id float64 706 1.8k | title stringlengths 1 343 | abstract stringlengths 6 6.09k | categories stringlengths 5 125 | processed_abstract stringlengths 2 5.96k | tokenized_abstract stringlengths 8 8.74k | centroid stringlengths 2.1k 2.17k |
|---|---|---|---|---|---|---|
1,802.0086 | The Black Hole Accretion Code: adaptive mesh refinement and constrained
transport | With the forthcoming VLBI images of Sgr A* and M87, simulations of accretion
flows onto black holes acquire a special importance to aid with the
interpretation of the observations and to test the predictions of different
accretion scenarios, including those coming from alternative theories of
gravity. The Black Hole Accretion Code (BHAC) is a new multidimensional
general-relativistic magnetohydrondynamics (GRMHD) module for the MPI-AMRVAC
framework. It exploits its adaptive mesh refinement techniques (AMR) to solve
the equations of ideal magnetohydrodynamics in arbitrary curved spacetimes with
a significant speedup and saving in computational cost. In a previous work,
this was shown using a Generalized Lagrange Multiplier (GLM) to enforce the
solenoidal constraint of the magnetic field. While GLM is fully compatible with
MPI-AMRVAC's AMR infrastructure, we found that simulations were sensible to the
divergence control technique employed, resulting in an improved behavior for
those using Constrained Transport (CT). However, cell-centered CT is
incompatible with AMR, and several modifications were required to make AMR
compatible with staggered CT. We present here preliminary results of these new
additions, which achieved machine precision fulfillment of the solenoidal
constraint and a significant speedup in a problem close to the intended
scientific application.
| gr-qc | with the forthcoming vlbi images of sgr a and m87 simulations of accretion flows onto black holes acquire a special importance to aid with the interpretation of the observations and to test the predictions of different accretion scenarios including those coming from alternative theories of gravity the black hole accretion code bhac is a new multidimensional generalrelativistic magnetohydrondynamics grmhd module for the mpiamrvac framework it exploits its adaptive mesh refinement techniques amr to solve the equations of ideal magnetohydrodynamics in arbitrary curved spacetimes with a significant speedup and saving in computational cost in a previous work this was shown using a generalized lagrange multiplier glm to enforce the solenoidal constraint of the magnetic field while glm is fully compatible with mpiamrvacs amr infrastructure we found that simulations were sensible to the divergence control technique employed resulting in an improved behavior for those using constrained transport ct however cellcentered ct is incompatible with amr and several modifications were required to make amr compatible with staggered ct we present here preliminary results of these new additions which achieved machine precision fulfillment of the solenoidal constraint and a significant speedup in a problem close to the intended scientific application | [['with', 'the', 'forthcoming', 'vlbi', 'images', 'of', 'sgr', 'a', 'and', 'm87', 'simulations', 'of', 'accretion', 'flows', 'onto', 'black', 'holes', 'acquire', 'a', 'special', 'importance', 'to', 'aid', 'with', 'the', 'interpretation', 'of', 'the', 'observations', 'and', 'to', 'test', 'the', 'predictions', 'of', 'different', 'accretion', 'scenarios', 'including', 'those', 'coming', 'from', 'alternative', 'theories', 'of', 'gravity', 'the', 'black', 'hole', 'accretion', 'code', 'bhac', 'is', 'a', 'new', 'multidimensional', 'generalrelativistic', 'magnetohydrondynamics', 'grmhd', 'module', 'for', 'the', 'mpiamrvac', 'framework', 'it', 'exploits', 'its', 'adaptive', 'mesh', 'refinement', 'techniques', 'amr', 'to', 'solve', 'the', 'equations', 'of', 'ideal', 'magnetohydrodynamics', 'in', 'arbitrary', 'curved', 'spacetimes', 'with', 'a', 'significant', 'speedup', 'and', 'saving', 'in', 'computational', 'cost', 'in', 'a', 'previous', 'work', 'this', 'was', 'shown', 'using', 'a', 'generalized', 'lagrange', 'multiplier', 'glm', 'to', 'enforce', 'the', 'solenoidal', 'constraint', 'of', 'the', 'magnetic', 'field', 'while', 'glm', 'is', 'fully', 'compatible', 'with', 'mpiamrvacs', 'amr', 'infrastructure', 'we', 'found', 'that', 'simulations', 'were', 'sensible', 'to', 'the', 'divergence', 'control', 'technique', 'employed', 'resulting', 'in', 'an', 'improved', 'behavior', 'for', 'those', 'using', 'constrained', 'transport', 'ct', 'however', 'cellcentered', 'ct', 'is', 'incompatible', 'with', 'amr', 'and', 'several', 'modifications', 'were', 'required', 'to', 'make', 'amr', 'compatible', 'with', 'staggered', 'ct', 'we', 'present', 'here', 'preliminary', 'results', 'of', 'these', 'new', 'additions', 'which', 'achieved', 'machine', 'precision', 'fulfillment', 'of', 'the', 'solenoidal', 'constraint', 'and', 'a', 'significant', 'speedup', 'in', 'a', 'problem', 'close', 'to', 'the', 'intended', 'scientific', 'application']] | [-0.09651092327255084, 0.024737360496653038, -0.0886185934296665, 0.05682614249220483, -0.1278648256052996, -0.14165222191866295, -0.01671923436440489, 0.35727213787817463, -0.24512034797196075, -0.3514487383858368, 0.09969801403409273, -0.23541504573485017, -0.0629077507519, 0.21951135055920512, -0.08363385738315272, 0.10583232901480347, 0.09772519031827598, -0.04350597436106122, -0.13405896770466627, -0.24201541655209666, 0.30695980007354573, 0.1507383086406578, 0.25008418823750783, -0.026628970119324465, 0.1208670263577099, -0.05936886528122824, -0.06510973724202954, 0.08755863318219781, -0.12563478560733746, 0.09915484704158854, 0.2396972732109584, 0.11168960709912107, 0.23652780808738827, -0.4574007286027688, -0.24301226159375275, 0.010330553381396554, 0.11955270527132317, 0.09965491607682012, -0.09894859145597085, -0.225431098236664, 0.11182544214069512, -0.2221569961978641, -0.10467798967087238, -0.07288300945332331, -0.046364943380467594, -0.0022365410782795247, -0.31445711272163795, 0.061363436626126394, 0.0403023660350975, 0.05512185037596939, -0.0912578180222292, -0.08604712245539407, -0.011119464741174862, 0.09001944322719901, 0.04361902366178686, 0.08910320659369692, 0.10990828666932036, -0.13486695297412843, -0.15630712125332757, 0.4034382795300527, -0.030044479828481515, -0.2124799724725872, 0.19856631758709237, -0.13979539252606402, -0.15148529303308153, 0.1223165113076846, 0.1795159286108905, 0.1293162081760268, -0.12926224270144226, 0.05812065702708217, -0.015594870206112638, 0.1567063986554204, 0.025290662821228663, -0.022054310952860517, 0.24080073317037606, 0.16661916925096595, 0.021611762506714493, 0.1253868629129786, -0.1074978348837455, -0.0977430845614998, -0.27853936979489535, -0.1590792688099456, -0.13259629106878773, 0.05285373842343688, -0.12382499127702477, -0.14231936265423428, 0.32961097694741387, 0.1825639771996066, 0.12748882958555074, 0.04600963943947548, 0.32434212399707624, 0.06203945602779997, 0.10244749856485083, 0.11106826519334362, 0.25048181580526024, 0.14659131476751616, 0.15543598176181778, -0.24149917444447055, 0.0031197828987190863, 0.0673424924913429] |
1,802.00861 | Spin wave emission by spin-orbit torque antennas | We study the generation of propagating spin waves in Ta/CoFeB waveguides by
spin-orbit torque antennas and compare them to conventional inductive antennas.
The spin-orbit torque was generated by a transverse microwave current across
the magnetic waveguide. The detected spin wave signals for an in-plane
magnetization across the waveguide (Damon-Eshbach configuration) exhibited the
expected phase rotation and amplitude decay upon propagation when the current
spreading was taken into account. Wavevectors up to about 6 rad/$\mu$m could be
excited by the spin-orbit torque antennas despite the current spreading,
presumably due to the non-uniformity of the microwave current. The relative
magnitude of generated anti-damping spin-Hall and Oersted fields was calculated
within an analytic model and it was found that they contribute approximately
equally to the total effective field generated by the spin-orbit torque
antenna. Due to the ellipticity of the precession in the ultrathin waveguide
and the different orientation of the anti-damping spin-Hall and Oersted fields,
the torque was however still dominated by the Oersted field. The prospects for
obtaining a pure spin-orbit torque response are discussed, as are the energy
efficiency and the scaling properties of spin-orbit torque antennas.
| cond-mat.mes-hall | we study the generation of propagating spin waves in tacofeb waveguides by spinorbit torque antennas and compare them to conventional inductive antennas the spinorbit torque was generated by a transverse microwave current across the magnetic waveguide the detected spin wave signals for an inplane magnetization across the waveguide damoneshbach configuration exhibited the expected phase rotation and amplitude decay upon propagation when the current spreading was taken into account wavevectors up to about 6 radmum could be excited by the spinorbit torque antennas despite the current spreading presumably due to the nonuniformity of the microwave current the relative magnitude of generated antidamping spinhall and oersted fields was calculated within an analytic model and it was found that they contribute approximately equally to the total effective field generated by the spinorbit torque antenna due to the ellipticity of the precession in the ultrathin waveguide and the different orientation of the antidamping spinhall and oersted fields the torque was however still dominated by the oersted field the prospects for obtaining a pure spinorbit torque response are discussed as are the energy efficiency and the scaling properties of spinorbit torque antennas | [['we', 'study', 'the', 'generation', 'of', 'propagating', 'spin', 'waves', 'in', 'tacofeb', 'waveguides', 'by', 'spinorbit', 'torque', 'antennas', 'and', 'compare', 'them', 'to', 'conventional', 'inductive', 'antennas', 'the', 'spinorbit', 'torque', 'was', 'generated', 'by', 'a', 'transverse', 'microwave', 'current', 'across', 'the', 'magnetic', 'waveguide', 'the', 'detected', 'spin', 'wave', 'signals', 'for', 'an', 'inplane', 'magnetization', 'across', 'the', 'waveguide', 'damoneshbach', 'configuration', 'exhibited', 'the', 'expected', 'phase', 'rotation', 'and', 'amplitude', 'decay', 'upon', 'propagation', 'when', 'the', 'current', 'spreading', 'was', 'taken', 'into', 'account', 'wavevectors', 'up', 'to', 'about', '6', 'radmum', 'could', 'be', 'excited', 'by', 'the', 'spinorbit', 'torque', 'antennas', 'despite', 'the', 'current', 'spreading', 'presumably', 'due', 'to', 'the', 'nonuniformity', 'of', 'the', 'microwave', 'current', 'the', 'relative', 'magnitude', 'of', 'generated', 'antidamping', 'spinhall', 'and', 'oersted', 'fields', 'was', 'calculated', 'within', 'an', 'analytic', 'model', 'and', 'it', 'was', 'found', 'that', 'they', 'contribute', 'approximately', 'equally', 'to', 'the', 'total', 'effective', 'field', 'generated', 'by', 'the', 'spinorbit', 'torque', 'antenna', 'due', 'to', 'the', 'ellipticity', 'of', 'the', 'precession', 'in', 'the', 'ultrathin', 'waveguide', 'and', 'the', 'different', 'orientation', 'of', 'the', 'antidamping', 'spinhall', 'and', 'oersted', 'fields', 'the', 'torque', 'was', 'however', 'still', 'dominated', 'by', 'the', 'oersted', 'field', 'the', 'prospects', 'for', 'obtaining', 'a', 'pure', 'spinorbit', 'torque', 'response', 'are', 'discussed', 'as', 'are', 'the', 'energy', 'efficiency', 'and', 'the', 'scaling', 'properties', 'of', 'spinorbit', 'torque', 'antennas']] | [-0.24028121539227595, 0.2195163004875486, 0.025704011781721988, 0.00932701075229273, -0.09766301284143601, -0.09588937434588149, -0.016904771086379326, 0.4240847608294859, -0.25803746542422684, -0.33254787242741035, -0.0005802317792850156, -0.2691217766203467, -0.09525386580724209, 0.25624155205979904, 0.048610687314692924, 0.004460390070728677, -0.030036657102911743, -0.03997996762915645, -0.020631188648797934, -0.17438795006025323, 0.26162346601436137, 0.058732942502785435, 0.3243234536300103, 0.05602064022483925, 0.06733676010582557, 0.020233322128963966, 0.059689310384333456, 0.003554722379332268, -0.09004909870790109, 0.04035176658503441, 0.2139401358035293, -0.0794881232363242, 0.1553416585409513, -0.4897052385113252, -0.18006289836240844, -0.01618060318913351, 0.12186748236517912, 0.18486197851097552, -0.03884774785343638, -0.3244389193533089, 0.02106570370773214, -0.17046723575631698, -0.12575677282963027, -0.036500771249574356, 0.049589925772330214, 0.022322487159286655, -0.27290189374859136, 0.04959293133350745, 0.11176061967247126, 0.06346871878611304, -0.06702413031160431, -0.10921147103942129, -0.09535549520965546, 0.06025280476931447, 0.12202055833955866, 0.08799896612437942, 0.19645073764390433, -0.13783005371023851, -0.12739720931076395, 0.30564517668017777, -0.06995734364365137, -0.16798157167310515, 0.09537632432916472, -0.2166119884198559, 0.062497546886324244, 0.15846589517839735, 0.1697789923643433, 0.0625286138033174, -0.140066982165961, 0.052090178542051925, 0.0632166435369522, 0.17323682748935415, 0.10232707109397418, 0.03722273268466515, 0.30676453389347563, 0.13873032063385973, 0.0381763733676835, 0.13880488744083172, -0.17426880607922857, -0.02228513136080476, -0.1990085457015397, -0.0945293525148118, -0.23226068391820157, 0.08697812629058414, -0.07788374647944077, -0.1064597922452395, 0.42857191268785266, 0.1754954269616514, 0.10800295373705286, -0.018077363773632133, 0.3703272512623219, 0.15285242036152732, 0.13362468351855353, 0.06089012274738922, 0.3744343259692272, 0.2476765305036679, 0.12253137781555133, -0.3408375967053636, 0.062458884972398, -0.06477909954921943] |
1,802.00862 | Projections of the Aldous chain on binary trees: Intertwining and
consistency | Consider the Aldous Markov chain on the space of rooted binary trees with $n$
labeled leaves in which at each transition a uniform random leaf is deleted and
reattached to a uniform random edge. Now, fix $1\le k < n$ and project the leaf
mass onto the subtree spanned by the first $k$ leaves. This yields a binary
tree with edge weights that we call a "decorated $k$-tree with total mass $n$."
We introduce label swapping dynamics for the Aldous chain so that, when it runs
in stationarity, the decorated $k$-trees evolve as Markov chains themselves,
and are projectively consistent over $k\le n$. The construction of projectively
consistent chains is a crucial step in the construction of the Aldous diffusion
on continuum trees by the present authors, which is the $n\rightarrow \infty$
continuum analogue of the Aldous chain and will be taken up elsewhere. Some of
our results have been generalized to Ford's alpha model trees.
| math.PR | consider the aldous markov chain on the space of rooted binary trees with n labeled leaves in which at each transition a uniform random leaf is deleted and reattached to a uniform random edge now fix 1le k n and project the leaf mass onto the subtree spanned by the first k leaves this yields a binary tree with edge weights that we call a decorated ktree with total mass n we introduce label swapping dynamics for the aldous chain so that when it runs in stationarity the decorated ktrees evolve as markov chains themselves and are projectively consistent over kle n the construction of projectively consistent chains is a crucial step in the construction of the aldous diffusion on continuum trees by the present authors which is the nrightarrow infty continuum analogue of the aldous chain and will be taken up elsewhere some of our results have been generalized to fords alpha model trees | [['consider', 'the', 'aldous', 'markov', 'chain', 'on', 'the', 'space', 'of', 'rooted', 'binary', 'trees', 'with', 'n', 'labeled', 'leaves', 'in', 'which', 'at', 'each', 'transition', 'a', 'uniform', 'random', 'leaf', 'is', 'deleted', 'and', 'reattached', 'to', 'a', 'uniform', 'random', 'edge', 'now', 'fix', '1le', 'k', 'n', 'and', 'project', 'the', 'leaf', 'mass', 'onto', 'the', 'subtree', 'spanned', 'by', 'the', 'first', 'k', 'leaves', 'this', 'yields', 'a', 'binary', 'tree', 'with', 'edge', 'weights', 'that', 'we', 'call', 'a', 'decorated', 'ktree', 'with', 'total', 'mass', 'n', 'we', 'introduce', 'label', 'swapping', 'dynamics', 'for', 'the', 'aldous', 'chain', 'so', 'that', 'when', 'it', 'runs', 'in', 'stationarity', 'the', 'decorated', 'ktrees', 'evolve', 'as', 'markov', 'chains', 'themselves', 'and', 'are', 'projectively', 'consistent', 'over', 'kle', 'n', 'the', 'construction', 'of', 'projectively', 'consistent', 'chains', 'is', 'a', 'crucial', 'step', 'in', 'the', 'construction', 'of', 'the', 'aldous', 'diffusion', 'on', 'continuum', 'trees', 'by', 'the', 'present', 'authors', 'which', 'is', 'the', 'nrightarrow', 'infty', 'continuum', 'analogue', 'of', 'the', 'aldous', 'chain', 'and', 'will', 'be', 'taken', 'up', 'elsewhere', 'some', 'of', 'our', 'results', 'have', 'been', 'generalized', 'to', 'fords', 'alpha', 'model', 'trees']] | [-0.12899977476786703, 0.22616467494217138, -0.03569556621594294, 0.03613792268499251, -0.0615236702284986, -0.15130873335405223, 0.08181090881116689, 0.40870638436848117, -0.29164235087171675, -0.23574178221545392, 0.0880269264531014, -0.29624634530215016, -0.08444526863961307, 0.07793271632774944, -0.05298044594725774, 0.03746437357678529, 0.10078384942945934, 0.07631511912500906, 0.011454942561085186, -0.2789013697194957, 0.3114357138142711, 0.08448385736499463, 0.20506964681009132, -0.026443942368871744, 0.10894995809724224, 0.04220727257911236, -0.023956000961122974, 0.01783652887811073, -0.17734881046184925, 0.05863822490097054, 0.23918153078205162, 0.11074283000592502, 0.2113307192410913, -0.3511442728580967, -0.18840733688857947, 0.15674237582622277, 0.16519641296096868, 0.07942278493466157, 0.04392576370117886, -0.28300541531324624, 0.09079093781840657, -0.1227183377850921, -0.12837673386618975, 0.014053208546172227, 0.05960616675626126, 0.010097727467936855, -0.24473428614174708, -0.005895237053834623, 0.14235474789935734, 0.014979679461929105, 0.024191859106142674, -0.1785751280645209, -0.07409327828265246, 0.10780509760126382, -0.021557941056427457, 0.08836297081828477, 0.06970172216514907, -0.03507842771829136, -0.153901171197574, 0.30704815402146307, -0.06416325454690283, -0.19707735268579377, 0.15469158902133426, -0.12102589214881582, -0.2346041697978733, 0.1494549690835899, 0.12006529622500943, 0.1417789031390942, -0.12214016121738935, 0.1526713714949907, -0.1195971819542108, 0.12249050359748634, 0.12090974130757874, -0.05779140776083354, 0.1801500250675505, 0.16848513382306743, 0.11431068687985141, 0.19036069932438793, -0.04546597369255558, -0.09430594734487034, -0.2680096226113458, -0.15311290910204634, -0.22133168526774932, 0.09421125379721484, -0.15572753201291387, -0.20235489724624542, 0.3120624443065495, 0.12113687942771409, 0.2644131764679426, 0.17935772428829824, 0.18071060780916484, 0.07534999032955497, 0.03167628888040781, 0.1097288356763461, 0.0652109020888137, 0.18611948756020397, 0.021722265239804982, -0.1414408279650454, 0.09365287845834128, 0.14081695065833627] |
1,802.00863 | Note: Formation of the nematic splay-bend in two-dimensional systems of
bow-shaped particles | Recently, Tavarone et al. (J. Chem. Phys. 143, 114505 (2015)) discussed phase
behavior of zig-zag and bow-shaped particles composed of three needles. The
authors presented very interesting results of extensive Monte Carlo simulations
with periodic boundary conditions in the constant-NVT and the constant-NPT
ensembles. In addition to isotropic, nematic, and smectic phases, they
identified a modulated nematic, which is actually the nematic splay-bend phase
($N_{SB}$), long-anticipated for bent-core systems (Europhys. Lett. 56, 247
(2001)). They also described isotropic-nematic and nematic-smectic transitions
using Density Functional Theory in mean-field approximation. The authors,
however, did not provided a theoretical description of the $N_{SB}$. Here, we
present a simple theory of a phase transition to the $N_{SB}$ phase to fill the
gap. In our study, we use Onsager-type Density Functional Theory with perfect
order approximation and Meyer parametrization of modulated structures. We
present results for arbitrary ratios of the length of central and side segments
and opening angles of bow-shaped particles.
| cond-mat.soft | recently tavarone et al j chem phys 143 114505 2015 discussed phase behavior of zigzag and bowshaped particles composed of three needles the authors presented very interesting results of extensive monte carlo simulations with periodic boundary conditions in the constantnvt and the constantnpt ensembles in addition to isotropic nematic and smectic phases they identified a modulated nematic which is actually the nematic splaybend phase n_sb longanticipated for bentcore systems europhys lett 56 247 2001 they also described isotropicnematic and nematicsmectic transitions using density functional theory in meanfield approximation the authors however did not provided a theoretical description of the n_sb here we present a simple theory of a phase transition to the n_sb phase to fill the gap in our study we use onsagertype density functional theory with perfect order approximation and meyer parametrization of modulated structures we present results for arbitrary ratios of the length of central and side segments and opening angles of bowshaped particles | [['recently', 'tavarone', 'et', 'al', 'j', 'chem', 'phys', '143', '114505', '2015', 'discussed', 'phase', 'behavior', 'of', 'zigzag', 'and', 'bowshaped', 'particles', 'composed', 'of', 'three', 'needles', 'the', 'authors', 'presented', 'very', 'interesting', 'results', 'of', 'extensive', 'monte', 'carlo', 'simulations', 'with', 'periodic', 'boundary', 'conditions', 'in', 'the', 'constantnvt', 'and', 'the', 'constantnpt', 'ensembles', 'in', 'addition', 'to', 'isotropic', 'nematic', 'and', 'smectic', 'phases', 'they', 'identified', 'a', 'modulated', 'nematic', 'which', 'is', 'actually', 'the', 'nematic', 'splaybend', 'phase', 'n_sb', 'longanticipated', 'for', 'bentcore', 'systems', 'europhys', 'lett', '56', '247', '2001', 'they', 'also', 'described', 'isotropicnematic', 'and', 'nematicsmectic', 'transitions', 'using', 'density', 'functional', 'theory', 'in', 'meanfield', 'approximation', 'the', 'authors', 'however', 'did', 'not', 'provided', 'a', 'theoretical', 'description', 'of', 'the', 'n_sb', 'here', 'we', 'present', 'a', 'simple', 'theory', 'of', 'a', 'phase', 'transition', 'to', 'the', 'n_sb', 'phase', 'to', 'fill', 'the', 'gap', 'in', 'our', 'study', 'we', 'use', 'onsagertype', 'density', 'functional', 'theory', 'with', 'perfect', 'order', 'approximation', 'and', 'meyer', 'parametrization', 'of', 'modulated', 'structures', 'we', 'present', 'results', 'for', 'arbitrary', 'ratios', 'of', 'the', 'length', 'of', 'central', 'and', 'side', 'segments', 'and', 'opening', 'angles', 'of', 'bowshaped', 'particles']] | [-0.1543769563237826, 0.17595468384283747, -0.09320325870066881, -0.01411341817279535, -0.023933216971664818, -0.09645778701634579, 0.08100691738955408, 0.3873585442482841, -0.17905150936746034, -0.3168429538814558, 0.02505947186252455, -0.28486306736477063, -0.19832136193320787, 0.09092491658197509, -0.016169075761294734, 0.05415148424970753, -0.02094580911501658, -0.08253125225123797, -0.11479347325356226, -0.21581269281283263, 0.19309719308930773, 0.06672946995069437, 0.25876939795501197, 0.03153670034401752, 0.047880923437879856, 0.02959083532001458, 0.022172858119863116, 0.04774190994128707, -0.265886809022525, 0.05548713052207056, 0.2595195331979007, -0.03726427119188741, 0.16444768138485505, -0.4369025266930169, -0.21076057033820284, 0.052957921917398086, 0.10363333389388239, 0.11381338342181194, -0.03777144242190677, -0.3228305323670308, 0.03406406610289564, -0.18231736393828019, -0.17958711776912845, -0.0827836936424955, 0.04765028313252758, 0.03609257526437028, -0.2548992035977445, 0.1408822671219241, 0.05346659937374342, 0.07584406632254374, -0.0597623024407654, -0.11888786692026199, -0.024007797244563586, 0.030326796398084197, 0.0052235936936222355, 0.058928032221540416, 0.09911413523642455, -0.0768021272732785, -0.12490153171158597, 0.3385512879106149, -0.01959885842489456, -0.11863695367945516, 0.21152481146375923, -0.1308490289573967, -0.15547168827037405, 0.1692736685166464, 0.12027264287196149, 0.11612044832345061, -0.1093928086772269, 0.06619790308463754, -0.05900237781796117, 0.18578716145304877, 0.08484685556427424, -0.03138402069985769, 0.1815738066554593, 0.12844906130720393, -0.04262367124042285, 0.12378728511177149, -0.08605652688652447, -0.1666258867830799, -0.2803853744202677, -0.15813425353734323, -0.1893507444902378, -0.002859321433432199, -0.0363637958768355, -0.19249326345553391, 0.36834196320254037, 0.12396795940783265, 0.18168502893250169, -0.02118185175098844, 0.19096906036382016, 0.04480586627428268, -0.03242452484646849, 0.09694233120668655, 0.2689368939103279, 0.19425356608063737, 0.10790171726832008, -0.19289268846958388, 0.006321377117868537, 0.07808872745098437] |
1,802.00864 | Onto2Vec: joint vector-based representation of biological entities and
their ontology-based annotations | We propose the Onto2Vec method, an approach to learn feature vectors for
biological entities based on their annotations to biomedical ontologies. Our
method can be applied to a wide range of bioinformatics research problems such
as similarity-based prediction of interactions between proteins, classification
of interaction types using supervised learning, or clustering.
| q-bio.QM cs.AI | we propose the onto2vec method an approach to learn feature vectors for biological entities based on their annotations to biomedical ontologies our method can be applied to a wide range of bioinformatics research problems such as similaritybased prediction of interactions between proteins classification of interaction types using supervised learning or clustering | [['we', 'propose', 'the', 'onto2vec', 'method', 'an', 'approach', 'to', 'learn', 'feature', 'vectors', 'for', 'biological', 'entities', 'based', 'on', 'their', 'annotations', 'to', 'biomedical', 'ontologies', 'our', 'method', 'can', 'be', 'applied', 'to', 'a', 'wide', 'range', 'of', 'bioinformatics', 'research', 'problems', 'such', 'as', 'similaritybased', 'prediction', 'of', 'interactions', 'between', 'proteins', 'classification', 'of', 'interaction', 'types', 'using', 'supervised', 'learning', 'or', 'clustering']] | [0.010958159882575274, -0.008531208249914926, -0.048137599538313224, 0.06684918128419667, -0.1647187190502882, -0.14399067532387563, 0.01992533292621374, 0.47222521126270295, -0.34387081675231457, -0.387986357472837, -0.008281964333727956, -0.27280564105138183, -0.20744613828603178, 0.21787566087674348, -0.0774349667970091, 0.06300487346947194, 0.12038615435361862, 0.05675440568476915, -0.04716818407177925, -0.2535332317277789, 0.30109297297894955, -0.017865272313356398, 0.36571095360442996, 0.07417147741653025, 0.12197747745551168, -0.0014765270380303263, -0.02142507123760879, 0.014946535788476467, -0.05851523597724736, 0.2590711118280888, 0.46886319752433336, 0.2641673848219216, 0.3674964415282011, -0.365871275216341, -0.28097658630926164, 0.1350252103805542, 0.18243446754291653, 0.13922945336438716, -0.030389617236796767, -0.3808303254470229, 0.06944219095632434, -0.1580631035938859, 0.0391832631547004, -0.20850518012419342, -0.018180289110168814, 0.04809460436925292, -0.2941556457802653, 0.0768865641579032, 0.042198100872337815, 0.11589335402473808, -0.10272649043239652, -0.10036218852736056, 0.07729674181435257, 0.18948048871941864, 0.054654554836452006, 0.0461409381008707, 0.187786159561947, -0.1899289122968912, -0.21254838060587644, 0.3873058696091175, -0.019080625344067813, -0.24390123814344405, 0.29225850163493305, 0.09133351009339094, -0.18717754293233158, 0.04357201039791107, 0.3046108812466264, 0.16169067406095564, -0.19228972758166493, -0.02344117736443877, 0.0017454602196812629, 0.21043828083202243, 0.04430391168221831, -0.04080933019518852, 0.2017798626422882, 0.29575802057981493, 0.0057323559187352656, 0.10474857329390944, -0.11682428216096014, -0.017807747526094316, -0.1762131106853485, -0.09068146442994475, -0.21729042952880262, -0.043507084855809806, -0.12720388800837099, -0.23052112674718955, 0.3671569894999266, 0.2457072694064118, 0.1962275816127658, 0.05910416590049863, 0.2693826836720109, -0.037793028084561225, 0.15149603175465018, -0.005111842323094607, 0.13316031623631716, 0.04783625174313784, 0.12865136136300862, -0.1349150722962804, 0.07299754277803004, 0.07575530715286732] |
1,802.00865 | Essential core of the Hawking--Ellis types | The Hawking-Ellis (Segre-Plebanski) classification of possible stress-energy
tensors is an essential tool in analyzing the implications of the Einstein
field equations in a more-or-less model-independent manner. In the current
article the basic idea is to simplify the Hawking-Ellis type I, II, III, and IV
classification by isolating the "essential core" of the type II, type III, and
type IV stress-energy tensors; this being done by subtracting (special cases
of) type I to simplify the (Lorentz invariant) eigenvalue structure as much as
possible without disturbing the eigenvector structure. We will denote these
"simplified cores" type II$_0$, type III$_0$, and type IV$_0$. These
"simplified cores" have very nice and simple algebraic properties. Furthermore,
types I and II$_0$ have very simple classical interpretations, while type
IV$_0$ is known to arise semi-classically (in renormalized expectation values
of standard stress-energy tensors). In contrast type III$_0$ stands out in that
it has neither a simple classical interpretation, nor even a simple
semi-classical interpretation. We will also consider the robustness of this
classification considering the stability of the different Hawking-Ellis types
under perturbations. We argue that types II and III are definitively unstable,
whereas types I and IV are stable.
| gr-qc hep-th | the hawkingellis segreplebanski classification of possible stressenergy tensors is an essential tool in analyzing the implications of the einstein field equations in a moreorless modelindependent manner in the current article the basic idea is to simplify the hawkingellis type i ii iii and iv classification by isolating the essential core of the type ii type iii and type iv stressenergy tensors this being done by subtracting special cases of type i to simplify the lorentz invariant eigenvalue structure as much as possible without disturbing the eigenvector structure we will denote these simplified cores type ii_0 type iii_0 and type iv_0 these simplified cores have very nice and simple algebraic properties furthermore types i and ii_0 have very simple classical interpretations while type iv_0 is known to arise semiclassically in renormalized expectation values of standard stressenergy tensors in contrast type iii_0 stands out in that it has neither a simple classical interpretation nor even a simple semiclassical interpretation we will also consider the robustness of this classification considering the stability of the different hawkingellis types under perturbations we argue that types ii and iii are definitively unstable whereas types i and iv are stable | [['the', 'hawkingellis', 'segreplebanski', 'classification', 'of', 'possible', 'stressenergy', 'tensors', 'is', 'an', 'essential', 'tool', 'in', 'analyzing', 'the', 'implications', 'of', 'the', 'einstein', 'field', 'equations', 'in', 'a', 'moreorless', 'modelindependent', 'manner', 'in', 'the', 'current', 'article', 'the', 'basic', 'idea', 'is', 'to', 'simplify', 'the', 'hawkingellis', 'type', 'i', 'ii', 'iii', 'and', 'iv', 'classification', 'by', 'isolating', 'the', 'essential', 'core', 'of', 'the', 'type', 'ii', 'type', 'iii', 'and', 'type', 'iv', 'stressenergy', 'tensors', 'this', 'being', 'done', 'by', 'subtracting', 'special', 'cases', 'of', 'type', 'i', 'to', 'simplify', 'the', 'lorentz', 'invariant', 'eigenvalue', 'structure', 'as', 'much', 'as', 'possible', 'without', 'disturbing', 'the', 'eigenvector', 'structure', 'we', 'will', 'denote', 'these', 'simplified', 'cores', 'type', 'ii_0', 'type', 'iii_0', 'and', 'type', 'iv_0', 'these', 'simplified', 'cores', 'have', 'very', 'nice', 'and', 'simple', 'algebraic', 'properties', 'furthermore', 'types', 'i', 'and', 'ii_0', 'have', 'very', 'simple', 'classical', 'interpretations', 'while', 'type', 'iv_0', 'is', 'known', 'to', 'arise', 'semiclassically', 'in', 'renormalized', 'expectation', 'values', 'of', 'standard', 'stressenergy', 'tensors', 'in', 'contrast', 'type', 'iii_0', 'stands', 'out', 'in', 'that', 'it', 'has', 'neither', 'a', 'simple', 'classical', 'interpretation', 'nor', 'even', 'a', 'simple', 'semiclassical', 'interpretation', 'we', 'will', 'also', 'consider', 'the', 'robustness', 'of', 'this', 'classification', 'considering', 'the', 'stability', 'of', 'the', 'different', 'hawkingellis', 'types', 'under', 'perturbations', 'we', 'argue', 'that', 'types', 'ii', 'and', 'iii', 'are', 'definitively', 'unstable', 'whereas', 'types', 'i', 'and', 'iv', 'are', 'stable']] | [-0.11013224020268748, 0.06160189017858563, -0.027493089637402154, 0.10009848630648223, -0.12268194869277677, -0.18734845962541188, -0.0011388754207916382, 0.3259011394056584, -0.24110131192018633, -0.24230045669209896, 0.11532832250603675, -0.21585804576917272, -0.16111241088794906, 0.13634955607042576, -0.06661141769599696, -0.044618951281607616, 0.03823983512028896, 0.04311122326180339, -0.11702981758655502, -0.21296550962995953, 0.3787096664388737, 0.026101167116275165, 0.26955793505407755, -0.0060837413443384095, 0.02398659467346069, -0.01787252044085784, -0.06343053426232106, 0.0848353341355348, -0.097978743312881, 0.07688084416120698, 0.21556376714370012, 0.1557442428865982, 0.2065111438022117, -0.41418504321957444, -0.16370054861440456, 0.1415868859661314, 0.14722164222453746, 0.1038607813456615, -0.009219074486029525, -0.2613745425237801, 0.10760160987948053, -0.16324602402210314, -0.1913745411518103, -0.09503615783717659, 0.060627556711323166, 0.008497923036917791, -0.21991173779599232, 0.10267911585586033, 0.10657511307877435, 0.02826002539081879, -0.0668167309245553, -0.13513269089589003, 0.009207954167032429, 0.06474438641483637, 0.021878844130106503, -0.05377113366473944, 0.08796961197180976, -0.13846122182622864, -0.08103111892187705, 0.3671088590827912, -0.04951383506316028, -0.19723952354141633, 0.20087108157053396, -0.0770616973152979, -0.17212919894050008, 0.0733191989570919, 0.08601042523941133, 0.12127359084789861, -0.13769654327264808, 0.12028790153934821, -0.0012646023067036224, 0.09777152425419364, 0.060479820613305604, 0.040514046033717575, 0.19969233765671546, 0.09064304465866838, -0.005754186863845204, 0.08881821189104964, -0.06767352756005542, -0.07558876592775622, -0.3770649547430233, -0.17357302502685115, -0.11635009599093796, 0.10872603739763875, -0.08994032983268618, -0.17093218714291356, 0.39530015691471193, 0.08589885739246335, 0.15771164264071597, 0.03468966080456075, 0.25023372530332566, 0.10215605129682855, 0.07941448608052512, 0.0719409710020607, 0.27228018872595894, 0.14809625816326685, 0.10058012244008793, -0.19152283900861106, 0.05116170527003901, 0.11140324519483948] |
1,802.00866 | Is Self-Interference in Full-Duplex Communications a Foe or a Friend? | This paper studies the potential of harvesting energy from the
self-interference of a full-duplex base station. The base station is equipped
with a self-interference cancellation switch, which is turned-off for a
fraction of the transmission period for harvesting the energy from the
self-interference that arises due to the downlink transmission. For the
remaining transmission period, the switch is on such that the uplink
transmission takes place simultaneously with the downlink transmission. A novel
energy-efficiency maximization problem is formulated for the joint design of
downlink beamformers, uplink power allocations and transmission time-splitting
factor. The optimization problem is nonconvex, and hence, a rapidly converging
iterative algorithm is proposed by employing the successive convex
approximation approach. Numerical simulation results show significant
improvement in the energy-efficiency by allowing self-energy recycling.
| cs.IT math.IT | this paper studies the potential of harvesting energy from the selfinterference of a fullduplex base station the base station is equipped with a selfinterference cancellation switch which is turnedoff for a fraction of the transmission period for harvesting the energy from the selfinterference that arises due to the downlink transmission for the remaining transmission period the switch is on such that the uplink transmission takes place simultaneously with the downlink transmission a novel energyefficiency maximization problem is formulated for the joint design of downlink beamformers uplink power allocations and transmission timesplitting factor the optimization problem is nonconvex and hence a rapidly converging iterative algorithm is proposed by employing the successive convex approximation approach numerical simulation results show significant improvement in the energyefficiency by allowing selfenergy recycling | [['this', 'paper', 'studies', 'the', 'potential', 'of', 'harvesting', 'energy', 'from', 'the', 'selfinterference', 'of', 'a', 'fullduplex', 'base', 'station', 'the', 'base', 'station', 'is', 'equipped', 'with', 'a', 'selfinterference', 'cancellation', 'switch', 'which', 'is', 'turnedoff', 'for', 'a', 'fraction', 'of', 'the', 'transmission', 'period', 'for', 'harvesting', 'the', 'energy', 'from', 'the', 'selfinterference', 'that', 'arises', 'due', 'to', 'the', 'downlink', 'transmission', 'for', 'the', 'remaining', 'transmission', 'period', 'the', 'switch', 'is', 'on', 'such', 'that', 'the', 'uplink', 'transmission', 'takes', 'place', 'simultaneously', 'with', 'the', 'downlink', 'transmission', 'a', 'novel', 'energyefficiency', 'maximization', 'problem', 'is', 'formulated', 'for', 'the', 'joint', 'design', 'of', 'downlink', 'beamformers', 'uplink', 'power', 'allocations', 'and', 'transmission', 'timesplitting', 'factor', 'the', 'optimization', 'problem', 'is', 'nonconvex', 'and', 'hence', 'a', 'rapidly', 'converging', 'iterative', 'algorithm', 'is', 'proposed', 'by', 'employing', 'the', 'successive', 'convex', 'approximation', 'approach', 'numerical', 'simulation', 'results', 'show', 'significant', 'improvement', 'in', 'the', 'energyefficiency', 'by', 'allowing', 'selfenergy', 'recycling']] | [-0.2893022121787662, -0.009850616282266047, -0.01957442811487006, -0.02661665475517068, -0.06563871817308523, -0.2350897017703761, 0.14478797376957825, 0.37866100216550486, -0.28694688015809605, -0.24244093403427136, 0.06250805080044157, -0.24381552478446375, -0.20899919038342815, 0.13327959916066556, -0.1161349829995916, 0.04295180463010356, 0.07558660607208453, 0.011384227490496068, -0.029712467838729186, -0.2382481035310775, 0.2637652318983797, 0.17274563610258084, 0.3792269423101393, 0.023895095390755506, 0.10200387092366342, 0.047373186087324506, 0.003379509223917026, -0.07173002030818708, -0.04366632326589694, 0.08580034154666853, 0.3315929579973546, 0.20869818450661287, 0.30036698326852823, -0.40443808606101406, -0.2974319506643547, 0.09729309122832049, 0.17581480002355954, 0.018757821817416698, -0.07559691247297451, -0.19392042309193622, 0.0967313378138305, -0.22644765096317446, 0.006015227294512211, 0.04399459378882533, -0.11523745951080133, 0.04963172172077946, -0.3979857917061992, 0.015433417219254706, -0.04264642622336627, 0.0052609772269894915, -0.11338926828096783, -0.11270070175773331, 0.023613722957966345, 0.12915182466958725, 0.0585774645054092, -0.020290232651556533, 0.06704523170917547, -0.048215899289408254, -0.09239607141168403, 0.38246904000166865, 0.0233116158859856, -0.20436334459199793, 0.08659800186028911, -0.07168302730479766, -0.04135683933938188, 0.2371205520808756, 0.24507460552859045, 0.0694181106814612, -0.1699708678486151, 0.06342752858726604, 0.010624369015059774, 0.1329214202299241, 0.11684270252636264, 0.05904008992504151, 0.1629310841542772, 0.21189532151246177, 0.19384998271411788, 0.12696042611995445, -0.1605277483883713, -0.12037289481137008, -0.21453517583006668, -0.12027731594733065, -0.2334922412040806, 0.075867809528958, -0.08312592023428315, -0.05535714216308579, 0.3834167211760013, 0.06789353827665014, 0.09848808220611693, 0.13428771685871724, 0.48611297978768275, 0.20250679087170975, 0.03986022455755451, 0.11293285404376331, 0.2324394066596315, 0.10813392793005776, 0.15944262451196822, -0.3239677953349042, 0.026338394622259315, -0.002462988730431313] |
1,802.00867 | Actions of semitopological groups | We investigate continuous transitive actions of semitopological groups on
spaces, as well as separately continuous transitive actions of topological
groups.
| math.GN | we investigate continuous transitive actions of semitopological groups on spaces as well as separately continuous transitive actions of topological groups | [['we', 'investigate', 'continuous', 'transitive', 'actions', 'of', 'semitopological', 'groups', 'on', 'spaces', 'as', 'well', 'as', 'separately', 'continuous', 'transitive', 'actions', 'of', 'topological', 'groups']] | [-0.1583157418295741, 0.2132752678822726, -0.008701353939250112, 0.1800175726413727, -0.12043791711330414, -0.10727212154306472, 0.10128836967051029, 0.5625824153423309, -0.34808882847428324, -0.14240390323102475, 0.2147720174631104, -0.2772975371219218, -0.1568548026494682, 0.20064830370247363, -0.2367706524208188, 0.0013293134048581122, -0.08005464933812619, 0.18225775556638837, -0.15796912778168917, -0.24034610968083142, 0.46519077755510807, -0.11699457485228777, 0.20554461609572172, -0.030991000309586524, 0.14823161978274585, 0.07792852217098697, -0.10959982834756374, 0.0783389525488019, -0.09971918929368258, -0.007571936771273613, 0.337279543094337, 0.008308206941001118, 0.20804024562239648, -0.31571653820574286, -0.26053044609725473, 0.23949717953801156, 0.041422975109890105, -0.08819490307942032, -0.007997255987720563, -0.4601205874234438, 0.06487950049340725, -0.22797753438353538, -0.02523257303982973, -0.17852734182961286, 0.10750378742814064, 0.041335365269333124, -0.11317511983215808, -0.03577172681689263, 0.15781420828425324, 0.1760592667851597, -0.15128638595342636, 0.03088674396276474, -0.20630574502283708, 0.29707017191685736, 0.017314735101535916, -0.008692937903106213, 0.1911105389939621, 0.03265527724288404, -0.2945945511572063, 0.4660779617726803, -0.06601549685001373, -0.2156049232929945, 0.26179661937057974, -0.12996992794796824, -0.3029982193838805, 0.01727418042719364, 0.2231294594705105, 0.2307870164513588, 0.014985784515738487, 0.15968191283755004, -0.14273514999076725, 0.09951072838157415, 0.01095370352268219, 0.09815130662173033, 0.151591813005507, 0.18318731598556043, 0.21312089320272207, 0.16997293578460812, 0.2160176394158043, 0.052056412320234814, -0.33887338265776634, -0.11759170424193144, -0.03974163332022727, 0.09533046656288206, -0.02849261285737157, -0.25389483517501504, 0.3882448624819517, 0.02263516765087843, 0.11075424998998643, 0.1941210696939379, 0.17508041802793742, -0.0737500710063614, -0.005375174805521965, 0.017801055894233288, -0.011499423533678055, 0.23972975872457028, -0.16225983451586218, -0.1412868846207857, -0.03965270323678851, 0.2840231703594327] |
1,802.00868 | Bayesian Renewables Scenario Generation via Deep Generative Networks | We present a method to generate renewable scenarios using Bayesian
probabilities by implementing the Bayesian generative adversarial
network~(Bayesian GAN), which is a variant of generative adversarial networks
based on two interconnected deep neural networks. By using a Bayesian
formulation, generators can be constructed and trained to produce scenarios
that capture different salient modes in the data, allowing for better diversity
and more accurate representation of the underlying physical process. Compared
to conventional statistical models that are often hard to scale or sample from,
this method is model-free and can generate samples extremely efficiently. For
validation, we use wind and solar times-series data from NREL integration data
sets to train the Bayesian GAN. We demonstrate that proposed method is able to
generate clusters of wind scenarios with different variance and mean value, and
is able to distinguish and generate wind and solar scenarios simultaneously
even if the historical data are intentionally mixed.
| math.OC cs.LG stat.ML | we present a method to generate renewable scenarios using bayesian probabilities by implementing the bayesian generative adversarial networkbayesian gan which is a variant of generative adversarial networks based on two interconnected deep neural networks by using a bayesian formulation generators can be constructed and trained to produce scenarios that capture different salient modes in the data allowing for better diversity and more accurate representation of the underlying physical process compared to conventional statistical models that are often hard to scale or sample from this method is modelfree and can generate samples extremely efficiently for validation we use wind and solar timesseries data from nrel integration data sets to train the bayesian gan we demonstrate that proposed method is able to generate clusters of wind scenarios with different variance and mean value and is able to distinguish and generate wind and solar scenarios simultaneously even if the historical data are intentionally mixed | [['we', 'present', 'a', 'method', 'to', 'generate', 'renewable', 'scenarios', 'using', 'bayesian', 'probabilities', 'by', 'implementing', 'the', 'bayesian', 'generative', 'adversarial', 'networkbayesian', 'gan', 'which', 'is', 'a', 'variant', 'of', 'generative', 'adversarial', 'networks', 'based', 'on', 'two', 'interconnected', 'deep', 'neural', 'networks', 'by', 'using', 'a', 'bayesian', 'formulation', 'generators', 'can', 'be', 'constructed', 'and', 'trained', 'to', 'produce', 'scenarios', 'that', 'capture', 'different', 'salient', 'modes', 'in', 'the', 'data', 'allowing', 'for', 'better', 'diversity', 'and', 'more', 'accurate', 'representation', 'of', 'the', 'underlying', 'physical', 'process', 'compared', 'to', 'conventional', 'statistical', 'models', 'that', 'are', 'often', 'hard', 'to', 'scale', 'or', 'sample', 'from', 'this', 'method', 'is', 'modelfree', 'and', 'can', 'generate', 'samples', 'extremely', 'efficiently', 'for', 'validation', 'we', 'use', 'wind', 'and', 'solar', 'timesseries', 'data', 'from', 'nrel', 'integration', 'data', 'sets', 'to', 'train', 'the', 'bayesian', 'gan', 'we', 'demonstrate', 'that', 'proposed', 'method', 'is', 'able', 'to', 'generate', 'clusters', 'of', 'wind', 'scenarios', 'with', 'different', 'variance', 'and', 'mean', 'value', 'and', 'is', 'able', 'to', 'distinguish', 'and', 'generate', 'wind', 'and', 'solar', 'scenarios', 'simultaneously', 'even', 'if', 'the', 'historical', 'data', 'are', 'intentionally', 'mixed']] | [0.018537586340024417, 0.0578247673509505, -0.07442727885302781, 0.1454334265358525, -0.11030255820470919, -0.17777878605298097, 0.030753118558724306, 0.45454848190182007, -0.2750556839179973, -0.3807013217178517, 0.07174865812635137, -0.2650014952687709, -0.16423445453261018, 0.21712010075188173, -0.11835827072769803, 0.0692455090969901, 0.11863842311990291, -0.03443535227616061, -0.012006381104681515, -0.2384989592434226, 0.314536563866153, 0.06587620300608812, 0.33484320530055356, -0.07224994385693122, 0.1419833700185104, -0.08367699226239483, 0.007747400958316038, 0.011077143020627882, -0.04800279201675424, 0.18898786374074, 0.27483807042688496, 0.20366006783028476, 0.27704183897316054, -0.4722802188782543, -0.2571350223331668, 0.12108229501360415, 0.11902203430865435, 0.1112246073918392, -0.029410267623485965, -0.2955182003944912, 0.1205917836741963, -0.1841334428203811, -0.01274238544166238, -0.16896815830529135, -0.060981009328962874, 0.008824163676714587, -0.3776175430132039, 0.049790074923794544, 0.0013185412710495044, 0.018198440637401985, -0.03516821011169595, -0.08350477541090558, -0.038340005389220964, 0.11469068337738164, 0.030356801546064686, 0.000637559332703584, 0.11487382094707065, -0.10915358344736766, -0.12906028590346344, 0.35484971867361725, -0.04615673819719345, -0.20755979756944995, 0.19945201043027058, -0.04791162143330146, -0.1193715298935871, 0.11135709068531659, 0.26035710657327965, 0.12109714075172907, -0.20708573634592478, -0.024500303061041458, 0.004321408976844493, 0.17340868827730618, -0.006380725614476524, -0.028697773348540068, 0.19674988397656495, 0.21951556256378457, 0.017831809799993197, 0.13154228888655195, -0.17432343699006778, -0.10414513806193668, -0.2112345875697148, -0.051668808364911764, -0.18614640187990897, 0.00592863131095749, -0.09908493250005272, -0.1434379436697611, 0.38253738184853286, 0.26629928604468406, 0.20537342528668825, 0.10129854900703059, 0.34701787619132723, 0.05630021829208071, 0.08623428952148897, 0.10747538854043005, 0.2004183727611894, 0.05949900525890841, 0.09520347926128131, -0.12208581685546423, 0.11711833321789417, -0.001139939181356622] |
1,802.00869 | Flip Graphs, Yoke Graphs and Diameter | In this paper we introduce Yoke graphs, a family of flip graphs that
generalizes several previously studied families of graphs: colored triangle
free triangulations, arc permutations and caterpillars. Our main result is the
computation of the diameter of an arbitrary Yoke graph.
| math.CO | in this paper we introduce yoke graphs a family of flip graphs that generalizes several previously studied families of graphs colored triangle free triangulations arc permutations and caterpillars our main result is the computation of the diameter of an arbitrary yoke graph | [['in', 'this', 'paper', 'we', 'introduce', 'yoke', 'graphs', 'a', 'family', 'of', 'flip', 'graphs', 'that', 'generalizes', 'several', 'previously', 'studied', 'families', 'of', 'graphs', 'colored', 'triangle', 'free', 'triangulations', 'arc', 'permutations', 'and', 'caterpillars', 'our', 'main', 'result', 'is', 'the', 'computation', 'of', 'the', 'diameter', 'of', 'an', 'arbitrary', 'yoke', 'graph']] | [-0.2158178553606073, 0.16520482662357003, -0.03155981604054216, 0.0245636674087672, -0.09749925633271535, -0.08033707497331004, -0.006268752403446429, 0.37454943500813986, -0.25472637659515296, -0.37301190441385623, 0.07617340043708239, -0.285637041686901, -0.1686821005506707, 0.1491657422323312, -0.20156163415092687, 0.052868689729144706, 0.14271243666076944, 0.03999368263231147, 0.003368667853508322, -0.2960282686655687, 0.3385515618581502, -0.011777818912551516, 0.15961064863950014, 0.06474781581865889, 0.0813132499544216, 0.04669898868139301, -0.00675238376217229, 0.06359790283299628, -0.23466966929377772, 0.13480303106119945, 0.19426261509458223, 0.08755508730454105, 0.10568480951977628, -0.34489164292989744, -0.15365751351540288, 0.2065450565091201, 0.10961634550421011, 0.08181918247802449, -0.015819982159882784, -0.22356615153451762, 0.09075237821698898, -0.15705095894546026, -0.13083701616796178, 0.047283822958845464, 0.0668191701351177, 0.012534003061730237, -0.2038517987932123, -0.08880663346055717, 0.23182144064811014, 0.06776828489576776, 0.09822422040936847, -0.19560974615714735, 0.03368630115541497, 0.1249127072154633, -0.07043304820434146, 0.04897029121362027, 0.03622338824373271, -0.05602186505261454, -0.2726962774371107, 0.303528265299974, 0.041550792642824706, -0.14705833455636388, 0.07793489253769319, -0.142674325588381, -0.23277879539611085, 0.11282593776871051, 0.12275226928648494, 0.21935060367520368, -0.15218024117694723, 0.12094548998616769, -0.18461556754828917, 0.0661906957448948, 0.2098318687418387, -0.03251414877983431, 0.1146293371649725, 0.14258721713641925, 0.1856743699782306, 0.3322321901186591, -0.0118295331263826, -0.07421106411631972, -0.31616640871479396, -0.12958130923410258, -0.20555420851867115, 0.027483672280574127, -0.21533582679382693, -0.29409427594925674, 0.46784738788292524, 0.1038177165624109, 0.1743760295156833, 0.17092749833439788, 0.2298020173218988, -0.05173546664272657, 0.09497147582338325, 0.1355960204022094, 0.0536713640516003, 0.24769137856284423, -0.033966822061865104, -0.11438016552433726, 0.052385207885111286, 0.19638173985073254] |
1,802.0087 | Box counting dimensions of generalised fractal nests | Fractal nests are sets defined as unions of unit $n$-spheres scaled by a
sequence of $k^{-\alpha}$ for some $\alpha>0$. In this article we generalise
the concept to subsets of such spheres and find the formulas for their box
counting dimensions. We introduce some novel classes of parameterised fractal
nests and apply these results to compute the dimensions with respect to these
parameters. We also show that these dimensions can be seen numerically. These
results motivate further research that may explain the unintuitive behaviour of
box counting dimensions for nest-type fractals, and in general the class of
sets where the box-counting dimension differs from the Hausdorff dimension.
| math.MG | fractal nests are sets defined as unions of unit nspheres scaled by a sequence of kalpha for some alpha0 in this article we generalise the concept to subsets of such spheres and find the formulas for their box counting dimensions we introduce some novel classes of parameterised fractal nests and apply these results to compute the dimensions with respect to these parameters we also show that these dimensions can be seen numerically these results motivate further research that may explain the unintuitive behaviour of box counting dimensions for nesttype fractals and in general the class of sets where the boxcounting dimension differs from the hausdorff dimension | [['fractal', 'nests', 'are', 'sets', 'defined', 'as', 'unions', 'of', 'unit', 'nspheres', 'scaled', 'by', 'a', 'sequence', 'of', 'kalpha', 'for', 'some', 'alpha0', 'in', 'this', 'article', 'we', 'generalise', 'the', 'concept', 'to', 'subsets', 'of', 'such', 'spheres', 'and', 'find', 'the', 'formulas', 'for', 'their', 'box', 'counting', 'dimensions', 'we', 'introduce', 'some', 'novel', 'classes', 'of', 'parameterised', 'fractal', 'nests', 'and', 'apply', 'these', 'results', 'to', 'compute', 'the', 'dimensions', 'with', 'respect', 'to', 'these', 'parameters', 'we', 'also', 'show', 'that', 'these', 'dimensions', 'can', 'be', 'seen', 'numerically', 'these', 'results', 'motivate', 'further', 'research', 'that', 'may', 'explain', 'the', 'unintuitive', 'behaviour', 'of', 'box', 'counting', 'dimensions', 'for', 'nesttype', 'fractals', 'and', 'in', 'general', 'the', 'class', 'of', 'sets', 'where', 'the', 'boxcounting', 'dimension', 'differs', 'from', 'the', 'hausdorff', 'dimension']] | [-0.05635299144224042, 0.12284737029002003, -0.015452841946500397, 0.1118515006432842, -0.036229995110382635, -0.09700117903717217, 0.009442299751875301, 0.3626170941406772, -0.261138744288612, -0.2558777447019349, 0.08641011438642938, -0.2953616282858309, -0.18227805724101406, 0.22488786269511496, -0.11536135929789106, 0.05150036775130069, -0.0019223973975472507, 0.0241955555132812, -0.05043368073889897, -0.2922200068809269, 0.40482754760554857, -0.04549499453888053, 0.2121543185164531, 0.055260460016628106, 0.04023882659773032, -0.04405918371464525, -0.047134707502222484, 0.13121712755057074, -0.2059784582645599, 0.16350404112821534, 0.24009841900496257, 0.1301021541646194, 0.19734521923320633, -0.3768984779715538, -0.20886459674331403, 0.1289905501307831, 0.18268039776012301, 0.046836770496641596, 0.01620754182693504, -0.26400342518907216, 0.11429390045148986, -0.13809586433316803, -0.2046498359380556, -0.14336141792369916, 0.0496810942001286, 0.05353091540746391, -0.25629553702615554, 0.011502170928343687, 0.10205236483099205, 0.05171788168982381, -0.045759467785024925, -0.1196488065722709, 0.021526534394139336, 0.14122425048789453, 0.04567765111014956, -0.03590920594565215, 0.0906694322691432, -0.06233522702851111, -0.17967971766456253, 0.3507137846645145, 0.013652254166525034, -0.26708548876146476, 0.2283186460756475, -0.184955886933243, -0.19012004113977865, 0.07391217092406892, 0.17577901808544993, 0.11283283102370444, -0.08255561069097547, 0.1173662965967586, -0.09890089242586068, 0.1354498306023223, 0.13535915396309325, 0.03959155532897317, 0.1375660117892992, 0.1092692876712092, 0.05604735631495714, 0.20758470401190043, -0.06976184806276467, -0.08740181237170916, -0.31826157451695986, -0.1646957010829023, -0.1805094248459985, 0.057564900975142205, -0.1632946184876519, -0.20274687618837647, 0.3534425684738727, 0.16444427075773654, 0.2443765161913775, 0.15032066318783022, 0.20926753645762802, 0.10159670036352639, 0.06023120133960176, 0.07445636965511811, 0.1478934110391752, 0.0694324046484239, 0.031848166395156155, -0.12525874174732182, -0.015853194129608925, 0.15281133573859865] |
1,802.00871 | Two-Phase Heating in Flaring Loops | We analyze and model a C5.7 two-ribbon solar flare observed by SDO, Hinode
and GOES on 2011 December 26. The flare is made of many loops formed and heated
successively over one and half hours, and their footpoints are brightened in
the UV 1600 A before enhanced soft X-ray and EUV missions are observed in flare
loops. Assuming that anchored at each brightened UV pixel is a half flaring
loop, we identify more than 6,700 half flaring loops, and infer the heating
rate of each loop from the UV light curve at the foot-point. In each half loop,
the heating rate consists of two phases, an intense impulsive heating followed
by a low-rate heating persistent for more than 20 minutes. Using these heating
rates, we simulate the evolution of their coronal temperatures and densities
with the model of "enthalpy-based thermal evolution of loops" (EBTEL). In the
model, suppression of thermal conduction is also considered. This model
successfully reproduces total soft X-ray and EUV light curves observed in
fifteen pass-bands by four instruments GOES, AIA, XRT, and EVE. In this flare,
a total energy of 4.9x10^30 ergs is required to heat the corona, around 40% of
this energy is in the slow heating phase. About two fifth of the total energy
used to heat the corona is radiated by the coronal plasmas, and the other three
fifth transported to the lower atmosphere by thermal conduction.
| astro-ph.SR | we analyze and model a c57 tworibbon solar flare observed by sdo hinode and goes on 2011 december 26 the flare is made of many loops formed and heated successively over one and half hours and their footpoints are brightened in the uv 1600 a before enhanced soft xray and euv missions are observed in flare loops assuming that anchored at each brightened uv pixel is a half flaring loop we identify more than 6700 half flaring loops and infer the heating rate of each loop from the uv light curve at the footpoint in each half loop the heating rate consists of two phases an intense impulsive heating followed by a lowrate heating persistent for more than 20 minutes using these heating rates we simulate the evolution of their coronal temperatures and densities with the model of enthalpybased thermal evolution of loops ebtel in the model suppression of thermal conduction is also considered this model successfully reproduces total soft xray and euv light curves observed in fifteen passbands by four instruments goes aia xrt and eve in this flare a total energy of 49x1030 ergs is required to heat the corona around 40 of this energy is in the slow heating phase about two fifth of the total energy used to heat the corona is radiated by the coronal plasmas and the other three fifth transported to the lower atmosphere by thermal conduction | [['we', 'analyze', 'and', 'model', 'a', 'c57', 'tworibbon', 'solar', 'flare', 'observed', 'by', 'sdo', 'hinode', 'and', 'goes', 'on', '2011', 'december', '26', 'the', 'flare', 'is', 'made', 'of', 'many', 'loops', 'formed', 'and', 'heated', 'successively', 'over', 'one', 'and', 'half', 'hours', 'and', 'their', 'footpoints', 'are', 'brightened', 'in', 'the', 'uv', '1600', 'a', 'before', 'enhanced', 'soft', 'xray', 'and', 'euv', 'missions', 'are', 'observed', 'in', 'flare', 'loops', 'assuming', 'that', 'anchored', 'at', 'each', 'brightened', 'uv', 'pixel', 'is', 'a', 'half', 'flaring', 'loop', 'we', 'identify', 'more', 'than', '6700', 'half', 'flaring', 'loops', 'and', 'infer', 'the', 'heating', 'rate', 'of', 'each', 'loop', 'from', 'the', 'uv', 'light', 'curve', 'at', 'the', 'footpoint', 'in', 'each', 'half', 'loop', 'the', 'heating', 'rate', 'consists', 'of', 'two', 'phases', 'an', 'intense', 'impulsive', 'heating', 'followed', 'by', 'a', 'lowrate', 'heating', 'persistent', 'for', 'more', 'than', '20', 'minutes', 'using', 'these', 'heating', 'rates', 'we', 'simulate', 'the', 'evolution', 'of', 'their', 'coronal', 'temperatures', 'and', 'densities', 'with', 'the', 'model', 'of', 'enthalpybased', 'thermal', 'evolution', 'of', 'loops', 'ebtel', 'in', 'the', 'model', 'suppression', 'of', 'thermal', 'conduction', 'is', 'also', 'considered', 'this', 'model', 'successfully', 'reproduces', 'total', 'soft', 'xray', 'and', 'euv', 'light', 'curves', 'observed', 'in', 'fifteen', 'passbands', 'by', 'four', 'instruments', 'goes', 'aia', 'xrt', 'and', 'eve', 'in', 'this', 'flare', 'a', 'total', 'energy', 'of', '49x1030', 'ergs', 'is', 'required', 'to', 'heat', 'the', 'corona', 'around', '40', 'of', 'this', 'energy', 'is', 'in', 'the', 'slow', 'heating', 'phase', 'about', 'two', 'fifth', 'of', 'the', 'total', 'energy', 'used', 'to', 'heat', 'the', 'corona', 'is', 'radiated', 'by', 'the', 'coronal', 'plasmas', 'and', 'the', 'other', 'three', 'fifth', 'transported', 'to', 'the', 'lower', 'atmosphere', 'by', 'thermal', 'conduction']] | [-0.08568930233013816, 0.2593891222972914, 0.013269365811319059, 0.139681660214435, -0.030605418141931295, -0.08969581633121787, 0.049386874304939835, 0.4572668475187223, -0.18661457434264495, -0.3790259137749672, 0.10176067563086941, -0.31418308357162206, -0.05228500329921472, 0.25140039743614734, -0.02678431090015661, 9.961769733465566e-05, 0.07290997579874439, -0.025744768546277993, -0.025537902073822272, -0.2394914091609876, 0.22598834409968574, 0.11539446552492806, 0.17357926635204107, 0.005109423775514909, 0.07505492134981162, -0.08879304540585445, -0.059554315741165896, -0.025256372187366902, -0.08855914016049642, 0.03474159008604555, 0.15128542712773196, 0.05865129679120306, 0.23188633276430634, -0.4677140781061788, -0.28944296456455926, 0.003876273262370699, 0.12915325044039114, -0.05044808990802768, 0.04108384381746372, -0.18090161125693502, 0.019225860250399223, -0.14641796407321916, -0.10370426336985789, 0.1125117045253162, 0.022365670006898843, -0.05125712147002622, -0.2372365996044868, 0.080351060793152, -0.0007048489060252905, 0.0760682160687103, -0.13501258337958555, -0.017087913864966612, -0.10829691344607172, 0.11856988106115625, 0.07734673181829717, 0.06021881254253395, 0.20353001991968306, -0.10241475075198306, -0.09044570802431955, 0.32083432580431437, -0.057168174118173896, 0.06250970803426001, 0.1712263531336593, -0.2267336393641895, -0.11019739436270701, 0.29092572903629904, 0.10489912099277215, 0.09031253809747607, -0.1700132294650318, -0.021107821258393132, -0.0007837803378010746, 0.1655330087694516, 0.10586656189066004, 0.006919017334815902, 0.2778756588392345, 0.12743245383447582, 0.01335333954469614, 0.19690789001695772, -0.24541093783898696, -0.05796880949617781, -0.28491941897062073, -0.10298558464229904, -0.06890426265730137, 0.06676443731352076, -0.07171091751190724, -0.16381972370034983, 0.44248559194665144, 0.13707450116309136, 0.21251488475988073, -0.03871335913045813, 0.3284142250285468, 0.13156178915885214, 0.054255582026912476, 0.20537356721590563, 0.3112283799152998, 0.1715779877157384, 0.2062348063211238, -0.24175208110005017, 0.018003423680182033, 0.08540987959713675] |
1,802.00872 | Load-Balanced Fractional Repetition Codes | We introduce load-balanced fractional repetition (LBFR) codes, which are a
strengthening of fractional repetition (FR) codes. LBFR codes have the
additional property that multiple node failures can be sequentially repaired by
downloading no more than one block from any other node. This allows for better
use of the network, and can additionally reduce the number of disk reads
necessary to repair multiple nodes. We characterize LBFR codes in terms of
their adjacency graphs, and use this characterization to present explicit
constructions LBFR codes with storage capacity comparable existing FR codes.
