.03662854889262235, 0.10507683294118507, -0.054345826478014496, -0.03776202651016507, -0.3501225894957728, -0.23514109012400006, -0.15305670680633435, 0.11261064445613124, -0.07005760927332882, -0.15248160401907426, 0.39634393012904107, 0.004184266572144438, 0.23115696806249256, 0.09090521791041664, 0.1539964535511396, -0.014516230973131056, 0.04104406141192184, 0.09380999535771338, 0.14388379927869913, 0.18284128632244542, -0.007445026037170954, -0.13245431138598857, -0.09316146260470892, 0.21087319832559384] |
1,803.00143 | The PPT square conjecture holds generically for some classes of
independent states | Let $|\psi\rangle\langle \psi|$ be a random pure state on
$\mathbb{C}^{d^2}\otimes \mathbb{C}^s$, where $\psi$ is a random unit vector
uniformly distributed on the sphere in $\mathbb{C}^{d^2}\otimes \mathbb{C}^s$.
Let $\rho_1$ be random induced states
$\rho_1=Tr_{\mathbb{C}^s}(|\psi\rangle\langle \psi |)$ whose distribution is
$\mu_{d^2,s}$; and let $\rho_2$ be random induced states following the same
distribution $\mu_{d^2,s}$ independent from $\rho_1$. Let $\rho$ be a random
state induced by the entanglement swapping of $\rho_1$ and $\rho_2$. We show
that the empirical spectrum of $\rho- {1\mkern -4mu{\rm l}}/d^2$ converges
almost surely to the Marcenko-Pastur law with parameter $c^2$ as $d\rightarrow
\infty$ and $s/d \rightarrow c$. As an application, we prove that the state
$\rho$ is separable generically if $\rho_1, \rho_2$ are PPT entangled.
| math-ph math.MP math.PR | let psiranglelangle psi be a random pure state on mathbbcd2otimes mathbbcs where psi is a random unit vector uniformly distributed on the sphere in mathbbcd2otimes mathbbcs let rho_1 be random induced states rho_1tr_mathbbcspsiranglelangle psi whose distribution is mu_d2s and let rho_2 be random induced states following the same distribution mu_d2s independent from rho_1 let rho be a random state induced by the entanglement swapping of rho_1 and rho_2 we show that the empirical spectrum of rho 1mkern 4murm ld2 converges almost surely to the marcenkopastur law with parameter c2 as drightarrow infty and sd rightarrow c as an application we prove that the state rho is separable generically if rho_1 rho_2 are ppt entangled | [['let', 'psiranglelangle', 'psi', 'be', 'a', 'random', 'pure', 'state', 'on', 'mathbbcd2otimes', 'mathbbcs', 'where', 'psi', 'is', 'a', 'random', 'unit', 'vector', 'uniformly', 'distributed', 'on', 'the', 'sphere', 'in', 'mathbbcd2otimes', 'mathbbcs', 'let', 'rho_1', 'be', 'random', 'induced', 'states', 'rho_1tr_mathbbcspsiranglelangle', 'psi', 'whose', 'distribution', 'is', 'mu_d2s', 'and', 'let', 'rho_2', 'be', 'random', 'induced', 'states', 'following', 'the', 'same', 'distribution', 'mu_d2s', 'independent', 'from', 'rho_1', 'let', 'rho', 'be', 'a', 'random', 'state', 'induced', 'by', 'the', 'entanglement', 'swapping', 'of', 'rho_1', 'and', 'rho_2', 'we', 'show', 'that', 'the', 'empirical', 'spectrum', 'of', 'rho', '1mkern', '4murm', 'ld2', 'converges', 'almost', 'surely', 'to', 'the', 'marcenkopastur', 'law', 'with', 'parameter', 'c2', 'as', 'drightarrow', 'infty', 'and', 'sd', 'rightarrow', 'c', 'as', 'an', 'application', 'we', 'prove', 'that', 'the', 'state', 'rho', 'is', 'separable', 'generically', 'if', 'rho_1', 'rho_2', 'are', 'ppt', 'entangled']] | [-0.15518117641573245, 0.3094950332362611, -0.12453879674299145, -0.06260323896894301, 0.041027938261768725, -0.2287472277607347, 0.005812053539008254, 0.347507327127288, -0.31036355796287646, -0.11953442422497385, 0.03127023117629952, -0.3369495451344915, -0.058794479199931166, 0.09737695381045341, -0.01958982522581827, 0.05744077206395988, 0.01503277624290621, 0.1281648657135314, -0.04360583714947526, -0.18177833174408045, 0.32070961712754137, -0.08236028471047867, 0.22936334764971486, 0.027302651964830903, 0.05662507531540644, 0.03150799252830867, 0.1090590779555364, -0.0023310593997110736, -0.17006542319764123, 0.014113786001030778, 0.2588398277337821, 0.12209245548104453, 0.2340964011656876, -0.3099206205378374, -0.1584671251212229, 0.2647912466968849, 0.14564102205869583, -0.054793050846541826, 0.038428973885393646, -0.3260359112744413, 0.15497285637231367, -0.15784810344635877, -0.14597690386152914, -0.08050718518712048, 0.08981722651295504, 0.018404553110605845, -0.4044894706282132, 0.09792818612515715, 0.07202952596543462, -3.5578354244243426e-05, -0.012059057671752459, -0.1807997683546383, -0.09511990247751181, 0.07855196572611776, -0.036066514042311544, 0.15110277606367642, 0.12825313985418035, -0.04875888136865677, -0.06124769901979785, 0.3274900241280502, -0.12751189018336107, -0.24807769225312853, 0.06814629340737637, -0.17222246890096873, -0.1051703864040802, 0.056395041701419034, 0.11517598257039867, 0.12688416530487165, -0.026913685594270233, 0.21104067516401145, -0.10241045453366032, 0.16439782647868478, 0.044483718431537166, 0.021607019002812932, 0.1457800556409155, 0.002459908255709792, 0.12353484531565798, 0.15667905217862493, -0.022518215485725482, -0.047899956327318305, -0.37840776799141235, -0.16879566571727958, -0.2886017269415359, 0.23300795460049556, -0.12014328629035999, -0.16342884319711407, 0.30435112218882115, -0.002799799375868631, 0.24112899903260734, 0.1015100796346746, 0.2018883631055085, 0.14300377629884026, -0.07304832883473521, 0.13973854153135898, 0.08013839778305858, 0.22216078293079264, -0.03985056167749582, -0.19393751387405297, 0.07537496792380961, 0.04630193239042781] |
1,803.00144 | Learning Longer-term Dependencies in RNNs with Auxiliary Losses | Despite recent advances in training recurrent neural networks (RNNs),
capturing long-term dependencies in sequences remains a fundamental challenge.
Most approaches use backpropagation through time (BPTT), which is difficult to
scale to very long sequences. This paper proposes a simple method that improves
the ability to capture long term dependencies in RNNs by adding an unsupervised
auxiliary loss to the original objective. This auxiliary loss forces RNNs to
either reconstruct previous events or predict next events in a sequence, making
truncated backpropagation feasible for long sequences and also improving full
BPTT. We evaluate our method on a variety of settings, including pixel-by-pixel
image classification with sequence lengths up to 16\,000, and a real document
classification benchmark. Our results highlight good performance and resource
efficiency of this approach over competitive baselines, including other
recurrent models and a comparable sized Transformer. Further analyses reveal
beneficial effects of the auxiliary loss on optimization and regularization, as
well as extreme cases where there is little to no backpropagation.
| cs.LG cs.AI stat.ML | despite recent advances in training recurrent neural networks rnns capturing longterm dependencies in sequences remains a fundamental challenge most approaches use backpropagation through time bptt which is difficult to scale to very long sequences this paper proposes a simple method that improves the ability to capture long term dependencies in rnns by adding an unsupervised auxiliary loss to the original objective this auxiliary loss forces rnns to either reconstruct previous events or predict next events in a sequence making truncated backpropagation feasible for long sequences and also improving full bptt we evaluate our method on a variety of settings including pixelbypixel image classification with sequence lengths up to 16000 and a real document classification benchmark our results highlight good performance and resource efficiency of this approach over competitive baselines including other recurrent models and a comparable sized transformer further analyses reveal beneficial effects of the auxiliary loss on optimization and regularization as well as extreme cases where there is little to no backpropagation | [['despite', 'recent', 'advances', 'in', 'training', 'recurrent', 'neural', 'networks', 'rnns', 'capturing', 'longterm', 'dependencies', 'in', 'sequences', 'remains', 'a', 'fundamental', 'challenge', 'most', 'approaches', 'use', 'backpropagation', 'through', 'time', 'bptt', 'which', 'is', 'difficult', 'to', 'scale', 'to', 'very', 'long', 'sequences', 'this', 'paper', 'proposes', 'a', 'simple', 'method', 'that', 'improves', 'the', 'ability', 'to', 'capture', 'long', 'term', 'dependencies', 'in', 'rnns', 'by', 'adding', 'an', 'unsupervised', 'auxiliary', 'loss', 'to', 'the', 'original', 'objective', 'this', 'auxiliary', 'loss', 'forces', 'rnns', 'to', 'either', 'reconstruct', 'previous', 'events', 'or', 'predict', 'next', 'events', 'in', 'a', 'sequence', 'making', 'truncated', 'backpropagation', 'feasible', 'for', 'long', 'sequences', 'and', 'also', 'improving', 'full', 'bptt', 'we', 'evaluate', 'our', 'method', 'on', 'a', 'variety', 'of', 'settings', 'including', 'pixelbypixel', 'image', 'classification', 'with', 'sequence', 'lengths', 'up', 'to', '16000', 'and', 'a', 'real', 'document', 'classification', 'benchmark', 'our', 'results', 'highlight', 'good', 'performance', 'and', 'resource', 'efficiency', 'of', 'this', 'approach', 'over', 'competitive', 'baselines', 'including', 'other', 'recurrent', 'models', 'and', 'a', 'comparable', 'sized', 'transformer', 'further', 'analyses', 'reveal', 'beneficial', 'effects', 'of', 'the', 'auxiliary', 'loss', 'on', 'optimization', 'and', 'regularization', 'as', 'well', 'as', 'extreme', 'cases', 'where', 'there', 'is', 'little', 'to', 'no', 'backpropagation']] | [-0.04990213436497364, -0.0077459128319815745, -0.04539660026285043, 0.12410387976675015, -0.10333558932186727, -0.16246024263091385, 0.05800466473639994, 0.4965495123796485, -0.3121623668392572, -0.3383113124687796, 0.08834649492455132, -0.2506931796155733, -0.20868679009659352, 0.20920061180510907, -0.1268818352122911, 0.09951182105649903, 0.18722359018480303, 0.021624263220300977, -0.07902072290204283, -0.32619532601609846, 0.23873290887810975, 0.07327023056975675, 0.30526089069484935, 0.010777266618961388, 0.14950489018760918, -0.013708457990658065, -0.04563405696571193, -0.04053709290248238, -0.02725205316867692, 0.13669190187281444, 0.2658876339812162, 0.14758826072000686, 0.3503337786761484, -0.413663531482174, -0.2786624136900792, 0.11847020372422698, 0.16174249376164623, 0.10519044647676058, -0.00875863689283606, -0.26160014328186865, 0.09263586560035685, -0.16397204061975829, 0.007750829130388294, -0.16302824060095508, 0.007918104632226235, 0.014066203911588785, -0.31405463210161366, 0.052500533046268005, 0.08625773310395632, 0.04434795350270586, -0.032643373819936546, -0.10034918060963345, 0.06059750442770224, 0.16440245206610649, 0.0829740718794141, 0.09106302292777534, 0.0828516681398625, -0.1800369770016412, -0.14680052399806723, 0.33024312163834557, -0.0995239084736484, -0.2075882783856677, 0.2357270428653738, -0.021341146238468178, -0.14194917772536628, 0.09407559118913544, 0.2153180886597081, 0.14455578669311445, -0.1579881357114785, -0.006906019022725409, -0.0007157392594163404, 0.20854860982012027, 0.07271541897036089, 0.025610281010958268, 0.16825740354634147, 0.2940632831430251, 0.021560815929298066, 0.12451815946731842, -0.10075996797303298, -0.10366154873480826, -0.2021969367030071, -0.0649790079300749, -0.1736156664491614, 0.025489100151512473, -0.09518397684547225, -0.17669338122170214, 0.40252913790754974, 0.22043280578875468, 0.24265859531598635, 0.14443151433480778, 0.36607571199901995, 0.017509745893874235, 0.15818267359656848, 0.0552355457080151, 0.1672358802502598, 0.06231607216913139, 0.1253984989684098, -0.18396026187146403, 0.0948232920701827, 0.02286975055738735] |
1,803.00145 | Critical behavior of magnetization in URhAl:Quasi-two-dimensional Ising
system with long-range interactions | The critical behavior of dc magnetization in the uranium ferromagnet URhAl
with the hexagonal ZrNiAl-type crystal structure has been studied around the
ferromagnetic transition temperature T_C. The critical exponent beta for the
temperature dependence of the spontaneous magnetization below T_C, gamma for
the magnetic susceptibility, and delta for the magnetic isotherm at T_C have
been obtained with a modified Arrott plot, a Kouvel-Fisher plot, the critical
isotherm analysis and the scaling analysis. We have determined the critical
exponents as beta = 0.287 +- 0.005, gamma = 1.47 +- 0.02, and delta = 6.08 +-
0.04 by the scaling analysis and the critical isotherm analysis. These critical
exponents satisfy the Widom scaling law delta=1+gamma/beta. URhAl has strong
uniaxial magnetic anisotropy, similar to its isostructural UCoAl that has been
regarded as a three-dimensional (3D) Ising system in previous studies. However,
the universality class of the critical phenomenon in URhAl does not belong to
the 3D Ising model (beta = 0.325, gamma = 1.241, and delta = 4.82) with
short-range exchange interactions between magnetic moments. The determined
exponents can be explained with the results of the renormalization group
approach for a two-dimensional (2D) Ising system coupled with long-range
interactions decaying as J(r)~r^-(d+sigma) with sigma = 1.44. We suggest that
the strong hybridization between the uranium 5f and rhodium 4d electrons in the
U-Rh_I layer in the hexagonal crystal structure is a source of the low
dimensional magnetic property. The present result is contrary to current
understandings of the physical properties in a series of isostructural UTX
uranium ferromagnets (T: transition metals, X: p-block elements) based on the
3D Ising model.
| cond-mat.str-el | the critical behavior of dc magnetization in the uranium ferromagnet urhal with the hexagonal zrnialtype crystal structure has been studied around the ferromagnetic transition temperature t_c the critical exponent beta for the temperature dependence of the spontaneous magnetization below t_c gamma for the magnetic susceptibility and delta for the magnetic isotherm at t_c have been obtained with a modified arrott plot a kouvelfisher plot the critical isotherm analysis and the scaling analysis we have determined the critical exponents as beta 0287 0005 gamma 147 002 and delta 608 004 by the scaling analysis and the critical isotherm analysis these critical exponents satisfy the widom scaling law delta1gammabeta urhal has strong uniaxial magnetic anisotropy similar to its isostructural ucoal that has been regarded as a threedimensional 3d ising system in previous studies however the universality class of the critical phenomenon in urhal does not belong to the 3d ising model beta 0325 gamma 1241 and delta 482 with shortrange exchange interactions between magnetic moments the determined exponents can be explained with the results of the renormalization group approach for a twodimensional 2d ising system coupled with longrange interactions decaying as jrrdsigma with sigma 144 we suggest that the strong hybridization between the uranium 5f and rhodium 4d electrons in the urh_i layer in the hexagonal crystal structure is a source of the low dimensional magnetic property the present result is contrary to current understandings of the physical properties in a series of isostructural utx uranium ferromagnets t transition metals x pblock elements based on the 3d ising model | [['the', 'critical', 'behavior', 'of', 'dc', 'magnetization', 'in', 'the', 'uranium', 'ferromagnet', 'urhal', 'with', 'the', 'hexagonal', 'zrnialtype', 'crystal', 'structure', 'has', 'been', 'studied', 'around', 'the', 'ferromagnetic', 'transition', 'temperature', 't_c', 'the', 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1,803.00146 | A Generic Top-N Recommendation Framework For Trading-off Accuracy,
Novelty, and Coverage | Standard collaborative filtering approaches for top-N recommendation are
biased toward popular items. As a result, they recommend items that users are
likely aware of and under-represent long-tail items. This is inadequate, both
for consumers who prefer novel items and because concentrating on popular items
poorly covers the item space, whereas high item space coverage increases
providers' revenue.
