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1,802.0356 | The Importance of Norm Regularization in Linear Graph Embedding:
Theoretical Analysis and Empirical Demonstration | Learning distributed representations for nodes in graphs is a crucial
primitive in network analysis with a wide spectrum of applications. Linear
graph embedding methods learn such representations by optimizing the likelihood
of both positive and negative edges while constraining the dimension of the
embedding vectors. We argue that the generalization performance of these
methods is not due to the dimensionality constraint as commonly believed, but
rather the small norm of embedding vectors. Both theoretical and empirical
evidence are provided to support this argument: (a) we prove that the
generalization error of these methods can be bounded by limiting the norm of
vectors, regardless of the embedding dimension; (b) we show that the
generalization performance of linear graph embedding methods is correlated with
the norm of embedding vectors, which is small due to the early stopping of SGD
and the vanishing gradients. We performed extensive experiments to validate our
analysis and showcased the importance of proper norm regularization in
practice.
| cs.LG | learning distributed representations for nodes in graphs is a crucial primitive in network analysis with a wide spectrum of applications linear graph embedding methods learn such representations by optimizing the likelihood of both positive and negative edges while constraining the dimension of the embedding vectors we argue that the generalization performance of these methods is not due to the dimensionality constraint as commonly believed but rather the small norm of embedding vectors both theoretical and empirical evidence are provided to support this argument a we prove that the generalization error of these methods can be bounded by limiting the norm of vectors regardless of the embedding dimension b we show that the generalization performance of linear graph embedding methods is correlated with the norm of embedding vectors which is small due to the early stopping of sgd and the vanishing gradients we performed extensive experiments to validate our analysis and showcased the importance of proper norm regularization in practice | [['learning', 'distributed', 'representations', 'for', 'nodes', 'in', 'graphs', 'is', 'a', 'crucial', 'primitive', 'in', 'network', 'analysis', 'with', 'a', 'wide', 'spectrum', 'of', 'applications', 'linear', 'graph', 'embedding', 'methods', 'learn', 'such', 'representations', 'by', 'optimizing', 'the', 'likelihood', 'of', 'both', 'positive', 'and', 'negative', 'edges', 'while', 'constraining', 'the', 'dimension', 'of', 'the', 'embedding', 'vectors', 'we', 'argue', 'that', 'the', 'generalization', 'performance', 'of', 'these', 'methods', 'is', 'not', 'due', 'to', 'the', 'dimensionality', 'constraint', 'as', 'commonly', 'believed', 'but', 'rather', 'the', 'small', 'norm', 'of', 'embedding', 'vectors', 'both', 'theoretical', 'and', 'empirical', 'evidence', 'are', 'provided', 'to', 'support', 'this', 'argument', 'a', 'we', 'prove', 'that', 'the', 'generalization', 'error', 'of', 'these', 'methods', 'can', 'be', 'bounded', 'by', 'limiting', 'the', 'norm', 'of', 'vectors', 'regardless', 'of', 'the', 'embedding', 'dimension', 'b', 'we', 'show', 'that', 'the', 'generalization', 'performance', 'of', 'linear', 'graph', 'embedding', 'methods', 'is', 'correlated', 'with', 'the', 'norm', 'of', 'embedding', 'vectors', 'which', 'is', 'small', 'due', 'to', 'the', 'early', 'stopping', 'of', 'sgd', 'and', 'the', 'vanishing', 'gradients', 'we', 'performed', 'extensive', 'experiments', 'to', 'validate', 'our', 'analysis', 'and', 'showcased', 'the', 'importance', 'of', 'proper', 'norm', 'regularization', 'in', 'practice']] | [-0.07935310040202297, 0.039805754076150605, -0.06359267757353285, 0.08413393993982049, -0.07952404591846485, -0.11323069944339306, 0.02717858530528281, 0.3846185258677546, -0.31328280546567727, -0.2838925331151523, 0.12657303190365457, -0.27975750801815363, -0.18443388822224904, 0.16215594677523998, -0.10215628918248622, 0.08382787115567322, 0.11794029645410911, 0.06446351082014423, -0.09394115333316508, -0.28531522483238364, 0.3227617345485286, 0.05286356926245509, 0.30736695500617883, 0.049672952952718584, 0.10220010843971428, -0.010958670143553484, -0.045055425517940485, 0.04837381197124977, -0.0743213612265477, 0.19054702790044015, 0.27448801092990915, 0.17006798916929783, 0.31734570860350214, -0.3702151551022657, -0.2313101235118185, 0.16491823618112822, 0.1492814622356691, 0.06889959327574913, -0.033400876894183223, -0.26748072192564887, 0.1255500873011894, -0.11215806480661128, -0.05541925562791667, -0.14903937215746949, 0.00708758472845708, 0.021525957492405677, -0.31711701191881225, 0.04265748880622006, 0.1259070215541752, 0.04407613672476786, -0.07668148101828758, -0.14162581067227717, 0.012863052543252707, 0.10153077940351136, 0.07076653449813712, 0.04561034834404833, 0.09878318094056826, -0.1291675800970117, -0.1280433944772062, 0.35366283494791706, -0.058852772374854145, -0.24144910847908765, 0.18626624644798395, -0.09328993199592304, -0.12294701183999672, 0.07482637246666511, 0.19998760720562636, 0.10958199311961543, -0.08212900252532472, 0.08553532022344382, -0.04521304619375546, 0.1476060762729074, 0.03176760176378273, 0.04107079969619477, 0.12615510836278773, 0.16417876075156643, 0.09006668327657401, 0.11923257932110848, -0.07437168339958543, -0.07358862261387443, -0.29046471847467265, -0.1412429436982709, -0.2528978426319857, 0.020259716474899946, -0.17695429977051527, -0.1626095261799835, 0.3896767215674403, 0.14947573876640988, 0.23795590084631862, 0.08442952843608463, 0.3111898759907147, 0.09409815825685952, 0.09009174541636142, 0.12654456090469077, 0.21370027698693597, 0.13831781968877083, 0.03137238498384707, -0.18211260332166562, 0.08985381534779971, 0.09520344615578227] |
1,802.03561 | Inducing Super-Approximation | Let $\Gamma_2\subseteq \Gamma_1$ be finitely generated subgroups of ${\rm
GL}_{n_0}(\mathbb{Z}[1/q_0])$. For $i=1$ or $2$, let $\mathbb{G}_i$ be the
Zariski-closure of $\Gamma_i$ in $({\rm GL}_{n_0})_{\mathbb{Q}}$,
$\mathbb{G}_i^{\circ}$ be the Zariski-connected component of $\mathbb{G}_i$,
and let $G_i$ be the closure of $\Gamma_i$ in $\prod_{p\nmid q_0}{\rm
GL}_{n_0}(\mathbb{Z}_p)$.
In this article we prove that, if $\mathbb{G}_1^{\circ}$ is the smallest
closed normal subgroup of $\mathbb{G}_1^{\circ}$ which contains
$\mathbb{G}_2^{\circ}$ and $\Gamma_2\curvearrowright G_2$ has spectral gap,
then $\Gamma_1\curvearrowright G_1$ has spectral gap.
| math.GR math.NT | let gamma_2subseteq gamma_1 be finitely generated subgroups of rm gl_n_0mathbbz1q_0 for i1 or 2 let mathbbg_i be the zariskiclosure of gamma_i in rm gl_n_0_mathbbq mathbbg_icirc be the zariskiconnected component of mathbbg_i and let g_i be the closure of gamma_i in prod_pnmid q_0rm gl_n_0mathbbz_p in this article we prove that if mathbbg_1circ is the smallest closed normal subgroup of mathbbg_1circ which contains mathbbg_2circ and gamma_2curvearrowright g_2 has spectral gap then gamma_1curvearrowright g_1 has spectral gap | [['let', 'gamma_2subseteq', 'gamma_1', 'be', 'finitely', 'generated', 'subgroups', 'of', 'rm', 'gl_n_0mathbbz1q_0', 'for', 'i1', 'or', '2', 'let', 'mathbbg_i', 'be', 'the', 'zariskiclosure', 'of', 'gamma_i', 'in', 'rm', 'gl_n_0_mathbbq', 'mathbbg_icirc', 'be', 'the', 'zariskiconnected', 'component', 'of', 'mathbbg_i', 'and', 'let', 'g_i', 'be', 'the', 'closure', 'of', 'gamma_i', 'in', 'prod_pnmid', 'q_0rm', 'gl_n_0mathbbz_p', 'in', 'this', 'article', 'we', 'prove', 'that', 'if', 'mathbbg_1circ', 'is', 'the', 'smallest', 'closed', 'normal', 'subgroup', 'of', 'mathbbg_1circ', 'which', 'contains', 'mathbbg_2circ', 'and', 'gamma_2curvearrowright', 'g_2', 'has', 'spectral', 'gap', 'then', 'gamma_1curvearrowright', 'g_1', 'has', 'spectral', 'gap']] | [-0.16063097875439009, 0.19487421323793894, -0.09503801348934865, -0.0532971519929499, -0.05730859618199895, -0.14444307558301647, -0.06685528686364829, 0.4157388224818949, -0.3278633186877784, -0.20399662403989646, 0.09780331916704585, -0.3063284374962924, -0.00010423293984416178, 0.18181663279232205, -0.07153441282620622, -0.15447451514428703, 0.03571621252823684, 0.1688533245380652, -0.04027401024112636, -0.17688592093980918, 0.37495359639495107, -0.15923135190949603, 0.1629807229264308, 0.09287315547876691, 0.02046200768011859, -0.03973892196949761, 0.059200891530362225, -0.004758842569633813, -0.1824202337426822, 0.08640717670826589, 0.3102215443248466, 0.11858329765293431, 0.24507339707559953, -0.35321956118410924, -0.13971640886265344, 0.31737832612973654, 0.24274238063243486, -0.14991073166138602, 0.03079379857044225, -0.23261677713717444, 0.2516973073736338, -0.1740128632508597, -0.1200927635023402, -0.0070889938496432065, 0.12180214397356671, -0.005139841098275225, -0.3181576920136557, 0.05336211280802549, 0.09128815437651287, 0.09873137397344335, 0.03297531079911327, -0.14670875700095953, -0.10573882571721481, 0.11173065123937519, 0.022201450742875873, 0.09802266230246336, 0.012185036798177477, -0.04956833261678832, -0.08763442061431075, 0.4196224383757276, -0.10606995273886596, -0.18305566468102447, 0.00704978843677347, -0.20784335100423482, -0.13114830180686915, 0.09972424925131312, 0.06798128161768792, 0.12314310123733545, -0.03862994419947519, 0.2556898790092002, -0.11502184117433883, 0.11680917848311996, 0.046187856139899314, -0.025673659920881865, 0.11802297308883172, 0.09940506636275578, 0.09387247253335633, 0.11603290215923877, -0.0009208789876646409, 0.15134949200802436, -0.36603158055725743, -0.1567711870549089, -0.19178800906796575, 0.19157117073250524, -0.09259049115274338, -0.15005198496756916, 0.4255604636618647, 0.024871867528911365, 0.1473500948730793, 0.05078911581037219, 0.18468494730670068, 0.1340344360382375, 0.04367032003099636, 0.155014703752694, 0.07911334226255194, 0.20771016693531963, -0.19135666717538388, -0.17905763082079967, 0.0033492705374324728, 0.09430198643703834] |
1,802.03562 | Towards a Lightweight Multi-Cloud DSL for Elastic and Transferable
Cloud-native Applications | Cloud-native applications are intentionally designed for the cloud in order
to leverage cloud platform features like horizontal scaling and elasticity -
benefits coming along with cloud platforms. In addition to classical (and very
often static) multi-tier deployment scenarios, cloud-native applications are
typically operated on much more complex but elastic infrastructures.
Furthermore, there is a trend to use elastic container platforms like
Kubernetes, Docker Swarm or Apache Mesos. However, especially multi-cloud use
cases are astonishingly complex to handle. In consequence, cloud-native
applications are prone to vendor lock-in. Very often TOSCA-based approaches are
used to tackle this aspect. But, these application topology defining approaches
are limited in supporting multi-cloud adaption of a cloud-native application at
runtime. In this paper, we analyzed several approaches to define cloud-native
applications being multi-cloud transferable at runtime. We have not found an
approach that fully satisfies all of our requirements. Therefore we introduce a
solution proposal that separates elastic platform definition from cloud
application definition. We present first considerations for a domain specific
language for application definition and demonstrate evaluation results on the
platform level showing that a cloud-native application can be transferred
between different cloud service providers like Azure and Google within minutes
and without downtime. The evaluation covers public and private cloud service
infrastructures provided by Amazon Web Services, Microsoft Azure, Google
Compute Engine and OpenStack.
| cs.SE | cloudnative applications are intentionally designed for the cloud in order to leverage cloud platform features like horizontal scaling and elasticity benefits coming along with cloud platforms in addition to classical and very often static multitier deployment scenarios cloudnative applications are typically operated on much more complex but elastic infrastructures furthermore there is a trend to use elastic container platforms like kubernetes docker swarm or apache mesos however especially multicloud use cases are astonishingly complex to handle in consequence cloudnative applications are prone to vendor lockin very often toscabased approaches are used to tackle this aspect but these application topology defining approaches are limited in supporting multicloud adaption of a cloudnative application at runtime in this paper we analyzed several approaches to define cloudnative applications being multicloud transferable at runtime we have not found an approach that fully satisfies all of our requirements therefore we introduce a solution proposal that separates elastic platform definition from cloud application definition we present first considerations for a domain specific language for application definition and demonstrate evaluation results on the platform level showing that a cloudnative application can be transferred between different cloud service providers like azure and google within minutes and without downtime the evaluation covers public and private cloud service infrastructures provided by amazon web services microsoft azure google compute engine and openstack | [['cloudnative', 'applications', 'are', 'intentionally', 'designed', 'for', 'the', 'cloud', 'in', 'order', 'to', 'leverage', 'cloud', 'platform', 'features', 'like', 'horizontal', 'scaling', 'and', 'elasticity', 'benefits', 'coming', 'along', 'with', 'cloud', 'platforms', 'in', 'addition', 'to', 'classical', 'and', 'very', 'often', 'static', 'multitier', 'deployment', 'scenarios', 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1,802.03563 | Coherent Scattering of Near-Resonant Light by a Dense, Microscopic Cloud
of Cold Two-Level Atoms: Experiment versus Theory | We measure the coherent scattering of low-intensity, near-resonant light by a
cloud of laser-cooled two-level rubidium atoms with a size comparable to the
wavelength of light. We isolate a two-level atomic structure by applying a 300G
magnetic field. We measure both the temporal and the steady-state coherent
optical response of the cloud for various detunings of the laser and for atom
numbers ranging from 5 to 100. We compare our results to a microscopic
coupled-dipole model and to a multi-mode, paraxial Maxwell-Bloch model. In the
low-intensity regime, both models are in excellent agreement, thus validating
the Maxwell-Bloch model. Comparing to the data, the models are found in very
good agreement for relatively low densities ($n/k^3\lesssim 0.1$), while
significant deviations start to occur at higher density. This disagreement
indicates that light scattering in dense, cold atomic ensembles is still not
quantitatively understood, even in pristine experimental conditions.
| physics.atom-ph cond-mat.quant-gas quant-ph | we measure the coherent scattering of lowintensity nearresonant light by a cloud of lasercooled twolevel rubidium atoms with a size comparable to the wavelength of light we isolate a twolevel atomic structure by applying a 300g magnetic field we measure both the temporal and the steadystate coherent optical response of the cloud for various detunings of the laser and for atom numbers ranging from 5 to 100 we compare our results to a microscopic coupleddipole model and to a multimode paraxial maxwellbloch model in the lowintensity regime both models are in excellent agreement thus validating the maxwellbloch model comparing to the data the models are found in very good agreement for relatively low densities nk3lesssim 01 while significant deviations start to occur at higher density this disagreement indicates that light scattering in dense cold atomic ensembles is still not quantitatively understood even in pristine experimental conditions | [['we', 'measure', 'the', 'coherent', 'scattering', 'of', 'lowintensity', 'nearresonant', 'light', 'by', 'a', 'cloud', 'of', 'lasercooled', 'twolevel', 'rubidium', 'atoms', 'with', 'a', 'size', 'comparable', 'to', 'the', 'wavelength', 'of', 'light', 'we', 'isolate', 'a', 'twolevel', 'atomic', 'structure', 'by', 'applying', 'a', '300g', 'magnetic', 'field', 'we', 'measure', 'both', 'the', 'temporal', 'and', 'the', 'steadystate', 'coherent', 'optical', 'response', 'of', 'the', 'cloud', 'for', 'various', 'detunings', 'of', 'the', 'laser', 'and', 'for', 'atom', 'numbers', 'ranging', 'from', '5', 'to', '100', 'we', 'compare', 'our', 'results', 'to', 'a', 'microscopic', 'coupleddipole', 'model', 'and', 'to', 'a', 'multimode', 'paraxial', 'maxwellbloch', 'model', 'in', 'the', 'lowintensity', 'regime', 'both', 'models', 'are', 'in', 'excellent', 'agreement', 'thus', 'validating', 'the', 'maxwellbloch', 'model', 'comparing', 'to', 'the', 'data', 'the', 'models', 'are', 'found', 'in', 'very', 'good', 'agreement', 'for', 'relatively', 'low', 'densities', 'nk3lesssim', '01', 'while', 'significant', 'deviations', 'start', 'to', 'occur', 'at', 'higher', 'density', 'this', 'disagreement', 'indicates', 'that', 'light', 'scattering', 'in', 'dense', 'cold', 'atomic', 'ensembles', 'is', 'still', 'not', 'quantitatively', 'understood', 'even', 'in', 'pristine', 'experimental', 'conditions']] | [-0.09255947277335258, 0.18215221805369547, -0.05166452603800029, 0.08642818848120755, 0.04118563411436204, -0.14255612165635, 0.07355068339966238, 0.4422097766354423, -0.2280992661763368, -0.29824797649835716, 0.016681773330341897, -0.28741115837775427, -0.05027272336602468, 0.22797263051856886, -2.0385623492043594e-06, 0.07900388701016019, 0.0625403975971557, -0.035663619486550835, -0.025631022340907104, -0.16145640433252115, 0.26581667820794574, 0.06946746189917984, 0.29909478404141704, 0.03411609892058989, 0.07269948971505952, -0.03804946404335827, 0.03237498596085814, -0.005301207965561028, -0.07711560731810911, 0.11201614735784524, 0.22847381641607578, 0.030224902333755944, 0.21362535361810747, -0.4703271288681647, -0.22688132030568245, 0.05378488263051057, 0.13428478409447867, 0.187239417544534, -0.07379685719015783, -0.27312170601578367, 0.02445651492874684, -0.13065062960898824, -0.1698276507764541, -0.0717427976911181, 0.009619480709481085, 0.051425338741632756, -0.28955201019316207, 0.05799217854208987, -0.006784599524890554, 0.07287376756811964, -0.08611694046071378, -0.0612777097744803, 0.016856787601839108, 0.05833021990376814, -0.034513175318113945, 0.037387513985118734, 0.15186910321133146, -0.16241285747784223, -0.05190708484372188, 0.4298426804100645, -0.15371603640117523, -0.10429212930812982, 0.21437087108477437, -0.20741559687102662, -0.05738027605452928, 0.18059665874481715, 0.1378810218701958, 0.08736348256211857, -0.1379358294905677, 0.02142629096152036, -0.06939938872137331, 0.2176525802833253, 0.10159104811509365, 0.08872907407941089, 0.2109056649247891, 0.18044198477191145, -0.03859746126008445, 0.12408619467971911, -0.12363667411665465, -0.11914922292344272, -0.24628228617767836, -0.0650520797642269, -0.16148127331880147, 0.02914195905353248, -0.08598269516700509, -0.10877587850173485, 0.3631346668277321, 0.19242684693428977, 0.20084781008933125, 0.026069792501371484, 0.3288594336088361, 0.11110799954722411, 0.01846671857517855, 0.013214546952653547, 0.29537232738502067, 0.1872773989360651, 0.08481125369957038, -0.2644476194014965, -0.0006184817034879636, -0.06329551784739156] |
1,802.03564 | Directional differentiability for elliptic quasi-variational
inequalities of obstacle type | The directional differentiability of the solution map of obstacle type
quasi-variational inequalities (QVIs) with respect to perturbations on the
forcing term is studied. The classical result of Mignot is then extended to the
quasi-variational case under assumptions that allow multiple solutions of the
QVI. The proof involves selection procedures for the solution set and
represents the directional derivative as the limit of a monotonic sequence of
directional derivatives associated to specific variational inequalities.
Additionally, estimates on the coincidence set and several simplifications
under higher regularity are studied. The theory is illustrated by a detailed
study of an application to thermoforming comprising of modelling, analysis and
some numerical experiments.
| math.OC math.AP | the directional differentiability of the solution map of obstacle type quasivariational inequalities qvis with respect to perturbations on the forcing term is studied the classical result of mignot is then extended to the quasivariational case under assumptions that allow multiple solutions of the qvi the proof involves selection procedures for the solution set and represents the directional derivative as the limit of a monotonic sequence of directional derivatives associated to specific variational inequalities additionally estimates on the coincidence set and several simplifications under higher regularity are studied the theory is illustrated by a detailed study of an application to thermoforming comprising of modelling analysis and some numerical experiments | [['the', 'directional', 'differentiability', 'of', 'the', 'solution', 'map', 'of', 'obstacle', 'type', 'quasivariational', 'inequalities', 'qvis', 'with', 'respect', 'to', 'perturbations', 'on', 'the', 'forcing', 'term', 'is', 'studied', 'the', 'classical', 'result', 'of', 'mignot', 'is', 'then', 'extended', 'to', 'the', 'quasivariational', 'case', 'under', 'assumptions', 'that', 'allow', 'multiple', 'solutions', 'of', 'the', 'qvi', 'the', 'proof', 'involves', 'selection', 'procedures', 'for', 'the', 'solution', 'set', 'and', 'represents', 'the', 'directional', 'derivative', 'as', 'the', 'limit', 'of', 'a', 'monotonic', 'sequence', 'of', 'directional', 'derivatives', 'associated', 'to', 'specific', 'variational', 'inequalities', 'additionally', 'estimates', 'on', 'the', 'coincidence', 'set', 'and', 'several', 'simplifications', 'under', 'higher', 'regularity', 'are', 'studied', 'the', 'theory', 'is', 'illustrated', 'by', 'a', 'detailed', 'study', 'of', 'an', 'application', 'to', 'thermoforming', 'comprising', 'of', 'modelling', 'analysis', 'and', 'some', 'numerical', 'experiments']] | [-0.13062603026628494, -0.01744559930654343, -0.06982654805195074, 0.0788283442162308, -0.10730313704671146, -0.11378154540695598, 0.033391521760549776, 0.3125327854965613, -0.28586657903218105, -0.2604305377631518, 0.1878921843980326, -0.28546423616462224, -0.14272664262800017, 0.2211844424290635, -0.06642345744446268, 0.09266339412294154, 0.04951766547398798, 0.04304300669241244, -0.08784093849891884, -0.20705971632002654, 0.3560107227536702, 0.014253924966345881, 0.24852358969363653, 0.03691601188674952, 0.1291084840097334, 0.007850493204336022, -0.0511514919823053, 0.017714690834840882, -0.17458034036323378, 0.14723326612446702, 0.19474812612309073, 0.11157931681182758, 0.3117037347439572, -0.40798894700672583, -0.2151159418259408, 0.09652549972775105, 0.0696963508891934, 0.058310840744525194, -0.03758231880552371, -0.29160861252963755, 0.08659678634700026, -0.11973836990513961, -0.19281041022024561, -0.10439314908200056, -0.021124561621903258, 0.09294953292766674, -0.3256820018150818, 0.09729084977227324, 0.08927329383773085, 0.03247616837175441, -0.1007793529141938, -0.07085421368932333, 0.004848615078159791, 0.03730839674979031, 0.09902356058646376, -0.009289219760048751, 0.07355911837385914, -0.11196598974187101, -0.10963377345214102, 0.3326155367333477, -0.07528785567634921, -0.2710593513596072, 0.20412073821856458, -0.09077884282516403, -0.14140607886207857, 0.10545319724462099, 0.12875024602252327, 0.15521817681788702, -0.1739167138133372, 0.10064340488839389, -0.049435633265143525, 0.1421326229818792, 0.1148281539681533, 0.027503324006727645, 0.08857233486155693, 0.1310908348108076, 0.15610805876274533, 0.18010171891024737, -0.04547498659742126, -0.14147283482391423, -0.3721413983464041, -0.15197543462055135, -0.16580473260832168, 0.03751363167778598, -0.13362735826220193, -0.16299046465047748, 0.3695858094980505, 0.10878840532269717, 0.13191111070086536, 0.08269395477956631, 0.231960577877221, 0.17706347233751815, 0.016698612653101994, -0.02213535299872823, 0.2095954111599359, 0.18114768640817117, 0.08389379761495114, -0.2005988434468457, 0.08880306391489352, 0.11530353732311349] |
1,802.03565 | About being the Tortoise or the Hare? - A Position Paper on Making Cloud
Applications too Fast and Furious for Attackers | Cloud applications expose - beside service endpoints - also potential or
actual vulnerabilities. And attackers have several advantages on their side.
They can select the weapons, the point of time and the point of attack. Very
often cloud application security engineering efforts focus to harden the
fortress walls but seldom assume that attacks may be successful. So, cloud
applications rely on their defensive walls but seldom attack intruders
actively. Biological systems are different. They accept that defensive "walls"
can be breached at several layers and therefore make use of an active and
adaptive defense system to attack potential intruders - an immune system. This
position paper proposes such an immune system inspired approach to ensure that
even undetected intruders can be purged out of cloud applications. This makes
it much harder for intruders to maintain a presence on victim systems.
Evaluation experiments with popular cloud service infrastructures (Amazon Web
Services, Google Compute Engine, Azure and OpenStack) showed that this could
minimize the undetected acting period of intruders down to minutes.
| cs.CR cs.DC cs.SE | cloud applications expose beside service endpoints also potential or actual vulnerabilities and attackers have several advantages on their side they can select the weapons the point of time and the point of attack very often cloud application security engineering efforts focus to harden the fortress walls but seldom assume that attacks may be successful so cloud applications rely on their defensive walls but seldom attack intruders actively biological systems are different they accept that defensive walls can be breached at several layers and therefore make use of an active and adaptive defense system to attack potential intruders an immune system this position paper proposes such an immune system inspired approach to ensure that even undetected intruders can be purged out of cloud applications this makes it much harder for intruders to maintain a presence on victim systems evaluation experiments with popular cloud service infrastructures amazon web services google compute engine azure and openstack showed that this could minimize the undetected acting period of intruders down to minutes | [['cloud', 'applications', 'expose', 'beside', 'service', 'endpoints', 'also', 'potential', 'or', 'actual', 'vulnerabilities', 'and', 'attackers', 'have', 'several', 'advantages', 'on', 'their', 'side', 'they', 'can', 'select', 'the', 'weapons', 'the', 'point', 'of', 'time', 'and', 'the', 'point', 'of', 'attack', 'very', 'often', 'cloud', 'application', 'security', 'engineering', 'efforts', 'focus', 'to', 'harden', 'the', 'fortress', 'walls', 'but', 'seldom', 'assume', 'that', 'attacks', 'may', 'be', 'successful', 'so', 'cloud', 'applications', 'rely', 'on', 'their', 'defensive', 'walls', 'but', 'seldom', 'attack', 'intruders', 'actively', 'biological', 'systems', 'are', 'different', 'they', 'accept', 'that', 'defensive', 'walls', 'can', 'be', 'breached', 'at', 'several', 'layers', 'and', 'therefore', 'make', 'use', 'of', 'an', 'active', 'and', 'adaptive', 'defense', 'system', 'to', 'attack', 'potential', 'intruders', 'an', 'immune', 'system', 'this', 'position', 'paper', 'proposes', 'such', 'an', 'immune', 'system', 'inspired', 'approach', 'to', 'ensure', 'that', 'even', 'undetected', 'intruders', 'can', 'be', 'purged', 'out', 'of', 'cloud', 'applications', 'this', 'makes', 'it', 'much', 'harder', 'for', 'intruders', 'to', 'maintain', 'a', 'presence', 'on', 'victim', 'systems', 'evaluation', 'experiments', 'with', 'popular', 'cloud', 'service', 'infrastructures', 'amazon', 'web', 'services', 'google', 'compute', 'engine', 'azure', 'and', 'openstack', 'showed', 'that', 'this', 'could', 'minimize', 'the', 'undetected', 'acting', 'period', 'of', 'intruders', 'down', 'to', 'minutes']] | [-0.1549215974972143, 0.06346909068327908, -0.0784238250312691, 0.08107131293313968, -0.1194017988456759, -0.2386754920600194, 0.118733403552463, 0.41372433198548986, -0.2679558613945446, -0.32028899374724684, 0.15796542681006345, -0.3171498217971293, -0.1584633156757601, 0.18685718865446121, -0.1406560842997623, 0.055831245219189966, 0.04371918957125574, 0.0012506288016203813, 0.05037107632612575, -0.3148775008551687, 0.2813963024433658, 0.06062866910779012, 0.31103501349553736, 0.11211232757920812, 0.013526889852465626, -0.03036396924969545, -0.009795918208330836, -0.02651890409653058, -0.040350399341003436, 0.06598481542743118, 0.31973700357244134, 0.1889917605980383, 0.3290444476987252, -0.50357834901103, -0.20762220795773817, 0.12448489805962333, 0.1856286001620357, 0.08731360527028714, -0.0365096226656631, -0.3260534547972019, 0.11186283745232713, -0.26562708122432677, -0.13397550996682958, -0.10167460329133549, 0.04109128638070277, 0.040453494351474335, -0.1781325873488025, -0.08699505197593223, 0.006064331652540825, 0.08767260891151152, -0.025474746601410702, -0.05857985770165318, -0.009482624257418389, 0.19810401927540358, 0.07267806655030229, 0.007390100099034831, 0.2981941067156365, -0.14578483545801374, -0.10541144509459595, 0.37813139040424615, 0.0494922701203861, -0.13751749408119052, 0.27501073380943364, 0.023910123994295113, -0.13973035855765292, 0.10690551205443408, 0.24370807006085168, 0.0810268002584532, -0.1762195031108435, -0.002514435097221062, 0.006424047443234992, 0.2239215121716735, 0.06620438955863658, 0.040008330483458, 0.24281548026218608, 0.13952422076606186, 0.12746225524704882, 0.12262430085039358, -0.07280422368889931, -0.09773976746444663, -0.16664951741081957, -0.14130626553753223, -0.1670297902345122, 0.01827352957710802, -0.017549562245216303, -0.16463079443759102, 0.29147325841498056, 0.2525967664599787, 0.12722096545621753, 0.014639477061475822, 0.39121064079735807, 0.0023813544184214876, 0.12724144090900077, 0.13105696420781984, 0.211369944647133, -0.06424740467967834, 0.15813696442846722, -0.1425778308617018, 0.1629508146911603, -0.025625845487090106] |
1,802.03566 | The possible members of the $5^1S_0$ meson nonet | The strong decays of the $5^1S_0$ $q\bar{q}$ states are evaluated in the
$^3P_0$ model with two types of space wave functions. Comparing the model
expectations with the experimental data for the $\pi(2360)$, $\eta(2320)$,
$X(2370)$, and $X(2500)$, we suggest that the $\pi(2360)$, $\eta(2320)$, and
$X(2500)$ can be assigned as the members of the $5^1S_0$ meson nonet, while the
$5^1S_0$ assignment for the $X(2370)$ is not favored by its width. The $5^1S_0$
kaon is predicted to have a mass of about 2418 MeV and a width of about 163 MeV
or 225 MeV.
| hep-ph | the strong decays of the 51s_0 qbarq states are evaluated in the 3p_0 model with two types of space wave functions comparing the model expectations with the experimental data for the pi2360 eta2320 x2370 and x2500 we suggest that the pi2360 eta2320 and x2500 can be assigned as the members of the 51s_0 meson nonet while the 51s_0 assignment for the x2370 is not favored by its width the 51s_0 kaon is predicted to have a mass of about 2418 mev and a width of about 163 mev or 225 mev | [['the', 'strong', 'decays', 'of', 'the', '51s_0', 'qbarq', 'states', 'are', 'evaluated', 'in', 'the', '3p_0', 'model', 'with', 'two', 'types', 'of', 'space', 'wave', 'functions', 'comparing', 'the', 'model', 'expectations', 'with', 'the', 'experimental', 'data', 'for', 'the', 'pi2360', 'eta2320', 'x2370', 'and', 'x2500', 'we', 'suggest', 'that', 'the', 'pi2360', 'eta2320', 'and', 'x2500', 'can', 'be', 'assigned', 'as', 'the', 'members', 'of', 'the', '51s_0', 'meson', 'nonet', 'while', 'the', '51s_0', 'assignment', 'for', 'the', 'x2370', 'is', 'not', 'favored', 'by', 'its', 'width', 'the', '51s_0', 'kaon', 'is', 'predicted', 'to', 'have', 'a', 'mass', 'of', 'about', '2418', 'mev', 'and', 'a', 'width', 'of', 'about', '163', 'mev', 'or', '225', 'mev']] | [-0.054239928298577496, 0.23218859712268863, -0.047135540660931154, 0.11670413539546091, -0.033575476174376215, -0.12150981279320261, 0.11231892062952804, 0.3660098098720727, -0.14702218210617646, -0.2863071444287394, 0.019316899991761685, -0.33483472812837095, 0.030420570296831848, 0.11140711847568464, 0.08931850025618679, 0.06246479664434773, 0.07367988278189402, 0.06196766374685038, -0.04192488484367142, -0.16054593587262828, 0.29452365695425636, 0.011371531115656489, 0.20405167784656952, 0.1441425240488744, 0.003121995650953875, -0.016860369305136833, 0.022459172983810807, -0.07688779147960549, -0.10183677697082356, 0.0634561645545934, 0.1685500784117854, 0.11007104778473967, 0.1831771497447253, -0.2670106837104776, -0.14267017727906114, 0.10401929608347375, 0.14941446456451263, 0.058703554284057755, 0.018935718683504993, -0.3888626898733083, 0.15537070074534629, -0.18043541603229857, -0.11688775434734279, -0.04521264510459445, 0.0040135498307749965, -0.015726128886741575, -0.30149151785089895, 0.11608201416020983, -0.058017344098914875, 0.015800365811904495, -0.11318686091356775, -0.27075576208783, -0.08089269059343954, 0.03894648701214137, 0.08442319516521575, 0.10311240035245257, 0.10265445617665903, -0.12791287945060248, -0.1525990141456256, 0.4359292362115524, -0.06032302699397119, -0.13684400939709623, 0.10280507210683956, -0.12319571351674333, -0.08129424415267167, 0.14144948450325245, 0.11381970726089614, 0.05362005011189017, -0.12984676873625328, 0.04154985604789422, -0.08802391805544135, 0.20751597721841228, 0.06226482242869025, 0.068953755034364, 0.1936918262685283, 0.16353483583249695, -0.06383248451270498, 0.05752035952303977, -0.09734297481501537, -0.06461501381558817, -0.29095049815650065, -0.1118040491638475, -0.10992396421981661, 0.032735358000853296, -0.02016413977760115, -0.08177638715499322, 0.40583190601319075, 0.06581192754543899, 0.2612582214459191, 0.03421901430139381, 0.23153309344596576, 0.10408710490446538, 0.10273517422336206, 0.07851984804977527, 0.300845689694868, 0.16384493988896773, 0.07297992476345866, -0.24558555619435363, 0.04842204578074344, -0.02395959347067924] |
1,802.03567 | Crit\`eres de qualit\'e d'un classifieur g\'en\'eraliste | This paper considers the problem of choosing a good classifier. For each
problem there exist an optimal classifier, but none are optimal, regarding the
error rate, in all cases. Because there exists a large number of classifiers, a
user would rather prefer an all-purpose classifier that is easy to adjust, in
the hope that it will do almost as good as the optimal. In this paper we
establish a list of criteria that a good generalist classifier should satisfy .
We first discuss data analytic, these criteria are presented. Six among the
most popular classifiers are selected and scored according to these criteria.
Tables allow to easily appreciate the relative values of each. In the end,
random forests turn out to be the best classifiers.
| stat.ML cs.LG | this paper considers the problem of choosing a good classifier for each problem there exist an optimal classifier but none are optimal regarding the error rate in all cases because there exists a large number of classifiers a user would rather prefer an allpurpose classifier that is easy to adjust in the hope that it will do almost as good as the optimal in this paper we establish a list of criteria that a good generalist classifier should satisfy we first discuss data analytic these criteria are presented six among the most popular classifiers are selected and scored according to these criteria tables allow to easily appreciate the relative values of each in the end random forests turn out to be the best classifiers | [['this', 'paper', 'considers', 'the', 'problem', 'of', 'choosing', 'a', 'good', 'classifier', 'for', 'each', 'problem', 'there', 'exist', 'an', 'optimal', 'classifier', 'but', 'none', 'are', 'optimal', 'regarding', 'the', 'error', 'rate', 'in', 'all', 'cases', 'because', 'there', 'exists', 'a', 'large', 'number', 'of', 'classifiers', 'a', 'user', 'would', 'rather', 'prefer', 'an', 'allpurpose', 'classifier', 'that', 'is', 'easy', 'to', 'adjust', 'in', 'the', 'hope', 'that', 'it', 'will', 'do', 'almost', 'as', 'good', 'as', 'the', 'optimal', 'in', 'this', 'paper', 'we', 'establish', 'a', 'list', 'of', 'criteria', 'that', 'a', 'good', 'generalist', 'classifier', 'should', 'satisfy', 'we', 'first', 'discuss', 'data', 'analytic', 'these', 'criteria', 'are', 'presented', 'six', 'among', 'the', 'most', 'popular', 'classifiers', 'are', 'selected', 'and', 'scored', 'according', 'to', 'these', 'criteria', 'tables', 'allow', 'to', 'easily', 'appreciate', 'the', 'relative', 'values', 'of', 'each', 'in', 'the', 'end', 'random', 'forests', 'turn', 'out', 'to', 'be', 'the', 'best', 'classifiers']] | [-0.0797119417299156, 0.024385368124996837, -0.07009567136711051, 0.11999513953079229, -0.11364291840788698, -0.21702193260388147, 0.12936294141353197, 0.4427573733452347, -0.23288251479220157, -0.2979467491048478, 0.10276601572836479, -0.2806777387515702, -0.14529220370816126, 0.21214417663201945, -0.12791021913665557, 0.0536865867119735, 0.09373303622593201, 0.09333215142232215, -0.042107683237484324, -0.3498313153151333, 0.29566273343892047, 0.08186901592829775, 0.29407014389101777, -0.016318354436436727, 0.07431883377026796, -0.018201104102415905, -0.03550307514266141, 0.03581587112214566, -0.1267244596617656, 0.11595448707943902, 0.3527659432842366, 0.1873718991372434, 0.33856256017761843, -0.3470924601837572, -0.11457463537695847, 0.1777932655813563, 0.1435462359682612, 0.13017395285198524, 0.0067026099836033195, -0.1953205082475418, 0.13834374199711508, -0.1489359644737335, -0.06964809610508382, -0.10772776124518245, -0.020043509009654722, 0.054666226938171615, -0.29540624214907085, -0.0020999033154258805, 0.05123647880927526, 0.019620084616866325, -0.07041315686318182, -0.1491410323013101, 0.0046536939203619, 0.17764667599957676, 0.07250195051986544, 0.03339515523064972, 0.07639394488726411, -0.11277121610774268, -0.11072251495469602, 0.38193028689091724, -0.006400633593946095, -0.21663484496868127, 0.20289417989589575, -0.06914789260143504, -0.15949604567515874, 0.09577005948015158, 0.1751800323883447, 0.09061172495656196, -0.18770956169934042, -0.028641117939954594, -0.09978519806698445, 0.17623558079433296, 0.07528876034437769, -0.0019860268502886738, 0.2096726763467755, 0.13893267653897526, 0.09510991435156264, 0.12637496624618857, -0.02599865194380043, -0.03541040546335881, -0.3223689905548048, -0.13479859878369158, -0.1437161169381016, -0.01978086232538185, -0.10891736686338141, -0.16981998732643983, 0.3537763193054425, 0.23026869048537205, 0.22488534183902364, 0.0750817618757156, 0.2500209117096083, 0.0731623595065978, 0.06564251708084246, 0.10984666295905388, 0.2571741312426785, 0.050289322734780366, 0.050499038928518855, -0.11066676477193757, 0.1213790503435678, 0.02824624998104428] |
1,802.03568 | Tips, guidelines and tools for managing multi-label datasets: the
mldr.datasets R package and the Cometa data repository | New proposals in the field of multi-label learning algorithms have been
growing in number steadily over the last few years. The experimentation
associated with each of them always goes through the same phases: selection of
datasets, partitioning, training, analysis of results and, finally, comparison
with existing methods. This last step is often hampered since it involves using
exactly the same datasets, partitioned in the same way and using the same
validation strategy. In this paper we present a set of tools whose objective is
to facilitate the management of multi-label datasets, aiming to standardize the
experimentation procedure. The two main tools are an R package, mldr.datasets,
and a web repository with datasets, Cometa. Together, these tools will simplify
the collection of datasets, their partitioning, documentation and export to
multiple formats, among other functions. Some tips, recommendations and
guidelines for a good experimental analysis of multi-label methods are also
presented.
| cs.LG | new proposals in the field of multilabel learning algorithms have been growing in number steadily over the last few years the experimentation associated with each of them always goes through the same phases selection of datasets partitioning training analysis of results and finally comparison with existing methods this last step is often hampered since it involves using exactly the same datasets partitioned in the same way and using the same validation strategy in this paper we present a set of tools whose objective is to facilitate the management of multilabel datasets aiming to standardize the experimentation procedure the two main tools are an r package mldrdatasets and a web repository with datasets cometa together these tools will simplify the collection of datasets their partitioning documentation and export to multiple formats among other functions some tips recommendations and guidelines for a good experimental analysis of multilabel methods are also presented | [['new', 'proposals', 'in', 'the', 'field', 'of', 'multilabel', 'learning', 'algorithms', 'have', 'been', 'growing', 'in', 'number', 'steadily', 'over', 'the', 'last', 'few', 'years', 'the', 'experimentation', 'associated', 'with', 'each', 'of', 'them', 'always', 'goes', 'through', 'the', 'same', 'phases', 'selection', 'of', 'datasets', 'partitioning', 'training', 'analysis', 'of', 'results', 'and', 'finally', 'comparison', 'with', 'existing', 'methods', 'this', 'last', 'step', 'is', 'often', 'hampered', 'since', 'it', 'involves', 'using', 'exactly', 'the', 'same', 'datasets', 'partitioned', 'in', 'the', 'same', 'way', 'and', 'using', 'the', 'same', 'validation', 'strategy', 'in', 'this', 'paper', 'we', 'present', 'a', 'set', 'of', 'tools', 'whose', 'objective', 'is', 'to', 'facilitate', 'the', 'management', 'of', 'multilabel', 'datasets', 'aiming', 'to', 'standardize', 'the', 'experimentation', 'procedure', 'the', 'two', 'main', 'tools', 'are', 'an', 'r', 'package', 'mldrdatasets', 'and', 'a', 'web', 'repository', 'with', 'datasets', 'cometa', 'together', 'these', 'tools', 'will', 'simplify', 'the', 'collection', 'of', 'datasets', 'their', 'partitioning', 'documentation', 'and', 'export', 'to', 'multiple', 'formats', 'among', 'other', 'functions', 'some', 'tips', 'recommendations', 'and', 'guidelines', 'for', 'a', 'good', 'experimental', 'analysis', 'of', 'multilabel', 'methods', 'are', 'also', 'presented']] | [-0.05566493071101726, 0.0032236666909021446, -0.08058281437823309, 0.031459905162808444, -0.09252750783284404, -0.15131660644915335, 0.04483722258346523, 0.4280062183738943, -0.25851633597393425, -0.3468169864571216, 0.13214487282457907, -0.297486338282929, -0.11556984594909056, 0.2519431434647769, -0.0756839534765122, 0.08662819270310658, 0.1621495552794995, 0.015804067811592905, -0.06384087053323988, -0.3513137037187916, 0.2968566052007432, 0.05817607454448736, 0.3391021030011023, 0.0356467355606558, 0.05147185906542934, -0.03040572287012715, -0.12304253698022542, 0.023824472807119697, -0.09935676938771797, 0.17588976355781144, 0.3378297051218446, 0.24276335688694348, 0.35435397052491197, -0.40666251461699504, -0.16890111903907085, 0.08303126676616315, 0.16834169620533987, 0.10468980594394416, -0.0437526907272903, -0.28316824020482095, 0.06565026450366872, -0.17786651463616088, -0.03619070002353009, -0.1145319508865387, -0.0035510004813257023, 0.04842128354713929, -0.24591751572783707, -0.02289938849700317, 0.0172525547463827, 0.09329132115639442, -0.04379879783040711, -0.16029006152312417, 0.010893525880425662, 0.18441777518906055, 0.10463794038890778, 0.028237220069368175, 0.08341014808951402, -0.12450925215641606, -0.16531509576606102, 0.35675589538173, -0.007230090093402965, -0.17819720409734516, 0.24410403578789258, -0.03805002533891286, -0.18093055951073259, 0.10776930965711268, 0.18335122736107531, 0.11162621789884304, -0.19146195470298433, 0.04679269089678708, -0.021330625636084954, 0.15692138636554434, 0.046285704586046694, -0.029858429707130606, 0.17518419714416472, 0.2459083072917194, 0.026413337050360582, 0.13367287532250605, -0.0646824359972597, -0.06942868771840863, -0.2740858081941252, -0.14435769140492288, -0.16222936460165446, -0.05983803689726913, -0.10843248967963134, -0.16240588139083717, 0.3987757527985338, 0.20138661305778058, 0.17290089855312693, 0.051509431824658294, 0.3547193942984351, 0.003587483453067799, 0.1283459250837387, 0.07731493058151939, 0.15317977828921617, 0.045085345543794286, 0.1306379153512, -0.13327712752796125, 0.051863497243497245, 0.017312653498964853] |
1,802.03569 | Persistence Fisher Kernel: A Riemannian Manifold Kernel for Persistence
Diagrams | Algebraic topology methods have recently played an important role for
statistical analysis with complicated geometric structured data such as shapes,
linked twist maps, and material data. Among them, \textit{persistent homology}
is a well-known tool to extract robust topological features, and outputs as
\textit{persistence diagrams} (PDs). However, PDs are point multi-sets which
can not be used in machine learning algorithms for vector data. To deal with
it, an emerged approach is to use kernel methods, and an appropriate geometry
for PDs is an important factor to measure the similarity of PDs. A popular
geometry for PDs is the \textit{Wasserstein metric}. However, Wasserstein
distance is not \textit{negative definite}. Thus, it is limited to build
positive definite kernels upon the Wasserstein distance \textit{without
approximation}. In this work, we rely upon the alternative \textit{Fisher
information geometry} to propose a positive definite kernel for PDs
\textit{without approximation}, namely the Persistence Fisher (PF) kernel.
Then, we analyze eigensystem of the integral operator induced by the proposed
kernel for kernel machines. Based on that, we derive generalization error
bounds via covering numbers and Rademacher averages for kernel machines with
the PF kernel. Additionally, we show some nice properties such as stability and
infinite divisibility for the proposed kernel. Furthermore, we also propose a
linear time complexity over the number of points in PDs for an approximation of
our proposed kernel with a bounded error. Throughout experiments with many
different tasks on various benchmark datasets, we illustrate that the PF kernel
compares favorably with other baseline kernels for PDs.
| stat.ML cs.LG math.AT | algebraic topology methods have recently played an important role for statistical analysis with complicated geometric structured data such as shapes linked twist maps and material data among them textitpersistent homology is a wellknown tool to extract robust topological features and outputs as textitpersistence diagrams pds however pds are point multisets which can not be used in machine learning algorithms for vector data to deal with it an emerged approach is to use kernel methods and an appropriate geometry for pds is an important factor to measure the similarity of pds a popular geometry for pds is the textitwasserstein metric however wasserstein distance is not textitnegative definite thus it is limited to build positive definite kernels upon the wasserstein distance textitwithout approximation in this work we rely upon the alternative textitfisher information geometry to propose a positive definite kernel for pds textitwithout approximation namely the persistence fisher pf kernel then we analyze eigensystem of the integral operator induced by the proposed kernel for kernel machines based on that we derive generalization error bounds via covering numbers and rademacher averages for kernel machines with the pf kernel additionally we show some nice properties such as stability and infinite divisibility for the proposed kernel furthermore we also propose a linear time complexity over the number of points in pds for an approximation of our proposed kernel with a bounded error throughout experiments with many different tasks on various benchmark datasets we illustrate that the pf kernel compares favorably with other baseline kernels for pds | [['algebraic', 'topology', 'methods', 'have', 'recently', 'played', 'an', 'important', 'role', 'for', 'statistical', 'analysis', 'with', 'complicated', 'geometric', 'structured', 'data', 'such', 'as', 'shapes', 'linked', 'twist', 'maps', 'and', 'material', 'data', 'among', 'them', 'textitpersistent', 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1,802.0357 | The recalibration of the UVES-POP stellar spectral library | We have re-reduced all spectra from the UVES-POP stellar spectral library
using the version 5.5.7 of the UVES pipeline and an algorithm we designed,
which allows us to remove ripples in regions where echelle orders are stitched.
These ripples are caused by the offset of a flat field with respect to a
science frame and under- or oversubtraction of scattered light. We have also
developed an approach to merge 6 UVES spectral chunks divided by gaps in the
spectral coverage by using synthetic stellar atmospheres to predict the flux
difference between the segments. At the end, we improved the flux calibration
quality to 2% or better for 85% of 430 spectra in the library.
| astro-ph.IM | we have rereduced all spectra from the uvespop stellar spectral library using the version 557 of the uves pipeline and an algorithm we designed which allows us to remove ripples in regions where echelle orders are stitched these ripples are caused by the offset of a flat field with respect to a science frame and under or oversubtraction of scattered light we have also developed an approach to merge 6 uves spectral chunks divided by gaps in the spectral coverage by using synthetic stellar atmospheres to predict the flux difference between the segments at the end we improved the flux calibration quality to 2 or better for 85 of 430 spectra in the library | [['we', 'have', 'rereduced', 'all', 'spectra', 'from', 'the', 'uvespop', 'stellar', 'spectral', 'library', 'using', 'the', 'version', '557', 'of', 'the', 'uves', 'pipeline', 'and', 'an', 'algorithm', 'we', 'designed', 'which', 'allows', 'us', 'to', 'remove', 'ripples', 'in', 'regions', 'where', 'echelle', 'orders', 'are', 'stitched', 'these', 'ripples', 'are', 'caused', 'by', 'the', 'offset', 'of', 'a', 'flat', 'field', 'with', 'respect', 'to', 'a', 'science', 'frame', 'and', 'under', 'or', 'oversubtraction', 'of', 'scattered', 'light', 'we', 'have', 'also', 'developed', 'an', 'approach', 'to', 'merge', '6', 'uves', 'spectral', 'chunks', 'divided', 'by', 'gaps', 'in', 'the', 'spectral', 'coverage', 'by', 'using', 'synthetic', 'stellar', 'atmospheres', 'to', 'predict', 'the', 'flux', 'difference', 'between', 'the', 'segments', 'at', 'the', 'end', 'we', 'improved', 'the', 'flux', 'calibration', 'quality', 'to', '2', 'or', 'better', 'for', '85', 'of', '430', 'spectra', 'in', 'the', 'library']] | [-0.027342888916711928, 0.07955263543433894, -0.09894927478908039, 0.045337266318056846, -0.05030755060234744, -0.08376611566595864, 0.043677050783707386, 0.455586742160417, -0.21014118160676667, -0.4191194194825489, 0.07394024484337454, -0.2963421271580094, -0.03278851692499429, 0.20241457117280284, -0.08954459809485757, 0.022551832446959196, 0.06903198285420474, -0.06498111028779756, -0.04723820432595778, -0.2504077244615346, 0.3121437357285207, 0.09249828945387874, 0.23926302923600337, -0.015914141023306894, 0.036897801467331875, -0.04980656897583813, -0.09659374314115236, 0.013701985737210825, -0.13672084527155548, 0.13024304093989103, 0.21169838838262908, 0.11141500088157491, 0.209006049105052, -0.3869601894137368, -0.16441864444743515, 0.04502200830744155, 0.1683970480961235, 0.0584721601713171, -0.012048862155592232, -0.2651429392658828, 0.09716059209313244, -0.1402417356720227, -0.10321432649537823, -0.01122198648865155, -0.02354896481438916, 0.031901144315366094, -0.24987122215730906, 0.022789935338088277, -0.023221949346183816, 0.10730444716714453, -0.10880832434601248, -0.11973595392191783, -0.05044210736492747, 0.1456594045484324, -0.016121857003829638, 0.014981870408774469, 0.07454398872428819, -0.06918901165032335, -0.04164678220261346, 0.3803203608513924, -0.14552426039192237, -0.09646135710972246, 0.16956200119385725, -0.14401765020662233, -0.11215384710955907, 0.20457024261224688, 0.18593000183943986, 0.09704955829013336, -0.1556165965617095, 0.011366429361882374, 0.03577326104175626, 0.2161907837481091, 0.12596078838728236, 0.02511260191207392, 0.24429146498605087, 0.09481141923338567, 0.005427473547905193, 0.12689114782730476, -0.22544289246332228, 0.004076451426698712, -0.22317001322415053, -0.09674886168846697, -0.13051732859406784, 0.0008951146890850444, -0.07955809086617369, -0.14388744650549548, 0.4309990462242511, 0.1480993214328855, 0.2167716931159559, 0.04436675665119924, 0.3109635807769863, 0.07566117347430504, 0.11460645672489415, 0.1172560974547084, 0.24395967055553278, 0.12104426777774566, 0.11705124663115528, -0.1934195219584038, -0.03413277919191802, 0.027689768238352577] |
1,802.03571 | Magnetic second-order topological insulators and semimetals | We propose magnetic second-order topological insulators (SOTIs). First, we
study a three-dimensional model. It is pointed out that the previously proposed
topological hinge insulator has actually surface states along the [001]
direction in addition to hinge states. We gap out these surface states by
introducing magnetization, obtaining a SOTI only with hinge states. The bulk
topological number is the $Z_2$ index protected by the combined symmetry of the
four-fold rotation and the inversion symmetry. We next study two dimensional
magnetic SOTIs, where the corner states are robust also in the presence of the
magnetization. Finally, we construct a magnetic second-order topological
semimetals by layering the two-dimensional magnetic SOTIs, where hinge-arc
states are robust also in the presence of the magnetization.
| cond-mat.mes-hall | we propose magnetic secondorder topological insulators sotis first we study a threedimensional model it is pointed out that the previously proposed topological hinge insulator has actually surface states along the 001 direction in addition to hinge states we gap out these surface states by introducing magnetization obtaining a soti only with hinge states the bulk topological number is the z_2 index protected by the combined symmetry of the fourfold rotation and the inversion symmetry we next study two dimensional magnetic sotis where the corner states are robust also in the presence of the magnetization finally we construct a magnetic secondorder topological semimetals by layering the twodimensional magnetic sotis where hingearc states are robust also in the presence of the magnetization | [['we', 'propose', 'magnetic', 'secondorder', 'topological', 'insulators', 'sotis', 'first', 'we', 'study', 'a', 'threedimensional', 'model', 'it', 'is', 'pointed', 'out', 'that', 'the', 'previously', 'proposed', 'topological', 'hinge', 'insulator', 'has', 'actually', 'surface', 'states', 'along', 'the', '001', 'direction', 'in', 'addition', 'to', 'hinge', 'states', 'we', 'gap', 'out', 'these', 'surface', 'states', 'by', 'introducing', 'magnetization', 'obtaining', 'a', 'soti', 'only', 'with', 'hinge', 'states', 'the', 'bulk', 'topological', 'number', 'is', 'the', 'z_2', 'index', 'protected', 'by', 'the', 'combined', 'symmetry', 'of', 'the', 'fourfold', 'rotation', 'and', 'the', 'inversion', 'symmetry', 'we', 'next', 'study', 'two', 'dimensional', 'magnetic', 'sotis', 'where', 'the', 'corner', 'states', 'are', 'robust', 'also', 'in', 'the', 'presence', 'of', 'the', 'magnetization', 'finally', 'we', 'construct', 'a', 'magnetic', 'secondorder', 'topological', 'semimetals', 'by', 'layering', 'the', 'twodimensional', 'magnetic', 'sotis', 'where', 'hingearc', 'states', 'are', 'robust', 'also', 'in', 'the', 'presence', 'of', 'the', 'magnetization']] | [-0.22204283102094627, 0.2204309456107136, -0.03793076224656416, 0.029684205788007678, -0.062210051749809446, -0.15177742562045446, 0.06834444838787328, 0.37632948726791293, -0.2688418712365214, -0.28347376751236053, 0.07042332435468947, -0.2739460603060091, -0.18521402487648697, 0.13365698561361922, -0.0019370747765353998, 0.061425176728908126, -0.0653293839021891, -0.03520689266068595, -0.1364891712510755, -0.21383730636737427, 0.3428036503165084, -0.05554623787580919, 0.3179456581588553, 0.05962423602619968, -0.0045734171935615416, -0.03107988883295104, 0.11580817486715167, 0.062056692379738106, -0.147555990483401, 0.06659441676182563, 0.1844744384132263, -0.0856575246571618, 0.1777633283357723, -0.45151264771443456, -0.21649133558815517, 0.03482036034287024, 0.08102569266558945, 0.17024811678135596, -0.09792808542701126, -0.3347034318364781, 0.09068945840689815, -0.12284514332824081, -0.12056489182174393, -0.15980124733011639, -0.044965445797718245, -0.08141941383105367, -0.19356001449060165, 0.08995653179370477, 0.07779681542496972, 0.09794071812902679, -0.08617794861970078, -0.060361016641504, -0.1807740983756713, 0.04781401730259927, 0.08329444217100396, 0.05349521905363796, 0.06594664037536767, -0.12869416391008637, -0.15987781066244014, 0.3374589361062571, -0.02839177069518355, -0.18057695790311368, 0.15385215915348224, -0.14939016117929568, -0.12544841627285136, 0.12827165981269434, 0.11804598464998377, 0.13360896401287436, -0.0622887153554113, 0.0760572351815625, -0.08494547720090682, 0.14134298486267255, -0.004479989534666559, 0.05256081425326587, 0.2723941568548384, 0.12588571711685978, 0.11039371487570579, 0.20883883117940746, -0.18973889545353614, -0.060930464694573847, -0.29526472727165504, -0.22054467772861489, -0.24344049843748203, 0.05282018688984779, -0.026541537159497208, -0.18580782191217446, 0.47506123042407156, 0.11345436666385826, 0.17988157985798484, -0.03579148354728808, 0.255926623944381, 0.12422773589286171, 0.05824139640674371, 0.09644310196720753, 0.2597225539918457, 0.16812588628811934, 0.03160247156599991, -0.25699776071006386, 0.03601847126289513, 0.08351781548728712] |
1,802.03572 | Junk News on Military Affairs and National Security: Social Media
Disinformation Campaigns Against US Military Personnel and Veterans | Social media provides political news and information for both active duty
military personnel and veterans. We analyze the subgroups of Twitter and
Facebook users who spend time consuming junk news from websites that target US
military personnel and veterans with conspiracy theories, misinformation, and
other forms of junk news about military affairs and national security issues.