Surprisingly, in some parameter regimes, our constructions of LBFR codes match
the parameters of the best constructions of FR codes.
| cs.IT math.IT | we introduce loadbalanced fractional repetition lbfr codes which are a strengthening of fractional repetition fr codes lbfr codes have the additional property that multiple node failures can be sequentially repaired by downloading no more than one block from any other node this allows for better use of the network and can additionally reduce the number of disk reads necessary to repair multiple nodes we characterize lbfr codes in terms of their adjacency graphs and use this characterization to present explicit constructions lbfr codes with storage capacity comparable existing fr codes surprisingly in some parameter regimes our constructions of lbfr codes match the parameters of the best constructions of fr codes | [['we', 'introduce', 'loadbalanced', 'fractional', 'repetition', 'lbfr', 'codes', 'which', 'are', 'a', 'strengthening', 'of', 'fractional', 'repetition', 'fr', 'codes', 'lbfr', 'codes', 'have', 'the', 'additional', 'property', 'that', 'multiple', 'node', 'failures', 'can', 'be', 'sequentially', 'repaired', 'by', 'downloading', 'no', 'more', 'than', 'one', 'block', 'from', 'any', 'other', 'node', 'this', 'allows', 'for', 'better', 'use', 'of', 'the', 'network', 'and', 'can', 'additionally', 'reduce', 'the', 'number', 'of', 'disk', 'reads', 'necessary', 'to', 'repair', 'multiple', 'nodes', 'we', 'characterize', 'lbfr', 'codes', 'in', 'terms', 'of', 'their', 'adjacency', 'graphs', 'and', 'use', 'this', 'characterization', 'to', 'present', 'explicit', 'constructions', 'lbfr', 'codes', 'with', 'storage', 'capacity', 'comparable', 'existing', 'fr', 'codes', 'surprisingly', 'in', 'some', 'parameter', 'regimes', 'our', 'constructions', 'of', 'lbfr', 'codes', 'match', 'the', 'parameters', 'of', 'the', 'best', 'constructions', 'of', 'fr', 'codes']] | [-0.19433728099525482, 0.03820238122716546, -0.019427575471557, 0.10134459253273566, -0.06767781544688412, -0.2827899030452086, 0.0623977517560971, 0.37374678586017, -0.2944900397041982, -0.310169762305238, 0.11600196309260685, -0.2310578636551244, -0.12253625386352228, 0.1873692854053595, -0.16113838242705572, 0.06936869573017412, 0.07761906786737117, 0.018907739425247365, -0.09895501493857327, -0.31853467519606715, 0.2929771108722145, 0.18743779042270034, 0.21437570418823848, -0.04272287787276913, -0.003677653974260796, -0.03583452885716476, -0.04303030615503138, -0.01695548181781884, -0.20573403271879545, 0.12243850528347221, 0.29384563409469344, 0.18101986765184186, 0.20517651115290142, -0.42415671047161924, -0.2619953265074979, 0.11926634475080804, 0.1398892391938716, 0.1824150577282787, -0.007045795170151079, -0.16807561626827175, 0.16263427341102876, -0.22606259177184918, 0.008374348818324506, -0.03257445316253738, -0.032421687076037584, 0.07035746415971186, -0.2908972120251168, -0.027476367155428637, 0.061274249496107755, 0.030953276877037503, -0.006168144366132434, -0.13641852429798085, 0.026822613772343507, 0.14069147240965818, -0.021321459698744796, 0.02420698064485226, 0.061587018752470614, -0.06511976076925004, -0.18753111479401757, 0.3342312234707854, -0.0060456525689170865, -0.20974182899914343, 0.1548830636988648, -0.0706469554242424, -0.13498901331966573, 0.1256916076071899, 0.1654714950826019, 0.1083677204114131, -0.1076117024206641, 0.05812643823446706, -0.03827068716372278, 0.1833485521884127, 0.15572418983263725, 0.1621890135035342, 0.14289954104884103, 0.052609638526866384, 0.07807705161606215, 0.19707492734675974, -0.03351862938566642, -0.03223681518469344, -0.2674404275360179, -0.11953691763126037, -0.17347435394979335, 0.0067466241392222315, -0.20510427984382576, -0.14866106980903582, 0.4178194486790083, 0.15729291961020367, 0.13019138390399432, 0.14630580582253805, 0.2654712312092835, -0.013944434967759827, 0.1608149094697596, 0.20694446710615672, 0.12310530174740549, 0.11768436018187045, 0.014142123855311762, -0.18562297051222149, 0.09587150753306395, 0.09069496437229893] |
1,802.00873 | Machine Learning Modeling of Wigner Intracule Functionals for Two
Electrons in One Dimension | In principle, many-electron correlation energy can be precisely computed from
a reduced Wigner distribution function ($\mathcal{W}$) thanks to a universal
functional transformation ($\mathcal{F}$), whose formal existence is akin to
that of the exchange-correlation functional in density functional theory. While
the exact dependence of $\mathcal{F}$ on $\mathcal{W}$ is unknown, a few
approximate parametric models have been proposed in the past. Here, for a
dataset of 923 one-dimensional external potentials with two interacting
electrons, we apply machine learning to model $\mathcal{F}$ within the kernel
Ansatz. We deal with over-fitting of the kernel to a specific region of
phase-space by a one-step regularization not depending on any hyperparameters.
Reference correlation energies have been computed by performing exact and
Hartree--Fock calculations using discrete variable representation. The
resulting models require $\mathcal{W}$ calculated at the Hartree--Fock level as
input while yielding monotonous decay in the predicted correlation energies of
new molecules reaching sub-chemical accuracy with training.
| physics.chem-ph | in principle manyelectron correlation energy can be precisely computed from a reduced wigner distribution function mathcalw thanks to a universal functional transformation mathcalf whose formal existence is akin to that of the exchangecorrelation functional in density functional theory while the exact dependence of mathcalf on mathcalw is unknown a few approximate parametric models have been proposed in the past here for a dataset of 923 onedimensional external potentials with two interacting electrons we apply machine learning to model mathcalf within the kernel ansatz we deal with overfitting of the kernel to a specific region of phasespace by a onestep regularization not depending on any hyperparameters reference correlation energies have been computed by performing exact and hartreefock calculations using discrete variable representation the resulting models require mathcalw calculated at the hartreefock level as input while yielding monotonous decay in the predicted correlation energies of new molecules reaching subchemical accuracy with training | [['in', 'principle', 'manyelectron', 'correlation', 'energy', 'can', 'be', 'precisely', 'computed', 'from', 'a', 'reduced', 'wigner', 'distribution', 'function', 'mathcalw', 'thanks', 'to', 'a', 'universal', 'functional', 'transformation', 'mathcalf', 'whose', 'formal', 'existence', 'is', 'akin', 'to', 'that', 'of', 'the', 'exchangecorrelation', 'functional', 'in', 'density', 'functional', 'theory', 'while', 'the', 'exact', 'dependence', 'of', 'mathcalf', 'on', 'mathcalw', 'is', 'unknown', 'a', 'few', 'approximate', 'parametric', 'models', 'have', 'been', 'proposed', 'in', 'the', 'past', 'here', 'for', 'a', 'dataset', 'of', '923', 'onedimensional', 'external', 'potentials', 'with', 'two', 'interacting', 'electrons', 'we', 'apply', 'machine', 'learning', 'to', 'model', 'mathcalf', 'within', 'the', 'kernel', 'ansatz', 'we', 'deal', 'with', 'overfitting', 'of', 'the', 'kernel', 'to', 'a', 'specific', 'region', 'of', 'phasespace', 'by', 'a', 'onestep', 'regularization', 'not', 'depending', 'on', 'any', 'hyperparameters', 'reference', 'correlation', 'energies', 'have', 'been', 'computed', 'by', 'performing', 'exact', 'and', 'hartreefock', 'calculations', 'using', 'discrete', 'variable', 'representation', 'the', 'resulting', 'models', 'require', 'mathcalw', 'calculated', 'at', 'the', 'hartreefock', 'level', 'as', 'input', 'while', 'yielding', 'monotonous', 'decay', 'in', 'the', 'predicted', 'correlation', 'energies', 'of', 'new', 'molecules', 'reaching', 'subchemical', 'accuracy', 'with', 'training']] | [-0.04689592095529473, 0.08153247884818471, -0.11732052857893825, 0.0984435929594899, -0.03952573459429804, -0.1599293665510396, 0.06680669880240855, 0.4000202307865124, -0.23480374309165566, -0.3110818576361459, 0.011449794489702823, -0.26547785457723694, -0.0818164793855742, 0.1363057968044191, 0.005018612394701602, 0.12290211479435831, 0.06723907002679154, 0.07830684928336, -0.1596156656382693, -0.21003784188350674, 0.31083178228617414, 0.045428111371052945, 0.26074125521485036, 0.020062271771239953, 0.12785009987927323, 0.007689729534502097, 0.03788378723778281, 0.012497096669378897, -0.10981002957397024, 0.129955064888181, 0.2672369008790174, 0.07853527102406893, 0.3233986524052468, -0.42177185648270954, -0.2555741469929348, 0.11626953131913038, 0.1289409907822241, 0.10618307295680396, -0.02554586207909797, -0.2773121241605122, 0.05358601663567926, -0.19979899203648555, -0.11758764311843145, -0.15375529232141155, 0.00972473629181517, 0.055380371064041437, -0.2898120228919957, 0.12792487421405369, 0.0013286649365128087, 0.05396200405333571, -0.07603965231687011, -0.1255236865902607, -0.04770313661886166, 0.05822244711882816, 0.02698269109132281, 0.08384966195952429, 0.14187361548995386, -0.11687520824547783, -0.08324718754621373, 0.33120363410023174, -0.08178068133033561, -0.24126555175589234, 0.13769988211546869, -0.12030630484973424, -0.14651738619275592, 0.15669883090233894, 0.14589537544044723, 0.11159061650501802, -0.1748743360055823, 0.15038835228813013, -0.027475348446399425, 0.17571779052026087, 0.0703887788644498, 0.011881852075582582, 0.12400229090837224, 0.13645216828405907, 0.009424954871553303, 0.10973721648169518, -0.07421706452256306, -0.11902758892431806, -0.29226170885282876, -0.08037421586548273, -0.23781943384982995, 0.05981756767405325, -0.08486298678570581, -0.2058573915566874, 0.4118215943711666, 0.11975713928658656, 0.18715127193112702, 0.07439466265042706, 0.23929815302171664, 0.1761475433056374, 0.08427848443451444, 0.06337412993967433, 0.20601540948647143, 0.12370895398561936, -0.0006583040320758251, -0.2143220565713092, 0.08060954938542673, 0.09276893831345709] |
1,802.00874 | Quantum phase diagram of spin-$1$ $J_1-J_2$ Heisenberg model on the
square lattice: an infinite projected entangled-pair state and density matrix
renormalization group study | We study the spin-$1$ Heisenberg model on the square lattice with the
antiferromagnetic nearest-neighbor $J_1$ and the next-nearest-neighbor $J_2$
couplings by using the infinite projected entangled-pair state (iPEPS) ansatz
and density matrix renormalization group (DMRG) calculation. The iPEPS
simulation, which studies the model directly in the thermodynamic limit, finds
a crossing of the ground state from the N\'eel magnetic state to the stripe
magnetic state at $J_2/J_1 \simeq 0.549$, showing a direct phase transition. In
the finite-size DMRG calculation on the cylinder geometry up to the cylinder
width $L_y = 10$, we find a very small intermediate regime $\sim 0.005 J_1$
between the two magnetic order phases, which may imply the absent intermediate
phase. Both calculations identify that the stripe order comes with a
first-order transition at $J_2/J_1 \simeq 0.549$. Our results indicate that
unlike the spin-$1/2$ $J_1-J_2$ square model, quantum fluctuations in the
spin-$1$ model may be not strong enough to stabilize an intermediate
non-magnetic phase.
| cond-mat.str-el quant-ph | we study the spin1 heisenberg model on the square lattice with the antiferromagnetic nearestneighbor j_1 and the nextnearestneighbor j_2 couplings by using the infinite projected entangledpair state ipeps ansatz and density matrix renormalization group dmrg calculation the ipeps simulation which studies the model directly in the thermodynamic limit finds a crossing of the ground state from the neel magnetic state to the stripe magnetic state at j_2j_1 simeq 0549 showing a direct phase transition in the finitesize dmrg calculation on the cylinder geometry up to the cylinder width l_y 10 we find a very small intermediate regime sim 0005 j_1 between the two magnetic order phases which may imply the absent intermediate phase both calculations identify that the stripe order comes with a firstorder transition at j_2j_1 simeq 0549 our results indicate that unlike the spin12 j_1j_2 square model quantum fluctuations in the spin1 model may be not strong enough to stabilize an intermediate nonmagnetic phase | [['we', 'study', 'the', 'spin1', 'heisenberg', 'model', 'on', 'the', 'square', 'lattice', 'with', 'the', 'antiferromagnetic', 'nearestneighbor', 'j_1', 'and', 'the', 'nextnearestneighbor', 'j_2', 'couplings', 'by', 'using', 'the', 'infinite', 'projected', 'entangledpair', 'state', 'ipeps', 'ansatz', 'and', 'density', 'matrix', 'renormalization', 'group', 'dmrg', 'calculation', 'the', 'ipeps', 'simulation', 'which', 'studies', 'the', 'model', 'directly', 'in', 'the', 'thermodynamic', 'limit', 'finds', 'a', 'crossing', 'of', 'the', 'ground', 'state', 'from', 'the', 'neel', 'magnetic', 'state', 'to', 'the', 'stripe', 'magnetic', 'state', 'at', 'j_2j_1', 'simeq', '0549', 'showing', 'a', 'direct', 'phase', 'transition', 'in', 'the', 'finitesize', 'dmrg', 'calculation', 'on', 'the', 'cylinder', 'geometry', 'up', 'to', 'the', 'cylinder', 'width', 'l_y', '10', 'we', 'find', 'a', 'very', 'small', 'intermediate', 'regime', 'sim', '0005', 'j_1', 'between', 'the', 'two', 'magnetic', 'order', 'phases', 'which', 'may', 'imply', 'the', 'absent', 'intermediate', 'phase', 'both', 'calculations', 'identify', 'that', 'the', 'stripe', 'order', 'comes', 'with', 'a', 'firstorder', 'transition', 'at', 'j_2j_1', 'simeq', '0549', 'our', 'results', 'indicate', 'that', 'unlike', 'the', 'spin12', 'j_1j_2', 'square', 'model', 'quantum', 'fluctuations', 'in', 'the', 'spin1', 'model', 'may', 'be', 'not', 'strong', 'enough', 'to', 'stabilize', 'an', 'intermediate', 'nonmagnetic', 'phase']] | [-0.21053771555531198, 0.24466169792765122, -0.02822847669132245, 0.05625057266129611, -0.00856703518030162, -0.14072150538245645, 0.08279928469952817, 0.37496285513043404, -0.23907582423028848, -0.2533418996638666, 0.06789761305210562, -0.3362971720411084, -0.053504238197675504, 0.09836658892769871, 0.17597071842875522, -0.022013073834853295, 0.012704015854232682, 0.029094130848534405, -0.18302256825830762, -0.2019852502256608, 0.2407535697290232, -0.018028521087193765, 0.2705026007949924, 0.056469661653794065, 0.029358473313876834, 0.028236629617495988, 0.16703699790956214, -0.005704236007892551, -0.21573390034432238, -0.008137530965121606, 0.22064260941876385, -0.11611871067912151, 0.16705884398797002, -0.3623047641550119, -0.14404409233695611, 0.0576342227958286, 0.1778983038578493, 0.1755930474580218, 0.015014218481388899, -0.3771293767405531, 0.022927008796101198, -0.2377936152759223, -0.1394307440843057, -0.13537250205916232, -0.0704115001628032, -0.04048028807916965, -0.27725522847284967, 0.15275420846107104, 0.05523159662381005, 0.055882432963279334, -0.05146671530420486, -0.15000689349686488, -0.08210307862221573, 0.06179234159823794, 0.05379307347361763, 0.12525406533714908, 0.08892293353206836, -0.11376106216070744, -0.09536502950896437, 0.35847111913160634, -0.05269305377106111, -0.10874104666977356, 0.150453160266177, -0.18751837744443597, -0.116085443389005, 0.1635554163561513, 0.0767591123969909, 0.05039491340255317, -0.05065697010081166, 0.10368457059181917, -0.026658110858615823, 0.2668710671401081, -0.06184916158254521, -0.01247732401959813, 0.23517076227551278, 0.1490184599832178, 0.06000537998998203, 0.1642259020568767, -0.16247779954051694, -0.21009423537478328, -0.28076326350371045, -0.10265416807781619, -0.24998293668091393, 0.044422789090346596, -0.18073408571795788, -0.19678433990786567, 0.33519782380086, 0.18512920979544736, 0.18990067481540907, 0.024809707429271955, 0.21371267348504028, 0.06526515049373326, 0.0283910856456854, 0.07531245423179382, 0.26252221760268396, 0.20779418137420017, 0.07341340516144648, -0.2978560821890521, 0.0007504947761145348, 0.11184302308948901] |
1,802.00875 | On taking advantage of multiple requests in error correcting codes | In most notions of locality in error correcting codes -- notably locally
recoverable codes (LRCs) and locally decodable codes (LDCs) -- a decoder seeks
to learn a single symbol of a message while looking at only a few symbols of
the corresponding codeword. However, suppose that one wants to recover r > 1
symbols of the message. The two extremes are repeating the single-query
algorithm r times (this is the intuition behind LRCs with availability,
primitive multiset batch codes, and PIR codes) or simply running a global
decoding algorithm to recover the whole thing. In this paper, we investigate
what can happen in between these two extremes: at what value of r does
repetition stop being a good idea? In order to begin to study this question we
introduce robust batch codes, which seek to find r symbols of the message using
m queries to the codeword, in the presence of erasures. We focus on the case
where r = m, which can be seen as a generalization of the MDS property.
Surprisingly, we show that for this notion of locality, repetition is optimal
even up to very large values of $r = \Omega(k)$.
| cs.IT math.IT | in most notions of locality in error correcting codes notably locally recoverable codes lrcs and locally decodable codes ldcs a decoder seeks to learn a single symbol of a message while looking at only a few symbols of the corresponding codeword however suppose that one wants to recover r 1 symbols of the message the two extremes are repeating the singlequery algorithm r times this is the intuition behind lrcs with availability primitive multiset batch codes and pir codes or simply running a global decoding algorithm to recover the whole thing in this paper we investigate what can happen in between these two extremes at what value of r does repetition stop being a good idea in order to begin to study this question we introduce robust batch codes which seek to find r symbols of the message using m queries to the codeword in the presence of erasures we focus on the case where r m which can be seen as a generalization of the mds property surprisingly we show that for this notion of locality repetition is optimal even up to very large values of r omegak | [['in', 'most', 'notions', 'of', 'locality', 'in', 'error', 'correcting', 'codes', 'notably', 'locally', 'recoverable', 'codes', 'lrcs', 'and', 'locally', 'decodable', 'codes', 'ldcs', 'a', 'decoder', 'seeks', 'to', 'learn', 'a', 'single', 'symbol', 'of', 'a', 'message', 'while', 'looking', 'at', 'only', 'a', 'few', 'symbols', 'of', 'the', 'corresponding', 'codeword', 'however', 'suppose', 'that', 'one', 'wants', 'to', 'recover', 'r', '1', 'symbols', 'of', 'the', 'message', 'the', 'two', 'extremes', 'are', 'repeating', 'the', 'singlequery', 'algorithm', 'r', 'times', 'this', 'is', 'the', 'intuition', 'behind', 'lrcs', 'with', 'availability', 'primitive', 'multiset', 'batch', 'codes', 'and', 'pir', 'codes', 'or', 'simply', 'running', 'a', 'global', 'decoding', 'algorithm', 'to', 'recover', 'the', 'whole', 'thing', 'in', 'this', 'paper', 'we', 'investigate', 'what', 'can', 'happen', 'in', 'between', 'these', 'two', 'extremes', 'at', 'what', 'value', 'of', 'r', 'does', 'repetition', 'stop', 'being', 'a', 'good', 'idea', 'in', 'order', 'to', 'begin', 'to', 'study', 'this', 'question', 'we', 'introduce', 'robust', 'batch', 'codes', 'which', 'seek', 'to', 'find', 'r', 'symbols', 'of', 'the', 'message', 'using', 'm', 'queries', 'to', 'the', 'codeword', 'in', 'the', 'presence', 'of', 'erasures', 'we', 'focus', 'on', 'the', 'case', 'where', 'r', 'm', 'which', 'can', 'be', 'seen', 'as', 'a', 'generalization', 'of', 'the', 'mds', 'property', 'surprisingly', 'we', 'show', 'that', 'for', 'this', 'notion', 'of', 'locality', 'repetition', 'is', 'optimal', 'even', 'up', 'to', 'very', 'large', 'values', 'of', 'r', 'omegak']] | [-0.1983514700525948, 0.07012976968486019, -0.06603412248844666, 0.09109823488896447, -0.04389223553949878, -0.28726832615088416, 0.08180589991234855, 0.3688380706601988, -0.34667053182853297, -0.2810840907422875, 0.12844166045185554, -0.2595479784917737, -0.11572750554932527, 0.1349217978678151, -0.15590605935791418, 0.06074297550836857, 0.04535412253742969, 0.10630337248442981, -0.11936864968285793, -0.3502915299401456, 0.29743963298658843, 0.13226957070991083, 0.20890722495735323, -0.03882360599304318, 0.06578876787769515, 0.005872075427471409, -0.031260752609731365, -0.021374182330551324, -0.16078626253882672, 0.10003252010595388, 0.32595663126829083, 0.20738038473649237, 0.27126386151932397, -0.3522997094594218, -0.17495382218449204, 0.1161897573176594, 0.13901662496386696, 0.15737185277483332, -0.008272920983409874, -0.18765752700098332, 0.18670665754483293, -0.16767281693623218, -0.00415757174416391, 0.024745626527835768, 0.013726924972303172, -0.010820987128970465, -0.31453458147330415, -0.03249431683351754, 0.08209181569083027, 0.04328788309421196, 0.01549699066807984, -0.07763237083853079, 0.057336688055238, 0.12049058598476073, 0.04401571532987334, 0.07584104262336734, 0.06827793175246193, -0.04425700559224145, -0.09687608815101345, 0.36736063590204276, -0.03476754191058082, -0.19116416221937885, 0.1288713195026118, -0.13688157924091215, -0.11700419229627759, 0.10567142526624064, 0.20251645784450586, 0.12189605708231549, -0.09926606013954534, 0.0851912298233608, -0.07129534966529165, 0.2155915645952706, 0.13557025207521975, 0.11271597316646308, 0.1609431635834789, 0.07263604913981109, 0.08597148073807595, 0.14791346357324747, -0.0478243526017722, -0.03084334311514068, -0.2863680789550205, -0.1275742634879573, -0.21896381241456658, 0.04943645865688465, -0.11642206317567139, -0.14621936332130442, 0.37301002708928926, 0.1654912298323498, 0.187800448230177, 0.14976513240777115, 0.25811067081633066, -0.013597445937597917, 0.11145521793021727, 0.21630616595465985, 0.14560137960133412, 0.10882070631579688, 0.03575274551154247, -0.14687885370945666, 0.09742956560982204, 0.09961851042690416] |
1,802.00876 | Observation of guided acoustic waves in a human skull | Human skull poses a significant barrier for the propagation of ultrasound
waves. Development of methods enabling more efficient ultrasound transmission
into and from the brain is therefore critical for the advancement of
ultrasound-mediated transcranial imaging or actuation techniques. We report on
the first observation of guided acoustic waves in the near-field of an ex vivo
human skull specimen in the frequency range between 0.2 and 1.5 MHz. In
contrast to what was previously observed for the guided wave propagation in
thin rodent skulls, the guided wave observed in a higher frequency regime
corresponds to a quasi-Rayleigh wave, mostly confined to the cortical bone
layer. The newly discovered near-field properties of the human skull are
expected to facilitate the development of more efficient diagnostic and
therapeutic techniques based on transcranial ultrasound.
| physics.med-ph cond-mat.mtrl-sci | human skull poses a significant barrier for the propagation of ultrasound waves development of methods enabling more efficient ultrasound transmission into and from the brain is therefore critical for the advancement of ultrasoundmediated transcranial imaging or actuation techniques we report on the first observation of guided acoustic waves in the nearfield of an ex vivo human skull specimen in the frequency range between 02 and 15 mhz in contrast to what was previously observed for the guided wave propagation in thin rodent skulls the guided wave observed in a higher frequency regime corresponds to a quasirayleigh wave mostly confined to the cortical bone layer the newly discovered nearfield properties of the human skull are expected to facilitate the development of more efficient diagnostic and therapeutic techniques based on transcranial ultrasound | [['human', 'skull', 'poses', 'a', 'significant', 'barrier', 'for', 'the', 'propagation', 'of', 'ultrasound', 'waves', 'development', 'of', 'methods', 'enabling', 'more', 'efficient', 'ultrasound', 'transmission', 'into', 'and', 'from', 'the', 'brain', 'is', 'therefore', 'critical', 'for', 'the', 'advancement', 'of', 'ultrasoundmediated', 'transcranial', 'imaging', 'or', 'actuation', 'techniques', 'we', 'report', 'on', 'the', 'first', 'observation', 'of', 'guided', 'acoustic', 'waves', 'in', 'the', 'nearfield', 'of', 'an', 'ex', 'vivo', 'human', 'skull', 'specimen', 'in', 'the', 'frequency', 'range', 'between', '02', 'and', '15', 'mhz', 'in', 'contrast', 'to', 'what', 'was', 'previously', 'observed', 'for', 'the', 'guided', 'wave', 'propagation', 'in', 'thin', 'rodent', 'skulls', 'the', 'guided', 'wave', 'observed', 'in', 'a', 'higher', 'frequency', 'regime', 'corresponds', 'to', 'a', 'quasirayleigh', 'wave', 'mostly', 'confined', 'to', 'the', 'cortical', 'bone', 'layer', 'the', 'newly', 'discovered', 'nearfield', 'properties', 'of', 'the', 'human', 'skull', 'are', 'expected', 'to', 'facilitate', 'the', 'development', 'of', 'more', 'efficient', 'diagnostic', 'and', 'therapeutic', 'techniques', 'based', 'on', 'transcranial', 'ultrasound']] | [-0.0751808593998878, 0.13097200720098603, -0.027375749147568757, 0.0010978966960325264, -0.12407928812675752, -0.07592463366902219, 0.03206434398889542, 0.41967044432575884, -0.21430527271989447, -0.26816074180488403, 0.08126493001338811, -0.26663187748680894, -0.21720328969486918, 0.2813410787896898, -0.04850933100455082, 0.10424571467133668, 0.03374819380696863, 0.003463515406474471, 0.022086274648944918, -0.07962182841192071, 0.21147781931347429, 0.0741323393339721, 0.3547947998301914, 0.042041510264747416, 0.08709935329567928, 0.006530480344708149, -0.01552894411322016, -0.05671069602607391, -0.1279021123639093, 0.1677899070442296, 0.3064525546506047, 0.12216886592796072, 0.309790306387899, -0.5175672661148173, -0.29078579646033736, 0.017041586633198536, 0.1828597891001174, 0.11023957053414331, -0.05652770823911012, -0.32712690338778955, 0.06466691546374932, -0.07981188469208204, -0.08878319449722767, 0.015324872642612229, 0.011498678396814144, -0.008712390452050245, -0.24305115815726683, 0.11621041134931147, 0.006168151861773087, 0.12959311905388649, -0.11609858293802693, -0.05290426786702413, 0.010116988757195381, 0.13328751452589552, 0.030521324062899043, 0.08252474847607888, 0.18126429601285893, -0.20262067441273338, -0.08961201411886857, 0.3440930861585702, 0.004747678446941651, -0.12821821978924652, 0.19883787455216337, -0.16948343413801362, 0.0019002112631614392, 0.18396467425538085, 0.21644916815969806, 0.10055768471748497, -0.14205162610249736, -0.04395598928363492, 0.06671003679276999, 0.20478176616777022, 0.1571054275541638, -0.02090160987483194, 0.200603785390894, 0.24199081046793322, -0.002489567419084219, 0.13089981319275326, -0.20523464308979994, 0.028784226476161096, -0.1914553073216946, -0.15544711064341335, -0.13545441996449462, -0.014503484807098106, -0.0479134753343085, -0.18013117710271706, 0.4098567700156799, 0.17270035456436184, 0.1409237514751462, -0.0024308335394240344, 0.3295625181085108, 0.053165075505295624, 0.1031822032125022, -0.02025069470400922, 0.3115637477182855, 0.11109725054926597, 0.15885948737055655, -0.23511363358881612, 0.07751948834540179, -0.019014671525488105] |
1,802.00877 | Small sphere limit of the quasi-local energy with anti de-Sitter space
reference | In [13], a new quasi-local energy is introduced for spacetimes with a
non-zero cosmological constant. In this article, we study the small sphere
limit of this newly defined quasi-local energy for spacetimes with a negative
cosmological constant. For such spacetimes, the anti de-Sitter space is used as
the reference for the quasi-local energy. Given a point $p$ in a spacetime $N$,
we consider a canonical family of surfaces approaching $p$ along its future
null cone and evaluate the limit of the quasi-local energy. The optimal
embedding equation which identifies the critical points of the quasi-local
energy is solved in order to evaluate the limit. Using the optimal embedding,
we show that the limit recovers the stress-energy tensor of the matter field at
$p$. For vacuum spacetimes, the quasi-local energy vanishes to a higher order.
In this case, the limit of the quasi-local energy is related to the
Bel-Robinson tensor at $p$.
| math.DG gr-qc | in 13 a new quasilocal energy is introduced for spacetimes with a nonzero cosmological constant in this article we study the small sphere limit of this newly defined quasilocal energy for spacetimes with a negative cosmological constant for such spacetimes the anti desitter space is used as the reference for the quasilocal energy given a point p in a spacetime n we consider a canonical family of surfaces approaching p along its future null cone and evaluate the limit of the quasilocal energy the optimal embedding equation which identifies the critical points of the quasilocal energy is solved in order to evaluate the limit using the optimal embedding we show that the limit recovers the stressenergy tensor of the matter field at p for vacuum spacetimes the quasilocal energy vanishes to a higher order in this case the limit of the quasilocal energy is related to the belrobinson tensor at p | [['in', '13', 'a', 'new', 'quasilocal', 'energy', 'is', 'introduced', 'for', 'spacetimes', 'with', 'a', 'nonzero', 'cosmological', 'constant', 'in', 'this', 'article', 'we', 'study', 'the', 'small', 'sphere', 'limit', 'of', 'this', 'newly', 'defined', 'quasilocal', 'energy', 'for', 'spacetimes', 'with', 'a', 'negative', 'cosmological', 'constant', 'for', 'such', 'spacetimes', 'the', 'anti', 'desitter', 'space', 'is', 'used', 'as', 'the', 'reference', 'for', 'the', 'quasilocal', 'energy', 'given', 'a', 'point', 'p', 'in', 'a', 'spacetime', 'n', 'we', 'consider', 'a', 'canonical', 'family', 'of', 'surfaces', 'approaching', 'p', 'along', 'its', 'future', 'null', 'cone', 'and', 'evaluate', 'the', 'limit', 'of', 'the', 'quasilocal', 'energy', 'the', 'optimal', 'embedding', 'equation', 'which', 'identifies', 'the', 'critical', 'points', 'of', 'the', 'quasilocal', 'energy', 'is', 'solved', 'in', 'order', 'to', 'evaluate', 'the', 'limit', 'using', 'the', 'optimal', 'embedding', 'we', 'show', 'that', 'the', 'limit', 'recovers', 'the', 'stressenergy', 'tensor', 'of', 'the', 'matter', 'field', 'at', 'p', 'for', 'vacuum', 'spacetimes', 'the', 'quasilocal', 'energy', 'vanishes', 'to', 'a', 'higher', 'order', 'in', 'this', 'case', 'the', 'limit', 'of', 'the', 'quasilocal', 'energy', 'is', 'related', 'to', 'the', 'belrobinson', 'tensor', 'at', 'p']] | [-0.18783537184955268, 0.11666865330436174, -0.07858566864430658, 0.10457365917402708, -0.04891851865717315, -0.09234901220346524, 0.001954695922230056, 0.2625715006266209, -0.21767402712921827, -0.25788077348473154, 0.04483082866379436, -0.28628869884530245, -0.05190661370242323, 0.13392774139327424, -0.03285486512009474, 0.07796918106726401, -0.010559138197402488, 0.1048727633573411, -0.12544667790335487, -0.21191810052502677, 0.41838289765234027, 0.10385278260816407, 0.2664318628229867, 0.04548730210259261, 0.1631907307487351, 0.007465682151289472, 0.018030428484896357, 0.051880635052652634, -0.19858207136327255, 0.08200654507744194, 0.2515564701714189, 0.057691353897483935, 0.2291147033714794, -0.344389924520491, -0.21417862686963862, 0.14805073707999772, 0.0992378827403392, 0.13002768762344927, -0.007581374830017421, -0.24515007822219256, 0.12030187658516502, -0.18314193821641683, -0.18124022218244557, -0.06261950777072632, 0.04301838799524707, -0.04209154041403818, -0.22437325064925515, 0.10645190936926481, 0.05416140064523117, -0.02230984301904574, -0.15438603833610723, -0.10472526032452946, -0.026815072701170745, 0.06109661756650798, 0.10062909044791013, 0.05680032869622062, 0.13187091448498484, -0.09487146183928176, -0.07559718786189888, 0.36489656930966674, -0.11184324803284142, -0.2598830534034217, 0.09186222662668157, -0.15387554393598613, -0.11710315289450747, 0.08505551277703008, 0.13133531182523298, 0.1920322554173651, -0.12855744386239853, 0.2094315502831916, -0.0013439117480587486, 0.0828190849585511, 0.12571292605431478, 0.015081965336761136, 0.24657204300966978, 0.09745087489078673, 0.11393628909949534, 0.14396389726771425, -0.06004172806560204, -0.09623008633235976, -0.41556490332332274, -0.2361929715913258, -0.23137579143466735, 0.11204085921381433, -0.1463486208958784, -0.18030136543640632, 0.3676192255672191, 0.10123636649145948, 0.19780076584479592, 0.06620038014802687, 0.2261981616206837, 0.12864036029065953, 0.02718288026170324, 0.1318352602126188, 0.2728525123663789, 0.10308677317044199, 0.13786370866309036, -0.20064375008647609, -0.07795290039568548, 0.09767879697105644] |
1,802.00878 | DECam Survey for Low-Mass Stars and Substellar Objects in the UCL and
LCC Subgroups of the Sco-Cen OB Association (SCOCENSUS) | Using images taken with the Dark Energy Camera (DECam), the first extensive
survey of low mass and substellar objects is made in the 15-20 Myr Upper
Centaurus Lupus (UCL) and Lower Centaurus Crux (LCC) subgroups of the Scorpius
Centaurus OB Association (Sco-Cen). Due to the size of our dataset (>2Tb) we
developed an extensive open source set of python libraries to reduce our
images, including astrometry, coaddition, and PSF photometry. Our survey
consists of 29$\times$3 deg$^2$ fields in the UCL and LCC subgroups of Sco-Cen
and the creation of a catalog with over 11 million point sources. We create a
prioritized list of candidate for members in UCL and LCC, with 118 \emph{best}
and another 348 \emph{good} candidates. We show that the luminosity and mass
functions of our low mass and substellar candidates are consistent with
measurements for the younger Upper Scorpius subgroup and estimates of a
universal IMF, with spectral types ranging from M1 down to L1.
| astro-ph.SR astro-ph.GA | using images taken with the dark energy camera decam the first extensive survey of low mass and substellar objects is made in the 1520 myr upper centaurus lupus ucl and lower centaurus crux lcc subgroups of the scorpius centaurus ob association scocen due to the size of our dataset 2tb we developed an extensive open source set of python libraries to reduce our images including astrometry coaddition and psf photometry our survey consists of 29times3 deg2 fields in the ucl and lcc subgroups of scocen and the creation of a catalog with over 11 million point sources we create a prioritized list of candidate for members in ucl and lcc with 118 emphbest and another 348 emphgood candidates we show that the luminosity and mass functions of our low mass and substellar candidates are consistent with measurements for the younger upper scorpius subgroup and estimates of a universal imf with spectral types ranging from m1 down to l1 | [['using', 'images', 'taken', 'with', 'the', 'dark', 'energy', 'camera', 'decam', 'the', 'first', 'extensive', 'survey', 'of', 'low', 'mass', 'and', 'substellar', 'objects', 'is', 'made', 'in', 'the', '1520', 'myr', 'upper', 'centaurus', 'lupus', 'ucl', 'and', 'lower', 'centaurus', 'crux', 'lcc', 'subgroups', 'of', 'the', 'scorpius', 'centaurus', 'ob', 'association', 'scocen', 'due', 'to', 'the', 'size', 'of', 'our', 'dataset', '2tb', 'we', 'developed', 'an', 'extensive', 'open', 'source', 'set', 'of', 'python', 'libraries', 'to', 'reduce', 'our', 'images', 'including', 'astrometry', 'coaddition', 'and', 'psf', 'photometry', 'our', 'survey', 'consists', 'of', '29times3', 'deg2', 'fields', 'in', 'the', 'ucl', 'and', 'lcc', 'subgroups', 'of', 'scocen', 'and', 'the', 'creation', 'of', 'a', 'catalog', 'with', 'over', '11', 'million', 'point', 'sources', 'we', 'create', 'a', 'prioritized', 'list', 'of', 'candidate', 'for', 'members', 'in', 'ucl', 'and', 'lcc', 'with', '118', 'emphbest', 'and', 'another', '348', 'emphgood', 'candidates', 'we', 'show', 'that', 'the', 'luminosity', 'and', 'mass', 'functions', 'of', 'our', 'low', 'mass', 'and', 'substellar', 'candidates', 'are', 'consistent', 'with', 'measurements', 'for', 'the', 'younger', 'upper', 'scorpius', 'subgroup', 'and', 'estimates', 'of', 'a', 'universal', 'imf', 'with', 'spectral', 'types', 'ranging', 'from', 'm1', 'down', 'to', 'l1']] | [-0.04874766327273578, 0.04292092428519167, -0.07700490114541772, 0.06563498932668246, -0.12455679517155752, -0.03943150402464641, 0.1686378529486366, 0.42171238544576156, -0.15500167555486163, -0.4393189832783089, 0.07893629955465141, -0.2886243811569726, 0.02732429445052567, 0.22453227822776312, -0.07675994862802327, -0.031890334242071286, 0.13125590129922599, -0.022901079777138643, 0.005724954241230033, -0.2906401163752143, 0.27897712002800873, 0.03944228256407838, 0.14978221711368325, -0.023443271429874957, 0.07130506182632719, -0.08750123317683271, -0.11189232220968758, -0.10303213189129168, -0.15415003578028774, 0.12936570372160835, 0.23357707102191994, 0.1673072888590515, 0.24057064392418864, -0.27339862027348805, -0.14899357080978987, 0.0594549101854985, 0.18910801616640618, -0.042191466266581885, -0.09787695796694607, -0.3672019213076848, 0.12356008216738701, -0.2041802578784812, -0.1573623841515002, 0.0842151935354102, 0.05930887426410873, 0.027829741477034986, -0.2448417654601284, 0.08462699694866434, -0.027879113738144484, 0.1456255359863952, -0.16924608322397733, -0.19081848042491728, -0.03462519101911965, 0.12667283350422692, -0.028908886306453496, 0.09752048318011639, 0.12740759278695363, -0.1755488278593895, -0.09649002119803275, 0.3680423937845402, -0.06508534810377452, 0.0038547152533936193, 0.2572937431769111, -0.1576317965444058, -0.20356991245912817, 0.13223373737239924, 0.19007233132232124, 0.11227149724921522, -0.1928320604030234, 0.04771731192065486, -0.07850460709932332, 0.21156497072829203, 0.039934855399247356, 0.04829034316189324, 0.23909401669740105, 0.14051531336437434, 0.05198116760509901, 0.16879157659609634, -0.29788215811818075, -0.029822896816767752, -0.2642977941238011, -0.11384955252735661, -0.09661270326110892, 0.03558026435702908, -0.14740541372772825, -0.14485781273516551, 0.3431572249242797, 0.12437075212484416, 0.13525514926415128, 0.08019015123733343, 0.2642855812565615, 0.006564639665530427, 0.1487234148856563, 0.18457300001934457, 0.2325771831943152, 0.1817711488931822, -0.015917449201552723, -0.1620629220519615, -0.026106088556564197, 0.010484023330112299] |
1,802.00879 | ATLAS: A High-Cadence All-Sky Survey System | Technology has advanced to the point that it is possible to image the entire
sky every night and process the data in real time. The sky is hardly static:
many interesting phenomena occur, including variable stationary objects such as
stars or QSOs, transient stationary objects such as supernovae or M dwarf
flares, and moving objects such as asteroids and the stars themselves. Funded
by NASA, we have designed and built a sky survey system for the purpose of
finding dangerous near-Earth asteroids (NEAs). This system, the "Asteroid
Terrestrial-impact Last Alert System" (ATLAS), has been optimized to produce
the best survey capability per unit cost, and therefore is an efficient and
competitive system for finding potentially hazardous asteroids (PHAs) but also
for tracking variables and finding transients. While carrying out its NASA
mission, ATLAS now discovers more bright ($m < 19$) supernovae candidates than
any ground based survey, frequently detecting very young explosions due to its
2 day cadence. ATLAS discovered the afterglow of a gamma-ray burst independent
of the high energy trigger and has released a variable star catalogue of
5$\times10^{6}$ sources. This, the first of a series of articles describing
ATLAS, is devoted to the design and performance of the ATLAS system. Subsequent
articles will describe in more detail the software, the survey strategy,
ATLAS-derived NEA population statistics, transient detections, and the first
data release of variable stars and transient lightcurves.
| astro-ph.IM | technology has advanced to the point that it is possible to image the entire sky every night and process the data in real time the sky is hardly static many interesting phenomena occur including variable stationary objects such as stars or qsos transient stationary objects such as supernovae or m dwarf flares and moving objects such as asteroids and the stars themselves funded by nasa we have designed and built a sky survey system for the purpose of finding dangerous nearearth asteroids neas this system the asteroid terrestrialimpact last alert system atlas has been optimized to produce the best survey capability per unit cost and therefore is an efficient and competitive system for finding potentially hazardous asteroids phas but also for tracking variables and finding transients while carrying out its nasa mission atlas now discovers more bright m 19 supernovae candidates than any ground based survey frequently detecting very young explosions due to its 2 day cadence atlas discovered the afterglow of a gammaray burst independent of the high energy trigger and has released a variable star catalogue of 5times106 sources this the first of a series of articles describing atlas is devoted to the design and performance of the atlas system subsequent articles will describe in more detail the software the survey strategy atlasderived nea population statistics transient detections and the first data release of variable stars and transient lightcurves | [['technology', 'has', 'advanced', 'to', 'the', 'point', 'that', 'it', 'is', 'possible', 'to', 'image', 'the', 'entire', 'sky', 'every', 'night', 'and', 'process', 'the', 'data', 'in', 'real', 'time', 'the', 'sky', 'is', 'hardly', 'static', 'many', 'interesting', 'phenomena', 'occur', 'including', 'variable', 'stationary', 'objects', 'such', 'as', 'stars', 'or', 'qsos', 'transient', 'stationary', 'objects', 'such', 'as', 'supernovae', 'or', 'm', 'dwarf', 'flares', 'and', 'moving', 'objects', 'such', 'as', 'asteroids', 'and', 'the', 'stars', 'themselves', 'funded', 'by', 'nasa', 'we', 'have', 'designed', 'and', 'built', 'a', 'sky', 'survey', 'system', 'for', 'the', 'purpose', 'of', 'finding', 'dangerous', 'nearearth', 'asteroids', 'neas', 'this', 'system', 'the', 'asteroid', 'terrestrialimpact', 'last', 'alert', 'system', 'atlas', 'has', 'been', 'optimized', 'to', 'produce', 'the', 'best', 'survey', 'capability', 'per', 'unit', 'cost', 'and', 'therefore', 'is', 'an', 'efficient', 'and', 'competitive', 'system', 'for', 'finding', 'potentially', 'hazardous', 'asteroids', 'phas', 'but', 'also', 'for', 'tracking', 'variables', 'and', 'finding', 'transients', 'while', 'carrying', 'out', 'its', 'nasa', 'mission', 'atlas', 'now', 'discovers', 'more', 'bright', 'm', '19', 'supernovae', 'candidates', 'than', 'any', 'ground', 'based', 'survey', 'frequently', 'detecting', 'very', 'young', 'explosions', 'due', 'to', 'its', '2', 'day', 'cadence', 'atlas', 'discovered', 'the', 'afterglow', 'of', 'a', 'gammaray', 'burst', 'independent', 'of', 'the', 'high', 'energy', 'trigger', 'and', 'has', 'released', 'a', 'variable', 'star', 'catalogue', 'of', '5times106', 'sources', 'this', 'the', 'first', 'of', 'a', 'series', 'of', 'articles', 'describing', 'atlas', 'is', 'devoted', 'to', 'the', 'design', 'and', 'performance', 'of', 'the', 'atlas', 'system', 'subsequent', 'articles', 'will', 'describe', 'in', 'more', 'detail', 'the', 'software', 'the', 'survey', 'strategy', 'atlasderived', 'nea', 'population', 'statistics', 'transient', 'detections', 'and', 'the', 'first', 'data', 'release', 'of', 'variable', 'stars', 'and', 'transient', 'lightcurves']] | [-0.0840306077203106, 0.11201850051299476, -0.07611223492346218, 0.10830215030460037, -0.14034145421287775, -0.08971090382167503, 0.06909019294759239, 0.3685478072087078, -0.16797329637262484, -0.38321241543162615, 0.13352030430636977, -0.3579907855363158, -0.07145737104077378, 0.25496364039895325, -0.09769821701901611, 0.054752415940469215, 0.15520417017092847, -0.05161617630586514, 0.03819755063665545, -0.3248567409474281, 0.22830581130996427, 0.13831333956154793, 0.15980030128772815, -0.10125855769374935, 0.12524902282140987, -0.04683745287095561, -0.10578984778333941, -0.05699907075158199, -0.10191991682909049, 0.051828010523003405, 0.2999282779617478, 0.2015605526998748, 0.23602382559531732, -0.3339213644563089, -0.19900297390944932, 0.13754482937004903, 0.14585917721219036, 0.03567939874128965, -0.06348748056119329, -0.3424183037658424, 0.06765584693867309, -0.22015010435994634, -0.1735361187880778, -0.016870803202745383, 0.1183489172152527, 0.05675787645184597, -0.18750472581874497, 0.02405986704840831, 0.0402250174443116, 0.11800305656823026, -0.12671024104904222, -0.09892017398100428, -0.051699542345823316, 0.13992259866234077, 0.04252808126760141, 0.06386719472787303, 0.14940908736240563, -0.1360429098252612, -0.07965892946636102, 0.39033648472927185, -0.005217545797716846, -0.0010191035902370577, 0.19830209952589306, -0.17160594300063245, -0.16815438278262382, 0.17855452001600972, 0.20828191421764053, 0.13294376916449477, -0.24210907828325973, 0.00341513327489931, 0.0360644122653985, 0.19213342208519033, 0.05428274278933911, 0.06500835502823896, 0.2884653613143398, 0.19843498027438056, 0.08360314660037503, 0.12150200228611736, -0.23837477485244365, -0.010247574548449611, -0.25939203198675226, -0.1329950140353859, -0.1740481185240914, 0.04348001985749959, -0.030727108164358904, -0.13999794573831859, 0.3835204983777974, 0.12806971541038997, 0.13958973708441075, -0.006556626390058385, 0.2909937260510481, 0.008659808935455817, 0.10047988539900753, 0.08869341262970525, 0.2772284692801211, 0.035663968718448735, 0.16237361833108993, -0.1380234357698456, 0.08573477497405332, 0.0030287335808996275] |
1,802.0088 | Study of SIC and RLS Channel Estimation for Large-Scale Antenna Systems
with 1-Bit ADCs | We propose a novel low-resolution-aware recursive least squares channel
estimation algorithm for uplink multi-user multiple-input multiple-output
systems. In order to reduce the energy consumption, 1-bit ADCs are used on each
receive antenna. The loss of performance can be recovered by the large-scale
antenna arrays at the receiver. The proposed adaptive channel estimator can
mitigate the distortions due to the coarse quantization. Moreover, we propose a
low-resolution-aware minimum mean square error based successive interference
canceler to successively mitigate the multiuser interference. Simulation
results show good performance of the system in terms of mean square error and
bit error rate.
| cs.IT math.IT | we propose a novel lowresolutionaware recursive least squares channel estimation algorithm for uplink multiuser multipleinput multipleoutput systems in order to reduce the energy consumption 1bit adcs are used on each receive antenna the loss of performance can be recovered by the largescale antenna arrays at the receiver the proposed adaptive channel estimator can mitigate the distortions due to the coarse quantization moreover we propose a lowresolutionaware minimum mean square error based successive interference canceler to successively mitigate the multiuser interference simulation results show good performance of the system in terms of mean square error and bit error rate | [['we', 'propose', 'a', 'novel', 'lowresolutionaware', 'recursive', 'least', 'squares', 'channel', 'estimation', 'algorithm', 'for', 'uplink', 'multiuser', 'multipleinput', 'multipleoutput', 'systems', 'in', 'order', 'to', 'reduce', 'the', 'energy', 'consumption', '1bit', 'adcs', 'are', 'used', 'on', 'each', 'receive', 'antenna', 'the', 'loss', 'of', 'performance', 'can', 'be', 'recovered', 'by', 'the', 'largescale', 'antenna', 'arrays', 'at', 'the', 'receiver', 'the', 'proposed', 'adaptive', 'channel', 'estimator', 'can', 'mitigate', 'the', 'distortions', 'due', 'to', 'the', 'coarse', 'quantization', 'moreover', 'we', 'propose', 'a', 'lowresolutionaware', 'minimum', 'mean', 'square', 'error', 'based', 'successive', 'interference', 'canceler', 'to', 'successively', 'mitigate', 'the', 'multiuser', 'interference', 'simulation', 'results', 'show', 'good', 'performance', 'of', 'the', 'system', 'in', 'terms', 'of', 'mean', 'square', 'error', 'and', 'bit', 'error', 'rate']] | [-0.26174390325512814, 0.009543814587083702, -0.005098707270713485, 0.04919974595113486, -0.03517227065844499, -0.23218558625109037, 0.0761436078013206, 0.391630861202578, -0.27302150882552473, -0.2849841189893837, 0.12477912991165127, -0.22936851825097063, -0.2323617714134102, 0.09789990624222829, -0.20555195522171502, 0.12081222274347816, 0.09357686016746626, 0.02002210706728986, -0.10223859026838018, -0.31628199453389616, 0.21791968170591458, 0.18912950671296946, 0.35111435857240336, -0.04629117765283326, 0.1586919740526652, 0.03537831051104075, 0.002514426491926519, -0.04445559855631306, -0.10514264679228773, 0.049160772720731945, 0.2992697036801362, 0.1524366116749921, 0.3210969668984109, -0.38623209709149536, -0.23187657426662592, 0.09796569039284879, 0.20889536160690597, 0.08620049612482591, -0.03730079297057106, -0.2643198964319059, 0.1515828179746714, -0.22094138102529912, 0.028327545773580064, 0.027452285786406423, -0.21508821993305974, 0.05145422731317124, -0.40623565634940656, 0.08365920976241481, -0.03184666511203561, 0.020823863247048338, 0.015579780815549346, -0.2244893086786686, 0.08161421637621005, 0.12206431708004022, -0.017444760990043988, -0.004891627121298593, 0.06811188732520962, -0.04610855428844082, -0.1323538645222897, 0.3267702343992471, -0.04358580333004915, -0.2949226893370553, 0.07578020200147578, -0.10769551986239242, -0.0154940379517419, 0.2228374454021758, 0.34231440927263124, 0.011240766683061208, -0.17324733964110517, -0.009362892799521322, 0.048505128323271564, 0.22211924595378188, 0.07730402996554514, 0.1622284979616501, 0.14446270870928635, 0.18193736972943025, 0.1709900762320363, 0.16884783805675843, -0.24033350901374098, -0.07159471622079003, -0.21169578099661335, -0.11610137774818102, -0.24099122719573124, -0.012035592114647888, -0.12983247440065404, -0.08348323820082813, 0.32005870224413824, 0.17237245232551074, 0.06709075000669275, 0.16567381862693523, 0.4427445115027379, 0.1430676163896462, 0.06961934179618802, 0.10945959827786654, 0.21106829998862683, 0.13462240204490644, 0.060595380897842804, -0.3046786629794432, 0.018550983760791014, 0.04592412076795436] |
1,802.00881 | A Protection Method in Active Distribution Grids with High Penetration
of Renewable Energy Sources | A protection method in active distribution networks is proposed in this
paper. In active distribution systems, fault currents flow in multiple
directions and presents a varying range of value, which poses a great challenge
of maintaining coordination among protective devices on feeders. The proposed
protection method addresses this challenge by simultaneously adjusting DG's
output power and protection devices' settings in pre-fault networks. Comparing
to previous protection solutions, the proposed method considers the influences
from renewable DG's intermittency, and explores the economic and protection
benefits of DG's active participation. The formulation of proposed method is
decomposed into two optimization sub-problems, coupling through the constraint
on fuse-recloser coordination. This decomposed mathematical structure
effectively extinguishes the non-linearity arising from reclosers' time-current
inverse characteristics, and greatly reduces computation efforts.
| math.OC | a protection method in active distribution networks is proposed in this paper in active distribution systems fault currents flow in multiple directions and presents a varying range of value which poses a great challenge of maintaining coordination among protective devices on feeders the proposed protection method addresses this challenge by simultaneously adjusting dgs output power and protection devices settings in prefault networks comparing to previous protection solutions the proposed method considers the influences from renewable dgs intermittency and explores the economic and protection benefits of dgs active participation the formulation of proposed method is decomposed into two optimization subproblems coupling through the constraint on fuserecloser coordination this decomposed mathematical structure effectively extinguishes the nonlinearity arising from reclosers timecurrent inverse characteristics and greatly reduces computation efforts | [['a', 'protection', 'method', 'in', 'active', 'distribution', 'networks', 'is', 'proposed', 'in', 'this', 'paper', 'in', 'active', 'distribution', 'systems', 'fault', 'currents', 'flow', 'in', 'multiple', 'directions', 'and', 'presents', 'a', 'varying', 'range', 'of', 'value', 'which', 'poses', 'a', 'great', 'challenge', 'of', 'maintaining', 'coordination', 'among', 'protective', 'devices', 'on', 'feeders', 'the', 'proposed', 'protection', 'method', 'addresses', 'this', 'challenge', 'by', 'simultaneously', 'adjusting', 'dgs', 'output', 'power', 'and', 'protection', 'devices', 'settings', 'in', 'prefault', 'networks', 'comparing', 'to', 'previous', 'protection', 'solutions', 'the', 'proposed', 'method', 'considers', 'the', 'influences', 'from', 'renewable', 'dgs', 'intermittency', 'and', 'explores', 'the', 'economic', 'and', 'protection', 'benefits', 'of', 'dgs', 'active', 'participation', 'the', 'formulation', 'of', 'proposed', 'method', 'is', 'decomposed', 'into', 'two', 'optimization', 'subproblems', 'coupling', 'through', 'the', 'constraint', 'on', 'fuserecloser', 'coordination', 'this', 'decomposed', 'mathematical', 'structure', 'effectively', 'extinguishes', 'the', 'nonlinearity', 'arising', 'from', 'reclosers', 'timecurrent', 'inverse', 'characteristics', 'and', 'greatly', 'reduces', 'computation', 'efforts']] | [-0.1824793413311976, 0.03033133173269815, -0.010442023520495315, 0.0024189144042778576, -0.030759684749607944, -0.1557426179919514, 0.08195102036277932, 0.33357097673779507, -0.29044382161170734, -0.3744058044108211, 0.07619235222037027, -0.24637390653694385, -0.16200586715255116, 0.1920559729342578, -0.12890187104697318, 0.06740735305977039, 0.02905203797747133, -0.11015681528913804, -0.004361200012785733, -0.21220441003039966, 0.26833048481570526, 0.06687950938329344, 0.4447158095045168, 0.03274663900345808, 0.11869350950554257, 0.016628038813741725, -0.053326781159724855, 0.03290174317500386, -0.03573996061650028, 0.17443665488409338, 0.2730004818934459, 0.15160182645910833, 0.3641952764642827, -0.44387932191984575, -0.257758131182035, 0.09007820337018395, 0.1465081628488346, 0.06162434314343254, -0.08220586217519996, -0.2527148999334847, 0.08480584311115814, -0.1995600122504211, -0.07291684257602281, -0.050644305765583014, -0.027232897757995325, 0.003099556974150607, -0.27842127651092596, 0.038018208943673824, 0.040269812275121206, 0.03198481618701557, -0.0894420546998144, -0.10608405949639492, -0.02303069541001784, 0.13839247649068923, 0.07618651902074086, -0.05449044236867521, 0.1715967912020803, -0.14567833934857158, -0.12278106918970703, 0.3853200670011097, 0.029871256563995707, -0.22177439413871233, 0.14859357443027443, -0.03746283622878436, -0.14355999952350118, 0.12872201470719252, 0.2651029473865313, 0.0743064590000746, -0.1615646610567995, 0.057700940953151585, 0.04099890129396417, 0.15689555925248405, 0.009393740750727107, 0.02110419044133703, 0.20002865739601863, 0.22405384419882884, 0.1368657766581803, 0.17223846235839252, -0.0670053027128919, -0.1356100171458999, -0.2170783533780553, -0.10856634175374372, -0.17129198515398397, 0.005104545984783622, -0.06992036637744664, -0.07965872298971918, 0.449364677560134, 0.18396026056382012, 0.1251189521848812, 0.03293884758929127, 0.38838016741038833, 0.06462309471823917, 0.09415131276397065, 0.0910342323715936, 0.20983581483119823, 0.07090172221021512, 0.14589466402291884, -0.2576150604195465, 0.12130503756467437, 0.0391645292316365] |
1,802.00882 | Proportional Representation in Approval-based Committee Voting and
Beyond | Proportional representation (PR) is one of the central principles in voting.