We present an approach that relies on historical rating data to learn user
long-tail novelty preferences. We integrate these preferences into a generic
re-ranking framework that customizes balance between accuracy and coverage. We
empirically validate that our proposedframework increases the novelty of
recommendations. Furthermore, by promoting long-tail items to the right group
of users, we significantly increase the system's coverage while scalably
maintaining accuracy. Our framework also enables personalization of existing
non-personalized algorithms, making them competitive with existing personalized
algorithms in key performance metrics, including accuracy and coverage.
| cs.IR | standard collaborative filtering approaches for topn recommendation are biased toward popular items as a result they recommend items that users are likely aware of and underrepresent longtail items this is inadequate both for consumers who prefer novel items and because concentrating on popular items poorly covers the item space whereas high item space coverage increases providers revenue we present an approach that relies on historical rating data to learn user longtail novelty preferences we integrate these preferences into a generic reranking framework that customizes balance between accuracy and coverage we empirically validate that our proposedframework increases the novelty of recommendations furthermore by promoting longtail items to the right group of users we significantly increase the systems coverage while scalably maintaining accuracy our framework also enables personalization of existing nonpersonalized algorithms making them competitive with existing personalized algorithms in key performance metrics including accuracy and coverage | [['standard', 'collaborative', 'filtering', 'approaches', 'for', 'topn', 'recommendation', 'are', 'biased', 'toward', 'popular', 'items', 'as', 'a', 'result', 'they', 'recommend', 'items', 'that', 'users', 'are', 'likely', 'aware', 'of', 'and', 'underrepresent', 'longtail', 'items', 'this', 'is', 'inadequate', 'both', 'for', 'consumers', 'who', 'prefer', 'novel', 'items', 'and', 'because', 'concentrating', 'on', 'popular', 'items', 'poorly', 'covers', 'the', 'item', 'space', 'whereas', 'high', 'item', 'space', 'coverage', 'increases', 'providers', 'revenue', 'we', 'present', 'an', 'approach', 'that', 'relies', 'on', 'historical', 'rating', 'data', 'to', 'learn', 'user', 'longtail', 'novelty', 'preferences', 'we', 'integrate', 'these', 'preferences', 'into', 'a', 'generic', 'reranking', 'framework', 'that', 'customizes', 'balance', 'between', 'accuracy', 'and', 'coverage', 'we', 'empirically', 'validate', 'that', 'our', 'proposedframework', 'increases', 'the', 'novelty', 'of', 'recommendations', 'furthermore', 'by', 'promoting', 'longtail', 'items', 'to', 'the', 'right', 'group', 'of', 'users', 'we', 'significantly', 'increase', 'the', 'systems', 'coverage', 'while', 'scalably', 'maintaining', 'accuracy', 'our', 'framework', 'also', 'enables', 'personalization', 'of', 'existing', 'nonpersonalized', 'algorithms', 'making', 'them', 'competitive', 'with', 'existing', 'personalized', 'algorithms', 'in', 'key', 'performance', 'metrics', 'including', 'accuracy', 'and', 'coverage']] | [-0.031085303127636306, -0.04365106765413253, -0.007434241444570944, 0.11330516155188282, -0.20594134161041844, -0.21750040121939188, 0.17168035567576428, 0.48156529935982284, -0.2233732738920177, -0.3486673176780136, 0.026573459903425425, -0.38246760565218413, -0.13139912843083343, 0.11185088292889607, -0.16978821486312276, 0.017306598925642902, 0.10139867169143851, 0.023587571557274915, -0.022472360415526457, -0.3969029746634381, 0.3046776036143355, 0.11173928897025892, 0.3627933039291141, 0.04998131078254017, 0.08015882379792554, 0.04774270883515581, -0.10480609824622257, 0.02673786074632921, -0.09735719939251591, 0.1690718467855024, 0.3972447750598399, 0.22733911974096876, 0.38560947192470646, -0.3382858387582625, -0.1555235296772379, 0.04760552397541081, 0.16604663582337606, 0.023322557640818156, -0.05845716059411643, -0.3369983268332564, 0.0919134132952119, -0.2247757024403351, 0.013438633914726475, -0.14187648900082828, -0.014814536879485887, 0.05513106306494188, -0.338915006936683, 0.028271024727978836, 0.029477711828399658, 0.018173265995250806, -0.03891407388598762, -0.16164185514531304, 0.03553047325759609, 0.19947416989210373, 0.11031530570795035, -0.036248736468324855, 0.17369133346882235, -0.16136148463920108, -0.1498751195354594, 0.4196969641424302, -0.025153836746337928, -0.18808504806333481, 0.19874155796909085, -0.04403388768938991, -0.1387401679615626, 0.1132040709134243, 0.23808146309278932, 0.10340743373186949, -0.14530058823422426, -0.018843724558893073, -0.04657677992428136, 0.210840196077091, 0.036696325508981116, 0.027407191826366924, 0.17983798911801488, 0.24119295452449782, 0.12850489543699142, 0.02488183690002188, 0.006519876269925994, -0.08743908525341088, -0.13461487836967637, -0.13681622284800848, -0.1495018125699264, -0.03766187241762964, -0.1600574547382065, -0.11841304843548844, 0.38446480940587613, 0.2522288451024603, 0.1577337866506746, 0.1470623947405127, 0.34521795996180216, 0.025772715997138422, 0.05552119921776466, 0.1176753627627881, 0.12233009612019588, -0.09527315102362384, 0.15682758410467715, -0.11197699776029266, 0.16184852924036225, -0.004379131300892267] |
1,803.00147 | Non-Hamiltonian Kelvin wave generation on vortices in Bose-Einstein
condensates | Ultra-cold quantum turbulence is expected to decay through a cascade of
Kelvin waves. These helical excitations couple vorticity to the quantum fluid
causing long wavelength phonon fluctuations in a Bose-Einstein condensate. This
interaction is hypothesized to be the route to relaxation for turbulent tangles
in quantum hydrodynamics. The local induction approximation is the lowest order
approximation to the Biot-Savart velocity field induced by a vortex line and,
because of its integrability, is thought to prohibit energy transfer by Kelvin
waves. Using the Biot-Savart description, we derive a generalization to the
local induction approximation which predicts that regions of large curvature
can reconfigure themselves as Kelvin wave packets. While this generalization
preserves the arclength metric, a quantity conserved under the Eulerian flow of
vortex lines, it also introduces a non-Hamiltonian structure on the geometric
properties of the vortex line. It is this non-Hamiltonian evolution of
curvature and torsion which provides a resolution to the missing Kelvin wave
motion. In this work, we derive corrections to the local induction
approximation in powers of curvature and state them for utilization in vortex
filament methods. Using the Hasimoto transformation, we arrive at a nonlinear
integro-differential equation which reduces to a modified nonlinear
Schr\"odinger type evolution of the curvature and torsion on the vortex line.
We show that this modification seeks to disperse localized curvature profiles.
At the same time, the non-Hamiltonian break in integrability bolsters the
deforming curvature profile and simulations show that this dynamic results in
Kelvin wave propagation along the dispersive vortex medium.
| cond-mat.quant-gas cond-mat.stat-mech math-ph math.MP | ultracold quantum turbulence is expected to decay through a cascade of kelvin waves these helical excitations couple vorticity to the quantum fluid causing long wavelength phonon fluctuations in a boseeinstein condensate this interaction is hypothesized to be the route to relaxation for turbulent tangles in quantum hydrodynamics the local induction approximation is the lowest order approximation to the biotsavart velocity field induced by a vortex line and because of its integrability is thought to prohibit energy transfer by kelvin waves using the biotsavart description we derive a generalization to the local induction approximation which predicts that regions of large curvature can reconfigure themselves as kelvin wave packets while this generalization preserves the arclength metric a quantity conserved under the eulerian flow of vortex lines it also introduces a nonhamiltonian structure on the geometric properties of the vortex line it is this nonhamiltonian evolution of curvature and torsion which provides a resolution to the missing kelvin wave motion in this work we derive corrections to the local induction approximation in powers of curvature and state them for utilization in vortex filament methods using the hasimoto transformation we arrive at a nonlinear integrodifferential equation which reduces to a modified nonlinear schrodinger type evolution of the curvature and torsion on the vortex line we show that this modification seeks to disperse localized curvature profiles at the same time the nonhamiltonian break in integrability bolsters the deforming curvature profile and simulations show that this dynamic results in kelvin wave propagation along the dispersive vortex medium | [['ultracold', 'quantum', 'turbulence', 'is', 'expected', 'to', 'decay', 'through', 'a', 'cascade', 'of', 'kelvin', 'waves', 'these', 'helical', 'excitations', 'couple', 'vorticity', 'to', 'the', 'quantum', 'fluid', 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1,803.00148 | Prospects for Detecting light bosons at the FCC-ee and CEPC | We look at the prospects for detecting light bosons, $X$, at proposed Z
factories assuming a production of $10^{12}$ Z bosons. Such a large yield is
within the design goals of future FCC-ee and CEPC colliders. Specifically we
look at the cases where $X$ is either a singlet scalar which mixes with the
standard model Higgs or a vector boson with mass $1\lesssim M_X \lesssim 80$
GeV. We find that several channels are particularly promising for discovery
prospects. In particular $Z\rightarrow f \bar{f} X$ and $Z \rightarrow V_Q X$
gives a promising signal above a very clean standard model background. We also
discuss several channels that have too large a background to be useful.
| hep-ph hep-ex | we look at the prospects for detecting light bosons x at proposed z factories assuming a production of 1012 z bosons such a large yield is within the design goals of future fccee and cepc colliders specifically we look at the cases where x is either a singlet scalar which mixes with the standard model higgs or a vector boson with mass 1lesssim m_x lesssim 80 gev we find that several channels are particularly promising for discovery prospects in particular zrightarrow f barf x and z rightarrow v_q x gives a promising signal above a very clean standard model background we also discuss several channels that have too large a background to be useful | [['we', 'look', 'at', 'the', 'prospects', 'for', 'detecting', 'light', 'bosons', 'x', 'at', 'proposed', 'z', 'factories', 'assuming', 'a', 'production', 'of', '1012', 'z', 'bosons', 'such', 'a', 'large', 'yield', 'is', 'within', 'the', 'design', 'goals', 'of', 'future', 'fccee', 'and', 'cepc', 'colliders', 'specifically', 'we', 'look', 'at', 'the', 'cases', 'where', 'x', 'is', 'either', 'a', 'singlet', 'scalar', 'which', 'mixes', 'with', 'the', 'standard', 'model', 'higgs', 'or', 'a', 'vector', 'boson', 'with', 'mass', '1lesssim', 'm_x', 'lesssim', '80', 'gev', 'we', 'find', 'that', 'several', 'channels', 'are', 'particularly', 'promising', 'for', 'discovery', 'prospects', 'in', 'particular', 'zrightarrow', 'f', 'barf', 'x', 'and', 'z', 'rightarrow', 'v_q', 'x', 'gives', 'a', 'promising', 'signal', 'above', 'a', 'very', 'clean', 'standard', 'model', 'background', 'we', 'also', 'discuss', 'several', 'channels', 'that', 'have', 'too', 'large', 'a', 'background', 'to', 'be', 'useful']] | [-0.10911542545031887, 0.2206452339969314, -0.020882253906803958, 0.13198595019077816, -0.038478557457493845, -0.24155055021197258, 0.05394857590037741, 0.3790246865695767, -0.22340651482278318, -0.24361961175230118, 0.05448666488110464, -0.30801503622920756, -0.009611426290990622, 0.20465207493480034, 0.0877464092888036, 0.02882250634495888, 0.06444484858953378, 0.020317290521537263, -0.0698756984222615, -0.2590091924689579, 0.2314304572350362, 0.059730224834199534, 0.19041892603497235, 0.07047397380882646, 0.08883213283690183, 0.01846963589859865, 0.03029910113949435, -0.09148190653434683, -0.11517390976453164, 0.04115242435466172, 0.23797790901408644, 0.10248743181868472, 0.19332880991421184, -0.2712115534313693, -0.12894202351341383, 0.21254681652460836, 0.13714589584994605, 0.051090504499274846, -0.1448152929844678, -0.3120579229455376, 0.16005562455138078, -0.1936813447493733, -0.10934847787741506, -0.03949969493694146, 0.01996493225982576, -0.06010595732981241, -0.3505772202390066, 0.0599581233545905, -0.036596393354110454, -0.00754992906902835, 0.04575979670513781, -0.21005052141845226, -0.04445186023714773, -0.02191461442373366, 0.07671643660420127, 0.11518169766733129, 0.15240221127067088, -0.20316525290131962, -0.11187514162156731, 0.41256207988287014, -0.13455930186982937, -0.1551760113001556, 0.21923993229702637, -0.2056445668680234, -0.1855330056819264, 0.1259919491008316, 0.2517535479682122, 0.09937533126784522, -0.15947371581569314, 0.18282970568683946, -0.018992085238559206, 0.15154984144681835, 0.03204469695654616, 0.09042736938174226, 0.29015045114098476, 0.21928794917103164, 0.04344371749647779, 0.050743569815000354, -0.14413494753177492, 0.029876624961105876, -0.40997535150432796, -0.17109764427183555, -0.0205341645908591, 0.1048489937869211, -0.04753162346554618, -0.029928122761479596, 0.3539258398405652, 0.14732492765873312, 0.2992025064917183, 0.03683104004614921, 0.267593417708811, 0.07414201763867025, 0.0519564671160065, 0.05157089190100107, 0.27379950531349895, 0.1129382038652374, 0.07188654534126583, -0.08594149578640466, -0.039724097047981466, -0.016208622408540624] |
1,803.00149 | Deep Learning for Causal Inference | In this paper, we propose deep learning techniques for econometrics,
specifically for causal inference and for estimating individual as well as
average treatment effects. The contribution of this paper is twofold: 1. For
generalized neighbor matching to estimate individual and average treatment
effects, we analyze the use of autoencoders for dimensionality reduction while
maintaining the local neighborhood structure among the data points in the
embedding space. This deep learning based technique is shown to perform better
than simple k nearest neighbor matching for estimating treatment effects,
especially when the data points have several features/covariates but reside in
a low dimensional manifold in high dimensional space. We also observe better
performance than manifold learning methods for neighbor matching. 2. Propensity
score matching is one specific and popular way to perform matching in order to
estimate average and individual treatment effects. We propose the use of deep
neural networks (DNNs) for propensity score matching, and present a network
called PropensityNet for this. This is a generalization of the logistic
regression technique traditionally used to estimate propensity scores and we
show empirically that DNNs perform better than logistic regression at
propensity score matching. Code for both methods will be made available shortly
on Github at: https://github.com/vikas84bf
| econ.EM cs.LG stat.ML | in this paper we propose deep learning techniques for econometrics specifically for causal inference and for estimating individual as well as average treatment effects the contribution of this paper is twofold 1 for generalized neighbor matching to estimate individual and average treatment effects we analyze the use of autoencoders for dimensionality reduction while maintaining the local neighborhood structure among the data points in the embedding space this deep learning based technique is shown to perform better than simple k nearest neighbor matching for estimating treatment effects especially when the data points have several featurescovariates but reside in a low dimensional manifold in high dimensional space we also observe better performance than manifold learning methods for neighbor matching 2 propensity score matching is one specific and popular way to perform matching in order to estimate average and individual treatment effects we propose the use of deep neural networks dnns for propensity score matching and present a network called propensitynet for this this is a generalization of the logistic regression technique traditionally used to estimate propensity scores and we show empirically that dnns perform better than logistic regression at propensity score matching code for both methods will be made available shortly on github at httpsgithubcomvikas84bf | [['in', 'this', 'paper', 'we', 'propose', 'deep', 'learning', 'techniques', 'for', 'econometrics', 'specifically', 'for', 'causal', 'inference', 'and', 'for', 'estimating', 'individual', 'as', 'well', 'as', 'average', 'treatment', 'effects', 'the', 'contribution', 'of', 'this', 'paper', 'is', 'twofold', '1', 'for', 'generalized', 'neighbor', 'matching', 'to', 'estimate', 'individual', 'and', 'average', 'treatment', 'effects', 'we', 'analyze', 'the', 'use', 'of', 'autoencoders', 'for', 'dimensionality', 'reduction', 'while', 'maintaining', 'the', 'local', 'neighborhood', 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1,803.0015 | Cavity-free quantum optomechanical cooling by atom-modulated radiation | We theoretically study the radiation-induced interaction between the
mechanical motion of an oscillating mirror and a remotely trapped atomic cloud.