(1) Over Twitter we find that there are significant and persistent interactions
between current and former military personnel and a broad network of extremist,
Russia-focused, and international conspiracy subgroups. (2) Over Facebook, we
find significant and persistent interactions between public pages for military
and veterans and subgroups dedicated to political conspiracy, and both sides of
the political spectrum. (3) Over Facebook, the users who are most interested in
conspiracy theories and the political right seem to be distributing the most
junk news, whereas users who are either in the military or are veterans are
among the most sophisticated news consumers, and share very little junk news
through the network.
| cs.SI | social media provides political news and information for both active duty military personnel and veterans we analyze the subgroups of twitter and facebook users who spend time consuming junk news from websites that target us military personnel and veterans with conspiracy theories misinformation and other forms of junk news about military affairs and national security issues 1 over twitter we find that there are significant and persistent interactions between current and former military personnel and a broad network of extremist russiafocused and international conspiracy subgroups 2 over facebook we find significant and persistent interactions between public pages for military and veterans and subgroups dedicated to political conspiracy and both sides of the political spectrum 3 over facebook the users who are most interested in conspiracy theories and the political right seem to be distributing the most junk news whereas users who are either in the military or are veterans are among the most sophisticated news consumers and share very little junk news through the network | [['social', 'media', 'provides', 'political', 'news', 'and', 'information', 'for', 'both', 'active', 'duty', 'military', 'personnel', 'and', 'veterans', 'we', 'analyze', 'the', 'subgroups', 'of', 'twitter', 'and', 'facebook', 'users', 'who', 'spend', 'time', 'consuming', 'junk', 'news', 'from', 'websites', 'that', 'target', 'us', 'military', 'personnel', 'and', 'veterans', 'with', 'conspiracy', 'theories', 'misinformation', 'and', 'other', 'forms', 'of', 'junk', 'news', 'about', 'military', 'affairs', 'and', 'national', 'security', 'issues', '1', 'over', 'twitter', 'we', 'find', 'that', 'there', 'are', 'significant', 'and', 'persistent', 'interactions', 'between', 'current', 'and', 'former', 'military', 'personnel', 'and', 'a', 'broad', 'network', 'of', 'extremist', 'russiafocused', 'and', 'international', 'conspiracy', 'subgroups', '2', 'over', 'facebook', 'we', 'find', 'significant', 'and', 'persistent', 'interactions', 'between', 'public', 'pages', 'for', 'military', 'and', 'veterans', 'and', 'subgroups', 'dedicated', 'to', 'political', 'conspiracy', 'and', 'both', 'sides', 'of', 'the', 'political', 'spectrum', '3', 'over', 'facebook', 'the', 'users', 'who', 'are', 'most', 'interested', 'in', 'conspiracy', 'theories', 'and', 'the', 'political', 'right', 'seem', 'to', 'be', 'distributing', 'the', 'most', 'junk', 'news', 'whereas', 'users', 'who', 'are', 'either', 'in', 'the', 'military', 'or', 'are', 'veterans', 'are', 'among', 'the', 'most', 'sophisticated', 'news', 'consumers', 'and', 'share', 'very', 'little', 'junk', 'news', 'through', 'the', 'network']] | [-0.12910228328944026, 0.0962996335668921, -0.0020966461452189833, 0.14192965060540633, -0.20085990392580266, -0.19618481786342384, 0.11534973707549818, 0.4245545899086609, -0.17147937125956536, -0.3533882803862869, 0.10631084345741107, -0.453073356005295, -0.14936004754116122, 0.19079628932749762, -0.12036221511321278, -0.09232002991981372, 0.011228253544597864, 0.060143764641862815, 0.08359096690915843, -0.3508375458612402, 0.29972300497763904, 0.04736138180909088, 0.3509872907193423, 0.10915217183746125, 0.002340988883544214, 0.030748219286068912, -0.15574815549615134, -0.06368881512984134, -0.030614328778383645, 0.11261999691132366, 0.44765602654164155, 0.25701914552213034, 0.40580369636598157, -0.44824134678829736, -0.1547132552737688, 0.13764918813183435, 0.0886745016458274, 0.042355855557749546, -0.0054647686874221375, -0.3955118015561843, 0.05460482272007162, -0.2963047749788796, -0.030004068766152713, -0.06418146224260875, 0.0843086264028055, 0.06684807257545067, -0.1689943959141468, 0.056815978020384184, -0.044494939218388825, 0.1750083474908024, -0.005323150863859621, -0.061292753645741355, -0.06276276271233744, 0.30265006897601915, 0.18638200810315406, -0.041698846662922465, 0.2055758277455447, -0.22597988752687986, -0.16182452001858774, 0.42345564056006146, 0.052288236475560995, -0.08268084714370894, 0.19127379632952465, -0.084118048859215, -0.1301389998634255, 0.028703646816699426, 0.2623294283360493, 0.07843271790572055, -0.13541807810685075, -0.04861178111977785, -0.03418807354301396, 0.189743696968923, 0.15279779505132265, -0.009079322764059393, 0.19670620159155167, 0.07903475246233184, 0.10677066073531466, -0.005171228189194552, 0.008536895027202441, -0.10734953735273604, -0.1689613634243426, -0.11561423214010486, -0.053231669195200795, 0.08025522269289047, -0.09816504409962629, -0.09760909133715691, 0.37222029939975315, 0.1365483514396701, 0.032080841390557994, -0.013085174482179487, 0.2541723753745872, -0.12868756061510706, 0.06808605711930574, 0.1597355669705061, 0.1429090490564704, -0.07811584407180618, 0.26054473154079283, -0.07884040141936743, 0.143589904685164, -0.06242791154399151] |
1,802.03573 | Social Media, News and Political Information during the US Election: Was
Polarizing Content Concentrated in Swing States? | US voters shared large volumes of polarizing political news and information
in the form of links to content from Russian, WikiLeaks and junk news sources.
Was this low quality political information distributed evenly around the
country, or concentrated in swing states and particular parts of the country?
In this data memo we apply a tested dictionary of sources about political news
and information being shared over Twitter over a ten day period around the 2016
Presidential Election. Using self-reported location information, we place a
third of users by state and create a simple index for the distribution of
polarizing content around the country. We find that (1) nationally, Twitter
users got more misinformation, polarizing and conspiratorial content than
professionally produced news. (2) Users in some states, however, shared more
polarizing political news and information than users in other states. (3)
Average levels of misinformation were higher in swing states than in
uncontested states, even when weighted for the relative size of the user
population in each state. We conclude with some observations about the impact
of strategically disseminated polarizing information on public life.
| cs.SI | us voters shared large volumes of polarizing political news and information in the form of links to content from russian wikileaks and junk news sources was this low quality political information distributed evenly around the country or concentrated in swing states and particular parts of the country in this data memo we apply a tested dictionary of sources about political news and information being shared over twitter over a ten day period around the 2016 presidential election using selfreported location information we place a third of users by state and create a simple index for the distribution of polarizing content around the country we find that 1 nationally twitter users got more misinformation polarizing and conspiratorial content than professionally produced news 2 users in some states however shared more polarizing political news and information than users in other states 3 average levels of misinformation were higher in swing states than in uncontested states even when weighted for the relative size of the user population in each state we conclude with some observations about the impact of strategically disseminated polarizing information on public life | [['us', 'voters', 'shared', 'large', 'volumes', 'of', 'polarizing', 'political', 'news', 'and', 'information', 'in', 'the', 'form', 'of', 'links', 'to', 'content', 'from', 'russian', 'wikileaks', 'and', 'junk', 'news', 'sources', 'was', 'this', 'low', 'quality', 'political', 'information', 'distributed', 'evenly', 'around', 'the', 'country', 'or', 'concentrated', 'in', 'swing', 'states', 'and', 'particular', 'parts', 'of', 'the', 'country', 'in', 'this', 'data', 'memo', 'we', 'apply', 'a', 'tested', 'dictionary', 'of', 'sources', 'about', 'political', 'news', 'and', 'information', 'being', 'shared', 'over', 'twitter', 'over', 'a', 'ten', 'day', 'period', 'around', 'the', '2016', 'presidential', 'election', 'using', 'selfreported', 'location', 'information', 'we', 'place', 'a', 'third', 'of', 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1,802.03574 | Radial Distributions of Power and Isotopic Concentrations in Candidate
Accident Tolerant Fuel U3Si2 and UO2/U3Si2 Fuel Pins with FeCrAl Cladding | Monte Carlo simulations show similarity on radial distributions of power and
isotopic concentrations at any effective full power depletion time among five
kinds of fuel-cladding combinations with the same cycle length, including the
normal UO2-zircaloy combination, the candidate Accident Tolerant Fuel (ATF)
UO2/U3Si2-FeCrAl combination, and three kinds of candidate ATF U3Si2-FeCrAl
combinations. An analytical formula f(x,s) including the fuel exposure (s) and
the relative radial (x) is proposed to describe the radial properties for all
five kinds of fuel-cladding combinations. f(x,s) has the form of the second
order polynomial term of s with the exponential type of coefficients depending
on x. It is shown that the suggested function f(x,s) gives a nice description
on the simulation data with rather small deviations and can immediately provide
radial distribution of power, burnup, and isotopic concentrations of 235U,
238U, 239Pu, and 241Pu at any fuel exposure and relative radius. It is useful
to discuss the fuel temperature through the present analytical formula. The
realistic radial power distribution gives flatter radial temperature
distribution compared with the uniform power distribution. Because of the
different thermal conductivities of fuels and claddings and the different
thicknesses of claddings, the present discussed five kinds of fuel-cladding
combinations have different radial temperature distributions, although their
radial power distributions are quite similar. The present work provides an
analytical formula to describe the radial properties of the ATF which is
expected to be helpful for further neutronic and multi-physics coupling
studies.
| physics.app-ph nucl-th | monte carlo simulations show similarity on radial distributions of power and isotopic concentrations at any effective full power depletion time among five kinds of fuelcladding combinations with the same cycle length including the normal uo2zircaloy combination the candidate accident tolerant fuel atf uo2u3si2fecral combination and three kinds of candidate atf u3si2fecral combinations an analytical formula fxs including the fuel exposure s and the relative radial x is proposed to describe the radial properties for all five kinds of fuelcladding combinations fxs has the form of the second order polynomial term of s with the exponential type of coefficients depending on x it is shown that the suggested function fxs gives a nice description on the simulation data with rather small deviations and can immediately provide radial distribution of power burnup and isotopic concentrations of 235u 238u 239pu and 241pu at any fuel exposure and relative radius it is useful to discuss the fuel temperature through the present analytical formula the realistic radial power distribution gives flatter radial temperature distribution compared with the uniform power distribution because of the different thermal conductivities of fuels and claddings and the different thicknesses of claddings the present discussed five kinds of fuelcladding combinations have different radial temperature distributions although their radial power distributions are quite similar the present work provides an analytical formula to describe the radial properties of the atf which is expected to be helpful for further neutronic and multiphysics coupling studies | [['monte', 'carlo', 'simulations', 'show', 'similarity', 'on', 'radial', 'distributions', 'of', 'power', 'and', 'isotopic', 'concentrations', 'at', 'any', 'effective', 'full', 'power', 'depletion', 'time', 'among', 'five', 'kinds', 'of', 'fuelcladding', 'combinations', 'with', 'the', 'same', 'cycle', 'length', 'including', 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1,802.03575 | On the computation of fusion over the affine Temperley-Lieb algebra | Fusion product originates in the algebraisation of the operator product
expansion in conformal field theory. Read and Saleur (2007) introduced an
analogue of fusion for modules over associative algebras, for example those
appearing in the description of 2d lattice models. The article extends their
definition for modules over the affine Temperley-Lieb algebra $\atl n$.
Since the regular Temperley-Lieb algebra $\tl n$ is a subalgebra of the
affine $\atl n$, there is a natural pair of adjoint induction-restriction
functors $(\Indar{}, \Resar{})$. The existence of an algebra morphism
$\phi:\atl n\to\tl n$ provides a second pair of adjoint functors
$(\Indphi{},\Resphi{})$. Two fusion products between $\atl{}$-modules are
proposed and studied. They are expressed in terms of these four functors. The
action of these functors is computed on the standard, cell and irreducible
$\atl n$-modules. As a byproduct, the Peirce decomposition of $\atl
n(q+q^{-1})$, when $q$ is not a root of unity, is given as direct sum of the
induction $\Indar{\TheS{n,k}}$ of standard $\tl n$-modules to $\atl n$-modules.
Examples of fusion products of various pairs of affine modules are given.
| math-ph math.MP | fusion product originates in the algebraisation of the operator product expansion in conformal field theory read and saleur 2007 introduced an analogue of fusion for modules over associative algebras for example those appearing in the description of 2d lattice models the article extends their definition for modules over the affine temperleylieb algebra atl n since the regular temperleylieb algebra tl n is a subalgebra of the affine atl n there is a natural pair of adjoint inductionrestriction functors indar resar the existence of an algebra morphism phiatl ntotl n provides a second pair of adjoint functors indphiresphi two fusion products between atlmodules are proposed and studied they are expressed in terms of these four functors the action of these functors is computed on the standard cell and irreducible atl nmodules as a byproduct the peirce decomposition of atl nqq1 when q is not a root of unity is given as direct sum of the induction indarthesnk of standard tl nmodules to atl nmodules examples of fusion products of various pairs of affine modules are given | [['fusion', 'product', 'originates', 'in', 'the', 'algebraisation', 'of', 'the', 'operator', 'product', 'expansion', 'in', 'conformal', 'field', 'theory', 'read', 'and', 'saleur', '2007', 'introduced', 'an', 'analogue', 'of', 'fusion', 'for', 'modules', 'over', 'associative', 'algebras', 'for', 'example', 'those', 'appearing', 'in', 'the', 'description', 'of', '2d', 'lattice', 'models', 'the', 'article', 'extends', 'their', 'definition', 'for', 'modules', 'over', 'the', 'affine', 'temperleylieb', 'algebra', 'atl', 'n', 'since', 'the', 'regular', 'temperleylieb', 'algebra', 'tl', 'n', 'is', 'a', 'subalgebra', 'of', 'the', 'affine', 'atl', 'n', 'there', 'is', 'a', 'natural', 'pair', 'of', 'adjoint', 'inductionrestriction', 'functors', 'indar', 'resar', 'the', 'existence', 'of', 'an', 'algebra', 'morphism', 'phiatl', 'ntotl', 'n', 'provides', 'a', 'second', 'pair', 'of', 'adjoint', 'functors', 'indphiresphi', 'two', 'fusion', 'products', 'between', 'atlmodules', 'are', 'proposed', 'and', 'studied', 'they', 'are', 'expressed', 'in', 'terms', 'of', 'these', 'four', 'functors', 'the', 'action', 'of', 'these', 'functors', 'is', 'computed', 'on', 'the', 'standard', 'cell', 'and', 'irreducible', 'atl', 'nmodules', 'as', 'a', 'byproduct', 'the', 'peirce', 'decomposition', 'of', 'atl', 'nqq1', 'when', 'q', 'is', 'not', 'a', 'root', 'of', 'unity', 'is', 'given', 'as', 'direct', 'sum', 'of', 'the', 'induction', 'indarthesnk', 'of', 'standard', 'tl', 'nmodules', 'to', 'atl', 'nmodules', 'examples', 'of', 'fusion', 'products', 'of', 'various', 'pairs', 'of', 'affine', 'modules', 'are', 'given']] | [-0.14590129102747554, 0.08065609338788823, -0.025604955474204737, 0.046027601429562415, -0.0796639545218361, -0.13395602567522916, -0.05222523355547026, 0.3400100752499378, -0.37773390536190954, -0.1914461912157873, 0.10271101361029074, -0.23144293647861766, -0.11390614265319474, 0.16630463063449202, -0.12200803877953105, -0.027710598603470953, 0.06718626710053667, 0.15612113063814798, -0.09781949546865695, -0.24585075300566095, 0.36717951182041786, 0.017812627572622038, 0.2599095904329669, 0.011638517516614969, 0.1330535658328476, 0.03610528021225494, -0.051617267455147234, -0.040917149627815466, -0.08132823278937867, 0.1351690855864324, 0.31324653749725473, 0.12743341949528564, 0.17520282911708365, -0.3615439872865281, -0.07003639180570127, 0.15294915323374336, 0.15952083629187933, 0.017962086947166365, 0.012507240503362776, -0.255127144653997, 0.07694132746236321, -0.29349517215790594, -0.06258787345810386, -0.057731882629994145, 0.08529563589247423, -0.012263887715219203, -0.28923490932936263, -0.015297203904720481, 0.1259312723333875, 0.1394432479610477, -0.08725738561661492, -0.14936779574407433, -0.057818544117460575, 0.09866865254219895, -0.07493202499961647, -0.001950231023846629, 0.136247289130704, -0.14408028909440213, -0.19399237356143084, 0.36385396424078653, -0.02459626114218981, -0.21793226130909013, 0.15810362639429862, -0.12267257643112088, -0.11287977778726442, 0.09833211439499955, 0.018395444392198753, 0.12077823256192628, -0.05925837658182927, 0.20665483848048197, -0.14499772836950547, 0.043287750753200425, 0.1362968982409262, 0.017818420712730126, 0.12627144677084898, 0.11791213563487246, -0.015998667061418115, 0.1428700555785152, 0.050259056402102646, -0.08707231259068285, -0.3735343481519979, -0.2044830699618571, -0.08293070804472723, 0.07155693166000102, -0.09212045414360383, -0.20047787142482526, 0.3999583883891384, 0.07818298176874498, 0.18026136154096997, 0.11023762618598645, 0.20866106467985152, 0.1019988117759077, 0.12821793434778953, -0.029345065656447126, 0.12361401932810372, 0.2715609878535845, 0.020725696024897614, -0.1327786477170062, -0.03454704080501984, 0.2550930040386578] |
1,802.03576 | Downlink User Association and Uplink Scheduling for Energy Harvesting
Users in Software-Defined Mobile Networks | In this paper we consider a heterogeneous network which consists of a macro
base station and some pico base stations utilizing massive MIMO and MIMO
techniques, respectively. A central software-defined mobile network (SDMN)
controller is adopted in order to provide user association and energy
scheduling. The users are considered battery limited and are capable of
simultaneous wireless information and power transfer (SWIPT) in order to
harvest energy and address the energy shortage issue. These users harvest
energy from the received signals in the downlink and consume it via their
uplink communications. This paper deals with the downlink user association by
jointly optimizing the overall sum-rate of the network and the harvested energy
by introducing an appropriate utility function. In this regard, the optimum
user association and power splitting factor for each user are calculated via
the downlink optimization stage. Then, the process of uplink scheduling is
defined as choosing the best users in each time epoch to transfer data as well
as optimizing their transmit power by solving Lyapunov drift-plus-penalty
function. Simulation results are provided in order to confirm the optimality of
the proposed algorithm in comparison with the previous user association and
uplink scheduling approaches in terms of providing fairness and battery
management among users.
| cs.IT math.IT | in this paper we consider a heterogeneous network which consists of a macro base station and some pico base stations utilizing massive mimo and mimo techniques respectively a central softwaredefined mobile network sdmn controller is adopted in order to provide user association and energy scheduling the users are considered battery limited and are capable of simultaneous wireless information and power transfer swipt in order to harvest energy and address the energy shortage issue these users harvest energy from the received signals in the downlink and consume it via their uplink communications this paper deals with the downlink user association by jointly optimizing the overall sumrate of the network and the harvested energy by introducing an appropriate utility function in this regard the optimum user association and power splitting factor for each user are calculated via the downlink optimization stage then the process of uplink scheduling is defined as choosing the best users in each time epoch to transfer data as well as optimizing their transmit power by solving lyapunov driftpluspenalty function simulation results are provided in order to confirm the optimality of the proposed algorithm in comparison with the previous user association and uplink scheduling approaches in terms of providing fairness and battery management among users | [['in', 'this', 'paper', 'we', 'consider', 'a', 'heterogeneous', 'network', 'which', 'consists', 'of', 'a', 'macro', 'base', 'station', 'and', 'some', 'pico', 'base', 'stations', 'utilizing', 'massive', 'mimo', 'and', 'mimo', 'techniques', 'respectively', 'a', 'central', 'softwaredefined', 'mobile', 'network', 'sdmn', 'controller', 'is', 'adopted', 'in', 'order', 'to', 'provide', 'user', 'association', 'and', 'energy', 'scheduling', 'the', 'users', 'are', 'considered', 'battery', 'limited', 'and', 'are', 'capable', 'of', 'simultaneous', 'wireless', 'information', 'and', 'power', 'transfer', 'swipt', 'in', 'order', 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1,802.03577 | Surface tension of the horizon | The idea of treating the horizon of a black hole as a stretched membrane with
surface tension has a long history. In this work, we discuss the microscopic
origin of the surface tension of the horizon in quantum pictures of spaces,
which are Bose-Einstein condensates of gravitons. The horizon is a phase
interface of gravitons, the surface tension of which is found to be a result of
the difference in the strength of the interaction between the gravitons on its
two sides. The gravitational source, such as a Schwarzschild black hole,
creates a transitional zone by changing the energy and distribution of its
surrounding gravitons. Archimedes' principle for gravity can be expressed as
follows: "the gravity on an object is equal to the weight of the gravitons that
it displaces."
| gr-qc hep-th | the idea of treating the horizon of a black hole as a stretched membrane with surface tension has a long history in this work we discuss the microscopic origin of the surface tension of the horizon in quantum pictures of spaces which are boseeinstein condensates of gravitons the horizon is a phase interface of gravitons the surface tension of which is found to be a result of the difference in the strength of the interaction between the gravitons on its two sides the gravitational source such as a schwarzschild black hole creates a transitional zone by changing the energy and distribution of its surrounding gravitons archimedes principle for gravity can be expressed as follows the gravity on an object is equal to the weight of the gravitons that it displaces | [['the', 'idea', 'of', 'treating', 'the', 'horizon', 'of', 'a', 'black', 'hole', 'as', 'a', 'stretched', 'membrane', 'with', 'surface', 'tension', 'has', 'a', 'long', 'history', 'in', 'this', 'work', 'we', 'discuss', 'the', 'microscopic', 'origin', 'of', 'the', 'surface', 'tension', 'of', 'the', 'horizon', 'in', 'quantum', 'pictures', 'of', 'spaces', 'which', 'are', 'boseeinstein', 'condensates', 'of', 'gravitons', 'the', 'horizon', 'is', 'a', 'phase', 'interface', 'of', 'gravitons', 'the', 'surface', 'tension', 'of', 'which', 'is', 'found', 'to', 'be', 'a', 'result', 'of', 'the', 'difference', 'in', 'the', 'strength', 'of', 'the', 'interaction', 'between', 'the', 'gravitons', 'on', 'its', 'two', 'sides', 'the', 'gravitational', 'source', 'such', 'as', 'a', 'schwarzschild', 'black', 'hole', 'creates', 'a', 'transitional', 'zone', 'by', 'changing', 'the', 'energy', 'and', 'distribution', 'of', 'its', 'surrounding', 'gravitons', 'archimedes', 'principle', 'for', 'gravity', 'can', 'be', 'expressed', 'as', 'follows', 'the', 'gravity', 'on', 'an', 'object', 'is', 'equal', 'to', 'the', 'weight', 'of', 'the', 'gravitons', 'that', 'it', 'displaces']] | [-0.1720892428348844, 0.17314998967888712, -0.13845225705478628, 0.08795799151904737, -0.07319658547639847, -0.08904153666673945, 0.008164026650886697, 0.28947290970334927, -0.23376696782831388, -0.28988992030230853, 0.09192318925007174, -0.2912457905781384, -0.0872588012779418, 0.1707128440710501, -0.043657610984519125, -0.019420757518114094, -0.004883471464451689, 0.07065936708822847, -0.05087863826013815, -0.19171081378769417, 0.37085436353316675, 0.07724348089854735, 0.22948417957657233, 0.08709239787422121, 0.12794892866021165, -0.026780269268112115, 0.052383468224881935, 0.053019599645183636, -0.11896453634684663, 0.08002523415220472, 0.1718298495572526, 0.08705551968171046, 0.21757516925079892, -0.43547067284010926, -0.2504391547722312, 0.05224745233829778, 0.14686166428101177, 0.16030027753709314, -0.027169454958325682, -0.28247958458960054, 0.011838331610824053, -0.1836624728802305, -0.16533448720852342, 0.06406983160772002, 0.06505347104886404, -0.0365551400428208, -0.17346664238900233, 0.10345460948659681, 0.06759970926034908, -0.044380069031523396, -0.08880972482192402, -0.03783740064917276, -0.07278515213670639, 0.12087930767940214, 0.14060136067072074, 0.06687522472885365, 0.18867503023133256, -0.15839039641611566, -0.08423570638092664, 0.37296598623864924, -0.09333612841482346, -0.18973765381826804, 0.18329867647889142, -0.17048427837614258, -0.028647294128313662, 0.10410073393454346, 0.12634153935225464, 0.1411360346694262, -0.12676387021246438, 0.11280531551500854, -0.015930065229678384, 0.14676195306058687, 0.13581589307958403, 0.047382720427301066, 0.3911598173982034, 0.1701572973245325, 0.018250885222537015, 0.18362838118516195, -0.1062798585706892, -0.06789818357165832, -0.3595562100661202, -0.20116219578060107, -0.2192803400138823, 0.04443975924514234, -0.14088532468292844, -0.21464124424741246, 0.3319443606484968, 0.07174651656084909, 0.1959943778669605, 0.02233293004369793, 0.24857340739466824, 0.1029284139862284, 0.06812257692576028, 0.05205775613251787, 0.32625863424573953, 0.13191822179091664, 0.09762305682256388, -0.26161226833310836, 0.004576215369385094, 0.07318638675273038] |
1,802.03578 | On the RR Lyrae stars in globulars: V. the complete Near-Infrared (JHKs)
census of omega Centauri RR Lyrae variables | We present a new complete Near-Infrared (NIR, $JHK_s$) census of RR Lyrae
stars (RRLs) in the globular $\omega$ Cen (NGC 5139). We collected 15,472
$JHK_s$ images with 4-8m class telescopes over 15 years (2000-2015) covering a
sky area around the cluster center of 60x34 arcmin$^2$. These images provided
calibrated photometry for 182 out of the 198 cluster RRL candidates with ten to
sixty measurements per band. We also provide new homogeneous estimates of the
photometric amplitude for 180 ($J$), 176 ($H$) and 174 ($K_s$) RRLs. These data
were supplemented with single-epoch $JK_s$ magnitudes from VHS and with
single-epoch $H$ magnitudes from 2MASS. Using proprietary optical and NIR data
together with new optical light curves (ASAS-SN) we also updated pulsation
periods for 59 candidate RRLs. As a whole, we provide $JHK_s$ magnitudes for 90
RRab (fundamentals), 103 RRc (first overtones) and one RRd (mixed--mode
pulsator). We found that NIR/optical photometric amplitude ratios increase when
moving from first overtone to fundamental and to long-period (P>0.7 days)
fundamental RRLs. Using predicted Period-Luminosity-Metallicity relations, we
derive a true distance modulus of 13.674$\pm$0.008$\pm$0.038 mag (statistical
error and standard deviation of the median)---based on spectroscopic iron
abundances---and of 13.698$\pm$0.004$\pm$0.048 mag---based on photometric iron
abundances. We also found evidence of possible systematics at the 5-10% level
in the zero-point of the PLs based on the five calibrating RRLs whose
parallaxes had been determined with HST
| astro-ph.SR astro-ph.GA | we present a new complete nearinfrared nir jhk_s census of rr lyrae stars rrls in the globular omega cen ngc 5139 we collected 15472 jhk_s images with 48m class telescopes over 15 years 20002015 covering a sky area around the cluster center of 60x34 arcmin2 these images provided calibrated photometry for 182 out of the 198 cluster rrl candidates with ten to sixty measurements per band we also provide new homogeneous estimates of the photometric amplitude for 180 j 176 h and 174 k_s rrls these data were supplemented with singleepoch jk_s magnitudes from vhs and with singleepoch h magnitudes from 2mass using proprietary optical and nir data together with new optical light curves asassn we also updated pulsation periods for 59 candidate rrls as a whole we provide jhk_s magnitudes for 90 rrab fundamentals 103 rrc first overtones and one rrd mixedmode pulsator we found that niroptical photometric amplitude ratios increase when moving from first overtone to fundamental and to longperiod p07 days fundamental rrls using predicted periodluminositymetallicity relations we derive a true distance modulus of 13674pm0008pm0038 mag statistical error and standard deviation of the medianbased on spectroscopic iron abundancesand of 13698pm0004pm0048 magbased on photometric iron abundances we also found evidence of possible systematics at the 510 level in the zeropoint of the pls based on the five calibrating rrls whose parallaxes had been determined with hst | [['we', 'present', 'a', 'new', 'complete', 'nearinfrared', 'nir', 'jhk_s', 'census', 'of', 'rr', 'lyrae', 'stars', 'rrls', 'in', 'the', 'globular', 'omega', 'cen', 'ngc', '5139', 'we', 'collected', '15472', 'jhk_s', 'images', 'with', '48m', 'class', 'telescopes', 'over', '15', 'years', '20002015', 'covering', 'a', 'sky', 'area', 'around', 'the', 'cluster', 'center', 'of', '60x34', 'arcmin2', 'these', 'images', 'provided', 'calibrated', 'photometry', 'for', '182', 'out', 'of', 'the', '198', 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1,802.03579 | On the weighted safe set problem on paths and cycles | Let $G$ be a graph, and let $w: V(G) \to \mathbb{R}$ be a weight function on
the vertices of $G$. For every subset $X$ of $V(G)$, let $w(X)=\sum_{v \in X}
w(v).$ A non-empty subset $S \subset V(G)$ is a weighted safe set of $(G,w)$
if, for every component $C$ of the subgraph induced by $S$ and every component
$D$ of $G-S$, we have $w(C) \geq w(D)$ whenever there is an edge between $C$
and $D$. If the subgraph of $G$ induced by a weighted safe set $S$ is
connected, then the set $S$ is called a connected weighted safe set of $(G,w)$.
The weighted safe number $s(G,w)$ and connected weighted safe number $cs(G,w)$
of $(G,w)$ are the minimum weights $w(S)$ among all weighted safe sets and all
connected weighted safe sets of $(G,w)$, respectively. It is easy to see that
for any pair $(G,w)$, ${s}(G,w) \le {cs}(G,w)$ by their definitions. In this
paper, we discuss the possible equality when $G$ is a path or a cycle. We also
give an answer to a problem due to Tittmann et al. [Eur. J. Combin. Vol. 32
(2011)] concerning subgraph component polynomials for cycles and complete
graphs.
| math.CO | let g be a graph and let w vg to mathbbr be a weight function on the vertices of g for every subset x of vg let wxsum_v in x wv a nonempty subset s subset vg is a weighted safe set of gw if for every component c of the subgraph induced by s and every component d of gs we have wc geq wd whenever there is an edge between c and d if the subgraph of g induced by a weighted safe set s is connected then the set s is called a connected weighted safe set of gw the weighted safe number sgw and connected weighted safe number csgw of gw are the minimum weights ws among all weighted safe sets and all connected weighted safe sets of gw respectively it is easy to see that for any pair gw sgw le csgw by their definitions in this paper we discuss the possible equality when g is a path or a cycle we also give an answer to a problem due to tittmann et al eur j combin vol 32 2011 concerning subgraph component polynomials for cycles and complete graphs | [['let', 'g', 'be', 'a', 'graph', 'and', 'let', 'w', 'vg', 'to', 'mathbbr', 'be', 'a', 'weight', 'function', 'on', 'the', 'vertices', 'of', 'g', 'for', 'every', 'subset', 'x', 'of', 'vg', 'let', 'wxsum_v', 'in', 'x', 'wv', 'a', 'nonempty', 'subset', 's', 'subset', 'vg', 'is', 'a', 'weighted', 'safe', 'set', 'of', 'gw', 'if', 'for', 'every', 'component', 'c', 'of', 'the', 'subgraph', 'induced', 'by', 's', 'and', 'every', 'component', 'd', 'of', 'gs', 'we', 'have', 'wc', 'geq', 'wd', 'whenever', 'there', 'is', 'an', 'edge', 'between', 'c', 'and', 'd', 'if', 'the', 'subgraph', 'of', 'g', 'induced', 'by', 'a', 'weighted', 'safe', 'set', 's', 'is', 'connected', 'then', 'the', 'set', 's', 'is', 'called', 'a', 'connected', 'weighted', 'safe', 'set', 'of', 'gw', 'the', 'weighted', 'safe', 'number', 'sgw', 'and', 'connected', 'weighted', 'safe', 'number', 'csgw', 'of', 'gw', 'are', 'the', 'minimum', 'weights', 'ws', 'among', 'all', 'weighted', 'safe', 'sets', 'and', 'all', 'connected', 'weighted', 'safe', 'sets', 'of', 'gw', 'respectively', 'it', 'is', 'easy', 'to', 'see', 'that', 'for', 'any', 'pair', 'gw', 'sgw', 'le', 'csgw', 'by', 'their', 'definitions', 'in', 'this', 'paper', 'we', 'discuss', 'the', 'possible', 'equality', 'when', 'g', 'is', 'a', 'path', 'or', 'a', 'cycle', 'we', 'also', 'give', 'an', 'answer', 'to', 'a', 'problem', 'due', 'to', 'tittmann', 'et', 'al', 'eur', 'j', 'combin', 'vol', '32', '2011', 'concerning', 'subgraph', 'component', 'polynomials', 'for', 'cycles', 'and', 'complete', 'graphs']] | [-0.18477139273571536, 0.13173608105156218, -0.016569290872315824, 0.0007146868952477611, -0.09839582107502755, -0.12601616134905677, 0.12806156405592772, 0.38035074932080903, -0.270027549551327, -0.26714583881044945, 0.03898945676375161, -0.3560317543726125, -0.11279782175250506, 0.15145981389488736, -0.11094092650681618, -0.0557906645147487, 0.11243937302795794, 0.0983409343137758, 0.02243061295747902, -0.2671109563171914, 0.2813952202459923, -0.06403102079367777, 0.13903317044062632, 0.07071851216102203, 0.0909845985005033, 0.06902449642708573, -0.00661643171939912, 0.090167299445014, -0.15787717755034192, 0.07395132270055325, 0.28605442777411155, 0.20746566547766096, 0.28383556634207, -0.3377875590872157, -0.15591065831472758, 0.27294709815262513, 0.05599397880753859, -0.048818688647860146, 0.07418623895666664, -0.22397347750658544, 0.2074827866911058, -0.1718053414336758, -0.09079283236772054, -0.011447378465606605, 0.21901342285138922, 0.013395669479212613, -0.32716518964792163, -0.01663548071700428, 0.07107166931457778, 0.003045672924774144, 0.053697896511486966, -0.15457817033377183, -0.10582947523762602, 0.03984865053014593, -0.1030000203089408, 0.1944365332342221, 0.025844934513765856, -0.06011475500930583, -0.1338513182630579, 0.35633345761872015, -0.05719213384092626, -0.13349128191460677, 0.11629769351294776, -0.10314860657928752, -0.129497267110418, 0.09152473991379682, 0.09655139744619554, 0.17813638493857814, -0.12728739487722943, 0.1760205684423958, -0.08309012988746321, 0.10616603342652166, 0.10928544983837193, 0.0031079090356590333, 0.17846659387189953, 0.12050915639630369, 0.17117608431093345, 0.09792977115326354, -0.02983667284438454, 0.1007255286990467, -0.3679416172080939, -0.10345492106773396, -0.20993158065139708, 0.0964999885969057, -0.12127166116894271, -0.20195117863039896, 0.37488823548596295, 0.0612741261483906, 0.18169909062761563, 0.065315381570724, 0.18058941143183666, 0.08621059332333669, -0.01149570062175065, 0.2116355978560494, 0.08901872994001819, 0.1859916077946057, -0.06425013512052075, -0.15054848643955943, 0.041414851950839826, 0.16010573716023502] |
1,802.0358 | Bees with attitude: the effect of gusts on flight dynamics | Flight is a complicated task at small scales in part due to the ubiquitous
unsteady air which contains it. Flying organisms deal with these difficulties
using active and passive control mechanisms to steer their body motion. Body
attitudes of flapping organisms are linked with their resultant flight
trajectories and performance, yet little is understood about how discrete
unsteady aerodynamic phenomena affect the interlaced dynamics of such systems.
In this study, we examined freely flying bumblebees subject to a single
discrete gust to emulate aerodynamic disturbances encountered in nature.
Bumblebees are expert commanders of the aerial domain as they persistently
forage within complex terrain elements. Physical obstacles such as flowers
produce local effects representative of a typified gust which threatens the
precise control of intricate maneuvers. By tracking the 3D dynamics of bees
flying through gusts, we determined the sequences of motion that permit flight
in three disturbance conditions. Bees repetitively executed a series of passive
impulsive maneuvers followed by active recovery maneuvers. Impulsive motion was
unique in each gust direction, maintaining control purely by passive
manipulation of the body. Bees pitched up and slowed-down at the beginning of
recovery in every disturbance, followed by corrective maneuvers which brought
attitudes back to their original state. Bees were displaced the most by the
sideward gust, displaying large lateral translations and roll deviations.
Upward gusts were easier for bees to fly through, causing only minor flight
changes and minimal recovery times. Downward gusts severely impaired the
control response of bees, inflicting strong adverse forces which sharply upset
trajectories. Bees used interesting control strategies when flying in each
disturbance, offering new insights into insect-scale flapping flight and
bio-inspired robotic systems.
| physics.flu-dyn physics.bio-ph | flight is a complicated task at small scales in part due to the ubiquitous unsteady air which contains it flying organisms deal with these difficulties using active and passive control mechanisms to steer their body motion body attitudes of flapping organisms are linked with their resultant flight trajectories and performance yet little is understood about how discrete unsteady aerodynamic phenomena affect the interlaced dynamics of such systems in this study we examined freely flying bumblebees subject to a single discrete gust to emulate aerodynamic disturbances encountered in nature bumblebees are expert commanders of the aerial domain as they persistently forage within complex terrain elements physical obstacles such as flowers produce local effects representative of a typified gust which threatens the precise control of intricate maneuvers by tracking the 3d dynamics of bees flying through gusts we determined the sequences of motion that permit flight in three disturbance conditions bees repetitively executed a series of passive impulsive maneuvers followed by active recovery maneuvers impulsive motion was unique in each gust direction maintaining control purely by passive manipulation of the body bees pitched up and sloweddown at the beginning of recovery in every disturbance followed by corrective maneuvers which brought attitudes back to their original state bees were displaced the most by the sideward gust displaying large lateral translations and roll deviations upward gusts were easier for bees to fly through causing only minor flight changes and minimal recovery times downward gusts severely impaired the control response of bees inflicting strong adverse forces which sharply upset trajectories bees used interesting control strategies when flying in each disturbance offering new insights into insectscale flapping flight and bioinspired robotic systems | [['flight', 'is', 'a', 'complicated', 'task', 'at', 'small', 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1,802.03581 | 2-gram-based Phonetic Feature Generation for Convolutional Neural
Network in Assessment of Trademark Similarity | A trademark is a mark used to identify various commodities. If same or
similar trademark is registered for the same or similar commodity, the
purchaser of the goods may be confused. Therefore, in the process of trademark
registration examination, the examiner judges whether the trademark is the same
or similar to the other applied or registered trademarks. The confusion in
trademarks is based on the visual, phonetic or conceptual similarity of the
marks. In this paper, we focus specifically on the phonetic similarity between
trademarks. We propose a method to generate 2D phonetic feature for
convolutional neural network in assessment of trademark similarity. This
proposed algorithm is tested with 12,553 trademark phonetic similar pairs and
34,020 trademark phonetic non-similar pairs from 2010 to 2016. As a result, we
have obtained approximately 92% judgment accuracy.
| cs.SD cs.CV eess.AS | a trademark is a mark used to identify various commodities if same or similar trademark is registered for the same or similar commodity the purchaser of the goods may be confused therefore in the process of trademark registration examination the examiner judges whether the trademark is the same or similar to the other applied or registered trademarks the confusion in trademarks is based on the visual phonetic or conceptual similarity of the marks in this paper we focus specifically on the phonetic similarity between trademarks we propose a method to generate 2d phonetic feature for convolutional neural network in assessment of trademark similarity this proposed algorithm is tested with 12553 trademark phonetic similar pairs and 34020 trademark phonetic nonsimilar pairs from 2010 to 2016 as a result we have obtained approximately 92 judgment accuracy | [['a', 'trademark', 'is', 'a', 'mark', 'used', 'to', 'identify', 'various', 'commodities', 'if', 'same', 'or', 'similar', 'trademark', 'is', 'registered', 'for', 'the', 'same', 'or', 'similar', 'commodity', 'the', 'purchaser', 'of', 'the', 'goods', 'may', 'be', 'confused', 'therefore', 'in', 'the', 'process', 'of', 'trademark', 'registration', 'examination', 'the', 'examiner', 'judges', 'whether', 'the', 'trademark', 'is', 'the', 'same', 'or', 'similar', 'to', 'the', 'other', 'applied', 'or', 'registered', 'trademarks', 'the', 'confusion', 'in', 'trademarks', 'is', 'based', 'on', 'the', 'visual', 'phonetic', 'or', 'conceptual', 'similarity', 'of', 'the', 'marks', 'in', 'this', 'paper', 'we', 'focus', 'specifically', 'on', 'the', 'phonetic', 'similarity', 'between', 'trademarks', 'we', 'propose', 'a', 'method', 'to', 'generate', '2d', 'phonetic', 'feature', 'for', 'convolutional', 'neural', 'network', 'in', 'assessment', 'of', 'trademark', 'similarity', 'this', 'proposed', 'algorithm', 'is', 'tested', 'with', '12553', 'trademark', 'phonetic', 'similar', 'pairs', 'and', '34020', 'trademark', 'phonetic', 'nonsimilar', 'pairs', 'from', '2010', 'to', '2016', 'as', 'a', 'result', 'we', 'have', 'obtained', 'approximately', '92', 'judgment', 'accuracy']] | [-0.01967429695518645, 0.03216300127766744, -0.12568246572504216, 0.12312941685825012, -0.09087594661325442, -0.18032101475436127, 0.032720262813558235, 0.40636251790618355, -0.23227631096079043, -0.3277432051501378, 0.03308548604378082, -0.31587373218358017, -0.16695142447221742, 0.16019281841440577, -0.14490229427478465, 0.03941742695557574, 0.05038848539786131, 0.11250830723226747, -0.03833274074801893, -0.27741748255423526, 0.2877698583302624, 0.04713715443556959, 0.3644830468392959, 0.013036634099218205, 0.09083729432049123, -0.005994411700433402, -0.09334453983429933, -0.04172483754122037, -0.0233833676302862, 0.15216647571122105, 0.29347791213711555, 0.17309033761337173, 0.29536453528668394, -0.3652033260741243, -0.13715353687152718, 0.125845808056924, 0.09645874541214046, 0.08324433417962582, -0.006106413496926314, -0.3663552063093944, 0.07972596533892845, -0.15166560142780794, 0.00959912725816001, -0.023718656827413448, 0.008815729270061252, -0.02278311643793572, -0.23110356720898187, 0.056040452334634734, 0.042314660280350254, 0.10397008749317717, -0.0425348190862375, -0.09593556341221274, -0.031617102052912356, 0.17546452414491354, 0.019129357125414324, 0.08316706112501296, 0.08907500375415, -0.15027144058231814, -0.14550338420314912, 0.3972129993154808, -0.06109138329823812, -0.17997746404161621, 0.22533468525525127, -0.06753061186860908, -0.1359616772335192, 0.06988023699677522, 0.21108711449161285, 0.05387740601012907, -0.17185908817037038, -0.04692743085439416, -0.07585049155309345, 0.2027914006982676, 0.12786164654729268, -0.020204053464994737, 0.2104189177714006, 0.19883123841348357, -0.037941661658061836, 0.13303683748227108, -0.11989918609050977, -0.028754174456204935, -0.19735500697579206, -0.1509954920814683, -0.1570447729688815, -0.013613980872006241, -0.025713544123639374, -0.16700389799238605, 0.4184147782789571, 0.20096719726853102, 0.20049535364766294, 0.015506283496506512, 0.2540802030513684, 0.042301679359022244, 0.1181941529626563, 0.03905958114685773, 0.2147180194804219, -0.039561528387959254, 0.1261233183851635, -0.12929873253398744, 0.1328407715208772, 0.0922743863934143] |
1,802.03582 | The full moment problem on subsets of probabilities and point
configurations | The aim of this paper is to study the full $K-$moment problem for measures
supported on some particular non-linear subsets $K$ of an infinite dimensional
vector space. We focus on the case of random measures, that is $K$ is a subset
of all non-negative Radon measures on $\mathbb{R}^d$. We consider as $K$ the
space of sub-probabilities, probabilities and point configurations on
$\mathbb{R}^d$. For each of these spaces we provide at least one representation
as a generalized basic closed semi-algebraic set to apply the main result in
[J. Funct. Anal., 267 (2014) no.5: 1382--1418]. We demonstrate that this main
result can be significantly improved by further considerations based on the
particular chosen representation of $K$. In the case when $K$ is a space of
point configurations, the correlation functions (also known as factorial moment
functions) are easier to handle than the ordinary moment functions. Hence, we
additionally express the main results in terms of correlation functions.
| math.FA math.PR | the aim of this paper is to study the full kmoment problem for measures supported on some particular nonlinear subsets k of an infinite dimensional vector space we focus on the case of random measures that is k is a subset of all nonnegative radon measures on mathbbrd we consider as k the space of subprobabilities probabilities and point configurations on mathbbrd for each of these spaces we provide at least one representation as a generalized basic closed semialgebraic set to apply the main result in j funct anal 267 2014 no5 13821418 we demonstrate that this main result can be significantly improved by further considerations based on the particular chosen representation of k in the case when k is a space of point configurations the correlation functions also known as factorial moment functions are easier to handle than the ordinary moment functions hence we additionally express the main results in terms of correlation functions | [['the', 'aim', 'of', 'this', 'paper', 'is', 'to', 'study', 'the', 'full', 'kmoment', 'problem', 'for', 'measures', 'supported', 'on', 'some', 'particular', 'nonlinear', 'subsets', 'k', 'of', 'an', 'infinite', 'dimensional', 'vector', 'space', 'we', 'focus', 'on', 'the', 'case', 'of', 'random', 'measures', 'that', 'is', 'k', 'is', 'a', 'subset', 'of', 'all', 'nonnegative', 'radon', 'measures', 'on', 'mathbbrd', 'we', 'consider', 'as', 'k', 'the', 'space', 'of', 'subprobabilities', 'probabilities', 'and', 'point', 'configurations', 'on', 'mathbbrd', 'for', 'each', 'of', 'these', 'spaces', 'we', 'provide', 'at', 'least', 'one', 'representation', 'as', 'a', 'generalized', 'basic', 'closed', 'semialgebraic', 'set', 'to', 'apply', 'the', 'main', 'result', 'in', 'j', 'funct', 'anal', '267', '2014', 'no5', '13821418', 'we', 'demonstrate', 'that', 'this', 'main', 'result', 'can', 'be', 'significantly', 'improved', 'by', 'further', 'considerations', 'based', 'on', 'the', 'particular', 'chosen', 'representation', 'of', 'k', 'in', 'the', 'case', 'when', 'k', 'is', 'a', 'space', 'of', 'point', 'configurations', 'the', 'correlation', 'functions', 'also', 'known', 'as', 'factorial', 'moment', 'functions', 'are', 'easier', 'to', 'handle', 'than', 'the', 'ordinary', 'moment', 'functions', 'hence', 'we', 'additionally', 'express', 'the', 'main', 'results', 'in', 'terms', 'of', 'correlation', 'functions']] | [-0.1042591973445287, 0.0838841902727628, -0.055429757097918614, 0.07050082571743653, -0.048322477478387894, -0.06610118503091869, 0.053076761907112656, 0.36197010340059504, -0.24915774475198751, -0.22116811871467754, 0.11477848273029344, -0.2885712683748673, -0.16798267881477288, 0.20261912985881364, -0.06874570705367898, 0.03511693936717861, 0.025727051331545986, 0.07114775895671402, -0.09439712004383015, -0.28549331231605796, 0.3912785580698279, -0.016400047776508418, 0.22576789057823476, 0.04855159282976506, 0.09580179695131294, 0.040511061114812986, -0.02559979296185904, 0.03133371289385014, -0.1464901463836439, 0.12571725073450069, 0.24707741339027492, 0.14213356376607336, 0.27869733094292526, -0.35588144714292747, -0.18590950805182552, 0.16019404487261318, 0.10051908169331308, 0.023020470138065505, 0.030897855162894464, -0.26529727860664326, 0.12225518941885359, -0.10151578581532816, -0.13249125228692576, -0.10228750052454036, 0.046908279835228245, 0.0473124634921916, -0.30573094315412974, 0.01847509979373118, 0.10879713242715286, 0.05295378037612715, -0.07718562530475406, -0.18992163918401098, 0.01049632149492847, 0.08324293810832376, 0.011908171017459454, 0.08990269807301267, 0.06232450563107538, -0.05076286318816368, -0.12497895706241062, 0.3743590858150152, -0.062095451578547345, -0.25886980252450004, 0.18772416633821634, -0.17578475694080778, -0.15979877073208199, 0.09315736853255847, 0.21044859478213623, 0.16627679848921748, -0.1260373694659553, 0.12830178619756552, -0.11086809864340752, 0.1310749989009791, 0.0727178105761851, 0.021326632532530792, 0.13091225297269382, 0.11003681287029454, 0.12065185848745255, 0.182990462008675, -0.04141955077191632, -0.06625764301842614, -0.34568103940341793, -0.1648127387758661, -0.20662153360443006, 0.060351657994651524, -0.0899699874026541, -0.15207190514584773, 0.37158862991088065, 0.12667015261964848, 0.21308575452494075, 0.07948170239531915, 0.24175445597277942, 0.14108353841821952, -0.0027892017566592863, 0.06909678779006784, 0.12739961706744843, 0.1309410197541629, 0.030194752511805763, -0.1418493185801054, 0.023327286696073667, 0.11889592339619508] |
1,802.03583 | Distributed One-class Learning | We propose a cloud-based filter trained to block third parties from uploading
privacy-sensitive images of others to online social media. The proposed filter
uses Distributed One-Class Learning, which decomposes the cloud-based filter
into multiple one-class classifiers. Each one-class classifier captures the
properties of a class of privacy-sensitive images with an autoencoder. The
multi-class filter is then reconstructed by combining the parameters of the
one-class autoencoders. The training takes place on edge devices (e.g.
smartphones) and therefore users do not need to upload their private and/or
sensitive images to the cloud. A major advantage of the proposed filter over
existing distributed learning approaches is that users cannot access, even
indirectly, the parameters of other users. Moreover, the filter can cope with
the imbalanced and complex distribution of the image content and the
independent probability of addition of new users. We evaluate the performance
of the proposed distributed filter using the exemplar task of blocking a user
from sharing privacy-sensitive images of other users. In particular, we
validate the behavior of the proposed multi-class filter with
non-privacy-sensitive images, the accuracy when the number of classes
increases, and the robustness to attacks when an adversary user has access to
privacy-sensitive images of other users.
| cs.LG stat.ML | we propose a cloudbased filter trained to block third parties from uploading privacysensitive images of others to online social media the proposed filter uses distributed oneclass learning which decomposes the cloudbased filter into multiple oneclass classifiers each oneclass classifier captures the properties of a class of privacysensitive images with an autoencoder the multiclass filter is then reconstructed by combining the parameters of the oneclass autoencoders the training takes place on edge devices eg smartphones and therefore users do not need to upload their private andor sensitive images to the cloud a major advantage of the proposed filter over existing distributed learning approaches is that users cannot access even indirectly the parameters of other users moreover the filter can cope with the imbalanced and complex distribution of the image content and the independent probability of addition of new users we evaluate the performance of the proposed distributed filter using the exemplar task of blocking a user from sharing privacysensitive images of other users in particular we validate the behavior of the proposed multiclass filter with nonprivacysensitive images the accuracy when the number of classes increases and the robustness to attacks when an adversary user has access to privacysensitive images of other users | [['we', 'propose', 'a', 'cloudbased', 'filter', 'trained', 'to', 'block', 'third', 'parties', 'from', 'uploading', 'privacysensitive', 'images', 'of', 'others', 'to', 'online', 'social', 'media', 'the', 'proposed', 'filter', 'uses', 'distributed', 'oneclass', 'learning', 'which', 'decomposes', 'the', 'cloudbased', 'filter', 'into', 'multiple', 'oneclass', 'classifiers', 'each', 'oneclass', 'classifier', 'captures', 'the', 'properties', 'of', 'a', 'class', 'of', 'privacysensitive', 'images', 'with', 'an', 'autoencoder', 'the', 'multiclass', 'filter', 'is', 'then', 'reconstructed', 'by', 'combining', 'the', 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1,802.03584 | Joint Learning for Pulmonary Nodule Segmentation, Attributes and
Malignancy Prediction | Refer to the literature of lung nodule classification, many studies adopt
Convolutional Neural Networks (CNN) to directly predict the malignancy of lung
nodules with original thoracic Computed Tomography (CT) and nodule location.