Elegant rules with compelling PR axiomatic properties have the potential to be
adopted for several important collective decision making settings. I survey
some recent ideas and results on axioms and rules for proportional
representation in committee voting.
| cs.GT | proportional representation pr is one of the central principles in voting elegant rules with compelling pr axiomatic properties have the potential to be adopted for several important collective decision making settings i survey some recent ideas and results on axioms and rules for proportional representation in committee voting | [['proportional', 'representation', 'pr', 'is', 'one', 'of', 'the', 'central', 'principles', 'in', 'voting', 'elegant', 'rules', 'with', 'compelling', 'pr', 'axiomatic', 'properties', 'have', 'the', 'potential', 'to', 'be', 'adopted', 'for', 'several', 'important', 'collective', 'decision', 'making', 'settings', 'i', 'survey', 'some', 'recent', 'ideas', 'and', 'results', 'on', 'axioms', 'and', 'rules', 'for', 'proportional', 'representation', 'in', 'committee', 'voting']] | [-0.047569923495757394, 0.04565779089656038, -0.12403502041706815, 0.10813913788539746, -0.18414481915533543, -0.20413093226185688, 0.14662353578023612, 0.3484386937537541, -0.2558882172452286, -0.26252240299557644, 0.10672641186101828, -0.316515516800185, -0.18347457970958203, 0.14821954327635467, -0.11036366461000095, 0.03574718137194092, -1.788118000452717e-05, 0.058331042159503944, -0.008158937601062158, -0.2984884854716559, 0.3064962056329629, 0.061441985152972244, 0.341441886111473, 0.016623331718922902, 0.10038279033809279, 0.06994918878384244, -0.05459808754191423, 0.054192160915893815, -0.1402920673523719, 0.14516462961910293, 0.34606353771717596, 0.22566217287870435, 0.34573036005410057, -0.40126138638394576, -0.10957231973103869, 0.04279940874160578, 0.06430258856077369, 0.0603063841505597, -0.026408505444123875, -0.2234848146714891, 0.05181390826085893, -0.2275682640223143, -0.1137131853805234, -0.16009909727533037, 0.06411155553845067, 0.053496277367230505, -0.2842888373415917, -0.009456732426770031, 0.07176245248410851, 0.13461770208474869, -0.09693240430594112, -0.20764679519925267, 0.0983403703624693, 0.07972232385266882, 0.05628802636541271, -0.015976452831334125, 0.11997896851971745, -0.17231221850185344, -0.20525427309136526, 0.4260099309807022, 0.04218382322384665, -0.16262383240973577, 0.15684345938886204, -0.06305630127705324, -0.21685281234870976, 0.0473509538108677, 0.11193212610669434, 0.08495455072261393, -0.15770704916212708, 0.037249997643812094, -0.0903111756973279, 0.10899917113905151, 0.026087566608718287, 0.0808521657794093, 0.2100796998323252, 0.15063326897992133, 0.027311664535469998, 0.07251212663686601, 0.005754811131434205, -0.1929816984338686, -0.27014898757139844, -0.12088574261482184, -0.1254405199821728, 0.00472888177804028, -0.10530207612646336, -0.07487775420304388, 0.3326537812948421, 0.1754717252527674, 0.10274797694122147, 0.02546784809480111, 0.2660548089382549, 0.09334883630314532, 0.130383706195668, 0.023080266691977158, 0.24071624199435368, 0.1421755124659588, 0.09323125918551038, -0.14049996207662238, 0.11171594190576191, 0.10216476438411821] |
1,802.00883 | Interplay between cost and benefits triggers nontrivial vaccination
uptake | The containment of epidemic spreading is a major challenge in science.
Vaccination, whenever available, is the best way to prevent the spreading,
because it eventually immunizes individuals. However, vaccines are not perfect,
and total immunization is not guaranteed. Imperfect immunization has driven the
emergence of anti-vaccine movements that totally alter the predictions about
the epidemic incidence. Here, we propose a mathematically solvable mean-field
vaccination model to mimic the spontaneous adoption of vaccines against
influenza-like diseases, and the expected epidemic incidence. The results are
in agreement with extensive Monte Carlo simulations of the epidemics and
vaccination co-evolutionary processes. Interestingly, the results reveal a
non-monotonic behavior on the vaccination coverage, that increases with the
imperfection of the vaccine and after decreases. This apparent counterintuitive
behavior is analyzed and understood from stability principles of the proposed
mathematical model.
| physics.soc-ph cs.GT | the containment of epidemic spreading is a major challenge in science vaccination whenever available is the best way to prevent the spreading because it eventually immunizes individuals however vaccines are not perfect and total immunization is not guaranteed imperfect immunization has driven the emergence of antivaccine movements that totally alter the predictions about the epidemic incidence here we propose a mathematically solvable meanfield vaccination model to mimic the spontaneous adoption of vaccines against influenzalike diseases and the expected epidemic incidence the results are in agreement with extensive monte carlo simulations of the epidemics and vaccination coevolutionary processes interestingly the results reveal a nonmonotonic behavior on the vaccination coverage that increases with the imperfection of the vaccine and after decreases this apparent counterintuitive behavior is analyzed and understood from stability principles of the proposed mathematical model | [['the', 'containment', 'of', 'epidemic', 'spreading', 'is', 'a', 'major', 'challenge', 'in', 'science', 'vaccination', 'whenever', 'available', 'is', 'the', 'best', 'way', 'to', 'prevent', 'the', 'spreading', 'because', 'it', 'eventually', 'immunizes', 'individuals', 'however', 'vaccines', 'are', 'not', 'perfect', 'and', 'total', 'immunization', 'is', 'not', 'guaranteed', 'imperfect', 'immunization', 'has', 'driven', 'the', 'emergence', 'of', 'antivaccine', 'movements', 'that', 'totally', 'alter', 'the', 'predictions', 'about', 'the', 'epidemic', 'incidence', 'here', 'we', 'propose', 'a', 'mathematically', 'solvable', 'meanfield', 'vaccination', 'model', 'to', 'mimic', 'the', 'spontaneous', 'adoption', 'of', 'vaccines', 'against', 'influenzalike', 'diseases', 'and', 'the', 'expected', 'epidemic', 'incidence', 'the', 'results', 'are', 'in', 'agreement', 'with', 'extensive', 'monte', 'carlo', 'simulations', 'of', 'the', 'epidemics', 'and', 'vaccination', 'coevolutionary', 'processes', 'interestingly', 'the', 'results', 'reveal', 'a', 'nonmonotonic', 'behavior', 'on', 'the', 'vaccination', 'coverage', 'that', 'increases', 'with', 'the', 'imperfection', 'of', 'the', 'vaccine', 'and', 'after', 'decreases', 'this', 'apparent', 'counterintuitive', 'behavior', 'is', 'analyzed', 'and', 'understood', 'from', 'stability', 'principles', 'of', 'the', 'proposed', 'mathematical', 'model']] | [-0.12204807506763, 0.12776064785169577, -0.0722400036174804, 0.11942546468474832, -0.06493269433545422, -0.17340944574992936, 0.12163245901743423, 0.3764858220759501, -0.22006211569532752, -0.2603484576562795, 0.13299805248580152, -0.31807148822847364, -0.25194473980505494, 0.11412630911956458, -0.09323507483784499, -0.0005692635947810625, 0.0855906109461116, -0.002769964021707037, 0.06857580427730234, -0.30926008891320134, 0.25759075446325397, 0.11143561511356105, 0.32590406525310184, 0.0813552360867025, 0.05675871458787249, 0.027870261916584933, -0.03283639834436185, 0.04646556337238915, -0.18895194024499593, 0.021392738715043543, 0.306707909302925, 0.1716457409653882, 0.32399445532563964, -0.4178252369721434, -0.25149867985047464, 0.1213395396984224, 0.15031193145342284, 0.19877604940044347, -0.02209288069592622, -0.27685107094750033, 0.03771737665493986, -0.2110003043772347, -0.1762071798638955, -0.011714596438583042, 0.0021793574699436997, 0.03212679360572721, -0.2589412844164728, 0.0908515436171707, -0.0044808825825466145, 0.12917501733988787, -0.02610430697851411, -0.087025359856771, -0.09146959610240371, 0.15839981108142842, 0.09534135098489169, -0.035094094375815635, 0.17468863252182004, -0.15569162357306637, -0.15517425746396324, 0.346725660470078, 0.007417760391497234, -0.13949602613149129, 0.17148370341634128, -0.13326655980199575, -0.067349090491319, 0.17314391917854882, 0.1738664089022691, 0.06517985538204214, -0.14576837584487537, -0.03590319767714228, -0.011606707543468297, 0.14974226749331843, 0.014075767345302529, -0.045339715723593375, 0.14582177256790005, 0.25813790282526455, 0.08140807172088925, 0.0696247734873097, -0.048088074201342666, -0.21020795932776337, -0.2233449430621938, -0.06688328513896453, -0.12200276491470259, 0.07904402108592808, -0.10749597266720827, -0.16561372126383123, 0.38680437050495686, 0.21041034714582918, 0.0964045719323513, 0.11505820291506043, 0.26656011466993323, 0.05801541818681159, 0.018596158488028085, 0.0618560170055864, 0.2400874535539257, 0.0802937757202994, 0.08969271404376782, -0.2916942639467515, 0.28295670636693265, -0.07548442198382344] |
1,802.00884 | A Model for Learned Bloom Filters and Related Structures | Recent work has suggested enhancing Bloom filters by using a pre-filter,
based on applying machine learning to model the data set the Bloom filter is
meant to represent. Here we model such learned Bloom filters, clarifying what
guarantees can and cannot be associated with such a structure.
| cs.DS | recent work has suggested enhancing bloom filters by using a prefilter based on applying machine learning to model the data set the bloom filter is meant to represent here we model such learned bloom filters clarifying what guarantees can and cannot be associated with such a structure | [['recent', 'work', 'has', 'suggested', 'enhancing', 'bloom', 'filters', 'by', 'using', 'a', 'prefilter', 'based', 'on', 'applying', 'machine', 'learning', 'to', 'model', 'the', 'data', 'set', 'the', 'bloom', 'filter', 'is', 'meant', 'to', 'represent', 'here', 'we', 'model', 'such', 'learned', 'bloom', 'filters', 'clarifying', 'what', 'guarantees', 'can', 'and', 'can', 'not', 'be', 'associated', 'with', 'such', 'a', 'structure']] | [0.001619596965610981, 0.04200222905880461, -0.12024541832154985, 0.05169394078014496, -0.19968645476425687, -0.20299891159326458, 0.05813129257876426, 0.4740343426043789, -0.3254571738555872, -0.31834061273063224, 0.1458852711172464, -0.25072662873814505, -0.23086390343011468, 0.16229639853312014, -0.14628433848459585, 0.1184150834645455, 0.030707925946141284, -0.004248911757410194, -0.0370556577302826, -0.2503788763860939, 0.2917158704949543, 0.12409977746816973, 0.2916004652603685, -0.027100696383665007, 0.10653383979661157, -0.003908445069100708, -0.12083971816658352, 0.03573111340908023, -0.08342572015438539, 0.1400847218950124, 0.26446151222626213, 0.21554368356980072, 0.3364701861331317, -0.3992226868479823, -0.22610363812418655, 0.11514196129670988, 0.14804679782052213, 0.12825587686772147, -0.05477383526401051, -0.3447139287600294, 0.13934485295855362, -0.15474374679130656, -0.007308016764000058, -0.156413757475093, -0.07600004102763099, 0.03017897390721676, -0.3039723186775518, -0.06700141114803652, 0.09377094697750483, 0.050668213225435466, -0.007907911625807174, -0.10273543100144404, -0.004987608345497089, 0.06456701262504794, -0.04924792290936845, 0.04897380436887033, 0.1444366634823382, -0.09969766086821134, -0.17940382504214844, 0.333432500405858, -0.08556489035254344, -0.2132168880198151, 0.20097966457736524, 0.006761030769363667, -0.10431770124705508, 0.04945693193197561, 0.1630429487170962, 0.04999554017558694, -0.17539576235503773, 0.05987042989848609, -0.0748874851463673, 0.19261556284618564, 0.031028055508310597, 0.06950808056474973, 0.17577864507135624, 0.2208486874199783, 0.023559597553685308, 0.10396695453043019, -0.1300979817424377, -0.04101977778433744, -0.21808965636106828, -0.11073281928353633, -0.23263804331751695, 0.002868108408013844, -0.018044104120235716, -0.1269499430588136, 0.3996194158680737, 0.23639063723385334, 0.23940187663538381, 0.026380599030138303, 0.2775880373083055, 0.1303319971560389, 0.16454370669089258, 0.09832036191558775, 0.19556977021663138, 0.05782018825508809, 0.08475117266061716, -0.08536308640032075, 0.10263182422932005, 0.09971786142947774] |
1,802.00885 | Measurement and subtraction of Schumann resonances at gravitational-wave
interferometers | Correlated magnetic noise from Schumann resonances threatens to contaminate
the observation of a stochastic gravitational-wave background in
interferometric detectors. In previous work, we reported on the first effort to
eliminate global correlated noise from the Schumann resonances using Wiener
filtering, demonstrating as much as a factor of two reduction in the coherence
between magnetometers on different continents. In this work, we present results
from dedicated magnetometer measurements at the Virgo and KAGRA sites, which
are the first results for subtraction using data from gravitational-wave
detector sites. We compare these measurements to a growing network of permanent
magnetometer stations, including at the LIGO sites. We show how dedicated
measurements can reduce coherence to a level consistent with uncorrelated
noise. We also show the effect of mutual magnetometer attraction, arguing that
magnetometers should be placed at least one meter from one another.
| gr-qc | correlated magnetic noise from schumann resonances threatens to contaminate the observation of a stochastic gravitationalwave background in interferometric detectors in previous work we reported on the first effort to eliminate global correlated noise from the schumann resonances using wiener filtering demonstrating as much as a factor of two reduction in the coherence between magnetometers on different continents in this work we present results from dedicated magnetometer measurements at the virgo and kagra sites which are the first results for subtraction using data from gravitationalwave detector sites we compare these measurements to a growing network of permanent magnetometer stations including at the ligo sites we show how dedicated measurements can reduce coherence to a level consistent with uncorrelated noise we also show the effect of mutual magnetometer attraction arguing that magnetometers should be placed at least one meter from one another | [['correlated', 'magnetic', 'noise', 'from', 'schumann', 'resonances', 'threatens', 'to', 'contaminate', 'the', 'observation', 'of', 'a', 'stochastic', 'gravitationalwave', 'background', 'in', 'interferometric', 'detectors', 'in', 'previous', 'work', 'we', 'reported', 'on', 'the', 'first', 'effort', 'to', 'eliminate', 'global', 'correlated', 'noise', 'from', 'the', 'schumann', 'resonances', 'using', 'wiener', 'filtering', 'demonstrating', 'as', 'much', 'as', 'a', 'factor', 'of', 'two', 'reduction', 'in', 'the', 'coherence', 'between', 'magnetometers', 'on', 'different', 'continents', 'in', 'this', 'work', 'we', 'present', 'results', 'from', 'dedicated', 'magnetometer', 'measurements', 'at', 'the', 'virgo', 'and', 'kagra', 'sites', 'which', 'are', 'the', 'first', 'results', 'for', 'subtraction', 'using', 'data', 'from', 'gravitationalwave', 'detector', 'sites', 'we', 'compare', 'these', 'measurements', 'to', 'a', 'growing', 'network', 'of', 'permanent', 'magnetometer', 'stations', 'including', 'at', 'the', 'ligo', 'sites', 'we', 'show', 'how', 'dedicated', 'measurements', 'can', 'reduce', 'coherence', 'to', 'a', 'level', 'consistent', 'with', 'uncorrelated', 'noise', 'we', 'also', 'show', 'the', 'effect', 'of', 'mutual', 'magnetometer', 'attraction', 'arguing', 'that', 'magnetometers', 'should', 'be', 'placed', 'at', 'least', 'one', 'meter', 'from', 'one', 'another']] | [-0.10641534488781222, 0.1462083196533578, -0.036833144228772395, 0.038564899113927305, -0.01912132697845144, -0.09670981990805427, 0.06051410666966279, 0.3778608539821497, -0.2101005455745118, -0.33236391340116306, 0.09330421019611614, -0.36715809535900396, -0.14007914901677784, 0.22354214868142402, -0.04630850091177438, 0.017180279327190615, 0.07970251654208238, 0.010102369589731097, -0.04593243364610576, -0.20415560725627335, 0.27360300555010325, 0.1243372954428196, 0.26562940129078927, -0.027356939749526125, 0.10154176006575913, -0.023911776270584335, -0.0615419223779879, 0.0063694820631228916, -0.0822911490289698, 0.05992186507761029, 0.2702744844036975, 0.10301688400546222, 0.2323119642146464, -0.4529528159115996, -0.1718323459200162, 0.08548345630822171, 0.10368844843781387, 0.15440509890738344, -0.025349133917396623, -0.3746683650650084, 0.032000749310412045, -0.15577650868965845, -0.09041123986576817, -0.03291317195731348, -0.025565605292961535, 0.039340300844716174, -0.2749441468489489, 0.06406852810988702, 0.01657490012834647, 0.06245717378374788, -0.05892041263702725, -0.1366419842400189, 0.04078376571913915, 0.11072354431796287, 0.008659597326602255, 0.033797368961261655, 0.18176676718119, -0.0805210190642226, -0.14523950584033238, 0.2843647919566138, -0.13006545685597562, -0.14675194114180548, 0.18590155391721055, -0.21676139860813107, -0.15584732600753862, 0.11327467496573393, 0.21774596270572927, 0.06716268871179117, -0.17993184522991734, -0.032743095989488734, 0.060163374137066836, 0.22760029417196556, 0.11102544256219907, 0.08492702216358988, 0.21466297185979782, 0.20517650263916168, 0.12483105138775759, 0.14855411949600758, -0.20546414187951345, -0.012568991644574062, -0.28404842762143484, -0.07354672284272964, -0.21218987983385368, 0.046113607942658875, -0.029749083771665547, -0.08323087883514485, 0.3501208981499076, 0.2417060814964186, 0.15668736417511744, -0.02724576484262278, 0.35033276630232907, 0.04057172923598305, 0.10744249111573612, 0.01089331056656582, 0.324342307378538, 0.11438219374509312, 0.10109337904224439, -0.1846472461292121, 0.02620457529223391, -0.04115979833295569] |
1,802.00886 | Lattices with exponentially large kissing numbers | We construct a sequence of lattices $\{L_{n_i}\subset \mathbb R^{n_i}\}$ for
$n_i\longrightarrow\infty$, with exponentially large kissing numbers, namely,
$\log_2\tau(L_{n_i})> 0.0338\cdot n_i -o(n_i)$. We also show that the maximum
lattice kissing number $ \tau^l_{n}$ in $n$ dimensions verifies
$\log_2\tau^l_{n}> 0.0219\cdot n -o(n)$.
| math.NT math.AG math.CO math.MG | we construct a sequence of lattices l_n_isubset mathbb rn_i for n_ilongrightarrowinfty with exponentially large kissing numbers namely log_2taul_n_i 00338cdot n_i on_i we also show that the maximum lattice kissing number taul_n in n dimensions verifies log_2taul_n 00219cdot n on | [['we', 'construct', 'a', 'sequence', 'of', 'lattices', 'l_n_isubset', 'mathbb', 'rn_i', 'for', 'n_ilongrightarrowinfty', 'with', 'exponentially', 'large', 'kissing', 'numbers', 'namely', 'log_2taul_n_i', '00338cdot', 'n_i', 'on_i', 'we', 'also', 'show', 'that', 'the', 'maximum', 'lattice', 'kissing', 'number', 'taul_n', 'in', 'n', 'dimensions', 'verifies', 'log_2taul_n', '00219cdot', 'n', 'on']] | [-0.25300690199946985, 0.24324188189348206, 0.05650249571772292, 0.029440247584716417, 0.047898631368298084, -0.17430031536787283, 0.05629279380445951, 0.3345510800136253, -0.20436768932268023, -0.2577937903115526, 0.05354561398780788, -0.30643288511782885, -0.1628573441557819, 0.163908603717573, -0.04467572251451202, 0.03381572188300197, 0.03612747411534656, 0.09636804859474069, 0.0026566828673821874, -0.36902152112452313, 0.2747276932641398, -0.11739258719899226, 0.20029996801167727, 0.04955510309082456, 0.07010942249326035, 0.01511726772878319, 0.06481240157154389, 0.055199616705067456, -0.2290335646623589, 0.12008414760930464, 0.1638241078471765, 0.09805687038169708, 0.20758684258908033, -0.3781580945942551, -0.14386073098285124, 0.1859336917696055, 0.23670528932052548, 0.06438127241563052, -0.014585767541575478, -0.13055858164443634, 0.15274100811802782, -0.1106891060298949, -0.1539270170906093, -0.07435227281530388, 0.1259178519831039, 0.1075744436820969, -0.33350410347338766, -0.0010996324790539802, 0.034648926579393446, 0.08036434190580621, 0.029097438062308356, -0.22990808155736886, 0.013814900681609288, 0.05463362776208669, 0.031011486193165183, 0.03207577628927538, -0.018851499451557174, -0.025770411361008883, -0.10954315331764519, 0.3574075477081351, -0.0652985640517727, -0.22703779137373203, 0.11981273035053164, -0.18728411063784733, -0.20444234882597812, 0.09513284786953591, 0.16510962671600282, 0.1610426902770996, 0.09416097177017946, 0.1567186403553933, -0.1880158045096323, 0.20133176291710697, 0.15215405225171708, 0.051745869815931655, 0.1135398794140201, 0.11613134510116652, 0.1304284460056806, 0.23058176500489935, -0.0494790318844025, -0.08923592162318528, -0.3130614354740828, -0.1602111396350665, -0.29489353034296073, 0.15643297036876902, -0.190333944367012, -0.16086254070978612, 0.24108355439966545, 0.0568810747936368, 0.27461334646795876, 0.18500729723018594, 0.12772741573280655, -0.0072579513343953295, 0.03149344794292119, 0.11310165134636918, 0.03548685117857531, 0.12497358518885449, -0.031285550227039494, -0.2003251746064052, -0.1292331426666351, 0.21100457300781272] |
1,802.00887 | A rigidity theorem for surfaces in Schwarzschild manifold | In this article, we prove a rigidity theorem for isometric embeddings into
the Schwarzschild manifold, by using the variational formula of quasi-local
mass.
| math.DG | in this article we prove a rigidity theorem for isometric embeddings into the schwarzschild manifold by using the variational formula of quasilocal mass | [['in', 'this', 'article', 'we', 'prove', 'a', 'rigidity', 'theorem', 'for', 'isometric', 'embeddings', 'into', 'the', 'schwarzschild', 'manifold', 'by', 'using', 'the', 'variational', 'formula', 'of', 'quasilocal', 'mass']] | [-0.08628274848603684, 0.04551898886490127, -0.1354150833339309, 0.1402780504077268, -0.07375607194136018, -0.0875932002723541, 0.01658047338866669, 0.3085996433282676, -0.23768732656279337, -0.259435143240768, 0.047469914845271924, -0.2476138025522232, -0.1296423349691474, 0.17395798789094324, -0.18088944422324066, 0.04150960562021836, 0.11087876261697839, 0.08033344068604967, -0.1754864044892399, -0.14525878498249728, 0.47397814172765484, -0.0465487256322218, 0.19877691907079323, 0.14029823543260928, 0.1810106348813228, 0.07248799089828263, 0.0460012708183216, 0.022500437279434307, -0.27982592742647167, 0.17844460070457147, 0.25345891595656134, 0.12390272103695442, 0.25284583627691737, -0.34511966906164004, -0.20325662847608328, 0.13633287710178157, 0.14009361236315707, 0.0907959361034243, -0.056951716997782176, -0.3785250633550079, 0.08817110779573736, -0.1863383378671563, -0.1703176769104017, -0.12637477943106837, 0.005729777374021385, -0.06292303449109844, -0.16616592957111803, 0.1322747629702739, 0.23217319502778674, 0.024508264725622925, -0.20591680988993333, -0.03518132124420093, -0.027862228498713153, 0.030108580912422876, 0.12552267491169597, 0.054978672657971794, 0.11141756247810047, 0.031865719493235585, -0.06224606459474434, 0.3250061391812304, -0.10524029705835425, -0.31201795026983903, -0.007997494028962177, -0.09588436501191767, -0.203395272159706, 0.05152137366973836, 0.16302671822030906, 0.23597526252674667, -0.20973611578507267, 0.1914045721616434, -0.07935540649392035, 0.05478032840334851, 0.16618985055095475, -0.032614535287670464, 0.17057605217332425, 0.11782037272401479, 0.15519404257445232, 0.20833368858565454, 0.003999069333076477, -0.04042437984405652, -0.3483658835048909, -0.2534035049763549, -0.2167928035006575, 0.20059397931048728, -0.19377411328984992, -0.14648488342114116, 0.3558574425785438, 0.061594866135198136, 0.20196365435486255, 0.19478891057002803, 0.2607775845605394, 0.05246438887780127, 0.05216492692251568, 0.1256992052025769, 0.24365998059511185, 0.22523456731932642, 0.044037411637280297, -0.07703850559039932, -0.156701292619919, 0.31316375793160306] |
1,802.00888 | To Numerical Modeling With Strong Orders 1.0, 1.5, and 2.0 of
Convergence for Multidimensional Dynamical Systems With Random Disturbances | The article is devoted to explicit one-step numerical methods with strong
orders 1.0, 1.5, and 2.0 of convergence for Ito stochastic differential
equations with multidimensional and non-commutative noise. For numerical
modeling of iterated Ito stochastic integrals with multiplicities 1 to 4 we use
the method of multiple Fourier-Legendre series converging in the sense of norm
in Hilbert space $L_2([t, T]^k),$ $k=1,2,3,4.$ The article is addressed to
engineers who use numerical modeling in stochastic control and for solving the
nonlinear filtering problem.
| math.PR | the article is devoted to explicit onestep numerical methods with strong orders 10 15 and 20 of convergence for ito stochastic differential equations with multidimensional and noncommutative noise for numerical modeling of iterated ito stochastic integrals with multiplicities 1 to 4 we use the method of multiple fourierlegendre series converging in the sense of norm in hilbert space l_2t tk k1234 the article is addressed to engineers who use numerical modeling in stochastic control and for solving the nonlinear filtering problem | [['the', 'article', 'is', 'devoted', 'to', 'explicit', 'onestep', 'numerical', 'methods', 'with', 'strong', 'orders', '10', '15', 'and', '20', 'of', 'convergence', 'for', 'ito', 'stochastic', 'differential', 'equations', 'with', 'multidimensional', 'and', 'noncommutative', 'noise', 'for', 'numerical', 'modeling', 'of', 'iterated', 'ito', 'stochastic', 'integrals', 'with', 'multiplicities', '1', 'to', '4', 'we', 'use', 'the', 'method', 'of', 'multiple', 'fourierlegendre', 'series', 'converging', 'in', 'the', 'sense', 'of', 'norm', 'in', 'hilbert', 'space', 'l_2t', 'tk', 'k1234', 'the', 'article', 'is', 'addressed', 'to', 'engineers', 'who', 'use', 'numerical', 'modeling', 'in', 'stochastic', 'control', 'and', 'for', 'solving', 'the', 'nonlinear', 'filtering', 'problem']] | [-0.0739545782706068, 0.004263656380597824, -0.046088959869747564, 0.06553449673164222, -0.06925095121065776, -0.10103643746086523, -0.008319687650159554, 0.3615287797939446, -0.3208712969136275, -0.2783274668050401, 0.1319960852075989, -0.27926566497779187, -0.17281099769896197, 0.2254693743608961, -0.10426421674474338, 0.1363905747593553, 0.046684967828224656, -0.04087774523671855, -0.1021643838569246, -0.3039668726326645, 0.317556277676313, 0.040584762661177436, 0.18393761268098274, -0.04858444865655016, 0.1595072597725156, 0.02101233389435543, -0.10776621396480887, -0.015281976471198033, -0.14629644446109455, 0.1489007290708545, 0.3243804442477815, 0.03198775606528844, 0.36854800267754423, -0.43392924901189994, -0.1587001978316241, 0.07954645397164571, 0.15414076830716056, 0.024887599694876025, -0.02338850390120053, -0.2548116546664617, 0.064159646489439, -0.16226131550645756, -0.14728605612698528, -0.1292170042080092, -0.001388634796495791, 0.08135890390402005, -0.3348924094512139, 0.07968791684618702, 0.07266342468318288, 0.09932393885367079, -0.05233646954415527, -0.12056120133234395, 0.0597585913621717, 0.04465618935402161, 0.050644234032657595, -0.017512490992997714, 0.05383982236301641, -0.06261978799730171, -0.15786324337665222, 0.30353016934047145, -0.08407112046067293, -0.2560553811605514, 0.15254844976934018, -0.14046051572824333, -0.13764127977422358, 0.1896191667995335, 0.16017854103335627, 0.1973775180723564, -0.14587385624785115, 0.12864914843252068, 0.015711682992354956, 0.10458103561612928, 0.09325926719854276, -0.01826432327751392, 0.03335283798981964, 0.16328034479814915, 0.10308000839740773, 0.09060294722080414, -0.03479394055673002, -0.200868743609775, -0.31696051602427167, -0.17983415446468387, -0.1120851516470681, 0.09142041309265259, -0.12336265026641302, -0.16102271245355593, 0.3325472851111381, 0.14990636263686566, 0.12064709937498894, 0.09598807447854384, 0.2690657381037319, 0.2006192450630076, -0.04546576970936673, 0.05571960618742454, 0.14448759916387957, 0.19787264251966535, 0.16537762950691912, -0.19134937715429215, 0.006997058788935344, 0.15207034872591862] |
1,802.00889 | Densely Connected Bidirectional LSTM with Applications to Sentence
Classification | Deep neural networks have recently been shown to achieve highly competitive
performance in many computer vision tasks due to their abilities of exploring
in a much larger hypothesis space. However, since most deep architectures like
stacked RNNs tend to suffer from the vanishing-gradient and overfitting
problems, their effects are still understudied in many NLP tasks. Inspired by
this, we propose a novel multi-layer RNN model called densely connected
bidirectional long short-term memory (DC-Bi-LSTM) in this paper, which
essentially represents each layer by the concatenation of its hidden state and
all preceding layers' hidden states, followed by recursively passing each
layer's representation to all subsequent layers. We evaluate our proposed model
on five benchmark datasets of sentence classification. DC-Bi-LSTM with depth up
to 20 can be successfully trained and obtain significant improvements over the
traditional Bi-LSTM with the same or even less parameters. Moreover, our model
has promising performance compared with the state-of-the-art approaches.
| cs.CL | deep neural networks have recently been shown to achieve highly competitive performance in many computer vision tasks due to their abilities of exploring in a much larger hypothesis space however since most deep architectures like stacked rnns tend to suffer from the vanishinggradient and overfitting problems their effects are still understudied in many nlp tasks inspired by this we propose a novel multilayer rnn model called densely connected bidirectional long shortterm memory dcbilstm in this paper which essentially represents each layer by the concatenation of its hidden state and all preceding layers hidden states followed by recursively passing each layers representation to all subsequent layers we evaluate our proposed model on five benchmark datasets of sentence classification dcbilstm with depth up to 20 can be successfully trained and obtain significant improvements over the traditional bilstm with the same or even less parameters moreover our model has promising performance compared with the stateoftheart approaches | [['deep', 'neural', 'networks', 'have', 'recently', 'been', 'shown', 'to', 'achieve', 'highly', 'competitive', 'performance', 'in', 'many', 'computer', 'vision', 'tasks', 'due', 'to', 'their', 'abilities', 'of', 'exploring', 'in', 'a', 'much', 'larger', 'hypothesis', 'space', 'however', 'since', 'most', 'deep', 'architectures', 'like', 'stacked', 'rnns', 'tend', 'to', 'suffer', 'from', 'the', 'vanishinggradient', 'and', 'overfitting', 'problems', 'their', 'effects', 'are', 'still', 'understudied', 'in', 'many', 'nlp', 'tasks', 'inspired', 'by', 'this', 'we', 'propose', 'a', 'novel', 'multilayer', 'rnn', 'model', 'called', 'densely', 'connected', 'bidirectional', 'long', 'shortterm', 'memory', 'dcbilstm', 'in', 'this', 'paper', 'which', 'essentially', 'represents', 'each', 'layer', 'by', 'the', 'concatenation', 'of', 'its', 'hidden', 'state', 'and', 'all', 'preceding', 'layers', 'hidden', 'states', 'followed', 'by', 'recursively', 'passing', 'each', 'layers', 'representation', 'to', 'all', 'subsequent', 'layers', 'we', 'evaluate', 'our', 'proposed', 'model', 'on', 'five', 'benchmark', 'datasets', 'of', 'sentence', 'classification', 'dcbilstm', 'with', 'depth', 'up', 'to', '20', 'can', 'be', 'successfully', 'trained', 'and', 'obtain', 'significant', 'improvements', 'over', 'the', 'traditional', 'bilstm', 'with', 'the', 'same', 'or', 'even', 'less', 'parameters', 'moreover', 'our', 'model', 'has', 'promising', 'performance', 'compared', 'with', 'the', 'stateoftheart', 'approaches']] | [-0.04140072348697222, 0.04758761278106924, -0.02147181217392154, 0.038823237845794424, -0.08623380039584212, -0.19676695828517246, 0.023804726301914464, 0.45811224316820404, -0.31437604046435963, -0.35459581239531374, 0.08181129790386123, -0.2617299287882171, -0.19488930656876885, 0.19757793175784366, -0.11207137758294676, 0.1039410805278026, 0.14850420754291857, 0.05277680282308319, -0.06837586392895226, -0.33585703823505747, 0.25945500367697305, 0.04867880719225077, 0.35077051808656695, -0.0022145409679768103, 0.1169158395762179, -0.07899369473032505, -0.007457993084268756, -0.008683042215462466, -0.012469403524358836, 0.1620354601953006, 0.29538592896177523, 0.11359659722344703, 0.32914517484975375, -0.47668921102196077, -0.293518181588032, 0.1011058578502066, 0.17339827430230992, 0.09205587172816393, 0.006113215971010311, -0.3170483968535204, 0.12478720348803264, -0.1994081898746605, 0.05414242903233216, -0.13759057378760287, -0.013982031754964333, -0.017824201413664646, -0.21892440588898118, 0.023771177281144487, 0.09439796240019631, 0.011750279532572785, -0.018749439230786993, -0.15886955713970427, 0.007602043212422274, 0.14716784790041085, 0.04684039078643607, 0.08752561559968486, 0.08542634600415727, -0.2139957048293702, -0.1727274578744669, 0.3388505879857436, -0.07444748351510787, -0.19502357408922438, 0.25317948476861646, -0.017399021606300247, -0.158032193705933, 0.08788238676875484, 0.21482266446217796, 0.11837870928471204, -0.16097494545361854, 0.007023890261054162, -0.0363485064135482, 0.19541875491942592, 0.0665777305681536, 0.021765902182025624, 0.20681972516814728, 0.29266053238969963, -0.004431543007511522, 0.14665745452758763, -0.11845167289451011, -0.09483565846828139, -0.13066968175625762, -0.07701404258263427, -0.14343490308483706, -0.02678162628470223, -0.0811193862846869, -0.14353988404961654, 0.4243465608929512, 0.22553951920793447, 0.24008587740411033, 0.11637049144988151, 0.3394614476833912, 0.027985591533137424, 0.20194820385420115, 0.12260919231736403, 0.2243682254130477, 0.05278143758379811, 0.10613399534563331, -0.10646499654151721, 0.11257064223462185, 0.021977163912938132] |
1,802.0089 | Modeling an Aquifer: Numerical Solution to the Groundwater Flow Equation | We present a model of groundwater dynamics under stationary flow and governed
by Darcy's Law of water motion through porous media, we apply it to study a 2D
aquifer with water table of constant slope comprised of an homogeneous and
isotropic media, the more realistic case of an homogeneous anisotropic soil is
also considered. Taking into account some geophysical parameters we develop a
computational routine, in the Finite Difference Method, that solves the
resulting elliptic partial equation, both in a homogeneous isotropic and
homogeneous anisotropic media. After calibration of the numerical model, this
routine is used to begin a study of the Ayamonte-Huelva aquifer in Spain, a
modest analysis of the system is given, we compute the average discharge vector
as well as its root mean square as a first predictive approximation of the flux
in this system, providing us a signal of the location of best exploitation;
long term goal is to develop a complete computational tool for the analysis of
groundwater dynamics.
| physics.geo-ph physics.flu-dyn | we present a model of groundwater dynamics under stationary flow and governed by darcys law of water motion through porous media we apply it to study a 2d aquifer with water table of constant slope comprised of an homogeneous and isotropic media the more realistic case of an homogeneous anisotropic soil is also considered taking into account some geophysical parameters we develop a computational routine in the finite difference method that solves the resulting elliptic partial equation both in a homogeneous isotropic and homogeneous anisotropic media after calibration of the numerical model this routine is used to begin a study of the ayamontehuelva aquifer in spain a modest analysis of the system is given we compute the average discharge vector as well as its root mean square as a first predictive approximation of the flux in this system providing us a signal of the location of best exploitation long term goal is to develop a complete computational tool for the analysis of groundwater dynamics | [['we', 'present', 'a', 'model', 'of', 'groundwater', 'dynamics', 'under', 'stationary', 'flow', 'and', 'governed', 'by', 'darcys', 'law', 'of', 'water', 'motion', 'through', 'porous', 'media', 'we', 'apply', 'it', 'to', 'study', 'a', '2d', 'aquifer', 'with', 'water', 'table', 'of', 'constant', 'slope', 'comprised', 'of', 'an', 'homogeneous', 'and', 'isotropic', 'media', 'the', 'more', 'realistic', 'case', 'of', 'an', 'homogeneous', 'anisotropic', 'soil', 'is', 'also', 'considered', 'taking', 'into', 'account', 'some', 'geophysical', 'parameters', 'we', 'develop', 'a', 'computational', 'routine', 'in', 'the', 'finite', 'difference', 'method', 'that', 'solves', 'the', 'resulting', 'elliptic', 'partial', 'equation', 'both', 'in', 'a', 'homogeneous', 'isotropic', 'and', 'homogeneous', 'anisotropic', 'media', 'after', 'calibration', 'of', 'the', 'numerical', 'model', 'this', 'routine', 'is', 'used', 'to', 'begin', 'a', 'study', 'of', 'the', 'ayamontehuelva', 'aquifer', 'in', 'spain', 'a', 'modest', 'analysis', 'of', 'the', 'system', 'is', 'given', 'we', 'compute', 'the', 'average', 'discharge', 'vector', 'as', 'well', 'as', 'its', 'root', 'mean', 'square', 'as', 'a', 'first', 'predictive', 'approximation', 'of', 'the', 'flux', 'in', 'this', 'system', 'providing', 'us', 'a', 'signal', 'of', 'the', 'location', 'of', 'best', 'exploitation', 'long', 'term', 'goal', 'is', 'to', 'develop', 'a', 'complete', 'computational', 'tool', 'for', 'the', 'analysis', 'of', 'groundwater', 'dynamics']] | [-0.0933700343901063, 0.08403450538182168, -0.08878550346529374, 0.03806250214011071, -0.061272282267640706, -0.09163805535577375, 0.011716920909603223, 0.3430544477573202, -0.27804219655898266, -0.28595233773469836, 0.13705083488979866, -0.2623547837566699, -0.1464275707165065, 0.1955758710920331, -0.01826949847256479, 0.07174877054307986, 0.038299283067027304, -0.007359637144633049, -0.034776513694722684, -0.21737969398464255, 0.2756787663571354, 0.06562377251341847, 0.2762826736916451, 0.02512316512225333, 0.14214312513444596, 0.009319864013489405, -0.06538241165133045, 0.059709600064377845, -0.16147926492221756, 0.10902669896983022, 0.21632240024720964, 0.06784939541669985, 0.2787750068297781, -0.44347437975093995, -0.2609239125544308, 0.09248615083987453, 0.10835343113303345, 0.10742897052957785, -0.034269814615647105, -0.2378387888879201, 0.036747725583338664, -0.18152248993104586, -0.18667939171177889, -0.04210822948003245, 0.013210106126826965, 0.029179445425037042, -0.2731893013404294, 0.1203905602242509, 0.0582717800565761, 0.08865789532164352, -0.11990548138423862, -0.0771243971661076, -0.0002544423282443563, 0.14957207642878445, 0.02096640081087733, -0.012472218742445569, 0.10117447409533147, -0.13300751109087577, -0.007379936888539718, 0.44190411614768343, -0.11692685509911893, -0.22137961518219415, 0.13465305005822994, -0.09827797945897364, -0.06786181800284542, 0.15031495677775766, 0.2384021350288295, 0.09474831925330296, -0.1843340383539765, 0.038542109683364564, -0.07827851923605415, 0.166130783392025, 0.010755541097327062, -0.07629919892885477, 0.16577478519360894, 0.24162277108075475, 0.0827714267523925, 0.18020594407421497, -0.07363728211872692, -0.1070668443386083, -0.3069406211014803, -0.18188124140324585, -0.14502500021327402, 0.062306313066989, -0.10454739859830117, -0.21731077109183544, 0.3888055654871135, 0.11935575154147174, 0.14265321502519165, 0.02380797476079201, 0.29971631332386306, 0.11237088961461045, -0.0016373622376615314, 0.08464983672023596, 0.18890213899895053, 0.13806545819467264, 0.14961252224123095, -0.20916915919626006, 0.06341094768497567, 0.05565482612265034] |
1,802.00891 | Joint Binary Neural Network for Multi-label Learning with Applications
to Emotion Classification | Recently the deep learning techniques have achieved success in multi-label
classification due to its automatic representation learning ability and the
end-to-end learning framework. Existing deep neural networks in multi-label
classification can be divided into two kinds: binary relevance neural network
(BRNN) and threshold dependent neural network (TDNN). However, the former needs
to train a set of isolate binary networks which ignore dependencies between
labels and have heavy computational load, while the latter needs an additional
threshold function mechanism to transform the multi-class probabilities to
multi-label outputs. In this paper, we propose a joint binary neural network
(JBNN), to address these shortcomings. In JBNN, the representation of the text
is fed to a set of logistic functions instead of a softmax function, and the
multiple binary classifications are carried out synchronously in one neural
network framework. Moreover, the relations between labels are captured via
training on a joint binary cross entropy (JBCE) loss. To better meet
multi-label emotion classification, we further proposed to incorporate the
prior label relations into the JBCE loss. The experimental results on the
benchmark dataset show that our model performs significantly better than the
state-of-the-art multi-label emotion classification methods, in both
classification performance and computational efficiency.
| cs.LG stat.ML | recently the deep learning techniques have achieved success in multilabel classification due to its automatic representation learning ability and the endtoend learning framework existing deep neural networks in multilabel classification can be divided into two kinds binary relevance neural network brnn and threshold dependent neural network tdnn however the former needs to train a set of isolate binary networks which ignore dependencies between labels and have heavy computational load while the latter needs an additional threshold function mechanism to transform the multiclass probabilities to multilabel outputs in this paper we propose a joint binary neural network jbnn to address these shortcomings in jbnn the representation of the text is fed to a set of logistic functions instead of a softmax function and the multiple binary classifications are carried out synchronously in one neural network framework moreover the relations between labels are captured via training on a joint binary cross entropy jbce loss to better meet multilabel emotion classification we further proposed to incorporate the prior label relations into the jbce loss the experimental results on the benchmark dataset show that our model performs significantly better than the stateoftheart multilabel emotion classification methods in both classification performance and computational efficiency | [['recently', 'the', 'deep', 'learning', 'techniques', 'have', 'achieved', 'success', 'in', 'multilabel', 'classification', 'due', 'to', 'its', 'automatic', 'representation', 'learning', 'ability', 'and', 'the', 'endtoend', 'learning', 'framework', 'existing', 'deep', 'neural', 'networks', 'in', 'multilabel', 'classification', 'can', 'be', 'divided', 'into', 'two', 'kinds', 'binary', 'relevance', 'neural', 'network', 'brnn', 'and', 'threshold', 'dependent', 'neural', 'network', 'tdnn', 'however', 'the', 'former', 'needs', 'to', 'train', 'a', 'set', 'of', 'isolate', 'binary', 'networks', 'which', 'ignore', 'dependencies', 'between', 'labels', 'and', 'have', 'heavy', 'computational', 'load', 'while', 'the', 'latter', 'needs', 'an', 'additional', 'threshold', 'function', 'mechanism', 'to', 'transform', 'the', 'multiclass', 'probabilities', 'to', 'multilabel', 'outputs', 'in', 'this', 'paper', 'we', 'propose', 'a', 'joint', 'binary', 'neural', 'network', 'jbnn', 'to', 'address', 'these', 'shortcomings', 'in', 'jbnn', 'the', 'representation', 'of', 'the', 'text', 'is', 'fed', 'to', 'a', 'set', 'of', 'logistic', 'functions', 'instead', 'of', 'a', 'softmax', 'function', 'and', 'the', 'multiple', 'binary', 'classifications', 'are', 'carried', 'out', 'synchronously', 'in', 'one', 'neural', 'network', 'framework', 'moreover', 'the', 'relations', 'between', 'labels', 'are', 'captured', 'via', 'training', 'on', 'a', 'joint', 'binary', 'cross', 'entropy', 'jbce', 'loss', 'to', 'better', 'meet', 'multilabel', 'emotion', 'classification', 'we', 'further', 'proposed', 'to', 'incorporate', 'the', 'prior', 'label', 'relations', 'into', 'the', 'jbce', 'loss', 'the', 'experimental', 'results', 'on', 'the', 'benchmark', 'dataset', 'show', 'that', 'our', 'model', 'performs', 'significantly', 'better', 'than', 'the', 'stateoftheart', 'multilabel', 'emotion', 'classification', 'methods', 'in', 'both', 'classification', 'performance', 'and', 'computational', 'efficiency']] | [-0.026433282663138248, -0.02926826563548858, -0.045105534012262256, 0.09499657623087748, -0.12112969024918782, -0.19360876531364063, 0.07618619929760312, 0.4666595897756708, -0.29350898903913986, -0.3119543407876522, 0.021421510801435662, -0.28300016544377193, -0.20224312474139225, 0.16971657989570538, -0.13990020494526012, 0.13574476693368828, 0.17365402277881423, 0.08107184151139779, -0.09507312638840328, -0.34575635506413305, 0.32500211553624236, 0.04896295462251426, 0.3794101434126974, 0.01360692543288072, 0.12118996559904936, -0.017196760926013573, -0.033440578589215876, -0.03036639522522306, -0.01357611821481558, 0.1758351556586627, 0.33371344287043964, 0.22280473217654687, 0.3479583570638146, -0.39520299095087325, -0.27915969288501985, 0.12272341148450207, 0.14541779426045906, 0.07660113107472753, 0.020442127640574025, -0.33175120463367935, 0.10212061446972, -0.22998011441758046, 0.09879515026337825, -0.1533695357111402, -0.035523640302320324, -0.013243154107103458, -0.28964030077346625, 0.038367374250904106, 0.11299311682546082, 0.020484207606372925, -0.08091029289644211, -0.13346449543172734, 0.012364364499584413, 0.15265599971798322, 0.03249999471769955, 0.08404653099819254, 0.1063569859506037, -0.2153981392450917, -0.15645466568664862, 0.3109509837407714, -0.03072193281535501, -0.25619498779042027, 0.21379815042138292, 0.007631677793912017, -0.1673962535121693, 0.09262204309865736, 0.30344334899041897, 0.09167377202986525, -0.184313169752176, -0.05435554656970243, -0.03154946487779037, 0.1980843475088477, 0.04828886199408235, -0.020167037199896116, 0.20092371790669858, 0.2888724044872782, -0.030005506901309275, 0.16428469999264986, -0.1565516157935445, -0.06787155074162934, -0.177540371791782, -0.06053409799217031, -0.17693538583146456, -0.02978607104279292, -0.11142939617342423, -0.1255893873814971, 0.3950002729844971, 0.2080227314781111, 0.24505895422532772, 0.15541803557485437, 0.3457762842759108, 0.06551691465846932, 0.14256807958516174, 0.08440183753099961, 0.19578303183512524, 0.04966387204133356, 0.09871395730461256, -0.18001911325607067, 0.09656977725621217, 0.09464602667348794] |
1,802.00892 | Left-Center-Right Separated Neural Network for Aspect-based Sentiment
Analysis with Rotatory Attention | Deep learning techniques have achieved success in aspect-based sentiment
analysis in recent years. However, there are two important issues that still
remain to be further studied, i.e., 1) how to efficiently represent the target
especially when the target contains multiple words; 2) how to utilize the
interaction between target and left/right contexts to capture the most
important words in them. In this paper, we propose an approach, called
left-center-right separated neural network with rotatory attention (LCR-Rot),
to better address the two problems. Our approach has two characteristics: 1) it
has three separated LSTMs, i.e., left, center and right LSTMs, corresponding to
three parts of a review (left context, target phrase and right context); 2) it
has a rotatory attention mechanism which models the relation between target and
left/right contexts. The target2context attention is used to capture the most
indicative sentiment words in left/right contexts. Subsequently, the
context2target attention is used to capture the most important word in the
target. This leads to a two-side representation of the target: left-aware
target and right-aware target. We compare our approach on three benchmark
datasets with ten related methods proposed recently. The results show that our
approach significantly outperforms the state-of-the-art techniques.
| cs.CL | deep learning techniques have achieved success in aspectbased sentiment analysis in recent years however there are two important issues that still remain to be further studied ie 1 how to efficiently represent the target especially when the target contains multiple words 2 how to utilize the interaction between target and leftright contexts to capture the most important words in them in this paper we propose an approach called leftcenterright separated neural network with rotatory attention lcrrot to better address the two problems our approach has two characteristics 1 it has three separated lstms ie left center and right lstms corresponding to three parts of a review left context target phrase and right context 2 it has a rotatory attention mechanism which models the relation between target and leftright contexts the target2context attention is used to capture the most indicative sentiment words in leftright contexts subsequently the context2target attention is used to capture the most important word in the target this leads to a twoside representation of the target leftaware target and rightaware target we compare our approach on three benchmark datasets with ten related methods proposed recently the results show that our approach significantly outperforms the stateoftheart techniques | [['deep', 'learning', 'techniques', 'have', 'achieved', 'success', 'in', 'aspectbased', 'sentiment', 'analysis', 'in', 'recent', 'years', 'however', 'there', 'are', 'two', 'important', 'issues', 'that', 'still', 'remain', 'to', 'be', 'further', 'studied', 'ie', '1', 'how', 'to', 'efficiently', 'represent', 'the', 'target', 'especially', 'when', 'the', 'target', 'contains', 'multiple', 'words', '2', 'how', 'to', 'utilize', 'the', 'interaction', 'between', 'target', 'and', 'leftright', 'contexts', 'to', 'capture', 'the', 'most', 'important', 'words', 'in', 'them', 'in', 'this', 'paper', 'we', 'propose', 'an', 'approach', 'called', 'leftcenterright', 'separated', 'neural', 'network', 'with', 'rotatory', 'attention', 'lcrrot', 'to', 'better', 'address', 'the', 'two', 'problems', 'our', 'approach', 'has', 'two', 'characteristics', '1', 'it', 'has', 'three', 'separated', 'lstms', 'ie', 'left', 'center', 'and', 'right', 'lstms', 'corresponding', 'to', 'three', 'parts', 'of', 'a', 'review', 'left', 'context', 'target', 'phrase', 'and', 'right', 'context', '2', 'it', 'has', 'a', 'rotatory', 'attention', 'mechanism', 'which', 'models', 'the', 'relation', 'between', 'target', 'and', 'leftright', 'contexts', 'the', 'target2context', 'attention', 'is', 'used', 'to', 'capture', 'the', 'most', 'indicative', 'sentiment', 'words', 'in', 'leftright', 'contexts', 'subsequently', 'the', 'context2target', 'attention', 'is', 'used', 'to', 'capture', 'the', 'most', 'important', 'word', 'in', 'the', 'target', 'this', 'leads', 'to', 'a', 'twoside', 'representation', 'of', 'the', 'target', 'leftaware', 'target', 'and', 'rightaware', 'target', 'we', 'compare', 'our', 'approach', 'on', 'three', 'benchmark', 'datasets', 'with', 'ten', 'related', 'methods', 'proposed', 'recently', 'the', 'results', 'show', 'that', 'our', 'approach', 'significantly', 'outperforms', 'the', 'stateoftheart', 'techniques']] | [-0.00845573998716039, 0.026623213157790815, -0.055314465128806965, 0.11171388360283648, -0.08654114792807377, -0.18907203193278596, -0.003523469851036983, 0.45355674665188417, -0.2820937600184455, -0.30059696736134356, 0.06970082698292875, -0.3030997968307929, -0.16347830999681415, 0.18759670768819584, -0.09171150562542607, 0.033665219486162336, 0.0688448858539535, 0.09765956986423892, -0.057511048905629046, -0.27597927338501904, 0.3254599034153216, 0.006777523275710943, 0.3387100317534835, 0.03452414545608917, 0.11766611175456394, -0.03960147102285797, -0.052128087748011843, -0.03520905066458605, -0.06652924504949927, 0.2010193545953977, 0.31707175823606804, 0.17523698074607333, 0.32261015708112, -0.41356101209142554, -0.22454870069001723, 0.0961515836691736, 0.15569371063854, 0.09318330584217922, -0.0434633445286939, -0.29783377250714693, 0.10512379526577813, -0.2007784671683718, 0.0028001575409083066, -0.09076028943976173, 0.03745690025486207, -0.0389311573417217, -0.22671416366574704, 0.011298676416724144, 0.11624427053296434, -0.0018388979515293613, -0.023337293931642005, -0.1271697815003184, 0.03909771446160448, 0.19502632090931607, 0.1156554437069038, 0.06482079549762905, 0.08692161621002015, -0.16778284477292496, -0.15026700580589628, 0.38064145021235163, -0.04590151327526352, -0.24091418110522986, 0.22567525515235806, -0.08809171092798351, -0.16441419602415408, 0.06614131451351568, 0.1941710590326693, 0.1367463435138537, -0.1457633720993575, 0.02669479437584717, -0.05700458270833527, 0.19551765371094612, 0.0910926668269288, 0.008324093539461805, 0.19761968588863965, 0.2519239611222777, 0.013940851122849077, 0.11320769835159201, -0.1383442343335446, -0.09194426140917737, -0.19192292347391535, -0.07584090618911432, -0.11484694990698092, -0.057311287464320536, -0.034233930445149476, -0.09294612987529642, 0.4267639911461932, 0.23505843586826813, 0.23699967697514998, 0.02920324177406049, 0.2949503006457235, 0.020451503734875587, 0.10899822659121128, 0.0604916828591134, 0.1901849066040692, 0.06395879339349146, 0.10512514892858842, -0.16239467983905342, 0.09082309003315459, 0.04642246090224944] |
1,802.00893 | D2D Big Data: Content Deliveries over Wireless Device-to-Device Sharing
in Large Scale Mobile Networks | Recently the topic of how to effectively offload cellular traffic onto
device-to-device (D2D) sharing among users in proximity has been gaining more
and more attention of global researchers and engineers. Users utilize wireless
short-range D2D communications for sharing contents locally, due to not only
the rapid sharing experience and free cost, but also high accuracy on
deliveries of interesting and popular contents, as well as strong social
impacts among friends. Nevertheless, the existing related studies are mostly
confined to small-scale datasets, limited dimensions of user features, or
unrealistic assumptions and hypotheses on user behaviors. In this article,
driven by emerging Big Data techniques, we propose to design a big data
platform, named D2D Big Data, in order to encourage the wireless D2D
communications among users effectively, to promote contents for providers
accurately, and to carry out offloading intelligence for operators efficiently.