When illuminated by continuous-wave radiation, the mirror motion will induce
red and blue sideband radiation, which respectively increases and reduces
motional excitation. We find that by suitably driving $\Lambda$-level atoms,
the mirror correlation with a specific radiation sideband could be converted
from the outgoing to the incoming radiation. Such process allows us to
manipulate the heating and cooling effects. Particularly, we develop an
optomechanical cooling strategy that can mutually cancel the heating effect of
the outgoing and incoming radiations, thus the motional ground state is
attainable by net cooling. Without the necessity of cavity installation or
perfect alignment, our proposal complements other efforts in quantum cooling of
macroscopic objects.
| quant-ph | we theoretically study the radiationinduced interaction between the mechanical motion of an oscillating mirror and a remotely trapped atomic cloud when illuminated by continuouswave radiation the mirror motion will induce red and blue sideband radiation which respectively increases and reduces motional excitation we find that by suitably driving lambdalevel atoms the mirror correlation with a specific radiation sideband could be converted from the outgoing to the incoming radiation such process allows us to manipulate the heating and cooling effects particularly we develop an optomechanical cooling strategy that can mutually cancel the heating effect of the outgoing and incoming radiations thus the motional ground state is attainable by net cooling without the necessity of cavity installation or perfect alignment our proposal complements other efforts in quantum cooling of macroscopic objects | [['we', 'theoretically', 'study', 'the', 'radiationinduced', 'interaction', 'between', 'the', 'mechanical', 'motion', 'of', 'an', 'oscillating', 'mirror', 'and', 'a', 'remotely', 'trapped', 'atomic', 'cloud', 'when', 'illuminated', 'by', 'continuouswave', 'radiation', 'the', 'mirror', 'motion', 'will', 'induce', 'red', 'and', 'blue', 'sideband', 'radiation', 'which', 'respectively', 'increases', 'and', 'reduces', 'motional', 'excitation', 'we', 'find', 'that', 'by', 'suitably', 'driving', 'lambdalevel', 'atoms', 'the', 'mirror', 'correlation', 'with', 'a', 'specific', 'radiation', 'sideband', 'could', 'be', 'converted', 'from', 'the', 'outgoing', 'to', 'the', 'incoming', 'radiation', 'such', 'process', 'allows', 'us', 'to', 'manipulate', 'the', 'heating', 'and', 'cooling', 'effects', 'particularly', 'we', 'develop', 'an', 'optomechanical', 'cooling', 'strategy', 'that', 'can', 'mutually', 'cancel', 'the', 'heating', 'effect', 'of', 'the', 'outgoing', 'and', 'incoming', 'radiations', 'thus', 'the', 'motional', 'ground', 'state', 'is', 'attainable', 'by', 'net', 'cooling', 'without', 'the', 'necessity', 'of', 'cavity', 'installation', 'or', 'perfect', 'alignment', 'our', 'proposal', 'complements', 'other', 'efforts', 'in', 'quantum', 'cooling', 'of', 'macroscopic', 'objects']] | [-0.11543448196449714, 0.2778729570313454, -0.05683436243643248, -0.00409214295008454, -0.07525869671890671, -0.13712848354737425, 0.058034718136822934, 0.4188589145566604, -0.2629148042796887, -0.3278479676462652, -0.005871121942195608, -0.2853951316216082, -0.0332163802434712, 0.19538622883567225, -0.04538784092815607, 0.004047323711389719, 0.057664657003506155, -0.055870914463560246, 0.028486329625367887, -0.15855110951883414, 0.29705878039244416, 0.10291796552230308, 0.3034550512801538, 0.04802527348330432, 0.10757690735608917, 0.019885825791013563, 0.04716605115199079, -0.042701683620842854, -0.06893342406754725, 0.05216416833532411, 0.18265327062618436, 0.07375524871686633, 0.23788526186416314, -0.4993915502992711, -0.19089004293430684, 0.1237955577509088, 0.1605367774663623, 0.1823745535121682, -0.07553236465901136, -0.27659500094289463, -0.04712536735387966, -0.14768632501183265, -0.13745811188867849, -0.06557785913117173, -0.029409763136113338, 0.015926061752216183, -0.26605078666795823, 0.028247617271750472, 0.08400902478926808, 0.0063143716872373756, -0.08392418957678167, -0.011572123743420424, -0.05219150594004264, 0.1128995006503772, -0.024922611067557072, 0.013668070966016877, 0.2469694880753315, -0.143166140450143, -0.08523434692183365, 0.3681247302208298, -0.08916738621399616, -0.12255987385287881, 0.1640411671503743, -0.1478548733672748, 0.005371469363359041, 0.19562148079709257, 0.11474943620490285, 0.10137780592596346, -0.12672064205247624, -0.04070677084066768, 0.06161925122286229, 0.1641607539531551, 0.14194314226565896, 0.08564322636383333, 0.28766179960870814, 0.10163268062481007, 0.058050066723611, 0.22556433185047015, -0.13913853035503349, -0.06765118134641197, -0.284296537446248, -0.10247375722433072, -0.15114866690385306, 0.08174694253322615, -0.037101051708220416, -0.0978012882487112, 0.32963135481140643, 0.16375487723918866, 0.13070388705029276, -0.05449242205918557, 0.3969373141610345, 0.12446440592166325, 0.05272998230336884, 0.028316695401047955, 0.32736708781169366, 0.14770936775165647, 0.06291666704179473, -0.388338745797284, 0.007093787453202314, -0.025052126959081766] |
1,803.00151 | A Simple Nearly-Optimal Restart Scheme For Speeding-Up First Order
Methods | We present a simple scheme for restarting first-order methods for convex
optimization problems. Restarts are made based only on achieving specified
decreases in objective values, the specified amounts being the same for all
optimization problems. Unlike existing restart schemes, the scheme makes no
attempt to learn parameter values characterizing the structure of an
optimization problem, nor does it require any special information that would
not be available in practice (unless the first-order method chosen to be
employed in the scheme itself requires special information). As immediate
corollaries to the main theorems, we show that when some well-known first-order
methods are employed in the scheme, the resulting complexity bounds are nearly
optimal for particular -- yet quite general -- classes of problems.
| math.OC | we present a simple scheme for restarting firstorder methods for convex optimization problems restarts are made based only on achieving specified decreases in objective values the specified amounts being the same for all optimization problems unlike existing restart schemes the scheme makes no attempt to learn parameter values characterizing the structure of an optimization problem nor does it require any special information that would not be available in practice unless the firstorder method chosen to be employed in the scheme itself requires special information as immediate corollaries to the main theorems we show that when some wellknown firstorder methods are employed in the scheme the resulting complexity bounds are nearly optimal for particular yet quite general classes of problems | [['we', 'present', 'a', 'simple', 'scheme', 'for', 'restarting', 'firstorder', 'methods', 'for', 'convex', 'optimization', 'problems', 'restarts', 'are', 'made', 'based', 'only', 'on', 'achieving', 'specified', 'decreases', 'in', 'objective', 'values', 'the', 'specified', 'amounts', 'being', 'the', 'same', 'for', 'all', 'optimization', 'problems', 'unlike', 'existing', 'restart', 'schemes', 'the', 'scheme', 'makes', 'no', 'attempt', 'to', 'learn', 'parameter', 'values', 'characterizing', 'the', 'structure', 'of', 'an', 'optimization', 'problem', 'nor', 'does', 'it', 'require', 'any', 'special', 'information', 'that', 'would', 'not', 'be', 'available', 'in', 'practice', 'unless', 'the', 'firstorder', 'method', 'chosen', 'to', 'be', 'employed', 'in', 'the', 'scheme', 'itself', 'requires', 'special', 'information', 'as', 'immediate', 'corollaries', 'to', 'the', 'main', 'theorems', 'we', 'show', 'that', 'when', 'some', 'wellknown', 'firstorder', 'methods', 'are', 'employed', 'in', 'the', 'scheme', 'the', 'resulting', 'complexity', 'bounds', 'are', 'nearly', 'optimal', 'for', 'particular', 'yet', 'quite', 'general', 'classes', 'of', 'problems']] | [-0.08144759275253956, 0.01707772400042829, -0.08013948776266154, 0.1039731874514152, -0.14163105411282859, -0.1988117776521552, 0.06258678054163123, 0.4223096497169062, -0.27812792972794603, -0.29745451162154435, 0.15645559687370889, -0.19604407429710782, -0.13461818640754253, 0.2503405103006032, -0.12524495605661087, 0.09466306750976652, 0.06994683410142775, 0.0425083010747027, -0.1336966237178495, -0.30427283834971236, 0.2838746057784169, 0.02248929434909853, 0.27047149898061734, 0.041779609870541246, 0.10756863453601558, 0.0025655842628799566, -0.010018054760323384, 0.057056581261059065, -0.12152719112739707, 0.08113663712371641, 0.3255984350811972, 0.18973413791836186, 0.3260829870397335, -0.4040678302613635, -0.20433042466076984, 0.16751311032665, 0.13578375489959696, 0.15923891637959525, -0.02480847093945823, -0.19781568189378546, 0.09569127914034017, -0.11275422786550299, -0.09566763431585136, -0.1073848648474184, -0.05969592208872322, 0.009228753771225945, -0.3220457951210541, 0.012616811124529658, 0.08316849438990113, -0.019814390516957315, -0.040012424126924837, -0.14167341107733453, 0.05309068484764014, 0.1174257578978319, 0.044035040241267, 0.01111708746776924, 0.10618580899449817, -0.09246928472181454, -0.14220116332862057, 0.4047284484880061, -0.005036587402334108, -0.2431006637859714, 0.17875419357721106, -0.05104926119347205, -0.18673776219278074, 0.16082706114965953, 0.14650186373531318, 0.19268981973883234, -0.12299117330031786, 0.08321882696712718, -0.027279618219668123, 0.19728007285460486, 0.04743085075717639, 0.046854780802499316, 0.10168602329115344, 0.11168787445865196, 0.14073417379575617, 0.11416636305438559, 0.0014748577275039518, -0.14809976513849712, -0.3025085271968107, -0.09615231093269687, -0.18854125589230136, -0.04210633578103235, -0.09413891368860598, -0.17543344105803702, 0.3132042125763299, 0.16388287293302098, 0.1744878294744662, 0.07769773427803978, 0.3395885306981807, 0.12829738930004703, 0.07619133673044813, 0.1377068659771985, 0.23750297449447974, 0.08241574009958685, 0.0843676892070084, -0.15937084696419976, 0.13858672968667596, 0.08568992767044727] |
1,803.00152 | A Global Information Based Adaptive Threshold for Grouping Large Scale
Global Optimization Problems | By taking the idea of divide-and-conquer, cooperative coevolution (CC)
provides a powerful architecture for large scale global optimization (LSGO)
problems, but its efficiency relies highly on the decomposition strategy. It
has been shown that differential grouping (DG) performs well on decomposing
LSGO problems by effectively detecting the interaction among decision
variables. However, its decomposition accuracy depends highly on the threshold.
To improve the decomposition accuracy of DG, a global information based
adaptive threshold setting algorithm (GIAT) is proposed in this paper. On the
one hand, by reducing the sensitivity of the indicator in DG to the roundoff
error and the magnitude of contribution weight of subcomponent, we proposed a
new indicator for two variables which is much more sensitive to their
interaction. On the other hand, instead of setting the threshold only based on
one pair of variables, the threshold is generated from the interaction
information for all pair of variables. By conducting the experiments on two
sets of LSGO benchmark functions, the correctness and robustness of this new
indicator and GIAT were verified.