However, these studies cannot tell how the CNN works in terms of predicting the
malignancy of the given nodule, e.g., it's hard to conclude that whether the
region within the nodule or the contextual information matters according to the
output of the CNN. In this paper, we propose an interpretable and multi-task
learning CNN -- Joint learning for \textbf{P}ulmonary \textbf{N}odule
\textbf{S}egmentation \textbf{A}ttributes and \textbf{M}alignancy
\textbf{P}rediction (PN-SAMP). It is able to not only accurately predict the
malignancy of lung nodules, but also provide semantic high-level attributes as
well as the areas of detected nodules. Moreover, the combination of nodule
segmentation, attributes and malignancy prediction is helpful to improve the
performance of each single task. In addition, inspired by the fact that
radiologists often change window widths and window centers to help to make
decision on uncertain nodules, PN-SAMP mixes multiple WW/WC together to gain
information for the raw CT input images. To verify the effectiveness of the
proposed method, the evaluation is implemented on the public LIDC-IDRI dataset,
which is one of the largest dataset for lung nodule malignancy prediction.
Experiments indicate that the proposed PN-SAMP achieves significant improvement
with respect to lung nodule classification, and promising performance on lung
nodule segmentation and attribute learning, compared with the-state-of-the-art
methods.
| cs.CV | refer to the literature of lung nodule classification many studies adopt convolutional neural networks cnn to directly predict the malignancy of lung nodules with original thoracic computed tomography ct and nodule location however these studies cannot tell how the cnn works in terms of predicting the malignancy of the given nodule eg its hard to conclude that whether the region within the nodule or the contextual information matters according to the output of the cnn in this paper we propose an interpretable and multitask learning cnn joint learning for textbfpulmonary textbfnodule textbfsegmentation textbfattributes and textbfmalignancy textbfprediction pnsamp it is able to not only accurately predict the malignancy of lung nodules but also provide semantic highlevel attributes as well as the areas of detected nodules moreover the combination of nodule segmentation attributes and malignancy prediction is helpful to improve the performance of each single task in addition inspired by the fact that radiologists often change window widths and window centers to help to make decision on uncertain nodules pnsamp mixes multiple wwwc together to gain information for the raw ct input images to verify the effectiveness of the proposed method the evaluation is implemented on the public lidcidri dataset which is one of the largest dataset for lung nodule malignancy prediction experiments indicate that the proposed pnsamp achieves significant improvement with respect to lung nodule classification and promising performance on lung nodule segmentation and attribute learning compared with thestateoftheart methods | [['refer', 'to', 'the', 'literature', 'of', 'lung', 'nodule', 'classification', 'many', 'studies', 'adopt', 'convolutional', 'neural', 'networks', 'cnn', 'to', 'directly', 'predict', 'the', 'malignancy', 'of', 'lung', 'nodules', 'with', 'original', 'thoracic', 'computed', 'tomography', 'ct', 'and', 'nodule', 'location', 'however', 'these', 'studies', 'can', 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1,802.03585 | Theory of Friedel oscillations in monolayer graphene and group-VI
dichalcogenides in a magnetic field | Friedel oscillations (FO) of electron density caused by a delta-like neutral
impurity in two-dimensional (2D) systems in a magnetic field are calculated.
Three 2D cases are considered: free electron gas, monolayer graphene and
group-VI dichalcogenides. An exact form of the renormalized Green's function is
used in the calculations, as obtained by a summation of the infinite Dyson
series and regularization procedure. Final results are valid for large ranges
of potential strengths $V_0$, electron densities $n_e$, magnetic fields $B$ and
distances from the impurity $r$. Realistic models for the impurities are used.
The first FO of induced density in WS$_2$ are described by the relation $\Delta
n(\vec{r}) \propto \sin(2\pi r/T_{FO})/r^2$, where $T_{FO} \propto
1/\sqrt{E_F}$. For weak impurity potentials, the amplitudes of FO are
proportional to $V_0$. For attractive potentials and high fields the total
electron density remains positive for all $r$. On the other hand, for low
fields, repulsive potentials and small $r$, the total electron density may
become negative, so that many-body effects should be taken into account.
| cond-mat.mes-hall cond-mat.other | friedel oscillations fo of electron density caused by a deltalike neutral impurity in twodimensional 2d systems in a magnetic field are calculated three 2d cases are considered free electron gas monolayer graphene and groupvi dichalcogenides an exact form of the renormalized greens function is used in the calculations as obtained by a summation of the infinite dyson series and regularization procedure final results are valid for large ranges of potential strengths v_0 electron densities n_e magnetic fields b and distances from the impurity r realistic models for the impurities are used the first fo of induced density in ws_2 are described by the relation delta nvecr propto sin2pi rt_for2 where t_fo propto 1sqrte_f for weak impurity potentials the amplitudes of fo are proportional to v_0 for attractive potentials and high fields the total electron density remains positive for all r on the other hand for low fields repulsive potentials and small r the total electron density may become negative so that manybody effects should be taken into account | [['friedel', 'oscillations', 'fo', 'of', 'electron', 'density', 'caused', 'by', 'a', 'deltalike', 'neutral', 'impurity', 'in', 'twodimensional', '2d', 'systems', 'in', 'a', 'magnetic', 'field', 'are', 'calculated', 'three', '2d', 'cases', 'are', 'considered', 'free', 'electron', 'gas', 'monolayer', 'graphene', 'and', 'groupvi', 'dichalcogenides', 'an', 'exact', 'form', 'of', 'the', 'renormalized', 'greens', 'function', 'is', 'used', 'in', 'the', 'calculations', 'as', 'obtained', 'by', 'a', 'summation', 'of', 'the', 'infinite', 'dyson', 'series', 'and', 'regularization', 'procedure', 'final', 'results', 'are', 'valid', 'for', 'large', 'ranges', 'of', 'potential', 'strengths', 'v_0', 'electron', 'densities', 'n_e', 'magnetic', 'fields', 'b', 'and', 'distances', 'from', 'the', 'impurity', 'r', 'realistic', 'models', 'for', 'the', 'impurities', 'are', 'used', 'the', 'first', 'fo', 'of', 'induced', 'density', 'in', 'ws_2', 'are', 'described', 'by', 'the', 'relation', 'delta', 'nvecr', 'propto', 'sin2pi', 'rt_for2', 'where', 't_fo', 'propto', '1sqrte_f', 'for', 'weak', 'impurity', 'potentials', 'the', 'amplitudes', 'of', 'fo', 'are', 'proportional', 'to', 'v_0', 'for', 'attractive', 'potentials', 'and', 'high', 'fields', 'the', 'total', 'electron', 'density', 'remains', 'positive', 'for', 'all', 'r', 'on', 'the', 'other', 'hand', 'for', 'low', 'fields', 'repulsive', 'potentials', 'and', 'small', 'r', 'the', 'total', 'electron', 'density', 'may', 'become', 'negative', 'so', 'that', 'manybody', 'effects', 'should', 'be', 'taken', 'into', 'account']] | [-0.1552229394349552, 0.2052000483901021, -0.009686407833501245, 0.1088618656542773, 0.02520860826850615, -0.19070893992585214, 0.002240196940009341, 0.3664961957344503, -0.2118930578147146, -0.2689054105825948, -0.015764469574346688, -0.33224905117443115, -0.0820805022154342, 0.18886563672655912, 0.06761609671468084, 0.04220546343448487, -0.027993483690432076, -0.02261542005069328, -0.07982978158009549, -0.1965786890620648, 0.2792054756770306, -0.0011082904554451957, 0.22833287035199729, 0.10229305042056226, 0.02615242459895936, 0.03441592353029233, 0.04175170054941466, 0.0761744145241237, -0.14872703312549593, 0.03767015442223937, 0.21598082596930468, -0.05743278701027686, 0.2513024692508307, -0.4610108107892853, -0.22079379043795846, 0.03380159373310479, 0.17190741282097544, 0.10700002837023048, -0.05946156799186473, -0.2667722264304757, 0.026147601781695177, -0.1699935654319371, -0.1323427730465703, -0.08623884406618097, 0.10528540276908174, 0.07733378426990274, -0.32680769354443656, 0.15201108666757743, -0.007518715390963762, 0.022321995923773624, -0.11610545371941319, -0.17887680623680352, -0.059730413701441706, 0.08034193815494126, 0.05618738835592839, 0.06835271972302122, 0.17348115208389406, -0.14891470444360466, -0.017374306022556442, 0.379771629612272, -0.1141173425412765, -0.16761406183976568, 0.15635126053980045, -0.17603020937831113, -0.06581005340329174, 0.14877962845405845, 0.11400047156564666, 0.09815894504021289, -0.13883885105958943, 0.15705140666514952, -0.00044480304050287516, 0.11825544835406948, 0.061730211566795, 0.04760057836573458, 0.23837171051312578, 0.06846435487157468, 0.032097200550505835, 0.07239172561199997, -0.1177274088177717, -0.03837229772047563, -0.26655697938845013, -0.11524778863796117, -0.2332842690961887, 0.052607796679843555, -0.09026959691660577, -0.19272007953488465, 0.3668095908028948, 0.09420841150479674, 0.19521273291020683, -0.012380539150344151, 0.2668954261493954, 0.19803600739617128, 0.07839327117491683, 0.03639192476191304, 0.2062112603788123, 0.17606841063014034, 0.055574853681592325, -0.24125416174418096, -0.0026382978158918293, 0.06356508642660849] |
1,802.03586 | Predicting the breaking strength of gravity water waves | We revisit the classical but as yet unresolved problem of predicting the
strength of breaking 2-D and 3-D gravity water waves, as quantified by the
amount of wave energy dissipated per breaking event. Following Duncan (1983),
the wave energy dissipation rate per unit length of breaking crest may be
related to the fifth moment of the wave speed and the non-dimensional breaking
strength parameter $b$. We use a finite-volume Navier-Stokes solver with LES
resolution and volume-of-fluid surface reconstruction (Derakhti and Kirby
2014a, 2016) to simulate wave packet evolution, breaking onset and
post-breaking evolution for representative cases of wave packets with breaking
due to dispersive focusing and to modulational instability. The present study
uses these results to investigate the relationship between the breaking
strength parameter $b$ and the breaking onset parameter $B$ proposed recently
by Barthelemy et al. (2018). The latter, formed from the local energy flux
normalized by the local energy density and the local crest speed, simplifies,
on the wave surface, to the ratio of fluid speed to crest speed. Following a
wave crest, when $B$ exceeds a generic threshold value at the wave crest
(Barthelemy et al., 2018), breaking is imminent. We find a robust relationship
between the breaking strength parameter $b$ and the rate of change of breaking
onset parameter, $dB/dt$ at the wave crest, as it transitions through the
generic breaking onset threshold ($B\sim 0.85$), scaled by the local period of
the breaking wave. This result significantly refines previous efforts to
express $b$ in terms of a wave packet steepness parameter, which is both
difficult to define robustly and which does not provide a generically accurate
forecast of the energy dissipated by breaking.
| physics.flu-dyn | we revisit the classical but as yet unresolved problem of predicting the strength of breaking 2d and 3d gravity water waves as quantified by the amount of wave energy dissipated per breaking event following duncan 1983 the wave energy dissipation rate per unit length of breaking crest may be related to the fifth moment of the wave speed and the nondimensional breaking strength parameter b we use a finitevolume navierstokes solver with les resolution and volumeoffluid surface reconstruction derakhti and kirby 2014a 2016 to simulate wave packet evolution breaking onset and postbreaking evolution for representative cases of wave packets with breaking due to dispersive focusing and to modulational instability the present study uses these results to investigate the relationship between the breaking strength parameter b and the breaking onset parameter b proposed recently by barthelemy et al 2018 the latter formed from the local energy flux normalized by the local energy density and the local crest speed simplifies on the wave surface to the ratio of fluid speed to crest speed following a wave crest when b exceeds a generic threshold value at the wave crest barthelemy et al 2018 breaking is imminent we find a robust relationship between the breaking strength parameter b and the rate of change of breaking onset parameter dbdt at the wave crest as it transitions through the generic breaking onset threshold bsim 085 scaled by the local period of the breaking wave this result significantly refines previous efforts to express b in terms of a wave packet steepness parameter which is both difficult to define robustly and which does not provide a generically accurate forecast of the energy dissipated by breaking | [['we', 'revisit', 'the', 'classical', 'but', 'as', 'yet', 'unresolved', 'problem', 'of', 'predicting', 'the', 'strength', 'of', 'breaking', '2d', 'and', '3d', 'gravity', 'water', 'waves', 'as', 'quantified', 'by', 'the', 'amount', 'of', 'wave', 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1,802.03587 | Network Flow-Based Refinement for Multilevel Hypergraph Partitioning | We present a refinement framework for multilevel hypergraph partitioning that
uses max-flow computations on pairs of blocks to improve the solution quality
of a $k$-way partition. The framework generalizes the flow-based improvement
algorithm of KaFFPa from graphs to hypergraphs and is integrated into the
hypergraph partitioner KaHyPar. By reducing the size of hypergraph flow
networks, improving the flow model used in KaFFPa, and developing techniques to
improve the running time of our algorithm, we obtain a partitioner that
computes the best solutions for a wide range of benchmark hypergraphs from
different application areas while still having a running time comparable to
that of hMetis.
| cs.DS | we present a refinement framework for multilevel hypergraph partitioning that uses maxflow computations on pairs of blocks to improve the solution quality of a kway partition the framework generalizes the flowbased improvement algorithm of kaffpa from graphs to hypergraphs and is integrated into the hypergraph partitioner kahypar by reducing the size of hypergraph flow networks improving the flow model used in kaffpa and developing techniques to improve the running time of our algorithm we obtain a partitioner that computes the best solutions for a wide range of benchmark hypergraphs from different application areas while still having a running time comparable to that of hmetis | [['we', 'present', 'a', 'refinement', 'framework', 'for', 'multilevel', 'hypergraph', 'partitioning', 'that', 'uses', 'maxflow', 'computations', 'on', 'pairs', 'of', 'blocks', 'to', 'improve', 'the', 'solution', 'quality', 'of', 'a', 'kway', 'partition', 'the', 'framework', 'generalizes', 'the', 'flowbased', 'improvement', 'algorithm', 'of', 'kaffpa', 'from', 'graphs', 'to', 'hypergraphs', 'and', 'is', 'integrated', 'into', 'the', 'hypergraph', 'partitioner', 'kahypar', 'by', 'reducing', 'the', 'size', 'of', 'hypergraph', 'flow', 'networks', 'improving', 'the', 'flow', 'model', 'used', 'in', 'kaffpa', 'and', 'developing', 'techniques', 'to', 'improve', 'the', 'running', 'time', 'of', 'our', 'algorithm', 'we', 'obtain', 'a', 'partitioner', 'that', 'computes', 'the', 'best', 'solutions', 'for', 'a', 'wide', 'range', 'of', 'benchmark', 'hypergraphs', 'from', 'different', 'application', 'areas', 'while', 'still', 'having', 'a', 'running', 'time', 'comparable', 'to', 'that', 'of', 'hmetis']] | [-0.06968747435334641, -0.02661132370793404, -0.09581513841000672, 0.02607706999473381, -0.06573298023655437, -0.12816861412004352, 0.06295160954379334, 0.3544346722953308, -0.2448024407002287, -0.39139693298448736, 0.09696492132990478, -0.24423180498146954, -0.10675792118910557, 0.20657958148289113, -0.08536075198879609, 0.11642805390203229, 0.13507288448118532, -0.008478218622398205, -0.01326695924329285, -0.25864545984838444, 0.2603447670497609, 0.03597018968475123, 0.2967694423859939, 0.08529421165719843, 0.09486112375564587, 0.02262476579590629, -0.015569682027069995, 0.08214409122368894, -0.12890849759137032, 0.17735293966800852, 0.25975208073102224, 0.2158201114910368, 0.2628398151674236, -0.4077092914376408, -0.19887394012990767, 0.12820350210397288, 0.1617428078875841, 0.11270635035963586, -0.00597031308503714, -0.24512126679362084, 0.13907445376949348, -0.1378300518442232, -0.03132786841776509, -0.06567598912023151, -0.009254200751176821, 0.006439825830546817, -0.3252063489232499, -0.015847876685886428, 0.06850408323673317, -0.01710983010259672, -0.007431633040631333, -0.1655963352487351, 0.04656344151813895, 0.14326268542432585, -0.05179698428687866, 0.06792898483063954, 0.09429213710917303, -0.13344709867781673, -0.1902738189091906, 0.35216501439348435, -0.08363762874012956, -0.15769407247828965, 0.1377955959805359, -0.0158788034459576, -0.16606236557261303, 0.12529750374289086, 0.2176500757341273, 0.1656809494150086, -0.086304971755733, 0.08323109044823258, -0.07043217197776987, 0.16877591620700863, 0.08004448214510027, 0.018294331102739446, 0.07678635441922009, 0.24899336090311408, 0.15106983937752935, 0.20043816729090536, -0.0027203874219244775, -0.08485669322040988, -0.22542004989652645, -0.14077213475740372, -0.1948411091042754, -0.03333419351614653, -0.18320024694073184, -0.1861884159107621, 0.4592871993708496, 0.19051617193214881, 0.19241673450093144, 0.1502145516883152, 0.3158425828979279, 0.05631431637336321, 0.08633533945808616, 0.16971176547052053, 0.13184447151434142, 0.126170111551451, 0.08008647210170658, -0.16615279561441498, 0.048540392926392645, 0.1416277647188578] |
1,802.03588 | Single SiV$^-$ centers in low-strain nanodiamonds with bulk-like
spectral properties and nano-manipulation capabilities | We report on the isolation of single SiV$^-$ centers in nanodiamonds. We
observe the fine-structure of single SiV$^-$ center with improved inhomogeneous
ensemble linewidth below the excited state splitting, stable optical
transitions, good polarization contrast and excellent spectral stability under
resonant excitation. Based on our experimental results we elaborate an
analytical strain model where we extract the ratio between strain coefficients
of excited and ground states as well the intrinsic zero-strain spin-orbit
splittings. The observed strain values are as low as best values in low-strain
bulk diamond. We achieve our results by means of H-plasma treatment of the
diamond surface and in combination with resonant and off-resonant excitation.
Our work paves the way for indistinguishable, single photon emission.
Furthermore, we demonstrate controlled nano-manipulation via atomic force
microscope cantilever of 1D- and 2D-alignments with a so-far unreached accuracy
of about 10nm, as well as new tools including dipole rotation and cluster
decomposition. Combined, our results show the potential to utilize SiV$^-$
centers in nanodiamonds for the controlled interfacing via optical coupling of
individually well-isolated atoms for bottom-up assemblies of complex quantum
systems.
| cond-mat.mes-hall cond-mat.mtrl-sci physics.optics quant-ph | we report on the isolation of single siv centers in nanodiamonds we observe the finestructure of single siv center with improved inhomogeneous ensemble linewidth below the excited state splitting stable optical transitions good polarization contrast and excellent spectral stability under resonant excitation based on our experimental results we elaborate an analytical strain model where we extract the ratio between strain coefficients of excited and ground states as well the intrinsic zerostrain spinorbit splittings the observed strain values are as low as best values in lowstrain bulk diamond we achieve our results by means of hplasma treatment of the diamond surface and in combination with resonant and offresonant excitation our work paves the way for indistinguishable single photon emission furthermore we demonstrate controlled nanomanipulation via atomic force microscope cantilever of 1d and 2dalignments with a sofar unreached accuracy of about 10nm as well as new tools including dipole rotation and cluster decomposition combined our results show the potential to utilize siv centers in nanodiamonds for the controlled interfacing via optical coupling of individually wellisolated atoms for bottomup assemblies of complex quantum systems | [['we', 'report', 'on', 'the', 'isolation', 'of', 'single', 'siv', 'centers', 'in', 'nanodiamonds', 'we', 'observe', 'the', 'finestructure', 'of', 'single', 'siv', 'center', 'with', 'improved', 'inhomogeneous', 'ensemble', 'linewidth', 'below', 'the', 'excited', 'state', 'splitting', 'stable', 'optical', 'transitions', 'good', 'polarization', 'contrast', 'and', 'excellent', 'spectral', 'stability', 'under', 'resonant', 'excitation', 'based', 'on', 'our', 'experimental', 'results', 'we', 'elaborate', 'an', 'analytical', 'strain', 'model', 'where', 'we', 'extract', 'the', 'ratio', 'between', 'strain', 'coefficients', 'of', 'excited', 'and', 'ground', 'states', 'as', 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1,802.03589 | Distributed Log Analysis on the Cloud Using MapReduce | In this paper we describe our work on designing a web based, distributed data
analysis system based on the popular MapReduce framework deployed on a small
cloud; developed specifically for analyzing web server logs. The log analysis
system consists of several cluster nodes, it splits the large log files on a
distributed file system and quickly processes them using MapReduce programming
model. The cluster is created using an open source cloud infrastructure, which
allows us to easily expand the computational power by adding new nodes. This
gives us the ability to automatically resize the cluster according to the data
analysis requirements. We implemented MapReduce programs for basic log analysis
needs like frequency analysis, error detection, busy hour detection etc. as
well as more complex analyses which require running several jobs. The system
can automatically identify and analyze several web server log types such as
Apache, IIS, Squid etc. We use open source projects for creating the cloud
infrastructure and running MapReduce jobs.
| cs.DC | in this paper we describe our work on designing a web based distributed data analysis system based on the popular mapreduce framework deployed on a small cloud developed specifically for analyzing web server logs the log analysis system consists of several cluster nodes it splits the large log files on a distributed file system and quickly processes them using mapreduce programming model the cluster is created using an open source cloud infrastructure which allows us to easily expand the computational power by adding new nodes this gives us the ability to automatically resize the cluster according to the data analysis requirements we implemented mapreduce programs for basic log analysis needs like frequency analysis error detection busy hour detection etc as well as more complex analyses which require running several jobs the system can automatically identify and analyze several web server log types such as apache iis squid etc we use open source projects for creating the cloud infrastructure and running mapreduce jobs | [['in', 'this', 'paper', 'we', 'describe', 'our', 'work', 'on', 'designing', 'a', 'web', 'based', 'distributed', 'data', 'analysis', 'system', 'based', 'on', 'the', 'popular', 'mapreduce', 'framework', 'deployed', 'on', 'a', 'small', 'cloud', 'developed', 'specifically', 'for', 'analyzing', 'web', 'server', 'logs', 'the', 'log', 'analysis', 'system', 'consists', 'of', 'several', 'cluster', 'nodes', 'it', 'splits', 'the', 'large', 'log', 'files', 'on', 'a', 'distributed', 'file', 'system', 'and', 'quickly', 'processes', 'them', 'using', 'mapreduce', 'programming', 'model', 'the', 'cluster', 'is', 'created', 'using', 'an', 'open', 'source', 'cloud', 'infrastructure', 'which', 'allows', 'us', 'to', 'easily', 'expand', 'the', 'computational', 'power', 'by', 'adding', 'new', 'nodes', 'this', 'gives', 'us', 'the', 'ability', 'to', 'automatically', 'resize', 'the', 'cluster', 'according', 'to', 'the', 'data', 'analysis', 'requirements', 'we', 'implemented', 'mapreduce', 'programs', 'for', 'basic', 'log', 'analysis', 'needs', 'like', 'frequency', 'analysis', 'error', 'detection', 'busy', 'hour', 'detection', 'etc', 'as', 'well', 'as', 'more', 'complex', 'analyses', 'which', 'require', 'running', 'several', 'jobs', 'the', 'system', 'can', 'automatically', 'identify', 'and', 'analyze', 'several', 'web', 'server', 'log', 'types', 'such', 'as', 'apache', 'iis', 'squid', 'etc', 'we', 'use', 'open', 'source', 'projects', 'for', 'creating', 'the', 'cloud', 'infrastructure', 'and', 'running', 'mapreduce', 'jobs']] | [-0.14258190612869198, -0.028215066761832632, -0.08559945971348587, 0.07220078396746014, -0.12239678084482382, -0.1944772360622193, 0.13538791869988342, 0.3560278765443299, -0.2722899277412827, -0.40700566075903216, 0.14251883502080923, -0.2800480236809012, -0.07207595939276579, 0.2299237760534096, -0.05698913037144567, 0.09100165790705769, 0.09307166286686688, 0.006924075177974171, 0.01992761700778458, -0.297984698195913, 0.2878052146291866, 0.08630190994360565, 0.3220023632819914, 0.011233275891339153, 0.033971370015304855, 0.03397405016190016, -0.0796737800076328, -0.024726261633829526, -0.06362782301306495, 0.12577739200428312, 0.30020604910110527, 0.2583706062287092, 0.27486169255226506, -0.4571739417086873, -0.12904141179320436, 0.05450583197446655, 0.19121655622313236, 0.04975514430865318, -0.03187473558285058, -0.30811535480955365, 0.09747775495167316, -0.21025570955725364, -0.08296695284051016, -0.09426770058610005, -0.009303415654066168, 0.027899360266962537, -0.22657372726035524, -0.05479614754540869, -0.03644274445635089, 0.04744943526550484, 0.003343388050533026, -0.08049227155883003, 0.05791426314196239, 0.16153350554515872, -0.009287024970866314, 0.025411100477486112, 0.2376443283132243, -0.047754834574233324, -0.09995833910314689, 0.3905184008296073, -0.03838304519066932, -0.10278629114920343, 0.19246786942900976, 0.03841619598707989, -0.24595988758575218, 0.0444423777829528, 0.2731986939159716, 0.08278081836150154, -0.22971458722949373, 0.07678990452270093, -0.0012948101351934452, 0.23725380694326934, 0.05533097345341733, -0.012123815341634148, 0.176788017090502, 0.24105163292821358, 0.07293478512768577, 0.16584140060607483, -0.08896266111662919, -0.10878268966485781, -0.23215516132337075, -0.1508723264042702, -0.16615703456925895, -0.02269654461674185, -0.12882766024079131, -0.16862193188043656, 0.37807286424665815, 0.19337678344828296, 0.15822965096981254, 0.0720703904078641, 0.38323780657424605, 0.04857108621127949, 0.15083059136192373, 0.19863110283723123, 0.04347737177769527, -0.020581541354423045, 0.2036849391836397, -0.15819910683749636, 0.0652629976120582, -0.0012258635388319324] |
1,802.0359 | Alternative explanation for the steep subthreshold slope in
Ferroelectric FETs | Since many years, sub-60mV/decade switching has been reported in
ferroelectric FETs. However, thus far these reports have lacked full physical
explanation since they typically use a negative capacitance in the
ferroelectric layer to be able to explain the experimental observations.
Because negative capacitance as such is not a physical concept, we propose an
alternative model that relies on the non-linear and non-equilibrium behavior of
the ferroelectric layer. It is shown that a steep subthreshold slope can be
obtained by a 2-step switching process, referred to as nucleation and
propagation. Making use of the concept of domain wall motion as known also from
fracture dynamics, we are able to explain the steep slope effect. A simple
mathematical model is added to further describe this phenomenon, and to further
investigate its eventual benefit for obtaining steep slope transistors in the
sub-10nm era.
| physics.app-ph | since many years sub60mvdecade switching has been reported in ferroelectric fets however thus far these reports have lacked full physical explanation since they typically use a negative capacitance in the ferroelectric layer to be able to explain the experimental observations because negative capacitance as such is not a physical concept we propose an alternative model that relies on the nonlinear and nonequilibrium behavior of the ferroelectric layer it is shown that a steep subthreshold slope can be obtained by a 2step switching process referred to as nucleation and propagation making use of the concept of domain wall motion as known also from fracture dynamics we are able to explain the steep slope effect a simple mathematical model is added to further describe this phenomenon and to further investigate its eventual benefit for obtaining steep slope transistors in the sub10nm era | [['since', 'many', 'years', 'sub60mvdecade', 'switching', 'has', 'been', 'reported', 'in', 'ferroelectric', 'fets', 'however', 'thus', 'far', 'these', 'reports', 'have', 'lacked', 'full', 'physical', 'explanation', 'since', 'they', 'typically', 'use', 'a', 'negative', 'capacitance', 'in', 'the', 'ferroelectric', 'layer', 'to', 'be', 'able', 'to', 'explain', 'the', 'experimental', 'observations', 'because', 'negative', 'capacitance', 'as', 'such', 'is', 'not', 'a', 'physical', 'concept', 'we', 'propose', 'an', 'alternative', 'model', 'that', 'relies', 'on', 'the', 'nonlinear', 'and', 'nonequilibrium', 'behavior', 'of', 'the', 'ferroelectric', 'layer', 'it', 'is', 'shown', 'that', 'a', 'steep', 'subthreshold', 'slope', 'can', 'be', 'obtained', 'by', 'a', '2step', 'switching', 'process', 'referred', 'to', 'as', 'nucleation', 'and', 'propagation', 'making', 'use', 'of', 'the', 'concept', 'of', 'domain', 'wall', 'motion', 'as', 'known', 'also', 'from', 'fracture', 'dynamics', 'we', 'are', 'able', 'to', 'explain', 'the', 'steep', 'slope', 'effect', 'a', 'simple', 'mathematical', 'model', 'is', 'added', 'to', 'further', 'describe', 'this', 'phenomenon', 'and', 'to', 'further', 'investigate', 'its', 'eventual', 'benefit', 'for', 'obtaining', 'steep', 'slope', 'transistors', 'in', 'the', 'sub10nm', 'era']] | [-0.06554711417030382, 0.10309601434191987, -0.12079097609296977, 0.054356448709604724, -0.14612141591932276, -0.15888443683012785, 0.05116831936719392, 0.42803732834407965, -0.26530464835089745, -0.30703823733404817, 0.08390156406885776, -0.22037308212543885, -0.2111652378636084, 0.21230520238001033, -0.0949628675966222, 0.056748703677833696, -0.005950936593664743, -0.022874953004149653, 0.002770002544422051, -0.16836289740326688, 0.24279843726054692, 0.0634624345201694, 0.3056552835535794, 0.09825571269641141, 0.08719018719112165, -0.07490696183827927, 0.040127127069524295, 0.034980290234356906, -0.124338650358309, 0.058430326660331204, 0.2474207241262356, 0.037287243945095284, 0.2530100386985075, -0.47139520955225117, -0.27021142775393864, 0.06503865326563794, 0.1589155498366106, 0.1304311371951116, -0.07564380977188886, -0.23200579724616285, 0.10877126703883032, -0.16369281362628282, -0.13966051966985524, -0.08232416276461545, 0.03764718594766563, -0.017572079904933573, -0.21466911481291379, 0.05535403215439434, 0.060683618403515584, 0.028992856866110573, -0.05271711302066905, -0.11231246320074324, -0.017756755397719558, 0.08959922742386676, 0.0650835601868788, 0.024926993727898427, 0.1620370951956875, -0.13171209047360868, -0.12864455462621002, 0.345901770061619, -0.023515746834949194, -0.14329567531543774, 0.18995257004720273, -0.14531810507500753, -0.0803111667356206, 0.12889608673804956, 0.13735950204272412, 0.07334008016590056, -0.16751347630278027, 0.03526722085067547, 0.02753859665914834, 0.18061873510818285, 0.06059340246376856, 0.03326858736226242, 0.22343951596362557, 0.22187521367286928, 0.025551785964086624, 0.141318458552881, -0.08082952930444597, -0.0637122260518841, -0.25953980770109475, -0.14255464258235243, -0.1752172533402608, 0.10105550148126104, -0.03090275008963813, -0.20540341381012905, 0.37500861556463533, 0.1646201017366307, 0.2152272304118001, 0.040500040681992505, 0.27960589852371664, 0.12928975954159372, 0.11868267262475096, 0.027586458934296808, 0.29564474214860004, 0.13451024900163733, 0.1650610706474123, -0.20414121144303798, 0.1550049672368914, -0.027274726414315994] |
1,802.03591 | New Reductions of a Matrix Generalized Heisenberg Ferromagnet Equation | We present in this report 1+1 dimensional nonlinear partial differential
equation integrable through inverse scattering transform. The integrable system
under consideration is a pseudo-Hermitian reduction of a matrix generalization
of classical 1+1 dimensional Heisenberg ferromagnet equation. We derive
recursion operators and describe the integrable hierarchy related to that
matrix equation.
| nlin.SI math-ph math.MP | we present in this report 11 dimensional nonlinear partial differential equation integrable through inverse scattering transform the integrable system under consideration is a pseudohermitian reduction of a matrix generalization of classical 11 dimensional heisenberg ferromagnet equation we derive recursion operators and describe the integrable hierarchy related to that matrix equation | [['we', 'present', 'in', 'this', 'report', '11', 'dimensional', 'nonlinear', 'partial', 'differential', 'equation', 'integrable', 'through', 'inverse', 'scattering', 'transform', 'the', 'integrable', 'system', 'under', 'consideration', 'is', 'a', 'pseudohermitian', 'reduction', 'of', 'a', 'matrix', 'generalization', 'of', 'classical', '11', 'dimensional', 'heisenberg', 'ferromagnet', 'equation', 'we', 'derive', 'recursion', 'operators', 'and', 'describe', 'the', 'integrable', 'hierarchy', 'related', 'to', 'that', 'matrix', 'equation']] | [-0.14222983473911882, 0.085686574280262, -0.0459347228705883, 0.11803321293089539, -0.13310146850068122, -0.1938832694850862, -0.05190011125523597, 0.26959102481603625, -0.32781242884229866, -0.16744968205690383, 0.13980771175120027, -0.3117183189600473, -0.23907941535115243, 0.13811994957271964, -0.004759191386401653, 0.11193898838013411, 0.08490747712552547, 0.00914269910659641, -0.20573192790616304, -0.23203779317438603, 0.40692361975088714, -0.07984283718280494, 0.23301519395783543, -0.014867152745136992, 0.23368352086283267, 0.06646268193377182, 0.011100756525993348, -0.07132052149623633, -0.1553626962059934, 0.0804598496761173, 0.274286734983325, -0.013332099728286266, 0.14332399556413292, -0.41970296010375024, -0.18135112909600137, 0.07152427005581558, 0.1654713276773691, 0.11223979710135609, -0.020377883897162975, -0.3004786534048617, -0.019588669650256633, -0.1601650005299598, -0.2004889550525695, -0.094388796556741, 0.005090161822736263, -0.044029439054429534, -0.23914896052330734, 0.17269926032982766, 0.11497742008417845, 0.0483388963402831, -0.11880287219770252, -0.10285469067050144, 0.0362599041312933, -0.046610115859657526, -0.05146221468690783, -0.01585673313587904, 0.04360827201977372, -0.045113058610877485, -0.09854440588504076, 0.3343433534353972, -0.09838983670808375, -0.32430951815098524, 0.09532271280884742, -0.1547023001126945, -0.1542289008013904, 0.10132841995800845, 0.1689972496405244, 0.047967294128611684, -0.23244790630415083, 0.21282538951258176, -0.08913021256215871, 0.15785546858329325, 0.027788814059458673, -0.0022767250891774895, 0.07241603497415781, 0.18587783571332694, 0.07844802612438798, 0.18206260338425637, 0.03201571743935347, -0.17428658954799175, -0.3099192290008068, -0.21998596711084248, -0.13035551579203455, 0.2231062345020473, -0.0973353103621048, -0.18534232407808304, 0.36915696669369935, 0.14231770444777794, 0.12453564938157796, 0.05883884266018868, 0.16796615047380328, 0.3121299156919122, 0.015734316259622575, 0.010221294164657592, 0.13520747404545547, 0.28273373703705146, 0.14153276060707867, -0.26459913202794266, -0.11648049706593155, 0.22801777066662907] |
1,802.03592 | Uniqueness results on phaseless inverse scattering with a reference ball | This paper is devoted to the uniqueness in inverse acoustic scattering
problems for the Helmholtz equation with phaseless far-field data. Some novel
techniques are developed to overcome the difficulty of translation invariance
induced by a single incident plane wave. In this paper, based on adding a
reference ball as an extra artificial impenetrable obstacle (resp. penetrable
homogeneous medium) to the inverse obstacle (resp. medium) scattering system
and then using superpositions of a fixed plane wave and some point sources as
the incident waves, we rigorously prove that the location and shape of the
obstacle as well as its boundary condition or the refractive index can be
uniquely determined by the modulus of far-field patterns. The reference ball
technique in conjunction with the superposition of incident waves brings in
several salient benefits. First, the framework of our theoretical analysis can
be applied to both the inverse obstacle and medium scattering problems. Second,
for inverse obstacle scattering, the underlying boundary condition could be of
a general type and be uniquely determined. Finally, only a single frequency is
needed.
| math.AP | this paper is devoted to the uniqueness in inverse acoustic scattering problems for the helmholtz equation with phaseless farfield data some novel techniques are developed to overcome the difficulty of translation invariance induced by a single incident plane wave in this paper based on adding a reference ball as an extra artificial impenetrable obstacle resp penetrable homogeneous medium to the inverse obstacle resp medium scattering system and then using superpositions of a fixed plane wave and some point sources as the incident waves we rigorously prove that the location and shape of the obstacle as well as its boundary condition or the refractive index can be uniquely determined by the modulus of farfield patterns the reference ball technique in conjunction with the superposition of incident waves brings in several salient benefits first the framework of our theoretical analysis can be applied to both the inverse obstacle and medium scattering problems second for inverse obstacle scattering the underlying boundary condition could be of a general type and be uniquely determined finally only a single frequency is needed | [['this', 'paper', 'is', 'devoted', 'to', 'the', 'uniqueness', 'in', 'inverse', 'acoustic', 'scattering', 'problems', 'for', 'the', 'helmholtz', 'equation', 'with', 'phaseless', 'farfield', 'data', 'some', 'novel', 'techniques', 'are', 'developed', 'to', 'overcome', 'the', 'difficulty', 'of', 'translation', 'invariance', 'induced', 'by', 'a', 'single', 'incident', 'plane', 'wave', 'in', 'this', 'paper', 'based', 'on', 'adding', 'a', 'reference', 'ball', 'as', 'an', 'extra', 'artificial', 'impenetrable', 'obstacle', 'resp', 'penetrable', 'homogeneous', 'medium', 'to', 'the', 'inverse', 'obstacle', 'resp', 'medium', 'scattering', 'system', 'and', 'then', 'using', 'superpositions', 'of', 'a', 'fixed', 'plane', 'wave', 'and', 'some', 'point', 'sources', 'as', 'the', 'incident', 'waves', 'we', 'rigorously', 'prove', 'that', 'the', 'location', 'and', 'shape', 'of', 'the', 'obstacle', 'as', 'well', 'as', 'its', 'boundary', 'condition', 'or', 'the', 'refractive', 'index', 'can', 'be', 'uniquely', 'determined', 'by', 'the', 'modulus', 'of', 'farfield', 'patterns', 'the', 'reference', 'ball', 'technique', 'in', 'conjunction', 'with', 'the', 'superposition', 'of', 'incident', 'waves', 'brings', 'in', 'several', 'salient', 'benefits', 'first', 'the', 'framework', 'of', 'our', 'theoretical', 'analysis', 'can', 'be', 'applied', 'to', 'both', 'the', 'inverse', 'obstacle', 'and', 'medium', 'scattering', 'problems', 'second', 'for', 'inverse', 'obstacle', 'scattering', 'the', 'underlying', 'boundary', 'condition', 'could', 'be', 'of', 'a', 'general', 'type', 'and', 'be', 'uniquely', 'determined', 'finally', 'only', 'a', 'single', 'frequency', 'is', 'needed']] | [-0.10816000073894182, 0.12002153197028581, -0.08331758615994742, 0.03635413643628867, -0.13228747899120208, -0.09398988041952676, 0.009425104136673988, 0.38806307363980025, -0.34871760101734917, -0.27540445827815513, 0.14351457251971375, -0.2832255957208955, -0.150673439732848, 0.17414022938067897, -0.03184214797511231, 0.1372707100040729, 0.04362365500723668, 0.025942296894341285, -0.02501411372875075, -0.1465651484398801, 0.36767392997121945, 0.02301472037056969, 0.2722346949488962, 0.07679551195707807, 0.10945803618696201, 0.05351686702686658, 0.0037170019204495475, 0.031289341696141164, -0.08871751974505639, 0.12335641534379894, 0.245243138907509, 0.08192186883719511, 0.2290814227625643, -0.44142460384914145, -0.25142142035342246, 0.1011122538792816, 0.16963157254742162, 0.11249843106964942, -0.06103929513946324, -0.3026127540188926, 0.06459792783822525, -0.07358262700621378, -0.22466029731069945, 0.011451342412990263, 0.004892540669640188, 0.028696715785785767, -0.2721132152152925, 0.05965546092589979, 0.06974591418541175, 0.02251473325304687, -0.09683400355358819, -0.06711017299147154, 0.024267093326092105, 0.09585692216933239, 0.031526896218896254, 0.04354165699359411, 0.1108650657988619, -0.12794874238368886, -0.0888426262377338, 0.41799825500823895, -0.0392497876356298, -0.25603499671607016, 0.13303238367370795, -0.11770652436693622, -0.026165920288556, 0.16772929214163346, 0.1926662819142538, 0.11802977989745242, -0.16533377317087303, 0.08630779934140843, -0.038404509222593995, 0.16066363845791123, 0.15168139057773675, -0.010085261803562224, 0.1766541019367816, 0.15610979754869317, 0.0844470426064535, 0.17069416987677952, -0.09686775305123195, 0.008288494197503578, -0.33668421618957917, -0.10765151551888928, -0.22032999009851748, 0.02301683250020274, -0.0864619421014471, -0.16705448630455066, 0.336994679575913, 0.12692471959003757, 0.17559144815789873, 0.010315030601552942, 0.3179650566717927, 0.16125361955610887, 0.013423957298403944, 0.05070483185012232, 0.24703544934395308, 0.16370872861906802, 0.1037353476064957, -0.21023453418671456, 0.02318940072225153, 0.06705820308178029] |
1,802.03593 | Dynamics of observables in rank-based models and performance of
functionally generated portfolios | In the seminal work [9], several macroscopic market observables have been
introduced, in an attempt to find characteristics capturing the diversity of a
financial market. Despite the crucial importance of such observables for
investment decisions, a concise mathematical description of their dynamics has
been missing. We fill this gap in the setting of rank-based models and expect
our ideas to extend to other models of large financial markets as well. The
results are then used to study the performance of multiplicatively and
additively functionally generated portfolios, in particular, over short-term
and medium-term horizons.
| math.PR q-fin.MF | in the seminal work 9 several macroscopic market observables have been introduced in an attempt to find characteristics capturing the diversity of a financial market despite the crucial importance of such observables for investment decisions a concise mathematical description of their dynamics has been missing we fill this gap in the setting of rankbased models and expect our ideas to extend to other models of large financial markets as well the results are then used to study the performance of multiplicatively and additively functionally generated portfolios in particular over shortterm and mediumterm horizons | [['in', 'the', 'seminal', 'work', '9', 'several', 'macroscopic', 'market', 'observables', 'have', 'been', 'introduced', 'in', 'an', 'attempt', 'to', 'find', 'characteristics', 'capturing', 'the', 'diversity', 'of', 'a', 'financial', 'market', 'despite', 'the', 'crucial', 'importance', 'of', 'such', 'observables', 'for', 'investment', 'decisions', 'a', 'concise', 'mathematical', 'description', 'of', 'their', 'dynamics', 'has', 'been', 'missing', 'we', 'fill', 'this', 'gap', 'in', 'the', 'setting', 'of', 'rankbased', 'models', 'and', 'expect', 'our', 'ideas', 'to', 'extend', 'to', 'other', 'models', 'of', 'large', 'financial', 'markets', 'as', 'well', 'the', 'results', 'are', 'then', 'used', 'to', 'study', 'the', 'performance', 'of', 'multiplicatively', 'and', 'additively', 'functionally', 'generated', 'portfolios', 'in', 'particular', 'over', 'shortterm', 'and', 'mediumterm', 'horizons']] | [-0.06787425631116475, 0.05380977384050712, -0.10977156597480017, 0.12275710279717841, -0.07181694731116295, -0.07573097262291177, 0.02410868778624522, 0.3955169689531128, -0.26408598229529395, -0.284722390284221, 0.12255939401145424, -0.28617178732328996, -0.1806082994607027, 0.19821293974515572, -0.1485705333085911, 0.06626784785961111, 0.033018363359314136, -0.013037090500195822, 0.012684394362612917, -0.31268831200256786, 0.27942051152919006, 0.08731400360783903, 0.2745053544540399, 0.06468335854605288, 0.08982918269792071, -0.028047477117969944, -0.09943064477955622, 0.05207575674379064, -0.14690310020320196, 0.16391737614908525, 0.33381167483137064, 0.1277963278805136, 0.37278285812866946, -0.46655830504593027, -0.2220946931109954, 0.13245485170734345, 0.07043937441732695, 0.07474191775245051, 0.023412096634587293, -0.24564296301073765, 0.03924241989490486, -0.2500137156876986, -0.11162380043978012, -0.15128651924271097, 0.04320568610884009, 0.013878885233494382, -0.23172538241394045, 0.03067858815593745, 0.0732453631664162, 0.06410672031943837, -0.05315883481694806, -0.12783626853538457, 0.005226097840775726, 0.18786700053881572, 0.14679416273069637, -0.06505651203965548, 0.09690547025992825, -0.13675757144798115, -0.207259997690437, 0.3608174557166715, -0.04682608010868231, -0.1738498964376988, 0.18885763916837914, -0.13647683019880005, -0.1348004914940365, 0.06072830420828635, 0.21445522994123478, 0.06624093179601015, -0.21752875300264488, 0.06874465938189357, -0.05283167224217166, 0.1308818525855019, 0.047567379495669755, 0.058541569297993055, 0.20324521108720733, 0.1901627664519374, 0.057184751168073665, 0.09810803990475633, -0.02970611509711792, -0.16133139799198798, -0.25221692708631355, -0.12186919523200261, -0.1433604465940246, 0.04366448491946944, -0.07803691237597221, -0.17808584541943606, 0.4273913994992292, 0.1891026336509454, 0.18247595374842965, 0.06769141638951917, 0.251603103105119, 0.052636512430004216, 0.036976224474088784, 0.05303490828842886, 0.22079587517361288, 0.059707754111338045, 0.1537586162204764, -0.14228860246297972, 0.1380906191864802, 0.009211300369051675] |
1,802.03594 | Online Learning for Effort Reduction in Interactive Neural Machine
Translation | Neural machine translation systems require large amounts of training data and
resources. Even with this, the quality of the translations may be insufficient
for some users or domains. In such cases, the output of the system must be
revised by a human agent. This can be done in a post-editing stage or following
an interactive machine translation protocol.
We explore the incremental update of neural machine translation systems
during the post-editing or interactive translation processes. Such
modifications aim to incorporate the new knowledge, from the edited sentences,
into the translation system. Updates to the model are performed on-the-fly, as
sentences are corrected, via online learning techniques. In addition, we
implement a novel interactive, adaptive system, able to react to
single-character interactions. This system greatly reduces the human effort
required for obtaining high-quality translations.
In order to stress our proposals, we conduct exhaustive experiments varying
the amount and type of data available for training. Results show that online
learning effectively achieves the objective of reducing the human effort
required during the post-editing or the interactive machine translation stages.
Moreover, these adaptive systems also perform well in scenarios with scarce
resources. We show that a neural machine translation system can be rapidly
adapted to a specific domain, exclusively by means of online learning
techniques.
| cs.CL | neural machine translation systems require large amounts of training data and resources even with this the quality of the translations may be insufficient for some users or domains in such cases the output of the system must be revised by a human agent this can be done in a postediting stage or following an interactive machine translation protocol we explore the incremental update of neural machine translation systems during the postediting or interactive translation processes such modifications aim to incorporate the new knowledge from the edited sentences into the translation system updates to the model are performed onthefly as sentences are corrected via online learning techniques in addition we implement a novel interactive adaptive system able to react to singlecharacter interactions this system greatly reduces the human effort required for obtaining highquality translations in order to stress our proposals we conduct exhaustive experiments varying the amount and type of data available for training results show that online learning effectively achieves the objective of reducing the human effort required during the postediting or the interactive machine translation stages moreover these adaptive systems also perform well in scenarios with scarce resources we show that a neural machine translation system can be rapidly adapted to a specific domain exclusively by means of online learning techniques | [['neural', 'machine', 'translation', 'systems', 'require', 'large', 'amounts', 'of', 'training', 'data', 'and', 'resources', 'even', 'with', 'this', 'the', 'quality', 'of', 'the', 'translations', 'may', 'be', 'insufficient', 'for', 'some', 'users', 'or', 'domains', 'in', 'such', 'cases', 'the', 'output', 'of', 'the', 'system', 'must', 'be', 'revised', 'by', 'a', 'human', 'agent', 'this', 'can', 'be', 'done', 'in', 'a', 'postediting', 'stage', 'or', 'following', 'an', 'interactive', 'machine', 'translation', 'protocol', 'we', 'explore', 'the', 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1,802.03595 | Deconstructing a galaxy: colour distributions of point sources in
Messier 83 | What do we see when we look at a nearby, well-resolved galaxy? Thousands of
individual sources are detected in multiband imaging observations of even a
fraction of a nearby galaxy, and characterizing those sources is a complex
process. This work analyses a ten-band photometric catalogue of nearly 70 000
point sources in a 7.3 square arcmin region of the nearby spiral galaxy Messier
83, made as part of the Early Release Science programme with the Hubble Space
Telescope's Wide Field Camera 3. Colour distributions were measured for both
broad-band and broad-and-narrow-band colours; colours made from broad-bands
with large wavelength differences generally had broader distributions although
B - V was an exception. Two- and three-dimensional colour spaces were generated
using various combinations of four bands and clustered with the K-Means and
Mean Shift algorithms. Neither algorithm was able to consistently segment the
colour distributions: while some distinct features in colour space were
apparent in visual examinations, these features were not compact or isolated
enough to be recognized as clusters in colour space. K-Means clustering of the
UBVI colour space was able to identify a group of objects more likely to be
star clusters. Mean Shift was successful in identifying outlying groups at the
edges of colour distributions. For identifying objects whose emission is
dominated by spectral lines, there was no clear benefit from combining
narrow-band photometry in multiple bands compared to a simple continuum
subtraction. The clustering analysis results are used to inform recommendations
for future surveys of nearby galaxies.
| astro-ph.GA | what do we see when we look at a nearby wellresolved galaxy thousands of individual sources are detected in multiband imaging observations of even a fraction of a nearby galaxy and characterizing those sources is a complex process this work analyses a tenband photometric catalogue of nearly 70 000 point sources in a 73 square arcmin region of the nearby spiral galaxy messier 83 made as part of the early release science programme with the hubble space telescopes wide field camera 3 colour distributions were measured for both broadband and broadandnarrowband colours colours made from broadbands with large wavelength differences generally had broader distributions although b v was an exception two and threedimensional colour spaces were generated using various combinations of four bands and clustered with the kmeans and mean shift algorithms neither algorithm was able to consistently segment the colour distributions while some distinct features in colour space were apparent in visual examinations these features were not compact or isolated enough to be recognized as clusters in colour space kmeans clustering of the ubvi colour space was able to identify a group of objects more likely to be star clusters mean shift was successful in identifying outlying groups at the edges of colour distributions for identifying objects whose emission is dominated by spectral lines there was no clear benefit from combining narrowband photometry in multiple bands compared to a simple continuum subtraction the clustering analysis results are used to inform recommendations for future surveys of nearby galaxies | [['what', 'do', 'we', 'see', 'when', 'we', 'look', 'at', 'a', 'nearby', 'wellresolved', 'galaxy', 'thousands', 'of', 'individual', 'sources', 'are', 'detected', 'in', 'multiband', 'imaging', 'observations', 'of', 'even', 'a', 'fraction', 'of', 'a', 'nearby', 'galaxy', 'and', 'characterizing', 'those', 'sources', 'is', 'a', 'complex', 'process', 'this', 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1,802.03596 | Deep Meta-Learning: Learning to Learn in the Concept Space | Few-shot learning remains challenging for meta-learning that learns a
learning algorithm (meta-learner) from many related tasks. In this work, we
argue that this is due to the lack of a good representation for meta-learning,
and propose deep meta-learning to integrate the representation power of deep
learning into meta-learning. The framework is composed of three modules, a
concept generator, a meta-learner, and a concept discriminator, which are
learned jointly. The concept generator, e.g. a deep residual net, extracts a
representation for each instance that captures its high-level concept, on which
the meta-learner performs few-shot learning, and the concept discriminator
recognizes the concepts. By learning to learn in the concept space rather than
in the complicated instance space, deep meta-learning can substantially improve
vanilla meta-learning, which is demonstrated on various few-shot image
recognition problems. For example, on 5-way-1-shot image recognition on
CIFAR-100 and CUB-200, it improves Matching Nets from 50.53% and 56.53% to
58.18% and 63.47%, improves MAML from 49.28% and 50.45% to 56.65% and 64.63%,
and improves Meta-SGD from 53.83% and 53.34% to 61.62% and 66.95%,
respectively.
| cs.LG | fewshot learning remains challenging for metalearning that learns a learning algorithm metalearner from many related tasks in this work we argue that this is due to the lack of a good representation for metalearning and propose deep metalearning to integrate the representation power of deep learning into metalearning the framework is composed of three modules a concept generator a metalearner and a concept discriminator which are learned jointly the concept generator eg a deep residual net extracts a representation for each instance that captures its highlevel concept on which the metalearner performs fewshot learning and the concept discriminator recognizes the concepts by learning to learn in the concept space rather than in the complicated instance space deep metalearning can substantially improve vanilla metalearning which is demonstrated on various fewshot image recognition problems for example on 5way1shot image recognition on cifar100 and cub200 it improves matching nets from 5053 and 5653 to 5818 and 6347 improves maml from 4928 and 5045 to 5665 and 6463 and improves metasgd from 5383 and 5334 to 6162 and 6695 respectively | [['fewshot', 'learning', 'remains', 'challenging', 'for', 'metalearning', 'that', 'learns', 'a', 'learning', 'algorithm', 'metalearner', 'from', 'many', 'related', 'tasks', 'in', 'this', 'work', 'we', 'argue', 'that', 'this', 'is', 'due', 'to', 'the', 'lack', 'of', 'a', 'good', 'representation', 'for', 'metalearning', 'and', 'propose', 'deep', 'metalearning', 'to', 'integrate', 'the', 'representation', 'power', 'of', 'deep', 'learning', 'into', 'metalearning', 'the', 'framework', 'is', 'composed', 'of', 'three', 'modules', 'a', 'concept', 'generator', 'a', 'metalearner', 'and', 'a', 'concept', 'discriminator', 'which', 'are', 'learned', 'jointly', 'the', 'concept', 'generator', 'eg', 'a', 'deep', 'residual', 'net', 'extracts', 'a', 'representation', 'for', 'each', 'instance', 'that', 'captures', 'its', 'highlevel', 'concept', 'on', 'which', 'the', 'metalearner', 'performs', 'fewshot', 'learning', 'and', 'the', 'concept', 'discriminator', 'recognizes', 'the', 'concepts', 'by', 'learning', 'to', 'learn', 'in', 'the', 'concept', 'space', 'rather', 'than', 'in', 'the', 'complicated', 'instance', 'space', 'deep', 'metalearning', 'can', 'substantially', 'improve', 'vanilla', 'metalearning', 'which', 'is', 'demonstrated', 'on', 'various', 'fewshot', 'image', 'recognition', 'problems', 'for', 'example', 'on', '5way1shot', 'image', 'recognition', 'on', 'cifar100', 'and', 'cub200', 'it', 'improves', 'matching', 'nets', 'from', '5053', 'and', '5653', 'to', '5818', 'and', '6347', 'improves', 'maml', 'from', '4928', 'and', '5045', 'to', '5665', 'and', '6463', 'and', 'improves', 'metasgd', 'from', '5383', 'and', '5334', 'to', '6162', 'and', '6695', 'respectively']] | [0.02344049990166262, -0.0066381001147871824, -0.09047999279845877, 0.07238191314251298, -0.16038612431536117, -0.19105133954408354, 0.0033235956958293567, 0.43648085366302763, -0.28966001108185285, -0.32954895252372784, 0.030195590869076985, -0.2513514223007474, -0.22883560546613924, 0.20405877140835363, -0.179315708365226, 0.047495112160414084, 0.11993702695356431, 0.04738668547616454, -0.0811025949238598, -0.24970708334646924, 0.301770238121032, 0.0240398074101319, 0.3463651754291543, 0.009878447557096942, 0.21286910611793006, -0.003454942946534669, 0.006454982372730622, -0.0391184018808417, -0.028055479945381764, 0.21082982986522783, 0.3186926274289437, 0.24609884749880137, 0.33849653087512793, -0.33665155162163385, -0.22979455161703566, 0.07185651304075026, 0.11834499676742834, 0.09181045791561775, -0.028346117496032986, -0.3540430170489334, 0.09837166884946719, -0.18954448449583342, 0.0789879679398023, -0.1670398012182147, -0.03983117417263531, -0.06030014891188309, -0.31633700070935383, -0.013629272789149736, 0.16135660799666804, 0.018847522636254627, -0.06301511721808072, -0.11857715014316215, 0.05001672540930214, 0.11819078688569672, -0.006780873922827213, 0.11593737503873151, 0.1432412975789075, -0.21114274608352554, -0.13654076886956978, 0.3385272264829156, -0.054634523337413504, -0.16968991975327247, 0.20333579828559656, 0.027816287688475255, -0.1683088816424603, 0.06816828171028183, 0.2220164449430719, 0.13631329784164348, -0.13714118156723723, 0.029983012693626424, -0.076748534383481, 0.14182972696430066, 0.04524216125583091, -0.04147269383034729, 0.1467640726957797, 0.29712237333552094, 0.048322240891117516, 0.1475085311267002, -0.11543226008962469, -0.08127785493668757, -0.181068553039205, -0.10767861373439344, -0.19357049335420612, -0.043058048966422416, -0.08694820915532982, -0.1320582830483449, 0.4070335664726489, 0.21736176152528422, 0.2467592711919886, 0.14756182554158878, 0.33567337654476537, 0.014154764581253828, 0.1341047477065953, 0.12250546774314988, 0.1744805442847377, 0.02588929021485934, 0.12154302091136227, -0.16485315068129297, 0.0821677628722129, 0.09834373284850204] |
1,802.03597 | Document Classification Using Distributed Machine Learning | In this paper, we investigate the performance and success rates of Na\"ive
Bayes Classification Algorithm for automatic classification of Turkish news
into predetermined categories like economy, life, health etc. We use Apache Big
Data technologies such as Hadoop, HDFS, Spark and Mahout, and apply these
distributed technologies to Machine Learning.