We deploy a big data platform and further utilize a large-scale dataset (3.56
TBytes) from a popular D2D sharing application (APP), which contains 866
million D2D sharing activities on 4.5 million files disseminated via nearly 850
million users in 13 weeks. By abstracting and analyzing multidimensional
features, including online behaviors, content properties, location relations,
structural characteristics, meeting dynamics, social arborescence, privacy
preservation policies and so on, we verify and evaluate the D2D Big Data
platform regarding predictive content propagating coverage. Finally, we discuss
challenges and opportunities regarding D2D Big Data and propose to unveil a
promising upcoming future of wireless D2D communications.
| cs.NI | recently the topic of how to effectively offload cellular traffic onto devicetodevice d2d sharing among users in proximity has been gaining more and more attention of global researchers and engineers users utilize wireless shortrange d2d communications for sharing contents locally due to not only the rapid sharing experience and free cost but also high accuracy on deliveries of interesting and popular contents as well as strong social impacts among friends nevertheless the existing related studies are mostly confined to smallscale datasets limited dimensions of user features or unrealistic assumptions and hypotheses on user behaviors in this article driven by emerging big data techniques we propose to design a big data platform named d2d big data in order to encourage the wireless d2d communications among users effectively to promote contents for providers accurately and to carry out offloading intelligence for operators efficiently we deploy a big data platform and further utilize a largescale dataset 356 tbytes from a popular d2d sharing application app which contains 866 million d2d sharing activities on 45 million files disseminated via nearly 850 million users in 13 weeks by abstracting and analyzing multidimensional features including online behaviors content properties location relations structural characteristics meeting dynamics social arborescence privacy preservation policies and so on we verify and evaluate the d2d big data platform regarding predictive content propagating coverage finally we discuss challenges and opportunities regarding d2d big data and propose to unveil a promising upcoming future of wireless d2d communications | [['recently', 'the', 'topic', 'of', 'how', 'to', 'effectively', 'offload', 'cellular', 'traffic', 'onto', 'devicetodevice', 'd2d', 'sharing', 'among', 'users', 'in', 'proximity', 'has', 'been', 'gaining', 'more', 'and', 'more', 'attention', 'of', 'global', 'researchers', 'and', 'engineers', 'users', 'utilize', 'wireless', 'shortrange', 'd2d', 'communications', 'for', 'sharing', 'contents', 'locally', 'due', 'to', 'not', 'only', 'the', 'rapid', 'sharing', 'experience', 'and', 'free', 'cost', 'but', 'also', 'high', 'accuracy', 'on', 'deliveries', 'of', 'interesting', 'and', 'popular', 'contents', 'as', 'well', 'as', 'strong', 'social', 'impacts', 'among', 'friends', 'nevertheless', 'the', 'existing', 'related', 'studies', 'are', 'mostly', 'confined', 'to', 'smallscale', 'datasets', 'limited', 'dimensions', 'of', 'user', 'features', 'or', 'unrealistic', 'assumptions', 'and', 'hypotheses', 'on', 'user', 'behaviors', 'in', 'this', 'article', 'driven', 'by', 'emerging', 'big', 'data', 'techniques', 'we', 'propose', 'to', 'design', 'a', 'big', 'data', 'platform', 'named', 'd2d', 'big', 'data', 'in', 'order', 'to', 'encourage', 'the', 'wireless', 'd2d', 'communications', 'among', 'users', 'effectively', 'to', 'promote', 'contents', 'for', 'providers', 'accurately', 'and', 'to', 'carry', 'out', 'offloading', 'intelligence', 'for', 'operators', 'efficiently', 'we', 'deploy', 'a', 'big', 'data', 'platform', 'and', 'further', 'utilize', 'a', 'largescale', 'dataset', '356', 'tbytes', 'from', 'a', 'popular', 'd2d', 'sharing', 'application', 'app', 'which', 'contains', '866', 'million', 'd2d', 'sharing', 'activities', 'on', '45', 'million', 'files', 'disseminated', 'via', 'nearly', '850', 'million', 'users', 'in', '13', 'weeks', 'by', 'abstracting', 'and', 'analyzing', 'multidimensional', 'features', 'including', 'online', 'behaviors', 'content', 'properties', 'location', 'relations', 'structural', 'characteristics', 'meeting', 'dynamics', 'social', 'arborescence', 'privacy', 'preservation', 'policies', 'and', 'so', 'on', 'we', 'verify', 'and', 'evaluate', 'the', 'd2d', 'big', 'data', 'platform', 'regarding', 'predictive', 'content', 'propagating', 'coverage', 'finally', 'we', 'discuss', 'challenges', 'and', 'opportunities', 'regarding', 'd2d', 'big', 'data', 'and', 'propose', 'to', 'unveil', 'a', 'promising', 'upcoming', 'future', 'of', 'wireless', 'd2d', 'communications']] | [-0.17657640307888756, -0.001013146633130916, -0.015411379868777875, 0.0786121376838493, -0.15889594867085816, -0.23467325168726927, 0.16305981867720562, 0.4193044708574137, -0.27527225024063035, -0.34806580510596, 0.08362551946452879, -0.3712613127496642, -0.16966774121658404, 0.1377641659593133, -0.10456646516355743, 0.03849060074215441, 0.07342962631807229, 0.014264253002625925, 0.02580597017649293, -0.30965147330856846, 0.2871817614027556, 0.10535549477927018, 0.40220583440379504, 0.08907824571780025, 0.0031587497097413236, 0.00216256153152742, -0.10074057039199205, -0.07456480731244808, -0.11566436504971508, 0.1822212520170451, 0.39112945226377543, 0.2420164622517816, 0.3486662031565275, -0.44868954453510024, -0.23944520591968985, 0.0704494057675448, 0.19437490351337158, -0.0005159783291457022, -0.09549403631422045, -0.3577183091633574, 0.14013925620710185, -0.2561897998445748, -0.0472522130372087, -0.09467115770467775, -0.00957000175195865, 0.0450997390916548, -0.26856904466533, -0.004646889213017483, -0.07707995462706581, 0.10642649963260493, -0.01133643819817817, -0.05071719830430308, 0.002311017415045527, 0.2105557204965778, 0.06430487673817809, -0.06018395236369468, 0.14218837738328388, -0.14257289085200286, -0.12722682394662205, 0.41591646802063587, 0.03931644623318242, -0.13334366679670664, 0.21351269261390277, -0.030795120162728392, -0.13536399300880883, 0.07248120650902022, 0.2947253669473657, 0.021469061299315327, -0.22513333063499827, 0.0036746083333352468, 0.013034105579246127, 0.16270943947420013, 0.0934944201685745, 0.10874444128104213, 0.20868769730584996, 0.22604922788148676, 0.11655771884101408, 0.0514201155093349, -0.051440342866246484, -0.12171725479314878, -0.1339642786104213, -0.12559157462480167, -0.17248198197514525, 0.04323636065818103, -0.08397351827620003, -0.05548376085642925, 0.38273598229389927, 0.17571937988048472, 0.13097414581120442, 0.02214970001366388, 0.36182837503044313, -0.03159302424178593, 0.1336461430503661, 0.1583130134637525, 0.11479289806400016, 0.0036805200643469523, 0.25590114043105716, -0.12151206499243485, 0.05649813967124547, -0.06646157737530063] |
1,802.00894 | Wireless MapReduce Distributed Computing | Motivated by mobile edge computing and wireless data centers, we study a
wireless distributed computing framework where the distributed nodes exchange
information over a wireless interference network. Our framework follows the
structure of MapReduce. This framework consists of Map, Shuffle, and Reduce
phases, where Map and Reduce are computation phases and Shuffle is a data
transmission phase. In our setting, we assume that the transmission is operated
over a wireless interference network. We demonstrate that, by duplicating the
computation work at a cluster of distributed nodes in the Map phase, one can
reduce the amount of transmission load required for the Shuffle phase. In this
work, we characterize the fundamental tradeoff between computation load and
communication load, under the assumption of one-shot linear schemes. The
proposed scheme is based on side information cancellation and zero-forcing, and
we prove that it is optimal in terms of computation-communication tradeoff. The
proposed scheme outperforms the naive TDMA scheme with single node transmission
at a time, as well as the coded TDMA scheme that allows coding across data, in
terms of the computation-communication tradeoff.
| cs.IT math.IT | motivated by mobile edge computing and wireless data centers we study a wireless distributed computing framework where the distributed nodes exchange information over a wireless interference network our framework follows the structure of mapreduce this framework consists of map shuffle and reduce phases where map and reduce are computation phases and shuffle is a data transmission phase in our setting we assume that the transmission is operated over a wireless interference network we demonstrate that by duplicating the computation work at a cluster of distributed nodes in the map phase one can reduce the amount of transmission load required for the shuffle phase in this work we characterize the fundamental tradeoff between computation load and communication load under the assumption of oneshot linear schemes the proposed scheme is based on side information cancellation and zeroforcing and we prove that it is optimal in terms of computationcommunication tradeoff the proposed scheme outperforms the naive tdma scheme with single node transmission at a time as well as the coded tdma scheme that allows coding across data in terms of the computationcommunication tradeoff | [['motivated', 'by', 'mobile', 'edge', 'computing', 'and', 'wireless', 'data', 'centers', 'we', 'study', 'a', 'wireless', 'distributed', 'computing', 'framework', 'where', 'the', 'distributed', 'nodes', 'exchange', 'information', 'over', 'a', 'wireless', 'interference', 'network', 'our', 'framework', 'follows', 'the', 'structure', 'of', 'mapreduce', 'this', 'framework', 'consists', 'of', 'map', 'shuffle', 'and', 'reduce', 'phases', 'where', 'map', 'and', 'reduce', 'are', 'computation', 'phases', 'and', 'shuffle', 'is', 'a', 'data', 'transmission', 'phase', 'in', 'our', 'setting', 'we', 'assume', 'that', 'the', 'transmission', 'is', 'operated', 'over', 'a', 'wireless', 'interference', 'network', 'we', 'demonstrate', 'that', 'by', 'duplicating', 'the', 'computation', 'work', 'at', 'a', 'cluster', 'of', 'distributed', 'nodes', 'in', 'the', 'map', 'phase', 'one', 'can', 'reduce', 'the', 'amount', 'of', 'transmission', 'load', 'required', 'for', 'the', 'shuffle', 'phase', 'in', 'this', 'work', 'we', 'characterize', 'the', 'fundamental', 'tradeoff', 'between', 'computation', 'load', 'and', 'communication', 'load', 'under', 'the', 'assumption', 'of', 'oneshot', 'linear', 'schemes', 'the', 'proposed', 'scheme', 'is', 'based', 'on', 'side', 'information', 'cancellation', 'and', 'zeroforcing', 'and', 'we', 'prove', 'that', 'it', 'is', 'optimal', 'in', 'terms', 'of', 'computationcommunication', 'tradeoff', 'the', 'proposed', 'scheme', 'outperforms', 'the', 'naive', 'tdma', 'scheme', 'with', 'single', 'node', 'transmission', 'at', 'a', 'time', 'as', 'well', 'as', 'the', 'coded', 'tdma', 'scheme', 'that', 'allows', 'coding', 'across', 'data', 'in', 'terms', 'of', 'the', 'computationcommunication', 'tradeoff']] | [-0.24487188586370193, 0.020397067587408755, -0.05370821500610974, -0.02803210025627373, -0.024124855734407903, -0.20238933684821758, 0.16304826683157847, 0.382748774646057, -0.3221075348349081, -0.2950791083586713, 0.07838078473254831, -0.2414265991900821, -0.2048039000015706, 0.16049843148017923, -0.13290550638743703, 0.06405951003026632, 0.060854945525837444, 0.023041942935540443, -0.03317545762466681, -0.27518382558223997, 0.306667396371227, 0.08680278692497975, 0.4040879389892022, 0.044062011508827305, 0.09921772932413862, 0.04773907193965796, -0.04728393720708684, -0.03479551935128661, -0.07064366056664666, 0.10257225638876359, 0.3095708753810161, 0.1660130636469047, 0.2620484813750308, -0.43463473185482954, -0.23652352924562162, 0.10185489948942429, 0.1719836094028627, 0.07319441417801298, -0.02240450159426675, -0.22714114375671166, 0.12006905469930239, -0.21645613958438237, 0.003560503693814907, -0.023987328525011738, -0.0735338838564025, 0.022074101491469062, -0.3370065905816672, 0.029300351821196575, -0.014279785285988408, 0.02413240365890993, -0.021488769972347655, -0.028923130511409708, 0.03674131996748555, 0.1533453542402842, -0.030209757945138135, -0.0022117028763103817, 0.10400741060471369, -0.07273545211064629, -0.14141750262222355, 0.3793764610981776, -0.007557687134895887, -0.17388796088440964, 0.09564629796400873, -0.04778501782841178, -0.11408450876673062, 0.09416327437178956, 0.1991186196398404, 0.0756238427789261, -0.1450678950282357, 0.06518711660141384, -0.029636193998157978, 0.15731844687316981, 0.08827238687469313, 0.09739732826904704, 0.11972490199485845, 0.20459917194934354, 0.1559423460948488, 0.1499541893381522, -0.1161075120856468, -0.1614484339952469, -0.250656672520563, -0.16522240422976514, -0.23202272068398694, -0.04949068084566129, -0.13015942509591696, -0.08151947631397181, 0.37360589671451533, 0.13424003845624005, 0.16518993349487168, 0.10324904168293061, 0.41154406148319445, 0.09358118779491634, 0.08874112538631178, 0.17450691565560797, 0.15051340428180993, 0.08673082810257458, 0.1395757391205229, -0.2320289125614282, 0.06566289770158215, 0.024095868949209235] |
1,802.00895 | Separation of Charge Instability and Lattice Symmetry Breaking in an
Organic Ferroelectric | We investigate the charge and lattice states in a quasi-one-dimensional
organic ferroelectric material, TTF-QCl$_{4}$, under pressures of up to 35 kbar
by nuclear quadrupole resonance experiments. The results reveal a global
pressure-temperature phase diagram, which spans the electronic and ionic
regimes of ferroelectric transitions, which have so far been studied
separately, in a single material. The revealed phase diagram clearly shows that
the charge-transfer instability and the lattice symmetry breaking, which
coincide in the electronic ferroelectric regime at low pressures, bifurcate at
a certain pressure, leading to the conventional ferroelectric regime. The
present results reveal that the crossover from electronic to ionic
ferroelectricity occurs through the separation of charge and lattice
instabilities.
| cond-mat.mtrl-sci | we investigate the charge and lattice states in a quasionedimensional organic ferroelectric material ttfqcl_4 under pressures of up to 35 kbar by nuclear quadrupole resonance experiments the results reveal a global pressuretemperature phase diagram which spans the electronic and ionic regimes of ferroelectric transitions which have so far been studied separately in a single material the revealed phase diagram clearly shows that the chargetransfer instability and the lattice symmetry breaking which coincide in the electronic ferroelectric regime at low pressures bifurcate at a certain pressure leading to the conventional ferroelectric regime the present results reveal that the crossover from electronic to ionic ferroelectricity occurs through the separation of charge and lattice instabilities | [['we', 'investigate', 'the', 'charge', 'and', 'lattice', 'states', 'in', 'a', 'quasionedimensional', 'organic', 'ferroelectric', 'material', 'ttfqcl_4', 'under', 'pressures', 'of', 'up', 'to', '35', 'kbar', 'by', 'nuclear', 'quadrupole', 'resonance', 'experiments', 'the', 'results', 'reveal', 'a', 'global', 'pressuretemperature', 'phase', 'diagram', 'which', 'spans', 'the', 'electronic', 'and', 'ionic', 'regimes', 'of', 'ferroelectric', 'transitions', 'which', 'have', 'so', 'far', 'been', 'studied', 'separately', 'in', 'a', 'single', 'material', 'the', 'revealed', 'phase', 'diagram', 'clearly', 'shows', 'that', 'the', 'chargetransfer', 'instability', 'and', 'the', 'lattice', 'symmetry', 'breaking', 'which', 'coincide', 'in', 'the', 'electronic', 'ferroelectric', 'regime', 'at', 'low', 'pressures', 'bifurcate', 'at', 'a', 'certain', 'pressure', 'leading', 'to', 'the', 'conventional', 'ferroelectric', 'regime', 'the', 'present', 'results', 'reveal', 'that', 'the', 'crossover', 'from', 'electronic', 'to', 'ionic', 'ferroelectricity', 'occurs', 'through', 'the', 'separation', 'of', 'charge', 'and', 'lattice', 'instabilities']] | [-0.16731351321596685, 0.21273760577487583, -0.07164572314401199, -0.012004420689835742, -0.01603436745862636, -0.11901275676817775, 0.13572415873735538, 0.3804685845048175, -0.2695495376514422, -0.2337567958276014, 0.03037359071832553, -0.2968260486115206, -0.13761025302212787, 0.11025951324483833, 0.09467740233584836, 0.0034376961925813745, -0.05400517411731385, -0.05353037625769371, -0.12674602337164786, -0.13287223649338656, 0.2614340145342253, -0.019935475150542753, 0.3168176629807095, 0.13204149347845348, 0.055130421108490715, -0.09098752634809562, 0.16529939511606284, 0.014577333395881159, -0.19201559306246643, -0.013111088234566253, 0.2968735234635706, -0.10992949133159945, 0.1677137098216393, -0.44620298755330007, -0.24051231874136236, -0.004000435507780797, 0.11449641311385979, 0.1759285393122768, -0.07801470161323343, -0.2710195018549089, 0.035624588646733006, -0.1336985076148365, -0.11534964411334882, -0.13679129824143005, -0.033913964170727645, 0.013928101666540176, -0.24283858717561843, 0.10360453775160955, 0.05046388420888835, 0.13469004750394406, -0.13126496080443398, -0.1555141106558343, -0.09279747516218875, 0.04643256548657879, 0.05922284097251323, 0.06598767772870692, 0.20486707761904713, -0.10892086302408495, -0.09382317768963608, 0.40534155292285456, 0.00401837992070763, -0.039097915258812344, 0.18086216957643964, -0.2593176455052385, -0.09538439363409001, 0.2649943353356542, 0.08352618838182173, 0.06779155839677178, -0.11518231606496884, 0.060108716524392786, 0.013795100781764533, 0.19494358454788993, 0.07774785868529868, 0.08465772354555828, 0.254606816179312, 0.17704125999070303, 0.0012274448621413998, 0.19483109693638645, -0.10965045615411909, -0.10750050012542454, -0.21870231000824017, -0.11532354645713165, -0.1676923459299278, 0.011742946578542123, -0.07177207690216852, -0.18061310277731568, 0.40085981381838925, 0.11503368523391383, 0.1722744132585085, -0.10702114278421242, 0.20470172950897264, 0.05553372086000603, 0.08893674243291891, 0.02564937917951931, 0.32274425914511085, 0.1513583940298607, 0.16307336376845702, -0.3464087887272776, 0.06890764889131124, 0.010397793739815956] |
1,802.00896 | A note on degenerate Stirling numbers of the first kind | Recently, the degenerate Stirling numbers of the first kind were introduced.
In this paper, we give some formulas for the degenerate Stirling numbers of the
first kind in the terms of the complete Bell polynomials with higher-order
harmonic number arguments. Also, we derive an identity connecting the
degenerate Stirling numbers of the first kind and the degenerate derangement
numbers by using probabilistic method.
| math.NT | recently the degenerate stirling numbers of the first kind were introduced in this paper we give some formulas for the degenerate stirling numbers of the first kind in the terms of the complete bell polynomials with higherorder harmonic number arguments also we derive an identity connecting the degenerate stirling numbers of the first kind and the degenerate derangement numbers by using probabilistic method | [['recently', 'the', 'degenerate', 'stirling', 'numbers', 'of', 'the', 'first', 'kind', 'were', 'introduced', 'in', 'this', 'paper', 'we', 'give', 'some', 'formulas', 'for', 'the', 'degenerate', 'stirling', 'numbers', 'of', 'the', 'first', 'kind', 'in', 'the', 'terms', 'of', 'the', 'complete', 'bell', 'polynomials', 'with', 'higherorder', 'harmonic', 'number', 'arguments', 'also', 'we', 'derive', 'an', 'identity', 'connecting', 'the', 'degenerate', 'stirling', 'numbers', 'of', 'the', 'first', 'kind', 'and', 'the', 'degenerate', 'derangement', 'numbers', 'by', 'using', 'probabilistic', 'method']] | [-0.19872689275218855, 0.14303825082583538, -0.014242608632360185, 0.08867556186178552, -0.12855819148558473, -0.06173738118793283, 0.05800881986469326, 0.2093493835113588, -0.2691044538675222, -0.3147563864255235, 0.04718107549429294, -0.2489695100793763, -0.19584610631009416, 0.23978311459474738, -0.04892524224516588, 0.06887616988803659, 0.01283142257422682, 0.07280056842321915, -0.045054940625079094, -0.2737105305025738, 0.40461675810908515, -0.03157728270346683, 0.18385802629211592, -0.015041975038392203, 0.08372008130841312, -0.04345833976560878, -0.019376443846831248, -0.03950751525542093, -0.17137269249245052, 0.14590254124431384, 0.2149557341001041, 0.09828467316748131, 0.2757059943581384, -0.42860092790353865, -0.04200572215227617, 0.17276754338176004, 0.08820435668652256, 0.07885242099799807, -0.019981509414575403, -0.25419674854400376, 0.024169784883732007, -0.21878035580148064, -0.2079139019894813, -0.12517512561605562, -0.03094940736061997, 0.17678733119770648, -0.24326625419041467, 0.0814814301284348, 0.12394170684828645, 0.08135389072436189, 0.05486485118597066, -0.15601147053438047, 0.04817283823199216, 0.07636323235633355, -0.01387405658643397, -0.13456013396618857, -0.08679957500111962, -0.08465473461408346, -0.19827467940067725, 0.37191889497141045, -0.023571554076163067, -0.22335041113316068, 0.05291617646931656, -0.17051953026315286, -0.28147959782342824, 0.10333043813801533, 0.0970599360704895, 0.20465568526987993, -0.0830949969619276, -0.003307165453276996, -0.11678208409261609, 0.06642658940501629, 0.21255467971047712, 0.03303987563898166, 0.15561105782491347, 0.01866403396522242, -0.018241972026843873, 0.2593413718369982, -0.04898511293152022, -0.09700255744927933, -0.3820667341055851, -0.2724019755417156, -0.24602473738588512, 0.03438721847025648, -0.06952728664420271, -0.15954180874876558, 0.3899251620566088, 0.09041408521847592, 0.15923146724641796, 0.09873988715902207, 0.2625277682968844, 0.20615984239275492, -0.0042306360685163075, -0.004190816455298946, 0.16233164056468505, 0.26629218483521117, 0.13064789313763853, -0.12911226959632977, 0.01440312690101564, 0.24325367976867018] |
1,802.00897 | Representations of quadratic combinatorial optimization problems: A case
study using the quadratic set covering problem | The objective function of a quadratic combinatorial optimization problem
(QCOP) can be represented by two data points, a quadratic cost matrix Q and a
linear cost vector c. Different, but equivalent, representations of the pair
(Q, c) for the same QCOP are well known in literature. Research papers often
state that without loss of generality we assume Q is symmetric, or
upper-triangular or positive semidefinite, etc. These representations however
have inherently different properties. Popular general purpose 0-1 QCOP solvers
such as GUROBI and CPLEX do not suggest a preferred representation of Q and c.
Our experimental analysis discloses that GUROBI prefers the upper triangular
representation of the matrix Q while CPLEX prefers the symmetric representation
in a statistically significant manner. Equivalent representations, although
preserve optimality, they could alter the corresponding lower bound values
obtained by various lower bounding schemes. For the natural lower bound of a
QCOP, symmetric representation produced tighter bounds, in general. Effect of
equivalent representations when CPLEX and GUROBI run in a heuristic mode are
also explored. Further, we review various equivalent representations of a QCOP
from the literature that have theoretical basis to be viewed as strong and
provide new theoretical insights for generating such equivalent representations
making use of constant value property and diagonalization (linearization) of
QCOP instances.
| math.OC cs.DM | the objective function of a quadratic combinatorial optimization problem qcop can be represented by two data points a quadratic cost matrix q and a linear cost vector c different but equivalent representations of the pair q c for the same qcop are well known in literature research papers often state that without loss of generality we assume q is symmetric or uppertriangular or positive semidefinite etc these representations however have inherently different properties popular general purpose 01 qcop solvers such as gurobi and cplex do not suggest a preferred representation of q and c our experimental analysis discloses that gurobi prefers the upper triangular representation of the matrix q while cplex prefers the symmetric representation in a statistically significant manner equivalent representations although preserve optimality they could alter the corresponding lower bound values obtained by various lower bounding schemes for the natural lower bound of a qcop symmetric representation produced tighter bounds in general effect of equivalent representations when cplex and gurobi run in a heuristic mode are also explored further we review various equivalent representations of a qcop from the literature that have theoretical basis to be viewed as strong and provide new theoretical insights for generating such equivalent representations making use of constant value property and diagonalization linearization of qcop instances | [['the', 'objective', 'function', 'of', 'a', 'quadratic', 'combinatorial', 'optimization', 'problem', 'qcop', 'can', 'be', 'represented', 'by', 'two', 'data', 'points', 'a', 'quadratic', 'cost', 'matrix', 'q', 'and', 'a', 'linear', 'cost', 'vector', 'c', 'different', 'but', 'equivalent', 'representations', 'of', 'the', 'pair', 'q', 'c', 'for', 'the', 'same', 'qcop', 'are', 'well', 'known', 'in', 'literature', 'research', 'papers', 'often', 'state', 'that', 'without', 'loss', 'of', 'generality', 'we', 'assume', 'q', 'is', 'symmetric', 'or', 'uppertriangular', 'or', 'positive', 'semidefinite', 'etc', 'these', 'representations', 'however', 'have', 'inherently', 'different', 'properties', 'popular', 'general', 'purpose', '01', 'qcop', 'solvers', 'such', 'as', 'gurobi', 'and', 'cplex', 'do', 'not', 'suggest', 'a', 'preferred', 'representation', 'of', 'q', 'and', 'c', 'our', 'experimental', 'analysis', 'discloses', 'that', 'gurobi', 'prefers', 'the', 'upper', 'triangular', 'representation', 'of', 'the', 'matrix', 'q', 'while', 'cplex', 'prefers', 'the', 'symmetric', 'representation', 'in', 'a', 'statistically', 'significant', 'manner', 'equivalent', 'representations', 'although', 'preserve', 'optimality', 'they', 'could', 'alter', 'the', 'corresponding', 'lower', 'bound', 'values', 'obtained', 'by', 'various', 'lower', 'bounding', 'schemes', 'for', 'the', 'natural', 'lower', 'bound', 'of', 'a', 'qcop', 'symmetric', 'representation', 'produced', 'tighter', 'bounds', 'in', 'general', 'effect', 'of', 'equivalent', 'representations', 'when', 'cplex', 'and', 'gurobi', 'run', 'in', 'a', 'heuristic', 'mode', 'are', 'also', 'explored', 'further', 'we', 'review', 'various', 'equivalent', 'representations', 'of', 'a', 'qcop', 'from', 'the', 'literature', 'that', 'have', 'theoretical', 'basis', 'to', 'be', 'viewed', 'as', 'strong', 'and', 'provide', 'new', 'theoretical', 'insights', 'for', 'generating', 'such', 'equivalent', 'representations', 'making', 'use', 'of', 'constant', 'value', 'property', 'and', 'diagonalization', 'linearization', 'of', 'qcop', 'instances']] | [-0.08628386880070006, 0.0595074204477687, -0.0660398057924295, 0.10424164686835165, -0.12552219461933864, -0.19909681722154499, 0.04626562643988592, 0.37482295535120047, -0.27669530567612516, -0.30958912289644563, 0.11662803464809633, -0.2462149618320307, -0.14818308158108676, 0.20413569455470085, -0.034342963639821665, 0.07168076754206881, 0.07743631949060173, 0.05504111831413665, -0.14545385658731022, -0.26460108911714786, 0.2842851928293163, 0.006022533448850255, 0.28081483547597635, 0.042273566446009096, 0.07396858191979008, -0.012824223818423602, -0.0047186233280631035, 0.03724483952162341, -0.09325467162716236, 0.13772798356864635, 0.2822128869702454, 0.16472415328069542, 0.2699651443574385, -0.404748680332387, -0.1512300821082413, 0.13783565822807295, 0.1641135582057628, 0.07065044391895929, -0.0626719210563586, -0.2417147210405641, 0.11446206180893645, -0.1663737776372232, -0.06620311123611922, -0.08512112179862887, 0.01681800085194234, 0.0004764044935543947, -0.30805337850983855, 0.05087041669564011, 0.09107578413188239, 0.06235154179493818, -0.07338011372690155, -0.24015194481837743, -0.003264785115716393, 0.07331813951288, 0.026787833471349637, 0.04554368701966768, 0.08431528556103787, -0.1197134147949097, -0.13689860527921782, 0.3848097090797385, -0.06069305545908395, -0.24887505815552433, 0.16778114190770033, -0.06804177586531415, -0.12078002887393212, 0.11018769329461868, 0.17426606412239684, 0.1379344715002242, -0.09197703976231407, 0.11512050709081595, -0.10931345649356539, 0.15502040321171337, 0.07292020976753302, 0.03508217136338441, 0.16281460168850148, 0.06907423423874266, 0.08075068212341639, 0.13650154029106626, 0.036827816810467644, -0.09504225779709542, -0.27096830897837737, -0.13368261081632227, -0.17440162993300196, 0.02617209362957305, -0.11687854407183026, -0.14130619164728975, 0.3518787908844405, 0.10658952479137046, 0.19784996769185498, 0.12300220103016202, 0.2705292849333633, 0.14979794728646204, 0.07470303248480714, 0.09059338578888752, 0.1976712457704502, 0.1022574268357977, 0.027282018364219108, -0.14909216071732623, 0.08405907205010855, 0.08732969253520731] |
1,802.00898 | Path Planning for Minimizing the Expected Cost until Success | Consider a general path planning problem of a robot on a graph with edge
costs, and where each node has a Boolean value of success or failure (with
respect to some task) with a given probability. The objective is to plan a path
for the robot on the graph that minimizes the expected cost until success. In
this paper, it is our goal to bring a foundational understanding to this
problem. We start by showing how this problem can be optimally solved by
formulating it as an infinite horizon Markov Decision Process, but with an
exponential space complexity. We then formally prove its NP-hardness. To
address the space complexity, we then propose a path planner, using a
game-theoretic framework, that asymptotically gets arbitrarily close to the
optimal solution. Moreover, we also propose two fast and non-myopic path
planners. To show the performance of our framework, we do extensive simulations
for two scenarios: a rover on Mars searching for an object for scientific
studies, and a robot looking for a connected spot to a remote station (with
real data from downtown San Francisco). Our numerical results show a
considerable performance improvement over existing state-of-the-art approaches.
| cs.RO | consider a general path planning problem of a robot on a graph with edge costs and where each node has a boolean value of success or failure with respect to some task with a given probability the objective is to plan a path for the robot on the graph that minimizes the expected cost until success in this paper it is our goal to bring a foundational understanding to this problem we start by showing how this problem can be optimally solved by formulating it as an infinite horizon markov decision process but with an exponential space complexity we then formally prove its nphardness to address the space complexity we then propose a path planner using a gametheoretic framework that asymptotically gets arbitrarily close to the optimal solution moreover we also propose two fast and nonmyopic path planners to show the performance of our framework we do extensive simulations for two scenarios a rover on mars searching for an object for scientific studies and a robot looking for a connected spot to a remote station with real data from downtown san francisco our numerical results show a considerable performance improvement over existing stateoftheart approaches | [['consider', 'a', 'general', 'path', 'planning', 'problem', 'of', 'a', 'robot', 'on', 'a', 'graph', 'with', 'edge', 'costs', 'and', 'where', 'each', 'node', 'has', 'a', 'boolean', 'value', 'of', 'success', 'or', 'failure', 'with', 'respect', 'to', 'some', 'task', 'with', 'a', 'given', 'probability', 'the', 'objective', 'is', 'to', 'plan', 'a', 'path', 'for', 'the', 'robot', 'on', 'the', 'graph', 'that', 'minimizes', 'the', 'expected', 'cost', 'until', 'success', 'in', 'this', 'paper', 'it', 'is', 'our', 'goal', 'to', 'bring', 'a', 'foundational', 'understanding', 'to', 'this', 'problem', 'we', 'start', 'by', 'showing', 'how', 'this', 'problem', 'can', 'be', 'optimally', 'solved', 'by', 'formulating', 'it', 'as', 'an', 'infinite', 'horizon', 'markov', 'decision', 'process', 'but', 'with', 'an', 'exponential', 'space', 'complexity', 'we', 'then', 'formally', 'prove', 'its', 'nphardness', 'to', 'address', 'the', 'space', 'complexity', 'we', 'then', 'propose', 'a', 'path', 'planner', 'using', 'a', 'gametheoretic', 'framework', 'that', 'asymptotically', 'gets', 'arbitrarily', 'close', 'to', 'the', 'optimal', 'solution', 'moreover', 'we', 'also', 'propose', 'two', 'fast', 'and', 'nonmyopic', 'path', 'planners', 'to', 'show', 'the', 'performance', 'of', 'our', 'framework', 'we', 'do', 'extensive', 'simulations', 'for', 'two', 'scenarios', 'a', 'rover', 'on', 'mars', 'searching', 'for', 'an', 'object', 'for', 'scientific', 'studies', 'and', 'a', 'robot', 'looking', 'for', 'a', 'connected', 'spot', 'to', 'a', 'remote', 'station', 'with', 'real', 'data', 'from', 'downtown', 'san', 'francisco', 'our', 'numerical', 'results', 'show', 'a', 'considerable', 'performance', 'improvement', 'over', 'existing', 'stateoftheart', 'approaches']] | [-0.10033261328671589, 0.03821942613653049, -0.09681917936169841, 0.035034662274961895, -0.11898943821457934, -0.13430481289571017, 0.11544935435198304, 0.43425102960846396, -0.2823451617540619, -0.31732045877730886, 0.12656433284990778, -0.2696189856127903, -0.1928322247803192, 0.19183633032737338, -0.16616946209333694, 0.09379569548104179, 0.14660322401173337, 0.07061844912763282, -0.019564474605506324, -0.27602481220149166, 0.28195338004650355, 0.059894717720903684, 0.24774981425119774, 0.04696268447766023, 0.1485311691169195, 0.017329263294442107, 0.03796691573753031, 0.06570964966409783, -0.1314796818839296, 0.13764066341124265, 0.3183848003403178, 0.20847838167605212, 0.3606369583339421, -0.4210759215344934, -0.1987945905852986, 0.1252751021127495, 0.1109599426680379, 0.08378841002596561, -0.040119538797166426, -0.29549796506762505, 0.09867871330044585, -0.18384568048185027, -0.09265022247204001, -0.027881600573591736, 0.0018915731469457298, -0.034469942951133266, -0.2985051141202104, -0.06487528444402828, 0.030846412476036967, 0.013832748841167879, -0.05365551019521891, -0.07225199172467704, 0.05341487827101127, 0.14572464222431702, 0.04465627739822237, 0.08649722992720985, 0.09745739459764625, -0.12134251536467813, -0.19753614105956302, 0.374540951057366, -0.012060361560290084, -0.21715550475091355, 0.17015329635277218, -0.07787437530320868, -0.14086924508434825, 0.10541180122317266, 0.19221336544808193, 0.12293027245593209, -0.14603484876588294, 0.06629789300345738, -0.06502610904083002, 0.14708700661125026, 0.02997328670330576, -0.034727806826398144, 0.1748125224307326, 0.23424216340171153, 0.17732420893741252, 0.15931565711063514, -0.052100265289407185, -0.11691546056554028, -0.24373970198482228, -0.1700378156401524, -0.18432588565532962, 0.015421351420299448, -0.09620897350426266, -0.1293793960051976, 0.36667874020514724, 0.19597885059432807, 0.1988857316145439, 0.15854943454260795, 0.34402897710115027, 0.09537946987901959, 0.0038162359377350084, 0.13715002476639018, 0.16718170061043208, -0.005751922081546262, 0.09585357188503978, -0.20286876738873147, 0.08552431723777416, 0.04884544908808371] |
1,802.00899 | Learning Parametric Closed-Loop Policies for Markov Potential Games | Multiagent systems where agents interact among themselves and with a
stochastic environment can be formalized as stochastic games. We study a
subclass named Markov potential games (MPGs) that appear often in economic and
engineering applications when the agents share a common resource. We consider
MPGs with continuous state-action variables, coupled constraints and nonconvex
rewards. Previous analysis followed a variational approach that is only valid
for very simple cases (convex rewards, invertible dynamics, and no coupled
constraints); or considered deterministic dynamics and provided open-loop (OL)
analysis, studying strategies that consist in predefined action sequences,
which are not optimal for stochastic environments. We present a closed-loop
(CL) analysis for MPGs and consider parametric policies that depend on the
current state. We provide easily verifiable, sufficient and necessary
conditions for a stochastic game to be an MPG, even for complex parametric
functions (e.g., deep neural networks); and show that a closed-loop Nash
equilibrium (NE) can be found (or at least approximated) by solving a related
optimal control problem (OCP). This is useful since solving an OCP--which is a
single-objective problem--is usually much simpler than solving the original set
of coupled OCPs that form the game--which is a multiobjective control problem.
This is a considerable improvement over the previously standard approach for
the CL analysis of MPGs, which gives no approximate solution if no NE belongs
to the chosen parametric family, and which is practical only for simple
parametric forms. We illustrate the theoretical contributions with an example
by applying our approach to a noncooperative communications engineering game.
We then solve the game with a deep reinforcement learning algorithm that learns
policies that closely approximates an exact variational NE of the game.
| cs.MA cs.GT cs.LG math.OC | multiagent systems where agents interact among themselves and with a stochastic environment can be formalized as stochastic games we study a subclass named markov potential games mpgs that appear often in economic and engineering applications when the agents share a common resource we consider mpgs with continuous stateaction variables coupled constraints and nonconvex rewards previous analysis followed a variational approach that is only valid for very simple cases convex rewards invertible dynamics and no coupled constraints or considered deterministic dynamics and provided openloop ol analysis studying strategies that consist in predefined action sequences which are not optimal for stochastic environments we present a closedloop cl analysis for mpgs and consider parametric policies that depend on the current state we provide easily verifiable sufficient and necessary conditions for a stochastic game to be an mpg even for complex parametric functions eg deep neural networks and show that a closedloop nash equilibrium ne can be found or at least approximated by solving a related optimal control problem ocp this is useful since solving an ocpwhich is a singleobjective problemis usually much simpler than solving the original set of coupled ocps that form the gamewhich is a multiobjective control problem this is a considerable improvement over the previously standard approach for the cl analysis of mpgs which gives no approximate solution if no ne belongs to the chosen parametric family and which is practical only for simple parametric forms we illustrate the theoretical contributions with an example by applying our approach to a noncooperative communications engineering game we then solve the game with a deep reinforcement learning algorithm that learns policies that closely approximates an exact variational ne of the game | [['multiagent', 'systems', 'where', 'agents', 'interact', 'among', 'themselves', 'and', 'with', 'a', 'stochastic', 'environment', 'can', 'be', 'formalized', 'as', 'stochastic', 'games', 'we', 'study', 'a', 'subclass', 'named', 'markov', 'potential', 'games', 'mpgs', 'that', 'appear', 'often', 'in', 'economic', 'and', 'engineering', 'applications', 'when', 'the', 'agents', 'share', 'a', 'common', 'resource', 'we', 'consider', 'mpgs', 'with', 'continuous', 'stateaction', 'variables', 'coupled', 'constraints', 'and', 'nonconvex', 'rewards', 'previous', 'analysis', 'followed', 'a', 'variational', 'approach', 'that', 'is', 'only', 'valid', 'for', 'very', 'simple', 'cases', 'convex', 'rewards', 'invertible', 'dynamics', 'and', 'no', 'coupled', 'constraints', 'or', 'considered', 'deterministic', 'dynamics', 'and', 'provided', 'openloop', 'ol', 'analysis', 'studying', 'strategies', 'that', 'consist', 'in', 'predefined', 'action', 'sequences', 'which', 'are', 'not', 'optimal', 'for', 'stochastic', 'environments', 'we', 'present', 'a', 'closedloop', 'cl', 'analysis', 'for', 'mpgs', 'and', 'consider', 'parametric', 'policies', 'that', 'depend', 'on', 'the', 'current', 'state', 'we', 'provide', 'easily', 'verifiable', 'sufficient', 'and', 'necessary', 'conditions', 'for', 'a', 'stochastic', 'game', 'to', 'be', 'an', 'mpg', 'even', 'for', 'complex', 'parametric', 'functions', 'eg', 'deep', 'neural', 'networks', 'and', 'show', 'that', 'a', 'closedloop', 'nash', 'equilibrium', 'ne', 'can', 'be', 'found', 'or', 'at', 'least', 'approximated', 'by', 'solving', 'a', 'related', 'optimal', 'control', 'problem', 'ocp', 'this', 'is', 'useful', 'since', 'solving', 'an', 'ocpwhich', 'is', 'a', 'singleobjective', 'problemis', 'usually', 'much', 'simpler', 'than', 'solving', 'the', 'original', 'set', 'of', 'coupled', 'ocps', 'that', 'form', 'the', 'gamewhich', 'is', 'a', 'multiobjective', 'control', 'problem', 'this', 'is', 'a', 'considerable', 'improvement', 'over', 'the', 'previously', 'standard', 'approach', 'for', 'the', 'cl', 'analysis', 'of', 'mpgs', 'which', 'gives', 'no', 'approximate', 'solution', 'if', 'no', 'ne', 'belongs', 'to', 'the', 'chosen', 'parametric', 'family', 'and', 'which', 'is', 'practical', 'only', 'for', 'simple', 'parametric', 'forms', 'we', 'illustrate', 'the', 'theoretical', 'contributions', 'with', 'an', 'example', 'by', 'applying', 'our', 'approach', 'to', 'a', 'noncooperative', 'communications', 'engineering', 'game', 'we', 'then', 'solve', 'the', 'game', 'with', 'a', 'deep', 'reinforcement', 'learning', 'algorithm', 'that', 'learns', 'policies', 'that', 'closely', 'approximates', 'an', 'exact', 'variational', 'ne', 'of', 'the', 'game']] | [-0.09441215023059736, 0.027855623007371006, -0.08359657103740822, 0.09479460511991585, -0.09725412124344571, -0.2276978197727691, 0.06386162030425939, 0.4124932103807276, -0.28822163376787846, -0.2712574573165991, 0.11928523606282067, -0.226255105890503, -0.19088006052357906, 0.17719803526489572, -0.0918911186504093, 0.04546291781928052, 0.08711645045360042, 0.025644532379546118, -0.013010223927399651, -0.24841783740811726, 0.28496485516450115, -0.002942059682893821, 0.23929657952521335, -0.020838663830548863, 0.14456426919691942, -0.0010210411589254033, 0.046601406644860455, 0.06594019175469426, -0.09294702585384419, 0.10607169229452583, 0.3250880181518468, 0.19626207493922926, 0.3741692122241313, -0.41442072789777407, -0.19328813344070858, 0.1655151872777126, 0.13010878228555983, 0.10252194189424203, -0.0541441302801567, -0.28364827294241296, 0.06568284943488173, -0.15133508843189866, -0.07160931484562091, -0.09218613230538639, -0.030992570937695828, 0.031080413381992415, -0.3766018783749843, 0.005198911003429781, 0.05184073605214838, 0.03588989373296499, -0.07394767338452353, -0.120387851533226, 0.01773061893965033, 0.08137915847780691, -0.022836451844566247, 0.021104850338094613, 0.12761255207217553, -0.13913871822201393, -0.1618185334203934, 0.3663012807238424, -0.041925636854456654, -0.23415894439528612, 0.18766548407518052, -0.013565056292043829, -0.16301120814401657, 0.13029809930954467, 0.19204889982769435, 0.17183892932731065, -0.21090424265865956, 0.07760550540258092, -0.08302463657476686, 0.19448671613904564, 0.030885857711122795, 0.00409809551963752, 0.14252993284639986, 0.19239707177991724, 0.16852347899228334, 0.12365946154152467, 0.04013853700069541, -0.21269577096470377, -0.26472503413869575, -0.09336840327956121, -0.13869822334159504, 0.03380490653645459, -0.07769869020076427, -0.149572793409567, 0.3491404238791967, 0.13059166243469172, 0.13650641174817627, 0.12213603991718794, 0.29237830069254744, 0.16834810913482215, 0.008315365455452015, 0.1254852233945646, 0.2116115092350678, 0.07142864931798117, 0.08443814356091686, -0.2032585138852962, 0.10702409658729184, 0.027152400871908122] |
1,802.009 | Gate-Induced Interfacial Superconductivity in 1T-SnSe2 | Layered metal chalcogenide materials provide a versatile platform to
investigate emergent phenomena and two-dimensional (2D) superconductivity
at/near the atomically thin limit. In particular, gate-induced interfacial
superconductivity realized by the use of an electric-double-layer transistor
(EDLT) has greatly extended the capability to electrically induce
superconductivity in oxides, nitrides and transition metal chalcogenides and
enable one to explore new physics, such as the Ising pairing mechanism.
Exploiting gate-induced superconductivity in various materials can provide us
with additional platforms to understand emergent interfacial superconductivity.
Here, we report the discovery of gate-induced 2D superconductivity in layered
1T-SnSe2, a typical member of the main-group metal dichalcogenide (MDC) family,
using an EDLT gating geometry. A superconducting transition temperature Tc
around 3.9 K was demonstrated at the EDL interface. The 2D nature of the
superconductivity therein was further confirmed based on 1) a 2D Tinkham
description of the angle-dependent upper critical field, 2) the existence of a
quantum creep state as well as a large ratio of the coherence length to the
thickness of superconductivity. Interestingly, the in-plane approaching zero
temperature was found to be 2-3 times higher than the Pauli limit, which might
be related to an electric field-modulated spin-orbit interaction. Such results
provide a new perspective to expand the material matrix available for
gate-induced 2D superconductivity and the fundamental understanding of
interfacial superconductivity.
| cond-mat.supr-con | layered metal chalcogenide materials provide a versatile platform to investigate emergent phenomena and twodimensional 2d superconductivity atnear the atomically thin limit in particular gateinduced interfacial superconductivity realized by the use of an electricdoublelayer transistor edlt has greatly extended the capability to electrically induce superconductivity in oxides nitrides and transition metal chalcogenides and enable one to explore new physics such as the ising pairing mechanism exploiting gateinduced superconductivity in various materials can provide us with additional platforms to understand emergent interfacial superconductivity here we report the discovery of gateinduced 2d superconductivity in layered 1tsnse2 a typical member of the maingroup metal dichalcogenide mdc family using an edlt gating geometry a superconducting transition temperature tc around 39 k was demonstrated at the edl interface the 2d nature of the superconductivity therein was further confirmed based on 1 a 2d tinkham description of the angledependent upper critical field 2 the existence of a quantum creep state as well as a large ratio of the coherence length to the thickness of superconductivity interestingly the inplane approaching zero temperature was found to be 23 times higher than the pauli limit which might be related to an electric fieldmodulated spinorbit interaction such results provide a new perspective to expand the material matrix available for gateinduced 2d superconductivity and the fundamental understanding of interfacial superconductivity | [['layered', 'metal', 'chalcogenide', 'materials', 'provide', 'a', 'versatile', 'platform', 'to', 'investigate', 'emergent', 'phenomena', 'and', 'twodimensional', '2d', 'superconductivity', 'atnear', 'the', 'atomically', 'thin', 'limit', 'in', 'particular', 'gateinduced', 'interfacial', 'superconductivity', 'realized', 'by', 'the', 'use', 'of', 'an', 'electricdoublelayer', 'transistor', 'edlt', 'has', 'greatly', 'extended', 'the', 'capability', 'to', 'electrically', 'induce', 'superconductivity', 'in', 'oxides', 'nitrides', 'and', 'transition', 'metal', 'chalcogenides', 'and', 'enable', 'one', 'to', 'explore', 'new', 'physics', 'such', 'as', 'the', 'ising', 'pairing', 'mechanism', 'exploiting', 'gateinduced', 'superconductivity', 'in', 'various', 'materials', 'can', 'provide', 'us', 'with', 'additional', 'platforms', 'to', 'understand', 'emergent', 'interfacial', 'superconductivity', 'here', 'we', 'report', 'the', 'discovery', 'of', 'gateinduced', '2d', 'superconductivity', 'in', 'layered', '1tsnse2', 'a', 'typical', 'member', 'of', 'the', 'maingroup', 'metal', 'dichalcogenide', 'mdc', 'family', 'using', 'an', 'edlt', 'gating', 'geometry', 'a', 'superconducting', 'transition', 'temperature', 'tc', 'around', '39', 'k', 'was', 'demonstrated', 'at', 'the', 'edl', 'interface', 'the', '2d', 'nature', 'of', 'the', 'superconductivity', 'therein', 'was', 'further', 'confirmed', 'based', 'on', '1', 'a', '2d', 'tinkham', 'description', 'of', 'the', 'angledependent', 'upper', 'critical', 'field', '2', 'the', 'existence', 'of', 'a', 'quantum', 'creep', 'state', 'as', 'well', 'as', 'a', 'large', 'ratio', 'of', 'the', 'coherence', 'length', 'to', 'the', 'thickness', 'of', 'superconductivity', 'interestingly', 'the', 'inplane', 'approaching', 'zero', 'temperature', 'was', 'found', 'to', 'be', '23', 'times', 'higher', 'than', 'the', 'pauli', 'limit', 'which', 'might', 'be', 'related', 'to', 'an', 'electric', 'fieldmodulated', 'spinorbit', 'interaction', 'such', 'results', 'provide', 'a', 'new', 'perspective', 'to', 'expand', 'the', 'material', 'matrix', 'available', 'for', 'gateinduced', '2d', 'superconductivity', 'and', 'the', 'fundamental', 'understanding', 'of', 'interfacial', 'superconductivity']] | [-0.1679694873510435, 0.18604934994475475, -0.016282265177554524, 0.01585407174419835, -0.07226188140620868, -0.19584351340265868, 0.11812404020848224, 0.3550482675739487, -0.24432188753480702, -0.2882674523363156, -0.006167471325153716, -0.2993646102425243, -0.17089968257957064, 0.19118025282415033, 0.03943963126764388, 0.032946603094798424, -0.10371664541822855, -0.07983266392978625, -0.14513489225029533, -0.2080591433962661, 0.2693313077931219, 0.033690123367101844, 0.3716086973457636, 0.13935186622411116, 0.017470009111013898, -0.033790569355462416, 0.1656083980798378, 0.020019257003398534, -0.19226053134164847, 0.07675398762706959, 0.2978931391938409, -0.1097862035194884, 0.20060631755443314, -0.45373141030836767, -0.2707211614290278, -0.039117117285779955, 0.1336766263777991, 0.15963876940479527, -0.11781336381239602, -0.2949034814971761, 0.06151487197177613, -0.1536175888375066, -0.13577343809229542, -0.0888940059893211, -0.01489764881000558, -0.03800063012629181, -0.22787415873702435, 0.04337919642987837, 0.06956130005837706, 0.10496683813315551, -0.08158559428660997, -0.12957917599754262, -0.04561602183207784, 0.04622317025060272, 0.02102094627262312, 0.06789656754256942, 0.1659193794416856, -0.1439645723375376, -0.12174183654937945, 0.36730960251474837, -0.043600583656957646, -0.04346774664411347, 0.2022155567816961, -0.14405447139709432, -0.0365384827628957, 0.15788012846857996, 0.14448371074808342, 0.06886817698371232, -0.15641759310243866, 0.07662977571899612, -0.015015406391373085, 0.1981638862240699, 0.014475487539553284, 0.10415966406063293, 0.29244045948197694, 0.27602614577664364, 0.030492143056351124, 0.13184339470822312, -0.09343577074831189, 0.0028403960790673983, -0.1985954361150153, -0.2614522360534566, -0.21390147064651538, 0.10671073653350835, -0.06515208409465034, -0.22790060382902896, 0.3715889483040053, 0.1881768495165786, 0.17303045661042507, -0.11392288368081563, 0.16654832184331894, 0.07203618551052952, 0.10437091567429117, 0.007062972893440572, 0.2634363058259204, 0.19228759148503624, 0.13283413068466757, -0.2595478880588257, 0.09227503223284599, 0.03488136420062902] |
1,802.00901 | Nucleation of superfluid-light domains in a quenched dynamics | Strong correlation effects emerge from light-matter interactions in coupled
resonator arrays, such as the Mott-insulator to superfluid phase transition of
atom-photon excitations. We demonstrate that the quenched dynamics of a
finite-sized complex array of coupled resonators induces a first-order like
phase transition. The latter is accompanied by domain nucleation that can be
used to manipulate the photonic transport properties of the emerging superfluid
phase; this in turn leads to an empirical scaling law. This universal behavior
emerges from the light-matter interaction and the topology of the array. The
validity of our results over a wide range of complex architectures might lead
to to a promising device for use in scaled quantum simulations.
| quant-ph cond-mat.mes-hall | strong correlation effects emerge from lightmatter interactions in coupled resonator arrays such as the mottinsulator to superfluid phase transition of atomphoton excitations we demonstrate that the quenched dynamics of a finitesized complex array of coupled resonators induces a firstorder like phase transition the latter is accompanied by domain nucleation that can be used to manipulate the photonic transport properties of the emerging superfluid phase this in turn leads to an empirical scaling law this universal behavior emerges from the lightmatter interaction and the topology of the array the validity of our results over a wide range of complex architectures might lead to to a promising device for use in scaled quantum simulations | [['strong', 'correlation', 'effects', 'emerge', 'from', 'lightmatter', 'interactions', 'in', 'coupled', 'resonator', 'arrays', 'such', 'as', 'the', 'mottinsulator', 'to', 'superfluid', 'phase', 'transition', 'of', 'atomphoton', 'excitations', 'we', 'demonstrate', 'that', 'the', 'quenched', 'dynamics', 'of', 'a', 'finitesized', 'complex', 'array', 'of', 'coupled', 'resonators', 'induces', 'a', 'firstorder', 'like', 'phase', 'transition', 'the', 'latter', 'is', 'accompanied', 'by', 'domain', 'nucleation', 'that', 'can', 'be', 'used', 'to', 'manipulate', 'the', 'photonic', 'transport', 'properties', 'of', 'the', 'emerging', 'superfluid', 'phase', 'this', 'in', 'turn', 'leads', 'to', 'an', 'empirical', 'scaling', 'law', 'this', 'universal', 'behavior', 'emerges', 'from', 'the', 'lightmatter', 'interaction', 'and', 'the', 'topology', 'of', 'the', 'array', 'the', 'validity', 'of', 'our', 'results', 'over', 'a', 'wide', 'range', 'of', 'complex', 'architectures', 'might', 'lead', 'to', 'to', 'a', 'promising', 'device', 'for', 'use', 'in', 'scaled', 'quantum', 'simulations']] | [-0.18509323313420672, 0.2075134791761489, -0.09471762174767459, 0.007535973102806436, -0.052921100972785746, -0.1393980559992737, 0.04749017254029501, 0.35750440192974303, -0.28530866133847405, -0.27656672826768564, 0.00739514785423775, -0.2814514917242507, -0.1835072826693899, 0.20140346889716707, 0.048042767335053735, 0.042349534260470785, 0.005504612428402262, -0.053368888321399154, -0.06451272508378939, -0.1403761844578964, 0.28645188256632537, 0.016713570839783642, 0.3421861186424004, 0.07386954006921899, 0.046813930289187865, -0.028992400591960177, 0.1142824250317582, 0.03681594372028485, -0.113197359847488, 0.07580402617376032, 0.2778411560154512, -0.008414469673880376, 0.21603549255606985, -0.45897358730767984, -0.2274725996955697, 0.0702954815891904, 0.1906830253262472, 0.15310010166805504, -0.06894131027365802, -0.3202286409629908, -0.005843358866903665, -0.1924432579960142, -0.14961742960417723, -0.11795843411735925, -0.03403880575206131, 0.038198504245623814, -0.2622092732012139, 0.05863847978512889, 0.06154524918695513, 0.015238966934183347, -0.008724025375808455, 0.009604936997805322, 0.01862247816461604, 0.1359802442405323, -0.038000921617952245, 0.02592888013272646, 0.1601281764991914, -0.16170183160491952, -0.10482447960852628, 0.39244583749677986, -0.07018317723746545, -0.1282870344917423, 0.22515997273980506, -0.1474124094113774, -0.06702156247670896, 0.1682777526917302, 0.19005037404349423, 0.05823876120777121, -0.0969423351385298, 0.04487189832590437, 0.01419832173269242, 0.21591138356598094, 0.0022687912652535097, 0.1051418098504655, 0.27108894189586863, 0.2332643992267549, 0.02142607162906123, 0.21635188074287726, -0.07079033474188431, -0.16367206112564808, -0.2721568404480682, -0.13796228216129489, -0.2207718665262551, 0.07657745323792499, -0.10800451032771109, -0.20496736738797544, 0.38695808753254823, 0.18273997996584512, 0.16283608984667808, -0.014635199880493539, 0.22077291895818366, 0.1089470013186136, 0.10458256271002549, -0.015430081852952884, 0.2918947902383349, 0.17326251962263736, 0.127722764914714, -0.2885272774292389, 0.019265185721451417, -8.41102612737034e-05] |
1,802.00902 | Invariant measure construction at a fixed mass | In this paper we analyze the derivative nonlinear Schr\"odinger equation on
$\mathbb{T}$ with randomized initial data in $\cap_{s < \frac{1}{2}}
H^{s}(\mathbb{T})$ according to a Wiener measure. We construct an invariant
measure at each sufficiently small, fixed mass $m$ through an argument that
emulates the divergence theorem in infinitely many dimensions. We also prove
that the density function needed to construct the Wiener measure is in $L^p$,
even after scaling of the Fourier coefficients of the intial data.