| cs.NE | by taking the idea of divideandconquer cooperative coevolution cc provides a powerful architecture for large scale global optimization lsgo problems but its efficiency relies highly on the decomposition strategy it has been shown that differential grouping dg performs well on decomposing lsgo problems by effectively detecting the interaction among decision variables however its decomposition accuracy depends highly on the threshold to improve the decomposition accuracy of dg a global information based adaptive threshold setting algorithm giat is proposed in this paper on the one hand by reducing the sensitivity of the indicator in dg to the roundoff error and the magnitude of contribution weight of subcomponent we proposed a new indicator for two variables which is much more sensitive to their interaction on the other hand instead of setting the threshold only based on one pair of variables the threshold is generated from the interaction information for all pair of variables by conducting the experiments on two sets of lsgo benchmark functions the correctness and robustness of this new indicator and giat were verified | [['by', 'taking', 'the', 'idea', 'of', 'divideandconquer', 'cooperative', 'coevolution', 'cc', 'provides', 'a', 'powerful', 'architecture', 'for', 'large', 'scale', 'global', 'optimization', 'lsgo', 'problems', 'but', 'its', 'efficiency', 'relies', 'highly', 'on', 'the', 'decomposition', 'strategy', 'it', 'has', 'been', 'shown', 'that', 'differential', 'grouping', 'dg', 'performs', 'well', 'on', 'decomposing', 'lsgo', 'problems', 'by', 'effectively', 'detecting', 'the', 'interaction', 'among', 'decision', 'variables', 'however', 'its', 'decomposition', 'accuracy', 'depends', 'highly', 'on', 'the', 'threshold', 'to', 'improve', 'the', 'decomposition', 'accuracy', 'of', 'dg', 'a', 'global', 'information', 'based', 'adaptive', 'threshold', 'setting', 'algorithm', 'giat', 'is', 'proposed', 'in', 'this', 'paper', 'on', 'the', 'one', 'hand', 'by', 'reducing', 'the', 'sensitivity', 'of', 'the', 'indicator', 'in', 'dg', 'to', 'the', 'roundoff', 'error', 'and', 'the', 'magnitude', 'of', 'contribution', 'weight', 'of', 'subcomponent', 'we', 'proposed', 'a', 'new', 'indicator', 'for', 'two', 'variables', 'which', 'is', 'much', 'more', 'sensitive', 'to', 'their', 'interaction', 'on', 'the', 'other', 'hand', 'instead', 'of', 'setting', 'the', 'threshold', 'only', 'based', 'on', 'one', 'pair', 'of', 'variables', 'the', 'threshold', 'is', 'generated', 'from', 'the', 'interaction', 'information', 'for', 'all', 'pair', 'of', 'variables', 'by', 'conducting', 'the', 'experiments', 'on', 'two', 'sets', 'of', 'lsgo', 'benchmark', 'functions', 'the', 'correctness', 'and', 'robustness', 'of', 'this', 'new', 'indicator', 'and', 'giat', 'were', 'verified']] | [-0.11261797669549407, 0.02086609522378895, -0.09703294806443642, 0.06510167588753257, -0.08093366196898874, -0.13163756919066358, 0.0752198472991315, 0.3492981955452367, -0.27171117440949, -0.33423458158747876, 0.13015558119717074, -0.23293021821611842, -0.14411184709616612, 0.216577950452162, -0.057612415256931684, 0.053030213968541344, 0.08130041284616603, 0.04644665237364554, -0.02678351970606072, -0.26450314299679945, 0.3470867205584465, 0.0790344555943468, 0.30950775820772747, 0.054981479419904306, 0.12319561290462654, 0.02114667123735904, -0.07915238251092, 0.031535582282569606, -0.044545994738063054, 0.1472551664770689, 0.20274247472048576, 0.15477861322294703, 0.3135640468193911, -0.36423471645278815, -0.17330993997842767, 0.08800620279074538, 0.13366643649114426, 0.06166646527167041, -0.035505435823176605, -0.2712149052057675, 0.10585464417208852, -0.15448763454468314, -0.04371718867400358, -0.08124162405350267, 0.012664632430451727, 0.0261667960527797, -0.30570780868414615, 0.06402011759454644, 0.04989823830776934, 0.026699062388002476, -0.026164541287661724, -0.1446326682831399, -0.014421341513001989, 0.1121823208644813, 0.011785928481447966, 0.026464444396984872, 0.13348060422089644, -0.13828606936582472, -0.12511226480155316, 0.3649805010415527, -0.034130592395275355, -0.2416367120079718, 0.21644432477512246, -0.07948516798205674, -0.13048211862008238, 0.12191217797700056, 0.1829391088052267, 0.15572944085308632, -0.13246907948700407, 0.044069474273656366, -0.01159135527843826, 0.18284792447568263, 0.02973163863346197, 0.03507197725987279, 0.12623718836775244, 0.22388722455172344, 0.11062764669606072, 0.12739485850981383, -0.09285543263053284, -0.10913337911154296, -0.2382763337430566, -0.12539126009810284, -0.19684648959624018, -0.03035354853856789, -0.10658084239168052, -0.14473325599490552, 0.4311081208938429, 0.17248208166457452, 0.1762165569200798, 0.039908205511638636, 0.33228316097412, 0.11454267361773947, 0.07818133067366732, 0.05327894272780869, 0.2508265071137007, 0.108514643167308, 0.04457488370149634, -0.23011939212284593, 0.1289626801338082, 0.11119755317841462] |
1,803.00153 | Drift and its mediation in terrestrial orbits | The slow deformation of terrestrial orbits in the medium range, subject to
lunisolar resonances, is well approximated by a family of Hamiltonian flow with
$2.5$ degree-of-freedom. The action variables of the system may experience
chaotic variations and large drift that we may quantify. Using variational
chaos indicators, we compute high-resolution portraits of the action space.
Such refined meshes allow to reveal the existence of tori and structures
filling chaotic regions. Our elaborate computations allow us to isolate precise
initial conditions near specific zones of interest and study their asymptotic
behaviour in time. Borrowing classical techniques of phase- space
visualisation, we highlight how the drift is mediated by the complement of the
numerically detected KAM tori.
| nlin.CD astro-ph.EP | the slow deformation of terrestrial orbits in the medium range subject to lunisolar resonances is well approximated by a family of hamiltonian flow with 25 degreeoffreedom the action variables of the system may experience chaotic variations and large drift that we may quantify using variational chaos indicators we compute highresolution portraits of the action space such refined meshes allow to reveal the existence of tori and structures filling chaotic regions our elaborate computations allow us to isolate precise initial conditions near specific zones of interest and study their asymptotic behaviour in time borrowing classical techniques of phase space visualisation we highlight how the drift is mediated by the complement of the numerically detected kam tori | [['the', 'slow', 'deformation', 'of', 'terrestrial', 'orbits', 'in', 'the', 'medium', 'range', 'subject', 'to', 'lunisolar', 'resonances', 'is', 'well', 'approximated', 'by', 'a', 'family', 'of', 'hamiltonian', 'flow', 'with', '25', 'degreeoffreedom', 'the', 'action', 'variables', 'of', 'the', 'system', 'may', 'experience', 'chaotic', 'variations', 'and', 'large', 'drift', 'that', 'we', 'may', 'quantify', 'using', 'variational', 'chaos', 'indicators', 'we', 'compute', 'highresolution', 'portraits', 'of', 'the', 'action', 'space', 'such', 'refined', 'meshes', 'allow', 'to', 'reveal', 'the', 'existence', 'of', 'tori', 'and', 'structures', 'filling', 'chaotic', 'regions', 'our', 'elaborate', 'computations', 'allow', 'us', 'to', 'isolate', 'precise', 'initial', 'conditions', 'near', 'specific', 'zones', 'of', 'interest', 'and', 'study', 'their', 'asymptotic', 'behaviour', 'in', 'time', 'borrowing', 'classical', 'techniques', 'of', 'phase', 'space', 'visualisation', 'we', 'highlight', 'how', 'the', 'drift', 'is', 'mediated', 'by', 'the', 'complement', 'of', 'the', 'numerically', 'detected', 'kam', 'tori']] | [-0.1872104614628884, 0.13067443059838335, -0.10735403117926225, 0.07600645778660217, -0.07156136456026656, -0.08548114448380859, 0.048351728932365126, 0.3297911957068288, -0.27695492066280997, -0.29890903302508853, 0.13389977693416016, -0.23411102016334948, -0.17557521677859453, 0.20107209387680758, -0.08842062442765936, 0.049690976953538864, 0.08682396206599863, -0.02359831451094183, -0.07156932991271353, -0.1936540169309212, 0.32177806754960964, 0.04581744063281171, 0.19344181600836632, -0.017825287214034924, 0.06533851405281736, -0.02445950237021822, -0.056387031786953624, 0.004225756842946715, -0.18893653113710057, 0.11193445279546406, 0.20393601824382684, 0.07708717872510137, 0.23760008585193884, -0.42922154574290566, -0.24005714469872738, 0.08207996875128669, 0.16248010262523008, 0.09191522713264694, 0.003392045766763065, -0.3189554727271847, 0.03150864538291226, -0.1392954549104299, -0.21100806117705675, -0.158159669286207, 0.018020522481073505, 0.04533175998367369, -0.25216944520564183, 0.0696788515166744, 0.044452079989866396, 0.11080571185473514, -0.07448286149326874, -0.026025752746500076, -0.03991178603458178, 0.17400347824086962, 0.04660856087037878, -0.021932405863038223, 0.175263849981939, -0.08778983718189208, -0.08466259225312134, 0.35776146435857065, -0.0669051876899017, -0.2075942277584387, 0.2502839684567374, -0.20937143150342466, -0.09833883320021888, 0.17744971108015464, 0.21178421373276607, 0.13620756025547567, -0.10324823552661616, 0.06069279092556352, 0.00689499549081792, 0.1435356791779075, 0.08414536194927084, 0.039842815304417974, 0.2003809496598399, 0.13379615114434906, 0.07061816145382498, 0.127052375058765, -0.11128734848259583, -0.15863767172331394, -0.2730124936722543, -0.10311382451884286, -0.1202426916393249, 0.02565314855993442, -0.11114564487146233, -0.16640368849039078, 0.3923218489786529, 0.1469239467083041, 0.19931947520083707, 0.004110333830644579, 0.23322349950347257, 0.09095643960526618, 0.03795043471188325, 0.07065708643311391, 0.2519238194663321, 0.13737788365991868, 0.08879323968582827, -0.2387493031271531, 0.03804206379648784, 0.10385935359593967] |
1,803.00154 | Structural and Thermal Stability of Graphyne and Graphdiyne Nanoscroll
Structures | Graphynes and graphdiynes are generic names for families of two-dimensional
carbon allotropes, where acetylenic groups connect benzenoid-like hexagonal
rings, with the co-existence of sp and sp2 hybridized carbon atoms. The main
differences between graphynes and graphdiynes are the number of acetylenic
groups (one and two for graphynes and graphdiynes, respectively). Similarly to
graphene nanoscrolls, graphyne and graphdiynes nanoscrolls are nanosized
membranes rolled up into papyrus-like structures. In this work we investigated
through fully atomistic reactive molecular dynamics simulations the structural
and thermal (up to 1000K) stability of alpha,beta,gamma-graphyne and
alpha,beta,gamma-graphdiyne scrolls. Our results show that stable nanoscrolls
can be formed for all the structures investigated here, although they are less
stable than corresponding graphene scrolls. This can be explained as a
consequence of the higher graphyne/graphdiyne structural porosity in relation
to graphene, which results in decreased {\pi}-{\pi} stacking interactions.
| cond-mat.mes-hall cond-mat.mtrl-sci | graphynes and graphdiynes are generic names for families of twodimensional carbon allotropes where acetylenic groups connect benzenoidlike hexagonal rings with the coexistence of sp and sp2 hybridized carbon atoms the main differences between graphynes and graphdiynes are the number of acetylenic groups one and two for graphynes and graphdiynes respectively similarly to graphene nanoscrolls graphyne and graphdiynes nanoscrolls are nanosized membranes rolled up into papyruslike structures in this work we investigated through fully atomistic reactive molecular dynamics simulations the structural and thermal up to 1000k stability of alphabetagammagraphyne and alphabetagammagraphdiyne scrolls our results show that stable nanoscrolls can be formed for all the structures investigated here although they are less stable than corresponding graphene scrolls this can be explained as a consequence of the higher graphynegraphdiyne structural porosity in relation to graphene which results in decreased pipi stacking interactions | [['graphynes', 'and', 'graphdiynes', 'are', 'generic', 'names', 'for', 'families', 'of', 'twodimensional', 'carbon', 'allotropes', 'where', 'acetylenic', 'groups', 'connect', 'benzenoidlike', 'hexagonal', 'rings', 'with', 'the', 'coexistence', 'of', 'sp', 'and', 'sp2', 'hybridized', 'carbon', 'atoms', 'the', 'main', 'differences', 'between', 'graphynes', 'and', 'graphdiynes', 'are', 'the', 'number', 'of', 'acetylenic', 'groups', 'one', 'and', 'two', 'for', 'graphynes', 'and', 'graphdiynes', 'respectively', 'similarly', 'to', 'graphene', 'nanoscrolls', 'graphyne', 'and', 'graphdiynes', 'nanoscrolls', 'are', 'nanosized', 'membranes', 'rolled', 'up', 'into', 'papyruslike', 'structures', 'in', 'this', 'work', 'we', 'investigated', 'through', 'fully', 'atomistic', 'reactive', 'molecular', 'dynamics', 'simulations', 'the', 'structural', 'and', 'thermal', 'up', 'to', '1000k', 'stability', 'of', 'alphabetagammagraphyne', 'and', 'alphabetagammagraphdiyne', 'scrolls', 'our', 'results', 'show', 'that', 'stable', 'nanoscrolls', 'can', 'be', 'formed', 'for', 'all', 'the', 'structures', 'investigated', 'here', 'although', 'they', 'are', 'less', 'stable', 'than', 'corresponding', 'graphene', 'scrolls', 'this', 'can', 'be', 'explained', 'as', 'a', 'consequence', 'of', 'the', 'higher', 'graphynegraphdiyne', 'structural', 'porosity', 'in', 'relation', 'to', 'graphene', 'which', 'results', 'in', 'decreased', 'pipi', 'stacking', 'interactions']] | [-0.1268960425854419, 0.17987375652193333, -0.0166586114460154, 0.013615893757108737, 0.03726836878599392, -0.14255988304330794, 0.041856454950929795, 0.4618573127314448, -0.2836537023074925, -0.2682197170952956, 0.02816027210145775, -0.2977432620608144, -0.15712558820291803, 0.16206894730804144, 0.024552162480109406, -0.021338908836521484, 0.0360796969625409, -0.11544635046825365, -0.0424714092053128, -0.239719472385736, 0.24565195880002447, -0.0024834785541450535, 0.2854452635920434, 0.06573596871975396, -0.02344516186405801, -0.09071609582407054, 0.10247643467349311, 0.06123821344226599, -0.168562653880077, 0.14824831008359238, 0.2625418332577856, -0.10333876533916703, 0.15718276944287396, -0.500959578535899, -0.18444779494570362, 0.00852166609838605, 0.17558874703805755, 0.10534337749618485, -0.03781471265376442, -0.25287509428130256, 0.16518425628611888, -0.1335467308802806, -0.09640225191359167, -0.09065680625951952, 0.012335339826273007, 0.05992012852113004, -0.1684420828052141, 0.06031926463938025, 0.05040735761393551, 0.06436599619617617, -0.11525807094035877, -0.18018238162276923, -0.17043629129141294, 0.08279857765969441, 0.00912736355740991, -0.06876475679897272, 0.18575423694802104, -0.05833261748085971, -0.0978715579404875, 0.4489063936803076, -0.024844816365576762, -0.1403028196775941, 0.23259234803694265, -0.10520412284035788, -0.14031694937773326, 0.15015725957022774, 0.10789279900405004, 0.1128281434101087, -0.13601186011391864, -0.0030091529160186097, -0.02554738529164482, 0.1211160256354897, 0.17261998444381688, 0.05134908714573141, 0.22993455007327376, 0.2036298643918363, 0.02486081857027279, 0.1391817908078708, -0.03421029416775262, -0.05865177594263245, -0.16213820426966305, -0.2206753465415979, -0.11317346828937945, 0.051182127178267194, -0.05567771850619465, -0.19645619263734532, 0.3705468676639376, 0.041527602876778, 0.17201126365129044, -0.005246654241690757, 0.18886345852407868, 0.0019068780941543756, 0.1262390793611606, 0.011407529938690089, 0.2298272291301853, 0.18127675654573572, 0.003695798254813309, -0.16540099721643384, 0.0248776969366224, -0.01063935394157414] |
1,803.00155 | Blue straggler stars beyond the Milky Way: a non-segregated population
in the Large Magellanic Cloud cluster NGC 2213 | Using the high-resolution observations obtained by the Hubble Space
Telescope, we analyzed the blue straggler stars (BSSs) in the Large Magellanic
Cloud cluster NGC 2213. We found that the radial distribution of BSSs is
consistent with that of the normal giant stars in NGC 2213, showing no evidence
of mass segregation. However, an analytic calculation carried out for these
BSSs shows that they are already dynamically old, because the estimated
half-mass relaxation time for these BSSs is significantly shorter than the
isochronal age of the cluster. We also performed direct N-body simulations for
a NGC 2213-like cluster to understand the dynamical processes that lead to this
none-segregated radial distribution of BSSs. Our numerical simulation shows
that the presence of black hole subsystems inside the cluster centre can
significantly affect the dynamical evolution of BSSs. The combined effects of
the delayed segregation, binary disruption and exchange interactions of BSS
progenitor binaries may result in this none-segregated radial distribution of
BSSs in NGC 2213.