| cs.IR cs.DC | in this paper we investigate the performance and success rates of naive bayes classification algorithm for automatic classification of turkish news into predetermined categories like economy life health etc we use apache big data technologies such as hadoop hdfs spark and mahout and apply these distributed technologies to machine learning | [['in', 'this', 'paper', 'we', 'investigate', 'the', 'performance', 'and', 'success', 'rates', 'of', 'naive', 'bayes', 'classification', 'algorithm', 'for', 'automatic', 'classification', 'of', 'turkish', 'news', 'into', 'predetermined', 'categories', 'like', 'economy', 'life', 'health', 'etc', 'we', 'use', 'apache', 'big', 'data', 'technologies', 'such', 'as', 'hadoop', 'hdfs', 'spark', 'and', 'mahout', 'and', 'apply', 'these', 'distributed', 'technologies', 'to', 'machine', 'learning']] | [-0.030124809639528394, -0.02583364211022854, -0.05959143685176969, 0.1194207960809581, -0.08429797537624836, -0.15632836155593396, 0.11509609299013392, 0.47546250181272626, -0.2882202671095729, -0.315568506186828, 0.15983574134064837, -0.29743070896714924, -0.13693545771297067, 0.25273309196345506, -0.17072691551875324, 0.09384124882519246, 0.1403630666807294, 0.07248799342662096, -0.03149686312302947, -0.35510387979447844, 0.28879548706347125, 0.05105442691594362, 0.47866201523691415, 0.018829155918210744, 0.05760027634212747, 0.014122617756947875, -0.07048377368599176, -0.06477182097733021, -0.07829961677081883, 0.1624720036238432, 0.380485014654696, 0.3531640861555934, 0.39137509480118754, -0.3948363591171801, -0.11428966373205185, 0.12633426683023571, 0.17079701831564306, 0.02938856744207442, -0.09832889258861542, -0.32888734782580287, 0.08368183698970824, -0.2884066693484783, -0.016711475402116774, -0.18494311230257154, -0.020044318633154035, 0.0330096254684031, -0.2088177541270852, -0.006056111250072718, 0.01187416549772024, 0.13661323527805508, -0.06218933751806617, -0.13988883044570685, 0.07371728306403384, 0.17732309225946666, 0.03094887625426054, 0.012763461200520396, 0.26074085032567385, -0.15815416255500167, -0.21012838765978814, 0.3740714336372912, -0.051725358963012696, -0.05580651327036321, 0.19766745157539845, 0.05545277026947588, -0.2358096351660788, -0.02857737200334668, 0.3588227736367844, 0.015959205725230276, -0.20135589272715151, 0.038345707558328286, 0.025979050174355506, 0.1315035916864872, 0.0904948490858078, -0.006537835765630007, 0.15104663534555585, 0.31924164813011885, -0.003846640307456255, 0.09224396497011185, -0.09203987136483192, -0.08834663173183799, -0.15641566014615818, -0.21664578814059496, -0.09898405686020852, 0.0393301527062431, -0.1099326718217344, -0.21698969271034002, 0.32643142667831854, 0.23562864925712346, 0.09071516866795719, 0.08416088361293078, 0.39552290603518486, -0.06623688549036161, 0.14653689423575997, 0.15273542660288514, 0.09406176035292446, -0.06037059314548969, 0.23947938717901707, -0.10186594200320542, 0.02805959416553378, 0.00920236824080348] |
1,802.03598 | The monoid of order isomorphisms of principal filters of a power of the
positive integers | Let $n$ be any positive integer and $\mathscr{I\!\!P\!F}(\mathbb{N}^n)$ be
the semigroup of all order isomorphisms between principal filters of the $n$-th
power of the set of positive integers $\mathbb{N}$ with the product order. We
study algebraic properties of the semigroup
$\mathscr{I\!\!P\!F}(\mathbb{N}^n)$. In particular, we show that
$\mathscr{I\!\!P\!F}(\mathbb{N}^n)$ is a bisimple, $E$-unitary, $F$-inverse
semigroup, describe Green's relations on $\mathscr{I\!\!P\!F}(\mathbb{N}^n)$
and its maximal subgroups. We show that the semigroup
$\mathscr{I\!\!P\!F}(\mathbb{N}^n)$ is isomorphic to the semidirect product of
the direct $n$-th power of the bicyclic monoid ${\mathscr{C}}^n(p,q)$ by the
group of permutation $\mathscr{S}_n$. Also we prove that every non-identity
congruence $\mathfrak{C}$ on the semigroup $\mathscr{I\!\!P\!F}(\mathbb{N}^n)$
is group and describe the least group congruence on
$\mathscr{I\!\!P\!F}(\mathbb{N}^n)$. We show that every Hausdorff
shift-continuous topology on $\mathscr{I\!\!P\!F}(\mathbb{N}^n)$ is discrete
and discuss embedding of the semigroup $\mathscr{I\!\!P\!F}(\mathbb{N}^n)$ into
compact-like topological semigroups.
| math.GR math.GN | let n be any positive integer and mathscripfmathbbnn be the semigroup of all order isomorphisms between principal filters of the nth power of the set of positive integers mathbbn with the product order we study algebraic properties of the semigroup mathscripfmathbbnn in particular we show that mathscripfmathbbnn is a bisimple eunitary finverse semigroup describe greens relations on mathscripfmathbbnn and its maximal subgroups we show that the semigroup mathscripfmathbbnn is isomorphic to the semidirect product of the direct nth power of the bicyclic monoid mathscrcnpq by the group of permutation mathscrs_n also we prove that every nonidentity congruence mathfrakc on the semigroup mathscripfmathbbnn is group and describe the least group congruence on mathscripfmathbbnn we show that every hausdorff shiftcontinuous topology on mathscripfmathbbnn is discrete and discuss embedding of the semigroup mathscripfmathbbnn into compactlike topological semigroups | [['let', 'n', 'be', 'any', 'positive', 'integer', 'and', 'mathscripfmathbbnn', 'be', 'the', 'semigroup', 'of', 'all', 'order', 'isomorphisms', 'between', 'principal', 'filters', 'of', 'the', 'nth', 'power', 'of', 'the', 'set', 'of', 'positive', 'integers', 'mathbbn', 'with', 'the', 'product', 'order', 'we', 'study', 'algebraic', 'properties', 'of', 'the', 'semigroup', 'mathscripfmathbbnn', 'in', 'particular', 'we', 'show', 'that', 'mathscripfmathbbnn', 'is', 'a', 'bisimple', 'eunitary', 'finverse', 'semigroup', 'describe', 'greens', 'relations', 'on', 'mathscripfmathbbnn', 'and', 'its', 'maximal', 'subgroups', 'we', 'show', 'that', 'the', 'semigroup', 'mathscripfmathbbnn', 'is', 'isomorphic', 'to', 'the', 'semidirect', 'product', 'of', 'the', 'direct', 'nth', 'power', 'of', 'the', 'bicyclic', 'monoid', 'mathscrcnpq', 'by', 'the', 'group', 'of', 'permutation', 'mathscrs_n', 'also', 'we', 'prove', 'that', 'every', 'nonidentity', 'congruence', 'mathfrakc', 'on', 'the', 'semigroup', 'mathscripfmathbbnn', 'is', 'group', 'and', 'describe', 'the', 'least', 'group', 'congruence', 'on', 'mathscripfmathbbnn', 'we', 'show', 'that', 'every', 'hausdorff', 'shiftcontinuous', 'topology', 'on', 'mathscripfmathbbnn', 'is', 'discrete', 'and', 'discuss', 'embedding', 'of', 'the', 'semigroup', 'mathscripfmathbbnn', 'into', 'compactlike', 'topological', 'semigroups']] | [-0.1874175472608344, 0.12111536426836568, -0.07492640941530805, 0.05386583554553164, -0.10457645787215425, -0.08173664617634406, -0.010402480486046636, 0.3694219712563085, -0.3948295527758698, -0.1098886476547429, 0.13376349989928052, -0.36948487294527393, -0.14048649650949024, 0.19478776325669253, -0.07715249428469123, -0.029630584498094817, 0.032249761198534434, 0.1651011602247529, -0.100701040807039, -0.23218207735176707, 0.40938595546917483, -0.06987285317683761, 0.20205540564369806, 0.0457432239958452, 0.1245436192363162, -0.021402321494833537, -0.03285462814594873, -0.003969400233913664, -0.1803787411907141, 0.06982733860301475, 0.26911453685412806, 0.10860478683995704, 0.23044221360098824, -0.374011865567864, -0.10021312787395084, 0.24332049509129403, 0.09222917962523008, -0.09973313559947367, -0.006478876494590871, -0.2792609051631933, 0.19078478533915724, -0.2110414928333326, -0.08167751661191384, -0.09777834625150938, 0.11834653388976937, 0.015233421019123247, -0.26191780318252067, -0.030172250440183703, 0.15213524926961822, 0.09531185328281329, -0.022481466538562072, -0.08416956543100462, -0.09627508667163608, 0.09070791657943504, -0.02819746705195443, -0.013063942128086857, 0.06441849842226613, 0.006691215362286929, -0.16151035950998915, 0.38186645297559374, -0.04831550477926309, -0.19187154472015347, 0.08231363656964139, -0.24591502577249863, -0.17824340701300764, 0.0890387432282847, 0.05820873472839594, 0.13765749695823726, 0.003182153231605436, 0.24467856744410132, -0.1989837423263287, 0.13354092968670142, 0.0460036298873919, 0.004850750097609831, 0.06495655767944161, 0.06538082118618838, 0.1101617896276752, 0.17706502714215525, 0.10841313498641478, 0.06804688170086592, -0.36207732449596125, -0.22010443794583393, -0.15944235047738237, 0.15518698532303626, -0.14948938323284505, -0.18424588749467424, 0.41132798445213475, 0.11931683769216761, 0.10122222697122418, 0.1443040448692486, 0.16814006515425828, 0.11994831703497877, 0.021174855002865308, 0.09706258420324461, 0.01473383843955217, 0.23851626806814843, -0.13628083069576832, -0.22743811033786077, -0.001037570586159938, 0.2864115152042359] |
1,802.03599 | Laplacian Dynamics on Cographs: Controllability Analysis through Joins
and Unions | In this paper, we examine the controllability of Laplacian dynamic networks
on cographs. Cographs appear in modeling a wide range of networks and include
as special instances, the threshold graphs. In this work, we present necessary
and sufficient conditions for the controllability of cographs, and provide an
efficient method for selecting a minimal set of input nodes from which the
network is controllable. In particular, we define a sibling partition in a
cograph and show that the network is controllable if all nodes of any cell of
this partition except one are chosen as control nodes. The key ingredient for
such characterizations is the intricate connection between the modularity of
cographs and their modal properties. Finally, we use these results to
characterize the controllability conditions for certain subclasses of cographs.
| math.OC cs.DM | in this paper we examine the controllability of laplacian dynamic networks on cographs cographs appear in modeling a wide range of networks and include as special instances the threshold graphs in this work we present necessary and sufficient conditions for the controllability of cographs and provide an efficient method for selecting a minimal set of input nodes from which the network is controllable in particular we define a sibling partition in a cograph and show that the network is controllable if all nodes of any cell of this partition except one are chosen as control nodes the key ingredient for such characterizations is the intricate connection between the modularity of cographs and their modal properties finally we use these results to characterize the controllability conditions for certain subclasses of cographs | [['in', 'this', 'paper', 'we', 'examine', 'the', 'controllability', 'of', 'laplacian', 'dynamic', 'networks', 'on', 'cographs', 'cographs', 'appear', 'in', 'modeling', 'a', 'wide', 'range', 'of', 'networks', 'and', 'include', 'as', 'special', 'instances', 'the', 'threshold', 'graphs', 'in', 'this', 'work', 'we', 'present', 'necessary', 'and', 'sufficient', 'conditions', 'for', 'the', 'controllability', 'of', 'cographs', 'and', 'provide', 'an', 'efficient', 'method', 'for', 'selecting', 'a', 'minimal', 'set', 'of', 'input', 'nodes', 'from', 'which', 'the', 'network', 'is', 'controllable', 'in', 'particular', 'we', 'define', 'a', 'sibling', 'partition', 'in', 'a', 'cograph', 'and', 'show', 'that', 'the', 'network', 'is', 'controllable', 'if', 'all', 'nodes', 'of', 'any', 'cell', 'of', 'this', 'partition', 'except', 'one', 'are', 'chosen', 'as', 'control', 'nodes', 'the', 'key', 'ingredient', 'for', 'such', 'characterizations', 'is', 'the', 'intricate', 'connection', 'between', 'the', 'modularity', 'of', 'cographs', 'and', 'their', 'modal', 'properties', 'finally', 'we', 'use', 'these', 'results', 'to', 'characterize', 'the', 'controllability', 'conditions', 'for', 'certain', 'subclasses', 'of', 'cographs']] | [-0.17165754074947193, 0.05116548286493456, -0.001785370478263268, 0.06810881751947678, -0.07172074792906642, -0.12766686875659686, 0.046958838948585956, 0.4161639573374906, -0.2870694223898821, -0.30661712407779235, 0.10169929276632432, -0.2203532253589051, -0.20523669599542, 0.1506313364988623, -0.08483483604907703, 0.05906230532922424, 0.11382862927320485, 0.062206888709512825, -0.02697599644575698, -0.21973963579736078, 0.3543676988185885, -0.0011202168794205555, 0.23757588809732205, 0.09662595042027533, 0.08691655148823674, 0.0023768315414110057, 0.036479171524898935, 0.06238002327736467, -0.18002774742853944, 0.11548725101523674, 0.28947805423432815, 0.17936049758170086, 0.2902097144128325, -0.41469926735242973, -0.1725294180930807, 0.18659154314261217, 0.11812401352665172, 0.08445907708239526, -0.006627871659405243, -0.23135609383909747, 0.1282910147869888, -0.11230295825606355, -0.0855064825907063, -0.0794949106621341, 0.018285860318260697, 0.06846975613242158, -0.2828977749611323, 0.01053616379817518, 0.11207351416635972, 0.032069501710625795, -0.046982881867398435, -0.08631873594489521, -0.024107759666199293, 0.1684055510120323, -0.058479382712035795, -0.062480416733664106, 0.040307366407404725, -0.14681822550483048, -0.13228584819544967, 0.381054331921041, 0.032767429379656765, -0.21058438321790443, 0.1700952200075755, -0.08264610475788896, -0.19609358357981993, 0.04061116414735667, 0.1881362421079897, 0.16118258128945645, -0.16009471365250647, 0.06427181341389838, -0.09825234090766082, 0.11181826348273227, 0.09470343893178954, 0.08569552698889031, 0.1251568256519162, 0.2039418158461698, 0.15650531571501722, 0.20464492024531444, -0.021582429139659954, -0.032326510326507, -0.3229663303336845, -0.15069243637486718, -0.18730205084788254, 0.02964496116530007, -0.1454819804839924, -0.20444189416101347, 0.4749508399516344, 0.17195337153971196, 0.21284564684431714, 0.12586591116313894, 0.22568486107047647, 0.08652428509902585, 0.057990079120589566, 0.09983809852542785, 0.175436784627919, 0.16777107834529417, 0.04190240587251118, -0.17421767071355135, 0.0937341528331252, 0.05974662642782697] |
1,802.036 | Regularity of Solutions to the Navier-Stokes equations in
$\dot{B}_{\infty,\infty}^{-1}$ | We prove that if $u$ is a suitable weak solution to the three dimensional
Navier-Stokes equations from the space
$L_{\infty}(0,T;\dot{B}_{\infty,\infty}^{-1})$, then all scaled energy
quantities of $u$ are bounded. As a consequence, it is shown that any axially
symmetric suitable weak solution $u$, belonging to
$L_{\infty}(0,T;\dot{B}_{\infty,\infty}^{-1})$, is smooth.
| math.AP | we prove that if u is a suitable weak solution to the three dimensional navierstokes equations from the space l_infty0tdotb_inftyinfty1 then all scaled energy quantities of u are bounded as a consequence it is shown that any axially symmetric suitable weak solution u belonging to l_infty0tdotb_inftyinfty1 is smooth | [['we', 'prove', 'that', 'if', 'u', 'is', 'a', 'suitable', 'weak', 'solution', 'to', 'the', 'three', 'dimensional', 'navierstokes', 'equations', 'from', 'the', 'space', 'l_infty0tdotb_inftyinfty1', 'then', 'all', 'scaled', 'energy', 'quantities', 'of', 'u', 'are', 'bounded', 'as', 'a', 'consequence', 'it', 'is', 'shown', 'that', 'any', 'axially', 'symmetric', 'suitable', 'weak', 'solution', 'u', 'belonging', 'to', 'l_infty0tdotb_inftyinfty1', 'is', 'smooth']] | [-0.15416490108169534, 0.08067724398215828, -0.04665765663325463, 0.06838314269375785, -0.07173023234202486, -0.16940439879165395, -0.061558699265689305, 0.33829155272763706, -0.36446318074898876, -0.15120053325739244, 0.11646680694842793, -0.2873982757370433, -0.09938052582615257, 0.17147439074419116, -0.007704008633837751, 0.05860491385480956, 0.07436596920065906, 0.0784198749486519, -0.10865989425624518, -0.21848336161802645, 0.40120022986894066, -0.1374347962277091, 0.24655274123601292, 0.028076229586630412, 0.1553777834886442, -0.07631540995435385, 0.08537225000074376, 0.09935081283417632, -0.14267158900221819, 0.0311353230221278, 0.23207378986737, 0.06650614553718301, 0.31228333792608715, -0.3628239895257613, -0.19112915853443352, 0.18903281600417002, 0.09907619832286044, 0.09003751577161577, -0.03448339708847925, -0.2899167153822339, 0.16749802227739408, -0.12068651700594826, -0.22990087727489678, -0.10578521496980735, 0.09974110211529162, 0.11496150147412783, -0.37602356339440396, 0.09043213174394939, 0.07866940201695441, -0.07367113121258824, -0.1457383411065878, -0.07346036450168036, -0.11006788830475314, 0.08916003835063589, 0.05898519289558348, 0.10994594705129124, 0.015697583242360015, -0.06351595072524303, 0.045762422018805926, 0.4179755541617456, -0.11449609591585139, -0.3191552995501653, 0.18698327704940154, -0.15350617288166415, -0.07133446486018923, 0.12261464824373632, 0.08775468162544396, 0.18286555773659569, -0.1758183080945974, 0.23583080861515002, -0.12294403909017211, 0.17937816129541834, 0.0786003931729204, -0.0034956789332563462, 0.09691854902421651, 0.08670266976822978, 0.1885924790378498, 0.1096024118039919, -0.037552997388917465, -0.07002772084113372, -0.38390731876311096, -0.13785434769385535, -0.18654092452134532, 0.1504779397509992, -0.08799306598856398, -0.19450961513148682, 0.29760485111087887, 0.08109315235101942, 0.16727373508088614, 0.023546012746089178, 0.2399991945889981, 0.16926744610384997, 0.011711382782896575, 0.13779830751414207, 0.1876560686075169, 0.12484751784485644, 0.0731164206062322, -0.13551043112681288, -0.00753026740099101, 0.17682481657444124] |
1,802.03601 | Deep Visual Domain Adaptation: A Survey | Deep domain adaption has emerged as a new learning technique to address the
lack of massive amounts of labeled data. Compared to conventional methods,
which learn shared feature subspaces or reuse important source instances with
shallow representations, deep domain adaption methods leverage deep networks to
learn more transferable representations by embedding domain adaptation in the
pipeline of deep learning. There have been comprehensive surveys for shallow
domain adaption, but few timely reviews the emerging deep learning based
methods. In this paper, we provide a comprehensive survey of deep domain
adaptation methods for computer vision applications with four major
contributions. First, we present a taxonomy of different deep domain adaption
scenarios according to the properties of data that define how two domains are
diverged. Second, we summarize deep domain adaption approaches into several
categories based on training loss, and analyze and compare briefly the
state-of-the-art methods under these categories. Third, we overview the
computer vision applications that go beyond image classification, such as face
recognition, semantic segmentation and object detection. Fourth, some potential
deficiencies of current methods and several future directions are highlighted.
| cs.CV | deep domain adaption has emerged as a new learning technique to address the lack of massive amounts of labeled data compared to conventional methods which learn shared feature subspaces or reuse important source instances with shallow representations deep domain adaption methods leverage deep networks to learn more transferable representations by embedding domain adaptation in the pipeline of deep learning there have been comprehensive surveys for shallow domain adaption but few timely reviews the emerging deep learning based methods in this paper we provide a comprehensive survey of deep domain adaptation methods for computer vision applications with four major contributions first we present a taxonomy of different deep domain adaption scenarios according to the properties of data that define how two domains are diverged second we summarize deep domain adaption approaches into several categories based on training loss and analyze and compare briefly the stateoftheart methods under these categories third we overview the computer vision applications that go beyond image classification such as face recognition semantic segmentation and object detection fourth some potential deficiencies of current methods and several future directions are highlighted | [['deep', 'domain', 'adaption', 'has', 'emerged', 'as', 'a', 'new', 'learning', 'technique', 'to', 'address', 'the', 'lack', 'of', 'massive', 'amounts', 'of', 'labeled', 'data', 'compared', 'to', 'conventional', 'methods', 'which', 'learn', 'shared', 'feature', 'subspaces', 'or', 'reuse', 'important', 'source', 'instances', 'with', 'shallow', 'representations', 'deep', 'domain', 'adaption', 'methods', 'leverage', 'deep', 'networks', 'to', 'learn', 'more', 'transferable', 'representations', 'by', 'embedding', 'domain', 'adaptation', 'in', 'the', 'pipeline', 'of', 'deep', 'learning', 'there', 'have', 'been', 'comprehensive', 'surveys', 'for', 'shallow', 'domain', 'adaption', 'but', 'few', 'timely', 'reviews', 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1,802.03602 | Revisiting Spatial-Dependent Propagation Model with Latest Observations
of Cosmic Ray Nuclei | Recently AMS-02 collaboration publish their measurements of light cosmic-ray
nuclei, including lithium, beryllium, boron, carbon and oxygen. All of them
reveal a prominent excess above $\sim$ 200 GV, coinciding with proton and
helium. Particularly, the secondary cosmic rays even harden than the primary
components above that break. One of the viable interpretations for above
anomalies is the spatial-dependent diffusion process. Such model has been
successfully applied to multiple observational phenomena, for example primary
cosmic ray nuclei, diffuse gamma ray and anisotropy. In this work, we
investigate the spatial-dependent propagation model in light of the new
observational data. We find that such model is able to explain the upturn of
secondary spectrum as well as the primary's. All the spectra can be well
reproduced and the calculated ratios are also in good agreement with the
observations.
| astro-ph.HE | recently ams02 collaboration publish their measurements of light cosmicray nuclei including lithium beryllium boron carbon and oxygen all of them reveal a prominent excess above sim 200 gv coinciding with proton and helium particularly the secondary cosmic rays even harden than the primary components above that break one of the viable interpretations for above anomalies is the spatialdependent diffusion process such model has been successfully applied to multiple observational phenomena for example primary cosmic ray nuclei diffuse gamma ray and anisotropy in this work we investigate the spatialdependent propagation model in light of the new observational data we find that such model is able to explain the upturn of secondary spectrum as well as the primarys all the spectra can be well reproduced and the calculated ratios are also in good agreement with the observations | [['recently', 'ams02', 'collaboration', 'publish', 'their', 'measurements', 'of', 'light', 'cosmicray', 'nuclei', 'including', 'lithium', 'beryllium', 'boron', 'carbon', 'and', 'oxygen', 'all', 'of', 'them', 'reveal', 'a', 'prominent', 'excess', 'above', 'sim', '200', 'gv', 'coinciding', 'with', 'proton', 'and', 'helium', 'particularly', 'the', 'secondary', 'cosmic', 'rays', 'even', 'harden', 'than', 'the', 'primary', 'components', 'above', 'that', 'break', 'one', 'of', 'the', 'viable', 'interpretations', 'for', 'above', 'anomalies', 'is', 'the', 'spatialdependent', 'diffusion', 'process', 'such', 'model', 'has', 'been', 'successfully', 'applied', 'to', 'multiple', 'observational', 'phenomena', 'for', 'example', 'primary', 'cosmic', 'ray', 'nuclei', 'diffuse', 'gamma', 'ray', 'and', 'anisotropy', 'in', 'this', 'work', 'we', 'investigate', 'the', 'spatialdependent', 'propagation', 'model', 'in', 'light', 'of', 'the', 'new', 'observational', 'data', 'we', 'find', 'that', 'such', 'model', 'is', 'able', 'to', 'explain', 'the', 'upturn', 'of', 'secondary', 'spectrum', 'as', 'well', 'as', 'the', 'primarys', 'all', 'the', 'spectra', 'can', 'be', 'well', 'reproduced', 'and', 'the', 'calculated', 'ratios', 'are', 'also', 'in', 'good', 'agreement', 'with', 'the', 'observations']] | [-0.046715510220922254, 0.1839290474196551, -0.03782068214689692, 0.15530380395860077, -0.0428270730017512, -0.1203584609255712, 0.026943389629876174, 0.41340477903959927, -0.25443727877853367, -0.37994724597643925, 0.006193474356809424, -0.3554770851197342, -0.04301653851375536, 0.20720938036597714, 0.016656392778235454, 0.01789140233067209, 0.03611369427138319, -0.005905973112762526, 0.001904971971762953, -0.18958173208472243, 0.25456471289249344, 0.17697600114538714, 0.24484277879388106, 0.08729260872911523, 0.03244660467651017, -0.04233430993087866, -0.04055539827655863, -0.014600125658843253, -0.11471451705384189, 0.06424355061586809, 0.22720748881498973, 0.09441736508074595, 0.09446291375724185, -0.40650987901759367, -0.31068903198120773, 0.13167313251437412, 0.17574053802010087, 0.06542492050987979, -0.09371569154232189, -0.2152853089971123, 0.0934312382126572, -0.17458135994772117, -0.16870528381593802, -0.011251102870813122, -0.0312475990469533, 0.06315176085465485, -0.20644347366911395, 0.07564456248116956, 0.01788611076599746, 0.04541378469309873, -0.11992059455046224, -0.15317945633842436, -0.03468076757810734, 0.09713665881297655, 0.11220256093678202, 0.033846772539739806, 0.14446199566677764, -0.07815228209796327, -0.07161709745242088, 0.4176386470595996, -0.0908472060869422, -0.014175983052700758, 0.18007585586229752, -0.1916599375133282, -0.18719108026060793, 0.1606285441632348, 0.13479100339528588, 0.06289028404970412, -0.1700797327897615, 0.03455063797085098, -0.013930608304562392, 0.1415544433053583, 0.06389469861363371, 0.018466930787663267, 0.2622429307136271, 0.1610229362802649, 0.008980063050155338, 0.04633559551374573, -0.1681132841003731, 0.00922491223792787, -0.2760311763496483, -0.13124924049091835, -0.1061410189692483, 0.039062354886145505, -0.07184542330559689, -0.11676327557199531, 0.36050538889787814, 0.10358359800043723, 0.24832024067743785, -0.02415065594120986, 0.3049206350481621, 0.06913216218645513, 0.04585465185033778, 0.07339814762860812, 0.3231957630081861, 0.16254994904932876, 0.11167214292756937, -0.2108286362003397, 0.09090881328684865, -0.060178442409744966] |
1,802.03603 | Running genetic algorithms on Hadoop for solving high dimensional
optimization problems | Hadoop is a popular MapReduce framework for developing parallel applications
in distributed environments. Several advantages of MapReduce such as
programming ease and ability to use commodity hardware make the applicability
of soft computing methods for parallel and distributed systems easier than
before. In this paper, we present the results of an experimental study on
running soft computing algorithms using Hadoop. This study shows how a simple
genetic algorithm running on Hadoop can be used to produce solutions for high
dimensional optimization problems. In addition, a simple but effective
technique, which did not need MapReduce chains, has been proposed.
| cs.DC | hadoop is a popular mapreduce framework for developing parallel applications in distributed environments several advantages of mapreduce such as programming ease and ability to use commodity hardware make the applicability of soft computing methods for parallel and distributed systems easier than before in this paper we present the results of an experimental study on running soft computing algorithms using hadoop this study shows how a simple genetic algorithm running on hadoop can be used to produce solutions for high dimensional optimization problems in addition a simple but effective technique which did not need mapreduce chains has been proposed | [['hadoop', 'is', 'a', 'popular', 'mapreduce', 'framework', 'for', 'developing', 'parallel', 'applications', 'in', 'distributed', 'environments', 'several', 'advantages', 'of', 'mapreduce', 'such', 'as', 'programming', 'ease', 'and', 'ability', 'to', 'use', 'commodity', 'hardware', 'make', 'the', 'applicability', 'of', 'soft', 'computing', 'methods', 'for', 'parallel', 'and', 'distributed', 'systems', 'easier', 'than', 'before', 'in', 'this', 'paper', 'we', 'present', 'the', 'results', 'of', 'an', 'experimental', 'study', 'on', 'running', 'soft', 'computing', 'algorithms', 'using', 'hadoop', 'this', 'study', 'shows', 'how', 'a', 'simple', 'genetic', 'algorithm', 'running', 'on', 'hadoop', 'can', 'be', 'used', 'to', 'produce', 'solutions', 'for', 'high', 'dimensional', 'optimization', 'problems', 'in', 'addition', 'a', 'simple', 'but', 'effective', 'technique', 'which', 'did', 'not', 'need', 'mapreduce', 'chains', 'has', 'been', 'proposed']] | [-0.08447593744435557, -0.014147977248322438, -0.11342564059601982, 0.053186122255580386, -0.13077098828721412, -0.1887060085891233, 0.030165582086311236, 0.4696512201002666, -0.25524821669357467, -0.3553804548609318, 0.133379327172499, -0.1719781045364786, -0.1600729042036953, 0.2762434035872242, -0.08903812343606307, 0.1284516288955905, 0.11206790521664887, -0.012032725392099547, -0.05696487672893064, -0.31566816791701985, 0.19570656966807662, 0.05986633415010815, 0.320487972301412, 0.06857818092827742, 0.04343358019951313, -0.009814969416023517, 0.021184712206488664, 0.0519376592484911, -0.05759841072306569, 0.16129977211394175, 0.3079942217624119, 0.2241537980616511, 0.3371309057752393, -0.49239174592099627, -0.1496009829035979, 0.09956710291456203, 0.21000332356791715, 0.0823488633364572, -0.11189830050167, -0.21665700070788058, 0.1267644313962332, -0.22513431725947528, -0.07236452301850124, -0.15993791573433852, -0.006287830858016197, -0.003073450762360376, -0.23744961785209573, -0.012809112498463055, -0.004611069773684959, 0.036264007274365545, 0.00829214471800975, -0.1257546424808703, 0.12619325207089452, 0.07874621556387568, 0.010714290529365974, 0.002512974208410905, 0.16687064079035605, -0.07465800925073919, -0.22215067680297915, 0.4117692147324584, -0.03177528026141345, -0.1891595976313158, 0.24311932770663644, 0.06296687265763021, -0.23647013927182678, 0.04795272537145992, 0.2868651710403133, 0.12952477906887627, -0.1994061491889309, 0.14748674169615178, 0.01985454302737299, 0.18058284546951858, 0.036006327157802116, -0.02134456290692395, 0.14494547962534185, 0.2610000349885347, 0.08037755687778093, 0.18917402897947183, -0.030809130305804466, -0.1368915141579144, -0.18380387281352767, -0.17119582864094754, -0.18620872549589115, -0.03627491022720909, -0.11438424760750163, -0.20470019415192, 0.36605397326757716, 0.21253037708514483, 0.1279462785269989, 0.09796019998849465, 0.42678108704941614, 0.04986851249360574, 0.13988805964247, 0.16944454813243023, 0.13227345052469827, 0.03580417320588414, 0.19931878524889446, -0.16491039003226526, 0.06751932870900752, -0.017243977360503406] |
1,802.03604 | Feature-Distributed SVRG for High-Dimensional Linear Classification | Linear classification has been widely used in many high-dimensional
applications like text classification. To perform linear classification for
large-scale tasks, we often need to design distributed learning methods on a
cluster of multiple machines. In this paper, we propose a new distributed
learning method, called feature-distributed stochastic variance reduced
gradient (FD-SVRG) for high-dimensional linear classification. Unlike most
existing distributed learning methods which are instance-distributed, FD-SVRG
is feature-distributed. FD-SVRG has lower communication cost than other
instance-distributed methods when the data dimensionality is larger than the
number of data instances. Experimental results on real data demonstrate that
FD-SVRG can outperform other state-of-the-art distributed methods for
high-dimensional linear classification in terms of both communication cost and
wall-clock time, when the dimensionality is larger than the number of instances
in training data.
| cs.LG cs.DC stat.ML | linear classification has been widely used in many highdimensional applications like text classification to perform linear classification for largescale tasks we often need to design distributed learning methods on a cluster of multiple machines in this paper we propose a new distributed learning method called featuredistributed stochastic variance reduced gradient fdsvrg for highdimensional linear classification unlike most existing distributed learning methods which are instancedistributed fdsvrg is featuredistributed fdsvrg has lower communication cost than other instancedistributed methods when the data dimensionality is larger than the number of data instances experimental results on real data demonstrate that fdsvrg can outperform other stateoftheart distributed methods for highdimensional linear classification in terms of both communication cost and wallclock time when the dimensionality is larger than the number of instances in training data | [['linear', 'classification', 'has', 'been', 'widely', 'used', 'in', 'many', 'highdimensional', 'applications', 'like', 'text', 'classification', 'to', 'perform', 'linear', 'classification', 'for', 'largescale', 'tasks', 'we', 'often', 'need', 'to', 'design', 'distributed', 'learning', 'methods', 'on', 'a', 'cluster', 'of', 'multiple', 'machines', 'in', 'this', 'paper', 'we', 'propose', 'a', 'new', 'distributed', 'learning', 'method', 'called', 'featuredistributed', 'stochastic', 'variance', 'reduced', 'gradient', 'fdsvrg', 'for', 'highdimensional', 'linear', 'classification', 'unlike', 'most', 'existing', 'distributed', 'learning', 'methods', 'which', 'are', 'instancedistributed', 'fdsvrg', 'is', 'featuredistributed', 'fdsvrg', 'has', 'lower', 'communication', 'cost', 'than', 'other', 'instancedistributed', 'methods', 'when', 'the', 'data', 'dimensionality', 'is', 'larger', 'than', 'the', 'number', 'of', 'data', 'instances', 'experimental', 'results', 'on', 'real', 'data', 'demonstrate', 'that', 'fdsvrg', 'can', 'outperform', 'other', 'stateoftheart', 'distributed', 'methods', 'for', 'highdimensional', 'linear', 'classification', 'in', 'terms', 'of', 'both', 'communication', 'cost', 'and', 'wallclock', 'time', 'when', 'the', 'dimensionality', 'is', 'larger', 'than', 'the', 'number', 'of', 'instances', 'in', 'training', 'data']] | [-0.06794225739976091, 0.01007465747440414, -0.02977952889106687, 0.05351340507284828, -0.11946639035558028, -0.19685228757049528, -0.01025414816479409, 0.41621513712790703, -0.2783534316347927, -0.32405723354613947, 0.1314226213219245, -0.26927904161059435, -0.1411825592982613, 0.3088683733386138, -0.14928161010386481, 0.14703441030184378, 0.12822449822429446, 0.06698463231535448, -0.09065748078489466, -0.36647486715247074, 0.2657484433561143, 0.01477392795946329, 0.37235607293945167, -0.060368049544312304, 0.06251038535828551, -0.005248785060229561, -0.03454798083722351, 0.036517957991529855, -0.015550476568668889, 0.18233008962124586, 0.37514991647819235, 0.2035867156922787, 0.38069815368151233, -0.3976437859086981, -0.24946202560057562, 0.1774879977963264, 0.17566174317893926, 0.13249080859015755, -0.024849978446464745, -0.25457794253053445, 0.08817707515892483, -0.13930305191165498, 0.035195253229880284, -0.1706530865649843, -0.013052708566969922, -0.006406155835476613, -0.28321297271477597, 0.08697690024235917, 0.04238343583042039, 0.0825693920262218, -0.028044524138808372, -0.17067242886180117, 0.06636164634067926, 0.07308839853982546, 0.029681685412557977, 0.026992880318674348, 0.1117025765875775, -0.14310228469220154, -0.1927523663522856, 0.3746496133070679, -0.035335960734007514, -0.2219830325639416, 0.24651658979916735, -0.0107366357133874, -0.19947400687998462, 0.11179365786994176, 0.3079714164543416, 0.12431850409953885, -0.14036408049967927, 0.06460963227108286, -0.035025095679767186, 0.18854664947326866, 0.008840413781018146, 0.014339888689949388, 0.06316667014823085, 0.27012019724990693, 0.1071595353400147, 0.10374956700346252, -0.1341507734295221, -0.10294667229379317, -0.16088752340076626, -0.11606129439878127, -0.24816430525313463, -0.04314493802526305, -0.1479457205107704, -0.1494367015848477, 0.3239368387167492, 0.19585945916121766, 0.1867290899878548, 0.13755380297023354, 0.383990932195898, 0.068814669953539, 0.12326205610447834, 0.1536207994250881, 0.15350098787401353, 0.027466234743535038, 0.1062827383002807, -0.16645076791305216, 0.08965347315965881, 0.019126257476126476] |
1,802.03605 | Combinets: Creativity via Recombination of Neural Networks | One of the defining characteristics of human creativity is the ability to
make conceptual leaps, creating something surprising from typical knowledge. In
comparison, deep neural networks often struggle to handle cases outside of
their training data, which is especially problematic for problems with limited
training data. Approaches exist to transfer knowledge from problems with
sufficient data to those with insufficient data, but they tend to require
additional training or a domain-specific method of transfer. We present a new
approach, conceptual expansion, that serves as a general representation for
reusing existing trained models to derive new models without backpropagation.
We evaluate our approach on few-shot variations of two tasks: image
classification and image generation, and outperform standard transfer learning
approaches.
| cs.LG cs.CV stat.ML | one of the defining characteristics of human creativity is the ability to make conceptual leaps creating something surprising from typical knowledge in comparison deep neural networks often struggle to handle cases outside of their training data which is especially problematic for problems with limited training data approaches exist to transfer knowledge from problems with sufficient data to those with insufficient data but they tend to require additional training or a domainspecific method of transfer we present a new approach conceptual expansion that serves as a general representation for reusing existing trained models to derive new models without backpropagation we evaluate our approach on fewshot variations of two tasks image classification and image generation and outperform standard transfer learning approaches | [['one', 'of', 'the', 'defining', 'characteristics', 'of', 'human', 'creativity', 'is', 'the', 'ability', 'to', 'make', 'conceptual', 'leaps', 'creating', 'something', 'surprising', 'from', 'typical', 'knowledge', 'in', 'comparison', 'deep', 'neural', 'networks', 'often', 'struggle', 'to', 'handle', 'cases', 'outside', 'of', 'their', 'training', 'data', 'which', 'is', 'especially', 'problematic', 'for', 'problems', 'with', 'limited', 'training', 'data', 'approaches', 'exist', 'to', 'transfer', 'knowledge', 'from', 'problems', 'with', 'sufficient', 'data', 'to', 'those', 'with', 'insufficient', 'data', 'but', 'they', 'tend', 'to', 'require', 'additional', 'training', 'or', 'a', 'domainspecific', 'method', 'of', 'transfer', 'we', 'present', 'a', 'new', 'approach', 'conceptual', 'expansion', 'that', 'serves', 'as', 'a', 'general', 'representation', 'for', 'reusing', 'existing', 'trained', 'models', 'to', 'derive', 'new', 'models', 'without', 'backpropagation', 'we', 'evaluate', 'our', 'approach', 'on', 'fewshot', 'variations', 'of', 'two', 'tasks', 'image', 'classification', 'and', 'image', 'generation', 'and', 'outperform', 'standard', 'transfer', 'learning', 'approaches']] | [0.0005142254355762686, -0.028943314412687004, -0.06927006552533928, 0.12795147176307603, -0.19150379721364869, -0.22243783315017568, 0.0474932685107024, 0.44810683339708995, -0.26418833524779173, -0.3836131225488767, 0.08511994989929299, -0.2624637874062447, -0.16439040416671635, 0.21621057049942619, -0.15550190032686756, 0.08227760207393214, 0.1767039845674103, 0.02821800869521724, -0.09640149810417954, -0.27224257234836136, 0.33249969505939725, 0.019877647657970655, 0.33880954721019046, -0.000962895666006483, 0.10587335144318327, -0.07384519101102922, -0.03920509486787898, -0.04297121354473714, -0.03366920043463171, 0.22973158749743922, 0.34695237684299624, 0.2514732998615804, 0.33394477601718753, -0.45142860838971216, -0.2553104195771191, 0.11701276387330614, 0.14668695640382395, 0.15935158857952475, -0.029667923987076125, -0.28981695506533905, 0.06494620157221817, -0.16033234050059142, -0.014732003713099035, -0.18811410984822682, -0.04901244104722468, -0.026486379790957235, -0.3048188495801903, 0.033679228114108985, 0.10866091450350555, 0.07095288149151607, -0.056285108368209866, -0.12499136835074506, 0.04379743948664923, 0.1908870669993564, 0.05958327316158615, 0.04184540723520918, 0.10945043562932973, -0.2165567172248432, -0.1433218088267105, 0.38648807988604805, -0.03224669597620348, -0.22292565546554177, 0.2610653436218365, -0.025061951576489487, -0.1378341612539121, 0.09949010509621947, 0.22547398609678582, 0.10444418272050489, -0.1558596736853238, -0.005783662733136669, 0.024810652968575472, 0.16219805879860788, 0.037179074578863734, -0.01769031259045885, 0.18666395328316776, 0.2447834260245206, -0.00012125357879842279, 0.10459106304377568, -0.08671840133748072, -0.10024821861967945, -0.23112902140730068, -0.07085441122567203, -0.20156308698679218, 0.011564750466965326, -0.06574575771443632, -0.1711805226514116, 0.34061090333559435, 0.26426600696550323, 0.20932924972862757, 0.11377381379305221, 0.36453556363322154, 0.008274667655184333, 0.17516780865942166, 0.08143176129987824, 0.16937780014372297, 0.02600236204765862, 0.16868128001439472, -0.12637111913176066, 0.09008958634231709, -0.0033269813969073925] |
1,802.03606 | Distributed NLP | In this paper we present the performance of parallel text processing with Map
Reduce on a cloud platform. Scientific papers in Turkish language are processed
using Zemberek NLP library. Experiments were run on a Hadoop cluster and
compared with the single machines performance.
| cs.DC cs.CL | in this paper we present the performance of parallel text processing with map reduce on a cloud platform scientific papers in turkish language are processed using zemberek nlp library experiments were run on a hadoop cluster and compared with the single machines performance | [['in', 'this', 'paper', 'we', 'present', 'the', 'performance', 'of', 'parallel', 'text', 'processing', 'with', 'map', 'reduce', 'on', 'a', 'cloud', 'platform', 'scientific', 'papers', 'in', 'turkish', 'language', 'are', 'processed', 'using', 'zemberek', 'nlp', 'library', 'experiments', 'were', 'run', 'on', 'a', 'hadoop', 'cluster', 'and', 'compared', 'with', 'the', 'single', 'machines', 'performance']] | [-0.060256865374477844, -0.04692749775547002, -0.06574301998175326, -0.030938097245303824, -0.09720682195343432, -0.11177767052625616, 0.03837648982880637, 0.4684296071174599, -0.25641975760282504, -0.39124357726957115, 0.0842902875072988, -0.32376335827367647, -0.06259748708855893, 0.28734138550325516, -0.07411743909497523, 0.10693444593233012, 0.25170315134649474, 0.05844023333685029, -0.02848099704299654, -0.3669510150239581, 0.25614793824830223, 0.07079266610422305, 0.36399001515071305, 0.0014830186652640502, 0.0298085663234815, -0.08782479402032636, -0.08257212321318332, -0.035258782495345385, -0.057331964606419206, 0.18565496735806977, 0.32301387378746377, 0.23494182218841853, 0.2953337742060068, -0.44646018400921356, -0.10812581448061835, 0.0014699968465027354, 0.15394026832655072, 0.032838560773858, -0.09422985972681393, -0.3067110415015902, 0.11151440006991227, -0.20445436874260417, 0.07076676584602822, -0.08284533870894284, -0.02736394617351748, 0.04487685069796585, -0.1825399861998102, -0.0069673834424022404, -0.011212224595337397, 0.17225693609742893, 0.0042496894907561086, -0.13871215749531984, 0.09164550046746929, 0.12336471781045907, -0.046047453014623554, 0.0958168508507134, 0.19460662810264953, -0.12821392060279668, -0.19479218107603846, 0.4377120598441079, -0.09677450354432776, -0.1918767291520323, 0.20259857581307492, 0.006411752702357869, -0.22402048691929805, 0.0016920072070899465, 0.30055984845828443, 0.0603882765752219, -0.19340508060884617, 0.1159430573170539, -0.020504804904617015, 0.2242997455455008, 0.1176364250048729, -0.03337911560776688, 0.20009469015834233, 0.2992057013990624, -0.06815800740427914, 0.2249295526888177, -0.09311494722385846, -0.060083404360782536, -0.2089386409858153, -0.19993993547368086, -0.1671408450984884, -0.05570273735516128, -0.02106810685966463, -0.1244341613103946, 0.3655136447355506, 0.19519111562875055, 0.1209178991793167, 0.09275993330049373, 0.37522462342998814, 0.015329896511199573, 0.14784637013716356, 0.1547556426930463, 0.08362319761709798, -0.01870786948018663, 0.25705766181151074, -0.14160320571889834, 0.031740976397746375, -0.028108303728956906] |
1,802.03607 | Microfluidic switchboards with integrated inertial pumps | Arrays of H-shape microfluidic channels connecting two different fluidic
reservoirs have been built with silicon/SU8 microfabrication technologies
utilized in production of thermal inkjet printheads. The fluids are delivered
to the channels via slots etched through the silicon wafer. Every H-shape
channel comprises four thermal inkjet resistors, one in each of the four legs.
The resistors vaporize water and generate drive bubbles that pump the fluids
from the bulk reservoirs into and out of the channels. By varying relative
frequencies of the four pumps, input fluids can be routed to any part of the
network in any proportion. Several fluidic operations including dilution,
mixing, dynamic valving, and routing have been demonstrated. Thus, a fully
integrated microfluidic switchboard that does not require external sources of
mechanical power has been achieved. A matrix formalism to describe flow in
complex switchboards has been developed and tested.
| physics.flu-dyn cond-mat.other | arrays of hshape microfluidic channels connecting two different fluidic reservoirs have been built with siliconsu8 microfabrication technologies utilized in production of thermal inkjet printheads the fluids are delivered to the channels via slots etched through the silicon wafer every hshape channel comprises four thermal inkjet resistors one in each of the four legs the resistors vaporize water and generate drive bubbles that pump the fluids from the bulk reservoirs into and out of the channels by varying relative frequencies of the four pumps input fluids can be routed to any part of the network in any proportion several fluidic operations including dilution mixing dynamic valving and routing have been demonstrated thus a fully integrated microfluidic switchboard that does not require external sources of mechanical power has been achieved a matrix formalism to describe flow in complex switchboards has been developed and tested | [['arrays', 'of', 'hshape', 'microfluidic', 'channels', 'connecting', 'two', 'different', 'fluidic', 'reservoirs', 'have', 'been', 'built', 'with', 'siliconsu8', 'microfabrication', 'technologies', 'utilized', 'in', 'production', 'of', 'thermal', 'inkjet', 'printheads', 'the', 'fluids', 'are', 'delivered', 'to', 'the', 'channels', 'via', 'slots', 'etched', 'through', 'the', 'silicon', 'wafer', 'every', 'hshape', 'channel', 'comprises', 'four', 'thermal', 'inkjet', 'resistors', 'one', 'in', 'each', 'of', 'the', 'four', 'legs', 'the', 'resistors', 'vaporize', 'water', 'and', 'generate', 'drive', 'bubbles', 'that', 'pump', 'the', 'fluids', 'from', 'the', 'bulk', 'reservoirs', 'into', 'and', 'out', 'of', 'the', 'channels', 'by', 'varying', 'relative', 'frequencies', 'of', 'the', 'four', 'pumps', 'input', 'fluids', 'can', 'be', 'routed', 'to', 'any', 'part', 'of', 'the', 'network', 'in', 'any', 'proportion', 'several', 'fluidic', 'operations', 'including', 'dilution', 'mixing', 'dynamic', 'valving', 'and', 'routing', 'have', 'been', 'demonstrated', 'thus', 'a', 'fully', 'integrated', 'microfluidic', 'switchboard', 'that', 'does', 'not', 'require', 'external', 'sources', 'of', 'mechanical', 'power', 'has', 'been', 'achieved', 'a', 'matrix', 'formalism', 'to', 'describe', 'flow', 'in', 'complex', 'switchboards', 'has', 'been', 'developed', 'and', 'tested']] | [-0.14766264388370992, 0.190525131130458, -0.05397565303969064, -0.09811212177654462, -0.008256463124416769, -0.22080970602276337, -0.0030138565260650857, 0.41164276437318353, -0.22812947598285974, -0.29065798321472747, 0.09012090695822346, -0.2860758609737137, -0.07223684791242704, 0.21576756849618894, -0.02265569238224998, 0.07734229238828577, 0.036204679968899915, -0.058333707221650655, 0.028465020563453437, -0.2105830630462151, 0.23905811769488666, 0.01950441566295922, 0.33328920863901396, 0.06206731432383614, 0.15618638486401842, -0.07165249411149749, -0.03319048905603787, 0.026396900238302934, -0.08659074928863057, 0.05101852190980156, 0.26390036251728555, 0.024899470782838763, 0.22249502172427518, -0.5479770231748782, -0.2771831024072266, 0.08709517937552716, 0.13960935360020293, 0.08227680136582681, -0.02329936632886529, -0.23811871360854378, 0.051873500598594545, -0.20561412837788728, -0.04355398229922035, -0.02513500157843477, 0.0023252832636769328, 0.035743863221640436, -0.22083212088327855, -0.02704507349325078, 0.00037369672567625197, 0.03011667537482156, -0.010884394593137716, -0.12333424189793211, -0.0622821635001206, 0.16700185675040952, -0.040064748996935254, -0.04705479200846249, 0.26174910483075237, -0.10384102378032237, -0.13342975924190667, 0.3405578140102859, -0.04338114446228636, -0.1993610109468656, 0.2116814327746397, -0.09164837043665881, -0.04816372258189534, 0.17533343741670251, 0.19163878388436778, 0.05128248739056289, -0.24306046419639774, 0.007546284693359796, 0.002619629233543362, 0.16821538844877587, 0.151209953542067, 0.02351358295418322, 0.22867365400972112, 0.19262414799470987, 0.0010024196609655128, 0.1904529387039864, -0.09055078754401101, -0.053557312530964345, -0.217814013813456, -0.17572124304697354, -0.15987692336119444, 0.037698375781370225, -0.050742990750586615, -0.13450482501274175, 0.3656870832367401, 0.10979822247422168, 0.12084902197155835, -0.024426803791097233, 0.3257733904357467, 0.027469802115644728, 0.1682476079956229, 0.0833216198853084, 0.2634814963010805, 0.159662363816668, 0.14850294523223836, -0.17830646557101448, 0.05871565870724486, -0.01568726991037173] |
1,802.03608 | MOEA/D with Angle-based Constrained Dominance Principle for Constrained
Multi-objective Optimization Problems | This paper proposes a novel constraint-handling mechanism named angle-based
constrained dominance principle (ACDP) embedded in a decomposition-based
multi-objective evolutionary algorithm (MOEA/D) to solve constrained
multi-objective optimization problems (CMOPs). To maintain the diversity of the
working population, ACDP utilizes the information of the angle of solutions to
adjust the dominance relation of solutions during the evolutionary process.