| math.AP | in this paper we analyze the derivative nonlinear schrodinger equation on mathbbt with randomized initial data in cap_s frac12 hsmathbbt according to a wiener measure we construct an invariant measure at each sufficiently small fixed mass m through an argument that emulates the divergence theorem in infinitely many dimensions we also prove that the density function needed to construct the wiener measure is in lp even after scaling of the fourier coefficients of the intial data | [['in', 'this', 'paper', 'we', 'analyze', 'the', 'derivative', 'nonlinear', 'schrodinger', 'equation', 'on', 'mathbbt', 'with', 'randomized', 'initial', 'data', 'in', 'cap_s', 'frac12', 'hsmathbbt', 'according', 'to', 'a', 'wiener', 'measure', 'we', 'construct', 'an', 'invariant', 'measure', 'at', 'each', 'sufficiently', 'small', 'fixed', 'mass', 'm', 'through', 'an', 'argument', 'that', 'emulates', 'the', 'divergence', 'theorem', 'in', 'infinitely', 'many', 'dimensions', 'we', 'also', 'prove', 'that', 'the', 'density', 'function', 'needed', 'to', 'construct', 'the', 'wiener', 'measure', 'is', 'in', 'lp', 'even', 'after', 'scaling', 'of', 'the', 'fourier', 'coefficients', 'of', 'the', 'intial', 'data']] | [-0.11909024433543285, 0.12220055063914818, -0.1346812147879973, 0.08982606659643352, -0.04356576315127313, -0.12230900585030516, 0.012656718996974329, 0.331587005207936, -0.29097597202907005, -0.2206421327839295, 0.11579310115426779, -0.2993758482237657, -0.1480521192153295, 0.17376153512236972, -0.0871168020243446, 0.09233888153607647, 0.028878900911659, 0.08686355384687583, -0.07899535442702472, -0.235902493459483, 0.36354777599374455, -0.017146393830577533, 0.22216770510499675, -0.004896772472808758, 0.14663093558512627, 0.020045658089220524, -0.04840845524643858, -0.055869506402814295, -0.22073853728148, 0.05315149621417125, 0.21922159374846767, 0.0638317832381775, 0.3198505341758331, -0.38654030886789165, -0.14096143281708162, 0.15799148683746655, 0.12401515811992188, 0.04767829826722542, -0.03171227719479551, -0.2604569562897086, 0.11296981438994408, -0.1057184566433231, -0.20776465420921644, -0.07927456554025411, 0.07093425101911029, 0.02016327700422456, -0.3324975805496797, 0.08678128948706823, 0.06771670425931613, 0.013043836032350858, -0.09755014159406225, -0.05344263143837452, -0.006145427419493595, 0.047074212599545716, 0.036847098839158814, 0.06966913179804882, 0.05961992597828309, -0.060558722189938026, -0.08485657790986201, 0.3074601010233164, -0.15160508981595436, -0.2777189425379038, 0.1324080329015851, -0.2041815551246206, -0.1715627268825968, 0.08670129795372002, 0.15793460999925932, 0.10295714840913812, -0.13398511499321708, 0.1607093678538998, -0.06569207079863797, 0.1922649709523345, 0.10872301640609901, 0.01661658553406596, 0.08001729014019171, 0.08705357854564985, 0.17336348298859472, 0.13939674017330012, -0.06473572948016226, -0.07723142314081391, -0.3317615358655651, -0.1467679988220334, -0.2406278753342728, 0.10331685752918322, -0.16596130022468666, -0.1836321377614513, 0.33237628440062206, 0.16195968515550097, 0.22579051414815088, 0.10510240815579891, 0.23839901196459928, 0.21777069771041474, 0.011455561324643593, 0.10598694490579268, 0.12305364315087597, 0.1326781009292851, 0.09635666497672597, -0.18942934760823846, -0.022622678646196923, 0.12140550216349463] |
1,802.00903 | Weak order in averaging principle for two-time-scale stochastic partial
differential equations | This work is devoted to averaging principle of a two-time-scale stochastic
partial differential equation on a bounded interval $[0, l]$, where both the
fast and slow components are directly perturbed by additive noises. Under some
regular conditions on drift coefficients, it is proved that the rate of weak
convergence for the slow variable to the averaged dynamics is of order
$1-\varepsilon$ for arbitrarily small $\varepsilon>0$. The proof is based on an
asymptotic expansion of solutions to Kolmogorov equations associated with the
multiple-time-scale system.
| math.PR | this work is devoted to averaging principle of a twotimescale stochastic partial differential equation on a bounded interval 0 l where both the fast and slow components are directly perturbed by additive noises under some regular conditions on drift coefficients it is proved that the rate of weak convergence for the slow variable to the averaged dynamics is of order 1varepsilon for arbitrarily small varepsilon0 the proof is based on an asymptotic expansion of solutions to kolmogorov equations associated with the multipletimescale system | [['this', 'work', 'is', 'devoted', 'to', 'averaging', 'principle', 'of', 'a', 'twotimescale', 'stochastic', 'partial', 'differential', 'equation', 'on', 'a', 'bounded', 'interval', '0', 'l', 'where', 'both', 'the', 'fast', 'and', 'slow', 'components', 'are', 'directly', 'perturbed', 'by', 'additive', 'noises', 'under', 'some', 'regular', 'conditions', 'on', 'drift', 'coefficients', 'it', 'is', 'proved', 'that', 'the', 'rate', 'of', 'weak', 'convergence', 'for', 'the', 'slow', 'variable', 'to', 'the', 'averaged', 'dynamics', 'is', 'of', 'order', '1varepsilon', 'for', 'arbitrarily', 'small', 'varepsilon0', 'the', 'proof', 'is', 'based', 'on', 'an', 'asymptotic', 'expansion', 'of', 'solutions', 'to', 'kolmogorov', 'equations', 'associated', 'with', 'the', 'multipletimescale', 'system']] | [-0.19341663349734015, 0.09619089030448195, -0.08400235564100096, 0.05095204925136528, -0.08105937101182539, -0.13042653169123883, 0.0030063325560270213, 0.3002951560788844, -0.3352187458953136, -0.19760838551559004, 0.17239922528009277, -0.25468907893511905, -0.10310166917112937, 0.2151061871414443, -0.07731550594255979, 0.10789979446163199, 0.05682342150529106, 0.06914804356329772, -0.04428114821988506, -0.25127193158918837, 0.3217225970464747, 0.019503700199076927, 0.22734906485217277, -0.01952295662366774, 0.18894743601264186, -0.0470829495615943, -0.024724708749430186, 0.010112319775598955, -0.1622126754405747, 0.08046707225941031, 0.19261626531201673, 0.037783292542963504, 0.3292900129860963, -0.3965587199368256, -0.17680771616063115, 0.08485113429915474, 0.13589579614506284, 0.08198131688208465, 0.009576644385177418, -0.2833618961319507, 0.1375789690919849, -0.09846493393654027, -0.16589771892808108, -0.07247047009597342, 0.08706228105149355, 0.09261653188991932, -0.34702817669295405, 0.11277879041294198, 0.14589967573039145, 0.01148768009741353, -0.05287322164681482, -0.06840884460961019, 0.040879239597503676, 0.05581828804282719, 0.08161710168751159, 0.017507053346440465, 0.06643838852823498, -0.07197008181099657, -0.02237980641110475, 0.3092133783993412, -0.14562251477597662, -0.2784112460507327, 0.15987623233421067, -0.14971210142732205, -0.11104001655770712, 0.1868238619708243, 0.1700637574330062, 0.18042057878281698, -0.17381387188222752, 0.1460506114708989, -0.00827853928084474, 0.18214142410623202, 0.075925222222121, 0.017146944295592517, 0.06395330700843808, 0.12143330908161644, 0.14900317896605983, 0.09751547434569213, -0.029462255277328687, -0.15383349976176958, -0.33249237168445644, -0.1008335347469694, -0.17965622813474522, 0.10238807080650186, -0.13214195032021261, -0.19595226198042104, 0.32400196777230283, 0.09139971336327403, 0.18615980861506548, 0.10864934636033084, 0.2715087546686451, 0.21886089390300842, -0.04280966096725988, 0.07613858143821449, 0.17526782125252838, 0.18623875096406653, 0.11656665139416435, -0.24559690783092056, 0.11985154158761163, 0.13741767802608834] |
1,802.00904 | Build a Compact Binary Neural Network through Bit-level Sensitivity and
Data Pruning | Convolutional neural network (CNN) has been widely used for vision-based
tasks. Due to the high computational complexity and memory storage requirement,
it is hard to directly deploy a full-precision CNN on embedded devices. The
hardware-friendly designs are needed for re-source-limited and
energy-constrained embed-ded devices. Emerging solutions are adopted for the
neural network compression, e.g., bina-ry/ternary weight network, pruned
network and quantized network. Among them, Binarized Neural Network (BNN) is
believed to be the most hardware-friendly framework due to its small network
size and low computational com-plexity. No existing work has further shrunk the
size of BNN. In this work, we explore the redun-dancy in BNN and build a
compact BNN (CBNN) based on the bit-level sensitivity analy-sis and bit-level
data pruning. The input data is converted to a high dimensional bit-sliced
for-mat. In post-training stage, we analyze the im-pact of different bit slices
to the accuracy. By pruning the redundant input bit slices and shrinking the
network size, we are able to build a more compact BNN. Our result shows that we
can further scale down the network size of the BNN up to 3.9x with no more than
1% accuracy drop. The actual runtime can be reduced up to 2x and 9.9x compared
with the baseline BNN and its full-precision counterpart, respectively.
| cs.CV | convolutional neural network cnn has been widely used for visionbased tasks due to the high computational complexity and memory storage requirement it is hard to directly deploy a fullprecision cnn on embedded devices the hardwarefriendly designs are needed for resourcelimited and energyconstrained embedded devices emerging solutions are adopted for the neural network compression eg binaryternary weight network pruned network and quantized network among them binarized neural network bnn is believed to be the most hardwarefriendly framework due to its small network size and low computational complexity no existing work has further shrunk the size of bnn in this work we explore the redundancy in bnn and build a compact bnn cbnn based on the bitlevel sensitivity analysis and bitlevel data pruning the input data is converted to a high dimensional bitsliced format in posttraining stage we analyze the impact of different bit slices to the accuracy by pruning the redundant input bit slices and shrinking the network size we are able to build a more compact bnn our result shows that we can further scale down the network size of the bnn up to 39x with no more than 1 accuracy drop the actual runtime can be reduced up to 2x and 99x compared with the baseline bnn and its fullprecision counterpart respectively | [['convolutional', 'neural', 'network', 'cnn', 'has', 'been', 'widely', 'used', 'for', 'visionbased', 'tasks', 'due', 'to', 'the', 'high', 'computational', 'complexity', 'and', 'memory', 'storage', 'requirement', 'it', 'is', 'hard', 'to', 'directly', 'deploy', 'a', 'fullprecision', 'cnn', 'on', 'embedded', 'devices', 'the', 'hardwarefriendly', 'designs', 'are', 'needed', 'for', 'resourcelimited', 'and', 'energyconstrained', 'embedded', 'devices', 'emerging', 'solutions', 'are', 'adopted', 'for', 'the', 'neural', 'network', 'compression', 'eg', 'binaryternary', 'weight', 'network', 'pruned', 'network', 'and', 'quantized', 'network', 'among', 'them', 'binarized', 'neural', 'network', 'bnn', 'is', 'believed', 'to', 'be', 'the', 'most', 'hardwarefriendly', 'framework', 'due', 'to', 'its', 'small', 'network', 'size', 'and', 'low', 'computational', 'complexity', 'no', 'existing', 'work', 'has', 'further', 'shrunk', 'the', 'size', 'of', 'bnn', 'in', 'this', 'work', 'we', 'explore', 'the', 'redundancy', 'in', 'bnn', 'and', 'build', 'a', 'compact', 'bnn', 'cbnn', 'based', 'on', 'the', 'bitlevel', 'sensitivity', 'analysis', 'and', 'bitlevel', 'data', 'pruning', 'the', 'input', 'data', 'is', 'converted', 'to', 'a', 'high', 'dimensional', 'bitsliced', 'format', 'in', 'posttraining', 'stage', 'we', 'analyze', 'the', 'impact', 'of', 'different', 'bit', 'slices', 'to', 'the', 'accuracy', 'by', 'pruning', 'the', 'redundant', 'input', 'bit', 'slices', 'and', 'shrinking', 'the', 'network', 'size', 'we', 'are', 'able', 'to', 'build', 'a', 'more', 'compact', 'bnn', 'our', 'result', 'shows', 'that', 'we', 'can', 'further', 'scale', 'down', 'the', 'network', 'size', 'of', 'the', 'bnn', 'up', 'to', '39x', 'with', 'no', 'more', 'than', '1', 'accuracy', 'drop', 'the', 'actual', 'runtime', 'can', 'be', 'reduced', 'up', 'to', '2x', 'and', '99x', 'compared', 'with', 'the', 'baseline', 'bnn', 'and', 'its', 'fullprecision', 'counterpart', 'respectively']] | [-0.07687093720272607, 0.015416681713543797, -0.026721802896541937, 0.05660503699280922, -0.08000584634989764, -0.18668336959410087, 0.06319523134193784, 0.44293121001308, -0.26780181824200966, -0.32999461748023734, 0.11454343951005992, -0.2424151234227228, -0.15561405895277858, 0.1692356686471957, -0.13827817363143674, 0.12371410133528978, 0.1042182946867879, 0.04106862271001554, -0.08402054045698054, -0.319920358128881, 0.25290282054084806, 0.11951043931061128, 0.33446330851014544, 0.0008901327448980927, 0.09167129036604087, -0.07198459301607339, -0.013468294969971746, -0.011252013704368747, -0.026277812520187994, 0.1798047577494411, 0.2662817665433149, 0.16711756608546027, 0.2717203123878988, -0.4748941593631325, -0.21933544269913005, 0.10713445868175389, 0.1446438217714238, 0.10135204617984557, 0.017048187642573417, -0.2699028051489196, 0.19022100378198648, -0.21295925368162058, 0.0014274117374909663, -0.12104836886103698, -0.00035904933173227084, -0.030729353624855068, -0.2485066966873139, -0.012912594733132995, 0.05525012556198649, -0.013753407652450117, 0.011751777530395122, -0.12992431652639502, -0.0063838357165438165, 0.12917986562281353, -0.019104582808196758, 0.10902183875220858, 0.14876227368825773, -0.15738028054991413, -0.1160994127240491, 0.32896102410608735, -0.007610834781668079, -0.2133148420310769, 0.19360192112019947, -0.025988079850800264, -0.1335254898069976, 0.1393651981083279, 0.24531603155225093, 0.0531865291913168, -0.12423876810088419, 0.01692453703732159, 0.03274014504335079, 0.23813719104289108, 0.07138436649411793, 0.04967932102449185, 0.1244392276154522, 0.3134952880402418, 0.05266346339295261, 0.1756864133924334, -0.1553190865729642, -0.0560287464811727, -0.1768485098565218, -0.11693591228575927, -0.20082871488436738, 0.030414643605301484, -0.1523804442539245, -0.10807936719477494, 0.38699319156690093, 0.17846122975582565, 0.20864628352416828, 0.1481998603420235, 0.3584591535083349, 0.06846200006625558, 0.2026888620201057, 0.1597871481158475, 0.18699272799689623, 0.0769773652563004, 0.12098419238960534, -0.15462818198674028, 0.07573171059219763, 0.019218465529594095] |
1,802.00905 | Hybrid Nodal Loop Metal: Unconventional Magnetoresponse and Material
Realization | A nodal loop is formed by band crossing along a one-dimensional closed
manifold, with each point on the loop a linear nodal point in the transverse
dimensions and can be classified as type-I or type-II depending on the band
dispersion. Here, we propose a class of nodal loops composed of both type-I and
type-II points, which are hence termed as hybrid nodal loops. Based on
firstprinciples calculations, we predict the realization of such loops in the
existing electride material Ca2As. For a hybrid loop, the Fermi surface
consists of coexisting electron and hole pockets that touch at isolated points
for an extended range of Fermi energies, without the need for fine-tuning. This
leads to unconventional magnetic responses, including the zero-field magnetic
breakdown and the momentum space Klein tunneling observable in the magnetic
quantum oscillations, as well as the peculiar anisotropy in the cyclotron
resonance.
| cond-mat.mes-hall cond-mat.mtrl-sci | a nodal loop is formed by band crossing along a onedimensional closed manifold with each point on the loop a linear nodal point in the transverse dimensions and can be classified as typei or typeii depending on the band dispersion here we propose a class of nodal loops composed of both typei and typeii points which are hence termed as hybrid nodal loops based on firstprinciples calculations we predict the realization of such loops in the existing electride material ca2as for a hybrid loop the fermi surface consists of coexisting electron and hole pockets that touch at isolated points for an extended range of fermi energies without the need for finetuning this leads to unconventional magnetic responses including the zerofield magnetic breakdown and the momentum space klein tunneling observable in the magnetic quantum oscillations as well as the peculiar anisotropy in the cyclotron resonance | [['a', 'nodal', 'loop', 'is', 'formed', 'by', 'band', 'crossing', 'along', 'a', 'onedimensional', 'closed', 'manifold', 'with', 'each', 'point', 'on', 'the', 'loop', 'a', 'linear', 'nodal', 'point', 'in', 'the', 'transverse', 'dimensions', 'and', 'can', 'be', 'classified', 'as', 'typei', 'or', 'typeii', 'depending', 'on', 'the', 'band', 'dispersion', 'here', 'we', 'propose', 'a', 'class', 'of', 'nodal', 'loops', 'composed', 'of', 'both', 'typei', 'and', 'typeii', 'points', 'which', 'are', 'hence', 'termed', 'as', 'hybrid', 'nodal', 'loops', 'based', 'on', 'firstprinciples', 'calculations', 'we', 'predict', 'the', 'realization', 'of', 'such', 'loops', 'in', 'the', 'existing', 'electride', 'material', 'ca2as', 'for', 'a', 'hybrid', 'loop', 'the', 'fermi', 'surface', 'consists', 'of', 'coexisting', 'electron', 'and', 'hole', 'pockets', 'that', 'touch', 'at', 'isolated', 'points', 'for', 'an', 'extended', 'range', 'of', 'fermi', 'energies', 'without', 'the', 'need', 'for', 'finetuning', 'this', 'leads', 'to', 'unconventional', 'magnetic', 'responses', 'including', 'the', 'zerofield', 'magnetic', 'breakdown', 'and', 'the', 'momentum', 'space', 'klein', 'tunneling', 'observable', 'in', 'the', 'magnetic', 'quantum', 'oscillations', 'as', 'well', 'as', 'the', 'peculiar', 'anisotropy', 'in', 'the', 'cyclotron', 'resonance']] | [-0.21231237810652157, 0.18675612788283105, 0.0021935828531590793, 0.07503828718172308, -0.08604602110604723, -0.16329480241658492, 0.1071313361537975, 0.3577938002495134, -0.23433002370728792, -0.2895199316130443, 0.03480572720592307, -0.29731906242538375, -0.11083695818214202, 0.21064122203880778, 0.022906078996097708, 0.014698316205356134, -0.007768680908150606, 0.024799164677677038, -0.13235238467186847, -0.13659273352070184, 0.3916841555920827, 0.0037746673467783974, 0.2884387234714325, 0.051652522588318046, 0.06375730168921026, -0.0017889364511816652, 0.11600714833703596, 0.06690299828431791, -0.0909159864356291, 0.06495566938196186, 0.22263420110737736, -0.08835237711290275, 0.18783688129062628, -0.43619103871132864, -0.24101622615309132, 0.024832910519431938, 0.1796084574710291, 0.10061526395155808, -0.06128919243379463, -0.273478975249259, 0.04634712523393255, -0.11693212469727114, -0.16421111774389874, -0.07914248719759337, -0.05961060745420156, -0.017862345889809873, -0.20521371350525977, 0.09034935687180165, 0.03890010890782114, 0.06883581096530929, -0.0849820902177061, -0.08161246130272251, -0.0848703088053222, 0.04871232363955489, 0.032992251730243956, 0.030718399603343147, 0.12884773976019734, -0.08196586045644969, -0.1798682677579093, 0.3662938162313214, -0.04395164759061136, -0.1264858751466589, 0.16090998001855805, -0.1595781662214834, -0.0958559225440364, 0.1719001120532752, 0.14467834463095539, 0.07533595286687458, -0.10375121845114794, 0.10214361601014185, -0.03056659299649878, 0.08906111573833822, 0.05151329474471562, 0.04377527827383963, 0.3177501988577676, 0.1783260949691953, 0.036139860560375076, 0.10554306964484414, -0.20903662634017012, -0.06552573813327386, -0.32085977304544483, -0.17142955991325548, -0.19057102901274925, 0.057218358246283935, -0.031926982985627086, -0.26029155469805626, 0.4472367524403844, 0.06492551166069258, 0.250176723567782, -0.058614429600139545, 0.2591349174646283, 0.12082908915720515, 0.10043929092638143, 0.10652670618211785, 0.23317074281012093, 0.07652333181455627, 0.055476091788280626, -0.28502186609210667, -0.0037240552743636334, 0.06322933901492116] |
1,802.00906 | Leader Tracking of Euler-Lagrange Agents on Directed Switching Networks
Using A Model-Independent Algorithm | In this paper, we propose a discontinuous distributed model-independent
algorithm for a directed network of Euler-Lagrange agents to track the
trajectory of a leader with non-constant velocity. We initially study a fixed
network and show that the leader tracking objective is achieved semi-globally
exponentially fast if the graph contains a directed spanning tree. By
model-independent, we mean that each agent executes its algorithm with no
knowledge of the parameter values of any agent's dynamics. Certain bounds on
the agent dynamics (including any disturbances) and network topology
information are used to design the control gain. This fact, combined with the
algorithm's model-independence, results in robustness to disturbances and
modelling uncertainties. Next, a continuous approximation of the algorithm is
proposed, which achieves practical tracking with an adjustable tracking error.
Last, we show that the algorithm is stable for networks that switch with an
explicitly computable dwell time. Numerical simulations are given to show the
algorithm's effectiveness.
| eess.SY cs.SY | in this paper we propose a discontinuous distributed modelindependent algorithm for a directed network of eulerlagrange agents to track the trajectory of a leader with nonconstant velocity we initially study a fixed network and show that the leader tracking objective is achieved semiglobally exponentially fast if the graph contains a directed spanning tree by modelindependent we mean that each agent executes its algorithm with no knowledge of the parameter values of any agents dynamics certain bounds on the agent dynamics including any disturbances and network topology information are used to design the control gain this fact combined with the algorithms modelindependence results in robustness to disturbances and modelling uncertainties next a continuous approximation of the algorithm is proposed which achieves practical tracking with an adjustable tracking error last we show that the algorithm is stable for networks that switch with an explicitly computable dwell time numerical simulations are given to show the algorithms effectiveness | [['in', 'this', 'paper', 'we', 'propose', 'a', 'discontinuous', 'distributed', 'modelindependent', 'algorithm', 'for', 'a', 'directed', 'network', 'of', 'eulerlagrange', 'agents', 'to', 'track', 'the', 'trajectory', 'of', 'a', 'leader', 'with', 'nonconstant', 'velocity', 'we', 'initially', 'study', 'a', 'fixed', 'network', 'and', 'show', 'that', 'the', 'leader', 'tracking', 'objective', 'is', 'achieved', 'semiglobally', 'exponentially', 'fast', 'if', 'the', 'graph', 'contains', 'a', 'directed', 'spanning', 'tree', 'by', 'modelindependent', 'we', 'mean', 'that', 'each', 'agent', 'executes', 'its', 'algorithm', 'with', 'no', 'knowledge', 'of', 'the', 'parameter', 'values', 'of', 'any', 'agents', 'dynamics', 'certain', 'bounds', 'on', 'the', 'agent', 'dynamics', 'including', 'any', 'disturbances', 'and', 'network', 'topology', 'information', 'are', 'used', 'to', 'design', 'the', 'control', 'gain', 'this', 'fact', 'combined', 'with', 'the', 'algorithms', 'modelindependence', 'results', 'in', 'robustness', 'to', 'disturbances', 'and', 'modelling', 'uncertainties', 'next', 'a', 'continuous', 'approximation', 'of', 'the', 'algorithm', 'is', 'proposed', 'which', 'achieves', 'practical', 'tracking', 'with', 'an', 'adjustable', 'tracking', 'error', 'last', 'we', 'show', 'that', 'the', 'algorithm', 'is', 'stable', 'for', 'networks', 'that', 'switch', 'with', 'an', 'explicitly', 'computable', 'dwell', 'time', 'numerical', 'simulations', 'are', 'given', 'to', 'show', 'the', 'algorithms', 'effectiveness']] | [-0.15802844294479915, 0.05688061740520779, -0.08933180165114243, 0.013727130880125785, -0.07907700256063518, -0.16769193642917876, 0.05626672026837341, 0.44773082200486164, -0.27695163523221944, -0.29710160625203474, 0.08520169462252698, -0.22323610641171115, -0.18940154714026042, 0.16523743619198922, -0.10255573886785317, 0.09141292642129588, 0.12396109722216021, 0.08087703397545914, -0.023455968779789938, -0.26453959033389834, 0.2617769942432944, 0.04542417015257449, 0.2352799832436873, 0.00026367481286915673, 0.17737431024948086, -0.001963795857449582, -0.002591800724773051, 0.055884372798254256, -0.13365858575036635, 0.09954175920839506, 0.24526199080388655, 0.1723158618831378, 0.33559625422545747, -0.39826255614034733, -0.20036238612147508, 0.14363404305942065, 0.1269693364413431, 0.1192416624587888, -0.044224002096929, -0.2983133883650911, 0.10820256937113772, -0.14548770146589582, -0.09229056802336369, -0.0733684377394036, 0.004426317401351286, 0.05697724611238371, -0.32983813209265667, 0.005356934995015526, 0.045089529437170206, 0.017886217051584805, -0.0630602971355842, -0.06299506264206554, -0.006860102451249183, 0.15786342426919825, 0.010724318481364905, 0.05502637323179561, 0.15041558644447064, -0.10313720046977252, -0.193162580566895, 0.3356789699619539, -0.047009997083442646, -0.22382739620500958, 0.13541507860645652, -0.0743306721802559, -0.1507131898678936, 0.15393610579844613, 0.21850253891973914, 0.1200504365419461, -0.140055872604263, 0.053022488893097415, -0.04891516529507451, 0.20497435861109914, 0.014069696052253923, -0.02507178397477278, 0.09507746183519046, 0.21419193676684972, 0.17797050046032997, 0.114711957830972, -0.060612656401361445, -0.1317679630652941, -0.2870697082933522, -0.10890908155578201, -0.18821284318246045, -0.02513705786353872, -0.13674110463015696, -0.168242268420774, 0.4085684876114904, 0.18056253736004135, 0.17832197665007082, 0.15441130422730023, 0.36271762063533264, 0.09343879132579963, 0.007654819670798523, 0.18158376934127762, 0.21409241259763284, 0.07084152885779206, 0.08627937113686296, -0.2238627758660269, 0.12918251047860346, 0.05656647804350435] |
1,802.00907 | Cuspidal integrals and subseries for $\mathrm{SL}(3)/K_{\epsilon}$ | We show that for the symmetric spaces
$\mathrm{SL}(3,\mathbb{R})/\mathrm{SO}(1,2)_{e}$ and
$\mathrm{SL}(3,\mathbb{C})/\mathrm{SU}(1,2)$ the cuspidal integrals are
absolutely convergent. We further determine the behavior of the corresponding
Radon transforms and relate the kernels of the Radon transforms to the
different series of representations occurring in the Plancherel decomposition
of these spaces. Finally we show that for the symmetric space
$\mathrm{SL}(3,\mathbb{H})/\mathrm{Sp}(1,2)$ the cuspidal integrals are not
convergent for all Schwartz functions.
| math.RT | we show that for the symmetric spaces mathrmsl3mathbbrmathrmso12_e and mathrmsl3mathbbcmathrmsu12 the cuspidal integrals are absolutely convergent we further determine the behavior of the corresponding radon transforms and relate the kernels of the radon transforms to the different series of representations occurring in the plancherel decomposition of these spaces finally we show that for the symmetric space mathrmsl3mathbbhmathrmsp12 the cuspidal integrals are not convergent for all schwartz functions | [['we', 'show', 'that', 'for', 'the', 'symmetric', 'spaces', 'mathrmsl3mathbbrmathrmso12_e', 'and', 'mathrmsl3mathbbcmathrmsu12', 'the', 'cuspidal', 'integrals', 'are', 'absolutely', 'convergent', 'we', 'further', 'determine', 'the', 'behavior', 'of', 'the', 'corresponding', 'radon', 'transforms', 'and', 'relate', 'the', 'kernels', 'of', 'the', 'radon', 'transforms', 'to', 'the', 'different', 'series', 'of', 'representations', 'occurring', 'in', 'the', 'plancherel', 'decomposition', 'of', 'these', 'spaces', 'finally', 'we', 'show', 'that', 'for', 'the', 'symmetric', 'space', 'mathrmsl3mathbbhmathrmsp12', 'the', 'cuspidal', 'integrals', 'are', 'not', 'convergent', 'for', 'all', 'schwartz', 'functions']] | [-0.11055115626368206, 0.11629060052473505, -0.17788310322794132, 0.12282934338145424, -0.056252163063618354, 0.007714285980910063, -0.042506971255534154, 0.4110367518442217, -0.33862426469568163, -0.10716857746592723, 0.14027982589141175, -0.28603614422900137, -0.1947132128989324, 0.2353320307447575, -0.08548333992439439, 0.08493633879697882, 0.04355549401952885, 0.0694620307040168, -0.1864381049745134, -0.27477155963060795, 0.4291242763574701, -0.09301859478000551, 0.24713253288064152, 0.018410603457596153, 0.08888542813656386, 0.015467988720047288, -0.077800111619581, -0.1130488427011187, -0.11222674609712158, 0.16290050624957075, 0.2767471302067861, 0.10768307101534447, 0.20391923782517551, -0.3957039367815014, -0.10977681355143432, 0.2157407379563665, 0.14851516096678097, -0.05307477763926727, -0.009605476021533832, -0.3170255358272698, 0.08331121716764756, -0.1278344515885692, -0.12098443851209595, -0.20793319202493876, 0.03963619826390641, 0.09055327486930764, -0.3084507304665749, 0.04284290956275072, 0.09798777068499476, 0.024669874226674438, -0.1817266196158016, -0.1356665007006086, -0.022497322133858688, 0.12403601285768673, 0.02013367129256949, -0.01727426579600433, 0.06777977843739791, -0.06819488854671363, -0.06893554372072686, 0.32090368852368556, -0.03840744672288565, -0.2731430348358117, 0.14076869789278135, -0.24051966461411212, -0.15547998154943343, 0.140393398120068, 0.1304730571282562, 0.17149975297797937, -0.0604715029039653, 0.11951647605565086, -0.06505115903564729, 0.02064372005588666, 0.12015215046994854, 0.010572178769507445, 0.12243948453397024, 0.006787930993596092, 0.09948916103166994, 0.18373343581333756, -0.056908470462076366, -0.06818734283842787, -0.3766736229881644, -0.24650853089406155, -0.17164646577839449, -0.0028136866710610775, -0.09862693856075566, -0.22813001154281665, 0.42422626987536205, 0.08532185650255997, 0.19052277732407674, 0.17457833752268925, 0.18172341043828055, 0.16724729847919662, 0.04488865449093282, 0.04843337101920042, 0.12426786635478493, 0.1421795435162494, 0.01686698489356786, -0.1739082922867965, -0.018734934243184398, 0.15842421997513156] |
1,802.00908 | The Power Allocation Game on Dynamic Networks: Subgame Perfection | In the game theory literature, there appears to be little research on
equilibrium selection for normal-form games with an infinite strategy space and
discontinuous utility functions. Moreover, many existing selection methods are
not applicable to games involving both cooperative and noncooperative scenarios
(e.g., "games on signed graphs"). With the purpose of equilibrium selection,
the power allocation game developed in \cite{allocation}, which is a static,
resource allocation game on signed graphs, will be reformulated into an
extensive form. Results about the subgame perfect Nash equilibria in the
extensive-form game will be given. This appears to be the first time that
subgame perfection based on time-varying graphs is used for equilibrium
selection in network games. This idea of subgame perfection proposed in the
paper may be extrapolated to other network games, which will be illustrated
with a simple example of congestion games.
| cs.GT cs.SI | in the game theory literature there appears to be little research on equilibrium selection for normalform games with an infinite strategy space and discontinuous utility functions moreover many existing selection methods are not applicable to games involving both cooperative and noncooperative scenarios eg games on signed graphs with the purpose of equilibrium selection the power allocation game developed in citeallocation which is a static resource allocation game on signed graphs will be reformulated into an extensive form results about the subgame perfect nash equilibria in the extensiveform game will be given this appears to be the first time that subgame perfection based on timevarying graphs is used for equilibrium selection in network games this idea of subgame perfection proposed in the paper may be extrapolated to other network games which will be illustrated with a simple example of congestion games | [['in', 'the', 'game', 'theory', 'literature', 'there', 'appears', 'to', 'be', 'little', 'research', 'on', 'equilibrium', 'selection', 'for', 'normalform', 'games', 'with', 'an', 'infinite', 'strategy', 'space', 'and', 'discontinuous', 'utility', 'functions', 'moreover', 'many', 'existing', 'selection', 'methods', 'are', 'not', 'applicable', 'to', 'games', 'involving', 'both', 'cooperative', 'and', 'noncooperative', 'scenarios', 'eg', 'games', 'on', 'signed', 'graphs', 'with', 'the', 'purpose', 'of', 'equilibrium', 'selection', 'the', 'power', 'allocation', 'game', 'developed', 'in', 'citeallocation', 'which', 'is', 'a', 'static', 'resource', 'allocation', 'game', 'on', 'signed', 'graphs', 'will', 'be', 'reformulated', 'into', 'an', 'extensive', 'form', 'results', 'about', 'the', 'subgame', 'perfect', 'nash', 'equilibria', 'in', 'the', 'extensiveform', 'game', 'will', 'be', 'given', 'this', 'appears', 'to', 'be', 'the', 'first', 'time', 'that', 'subgame', 'perfection', 'based', 'on', 'timevarying', 'graphs', 'is', 'used', 'for', 'equilibrium', 'selection', 'in', 'network', 'games', 'this', 'idea', 'of', 'subgame', 'perfection', 'proposed', 'in', 'the', 'paper', 'may', 'be', 'extrapolated', 'to', 'other', 'network', 'games', 'which', 'will', 'be', 'illustrated', 'with', 'a', 'simple', 'example', 'of', 'congestion', 'games']] | [-0.11991824213515169, 8.549982469828268e-05, -0.15605955247634223, 0.10293627915504788, -0.09395802889817527, -0.2117293351196817, 0.08555314466024616, 0.43765238455629774, -0.2891106887587479, -0.2908863306311624, 0.11998694214520843, -0.22103451621452613, -0.1757161758224746, 0.07873668424519045, -0.17175426248993192, 0.03394620630757085, 0.07177469300472045, 0.05972543968902236, 0.08509761748802183, -0.29925028928555547, 0.3291069797339982, 0.05487481355667114, 0.27916142433615665, 0.06895695546242808, 0.061117138231306205, -0.0016534425484548722, 0.023369304027541407, 0.14614682884234104, -0.13927314990126302, 0.05374989255797118, 0.38020599449186454, 0.21272822382327702, 0.3842467107111588, -0.40496728343090843, -0.18038894406054168, 0.20320771784255548, 0.09334435110577033, 0.10761468109641491, -0.040285151774462846, -0.2729057481462535, 0.07328741736710072, -0.19648512329856332, -0.038527176964895, -0.06216946236901484, -0.035825400535083776, 0.04813738831185869, -0.3394492109006803, -0.06105733720386135, 0.007062658872122743, 0.06756796223510589, -0.06743391402331846, -0.15406995271831483, 0.0035907610951523695, 0.1491788772234161, -0.01764430013551776, -0.03280785986488419, 0.11505826152861118, -0.12699799686005073, -0.27664967495781767, 0.4218991145558123, -0.04279618310475988, -0.2018136691435107, 0.1529480829086554, -0.03808875689781936, -0.16311590256019762, 0.11630532974543582, 0.1716097824807678, 0.17669512378218186, -0.14615244400754038, 0.0221819552917233, -0.11636181571520865, 0.16972999671540623, 0.06614427079912275, 0.008431770163588226, 0.16490506592090243, 0.17204807866364719, 0.1943355150450121, 0.14981135298439768, 0.054215509950050284, -0.2483763975823032, -0.23955962221537316, -0.07880129753611982, -0.15298845392784902, 0.03123900117030384, -0.10430535774524158, -0.170184529872079, 0.3469570316723548, 0.11239969208587094, 0.026171542251748697, 0.11703095045273325, 0.2824304706683116, 0.10992351876172636, 0.003917390923015773, 0.0948710563925228, 0.1882708816822352, 0.1018847684642034, 0.17906827619299293, -0.18288092760028252, 0.12667852531386806, 0.049035704308854684] |
1,802.00909 | Brauer characters and normal Sylow $p$-subgroups | In this paper, we study some variations of the well-known It\^{o}-Michler
theorem for $p$-Brauer characters using various inequalities involving the
$p$-Brauer character degrees of finite groups. Several new criteria for the
existence of a normal Sylow $p$-subgroup of finite groups are obtained using
the $p$-parts and $p'$-parts of the $p$-Brauer character degrees.
| math.GR math.RT | in this paper we study some variations of the wellknown itomichler theorem for pbrauer characters using various inequalities involving the pbrauer character degrees of finite groups several new criteria for the existence of a normal sylow psubgroup of finite groups are obtained using the pparts and pparts of the pbrauer character degrees | [['in', 'this', 'paper', 'we', 'study', 'some', 'variations', 'of', 'the', 'wellknown', 'itomichler', 'theorem', 'for', 'pbrauer', 'characters', 'using', 'various', 'inequalities', 'involving', 'the', 'pbrauer', 'character', 'degrees', 'of', 'finite', 'groups', 'several', 'new', 'criteria', 'for', 'the', 'existence', 'of', 'a', 'normal', 'sylow', 'psubgroup', 'of', 'finite', 'groups', 'are', 'obtained', 'using', 'the', 'pparts', 'and', 'pparts', 'of', 'the', 'pbrauer', 'character', 'degrees']] | [-0.20070321252569556, 0.1109579997817211, -0.11823193582061392, 0.058906596232331, -0.08212574055561653, -0.08010700806223142, 0.03750763731099701, 0.28249426605179906, -0.3127074061104885, -0.2229944572855647, 0.10729564493298173, -0.24858114352593055, -0.09702665742056873, 0.2467190925246821, -0.0942820345159047, -0.028621712332376495, 0.012795152500844918, 0.07235376110586983, -0.10980374452013236, -0.2837940324456073, 0.3722245626581403, -0.12027262043781005, 0.22843036846293566, 0.041962792058117114, 0.0788415035696324, 0.05677156133326487, -0.008246503895721756, -0.01577626086341647, -0.14348606621989837, 0.16546598756954503, 0.28316670798589116, 0.041578346041765496, 0.24396535659840224, -0.34891542272928816, -0.14873752562114254, 0.1893012781197635, 0.08902805419907403, 0.049419484387796656, -0.04402450153317589, -0.30508985843222874, 0.14924507127859846, -0.2321443840315064, -0.17244869692680928, -0.08048911564625226, 0.032810763276826874, 0.06106778642144771, -0.21402369156623116, 0.07705712700799967, 0.11615447642711493, 0.2108857530334857, -0.06705312666375764, -0.1969513580059776, 0.04511926551304686, 0.15032920753583312, 0.03999076600974569, -0.11173985665216325, 0.022326422050984826, -0.14275037279227176, -0.13842409321715912, 0.38807459311703074, -0.03679256499386751, -0.17925535625993058, 0.17454835808334443, -0.15545610974256235, -0.21397340685451546, 0.10327328094997658, 0.1192837174480351, 0.18302627420052886, -0.08049434030321069, 0.13024079396564048, -0.14682435961619306, 0.07248259500528757, 0.125808599879607, 0.03557787488143031, 0.0754111295637603, 0.02572076305603752, -0.004804901545867324, 0.1763104152901528, -0.017617894981343012, 0.013470789077333532, -0.35999861478474204, -0.1981211377021212, -0.12800692582431322, 0.04058683115559129, -0.12104412641957214, -0.17580038746998, 0.4471399321292455, 0.07039548383461526, 0.09348624364401285, 0.10167554395201688, 0.13040266279131174, 0.05988595269333858, 0.05468613481256258, 0.017434988206682298, 0.08961334754097222, 0.26438128507624453, -0.09472890130172555, -0.20159130113629195, -0.0063727081556303, 0.18809573104282698] |
1,802.0091 | GeniePath: Graph Neural Networks with Adaptive Receptive Paths | We present, GeniePath, a scalable approach for learning adaptive receptive
fields of neural networks defined on permutation invariant graph data. In
GeniePath, we propose an adaptive path layer consists of two complementary
functions designed for breadth and depth exploration respectively, where the
former learns the importance of different sized neighborhoods, while the latter
extracts and filters signals aggregated from neighbors of different hops away.
Our method works in both transductive and inductive settings, and extensive
experiments compared with competitive methods show that our approaches yield
state-of-the-art results on large graphs.
| cs.LG | we present geniepath a scalable approach for learning adaptive receptive fields of neural networks defined on permutation invariant graph data in geniepath we propose an adaptive path layer consists of two complementary functions designed for breadth and depth exploration respectively where the former learns the importance of different sized neighborhoods while the latter extracts and filters signals aggregated from neighbors of different hops away our method works in both transductive and inductive settings and extensive experiments compared with competitive methods show that our approaches yield stateoftheart results on large graphs | [['we', 'present', 'geniepath', 'a', 'scalable', 'approach', 'for', 'learning', 'adaptive', 'receptive', 'fields', 'of', 'neural', 'networks', 'defined', 'on', 'permutation', 'invariant', 'graph', 'data', 'in', 'geniepath', 'we', 'propose', 'an', 'adaptive', 'path', 'layer', 'consists', 'of', 'two', 'complementary', 'functions', 'designed', 'for', 'breadth', 'and', 'depth', 'exploration', 'respectively', 'where', 'the', 'former', 'learns', 'the', 'importance', 'of', 'different', 'sized', 'neighborhoods', 'while', 'the', 'latter', 'extracts', 'and', 'filters', 'signals', 'aggregated', 'from', 'neighbors', 'of', 'different', 'hops', 'away', 'our', 'method', 'works', 'in', 'both', 'transductive', 'and', 'inductive', 'settings', 'and', 'extensive', 'experiments', 'compared', 'with', 'competitive', 'methods', 'show', 'that', 'our', 'approaches', 'yield', 'stateoftheart', 'results', 'on', 'large', 'graphs']] | [-0.0605678596158131, 0.0411368308349748, -0.04981134093726392, 0.028464487356938083, -0.07456465606016784, -0.16968468146462162, 0.0412337547565006, 0.47664627741852944, -0.26507781456563284, -0.3022496805272319, 0.03925948435260745, -0.2808440139263191, -0.18839293417973255, 0.19807392955673012, -0.08252008554568006, 0.04803019054022364, 0.13966365677928974, 0.023932196460505525, -0.046531591381856495, -0.2702866724288139, 0.30126870729145594, 0.02668637678090652, 0.35774875740224327, -0.01194668505658311, 0.16738717680908105, 0.035162924287512644, -0.0823998632682064, 0.0742868909384759, -0.113141802846423, 0.17951383322096345, 0.30947593823921954, 0.1773990444860167, 0.3004241360097446, -0.42043829565359786, -0.21157132891345432, 0.06853708687809888, 0.13499784845308485, 0.08250269587733783, -0.047141276927008716, -0.3246482386814684, 0.07814688647208227, -0.09845853337637064, 0.02349507734602825, -0.14011929007458754, -0.031099120922259648, 0.039658250265347306, -0.3351013791451061, 0.01633073098227297, 0.08150788049599876, 0.06976114022968845, -0.03556613810360432, -0.19987346955299887, 0.042577124057887966, 0.14836186596552248, -0.018373142001854085, 0.015650616942862558, 0.1479780439985916, -0.148504471213197, -0.1906719610349021, 0.29497919768222014, -0.07227952934706329, -0.1837112557788549, 0.23169756310843778, -0.018618459000539257, -0.14411765028489754, 0.10245981414548376, 0.23797314734838437, 0.17684167221357877, -0.10531780105190013, 0.045678693809102035, -0.046909725992009044, 0.15414732723200525, 0.02573065050951713, 0.014615552388766611, 0.13271157417305154, 0.23838479299394583, 0.0948759029858841, 0.15943045657265678, -0.12489411761370403, -0.10345037438144739, -0.21865952096612257, -0.0651028825562786, -0.21852799588867294, -0.06639961720528928, -0.17430054432836847, -0.12641547873210418, 0.4165717385476455, 0.23038212735925548, 0.20068503531034698, 0.16676893695892597, 0.35060222152705217, -0.007970789984524758, 0.13303461244372142, 0.13278380046937277, 0.17407357250340283, 0.04933391668071801, 0.08722528100108982, -0.1456252022035187, 0.05179144641045819, 0.05725812617774037] |
1,802.00911 | Segregation growth and self-organization of ordered S atomic
superlattices confined at interface between graphene and substrates | Ordered atomic-scale superlattices on surface hold great interest both for
basic science and for potential applications in advanced technology. However,
controlled fabrication of superlattices down to atomic scale has proven
exceptionally challenging. Here we demonstrate the segregation-growth and
self-organization of ordered S atomic superlattices confined at the interface
between graphene and S-rich Cu substrates. Scanning tunneling microscope (STM)
studies show that, by finely controlling the growth temperature, we obtain
well-ordered S (sub)nanometer-cluster superlattice and monoatomic superlattices
with various periods at the interface. These atomic superlattices are stable in
atmospheric environment and robust even after high-temperature annealing (~ 350
oC). Our experiments demonstrate that the S monoatomic superlattice can drive
graphene into the electronic Kekul\'e distortion phase when the period of the
ordered S adatoms is commensurate with graphene lattice. Our results not only
open a road to realize atomic-scale superlattices at interfaces, but also
provide a new route to realize exotic electronic states in graphene.
| cond-mat.mtrl-sci cond-mat.mes-hall | ordered atomicscale superlattices on surface hold great interest both for basic science and for potential applications in advanced technology however controlled fabrication of superlattices down to atomic scale has proven exceptionally challenging here we demonstrate the segregationgrowth and selforganization of ordered s atomic superlattices confined at the interface between graphene and srich cu substrates scanning tunneling microscope stm studies show that by finely controlling the growth temperature we obtain wellordered s subnanometercluster superlattice and monoatomic superlattices with various periods at the interface these atomic superlattices are stable in atmospheric environment and robust even after hightemperature annealing 350 oc our experiments demonstrate that the s monoatomic superlattice can drive graphene into the electronic kekule distortion phase when the period of the ordered s adatoms is commensurate with graphene lattice our results not only open a road to realize atomicscale superlattices at interfaces but also provide a new route to realize exotic electronic states in graphene | [['ordered', 'atomicscale', 'superlattices', 'on', 'surface', 'hold', 'great', 'interest', 'both', 'for', 'basic', 'science', 'and', 'for', 'potential', 'applications', 'in', 'advanced', 'technology', 'however', 'controlled', 'fabrication', 'of', 'superlattices', 'down', 'to', 'atomic', 'scale', 'has', 'proven', 'exceptionally', 'challenging', 'here', 'we', 'demonstrate', 'the', 'segregationgrowth', 'and', 'selforganization', 'of', 'ordered', 's', 'atomic', 'superlattices', 'confined', 'at', 'the', 'interface', 'between', 'graphene', 'and', 'srich', 'cu', 'substrates', 'scanning', 'tunneling', 'microscope', 'stm', 'studies', 'show', 'that', 'by', 'finely', 'controlling', 'the', 'growth', 'temperature', 'we', 'obtain', 'wellordered', 's', 'subnanometercluster', 'superlattice', 'and', 'monoatomic', 'superlattices', 'with', 'various', 'periods', 'at', 'the', 'interface', 'these', 'atomic', 'superlattices', 'are', 'stable', 'in', 'atmospheric', 'environment', 'and', 'robust', 'even', 'after', 'hightemperature', 'annealing', '350', 'oc', 'our', 'experiments', 'demonstrate', 'that', 'the', 's', 'monoatomic', 'superlattice', 'can', 'drive', 'graphene', 'into', 'the', 'electronic', 'kekule', 'distortion', 'phase', 'when', 'the', 'period', 'of', 'the', 'ordered', 's', 'adatoms', 'is', 'commensurate', 'with', 'graphene', 'lattice', 'our', 'results', 'not', 'only', 'open', 'a', 'road', 'to', 'realize', 'atomicscale', 'superlattices', 'at', 'interfaces', 'but', 'also', 'provide', 'a', 'new', 'route', 'to', 'realize', 'exotic', 'electronic', 'states', 'in', 'graphene']] | [-0.16167214662786328, 0.23974468818969877, -0.036223552149941066, 0.0011740685716320417, 0.01747100648323172, -0.21139717977769723, 0.11819441855799309, 0.4886200059125641, -0.30825685009384823, -0.30518505293998477, 0.023702706873576205, -0.2781400306123128, -0.15835418550934838, 0.23137375248571565, 0.036967505085110465, 0.05483610824586848, 0.020703142729440804, -0.14896354585480845, -0.07975639519741219, -0.27070092055854683, 0.21308772335147583, 0.0012093315691393066, 0.31558681276050937, 0.11499138351676888, 0.03027738567049566, -0.014338984757686328, 0.14361112287583397, -0.010801425206744554, -0.19280211834934322, 0.09591056195533479, 0.28441452880782125, -0.1352833470507329, 0.18251055704221422, -0.5478938262247922, -0.22297584844024018, -0.05055965550893329, 0.12786115635351866, 0.1481863348811825, -0.12100448902760706, -0.2772957847107471, 0.08669758884628352, -0.07298170924676876, -0.1228585564439479, -0.12443498405627906, 0.025530893012489144, -0.0006717537490706155, -0.21235231499227475, 0.026402168845762436, 0.03585900797349352, 0.13694439022372917, -0.10256395254615008, -0.09503784757965293, -0.10944854382710457, 0.035950310656373496, -0.03670318366647208, 0.05666725668829473, 0.19575677844420025, -0.06623540445128272, -0.1246139494959559, 0.381372830707972, -0.023142419238482898, -0.05237712417606657, 0.21698178610600544, -0.18784409283206946, -0.06698792393772716, 0.12644746120081676, 0.09969980295098044, 0.0630894939623479, -0.14123279939198446, 0.0988503465452691, 0.0053282925724296975, 0.2019846641947209, 0.1413458130509274, 0.10212792535802644, 0.2887932480508952, 0.23896715686944509, 0.10512937307756051, 0.11463654100340423, -0.10463972353241652, 0.0013215897610950236, -0.16882185894677318, -0.20451061566066192, -0.22319691763320743, 0.062466633792657954, -0.06464387201288589, -0.21125232548097542, 0.3779765536432694, 0.15716682732301324, 0.1352485954350988, -0.10533958119738504, 0.22251433017663658, 0.02391758139894687, 0.05793908744942057, -0.03479257781720279, 0.17323346943962142, 0.130008263707982, 0.11129088222208482, -0.23183591691409483, 0.09395094371039274, -0.04743031998422291] |
1,802.00912 | Active, Continual Fine Tuning of Convolutional Neural Networks for
Reducing Annotation Efforts | The splendid success of convolutional neural networks (CNNs) in computer
vision is largely attributable to the availability of massive annotated
datasets, such as ImageNet and Places. However, in medical imaging, it is
challenging to create such large annotated datasets, as annotating medical
images is not only tedious, laborious, and time consuming, but it also demands
costly, specialty-oriented skills, which are not easily accessible. To
dramatically reduce annotation cost, this paper presents a novel method to
naturally integrate active learning and transfer learning (fine-tuning) into a
single framework, which starts directly with a pre-trained CNN to seek "worthy"
samples for annotation and gradually enhances the (fine-tuned) CNN via
continual fine-tuning. We have evaluated our method using three distinct
medical imaging applications, demonstrating that it can reduce annotation
efforts by at least half compared with random selection.
| cs.LG cs.CV stat.ML | the splendid success of convolutional neural networks cnns in computer vision is largely attributable to the availability of massive annotated datasets such as imagenet and places however in medical imaging it is challenging to create such large annotated datasets as annotating medical images is not only tedious laborious and time consuming but it also demands costly specialtyoriented skills which are not easily accessible to dramatically reduce annotation cost this paper presents a novel method to naturally integrate active learning and transfer learning finetuning into a single framework which starts directly with a pretrained cnn to seek worthy samples for annotation and gradually enhances the finetuned cnn via continual finetuning we have evaluated our method using three distinct medical imaging applications demonstrating that it can reduce annotation efforts by at least half compared with random selection | [['the', 'splendid', 'success', 'of', 'convolutional', 'neural', 'networks', 'cnns', 'in', 'computer', 'vision', 'is', 'largely', 'attributable', 'to', 'the', 'availability', 'of', 'massive', 'annotated', 'datasets', 'such', 'as', 'imagenet', 'and', 'places', 'however', 'in', 'medical', 'imaging', 'it', 'is', 'challenging', 'to', 'create', 'such', 'large', 'annotated', 'datasets', 'as', 'annotating', 'medical', 'images', 'is', 'not', 'only', 'tedious', 'laborious', 'and', 'time', 'consuming', 'but', 'it', 'also', 'demands', 'costly', 'specialtyoriented', 'skills', 'which', 'are', 'not', 'easily', 'accessible', 'to', 'dramatically', 'reduce', 'annotation', 'cost', 'this', 'paper', 'presents', 'a', 'novel', 'method', 'to', 'naturally', 'integrate', 'active', 'learning', 'and', 'transfer', 'learning', 'finetuning', 'into', 'a', 'single', 'framework', 'which', 'starts', 'directly', 'with', 'a', 'pretrained', 'cnn', 'to', 'seek', 'worthy', 'samples', 'for', 'annotation', 'and', 'gradually', 'enhances', 'the', 'finetuned', 'cnn', 'via', 'continual', 'finetuning', 'we', 'have', 'evaluated', 'our', 'method', 'using', 'three', 'distinct', 'medical', 'imaging', 'applications', 'demonstrating', 'that', 'it', 'can', 'reduce', 'annotation', 'efforts', 'by', 'at', 'least', 'half', 'compared', 'with', 'random', 'selection']] | [0.0030963435949785496, 0.04487009545259956, 0.005014701687327739, 0.06561256605443726, -0.16862240998824432, -0.22605503092297533, 0.027446051420984482, 0.4595568067439373, -0.27044360111328536, -0.40319340801172293, 0.09658876664192874, -0.26080612458007185, -0.17225000668150275, 0.20875492582181052, -0.17320399581969007, 0.07335272590184945, 0.21044347080603631, 0.020461683898274578, -0.03302728266971283, -0.3269138221260605, 0.2692248745849217, 0.03865154563630028, 0.3620608364190184, 0.04393491807303378, 0.11387457279140936, -0.05942573621799586, -0.04953208052094525, -0.01938481412397058, -0.008460532651735042, 0.18205637215122358, 0.3873456810694883, 0.22589953612786398, 0.3827983546592017, -0.43720059904422776, -0.2370021972829587, 0.10855208495238695, 0.2043663982906615, 0.11757884231772198, -0.05360731008504309, -0.3436971268991926, 0.08207371379874313, -0.15837163545053154, 0.05262927431960715, -0.19691931919382413, -0.009142802259363512, -0.09004975157814211, -0.27012380915902445, 0.04520003284729927, 0.0009848900667544622, 0.08344254487388726, -0.01951354907116672, -0.09019345587202862, -0.0051403563667728166, 0.17509626612456433, 0.041623207443365964, 0.0897204658162516, 0.162029076093085, -0.2340756518151194, -0.08889888082422427, 0.3606088291436656, 0.0059498728656057105, -0.20525218637698373, 0.25434495589987777, -0.015113176373111556, -0.17932477579174091, 0.16222351662286405, 0.21148373988811484, 0.12518159937375892, -0.18356204445737956, -0.0027596962850179466, 0.022059225355650285, 0.21603252961814626, 0.05665643371853616, -0.02291718724100336, 0.18199734405072324, 0.2901449209850615, -0.0009526676800685809, 0.13616586772641584, -0.11632572984628713, -0.02290074532823776, -0.19314097320146636, -0.11011145289452162, -0.2402883027471713, 0.01373808767404164, -0.05161802610947139, -0.15680256332861564, 0.33461779760502613, 0.27347309528780517, 0.1885520958405947, 0.08518169904231969, 0.36949304889526163, -0.0036233655053691297, 0.24450434607439744, 0.07129615232862754, 0.1604087881660506, -0.050056808341447434, 0.17823311170118292, -0.15441479914779985, 0.08757241838937452, 0.0120404373659794] |
1,802.00913 | Geodesic conformal transformation optics: manipulating light with
continuous refractive index profile | Conformal transformation optics provides a simple scheme for manipulating
light rays with inhomogeneous isotropic dielectrics. However, there is usually
discontinuity for refractive index profile at branch cuts of different virtual
Riemann sheets, hence compromising the functionalities. To deal with that, we
present a special method for conformal transformation optics based on the
concept of geodesic lens. The requirement is a continuous refractive index
profile of dielectrics, which shows almost perfect performance of designed
devices. We demonstrate such a proposal by achieving conformal transparency and
reflection. We can further achieve conformal invisible cloaks by two techniques
with perfect electromagnetic conductors. The geodesic concept may also find
applications in other waves that obey the Helmholtz equation in two dimensions.
| physics.optics | conformal transformation optics provides a simple scheme for manipulating light rays with inhomogeneous isotropic dielectrics however there is usually discontinuity for refractive index profile at branch cuts of different virtual riemann sheets hence compromising the functionalities to deal with that we present a special method for conformal transformation optics based on the concept of geodesic lens the requirement is a continuous refractive index profile of dielectrics which shows almost perfect performance of designed devices we demonstrate such a proposal by achieving conformal transparency and reflection we can further achieve conformal invisible cloaks by two techniques with perfect electromagnetic conductors the geodesic concept may also find applications in other waves that obey the helmholtz equation in two dimensions | [['conformal', 'transformation', 'optics', 'provides', 'a', 'simple', 'scheme', 'for', 'manipulating', 'light', 'rays', 'with', 'inhomogeneous', 'isotropic', 'dielectrics', 'however', 'there', 'is', 'usually', 'discontinuity', 'for', 'refractive', 'index', 'profile', 'at', 'branch', 'cuts', 'of', 'different', 'virtual', 'riemann', 'sheets', 'hence', 'compromising', 'the', 'functionalities', 'to', 'deal', 'with', 'that', 'we', 'present', 'a', 'special', 'method', 'for', 'conformal', 'transformation', 'optics', 'based', 'on', 'the', 'concept', 'of', 'geodesic', 'lens', 'the', 'requirement', 'is', 'a', 'continuous', 'refractive', 'index', 'profile', 'of', 'dielectrics', 'which', 'shows', 'almost', 'perfect', 'performance', 'of', 'designed', 'devices', 'we', 'demonstrate', 'such', 'a', 'proposal', 'by', 'achieving', 'conformal', 'transparency', 'and', 'reflection', 'we', 'can', 'further', 'achieve', 'conformal', 'invisible', 'cloaks', 'by', 'two', 'techniques', 'with', 'perfect', 'electromagnetic', 'conductors', 'the', 'geodesic', 'concept', 'may', 'also', 'find', 'applications', 'in', 'other', 'waves', 'that', 'obey', 'the', 'helmholtz', 'equation', 'in', 'two', 'dimensions']] | [-0.14480552066746366, 0.13634895332730734, -0.10629113929935245, -0.0017968679093715982, -0.15063799987746102, -0.19769157480416644, -0.025666334668301746, 0.4567291891664012, -0.2203140285390461, -0.29048185812261623, 0.03471013263035088, -0.2693308459288385, -0.19261557733815196, 0.23141508945860925, -0.05615146841026015, 0.11965596463041714, 0.006340494446066392, -0.02618634718287195, -0.08662555798585726, -0.18763339031949386, 0.332327753607916, 0.015401350198966315, 0.3631423241100632, 0.06956144731340563, 0.13183900236120272, 0.03970358225031414, -0.0027403204441150157, 0.029813190364939533, -0.09692063451554073, 0.10429697659893487, 0.2678522065234108, 0.045105519451790005, 0.20286961044304264, -0.42252245421658674, -0.2681939800023141, 0.046175720874602214, 0.11132118245935003, 0.08800966132523763, -0.11983563140150096, -0.25531595542581165, 0.062221349049837164, -0.10802505845920397, -0.21390520866239407, -0.03914821324432189, -0.0354954131727672, -0.014560913528777404, -0.22825135092418164, 0.029842454050485995, 0.06292584054092439, 0.04177210429030606, -0.039960165111707226, -0.042672576785732344, 0.009737257921319997, 0.030703569734548658, 0.01541241261168805, -0.03386660435428031, 0.12479625843439458, -0.1484190820911342, -0.11132983729227358, 0.4099190017033337, -0.05784509309999591, -0.25032076844547546, 0.14151161622542602, -0.10874104957219659, -0.06563648619712928, 0.14840529970207816, 0.15461436960571068, 0.09593600254808353, -0.12728274082685384, 0.10206025181304361, -0.004382651721119371, 0.1583999652797595, 0.16411757490768805, 0.042351631992934353, 0.2337540073069529, 0.10774635284956004, 0.07810219661501817, 0.16107726292177224, -0.04090864526736749, -0.004859705001879961, -0.31087821419549805, -0.19812693380010435, -0.19285606675080827, 0.05526680264096612, -0.15395333107464357, -0.22230345975512114, 0.35387833421147785, 0.11882874733643033, 0.10581037145243305, 0.03439323467294025, 0.3152562919844929, 0.10856424752945223, 0.07974138381516832, 0.08777296368000853, 0.2854298732639489, 0.11738193728045648, 0.10409332621189901, -0.15981733130239364, -0.013930905148641676, 0.0785751154120916] |
1,802.00914 | Arrow Update Synthesis | In this contribution we present arbitrary arrow update model logic (AAUML).