| astro-ph.SR astro-ph.GA | using the highresolution observations obtained by the hubble space telescope we analyzed the blue straggler stars bsss in the large magellanic cloud cluster ngc 2213 we found that the radial distribution of bsss is consistent with that of the normal giant stars in ngc 2213 showing no evidence of mass segregation however an analytic calculation carried out for these bsss shows that they are already dynamically old because the estimated halfmass relaxation time for these bsss is significantly shorter than the isochronal age of the cluster we also performed direct nbody simulations for a ngc 2213like cluster to understand the dynamical processes that lead to this nonesegregated radial distribution of bsss our numerical simulation shows that the presence of black hole subsystems inside the cluster centre can significantly affect the dynamical evolution of bsss the combined effects of the delayed segregation binary disruption and exchange interactions of bss progenitor binaries may result in this nonesegregated radial distribution of bsss in ngc 2213 | [['using', 'the', 'highresolution', 'observations', 'obtained', 'by', 'the', 'hubble', 'space', 'telescope', 'we', 'analyzed', 'the', 'blue', 'straggler', 'stars', 'bsss', 'in', 'the', 'large', 'magellanic', 'cloud', 'cluster', 'ngc', '2213', 'we', 'found', 'that', 'the', 'radial', 'distribution', 'of', 'bsss', 'is', 'consistent', 'with', 'that', 'of', 'the', 'normal', 'giant', 'stars', 'in', 'ngc', '2213', 'showing', 'no', 'evidence', 'of', 'mass', 'segregation', 'however', 'an', 'analytic', 'calculation', 'carried', 'out', 'for', 'these', 'bsss', 'shows', 'that', 'they', 'are', 'already', 'dynamically', 'old', 'because', 'the', 'estimated', 'halfmass', 'relaxation', 'time', 'for', 'these', 'bsss', 'is', 'significantly', 'shorter', 'than', 'the', 'isochronal', 'age', 'of', 'the', 'cluster', 'we', 'also', 'performed', 'direct', 'nbody', 'simulations', 'for', 'a', 'ngc', '2213like', 'cluster', 'to', 'understand', 'the', 'dynamical', 'processes', 'that', 'lead', 'to', 'this', 'nonesegregated', 'radial', 'distribution', 'of', 'bsss', 'our', 'numerical', 'simulation', 'shows', 'that', 'the', 'presence', 'of', 'black', 'hole', 'subsystems', 'inside', 'the', 'cluster', 'centre', 'can', 'significantly', 'affect', 'the', 'dynamical', 'evolution', 'of', 'bsss', 'the', 'combined', 'effects', 'of', 'the', 'delayed', 'segregation', 'binary', 'disruption', 'and', 'exchange', 'interactions', 'of', 'bss', 'progenitor', 'binaries', 'may', 'result', 'in', 'this', 'nonesegregated', 'radial', 'distribution', 'of', 'bsss', 'in', 'ngc', '2213']] | [-0.11883212305581982, 0.09714914622664256, -0.1552217344437878, 0.11962165355202069, -0.07172563218704935, -0.012076839082727924, 0.09267376759528832, 0.412737211323806, -0.14196253353554122, -0.3242258760761821, 0.005496588006978796, -0.24267962847985677, -0.0608503843698674, 0.2306202640200514, -0.03396124662479983, -0.04957958615718866, 0.20045600146488668, -0.06378668427186192, -0.034076878130318795, -0.30787216548064034, 0.3269264404530163, 0.05740826172057141, 0.15779719231123068, -0.06875548403382113, 0.01621122844517231, -0.0818348594695189, -0.058920164914719714, -0.02287171730415093, -0.14251047894092309, 0.014120249847922497, 0.21295909311054215, 0.126905910532148, 0.23634746004153723, -0.3735361132981642, -0.20583464782802188, 0.062290987119945525, 0.3008203925594172, 0.05507618954124035, -0.11463565537530296, -0.28620148449330796, 0.11568512876061196, -0.21060797619873528, -0.2152945460083912, 0.07367089716424732, 0.07892787415510828, 0.046883447112591715, -0.22140924751984817, 0.18853895550387995, -0.0032281759652637134, 0.07496242383319252, -0.1553854642277759, -0.15030025782754775, -0.064120022569045, 0.10901791732406842, 0.027654532759022788, 0.02906846179357443, 0.16100390618736563, -0.07420368141746558, -0.009804712977471217, 0.3580639198328714, -0.03707493686326999, -0.01585635090094505, 0.23285457432861645, -0.21548057411381472, -0.18275217710754033, 0.06926717197247224, 0.13725411469840193, 0.12973759060655282, -0.20453782118095448, -0.010452967758346114, -0.039484436556392305, 0.23381490634648866, 0.059227307003471744, 0.029391515369632474, 0.2898404510053308, 0.09910641984176477, 0.011608993494004573, 0.10820376701981017, -0.21851055993136229, -0.16307382289109365, -0.17361045246885257, -0.08624060767989769, -0.131542197613151, 0.055094469823353226, -0.1867366751540436, -0.14464089653378576, 0.2860257550898307, 0.09346770249825534, 0.180543637360042, 0.026675987524238897, 0.2762650988425667, 0.07932915880400453, 0.1419464518570886, 0.1411771333378113, 0.29907789384747074, 0.187732753368774, 0.09153055227742349, -0.3518307144759492, 0.08881753756472133, -0.0490767452865839] |
1,803.00156 | Autoencoding topology | The problem of learning a manifold structure on a dataset is framed in terms
of a generative model, to which we use ideas behind autoencoders (namely
adversarial/Wasserstein autoencoders) to fit deep neural networks. From a
machine learning perspective, the resulting structure, an atlas of a manifold,
may be viewed as a combination of dimensionality reduction and "fuzzy"
clustering.
| stat.ML cs.LG | the problem of learning a manifold structure on a dataset is framed in terms of a generative model to which we use ideas behind autoencoders namely adversarialwasserstein autoencoders to fit deep neural networks from a machine learning perspective the resulting structure an atlas of a manifold may be viewed as a combination of dimensionality reduction and fuzzy clustering | [['the', 'problem', 'of', 'learning', 'a', 'manifold', 'structure', 'on', 'a', 'dataset', 'is', 'framed', 'in', 'terms', 'of', 'a', 'generative', 'model', 'to', 'which', 'we', 'use', 'ideas', 'behind', 'autoencoders', 'namely', 'adversarialwasserstein', 'autoencoders', 'to', 'fit', 'deep', 'neural', 'networks', 'from', 'a', 'machine', 'learning', 'perspective', 'the', 'resulting', 'structure', 'an', 'atlas', 'of', 'a', 'manifold', 'may', 'be', 'viewed', 'as', 'a', 'combination', 'of', 'dimensionality', 'reduction', 'and', 'fuzzy', 'clustering']] | [-0.02523102438620602, -0.031403665256986735, -0.151201281696558, 0.11798140768721503, -0.1442871982591194, -0.15656903224360003, 0.014839489454109418, 0.39085377360645096, -0.3630584597489552, -0.3109608844510819, 0.07775540617417152, -0.25634524833999184, -0.2502490094160302, 0.14764730164601483, -0.15006963408699162, 0.03650311687797831, 0.11553921014593359, 0.045829456087136476, -0.0966333307227806, -0.22768647941809736, 0.3471867471377839, 0.06466093153249587, 0.32716615371438756, -0.050196761592176925, 0.1928897940128911, -0.031002829194461044, 0.04228338559991435, 0.04930073003235616, -0.048725692306138706, 0.2390581984991992, 0.29409145856717306, 0.21627313188652983, 0.3373429530888404, -0.3923276393886721, -0.2786123245186581, 0.11065892966692908, 0.16530626979574822, 0.10459521223929825, -0.013731831208835438, -0.3303141728729794, 0.04329021770523436, -0.17270345904194473, -0.016740434696865186, -0.15144375958398246, -0.08510716674489933, -0.07270911667626678, -0.2682892211831355, -0.018094582086951846, 0.09911910927315291, 0.07471015211194754, -0.06824771234798327, -0.11135882415334906, 0.009006639795475885, 0.06647717938023179, 0.008481715963091375, 0.12622705632984116, 0.1338066656403897, -0.22982611358492522, -0.20493258959041877, 0.30784518323969423, -0.07317700879158158, -0.24132278672697252, 0.1734737050964644, 0.06497516465095575, -0.18659252741194346, 0.07089757043541524, 0.3053649329629384, 0.09039727227533595, -0.15315607847163878, 0.07141189120215642, -0.07452376720315793, 0.16975448470922155, 0.002455495395942738, -0.03195947792438235, 0.17049316310307436, 0.32675733884976227, 0.019558508210537725, 0.13982615083978886, -0.1541431257297007, -0.09680712553100627, -0.23257395239514217, -0.10063350288883637, -0.20146236905272594, 0.09043859378353022, -0.10341133649451781, -0.19685551847674346, 0.3874430364128529, 0.09810168025484145, 0.32001596050369635, 0.0986391607234908, 0.30210010472096893, 0.0312056807094466, 0.13093871878982896, 0.06539820776762147, 0.17008097342771003, 0.13279217991389727, 0.10388421869362917, -0.10798359687211351, 0.039724544278885186, 0.11486266930015725] |
1,803.00157 | GOODS-ALMA: 1.1 mm galaxy survey - I. Source catalogue and optically
dark galaxies | We present a 69 arcmin$^2$ ALMA survey at 1.1mm, GOODS-ALMA, matching the
deepest HST-WFC3 H-band part of the GOODS-South field. We taper the 0"24
original image with a homogeneous and circular synthesized beam of 0"60 to
reduce the number of independent beams - thus reducing the number of purely
statistical spurious detections - and optimize the sensitivity to point
sources. We extract a catalogue of galaxies purely selected by ALMA and
identify sources with and without HST counterparts down to a 5$\sigma$ limiting
depth of H=28.2 AB (HST/WFC3 F160W). ALMA detects 20 sources brighter than 0.7
mJy in the 0"60 tapered mosaic (rms sensitivity =0.18 mJy/beam) with a purity
greater than 80%. Among these detections, we identify three sources with no HST
nor Spitzer-IRAC counterpart, consistent with the expected number of spurious
galaxies from the analysis of the inverted image; their definitive status will
require additional investigation. An additional three sources with HST
counterparts are detected either at high significance in the higher resolution
map, or with different detection-algorithm parameters ensuring a purity greater
than 80%. Hence we identify in total 20 robust detections. Our wide contiguous
survey allows us to push further in redshift the blind detection of massive
galaxies with ALMA with a median redshift of $z$=2.92 and a median stellar mass
of M$_{\star}$ = 1.1 $\times 10^{11}$M$_\odot$. Our sample includes 20%
HST-dark galaxies (4 out of 20), all detected in the mid-infrared with IRAC.
The near-infrared based photometric redshifts of two of them $z\sim$4.3 and
4.8) suggest that these sources have redshifts $z$>4. At least 40% of the ALMA
sources host an X-ray AGN, compared to 14% for other galaxies of similar mass
and redshift. The wide area of our ALMA survey provides lower values at the
bright end of number counts than single-dish telescopes
| astro-ph.GA | we present a 69 arcmin2 alma survey at 11mm goodsalma matching the deepest hstwfc3 hband part of the goodssouth field we taper the 024 original image with a homogeneous and circular synthesized beam of 060 to reduce the number of independent beams thus reducing the number of purely statistical spurious detections and optimize the sensitivity to point sources we extract a catalogue of galaxies purely selected by alma and identify sources with and without hst counterparts down to a 5sigma limiting depth of h282 ab hstwfc3 f160w alma detects 20 sources brighter than 07 mjy in the 060 tapered mosaic rms sensitivity 018 mjybeam with a purity greater than 80 among these detections we identify three sources with no hst nor spitzerirac counterpart consistent with the expected number of spurious galaxies from the analysis of the inverted image their definitive status will require additional investigation an additional three sources with hst counterparts are detected either at high significance in the higher resolution map or with different detectionalgorithm parameters ensuring a purity greater than 80 hence we identify in total 20 robust detections our wide contiguous survey allows us to push further in redshift the blind detection of massive galaxies with alma with a median redshift of z292 and a median stellar mass of m_star 11 times 1011m_odot our sample includes 20 hstdark galaxies 4 out of 20 all detected in the midinfrared with irac the nearinfrared based photometric redshifts of two of them zsim43 and 48 suggest that these sources have redshifts z4 at least 40 of the alma sources host an xray agn compared to 14 for other galaxies of similar mass and redshift the wide area of our alma survey provides lower values at the bright end of number counts than singledish telescopes | [['we', 'present', 'a', '69', 'arcmin2', 'alma', 'survey', 'at', '11mm', 'goodsalma', 'matching', 'the', 'deepest', 'hstwfc3', 'hband', 'part', 'of', 'the', 'goodssouth', 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1,803.00158 | Modeling reverse thinking for machine learning | Human inertial thinking schemes can be formed through learning, which are
then applied to quickly solve similar problems later. However, when problems
are significantly different, inertial thinking generally presents the solutions
that are definitely imperfect. In such cases, people will apply creative
thinking, such as reverse thinking, to solve problems. Similarly, machine
learning methods also form inertial thinking schemes through learning the
knowledge from a large amount of data. However, when the testing data are
vastly difference, the formed inertial thinking schemes will inevitably
generate errors. This kind of inertial thinking is called illusion inertial
thinking. Because all machine learning methods do not consider illusion
inertial thinking, in this paper we propose a new method that uses reverse
thinking to correct illusion inertial thinking, which increases the
generalization ability of machine learning methods. Experimental results on
benchmark datasets are used to validate the proposed method.