This paper uses 14 benchmark instances to evaluate the performance of the
MOEA/D with ACDP (MOEA/D-ACDP). Additionally, an engineering optimization
problem (which is I-beam optimization problem) is optimized. The proposed
MOEA/D-ACDP, and four other decomposition-based CMOEAs, including C-MOEA/D,
MOEA/D-CDP, MOEA/D-Epsilon and MOEA/D-SR are tested by the above benchmarks and
the engineering application. The experimental results manifest that MOEA/D-ACDP
is significantly better than the other four CMOEAs on these test instances and
the real-world case, which indicates that ACDP is more effective for solving
CMOPs.
| cs.NE | this paper proposes a novel constrainthandling mechanism named anglebased constrained dominance principle acdp embedded in a decompositionbased multiobjective evolutionary algorithm moead to solve constrained multiobjective optimization problems cmops to maintain the diversity of the working population acdp utilizes the information of the angle of solutions to adjust the dominance relation of solutions during the evolutionary process this paper uses 14 benchmark instances to evaluate the performance of the moead with acdp moeadacdp additionally an engineering optimization problem which is ibeam optimization problem is optimized the proposed moeadacdp and four other decompositionbased cmoeas including cmoead moeadcdp moeadepsilon and moeadsr are tested by the above benchmarks and the engineering application the experimental results manifest that moeadacdp is significantly better than the other four cmoeas on these test instances and the realworld case which indicates that acdp is more effective for solving cmops | [['this', 'paper', 'proposes', 'a', 'novel', 'constrainthandling', 'mechanism', 'named', 'anglebased', 'constrained', 'dominance', 'principle', 'acdp', 'embedded', 'in', 'a', 'decompositionbased', 'multiobjective', 'evolutionary', 'algorithm', 'moead', 'to', 'solve', 'constrained', 'multiobjective', 'optimization', 'problems', 'cmops', 'to', 'maintain', 'the', 'diversity', 'of', 'the', 'working', 'population', 'acdp', 'utilizes', 'the', 'information', 'of', 'the', 'angle', 'of', 'solutions', 'to', 'adjust', 'the', 'dominance', 'relation', 'of', 'solutions', 'during', 'the', 'evolutionary', 'process', 'this', 'paper', 'uses', '14', 'benchmark', 'instances', 'to', 'evaluate', 'the', 'performance', 'of', 'the', 'moead', 'with', 'acdp', 'moeadacdp', 'additionally', 'an', 'engineering', 'optimization', 'problem', 'which', 'is', 'ibeam', 'optimization', 'problem', 'is', 'optimized', 'the', 'proposed', 'moeadacdp', 'and', 'four', 'other', 'decompositionbased', 'cmoeas', 'including', 'cmoead', 'moeadcdp', 'moeadepsilon', 'and', 'moeadsr', 'are', 'tested', 'by', 'the', 'above', 'benchmarks', 'and', 'the', 'engineering', 'application', 'the', 'experimental', 'results', 'manifest', 'that', 'moeadacdp', 'is', 'significantly', 'better', 'than', 'the', 'other', 'four', 'cmoeas', 'on', 'these', 'test', 'instances', 'and', 'the', 'realworld', 'case', 'which', 'indicates', 'that', 'acdp', 'is', 'more', 'effective', 'for', 'solving', 'cmops']] | [-0.0724533290238783, -0.006334168741729935, -0.08148129604817134, 0.07928654461437278, -0.11731675346951476, -0.17139731536170455, 0.031295741306912664, 0.3520934416865595, -0.254650425386997, -0.36938057699167043, 0.12562254237818446, -0.21850403447354064, -0.1783021820990516, 0.19742416582747008, -0.07190595019488133, 0.07164163003581837, 0.08711571969333098, -0.05072553009263796, -0.06393230704443274, -0.2561377489800304, 0.27799436621026224, 0.04850185780430869, 0.30680811797141383, 0.035026457458449754, 0.12137067742082927, -0.02056303047474554, -0.003079598047603056, 0.028160960472141987, -0.0867984203188119, 0.15373886118495583, 0.26683198097128447, 0.2246225385109244, 0.2957218176159311, -0.3364977502002776, -0.17746154239012613, 0.09694845363390532, 0.14683307700274553, 0.05220209218045737, -0.035209055163871614, -0.24650829275835492, 0.08574934597730154, -0.1228333895928863, -0.05160736660774556, -0.05304474240010219, -0.043463060427993104, -0.01023777449091481, -0.3027480091068813, 0.03157401013438483, 0.003463295536551695, -0.005204061428151971, -0.09183870618760907, -0.18749805836663616, 0.03342810901645705, 0.05982150481812817, 0.0440299338600414, 0.040011877471266194, 0.12490008942273667, -0.09917483349256812, -0.1982879396544756, 0.3917981932649938, -0.0038825499733261726, -0.19342184271127413, 0.190579239856502, 0.022258818528125827, -0.15021690042528638, 0.10358743038256284, 0.2069733901780179, 0.19222236951652297, -0.17625132280823996, 0.033394046853088756, -0.0481940350501979, 0.15691631781808335, 0.03428531106119212, -0.04087517431287242, 0.11752081564091575, 0.2413670676324865, 0.09943244274263438, 0.16285105325908086, -0.06885123513002457, -0.15289559228791313, -0.1864030721903906, -0.11088919967895361, -0.11679925528696651, -0.045910081077407267, -0.09593635840004766, -0.1265848284124953, 0.4082861128497681, 0.19488633113826137, 0.12241961387749896, 0.08775741093320498, 0.30637794089831893, 0.07946283505683456, 0.060644899505415004, 0.08726555597465888, 0.23087290802839874, 0.056656511066642275, 0.10644826973895642, -0.26066713146329357, 0.07521566088988091, 0.05226536079061975] |
1,802.03609 | The Strong Gravitational Lens Finding Challenge | Large scale imaging surveys will increase the number of galaxy-scale strong
lensing candidates by maybe three orders of magnitudes beyond the number known
today. Finding these rare objects will require picking them out of at least
tens of millions of images and deriving scientific results from them will
require quantifying the efficiency and bias of any search method. To achieve
these objectives automated methods must be developed. Because gravitational
lenses are rare objects reducing false positives will be particularly
important. We present a description and results of an open gravitational lens
finding challenge. Participants were asked to classify 100,000 candidate
objects as to whether they were gravitational lenses or not with the goal of
developing better automated methods for finding lenses in large data sets. A
variety of methods were used including visual inspection, arc and ring finders,
support vector machines (SVM) and convolutional neural networks (CNN). We find
that many of the methods will be easily fast enough to analyse the anticipated
data flow. In test data, several methods are able to identify upwards of half
the lenses after applying some thresholds on the lens characteristics such as
lensed image brightness, size or contrast with the lens galaxy without making a
single false-positive identification. This is significantly better than direct
inspection by humans was able to do. (abridged)
| astro-ph.GA astro-ph.CO astro-ph.IM | large scale imaging surveys will increase the number of galaxyscale strong lensing candidates by maybe three orders of magnitudes beyond the number known today finding these rare objects will require picking them out of at least tens of millions of images and deriving scientific results from them will require quantifying the efficiency and bias of any search method to achieve these objectives automated methods must be developed because gravitational lenses are rare objects reducing false positives will be particularly important we present a description and results of an open gravitational lens finding challenge participants were asked to classify 100000 candidate objects as to whether they were gravitational lenses or not with the goal of developing better automated methods for finding lenses in large data sets a variety of methods were used including visual inspection arc and ring finders support vector machines svm and convolutional neural networks cnn we find that many of the methods will be easily fast enough to analyse the anticipated data flow in test data several methods are able to identify upwards of half the lenses after applying some thresholds on the lens characteristics such as lensed image brightness size or contrast with the lens galaxy without making a single falsepositive identification this is significantly better than direct inspection by humans was able to do abridged | [['large', 'scale', 'imaging', 'surveys', 'will', 'increase', 'the', 'number', 'of', 'galaxyscale', 'strong', 'lensing', 'candidates', 'by', 'maybe', 'three', 'orders', 'of', 'magnitudes', 'beyond', 'the', 'number', 'known', 'today', 'finding', 'these', 'rare', 'objects', 'will', 'require', 'picking', 'them', 'out', 'of', 'at', 'least', 'tens', 'of', 'millions', 'of', 'images', 'and', 'deriving', 'scientific', 'results', 'from', 'them', 'will', 'require', 'quantifying', 'the', 'efficiency', 'and', 'bias', 'of', 'any', 'search', 'method', 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1,802.0361 | On the additive complexity of a Thue-Morse like sequence | In this paper, we study the additive complexity $\rho^{+}_{\mathbf{t}}(n)$ of
a Thue-Morse like sequence $\mathbf{t}=\sigma^{\infty}(0)$ with the morphism
$\sigma: 0\to 01, 1\to 12, 2\to 20$. We show that
$\rho^{+}_{\mathbf{t}}(n)=2\lfloor\log_2(n)\rfloor+3$ for all integers $n\geq
1$. Consequently, $(\rho_{\mathbf{t}}(n))_{n\geq 1}$ is a $2$-regular sequence.
| math.CO | in this paper we study the additive complexity rho_mathbftn of a thuemorse like sequence mathbftsigmainfty0 with the morphism sigma 0to 01 1to 12 2to 20 we show that rho_mathbftn2lfloorlog_2nrfloor3 for all integers ngeq 1 consequently rho_mathbftn_ngeq 1 is a 2regular sequence | [['in', 'this', 'paper', 'we', 'study', 'the', 'additive', 'complexity', 'rho_mathbftn', 'of', 'a', 'thuemorse', 'like', 'sequence', 'mathbftsigmainfty0', 'with', 'the', 'morphism', 'sigma', '0to', '01', '1to', '12', '2to', '20', 'we', 'show', 'that', 'rho_mathbftn2lfloorlog_2nrfloor3', 'for', 'all', 'integers', 'ngeq', '1', 'consequently', 'rho_mathbftn_ngeq', '1', 'is', 'a', '2regular', 'sequence']] | [-0.166062788760944, 0.18191282320264224, 0.0060991033647410774, 0.11003814378400913, 0.04652822925079916, -0.129379246258051, 0.02771334595210548, 0.392416658433708, -0.35185445580832864, -0.23911428995229103, 0.07045176341764729, -0.3091841709195963, -0.1818161679280771, 0.09996486289074293, -0.08266409114003181, -0.031317635848363105, 0.04760064491750421, 0.09266466883363554, -0.05242275068460888, -0.2666646268822857, 0.3390096383355322, -0.12968813960213918, 0.10547690707686785, -0.0428045773375276, 0.07021228850440034, 0.0006744670052383397, 0.0319186697235784, -0.0673532056043277, -0.23340883753612493, 0.045998325517929685, 0.26434995294422714, 0.12693137800119617, 0.2684727510487711, -0.2678594230296644, -0.11339059613040976, 0.23051520585510377, 0.1954357196652406, 0.018443759681807982, -0.05013613499751365, -0.1703139810848075, 0.2537118288612849, -0.17756182903650444, -0.067523952749734, 0.0039057462048288937, 0.13097397837083083, 0.033428861426088856, -0.3281627765579804, 0.0007600614901732754, 0.2072623807030755, 0.08947198717175303, -0.033077940118272565, -0.22163475956767797, 0.04902389580132188, 0.0929980195454649, -0.0339741990964457, 0.18035567462809282, 0.012194790726376546, -0.05941097050074588, -0.1364011483210315, 0.34854835080536634, -0.11420444784862166, -0.19783210225806042, 0.07061564897161883, -0.2114919498311104, -0.26888970086804115, 0.15120102565836263, 0.03572268913912184, 0.12346850017815628, -0.032867166238862114, 0.1973751931879166, -0.06110024915353672, 0.26684276950923175, 0.14106058914566766, -0.0006254845978440466, 0.06967819270652693, 0.16355024872196688, 0.015630128378098882, 0.13062727501666224, -0.07584877785395931, 0.03417550459051052, -0.3301779832942663, -0.18429833828043696, -0.09781651435784895, 0.22396725348222093, -0.1344363178327881, -0.12342250313156762, 0.34834444904196504, 0.11849960068089736, 0.20045495164152738, 0.2371749108204165, 0.19966967061564728, 0.07922119846114435, -0.04646001333320463, 0.05110219827022504, 0.044176838310385075, 0.13729833610154488, -0.01751072009131816, -0.13401589518714999, -0.09118037141003721, 0.10207695400694737] |
1,802.03611 | Searching isomorphic graphs | To determine that two given undirected graphs are isomorphic, we construct
for them auxiliary graphs, using the breadth-first search. This makes
capability to position vertices in each digraph with respect to each other. If
the given graphs are isomorphic, in each of them we can find such positionally
equivalent auxiliary digraphs that have the same mutual positioning of
vertices. Obviously, if the given graphs are isomorphic, then such equivalent
digraphs exist. Proceeding from the arrangement of vertices in one of the
digraphs, we try to determine the corresponding vertices in another digraph. As
a result we develop the algorithm for constructing a bijective mapping between
vertices of the given graphs if they are isomorphic. The running time of the
algorithm equal to $O(n^5)$, where $n$ is the number of graph vertices.
| cs.DS | to determine that two given undirected graphs are isomorphic we construct for them auxiliary graphs using the breadthfirst search this makes capability to position vertices in each digraph with respect to each other if the given graphs are isomorphic in each of them we can find such positionally equivalent auxiliary digraphs that have the same mutual positioning of vertices obviously if the given graphs are isomorphic then such equivalent digraphs exist proceeding from the arrangement of vertices in one of the digraphs we try to determine the corresponding vertices in another digraph as a result we develop the algorithm for constructing a bijective mapping between vertices of the given graphs if they are isomorphic the running time of the algorithm equal to on5 where n is the number of graph vertices | [['to', 'determine', 'that', 'two', 'given', 'undirected', 'graphs', 'are', 'isomorphic', 'we', 'construct', 'for', 'them', 'auxiliary', 'graphs', 'using', 'the', 'breadthfirst', 'search', 'this', 'makes', 'capability', 'to', 'position', 'vertices', 'in', 'each', 'digraph', 'with', 'respect', 'to', 'each', 'other', 'if', 'the', 'given', 'graphs', 'are', 'isomorphic', 'in', 'each', 'of', 'them', 'we', 'can', 'find', 'such', 'positionally', 'equivalent', 'auxiliary', 'digraphs', 'that', 'have', 'the', 'same', 'mutual', 'positioning', 'of', 'vertices', 'obviously', 'if', 'the', 'given', 'graphs', 'are', 'isomorphic', 'then', 'such', 'equivalent', 'digraphs', 'exist', 'proceeding', 'from', 'the', 'arrangement', 'of', 'vertices', 'in', 'one', 'of', 'the', 'digraphs', 'we', 'try', 'to', 'determine', 'the', 'corresponding', 'vertices', 'in', 'another', 'digraph', 'as', 'a', 'result', 'we', 'develop', 'the', 'algorithm', 'for', 'constructing', 'a', 'bijective', 'mapping', 'between', 'vertices', 'of', 'the', 'given', 'graphs', 'if', 'they', 'are', 'isomorphic', 'the', 'running', 'time', 'of', 'the', 'algorithm', 'equal', 'to', 'on5', 'where', 'n', 'is', 'the', 'number', 'of', 'graph', 'vertices']] | [-0.145100194264118, 0.12150357571476715, -0.01505289310660526, 0.04268975196342261, -0.14449039153498774, -0.1962525863200426, 0.032034346776950454, 0.4623503222890938, -0.2983361569211911, -0.3168959105871653, 0.07265518889993515, -0.3381323798751558, -0.1446829790494739, 0.07022965046619811, -0.09883134588172646, -0.015514929298993969, 0.13386823324031616, 0.16375334664667382, 0.0019255303129027705, -0.2903397537331108, 0.30281100432097, -0.0413110657872362, 0.14865749937176476, 0.044139456259840316, 0.09971291503605952, 0.020431226865878997, -0.02415893542160856, 0.06884826846307016, -0.16412824475197993, 0.07713046329422989, 0.30185110973485724, 0.17253171806827983, 0.20836438295850887, -0.39972436150806095, -0.060090779575682776, 0.27712968660106185, 0.13930212608929582, 0.05508804505065311, 0.050641998147435544, -0.23316222919819465, 0.10997274437766408, -0.08826895315868367, -0.07280378185838234, 0.010403611738135228, 0.08405505793297109, 0.059054194004026075, -0.2563909903552582, -0.06755089358149366, 0.05484946344389033, 0.011822186723237729, 0.03971106434624841, -0.11663449982639744, -0.07716845905138109, 0.16300978937776375, -0.05368025829297497, 0.06829142001349095, 0.03097330673619081, -0.08245480646119802, -0.2165370807762592, 0.3935559070633568, 0.004921243575806836, -0.20350438861339887, 0.14393693166482796, -0.1139583450614098, -0.20073280958032222, 0.12262281719133088, 0.11842369717609791, 0.16131776398161554, -0.10292070324155893, 0.0807327582138299, -0.11049397454511745, 0.11221590401953128, 0.11920292881665794, 0.01506525122397046, 0.13205114181885738, 0.11797064657586108, 0.1700070052716938, 0.21562831373289867, 0.035991060916771615, -0.009737091778798867, -0.3015526263896404, -0.0945085259154439, -0.2185998551391122, 0.03514855416520065, -0.17584597351356546, -0.18663125667891656, 0.41452770214527845, 0.1764839381043522, 0.21701093009168296, 0.11199344211890724, 0.22654748133575645, 0.05557874474187474, 0.07112461844644019, 0.18337313844004774, 0.15725009013962654, 0.17135998400511177, -0.049712745369703486, -0.146397668691903, 0.07112849291719724, 0.17743777795090476] |
1,802.03612 | 2017 Update of the Discoveries of Nuclides | The 2017 update of the discovery of nuclide project is presented. 34 new
nuclides were observed for the first time in 2017. However, the assignment of
six previously identified nuclides had to be retracted.
| nucl-ex | the 2017 update of the discovery of nuclide project is presented 34 new nuclides were observed for the first time in 2017 however the assignment of six previously identified nuclides had to be retracted | [['the', '2017', 'update', 'of', 'the', 'discovery', 'of', 'nuclide', 'project', 'is', 'presented', '34', 'new', 'nuclides', 'were', 'observed', 'for', 'the', 'first', 'time', 'in', '2017', 'however', 'the', 'assignment', 'of', 'six', 'previously', 'identified', 'nuclides', 'had', 'to', 'be', 'retracted']] | [-0.03338906470247928, 0.11173145188128247, -0.05841827677453265, 0.032308103431098384, -0.053540723727029914, -0.07981132973423775, 0.10966373267857467, 0.31864646197680163, -0.1430200021713972, -0.4136081033331506, 0.12038975937278285, -0.26596476307467504, -0.03343771995380795, 0.17011015481479905, -0.05672927455538336, 0.07417236301390563, 0.0700298356001868, 0.03831089290735476, 0.0017254480215556482, -0.3842324849546832, 0.21836886533042965, 0.14385139701120994, 0.19930855086182847, -0.0022781067801749006, 0.012112622395815217, -0.08476930566351204, -0.15289742669419332, -0.10331929086104912, -0.11098918941912844, 0.06373680270660449, 0.25940813463838663, 0.19378033072194634, 0.207952710087685, -0.34426151747431827, -0.15163875536938362, 0.057638434624737674, 0.1086400772773606, 0.0977917753488702, -0.06557489822016042, -0.3287232231129618, 0.0743817329792578, -0.1954263214241056, -0.10523720321190708, -0.010823649518630084, 0.1426164524848847, 0.04067126983050805, -0.1960816666867365, 0.0408384405377814, -0.023407892214463037, 0.06610728753388256, -0.12116627688245739, -0.23629197883693612, 0.012348971521372305, 0.12851906118585782, 0.03650035467409693, 0.04418840231921743, 0.10912090446799994, -0.030753884081016567, -0.17457605136887117, 0.3652519282117924, 0.004108499817769317, 0.01426864809849683, 0.11778367321719141, -0.1551454564666047, -0.2292083628640017, 0.2267793710617458, 0.19611581469721653, 0.1537064138580771, -0.26750960321549105, 0.050461282131864744, -0.039532175506739056, 0.1623975735376863, 0.11518233883030274, -0.027630072114441323, 0.12283168182543971, 0.2080718263545457, -0.03276543171309373, 0.0608070697327254, -0.1538628909517737, -0.0482083962244146, -0.30101015889907584, -0.21376586023389416, -0.13952116859967217, -0.0004821580369025469, 0.10979278806103941, -0.05809904932154014, 0.4292547518716139, 0.05908099556451335, 0.17796308306210182, -0.08051433864131789, 0.1339417170623646, 0.06954814877141924, 0.11969964299350977, 0.07492219206110082, 0.40277532126535387, 0.0725831019157982, 0.12964950589572682, -0.1966349674963995, 0.15939584477147198, 0.0391495109994567] |
1,802.03613 | Security level analysis of academic information systems based on
standard ISO 27002:2003 using SSE-CMM | This research was conducted to find out the level of information security in
an organization to give recommendations improvements in information security
management at the organization. This research uses the ISO 27002 by involving
the entire clause that exists in ISO 27002 check-lists. Based on the analysis
results, 13 objective controls and 43 security controls were scattered in 3
clauses of ISO 27002. From the analysis it was concluded that the maturity
level of information system security governance was 2.51, which means the level
of security is still at level 2 planned and tracked is planned and tracked
actively) but is approaching level 3 well defined.
| cs.CY | this research was conducted to find out the level of information security in an organization to give recommendations improvements in information security management at the organization this research uses the iso 27002 by involving the entire clause that exists in iso 27002 checklists based on the analysis results 13 objective controls and 43 security controls were scattered in 3 clauses of iso 27002 from the analysis it was concluded that the maturity level of information system security governance was 251 which means the level of security is still at level 2 planned and tracked is planned and tracked actively but is approaching level 3 well defined | [['this', 'research', 'was', 'conducted', 'to', 'find', 'out', 'the', 'level', 'of', 'information', 'security', 'in', 'an', 'organization', 'to', 'give', 'recommendations', 'improvements', 'in', 'information', 'security', 'management', 'at', 'the', 'organization', 'this', 'research', 'uses', 'the', 'iso', '27002', 'by', 'involving', 'the', 'entire', 'clause', 'that', 'exists', 'in', 'iso', '27002', 'checklists', 'based', 'on', 'the', 'analysis', 'results', '13', 'objective', 'controls', 'and', '43', 'security', 'controls', 'were', 'scattered', 'in', '3', 'clauses', 'of', 'iso', '27002', 'from', 'the', 'analysis', 'it', 'was', 'concluded', 'that', 'the', 'maturity', 'level', 'of', 'information', 'system', 'security', 'governance', 'was', '251', 'which', 'means', 'the', 'level', 'of', 'security', 'is', 'still', 'at', 'level', '2', 'planned', 'and', 'tracked', 'is', 'planned', 'and', 'tracked', 'actively', 'but', 'is', 'approaching', 'level', '3', 'well', 'defined']] | [-0.10942569171997744, 0.040255996087958094, -0.06677927601564113, 0.08114717120221236, -0.043232009245328745, -0.12577631687394009, 0.07817751083101304, 0.333304714112771, -0.2150778420234345, -0.3671514834540914, 0.1523489537692787, -0.3163788027991101, -0.08754915145735415, 0.1844270345271688, -0.07991874850583526, 0.05170140028404037, -0.0025028021839218883, 0.03758168661799985, -0.005296942426489209, -0.29176819725137837, 0.2814310846041958, 0.1142503424319175, 0.3320991115442495, 0.10398522457371484, 0.07761802098094397, -0.0003918868133727953, -0.06398604763671756, -0.0431542304731062, -0.08786953260195728, 0.13701749966482035, 0.34912078518789474, 0.23301896581619555, 0.361807487975314, -0.3530636836229911, -0.11883832379200338, -0.005710487328645475, 0.10826500864558906, -0.0014952929429094886, 0.02135062675795711, -0.31149721289062343, 0.07124474836636405, -0.17365773265548753, -0.11614328041180687, -0.03796519696457698, -0.0062657723835897895, 0.010547764397243847, -0.2327869705150765, -0.007071807330368825, 0.013042888455839723, 0.16650959786097957, -0.07403642821723139, -0.12845342348764632, -0.05448401684407145, 0.17065095454168874, 0.004489043966937318, 0.07081367823933384, 0.18952931321823513, -0.14116891864950787, -0.1140440924066769, 0.3406157080141195, -0.016862794744808507, -0.14951968169771135, 0.14384403980758814, -0.11490840211791813, -0.18565317058830327, 0.13837234935073076, 0.12299227108300295, 0.00883020350779846, -0.18055820329782535, 0.06342142223619926, 0.004588222157491266, 0.29193050389722836, 0.08492111840794953, 0.0033276634387461083, 0.18458660834310753, 0.22676454974725, 0.0655248337337431, 0.08891919466341792, -0.09639777340442997, -0.0971271702844017, -0.28489141232984244, -0.14875525557548622, -0.12485216187207766, 0.013392469964324022, 0.0016784849503892474, -0.047425401309188805, 0.347514389451804, 0.18590479614740274, 0.08526932998595513, 0.02208619934994341, 0.3123697496473983, 0.0645528472344971, 0.10345024818209668, 0.12253653383286635, 0.26792362254587404, -0.02642456730080874, 0.163394192563159, -0.12074125437549192, 0.13364505447570305, -0.004955393420356624] |
1,802.03614 | Rigidity of manifolds admitting stable solutions of an elliptic problem | In this paper, we study geometric rigidity of Riemannian manifolds admitting
stable solutions of certain elliptic problems (stability in a variational
sense), that is, under suitable hypotheses, we are able to characterize the
Riemannian manifold which admits a stable solution. Furthermore, under the
non-negativity of the weighted Ricci curvature, we deduce several data about
the stable solution and a splitting result for the manifold.
| math.DG math.AP | in this paper we study geometric rigidity of riemannian manifolds admitting stable solutions of certain elliptic problems stability in a variational sense that is under suitable hypotheses we are able to characterize the riemannian manifold which admits a stable solution furthermore under the nonnegativity of the weighted ricci curvature we deduce several data about the stable solution and a splitting result for the manifold | [['in', 'this', 'paper', 'we', 'study', 'geometric', 'rigidity', 'of', 'riemannian', 'manifolds', 'admitting', 'stable', 'solutions', 'of', 'certain', 'elliptic', 'problems', 'stability', 'in', 'a', 'variational', 'sense', 'that', 'is', 'under', 'suitable', 'hypotheses', 'we', 'are', 'able', 'to', 'characterize', 'the', 'riemannian', 'manifold', 'which', 'admits', 'a', 'stable', 'solution', 'furthermore', 'under', 'the', 'nonnegativity', 'of', 'the', 'weighted', 'ricci', 'curvature', 'we', 'deduce', 'several', 'data', 'about', 'the', 'stable', 'solution', 'and', 'a', 'splitting', 'result', 'for', 'the', 'manifold']] | [-0.20054354902822524, 0.031910588542814367, -0.135039906235761, 0.11878717478248291, -0.11753580867662095, -0.15911837093153736, -0.037727908847955405, 0.3576271393103525, -0.3038166032638401, -0.22083675852627493, 0.17840802687351243, -0.23333238397026435, -0.1769176435773261, 0.16186346714675892, -0.1635878995002713, 0.07957815054396633, 0.13160409895135672, 0.09510992813738994, -0.12210579473685357, -0.23741920700012997, 0.48622557800263166, -0.03020027843740536, 0.24355851986911148, 0.10003010293439729, 0.17926847197122697, -0.06778247397596715, 0.08442138470127247, 0.07602784701884957, -0.24086357166072503, 0.16832415347744245, 0.2518569441162981, 0.09506449203763623, 0.2397309408406727, -0.37290068044967484, -0.20690779041615315, 0.19095123693841742, 0.07852841163548874, 0.044460000141043565, -0.059080817273297725, -0.27756181699805893, 0.16071687568182824, -0.04118237676448189, -0.20396616731068207, -0.17507989614387043, -0.049390908039640635, -0.0021945691587461624, -0.2779123991676897, 0.0480154299075366, 0.14049489205353893, 0.0184364076849306, -0.17259431698039407, -0.04515048742177896, -0.01988884064667218, 0.04653890290501295, 0.08221215401499649, -0.01679517670709174, 0.06644088028406259, -0.05081960481766146, -0.06773678906938585, 0.3552824123762548, -0.12092556551215239, -0.30506284436705755, 0.10809552836872172, -0.05804934090701863, -0.18184261914575472, 0.1243776178889675, 0.1557812934552203, 0.2485620325023774, -0.11219557875665487, 0.12950774482942506, -0.08645347290439531, 0.08311105073516956, 0.11403009764762828, -0.01177242102130549, 0.07768237258278532, 0.1292016297156806, 0.26140100523480214, 0.13218695033719996, -0.020916419220156968, -0.13837316191347782, -0.33066505334863905, -0.2011350739048794, -0.09931003987730946, 0.16946324205491692, -0.14999718229205428, -0.19367563408741262, 0.4204825210617855, 0.025075433841266204, 0.18225441507820506, 0.14479199320885527, 0.21413711173227057, 0.055987400643061846, -0.04044722698745318, 0.10294232911110157, 0.2267858920240542, 0.22749771372036776, 0.06600948720006272, -0.13583303487030207, -0.02047669953753939, 0.11549257847946137] |
1,802.03615 | Geometry, packing, and evolutionary paths to increased multicellular
size | The evolutionary transition to multicellularity transformed life on earth,
allowing for the evolution of large, complex organisms. While multicellularity
can be strongly advantageous, its earliest stages bring unique physical
challenges. Nascent multicellular organisms must contend with a novel
constraint: intercellular stresses arising from cell-cell interactions that can
limit multicellular size. Among the possible evolutionary routes to overcoming
this size limit, two appear obvious: multicellular organisms can increase
intercellular bond strength, allowing them to tolerate larger stresses, or,
they can slow the rate of stress accumulation by altering their internal
structure. Recent experiments demonstrated that multicellular 'snowflake yeast'
readily find a solution to this problem via the latter route. By evolving more
elongated cells, which decreases cellular packing fraction and thus the rate of
internal stress accumulation during growth, snowflake yeast evolve to delay
fracture and grow larger. However, it is unclear if snowflake yeast evolved
large size along an optimal path, or if the observed path to large size was
taken due to proximate biological reasons. Here, we examine the geometric
efficiency of both strategies using a minimal model that was previously
demonstrated to replicate many experimentally observed structural properties of
snowflake yeast. We find that changing geometry is a far more efficient route
to large size than evolving increased intercellular adhesion. In fact,
increasing cellular aspect ratio is on average ~13 times more effective at
increasing the number of cells in a cluster than increasing bond strength.
These results suggest that geometrically-imposed physical constraints may have
been a key early selective force guiding the emergence of multicellular
complexity.
| physics.bio-ph cond-mat.soft | the evolutionary transition to multicellularity transformed life on earth allowing for the evolution of large complex organisms while multicellularity can be strongly advantageous its earliest stages bring unique physical challenges nascent multicellular organisms must contend with a novel constraint intercellular stresses arising from cellcell interactions that can limit multicellular size among the possible evolutionary routes to overcoming this size limit two appear obvious multicellular organisms can increase intercellular bond strength allowing them to tolerate larger stresses or they can slow the rate of stress accumulation by altering their internal structure recent experiments demonstrated that multicellular snowflake yeast readily find a solution to this problem via the latter route by evolving more elongated cells which decreases cellular packing fraction and thus the rate of internal stress accumulation during growth snowflake yeast evolve to delay fracture and grow larger however it is unclear if snowflake yeast evolved large size along an optimal path or if the observed path to large size was taken due to proximate biological reasons here we examine the geometric efficiency of both strategies using a minimal model that was previously demonstrated to replicate many experimentally observed structural properties of snowflake yeast we find that changing geometry is a far more efficient route to large size than evolving increased intercellular adhesion in fact increasing cellular aspect ratio is on average 13 times more effective at increasing the number of cells in a cluster than increasing bond strength these results suggest that geometricallyimposed physical constraints may have been a key early selective force guiding the emergence of multicellular complexity | [['the', 'evolutionary', 'transition', 'to', 'multicellularity', 'transformed', 'life', 'on', 'earth', 'allowing', 'for', 'the', 'evolution', 'of', 'large', 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1,802.03616 | Disjointness of continuous g-frames and Riesz-type continuous g-frames | In this paper we introduce concepts of disjoint, strongly disjoint and weakly
disjoint continuous $g$-frames in Hilbert spaces and we get some equivalent
conditions to these notions. We also construct a continuous g-frame by disjoint
continuous g-frames. Furthermore, we provide some results related to the
Riesz-type continuous $g$-frames.
| math.FA | in this paper we introduce concepts of disjoint strongly disjoint and weakly disjoint continuous gframes in hilbert spaces and we get some equivalent conditions to these notions we also construct a continuous gframe by disjoint continuous gframes furthermore we provide some results related to the riesztype continuous gframes | [['in', 'this', 'paper', 'we', 'introduce', 'concepts', 'of', 'disjoint', 'strongly', 'disjoint', 'and', 'weakly', 'disjoint', 'continuous', 'gframes', 'in', 'hilbert', 'spaces', 'and', 'we', 'get', 'some', 'equivalent', 'conditions', 'to', 'these', 'notions', 'we', 'also', 'construct', 'a', 'continuous', 'gframe', 'by', 'disjoint', 'continuous', 'gframes', 'furthermore', 'we', 'provide', 'some', 'results', 'related', 'to', 'the', 'riesztype', 'continuous', 'gframes']] | [-0.11058859969489276, 0.19618393823232813, -0.06039681743520001, 0.08345507282501785, -0.0748509816621663, -0.11186414112065297, 0.04207029120152583, 0.4646327624407907, -0.36009691019232076, -0.10469530935127598, 0.13538160323999668, -0.2307020533674707, -0.17903361500551304, 0.1533988123604407, -0.20845499221468344, 0.055244393258666, 0.059469809668371454, -0.0030872792746473956, -0.09567172511015087, -0.29764556645629153, 0.43266359996050596, -0.1275465752890644, 0.18703492071169117, 0.07221078148965414, 0.09510218049399555, 0.035487665804491066, -0.05976367854358008, 0.0947245026472956, -0.2885107911230686, 0.16375152545515448, 0.3124465749133378, 0.19888092691932494, 0.3142592817894183, -0.3997869271164139, -0.13107519038021564, 0.19735652728316685, 0.08314194347864638, -0.03495685019394538, -0.019821041751129087, -0.32868704355011386, 0.05417579426042115, -0.11503658748309438, -0.049749440625115916, -0.15551056953457496, -0.011776160293569168, 0.07166190480347723, -0.26212147204205394, -0.0016402000771146656, 0.13025675372531018, 0.0326742590793098, -0.11168401619458261, -0.012857519497629255, -0.00278875112417154, 0.0670426548361623, -0.04175749762604634, 0.027014017483452335, 0.04746481749073913, 0.017490720846884262, -0.11840402835514396, 0.29610241452852887, -0.08758107674536102, -0.2847041793478032, 0.2547013032405327, -0.13692085802055468, -0.21132959021876255, 0.0630220493961436, 0.11838814024425422, 0.14805118755126992, -0.1653782504533107, 0.08346501624570617, -0.14581256381158406, 0.06315409152981981, 0.10526137308139975, 0.13490736372962905, 0.11839616602325502, 0.05284998155548237, 0.14098263182677329, 0.2694030336812527, 0.09395861013520819, -0.050666263020199644, -0.3629499593904863, -0.1578078192154256, -0.09948340002059315, 0.011536339996382594, -0.057359084331134604, -0.19417526018999828, 0.39272636982301873, 0.16068326136640584, 0.2375440823379904, 0.1453100145639231, 0.24963452434167266, 0.06890391701987635, -0.027877701679244637, 0.08259900032620256, 0.14572615023765442, 0.12010454421882362, 0.004366027385306855, -0.06817498731349285, -0.06079255177367789, 0.19456898162995154] |
1,802.03617 | Optimize transfer learning for lung diseases in bronchoscopy using a new
concept: sequential fine-tuning | Bronchoscopy inspection as a follow-up procedure from the radiological
imaging plays a key role in lung disease diagnosis and determining treatment
plans for the patients. Doctors needs to make a decision whether to biopsy the
patients timely when performing bronchoscopy. However, the doctors also needs
to be very selective with biopsies as biopsies may cause uncontrollable
bleeding of the lung tissue which is life-threaten. To help doctors to be more
selective on biopsies and provide a second opinion on diagnosis, in this work,
we propose a computer-aided diagnosis (CAD) system for lung diseases including
cancers and tuberculosis (TB). The system is developed based on transfer
learning. We propose a novel transfer learning method: sentential fine-tuning .
Compared to traditional fine-tuning methods, our methods achieves the best
performance. We obtained a overall accuracy of 77.0% a dataset of 81 normal
cases, 76 tuberculosis cases and 277 lung cancer cases while the other
traditional transfer learning methods achieve an accuracy of 73% and 68%. . The
detection accuracy of our method for cancers, TB and normal cases are 87%, 54%
and 91% respectively. This indicates that the CAD system has potential to
improve lung disease diagnosis accuracy in bronchoscopy and it also might be
used to be more selective with biopsies.
| cs.CV | bronchoscopy inspection as a followup procedure from the radiological imaging plays a key role in lung disease diagnosis and determining treatment plans for the patients doctors needs to make a decision whether to biopsy the patients timely when performing bronchoscopy however the doctors also needs to be very selective with biopsies as biopsies may cause uncontrollable bleeding of the lung tissue which is lifethreaten to help doctors to be more selective on biopsies and provide a second opinion on diagnosis in this work we propose a computeraided diagnosis cad system for lung diseases including cancers and tuberculosis tb the system is developed based on transfer learning we propose a novel transfer learning method sentential finetuning compared to traditional finetuning methods our methods achieves the best performance we obtained a overall accuracy of 770 a dataset of 81 normal cases 76 tuberculosis cases and 277 lung cancer cases while the other traditional transfer learning methods achieve an accuracy of 73 and 68 the detection accuracy of our method for cancers tb and normal cases are 87 54 and 91 respectively this indicates that the cad system has potential to improve lung disease diagnosis accuracy in bronchoscopy and it also might be used to be more selective with biopsies | [['bronchoscopy', 'inspection', 'as', 'a', 'followup', 'procedure', 'from', 'the', 'radiological', 'imaging', 'plays', 'a', 'key', 'role', 'in', 'lung', 'disease', 'diagnosis', 'and', 'determining', 'treatment', 'plans', 'for', 'the', 'patients', 'doctors', 'needs', 'to', 'make', 'a', 'decision', 'whether', 'to', 'biopsy', 'the', 'patients', 'timely', 'when', 'performing', 'bronchoscopy', 'however', 'the', 'doctors', 'also', 'needs', 'to', 'be', 'very', 'selective', 'with', 'biopsies', 'as', 'biopsies', 'may', 'cause', 'uncontrollable', 'bleeding', 'of', 'the', 'lung', 'tissue', 'which', 'is', 'lifethreaten', 'to', 'help', 'doctors', 'to', 'be', 'more', 'selective', 'on', 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1,802.03618 | Invertibility of Multipliers for Continuous G-frames | In this paper we study the concept of multipliers for continuous $g$-Bessel
families in Hilbert spaces. We present necessary conditions for invertibility
of multipliers for continuous $g$-Bessel families and sufficient conditions for
invertibility of multipliers for continuous $g$-frames.
| math.FA | in this paper we study the concept of multipliers for continuous gbessel families in hilbert spaces we present necessary conditions for invertibility of multipliers for continuous gbessel families and sufficient conditions for invertibility of multipliers for continuous gframes | [['in', 'this', 'paper', 'we', 'study', 'the', 'concept', 'of', 'multipliers', 'for', 'continuous', 'gbessel', 'families', 'in', 'hilbert', 'spaces', 'we', 'present', 'necessary', 'conditions', 'for', 'invertibility', 'of', 'multipliers', 'for', 'continuous', 'gbessel', 'families', 'and', 'sufficient', 'conditions', 'for', 'invertibility', 'of', 'multipliers', 'for', 'continuous', 'gframes']] | [-0.1257630016066526, 0.10084433716378714, -0.034821898492641355, 0.08356656377681677, -0.05291653268872515, -0.057961197030779565, -0.012895332597269627, 0.46414357972772496, -0.3014687579125166, -0.12998379453232414, 0.2087424467011404, -0.1059145669599897, -0.1275988657419619, 0.2464769936019653, -0.17196065858986817, 0.15929089819914416, 0.034424346528555215, -0.04054437415968431, -0.20746741799245538, -0.2397105113456124, 0.4331444036215544, -0.04046105051805314, 0.18647256917565277, 0.05205427129801951, 0.09968994839705135, 0.0750622435497414, 0.007176302820689192, -0.10346725632093455, -0.21427919137242593, 0.12517340554806747, 0.2716515129922252, 0.14031279337053237, 0.32786821064196137, -0.39728562612282603, -0.1455514614568337, 0.2192414862495896, 0.10265056416392326, -0.07368763124472216, -0.13106841861123317, -0.2906212664552425, 0.09554066028642028, -0.10062938252169835, -0.15477049938942256, -0.16650144452241397, 0.040610233615887795, 0.10132811306117985, -0.40485063097194623, 0.06483204042735069, 0.16141742953148328, 0.11916854830556795, -0.22612496632102289, -0.04467101904906725, 0.04330309127506457, 0.048294590614540014, -0.051972477157649244, -0.11174285190645605, 0.012995290741520492, 0.004559919048149727, -0.0754771088985236, 0.29109663163360794, -0.02250392913763215, -0.3191071361989567, 0.17906752963991543, -0.13061266921852765, -0.22102284225586213, 0.030134457800733417, 0.1760060775927023, 0.21296707079990915, -0.17898939728834912, 0.13020588988039986, -0.12201426433105218, 0.028124363639539008, 0.12397800447223217, 0.11551544094752324, 0.050460647399488245, 0.07328944307702936, 0.26965569219502966, 0.21778841757163478, 0.03313581232520703, -0.011183117962086965, -0.42005813553145055, -0.271776644414977, -0.12654126121809608, -0.04093926517586959, -0.018870496950464593, -0.173086179371335, 0.494767635472511, 0.16244827105516665, 0.12813587457333742, 0.10910442976379081, 0.19954968047769447, 0.08523026767254219, -0.011227779335489399, 0.030715349865587133, 0.16077615712818347, 0.20339846199280337, 0.10893608394422029, -0.11455178947040909, -0.016207301158972672, 0.2711636394531907] |
1,802.03619 | A note on local BRST cohomology of Yang-Mills type theories with free
abelian factors | We extend previous work on antifield dependent local BRST cohomology for
matter coupled gauge theories of Yang-Mills type to the case of gauge groups
that involve free abelian factors. More precisely, we first investigate in a
model independent way how the dynamics enters the computation of the cohomology
for a general class of Lagrangians in general spacetime dimensions. We then
discuss explicit solutions in the case of specific models. Our analysis has
implications for the structure of characteristic cohomology and for consistent
deformations of the classical models, as well as for divergences/counterterms
and for gauge anomalies that may appear during perturbative quantization.
| hep-th | we extend previous work on antifield dependent local brst cohomology for matter coupled gauge theories of yangmills type to the case of gauge groups that involve free abelian factors more precisely we first investigate in a model independent way how the dynamics enters the computation of the cohomology for a general class of lagrangians in general spacetime dimensions we then discuss explicit solutions in the case of specific models our analysis has implications for the structure of characteristic cohomology and for consistent deformations of the classical models as well as for divergencescounterterms and for gauge anomalies that may appear during perturbative quantization | [['we', 'extend', 'previous', 'work', 'on', 'antifield', 'dependent', 'local', 'brst', 'cohomology', 'for', 'matter', 'coupled', 'gauge', 'theories', 'of', 'yangmills', 'type', 'to', 'the', 'case', 'of', 'gauge', 'groups', 'that', 'involve', 'free', 'abelian', 'factors', 'more', 'precisely', 'we', 'first', 'investigate', 'in', 'a', 'model', 'independent', 'way', 'how', 'the', 'dynamics', 'enters', 'the', 'computation', 'of', 'the', 'cohomology', 'for', 'a', 'general', 'class', 'of', 'lagrangians', 'in', 'general', 'spacetime', 'dimensions', 'we', 'then', 'discuss', 'explicit', 'solutions', 'in', 'the', 'case', 'of', 'specific', 'models', 'our', 'analysis', 'has', 'implications', 'for', 'the', 'structure', 'of', 'characteristic', 'cohomology', 'and', 'for', 'consistent', 'deformations', 'of', 'the', 'classical', 'models', 'as', 'well', 'as', 'for', 'divergencescounterterms', 'and', 'for', 'gauge', 'anomalies', 'that', 'may', 'appear', 'during', 'perturbative', 'quantization']] | [-0.13216842754656394, 0.1285314137429589, -0.09401612612188305, 0.1036522675324047, -0.08521404861942819, -0.1175941370685797, -0.008448308508953836, 0.31913981178203726, -0.22530550014983752, -0.25708310797552364, 0.0830495794773987, -0.20833459547674743, -0.19659361043613, 0.16109707222854958, -0.07698351340708415, 0.014796540913365708, -0.0073635285249957355, 0.08144207910108979, -0.10606708436383691, -0.26445031228549043, 0.4024624582103426, 0.008483882513966891, 0.2478377119831667, 0.029679201663204348, 0.08736592278087346, 0.03047257797829438, -0.03558436952678874, 0.008903297729384486, -0.12831839630083472, 0.09937144684291786, 0.24928553901604977, 0.03370385423311583, 0.15202837622042117, -0.4437194209140126, -0.251263196598554, 0.10309229491020341, 0.1594605999053594, 0.1504000042874071, -0.026087614027166618, -0.25301824325216143, 0.06804945940465325, -0.18270537169133821, -0.1626450139648825, -0.14258755439671106, 0.037209487824451805, -0.03810763263171262, -0.24670123137213235, 0.10556956294158676, 0.031532506620981685, 0.058279369220063826, -0.10879649818957884, -0.07771517895853682, -0.018223207268753264, 0.08943269261571643, 0.07730943548128857, -0.003019215700465559, 0.1229606649912156, -0.19088190625707555, -0.15417550788675943, 0.41370956647661655, -0.08799239556019259, -0.23117624824903538, 0.1684389985673646, -0.1269719347837243, -0.2673497014300953, 0.04277753377088926, 0.14160041753571517, 0.2028161179514067, -0.07774275102442832, 0.19611903563911778, -0.03475875254388493, 0.09332643628655242, 0.05321966774651025, 0.03758701422864959, 0.19087145921064191, 0.10209905433164228, 0.05125970254642981, 0.11895758499123968, 0.022308631650273605, -0.13674933793908445, -0.43088931446470835, -0.18333287627463885, -0.07526475936174393, 0.08745339863569393, -0.10052194463009577, -0.13657409697079495, 0.4254394000635879, 0.1590663541327657, 0.1429807923199369, 0.09941376447862033, 0.2163635216063202, 0.09480244743218184, 0.07750444530998127, 0.015295743356726252, 0.21152380760759115, 0.18123720041798927, 0.04399356540606016, -0.18728330696352047, -0.04474676463644638, 0.14264714034126685] |
1,802.0362 | Optimal approximation of continuous functions by very deep ReLU networks | We consider approximations of general continuous functions on
finite-dimensional cubes by general deep ReLU neural networks and study the
approximation rates with respect to the modulus of continuity of the function
and the total number of weights $W$ in the network. We establish the complete
phase diagram of feasible approximation rates and show that it includes two
distinct phases. One phase corresponds to slower approximations that can be
achieved with constant-depth networks and continuous weight assignments. The
other phase provides faster approximations at the cost of depths necessarily
growing as a power law $L\sim W^{\alpha}, 0<\alpha\le 1,$ and with necessarily
discontinuous weight assignments. In particular, we prove that constant-width
fully-connected networks of depth $L\sim W$ provide the fastest possible
approximation rate $\|f-\widetilde f\|_\infty = O(\omega_f(O(W^{-2/\nu})))$
that cannot be achieved with less deep networks.
| cs.NE | we consider approximations of general continuous functions on finitedimensional cubes by general deep relu neural networks and study the approximation rates with respect to the modulus of continuity of the function and the total number of weights w in the network we establish the complete phase diagram of feasible approximation rates and show that it includes two distinct phases one phase corresponds to slower approximations that can be achieved with constantdepth networks and continuous weight assignments the other phase provides faster approximations at the cost of depths necessarily growing as a power law lsim walpha 0alphale 1 and with necessarily discontinuous weight assignments in particular we prove that constantwidth fullyconnected networks of depth lsim w provide the fastest possible approximation rate fwidetilde f_infty oomega_fow2nu that cannot be achieved with less deep networks | [['we', 'consider', 'approximations', 'of', 'general', 'continuous', 'functions', 'on', 'finitedimensional', 'cubes', 'by', 'general', 'deep', 'relu', 'neural', 'networks', 'and', 'study', 'the', 'approximation', 'rates', 'with', 'respect', 'to', 'the', 'modulus', 'of', 'continuity', 'of', 'the', 'function', 'and', 'the', 'total', 'number', 'of', 'weights', 'w', 'in', 'the', 'network', 'we', 'establish', 'the', 'complete', 'phase', 'diagram', 'of', 'feasible', 'approximation', 'rates', 'and', 'show', 'that', 'it', 'includes', 'two', 'distinct', 'phases', 'one', 'phase', 'corresponds', 'to', 'slower', 'approximations', 'that', 'can', 'be', 'achieved', 'with', 'constantdepth', 'networks', 'and', 'continuous', 'weight', 'assignments', 'the', 'other', 'phase', 'provides', 'faster', 'approximations', 'at', 'the', 'cost', 'of', 'depths', 'necessarily', 'growing', 'as', 'a', 'power', 'law', 'lsim', 'walpha', '0alphale', '1', 'and', 'with', 'necessarily', 'discontinuous', 'weight', 'assignments', 'in', 'particular', 'we', 'prove', 'that', 'constantwidth', 'fullyconnected', 'networks', 'of', 'depth', 'lsim', 'w', 'provide', 'the', 'fastest', 'possible', 'approximation', 'rate', 'fwidetilde', 'f_infty', 'oomega_fow2nu', 'that', 'can', 'not', 'be', 'achieved', 'with', 'less', 'deep', 'networks']] | [-0.12256800697561894, 0.10744480822832529, -0.033795842029978984, 0.03971160626934685, -0.028684342800788867, -0.18468634550680071, 0.07936541852008068, 0.43117462416541164, -0.2963860587906519, -0.23370866328060186, 0.0802533366017129, -0.24264358490250493, -0.1524225572472481, 0.18069287102299791, -0.048071722500026226, 0.06669440940875589, 0.06480271018728728, 0.03674509022087179, -0.10162037466243905, -0.2718880817539393, 0.28748235013069345, -0.004849372321885534, 0.26751953543995627, 0.009384563061202528, 0.08655775610498821, -0.03319045504597762, 0.015117710680196076, -0.00795311364256386, -0.14057641745236601, 0.09397506845745308, 0.24158813433280882, 0.11655720313548872, 0.2826444495839017, -0.42547558264394525, -0.20845463792331345, 0.18768041673825542, 0.11936641868402939, 0.04652626757716177, 0.0324083607784252, -0.21467818932621988, 0.12016495370362949, -0.16610477081293823, -0.10702066733110838, -0.09846819457526937, 0.014891000382801277, 0.0671289822909597, -0.30496830849744777, 0.09661175922902998, 0.07682903398198025, -0.009818295079091464, -0.038015272514539136, -0.16114511463670272, -0.026125613091698356, 0.07650267174254165, -0.013319572992800585, 0.09249837490559364, 0.05741345803857646, -0.17281277318492191, -0.10094450972263368, 0.3289218884857215, -0.07443438980813699, -0.18737629288629779, 0.17229556268123725, -0.14546530532576446, -0.11293292049858861, 0.14625167130769892, 0.18350549973902924, 0.1235065400889361, -0.09028426907913945, 0.06848982177836507, -0.02592775545737045, 0.18901828050129968, 0.06538981445602902, 0.053969789307941295, 0.11144887235811876, 0.15555484554346452, 0.10427520458252375, 0.14118826851997, -0.04784934393144461, -0.10823719463007211, -0.2879533138950817, -0.1123519278636188, -0.18449412837965798, 0.052641602170956675, -0.1498257057088781, -0.1606417878367405, 0.37374166484463617, 0.09469687530881092, 0.2249328846481579, 0.18558676383024408, 0.26147468680274166, 0.15259060412840847, 0.10578311285899795, 0.10661662619015316, 0.21804057296635654, 0.12832958785504217, 0.07376493617824727, -0.1442789537965441, 0.07971194549508123, 0.09893019458293233] |
1,802.03621 | Preliminary result on stochastic system control theory for aperiod
sample-data systems | In this paper, we obtain some preliminary results on stochastic control
theory for time-varying linear systems both continuous and discrete, and
further apply to aperiod sample-data linear systems. The Ito's lemma is
utilized in this proposed theory, and deduced that the stability of a linear
time-varying system is determined by the eigenvalues expectation of system
matrix, which coincidences with the stable conditions for time-invariant
system, i.e. Hurwitz for continuous systems or inside the unit circle for
discrete systems. The control method for aperiod time-invariant sample-data
system is also derived. It is shown that the stable condition is determined by
the expectation of the sample-interval but the up-bound and the aperiod
interval can be arbitrarily large even infinity. To verify the efficiency of
our theory, serval experiments are demonstrated in the final of the paper.
| cs.SY | in this paper we obtain some preliminary results on stochastic control theory for timevarying linear systems both continuous and discrete and further apply to aperiod sampledata linear systems the itos lemma is utilized in this proposed theory and deduced that the stability of a linear timevarying system is determined by the eigenvalues expectation of system matrix which coincidences with the stable conditions for timeinvariant system ie hurwitz for continuous systems or inside the unit circle for discrete systems the control method for aperiod timeinvariant sampledata system is also derived it is shown that the stable condition is determined by the expectation of the sampleinterval but the upbound and the aperiod interval can be arbitrarily large even infinity to verify the efficiency of our theory serval experiments are demonstrated in the final of the paper | [['in', 'this', 'paper', 'we', 'obtain', 'some', 'preliminary', 'results', 'on', 'stochastic', 'control', 'theory', 'for', 'timevarying', 'linear', 'systems', 'both', 'continuous', 'and', 'discrete', 'and', 'further', 'apply', 'to', 'aperiod', 'sampledata', 'linear', 'systems', 'the', 'itos', 'lemma', 'is', 'utilized', 'in', 'this', 'proposed', 'theory', 'and', 'deduced', 'that', 'the', 'stability', 'of', 'a', 'linear', 'timevarying', 'system', 'is', 'determined', 'by', 'the', 'eigenvalues', 'expectation', 'of', 'system', 'matrix', 'which', 'coincidences', 'with', 'the', 'stable', 'conditions', 'for', 'timeinvariant', 'system', 'ie', 'hurwitz', 'for', 'continuous', 'systems', 'or', 'inside', 'the', 'unit', 'circle', 'for', 'discrete', 'systems', 'the', 'control', 'method', 'for', 'aperiod', 'timeinvariant', 'sampledata', 'system', 'is', 'also', 'derived', 'it', 'is', 'shown', 'that', 'the', 'stable', 'condition', 'is', 'determined', 'by', 'the', 'expectation', 'of', 'the', 'sampleinterval', 'but', 'the', 'upbound', 'and', 'the', 'aperiod', 'interval', 'can', 'be', 'arbitrarily', 'large', 'even', 'infinity', 'to', 'verify', 'the', 'efficiency', 'of', 'our', 'theory', 'serval', 'experiments', 'are', 'demonstrated', 'in', 'the', 'final', 'of', 'the', 'paper']] | [-0.13283712035979878, 0.08718317396459029, -0.08550618928998258, 0.04818206688188101, -0.00969996025863414, -0.15289434744397912, 0.030407645862203826, 0.3291299061595716, -0.2922591898664409, -0.244764646370613, 0.1701278036193936, -0.2202503594876775, -0.18547136309225057, 0.2358154537576025, -0.0816829606399441, 0.1326508152397406, 0.07138556712060094, 0.0687275004403835, -0.05251022231602816, -0.255599607586522, 0.3111571744154913, 0.035978397178096755, 0.25051892786392604, 0.035736524909170286, 0.14421055441110212, 0.02560843011738281, -0.0010505360127850013, 0.0324214447830331, -0.08309411318590389, 0.1237236831114966, 0.23339985491651477, 0.10347896306528569, 0.25960292588129186, -0.3834380527647833, -0.2230506045179385, 0.10990560346875679, 0.11468042565529961, 0.08539570676370975, -0.020790845537593475, -0.29969762020151725, 0.14302545021090543, -0.14364426988035892, -0.14515581365787622, -0.09280062813107207, 0.00868917507063033, 0.05350240061913306, -0.32099027351730247, 0.06223607863891326, 0.0900515757719845, 0.04024003301112151, -0.10216269329380752, -0.07537344749664154, -0.025103663664071286, 0.10855731916098828, 0.008901139627789345, -0.018449997082246806, 0.0961934215297471, -0.05716569928335957, -0.10408557121021052, 0.351291590730067, -0.05945350626963332, -0.24649143698193118, 0.16250964373819332, -0.13884737195136646, -0.12773033649357263, 0.10297924948672792, 0.17998699939132415, 0.14648875019117963, -0.1539965803121132, 0.11046983464797394, -0.06683677830027812, 0.1692910026053362, 0.024077565792002573, 0.008079455990809947, 0.1457501579019608, 0.11963703885505145, 0.1203452603341165, 0.1345300553939799, -0.012494107087453207, -0.14189915290582134, -0.29657569570664427, -0.12761820723641326, -0.1807519953334563, 0.006629697438752787, -0.07073791748729436, -0.1570419196229495, 0.36656608130557067, 0.1233810068489137, 0.17265144320360074, 0.11459928438522514, 0.2612947156920916, 0.20179349681182596, 0.004435655615066715, 0.04431002564855258, 0.23721104897457268, 0.16813354576923745, 0.10080626897979528, -0.23340520591679678, 0.05387027643860854, 0.06406373729943439] |
1,802.03622 | Optimal preconditioners for systems defined by functions of Toeplitz
matrices | We propose several circulant preconditioners for systems defined by some
functions $g$ of Toeplitz matrices $A_n$. In this paper we are interested in
solving $g(A_n)\mathbf{x}=\mathbf{b}$ by the preconditioned conjugate method or
the preconditioned minimal residual method, namely in the cases when $g(z)$ are
the functions $e^{z}$, $\sin{z}$ and $\cos{z}$. Numerical results are given to
show the effectiveness of the proposed preconditioners.