This is a dynamic epistemic logic or update logic. In update logics,
static/basic modalities are interpreted on a given relational model whereas
dynamic/update modalities induce transformations (updates) of relational
models. In AAUML the update modalities formalize the execution of arrow update
models, and there is also a modality for quantification over arrow update
models. Arrow update models are an alternative to the well-known action models.
We provide an axiomatization of AAUML. The axiomatization is a rewrite system
allowing to eliminate arrow update modalities from any given formula, while
preserving truth. Thus, AAUML is decidable and equally expressive as the base
multi-agent modal logic. Our main result is to establish arrow update
synthesis: if there is an arrow update model after which phi, we can construct
(synthesize) that model from phi. We also point out some pregnant differences
in update expressivity between arrow update logics, action model logics, and
refinement modal logic.
| cs.LO | in this contribution we present arbitrary arrow update model logic aauml this is a dynamic epistemic logic or update logic in update logics staticbasic modalities are interpreted on a given relational model whereas dynamicupdate modalities induce transformations updates of relational models in aauml the update modalities formalize the execution of arrow update models and there is also a modality for quantification over arrow update models arrow update models are an alternative to the wellknown action models we provide an axiomatization of aauml the axiomatization is a rewrite system allowing to eliminate arrow update modalities from any given formula while preserving truth thus aauml is decidable and equally expressive as the base multiagent modal logic our main result is to establish arrow update synthesis if there is an arrow update model after which phi we can construct synthesize that model from phi we also point out some pregnant differences in update expressivity between arrow update logics action model logics and refinement modal logic | [['in', 'this', 'contribution', 'we', 'present', 'arbitrary', 'arrow', 'update', 'model', 'logic', 'aauml', 'this', 'is', 'a', 'dynamic', 'epistemic', 'logic', 'or', 'update', 'logic', 'in', 'update', 'logics', 'staticbasic', 'modalities', 'are', 'interpreted', 'on', 'a', 'given', 'relational', 'model', 'whereas', 'dynamicupdate', 'modalities', 'induce', 'transformations', 'updates', 'of', 'relational', 'models', 'in', 'aauml', 'the', 'update', 'modalities', 'formalize', 'the', 'execution', 'of', 'arrow', 'update', 'models', 'and', 'there', 'is', 'also', 'a', 'modality', 'for', 'quantification', 'over', 'arrow', 'update', 'models', 'arrow', 'update', 'models', 'are', 'an', 'alternative', 'to', 'the', 'wellknown', 'action', 'models', 'we', 'provide', 'an', 'axiomatization', 'of', 'aauml', 'the', 'axiomatization', 'is', 'a', 'rewrite', 'system', 'allowing', 'to', 'eliminate', 'arrow', 'update', 'modalities', 'from', 'any', 'given', 'formula', 'while', 'preserving', 'truth', 'thus', 'aauml', 'is', 'decidable', 'and', 'equally', 'expressive', 'as', 'the', 'base', 'multiagent', 'modal', 'logic', 'our', 'main', 'result', 'is', 'to', 'establish', 'arrow', 'update', 'synthesis', 'if', 'there', 'is', 'an', 'arrow', 'update', 'model', 'after', 'which', 'phi', 'we', 'can', 'construct', 'synthesize', 'that', 'model', 'from', 'phi', 'we', 'also', 'point', 'out', 'some', 'pregnant', 'differences', 'in', 'update', 'expressivity', 'between', 'arrow', 'update', 'logics', 'action', 'model', 'logics', 'and', 'refinement', 'modal', 'logic']] | [-0.08216401475074235, 0.05799920390954867, -0.12061839710804634, 0.10409503581468016, -0.1953469535917975, -0.21598828218411653, 0.08572980538447154, 0.43628333136439323, -0.369706520321779, -0.2863857492164243, 0.05380184401001316, -0.22445496628060937, -0.07513506595678336, 0.06713341683207545, -0.15079341110831593, 0.007370033190818503, 0.03938778500742046, 0.10707250376308367, -0.06134381195734022, -0.22687560944923463, 0.23501737774495268, -0.012289135507307947, 0.1871932221809402, -0.029052107865572906, 0.15276463799746126, 0.034179103445785584, -0.03429496881435625, 0.044120314633619276, -0.07748243000555703, 0.10558402347960509, 0.3312891179048393, 0.3059064807224786, 0.24718768276215997, -0.4373366729472764, -0.12252628148635267, 0.10800934443541337, 0.09197792458580807, 0.13544958933271117, 0.06176610580223496, -0.3005310212029144, 0.02190069034404587, -0.2406208489330311, 2.5541969807818533e-05, -0.14539960552356207, 0.07857452491298318, -0.027081220079344347, -0.27060345451536705, -0.031545380122042846, 0.19134443503207876, 0.11836872799904086, -0.11511849443049868, -0.04870054651000828, 0.02377280285145389, 0.06756477706803707, -0.030431310224957996, 0.045953956909943375, 0.11593516674183775, -0.08251447557995562, -0.27084189538727516, 0.3494737810047809, -0.02809014087979449, -0.20334377616727578, 0.09695251495868433, -0.030518228611617813, -0.21983007090748288, 0.04694447045912966, 0.07267922537575941, 0.11331493001198396, -0.12349290886777453, 0.12764786436637224, -0.03975682912277989, 0.2520577322700774, 0.1010707747307606, 0.05120530438471178, 0.20682131926878355, 0.21475337452429813, 0.03947293176970561, 0.15804076302447356, 0.09169028314499883, -0.16594654322107089, -0.364134364961501, -0.15787402810528875, -0.05198060658003669, -0.02520341680092315, -0.10056884541381805, -0.1531462086015381, 0.3258885803865269, 0.25615948624617885, 0.16118231851723977, 0.21062831922099576, 0.34511766699142754, 0.10668705866519304, 0.08654429540911224, 0.04272808669047663, 0.10099623634396267, 0.12815855457884026, 0.14485355015858659, -0.17837968722451478, 0.19493973444623408, 0.11604124398290878] |
1,802.00915 | The Legendre Spectral-Collocation method for a class of fractional
integral equations | In this paper, we consider spectral-collocation method base on
Legendre-Gauss-Lobatto point. We present a computational method for solving a
class of fractional integral equation of the second kind. Then based on
Legendre-Gauss-Lobatto point and using, we derive a system of algebraic
equations. The method is illustrated by applications and the results obtained
are compared with the exact solutions in open literature. The obtained
numerical results show that our proposed method is efficient and accurate for
fractional integral equations of second kind. In addition, we prove that the
error of the approximate solution decay exponentially in L^2 norm.
| math.NA | in this paper we consider spectralcollocation method base on legendregausslobatto point we present a computational method for solving a class of fractional integral equation of the second kind then based on legendregausslobatto point and using we derive a system of algebraic equations the method is illustrated by applications and the results obtained are compared with the exact solutions in open literature the obtained numerical results show that our proposed method is efficient and accurate for fractional integral equations of second kind in addition we prove that the error of the approximate solution decay exponentially in l2 norm | [['in', 'this', 'paper', 'we', 'consider', 'spectralcollocation', 'method', 'base', 'on', 'legendregausslobatto', 'point', 'we', 'present', 'a', 'computational', 'method', 'for', 'solving', 'a', 'class', 'of', 'fractional', 'integral', 'equation', 'of', 'the', 'second', 'kind', 'then', 'based', 'on', 'legendregausslobatto', 'point', 'and', 'using', 'we', 'derive', 'a', 'system', 'of', 'algebraic', 'equations', 'the', 'method', 'is', 'illustrated', 'by', 'applications', 'and', 'the', 'results', 'obtained', 'are', 'compared', 'with', 'the', 'exact', 'solutions', 'in', 'open', 'literature', 'the', 'obtained', 'numerical', 'results', 'show', 'that', 'our', 'proposed', 'method', 'is', 'efficient', 'and', 'accurate', 'for', 'fractional', 'integral', 'equations', 'of', 'second', 'kind', 'in', 'addition', 'we', 'prove', 'that', 'the', 'error', 'of', 'the', 'approximate', 'solution', 'decay', 'exponentially', 'in', 'l2', 'norm']] | [-0.10632505801688764, -0.033771354028206205, -0.09155255210731111, 0.06294686915280931, -0.05274732810320314, -0.09712647423436194, 0.04836119141924128, 0.33403170751119704, -0.2556389366857439, -0.2601033137611968, 0.15970114521567047, -0.26270412899476964, -0.2096486062425928, 0.26742433834120094, -0.04655232836405948, 0.09998960919274959, 0.12277770380743962, 0.051356308822299246, -0.10511492466381223, -0.2319542895066408, 0.35121264273166347, -0.030709580844748265, 0.23305528035829054, 0.02062031700031006, 0.15730578963616, -0.04796482268096783, -0.0636885941614272, 0.03401730746779706, -0.15818993585048, 0.1817380693961972, 0.22370424740659745, 0.07291367496171794, 0.28388966297365953, -0.4124915776774287, -0.18612137218918076, 0.06665698405038373, 0.15192379617641114, 0.12086728026072696, -0.10002515015621986, -0.2859150125385867, 0.12819379599784145, -0.14247039139040352, -0.17548072500845668, -0.10921594027199388, -0.020926195014383375, 0.08341521983870219, -0.3144820538570279, 0.10449159888493031, 0.03770778724712502, 0.020130547992501063, -0.12352148660449024, -0.1410955405270815, 0.06929676543162733, 0.045143848995572514, 0.024106769912549744, 0.010153609500794681, 0.02312260587728515, -0.08457766026594513, -0.11102835093288846, 0.3425100357511762, -0.09336360811601518, -0.2636386993312344, 0.10473587264825311, -0.09502801217724444, -0.14743256284231224, 0.12163168251437624, 0.170274945460836, 0.19652705868110829, -0.13626248672719776, 0.11972531476886175, -0.05081970807280123, 0.13388187679901392, 0.02913741700677681, -0.0031555844470858574, 0.05398555990001283, 0.17320643983224465, 0.10532501915028107, 0.17817790983916865, -0.06136019557676057, -0.13180152589896904, -0.34111166207753507, -0.19485730135596727, -0.21432116758270361, 0.026926601454423566, -0.07876571756502561, -0.1425660996765052, 0.3712414987577298, 0.18193753998841822, 0.1297626953245592, 0.09621454535301813, 0.30000590045273917, 0.20216211416043303, -0.01846468966147027, 0.10406865626957613, 0.20209904959828584, 0.10380465394250818, 0.1047186360108791, -0.22635706177175755, 0.0025620565116021435, 0.18846408027637096] |
1,802.00916 | Three-dimensional black holes and solitons in higher-dimensional
theories with compactification | Several types of static solutions to Einstein's equations coupled with
antisymmetric tensor fields are found in $(2+N+1)$-dimensional spacetime. The
solutions describe a product of a three-dimensional radially symmetric
spacetime and an internal maximally symmetric manifold. The scale of the
internal space may depend on the radial distance from the origin in these
solutions.
| gr-qc | several types of static solutions to einsteins equations coupled with antisymmetric tensor fields are found in 2n1dimensional spacetime the solutions describe a product of a threedimensional radially symmetric spacetime and an internal maximally symmetric manifold the scale of the internal space may depend on the radial distance from the origin in these solutions | [['several', 'types', 'of', 'static', 'solutions', 'to', 'einsteins', 'equations', 'coupled', 'with', 'antisymmetric', 'tensor', 'fields', 'are', 'found', 'in', '2n1dimensional', 'spacetime', 'the', 'solutions', 'describe', 'a', 'product', 'of', 'a', 'threedimensional', 'radially', 'symmetric', 'spacetime', 'and', 'an', 'internal', 'maximally', 'symmetric', 'manifold', 'the', 'scale', 'of', 'the', 'internal', 'space', 'may', 'depend', 'on', 'the', 'radial', 'distance', 'from', 'the', 'origin', 'in', 'these', 'solutions']] | [-0.18754181256166325, 0.1456206698645508, -0.0566531022799746, 0.03326215990379734, -0.10365423721806058, -0.09129976499210692, -0.11966848422615033, 0.3291214112157248, -0.24071997368954262, -0.23308761695505315, 0.1287969637196511, -0.26029170039197747, -0.12549612266099397, 0.11553667068595665, 0.005396343029613765, -0.0032446474574928013, -0.011583635970106665, 0.09656956120622608, -0.14320160647775135, -0.18720610313258082, 0.45559523873171714, -0.017037808297658868, 0.2920445718995805, -0.0421576136898882, 0.11858842830735979, -0.06138370327985371, -0.043845772567503855, 0.062002878574070185, -0.11995936138116105, 0.09770272568500829, 0.17762380501009384, 0.10016834014534669, 0.17205603672894385, -0.4706001395605645, -0.19081419764332613, 0.09923030195300872, 0.13785313632128374, 0.15509974879195104, -0.034121113611859676, -0.35125103415394165, 0.02399624842834079, -0.13604390135316072, -0.23025714950460308, -0.07851092771293139, 0.04776356725971091, 0.02852273113885016, -0.21688114510813974, 0.08455088095001455, 0.024167874707172642, -0.015034672733888312, -0.20684158620160986, -0.07641121690635974, -0.08226567084180578, 0.079728224139028, 0.08210838520635833, 0.030311848080875177, 0.08758151689368598, -0.10957808658164346, -0.11770994154701256, 0.38686482764710234, -0.07103860960781293, -0.3669976398770539, 0.189785685530811, -0.15278203518323177, -0.04881945288880676, 0.11832045561212273, 0.20513706570842638, 0.20833509795824592, -0.12954233096525916, 0.17065496714500147, -0.040966237209877875, 0.1573074075419737, 0.10750318147277213, 0.040387733118995184, 0.2545830988377895, 0.033674041424298064, 0.07738999251214752, 0.1183325190993272, -0.01361529667475173, -0.17124440785462283, -0.3392283078634514, -0.13797467725597462, -0.12141134408478327, 0.12779691609782431, -0.2066491731914213, -0.22048754473480414, 0.3834621298372886, 0.028501685298012815, 0.11040772907194277, -0.01990979356374943, 0.19303642204558513, 0.05961975369180711, 0.05997952657207003, 0.13813413106748518, 0.28513539108043573, 0.1938919613559572, 0.10152812311777247, -0.20282246021747166, -0.055335307695885315, 0.08582284255831871] |
1,802.00917 | Delay Analysis of Random Scheduling and Round Robin in Small Cell
Networks | We analyze the delay performance of small cell networks operating under
random scheduling (RS) and round robin (RR) protocols. Based on stochastic
geometry and queuing theory, we derive accurate and tractable expressions for
the distribution of mean delay, which accounts for the impact of random traffic
arrivals, queuing interactions, and failed packet retransmissions. Our analysis
asserts that RR outperforms RS in terms of mean delay, regardless of traffic
statistic. Moreover, the gain from RR is more pronounced in the presence of
heavy traffic, which confirms the importance of accounting fairness in the
design of scheduling policy. We also find that constrained on the same delay
outage probability, RR is able to support more user equipments (UEs) than that
of RS, demonstrating it as an appropriate candidate for the traffic scheduling
policy of internet-of-things (IoT) network.
| cs.IT math.IT | we analyze the delay performance of small cell networks operating under random scheduling rs and round robin rr protocols based on stochastic geometry and queuing theory we derive accurate and tractable expressions for the distribution of mean delay which accounts for the impact of random traffic arrivals queuing interactions and failed packet retransmissions our analysis asserts that rr outperforms rs in terms of mean delay regardless of traffic statistic moreover the gain from rr is more pronounced in the presence of heavy traffic which confirms the importance of accounting fairness in the design of scheduling policy we also find that constrained on the same delay outage probability rr is able to support more user equipments ues than that of rs demonstrating it as an appropriate candidate for the traffic scheduling policy of internetofthings iot network | [['we', 'analyze', 'the', 'delay', 'performance', 'of', 'small', 'cell', 'networks', 'operating', 'under', 'random', 'scheduling', 'rs', 'and', 'round', 'robin', 'rr', 'protocols', 'based', 'on', 'stochastic', 'geometry', 'and', 'queuing', 'theory', 'we', 'derive', 'accurate', 'and', 'tractable', 'expressions', 'for', 'the', 'distribution', 'of', 'mean', 'delay', 'which', 'accounts', 'for', 'the', 'impact', 'of', 'random', 'traffic', 'arrivals', 'queuing', 'interactions', 'and', 'failed', 'packet', 'retransmissions', 'our', 'analysis', 'asserts', 'that', 'rr', 'outperforms', 'rs', 'in', 'terms', 'of', 'mean', 'delay', 'regardless', 'of', 'traffic', 'statistic', 'moreover', 'the', 'gain', 'from', 'rr', 'is', 'more', 'pronounced', 'in', 'the', 'presence', 'of', 'heavy', 'traffic', 'which', 'confirms', 'the', 'importance', 'of', 'accounting', 'fairness', 'in', 'the', 'design', 'of', 'scheduling', 'policy', 'we', 'also', 'find', 'that', 'constrained', 'on', 'the', 'same', 'delay', 'outage', 'probability', 'rr', 'is', 'able', 'to', 'support', 'more', 'user', 'equipments', 'ues', 'than', 'that', 'of', 'rs', 'demonstrating', 'it', 'as', 'an', 'appropriate', 'candidate', 'for', 'the', 'traffic', 'scheduling', 'policy', 'of', 'internetofthings', 'iot', 'network']] | [-0.23934142804980554, 0.011042234678515579, -0.06813133685823737, 0.08389589609405784, -0.06847263997489655, -0.23160469425773178, 0.1822877144156438, 0.3871654007583857, -0.18828120195582784, -0.2782844166719803, 0.05586620741437569, -0.24775810886036467, -0.2014053178416496, 0.18461571090026863, -0.16883433617099566, 0.07705662805173132, 0.07026561128786178, 0.08662192815983737, 0.0012691473881541579, -0.3029824159073609, 0.2688441574332063, 0.073735613912275, 0.36501828917750606, 0.015115241726636197, 0.06007143516714374, 0.051913498262702314, -0.042369458073301725, -0.0030683470717458813, -0.07656262992720629, 0.0591308705267255, 0.2561732770392188, 0.18610335993408053, 0.26030361456451595, -0.4430580048373452, -0.2728733270308348, 0.08997290438317039, 0.15228438952191803, 0.03524956891692623, -0.015884542359358253, -0.27125811717576453, 0.14339019614099352, -0.20497420975179584, -0.057788393117435694, 0.021340054742715976, -0.0017850184882128681, 0.10290899818970098, -0.3279816786448161, 0.04172651880416433, -0.03987691365872268, 0.022763080453431166, -0.06596209893899935, -0.10596195479372034, -0.025868953966225187, 0.14863745195929098, 0.09597648272497786, -0.04518705356383213, 0.12572469512276627, -0.11798213183258971, -0.1284765471092046, 0.4092062268337166, -0.02652706836956053, -0.16707292471632915, 0.11070868632135292, -0.0488463267373542, -0.1441925336641294, 0.1319627733538962, 0.25808737696734846, 0.0769204233876533, -0.19386995098105184, -0.016751775154154058, -0.004586449893260443, 0.15888523402175417, 0.0977831563860592, 0.0898273623858889, 0.10573695915475212, 0.2393150508955673, 0.1592322900960291, 0.09931364656874427, -0.13828988228062236, -0.17203076593577862, -0.26808958462335997, -0.12190082923205207, -0.14854273056542433, 0.03979059841647675, -0.17638784784573577, -0.11108614006597135, 0.36732073653903274, 0.15052568034518993, 0.10939196315766485, 0.1709019809357684, 0.3440537534792114, 0.13430166705683977, 0.0371981222803394, 0.195394158501316, 0.1564279911901664, 0.08748241488614844, 0.16100972314123754, -0.2731608497979188, 0.1376370010914764, -0.011176268321772416] |
1,802.00918 | Typicality Matching for Pairs of Correlated Graphs | In this paper, the problem of matching pairs of correlated random graphs with
multi-valued edge attributes is considered. Graph matching problems of this
nature arise in several settings of practical interest including social network
de-anonymization, study of biological data, web graphs, etc. An achievable
region for successful matching is derived by analyzing a new matching algorithm
that we refer to as typicality matching. The algorithm operates by
investigating the joint typicality of the adjacency matrices of the two
correlated graphs. Our main result shows that the achievable region depends on
the mutual information between the variables corresponding to the edge
probabilities of the two graphs. The result is based on bounds on the
typicality of permutations of sequences of random variables that might be of
independent interest.
| cs.IT math.IT | in this paper the problem of matching pairs of correlated random graphs with multivalued edge attributes is considered graph matching problems of this nature arise in several settings of practical interest including social network deanonymization study of biological data web graphs etc an achievable region for successful matching is derived by analyzing a new matching algorithm that we refer to as typicality matching the algorithm operates by investigating the joint typicality of the adjacency matrices of the two correlated graphs our main result shows that the achievable region depends on the mutual information between the variables corresponding to the edge probabilities of the two graphs the result is based on bounds on the typicality of permutations of sequences of random variables that might be of independent interest | [['in', 'this', 'paper', 'the', 'problem', 'of', 'matching', 'pairs', 'of', 'correlated', 'random', 'graphs', 'with', 'multivalued', 'edge', 'attributes', 'is', 'considered', 'graph', 'matching', 'problems', 'of', 'this', 'nature', 'arise', 'in', 'several', 'settings', 'of', 'practical', 'interest', 'including', 'social', 'network', 'deanonymization', 'study', 'of', 'biological', 'data', 'web', 'graphs', 'etc', 'an', 'achievable', 'region', 'for', 'successful', 'matching', 'is', 'derived', 'by', 'analyzing', 'a', 'new', 'matching', 'algorithm', 'that', 'we', 'refer', 'to', 'as', 'typicality', 'matching', 'the', 'algorithm', 'operates', 'by', 'investigating', 'the', 'joint', 'typicality', 'of', 'the', 'adjacency', 'matrices', 'of', 'the', 'two', 'correlated', 'graphs', 'our', 'main', 'result', 'shows', 'that', 'the', 'achievable', 'region', 'depends', 'on', 'the', 'mutual', 'information', 'between', 'the', 'variables', 'corresponding', 'to', 'the', 'edge', 'probabilities', 'of', 'the', 'two', 'graphs', 'the', 'result', 'is', 'based', 'on', 'bounds', 'on', 'the', 'typicality', 'of', 'permutations', 'of', 'sequences', 'of', 'random', 'variables', 'that', 'might', 'be', 'of', 'independent', 'interest']] | [-0.17242854124477763, 0.07076841061231295, -0.05007457581356051, 0.07588962341317775, -0.06532963373749627, -0.10599344604883314, 0.08833870350467554, 0.36426868751703756, -0.2778833601403776, -0.32493152352431276, 0.10584203567627201, -0.26740734035691877, -0.1829202024148792, 0.1648427271907489, -0.12031455380579927, 0.06542281942867388, 0.07765405320866751, 0.07611981814536523, -0.02638524200393283, -0.2617043550660207, 0.369871883880435, 0.04149506422214386, 0.30093051367036, 0.06297645305180702, 0.07068741757983679, 0.05518950346681312, -0.075102302106464, 0.03307707729753048, -0.11558390055744364, 0.15014256251605565, 0.26910420329727996, 0.22515454745202235, 0.26663496291748884, -0.37554561236770606, -0.20026040135875461, 0.13597395864689327, 0.12469160566051439, 0.08782719479375116, -0.01635339130797812, -0.2927329565112398, 0.07630050087376959, -0.11006315856966681, -0.02355315344862816, -0.00246804860772521, -0.0009286198765039444, 0.04506025216945513, -0.32197229770987523, 0.04168546403846227, 0.10214937617635633, 0.03319980459119098, 0.03221640550150118, -0.13060313996338235, 0.022786172475357813, 0.17300419538743853, 0.024288595900650863, -0.017790762985337848, 0.11339067387959267, -0.13043865352121042, -0.19667678580465398, 0.35580342676578547, -0.006106027627481133, -0.15735417356582607, 0.14614676410244323, -0.09065677487020066, -0.1988082944899504, 0.09088736728079412, 0.20347947285576598, 0.13997893354958144, -0.16042458493075532, 0.04824527938058233, -0.13255253467914158, 0.13195405928665538, 0.07933097595246288, 0.08796225229688868, 0.1673766974007755, 0.16079733547879252, 0.10869437515530295, 0.18727869179932122, -0.03235105126106188, -0.11528729384501032, -0.24923139793695662, -0.07714741234117606, -0.28179223147792903, -0.038789043576991936, -0.19065167656989665, -0.19542800265045351, 0.41694898140712044, 0.17159342616943157, 0.21956321837079865, 0.07172875973857998, 0.25886660262824984, 0.07939037529927656, 0.0017485603090654324, 0.08537412674246928, 0.17885905985287795, 0.15678198012618685, 0.04509248909005147, -0.17991161832813263, 0.13241093186111608, 0.07820159940261716] |
1,802.00919 | Precursor of Superfluidity in a Strongly Interacting Fermi Gas with
Negative Effective Range | We theoretically investigate the effects of pairing fluctuations in an
ultracold Fermi gas near a Feshbach resonance with a negative effective range.
By employing a many-body T-matrix theory with a coupled boson-fermion model, we
show that the single-particle density of states exhibits the so-called
pseudogap phenomenon which is a precursor of superfluidity induced by strong
pairing fluctuations. We clarify the region where strong pairing fluctuations
play a crucial role in single-particle properties, from the broad-resonance
region to the narrow-resonance limit at the divergent two-body scattering
length. We also extrapolate the effects of pairing fluctuations to the
positive-effective-range region from our results near the narrow Feshbach
resonance. Results shown in this paper are relevant to the connection between
ultracold Fermi gases and low-density neutron matter from the viewpoint of
finite-effective-range corrections.
| cond-mat.quant-gas nucl-th | we theoretically investigate the effects of pairing fluctuations in an ultracold fermi gas near a feshbach resonance with a negative effective range by employing a manybody tmatrix theory with a coupled bosonfermion model we show that the singleparticle density of states exhibits the socalled pseudogap phenomenon which is a precursor of superfluidity induced by strong pairing fluctuations we clarify the region where strong pairing fluctuations play a crucial role in singleparticle properties from the broadresonance region to the narrowresonance limit at the divergent twobody scattering length we also extrapolate the effects of pairing fluctuations to the positiveeffectiverange region from our results near the narrow feshbach resonance results shown in this paper are relevant to the connection between ultracold fermi gases and lowdensity neutron matter from the viewpoint of finiteeffectiverange corrections | [['we', 'theoretically', 'investigate', 'the', 'effects', 'of', 'pairing', 'fluctuations', 'in', 'an', 'ultracold', 'fermi', 'gas', 'near', 'a', 'feshbach', 'resonance', 'with', 'a', 'negative', 'effective', 'range', 'by', 'employing', 'a', 'manybody', 'tmatrix', 'theory', 'with', 'a', 'coupled', 'bosonfermion', 'model', 'we', 'show', 'that', 'the', 'singleparticle', 'density', 'of', 'states', 'exhibits', 'the', 'socalled', 'pseudogap', 'phenomenon', 'which', 'is', 'a', 'precursor', 'of', 'superfluidity', 'induced', 'by', 'strong', 'pairing', 'fluctuations', 'we', 'clarify', 'the', 'region', 'where', 'strong', 'pairing', 'fluctuations', 'play', 'a', 'crucial', 'role', 'in', 'singleparticle', 'properties', 'from', 'the', 'broadresonance', 'region', 'to', 'the', 'narrowresonance', 'limit', 'at', 'the', 'divergent', 'twobody', 'scattering', 'length', 'we', 'also', 'extrapolate', 'the', 'effects', 'of', 'pairing', 'fluctuations', 'to', 'the', 'positiveeffectiverange', 'region', 'from', 'our', 'results', 'near', 'the', 'narrow', 'feshbach', 'resonance', 'results', 'shown', 'in', 'this', 'paper', 'are', 'relevant', 'to', 'the', 'connection', 'between', 'ultracold', 'fermi', 'gases', 'and', 'lowdensity', 'neutron', 'matter', 'from', 'the', 'viewpoint', 'of', 'finiteeffectiverange', 'corrections']] | [-0.15229692678911405, 0.23127717653568558, -0.12253991777341314, 0.13570236543002795, 0.006726028401345953, -0.08873796543311768, 0.07272379759948437, 0.30034049699361637, -0.2484476196425637, -0.22907684290209623, -0.06675417929444111, -0.3286759921488917, -0.14512842809268106, 0.14340432877579015, 0.06504093293640506, 0.01952369309463135, 0.004009031983987083, 0.0017985754360364179, -0.11465085859549796, -0.16909137985386746, 0.39263446599347734, 0.08878476948107915, 0.27781418636147903, 0.1751514641406733, 0.02021168289324782, 0.022410159248594692, 0.1040954706295738, -0.013430500300381128, -0.17098449100425153, 0.07178641878220612, 0.2790089211624673, -0.08901598334576435, 0.2294093258825578, -0.40890899021178484, -0.24515042856599636, 0.07112996257460258, 0.18970196401172384, 0.17751970976924772, -0.041076372157576986, -0.3412996807142975, -0.028926084441404175, -0.21006464228899344, -0.17880695939291358, -0.10996587146220245, -0.0064401568507585, -0.00941205066152093, -0.25536571304161676, 0.10222128603843914, 0.05931165992964323, 0.06850973382504202, -0.07228172098939109, -0.08036390036403195, 0.03525108636831718, 0.05375106635551137, 0.045144679044065335, 0.03664917188263902, 0.16692877967482594, -0.1918543198308031, -0.03236046019120244, 0.3508359595957234, -0.11063921634349826, -0.09876705316025085, 0.1883162838025532, -0.19149006288461973, -0.07561595567802745, 0.1829932221233493, 0.12322001281289721, 0.06641964203729404, -0.11693468013517999, 0.0933276648723931, -0.07017192984546175, 0.14811716976073225, 0.02253769117216836, 0.11204315244960855, 0.29950180313527935, 0.16861875418676284, 0.009939338447247434, 0.14118663139098625, -0.17685419366526323, -0.12852980549205242, -0.3041663477160623, -0.0657283487165068, -0.19979520277010174, -0.004356195519295132, 0.00043319024553056806, -0.14770248893893134, 0.35208081524437806, 0.1587436004417149, 0.2409905560395321, -0.06841791151953112, 0.22636375583590954, 0.13382891124924717, 0.05171076718747147, 0.0601253835264388, 0.28602017229056265, 0.18988578132716719, 0.05285322008742474, -0.3688628193427347, -4.272911638960125e-05, 0.06075597982706986] |
1,802.0092 | Stability of the Euler Resting N-Body Relative Equlilbria | The stability of a system of $N$ equal sized mutually gravitating spheres
resting on each other in a straight line and rotating in inertial space is
considered. This is a generalization of the "Euler Resting" configurations
previously analyzed in the finite density 3 and 4 body problems. Specific
questions for the general case are how rapidly the system must spin for the
configuration to stabilize, how rapidly it can spin before the components
separate from each other, and how these results change as a function of $N$.
This paper shows that the Euler Resting configuration can only be stable for up
to 5 bodies, and that for 6 or more bodies the configuration can never be
stable. This places an ideal limit of 5:1 on the aspect ratio of a rubble pile
body's shape.
| astro-ph.EP | the stability of a system of n equal sized mutually gravitating spheres resting on each other in a straight line and rotating in inertial space is considered this is a generalization of the euler resting configurations previously analyzed in the finite density 3 and 4 body problems specific questions for the general case are how rapidly the system must spin for the configuration to stabilize how rapidly it can spin before the components separate from each other and how these results change as a function of n this paper shows that the euler resting configuration can only be stable for up to 5 bodies and that for 6 or more bodies the configuration can never be stable this places an ideal limit of 51 on the aspect ratio of a rubble pile bodys shape | [['the', 'stability', 'of', 'a', 'system', 'of', 'n', 'equal', 'sized', 'mutually', 'gravitating', 'spheres', 'resting', 'on', 'each', 'other', 'in', 'a', 'straight', 'line', 'and', 'rotating', 'in', 'inertial', 'space', 'is', 'considered', 'this', 'is', 'a', 'generalization', 'of', 'the', 'euler', 'resting', 'configurations', 'previously', 'analyzed', 'in', 'the', 'finite', 'density', '3', 'and', '4', 'body', 'problems', 'specific', 'questions', 'for', 'the', 'general', 'case', 'are', 'how', 'rapidly', 'the', 'system', 'must', 'spin', 'for', 'the', 'configuration', 'to', 'stabilize', 'how', 'rapidly', 'it', 'can', 'spin', 'before', 'the', 'components', 'separate', 'from', 'each', 'other', 'and', 'how', 'these', 'results', 'change', 'as', 'a', 'function', 'of', 'n', 'this', 'paper', 'shows', 'that', 'the', 'euler', 'resting', 'configuration', 'can', 'only', 'be', 'stable', 'for', 'up', 'to', '5', 'bodies', 'and', 'that', 'for', '6', 'or', 'more', 'bodies', 'the', 'configuration', 'can', 'never', 'be', 'stable', 'this', 'places', 'an', 'ideal', 'limit', 'of', '51', 'on', 'the', 'aspect', 'ratio', 'of', 'a', 'rubble', 'pile', 'bodys', 'shape']] | [-0.12728609808689373, 0.1789627729632949, -0.08206911904758203, 0.019714644846833076, -0.012451104903176649, -0.1274408945855476, 0.011509901599840387, 0.31555132592903146, -0.24474003120436708, -0.2901057682917523, 0.12611105981189064, -0.2630018509104292, -0.105661578043781, 0.18198990228293993, -0.05368160814700176, 0.02220924942295498, 0.0460852265927885, 0.07964111228887019, -0.0457352388983787, -0.25984050710807177, 0.33247925801230455, 0.0019595776406575497, 0.2159101598741445, -0.004243353896065435, 0.07898003206529251, -0.010680116900701576, 0.06569147152381379, 0.08405800923030066, -0.09924746388206762, 0.08187337509580582, 0.2170968576501221, 0.10368432342630825, 0.2368264305335817, -0.43379951874489214, -0.18260609751233636, 0.0900617937953559, 0.1620652715644257, 0.11084938690816956, 0.024069929873695033, -0.23303336338591832, 0.12017087941926752, -0.18143527826064948, -0.18472819193266332, -0.026125630815582935, 0.11670958186819483, 0.03404782070263998, -0.22860042178836554, 0.01684119775474294, 0.10433990047521778, 0.033142898614547014, -0.113026604991156, -0.1164458296583173, -0.02385284406577807, 0.15888856966986753, 0.03469671043845366, 0.03680252319952445, 0.16653434388859045, -0.0983676365559309, -0.07044810476476576, 0.39273315801549313, -0.038500614092448975, -0.2840086250051634, 0.2163245558266097, -0.15237258257406916, -0.10509680087136021, 0.13977124081723002, 0.1717561614949868, 0.15477149092084938, -0.10132564516710256, 0.02921223361827016, -0.07499310902907137, 0.20371402435199315, 0.11200096888759577, -0.043204927397805, 0.25609473592198606, 0.1499671855413202, 0.09518445615169945, 0.14366698756191962, -0.09702437094746352, -0.08640288817696273, -0.2725173793385611, -0.18033595369614439, -0.16122117827371904, 0.06342337159578924, -0.08739744705876071, -0.1544906002573179, 0.35831896373098576, 0.09092196020649124, 0.20035276958830098, 0.028118669256957163, 0.29188207014283135, 0.07194748551518754, 0.041600268236847954, 0.06681176563446868, 0.26763393670153707, 0.0969739753714721, 0.09765075070700094, -0.1815943391898077, 0.03681162263113839, 0.029708058488166045] |
1,802.00921 | A deep tree-based model for software defect prediction | Defects are common in software systems and can potentially cause various
problems to software users. Different methods have been developed to quickly
predict the most likely locations of defects in large code bases. Most of them
focus on designing features (e.g. complexity metrics) that correlate with
potentially defective code. Those approaches however do not sufficiently
capture the syntax and different levels of semantics of source code, an
important capability for building accurate prediction models. In this paper, we
develop a novel prediction model which is capable of automatically learning
features for representing source code and using them for defect prediction. Our
prediction system is built upon the powerful deep learning, tree-structured
Long Short Term Memory network which directly matches with the Abstract Syntax
Tree representation of source code. An evaluation on two datasets, one from
open source projects contributed by Samsung and the other from the public
PROMISE repository, demonstrates the effectiveness of our approach for both
within-project and cross-project predictions.
| cs.SE | defects are common in software systems and can potentially cause various problems to software users different methods have been developed to quickly predict the most likely locations of defects in large code bases most of them focus on designing features eg complexity metrics that correlate with potentially defective code those approaches however do not sufficiently capture the syntax and different levels of semantics of source code an important capability for building accurate prediction models in this paper we develop a novel prediction model which is capable of automatically learning features for representing source code and using them for defect prediction our prediction system is built upon the powerful deep learning treestructured long short term memory network which directly matches with the abstract syntax tree representation of source code an evaluation on two datasets one from open source projects contributed by samsung and the other from the public promise repository demonstrates the effectiveness of our approach for both withinproject and crossproject predictions | [['defects', 'are', 'common', 'in', 'software', 'systems', 'and', 'can', 'potentially', 'cause', 'various', 'problems', 'to', 'software', 'users', 'different', 'methods', 'have', 'been', 'developed', 'to', 'quickly', 'predict', 'the', 'most', 'likely', 'locations', 'of', 'defects', 'in', 'large', 'code', 'bases', 'most', 'of', 'them', 'focus', 'on', 'designing', 'features', 'eg', 'complexity', 'metrics', 'that', 'correlate', 'with', 'potentially', 'defective', 'code', 'those', 'approaches', 'however', 'do', 'not', 'sufficiently', 'capture', 'the', 'syntax', 'and', 'different', 'levels', 'of', 'semantics', 'of', 'source', 'code', 'an', 'important', 'capability', 'for', 'building', 'accurate', 'prediction', 'models', 'in', 'this', 'paper', 'we', 'develop', 'a', 'novel', 'prediction', 'model', 'which', 'is', 'capable', 'of', 'automatically', 'learning', 'features', 'for', 'representing', 'source', 'code', 'and', 'using', 'them', 'for', 'defect', 'prediction', 'our', 'prediction', 'system', 'is', 'built', 'upon', 'the', 'powerful', 'deep', 'learning', 'treestructured', 'long', 'short', 'term', 'memory', 'network', 'which', 'directly', 'matches', 'with', 'the', 'abstract', 'syntax', 'tree', 'representation', 'of', 'source', 'code', 'an', 'evaluation', 'on', 'two', 'datasets', 'one', 'from', 'open', 'source', 'projects', 'contributed', 'by', 'samsung', 'and', 'the', 'other', 'from', 'the', 'public', 'promise', 'repository', 'demonstrates', 'the', 'effectiveness', 'of', 'our', 'approach', 'for', 'both', 'withinproject', 'and', 'crossproject', 'predictions']] | [-0.04199454719374269, 0.009329296233637189, -0.05293824794407217, 0.10542805623682505, -0.09240333298915383, -0.20739877275451554, 0.016432849332395225, 0.4171833166540224, -0.26472632673291363, -0.3602432230927096, 0.09589564281310424, -0.29059900151947454, -0.13448059456914618, 0.23762902334514682, -0.06901441895264138, 0.07686277865164665, 0.13316138583557527, 0.025466913114423336, -0.024367465197541807, -0.254005241751532, 0.3300234979128477, 0.11212299130578607, 0.33365355673251873, 0.05144027102397327, 0.06165608817202427, -0.02884350630153919, -0.06919025889921772, -0.016091205429152718, -0.05182377463344937, 0.1819885015395117, 0.33336125705811004, 0.22060396374052865, 0.28403904511136296, -0.4336769980210695, -0.24431508139169178, 0.033055953253407655, 0.1268112048081387, 0.15585367853533816, -0.03721009169470935, -0.2952410614223549, 0.09332204224564922, -0.1936216122783165, -0.037607648250826214, -0.1054365182583226, -0.0037282751381443524, 0.001152290180267662, -0.2255084133180587, -0.02981596863871984, 0.04857045560005057, 0.06960946900093269, -0.056856783478584444, -0.1263714949340091, 0.005191382812480702, 0.20641513507500847, 0.030497249482656533, 0.06022991792165807, 0.11531647217896715, -0.1497737514082869, -0.15448207043325354, 0.3715328596856283, -0.05194338521434237, -0.1846873694799424, 0.24780035553129842, -0.008887447211428643, -0.176017056760141, 0.11819084814998397, 0.22699614089583944, 0.11147535447557659, -0.18731981054561572, 0.02396202091270803, 0.020118068973099965, 0.2124167256836739, 0.01511017390185539, 0.014682461091126343, 0.2479162738132088, 0.20647322187657172, -0.027332861028519107, 0.11375613759672384, -0.0713007983940318, -0.06912106959733487, -0.2161385428727321, -0.10531949075088931, -0.13926855450559708, -0.04137782981720108, -0.10313208344757993, -0.18994507789235818, 0.4052930670379667, 0.22803426208842245, 0.13696561585038014, 0.08203549707389396, 0.3036960903497309, -0.005463269517485121, 0.15408432808189843, 0.1249274436474101, 0.1332714125132246, 0.0037840123140825805, 0.07519551278063044, -0.1685361769108206, 0.14490069098111172, 0.038297554350787806] |
1,802.00922 | Realizing Uncertainty-Aware Timing Stack in Embedded Operating System | Time awareness is critical to a broad range of emerging applications -- in
Cyber-Physical Systems and Internet of Things -- running on commodity platforms
and operating systems. Traditionally, time is synchronized across devices
through a best-effort background service whose performance is neither
observable nor controllable, thus consuming system resources independently of
application needs while not allowing the applications and OS services to adapt
to changes in uncertainty in system time. We advocate for rethinking how time
is managed in a system stack. In this paper, we propose a new clock model that
characterizes various sources of timing uncertainties in true time. We then
present a Kalman filter based time synchronization protocol that adapts to the
uncertainties exposed by the clock model. Our realization of a
uncertainty-aware clock model and synchronization protocol is based on a
standard embedded Linux platform.
| cs.RO cs.NI cs.OS cs.SY | time awareness is critical to a broad range of emerging applications in cyberphysical systems and internet of things running on commodity platforms and operating systems traditionally time is synchronized across devices through a besteffort background service whose performance is neither observable nor controllable thus consuming system resources independently of application needs while not allowing the applications and os services to adapt to changes in uncertainty in system time we advocate for rethinking how time is managed in a system stack in this paper we propose a new clock model that characterizes various sources of timing uncertainties in true time we then present a kalman filter based time synchronization protocol that adapts to the uncertainties exposed by the clock model our realization of a uncertaintyaware clock model and synchronization protocol is based on a standard embedded linux platform | [['time', 'awareness', 'is', 'critical', 'to', 'a', 'broad', 'range', 'of', 'emerging', 'applications', 'in', 'cyberphysical', 'systems', 'and', 'internet', 'of', 'things', 'running', 'on', 'commodity', 'platforms', 'and', 'operating', 'systems', 'traditionally', 'time', 'is', 'synchronized', 'across', 'devices', 'through', 'a', 'besteffort', 'background', 'service', 'whose', 'performance', 'is', 'neither', 'observable', 'nor', 'controllable', 'thus', 'consuming', 'system', 'resources', 'independently', 'of', 'application', 'needs', 'while', 'not', 'allowing', 'the', 'applications', 'and', 'os', 'services', 'to', 'adapt', 'to', 'changes', 'in', 'uncertainty', 'in', 'system', 'time', 'we', 'advocate', 'for', 'rethinking', 'how', 'time', 'is', 'managed', 'in', 'a', 'system', 'stack', 'in', 'this', 'paper', 'we', 'propose', 'a', 'new', 'clock', 'model', 'that', 'characterizes', 'various', 'sources', 'of', 'timing', 'uncertainties', 'in', 'true', 'time', 'we', 'then', 'present', 'a', 'kalman', 'filter', 'based', 'time', 'synchronization', 'protocol', 'that', 'adapts', 'to', 'the', 'uncertainties', 'exposed', 'by', 'the', 'clock', 'model', 'our', 'realization', 'of', 'a', 'uncertaintyaware', 'clock', 'model', 'and', 'synchronization', 'protocol', 'is', 'based', 'on', 'a', 'standard', 'embedded', 'linux', 'platform']] | [-0.20052172810546237, 0.07698418329857344, -0.07214360361932402, 0.003485690224507864, -0.06267195357431243, -0.2045184605972447, 0.11664045154160385, 0.4275257227140187, -0.25660272691752356, -0.3258867494721156, 0.14072129388006716, -0.2119930359321633, -0.1269127879509308, 0.2276937274286102, -0.13898856701315754, 0.08244198499193048, 0.052608201587844614, 0.014776368882085611, -0.010653791721420783, -0.2174842599915327, 0.25974283266105574, 0.07833611082802289, 0.3153300181874176, 0.026678520387351294, 0.09699485568423377, -0.003784423667672396, 0.0015122422125924677, -0.03611805885826258, -0.04897273069191945, 0.09866548943156793, 0.2830288193739244, 0.18073796639966705, 0.323002777873015, -0.44061646473179333, -0.2082640035281869, 0.12192342988966139, 0.1496650751316444, 0.0813507198910127, -0.02565642762895211, -0.31323145779808237, 0.06492016323073938, -0.22952200517051574, -0.09340356788621369, -0.050537213917658495, 0.030003847965603545, 0.0381128723273414, -0.24752323540695773, -0.0009842474577798895, -0.0038333494882405237, 0.04707311794508493, -0.05020337847342297, 0.010270112696321287, 0.03639314487579884, 0.15175225571423334, -0.029116392785676477, -0.019379506579321557, 0.19229382363549113, -0.07815308127650841, -0.14761148961804751, 0.4018427518733444, -0.055744617141784934, -0.15749453539776542, 0.18734745392456895, -0.05064909831947056, -0.158766898053807, 0.06326747188526444, 0.2442249478636323, 0.05927404602581676, -0.21286446398572764, 0.08459983860570801, 0.04626385444761628, 0.2784032181710902, -0.00536382867509404, 0.07860438971793836, 0.17894777907466475, 0.2565274385286726, 0.1067818259840736, 0.0953482543968045, -0.03318615162228472, -0.12959293626932034, -0.2494106724565971, -0.15275481941661098, -0.17407505911006763, 0.010282184316413001, -0.05223701874186781, -0.14175925737401526, 0.39253695008691647, 0.24952926205974207, 0.1304223096129029, 0.059482205086209244, 0.39214879704000305, 0.07496802292400495, 0.08617397589459472, 0.11347299449512885, 0.14042019328088873, 0.010708364034343484, 0.20309770618986855, -0.15465532630226528, 0.11848504128814913, -0.011053273675242697] |
1,802.00923 | Multi-attention Recurrent Network for Human Communication Comprehension | Human face-to-face communication is a complex multimodal signal. We use words
(language modality), gestures (vision modality) and changes in tone (acoustic
modality) to convey our intentions. Humans easily process and understand
face-to-face communication, however, comprehending this form of communication
remains a significant challenge for Artificial Intelligence (AI). AI must
understand each modality and the interactions between them that shape human
communication. In this paper, we present a novel neural architecture for
understanding human communication called the Multi-attention Recurrent Network
(MARN). The main strength of our model comes from discovering interactions
between modalities through time using a neural component called the
Multi-attention Block (MAB) and storing them in the hybrid memory of a
recurrent component called the Long-short Term Hybrid Memory (LSTHM). We
perform extensive comparisons on six publicly available datasets for multimodal
sentiment analysis, speaker trait recognition and emotion recognition. MARN
shows state-of-the-art performance on all the datasets.
| cs.AI cs.CL cs.LG | human facetoface communication is a complex multimodal signal we use words language modality gestures vision modality and changes in tone acoustic modality to convey our intentions humans easily process and understand facetoface communication however comprehending this form of communication remains a significant challenge for artificial intelligence ai ai must understand each modality and the interactions between them that shape human communication in this paper we present a novel neural architecture for understanding human communication called the multiattention recurrent network marn the main strength of our model comes from discovering interactions between modalities through time using a neural component called the multiattention block mab and storing them in the hybrid memory of a recurrent component called the longshort term hybrid memory lsthm we perform extensive comparisons on six publicly available datasets for multimodal sentiment analysis speaker trait recognition and emotion recognition marn shows stateoftheart performance on all the datasets | [['human', 'facetoface', 'communication', 'is', 'a', 'complex', 'multimodal', 'signal', 'we', 'use', 'words', 'language', 'modality', 'gestures', 'vision', 'modality', 'and', 'changes', 'in', 'tone', 'acoustic', 'modality', 'to', 'convey', 'our', 'intentions', 'humans', 'easily', 'process', 'and', 'understand', 'facetoface', 'communication', 'however', 'comprehending', 'this', 'form', 'of', 'communication', 'remains', 'a', 'significant', 'challenge', 'for', 'artificial', 'intelligence', 'ai', 'ai', 'must', 'understand', 'each', 'modality', 'and', 'the', 'interactions', 'between', 'them', 'that', 'shape', 'human', 'communication', 'in', 'this', 'paper', 'we', 'present', 'a', 'novel', 'neural', 'architecture', 'for', 'understanding', 'human', 'communication', 'called', 'the', 'multiattention', 'recurrent', 'network', 'marn', 'the', 'main', 'strength', 'of', 'our', 'model', 'comes', 'from', 'discovering', 'interactions', 'between', 'modalities', 'through', 'time', 'using', 'a', 'neural', 'component', 'called', 'the', 'multiattention', 'block', 'mab', 'and', 'storing', 'them', 'in', 'the', 'hybrid', 'memory', 'of', 'a', 'recurrent', 'component', 'called', 'the', 'longshort', 'term', 'hybrid', 'memory', 'lsthm', 'we', 'perform', 'extensive', 'comparisons', 'on', 'six', 'publicly', 'available', 'datasets', 'for', 'multimodal', 'sentiment', 'analysis', 'speaker', 'trait', 'recognition', 'and', 'emotion', 'recognition', 'marn', 'shows', 'stateoftheart', 'performance', 'on', 'all', 'the', 'datasets']] | [-0.12424768627063706, 0.009989922172484957, -0.03517959437722682, 0.08797405013618019, -0.16664063751691205, -0.23400289895722554, 0.0222562305339403, 0.474734863988599, -0.2966760249212891, -0.2935679847371391, 0.019867255481523873, -0.31400269562961397, -0.27036205159664967, 0.17179896916796575, -0.14242936767713757, 0.026518135312564518, 0.1654863562419073, 0.10366266339995145, 0.05666962122943785, -0.21861620154310982, 0.27382131839083146, -0.010880638078666058, 0.33601443824313937, 0.008397215182202405, 0.13048360376384388, 0.00602098557223775, -0.04225072323433345, -0.10095088444270041, -0.023241804628231052, 0.2192517797345138, 0.36446736792742346, 0.23691575925442435, 0.3557147635653818, -0.43594917770297753, -0.23323402169230237, 0.08302283867382679, 0.14606868491514402, 0.07436952515537724, -0.03202190880701706, -0.37379022560963016, 0.03482194479276128, -0.19237716960384935, 0.06313639027731759, -0.10756752352059192, 0.02760921285818743, -0.04698915060706196, -0.2600412188441537, 0.011306356195183028, 0.09367367408561464, 0.1608002260073918, -0.04904269419458448, -0.06146781226987539, 0.054669705804811196, 0.23419921361480137, 0.01749205490796776, 0.06141216908486522, 0.15715804004243442, -0.21084457040023136, -0.17908182363247588, 0.338453896331037, -0.04345443076929267, -0.20651317646942374, 0.24215515050086744, -0.006434793710759302, -0.1725540081336841, 0.02889148066215119, 0.24619377593250097, 0.04622792616030391, -0.2176737517599954, 0.00048684433805851304, 0.0010345935010585656, 0.25925589306121627, 0.05654993957561143, 0.016911018180477175, 0.1977657052279659, 0.3152210578087996, -0.037203839126353465, 0.11563740578974235, -0.11872416605490266, -0.07279393162743068, -0.16078485538042625, -0.12749992515004815, -0.1651205799509106, -0.022863350689037366, -0.09963948965707373, -0.12595924660785213, 0.4000492148033223, 0.23439372474086934, 0.13938841202809493, 0.1484088918018364, 0.4041681738228214, -0.026064417236262842, 0.15573083930870607, 0.050280931915337106, 0.13685561527916212, -0.019636306518904207, 0.1862011414914563, -0.2052197936545348, 0.1279112030942069, 0.02321961642794159] |
1,802.00924 | Multimodal Sentiment Analysis with Word-Level Fusion and Reinforcement
Learning | With the increasing popularity of video sharing websites such as YouTube and
Facebook, multimodal sentiment analysis has received increasing attention from
the scientific community. Contrary to previous works in multimodal sentiment
analysis which focus on holistic information in speech segments such as bag of
words representations and average facial expression intensity, we develop a
novel deep architecture for multimodal sentiment analysis that performs
modality fusion at the word level. In this paper, we propose the Gated
Multimodal Embedding LSTM with Temporal Attention (GME-LSTM(A)) model that is
composed of 2 modules. The Gated Multimodal Embedding alleviates the
difficulties of fusion when there are noisy modalities. The LSTM with Temporal
Attention performs word level fusion at a finer fusion resolution between input
modalities and attends to the most important time steps. As a result, the
GME-LSTM(A) is able to better model the multimodal structure of speech through
time and perform better sentiment comprehension. We demonstrate the
effectiveness of this approach on the publicly-available Multimodal Corpus of
Sentiment Intensity and Subjectivity Analysis (CMU-MOSI) dataset by achieving
state-of-the-art sentiment classification and regression results. Qualitative
analysis on our model emphasizes the importance of the Temporal Attention Layer
in sentiment prediction because the additional acoustic and visual modalities
are noisy. We also demonstrate the effectiveness of the Gated Multimodal
Embedding in selectively filtering these noisy modalities out. Our results and
analysis open new areas in the study of sentiment analysis in human
communication and provide new models for multimodal fusion.