| cs.LG cs.AI stat.ML | human inertial thinking schemes can be formed through learning which are then applied to quickly solve similar problems later however when problems are significantly different inertial thinking generally presents the solutions that are definitely imperfect in such cases people will apply creative thinking such as reverse thinking to solve problems similarly machine learning methods also form inertial thinking schemes through learning the knowledge from a large amount of data however when the testing data are vastly difference the formed inertial thinking schemes will inevitably generate errors this kind of inertial thinking is called illusion inertial thinking because all machine learning methods do not consider illusion inertial thinking in this paper we propose a new method that uses reverse thinking to correct illusion inertial thinking which increases the generalization ability of machine learning methods experimental results on benchmark datasets are used to validate the proposed method | [['human', 'inertial', 'thinking', 'schemes', 'can', 'be', 'formed', 'through', 'learning', 'which', 'are', 'then', 'applied', 'to', 'quickly', 'solve', 'similar', 'problems', 'later', 'however', 'when', 'problems', 'are', 'significantly', 'different', 'inertial', 'thinking', 'generally', 'presents', 'the', 'solutions', 'that', 'are', 'definitely', 'imperfect', 'in', 'such', 'cases', 'people', 'will', 'apply', 'creative', 'thinking', 'such', 'as', 'reverse', 'thinking', 'to', 'solve', 'problems', 'similarly', 'machine', 'learning', 'methods', 'also', 'form', 'inertial', 'thinking', 'schemes', 'through', 'learning', 'the', 'knowledge', 'from', 'a', 'large', 'amount', 'of', 'data', 'however', 'when', 'the', 'testing', 'data', 'are', 'vastly', 'difference', 'the', 'formed', 'inertial', 'thinking', 'schemes', 'will', 'inevitably', 'generate', 'errors', 'this', 'kind', 'of', 'inertial', 'thinking', 'is', 'called', 'illusion', 'inertial', 'thinking', 'because', 'all', 'machine', 'learning', 'methods', 'do', 'not', 'consider', 'illusion', 'inertial', 'thinking', 'in', 'this', 'paper', 'we', 'propose', 'a', 'new', 'method', 'that', 'uses', 'reverse', 'thinking', 'to', 'correct', 'illusion', 'inertial', 'thinking', 'which', 'increases', 'the', 'generalization', 'ability', 'of', 'machine', 'learning', 'methods', 'experimental', 'results', 'on', 'benchmark', 'datasets', 'are', 'used', 'to', 'validate', 'the', 'proposed', 'method']] | [-0.00691177919185881, 0.15008171958977293, -0.1311919404193759, 0.10518167574713327, -0.18977048700868054, -0.21051108925121612, -0.013502076293084898, 0.38677838932208974, -0.3034339308770823, -0.3298766839446436, 0.036153174842033405, -0.20873472368473123, -0.20032392698259832, 0.2635366045075051, -0.19114221723686003, 0.06099711770898309, 0.13368487806733825, 0.04216479303853201, -0.06287456108016315, -0.26579808968055096, 0.3046013138096394, 0.01926181261459815, 0.3838718828713072, -0.019499883999855354, 0.09852992653814627, -0.06523381609726568, -0.036648977133606014, 0.05754262490511886, -0.06910717773365643, 0.14143209023208453, 0.3699758267479724, 0.23746712741859513, 0.43388925341182744, -0.43485391509943994, -0.20070160157207784, 0.09430548286643521, 0.20550094525297655, 0.16038525512949806, -0.04705816209075395, -0.319652752167192, 0.060245120275267884, -0.16700395718216896, -0.0839987854762324, -0.12460868888020772, -0.03586456804247252, 0.006949699729086895, -0.2448215223013841, 0.02583123229841445, 0.10994789980608842, 0.0771388591304301, -0.05595130581601426, -0.0997992157197461, 0.13322191765082295, 0.13035327612607064, 0.0950886627450457, -0.01898175878209415, 0.21304295309979854, -0.1334108438322738, -0.17563200237486384, 0.4225715119777055, 0.016717219198572223, -0.2368023061121268, 0.22363922420632223, -0.028153312437493225, -0.09466090499812833, 0.0885889274871041, 0.24795335456728934, 0.10633434679294969, -0.11252953558618299, 0.007265817003067711, -0.02326776942816274, 0.1475572098592489, 0.06439566367851762, -0.03491017512720207, 0.18587333038449289, 0.14122896464541554, 0.04993602623279881, 0.042765293373918996, -0.028640174077309927, -0.12381020565035528, -0.22923238786130115, -0.168517740584653, -0.15637474497378773, 0.00896593383202281, -0.02928098415120922, -0.11630917594864451, 0.3016378981462712, 0.280973077687467, 0.12966707501046618, 0.042119093308353735, 0.37735926128666974, 0.04037613045536624, 0.10669374315084183, 0.11210485028488369, 0.239691323295621, 0.026341334518430563, 0.18569730030501197, -0.17401957565774048, 0.07658734841047433, 0.06496893405946422] |
1,803.00159 | A Class-Incremental Learning Method Based on One Class Support Vector
Machine | A method based on one class support vector machine (OCSVM) is proposed for
class incremental learning. Several OCSVM models divide the input space into
several parts. Then, the 1VS1 classifiers are constructed for the confuse part
by using the support vectors. During the class incremental learning process,
the OCSVM of the new class is trained at first. Then the support vectors of the
old classes and the support vectors of the new class are reused to train 1VS1
classifiers for the confuse part. In order to bring more information to the
certain support vectors, the support vectors are at the boundary of the
distribution of samples as much as possible when the OCSVM is built. Compared
with the traditional methods, the proposed method retains the original model
and thus reduces memory consumption and training time cost. Various experiments
on different datasets also verify the efficiency of the proposed method.
| cs.CV | a method based on one class support vector machine ocsvm is proposed for class incremental learning several ocsvm models divide the input space into several parts then the 1vs1 classifiers are constructed for the confuse part by using the support vectors during the class incremental learning process the ocsvm of the new class is trained at first then the support vectors of the old classes and the support vectors of the new class are reused to train 1vs1 classifiers for the confuse part in order to bring more information to the certain support vectors the support vectors are at the boundary of the distribution of samples as much as possible when the ocsvm is built compared with the traditional methods the proposed method retains the original model and thus reduces memory consumption and training time cost various experiments on different datasets also verify the efficiency of the proposed method | [['a', 'method', 'based', 'on', 'one', 'class', 'support', 'vector', 'machine', 'ocsvm', 'is', 'proposed', 'for', 'class', 'incremental', 'learning', 'several', 'ocsvm', 'models', 'divide', 'the', 'input', 'space', 'into', 'several', 'parts', 'then', 'the', '1vs1', 'classifiers', 'are', 'constructed', 'for', 'the', 'confuse', 'part', 'by', 'using', 'the', 'support', 'vectors', 'during', 'the', 'class', 'incremental', 'learning', 'process', 'the', 'ocsvm', 'of', 'the', 'new', 'class', 'is', 'trained', 'at', 'first', 'then', 'the', 'support', 'vectors', 'of', 'the', 'old', 'classes', 'and', 'the', 'support', 'vectors', 'of', 'the', 'new', 'class', 'are', 'reused', 'to', 'train', '1vs1', 'classifiers', 'for', 'the', 'confuse', 'part', 'in', 'order', 'to', 'bring', 'more', 'information', 'to', 'the', 'certain', 'support', 'vectors', 'the', 'support', 'vectors', 'are', 'at', 'the', 'boundary', 'of', 'the', 'distribution', 'of', 'samples', 'as', 'much', 'as', 'possible', 'when', 'the', 'ocsvm', 'is', 'built', 'compared', 'with', 'the', 'traditional', 'methods', 'the', 'proposed', 'method', 'retains', 'the', 'original', 'model', 'and', 'thus', 'reduces', 'memory', 'consumption', 'and', 'training', 'time', 'cost', 'various', 'experiments', 'on', 'different', 'datasets', 'also', 'verify', 'the', 'efficiency', 'of', 'the', 'proposed', 'method']] | [-0.029902487620555064, 0.037608505448563985, -0.05024105535457598, 0.05667757727610326, -0.12202415192474755, -0.1390301115367868, 0.03903412338871284, 0.3711293946526595, -0.2848834783813897, -0.2753667411138147, 0.1274296908253404, -0.261775418879283, -0.13858129200213057, 0.222446791247946, -0.05424598966343891, 0.09353114838318137, 0.09656926420135986, 0.09872830569847243, -0.05174608477594443, -0.3369522056098552, 0.3698183265897514, 0.029319361020223356, 0.3614857866172522, -0.04823966524344963, 0.12544132672537794, -0.024685541627266064, -0.04061359959191974, -0.012922469144956608, -0.006266236808401026, 0.16904291228685003, 0.25478013752344486, 0.2109727210008958, 0.3352278785017513, -0.38425630149775303, -0.22408761706003208, 0.10750799894882929, 0.09874341049819135, 0.09531055303415886, -0.010355756621696086, -0.3047703575067872, 0.13161328872947034, -0.14595271825584138, -0.04330338404557649, -0.1338456795822779, -0.03701970834697553, 0.015505399054928913, -0.30101821174057536, 0.001659204492512653, 0.08227921034765724, 0.016674908331201582, -0.1126566938212104, -0.16676676654313136, -0.007000761685561664, 0.1259592058437473, 0.04591763527784965, 0.05435543402251256, 0.09949818364775821, -0.11727915964895647, -0.1438847432669327, 0.3509318249287621, -0.04451487844437986, -0.23100076518302795, 0.2154073717288248, -0.024628437164820882, -0.12220136629067511, 0.12730675602906802, 0.24119515156673405, 0.09530590631180262, -0.12132739009315714, 0.023371062474715956, -0.026218695621531233, 0.147604241117135, 0.01778012069235962, 0.007444920855880584, 0.1716904851159506, 0.19937543902544605, 0.019567273594689048, 0.16762762217026128, -0.13319372425587137, -0.06412338171874629, -0.2726645525044843, -0.17031017185232583, -0.22390132891201733, -0.08784863716085135, -0.10721844562218796, -0.1262198111976828, 0.41629044930044334, 0.20071376140398167, 0.22191915860174585, 0.14293618364597127, 0.32155400977138704, 0.06613721702194729, 0.1403944184515319, 0.12318496690162976, 0.1904255404112073, 0.06759158473293103, 0.10894212959921801, -0.15891648038648282, 0.09708790441087428, 0.08536430844574566] |
1,803.0016 | Buckling of thin composite plates reinforced with randomly oriented,
straight single-walled carbon nanotubes using B3-Spline finite strip method | This paper is devoted to the mechanical buckling analysis of thin composite
plates under straight single-walled carbon nanotubes reinforcement with uniform
distribution and random orientations. In order to develop the fundamental
equations, the B3-Spline finite strip method along with the classical plate
theory is employed and the total potential energy is minimized which leads to
an eigenvalue problem. For deriving the effective modulus of thin composite
plates reinforced with carbon nanotubes, the Mori-Tanaka method is used in
which each straight carbon nanotube is modeled as a fiber with transversely
isotropic elastic properties. The results of our numerical experiments
including the critical buckling loads for rectangular thin composite plates
reinforced by carbon nanotubes with various boundary conditions and different
volume fractions of nanotubes are provided and the positive effect of using
carbon nanotubes reinforcement in mechanical buckling of thin plates is
illustrated.
| cs.CE | this paper is devoted to the mechanical buckling analysis of thin composite plates under straight singlewalled carbon nanotubes reinforcement with uniform distribution and random orientations in order to develop the fundamental equations the b3spline finite strip method along with the classical plate theory is employed and the total potential energy is minimized which leads to an eigenvalue problem for deriving the effective modulus of thin composite plates reinforced with carbon nanotubes the moritanaka method is used in which each straight carbon nanotube is modeled as a fiber with transversely isotropic elastic properties the results of our numerical experiments including the critical buckling loads for rectangular thin composite plates reinforced by carbon nanotubes with various boundary conditions and different volume fractions of nanotubes are provided and the positive effect of using carbon nanotubes reinforcement in mechanical buckling of thin plates is illustrated | [['this', 'paper', 'is', 'devoted', 'to', 'the', 'mechanical', 'buckling', 'analysis', 'of', 'thin', 'composite', 'plates', 'under', 'straight', 'singlewalled', 'carbon', 'nanotubes', 'reinforcement', 'with', 'uniform', 'distribution', 'and', 'random', 'orientations', 'in', 'order', 'to', 'develop', 'the', 'fundamental', 'equations', 'the', 'b3spline', 'finite', 'strip', 'method', 'along', 'with', 'the', 'classical', 'plate', 'theory', 'is', 'employed', 'and', 'the', 'total', 'potential', 'energy', 'is', 'minimized', 'which', 'leads', 'to', 'an', 'eigenvalue', 'problem', 'for', 'deriving', 'the', 'effective', 'modulus', 'of', 'thin', 'composite', 'plates', 'reinforced', 'with', 'carbon', 'nanotubes', 'the', 'moritanaka', 'method', 'is', 'used', 'in', 'which', 'each', 'straight', 'carbon', 'nanotube', 'is', 'modeled', 'as', 'a', 'fiber', 'with', 'transversely', 'isotropic', 'elastic', 'properties', 'the', 'results', 'of', 'our', 'numerical', 'experiments', 'including', 'the', 'critical', 'buckling', 'loads', 'for', 'rectangular', 'thin', 'composite', 'plates', 'reinforced', 'by', 'carbon', 'nanotubes', 'with', 'various', 'boundary', 'conditions', 'and', 'different', 'volume', 'fractions', 'of', 'nanotubes', 'are', 'provided', 'and', 'the', 'positive', 'effect', 'of', 'using', 'carbon', 'nanotubes', 'reinforcement', 'in', 'mechanical', 'buckling', 'of', 'thin', 'plates', 'is', 'illustrated']] | [-0.11550668058584311, 0.13678602427337735, -0.03935245021712035, -0.08692188273499986, -0.01995487430997725, -0.1489758911509333, 0.014451318048772269, 0.4895950451360217, -0.26013142996234817, -0.2494521381084009, 0.06999314230633899, -0.2682291769954775, -0.11444722320884466, 0.1637838228106765, -0.03615929832282875, 0.11586136328322547, 0.07008179036534525, -0.07047050352807023, -0.028051335577453887, -0.189208469228054, 0.27465495867987294, 0.031080101929338914, 0.38135573867974537, 0.06402418873331044, 0.05339761733942266, 0.006776042117936803, 0.042424843205870796, 0.09396906500748758, -0.19092683717774758, 0.14813096280475813, 0.21313555300335532, -0.1126273307782997, 0.2316300145749535, -0.4989812817690628, -0.18708093950657972, 0.048868213460913724, 0.10898455723820787, 0.09404798437442098, -0.023322364032667662, -0.23642862308770418, 0.1026699292673064, -0.11275592637580953, -0.17346839050296695, 0.01570788375767214, 0.003776124937992011, 0.07206109552976808, -0.22629635070084728, 0.051593915722956546, 0.034209710300534164, 0.059600234671961516, -0.09862102929702295, -0.17885901099070906, -0.044279463673176776, 0.06280372345021792, 0.10616473163245246, -0.0451136327972303, 0.23838448428016687, -0.09835489857110329, -0.035688425271239665, 0.43287711343062774, -0.0724477437765537, -0.19201645216505442, 0.1593388126165207, -0.05419879157229194, -0.018928105141302304, 0.16997553389186837, 0.1377421705917056, 0.12895937996051673, -0.18829771995744002, 0.017774994436851037, -0.04988768739692335, 0.1203831166960299, 0.11991474109735074, -0.04858028385122972, 0.23771662595016615, 0.26325425077229736, 0.022896903227748615, 0.2242724179868154, -0.0859405084538074, -0.04985295970192445, -0.276618140512645, -0.19122947527544706, -0.16348620788526855, 0.055088147518524366, -0.11618997512476718, -0.2873348229464942, 0.31685196473928434, 0.024526197717308865, 0.13201227130567922, 0.022574608807917684, 0.27549884159650123, 0.08480254272331617, 0.06707738568407616, -0.012302947154135576, 0.2710780189455753, 0.26030388060857407, 0.10226973677760855, -0.2049481808839898, 0.02823669866525701, 0.058952534132237944] |
1,803.00161 | Reciprocal Sum of Palindromes | A positive integer $n$ is said to be a palindrome in base $b$ (or $b$-adic
palindrome) if the representation of $n = (a_k a_{k-1} \cdots a_0)_b$ in base
$b$ with $a_k \neq 0$ has the symmetric property $a_{k-i} = a_i$ for every
$i=0,1,2,\ldots ,k$. Let $s_b$ be the reciprocal sum of all $b$-adic
palindromes. It is not difficult to show that $s_b$ converges. In this article,
we obtain upper and lower bounds for $s_b$ and the inequality $s_{b} <s_{b'}$
for $2\leq b<b'$. Its consequences and some numerical data are also given.