| math.NA | we propose several circulant preconditioners for systems defined by some functions g of toeplitz matrices a_n in this paper we are interested in solving ga_nmathbfxmathbfb by the preconditioned conjugate method or the preconditioned minimal residual method namely in the cases when gz are the functions ez sinz and cosz numerical results are given to show the effectiveness of the proposed preconditioners | [['we', 'propose', 'several', 'circulant', 'preconditioners', 'for', 'systems', 'defined', 'by', 'some', 'functions', 'g', 'of', 'toeplitz', 'matrices', 'a_n', 'in', 'this', 'paper', 'we', 'are', 'interested', 'in', 'solving', 'ga_nmathbfxmathbfb', 'by', 'the', 'preconditioned', 'conjugate', 'method', 'or', 'the', 'preconditioned', 'minimal', 'residual', 'method', 'namely', 'in', 'the', 'cases', 'when', 'gz', 'are', 'the', 'functions', 'ez', 'sinz', 'and', 'cosz', 'numerical', 'results', 'are', 'given', 'to', 'show', 'the', 'effectiveness', 'of', 'the', 'proposed', 'preconditioners']] | [-0.11522747469134628, 0.05944208680496862, -0.0011689423738668362, 0.02996794430655427, -0.0025263311186184485, -0.1411321778354856, -0.038606578963420665, 0.40848900796069454, -0.3035022009862587, -0.23525892220592748, 0.14176941877425028, -0.2808100493003925, -0.2283321763893279, 0.22333546637479837, -0.10694842658316096, 0.12049174757363895, 0.11164694697363302, -0.006868134586450954, -0.17693073766616482, -0.296606896755596, 0.36322221818069617, -0.005939761366850386, 0.18134954053287705, 0.008886870190811655, 0.07556555190434058, -0.06567754591815174, -0.043338680841649574, -0.03380507053980182, -0.0949175947656234, 0.09539490773652991, 0.2868699647951871, 0.14535829378291965, 0.32253737170249225, -0.3737440861451129, -0.1277064506857035, 0.15649477263602118, 0.18843220338070144, 0.025102484117572505, -0.08667026612286767, -0.2569244715540359, 0.16336629015083115, -0.14245194980564216, -0.10798399381649991, -0.1431228966296961, -0.06407971669298908, 0.10381454791640862, -0.3215061566171547, 0.05685977086735269, 0.053935408545657995, 0.03500137087733795, -0.03275354000506923, -0.22456863538051644, 0.021721634982774653, -0.00474736139246185, 0.0380772525522237, -0.058934510514761014, 0.06002343515865505, -0.04507128473293657, -0.08626878634095192, 0.3538667961023748, -0.036002292693592605, -0.3244676451198757, 0.09587190635502338, -0.09634495335631073, -0.16154885898188998, 0.040619935095310214, 0.1709705416113138, 0.21336684074097623, -0.11279093650834209, 0.1523984302136038, -0.06928426617135604, 0.059406688126424946, 0.02534504293774565, -0.04687545684476693, 0.05290578425241013, 0.07591954024974257, 0.09571717876824551, 0.1647090668673627, -0.0007469006503621737, -0.028123037548114856, -0.28661318123340607, -0.11560875955037772, -0.21606710774358362, -0.03374312478893747, -0.13690549316791778, -0.1697805236869802, 0.4337790829439958, 0.1397724992595613, 0.14163615570093194, 0.07414959811527903, 0.2762159015517682, 0.16247557500222076, 0.05654817505783285, 0.14726610901610304, 0.13168544407089938, 0.20133313776459544, 0.045361107727512714, -0.2529677062916259, -0.0012467712784806886, 0.21142996271761755] |
1,802.03623 | Difffusion Dynamics on the Coexistence Subspace in a Stochastic
Evolutionary Game | Frequency-dependent selection reflects the interaction between different
species as they battle for limited resources in their environment. In a
stochastic evolutionary game the species relative fitnesses guides the
evolutionary dynamics which fluctuate due to random drift. Dependence of
species selection advantages on the environment introduces additional
possibilities for the evolutionary dynamics. We analyse a simple model in which
a random environment allows competing species to coexist for a long time before
a fixation of a single species happens. In our analysis we use stability in a
linear combination of competing species to approximate the stochastic dynamics
of the system by a diffusion on a one dimensional co-existence region. Our
method significantly simplifies calculating the probability of first extinction
and its expected time, and demonstrates a rigorous model reduction technique
for evaluating quasistationary properties of a stochastic evolutionary model.
| math.PR q-bio.PE | frequencydependent selection reflects the interaction between different species as they battle for limited resources in their environment in a stochastic evolutionary game the species relative fitnesses guides the evolutionary dynamics which fluctuate due to random drift dependence of species selection advantages on the environment introduces additional possibilities for the evolutionary dynamics we analyse a simple model in which a random environment allows competing species to coexist for a long time before a fixation of a single species happens in our analysis we use stability in a linear combination of competing species to approximate the stochastic dynamics of the system by a diffusion on a one dimensional coexistence region our method significantly simplifies calculating the probability of first extinction and its expected time and demonstrates a rigorous model reduction technique for evaluating quasistationary properties of a stochastic evolutionary model | [['frequencydependent', 'selection', 'reflects', 'the', 'interaction', 'between', 'different', 'species', 'as', 'they', 'battle', 'for', 'limited', 'resources', 'in', 'their', 'environment', 'in', 'a', 'stochastic', 'evolutionary', 'game', 'the', 'species', 'relative', 'fitnesses', 'guides', 'the', 'evolutionary', 'dynamics', 'which', 'fluctuate', 'due', 'to', 'random', 'drift', 'dependence', 'of', 'species', 'selection', 'advantages', 'on', 'the', 'environment', 'introduces', 'additional', 'possibilities', 'for', 'the', 'evolutionary', 'dynamics', 'we', 'analyse', 'a', 'simple', 'model', 'in', 'which', 'a', 'random', 'environment', 'allows', 'competing', 'species', 'to', 'coexist', 'for', 'a', 'long', 'time', 'before', 'a', 'fixation', 'of', 'a', 'single', 'species', 'happens', 'in', 'our', 'analysis', 'we', 'use', 'stability', 'in', 'a', 'linear', 'combination', 'of', 'competing', 'species', 'to', 'approximate', 'the', 'stochastic', 'dynamics', 'of', 'the', 'system', 'by', 'a', 'diffusion', 'on', 'a', 'one', 'dimensional', 'coexistence', 'region', 'our', 'method', 'significantly', 'simplifies', 'calculating', 'the', 'probability', 'of', 'first', 'extinction', 'and', 'its', 'expected', 'time', 'and', 'demonstrates', 'a', 'rigorous', 'model', 'reduction', 'technique', 'for', 'evaluating', 'quasistationary', 'properties', 'of', 'a', 'stochastic', 'evolutionary', 'model']] | [-0.11467811343828689, 0.11818159903413143, -0.11406273509551218, 0.08336165642780859, -0.021827550918754678, -0.14095278182809334, 0.12005150911189022, 0.3665838569463433, -0.278740706394656, -0.2801873014929394, 0.040544075532850096, -0.2132282586570237, -0.14237438281998038, 0.10706638193021165, -0.023199135803936413, -0.014235976216656602, 0.05535670457934232, -0.005173628798017845, -0.0016410684363201153, -0.21768770552159328, 0.2622166681626434, 0.058440016565736434, 0.2801480050539544, -0.017686850168620764, 0.13668357279823176, 0.00317638300433485, -0.04043225121036496, 0.039687804810270885, -0.11191782140024546, 0.09055030910193623, 0.20809007751244202, 0.1404199463625749, 0.32196606628164864, -0.4101734268951459, -0.2668323200735925, 0.10383383701622918, 0.15449984777871473, 0.1731670597100806, -0.04402165372343059, -0.2570065110651911, -0.015856961927214717, -0.17356927228958238, -0.145697099182829, -0.03685103711145728, 0.03545684898542105, 0.05490618072571638, -0.2868937237367061, 0.08081538465963947, 0.03474814342901126, 0.051702586502052734, -0.07321116797232906, -0.1131349553926038, -0.020030486395758024, 0.15435110530276122, 0.01883153804574513, -0.04497179137947767, 0.14286131636185598, -0.13999773355174946, -0.13335963121304908, 0.36543927179055585, -0.11010726848853401, -0.20811835816467478, 0.27868665679884347, -0.09872383402619997, -0.15205022639196797, 0.16350414999085816, 0.2139687205152755, 0.1472406646243526, -0.18695854811348778, 0.04258795467161479, 0.026570752566761297, 0.15764619863551596, 0.013664229506410766, 0.007632778903496438, 0.2024430830868474, 0.2537942999076314, 0.08914398692845218, 0.13102853027563813, -0.07038133373767462, -0.21547124556778674, -0.22977117079553072, -0.14223999886577815, -0.13174144808958838, 0.03281304318090037, -0.125589861109156, -0.17125697484563873, 0.42292422927457135, 0.1764792377889271, 0.1958355362918498, 0.06256181200244126, 0.2987494153962673, 0.08696325097411899, 0.009847007848767807, 0.014677435364886322, 0.17458741227164865, 0.1166353730520274, 0.11576340909021487, -0.27804948721566924, 0.17053927271964325, 0.04405526345784681] |
1,802.03624 | Lectures on the Euler characteristic of affine manifolds | These are lecture notes prepared for the summer school "Geometric, algebraic
and topological methods in quantum field theory", held in Villa de Leyva in
July 2017. Our goal is to provide an introduction to a conjecture of Chern that
states that the Euler characteristic of a closed affine manifold vanishes. We
present part of the history and motivation for the conjecture as well as some
recent developments. All comments and corrections are most welcome!
| math.DG math.AT | these are lecture notes prepared for the summer school geometric algebraic and topological methods in quantum field theory held in villa de leyva in july 2017 our goal is to provide an introduction to a conjecture of chern that states that the euler characteristic of a closed affine manifold vanishes we present part of the history and motivation for the conjecture as well as some recent developments all comments and corrections are most welcome | [['these', 'are', 'lecture', 'notes', 'prepared', 'for', 'the', 'summer', 'school', 'geometric', 'algebraic', 'and', 'topological', 'methods', 'in', 'quantum', 'field', 'theory', 'held', 'in', 'villa', 'de', 'leyva', 'in', 'july', '2017', 'our', 'goal', 'is', 'to', 'provide', 'an', 'introduction', 'to', 'a', 'conjecture', 'of', 'chern', 'that', 'states', 'that', 'the', 'euler', 'characteristic', 'of', 'a', 'closed', 'affine', 'manifold', 'vanishes', 'we', 'present', 'part', 'of', 'the', 'history', 'and', 'motivation', 'for', 'the', 'conjecture', 'as', 'well', 'as', 'some', 'recent', 'developments', 'all', 'comments', 'and', 'corrections', 'are', 'most', 'welcome']] | [-0.14148127872219965, 0.14937373024991216, -0.14952117010850358, 0.061019458847334354, -0.10956703445864716, -0.14229660692020646, -0.06265414419514756, 0.2672288646116047, -0.1930342185125384, -0.3126629328204168, 0.14459598545973013, -0.2845227366369973, -0.20443848884865842, 0.2020373109451218, -0.15646801552911466, -0.002067878268143112, 0.034988046158105135, 0.020190791350024804, -0.05227786157204694, -0.3910334408031525, 0.3327016777900123, 0.0794413906468607, 0.2544010267655894, 0.12425937572953517, 0.10659804880442852, 0.0001412190786386664, -0.030050763149267516, 0.004360992801249833, -0.1566520286545022, 0.13288369021815802, 0.37346947840037376, 0.10145114764973924, 0.26149749745791023, -0.4559363611294208, -0.11273771710064565, 0.001351660007384379, 0.03722735496734999, 0.13111066250476633, -0.05422027051020917, -0.3148074737573798, 0.04457215454454559, -0.14897380628295848, -0.15198550889948131, -0.046061063802020775, 0.05112544118860938, -0.02744355528439219, -0.1355965293541148, 0.014977622453148503, 0.09490093891036923, 0.162338885239552, -0.05780932920350932, -0.14804336644954527, -0.0015746479463839048, 0.12680281914858701, 0.025572109045586676, 0.08309543481398676, 0.07411812769400107, -0.1315312812331359, -0.186726877761602, 0.35817693302256837, -0.07511892990284674, -0.09636011208734803, 0.13651666547347968, -0.11033988273324999, -0.2207288987827613, 0.0675697145933235, 0.17201509613644433, 0.1292056683859964, -0.05597054939465346, 0.14597750424070763, -0.02278269885829178, 0.08028446609859129, 0.05997619309739487, 0.013190396444720996, 0.2294154563789709, 0.05061249146741387, -0.0008989430561258987, 0.06560316935773133, -0.011477815308230551, -0.12379529456154921, -0.40508858960222555, -0.2208834064281168, -0.16952864813297433, 0.10059562156832702, 0.03798675542831818, -0.17158094239798752, 0.3796529662291947, 0.11427391126651217, 0.15863332718711448, 0.052982121948278636, 0.2021046285895077, 0.06356095284934633, -0.042866237553597954, 0.0768337448165324, 0.18666150400964102, 0.1848265689269111, 0.15336071106454208, -0.097405671337116, -0.007764432697267448, 0.16023417196010012] |
1,802.03625 | The Follower Count Fallacy: Detecting Twitter Users with Manipulated
Follower Count | Online Social Networks (OSN) are increasingly being used as platform for an
effective communication, to engage with other users, and to create a social
worth via number of likes, followers and shares. Such metrics and crowd-sourced
ratings give the OSN user a sense of social reputation which she tries to
maintain and boost to be more influential. Users artificially bolster their
social reputation via black-market web services. In this work, we identify
users which manipulate their projected follower count using an unsupervised
local neighborhood detection method. We identify a neighborhood of the user
based on a robust set of features which reflect user similarity in terms of the
expected follower count. We show that follower count estimation using our
method has 84.2% accuracy with a low error rate. In addition, we estimate the
follower count of the user under suspicion by finding its neighborhood drawn
from a large random sample of Twitter. We show that our method is highly
tolerant to synthetic manipulation of followers. Using the deviation of
predicted follower count from the displayed count, we are also able to detect
customers with a high precision of 98.62%
| cs.SI | online social networks osn are increasingly being used as platform for an effective communication to engage with other users and to create a social worth via number of likes followers and shares such metrics and crowdsourced ratings give the osn user a sense of social reputation which she tries to maintain and boost to be more influential users artificially bolster their social reputation via blackmarket web services in this work we identify users which manipulate their projected follower count using an unsupervised local neighborhood detection method we identify a neighborhood of the user based on a robust set of features which reflect user similarity in terms of the expected follower count we show that follower count estimation using our method has 842 accuracy with a low error rate in addition we estimate the follower count of the user under suspicion by finding its neighborhood drawn from a large random sample of twitter we show that our method is highly tolerant to synthetic manipulation of followers using the deviation of predicted follower count from the displayed count we are also able to detect customers with a high precision of 9862 | [['online', 'social', 'networks', 'osn', 'are', 'increasingly', 'being', 'used', 'as', 'platform', 'for', 'an', 'effective', 'communication', 'to', 'engage', 'with', 'other', 'users', 'and', 'to', 'create', 'a', 'social', 'worth', 'via', 'number', 'of', 'likes', 'followers', 'and', 'shares', 'such', 'metrics', 'and', 'crowdsourced', 'ratings', 'give', 'the', 'osn', 'user', 'a', 'sense', 'of', 'social', 'reputation', 'which', 'she', 'tries', 'to', 'maintain', 'and', 'boost', 'to', 'be', 'more', 'influential', 'users', 'artificially', 'bolster', 'their', 'social', 'reputation', 'via', 'blackmarket', 'web', 'services', 'in', 'this', 'work', 'we', 'identify', 'users', 'which', 'manipulate', 'their', 'projected', 'follower', 'count', 'using', 'an', 'unsupervised', 'local', 'neighborhood', 'detection', 'method', 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0.012880922033837897, 0.13341477593317866, -0.170171142469081, 0.15272869486014973, 0.0007105848883239037] |
1,802.03626 | Infrared spectra of C2H4 dimer and trimer | Spectra of ethylene dimers and trimers are studied in the nu11 and (for the
dimer) nu9 fundamental band regions of C2H4 (~2990 and 3100 cm-1) using a
tunable optical parametric oscillator source to probe a pulsed supersonic slit
jet expansion. The deuterated trimer has been observed previously, but this
represents the first rotationally resolved spectrum of (C2H4)3. The results
support the previously determined cross-shaped (D2d) dimer and barrel-shaped
(C3h or C3) trimer structures. However, the dimer spectrum in the nu9
fundamental region of C2H4 is apparently very perturbed and a previous
rotational analysis is not well verified.
| physics.atm-clus | spectra of ethylene dimers and trimers are studied in the nu11 and for the dimer nu9 fundamental band regions of c2h4 2990 and 3100 cm1 using a tunable optical parametric oscillator source to probe a pulsed supersonic slit jet expansion the deuterated trimer has been observed previously but this represents the first rotationally resolved spectrum of c2h43 the results support the previously determined crossshaped d2d dimer and barrelshaped c3h or c3 trimer structures however the dimer spectrum in the nu9 fundamental region of c2h4 is apparently very perturbed and a previous rotational analysis is not well verified | [['spectra', 'of', 'ethylene', 'dimers', 'and', 'trimers', 'are', 'studied', 'in', 'the', 'nu11', 'and', 'for', 'the', 'dimer', 'nu9', 'fundamental', 'band', 'regions', 'of', 'c2h4', '2990', 'and', '3100', 'cm1', 'using', 'a', 'tunable', 'optical', 'parametric', 'oscillator', 'source', 'to', 'probe', 'a', 'pulsed', 'supersonic', 'slit', 'jet', 'expansion', 'the', 'deuterated', 'trimer', 'has', 'been', 'observed', 'previously', 'but', 'this', 'represents', 'the', 'first', 'rotationally', 'resolved', 'spectrum', 'of', 'c2h43', 'the', 'results', 'support', 'the', 'previously', 'determined', 'crossshaped', 'd2d', 'dimer', 'and', 'barrelshaped', 'c3h', 'or', 'c3', 'trimer', 'structures', 'however', 'the', 'dimer', 'spectrum', 'in', 'the', 'nu9', 'fundamental', 'region', 'of', 'c2h4', 'is', 'apparently', 'very', 'perturbed', 'and', 'a', 'previous', 'rotational', 'analysis', 'is', 'not', 'well', 'verified']] | [-0.1273780359867184, 0.11882863421730387, -0.027535201409288373, 0.0259307303604146, -0.003125679058333238, -0.17435431773871338, 0.0234368409680125, 0.47299377476641286, -0.18703250753848502, -0.25400212183012627, 0.057176200525039654, -0.24565224058460444, -0.06733472925892177, 0.14881665739145924, 0.057933947437353105, 0.02444107212553111, 0.02803558424542037, -0.05276258023999011, 0.017194048258412902, -0.12032929908309598, 0.23081612762386308, 0.08851877141084212, 0.23759215019526891, 0.1022624506391973, 0.02794249005576906, -0.09103473382128868, 0.03431666056470325, -0.07946495023012783, -0.19619087319491277, 0.12546194368042052, 0.24539175193785923, -0.02649913804877239, 0.13555786198412534, -0.35022778873099014, -0.23319288168568164, 0.03135559103005411, 0.21912429622898344, 0.12080973489325213, 0.005012328644321921, -0.29474859583812457, -0.008435886319299849, -0.16133990178544386, -0.14729528495809063, -0.03807744831283344, 0.006527526653371751, 0.032061689923959115, -0.20937398451011782, 0.11261376320423248, 0.02789410726715384, 0.10027725957722093, -0.12194513914194734, -0.14241142697574105, -0.11854740266426234, 0.0730818382750537, -0.027291004696356442, 0.024140182014283102, 0.17181967258996642, -0.08656274036184186, -0.08289210184011608, 0.413900065400109, -0.07887954831312527, -0.05377202924955782, 0.19581065911067222, -0.2026084138196893, -0.15649543551747533, 0.240497426110475, 0.02515664627329291, 0.13132180371515764, -0.1125980512327563, 0.04655650648722561, -0.06944025802658871, 0.23747525789076462, 0.1598426678344064, 0.05938616779894801, 0.2130513278922687, 0.13864381940220483, 0.010682667428530598, 0.15647655937209493, -0.15221765669533247, -0.08483276277547702, -0.17246063091927985, -0.13190733663820234, -0.21142509049968794, 0.00040023116298470995, -0.01479992009429528, -0.14811229457700392, 0.37687485752394423, -0.06712064059684053, 0.1658468326252963, -0.05231869797232017, 0.2467134272446856, 0.08903044278849848, 0.0627577731841787, 0.028257890342501923, 0.3192727061347493, 0.19987525960944671, 0.07766152821083476, -0.2523986009473447, 0.014884092083472448, -0.004753532287092336] |
1,802.03627 | Detecting Multiple Change Points Using Adaptive Regression Splines with
Application to Neural Recordings | Time series, as frequently the case in neuroscience, are rarely stationary,
but often exhibit abrupt changes due to attractor transitions or bifurcations
in the dynamical systems producing them. A plethora of methods for detecting
such change points in time series statistics have been developed over the
years, in addition to test criteria to evaluate their significance. Issues to
consider when developing change point analysis methods include computational
demands, difficulties arising from either limited amount of data or a large
number of covariates, and arriving at statistical tests with sufficient power
to detect as many changes as contained in potentially high-dimensional time
series. Here, a general method called Paired Adaptive Regressors for Cumulative
Sum is developed for detecting multiple change points in the mean of
multivariate time series. The method's advantages over alternative approaches
are demonstrated through a series of simulation experiments. This is followed
by a real data application to neural recordings from rat medial prefrontal
cortex during learning. Finally, the method's flexibility to incorporate useful
features from state-of-the-art change point detection techniques is discussed,
along with potential drawbacks and suggestions to remedy them.
| stat.ME q-bio.NC q-bio.QM | time series as frequently the case in neuroscience are rarely stationary but often exhibit abrupt changes due to attractor transitions or bifurcations in the dynamical systems producing them a plethora of methods for detecting such change points in time series statistics have been developed over the years in addition to test criteria to evaluate their significance issues to consider when developing change point analysis methods include computational demands difficulties arising from either limited amount of data or a large number of covariates and arriving at statistical tests with sufficient power to detect as many changes as contained in potentially highdimensional time series here a general method called paired adaptive regressors for cumulative sum is developed for detecting multiple change points in the mean of multivariate time series the methods advantages over alternative approaches are demonstrated through a series of simulation experiments this is followed by a real data application to neural recordings from rat medial prefrontal cortex during learning finally the methods flexibility to incorporate useful features from stateoftheart change point detection techniques is discussed along with potential drawbacks and suggestions to remedy them | [['time', 'series', 'as', 'frequently', 'the', 'case', 'in', 'neuroscience', 'are', 'rarely', 'stationary', 'but', 'often', 'exhibit', 'abrupt', 'changes', 'due', 'to', 'attractor', 'transitions', 'or', 'bifurcations', 'in', 'the', 'dynamical', 'systems', 'producing', 'them', 'a', 'plethora', 'of', 'methods', 'for', 'detecting', 'such', 'change', 'points', 'in', 'time', 'series', 'statistics', 'have', 'been', 'developed', 'over', 'the', 'years', 'in', 'addition', 'to', 'test', 'criteria', 'to', 'evaluate', 'their', 'significance', 'issues', 'to', 'consider', 'when', 'developing', 'change', 'point', 'analysis', 'methods', 'include', 'computational', 'demands', 'difficulties', 'arising', 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1,802.03628 | Learning Correlation Space for Time Series | We propose an approximation algorithm for efficient correlation search in
time series data. In our method, we use Fourier transform and neural network to
embed time series into a low-dimensional Euclidean space. The given space is
learned such that time series correlation can be effectively approximated from
Euclidean distance between corresponding embedded vectors. Therefore, search
for correlated time series can be done using an index in the embedding space
for efficient nearest neighbor search. Our theoretical analysis illustrates
that our method's accuracy can be guaranteed under certain regularity
conditions. We further conduct experiments on real-world datasets and the
results show that our method indeed outperforms the baseline solution. In
particular, for approximation of correlation, our method reduces the
approximation loss by a half in most test cases compared to the baseline
solution. For top-$k$ highest correlation search, our method improves the
precision from 5\% to 20\% while the query time is similar to the baseline
approach query time.
| cs.LG stat.ML | we propose an approximation algorithm for efficient correlation search in time series data in our method we use fourier transform and neural network to embed time series into a lowdimensional euclidean space the given space is learned such that time series correlation can be effectively approximated from euclidean distance between corresponding embedded vectors therefore search for correlated time series can be done using an index in the embedding space for efficient nearest neighbor search our theoretical analysis illustrates that our methods accuracy can be guaranteed under certain regularity conditions we further conduct experiments on realworld datasets and the results show that our method indeed outperforms the baseline solution in particular for approximation of correlation our method reduces the approximation loss by a half in most test cases compared to the baseline solution for topk highest correlation search our method improves the precision from 5 to 20 while the query time is similar to the baseline approach query time | [['we', 'propose', 'an', 'approximation', 'algorithm', 'for', 'efficient', 'correlation', 'search', 'in', 'time', 'series', 'data', 'in', 'our', 'method', 'we', 'use', 'fourier', 'transform', 'and', 'neural', 'network', 'to', 'embed', 'time', 'series', 'into', 'a', 'lowdimensional', 'euclidean', 'space', 'the', 'given', 'space', 'is', 'learned', 'such', 'that', 'time', 'series', 'correlation', 'can', 'be', 'effectively', 'approximated', 'from', 'euclidean', 'distance', 'between', 'corresponding', 'embedded', 'vectors', 'therefore', 'search', 'for', 'correlated', 'time', 'series', 'can', 'be', 'done', 'using', 'an', 'index', 'in', 'the', 'embedding', 'space', 'for', 'efficient', 'nearest', 'neighbor', 'search', 'our', 'theoretical', 'analysis', 'illustrates', 'that', 'our', 'methods', 'accuracy', 'can', 'be', 'guaranteed', 'under', 'certain', 'regularity', 'conditions', 'we', 'further', 'conduct', 'experiments', 'on', 'realworld', 'datasets', 'and', 'the', 'results', 'show', 'that', 'our', 'method', 'indeed', 'outperforms', 'the', 'baseline', 'solution', 'in', 'particular', 'for', 'approximation', 'of', 'correlation', 'our', 'method', 'reduces', 'the', 'approximation', 'loss', 'by', 'a', 'half', 'in', 'most', 'test', 'cases', 'compared', 'to', 'the', 'baseline', 'solution', 'for', 'topk', 'highest', 'correlation', 'search', 'our', 'method', 'improves', 'the', 'precision', 'from', '5', 'to', '20', 'while', 'the', 'query', 'time', 'is', 'similar', 'to', 'the', 'baseline', 'approach', 'query', 'time']] | [-0.05440991201815255, -0.018343851140286197, -0.1119490028624246, 0.09779197174784597, -0.06096187544267483, -0.1131555536806825, 0.0731465538707267, 0.46112107051701484, -0.28702729310961206, -0.3238952319591624, 0.09839399939600402, -0.28364942950208355, -0.16003506346252053, 0.23845078541802925, -0.018478296676509177, 0.0861256168013191, 0.12434559762094735, 0.0302175782794793, -0.11423258146394893, -0.3180844676916641, 0.2578847988367175, 0.07359517510697434, 0.3071368483449273, -0.0020670462676023334, 0.06820106893981205, 0.006200815462566252, -0.05331179747856045, 0.0307687352910704, -0.04960544554061143, 0.11067660907401314, 0.27109638719872536, 0.18139968509196386, 0.2754155355461437, -0.3875655086498849, -0.2050059972938056, 0.10260565441891645, 0.1709686943058726, 0.07139580371825369, -0.045718573136594075, -0.31089657994836, 0.0706205725923322, -0.11844297936716694, -0.04540493373782669, -0.17586756365659612, -0.009362321685928899, -0.012786228755338684, -0.3343735860802139, 0.0854869635784527, 0.03197537343418579, -0.012867906384311522, -0.07522426482733587, -0.09471992488738291, 0.07937973916023687, 0.13164095195670483, 0.023209741940321046, 0.10910332416879791, 0.05944730748446022, -0.05825034658943268, -0.14641338487559505, 0.3721582975851584, -0.13155285873792216, -0.2262223080873395, 0.17022023611713813, -0.1168001505127766, -0.12902896734001704, 0.1315623951509972, 0.22896374786520213, 0.1606006084976694, -0.12542039414136846, 0.05649404023122632, -0.056345072003152176, 0.20936993051532515, 0.05152573716926943, -0.0022905285682387746, 0.116527217345996, 0.2337819187875389, 0.09435029370825196, 0.1337038471173389, -0.1218112412312575, -0.09463705813300007, -0.2619992224381694, -0.1574876214483682, -0.23797850101549603, -0.035858843006234896, -0.17424386961712013, -0.10169373068934943, 0.4076445261546988, 0.2255470390472844, 0.1921417691650553, 0.14297391104959895, 0.30099355184201954, 0.09722556449514165, 0.07435898200160812, 0.11266806318507164, 0.19042554564326059, -0.009995266432649915, 0.06935690192924222, -0.1934788430940737, 0.07292683343579874, 0.10607961934837926] |
1,802.03629 | Astrolabe: Curating, Linking and Computing Astronomy's Dark Data | Where appropriate repositories are not available to support all relevant
astronomical data products, data can fall into darkness: unseen and unavailable
for future reference and re-use. Some data in this category are legacy or old
data, but newer datasets are also often uncurated and could remain "dark". This
paper provides a description of the design motivation and development of
Astrolabe, a cyberinfrastructure project that addresses a set of community
recommendations for locating and ensuring the long-term curation of dark or
otherwise at-risk data and integrated computing. This paper also describes the
outcomes of the series of community workshops that informed creation of
Astrolabe. According to participants in these workshops, much astronomical dark
data currently exist that are not curated elsewhere, as well as software that
can only be executed by a few individuals and therefore becomes unusable
because of changes in computing platforms. Astronomical research questions and
challenges would be better addressed with integrated data and computational
resources that fall outside the scope of existing observatory and space mission
projects. As a solution, the design of the Astrolabe system is aimed at
developing new resources for management of astronomical data. The project is
based in CyVerse cyberinfrastructure technology and is a collaboration between
the University of Arizona and the American Astronomical Society. Overall the
project aims to support open access to research data by leveraging existing
cyberinfrastructure resources and promoting scientific discovery by making
potentially-useful data in a computable format broadly available to the
astronomical community.
| astro-ph.IM cs.DL | where appropriate repositories are not available to support all relevant astronomical data products data can fall into darkness unseen and unavailable for future reference and reuse some data in this category are legacy or old data but newer datasets are also often uncurated and could remain dark this paper provides a description of the design motivation and development of astrolabe a cyberinfrastructure project that addresses a set of community recommendations for locating and ensuring the longterm curation of dark or otherwise atrisk data and integrated computing this paper also describes the outcomes of the series of community workshops that informed creation of astrolabe according to participants in these workshops much astronomical dark data currently exist that are not curated elsewhere as well as software that can only be executed by a few individuals and therefore becomes unusable because of changes in computing platforms astronomical research questions and challenges would be better addressed with integrated data and computational resources that fall outside the scope of existing observatory and space mission projects as a solution the design of the astrolabe system is aimed at developing new resources for management of astronomical data the project is based in cyverse cyberinfrastructure technology and is a collaboration between the university of arizona and the american astronomical society overall the project aims to support open access to research data by leveraging existing cyberinfrastructure resources and promoting scientific discovery by making potentiallyuseful data in a computable format broadly available to the astronomical community | [['where', 'appropriate', 'repositories', 'are', 'not', 'available', 'to', 'support', 'all', 'relevant', 'astronomical', 'data', 'products', 'data', 'can', 'fall', 'into', 'darkness', 'unseen', 'and', 'unavailable', 'for', 'future', 'reference', 'and', 'reuse', 'some', 'data', 'in', 'this', 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1,802.0363 | Solution to Briot and Bouquet problem on singularities of differential
equations | We solve Briot and Bouquet problem (1856) on the existence of non-monodromic
(multivalued) solutions for singularities of differential equations in the
complex domain. The solution is an application of hedgehog dynamics for
indifferent irrational fixed points. We present an important simplification by
only using a local hedgehog for which we give a simpler and direct construction
of quasi-invariant curves which does not rely on complex renormalization.
| math.DS math.CV | we solve briot and bouquet problem 1856 on the existence of nonmonodromic multivalued solutions for singularities of differential equations in the complex domain the solution is an application of hedgehog dynamics for indifferent irrational fixed points we present an important simplification by only using a local hedgehog for which we give a simpler and direct construction of quasiinvariant curves which does not rely on complex renormalization | [['we', 'solve', 'briot', 'and', 'bouquet', 'problem', '1856', 'on', 'the', 'existence', 'of', 'nonmonodromic', 'multivalued', 'solutions', 'for', 'singularities', 'of', 'differential', 'equations', 'in', 'the', 'complex', 'domain', 'the', 'solution', 'is', 'an', 'application', 'of', 'hedgehog', 'dynamics', 'for', 'indifferent', 'irrational', 'fixed', 'points', 'we', 'present', 'an', 'important', 'simplification', 'by', 'only', 'using', 'a', 'local', 'hedgehog', 'for', 'which', 'we', 'give', 'a', 'simpler', 'and', 'direct', 'construction', 'of', 'quasiinvariant', 'curves', 'which', 'does', 'not', 'rely', 'on', 'complex', 'renormalization']] | [-0.19223188268369995, -0.007648519811027654, -0.10552171927702148, 0.07508910627257137, -0.14651917346418486, -0.11280251650896389, 0.06006217814501724, 0.31773198474547826, -0.24345307282055728, -0.2199822801303526, 0.14197230789977766, -0.251593676788616, -0.17731264028407168, 0.22059208710561506, -0.07692014760687016, 0.061465302380383946, 0.04277588265176746, 0.04000546929455595, -0.07312507787719369, -0.21887616887397598, 0.4126584841578733, -0.05252981036028359, 0.22038684933613695, 0.05703465378610417, 0.17855837239767425, 0.04367804541107034, 0.0059658744430635124, 0.03210044633306097, -0.17333658415373066, 0.10642332699171675, 0.22625317527490552, 0.09936889646633063, 0.2244632660294883, -0.43456275420612656, -0.17701992874526695, 0.13873960271303076, 0.1822799649744411, 0.11804720530562918, -0.08124708760624344, -0.26181474002078176, 0.09137345144699793, -0.08833720537950285, -0.18961443669104483, -0.1424640940822428, 0.039859101780166384, 0.03562754578888416, -0.24210583275271347, 0.04700003324251156, 0.0867484220652841, 0.11754494332126342, -0.10059789076331072, -0.060877731535583735, -0.01015432214262546, 0.08345224241202231, 0.003328038721519988, 0.039919026545248926, 0.06522039767151, -0.1315559720969759, -0.11204217299382435, 0.37683954354724847, -0.06881800713381381, -0.29749820170763996, 0.1665960699756397, -0.07042579299741192, -0.15530830221177894, 0.16280691202518938, 0.14818819348147372, 0.20821651084406767, -0.10786885621200781, 0.13106782269278483, -0.08838186346110888, 0.15973002903047018, 0.0763456895656418, -0.03953762449964415, 0.15617363062119693, 0.11008006607880816, 0.15884331068082247, 0.1080450531317183, 0.010236825946776662, -0.15162619249895215, -0.3417257062974386, -0.1596148100256869, -0.12516198781668209, 0.12363422307339533, -0.09946259430307691, -0.2552361509006005, 0.4006566069147084, 0.09258579048037063, 0.2175723707623547, 0.032428305992652895, 0.2296385081644985, 0.14590622892865213, 0.028786377047254064, 0.045544820408395026, 0.18270449923875276, 0.08659658930264413, 0.06061251762730535, -0.20370791204913985, 0.011500958367832936, 0.16212230516248383] |
1,802.03631 | Commutative rings over which the support of any module is the collection
of prime ideals containing the annihilator | The support of any module over a commutative ring is defined as the
collection of all prime ideals of the ring at which the localization of the
module is non-zero. For finitely generated modules, the support is the
collection of all prime ideals containing the annihilator of the module. In
this article, we raise the natural question that over which commutative rings,
the support of every module is the collection of all the prime ideals of its
annihilator. We completely classify such rings, and in the process it also
comes out that it is enough to require that only for countably generated
modules, the support is the collection of all prime ideals containing the
annihilator of the module.
| math.AC | the support of any module over a commutative ring is defined as the collection of all prime ideals of the ring at which the localization of the module is nonzero for finitely generated modules the support is the collection of all prime ideals containing the annihilator of the module in this article we raise the natural question that over which commutative rings the support of every module is the collection of all the prime ideals of its annihilator we completely classify such rings and in the process it also comes out that it is enough to require that only for countably generated modules the support is the collection of all prime ideals containing the annihilator of the module | [['the', 'support', 'of', 'any', 'module', 'over', 'a', 'commutative', 'ring', 'is', 'defined', 'as', 'the', 'collection', 'of', 'all', 'prime', 'ideals', 'of', 'the', 'ring', 'at', 'which', 'the', 'localization', 'of', 'the', 'module', 'is', 'nonzero', 'for', 'finitely', 'generated', 'modules', 'the', 'support', 'is', 'the', 'collection', 'of', 'all', 'prime', 'ideals', 'containing', 'the', 'annihilator', 'of', 'the', 'module', 'in', 'this', 'article', 'we', 'raise', 'the', 'natural', 'question', 'that', 'over', 'which', 'commutative', 'rings', 'the', 'support', 'of', 'every', 'module', 'is', 'the', 'collection', 'of', 'all', 'the', 'prime', 'ideals', 'of', 'its', 'annihilator', 'we', 'completely', 'classify', 'such', 'rings', 'and', 'in', 'the', 'process', 'it', 'also', 'comes', 'out', 'that', 'it', 'is', 'enough', 'to', 'require', 'that', 'only', 'for', 'countably', 'generated', 'modules', 'the', 'support', 'is', 'the', 'collection', 'of', 'all', 'prime', 'ideals', 'containing', 'the', 'annihilator', 'of', 'the', 'module']] | [-0.20286033182578572, 0.04561180614282267, -0.030500098650533138, 0.0010047340169491027, -0.05146602019115146, -0.11975569272476991, -0.07820059708714233, 0.3186388995372138, -0.40309217737956066, -0.1519839793948804, 0.1394349907236805, -0.27119162833412824, -0.06279622067271148, 0.23006091438139142, -0.09487293128287262, -0.039177982492265846, 0.07729172944959442, 0.16108918465058303, 0.04021571353827682, -0.34477164661038223, 0.4390742998181495, 0.05900995779419476, 0.19661704595476956, 0.03721316045907847, 0.1346828922021629, 0.02077741048030429, -0.040965908763425075, 0.014661785641337856, -0.09528483871770856, 0.13142793850531265, 0.3330688824151027, 0.14957768073842181, 0.24331626545448423, -0.3470672236218303, -0.06601272437374218, 0.23497884725299428, 0.12123531395948287, -0.015985974562512238, -0.020429713746248666, -0.16904808892631681, 0.21676734800576786, -0.25753297818966714, -0.11159257190617716, -0.0824114588420775, 0.08752696255569235, 0.044505611890769105, -0.30267478243084783, -0.08061640668029983, 0.14758348951147773, 0.16495886519235575, -0.06960695380664636, -0.05665394744314885, -0.04467372779766003, 0.11865841913415845, -0.06180399474396836, -0.013363999425102089, 0.10678084546224594, -0.12801735504269915, -0.12916021159518573, 0.3722705413918879, -0.0033195149599894156, -0.1884530547501172, 0.17544892694903697, -0.22968804749454988, -0.09290466541266543, 0.17209083421976637, 0.006304869104681884, 0.10047392240109837, -0.03212781112356964, 0.18714793279749278, -0.1941660325500672, 0.07651231660491835, 0.07177340341100501, 0.05046850608235572, 0.23289778138810802, 0.13840128973884097, 0.04832285980474977, 0.1261862222641959, -0.020207137153562853, 0.055083311691690805, -0.3939906310732082, -0.24909911314183372, -0.1803286161107974, 0.07029738760616441, -0.03720676756483183, -0.19846202288478046, 0.48846614482354817, 0.16163528646673944, 0.1621040698551273, 0.08290189826837346, 0.2536450607733706, 0.01830579989917292, 0.15133331546765136, 0.07017614794710687, 0.10690736022414797, 0.17371845226419175, -0.031268954713102745, -0.10748578775555716, 0.010761223796565654, 0.10825476543662155] |
1,802.03632 | Classifying spaces for commutativity of low-dimensional Lie groups | For each of the groups $G = O(2), SU(2), U(2)$, we compute the integral and
$\mathbb{F}_2$-cohomology rings of $B_\text{com} G$ (the classifying space for
commutativity of $G$), the action of the Steenrod algebra on the mod 2
cohomology, the homotopy type of $E_\text{com} G$ (the homotopy fiber of the
inclusion $B_\text{com} G \to BG$), and some low-dimensional homotopy groups of
$B_\text{com} G$.
| math.AT | for each of the groups g o2 su2 u2 we compute the integral and mathbbf_2cohomology rings of b_textcom g the classifying space for commutativity of g the action of the steenrod algebra on the mod 2 cohomology the homotopy type of e_textcom g the homotopy fiber of the inclusion b_textcom g to bg and some lowdimensional homotopy groups of b_textcom g | [['for', 'each', 'of', 'the', 'groups', 'g', 'o2', 'su2', 'u2', 'we', 'compute', 'the', 'integral', 'and', 'mathbbf_2cohomology', 'rings', 'of', 'b_textcom', 'g', 'the', 'classifying', 'space', 'for', 'commutativity', 'of', 'g', 'the', 'action', 'of', 'the', 'steenrod', 'algebra', 'on', 'the', 'mod', '2', 'cohomology', 'the', 'homotopy', 'type', 'of', 'e_textcom', 'g', 'the', 'homotopy', 'fiber', 'of', 'the', 'inclusion', 'b_textcom', 'g', 'to', 'bg', 'and', 'some', 'lowdimensional', 'homotopy', 'groups', 'of', 'b_textcom', 'g']] | [-0.24888177156069521, 0.06383309836925591, -0.08254373772859068, 0.018785126444141743, -0.10084488725889537, -0.09844574209113242, 0.00040442364239054966, 0.3508961470245178, -0.3358520913269308, -0.24093303126204063, 0.08460774344502617, -0.22315117424764372, -0.10602416442233627, 0.12959355294262453, -0.1500196269066152, -0.07914352530645112, 0.03283408914803196, 0.20542317154517364, -0.08217856339257905, -0.2782135062487954, 0.39727577144059084, -0.08918415793707057, 0.16767938916567246, -0.04938293283931532, 0.09545465924982298, 0.029310113174240975, -0.017197108953812365, -0.034850218657392344, -0.16083322294940383, 0.15363314759254582, 0.31055730489729827, -0.006178428568893065, 0.19289291668998992, -0.33213606148453084, -0.11767876291868545, 0.22217331256843725, 0.06564916443016569, -0.07616351006584147, 0.051890843526176095, -0.3203935309360593, 0.1366961083801116, -0.2152644515588043, -0.05591980028518681, -0.06605558826635449, 0.17309850057334467, 0.018447772615541844, -0.1994275006828672, -0.053175243596404284, 0.060085792173394714, 0.1452304707461242, -0.0555687612080473, -0.09924432445885771, -0.15092779560235597, 0.1378326161006876, -0.0411379050337157, 0.047894057052178404, 0.14469953008406497, -0.1385629266279481, -0.09421373299180956, 0.45048261326500927, -0.06281007652691865, -0.10913506709039211, 0.09167995834249561, -0.20042936582812818, -0.25115041958831125, 0.13564434806989917, -0.0004007912288277836, 0.18925257803777518, 0.05675626606127973, 0.23693689924735828, -0.07265930453915212, 0.046162700343687656, 0.05068739243167437, -0.03236084117285781, 0.05554683699067366, 0.10042683862275996, 0.11057816383476227, 0.09771291449097759, -0.0046331120686510865, 0.10480080213460882, -0.394428305533099, -0.20880555596313108, -0.11158785789068472, 0.15164772175662852, -0.16168687261307566, -0.13548896704026955, 0.4431353028216478, 0.04638379329215672, 0.09683294474715524, 0.1254726574140585, 0.15869728261131352, 0.004842807570377649, 0.08406642964899035, 0.028373293779885112, 0.09650483093928877, 0.32015837330402713, -0.12460939806365108, -0.23446013838431592, -0.16007845827488829, 0.32386160540883824] |
1,802.03633 | Tipping the magnetic instability in paramagnetic Sr$_3$Ru$_2$O$_7$ by Fe
impurities | We report the magnetic and electronic properties of the bilayer ruthenate
Sr$_3$Ru$_2$O$_7$ upon Fe substitution for Ru. We find that
Sr$_3$(Ru$_{1-x}$Fe$_x$)$_2$O$_7$ shows a spin-glass-like phase below 4 K for
$x$ = 0.01 and commensurate E-type antiferromagnetically ordered insulating
ground state characterized by the propagation vector $q_c$ = (0.25 0.25 0) for
$x$ $\geq$ 0.03, respectively, in contrast to the paramagnetic metallic state
in the parent compound with strong spin fluctuations occurring at wave vectors
$q$ = (0.09 0 0) and (0.25 0 0). The observed antiferromagnetic ordering is
quasi-two-dimensional with very short correlation length along the $c$ axis, a
feature similar to the Mn-doped Sr$_3$Ru$_2$O$_7$. Our results suggest that
this ordered ground state is associated with the intrinsic magnetic instability
in the pristine compound, which can be readily tipped by the local magnetic
coupling between the 3$d$ orbitals of the magnetic dopants and Ru 4$d$
orbitals.
| cond-mat.str-el | we report the magnetic and electronic properties of the bilayer ruthenate sr_3ru_2o_7 upon fe substitution for ru we find that sr_3ru_1xfe_x_2o_7 shows a spinglasslike phase below 4 k for x 001 and commensurate etype antiferromagnetically ordered insulating ground state characterized by the propagation vector q_c 025 025 0 for x geq 003 respectively in contrast to the paramagnetic metallic state in the parent compound with strong spin fluctuations occurring at wave vectors q 009 0 0 and 025 0 0 the observed antiferromagnetic ordering is quasitwodimensional with very short correlation length along the c axis a feature similar to the mndoped sr_3ru_2o_7 our results suggest that this ordered ground state is associated with the intrinsic magnetic instability in the pristine compound which can be readily tipped by the local magnetic coupling between the 3d orbitals of the magnetic dopants and ru 4d orbitals | [['we', 'report', 'the', 'magnetic', 'and', 'electronic', 'properties', 'of', 'the', 'bilayer', 'ruthenate', 'sr_3ru_2o_7', 'upon', 'fe', 'substitution', 'for', 'ru', 'we', 'find', 'that', 'sr_3ru_1xfe_x_2o_7', 'shows', 'a', 'spinglasslike', 'phase', 'below', '4', 'k', 'for', 'x', '001', 'and', 'commensurate', 'etype', 'antiferromagnetically', 'ordered', 'insulating', 'ground', 'state', 'characterized', 'by', 'the', 'propagation', 'vector', 'q_c', '025', '025', '0', 'for', 'x', 'geq', '003', 'respectively', 'in', 'contrast', 'to', 'the', 'paramagnetic', 'metallic', 'state', 'in', 'the', 'parent', 'compound', 'with', 'strong', 'spin', 'fluctuations', 'occurring', 'at', 'wave', 'vectors', 'q', '009', '0', '0', 'and', '025', '0', '0', 'the', 'observed', 'antiferromagnetic', 'ordering', 'is', 'quasitwodimensional', 'with', 'very', 'short', 'correlation', 'length', 'along', 'the', 'c', 'axis', 'a', 'feature', 'similar', 'to', 'the', 'mndoped', 'sr_3ru_2o_7', 'our', 'results', 'suggest', 'that', 'this', 'ordered', 'ground', 'state', 'is', 'associated', 'with', 'the', 'intrinsic', 'magnetic', 'instability', 'in', 'the', 'pristine', 'compound', 'which', 'can', 'be', 'readily', 'tipped', 'by', 'the', 'local', 'magnetic', 'coupling', 'between', 'the', '3d', 'orbitals', 'of', 'the', 'magnetic', 'dopants', 'and', 'ru', '4d', 'orbitals']] | [-0.21002746966969169, 0.28550857905967447, 0.04064418465017118, 0.016029816710318005, 0.010272489636021495, -0.14840953055200246, 0.0977207630771068, 0.41703114734674, -0.2895666496880667, -0.26973975486528706, -0.030825439177315787, -0.37681942726944534, -0.07148029890344282, 0.07549603321937136, 0.11012440029544834, -0.050804108059233376, -0.0646460635758097, 0.0018773152899574225, -0.19549635290676518, -0.1856080946810937, 0.2536733291689044, -0.033703298983492065, 0.29510757697559237, 0.020062366251269698, 0.021231000960975046, -0.0030936695976366457, 0.2250821886413639, 0.007896708632806237, -0.18241063482999148, 0.011059414815078949, 0.26936997103334315, -0.10688782687803966, 0.16456786833021422, -0.3652698698676598, -0.15368054351977622, -0.025987912195695326, 0.15235890598941437, 0.09359387022321781, -0.05264098986714396, -0.31775939062854247, 0.11354346138967897, -0.0963360696545684, -0.14541889246246567, -0.08257072657661539, 0.008755708887228663, -0.00927954431111649, -0.27608289353182197, 0.14898264706528314, 0.11956226017693398, 0.13170300195777368, -0.15562178008586272, -0.2112879467190204, -0.1486428716190448, 0.00817794882049653, 0.06696031916014274, 0.18966797118248996, 0.11186785636876355, -0.05957181877817329, -0.08109619412333442, 0.34364273951677476, -0.05736444487800481, -0.0648850024838499, 0.13401956638504922, -0.2518901107773352, -0.10741761095270934, 0.23487770044192358, 0.0627955187531844, 0.10323698696693484, -0.04464152596012073, 0.0905716093896534, 0.0056466630474101874, 0.24034522959313184, -0.002336950254508517, 0.07476714787788322, 0.2623617958513574, 0.15147387714120708, 0.016741624185208782, 0.11850259484949482, -0.17347389247152023, -0.02384451300259942, -0.18169042501519894, -0.19566288920985142, -0.20679448966533734, 0.1093721758203481, -0.12217807592324008, -0.17670236876181944, 0.32691615945528407, 0.15285962600257694, 0.19983258354149655, -0.0754298254868842, 0.17522196596960576, 0.04329501545223945, -0.00022852042293123586, 0.046922234524841566, 0.22439984818526976, 0.19062342303952623, 0.1242204045837509, -0.2637841764273463, 0.1186363174724327, -0.00014718633774720447] |
1,802.03634 | On the Tractability of (k,i)-Coloring | In an undirected graph, a proper (k,i)-coloring is an assignment of a set of
k colors to each vertex such that any two adjacent vertices have at most i
common colors. The (k,i)-coloring problem is to compute the minimum number of
colors required for a proper (k,i)-coloring. This is a generalization of the
classic graph coloring problem. We show a parameterized algorithm for the
(k,i)-coloring problem with the size of the feedback vertex set as a parameter.