| cs.LG cs.AI cs.CL stat.ML | with the increasing popularity of video sharing websites such as youtube and facebook multimodal sentiment analysis has received increasing attention from the scientific community contrary to previous works in multimodal sentiment analysis which focus on holistic information in speech segments such as bag of words representations and average facial expression intensity we develop a novel deep architecture for multimodal sentiment analysis that performs modality fusion at the word level in this paper we propose the gated multimodal embedding lstm with temporal attention gmelstma model that is composed of 2 modules the gated multimodal embedding alleviates the difficulties of fusion when there are noisy modalities the lstm with temporal attention performs word level fusion at a finer fusion resolution between input modalities and attends to the most important time steps as a result the gmelstma is able to better model the multimodal structure of speech through time and perform better sentiment comprehension we demonstrate the effectiveness of this approach on the publiclyavailable multimodal corpus of sentiment intensity and subjectivity analysis cmumosi dataset by achieving stateoftheart sentiment classification and regression results qualitative analysis on our model emphasizes the importance of the temporal attention layer in sentiment prediction because the additional acoustic and visual modalities are noisy we also demonstrate the effectiveness of the gated multimodal embedding in selectively filtering these noisy modalities out our results and analysis open new areas in the study of sentiment analysis in human communication and provide new models for multimodal fusion | [['with', 'the', 'increasing', 'popularity', 'of', 'video', 'sharing', 'websites', 'such', 'as', 'youtube', 'and', 'facebook', 'multimodal', 'sentiment', 'analysis', 'has', 'received', 'increasing', 'attention', 'from', 'the', 'scientific', 'community', 'contrary', 'to', 'previous', 'works', 'in', 'multimodal', 'sentiment', 'analysis', 'which', 'focus', 'on', 'holistic', 'information', 'in', 'speech', 'segments', 'such', 'as', 'bag', 'of', 'words', 'representations', 'and', 'average', 'facial', 'expression', 'intensity', 'we', 'develop', 'a', 'novel', 'deep', 'architecture', 'for', 'multimodal', 'sentiment', 'analysis', 'that', 'performs', 'modality', 'fusion', 'at', 'the', 'word', 'level', 'in', 'this', 'paper', 'we', 'propose', 'the', 'gated', 'multimodal', 'embedding', 'lstm', 'with', 'temporal', 'attention', 'gmelstma', 'model', 'that', 'is', 'composed', 'of', '2', 'modules', 'the', 'gated', 'multimodal', 'embedding', 'alleviates', 'the', 'difficulties', 'of', 'fusion', 'when', 'there', 'are', 'noisy', 'modalities', 'the', 'lstm', 'with', 'temporal', 'attention', 'performs', 'word', 'level', 'fusion', 'at', 'a', 'finer', 'fusion', 'resolution', 'between', 'input', 'modalities', 'and', 'attends', 'to', 'the', 'most', 'important', 'time', 'steps', 'as', 'a', 'result', 'the', 'gmelstma', 'is', 'able', 'to', 'better', 'model', 'the', 'multimodal', 'structure', 'of', 'speech', 'through', 'time', 'and', 'perform', 'better', 'sentiment', 'comprehension', 'we', 'demonstrate', 'the', 'effectiveness', 'of', 'this', 'approach', 'on', 'the', 'publiclyavailable', 'multimodal', 'corpus', 'of', 'sentiment', 'intensity', 'and', 'subjectivity', 'analysis', 'cmumosi', 'dataset', 'by', 'achieving', 'stateoftheart', 'sentiment', 'classification', 'and', 'regression', 'results', 'qualitative', 'analysis', 'on', 'our', 'model', 'emphasizes', 'the', 'importance', 'of', 'the', 'temporal', 'attention', 'layer', 'in', 'sentiment', 'prediction', 'because', 'the', 'additional', 'acoustic', 'and', 'visual', 'modalities', 'are', 'noisy', 'we', 'also', 'demonstrate', 'the', 'effectiveness', 'of', 'the', 'gated', 'multimodal', 'embedding', 'in', 'selectively', 'filtering', 'these', 'noisy', 'modalities', 'out', 'our', 'results', 'and', 'analysis', 'open', 'new', 'areas', 'in', 'the', 'study', 'of', 'sentiment', 'analysis', 'in', 'human', 'communication', 'and', 'provide', 'new', 'models', 'for', 'multimodal', 'fusion']] | [-0.0052725323186066635, -0.014463295499287947, -0.04633555358326009, 0.0734792272305489, -0.13091465754287618, -0.17750802407953192, 0.03485020286842207, 0.5043844300248811, -0.2719901573648135, -0.279695342183287, 0.03795469423448783, -0.3329445413541806, -0.20518649160751834, 0.17052181481593384, -0.13574994040877245, 0.07007502566847208, 0.1752480881735953, 0.12624534408195248, -0.008361764537548276, -0.2659420482583655, 0.3204425767583858, 0.052119200862630145, 0.40716958186623964, 0.05601659409230366, 0.11490667993309077, 0.018881024591841145, -0.11265421271927005, -0.05708741321454513, -0.025669193387582716, 0.22684430758298774, 0.37432694160814517, 0.21019979311671125, 0.3589020079819107, -0.3786994868783663, -0.2537606063847627, 0.020640962927204198, 0.15282883629960267, 0.09630527645223909, -0.04589348080755439, -0.3862579276174678, 0.08341251646363683, -0.1795065223830617, 0.09975894597355137, -0.1530893420746465, 0.01866707185677473, -0.01790838592194979, -0.259358303159856, 0.07851381395753623, 0.13025071847163666, 0.12225206247704776, -0.05978062378795222, -0.11142472246482087, 0.011321465773179478, 0.2105215376633542, 0.07538016026895648, 0.03364274180875859, 0.1432697939620299, -0.21256758872017698, -0.1619934636821267, 0.32730573276637503, -0.0754485260666236, -0.2205496882416957, 0.1981539431567153, -0.061931206278611144, -0.18092273693160768, 0.05414178203095786, 0.25169213683705743, 0.08506930993553019, -0.1281676395314758, -0.005161146840555941, -0.06469475707866221, 0.25934737491811705, 0.09125124234551588, 0.016435298092926067, 0.1935887238652256, 0.3256393540582979, -0.011983250920888793, 0.14781073687542076, -0.15636353113077953, -0.055736679574126896, -0.1875886624269342, -0.09415503622021108, -0.1266021391867633, -0.056418127396252175, -0.11588232599615933, -0.13096978880186794, 0.4486210096117372, 0.24400481584006573, 0.22625108704133215, 0.10387610697479246, 0.3695543504540356, 0.019748123312181565, 0.09653009255165505, 0.028331544379707564, 0.10507098727770675, 0.0020834274089574444, 0.19010235049425628, -0.17015349767624915, 0.07550073063935257, 0.05607156433489684] |
1,802.00925 | First-principles theory of magnetic multipoles in condensed matter
systems | The multipole concept, which characterizes the spacial distribution of scalar
and vector objects by their angular dependence, has already become widely used
in various areas of physics. In recent years it has become employed to
systematically classify the anisotropic distribution of electrons and
magnetization around atoms in solid state materials. This has been fuelled by
the discovery of several physical phenomena that exhibit unusual higher rank
multipole moments, beyond that of the conventional degrees of freedom as charge
and magnetic dipole moment. Moreover, the higher rank electric/magnetic
multipole moments have been suggested as promising order parameters in exotic
hidden order phases. While the experimental investigations of such anomalous
phases have provided encouraging observations of multipolar order, theoretical
approaches have developed at a slower pace. In particular, a materials'
specific theory has been missing. The multipole concept has furthermore been
recognized as the key quantity which characterizes the resultant configuration
of magnetic moments in a cluster of atomic moments. This cluster multipole
moment has then been introduced as macroscopic order parameter for a
noncollinear antiferromagnetic structure in crystals that can explain unusual
physical phenomena whose appearance is determined by the magnetic point group
symmetry. It is the purpose of this review to discuss the recent developments
in the first-principles theory investigating multipolar degrees of freedom in
condensed matter systems. These recent developments exemplify that ab initio
electronic structure calculations can unveil detailed insight in the mechanism
of physical phenomena caused by the unconventional, multipole degree of
freedom.
| cond-mat.str-el cond-mat.mtrl-sci cond-mat.supr-con | the multipole concept which characterizes the spacial distribution of scalar and vector objects by their angular dependence has already become widely used in various areas of physics in recent years it has become employed to systematically classify the anisotropic distribution of electrons and magnetization around atoms in solid state materials this has been fuelled by the discovery of several physical phenomena that exhibit unusual higher rank multipole moments beyond that of the conventional degrees of freedom as charge and magnetic dipole moment moreover the higher rank electricmagnetic multipole moments have been suggested as promising order parameters in exotic hidden order phases while the experimental investigations of such anomalous phases have provided encouraging observations of multipolar order theoretical approaches have developed at a slower pace in particular a materials specific theory has been missing the multipole concept has furthermore been recognized as the key quantity which characterizes the resultant configuration of magnetic moments in a cluster of atomic moments this cluster multipole moment has then been introduced as macroscopic order parameter for a noncollinear antiferromagnetic structure in crystals that can explain unusual physical phenomena whose appearance is determined by the magnetic point group symmetry it is the purpose of this review to discuss the recent developments in the firstprinciples theory investigating multipolar degrees of freedom in condensed matter systems these recent developments exemplify that ab initio electronic structure calculations can unveil detailed insight in the mechanism of physical phenomena caused by the unconventional multipole degree of freedom | [['the', 'multipole', 'concept', 'which', 'characterizes', 'the', 'spacial', 'distribution', 'of', 'scalar', 'and', 'vector', 'objects', 'by', 'their', 'angular', 'dependence', 'has', 'already', 'become', 'widely', 'used', 'in', 'various', 'areas', 'of', 'physics', 'in', 'recent', 'years', 'it', 'has', 'become', 'employed', 'to', 'systematically', 'classify', 'the', 'anisotropic', 'distribution', 'of', 'electrons', 'and', 'magnetization', 'around', 'atoms', 'in', 'solid', 'state', 'materials', 'this', 'has', 'been', 'fuelled', 'by', 'the', 'discovery', 'of', 'several', 'physical', 'phenomena', 'that', 'exhibit', 'unusual', 'higher', 'rank', 'multipole', 'moments', 'beyond', 'that', 'of', 'the', 'conventional', 'degrees', 'of', 'freedom', 'as', 'charge', 'and', 'magnetic', 'dipole', 'moment', 'moreover', 'the', 'higher', 'rank', 'electricmagnetic', 'multipole', 'moments', 'have', 'been', 'suggested', 'as', 'promising', 'order', 'parameters', 'in', 'exotic', 'hidden', 'order', 'phases', 'while', 'the', 'experimental', 'investigations', 'of', 'such', 'anomalous', 'phases', 'have', 'provided', 'encouraging', 'observations', 'of', 'multipolar', 'order', 'theoretical', 'approaches', 'have', 'developed', 'at', 'a', 'slower', 'pace', 'in', 'particular', 'a', 'materials', 'specific', 'theory', 'has', 'been', 'missing', 'the', 'multipole', 'concept', 'has', 'furthermore', 'been', 'recognized', 'as', 'the', 'key', 'quantity', 'which', 'characterizes', 'the', 'resultant', 'configuration', 'of', 'magnetic', 'moments', 'in', 'a', 'cluster', 'of', 'atomic', 'moments', 'this', 'cluster', 'multipole', 'moment', 'has', 'then', 'been', 'introduced', 'as', 'macroscopic', 'order', 'parameter', 'for', 'a', 'noncollinear', 'antiferromagnetic', 'structure', 'in', 'crystals', 'that', 'can', 'explain', 'unusual', 'physical', 'phenomena', 'whose', 'appearance', 'is', 'determined', 'by', 'the', 'magnetic', 'point', 'group', 'symmetry', 'it', 'is', 'the', 'purpose', 'of', 'this', 'review', 'to', 'discuss', 'the', 'recent', 'developments', 'in', 'the', 'firstprinciples', 'theory', 'investigating', 'multipolar', 'degrees', 'of', 'freedom', 'in', 'condensed', 'matter', 'systems', 'these', 'recent', 'developments', 'exemplify', 'that', 'ab', 'initio', 'electronic', 'structure', 'calculations', 'can', 'unveil', 'detailed', 'insight', 'in', 'the', 'mechanism', 'of', 'physical', 'phenomena', 'caused', 'by', 'the', 'unconventional', 'multipole', 'degree', 'of', 'freedom']] | [-0.13658612375564086, 0.1834553231767235, -0.0909542101770342, 0.05294576027204389, -0.09641496562190748, -0.06798206113006283, -0.004567725371902551, 0.34673597990739635, -0.23275808292456213, -0.32574120788963706, 0.036269024615478894, -0.27035582047220835, -0.1511340761288425, 0.13671299271811768, 0.046409958403754406, 0.04699141183883194, -0.05877165557869627, 0.03694156184035346, -0.09264847830437668, -0.21121803907146564, 0.28123263377559987, 0.0731822093906131, 0.28449004546671014, 0.06639029940862845, 0.0877647491425609, -0.027422086935778096, 0.017225929698717547, 0.04448253286972157, -0.12272574561631508, 0.10591538987567374, 0.2602440601089833, 0.028755018638918505, 0.23249712307384707, -0.4616254177777384, -0.2735953159872594, 0.059393336835536895, 0.17132802409540135, 0.13608214555012926, -0.09191673897524796, -0.26298527446446746, 0.06388984816111018, -0.19274427627580765, -0.20217673268762598, -0.19052616399627678, 0.058294444595751296, 0.027812882058456827, -0.21242150587902398, 0.0733360314620583, 0.07224006183294966, 0.10297210524646008, -0.03895192789763299, -0.1810066075858327, -0.008292358467540877, 0.06918892134889597, 0.09908544920868528, 0.0433388054569638, 0.1095362279390939, -0.13508822534770926, -0.16263853301477763, 0.3819124809678311, 0.010980130220589236, -0.12815608087644584, 0.1780647883263636, -0.19353265762889882, -0.16872856434154498, 0.1426948074789006, 0.15904145322293345, 0.10102805294824302, -0.15934358594221693, 0.11616734698404392, 0.0022863144393607112, 0.14109715905901313, 0.04168670345854953, 0.10892550168441593, 0.2743123995056906, 0.15709571684123871, -0.0044958350860051875, 0.11463583065920918, -0.09671164267389785, -0.10593413613922137, -0.2080653916961112, -0.13862332864475202, -0.22224015314737713, 0.031089596767394947, -0.06752309201426503, -0.13491773556509032, 0.43009377847145486, 0.15082671983993085, 0.14646320111625563, -0.11229968494243496, 0.2676258750735002, 0.08624742369591523, 0.09381600382938467, 0.010762442017875127, 0.2995495363810607, 0.19653915933700566, 0.09993390556012197, -0.21226005062150458, 0.09397415323675103, 0.05529184593644932] |
1,802.00926 | On the Minimax Misclassification Ratio of Hypergraph Community Detection | Community detection in hypergraphs is explored. Under a generative hypergraph
model called "d-wise hypergraph stochastic block model" (d-hSBM) which
naturally extends the Stochastic Block Model from graphs to d-uniform
hypergraphs, the asymptotic minimax mismatch ratio is characterized. For
proving the achievability, we propose a two-step polynomial time algorithm that
achieves the fundamental limit. The first step of the algorithm is a hypergraph
spectral clustering method which achieves partial recovery to a certain
precision level. The second step is a local refinement method which leverages
the underlying probabilistic model along with parameter estimation from the
outcome of the first step. To characterize the asymptotic performance of the
proposed algorithm, we first derive a sufficient condition for attaining weak
consistency in the hypergraph spectral clustering step. Then, under the
guarantee of weak consistency in the first step, we upper bound the worst-case
risk attained in the local refinement step by an exponentially decaying
function of the size of the hypergraph and characterize the decaying rate. For
proving the converse, the lower bound of the minimax mismatch ratio is set by
finding a smaller parameter space which contains the most dominant error
events, inspired by the analysis in the achievability part. It turns out that
the minimax mismatch ratio decays exponentially fast to zero as the number of
nodes tends to infinity, and the rate function is a weighted combination of
several divergence terms, each of which is the Renyi divergence of order 1/2
between two Bernoulli's. The Bernoulli's involved in the characterization of
the rate function are those governing the random instantiation of hyperedges in
d-hSBM. Experimental results on synthetic data validate our theoretical finding
that the refinement step is critical in achieving the optimal statistical
limit.
| cs.IT math.IT math.ST stat.ML stat.TH | community detection in hypergraphs is explored under a generative hypergraph model called dwise hypergraph stochastic block model dhsbm which naturally extends the stochastic block model from graphs to duniform hypergraphs the asymptotic minimax mismatch ratio is characterized for proving the achievability we propose a twostep polynomial time algorithm that achieves the fundamental limit the first step of the algorithm is a hypergraph spectral clustering method which achieves partial recovery to a certain precision level the second step is a local refinement method which leverages the underlying probabilistic model along with parameter estimation from the outcome of the first step to characterize the asymptotic performance of the proposed algorithm we first derive a sufficient condition for attaining weak consistency in the hypergraph spectral clustering step then under the guarantee of weak consistency in the first step we upper bound the worstcase risk attained in the local refinement step by an exponentially decaying function of the size of the hypergraph and characterize the decaying rate for proving the converse the lower bound of the minimax mismatch ratio is set by finding a smaller parameter space which contains the most dominant error events inspired by the analysis in the achievability part it turns out that the minimax mismatch ratio decays exponentially fast to zero as the number of nodes tends to infinity and the rate function is a weighted combination of several divergence terms each of which is the renyi divergence of order 12 between two bernoullis the bernoullis involved in the characterization of the rate function are those governing the random instantiation of hyperedges in dhsbm experimental results on synthetic data validate our theoretical finding that the refinement step is critical in achieving the optimal statistical limit | [['community', 'detection', 'in', 'hypergraphs', 'is', 'explored', 'under', 'a', 'generative', 'hypergraph', 'model', 'called', 'dwise', 'hypergraph', 'stochastic', 'block', 'model', 'dhsbm', 'which', 'naturally', 'extends', 'the', 'stochastic', 'block', 'model', 'from', 'graphs', 'to', 'duniform', 'hypergraphs', 'the', 'asymptotic', 'minimax', 'mismatch', 'ratio', 'is', 'characterized', 'for', 'proving', 'the', 'achievability', 'we', 'propose', 'a', 'twostep', 'polynomial', 'time', 'algorithm', 'that', 'achieves', 'the', 'fundamental', 'limit', 'the', 'first', 'step', 'of', 'the', 'algorithm', 'is', 'a', 'hypergraph', 'spectral', 'clustering', 'method', 'which', 'achieves', 'partial', 'recovery', 'to', 'a', 'certain', 'precision', 'level', 'the', 'second', 'step', 'is', 'a', 'local', 'refinement', 'method', 'which', 'leverages', 'the', 'underlying', 'probabilistic', 'model', 'along', 'with', 'parameter', 'estimation', 'from', 'the', 'outcome', 'of', 'the', 'first', 'step', 'to', 'characterize', 'the', 'asymptotic', 'performance', 'of', 'the', 'proposed', 'algorithm', 'we', 'first', 'derive', 'a', 'sufficient', 'condition', 'for', 'attaining', 'weak', 'consistency', 'in', 'the', 'hypergraph', 'spectral', 'clustering', 'step', 'then', 'under', 'the', 'guarantee', 'of', 'weak', 'consistency', 'in', 'the', 'first', 'step', 'we', 'upper', 'bound', 'the', 'worstcase', 'risk', 'attained', 'in', 'the', 'local', 'refinement', 'step', 'by', 'an', 'exponentially', 'decaying', 'function', 'of', 'the', 'size', 'of', 'the', 'hypergraph', 'and', 'characterize', 'the', 'decaying', 'rate', 'for', 'proving', 'the', 'converse', 'the', 'lower', 'bound', 'of', 'the', 'minimax', 'mismatch', 'ratio', 'is', 'set', 'by', 'finding', 'a', 'smaller', 'parameter', 'space', 'which', 'contains', 'the', 'most', 'dominant', 'error', 'events', 'inspired', 'by', 'the', 'analysis', 'in', 'the', 'achievability', 'part', 'it', 'turns', 'out', 'that', 'the', 'minimax', 'mismatch', 'ratio', 'decays', 'exponentially', 'fast', 'to', 'zero', 'as', 'the', 'number', 'of', 'nodes', 'tends', 'to', 'infinity', 'and', 'the', 'rate', 'function', 'is', 'a', 'weighted', 'combination', 'of', 'several', 'divergence', 'terms', 'each', 'of', 'which', 'is', 'the', 'renyi', 'divergence', 'of', 'order', '12', 'between', 'two', 'bernoullis', 'the', 'bernoullis', 'involved', 'in', 'the', 'characterization', 'of', 'the', 'rate', 'function', 'are', 'those', 'governing', 'the', 'random', 'instantiation', 'of', 'hyperedges', 'in', 'dhsbm', 'experimental', 'results', 'on', 'synthetic', 'data', 'validate', 'our', 'theoretical', 'finding', 'that', 'the', 'refinement', 'step', 'is', 'critical', 'in', 'achieving', 'the', 'optimal', 'statistical', 'limit']] | [-0.11726199523582824, 0.051691542318110954, -0.08859744659286031, 0.06621674188394626, -0.04889340296407418, -0.14029716475876608, 0.08278230593462438, 0.3107363643172418, -0.29093185000001476, -0.2958836380389022, 0.10403487358206073, -0.23835750684708146, -0.1329704505011321, 0.1426239496244236, -0.07627930212930477, 0.10752923379597902, 0.06063803858633808, 0.06138113333247997, -0.030769918217404348, -0.27183935223770483, 0.30179327349145585, 0.08549601313832476, 0.3084718720928203, 0.041677251878990906, 0.09238107365867873, -0.004655585792113109, -0.021398956819137973, 0.01699226244746431, -0.1626350476655954, 0.11770737248838159, 0.23087912858151188, 0.16851509739101828, 0.32761962660473143, -0.34707264242181224, -0.15418148971147927, 0.14064327856466544, 0.15382560473671775, 0.10031756451443931, -0.01263964864015645, -0.24589815600643547, 0.12010023165362568, -0.11490527210615628, -0.07854873405657577, -0.028566338015759737, -0.022249723797172057, 0.012508445284444536, -0.35946768310987276, 0.07757326977018122, 0.11318568858341067, 0.013115542807411435, -0.02481127747699829, -0.13291548601888095, 0.01681213391214188, 0.11245079592772013, 0.03490473418669321, 0.035756488728140357, 0.09217130455974977, -0.11044932259549058, -0.11777273482435742, 0.31567732888757655, -0.08354906221669062, -0.18510511778492494, 0.1258459466788424, -0.11841915473144489, -0.153744800245001, 0.140513070169573, 0.1576475741493091, 0.1355666949005646, -0.12236296981671832, 0.08785533615389622, -0.06310618197867916, 0.1628929774639733, 0.07054309963739012, 0.04222629399799047, 0.10341812619633393, 0.20657175703323036, 0.16285678798122194, 0.18878483172759492, -0.09139252193815112, -0.09973577323925843, -0.33776528424132524, -0.14107801398474645, -0.24236996721372653, -0.003139844845055665, -0.19748325453036178, -0.16583938897972395, 0.39565971914132525, 0.1414056074436978, 0.2092973605421941, 0.14756904458734058, 0.30182579736136456, 0.14204796229748512, 0.008358104667024089, 0.10082309833536905, 0.2291824096731937, 0.15706973216848533, 0.03170968397716291, -0.2106972083412547, 0.13230028237436664, 0.15467167858037664] |
1,802.00927 | Memory Fusion Network for Multi-view Sequential Learning | Multi-view sequential learning is a fundamental problem in machine learning
dealing with multi-view sequences. In a multi-view sequence, there exists two
forms of interactions between different views: view-specific interactions and
cross-view interactions. In this paper, we present a new neural architecture
for multi-view sequential learning called the Memory Fusion Network (MFN) that
explicitly accounts for both interactions in a neural architecture and
continuously models them through time. The first component of the MFN is called
the System of LSTMs, where view-specific interactions are learned in isolation
through assigning an LSTM function to each view. The cross-view interactions
are then identified using a special attention mechanism called the Delta-memory
Attention Network (DMAN) and summarized through time with a Multi-view Gated
Memory. Through extensive experimentation, MFN is compared to various proposed
approaches for multi-view sequential learning on multiple publicly available
benchmark datasets. MFN outperforms all the existing multi-view approaches.
Furthermore, MFN outperforms all current state-of-the-art models, setting new
state-of-the-art results for these multi-view datasets.
| cs.LG cs.AI | multiview sequential learning is a fundamental problem in machine learning dealing with multiview sequences in a multiview sequence there exists two forms of interactions between different views viewspecific interactions and crossview interactions in this paper we present a new neural architecture for multiview sequential learning called the memory fusion network mfn that explicitly accounts for both interactions in a neural architecture and continuously models them through time the first component of the mfn is called the system of lstms where viewspecific interactions are learned in isolation through assigning an lstm function to each view the crossview interactions are then identified using a special attention mechanism called the deltamemory attention network dman and summarized through time with a multiview gated memory through extensive experimentation mfn is compared to various proposed approaches for multiview sequential learning on multiple publicly available benchmark datasets mfn outperforms all the existing multiview approaches furthermore mfn outperforms all current stateoftheart models setting new stateoftheart results for these multiview datasets | [['multiview', 'sequential', 'learning', 'is', 'a', 'fundamental', 'problem', 'in', 'machine', 'learning', 'dealing', 'with', 'multiview', 'sequences', 'in', 'a', 'multiview', 'sequence', 'there', 'exists', 'two', 'forms', 'of', 'interactions', 'between', 'different', 'views', 'viewspecific', 'interactions', 'and', 'crossview', 'interactions', 'in', 'this', 'paper', 'we', 'present', 'a', 'new', 'neural', 'architecture', 'for', 'multiview', 'sequential', 'learning', 'called', 'the', 'memory', 'fusion', 'network', 'mfn', 'that', 'explicitly', 'accounts', 'for', 'both', 'interactions', 'in', 'a', 'neural', 'architecture', 'and', 'continuously', 'models', 'them', 'through', 'time', 'the', 'first', 'component', 'of', 'the', 'mfn', 'is', 'called', 'the', 'system', 'of', 'lstms', 'where', 'viewspecific', 'interactions', 'are', 'learned', 'in', 'isolation', 'through', 'assigning', 'an', 'lstm', 'function', 'to', 'each', 'view', 'the', 'crossview', 'interactions', 'are', 'then', 'identified', 'using', 'a', 'special', 'attention', 'mechanism', 'called', 'the', 'deltamemory', 'attention', 'network', 'dman', 'and', 'summarized', 'through', 'time', 'with', 'a', 'multiview', 'gated', 'memory', 'through', 'extensive', 'experimentation', 'mfn', 'is', 'compared', 'to', 'various', 'proposed', 'approaches', 'for', 'multiview', 'sequential', 'learning', 'on', 'multiple', 'publicly', 'available', 'benchmark', 'datasets', 'mfn', 'outperforms', 'all', 'the', 'existing', 'multiview', 'approaches', 'furthermore', 'mfn', 'outperforms', 'all', 'current', 'stateoftheart', 'models', 'setting', 'new', 'stateoftheart', 'results', 'for', 'these', 'multiview', 'datasets']] | [-0.09656343041045695, -0.0016549392638587745, -0.05459057391398465, 0.059747015665464615, -0.09611718976430549, -0.22813738249994306, -0.0015765425188015707, 0.49030080643592416, -0.30602160613891316, -0.2735797481102325, 0.023148289710277496, -0.29053997409946475, -0.21697271286700823, 0.17619101002071955, -0.10091736976358091, 0.07087182404071393, 0.16905156093843351, 0.05553691792200603, -0.07103311841561233, -0.29062747666091654, 0.32797905664763743, -0.0019837865101242935, 0.3573630908619821, -0.0097534088099734, 0.17701070602427912, 0.027816154721470678, -0.04298405176583932, 0.00690607344084535, -0.02625653130800054, 0.17065976575878883, 0.309908445178893, 0.21680637127112434, 0.3448957389463549, -0.40876744452701963, -0.24253202770888713, 0.08837034885326157, 0.13837677415066996, 0.07382330370894112, -0.05105707850900149, -0.3573378269092081, 0.055913881272922114, -0.20749836970013652, 0.1180368738266298, -0.1663280780113433, -0.0387883249045529, -0.02064676914850007, -0.31990037843446184, 0.009441853411241163, 0.05793801826997573, 0.05366408909665224, -0.06885017433799571, -0.12299014177312206, 0.029296134563122432, 0.16873768630642907, 0.03945160430482941, 0.06255356430058709, 0.1009119159465378, -0.1931897047632345, -0.21240709979782713, 0.3389502584887958, -0.06510190834487956, -0.2134733967257444, 0.22533411400172837, 0.04468307705011843, -0.20270263211604972, 0.07847168163601771, 0.23300387471283768, 0.12132172869599384, -0.19999385150785773, 0.03932168080669579, -0.07965087839720413, 0.13916368368631288, 0.00968462705427075, -0.013035546062710863, 0.17334823096778407, 0.32439344598454717, -0.0329144594305837, 0.13287146234601413, -0.148190409659664, -0.11927707464960606, -0.20279131138802836, -0.06364093555473023, -0.18687815770003596, -0.08369495418378815, -0.12572237074359607, -0.11194545758876556, 0.3963533477938693, 0.2396721480116364, 0.21590329627708896, 0.13247402127823496, 0.3967901468703593, -0.030735058252994687, 0.1517362829090981, 0.11462663703233651, 0.1555180585486178, -0.014096175544600216, 0.12194797398106004, -0.14669380036923133, 0.07073682568325973, 0.07420066005538756] |
1,802.00928 | NMR Evidence of Charge Fluctuations in Multiferroic CuBr2 | We report combined magnetic susceptibility, dielectric constant, nuclear
quadruple resonance (NQR) and zero-field nuclear magnetic resonance (NMR)
measurements on single crystals of multiferroics CuBr$_2$. High quality of the
sample is demonstrated by the sharp magnetic and magnetic-driven ferroelectric
transition at $T_N=T_C\approx$ 74~K. The zero-field $^{79}$Br and $^{81}$Br NMR
are resolved below $T_N$. The spin-lattice relaxation rates reveal charge
fluctuations when cooled below 60~K. Evidences of an increase of NMR linewidth,
a reduction of dielectric constant, and an increase of magnetic susceptibility
are also seen at low temperatures. These data suggest an emergent instability
which competes with the spiral magnetic ordering and the ferroelectricity.
Candidate mechanisms are discussed based on the quasi-one-dimensional (1D)
nature of the magnetic system.
| cond-mat.str-el | we report combined magnetic susceptibility dielectric constant nuclear quadruple resonance nqr and zerofield nuclear magnetic resonance nmr measurements on single crystals of multiferroics cubr_2 high quality of the sample is demonstrated by the sharp magnetic and magneticdriven ferroelectric transition at t_nt_capprox 74k the zerofield 79br and 81br nmr are resolved below t_n the spinlattice relaxation rates reveal charge fluctuations when cooled below 60k evidences of an increase of nmr linewidth a reduction of dielectric constant and an increase of magnetic susceptibility are also seen at low temperatures these data suggest an emergent instability which competes with the spiral magnetic ordering and the ferroelectricity candidate mechanisms are discussed based on the quasionedimensional 1d nature of the magnetic system | [['we', 'report', 'combined', 'magnetic', 'susceptibility', 'dielectric', 'constant', 'nuclear', 'quadruple', 'resonance', 'nqr', 'and', 'zerofield', 'nuclear', 'magnetic', 'resonance', 'nmr', 'measurements', 'on', 'single', 'crystals', 'of', 'multiferroics', 'cubr_2', 'high', 'quality', 'of', 'the', 'sample', 'is', 'demonstrated', 'by', 'the', 'sharp', 'magnetic', 'and', 'magneticdriven', 'ferroelectric', 'transition', 'at', 't_nt_capprox', '74k', 'the', 'zerofield', '79br', 'and', '81br', 'nmr', 'are', 'resolved', 'below', 't_n', 'the', 'spinlattice', 'relaxation', 'rates', 'reveal', 'charge', 'fluctuations', 'when', 'cooled', 'below', '60k', 'evidences', 'of', 'an', 'increase', 'of', 'nmr', 'linewidth', 'a', 'reduction', 'of', 'dielectric', 'constant', 'and', 'an', 'increase', 'of', 'magnetic', 'susceptibility', 'are', 'also', 'seen', 'at', 'low', 'temperatures', 'these', 'data', 'suggest', 'an', 'emergent', 'instability', 'which', 'competes', 'with', 'the', 'spiral', 'magnetic', 'ordering', 'and', 'the', 'ferroelectricity', 'candidate', 'mechanisms', 'are', 'discussed', 'based', 'on', 'the', 'quasionedimensional', '1d', 'nature', 'of', 'the', 'magnetic', 'system']] | [-0.1846297038085922, 0.23717922624136759, 0.03532016698450765, -0.01155053635879436, -0.07252331437714594, -0.13907336671183115, 0.0657730731917055, 0.4020728250932798, -0.23641909267385736, -0.3047856613830255, 0.029251044824425327, -0.3366791461185928, -0.06322949866380281, 0.2151648367968543, 0.1179793340464433, 0.0063270179824413434, -0.08425394091918542, 0.04049461679441217, -0.1231129861205996, -0.17152198758342643, 0.23802051864760487, 0.05495946333547564, 0.3292351474070497, 0.13257291847880798, 0.05305613190897642, -0.05430668472565645, 0.1688974601887534, 0.007433181775635795, -0.16518517108960895, -0.005714074553300937, 0.2446046427888959, -0.08218895063915274, 0.1539696694408919, -0.42044838739265716, -0.18832052027267454, -0.011807671684788116, 0.15073613194736504, 0.14126499357077768, -0.10373831924925182, -0.27996073970203533, 0.04235815359514386, -0.05388851729609693, -0.0979626668061093, -0.19606918042623683, -0.06672650829709151, 0.024048587225638983, -0.27821450249961854, 0.18859467603314234, 0.10044259287155512, 0.22054142285579523, -0.19596073923125995, -0.16930351845956848, 0.00673466584728949, -0.00934406839338695, 0.06582507163310718, 0.10930733231259837, 0.24471120547049827, -0.07901786447486334, -0.15206627552642635, 0.2732880616871019, -0.051176719306743494, 0.02704488596952471, 0.13011206335004158, -0.27231777826601866, -0.09017414495495982, 0.2379453525911167, 0.10694464061637982, 0.09338948296039905, -0.11321218660075254, 0.01703546948607382, 0.03248272786170179, 0.25213984731919736, 0.01724821526410156, 0.08507372320041452, 0.26033750737674144, 0.20185468596071332, -0.014820428246534184, 0.16292219353316909, -0.16415564105600902, -0.00861287004265346, -0.19991729041692197, -0.10405920605019976, -0.2261666686834743, 0.06237279361761339, -0.09429181085655919, -0.1669491989938379, 0.32392539277574733, 0.1068241151229462, 0.1901443501566351, -0.10460206271014422, 0.2587289414817892, 0.08158455554522541, 0.09528826615413684, 0.02922404995351507, 0.281460293913832, 0.26017183130974636, 0.1644073783567077, -0.42406628830276694, 0.07085754120111744, -0.07466481577629518] |
1,802.00929 | On OTFS Modulation for High-Doppler Fading Channels | Orthogonal time frequency space (OTFS) modulation is a 2-dimensional (2D)
modulation scheme designed in the delay-Doppler domain, unlike traditional
modulation schemes which are designed in the time-frequency domain. Through a
series of 2D transformations, OTFS converts a doubly-dispersive channel into an
almost non-fading channel in the delay-Doppler domain. In this domain, each
symbol in a frame experiences an almost constant fade, thus achieving
significant performance gains over existing modulation schemes such as OFDM.
The sparse delay-Doppler impulse response which reflects the actual physical
geometry of the wireless channel enables efficient channel estimation,
especially in high-Doppler fading channels. This paper investigates OTFS from a
signal detection and channel estimation perspective, and proposes a Markov
chain Monte-Carlo sampling based detection scheme and a pseudo-random noise
(PN) pilot based channel estimation scheme in the delay-Doppler domain.
| cs.IT math.IT | orthogonal time frequency space otfs modulation is a 2dimensional 2d modulation scheme designed in the delaydoppler domain unlike traditional modulation schemes which are designed in the timefrequency domain through a series of 2d transformations otfs converts a doublydispersive channel into an almost nonfading channel in the delaydoppler domain in this domain each symbol in a frame experiences an almost constant fade thus achieving significant performance gains over existing modulation schemes such as ofdm the sparse delaydoppler impulse response which reflects the actual physical geometry of the wireless channel enables efficient channel estimation especially in highdoppler fading channels this paper investigates otfs from a signal detection and channel estimation perspective and proposes a markov chain montecarlo sampling based detection scheme and a pseudorandom noise pn pilot based channel estimation scheme in the delaydoppler domain | [['orthogonal', 'time', 'frequency', 'space', 'otfs', 'modulation', 'is', 'a', '2dimensional', '2d', 'modulation', 'scheme', 'designed', 'in', 'the', 'delaydoppler', 'domain', 'unlike', 'traditional', 'modulation', 'schemes', 'which', 'are', 'designed', 'in', 'the', 'timefrequency', 'domain', 'through', 'a', 'series', 'of', '2d', 'transformations', 'otfs', 'converts', 'a', 'doublydispersive', 'channel', 'into', 'an', 'almost', 'nonfading', 'channel', 'in', 'the', 'delaydoppler', 'domain', 'in', 'this', 'domain', 'each', 'symbol', 'in', 'a', 'frame', 'experiences', 'an', 'almost', 'constant', 'fade', 'thus', 'achieving', 'significant', 'performance', 'gains', 'over', 'existing', 'modulation', 'schemes', 'such', 'as', 'ofdm', 'the', 'sparse', 'delaydoppler', 'impulse', 'response', 'which', 'reflects', 'the', 'actual', 'physical', 'geometry', 'of', 'the', 'wireless', 'channel', 'enables', 'efficient', 'channel', 'estimation', 'especially', 'in', 'highdoppler', 'fading', 'channels', 'this', 'paper', 'investigates', 'otfs', 'from', 'a', 'signal', 'detection', 'and', 'channel', 'estimation', 'perspective', 'and', 'proposes', 'a', 'markov', 'chain', 'montecarlo', 'sampling', 'based', 'detection', 'scheme', 'and', 'a', 'pseudorandom', 'noise', 'pn', 'pilot', 'based', 'channel', 'estimation', 'scheme', 'in', 'the', 'delaydoppler', 'domain']] | [-0.26109175361111703, 0.026474941844213402, -0.0827680970450226, -0.024856041280090585, -0.08209170668063812, -0.20915929760076665, 0.07950993565425865, 0.4527312838157317, -0.29282500200617806, -0.17891331486880108, 0.13104050228620803, -0.14656148176934375, -0.19168116480700279, 0.19353742874752647, -0.1267749320722668, 0.11472861282527447, 0.044078829012026914, 0.014597100940098832, -0.11243601577160389, -0.18653854975630915, 0.23666346652951456, 0.08328432369368025, 0.42769124920136836, -0.0874933333493265, 0.13895615380733534, 0.08793250255328708, -0.09887804682331539, -0.14204053174992287, -0.08476656422945075, 0.03399666019407262, 0.33373859109833165, 0.11506925651775603, 0.24846787924381128, -0.3585634573640239, -0.32296505360338923, 0.08095851835017477, 0.2151087877687235, 0.11697745358893522, -0.06755343526759346, -0.32814556490489866, 0.06702993014701326, -0.21540563657032535, 0.0540647948284623, 0.051805189602371106, -0.06943336840284835, -0.0008000418284725874, -0.3787682870336409, 0.04979421813881263, 0.07425893725533235, 0.04873109674711425, -0.04378249753210554, -0.11441907097578496, 0.10965418132526197, 0.13431755344460444, -0.016464618053917485, 0.04596462102494854, 0.07414967801091016, -0.06947797065586749, -0.11627206026359384, 0.3525280756718318, -0.07842507719629466, -0.3070290537859152, 0.11440728106133331, -0.10788214173959218, -0.011800412190167751, 0.2276658605490076, 0.26913871116064403, 0.05259077545059355, -0.16486706355975284, 0.05026009990667392, 0.012905968477445325, 0.2237420202061338, 0.10029390623050749, 0.14956768082552835, 0.130736938518423, 0.1990565918806128, 0.1282279287312614, 0.14093544919575965, -0.18166921179587567, -0.10581081997274064, -0.22446939737108865, -0.11781104057292013, -0.22876639895778345, -0.04039797221163386, -0.0823545818619957, -0.1383031345763825, 0.41295980899601836, 0.11287206623601514, 0.12757368403297842, 0.04710877512903758, 0.43456300325635683, 0.09671861214713803, 0.03282105741511218, 0.09161493072594355, 0.12937598286592228, 0.13078454176762275, 0.13294213542389102, -0.18742156485305692, 0.03937504257432612, 0.016625811634287658] |
1,802.0093 | Mixed Precision Training of Convolutional Neural Networks using Integer
Operations | The state-of-the-art (SOTA) for mixed precision training is dominated by
variants of low precision floating point operations, and in particular, FP16
accumulating into FP32 Micikevicius et al. (2017). On the other hand, while a
lot of research has also happened in the domain of low and mixed-precision
Integer training, these works either present results for non-SOTA networks (for
instance only AlexNet for ImageNet-1K), or relatively small datasets (like
CIFAR-10). In this work, we train state-of-the-art visual understanding neural
networks on the ImageNet-1K dataset, with Integer operations on General Purpose
(GP) hardware. In particular, we focus on Integer Fused-Multiply-and-Accumulate
(FMA) operations which take two pairs of INT16 operands and accumulate results
into an INT32 output.We propose a shared exponent representation of tensors and
develop a Dynamic Fixed Point (DFP) scheme suitable for common neural network
operations. The nuances of developing an efficient integer convolution kernel
is examined, including methods to handle overflow of the INT32 accumulator. We
implement CNN training for ResNet-50, GoogLeNet-v1, VGG-16 and AlexNet; and
these networks achieve or exceed SOTA accuracy within the same number of
iterations as their FP32 counterparts without any change in hyper-parameters
and with a 1.8X improvement in end-to-end training throughput. To the best of
our knowledge these results represent the first INT16 training results on GP
hardware for ImageNet-1K dataset using SOTA CNNs and achieve highest reported
accuracy using half-precision
| cs.NE cs.LG cs.NA | the stateoftheart sota for mixed precision training is dominated by variants of low precision floating point operations and in particular fp16 accumulating into fp32 micikevicius et al 2017 on the other hand while a lot of research has also happened in the domain of low and mixedprecision integer training these works either present results for nonsota networks for instance only alexnet for imagenet1k or relatively small datasets like cifar10 in this work we train stateoftheart visual understanding neural networks on the imagenet1k dataset with integer operations on general purpose gp hardware in particular we focus on integer fusedmultiplyandaccumulate fma operations which take two pairs of int16 operands and accumulate results into an int32 outputwe propose a shared exponent representation of tensors and develop a dynamic fixed point dfp scheme suitable for common neural network operations the nuances of developing an efficient integer convolution kernel is examined including methods to handle overflow of the int32 accumulator we implement cnn training for resnet50 googlenetv1 vgg16 and alexnet and these networks achieve or exceed sota accuracy within the same number of iterations as their fp32 counterparts without any change in hyperparameters and with a 18x improvement in endtoend training throughput to the best of our knowledge these results represent the first int16 training results on gp hardware for imagenet1k dataset using sota cnns and achieve highest reported accuracy using halfprecision | [['the', 'stateoftheart', 'sota', 'for', 'mixed', 'precision', 'training', 'is', 'dominated', 'by', 'variants', 'of', 'low', 'precision', 'floating', 'point', 'operations', 'and', 'in', 'particular', 'fp16', 'accumulating', 'into', 'fp32', 'micikevicius', 'et', 'al', '2017', 'on', 'the', 'other', 'hand', 'while', 'a', 'lot', 'of', 'research', 'has', 'also', 'happened', 'in', 'the', 'domain', 'of', 'low', 'and', 'mixedprecision', 'integer', 'training', 'these', 'works', 'either', 'present', 'results', 'for', 'nonsota', 'networks', 'for', 'instance', 'only', 'alexnet', 'for', 'imagenet1k', 'or', 'relatively', 'small', 'datasets', 'like', 'cifar10', 'in', 'this', 'work', 'we', 'train', 'stateoftheart', 'visual', 'understanding', 'neural', 'networks', 'on', 'the', 'imagenet1k', 'dataset', 'with', 'integer', 'operations', 'on', 'general', 'purpose', 'gp', 'hardware', 'in', 'particular', 'we', 'focus', 'on', 'integer', 'fusedmultiplyandaccumulate', 'fma', 'operations', 'which', 'take', 'two', 'pairs', 'of', 'int16', 'operands', 'and', 'accumulate', 'results', 'into', 'an', 'int32', 'outputwe', 'propose', 'a', 'shared', 'exponent', 'representation', 'of', 'tensors', 'and', 'develop', 'a', 'dynamic', 'fixed', 'point', 'dfp', 'scheme', 'suitable', 'for', 'common', 'neural', 'network', 'operations', 'the', 'nuances', 'of', 'developing', 'an', 'efficient', 'integer', 'convolution', 'kernel', 'is', 'examined', 'including', 'methods', 'to', 'handle', 'overflow', 'of', 'the', 'int32', 'accumulator', 'we', 'implement', 'cnn', 'training', 'for', 'resnet50', 'googlenetv1', 'vgg16', 'and', 'alexnet', 'and', 'these', 'networks', 'achieve', 'or', 'exceed', 'sota', 'accuracy', 'within', 'the', 'same', 'number', 'of', 'iterations', 'as', 'their', 'fp32', 'counterparts', 'without', 'any', 'change', 'in', 'hyperparameters', 'and', 'with', 'a', '18x', 'improvement', 'in', 'endtoend', 'training', 'throughput', 'to', 'the', 'best', 'of', 'our', 'knowledge', 'these', 'results', 'represent', 'the', 'first', 'int16', 'training', 'results', 'on', 'gp', 'hardware', 'for', 'imagenet1k', 'dataset', 'using', 'sota', 'cnns', 'and', 'achieve', 'highest', 'reported', 'accuracy', 'using', 'halfprecision']] | [-0.08174979028509878, 0.010452085910626654, -0.005885389742415843, 0.03569757839752395, -0.07746507087535516, -0.19945144546090052, 0.07590356835542213, 0.43156970143659945, -0.20886129731901948, -0.35879540007663974, 0.10528980160384106, -0.24056727092162014, -0.15622618287126747, 0.2416667508339475, -0.13335751913757055, 0.09584756117365366, 0.16928036509449124, 0.03452235219974515, -0.10499881141938679, -0.3559068238395779, 0.2672372609953644, 0.04580884608717789, 0.3042189220261307, 0.00800479557220995, 0.12996181498982284, -0.04398256254066295, -0.01236861077331673, -0.04520373829958721, -0.04262825494649213, 0.14238206104379694, 0.27282476462338356, 0.16291023528741655, 0.30837420430974266, -0.45336518713503804, -0.19759252537554556, 0.09195698805179002, 0.1169068554561551, 0.08387484006727074, -0.01325780058860642, -0.30116008175964204, 0.1360761114335277, -0.18007206917663507, 0.0284346940525628, -0.1549482660019907, 0.006419870536774397, 0.011807799287761963, -0.2934339702419868, 0.018798880617496336, 0.09558452180813178, 0.09306048063151197, -0.0330860857561299, -0.19508648007935117, 0.03865737028509686, 0.13417840443141416, -0.03398894880622745, 0.08062883157714552, 0.11177073497914734, -0.18433080963820322, -0.17097998451902915, 0.3413030534786164, -0.06014197641864009, -0.18856287989423637, 0.18814618346802547, -0.03218738408225221, -0.16877996930510764, 0.0946819964911652, 0.25654625350720495, 0.1032358469054906, -0.09858279873044529, 0.03608173035846707, -0.013440006186143248, 0.19301899700610334, 0.10489081679800645, 0.0020263736808983344, 0.11811462528533687, 0.26888733136746257, 0.021823870814892003, 0.12303783394499904, -0.12991155414637007, -0.06673693190752784, -0.20776256790316297, -0.123564338837024, -0.21109850844581945, -0.009856522140355348, -0.10375064930722647, -0.12201893643407714, 0.3807438196045625, 0.19545734339872153, 0.19835202957059198, 0.14701996425459365, 0.34407763659031293, 0.024458875185425537, 0.136064510167696, 0.1521476173496788, 0.1669746149638924, -0.005307739120993524, 0.11034401600492623, -0.1513734897000527, 0.05403679238692131, 0.047595827951744295] |
1,802.00931 | Deep Learning Framework for Multi-class Breast Cancer Histology Image
Classification | In this work, we present a deep learning framework for multi-class breast
cancer image classification as our submission to the International Conference
on Image Analysis and Recognition (ICIAR) 2018 Grand Challenge on BreAst Cancer
Histology images (BACH). As these histology images are too large to fit into
GPU memory, we first propose using Inception V3 to perform patch level
classification. The patch level predictions are then passed through an ensemble
fusion framework involving majority voting, gradient boosting machine (GBM),
and logistic regression to obtain the image level prediction. We improve the
sensitivity of the Normal and Benign predicted classes by designing a Dual Path
Network (DPN) to be used as a feature extractor where these extracted features
are further sent to a second layer of ensemble prediction fusion using GBM,
logistic regression, and support vector machine (SVM) to refine predictions.
Experimental results demonstrate our framework shows a 12.5$\%$ improvement
over the state-of-the-art model.
| cs.CV | in this work we present a deep learning framework for multiclass breast cancer image classification as our submission to the international conference on image analysis and recognition iciar 2018 grand challenge on breast cancer histology images bach as these histology images are too large to fit into gpu memory we first propose using inception v3 to perform patch level classification the patch level predictions are then passed through an ensemble fusion framework involving majority voting gradient boosting machine gbm and logistic regression to obtain the image level prediction we improve the sensitivity of the normal and benign predicted classes by designing a dual path network dpn to be used as a feature extractor where these extracted features are further sent to a second layer of ensemble prediction fusion using gbm logistic regression and support vector machine svm to refine predictions experimental results demonstrate our framework shows a 125 improvement over the stateoftheart model | [['in', 'this', 'work', 'we', 'present', 'a', 'deep', 'learning', 'framework', 'for', 'multiclass', 'breast', 'cancer', 'image', 'classification', 'as', 'our', 'submission', 'to', 'the', 'international', 'conference', 'on', 'image', 'analysis', 'and', 'recognition', 'iciar', '2018', 'grand', 'challenge', 'on', 'breast', 'cancer', 'histology', 'images', 'bach', 'as', 'these', 'histology', 'images', 'are', 'too', 'large', 'to', 'fit', 'into', 'gpu', 'memory', 'we', 'first', 'propose', 'using', 'inception', 'v3', 'to', 'perform', 'patch', 'level', 'classification', 'the', 'patch', 'level', 'predictions', 'are', 'then', 'passed', 'through', 'an', 'ensemble', 'fusion', 'framework', 'involving', 'majority', 'voting', 'gradient', 'boosting', 'machine', 'gbm', 'and', 'logistic', 'regression', 'to', 'obtain', 'the', 'image', 'level', 'prediction', 'we', 'improve', 'the', 'sensitivity', 'of', 'the', 'normal', 'and', 'benign', 'predicted', 'classes', 'by', 'designing', 'a', 'dual', 'path', 'network', 'dpn', 'to', 'be', 'used', 'as', 'a', 'feature', 'extractor', 'where', 'these', 'extracted', 'features', 'are', 'further', 'sent', 'to', 'a', 'second', 'layer', 'of', 'ensemble', 'prediction', 'fusion', 'using', 'gbm', 'logistic', 'regression', 'and', 'support', 'vector', 'machine', 'svm', 'to', 'refine', 'predictions', 'experimental', 'results', 'demonstrate', 'our', 'framework', 'shows', 'a', '125', 'improvement', 'over', 'the', 'stateoftheart', 'model']] | [0.03670559374895555, -0.043631646324773135, -0.06702048298933246, 0.07790801889229615, -0.08129629600805298, -0.1954989090190555, 0.0449913327986971, 0.4239660133090284, -0.24822628007763448, -0.32089221042282634, 0.1037277742372704, -0.2917957833033737, -0.19092149630164593, 0.1901600707514102, -0.12793833410726507, 0.1177764522216855, 0.17379710542683313, 0.056825517877644185, -0.021259721152460068, -0.3542382481014904, 0.24858245212627345, 0.09275119792912734, 0.38854097435555235, -0.0026976032048658606, 0.1164885115873653, -0.006645487619524371, -0.05403933993972886, -0.02566450283317857, -0.03630028202448016, 0.1867636437649577, 0.3420457494326447, 0.19697003328284018, 0.31447720428125236, -0.38264063020157657, -0.2233596375437295, 0.08100551587766876, 0.12025340423414033, 0.08655665679743477, -0.018755578104211808, -0.35273985176353284, 0.09020281133768483, -0.18592727506301956, 0.022616432227949106, -0.1642899951605382, -0.05704921736454993, -0.04517759649947383, -0.3050590050802414, 0.08272242931393432, 0.04959742522804566, 0.09459560247004227, -0.10237110047327246, -0.13325537831881662, 0.02919316886820727, 0.14127058338897389, 0.033702568771177596, 0.12432065194307558, 0.19172845443073824, -0.20098642284032323, -0.17506478054546357, 0.3395083967224981, -0.08389747260881016, -0.14787034345242908, 0.1810563720878384, -0.014160853819739, -0.1584062179507919, 0.08592753611456334, 0.28299552641711595, 0.059315350746699406, -0.16022604342330904, -0.04516665911868069, -0.02805104640805546, 0.1803489795024659, 0.06764738798579749, -0.10378547090416153, 0.17193332369717218, 0.287339675830665, -0.05365228522690683, 0.13644251768900312, -0.22911067501173105, -0.04483036811652234, -0.25807178824275634, -0.13101543563728532, -0.13127233979164385, -0.023436202033836808, -0.07123985289393626, -0.16068976573962596, 0.40243516692563014, 0.20854768554186998, 0.2385074426998305, 0.13229389682874765, 0.32208138750866055, 0.013422158994906721, 0.14051700183768678, 0.05538742938094158, 0.2041036522729639, 0.04311495125324167, 0.07638690372916802, -0.13934199306432896, 0.036401490677829856, 0.12370508894768659] |
1,802.00932 | Demand-driven Alias Analysis : Formalizing Bidirectional Analyses for
Soundness and Precision | A demand-driven approach to program analysis have been viewed as efficient
algorithms to compute only the information required to serve a target demand.
In contrast, an exhaustive approach computes all information in anticipation of
it being used later. However, for a given set of demands, demand-driven methods
are believed to compute the same information that would be computed by the
corresponding exhaustive methods. We investigate the precision and
bidirectional nature of demand-driven methods and show that:
(a) demand-driven methods can be formalized inherently as bidirectional data
flow analysis because the demands are propagated against the control flow and
the information to satisfy the demands is propagated along the control flow. We
extend the formalization of the Meet Over Paths solution to bidirectional
flows. This formalization helps us to prove the soundness and precision of our
analysis, and
(b) since a demand-driven method computes only the required information to
meet a demand, it should be able to reduce the imprecision caused by data
abstractions and hence should be more precise than an exhaustive method. We
show that while this is indeed the case with Java, for C/C++, the precision
critically hinges on how indirect assignments are handled. We use this insight
and propose a demand-driven alias analysis that is more precise than an
exhaustive analysis for C/C++ too.
We have chosen devirtualization as an application. Our measurements show that
our method is not only more efficient but more precise than the existing
demand-driven method, as well as the corresponding exhaustive method.
| cs.PL | a demanddriven approach to program analysis have been viewed as efficient algorithms to compute only the information required to serve a target demand in contrast an exhaustive approach computes all information in anticipation of it being used later however for a given set of demands demanddriven methods are believed to compute the same information that would be computed by the corresponding exhaustive methods we investigate the precision and bidirectional nature of demanddriven methods and show that a demanddriven methods can be formalized inherently as bidirectional data flow analysis because the demands are propagated against the control flow and the information to satisfy the demands is propagated along the control flow we extend the formalization of the meet over paths solution to bidirectional flows this formalization helps us to prove the soundness and precision of our analysis and b since a demanddriven method computes only the required information to meet a demand it should be able to reduce the imprecision caused by data abstractions and hence should be more precise than an exhaustive method we show that while this is indeed the case with java for cc the precision critically hinges on how indirect assignments are handled we use this insight and propose a demanddriven alias analysis that is more precise than an exhaustive analysis for cc too we have chosen devirtualization as an application our measurements show that our method is not only more efficient but more precise than the existing demanddriven method as well as the corresponding exhaustive method | [['a', 'demanddriven', 'approach', 'to', 'program', 'analysis', 'have', 'been', 'viewed', 'as', 'efficient', 'algorithms', 'to', 'compute', 'only', 'the', 'information', 'required', 'to', 'serve', 'a', 'target', 'demand', 'in', 'contrast', 'an', 'exhaustive', 'approach', 'computes', 'all', 'information', 'in', 'anticipation', 'of', 'it', 'being', 'used', 'later', 'however', 'for', 'a', 'given', 'set', 'of', 'demands', 'demanddriven', 'methods', 'are', 'believed', 'to', 'compute', 'the', 'same', 'information', 'that', 'would', 'be', 'computed', 'by', 'the', 'corresponding', 'exhaustive', 'methods', 'we', 'investigate', 'the', 'precision', 'and', 'bidirectional', 'nature', 'of', 'demanddriven', 'methods', 'and', 'show', 'that', 'a', 'demanddriven', 'methods', 'can', 'be', 'formalized', 'inherently', 'as', 'bidirectional', 'data', 'flow', 'analysis', 'because', 'the', 'demands', 'are', 'propagated', 'against', 'the', 'control', 'flow', 'and', 'the', 'information', 'to', 'satisfy', 'the', 'demands', 'is', 'propagated', 'along', 'the', 'control', 'flow', 'we', 'extend', 'the', 'formalization', 'of', 'the', 'meet', 'over', 'paths', 'solution', 'to', 'bidirectional', 'flows', 'this', 'formalization', 'helps', 'us', 'to', 'prove', 'the', 'soundness', 'and', 'precision', 'of', 'our', 'analysis', 'and', 'b', 'since', 'a', 'demanddriven', 'method', 'computes', 'only', 'the', 'required', 'information', 'to', 'meet', 'a', 'demand', 'it', 'should', 'be', 'able', 'to', 'reduce', 'the', 'imprecision', 'caused', 'by', 'data', 'abstractions', 'and', 'hence', 'should', 'be', 'more', 'precise', 'than', 'an', 'exhaustive', 'method', 'we', 'show', 'that', 'while', 'this', 'is', 'indeed', 'the', 'case', 'with', 'java', 'for', 'cc', 'the', 'precision', 'critically', 'hinges', 'on', 'how', 'indirect', 'assignments', 'are', 'handled', 'we', 'use', 'this', 'insight', 'and', 'propose', 'a', 'demanddriven', 'alias', 'analysis', 'that', 'is', 'more', 'precise', 'than', 'an', 'exhaustive', 'analysis', 'for', 'cc', 'too', 'we', 'have', 'chosen', 'devirtualization', 'as', 'an', 'application', 'our', 'measurements', 'show', 'that', 'our', 'method', 'is', 'not', 'only', 'more', 'efficient', 'but', 'more', 'precise', 'than', 'the', 'existing', 'demanddriven', 'method', 'as', 'well', 'as', 'the', 'corresponding', 'exhaustive', 'method']] | [-0.07626209760469994, 0.020369551315890574, -0.11736879383314805, 0.09464072219393474, -0.11912105923488617, -0.15484302416705642, 0.07607681744069938, 0.3942505680174234, -0.26712350719926287, -0.3292555420801612, 0.14723301242128103, -0.24628142525916957, -0.1291471320488696, 0.24819033733946852, -0.08461540171420238, 0.0579623036782246, 0.10519320789180382, 0.04828194771747453, -0.05553307351478596, -0.24348486136891936, 0.2367418450186785, 0.0727824715272819, 0.3020398051994123, 0.04110122430998548, 0.06191229340283716, 0.01199438479583127, -0.08550946298359247, 0.05088360771073036, -0.09830282416069495, 0.16933651443899994, 0.27723031184586033, 0.1997911219885311, 0.28374723427326803, -0.43827009054805977, -0.2005692040005392, 0.10348614490961275, 0.1648041247544496, 0.13749892358017346, -0.004558909705692311, -0.2607751793020506, 0.13831762025828462, -0.16794785211259222, -0.06959568939347523, -0.16296530173564083, -0.015188991406326553, 0.00016381563623454496, -0.28719533169941314, 0.00826748659138579, 0.05949183595394828, 0.02702698121258772, -0.02185032598442402, -0.0730990447799664, -0.017868731512190646, 0.15004388431468643, 0.02826403468018809, 0.05116902073577839, 0.11494283762435059, -0.09611759617659132, -0.13288646077654925, 0.39729853917706204, -0.024831210170443783, -0.22331049547616735, 0.17939770424992862, -0.041403979532435596, -0.12967404510440628, 0.12644810194288003, 0.17896759736968343, 0.14602514198536135, -0.1654001942248529, 0.005089955410244968, -0.021224655353101383, 0.20517337610374717, 0.02083824305275238, 0.03502256003391731, 0.17221254797888, 0.19343307555230507, 0.11043083387610304, 0.10302695572136875, -0.04465688906336974, -0.07255407966239505, -0.26881332741115227, -0.16976840367697807, -0.15828361620810483, -0.001663114045628339, -0.028104201662431313, -0.13299172721152386, 0.34094437180894865, 0.2241984359474935, 0.1757937578576052, 0.07978672597063594, 0.354249852111032, 0.09540747789051018, 0.10083593658277257, 0.12383039276598089, 0.22786499018758447, 0.06252291206790352, 0.11916428667387502, -0.1747636432033855, 0.11832365467321561, 0.040798008973040735] |
1,802.00933 | The $L_p$ dual Minkowski problem for $p>1$ and $q>0$ | General $L_p$ dual curvature measures have recently been introduced by
Lutwak, Yang and Zhang. These new measures unify several other geometric
measures of the Brunn-Minkowski theory and the dual Brunn-Minkowski theory.