| math.CA | a positive integer n is said to be a palindrome in base b or badic palindrome if the representation of n a_k a_k1 cdots a_0_b in base b with a_k neq 0 has the symmetric property a_ki a_i for every i012ldots k let s_b be the reciprocal sum of all badic palindromes it is not difficult to show that s_b converges in this article we obtain upper and lower bounds for s_b and the inequality s_b s_b for 2leq bb its consequences and some numerical data are also given | [['a', 'positive', 'integer', 'n', 'is', 'said', 'to', 'be', 'a', 'palindrome', 'in', 'base', 'b', 'or', 'badic', 'palindrome', 'if', 'the', 'representation', 'of', 'n', 'a_k', 'a_k1', 'cdots', 'a_0_b', 'in', 'base', 'b', 'with', 'a_k', 'neq', '0', 'has', 'the', 'symmetric', 'property', 'a_ki', 'a_i', 'for', 'every', 'i012ldots', 'k', 'let', 's_b', 'be', 'the', 'reciprocal', 'sum', 'of', 'all', 'badic', 'palindromes', 'it', 'is', 'not', 'difficult', 'to', 'show', 'that', 's_b', 'converges', 'in', 'this', 'article', 'we', 'obtain', 'upper', 'and', 'lower', 'bounds', 'for', 's_b', 'and', 'the', 'inequality', 's_b', 's_b', 'for', '2leq', 'bb', 'its', 'consequences', 'and', 'some', 'numerical', 'data', 'are', 'also', 'given']] | [-0.19333966630885654, 0.18487125660838752, -0.032877256052739834, -0.0005412852035664108, -0.064696870302653, -0.24485749633040751, 0.0532121429669446, 0.3496351034070055, -0.31288469848276557, -0.13656039103523068, 0.09700082775189317, -0.37292617979063386, -0.11026202111997395, 0.11207669172114854, -0.06263715154009647, -0.04068924219082591, -0.020133376025177282, 0.16716396394346295, -0.08596487580022465, -0.2691840932800852, 0.23797454286752076, -0.1078444923349153, 0.17261699599149669, 0.07031219897941611, 0.04028235182257091, -0.018391920248282023, 0.05233088634030401, -0.0325663672922709, -0.22248885992838502, 0.04443879790026052, 0.31264236978330145, 0.18988269568024868, 0.246180204642487, -0.2980343058404925, -0.03916569025223625, 0.28517402945107767, 0.23201953232737965, -0.07884502952554445, 0.005715941678432898, -0.1913338946820847, 0.2000559472676966, -0.15590513429079933, -0.1301170913288477, -0.0917697539338949, 0.17365423870412097, 0.002101771469260084, -0.405956183565545, 0.06445973949612589, 0.10638341699437849, 0.04584823836453377, -0.0059121052624829026, -0.251312930136919, -0.04493988438070506, 0.08755610456312846, 0.02549985948222122, 0.1218830439484218, 0.00034428141601704833, -0.007603473744579944, -0.07496413403998889, 0.3678114367006668, -0.04988724735147994, -0.275472484711001, 0.036201272529132404, -0.20447416036207786, -0.1677483770321926, 0.08260545105641258, 0.038853953271333515, 0.18474991021185427, -0.055842125879322316, 0.2211265594988858, -0.13595315839709907, 0.13180184103521764, 0.13558822210300073, 0.060687045208124936, 0.13398668495402938, -0.0040981717215015975, 0.09883444554482897, 0.11436910915252721, 0.008118431014276442, 0.0592986236957983, -0.36117157467437544, -0.17615039446444689, -0.24247102346405086, 0.16778465177099108, -0.13575367765429552, -0.12305653176468465, 0.2393182245452471, 0.07977484944731468, 0.21092302243535718, 0.15222761016901068, 0.22254658356608673, 0.10519410526358533, 0.03636534675961125, 0.12843052472319755, 0.0349760898111265, 0.17399775440237303, -0.033501116152124845, -0.15805353974687986, 0.019192957664550894, 0.15154255937313896] |
1,803.00162 | Towards Cooperation in Sequential Prisoner's Dilemmas: a Deep Multiagent
Reinforcement Learning Approach | The Iterated Prisoner's Dilemma has guided research on social dilemmas for
decades. However, it distinguishes between only two atomic actions: cooperate
and defect. In real-world prisoner's dilemmas, these choices are temporally
extended and different strategies may correspond to sequences of actions,
reflecting grades of cooperation. We introduce a Sequential Prisoner's Dilemma
(SPD) game to better capture the aforementioned characteristics. In this work,
we propose a deep multiagent reinforcement learning approach that investigates
the evolution of mutual cooperation in SPD games. Our approach consists of two
phases. The first phase is offline: it synthesizes policies with different
cooperation degrees and then trains a cooperation degree detection network. The
second phase is online: an agent adaptively selects its policy based on the
detected degree of opponent cooperation. The effectiveness of our approach is
demonstrated in two representative SPD 2D games: the Apple-Pear game and the
Fruit Gathering game. Experimental results show that our strategy can avoid
being exploited by exploitative opponents and achieve cooperation with
cooperative opponents.
| cs.AI cs.GT cs.LG cs.MA | the iterated prisoners dilemma has guided research on social dilemmas for decades however it distinguishes between only two atomic actions cooperate and defect in realworld prisoners dilemmas these choices are temporally extended and different strategies may correspond to sequences of actions reflecting grades of cooperation we introduce a sequential prisoners dilemma spd game to better capture the aforementioned characteristics in this work we propose a deep multiagent reinforcement learning approach that investigates the evolution of mutual cooperation in spd games our approach consists of two phases the first phase is offline it synthesizes policies with different cooperation degrees and then trains a cooperation degree detection network the second phase is online an agent adaptively selects its policy based on the detected degree of opponent cooperation the effectiveness of our approach is demonstrated in two representative spd 2d games the applepear game and the fruit gathering game experimental results show that our strategy can avoid being exploited by exploitative opponents and achieve cooperation with cooperative opponents | [['the', 'iterated', 'prisoners', 'dilemma', 'has', 'guided', 'research', 'on', 'social', 'dilemmas', 'for', 'decades', 'however', 'it', 'distinguishes', 'between', 'only', 'two', 'atomic', 'actions', 'cooperate', 'and', 'defect', 'in', 'realworld', 'prisoners', 'dilemmas', 'these', 'choices', 'are', 'temporally', 'extended', 'and', 'different', 'strategies', 'may', 'correspond', 'to', 'sequences', 'of', 'actions', 'reflecting', 'grades', 'of', 'cooperation', 'we', 'introduce', 'a', 'sequential', 'prisoners', 'dilemma', 'spd', 'game', 'to', 'better', 'capture', 'the', 'aforementioned', 'characteristics', 'in', 'this', 'work', 'we', 'propose', 'a', 'deep', 'multiagent', 'reinforcement', 'learning', 'approach', 'that', 'investigates', 'the', 'evolution', 'of', 'mutual', 'cooperation', 'in', 'spd', 'games', 'our', 'approach', 'consists', 'of', 'two', 'phases', 'the', 'first', 'phase', 'is', 'offline', 'it', 'synthesizes', 'policies', 'with', 'different', 'cooperation', 'degrees', 'and', 'then', 'trains', 'a', 'cooperation', 'degree', 'detection', 'network', 'the', 'second', 'phase', 'is', 'online', 'an', 'agent', 'adaptively', 'selects', 'its', 'policy', 'based', 'on', 'the', 'detected', 'degree', 'of', 'opponent', 'cooperation', 'the', 'effectiveness', 'of', 'our', 'approach', 'is', 'demonstrated', 'in', 'two', 'representative', 'spd', '2d', 'games', 'the', 'applepear', 'game', 'and', 'the', 'fruit', 'gathering', 'game', 'experimental', 'results', 'show', 'that', 'our', 'strategy', 'can', 'avoid', 'being', 'exploited', 'by', 'exploitative', 'opponents', 'and', 'achieve', 'cooperation', 'with', 'cooperative', 'opponents']] | [-0.12845368383142802, 0.06767711625088407, -0.11752579495219923, 0.06884320783173302, -0.07884308242625217, -0.22777258953301055, 0.07992765586823225, 0.5011196814324116, -0.265941700378501, -0.30034451732389267, 0.0234665986712704, -0.2506235147621937, -0.2776598745173343, 0.021292250606276823, -0.1578207024935315, -0.03255042042221204, 0.09723658160669957, 0.022963845457813544, 0.060806914269583436, -0.33747335942958984, 0.3717504500627245, 0.025694646013484666, 0.31531476707157974, 0.0069036311824896895, 0.14979836120406484, 0.027910646002898675, -0.015406024986210779, 0.02017240396277729, -0.08306526908377347, 0.11528966341042755, 0.35433431464338266, 0.21277313938977696, 0.4385205032871809, -0.41836414758752033, -0.18124051357437743, 0.11039902003646669, 0.12616017896102788, 0.1119326526006661, -0.026346844037506367, -0.36321449400459577, 0.04229697442000232, -0.2068517234436557, -0.01988943766024554, -0.07525965822406294, -0.07096634804634605, 0.007891640832318312, -0.31395452784192635, -0.05064553888117122, 0.03129428476606291, 0.08747819410040747, -0.056933886747063345, -0.11057299895711788, -0.011116540174442911, 0.21172705461645908, 0.03710241128105625, -0.06223233756126564, 0.14836954369164276, -0.14895788842965507, -0.27287187526093387, 0.36472416917914835, 0.017827779128466073, -0.1577887031158841, 0.20529209180002486, -0.05585249126528563, -0.10591411110717894, 0.08780339006732059, 0.18013962711031525, 0.15169765722864056, -0.12359689064636235, -0.013212425096553931, -0.07306001492685116, 0.18837232107549293, 0.05117284099957565, 0.00668510588215346, 0.1525893893139721, 0.2588241264556262, 0.16217470751143992, 0.08399498844434075, -0.02089994174155702, -0.24428727344905093, -0.1643708439035591, -0.07998741901033876, -0.14717660178203226, -0.013502032961696386, -0.12613681320158876, -0.05927148316532555, 0.3848296825137989, 0.16474182998318587, 0.11394130195087822, 0.09877927131133109, 0.30394208804056866, 0.01853749751632965, 0.031473906631950606, 0.03341853590227845, 0.21342978943082527, 0.02351324708007912, 0.1562119396261462, -0.2555945998261095, 0.16068242687629763, 0.047827157464514415] |
1,803.00163 | The Historical Development of Algebraic Geometry: a lecture by Jean
Dieudonn\'e | This article is a transcription of a video of a 1972 lecture by Jean
Dieudonn\'e, enhanced with composite still images from the video. The lecture
covers the same material as an earlier paper and lecture notes by Dieudonn\'e,
but the live lecture has a character of its own.
| math.HO | this article is a transcription of a video of a 1972 lecture by jean dieudonne enhanced with composite still images from the video the lecture covers the same material as an earlier paper and lecture notes by dieudonne but the live lecture has a character of its own | [['this', 'article', 'is', 'a', 'transcription', 'of', 'a', 'video', 'of', 'a', '1972', 'lecture', 'by', 'jean', 'dieudonne', 'enhanced', 'with', 'composite', 'still', 'images', 'from', 'the', 'video', 'the', 'lecture', 'covers', 'the', 'same', 'material', 'as', 'an', 'earlier', 'paper', 'and', 'lecture', 'notes', 'by', 'dieudonne', 'but', 'the', 'live', 'lecture', 'has', 'a', 'character', 'of', 'its', 'own']] | [-0.08315632563608233, 0.13020946758024365, -0.15490786167113887, -0.0019805651681963354, -0.09947662220414107, -0.10818512139182228, -0.010686011885506256, 0.2714603965481122, -0.21911523246672004, -0.3265784477892642, 0.18007874560620016, -0.2836379149230197, -0.227705380685317, 0.14067173479512954, -0.23370636508722478, -0.07818422986626199, 0.0997599935993397, 0.05027697503101081, -0.08372969628544524, -0.37172951611379784, 0.3128434958246847, 0.12681194813922048, 0.2828102900966769, 0.038239684799918905, 0.1556485461187549, -0.007564135536085814, -0.08434997033327818, -0.027346983978835244, -0.09813601001709078, 0.19307696195149524, 0.3368579403613694, 0.0844856328330934, 0.32865820369139936, -0.40832279610913247, -0.10064293574153756, -0.03612206182636631, 0.11922400182811543, 0.09864663045542936, -0.08395521521257858, -0.3292593395453878, 0.07354697431825723, -0.18458940364265194, -0.04741051282811289, 0.044466170210701726, 0.04346000367513625, -0.012545558041892946, -0.14710884826490656, 0.035279152517129354, 0.1625583830755204, 0.1989286121800736, -0.038895393331282925, -0.10247402189997956, 0.043121462455019355, 0.10919267124457595, 0.0487549599444416, 0.11352468068556239, 0.11315983262223502, -0.2108992387414522, -0.13647791766561568, 0.42653922946192324, -0.06845640344545245, -0.07369148482879002, 0.1456612740488102, -0.06926281036188205, -0.12602105502931712, 0.16273711406878041, 0.174679068402232, 0.13627816682371, -0.1960716802471628, 0.10445371772584622, -0.13004851685551935, 0.18743961459646621, 0.09056765103014186, -0.008457457025845846, 0.20338933336703727, 0.11712839043078323, -0.10656165418913588, 0.0985208780815204, -0.002843572525307536, -0.00792594711917142, -0.3459940985000382, -0.23676684846092635, -0.2252888286796709, 0.06153529434232041, 0.0641147555728215, -0.1337837690177063, 0.45531927107367665, 0.10358499906336267, 0.1750497478642501, 0.05537602251085142, 0.2300745123066008, 0.010191217838534309, 0.0028562962058155486, -0.022806918879117195, 0.12359992554411292, 0.08035307900475648, 0.20693882384027043, -0.04090912464016583, 0.004603176038168992, 0.18655605396876732] |
1,803.00164 | Turing instability and Turing-Hopf bifurcation in diffusive Schnakenberg
systems with gene expression time delay | For delayed reaction-diffusion Schnakenberg systems with Neumann boundary
conditions, critical conditions for Turing instability are derived, which are
necessary and sufficient. And existence conditions for Turing, Hopf and
Turing-Hopf bifurcations are established. Normal forms truncated to order 3 at
Turing-Hopf singularity of codimension 2, are derived. By investigating
Turing-Hopf bifurcation, the parameter regions for the stability of a periodic
solution, a pair of spatially inhomogeneous steady states and a pair of
spatially inhomogeneous periodic solutions, are derived in $(\tau,\varepsilon)$
parameter plane ($\tau$ for time delay, $\varepsilon$ for diffusion rate). It
is revealed that joint effects of diffusion and delay can lead to the
occurrence of mixed spatial and temporal patterns. Moreover, it is also
demonstrated that various spatially inhomogeneous patterns with different
spatial frequencies can be achieved via changing the diffusion rate. And, the
phenomenon that time delay may induce a failure of Turing instability observed
by Gaffney and Monk (2006) are theoretically explained.