Our algorithm does not use tree-width machinery, thus answering a question of
Majumdar, Neogi, Raman and Tale [CALDAM 2017]. We also give a faster and
simpler exact algorithm for (k, k-1)-coloring. From the hardness perspective,
we show that the (k,i)-coloring problem is NP-complete for any fixed values i,
k, whenever i<k, thereby settling a conjecture of Mendez-Diaz and Zabala [1999]
and again asked by Majumdar, Neogi, Raman and Tale. The NP-completeness result
improves the partial NP-completeness shown in the preliminary version of this
paper published in CALDAM 2018.
| cs.DS | in an undirected graph a proper kicoloring is an assignment of a set of k colors to each vertex such that any two adjacent vertices have at most i common colors the kicoloring problem is to compute the minimum number of colors required for a proper kicoloring this is a generalization of the classic graph coloring problem we show a parameterized algorithm for the kicoloring problem with the size of the feedback vertex set as a parameter our algorithm does not use treewidth machinery thus answering a question of majumdar neogi raman and tale caldam 2017 we also give a faster and simpler exact algorithm for k k1coloring from the hardness perspective we show that the kicoloring problem is npcomplete for any fixed values i k whenever ik thereby settling a conjecture of mendezdiaz and zabala 1999 and again asked by majumdar neogi raman and tale the npcompleteness result improves the partial npcompleteness shown in the preliminary version of this paper published in caldam 2018 | [['in', 'an', 'undirected', 'graph', 'a', 'proper', 'kicoloring', 'is', 'an', 'assignment', 'of', 'a', 'set', 'of', 'k', 'colors', 'to', 'each', 'vertex', 'such', 'that', 'any', 'two', 'adjacent', 'vertices', 'have', 'at', 'most', 'i', 'common', 'colors', 'the', 'kicoloring', 'problem', 'is', 'to', 'compute', 'the', 'minimum', 'number', 'of', 'colors', 'required', 'for', 'a', 'proper', 'kicoloring', 'this', 'is', 'a', 'generalization', 'of', 'the', 'classic', 'graph', 'coloring', 'problem', 'we', 'show', 'a', 'parameterized', 'algorithm', 'for', 'the', 'kicoloring', 'problem', 'with', 'the', 'size', 'of', 'the', 'feedback', 'vertex', 'set', 'as', 'a', 'parameter', 'our', 'algorithm', 'does', 'not', 'use', 'treewidth', 'machinery', 'thus', 'answering', 'a', 'question', 'of', 'majumdar', 'neogi', 'raman', 'and', 'tale', 'caldam', '2017', 'we', 'also', 'give', 'a', 'faster', 'and', 'simpler', 'exact', 'algorithm', 'for', 'k', 'k1coloring', 'from', 'the', 'hardness', 'perspective', 'we', 'show', 'that', 'the', 'kicoloring', 'problem', 'is', 'npcomplete', 'for', 'any', 'fixed', 'values', 'i', 'k', 'whenever', 'ik', 'thereby', 'settling', 'a', 'conjecture', 'of', 'mendezdiaz', 'and', 'zabala', '1999', 'and', 'again', 'asked', 'by', 'majumdar', 'neogi', 'raman', 'and', 'tale', 'the', 'npcompleteness', 'result', 'improves', 'the', 'partial', 'npcompleteness', 'shown', 'in', 'the', 'preliminary', 'version', 'of', 'this', 'paper', 'published', 'in', 'caldam', '2018']] | [-0.09276167034315935, 0.0626592300878322, -0.0757370417601653, 0.020369121569055546, -0.1374334576259709, -0.15480917233301775, 0.11837343681276528, 0.37122603711418667, -0.27875187994007466, -0.33772041363924554, 0.06335191267006146, -0.2812262486351088, -0.1662353695358468, 0.14444557741237216, -0.1291602532809624, 0.02779363817949794, 0.11686878226685656, 0.031674674586864494, 0.03245277537616512, -0.2939956992458629, 0.26747236395390306, -0.002659744774639795, 0.16471129954097014, 0.09853913228298668, 0.0816129922124121, 0.05094508119693757, -0.015657840670923455, 0.07417785657549135, -0.17328427201610344, 0.06048680335295068, 0.2548111385512578, 0.20564578466494626, 0.29635542294281664, -0.35234499237016786, -0.14998548947528145, 0.15540899653827678, 0.11385650630698457, 0.11535050053314506, -0.0033954117871537993, -0.18349942756681173, 0.12103468424027573, -0.1057503285885588, -0.0908181837824751, 0.029688132343271487, 0.12306947436749557, -0.04527165875438359, -0.30043928641413303, -0.014797408766108801, 0.13641868694095954, 0.037873372428662205, -0.020339938762591846, -0.1660196114054445, 0.022187053940636164, 0.09372756517745696, -0.03871834424288967, 0.1032894258785262, 0.0063943971755996915, -0.13645876818808103, -0.18359598389803136, 0.37961829316366136, -0.010322321330114632, -0.12484646137049303, 0.14849382986703627, -0.06393475455373432, -0.18486899781465246, 0.12142557851215706, 0.10820183650107135, 0.17029718268379757, -0.09983941361686079, 0.10488540455632654, -0.15741640133075888, 0.14961068598624272, 0.10572598329922066, -0.013147946270300618, 0.11007292489108594, 0.12894774365969758, 0.1309352736887699, 0.16381709862469485, 0.00597350594375424, 0.018586226291932413, -0.2799529661277097, -0.12789559921123017, -0.19933298436329477, 0.04963245489378534, -0.13011745424380106, -0.15749161346882576, 0.3834135067837808, 0.15020384654974495, 0.21869701715009404, 0.12330250517771685, 0.27402001927029107, 0.1002567332914715, 0.0003294607600833796, 0.1638607944397232, 0.15441725156093133, 0.13016906027723388, 0.036896578177715404, -0.22408657552471054, 0.05194789831986344, 0.1470280213552657] |
1,802.03635 | The matter-gravity entanglement hypothesis | I outline some of my work (some dating back to 1998, some more recent) on my
matter-gravity entanglement hypothesis, according to which the entropy of a
closed quantum gravitational system is equal to the system's matter-gravity
entanglement entropy. The main arguments presented are: (1) this hypothesis is
capable of resolving the second-law puzzle, i.e. the puzzle as to how the
entropy increase of a closed system can be reconciled with the asssumption of
unitary time-evolution; (2) the black hole information loss puzzle may be
regarded as a special case of this second law puzzle and therefore the same
resolution applies to it; (3) the black hole thermal atmosphere puzzle (which I
recall) can be resolved by adopting a radically different-from-usual
description of quantum black hole equilibrium states, according to which they
are total pure states, entangled between matter and gravity so that the partial
states of matter and gravity are each approximately thermal equilibrium states
(at the Hawking temperature); (4) the Susskind-Horowitz-Polchinski
string-theoretic understanding of black hole entropy as the logarithm of the
degeneracy of a long string (which is the weak string coupling limit of a black
hole) cannot be correct but should be replaced by a modified understanding
according to which it is the entanglement entropy between a long string and its
stringy atmosphere, when in a total pure equilibrium state in a suitable box,
which (in line with (3)) goes over, at strong-coupling, to a black hole in
equilibrium with its thermal atmosphere. The modified understanding in (4) is
based on a general result, which I describe, about the likely state of a
quantum system weakly coupled to an energy-bath when the total state is a
random pure state with given energy. This result generalizes Goldstein et al.'s
'canonical typicality' result to systems which are not necessarily small.
| hep-th cond-mat.stat-mech gr-qc quant-ph | i outline some of my work some dating back to 1998 some more recent on my mattergravity entanglement hypothesis according to which the entropy of a closed quantum gravitational system is equal to the systems mattergravity entanglement entropy the main arguments presented are 1 this hypothesis is capable of resolving the secondlaw puzzle ie the puzzle as to how the entropy increase of a closed system can be reconciled with the asssumption of unitary timeevolution 2 the black hole information loss puzzle may be regarded as a special case of this second law puzzle and therefore the same resolution applies to it 3 the black hole thermal atmosphere puzzle which i recall can be resolved by adopting a radically differentfromusual description of quantum black hole equilibrium states according to which they are total pure states entangled between matter and gravity so that the partial states of matter and gravity are each approximately thermal equilibrium states at the hawking temperature 4 the susskindhorowitzpolchinski stringtheoretic understanding of black hole entropy as the logarithm of the degeneracy of a long string which is the weak string coupling limit of a black hole cannot be correct but should be replaced by a modified understanding according to which it is the entanglement entropy between a long string and its stringy atmosphere when in a total pure equilibrium state in a suitable box which in line with 3 goes over at strongcoupling to a black hole in equilibrium with its thermal atmosphere the modified understanding in 4 is based on a general result which i describe about the likely state of a quantum system weakly coupled to an energybath when the total state is a random pure state with given energy this result generalizes goldstein et als canonical typicality result to systems which are not necessarily small | [['i', 'outline', 'some', 'of', 'my', 'work', 'some', 'dating', 'back', 'to', '1998', 'some', 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1,802.03636 | Probabilistic modelling and reconstruction of strain | This paper deals with modelling and reconstruction of strain fields, relying
upon data generated from neutron Bragg-edge measurements. We propose a
probabilistic approach in which the strain field is modelled as a Gaussian
process, assigned a covariance structure customised by incorporation of the
so-called equilibrium constraints. The computational complexity is
significantly reduced by utilising an approximation scheme well suited for the
problem. We illustrate the method on simulations and real data. The results
indicate a high potential and can hopefully inspire the concept of
probabilistic modelling to be used within other tomographic applications as
well.
| physics.data-an | this paper deals with modelling and reconstruction of strain fields relying upon data generated from neutron braggedge measurements we propose a probabilistic approach in which the strain field is modelled as a gaussian process assigned a covariance structure customised by incorporation of the socalled equilibrium constraints the computational complexity is significantly reduced by utilising an approximation scheme well suited for the problem we illustrate the method on simulations and real data the results indicate a high potential and can hopefully inspire the concept of probabilistic modelling to be used within other tomographic applications as well | [['this', 'paper', 'deals', 'with', 'modelling', 'and', 'reconstruction', 'of', 'strain', 'fields', 'relying', 'upon', 'data', 'generated', 'from', 'neutron', 'braggedge', 'measurements', 'we', 'propose', 'a', 'probabilistic', 'approach', 'in', 'which', 'the', 'strain', 'field', 'is', 'modelled', 'as', 'a', 'gaussian', 'process', 'assigned', 'a', 'covariance', 'structure', 'customised', 'by', 'incorporation', 'of', 'the', 'socalled', 'equilibrium', 'constraints', 'the', 'computational', 'complexity', 'is', 'significantly', 'reduced', 'by', 'utilising', 'an', 'approximation', 'scheme', 'well', 'suited', 'for', 'the', 'problem', 'we', 'illustrate', 'the', 'method', 'on', 'simulations', 'and', 'real', 'data', 'the', 'results', 'indicate', 'a', 'high', 'potential', 'and', 'can', 'hopefully', 'inspire', 'the', 'concept', 'of', 'probabilistic', 'modelling', 'to', 'be', 'used', 'within', 'other', 'tomographic', 'applications', 'as', 'well']] | [-0.042650994805521085, 0.06247798914325693, -0.0966136488744891, 0.04831554573467752, -0.07385760203592087, -0.09941555590515858, 0.05870909467952228, 0.3760762173878519, -0.2975544146605228, -0.3414784411262525, 0.12474317303000901, -0.21458054339807284, -0.16606094952623704, 0.23505473593740087, -0.06228311529236012, 0.08530650861242688, 0.07187889281072114, -0.011224377336666772, -0.06334978984178681, -0.1964194131512685, 0.30570374921356375, 0.10808605840919834, 0.31846015445495907, 0.023143132736808375, 0.0901218536971627, 0.04626564167341904, -0.06732386693750557, 0.0683940797259933, -0.10699622621024518, 0.13920924328390116, 0.24839716163512907, 0.15898242899167694, 0.2688928111235758, -0.44321849467722996, -0.25969501694566327, 0.04723589660501794, 0.1308931436046566, 0.1347373060529782, -0.07109762529818046, -0.29312134915472643, 0.057002014264856515, -0.16429613664697268, -0.12396365436480233, -0.11762295595835895, -0.10411269129376466, 0.04093241566105893, -0.3145201853212998, 0.07718201872745627, 0.02535071008176984, 0.053565866446220564, -0.08348999316313942, -0.10734435490969764, 0.025095129846350143, 0.07851929253972087, -0.0010853599245341397, 0.039670392699343596, 0.13966343568049763, -0.10821050343986012, -0.1415569471489442, 0.4123877113586978, -0.07499216035449584, -0.22517397858594593, 0.1665890973845595, -0.08039739879926569, -0.11948491684406211, 0.11182511997850318, 0.19697490699197118, 0.12670238030780304, -0.1441878279873268, 0.08058975412767674, 0.03105679704366546, 0.1575561068167812, 0.0004393636812701037, -0.016303525021985957, 0.17745803000503466, 0.2153390124329905, 0.04463744473790652, 0.13934210475693506, -0.08677646713086257, -0.05733949193779967, -0.2728522205186125, -0.1305827100222048, -0.22604613283551053, 0.014197497361486679, -0.0865373245484845, -0.14125664351311953, 0.36829662447390116, 0.17553353997339544, 0.1949556424723644, 0.04786428661257225, 0.3312421823704713, 0.1271124918914498, 0.053973274727008844, 0.026708199868076727, 0.19370633290811, 0.14634801387100627, 0.09616663782906375, -0.1784087932399033, 0.07906411190174129, 0.0018173609419088615] |
1,802.03637 | Effect of predomination and vertex removal on the game total domination
number of a graph | The game total domination number, ${\gamma_{g}^{t}}$, was introduced by
Henning et al.\ in 2015. In this paper we study the effect of vertex
predomination on the game total domination number. We prove that
${\gamma_{g}^{t}}(G|v) \geq {\gamma_{g}^{t}}(G) - 2$ holds for all vertices $v$
of a graph $G$ and present infinite families attaining the equality. To achieve
this, some new variations of the total domination game are introduced. The
effect of vertex removal is also studied. We show that ${\gamma_{g}^{t}}(G)
\leq {\gamma_{g}^{t}}(G-v) + 4$ and ${\gamma_{g}^{t}}'(G) \leq
{\gamma_{g}^{t}}'(G-v) + 4$.
| math.CO | the game total domination number gamma_gt was introduced by henning et al in 2015 in this paper we study the effect of vertex predomination on the game total domination number we prove that gamma_gtgv geq gamma_gtg 2 holds for all vertices v of a graph g and present infinite families attaining the equality to achieve this some new variations of the total domination game are introduced the effect of vertex removal is also studied we show that gamma_gtg leq gamma_gtgv 4 and gamma_gtg leq gamma_gtgv 4 | [['the', 'game', 'total', 'domination', 'number', 'gamma_gt', 'was', 'introduced', 'by', 'henning', 'et', 'al', 'in', '2015', 'in', 'this', 'paper', 'we', 'study', 'the', 'effect', 'of', 'vertex', 'predomination', 'on', 'the', 'game', 'total', 'domination', 'number', 'we', 'prove', 'that', 'gamma_gtgv', 'geq', 'gamma_gtg', '2', 'holds', 'for', 'all', 'vertices', 'v', 'of', 'a', 'graph', 'g', 'and', 'present', 'infinite', 'families', 'attaining', 'the', 'equality', 'to', 'achieve', 'this', 'some', 'new', 'variations', 'of', 'the', 'total', 'domination', 'game', 'are', 'introduced', 'the', 'effect', 'of', 'vertex', 'removal', 'is', 'also', 'studied', 'we', 'show', 'that', 'gamma_gtg', 'leq', 'gamma_gtgv', '4', 'and', 'gamma_gtg', 'leq', 'gamma_gtgv', '4']] | [-0.1663381994391481, 0.1687936663206312, -0.018625049973793682, 0.03184515719407327, -0.06129635262325229, -0.09111845926270776, 0.03681303552834184, 0.31125794592801304, -0.20944307169078716, -0.3918221076462595, 0.04584563267694432, -0.3288053558429792, -0.17077583613406336, 0.0820349898366701, -0.13750216078811459, -0.028696812168207197, 0.04663574658999486, 0.04180949480671968, 0.055434196339254935, -0.3677281707797582, 0.31006091909616124, -0.029874050889962485, 0.15150113496375048, 0.10680947938401784, 0.10797520256268658, 0.07217901014900278, -0.020109354850969145, 0.09132587011637432, -0.256127551559205, 0.05559406878906191, 0.18653731541486368, 0.18521526888556158, 0.28003168995270417, -0.3548609181750743, -0.15721185569695773, 0.208501491728904, 0.08003646175798383, 0.02027011353389493, -0.01929713203987506, -0.18225760737966215, 0.16383219424668433, -0.1862459489853964, -0.07613461162518001, 0.033308695314363354, 0.15338883063356792, 0.00012013544273074894, -0.2911362828398567, 0.022383235918477157, 0.08982117431947872, 0.043531599862035364, 0.030034567656305928, -0.21099566907200606, -0.038167175149456375, 0.06403960366289885, -0.07171215697114046, 0.03444374408960963, -0.005200287576631776, -0.11186438608759393, -0.14196923798659727, 0.2983285406205271, -0.04243297863216694, -0.06491196280278798, 0.0985148637832719, -0.13699026271102152, -0.18687487053795762, 0.062254046951974965, 0.1307193640303532, 0.18791416946000286, -0.08510600275448746, 0.15060829126603148, -0.11234302456224603, 0.11702394899995332, 0.16432441331423997, 0.006482316364002015, 0.03204289089799637, 0.10696265384432904, 0.14015578097175985, 0.17327865684360622, -0.04211318286113618, 0.012942427898884662, -0.3217586064378598, -0.13455198235100224, -0.17292204085831708, 0.05659853955841668, -0.10479900872278017, -0.09625346691436357, 0.36469682004480136, 0.14218716664306288, 0.16014682854958145, 0.11211474819685377, 0.18646697267623885, 0.0909170442526894, -0.028310348119010172, 0.16528862991392435, 0.19032396040919203, 0.19603289179559352, 0.04495775541623256, -0.19246487664834908, 0.02050465583687626, 0.11929902135549734] |
1,802.03638 | Beyond Markov Logic: Efficient Mining of Prediction Rules in Large
Graphs | Graph representations of large knowledge bases may comprise billions of
edges. Usually built upon human-generated ontologies, several knowledge bases
do not feature declared ontological rules and are far from being complete.
Current rule mining approaches rely on schemata or store the graph in-memory,
which can be unfeasible for large graphs. In this paper, we introduce
HornConcerto, an algorithm to discover Horn clauses in large graphs without the
need of a schema. Using a standard fact-based confidence score, we can mine
close Horn rules having an arbitrary body size. We show that our method can
outperform existing approaches in terms of runtime and memory consumption and
mine high-quality rules for the link prediction task, achieving
state-of-the-art results on a widely-used benchmark. Moreover, we find that
rules alone can perform inference significantly faster than embedding-based
methods and achieve accuracies on link prediction comparable to
resource-demanding approaches such as Markov Logic Networks.
| cs.DB cs.AI | graph representations of large knowledge bases may comprise billions of edges usually built upon humangenerated ontologies several knowledge bases do not feature declared ontological rules and are far from being complete current rule mining approaches rely on schemata or store the graph inmemory which can be unfeasible for large graphs in this paper we introduce hornconcerto an algorithm to discover horn clauses in large graphs without the need of a schema using a standard factbased confidence score we can mine close horn rules having an arbitrary body size we show that our method can outperform existing approaches in terms of runtime and memory consumption and mine highquality rules for the link prediction task achieving stateoftheart results on a widelyused benchmark moreover we find that rules alone can perform inference significantly faster than embeddingbased methods and achieve accuracies on link prediction comparable to resourcedemanding approaches such as markov logic networks | [['graph', 'representations', 'of', 'large', 'knowledge', 'bases', 'may', 'comprise', 'billions', 'of', 'edges', 'usually', 'built', 'upon', 'humangenerated', 'ontologies', 'several', 'knowledge', 'bases', 'do', 'not', 'feature', 'declared', 'ontological', 'rules', 'and', 'are', 'far', 'from', 'being', 'complete', 'current', 'rule', 'mining', 'approaches', 'rely', 'on', 'schemata', 'or', 'store', 'the', 'graph', 'inmemory', 'which', 'can', 'be', 'unfeasible', 'for', 'large', 'graphs', 'in', 'this', 'paper', 'we', 'introduce', 'hornconcerto', 'an', 'algorithm', 'to', 'discover', 'horn', 'clauses', 'in', 'large', 'graphs', 'without', 'the', 'need', 'of', 'a', 'schema', 'using', 'a', 'standard', 'factbased', 'confidence', 'score', 'we', 'can', 'mine', 'close', 'horn', 'rules', 'having', 'an', 'arbitrary', 'body', 'size', 'we', 'show', 'that', 'our', 'method', 'can', 'outperform', 'existing', 'approaches', 'in', 'terms', 'of', 'runtime', 'and', 'memory', 'consumption', 'and', 'mine', 'highquality', 'rules', 'for', 'the', 'link', 'prediction', 'task', 'achieving', 'stateoftheart', 'results', 'on', 'a', 'widelyused', 'benchmark', 'moreover', 'we', 'find', 'that', 'rules', 'alone', 'can', 'perform', 'inference', 'significantly', 'faster', 'than', 'embeddingbased', 'methods', 'and', 'achieve', 'accuracies', 'on', 'link', 'prediction', 'comparable', 'to', 'resourcedemanding', 'approaches', 'such', 'as', 'markov', 'logic', 'networks']] | [-0.057353436183397256, 0.0063747559893149505, -0.043448289791814275, 0.09251708136100599, -0.19192190025933087, -0.1511436872266082, 0.12222944390786988, 0.4410761991213705, -0.2410008545077683, -0.36324645465592276, 0.09588014276625866, -0.2809436508207393, -0.11906706670620101, 0.1849939120938133, -0.11753821600782308, 0.06815137199324404, 0.1594202074346548, 0.08247850566309549, -0.05218538246589486, -0.26182086992500714, 0.270319243191389, 0.02401489663139187, 0.32910372009123295, 0.03115347944313308, 0.07949758060213581, -0.03193509917882447, -0.0494003090382132, 0.036266244216381596, -0.06272948160856047, 0.16572746087016688, 0.3476117569496981, 0.25660786570074995, 0.26382045810129745, -0.4631113777054329, -0.15029504377319403, 0.13689568523959433, 0.15326556607898376, 0.1371709359502986, 0.05177609435348721, -0.27875897895097984, 0.10347781178692507, -0.20054504393310463, 0.008482475521206553, -0.1971049083717721, -0.03798716967670574, 0.01594745208244972, -0.29102722472335035, 0.007828114472480642, 0.11513759328266354, 0.07046110071619419, 0.012496153400845927, -0.18323382659110468, 0.04649189988157485, 0.1197404432491198, -0.03682296820271232, 0.039659654679141836, 0.1350012949925086, -0.1359170585657577, -0.2527392057912751, 0.33979158978783397, -0.05258612195786948, -0.21968124972851444, 0.19403099808826213, -0.024146860116161406, -0.18510958067191816, 0.10238409378389651, 0.1846305909440727, 0.14011216517758077, -0.16413440077716634, 0.03234487897999635, -0.04020326616041161, 0.2360351013894369, 0.10501686903031392, 0.012103646918124444, 0.19537402133180484, 0.22674183573611584, 0.049016870160247326, 0.09710835377651116, -0.0388860724995391, -0.0788323717975259, -0.20201872208161983, -0.09847457588310479, -0.1796394482324202, -0.009665582681328807, -0.13898390741048167, -0.18180153736643292, 0.3335809358899054, 0.24080341616472406, 0.15844880177629986, 0.1568505343410023, 0.31698044079884485, 0.02478729109153957, 0.18530084100249852, 0.12738934154834636, 0.16713115845404164, -0.01198731068949643, 0.09006073214805911, -0.10354061114973061, 0.13126337726292125, 0.05687019060919615] |
1,802.03639 | Martingale Characterizations of Risk-Averse Stochastic Optimization
Problems | This paper addresses risk awareness of stochastic optimization problems.
Nested risk measures appear naturally in this context, as they allow beneficial
reformulations for algorithmic treatments. The reformulations presented extend
usual Hamilton-Jacobi-Bellman equations in dynamic optimization by involving
risk awareness in the problem formulation.
Nested risk measures are built on risk measures, which originate by
conditioning on the history of a stochastic process. We derive martingale
properties of these risk measures and use them to prove continuity. It is
demonstrated that stochastic optimization problems, which incorporate risk
awareness via nesting risk measures, are continuous with respect to the natural
distance governing these optimization problems, the nested distance.
| math.OC | this paper addresses risk awareness of stochastic optimization problems nested risk measures appear naturally in this context as they allow beneficial reformulations for algorithmic treatments the reformulations presented extend usual hamiltonjacobibellman equations in dynamic optimization by involving risk awareness in the problem formulation nested risk measures are built on risk measures which originate by conditioning on the history of a stochastic process we derive martingale properties of these risk measures and use them to prove continuity it is demonstrated that stochastic optimization problems which incorporate risk awareness via nesting risk measures are continuous with respect to the natural distance governing these optimization problems the nested distance | [['this', 'paper', 'addresses', 'risk', 'awareness', 'of', 'stochastic', 'optimization', 'problems', 'nested', 'risk', 'measures', 'appear', 'naturally', 'in', 'this', 'context', 'as', 'they', 'allow', 'beneficial', 'reformulations', 'for', 'algorithmic', 'treatments', 'the', 'reformulations', 'presented', 'extend', 'usual', 'hamiltonjacobibellman', 'equations', 'in', 'dynamic', 'optimization', 'by', 'involving', 'risk', 'awareness', 'in', 'the', 'problem', 'formulation', 'nested', 'risk', 'measures', 'are', 'built', 'on', 'risk', 'measures', 'which', 'originate', 'by', 'conditioning', 'on', 'the', 'history', 'of', 'a', 'stochastic', 'process', 'we', 'derive', 'martingale', 'properties', 'of', 'these', 'risk', 'measures', 'and', 'use', 'them', 'to', 'prove', 'continuity', 'it', 'is', 'demonstrated', 'that', 'stochastic', 'optimization', 'problems', 'which', 'incorporate', 'risk', 'awareness', 'via', 'nesting', 'risk', 'measures', 'are', 'continuous', 'with', 'respect', 'to', 'the', 'natural', 'distance', 'governing', 'these', 'optimization', 'problems', 'the', 'nested', 'distance']] | [-0.06770516549816953, 0.01623813955570167, -0.07857727348576037, 0.20139542565731355, -0.09737633449851342, -0.13614091884678686, 0.06984859270171069, 0.3748874379484475, -0.37654169566077095, -0.23678004296050617, 0.15742687904825084, -0.26299041164336057, -0.17564361403643522, 0.1747746130754679, -0.21784206649358823, 0.0965121205597413, 0.07757054245591445, -0.03497940574043413, -0.051841475081603694, -0.2563873741046508, 0.34595158550325994, 0.03209980159013901, 0.2843938567412068, 0.044493711698484026, 0.12867284901352283, 0.022050339240967384, -0.04204920370940049, 0.05245826131760863, -0.14245054566550985, 0.18972617798849484, 0.3600644234849035, 0.23767982463941048, 0.44350482533984587, -0.4245498921757318, -0.24625848880353965, 0.12462945452908862, 0.0694585475833968, -0.001975354549633163, 0.018955308728728373, -0.31899722177043277, -0.03544123916236578, -0.1392213294923938, -0.09637822725651961, -0.09701722450576336, -0.08876738621730287, 0.038235986192341684, -0.2836876931635417, 0.09670605900574406, 0.04263211199838036, 0.034257358617702814, -0.0699655705500605, -0.13235067510274504, 0.029870636111019918, 0.06262741567795428, 0.13524832467977968, -0.03106025328754254, 0.16503473832974877, -0.04175315090490259, -0.22843053863375043, 0.32561280855135816, 0.0120039476797793, -0.2887807172231095, 0.1769588918596471, -0.05012148667241113, -0.16703594753624135, 0.11415618384379964, 0.2502724066517263, 0.0915416473048335, -0.2546622155515372, 0.07900383377849687, -0.006830496416989503, 0.09328484394640293, 0.06581780003858204, 0.04363438597757299, 0.1224162039986179, 0.14229922877835496, 0.1933044367294126, 0.1374916067118493, 0.011849601500182202, -0.2026768282785098, -0.2670786275510799, -0.12499038542550549, -0.08416688334600965, 0.024133293832353544, -0.1420039385995105, -0.2072993368042176, 0.327229417191669, 0.19324104161933064, 0.11378651376778506, 0.12157639600380482, 0.27313638510147353, 0.18313895453993267, 0.029622926780918858, 0.06731352480216268, 0.1560406197556558, 0.10662257471198726, 0.05692332176456474, -0.21305867378107923, 0.1784029788955967, 0.11846720972010549] |
1,802.0364 | Noise Analysis for High-Fidelity Quantum Entangling Gates in an
Anharmonic Linear Paul Trap | The realization of high fidelity quantum gates in a multi-qubit system, with
a typical target set at 99.9%, is a critical requirement for the implementation
of fault-tolerant quantum computation. To reach this level of fidelity, one
needs to carefully analyze the noises and imperfections in the experimental
system and optimize the gate operations to mitigate their effects. Here, we
consider one of the leading experimental systems for the fault-tolerant quantum
computation, ions in an anharmonic linear Paul trap, and optimize entangling
quantum gates using segmented laser pulses with the assistance of all the
collective transverse phonon modes of the ion crystal. We present detailed
analyses of the effects of various kinds of intrinsic experimental noises as
well as errors from imperfect experimental controls. Through explicit
calculations, we find the requirements on these relevant noise levels and
control precisions to achieve the targeted high fidelity of 99.9% for the
entangling quantum gates in a multi-ion crystal.
| quant-ph | the realization of high fidelity quantum gates in a multiqubit system with a typical target set at 999 is a critical requirement for the implementation of faulttolerant quantum computation to reach this level of fidelity one needs to carefully analyze the noises and imperfections in the experimental system and optimize the gate operations to mitigate their effects here we consider one of the leading experimental systems for the faulttolerant quantum computation ions in an anharmonic linear paul trap and optimize entangling quantum gates using segmented laser pulses with the assistance of all the collective transverse phonon modes of the ion crystal we present detailed analyses of the effects of various kinds of intrinsic experimental noises as well as errors from imperfect experimental controls through explicit calculations we find the requirements on these relevant noise levels and control precisions to achieve the targeted high fidelity of 999 for the entangling quantum gates in a multiion crystal | [['the', 'realization', 'of', 'high', 'fidelity', 'quantum', 'gates', 'in', 'a', 'multiqubit', 'system', 'with', 'a', 'typical', 'target', 'set', 'at', '999', 'is', 'a', 'critical', 'requirement', 'for', 'the', 'implementation', 'of', 'faulttolerant', 'quantum', 'computation', 'to', 'reach', 'this', 'level', 'of', 'fidelity', 'one', 'needs', 'to', 'carefully', 'analyze', 'the', 'noises', 'and', 'imperfections', 'in', 'the', 'experimental', 'system', 'and', 'optimize', 'the', 'gate', 'operations', 'to', 'mitigate', 'their', 'effects', 'here', 'we', 'consider', 'one', 'of', 'the', 'leading', 'experimental', 'systems', 'for', 'the', 'faulttolerant', 'quantum', 'computation', 'ions', 'in', 'an', 'anharmonic', 'linear', 'paul', 'trap', 'and', 'optimize', 'entangling', 'quantum', 'gates', 'using', 'segmented', 'laser', 'pulses', 'with', 'the', 'assistance', 'of', 'all', 'the', 'collective', 'transverse', 'phonon', 'modes', 'of', 'the', 'ion', 'crystal', 'we', 'present', 'detailed', 'analyses', 'of', 'the', 'effects', 'of', 'various', 'kinds', 'of', 'intrinsic', 'experimental', 'noises', 'as', 'well', 'as', 'errors', 'from', 'imperfect', 'experimental', 'controls', 'through', 'explicit', 'calculations', 'we', 'find', 'the', 'requirements', 'on', 'these', 'relevant', 'noise', 'levels', 'and', 'control', 'precisions', 'to', 'achieve', 'the', 'targeted', 'high', 'fidelity', 'of', '999', 'for', 'the', 'entangling', 'quantum', 'gates', 'in', 'a', 'multiion', 'crystal']] | [-0.14125480649151628, 0.16810667753123842, -0.00311053569668964, 0.021228571626688203, 0.047202977529096024, -0.2112821268037923, 0.11677013344371752, 0.3825987763102016, -0.22022475950540074, -0.3233807333775105, 0.07151016458087871, -0.2687586322727223, -0.06813936809513478, 0.24760535621805296, -0.07321657462068082, 0.17941152517563633, 0.0684999001865846, -0.00199561077170074, -0.08755476974369113, -0.2533574152452451, 0.23986243600806884, 0.1015496008961852, 0.29477582837364846, 0.04132072675612665, 0.12340960748974354, -0.01214475205708896, 0.05753801866329365, -0.04128660601751518, -0.10240802229682525, 0.10792090192497257, 0.27357430138264693, 0.05274849323435656, 0.25695743729029935, -0.47551841965365793, -0.17817192995590309, 0.04977697904011415, 0.08020661632650562, 0.22768983835534704, -0.03555682318119873, -0.29730923161241074, 0.038419574158146015, -0.16588820912424596, -0.08162362068110417, -0.14301141642274395, -0.025374773409097426, 0.021202347815156945, -0.2567401236824451, 0.04267187378399315, 0.033104428980169034, 0.0875808028424921, -0.010322527280978619, -0.06230888545603281, 0.03152289479865783, 0.1418416499881254, -0.09611816861171035, -0.0054630204845565345, 0.21594698818460586, -0.12775849943169423, -0.18076240714759595, 0.3712108220784895, -0.004955578478233468, -0.19588770120134277, 0.12908313439618196, -0.13029352613920045, -0.11085983053030025, 0.07877626986836174, 0.1825342474693072, 0.051735730024219884, -0.11526337188458251, 0.04578484311970251, 0.0993587413712615, 0.20711859698501706, 0.04832558282275474, 0.16030902373153835, 0.188368534747391, 0.15394852541447165, 0.059222684271873965, 0.21251166215806733, -0.1064694517367189, -0.0853347043641993, -0.35223567085641044, -0.16516717244840917, -0.1806245250454653, 0.041290930640553274, -0.05355141582756093, -0.13990693017239533, 0.4267705333088675, 0.1733328104492337, 0.15075881252245557, 0.0018977007062564934, 0.35104517367623383, 0.11227867972310407, 0.05756977427480442, 0.037143105390151185, 0.23887759655368546, 0.14149113260209562, 0.047051306931121695, -0.3158982145885426, 0.03643991215935638, -0.04854035170208062] |
1,802.03641 | Same Same, but Different: A Descriptive Differentiation of Intra-cloud
Iaas Services | Users of cloud computing are overwhelmed with choice, even within the
services offered by one provider. As such, many users select cloud services
based on description alone. We investigate the degree to which such strategy is
optimal. In this quantitative study, we investigate the services of 2 of major
IaaS providers. We use 2 representative applications to obtain longitudinal
observations over 7 days of the week and over different times of the day,
totalling over 14,000 executions. We give evidence of significant variations of
performance offered within IaaS services, calling for brokers to use automated
and adaptive decision making processes with means for incorporating expressive
user constraints.
| cs.DC | users of cloud computing are overwhelmed with choice even within the services offered by one provider as such many users select cloud services based on description alone we investigate the degree to which such strategy is optimal in this quantitative study we investigate the services of 2 of major iaas providers we use 2 representative applications to obtain longitudinal observations over 7 days of the week and over different times of the day totalling over 14000 executions we give evidence of significant variations of performance offered within iaas services calling for brokers to use automated and adaptive decision making processes with means for incorporating expressive user constraints | [['users', 'of', 'cloud', 'computing', 'are', 'overwhelmed', 'with', 'choice', 'even', 'within', 'the', 'services', 'offered', 'by', 'one', 'provider', 'as', 'such', 'many', 'users', 'select', 'cloud', 'services', 'based', 'on', 'description', 'alone', 'we', 'investigate', 'the', 'degree', 'to', 'which', 'such', 'strategy', 'is', 'optimal', 'in', 'this', 'quantitative', 'study', 'we', 'investigate', 'the', 'services', 'of', '2', 'of', 'major', 'iaas', 'providers', 'we', 'use', '2', 'representative', 'applications', 'to', 'obtain', 'longitudinal', 'observations', 'over', '7', 'days', 'of', 'the', 'week', 'and', 'over', 'different', 'times', 'of', 'the', 'day', 'totalling', 'over', '14000', 'executions', 'we', 'give', 'evidence', 'of', 'significant', 'variations', 'of', 'performance', 'offered', 'within', 'iaas', 'services', 'calling', 'for', 'brokers', 'to', 'use', 'automated', 'and', 'adaptive', 'decision', 'making', 'processes', 'with', 'means', 'for', 'incorporating', 'expressive', 'user', 'constraints']] | [-0.13366555071693723, 0.008245304348730595, -0.030324261757397206, 0.0610487608012752, -0.10791522451615501, -0.1527040185076054, 0.13196079235819874, 0.43761220607453977, -0.25160051936666683, -0.35629148363510027, 0.11202870505082935, -0.2812386061473985, -0.07999712755799154, 0.19668978416125907, -0.08013714145364187, 0.00903011653920479, 0.05271419354716629, 0.004935161706721671, -0.008113552696532874, -0.33437617401653363, 0.275769170743596, 0.04209197510173109, 0.26865157831425424, 0.07028403998326475, 0.07645265884244833, 0.05446717203551225, -0.11052172767677318, -0.03448908689875747, -0.13054149724960884, 0.16098875598764378, 0.3260000801784482, 0.24570013149684974, 0.3240091999836057, -0.44391052149862886, -0.1545956744148753, 0.08851546612263561, 0.14193003633980003, 0.004733649259824828, -0.01384259316151979, -0.27547338333363847, 0.12825711301710702, -0.2716447993990297, -0.11476993999034862, -0.0854185504891048, 0.01882152011287268, 0.05517076058247971, -0.2991041452797983, -0.0179300948533569, -0.052421211001733053, 0.14154870238015863, -0.06433242342704645, -0.08378012261242458, 0.033915880777938105, 0.18880619894208334, 0.060654835782022534, -0.03148717026522157, 0.15861692416882056, -0.14869538357994894, -0.14622865608843288, 0.42141988081422366, -0.03412095686229311, -0.06623986118748375, 0.20904864081416594, -0.061904488370762525, -0.16417042223005512, 0.1032536237840062, 0.23747546680132361, 0.05663947249266589, -0.1838954012736539, -0.005417169009344315, -0.012155617443109192, 0.18772480640316677, 0.0832066445319835, 0.07188727926880678, 0.2162231478957616, 0.22600813475480983, 0.11511453496951557, 0.09702845545328945, -0.03856803826056421, -0.11164432579529619, -0.19600650890094934, -0.1623372949189811, -0.10649364049520309, 0.06027563283150302, -0.11968723864149886, -0.11718893320534692, 0.38864450774233894, 0.206568333945712, 0.13422447564863713, 0.06673587710818572, 0.3565200337113064, 0.06887334362259918, 0.11609789999811171, 0.1457589781651639, 0.13601555606506974, -0.03919283993254964, 0.1514308042008273, -0.10157439695392674, 0.10961053385529841, -0.02908200132617048] |
1,802.03642 | Graph Planning with Expected Finite Horizon | Graph planning gives rise to fundamental algorithmic questions such as
shortest path, traveling salesman problem, etc. A classical problem in discrete
planning is to consider a weighted graph and construct a path that maximizes
the sum of weights for a given time horizon $T$. However, in many scenarios,
the time horizon is not fixed, but the stopping time is chosen according to
some distribution such that the expected stopping time is $T$. If the stopping
time distribution is not known, then to ensure robustness, the distribution is
chosen by an adversary, to represent the worst-case scenario.
A stationary plan for every vertex always chooses the same outgoing edge. For
fixed horizon or fixed stopping-time distribution, stationary plans are not
sufficient for optimality. Quite surprisingly we show that when an adversary
chooses the stopping-time distribution with expected stopping time $T$, then
stationary plans are sufficient. While computing optimal stationary plans for
fixed horizon is NP-complete, we show that computing optimal stationary plans
under adversarial stopping-time distribution can be achieved in polynomial
time. Consequently, our polynomial-time algorithm for adversarial stopping time
also computes an optimal plan among all possible plans.
| cs.AI | graph planning gives rise to fundamental algorithmic questions such as shortest path traveling salesman problem etc a classical problem in discrete planning is to consider a weighted graph and construct a path that maximizes the sum of weights for a given time horizon t however in many scenarios the time horizon is not fixed but the stopping time is chosen according to some distribution such that the expected stopping time is t if the stopping time distribution is not known then to ensure robustness the distribution is chosen by an adversary to represent the worstcase scenario a stationary plan for every vertex always chooses the same outgoing edge for fixed horizon or fixed stoppingtime distribution stationary plans are not sufficient for optimality quite surprisingly we show that when an adversary chooses the stoppingtime distribution with expected stopping time t then stationary plans are sufficient while computing optimal stationary plans for fixed horizon is npcomplete we show that computing optimal stationary plans under adversarial stoppingtime distribution can be achieved in polynomial time consequently our polynomialtime algorithm for adversarial stopping time also computes an optimal plan among all possible plans | [['graph', 'planning', 'gives', 'rise', 'to', 'fundamental', 'algorithmic', 'questions', 'such', 'as', 'shortest', 'path', 'traveling', 'salesman', 'problem', 'etc', 'a', 'classical', 'problem', 'in', 'discrete', 'planning', 'is', 'to', 'consider', 'a', 'weighted', 'graph', 'and', 'construct', 'a', 'path', 'that', 'maximizes', 'the', 'sum', 'of', 'weights', 'for', 'a', 'given', 'time', 'horizon', 't', 'however', 'in', 'many', 'scenarios', 'the', 'time', 'horizon', 'is', 'not', 'fixed', 'but', 'the', 'stopping', 'time', 'is', 'chosen', 'according', 'to', 'some', 'distribution', 'such', 'that', 'the', 'expected', 'stopping', 'time', 'is', 't', 'if', 'the', 'stopping', 'time', 'distribution', 'is', 'not', 'known', 'then', 'to', 'ensure', 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1,802.03643 | Electroforming Free Controlled Bipolar Resistive Switching in
Al/CoFe2O4/FTO device with Self-Compliance Effect | Controlled bipolar resistive switching (BRS) has been observed in
nanostructured CoFe2O4 films using Al(aluminum)/CoFe2O4/FTO(fluorine-doped tin
oxide) device. The fabricated device shows electroforming-free uniform BRS with
two clearly distinguished and stable resistance states without any application
of compliance current (CC), with a resistance ratio of high resistance state
(HRS) and low resistance state (LRS) > 102. Small switching voltage (< 1 volt)
and lower current in both the resistance states confirms the fabrication of low
power consumption device. In the LRS, the conduction mechanism was found to be
of Ohmic in nature, while the high-resistance state (HRS/OFF state) was
governed by space charge-limited conduction mechanism, which indicates the
presence of an interfacial layer with imperfect microstructure near the top
Al/CFO interface. The device shows nonvolatile behavior with good endurance
properties, acceptable resistance ratio, uniform resistive switching due to
stable, less random filament formation/rupture and a control over the resistive
switching properties by choosing different stop voltages, which makes the
device suitable for its application in future nonvolatile resistive random
access memory (ReRAM).
| cond-mat.mtrl-sci | controlled bipolar resistive switching brs has been observed in nanostructured cofe2o4 films using alaluminumcofe2o4ftofluorinedoped tin oxide device the fabricated device shows electroformingfree uniform brs with two clearly distinguished and stable resistance states without any application of compliance current cc with a resistance ratio of high resistance state hrs and low resistance state lrs 102 small switching voltage 1 volt and lower current in both the resistance states confirms the fabrication of low power consumption device in the lrs the conduction mechanism was found to be of ohmic in nature while the highresistance state hrsoff state was governed by space chargelimited conduction mechanism which indicates the presence of an interfacial layer with imperfect microstructure near the top alcfo interface the device shows nonvolatile behavior with good endurance properties acceptable resistance ratio uniform resistive switching due to stable less random filament formationrupture and a control over the resistive switching properties by choosing different stop voltages which makes the device suitable for its application in future nonvolatile resistive random access memory reram | [['controlled', 'bipolar', 'resistive', 'switching', 'brs', 'has', 'been', 'observed', 'in', 'nanostructured', 'cofe2o4', 'films', 'using', 'alaluminumcofe2o4ftofluorinedoped', 'tin', 'oxide', 'device', 'the', 'fabricated', 'device', 'shows', 'electroformingfree', 'uniform', 'brs', 'with', 'two', 'clearly', 'distinguished', 'and', 'stable', 'resistance', 'states', 'without', 'any', 'application', 'of', 'compliance', 'current', 'cc', 'with', 'a', 'resistance', 'ratio', 'of', 'high', 'resistance', 'state', 'hrs', 'and', 'low', 'resistance', 'state', 'lrs', '102', 'small', 'switching', 'voltage', '1', 'volt', 'and', 'lower', 'current', 'in', 'both', 'the', 'resistance', 'states', 'confirms', 'the', 'fabrication', 'of', 'low', 'power', 'consumption', 'device', 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1,802.03644 | Learning to Match via Inverse Optimal Transport | We propose a unified data-driven framework based on inverse optimal transport
that can learn adaptive, nonlinear interaction cost function from noisy and
incomplete empirical matching matrix and predict new matching in various
matching contexts. We emphasize that the discrete optimal transport plays the
role of a variational principle which gives rise to an optimization-based
framework for modeling the observed empirical matching data. Our formulation
leads to a non-convex optimization problem which can be solved efficiently by
an alternating optimization method. A key novel aspect of our formulation is
the incorporation of marginal relaxation via regularized Wasserstein distance,
significantly improving the robustness of the method in the face of noisy or
missing empirical matching data. Our model falls into the category of
prescriptive models, which not only predict potential future matching, but is
also able to explain what leads to empirical matching and quantifies the impact
of changes in matching factors. The proposed approach has wide applicability
including predicting matching in online dating, labor market, college
application and crowdsourcing. We back up our claims with numerical experiments
on both synthetic data and real world data sets.
| stat.ML cs.LG | we propose a unified datadriven framework based on inverse optimal transport that can learn adaptive nonlinear interaction cost function from noisy and incomplete empirical matching matrix and predict new matching in various matching contexts we emphasize that the discrete optimal transport plays the role of a variational principle which gives rise to an optimizationbased framework for modeling the observed empirical matching data our formulation leads to a nonconvex optimization problem which can be solved efficiently by an alternating optimization method a key novel aspect of our formulation is the incorporation of marginal relaxation via regularized wasserstein distance significantly improving the robustness of the method in the face of noisy or missing empirical matching data our model falls into the category of prescriptive models which not only predict potential future matching but is also able to explain what leads to empirical matching and quantifies the impact of changes in matching factors the proposed approach has wide applicability including predicting matching in online dating labor market college application and crowdsourcing we back up our claims with numerical experiments on both synthetic data and real world data sets | [['we', 'propose', 'a', 'unified', 'datadriven', 'framework', 'based', 'on', 'inverse', 'optimal', 'transport', 'that', 'can', 'learn', 'adaptive', 'nonlinear', 'interaction', 'cost', 'function', 'from', 'noisy', 'and', 'incomplete', 'empirical', 'matching', 'matrix', 'and', 'predict', 'new', 'matching', 'in', 'various', 'matching', 'contexts', 'we', 'emphasize', 'that', 'the', 'discrete', 'optimal', 'transport', 'plays', 'the', 'role', 'of', 'a', 'variational', 'principle', 'which', 'gives', 'rise', 'to', 'an', 'optimizationbased', 'framework', 'for', 'modeling', 'the', 'observed', 'empirical', 'matching', 'data', 'our', 'formulation', 'leads', 'to', 'a', 'nonconvex', 'optimization', 'problem', 'which', 'can', 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1,802.03645 | Link diagrams in Seifert manifolds and applications to skein modules | In this survey paper we present results about link diagrams in Seifert
manifolds using arrow diagrams, starting with link diagrams in $F\times S^1$
and $N\hat{\times}S^1$, where $F$ is an orientable and $N$ an unorientable
surface. Reidemeister moves for such arrow diagrams make the study of link
invariants possible. Transitions between arrow diagrams and alternative
diagrams are presented. We recall results about %the knot group presentation
for lens spaces and the Kauffman bracket and HOMFLYPT skein modules of some
Seifert manifolds using arrow diagrams, namely lens spaces, a product of a disk
with two holes times $S^1$, $\mathbb{R}P^3 \# \mathbb{R}P^3$, and prism
manifolds. We also present new bases of the Kauffman bracket and HOMFLYPT skein
modules of the solid torus and lens spaces.
| math.GN math.GT | in this survey paper we present results about link diagrams in seifert manifolds using arrow diagrams starting with link diagrams in ftimes s1 and nhattimess1 where f is an orientable and n an unorientable surface reidemeister moves for such arrow diagrams make the study of link invariants possible transitions between arrow diagrams and alternative diagrams are presented we recall results about the knot group presentation for lens spaces and the kauffman bracket and homflypt skein modules of some seifert manifolds using arrow diagrams namely lens spaces a product of a disk with two holes times s1 mathbbrp3 mathbbrp3 and prism manifolds we also present new bases of the kauffman bracket and homflypt skein modules of the solid torus and lens spaces | [['in', 'this', 'survey', 'paper', 'we', 'present', 'results', 'about', 'link', 'diagrams', 'in', 'seifert', 'manifolds', 'using', 'arrow', 'diagrams', 'starting', 'with', 'link', 'diagrams', 'in', 'ftimes', 's1', 'and', 'nhattimess1', 'where', 'f', 'is', 'an', 'orientable', 'and', 'n', 'an', 'unorientable', 'surface', 'reidemeister', 'moves', 'for', 'such', 'arrow', 'diagrams', 'make', 'the', 'study', 'of', 'link', 'invariants', 'possible', 'transitions', 'between', 'arrow', 'diagrams', 'and', 'alternative', 'diagrams', 'are', 'presented', 'we', 'recall', 'results', 'about', 'the', 'knot', 'group', 'presentation', 'for', 'lens', 'spaces', 'and', 'the', 'kauffman', 'bracket', 'and', 'homflypt', 'skein', 'modules', 'of', 'some', 'seifert', 'manifolds', 'using', 'arrow', 'diagrams', 'namely', 'lens', 'spaces', 'a', 'product', 'of', 'a', 'disk', 'with', 'two', 'holes', 'times', 's1', 'mathbbrp3', 'mathbbrp3', 'and', 'prism', 'manifolds', 'we', 'also', 'present', 'new', 'bases', 'of', 'the', 'kauffman', 'bracket', 'and', 'homflypt', 'skein', 'modules', 'of', 'the', 'solid', 'torus', 'and', 'lens', 'spaces']] | [-0.2569479422216924, 0.10144945283173001, -0.11461574559410413, 0.12315500433711955, -0.1394351269157293, -0.16701229114551097, 0.00597280367607406, 0.4105632151477039, -0.2789728357301404, -0.3091381168613831, 0.06399042027090521, -0.24486916743723366, -0.1954537986641905, 0.16895484801304217, -0.14342655411067728, -0.0741972258547321, 0.0888326676019157, 0.01623872291917602, -0.14940795111518432, -0.2633434545481578, 0.4074537616785771, -0.036815521396541345, 0.12599062554848692, 0.03616662724331642, 0.1262010301773747, -0.0030196191005719204, -0.06413668396805103, 2.2471932849536338e-05, -0.2767313536644603, 0.12330500750783055, 0.26708965594492234, 0.059806412334243456, -0.00927851979310314, -0.38150615178359054, -0.08432446577353403, 0.121081145425948, 0.1629041447653435, -0.024064940105502804, 0.03621276408278694, -0.29727838723144184, 0.002150555847523113, -0.20069696344435214, -0.13325401772744955, -0.026935718286161623, 0.07863853188464417, -0.001846810579687978, -0.12687722893121342, -0.04026963290743879, 0.0533932706185927, 0.16482458509660017, -0.030984820460435002, -0.0995065858742843, -0.07442581340049705, 0.15102157656801865, -0.06113962054757091, 0.1141261644603219, 0.10187445976383364, -0.16068564222077839, -0.21697747687576338, 0.34539762417164943, -0.0348028480540961, -0.23378748809664102, 0.12373190505507713, -0.13256217969756107, -0.2429182342758092, 0.16633746856144474, -0.011527707326846819, 0.10511103922811647, -0.05764898638396213, 0.10541605121300866, -0.08763530316452185, 0.07344297162878016, 0.12314412623333434, -0.018580226716585456, 0.21028542130370625, 0.06342491274505543, 0.055269815229500335, 0.19326941039180384, -0.025316916244143308, -0.08118524937987483, -0.3356746589144071, -0.3375524830383559, -0.07345058287804325, 0.11445698660099879, -0.1777519000568039, -0.1622267035767436, 0.3326482703210786, 0.02887884865825375, 0.14378703641632457, 0.17095425290923838, 0.2556902138299544, -0.0007574930243814985, 0.02295512183724592, 0.0671122946155568, 0.10943578522540823, 0.24400682263076306, 0.02706216304601791, -0.07226422805106267, -0.046858741387647264, 0.2677274349882888] |
1,802.03646 | On the Universal Approximability and Complexity Bounds of Quantized ReLU
Neural Networks | Compression is a key step to deploy large neural networks on
resource-constrained platforms. As a popular compression technique,
quantization constrains the number of distinct weight values and thus reducing
the number of bits required to represent and store each weight. In this paper,
we study the representation power of quantized neural networks. First, we prove
the universal approximability of quantized ReLU networks on a wide class of
functions. Then we provide upper bounds on the number of weights and the memory
size for a given approximation error bound and the bit-width of weights for
function-independent and function-dependent structures. Our results reveal
that, to attain an approximation error bound of $\epsilon$, the number of
weights needed by a quantized network is no more than
$\mathcal{O}\left(\log^5(1/\epsilon)\right)$ times that of an unquantized
network. This overhead is of much lower order than the lower bound of the
number of weights needed for the error bound, supporting the empirical success
of various quantization techniques. To the best of our knowledge, this is the
first in-depth study on the complexity bounds of quantized neural networks.
| cs.LG cs.CV | compression is a key step to deploy large neural networks on resourceconstrained platforms as a popular compression technique quantization constrains the number of distinct weight values and thus reducing the number of bits required to represent and store each weight in this paper we study the representation power of quantized neural networks first we prove the universal approximability of quantized relu networks on a wide class of functions then we provide upper bounds on the number of weights and the memory size for a given approximation error bound and the bitwidth of weights for functionindependent and functiondependent structures our results reveal that to attain an approximation error bound of epsilon the number of weights needed by a quantized network is no more than mathcaloleftlog51epsilonright times that of an unquantized network this overhead is of much lower order than the lower bound of the number of weights needed for the error bound supporting the empirical success of various quantization techniques to the best of our knowledge this is the first indepth study on the complexity bounds of quantized neural networks | [['compression', 'is', 'a', 'key', 'step', 'to', 'deploy', 'large', 'neural', 'networks', 'on', 'resourceconstrained', 'platforms', 'as', 'a', 'popular', 'compression', 'technique', 'quantization', 'constrains', 'the', 'number', 'of', 'distinct', 'weight', 'values', 'and', 'thus', 'reducing', 'the', 'number', 'of', 'bits', 'required', 'to', 'represent', 'and', 'store', 'each', 'weight', 'in', 'this', 'paper', 'we', 'study', 'the', 'representation', 'power', 'of', 'quantized', 'neural', 'networks', 'first', 'we', 'prove', 'the', 'universal', 'approximability', 'of', 'quantized', 'relu', 'networks', 'on', 'a', 'wide', 'class', 'of', 'functions', 'then', 'we', 'provide', 'upper', 'bounds', 'on', 'the', 'number', 'of', 'weights', 'and', 'the', 'memory', 'size', 'for', 'a', 'given', 'approximation', 'error', 'bound', 'and', 'the', 'bitwidth', 'of', 'weights', 'for', 'functionindependent', 'and', 'functiondependent', 'structures', 'our', 'results', 'reveal', 'that', 'to', 'attain', 'an', 'approximation', 'error', 'bound', 'of', 'epsilon', 'the', 'number', 'of', 'weights', 'needed', 'by', 'a', 'quantized', 'network', 'is', 'no', 'more', 'than', 'mathcaloleftlog51epsilonright', 'times', 'that', 'of', 'an', 'unquantized', 'network', 'this', 'overhead', 'is', 'of', 'much', 'lower', 'order', 'than', 'the', 'lower', 'bound', 'of', 'the', 'number', 'of', 'weights', 'needed', 'for', 'the', 'error', 'bound', 'supporting', 'the', 'empirical', 'success', 'of', 'various', 'quantization', 'techniques', 'to', 'the', 'best', 'of', 'our', 'knowledge', 'this', 'is', 'the', 'first', 'indepth', 'study', 'on', 'the', 'complexity', 'bounds', 'of', 'quantized', 'neural', 'networks']] | [-0.1572201784869126, 0.06261872158975783, -0.02292332779826586, 0.04138190065356666, -0.06610976753882684, -0.13440093456762237, 0.11962458895490437, 0.3453227986008097, -0.2483748985128214, -0.32478647688035406, 0.07393661182436903, -0.2211094244809474, -0.19771885820585539, 0.22006173360840245, -0.08774558088536989, 0.10660749967389221, 0.06706175552207534, 0.10001908121436524, -0.06576681602877792, -0.3304047703692912, 0.3021059674704132, 0.10113162104971707, 0.3029800517179367, 0.038510295793961334, 0.080018682583593, -0.04971714951045348, 0.003707382421530152, -0.029010528934025073, -0.13010170149505415, 0.20997871212019337, 0.21818375269393042, 0.1365186599317391, 0.33111291933320675, -0.4445712018737012, -0.1968887064469147, 0.12937835302982625, 0.13782720624385034, 0.11903292827550216, 0.00942131637496608, -0.22275881809287698, 0.15325261814793373, -0.1511885477479884, -0.022338437787283442, -0.08090452328955723, 0.006605431568048536, 0.019650194719176044, -0.3014960321255744, 0.007021128078975246, 0.08200750564156616, 0.015063180428854647, -0.05187150390324114, -0.1715858953105191, 0.03473645068723498, 0.1452621084580061, 0.0004224972536334884, 0.043423168126383194, 0.07282001535423983, -0.19201187159745076, -0.11920151762969497, 0.3125382177426485, -0.03461601952565071, -0.21199154680315932, 0.12284247270432738, -0.06876587472187808, -0.1396958028192574, 0.11189315702482047, 0.2415645223855446, 0.11036202090524011, -0.07030648899811852, 0.03379677094540185, -0.059497494234750835, 0.2121677261508875, 0.06646974951996978, 0.10723590150825406, 0.11915577997027313, 0.20967894636547077, 0.12316993528998084, 0.16419744372267886, -0.10205775026842362, -0.07222036941708355, -0.2890053554832683, -0.15808349892649654, -0.2650003035179218, 0.043025537092754314, -0.15077630546834192, -0.1643909971232363, 0.39128268006820716, 0.1582722258333138, 0.24981551668422539, 0.19186557070904617, 0.33604545787103096, 0.12635546866712283, 0.0996241838708099, 0.13498565744566546, 0.18814521659452385, 0.13430797800784772, 0.05652657452718181, -0.18364029773050594, 0.08898050424091697, 0.08038064259456965] |
1,802.03647 | Quantum correlations as probes of chaos and ergodicity | Long-time average behavior of quantum correlations in a multi-qubit system,
collectively modeled as a kicked top, is addressed here. The behavior of
dynamical generation of quantum correlations such as entanglement, discord,
concurrence, as previously studied, and Bell correlation function and tangle,
as identified in this study, is a function of initially localized coherent
states. Their long-time average reproduces coarse-grained classical phase space
structures of the kicked top which contrast, often starkly, chaotic and regular
regions. Apart from providing numerical evidence of such correspondence in the
semiclassical regime of a large number of qubits, we use data from a recent
transmons based experiment to explore this in the deep quantum regime of a
3-qubit kicked top. The degree to which quantum correlations can be regarded as
a quantum signature of chaos, and in what ways the various correlation measures
are similar or distinct are discussed.
| quant-ph | longtime average behavior of quantum correlations in a multiqubit system collectively modeled as a kicked top is addressed here the behavior of dynamical generation of quantum correlations such as entanglement discord concurrence as previously studied and bell correlation function and tangle as identified in this study is a function of initially localized coherent states their longtime average reproduces coarsegrained classical phase space structures of the kicked top which contrast often starkly chaotic and regular regions apart from providing numerical evidence of such correspondence in the semiclassical regime of a large number of qubits we use data from a recent transmons based experiment to explore this in the deep quantum regime of a 3qubit kicked top the degree to which quantum correlations can be regarded as a quantum signature of chaos and in what ways the various correlation measures are similar or distinct are discussed | [['longtime', 'average', 'behavior', 'of', 'quantum', 'correlations', 'in', 'a', 'multiqubit', 'system', 'collectively', 'modeled', 'as', 'a', 'kicked', 'top', 'is', 'addressed', 'here', 'the', 'behavior', 'of', 'dynamical', 'generation', 'of', 'quantum', 'correlations', 'such', 'as', 'entanglement', 'discord', 'concurrence', 'as', 'previously', 'studied', 'and', 'bell', 'correlation', 'function', 'and', 'tangle', 'as', 'identified', 'in', 'this', 'study', 'is', 'a', 'function', 'of', 'initially', 'localized', 'coherent', 'states', 'their', 'longtime', 'average', 'reproduces', 'coarsegrained', 'classical', 'phase', 'space', 'structures', 'of', 'the', 'kicked', 'top', 'which', 'contrast', 'often', 'starkly', 'chaotic', 'and', 'regular', 'regions', 'apart', 'from', 'providing', 'numerical', 'evidence', 'of', 'such', 'correspondence', 'in', 'the', 'semiclassical', 'regime', 'of', 'a', 'large', 'number', 'of', 'qubits', 'we', 'use', 'data', 'from', 'a', 'recent', 'transmons', 'based', 'experiment', 'to', 'explore', 'this', 'in', 'the', 'deep', 'quantum', 'regime', 'of', 'a', '3qubit', 'kicked', 'top', 'the', 'degree', 'to', 'which', 'quantum', 'correlations', 'can', 'be', 'regarded', 'as', 'a', 'quantum', 'signature', 'of', 'chaos', 'and', 'in', 'what', 'ways', 'the', 'various', 'correlation', 'measures', 'are', 'similar', 'or', 'distinct', 'are', 'discussed']] | [-0.164859902449987, 0.19530932183584404, -0.11451827941022606, 0.1233519640000951, 0.049249666910327505, -0.17910924475048282, 0.029563716843970016, 0.3193239129614085, -0.2654355270801716, -0.2752920974809159, 0.04642796758303626, -0.29105411044535384, -0.1710204262684177, 0.20254609626944228, 0.004594353066446881, 0.12578548826341931, 0.03624492608469154, 0.03131150683717957, -0.06244194725230853, -0.19243752656297552, 0.3020586347960893, 0.040056905603099345, 0.2638167412159318, 0.007599003642098978, 0.06652432737448383, -0.019276203437281463, 0.03419540352964153, 0.039770259625179785, -0.10004245886941338, 0.057950789074513724, 0.24008185168107352, 0.08486655830832508, 0.2436370637216088, -0.4032841993418212, -0.20614951529488382, 0.0828418491097788, 0.17657545889172374, 0.16681850536487117, -0.03190220345924091, -0.3456329440361717, -0.00012352794146863744, -0.20230394533195473, -0.11855514986544019, -0.08717000868313739, 0.026286323916994862, 0.02893748056602716, -0.21551559816968316, 0.132921894750325, 0.06575593073668845, 0.09935020771485546, 0.04957024538372126, -0.029593356677246954, -0.01359880370485674, 0.15274438885939567, -0.025556593638611957, 0.027469853305471286, 0.14421717080363627, -0.16359727852007686, -0.1890437725289505, 0.33378477417055463, -0.06673667882649978, -0.1855902212102794, 0.22316474650061638, -0.16923257240301204, -0.0999510061293323, 0.03803189190026993, 0.14813275227788836, 0.11078097581169762, -0.139150535553199, 0.029649340442524083, -0.058344904178132616, 0.16938194951378843, 0.0599424474251767, 0.15624439036037074, 0.23251407830199847, 0.13593187675319818, 0.023764740222961538, 0.20904806946944315, -0.06899788435031143, -0.2172866558701369, -0.2882682279806532, -0.17233651026617736, -0.23534244491505282, 0.09671480060205795, -0.04644346867078437, -0.1737127884553047, 0.4252070211255664, 0.10813294129628856, 0.23075401988333194, 0.005461797132738866, 0.24943935008357382, 0.10931163287265615, 0.04429119160825697, 0.03321152503162415, 0.2535117474746787, 0.15381693803162003, 0.08856659787124954, -0.22020846387992302, 0.06459175631769337, 0.0317065038559829] |
1,802.03648 | Tur\'an, involution and shifting | We propose a strengthening of the conclusion in Tur\'an's (3,4)-conjecture in
terms of algebraic shifting, and show that its analogue for graphs does hold.
In another direction, we generalize the Mantel-Tur\'an theorem by weakening its
assumption: for any graph G on n vertices and any involution on its vertex set,
if for any 3-set S of the vertices, the number of edges in G spanned by S, plus
the number of edges in G spanned by the image of S under the involution, is at
least 2, then the number of edges in G is at least the Mantel-Tur\'an bound,
namely the number achieved by two disjoint cliques of sizes n/2 rounded up and
down.
| math.CO | we propose a strengthening of the conclusion in turans 34conjecture in terms of algebraic shifting and show that its analogue for graphs does hold in another direction we generalize the mantelturan theorem by weakening its assumption for any graph g on n vertices and any involution on its vertex set if for any 3set s of the vertices the number of edges in g spanned by s plus the number of edges in g spanned by the image of s under the involution is at least 2 then the number of edges in g is at least the mantelturan bound namely the number achieved by two disjoint cliques of sizes n2 rounded up and down | [['we', 'propose', 'a', 'strengthening', 'of', 'the', 'conclusion', 'in', 'turans', '34conjecture', 'in', 'terms', 'of', 'algebraic', 'shifting', 'and', 'show', 'that', 'its', 'analogue', 'for', 'graphs', 'does', 'hold', 'in', 'another', 'direction', 'we', 'generalize', 'the', 'mantelturan', 'theorem', 'by', 'weakening', 'its', 'assumption', 'for', 'any', 'graph', 'g', 'on', 'n', 'vertices', 'and', 'any', 'involution', 'on', 'its', 'vertex', 'set', 'if', 'for', 'any', '3set', 's', 'of', 'the', 'vertices', 'the', 'number', 'of', 'edges', 'in', 'g', 'spanned', 'by', 's', 'plus', 'the', 'number', 'of', 'edges', 'in', 'g', 'spanned', 'by', 'the', 'image', 'of', 's', 'under', 'the', 'involution', 'is', 'at', 'least', '2', 'then', 'the', 'number', 'of', 'edges', 'in', 'g', 'is', 'at', 'least', 'the', 'mantelturan', 'bound', 'namely', 'the', 'number', 'achieved', 'by', 'two', 'disjoint', 'cliques', 'of', 'sizes', 'n2', 'rounded', 'up', 'and', 'down']] | [-0.18525773545167926, 0.14286539134835557, -0.028643154730421623, -0.029992841252741136, -0.04837949519527943, -0.12911409759960538, 0.08969616002702553, 0.3361785026001079, -0.25669734723799464, -0.34193070146388244, 0.06766485406842548, -0.31887328575040946, -0.09724965231933831, 0.1196393970291995, -0.08659141102439857, -0.03338168827602723, 0.08036806125892326, 0.13011741343195485, -0.018159098471837103, -0.2868429849178418, 0.3225335701302226, -0.07263761427852192, 0.16462338494810475, 0.07172918833956439, 0.07651767750004572, 0.07479026948983249, -0.00015775784933274345, 0.0698858954295117, -0.11336986868816114, 0.08961312677794402, 0.21293026388489775, 0.1309188532925743, 0.268303092190763, -0.4170541363169572, -0.15152736433083192, 0.1900240836049696, 0.1142585262922304, 0.004249549298297747, 0.04751573571411427, -0.20140388568480766, 0.15147707904023783, -0.08592045505065471, -0.14206013847225613, 0.039416662334198396, 0.11783440785490841, -0.015843825132053877, -0.25814274209551513, -0.0024128996301442385, 0.14929629100619682, 0.0635977745945898, 0.06010228867463801, -0.17364663853472198, -0.0863118658734103, 0.07127667739405297, -0.020986289155968865, 0.0798336721834078, 0.03923001034334967, -0.13466799210956587, -0.13116856490211962, 0.3522024098118501, -0.06678570659382135, -0.1665940733343762, 0.12508309423667793, -0.16189492523920176, -0.1537601518669232, 0.1287133909396029, 0.09458751582341003, 0.14627860516442784, -0.05129808337993121, 0.16181821485692385, -0.11544649178748452, 0.09376897593860381, 0.1636106787648584, 0.0008124331242080578, 0.13580022220100676, 0.10766267205222643, 0.1643517617994803, 0.16966192886632467, -0.032049103818800564, 0.07165855948447383, -0.3497617998426514, -0.12408840214993688, -0.25708180320881574, 0.07115538851524304, -0.17671791390460254, -0.11516847187054477, 0.4118921065382892, 0.10149021683277429, 0.2436190380872826, 0.09163530624417555, 0.21407378049168205, 0.057980855760173915, 0.08274938897479192, 0.153851810821964, 0.1451966500026174, 0.1603735157072411, -0.07301949772019205, -0.1921322351429678, 0.05999751081565462, 0.17534753427441632] |
1,802.03649 | Low-Rank Methods in Event Detection and Subsampled Point-to-Subspace
Proximity Tests | Monitoring of streamed data to detect abnormal behaviour (variously known as
event detection, anomaly detection, change detection, or outlier detection)
underlies many applications of the Internet of Things. There, one often
collects data from a variety of sources, with asynchronous sampling, and
missing data. In this setting, one can predict abnormal behavior using low-rank
techniques. In particular, we assume that normal observations come from a
low-rank subspace, prior to being corrupted by a uniformly distributed noise.
Correspondingly, we aim to recover a representation of the subspace, and
perform event detection by running point-to-subspace distance query for
incoming data. In particular, we use a variant of low-rank factorisation, which
considers interval uncertainty sets around "known entries", on a suitable
flattening of the input data to obtain a low-rank model. On-line, we compute
the distance of incoming data to the low-rank normal subspace and update the
subspace to keep it consistent with the seasonal changes present. For the
distance computation, we suggest to consider subsampling. We bound the
one-sided error as a function of the number of coordinates employed using
techniques from learning theory and computational geometry. In our experimental
evaluation, we have tested the ability of the proposed algorithm to identify
samples of abnormal behavior in induction-loop data from Dublin, Ireland.
| cs.DS cs.CG | monitoring of streamed data to detect abnormal behaviour variously known as event detection anomaly detection change detection or outlier detection underlies many applications of the internet of things there one often collects data from a variety of sources with asynchronous sampling and missing data in this setting one can predict abnormal behavior using lowrank techniques in particular we assume that normal observations come from a lowrank subspace prior to being corrupted by a uniformly distributed noise correspondingly we aim to recover a representation of the subspace and perform event detection by running pointtosubspace distance query for incoming data in particular we use a variant of lowrank factorisation which considers interval uncertainty sets around known entries on a suitable flattening of the input data to obtain a lowrank model online we compute the distance of incoming data to the lowrank normal subspace and update the subspace to keep it consistent with the seasonal changes present for the distance computation we suggest to consider subsampling we bound the onesided error as a function of the number of coordinates employed using techniques from learning theory and computational geometry in our experimental evaluation we have tested the ability of the proposed algorithm to identify samples of abnormal behavior in inductionloop data from dublin ireland | [['monitoring', 'of', 'streamed', 'data', 'to', 'detect', 'abnormal', 'behaviour', 'variously', 'known', 'as', 'event', 'detection', 'anomaly', 'detection', 'change', 'detection', 'or', 'outlier', 'detection', 'underlies', 'many', 'applications', 'of', 'the', 'internet', 'of', 'things', 'there', 'one', 'often', 'collects', 'data', 'from', 'a', 'variety', 'of', 'sources', 'with', 'asynchronous', 'sampling', 'and', 'missing', 'data', 'in', 'this', 'setting', 'one', 'can', 'predict', 'abnormal', 'behavior', 'using', 'lowrank', 'techniques', 'in', 'particular', 'we', 'assume', 'that', 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1,802.0365 | Achieving Efficient Realization of Kalman Filter on CGRA through
Algorithm-Architecture Co-design | In this paper, we present efficient realization of Kalman Filter (KF) that
can achieve up to 65% of the theoretical peak performance of underlying
architecture platform. KF is realized using Modified Faddeeva Algorithm (MFA)
as a basic building block due to its versatility and REDEFINE Coarse Grained
Reconfigurable Architecture (CGRA) is used as a platform for experiments since
REDEFINE is capable of supporting realization of a set algorithmic compute
structures at run-time on a Reconfigurable Data-path (RDP). We perform several
hardware and software based optimizations in the realization of KF to achieve
116% improvement in terms of Gflops over the first realization of KF. Overall,
with the presented approach for KF, 4-105x performance improvement in terms of
Gflops/watt over several academically and commercially available realizations
of KF is attained. In REDEFINE, we show that our implementation is scalable and
the performance attained is commensurate with the underlying hardware resources
| cs.MS cs.AR | in this paper we present efficient realization of kalman filter kf that can achieve up to 65 of the theoretical peak performance of underlying architecture platform kf is realized using modified faddeeva algorithm mfa as a basic building block due to its versatility and redefine coarse grained reconfigurable architecture cgra is used as a platform for experiments since redefine is capable of supporting realization of a set algorithmic compute structures at runtime on a reconfigurable datapath rdp we perform several hardware and software based optimizations in the realization of kf to achieve 116 improvement in terms of gflops over the first realization of kf overall with the presented approach for kf 4105x performance improvement in terms of gflopswatt over several academically and commercially available realizations of kf is attained in redefine we show that our implementation is scalable and the performance attained is commensurate with the underlying hardware resources | [['in', 'this', 'paper', 'we', 'present', 'efficient', 'realization', 'of', 'kalman', 'filter', 'kf', 'that', 'can', 'achieve', 'up', 'to', '65', 'of', 'the', 'theoretical', 'peak', 'performance', 'of', 'underlying', 'architecture', 'platform', 'kf', 'is', 'realized', 'using', 'modified', 'faddeeva', 'algorithm', 'mfa', 'as', 'a', 'basic', 'building', 'block', 'due', 'to', 'its', 'versatility', 'and', 'redefine', 'coarse', 'grained', 'reconfigurable', 'architecture', 'cgra', 'is', 'used', 'as', 'a', 'platform', 'for', 'experiments', 'since', 'redefine', 'is', 'capable', 'of', 'supporting', 'realization', 'of', 'a', 'set', 'algorithmic', 'compute', 'structures', 'at', 'runtime', 'on', 'a', 'reconfigurable', 'datapath', 'rdp', 'we', 'perform', 'several', 'hardware', 'and', 'software', 'based', 'optimizations', 'in', 'the', 'realization', 'of', 'kf', 'to', 'achieve', '116', 'improvement', 'in', 'terms', 'of', 'gflops', 'over', 'the', 'first', 'realization', 'of', 'kf', 'overall', 'with', 'the', 'presented', 'approach', 'for', 'kf', '4105x', 'performance', 'improvement', 'in', 'terms', 'of', 'gflopswatt', 'over', 'several', 'academically', 'and', 'commercially', 'available', 'realizations', 'of', 'kf', 'is', 'attained', 'in', 'redefine', 'we', 'show', 'that', 'our', 'implementation', 'is', 'scalable', 'and', 'the', 'performance', 'attained', 'is', 'commensurate', 'with', 'the', 'underlying', 'hardware', 'resources']] | [-0.12928091692486526, -0.009048161676194443, -0.06875334512064787, -0.03658851085630296, -0.0818100259466273, -0.16139139894182114, 0.017838804448507977, 0.4464697762946221, -0.24173573359242967, -0.33168856467346886, 0.10658665519833842, -0.15919584008500082, -0.1858423546934343, 0.27794526113398577, -0.09920186583405814, 0.06993109038425295, 0.05581102370146058, 0.028768165137719463, -0.07894341434616395, -0.2890103796616549, 0.18566407249749614, 0.12777438628285923, 0.3089060344547663, 0.04718969639971211, 0.1425690377458326, -0.04852235834806453, 0.014683046878466534, -0.008086227089783328, -0.08126209157086811, 0.16708358089011666, 0.2878684511367307, 0.1558942206713648, 0.27380208748493445, -0.3886041473974851, -0.1476744519038491, 0.05172299146274659, 0.14440281677210853, 0.0667345976601339, -0.04440047916865002, -0.2631774213341241, 0.14847039117169492, -0.21418401474963772, -0.08915356076495545, -0.13029550502353623, -0.0439077942656523, -0.010851344769361202, -0.26521049190695695, -0.005378130412773808, 0.04382780334924826, 0.07938717237384235, -0.008704696090520686, -0.14508420292829238, 0.04848686313395061, 0.05546953258217891, -0.08085613912978284, 0.032787747415273474, 0.16424454846482323, -0.11029716901956252, -0.18182578307853356, 0.4022420854181857, -0.08610324690596678, -0.18254511592307165, 0.1687118415091489, -0.00904149209413476, -0.10696476837777458, 0.14477262102583474, 0.19167981596940473, 0.040704341268645146, -0.12467213356993287, 0.10112893360358588, -0.004557474598137511, 0.19272987746690223, 0.038616069663328596, 0.08696141774212388, 0.12043421524356597, 0.279106447094032, 0.07021698883004687, 0.1710564050757278, -0.05923174673021217, -0.09614900302541493, -0.2753421711993822, -0.19919762951699463, -0.2055774314259808, -0.032099772795304855, -0.05932303735745655, -0.14394340195419342, 0.406641909582037, 0.23814248869736754, 0.14894588914270374, 0.09162767276142698, 0.325424394447583, 0.04328422746442123, 0.10105804215827202, 0.08836442990719366, 0.1712748702691012, 0.08559858175372151, 0.08641479104423795, -0.19615449371898697, 0.044504160593213465, 0.01317030518485636] |
1,802.03651 | Deep learning with t-exponential Bayesian kitchen sinks | Bayesian learning has been recently considered as an effective means of
accounting for uncertainty in trained deep network parameters. This is of
crucial importance when dealing with small or sparse training datasets. On the
other hand, shallow models that compute weighted sums of their inputs, after
passing them through a bank of arbitrary randomized nonlinearities, have been
recently shown to enjoy good test error bounds that depend on the number of
nonlinearities. Inspired from these advances, in this paper we examine novel
deep network architectures, where each layer comprises a bank of arbitrary
nonlinearities, linearly combined using multiple alternative sets of weights.
We effect model training by means of approximate inference based on a
t-divergence measure; this generalizes the Kullback-Leibler divergence in the
context of the t-exponential family of distributions. We adopt the
t-exponential family since it can more flexibly accommodate real-world data,
that entail outliers and distributions with fat tails, compared to conventional
Gaussian model assumptions. We extensively evaluate our approach using several
challenging benchmarks, and provide comparative results to related
state-of-the-art techniques.
| cs.LG stat.ML | bayesian learning has been recently considered as an effective means of accounting for uncertainty in trained deep network parameters this is of crucial importance when dealing with small or sparse training datasets on the other hand shallow models that compute weighted sums of their inputs after passing them through a bank of arbitrary randomized nonlinearities have been recently shown to enjoy good test error bounds that depend on the number of nonlinearities inspired from these advances in this paper we examine novel deep network architectures where each layer comprises a bank of arbitrary nonlinearities linearly combined using multiple alternative sets of weights we effect model training by means of approximate inference based on a tdivergence measure this generalizes the kullbackleibler divergence in the context of the texponential family of distributions we adopt the texponential family since it can more flexibly accommodate realworld data that entail outliers and distributions with fat tails compared to conventional gaussian model assumptions we extensively evaluate our approach using several challenging benchmarks and provide comparative results to related stateoftheart techniques | [['bayesian', 'learning', 'has', 'been', 'recently', 'considered', 'as', 'an', 'effective', 'means', 'of', 'accounting', 'for', 'uncertainty', 'in', 'trained', 'deep', 'network', 'parameters', 'this', 'is', 'of', 'crucial', 'importance', 'when', 'dealing', 'with', 'small', 'or', 'sparse', 'training', 'datasets', 'on', 'the', 'other', 'hand', 'shallow', 'models', 'that', 'compute', 'weighted', 'sums', 'of', 'their', 'inputs', 'after', 'passing', 'them', 'through', 'a', 'bank', 'of', 'arbitrary', 'randomized', 'nonlinearities', 'have', 'been', 'recently', 'shown', 'to', 'enjoy', 'good', 'test', 'error', 'bounds', 'that', 'depend', 'on', 'the', 'number', 'of', 'nonlinearities', 'inspired', 'from', 'these', 'advances', 'in', 'this', 'paper', 'we', 'examine', 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1,802.03652 | Approximating Sparse Graphs: The Random Overlapping Communities Model | How can we approximate sparse graphs and sequences of sparse graphs (with
unbounded average degree)? We consider convergence in the first $k$ moments of
the graph spectrum (equivalent to the numbers of closed $k$-walks)
appropriately normalized. We introduce a simple, easy to sample, random graph
model that captures the limiting spectra of many sequences of interest,
including the sequence of hypercube graphs. The Random Overlapping Communities
(ROC) model is specified by a distribution on pairs $(s,q)$, $s \in
\mathbb{Z}_+, q \in (0,1]$. A graph on $n$ vertices with average degree $d$ is
generated by repeatedly picking pairs $(s,q)$ from the distribution, adding an
Erd\H{o}s-R\'{e}nyi random graph of edge density $q$ on a subset of vertices
chosen by including each vertex with probability $s/n$, and repeating this
process so that the expected degree is $d$. Our proof of convergence to a ROC
random graph is based on the Stieltjes moment condition. We also show that the
model is an effective approximation for individual graphs. For almost all
possible triangle-to-edge and four-cycle-to-edge ratios, there exists a pair
$(s,q)$ such that the ROC model with this single community type produces graphs
with both desired ratios, a property that cannot be achieved by stochastic
block models of bounded description size. Moreover, ROC graphs exhibit an
inverse relationship between degree and clustering coefficient, a
characteristic of many real-world networks.
| math.CO cs.DM | how can we approximate sparse graphs and sequences of sparse graphs with unbounded average degree we consider convergence in the first k moments of the graph spectrum equivalent to the numbers of closed kwalks appropriately normalized we introduce a simple easy to sample random graph model that captures the limiting spectra of many sequences of interest including the sequence of hypercube graphs the random overlapping communities roc model is specified by a distribution on pairs sq s in mathbbz_ q in 01 a graph on n vertices with average degree d is generated by repeatedly picking pairs sq from the distribution adding an erdhosrenyi random graph of edge density q on a subset of vertices chosen by including each vertex with probability sn and repeating this process so that the expected degree is d our proof of convergence to a roc random graph is based on the stieltjes moment condition we also show that the model is an effective approximation for individual graphs for almost all possible triangletoedge and fourcycletoedge ratios there exists a pair sq such that the roc model with this single community type produces graphs with both desired ratios a property that cannot be achieved by stochastic block models of bounded description size moreover roc graphs exhibit an inverse relationship between degree and clustering coefficient a characteristic of many realworld networks | [['how', 'can', 'we', 'approximate', 'sparse', 'graphs', 'and', 'sequences', 'of', 'sparse', 'graphs', 'with', 'unbounded', 'average', 'degree', 'we', 'consider', 'convergence', 'in', 'the', 'first', 'k', 'moments', 'of', 'the', 'graph', 'spectrum', 'equivalent', 'to', 'the', 'numbers', 'of', 'closed', 'kwalks', 'appropriately', 'normalized', 'we', 'introduce', 'a', 'simple', 'easy', 'to', 'sample', 'random', 'graph', 'model', 'that', 'captures', 'the', 'limiting', 'spectra', 'of', 'many', 'sequences', 'of', 'interest', 'including', 'the', 'sequence', 'of', 'hypercube', 'graphs', 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1,802.03653 | On Symplectic Optimization | Accelerated gradient methods have had significant impact in machine learning
-- in particular the theoretical side of machine learning -- due to their
ability to achieve oracle lower bounds. But their heuristic construction has
hindered their full integration into the practical machine-learning algorithmic
toolbox, and has limited their scope. In this paper we build on recent work
which casts acceleration as a phenomenon best explained in continuous time, and
we augment that picture by providing a systematic methodology for converting
continuous-time dynamics into discrete-time algorithms while retaining oracle
rates. Our framework is based on ideas from Hamiltonian dynamical systems and
symplectic integration. These ideas have had major impact in many areas in
applied mathematics, but have not yet been seen to have a relationship with
optimization.
| stat.CO | accelerated gradient methods have had significant impact in machine learning in particular the theoretical side of machine learning due to their ability to achieve oracle lower bounds but their heuristic construction has hindered their full integration into the practical machinelearning algorithmic toolbox and has limited their scope in this paper we build on recent work which casts acceleration as a phenomenon best explained in continuous time and we augment that picture by providing a systematic methodology for converting continuoustime dynamics into discretetime algorithms while retaining oracle rates our framework is based on ideas from hamiltonian dynamical systems and symplectic integration these ideas have had major impact in many areas in applied mathematics but have not yet been seen to have a relationship with optimization | [['accelerated', 'gradient', 'methods', 'have', 'had', 'significant', 'impact', 'in', 'machine', 'learning', 'in', 'particular', 'the', 'theoretical', 'side', 'of', 'machine', 'learning', 'due', 'to', 'their', 'ability', 'to', 'achieve', 'oracle', 'lower', 'bounds', 'but', 'their', 'heuristic', 'construction', 'has', 'hindered', 'their', 'full', 'integration', 'into', 'the', 'practical', 'machinelearning', 'algorithmic', 'toolbox', 'and', 'has', 'limited', 'their', 'scope', 'in', 'this', 'paper', 'we', 'build', 'on', 'recent', 'work', 'which', 'casts', 'acceleration', 'as', 'a', 'phenomenon', 'best', 'explained', 'in', 'continuous', 'time', 'and', 'we', 'augment', 'that', 'picture', 'by', 'providing', 'a', 'systematic', 'methodology', 'for', 'converting', 'continuoustime', 'dynamics', 'into', 'discretetime', 'algorithms', 'while', 'retaining', 'oracle', 'rates', 'our', 'framework', 'is', 'based', 'on', 'ideas', 'from', 'hamiltonian', 'dynamical', 'systems', 'and', 'symplectic', 'integration', 'these', 'ideas', 'have', 'had', 'major', 'impact', 'in', 'many', 'areas', 'in', 'applied', 'mathematics', 'but', 'have', 'not', 'yet', 'been', 'seen', 'to', 'have', 'a', 'relationship', 'with', 'optimization']] | [-0.027773198371212327, 0.019442749485718988, -0.14715218225929647, 0.05607148817941174, -0.12713058124063537, -0.15995526539946667, 0.0836762175904662, 0.44796296743856323, -0.25703433426021927, -0.35209092446753093, 0.12066608572175967, -0.2023112856174609, -0.16030218255435746, 0.2483661764924745, -0.13689635542311496, 0.10492121814299495, 0.0610634776493234, -0.016053855231392286, -0.0670354995001558, -0.28678136211331756, 0.24082865395710892, 0.07492930884778147, 0.29167868872924196, 0.06520216329109614, 0.0995724884710545, -0.02924239732730653, -0.049403484501395256, 0.022748092166899193, -0.09483218366767687, 0.15970013711616518, 0.28943652692466837, 0.19368537995896692, 0.3862821987619804, -0.470386202522223, -0.25730907124436214, 0.11383920370210562, 0.19902359196285327, 0.14276525527032302, -0.1039557253900585, -0.2787583092802144, 0.04396623127526724, -0.18479406114126887, -0.05462987514047493, -0.13273754373433128, 0.0028522944175488046, 0.007071516446059479, -0.16247031680541113, 0.02988911726166524, 0.10996395149225963, 0.0891128002643405, -0.022259318889419157, -0.14722740093636658, 0.07334918135799648, 0.12870369659316155, 0.06546579030409996, 0.05890671034989458, 0.12784581611706544, -0.10067787264556353, -0.1899864473624245, 0.3633734793026721, -0.016780402762026737, -0.19374611127533437, 0.22766240448653396, -0.08159819892757843, -0.18666251577932627, 0.12116830249226862, 0.2583306013947285, 0.07083508447037949, -0.1603514217713382, 0.12495367317845399, 0.01794187181569155, 0.11970420547520681, 0.022953753539871787, 0.04961220072882791, 0.18401136968262313, 0.20780620929003962, 0.03778920242996631, 0.09884627378027465, -0.0048484518342921815, -0.1692885055296844, -0.18845457847102456, -0.10199179016865767, -0.1642537386487088, 0.013991130917871018, -0.04600942380420747, -0.14424048015573662, 0.3662301012269792, 0.2144486714255876, 0.17377818749105978, 0.07676991074432378, 0.3133697138588515, 0.10995486900661021, 0.08124362041933401, 0.09142610405556738, 0.2740504631618669, 0.11570416585609858, 0.1228167412881439, -0.15129175918334495, 0.13389813927126176, 0.05543687337100686] |
1,802.03654 | Beyond the One Step Greedy Approach in Reinforcement Learning | The famous Policy Iteration algorithm alternates between policy improvement
and policy evaluation. Implementations of this algorithm with several variants
of the latter evaluation stage, e.g, $n$-step and trace-based returns, have
been analyzed in previous works. However, the case of multiple-step lookahead
policy improvement, despite the recent increase in empirical evidence of its
strength, has to our knowledge not been carefully analyzed yet. In this work,
we introduce the first such analysis. Namely, we formulate variants of
multiple-step policy improvement, derive new algorithms using these definitions
and prove their convergence. Moreover, we show that recent prominent
Reinforcement Learning algorithms are, in fact, instances of our framework. We
thus shed light on their empirical success and give a recipe for deriving new
algorithms for future study.
| cs.AI cs.LG stat.ML | the famous policy iteration algorithm alternates between policy improvement and policy evaluation implementations of this algorithm with several variants of the latter evaluation stage eg nstep and tracebased returns have been analyzed in previous works however the case of multiplestep lookahead policy improvement despite the recent increase in empirical evidence of its strength has to our knowledge not been carefully analyzed yet in this work we introduce the first such analysis namely we formulate variants of multiplestep policy improvement derive new algorithms using these definitions and prove their convergence moreover we show that recent prominent reinforcement learning algorithms are in fact instances of our framework we thus shed light on their empirical success and give a recipe for deriving new algorithms for future study | [['the', 'famous', 'policy', 'iteration', 'algorithm', 'alternates', 'between', 'policy', 'improvement', 'and', 'policy', 'evaluation', 'implementations', 'of', 'this', 'algorithm', 'with', 'several', 'variants', 'of', 'the', 'latter', 'evaluation', 'stage', 'eg', 'nstep', 'and', 'tracebased', 'returns', 'have', 'been', 'analyzed', 'in', 'previous', 'works', 'however', 'the', 'case', 'of', 'multiplestep', 'lookahead', 'policy', 'improvement', 'despite', 'the', 'recent', 'increase', 'in', 'empirical', 'evidence', 'of', 'its', 'strength', 'has', 'to', 'our', 'knowledge', 'not', 'been', 'carefully', 'analyzed', 'yet', 'in', 'this', 'work', 'we', 'introduce', 'the', 'first', 'such', 'analysis', 'namely', 'we', 'formulate', 'variants', 'of', 'multiplestep', 'policy', 'improvement', 'derive', 'new', 'algorithms', 'using', 'these', 'definitions', 'and', 'prove', 'their', 'convergence', 'moreover', 'we', 'show', 'that', 'recent', 'prominent', 'reinforcement', 'learning', 'algorithms', 'are', 'in', 'fact', 'instances', 'of', 'our', 'framework', 'we', 'thus', 'shed', 'light', 'on', 'their', 'empirical', 'success', 'and', 'give', 'a', 'recipe', 'for', 'deriving', 'new', 'algorithms', 'for', 'future', 'study']] | [-0.05261353488951441, -0.04167407208062228, -0.12092665464226757, 0.07251476196219184, -0.08661977034963426, -0.13468104045117094, 0.12426874278143289, 0.49449608405871737, -0.21888286758011447, -0.33473969987197405, 0.11377479274561929, -0.21745759565683623, -0.22154363174171698, 0.23457150407803937, -0.11121334367236423, 0.08769350501872043, 0.10276056854655186, 0.010870112290215348, -0.0762882385939637, -0.31671856509544133, 0.27446934641263776, 0.0490311338085561, 0.3175178763152461, 0.06269649211167087, 0.09897915635918898, -0.008312693507891268, -0.0705732355014451, 0.016580751296245463, -0.1337687954506222, 0.14448320141602908, 0.249946728309301, 0.22711063086828817, 0.36967393569648266, -0.4000678821016223, -0.1955438327239526, 0.12631520448297623, 0.1607995722840696, 0.12210326266828023, -0.09057149557667153, -0.27442488636663787, 0.08450571755407334, -0.1728244285951669, -0.04823468465756084, -0.14566332223494688, -0.007854924315676814, 0.036263433120761725, -0.26690308042546557, 0.013028781410426862, 0.08878855397098608, 0.07348431538106993, -0.04417888673807254, -0.19308026804919204, 0.1021025204587908, 0.13125498195163784, 0.11733022272857206, 0.02284194338097868, 0.10023594120397203, -0.091301865639135, -0.2330614879573395, 0.304123081627392, -0.05275217455721671, -0.12100289894207832, 0.15968606594739662, -0.04076391854511213, -0.20139732889102532, 0.06669532976323558, 0.21512758293242623, 0.13374177481220553, -0.12196785964882903, 0.05886810448306102, -0.0718555214604543, 0.10989430123534534, 0.04053475759921581, 0.03340591892464868, 0.10165570680833151, 0.22778505056105072, 0.07373215081808608, 0.1474655909508827, -0.03325448523936493, -0.15733963819108002, -0.2418086891393027, -0.1490895565211465, -0.13149433434685512, -0.028958728814740747, -0.09757702059681422, -0.12321842975178433, 0.37193932576525596, 0.21532906012608663, 0.17485746372521163, 0.13489333477111592, 0.32922336973640465, 0.10925395689827705, 0.0288307422005962, 0.13145687936564848, 0.264469652209476, 0.04449835042619417, 0.12775673227940476, -0.20240254176690453, 0.14158372166604105, 0.0762258269073021] |
1,802.03655 | Sparse Dowker Nerves | We propose sparse versions of filtered simplicial complexes used to compte
persistent homology of point clouds and of networks. In particular we extend a
slight variation of the Sparse \v{C}ech Complex of Cavanna, Jahanseir and
Sheehy from point clouds in Cartesian space to point clouds in arbitrary metric
spaces. Along the way we formulate interleaving in terms of strict
$2$-categories, and we introduce the concept of Dowker dissimilarities that can
be considered as a common generalization of metric spaces and networks.
| math.AT | we propose sparse versions of filtered simplicial complexes used to compte persistent homology of point clouds and of networks in particular we extend a slight variation of the sparse vcech complex of cavanna jahanseir and sheehy from point clouds in cartesian space to point clouds in arbitrary metric spaces along the way we formulate interleaving in terms of strict 2categories and we introduce the concept of dowker dissimilarities that can be considered as a common generalization of metric spaces and networks | [['we', 'propose', 'sparse', 'versions', 'of', 'filtered', 'simplicial', 'complexes', 'used', 'to', 'compte', 'persistent', 'homology', 'of', 'point', 'clouds', 'and', 'of', 'networks', 'in', 'particular', 'we', 'extend', 'a', 'slight', 'variation', 'of', 'the', 'sparse', 'vcech', 'complex', 'of', 'cavanna', 'jahanseir', 'and', 'sheehy', 'from', 'point', 'clouds', 'in', 'cartesian', 'space', 'to', 'point', 'clouds', 'in', 'arbitrary', 'metric', 'spaces', 'along', 'the', 'way', 'we', 'formulate', 'interleaving', 'in', 'terms', 'of', 'strict', '2categories', 'and', 'we', 'introduce', 'the', 'concept', 'of', 'dowker', 'dissimilarities', 'that', 'can', 'be', 'considered', 'as', 'a', 'common', 'generalization', 'of', 'metric', 'spaces', 'and', 'networks']] | [-0.15815130900591612, 0.028138719463744497, -0.07077903958344006, 0.1359368059312618, -0.05505216414038139, -0.05374154793923792, 0.05756219784151527, 0.41919096002850353, -0.33451549242003054, -0.2035927209955982, 0.058819951849129005, -0.20860821686543618, -0.2284402523979639, 0.11756246828155804, -0.21133262628712987, -0.005750509415151952, 0.06421884546009234, 0.009529479985608708, -0.08393810059627684, -0.2277251259523998, 0.43923731939277694, 0.02953883410354817, 0.21371568310298497, 0.02239270595527148, 0.14380212297333003, -0.005581557332196191, -0.04209401585821864, 0.09910689205295241, -0.13014316906951018, 0.1987008117190916, 0.2852484736874511, 0.12980697091220866, 0.21193923041978968, -0.40074682004655465, -0.18476560538563924, 0.18266001472226048, 0.12712438212253765, 0.05275287451753039, 0.026862237664363995, -0.32462571155798586, 0.12592565254720894, -0.1866354121932689, -0.08277170266661368, -0.10486675544252878, -0.009155687628478943, 0.061463643861580874, -0.21940227628603012, 0.012162667380858071, 0.05231776683815295, 0.07271635492094292, -0.06649662485888487, -0.07702508073630213, -0.006739448767768432, 0.11916305440702016, -0.04810125617121783, 0.054960426066919595, 0.12070549621329277, -0.0825706175322161, -0.1696057454741831, 0.4304043203002856, -0.08985967642945956, -0.22477404497375217, 0.18114200375879866, -0.10333578753037544, -0.1822460513220086, 0.08458742563011526, 0.15888604495226394, 0.14976372632280557, -0.06271768926607468, 0.09887944778745782, -0.06561287361656941, 0.06573104308409722, 0.12048849548342862, 0.06251550374458296, 0.15800222400959016, 0.11875748799088164, 0.14044908962295025, 0.20576273646246926, -0.0928143356408028, -0.12330477862011735, -0.25835593768595894, -0.19342373542581934, -0.14695755314506306, 0.05128725570968435, -0.16339552289525583, -0.21133077315561757, 0.3413463666433869, 0.13804200632922067, 0.2175750292180389, 0.10406595186766567, 0.2771058691123241, 0.005279933265999809, 0.050357713540898096, 0.05170972405378766, 0.12535239279836038, 0.18039604136130855, 0.08953669074557344, -0.05145231981754754, 0.017503763159996348, 0.17404342317788662] |
1,802.03656 | TextZoo, a New Benchmark for Reconsidering Text Classification | Text representation is a fundamental concern in Natural Language Processing,
especially in text classification. Recently, many neural network approaches
with delicate representation model (e.g. FASTTEXT, CNN, RNN and many hybrid
models with attention mechanisms) claimed that they achieved state-of-art in
specific text classification datasets. However, it lacks an unified benchmark
to compare these models and reveals the advantage of each sub-components for
various settings. We re-implement more than 20 popular text representation
models for classification in more than 10 datasets. In this paper, we
reconsider the text classification task in the perspective of neural network
and get serval effects with analysis of the above results.
| cs.CL | text representation is a fundamental concern in natural language processing especially in text classification recently many neural network approaches with delicate representation model eg fasttext cnn rnn and many hybrid models with attention mechanisms claimed that they achieved stateofart in specific text classification datasets however it lacks an unified benchmark to compare these models and reveals the advantage of each subcomponents for various settings we reimplement more than 20 popular text representation models for classification in more than 10 datasets in this paper we reconsider the text classification task in the perspective of neural network and get serval effects with analysis of the above results | [['text', 'representation', 'is', 'a', 'fundamental', 'concern', 'in', 'natural', 'language', 'processing', 'especially', 'in', 'text', 'classification', 'recently', 'many', 'neural', 'network', 'approaches', 'with', 'delicate', 'representation', 'model', 'eg', 'fasttext', 'cnn', 'rnn', 'and', 'many', 'hybrid', 'models', 'with', 'attention', 'mechanisms', 'claimed', 'that', 'they', 'achieved', 'stateofart', 'in', 'specific', 'text', 'classification', 'datasets', 'however', 'it', 'lacks', 'an', 'unified', 'benchmark', 'to', 'compare', 'these', 'models', 'and', 'reveals', 'the', 'advantage', 'of', 'each', 'subcomponents', 'for', 'various', 'settings', 'we', 'reimplement', 'more', 'than', '20', 'popular', 'text', 'representation', 'models', 'for', 'classification', 'in', 'more', 'than', '10', 'datasets', 'in', 'this', 'paper', 'we', 'reconsider', 'the', 'text', 'classification', 'task', 'in', 'the', 'perspective', 'of', 'neural', 'network', 'and', 'get', 'serval', 'effects', 'with', 'analysis', 'of', 'the', 'above', 'results']] | [-0.030085023554662863, -0.056086952433161925, -0.04631978846598594, 0.11253289458303091, -0.09737126597735499, -0.21382103770350416, -0.006938927220402374, 0.44054546494569097, -0.24482925583918888, -0.3399105148478633, 0.0762708902891193, -0.30548721731063866, -0.1974357250102219, 0.24536495751789994, -0.13042776415213234, 0.057685581639054274, 0.15002312789715472, 0.07331058335756617, -0.07284898689083223, -0.2833656094613529, 0.29740988020785153, 0.009374406024636257, 0.3752896212750957, 0.017402822817564896, 0.07759317248904457, -0.05794818639489157, -0.06468250622884149, -0.05418326966630827, -0.04426798620517643, 0.1834534856357745, 0.3365294162408169, 0.1902455982796493, 0.2967513150491175, -0.41606822620545114, -0.2805838876270822, 0.09749674079052749, 0.18179636706329239, 0.11853715837517353, -0.014011960686461645, -0.31924768284495386, 0.11574925007742076, -0.18654740964993835, 0.07855524619392651, -0.15256390726814667, 0.039676734095528014, -0.06568909736372353, -0.23384133011346594, 0.046243922374699084, 0.13119860662679586, 0.10619416573040542, -0.050681389333857664, -0.16289443111135846, 0.05461383214819112, 0.1347826286839942, 0.07239818147694072, 0.07483293509022111, 0.08787421239983467, -0.2558242958016871, -0.15447134533564427, 0.4163632870962222, -0.05370476990855033, -0.22845837606915406, 0.24227969673950048, -0.020809790574102885, -0.23566996236996993, 0.038907172357929606, 0.20626910217328087, 0.09635042378545872, -0.16153028461461266, -0.0023838524035887706, -0.04962054893507489, 0.20211052244883917, 0.05836219976523093, 0.013699436325225092, 0.15977592202169555, 0.3265327312761829, -0.03421029566144127, 0.1000340146256522, -0.08999599258725842, -0.07887369707404147, -0.1615027073549018, -0.09349903421742575, -0.1243957859419641, -0.029035033018416947, -0.12796550210789845, -0.1188376251075949, 0.4171624949290639, 0.2481738047032628, 0.20007855689507864, 0.0969846011627288, 0.31772961472826344, 0.017898295615755375, 0.12223752818646885, 0.08049171886218356, 0.1370211373748524, 0.029917120126386483, 0.14725736094017824, -0.06061542737519457, 0.04005146901167574, 0.041906081175520304] |
1,802.03657 | Generalized Fitch Graphs: Edge-labeled Graphs that are explained by
Edge-labeled Trees | Fitch graphs $G=(X,E)$ are di-graphs that are explained by
$\{\otimes,1\}$-edge-labeled rooted trees with leaf set $X$: there is an arc
$xy\in E$ if and only if the unique path in $T$ that connects the least common
ancestor $\textrm{lca}(x,y)$ of $x$ and $y$ with $y$ contains at least one edge
with label $1$. In practice, Fitch graphs represent xenology relations, i.e.,
pairs of genes $x$ and $y$ for which a horizontal gene transfer happened along
the path from $\textrm{lca}(x,y)$ to $y$. In this contribution, we generalize
the concept of xenology and Fitch graphs and consider complete di-graphs
$K_{|X|}$ with vertex set $X$ and a map $\epsilon$ that assigns to each arc
$xy$ a unique label $\epsilon(x,y)\in M\cup \{\otimes\}$, where $M$ denotes an
arbitrary set of symbols. A di-graph $(K_{|X|},\epsilon)$ is a generalized
Fitch graph if there is an $M\cup \{\otimes\}$-edge-labeled tree $(T,\lambda)$
that can explain $(K_{|X|},\epsilon)$. We provide a simple characterization of
generalized Fitch graphs $(K_{|X|},\epsilon)$ and give an $O(|X|^2)$-time
algorithm for their recognition as well as for the reconstruction of the unique
least resolved phylogenetic tree that explains $(K_{|X|},\epsilon)$.
| cs.DM math.CO | fitch graphs gxe are digraphs that are explained by otimes1edgelabeled rooted trees with leaf set x there is an arc xyin e if and only if the unique path in t that connects the least common ancestor textrmlcaxy of x and y with y contains at least one edge with label 1 in practice fitch graphs represent xenology relations ie pairs of genes x and y for which a horizontal gene transfer happened along the path from textrmlcaxy to y in this contribution we generalize the concept of xenology and fitch graphs and consider complete digraphs k_x with vertex set x and a map epsilon that assigns to each arc xy a unique label epsilonxyin mcup otimes where m denotes an arbitrary set of symbols a digraph k_xepsilon is a generalized fitch graph if there is an mcup otimesedgelabeled tree tlambda that can explain k_xepsilon we provide a simple characterization of generalized fitch graphs k_xepsilon and give an ox2time algorithm for their recognition as well as for the reconstruction of the unique least resolved phylogenetic tree that explains k_xepsilon | [['fitch', 'graphs', 'gxe', 'are', 'digraphs', 'that', 'are', 'explained', 'by', 'otimes1edgelabeled', 'rooted', 'trees', 'with', 'leaf', 'set', 'x', 'there', 'is', 'an', 'arc', 'xyin', 'e', 'if', 'and', 'only', 'if', 'the', 'unique', 'path', 'in', 't', 'that', 'connects', 'the', 'least', 'common', 'ancestor', 'textrmlcaxy', 'of', 'x', 'and', 'y', 'with', 'y', 'contains', 'at', 'least', 'one', 'edge', 'with', 'label', '1', 'in', 'practice', 'fitch', 'graphs', 'represent', 'xenology', 'relations', 'ie', 'pairs', 'of', 'genes', 'x', 'and', 'y', 'for', 'which', 'a', 'horizontal', 'gene', 'transfer', 'happened', 'along', 'the', 'path', 'from', 'textrmlcaxy', 'to', 'y', 'in', 'this', 'contribution', 'we', 'generalize', 'the', 'concept', 'of', 'xenology', 'and', 'fitch', 'graphs', 'and', 'consider', 'complete', 'digraphs', 'k_x', 'with', 'vertex', 'set', 'x', 'and', 'a', 'map', 'epsilon', 'that', 'assigns', 'to', 'each', 'arc', 'xy', 'a', 'unique', 'label', 'epsilonxyin', 'mcup', 'otimes', 'where', 'm', 'denotes', 'an', 'arbitrary', 'set', 'of', 'symbols', 'a', 'digraph', 'k_xepsilon', 'is', 'a', 'generalized', 'fitch', 'graph', 'if', 'there', 'is', 'an', 'mcup', 'otimesedgelabeled', 'tree', 'tlambda', 'that', 'can', 'explain', 'k_xepsilon', 'we', 'provide', 'a', 'simple', 'characterization', 'of', 'generalized', 'fitch', 'graphs', 'k_xepsilon', 'and', 'give', 'an', 'ox2time', 'algorithm', 'for', 'their', 'recognition', 'as', 'well', 'as', 'for', 'the', 'reconstruction', 'of', 'the', 'unique', 'least', 'resolved', 'phylogenetic', 'tree', 'that', 'explains', 'k_xepsilon']] | [-0.16018178153680798, 0.11738409483374629, -0.03767495880420239, 0.05439093989667326, -0.090629492498762, -0.19047453172095766, 0.056833509829519804, 0.41312806041254474, -0.30288554374882254, -0.22792828060764647, 0.033441510791365446, -0.32601715969406286, -0.1421031885177457, 0.11377446481727893, -0.11402986354848661, -0.051425844261347387, 0.11523770514574182, 0.12934329713754641, -0.004362834842251129, -0.2205124335880647, 0.3090650813298763, -0.042125292360578055, 0.1586618900105271, 0.023232783598696134, 0.18976796656911601, 0.034140698977805285, 0.00971077470515311, 0.06890163736988526, -0.15375610012154806, 0.0787262669602958, 0.29583654194569614, 0.19310486592309323, 0.2155742697374669, -0.32884642352872245, -0.14966912399385277, 0.1867642754236656, 0.117979866589791, 0.007184989448243784, 0.019907524984200738, -0.21065962265886237, 0.15275261789957173, -0.10996335171259983, -0.06579894574111425, -0.021600711636509035, 0.11057935408558804, 0.002555631311082771, -0.2855773871559973, 0.0006604909390814042, 0.11084653482156015, 0.04515050314474485, 0.0534832908858066, -0.1693691377382661, -0.10638809718751494, 0.10631962452013842, -0.04654921106980694, 0.15592255642317301, 0.0050349590648504005, -0.0776176415356899, -0.1818811226706155, 0.3458636910089351, -0.03305824531102284, -0.17750440903460188, 0.13116350220354822, -0.13611193513901462, -0.1953756834240361, 0.11430078995214894, 0.08486463164243137, 0.14039743132620103, -0.13513361976207422, 0.12394982768051768, -0.1071448076280531, 0.11208640739343242, 0.11721129986226042, -0.022137589982813063, 0.15411042048963597, 0.15182488631306357, 0.14277414860193097, 0.11396214000764976, -0.041657754020883855, 0.03872648294950496, -0.30859797031247205, -0.13944444733784894, -0.18721384933013793, 0.07613188234182086, -0.13512709293714012, -0.21815669253425446, 0.3591045520639833, 0.09072424251736932, 0.25434352793714643, 0.09278974706923449, 0.18205215992955115, 0.043043755222905315, 0.020593767933213727, 0.15597245673988167, 0.0610895689510918, 0.16567543830394185, -0.022404453321494634, -0.1377810975368474, 0.10360421579347309, 0.1345629122271741] |
1,802.03658 | A geometric approach to integer factorization | We give a geometric approach to integer factorization. This approach is based
on special approximations of segments of the curve that is represented by
$y=n/x$, where $n$ is the integer whose factorization we need.
| math.NT cs.IT math.AG math.IT math.NA | we give a geometric approach to integer factorization this approach is based on special approximations of segments of the curve that is represented by ynx where n is the integer whose factorization we need | [['we', 'give', 'a', 'geometric', 'approach', 'to', 'integer', 'factorization', 'this', 'approach', 'is', 'based', 'on', 'special', 'approximations', 'of', 'segments', 'of', 'the', 'curve', 'that', 'is', 'represented', 'by', 'ynx', 'where', 'n', 'is', 'the', 'integer', 'whose', 'factorization', 'we', 'need']] | [-0.15569750702177937, 0.06699854869282607, -0.1868064838151137, 0.010901482825195699, -0.1134951333515346, -0.1842033777696391, 0.018807037271361685, 0.3832111569742362, -0.27942351940454857, -0.2655203433876688, 0.1159195877668081, -0.24172663023328464, -0.23016049719246273, 0.13722567181244041, -0.06038797588906053, 0.06096676974134012, 0.036900212367375694, 0.05928426218981093, -0.07503054487857629, -0.26605018116815976, 0.3732416560365395, -0.05293934419751167, 0.17922112955288452, 0.06403806653565192, 0.13451205761256543, 0.053813313162236504, -0.0006897128328229442, 0.05717759443954988, -0.1043443896917531, 0.1916457609640378, 0.2789287871935151, 0.14397360236299309, 0.23519491788112756, -0.39925026109047007, -0.12404312589911348, 0.124889173603151, 0.1366022256993209, 0.07776149249438083, 0.024358203842756197, -0.19546665042413003, 0.17426745689502268, -0.10926863844647552, -0.07779998940443902, -0.09496736564588817, 0.0691484132160743, 0.00881580930824081, -0.33852005704785837, 0.0039962591307068415, 0.09940643275551724, 0.04584962660164544, 0.006041579597601385, -0.16814447290292292, 0.1115568359805779, 0.043220609849826855, 0.008100572133888349, 0.055358668619936165, 0.03467935241871711, -0.047004709210754794, -0.1167480000576964, 0.4126842175469254, -0.04262600793982997, -0.28098616613583133, 0.0653693983648819, -0.08827501460627625, -0.14458750225039144, 0.12267719397222566, 0.16834292786590982, 0.18206222027991759, -0.06569328607822006, 0.14515727907983642, -0.1336887733672153, 0.1532476761124351, 0.018112870892792038, -0.029127964199605314, 0.1670153049018347, 0.15014621152570753, 0.04728383247768789, 0.16687487438321114, -0.03842302910616678, -0.09700513868169351, -0.350711299840248, -0.132160880123124, -0.27219559478037286, 0.11082225064323707, -0.10818507101838336, -0.21564807697679056, 0.37885037708011543, 0.10663483833724802, 0.23592711721236506, 0.09000477496054814, 0.3373262741123185, 0.2111533662078507, 0.037164689803665336, 0.0670253154257256, 0.09434049750085581, 0.08485541535738969, -0.017886984966356646, -0.17279116672491937, 0.0616816140316201, 0.20199840665428023] |
1,802.03659 | Backward Stochastic Volterra Integral Equations--- Representation of
Adapted Solutions | For backward stochastic Volterra integral equations (BSVIEs, for short),
under some mild conditions, the so-called adapted solutions or adapted
M-solutions uniquely exist. However, satisfactory regularity of the solutions
is difficult to obtain in general. Inspired by the decoupling idea of
forward-backward stochastic differential equations, in this paper, for a class
of BSVIEs, a representation of adapted M-solutions is established by means of
the so-called representation partial differential equations and (forward)
stochastic differential equations. Well-posedness of the representation partial
differential equations are also proved in certain sense.
| math.PR | for backward stochastic volterra integral equations bsvies for short under some mild conditions the socalled adapted solutions or adapted msolutions uniquely exist however satisfactory regularity of the solutions is difficult to obtain in general inspired by the decoupling idea of forwardbackward stochastic differential equations in this paper for a class of bsvies a representation of adapted msolutions is established by means of the socalled representation partial differential equations and forward stochastic differential equations wellposedness of the representation partial differential equations are also proved in certain sense | [['for', 'backward', 'stochastic', 'volterra', 'integral', 'equations', 'bsvies', 'for', 'short', 'under', 'some', 'mild', 'conditions', 'the', 'socalled', 'adapted', 'solutions', 'or', 'adapted', 'msolutions', 'uniquely', 'exist', 'however', 'satisfactory', 'regularity', 'of', 'the', 'solutions', 'is', 'difficult', 'to', 'obtain', 'in', 'general', 'inspired', 'by', 'the', 'decoupling', 'idea', 'of', 'forwardbackward', 'stochastic', 'differential', 'equations', 'in', 'this', 'paper', 'for', 'a', 'class', 'of', 'bsvies', 'a', 'representation', 'of', 'adapted', 'msolutions', 'is', 'established', 'by', 'means', 'of', 'the', 'socalled', 'representation', 'partial', 'differential', 'equations', 'and', 'forward', 'stochastic', 'differential', 'equations', 'wellposedness', 'of', 'the', 'representation', 'partial', 'differential', 'equations', 'are', 'also', 'proved', 'in', 'certain', 'sense']] | [-0.14652502130572198, 0.00927007547038239, -0.0971712276209579, 0.14970967257639048, -0.1568977302021542, -0.13933749530507727, -0.05950402750005556, 0.23760813504977282, -0.3756656879125986, -0.21561636240763027, 0.17124358899647715, -0.21888935358025308, -0.17263119625804815, 0.20065640880953695, -0.12560791397727158, 0.1459862164351656, 0.09011684754473526, 0.01617707464529938, -0.1030763860217967, -0.22893991470770086, 0.39038372641905794, -0.05996082534710335, 0.21810681379920002, -0.03842009913493056, 0.24311890637198852, 0.01643864257087888, -0.08101488413224213, 0.025134025033303472, -0.1657726966429415, 0.0969626727773873, 0.31562908826027675, 0.06699941899670764, 0.27727471961271627, -0.458584961990371, -0.18381667545411823, 0.07684574068285698, 0.1303358159591796, 0.10375144597965964, -0.06419361618260831, -0.33084228769118007, 0.11260470847036083, -0.09261542414098459, -0.2143383846444966, -0.10088299088136748, -0.006269726907661141, 0.11403721680750983, -0.33422695267891384, 0.09690995854129311, 0.11425496203564973, 0.028926200967087128, -0.10895372075461891, -0.06118199504711557, 0.0034656986820262446, -0.015277510576578246, 0.029191797168730476, -0.04935840917426313, -0.02464776774218609, -0.112141634735582, -0.09704436017010837, 0.3155187095385478, -0.12807612068582933, -0.3493918047264911, 0.137420549869624, -0.11153670775346718, -0.17005577854010775, 0.1796211185513151, 0.13927414358138693, 0.22986374410454097, -0.29663168808423673, 0.14968990498739512, -0.05341045355944093, 0.07954346294363224, 0.12387752584398312, 0.010521620217586196, 0.024890986509447873, 0.12392385720361977, 0.14698451779089694, 0.06778284264620134, 0.055431512384783735, -0.19632568193036457, -0.33566119383240856, -0.14096664897231168, -0.03492791845658144, 0.08965440665582888, -0.0670718205678155, -0.1524099540831738, 0.3207202704195583, 0.11348173143558724, 0.1188763858273972, 0.09098123192386486, 0.2023460943858291, 0.2743008670418762, -0.02120619451323914, 0.03737775513057619, 0.17773330300067672, 0.23627397918878773, 0.21013344970510103, -0.19770913545602695, 0.11185422348844018, 0.21142322504026598] |
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