| math.DS | for delayed reactiondiffusion schnakenberg systems with neumann boundary conditions critical conditions for turing instability are derived which are necessary and sufficient and existence conditions for turing hopf and turinghopf bifurcations are established normal forms truncated to order 3 at turinghopf singularity of codimension 2 are derived by investigating turinghopf bifurcation the parameter regions for the stability of a periodic solution a pair of spatially inhomogeneous steady states and a pair of spatially inhomogeneous periodic solutions are derived in tauvarepsilon parameter plane tau for time delay varepsilon for diffusion rate it is revealed that joint effects of diffusion and delay can lead to the occurrence of mixed spatial and temporal patterns moreover it is also demonstrated that various spatially inhomogeneous patterns with different spatial frequencies can be achieved via changing the diffusion rate and the phenomenon that time delay may induce a failure of turing instability observed by gaffney and monk 2006 are theoretically explained | [['for', 'delayed', 'reactiondiffusion', 'schnakenberg', 'systems', 'with', 'neumann', 'boundary', 'conditions', 'critical', 'conditions', 'for', 'turing', 'instability', 'are', 'derived', 'which', 'are', 'necessary', 'and', 'sufficient', 'and', 'existence', 'conditions', 'for', 'turing', 'hopf', 'and', 'turinghopf', 'bifurcations', 'are', 'established', 'normal', 'forms', 'truncated', 'to', 'order', '3', 'at', 'turinghopf', 'singularity', 'of', 'codimension', '2', 'are', 'derived', 'by', 'investigating', 'turinghopf', 'bifurcation', 'the', 'parameter', 'regions', 'for', 'the', 'stability', 'of', 'a', 'periodic', 'solution', 'a', 'pair', 'of', 'spatially', 'inhomogeneous', 'steady', 'states', 'and', 'a', 'pair', 'of', 'spatially', 'inhomogeneous', 'periodic', 'solutions', 'are', 'derived', 'in', 'tauvarepsilon', 'parameter', 'plane', 'tau', 'for', 'time', 'delay', 'varepsilon', 'for', 'diffusion', 'rate', 'it', 'is', 'revealed', 'that', 'joint', 'effects', 'of', 'diffusion', 'and', 'delay', 'can', 'lead', 'to', 'the', 'occurrence', 'of', 'mixed', 'spatial', 'and', 'temporal', 'patterns', 'moreover', 'it', 'is', 'also', 'demonstrated', 'that', 'various', 'spatially', 'inhomogeneous', 'patterns', 'with', 'different', 'spatial', 'frequencies', 'can', 'be', 'achieved', 'via', 'changing', 'the', 'diffusion', 'rate', 'and', 'the', 'phenomenon', 'that', 'time', 'delay', 'may', 'induce', 'a', 'failure', 'of', 'turing', 'instability', 'observed', 'by', 'gaffney', 'and', 'monk', '2006', 'are', 'theoretically', 'explained']] | [-0.21591394513029777, 0.1619515589048806, -0.06511461393286784, 0.087942653545635, -0.05266980220734148, -0.18259544780757886, 0.014573095806149973, 0.3517419324352827, -0.3152763574951773, -0.22927412698638863, 0.158885837228987, -0.18284469338596548, -0.15491221524969814, 0.19882980982462564, -0.014116916415932912, 0.0759970149690334, 0.057235968121163106, 0.011776858417232052, -0.018000912543666227, -0.2097471916539826, 0.3665112414537302, 0.011217376000584207, 0.26547232040149327, 0.020964575955778164, 0.07284784614674601, -0.08061839868710224, 0.006593044486590752, 0.04861302676708042, -0.18733740685308115, 0.001236994754982613, 0.2357428329540234, 0.07988874318787305, 0.22470463579824937, -0.4383448463639403, -0.25868246352507007, 0.10855636491860245, 0.1583831805942787, 0.11318816274961493, -0.024102698259049527, -0.31813596843382697, 0.10624695626190966, -0.09922309854949822, -0.19231441718666187, -0.06685791776996422, 0.06487513835438424, 0.06556264462795572, -0.33622420598064956, 0.17078324674542694, 0.07913563324418528, 0.07949239296802119, -0.09321130667904429, -0.02019955839633065, -0.08645675017945834, 0.09653889588110783, 0.005890590379172876, -0.03347010640549212, 0.06208726806087523, -0.09645217875310895, -0.11799711036477603, 0.31082477003603787, -0.04786000164146566, -0.2117574554520889, 0.2274351051325295, -0.1522422495702878, -0.05344557908540166, 0.19077513321588924, 0.14063816398284792, 0.09139910252198723, -0.14158712383177058, 0.022055524733112335, 0.013814738897600643, 0.14007865859072327, 0.1660565120880218, 0.021717018706517086, 0.15155102745964635, 0.1461570626208437, 0.0663099835620694, 0.11550495213010269, -0.07884432076099115, -0.1360572590781802, -0.2918319615285868, -0.09351015490254545, -0.09182893153465675, 0.0392653119286752, -0.11006572996524887, -0.15981161638217814, 0.37453353961357494, 0.06194576911789554, 0.18647185604702804, 0.0390959783994404, 0.18732454863017778, 0.15874180281222827, -0.015332620980498058, 0.05208039255751894, 0.21290193600701837, 0.13207951617682828, 0.11853022553221462, -0.24012508746114822, 0.1033081542609223, 0.08496462693742288] |
1,803.00165 | Minimizing the number of 5-cycles in graphs with given edge-density | Motivated by the work of Razborov about the minimal density of triangles in
graphs we study the minimal density of the 5-cycle $C_5$. We show that every
graph of order $n$ and size $\left( 1-\frac{1}{k}\right)\binom{n}{2}$, where
$k\ge 3$ is an integer, contains at least \[ \left( \frac{1}{10} -\frac{1}{2k}
+ \frac{1}{k^2} - \frac{1}{k^3} + \frac{2}{5 k^4} \right)n^5 +o(n^5) \] copies
of $C_5$. This bound is optimal, since a matching upper bound is given by the
balanced complete $k$-partite graph. The proof is based on the flag algebras
framework. We also provide a stability result. An SDP solver is not necessary
to verify our proofs.
| math.CO | motivated by the work of razborov about the minimal density of triangles in graphs we study the minimal density of the 5cycle c_5 we show that every graph of order n and size left 1frac1krightbinomn2 where kge 3 is an integer contains at least left frac110 frac12k frac1k2 frac1k3 frac25 k4 rightn5 on5 copies of c_5 this bound is optimal since a matching upper bound is given by the balanced complete kpartite graph the proof is based on the flag algebras framework we also provide a stability result an sdp solver is not necessary to verify our proofs | [['motivated', 'by', 'the', 'work', 'of', 'razborov', 'about', 'the', 'minimal', 'density', 'of', 'triangles', 'in', 'graphs', 'we', 'study', 'the', 'minimal', 'density', 'of', 'the', '5cycle', 'c_5', 'we', 'show', 'that', 'every', 'graph', 'of', 'order', 'n', 'and', 'size', 'left', '1frac1krightbinomn2', 'where', 'kge', '3', 'is', 'an', 'integer', 'contains', 'at', 'least', 'left', 'frac110', 'frac12k', 'frac1k2', 'frac1k3', 'frac25', 'k4', 'rightn5', 'on5', 'copies', 'of', 'c_5', 'this', 'bound', 'is', 'optimal', 'since', 'a', 'matching', 'upper', 'bound', 'is', 'given', 'by', 'the', 'balanced', 'complete', 'kpartite', 'graph', 'the', 'proof', 'is', 'based', 'on', 'the', 'flag', 'algebras', 'framework', 'we', 'also', 'provide', 'a', 'stability', 'result', 'an', 'sdp', 'solver', 'is', 'not', 'necessary', 'to', 'verify', 'our', 'proofs']] | [-0.18132589231674118, 0.08490938143774034, -0.055563329331772895, 0.03018154250457883, -0.06352592469438126, -0.14872481908992324, 0.04165387557280299, 0.31905439976406724, -0.21388222502152388, -0.32007428762551987, 0.14149100949963261, -0.3043542718416766, -0.15361077462224976, 0.12143979676459965, -0.11804478372398176, 0.0029730117169061774, 0.0640858815492768, 0.10077053128870352, -0.010699057708935518, -0.3222552055425599, 0.27756675243573753, -0.015447716974947406, 0.15622702044876, 0.16135080376345862, 0.07157323581509684, 0.016442928465671445, 0.005713182741678075, 0.016102083046969615, -0.24079902402751796, 0.08635960193718538, 0.22723872024369868, 0.16779975617598547, 0.2345850568068655, -0.3822364878335257, -0.08159245535927384, 0.17632423071190714, 0.1366954719745799, 0.06917801917971748, 0.003137436463791681, -0.2060393972935057, 0.16408784776362345, -0.1084020928626782, -0.105589158587942, -0.022633854329193893, 0.08142724251864772, -0.05217635867087857, -0.300282258976643, 0.02422760194381944, 0.15781102333413927, 0.043044513794532245, 0.01950171316826814, -0.1681080160319413, -0.03694113411352431, 0.056561875630072075, -0.07349799685425272, 0.06634132703888769, -0.0030902218582157634, -0.0909382114612718, -0.16576746977838125, 0.32511544490331096, -0.0309744772158171, -0.16277245767904738, 0.07262729016549298, -0.1326802165296517, -0.18016913904269274, 0.11945517982699369, 0.06671303413612278, 0.1918678569166284, -0.07166526893545923, 0.1731700034021694, -0.17668739695249028, 0.19908670309048734, 0.13639756238186046, -0.011189151717055786, 0.08353422261578472, 0.1647822651472923, 0.18253695323180996, 0.1644140168436264, 0.0353195197022471, 0.010410586278885603, -0.3302351371140072, -0.1429088444069126, -0.2397542732032506, 0.08967106906271319, -0.1514938811181836, -0.15555824133518495, 0.3717182203735176, 0.09757368727985426, 0.15619533288439638, 0.13275351137118904, 0.26459000575674796, 0.09435847410074386, 0.02040548228021515, 0.18369299102280484, 0.12151260155645248, 0.18132739617538296, -0.05884364076497915, -0.16811864952997943, 0.04278978548551861, 0.1673599822691789] |
1,803.00166 | Round-Robin Differential Phase-Shift Quantum Key Distribution with
Twisted Photons | Quantum key distribution (QKD) offers the possibility for two individuals to
communicate a securely encrypted message. From the time of its inception in
1984 by Bennett and Brassard, QKD has been the result of intense research. One
technical challenge is the monitoring of signal disturbance in a QKD system to
bound the information leakage towards an unwanted eavesdropper. Recently, the
round-robin differential phase-shift (RRDPS) protocol, which encodes bits of
information in a high-dimensional state space, was proposed to solve this exact
problem. Since its introduction, many realizations of the RRDPS protocol were
demonstrated using trains of coherent pulses. Here, we propose and
experimentally demonstrate an implementation of the RRDPS protocol using the
photonic orbital angular momentum degree of freedom. In particular, we show
that Alice's generation stage and Bob's detection stage can each be reduced to
a single phase element, greatly simplifying its implementation. Our scheme
offers a practical demonstration of the RRDPS protocol which will suppress the
need for monitoring signal disturbance in free-space channels.
| quant-ph physics.optics | quantum key distribution qkd offers the possibility for two individuals to communicate a securely encrypted message from the time of its inception in 1984 by bennett and brassard qkd has been the result of intense research one technical challenge is the monitoring of signal disturbance in a qkd system to bound the information leakage towards an unwanted eavesdropper recently the roundrobin differential phaseshift rrdps protocol which encodes bits of information in a highdimensional state space was proposed to solve this exact problem since its introduction many realizations of the rrdps protocol were demonstrated using trains of coherent pulses here we propose and experimentally demonstrate an implementation of the rrdps protocol using the photonic orbital angular momentum degree of freedom in particular we show that alices generation stage and bobs detection stage can each be reduced to a single phase element greatly simplifying its implementation our scheme offers a practical demonstration of the rrdps protocol which will suppress the need for monitoring signal disturbance in freespace channels | [['quantum', 'key', 'distribution', 'qkd', 'offers', 'the', 'possibility', 'for', 'two', 'individuals', 'to', 'communicate', 'a', 'securely', 'encrypted', 'message', 'from', 'the', 'time', 'of', 'its', 'inception', 'in', '1984', 'by', 'bennett', 'and', 'brassard', 'qkd', 'has', 'been', 'the', 'result', 'of', 'intense', 'research', 'one', 'technical', 'challenge', 'is', 'the', 'monitoring', 'of', 'signal', 'disturbance', 'in', 'a', 'qkd', 'system', 'to', 'bound', 'the', 'information', 'leakage', 'towards', 'an', 'unwanted', 'eavesdropper', 'recently', 'the', 'roundrobin', 'differential', 'phaseshift', 'rrdps', 'protocol', 'which', 'encodes', 'bits', 'of', 'information', 'in', 'a', 'highdimensional', 'state', 'space', 'was', 'proposed', 'to', 'solve', 'this', 'exact', 'problem', 'since', 'its', 'introduction', 'many', 'realizations', 'of', 'the', 'rrdps', 'protocol', 'were', 'demonstrated', 'using', 'trains', 'of', 'coherent', 'pulses', 'here', 'we', 'propose', 'and', 'experimentally', 'demonstrate', 'an', 'implementation', 'of', 'the', 'rrdps', 'protocol', 'using', 'the', 'photonic', 'orbital', 'angular', 'momentum', 'degree', 'of', 'freedom', 'in', 'particular', 'we', 'show', 'that', 'alices', 'generation', 'stage', 'and', 'bobs', 'detection', 'stage', 'can', 'each', 'be', 'reduced', 'to', 'a', 'single', 'phase', 'element', 'greatly', 'simplifying', 'its', 'implementation', 'our', 'scheme', 'offers', 'a', 'practical', 'demonstration', 'of', 'the', 'rrdps', 'protocol', 'which', 'will', 'suppress', 'the', 'need', 'for', 'monitoring', 'signal', 'disturbance', 'in', 'freespace', 'channels']] | [-0.22359727219824332, 0.09033627587202965, -0.1315015613449266, -0.014551475942513558, -0.03215041422812515, -0.2334624283374792, 0.09111276760989104, 0.376610233109018, -0.27379744949032475, -0.2990845628335219, 0.07836885184136567, -0.22877849575766104, -0.10586011658018701, 0.22535789045449123, -0.1381512126847857, 0.1229768389999356, 0.07440795810912113, 0.005598408019966839, -0.010861989660525179, -0.2694769643740841, 0.26562570538568164, 0.09130469608297347, 0.33011376145379956, 0.03254716730317527, 0.16567667453347829, 0.03502473263041935, -0.025700870219685406, -0.13917883000556094, -0.04778286943189184, 0.07630371563038396, 0.2955268884655537, 0.17677089880663813, 0.3153591191687857, -0.39507983969412297, -0.21339131895654712, 0.09741085456931088, 0.1450331159805983, 0.2108636039508372, -0.07680350920207889, -0.3553689756060401, 0.0442610433050263, -0.2279508621846529, -0.11631295830852924, -0.04840376051367227, -0.017071828464921338, -0.02391696068127247, -0.25633096837449865, 0.015397454401567937, 0.04415520852437265, 0.01745701163757525, 0.04842508602617392, -0.036646849786475616, 0.02088194516913808, 0.1371836157274399, -0.04673041166816221, 0.027575700566262365, 0.1436407567806267, -0.09209861163279962, -0.17629739344793272, 0.30769908664669526, -0.017365593610454845, -0.15773222955078425, 0.07870632179112297, -0.07564366887413325, -0.09460269224252103, 0.12544718378010283, 0.17060379841903517, 0.0754314780446381, -0.15706469731598935, 0.006205754157180999, -0.015350023407982775, 0.24118612921117882, 0.07311233094366021, 0.15137327618683766, 0.1488945517290653, 0.1573134857715186, 0.07482100950227209, 0.14585660942226153, -0.11865316205994642, -0.13900485363471918, -0.25103674804881576, -0.2096391059987735, -0.24455540378701435, 0.03594276158935783, -0.035895214177382666, -0.05519703234383186, 0.41933314734725796, 0.20311885745845265, 0.11711895273274642, -0.018033864962621535, 0.42207572270319405, 0.0745583388902505, 0.055006278179451286, 0.09309226385295301, 0.26510287505148133, 0.13205256373677626, 0.11605619794004653, -0.20292468323913707, 0.15076504502703536, -0.00955139808581177] |
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