$L_p$ dual curvature measures arise from $q$th dual inrinsic volumes by means
of Alexandrov-type variational formulas. Lutwak, Yang and Zhang formulated the
$L_p$ dual Minkowski problem, which concerns the characterization of $L_p$ dual
curvature measures. In this paper, we solve the existence part of the $L_{p}$
dual Minkowski problem for $p>1$ and $q>0$, and we also discuss the regularity
of the solution.
| math.AP | general l_p dual curvature measures have recently been introduced by lutwak yang and zhang these new measures unify several other geometric measures of the brunnminkowski theory and the dual brunnminkowski theory l_p dual curvature measures arise from qth dual inrinsic volumes by means of alexandrovtype variational formulas lutwak yang and zhang formulated the l_p dual minkowski problem which concerns the characterization of l_p dual curvature measures in this paper we solve the existence part of the l_p dual minkowski problem for p1 and q0 and we also discuss the regularity of the solution | [['general', 'l_p', 'dual', 'curvature', 'measures', 'have', 'recently', 'been', 'introduced', 'by', 'lutwak', 'yang', 'and', 'zhang', 'these', 'new', 'measures', 'unify', 'several', 'other', 'geometric', 'measures', 'of', 'the', 'brunnminkowski', 'theory', 'and', 'the', 'dual', 'brunnminkowski', 'theory', 'l_p', 'dual', 'curvature', 'measures', 'arise', 'from', 'qth', 'dual', 'inrinsic', 'volumes', 'by', 'means', 'of', 'alexandrovtype', 'variational', 'formulas', 'lutwak', 'yang', 'and', 'zhang', 'formulated', 'the', 'l_p', 'dual', 'minkowski', 'problem', 'which', 'concerns', 'the', 'characterization', 'of', 'l_p', 'dual', 'curvature', 'measures', 'in', 'this', 'paper', 'we', 'solve', 'the', 'existence', 'part', 'of', 'the', 'l_p', 'dual', 'minkowski', 'problem', 'for', 'p1', 'and', 'q0', 'and', 'we', 'also', 'discuss', 'the', 'regularity', 'of', 'the', 'solution']] | [-0.06850435291119578, 0.08291374208815598, -0.07870055161133084, 0.16275717930883452, -0.08177278759767828, -0.12289951060412695, -0.04550025251861054, 0.27115901762052724, -0.3226798021679987, -0.19393306859242526, 0.14242033617262242, -0.29413073460328515, -0.211895408838957, 0.13814890946504538, -0.19998672583729468, 0.11880879560687943, -0.08471227520266952, -0.0008104563812198846, -0.1349620319619451, -0.28997845300878194, 0.3382765649635669, 0.014482477922802385, 0.30310510283680225, 0.12213566150318872, 0.10526841240125182, 0.030640765245113034, -0.053756324037828526, 0.08736544233017965, -0.2788579373205405, 0.22448265250600147, 0.23975071107017118, 0.16053727565525586, 0.29287366754294414, -0.38400987726028846, -0.2516879485641687, 0.1601696697914082, 0.0624581359974716, -0.015198028764090217, -0.041008932607086455, -0.3065665298667939, 0.053671445900007435, -0.09473560301020094, -0.16389116402917905, -0.08482427670083859, 0.02768741501495242, 0.023027576273307204, -0.22964322283540084, 0.13219540028892326, 0.11189003329238166, 0.031659981407953994, -0.17269416628709144, -0.14469160910812207, 0.016345759574070817, 0.025097139630183254, 0.09133170431722766, 0.12087314633612076, 0.002630862929989867, -0.06036452186526731, -0.21126001721005078, 0.28635712030708144, -0.02800050759991712, -0.27516266515316523, 0.10534684798546115, -0.13708219204462416, -0.1525507732446346, 0.037779979650741036, 0.11536983144469559, 0.177694135722603, -0.10160303913542758, 0.2282425116149091, -0.10530280027255091, 0.0006466184985702453, 0.1675533569296417, 0.0970605249477439, 0.07806518337810817, 0.0005996829992079216, 0.12800367168181212, 0.1774908218883312, -0.023051510357459927, -0.13481792474529988, -0.33670702906649397, -0.22452600174785955, -0.17642156147074117, 0.07738524617667755, -0.15929225803167513, -0.12036278121092397, 0.3147014016589231, 0.05682521387833454, 0.1350575916307128, 0.09761364409654483, 0.1714008741875422, 0.11179848383788181, -0.006562657372737506, 0.07434566592564806, 0.24491845539001666, 0.24164178863461333, 0.08539658636057182, -0.17030589850416974, -0.06923577351414639, 0.3175850769494782] |
1,802.00934 | Incorporating Literals into Knowledge Graph Embeddings | Knowledge graphs, on top of entities and their relationships, contain other
important elements: literals. Literals encode interesting properties (e.g. the
height) of entities that are not captured by links between entities alone. Most
of the existing work on embedding (or latent feature) based knowledge graph
analysis focuses mainly on the relations between entities. In this work, we
study the effect of incorporating literal information into existing link
prediction methods. Our approach, which we name LiteralE, is an extension that
can be plugged into existing latent feature methods. LiteralE merges entity
embeddings with their literal information using a learnable, parametrized
function, such as a simple linear or nonlinear transformation, or a multilayer
neural network. We extend several popular embedding models based on LiteralE
and evaluate their performance on the task of link prediction. Despite its
simplicity, LiteralE proves to be an effective way to incorporate literal
information into existing embedding based methods, improving their performance
on different standard datasets, which we augmented with their literals and
provide as testbed for further research.
| cs.AI stat.ML | knowledge graphs on top of entities and their relationships contain other important elements literals literals encode interesting properties eg the height of entities that are not captured by links between entities alone most of the existing work on embedding or latent feature based knowledge graph analysis focuses mainly on the relations between entities in this work we study the effect of incorporating literal information into existing link prediction methods our approach which we name literale is an extension that can be plugged into existing latent feature methods literale merges entity embeddings with their literal information using a learnable parametrized function such as a simple linear or nonlinear transformation or a multilayer neural network we extend several popular embedding models based on literale and evaluate their performance on the task of link prediction despite its simplicity literale proves to be an effective way to incorporate literal information into existing embedding based methods improving their performance on different standard datasets which we augmented with their literals and provide as testbed for further research | [['knowledge', 'graphs', 'on', 'top', 'of', 'entities', 'and', 'their', 'relationships', 'contain', 'other', 'important', 'elements', 'literals', 'literals', 'encode', 'interesting', 'properties', 'eg', 'the', 'height', 'of', 'entities', 'that', 'are', 'not', 'captured', 'by', 'links', 'between', 'entities', 'alone', 'most', 'of', 'the', 'existing', 'work', 'on', 'embedding', 'or', 'latent', 'feature', 'based', 'knowledge', 'graph', 'analysis', 'focuses', 'mainly', 'on', 'the', 'relations', 'between', 'entities', 'in', 'this', 'work', 'we', 'study', 'the', 'effect', 'of', 'incorporating', 'literal', 'information', 'into', 'existing', 'link', 'prediction', 'methods', 'our', 'approach', 'which', 'we', 'name', 'literale', 'is', 'an', 'extension', 'that', 'can', 'be', 'plugged', 'into', 'existing', 'latent', 'feature', 'methods', 'literale', 'merges', 'entity', 'embeddings', 'with', 'their', 'literal', 'information', 'using', 'a', 'learnable', 'parametrized', 'function', 'such', 'as', 'a', 'simple', 'linear', 'or', 'nonlinear', 'transformation', 'or', 'a', 'multilayer', 'neural', 'network', 'we', 'extend', 'several', 'popular', 'embedding', 'models', 'based', 'on', 'literale', 'and', 'evaluate', 'their', 'performance', 'on', 'the', 'task', 'of', 'link', 'prediction', 'despite', 'its', 'simplicity', 'literale', 'proves', 'to', 'be', 'an', 'effective', 'way', 'to', 'incorporate', 'literal', 'information', 'into', 'existing', 'embedding', 'based', 'methods', 'improving', 'their', 'performance', 'on', 'different', 'standard', 'datasets', 'which', 'we', 'augmented', 'with', 'their', 'literals', 'and', 'provide', 'as', 'testbed', 'for', 'further', 'research']] | [-0.05103765017957541, -0.0003499540155522024, -0.07833757990023546, 0.08419930389864447, -0.19662434644506951, -0.16643729540710037, 0.0727543456157212, 0.4252502084845862, -0.3031449911507304, -0.33686451117742305, 0.06904762964464223, -0.3018605442205716, -0.17876591193435268, 0.16647895672413035, -0.07205342430191125, 0.030076311092772003, 0.08594057563221223, 0.09000063728461483, -0.07326023978840189, -0.25046364569642354, 0.3506656699318286, 0.011577921886846684, 0.3104561638013509, 0.056216722030236185, 0.1139249113634806, 0.014847680539633448, -0.08522857906846928, 0.026030212759900816, -0.07044368820601586, 0.21188542438801705, 0.287941618299263, 0.23274059186836607, 0.2727949527005137, -0.4584050105601462, -0.24027469573945504, 0.08139183868964513, 0.12056635162677025, 0.06440078749735663, 0.002853315523743891, -0.3337186977474226, 0.0644199154394194, -0.17290837157038394, 0.033669888371416525, -0.13692490188595538, -0.03137936486388769, 0.0051316565041544665, -0.22592780926695083, 0.011503964998075645, 0.15702039519139724, 0.06600951578206661, -0.02324425871688219, -0.14519144571522435, 0.004221444125750173, 0.17565041754805968, 0.025637880776299447, 0.01471225303742076, 0.13843632416189675, -0.14896098774961128, -0.16711840721981447, 0.3677292590557832, -0.05677181640394816, -0.26237409849438753, 0.1973090087382221, 0.014235044739682946, -0.16222765087150037, 0.0451816019536764, 0.20409570474379113, 0.10424850452609621, -0.174622784338739, 0.04131658400214217, -0.044951038944333443, 0.19711365239770964, 0.04761936439321055, 0.06465526519626466, 0.22173372395500018, 0.2174613716690774, -0.0005208100818576869, 0.11690287813115374, -0.04717093858083612, -0.09166267607701474, -0.20294049651530838, -0.13338025333598114, -0.1747948932597599, -0.03033241885841881, -0.12399786364871597, -0.15717389508373816, 0.3933850545436144, 0.21047267018087557, 0.23727617257857445, 0.07940789081880616, 0.3296825543293806, 0.0370739026870633, 0.12920049901599642, 0.08057912391826935, 0.14722962041347348, 0.05854041073525771, 0.04476871252691711, -0.11464148302622444, 0.1287205097107598, 0.11335994288657536] |
1,802.00935 | Randomness induced spin-liquid-like phase in the spin-$1/2$ $J_1 - J_2$
triangular Heisenberg model | We study the effects of bond randomness in the spin-$1/2$ $J_1-J_2$
triangular Heisenberg model using exact diagonalization and density matrix
renormalization group. With increasing bond randomness, we identify a
randomness induced spin-liquid-like phase without any magnetic order, dimer
order, spin glass order, or valence-bond glass order. The finite-size scaling
of gaps suggests the gapless nature of both spin triplet and singlet
excitations, which is further supported by the broad continuum of dynamical
spin structure factor. By studying the bipartite entanglement spectrum of the
system on cylinder geometry, we identify the features of the low-lying
entanglement spectrum in the spin-liquid-like phase, which may distinguish this
randomness induced spin-liquid-like phase and the intrinsic spin liquid phase
in the clean $J_1 - J_2$ triangular Heisenberg model. We further discuss the
nature of this spin-liquid-like phase and the indication of our results for
understanding spin-liquid-like materials with triangular-lattice structure.
| cond-mat.str-el | we study the effects of bond randomness in the spin12 j_1j_2 triangular heisenberg model using exact diagonalization and density matrix renormalization group with increasing bond randomness we identify a randomness induced spinliquidlike phase without any magnetic order dimer order spin glass order or valencebond glass order the finitesize scaling of gaps suggests the gapless nature of both spin triplet and singlet excitations which is further supported by the broad continuum of dynamical spin structure factor by studying the bipartite entanglement spectrum of the system on cylinder geometry we identify the features of the lowlying entanglement spectrum in the spinliquidlike phase which may distinguish this randomness induced spinliquidlike phase and the intrinsic spin liquid phase in the clean j_1 j_2 triangular heisenberg model we further discuss the nature of this spinliquidlike phase and the indication of our results for understanding spinliquidlike materials with triangularlattice structure | [['we', 'study', 'the', 'effects', 'of', 'bond', 'randomness', 'in', 'the', 'spin12', 'j_1j_2', 'triangular', 'heisenberg', 'model', 'using', 'exact', 'diagonalization', 'and', 'density', 'matrix', 'renormalization', 'group', 'with', 'increasing', 'bond', 'randomness', 'we', 'identify', 'a', 'randomness', 'induced', 'spinliquidlike', 'phase', 'without', 'any', 'magnetic', 'order', 'dimer', 'order', 'spin', 'glass', 'order', 'or', 'valencebond', 'glass', 'order', 'the', 'finitesize', 'scaling', 'of', 'gaps', 'suggests', 'the', 'gapless', 'nature', 'of', 'both', 'spin', 'triplet', 'and', 'singlet', 'excitations', 'which', 'is', 'further', 'supported', 'by', 'the', 'broad', 'continuum', 'of', 'dynamical', 'spin', 'structure', 'factor', 'by', 'studying', 'the', 'bipartite', 'entanglement', 'spectrum', 'of', 'the', 'system', 'on', 'cylinder', 'geometry', 'we', 'identify', 'the', 'features', 'of', 'the', 'lowlying', 'entanglement', 'spectrum', 'in', 'the', 'spinliquidlike', 'phase', 'which', 'may', 'distinguish', 'this', 'randomness', 'induced', 'spinliquidlike', 'phase', 'and', 'the', 'intrinsic', 'spin', 'liquid', 'phase', 'in', 'the', 'clean', 'j_1', 'j_2', 'triangular', 'heisenberg', 'model', 'we', 'further', 'discuss', 'the', 'nature', 'of', 'this', 'spinliquidlike', 'phase', 'and', 'the', 'indication', 'of', 'our', 'results', 'for', 'understanding', 'spinliquidlike', 'materials', 'with', 'triangularlattice', 'structure']] | [-0.19718899952648725, 0.25080465271053576, -0.0454989892249513, 0.06240850392997446, -0.02021720921362026, -0.1347729428582372, 0.06801422539865598, 0.3831584423573481, -0.24683631098014303, -0.24105976149924876, 0.05778539162080657, -0.3195228261659698, -0.12336942827257896, 0.05225059169364007, 0.11062744452566323, 0.02427891531907436, -0.04046946643211413, -0.007973735377567613, -0.1299642257573497, -0.176246928883807, 0.3200342743625192, 0.0052756950026378036, 0.2953229086059663, 0.08692682275432162, -0.0003040848791392313, 0.05494355313486368, 0.10421440555182926, -0.010867184099172138, -0.2036440874928505, 0.06735408337974352, 0.22132139791129804, -0.09258707435866301, 0.12109039746186075, -0.40420479310624713, -0.22074747434170502, 0.09125909359297818, 0.11945511080719168, 0.16835182196518872, -0.014126768155417975, -0.3580942131811753, -0.0005547323315921756, -0.23712100488612325, -0.152389256241602, -0.14536943551987255, -0.07385614824306685, -0.04570615478041873, -0.22976827707720482, 0.13032742274744022, 0.14371181416431178, 0.1114292429604878, -0.034284829559813566, -0.09258213332294771, -0.07806998954199823, 0.10845167629223498, 0.04281093628540273, 0.03290513416060195, 0.09978437600916044, -0.1378523109985205, -0.1704657299317963, 0.375255200048236, -0.028487892004098587, -0.11450973158288333, 0.1411917753955802, -0.20027101014178092, -0.11793038029120201, 0.1341928173399841, 0.09457256829813963, 0.021061730089261092, -0.09250046209086173, 0.07795051474790347, -0.015102517080473868, 0.26354515467149514, -0.04982915996677346, 0.08982040721619139, 0.24107199748848668, 0.1726944456847074, 0.03735371176864848, 0.21429528833282852, -0.11769445641968762, -0.16637226699903193, -0.22848730456704894, -0.16040689369725036, -0.26285529354128, 0.0725874359615975, -0.1738490804915879, -0.18064344016602263, 0.43539402986385134, 0.1443714593761898, 0.14082584593496802, -0.046737985114709266, 0.19208431822415958, 0.05726967925713527, 0.019393665735454608, 0.03729849371140719, 0.22584974005197486, 0.20487415234027948, 0.031433910677959725, -0.3246019577329409, 0.052632612354096234, 0.09499172979687703] |
1,802.00936 | Frequency of Rational Fractions on [0, 1] | In this paper, the authors design a trial to count rational ratios on the
interval [0, 1], and plot a normalized frequency statistical graph. Patterns,
symmetry and co-linear properties reflected in the graph are confirmed. The
main objective is to present a new view of Farey sequence and to explain the
inner principle of its procedure. In addition, we compare Farey sequence and
Continued fraction in terms of numerical approximation track and clarify the
internal reason why we iteratively choose mediant as the next suitable
approximation for the first time. Besides, all sorts of Fibonacci-Lucas
sequences emerge from the statistical graph.
| math.HO | in this paper the authors design a trial to count rational ratios on the interval 0 1 and plot a normalized frequency statistical graph patterns symmetry and colinear properties reflected in the graph are confirmed the main objective is to present a new view of farey sequence and to explain the inner principle of its procedure in addition we compare farey sequence and continued fraction in terms of numerical approximation track and clarify the internal reason why we iteratively choose mediant as the next suitable approximation for the first time besides all sorts of fibonaccilucas sequences emerge from the statistical graph | [['in', 'this', 'paper', 'the', 'authors', 'design', 'a', 'trial', 'to', 'count', 'rational', 'ratios', 'on', 'the', 'interval', '0', '1', 'and', 'plot', 'a', 'normalized', 'frequency', 'statistical', 'graph', 'patterns', 'symmetry', 'and', 'colinear', 'properties', 'reflected', 'in', 'the', 'graph', 'are', 'confirmed', 'the', 'main', 'objective', 'is', 'to', 'present', 'a', 'new', 'view', 'of', 'farey', 'sequence', 'and', 'to', 'explain', 'the', 'inner', 'principle', 'of', 'its', 'procedure', 'in', 'addition', 'we', 'compare', 'farey', 'sequence', 'and', 'continued', 'fraction', 'in', 'terms', 'of', 'numerical', 'approximation', 'track', 'and', 'clarify', 'the', 'internal', 'reason', 'why', 'we', 'iteratively', 'choose', 'mediant', 'as', 'the', 'next', 'suitable', 'approximation', 'for', 'the', 'first', 'time', 'besides', 'all', 'sorts', 'of', 'fibonaccilucas', 'sequences', 'emerge', 'from', 'the', 'statistical', 'graph']] | [-0.11236757913604378, 0.07238027779851108, -0.0959969468228519, 0.11693691303953528, -0.07956257557030767, -0.05461785685736686, 0.11007549585425296, 0.38175337061285974, -0.30041706634685394, -0.3059585468377918, 0.08467028820188716, -0.2663807740621269, -0.1696218801289797, 0.13791449619922788, -0.07555538696236909, 0.04548851080413442, 0.03310318635427393, 0.05180272014811635, -0.06302350086160004, -0.2187676888794522, 0.2974369248002768, 0.007088615139946341, 0.20895265916595235, -0.013974036206491292, 0.08962352861999534, 0.007236567073268816, -0.0675342476554215, -0.011521699568256737, -0.16890111222397536, 0.10270295295165852, 0.24787241458427162, 0.15699794309679418, 0.2653571178019047, -0.3936128350161016, -0.12191212072037161, 0.1303157859062776, 0.1693307553534396, 0.06171940376516431, -0.01582596934051253, -0.22115218369755893, 0.11049850054085254, -0.13102372194407508, -0.14173010421916843, -0.07875014293007553, 0.0112373529933393, 0.06683053853921592, -0.23632914734072982, 0.03432187693659216, 0.07049542330205441, 0.05147910981439054, -0.03303529528551735, -0.13899598289979623, 0.010900103924795986, 0.15215991019504144, 0.06601529816864059, 0.0450729932799004, 0.07912157679558732, -0.06313988398527727, -0.12610774459317325, 0.3627920874208212, -0.02154793350957334, -0.1756163830496371, 0.1287186946021393, -0.15689553339965642, -0.19010289677418768, 0.11581873616552912, 0.1722260928968899, 0.11226747575215995, -0.1243778557504993, 0.047037823460414074, -0.01580375144490972, 0.16803403153782712, 0.11049913017312064, 0.003943678911309689, 0.18481387495994567, 0.12995797514915466, 0.037444428401067854, 0.1704126786068082, -0.06278115551685914, -0.10085600168909878, -0.3222274107672274, -0.1624388575227931, -0.19246764241834172, 0.0030999568151310088, -0.1135125875652011, -0.16622085528913885, 0.43194517033174634, 0.1484714144701138, 0.2075032752752304, 0.10557980405166745, 0.27055273508187383, 0.11294937889324501, -0.00196625477517955, 0.019836442134110257, 0.1738865452259779, 0.13783786084502936, 0.07820310661918484, -0.19432129916967825, 0.05595231995219365, 0.10811102027539164] |
1,802.00937 | ProFound: Source Extraction and Application to Modern Survey Data | We introduce ProFound, a source finding and image analysis package. ProFound
provides methods to detect sources in noisy images, generate segmentation maps
identifying the pixels belonging to each source, and measure statistics like
flux, size and ellipticity. These inputs are key requirements of ProFit, our
recently released galaxy profiling package, where the design aim is that these
two software packages will be used in unison to semi-automatically profile
large samples of galaxies. The key novel feature introduced in ProFound is that
all photometry is executed on dilated segmentation maps that fully contain the
identifiable flux, rather than using more traditional circular or ellipse based
photometry. Also, to be less sensitive to pathological segmentation issues, the
de-blending is made across saddle points in flux. We apply ProFound in a number
of simulated and real world cases, and demonstrate that it behaves reasonably
given its stated design goals. In particular, it offers good initial parameter
estimation for ProFit, and also segmentation maps that follow the sometimes
complex geometry of resolved sources, whilst capturing nearly all of the flux.
A number of bulge-disc decomposition projects are already making use of the
ProFound and ProFit pipeline, and adoption is being encouraged by publicly
releasing the software for the open source R data analysis platform under an
LGPL-3 license on GitHub (github.com/asgr/ProFound).
| astro-ph.IM | we introduce profound a source finding and image analysis package profound provides methods to detect sources in noisy images generate segmentation maps identifying the pixels belonging to each source and measure statistics like flux size and ellipticity these inputs are key requirements of profit our recently released galaxy profiling package where the design aim is that these two software packages will be used in unison to semiautomatically profile large samples of galaxies the key novel feature introduced in profound is that all photometry is executed on dilated segmentation maps that fully contain the identifiable flux rather than using more traditional circular or ellipse based photometry also to be less sensitive to pathological segmentation issues the deblending is made across saddle points in flux we apply profound in a number of simulated and real world cases and demonstrate that it behaves reasonably given its stated design goals in particular it offers good initial parameter estimation for profit and also segmentation maps that follow the sometimes complex geometry of resolved sources whilst capturing nearly all of the flux a number of bulgedisc decomposition projects are already making use of the profound and profit pipeline and adoption is being encouraged by publicly releasing the software for the open source r data analysis platform under an lgpl3 license on github githubcomasgrprofound | [['we', 'introduce', 'profound', 'a', 'source', 'finding', 'and', 'image', 'analysis', 'package', 'profound', 'provides', 'methods', 'to', 'detect', 'sources', 'in', 'noisy', 'images', 'generate', 'segmentation', 'maps', 'identifying', 'the', 'pixels', 'belonging', 'to', 'each', 'source', 'and', 'measure', 'statistics', 'like', 'flux', 'size', 'and', 'ellipticity', 'these', 'inputs', 'are', 'key', 'requirements', 'of', 'profit', 'our', 'recently', 'released', 'galaxy', 'profiling', 'package', 'where', 'the', 'design', 'aim', 'is', 'that', 'these', 'two', 'software', 'packages', 'will', 'be', 'used', 'in', 'unison', 'to', 'semiautomatically', 'profile', 'large', 'samples', 'of', 'galaxies', 'the', 'key', 'novel', 'feature', 'introduced', 'in', 'profound', 'is', 'that', 'all', 'photometry', 'is', 'executed', 'on', 'dilated', 'segmentation', 'maps', 'that', 'fully', 'contain', 'the', 'identifiable', 'flux', 'rather', 'than', 'using', 'more', 'traditional', 'circular', 'or', 'ellipse', 'based', 'photometry', 'also', 'to', 'be', 'less', 'sensitive', 'to', 'pathological', 'segmentation', 'issues', 'the', 'deblending', 'is', 'made', 'across', 'saddle', 'points', 'in', 'flux', 'we', 'apply', 'profound', 'in', 'a', 'number', 'of', 'simulated', 'and', 'real', 'world', 'cases', 'and', 'demonstrate', 'that', 'it', 'behaves', 'reasonably', 'given', 'its', 'stated', 'design', 'goals', 'in', 'particular', 'it', 'offers', 'good', 'initial', 'parameter', 'estimation', 'for', 'profit', 'and', 'also', 'segmentation', 'maps', 'that', 'follow', 'the', 'sometimes', 'complex', 'geometry', 'of', 'resolved', 'sources', 'whilst', 'capturing', 'nearly', 'all', 'of', 'the', 'flux', 'a', 'number', 'of', 'bulgedisc', 'decomposition', 'projects', 'are', 'already', 'making', 'use', 'of', 'the', 'profound', 'and', 'profit', 'pipeline', 'and', 'adoption', 'is', 'being', 'encouraged', 'by', 'publicly', 'releasing', 'the', 'software', 'for', 'the', 'open', 'source', 'r', 'data', 'analysis', 'platform', 'under', 'an', 'lgpl3', 'license', 'on', 'github', 'githubcomasgrprofound']] | [-0.06692885060383137, 0.021899210829030572, -0.09126336837057457, 0.08140158963194767, -0.1025762687393919, -0.1724998207156395, 0.004033693085359626, 0.4403722289646434, -0.22755055338642452, -0.33990106150551247, 0.12673630027728544, -0.2924912558305402, -0.11008542410617825, 0.24385045878183054, -0.10011306429866615, 0.04676418359862467, 0.12436499560988226, -0.026137706810651824, -0.01254038109584863, -0.27572123597235265, 0.2932253512434748, 0.08235970270754986, 0.2895083303659407, 0.028577067365945685, 0.08115317237855824, -0.030135592907060717, -0.09565721832102078, 0.012420130643489714, -0.09907942089813268, 0.12167462728116228, 0.2843304097479166, 0.20989852724958963, 0.2624103113467437, -0.38745094354502685, -0.1882404746011246, 0.08235945939086378, 0.1301910111160819, 0.061749376933805125, -0.055782639977947246, -0.29646614874973026, 0.06679326890491295, -0.13437821605177813, -0.09859685703224047, -0.101961662847722, 0.02110221380595291, -0.00021039427956566216, -0.23335122706065345, 0.04392639635276959, 0.013297722895824631, 0.07599713368731183, -0.04759410858360054, -0.11450488900462555, -0.04317685178762605, 0.18467738525292207, 0.010682225507835662, 0.07099679741293712, 0.16888639018979185, -0.14549166457592186, -0.061766468718388055, 0.3726085830566495, -0.02165653026821146, -0.17224978047076525, 0.171238008671072, -0.10742584011019315, -0.1829885803206369, 0.12895840820423218, 0.18943470305394988, 0.08497517788241249, -0.19650837024240647, 0.009992959322191255, -0.013224145202615927, 0.21914212342871484, 0.0682286967848276, 0.003676298599714565, 0.22633129952388795, 0.16214278568080517, 0.06743374800277051, 0.1495469992817921, -0.13571385610198905, -0.05478512041032488, -0.2552806079560934, -0.11970158569774655, -0.17869184787038628, 0.008892634865113122, -0.08042606589611793, -0.18998203253709214, 0.3931840825219487, 0.21249381786992036, 0.16574162284865282, 0.020463842922837885, 0.34319485673291045, 0.03197678760571275, 0.126432933949129, 0.11699701581193611, 0.18347805010284796, 0.04626014890842313, 0.07982260423041014, -0.12939167826674705, 0.06989653642440952, -0.01027759923491367] |
1,802.00938 | DeepProcess: Supporting business process execution using a MANN-based
recommender system | Process-aware Recommender systems can provide critical decision support
functionality to aid business process execution by recommending what actions to
take next. Based on recent advances in the field of deep learning, we present a
novel memory-augmented neural network (MANN) based approach for constructing a
process-aware recommender system. We propose a novel network architecture,
namely Write-Protected Dual Controller Memory-Augmented Neural Network
(DCw-MANN), for building prescriptive models. To evaluate the feasibility and
usefulness of our approach, we consider three real-world datasets and show that
our approach leads to better performance on several baselines for the task of
suffix recommendation and next task prediction.
| cs.NE | processaware recommender systems can provide critical decision support functionality to aid business process execution by recommending what actions to take next based on recent advances in the field of deep learning we present a novel memoryaugmented neural network mann based approach for constructing a processaware recommender system we propose a novel network architecture namely writeprotected dual controller memoryaugmented neural network dcwmann for building prescriptive models to evaluate the feasibility and usefulness of our approach we consider three realworld datasets and show that our approach leads to better performance on several baselines for the task of suffix recommendation and next task prediction | [['processaware', 'recommender', 'systems', 'can', 'provide', 'critical', 'decision', 'support', 'functionality', 'to', 'aid', 'business', 'process', 'execution', 'by', 'recommending', 'what', 'actions', 'to', 'take', 'next', 'based', 'on', 'recent', 'advances', 'in', 'the', 'field', 'of', 'deep', 'learning', 'we', 'present', 'a', 'novel', 'memoryaugmented', 'neural', 'network', 'mann', 'based', 'approach', 'for', 'constructing', 'a', 'processaware', 'recommender', 'system', 'we', 'propose', 'a', 'novel', 'network', 'architecture', 'namely', 'writeprotected', 'dual', 'controller', 'memoryaugmented', 'neural', 'network', 'dcwmann', 'for', 'building', 'prescriptive', 'models', 'to', 'evaluate', 'the', 'feasibility', 'and', 'usefulness', 'of', 'our', 'approach', 'we', 'consider', 'three', 'realworld', 'datasets', 'and', 'show', 'that', 'our', 'approach', 'leads', 'to', 'better', 'performance', 'on', 'several', 'baselines', 'for', 'the', 'task', 'of', 'suffix', 'recommendation', 'and', 'next', 'task', 'prediction']] | [-0.07511566458735615, -0.060861568562686444, -0.03859627262922004, 0.036086030669484896, -0.1649297880497761, -0.2004974439041689, 0.06513473104962031, 0.4605238061398268, -0.23248957172036172, -0.3033370760269463, 0.07934522194671445, -0.23587600041879342, -0.28872653785627334, 0.21654005849733948, -0.13473916023038326, 0.11602470629382879, 0.1461400443967432, 0.03491023319336819, -0.04704675423214212, -0.31940489197149874, 0.34402500458061697, 0.04967145070171682, 0.3789640862634405, 0.045843655630014836, 0.12970763308927416, 0.009630203961860389, -0.005130906731355935, -0.04001600523828529, -0.05259048347514181, 0.22479711735621094, 0.3305831797623614, 0.28245650817174467, 0.36715457586571576, -0.42914866290055215, -0.22685782541520894, 0.0497773417760618, 0.13653511413373054, 0.07249535983559326, -0.0485600787261501, -0.350352582288906, 0.10154602120514028, -0.256049833856523, 0.024545795382000506, -0.19965108667267487, -0.040364236145978796, -0.029327819138998165, -0.30930585273425093, -0.05010062078014016, 0.06365968365105801, 0.02939966108649969, -0.038652600259520116, -0.1209746247890871, 0.08138338586315513, 0.1598320680297911, -0.014273814751359169, 0.04261172632221132, 0.15208097063004972, -0.15510468556247362, -0.2665718383342028, 0.3511239232867956, -0.06389647869160399, -0.18881180686876178, 0.1669888146361336, 0.044637897522188726, -0.22304471254348754, 0.023474664120003583, 0.3108341799210757, 0.09691498142667115, -0.2017340803006664, -0.00527986183471512, -0.03630745087517426, 0.16624463602900505, -0.028154976840596646, -0.02166266321670264, 0.207542112164665, 0.3785168016701937, 0.04112987510161474, 0.13605383538641036, -0.0691842392552644, -0.10989761323668062, -0.16029059856198727, -0.11995505970902741, -0.14199673086637632, -0.014148186643142253, -0.11426236600571428, -0.1332590744458139, 0.3960285166464746, 0.32526190464850513, 0.1621451617917046, 0.1726475619408302, 0.3521557227929588, 0.02381064762477763, 0.09525975129567087, 0.09064982872339897, 0.15066207147901878, -0.014178847081493586, 0.1741403873451054, -0.1835113772843033, 0.07300135657307692, 0.10519479380454869] |
1,802.00939 | Recent Advances in Efficient Computation of Deep Convolutional Neural
Networks | Deep neural networks have evolved remarkably over the past few years and they
are currently the fundamental tools of many intelligent systems. At the same
time, the computational complexity and resource consumption of these networks
also continue to increase. This will pose a significant challenge to the
deployment of such networks, especially in real-time applications or on
resource-limited devices. Thus, network acceleration has become a hot topic
within the deep learning community. As for hardware implementation of deep
neural networks, a batch of accelerators based on FPGA/ASIC have been proposed
in recent years. In this paper, we provide a comprehensive survey of recent
advances in network acceleration, compression and accelerator design from both
algorithm and hardware points of view. Specifically, we provide a thorough
analysis of each of the following topics: network pruning, low-rank
approximation, network quantization, teacher-student networks, compact network
design and hardware accelerators. Finally, we will introduce and discuss a few
possible future directions.
| cs.CV | deep neural networks have evolved remarkably over the past few years and they are currently the fundamental tools of many intelligent systems at the same time the computational complexity and resource consumption of these networks also continue to increase this will pose a significant challenge to the deployment of such networks especially in realtime applications or on resourcelimited devices thus network acceleration has become a hot topic within the deep learning community as for hardware implementation of deep neural networks a batch of accelerators based on fpgaasic have been proposed in recent years in this paper we provide a comprehensive survey of recent advances in network acceleration compression and accelerator design from both algorithm and hardware points of view specifically we provide a thorough analysis of each of the following topics network pruning lowrank approximation network quantization teacherstudent networks compact network design and hardware accelerators finally we will introduce and discuss a few possible future directions | [['deep', 'neural', 'networks', 'have', 'evolved', 'remarkably', 'over', 'the', 'past', 'few', 'years', 'and', 'they', 'are', 'currently', 'the', 'fundamental', 'tools', 'of', 'many', 'intelligent', 'systems', 'at', 'the', 'same', 'time', 'the', 'computational', 'complexity', 'and', 'resource', 'consumption', 'of', 'these', 'networks', 'also', 'continue', 'to', 'increase', 'this', 'will', 'pose', 'a', 'significant', 'challenge', 'to', 'the', 'deployment', 'of', 'such', 'networks', 'especially', 'in', 'realtime', 'applications', 'or', 'on', 'resourcelimited', 'devices', 'thus', 'network', 'acceleration', 'has', 'become', 'a', 'hot', 'topic', 'within', 'the', 'deep', 'learning', 'community', 'as', 'for', 'hardware', 'implementation', 'of', 'deep', 'neural', 'networks', 'a', 'batch', 'of', 'accelerators', 'based', 'on', 'fpgaasic', 'have', 'been', 'proposed', 'in', 'recent', 'years', 'in', 'this', 'paper', 'we', 'provide', 'a', 'comprehensive', 'survey', 'of', 'recent', 'advances', 'in', 'network', 'acceleration', 'compression', 'and', 'accelerator', 'design', 'from', 'both', 'algorithm', 'and', 'hardware', 'points', 'of', 'view', 'specifically', 'we', 'provide', 'a', 'thorough', 'analysis', 'of', 'each', 'of', 'the', 'following', 'topics', 'network', 'pruning', 'lowrank', 'approximation', 'network', 'quantization', 'teacherstudent', 'networks', 'compact', 'network', 'design', 'and', 'hardware', 'accelerators', 'finally', 'we', 'will', 'introduce', 'and', 'discuss', 'a', 'few', 'possible', 'future', 'directions']] | [-0.13211386856169272, 0.004854253111914612, -0.055089264772295095, 0.024414589177840985, -0.08905961946733534, -0.15841333205119157, 0.03812725471335654, 0.45988780460678613, -0.2635760923477415, -0.31050385932664937, 0.12536173471912312, -0.2314579950687356, -0.21611108764773235, 0.23162017401325732, -0.1063896560181792, 0.11785328093295296, 0.13968800967230868, 0.019605980467018433, -0.06324199805571996, -0.3056945604651283, 0.2757549113474595, 0.12160748018355569, 0.32844128573719317, 0.05446381545446526, 0.0922505371660615, -0.051079022143639095, -0.037859979849786334, -0.014063536951419873, -0.05161514107525223, 0.19659990690147075, 0.31643013732555586, 0.2275159178310647, 0.3944106702095805, -0.5200032382630385, -0.28263791932318455, 0.10826917928016673, 0.16801604715426668, 0.1092656733130869, -0.07694161409521118, -0.2653645212890413, 0.0811172465682597, -0.23084589349589527, -0.029142023553830745, -0.1210151314511537, 0.012014873010906367, 0.04163056143368475, -0.18067125438709553, -0.0439175097766192, 0.0251654742930371, 0.060728015777362414, -0.00751287001978012, -0.14677155522169927, 0.08990175610121626, 0.1279355339199985, 0.009901443526196556, 0.05620800170342987, 0.14631395391487062, -0.18523824720894203, -0.16160266043675037, 0.3420235951646016, 0.011353992571597561, -0.1165399147460285, 0.21069586887740746, -0.0431585090700537, -0.2327557229735435, 0.07702464475938214, 0.3185250801123822, 0.06939844240267308, -0.17529132607607886, 0.046539030760938585, -0.001478491942040049, 0.12546674932356183, 0.029954492841333818, 0.06500480967821577, 0.2320534642875338, 0.3340855997043829, 0.07198786748631392, 0.09202961419419481, -0.11891015032485414, -0.071663193708026, -0.21028501858027318, -0.11653693945099337, -0.16180135406410465, 0.019962171963225746, -0.06671107308107029, -0.1303568511741618, 0.4210819389181546, 0.18781264662408295, 0.16879507132584992, 0.1012771415489857, 0.36314251591475344, 0.011730992680755802, 0.12365443703646843, 0.12377130382288343, 0.2169097545228373, 0.05737006929195605, 0.20979203809512023, -0.11586500966222957, 0.04583239592223739, -0.011711397509759244] |
1,802.0094 | Oscillating Modes of Driven Colloids in Overdamped Systems | Microscopic particles suspended in liquids are the prime example of an
overdamped system because viscous forces dominate over inertial effects. Apart
from their use as model systems, they receive considerable attention as
sensitive probes from which forces on molecular scales can be inferred. The
interpretation of such experiments rests on the assumption, that, even if the
particles are driven, the liquid remains in equilibrium, and all modes are
overdamped. Here, we experimentally demonstrate that this is no longer valid
when a particle is forced through a viscoelastic fluid. Even at small driving
velocities where Stokes law remains valid, we observe particle oscillations
with periods up to several tens of seconds. We attribute these to
non-equilibrium fluctuations of the fluid, which are excited by the particle's
motion. The observed oscillatory dynamics is in quantitative agreement with an
overdamped Langevin equation with negative friction-memory term and which is
equivalent to the motion of a stochastically driven underdamped oscillator.
This fundamentally new oscillatory mode will largely expand the variety of
model systems but has also considerable implications on how molecular forces
are determined by colloidal probe particles under natural viscoelastic
conditions.
| cond-mat.soft | microscopic particles suspended in liquids are the prime example of an overdamped system because viscous forces dominate over inertial effects apart from their use as model systems they receive considerable attention as sensitive probes from which forces on molecular scales can be inferred the interpretation of such experiments rests on the assumption that even if the particles are driven the liquid remains in equilibrium and all modes are overdamped here we experimentally demonstrate that this is no longer valid when a particle is forced through a viscoelastic fluid even at small driving velocities where stokes law remains valid we observe particle oscillations with periods up to several tens of seconds we attribute these to nonequilibrium fluctuations of the fluid which are excited by the particles motion the observed oscillatory dynamics is in quantitative agreement with an overdamped langevin equation with negative frictionmemory term and which is equivalent to the motion of a stochastically driven underdamped oscillator this fundamentally new oscillatory mode will largely expand the variety of model systems but has also considerable implications on how molecular forces are determined by colloidal probe particles under natural viscoelastic conditions | [['microscopic', 'particles', 'suspended', 'in', 'liquids', 'are', 'the', 'prime', 'example', 'of', 'an', 'overdamped', 'system', 'because', 'viscous', 'forces', 'dominate', 'over', 'inertial', 'effects', 'apart', 'from', 'their', 'use', 'as', 'model', 'systems', 'they', 'receive', 'considerable', 'attention', 'as', 'sensitive', 'probes', 'from', 'which', 'forces', 'on', 'molecular', 'scales', 'can', 'be', 'inferred', 'the', 'interpretation', 'of', 'such', 'experiments', 'rests', 'on', 'the', 'assumption', 'that', 'even', 'if', 'the', 'particles', 'are', 'driven', 'the', 'liquid', 'remains', 'in', 'equilibrium', 'and', 'all', 'modes', 'are', 'overdamped', 'here', 'we', 'experimentally', 'demonstrate', 'that', 'this', 'is', 'no', 'longer', 'valid', 'when', 'a', 'particle', 'is', 'forced', 'through', 'a', 'viscoelastic', 'fluid', 'even', 'at', 'small', 'driving', 'velocities', 'where', 'stokes', 'law', 'remains', 'valid', 'we', 'observe', 'particle', 'oscillations', 'with', 'periods', 'up', 'to', 'several', 'tens', 'of', 'seconds', 'we', 'attribute', 'these', 'to', 'nonequilibrium', 'fluctuations', 'of', 'the', 'fluid', 'which', 'are', 'excited', 'by', 'the', 'particles', 'motion', 'the', 'observed', 'oscillatory', 'dynamics', 'is', 'in', 'quantitative', 'agreement', 'with', 'an', 'overdamped', 'langevin', 'equation', 'with', 'negative', 'frictionmemory', 'term', 'and', 'which', 'is', 'equivalent', 'to', 'the', 'motion', 'of', 'a', 'stochastically', 'driven', 'underdamped', 'oscillator', 'this', 'fundamentally', 'new', 'oscillatory', 'mode', 'will', 'largely', 'expand', 'the', 'variety', 'of', 'model', 'systems', 'but', 'has', 'also', 'considerable', 'implications', 'on', 'how', 'molecular', 'forces', 'are', 'determined', 'by', 'colloidal', 'probe', 'particles', 'under', 'natural', 'viscoelastic', 'conditions']] | [-0.13471885362794414, 0.2689798722545713, -0.10629055172175089, 0.029124542693532804, -0.046791730243164825, -0.161863928461537, -0.014986129193741052, 0.3516347210675, -0.29159767719553437, -0.290648420209334, 0.06152266815347089, -0.27472356381221613, -0.13363347782250395, 0.21781832955274633, -0.020231941236770728, 0.02997030599545787, 0.07064208355458344, 0.010491124402861146, 0.03073164543951239, -0.16464881493117323, 0.2595900237052278, 0.03934577282861792, 0.23559000661078702, 0.03704936809204071, 0.13860227253766141, -0.05784936372643924, 0.02404131918627629, 0.056900541218406056, -0.15062543060842223, 0.022165010787825533, 0.19951962697304745, -0.031615309313905826, 0.25816075504573194, -0.4883064786420468, -0.23274810023526138, 0.10241711381544237, 0.1823370330677527, 0.1567936265762686, -0.03978924005962211, -0.25982082401088535, 0.019332573776516843, -0.14065188322873676, -0.1313862344604932, -0.10419166837395592, 0.07568897302436797, 0.06801608739091447, -0.22498101791376895, 0.1471338161728955, 0.07312199045321281, 0.061145551872494465, -0.08955488793170926, -0.06643082484550634, -0.002136205209737873, 0.09491665403680492, 0.0914313418580279, -0.01716135718914516, 0.23394012606060283, -0.14830325493641336, -0.06153493974716427, 0.441112619162323, -0.07546830193617725, -0.23588503054066132, 0.2606177056073784, -0.16117713556819183, -0.07491623857531717, 0.17779372325216863, 0.1583490929043233, 0.10271377720638025, -0.16906214334771977, 0.034605419246435044, -0.04067862962676402, 0.18224689220875143, 0.06355585866479482, 0.02543654234454697, 0.2620337684016256, 0.17533904315659746, 0.0164275684388962, 0.11465742137161068, -0.07851358008937336, -0.15464446571142598, -0.2800980648847626, -0.09738096073120434, -0.17732606942122328, 0.06919243059340208, -0.05590646708602669, -0.1353124145899008, 0.3346112125371408, 0.16666077919314332, 0.15579651954539, 0.0453094381230191, 0.27396447938134805, 0.11044233011076834, 0.03008252073622204, 0.05540410006906897, 0.30760486059906805, 0.10475852387025952, 0.10356708630648029, -0.239633271099631, 0.060569974749453644, -0.00032128550032562114] |
1,802.00941 | Learning the Synthesizability of Dynamic Texture Samples | A dynamic texture (DT) refers to a sequence of images that exhibit temporal
regularities and has many applications in computer vision and graphics. Given
an exemplar of dynamic texture, it is a dynamic but challenging task to
generate new samples with high quality that are perceptually similar to the
input exemplar, which is known to be {\em example-based dynamic texture
synthesis (EDTS)}. Numerous approaches have been devoted to this problem, in
the past decades, but none them are able to tackle all kinds of dynamic
textures equally well. In this paper, we investigate the synthesizability of
dynamic texture samples: {\em given a dynamic texture sample, how synthesizable
it is by using EDTS, and which EDTS method is the most suitable to synthesize
it?} To this end, we propose to learn regression models to connect dynamic
texture samples with synthesizability scores, with the help of a compiled
dynamic texture dataset annotated in terms of synthesizability. More precisely,
we first define the synthesizability of DT samples and characterize them by a
set of spatiotemporal features. Based on these features and an annotated
dynamic texture dataset, we then train regression models to predict the
synthesizability scores of texture samples and learn classifiers to select the
most suitable EDTS methods. We further complete the selection, partition and
synthesizability prediction of dynamic texture samples in a hierarchical
scheme. We finally apply the learned synthesizability to detecting
synthesizable regions in videos. The experiments demonstrate that our method
can effectively learn and predict the synthesizability of DT samples.
| cs.CV | a dynamic texture dt refers to a sequence of images that exhibit temporal regularities and has many applications in computer vision and graphics given an exemplar of dynamic texture it is a dynamic but challenging task to generate new samples with high quality that are perceptually similar to the input exemplar which is known to be em examplebased dynamic texture synthesis edts numerous approaches have been devoted to this problem in the past decades but none them are able to tackle all kinds of dynamic textures equally well in this paper we investigate the synthesizability of dynamic texture samples em given a dynamic texture sample how synthesizable it is by using edts and which edts method is the most suitable to synthesize it to this end we propose to learn regression models to connect dynamic texture samples with synthesizability scores with the help of a compiled dynamic texture dataset annotated in terms of synthesizability more precisely we first define the synthesizability of dt samples and characterize them by a set of spatiotemporal features based on these features and an annotated dynamic texture dataset we then train regression models to predict the synthesizability scores of texture samples and learn classifiers to select the most suitable edts methods we further complete the selection partition and synthesizability prediction of dynamic texture samples in a hierarchical scheme we finally apply the learned synthesizability to detecting synthesizable regions in videos the experiments demonstrate that our method can effectively learn and predict the synthesizability of dt samples | [['a', 'dynamic', 'texture', 'dt', 'refers', 'to', 'a', 'sequence', 'of', 'images', 'that', 'exhibit', 'temporal', 'regularities', 'and', 'has', 'many', 'applications', 'in', 'computer', 'vision', 'and', 'graphics', 'given', 'an', 'exemplar', 'of', 'dynamic', 'texture', 'it', 'is', 'a', 'dynamic', 'but', 'challenging', 'task', 'to', 'generate', 'new', 'samples', 'with', 'high', 'quality', 'that', 'are', 'perceptually', 'similar', 'to', 'the', 'input', 'exemplar', 'which', 'is', 'known', 'to', 'be', 'em', 'examplebased', 'dynamic', 'texture', 'synthesis', 'edts', 'numerous', 'approaches', 'have', 'been', 'devoted', 'to', 'this', 'problem', 'in', 'the', 'past', 'decades', 'but', 'none', 'them', 'are', 'able', 'to', 'tackle', 'all', 'kinds', 'of', 'dynamic', 'textures', 'equally', 'well', 'in', 'this', 'paper', 'we', 'investigate', 'the', 'synthesizability', 'of', 'dynamic', 'texture', 'samples', 'em', 'given', 'a', 'dynamic', 'texture', 'sample', 'how', 'synthesizable', 'it', 'is', 'by', 'using', 'edts', 'and', 'which', 'edts', 'method', 'is', 'the', 'most', 'suitable', 'to', 'synthesize', 'it', 'to', 'this', 'end', 'we', 'propose', 'to', 'learn', 'regression', 'models', 'to', 'connect', 'dynamic', 'texture', 'samples', 'with', 'synthesizability', 'scores', 'with', 'the', 'help', 'of', 'a', 'compiled', 'dynamic', 'texture', 'dataset', 'annotated', 'in', 'terms', 'of', 'synthesizability', 'more', 'precisely', 'we', 'first', 'define', 'the', 'synthesizability', 'of', 'dt', 'samples', 'and', 'characterize', 'them', 'by', 'a', 'set', 'of', 'spatiotemporal', 'features', 'based', 'on', 'these', 'features', 'and', 'an', 'annotated', 'dynamic', 'texture', 'dataset', 'we', 'then', 'train', 'regression', 'models', 'to', 'predict', 'the', 'synthesizability', 'scores', 'of', 'texture', 'samples', 'and', 'learn', 'classifiers', 'to', 'select', 'the', 'most', 'suitable', 'edts', 'methods', 'we', 'further', 'complete', 'the', 'selection', 'partition', 'and', 'synthesizability', 'prediction', 'of', 'dynamic', 'texture', 'samples', 'in', 'a', 'hierarchical', 'scheme', 'we', 'finally', 'apply', 'the', 'learned', 'synthesizability', 'to', 'detecting', 'synthesizable', 'regions', 'in', 'videos', 'the', 'experiments', 'demonstrate', 'that', 'our', 'method', 'can', 'effectively', 'learn', 'and', 'predict', 'the', 'synthesizability', 'of', 'dt', 'samples']] | [-0.0061678839012594576, 0.03546885612888718, -0.08883005488326261, 0.08046466585821974, -0.127734333593992, -0.13537553531039817, 0.021657532983574555, 0.46529308256815627, -0.29758719517107296, -0.3624935278954704, 0.09161041928334644, -0.2733762507397399, -0.20162170055643303, 0.14803716891716082, -0.10765584022923562, 0.10432180029245472, 0.06471964230657039, 0.036072226682774336, -0.07004107346858429, -0.2705771334736471, 0.2821633116271629, -0.00015352244778309924, 0.33305687924097493, -0.03467986740049465, 0.12599203295113362, -0.03365066994827582, -0.03156202599458189, 0.03500934807487218, -0.08996531471980071, 0.16500072924845083, 0.3221658593477865, 0.17564263757767223, 0.25098125655315373, -0.39728998134929694, -0.2067806941848545, 0.08135479391483864, 0.1003907631630026, 0.12316095042209405, -0.03752493076992316, -0.29808707162737846, 0.15827792561223425, -0.11568222331790813, -0.011500102209958778, -0.19504852453061192, 0.010711669904714175, -0.022662446810260965, -0.3028837466441778, 0.04202987572115167, 0.06065924292703, 0.038121939480928076, -0.07039521460094864, -0.09146281369902431, 0.013471338841359157, 0.17661346831318153, 0.029095245094828873, 0.026025469044772574, 0.1136516430025616, -0.17261750200662235, -0.1233904576262986, 0.37709591188931846, -0.013052347527336389, -0.21064727572238154, 0.22236016787963353, -0.07530954941379953, -0.14858138470481932, 0.09837583058042473, 0.23066910888500644, 0.17357203521509748, -0.20149542281470925, -0.019778136150963396, -0.0723908961494546, 0.20530678091043852, 0.037826664577909855, -0.02567344600431235, 0.200989727810233, 0.23486980803837754, 0.009727545875445586, 0.17441555978572626, -0.13886930911260958, -0.012623122358969245, -0.17254249432098223, -0.10947115424105831, -0.193049430543375, -0.03747229623994178, -0.07262054353579507, -0.19445504835732347, 0.4482183576375495, 0.2565719217778113, 0.24852527685433745, 0.07017175390049296, 0.29252548687497665, 0.026854832217051365, 0.11323834521755338, 0.04041424058206135, 0.13668396082183576, 0.02984991111603391, 0.10279530859496668, -0.16501641508555448, 0.10332666003920761, 0.051070644132481274] |
1,802.00942 | Angle-dependent magic wavelengths for the $4s_{1/2}\to3d_{5/2,3/2}$
transitions of Ca$^{+}$ ions | The dynamic polarizabilities of the atomic states with angular momentum $j>
\frac12$ are sensitive to the angle between the quantization axis $\hat{e}_z$
and the polarization vector $\hat{\mathbf{\epsilon}}$ owing to the contribution
of anisotropic tensor polarizabilities. The magic wavelength, at which the
differential Stark shift of an atomic transition nullifies, depends on this
angle. We identified the magic wavelengths for the
$4s_{\frac12}\to3d_{\frac32,\frac52}$ transitions of Ca$^{+}$ ions at different
angles between $\hat{e}_z$ and $\hat{\mathbf{\epsilon}}$ in the case of
linearly polarized light. We found that the magic wavelengths near 395.79 nm,
which lie between the $4s_{\frac12}\to4p_{\frac12}$ and $4s_{\frac12}\to
4p_{\frac32}$ transition wavelengths, remain unsensitive to the angle, while
the magic wavelengths, which are longer than the $3d_{\frac52}\to 4p_{\frac32}$
resonant transition wavelength (854.21 nm), are very sensitive to the angle.
| physics.atom-ph | the dynamic polarizabilities of the atomic states with angular momentum j frac12 are sensitive to the angle between the quantization axis hate_z and the polarization vector hatmathbfepsilon owing to the contribution of anisotropic tensor polarizabilities the magic wavelength at which the differential stark shift of an atomic transition nullifies depends on this angle we identified the magic wavelengths for the 4s_frac12to3d_frac32frac52 transitions of ca ions at different angles between hate_z and hatmathbfepsilon in the case of linearly polarized light we found that the magic wavelengths near 39579 nm which lie between the 4s_frac12to4p_frac12 and 4s_frac12to 4p_frac32 transition wavelengths remain unsensitive to the angle while the magic wavelengths which are longer than the 3d_frac52to 4p_frac32 resonant transition wavelength 85421 nm are very sensitive to the angle | [['the', 'dynamic', 'polarizabilities', 'of', 'the', 'atomic', 'states', 'with', 'angular', 'momentum', 'j', 'frac12', 'are', 'sensitive', 'to', 'the', 'angle', 'between', 'the', 'quantization', 'axis', 'hate_z', 'and', 'the', 'polarization', 'vector', 'hatmathbfepsilon', 'owing', 'to', 'the', 'contribution', 'of', 'anisotropic', 'tensor', 'polarizabilities', 'the', 'magic', 'wavelength', 'at', 'which', 'the', 'differential', 'stark', 'shift', 'of', 'an', 'atomic', 'transition', 'nullifies', 'depends', 'on', 'this', 'angle', 'we', 'identified', 'the', 'magic', 'wavelengths', 'for', 'the', '4s_frac12to3d_frac32frac52', 'transitions', 'of', 'ca', 'ions', 'at', 'different', 'angles', 'between', 'hate_z', 'and', 'hatmathbfepsilon', 'in', 'the', 'case', 'of', 'linearly', 'polarized', 'light', 'we', 'found', 'that', 'the', 'magic', 'wavelengths', 'near', '39579', 'nm', 'which', 'lie', 'between', 'the', '4s_frac12to4p_frac12', 'and', '4s_frac12to', '4p_frac32', 'transition', 'wavelengths', 'remain', 'unsensitive', 'to', 'the', 'angle', 'while', 'the', 'magic', 'wavelengths', 'which', 'are', 'longer', 'than', 'the', '3d_frac52to', '4p_frac32', 'resonant', 'transition', 'wavelength', '85421', 'nm', 'are', 'very', 'sensitive', 'to', 'the', 'angle']] | [-0.1353279909969348, 0.2744966420113443, 0.0030663541801597763, 0.02413723144639769, -0.028906572557499875, -0.15289352044017743, 0.052759807492079945, 0.46396179510199503, -0.2529423096095738, -0.2978994247098656, 0.031649681394312366, -0.2879263170063496, -0.023001338168978692, 0.1378927950862461, 0.067887059437192, 9.242694460503433e-05, -0.015067373151364534, -0.027534888182645257, -0.07388390909839908, -0.09973160241447065, 0.31418179633176846, 0.11269029037136098, 0.26672672389566127, 0.07080069735808217, 0.06551891369542674, 0.020865149775762922, 0.03780708455521128, -0.03508715291871973, -0.11454895700197718, 0.09335721656022107, 0.23841964197547538, -0.030365667010293057, 0.13423391668045004, -0.3481697427513807, -0.09014445545718722, 0.06931072938984827, 0.09031360924000974, 0.1282242449314293, 0.047545571672811135, -0.27848675968327924, -0.01751392746101255, -0.05365811121528563, -0.1667609949717703, -0.013277592756987915, 0.08016604521190343, 0.012795633141902964, -0.2341628755159352, 0.038511733463762896, 0.002424765062396941, 0.090159057725269, -0.05641282815891115, -0.17754344959216922, -0.06608547809655252, 0.0844875693159259, 0.059207843616604805, 0.04800664731908751, 0.11544941420383427, -0.09590281038747533, -0.07698773993584125, 0.3765169662838478, -0.042242443843608035, -0.09978160459710204, 0.1309200673971487, -0.23440127638371094, -0.054890164590197736, 0.2192964378906333, 0.12351963195301917, 0.1709840422577184, -0.02088554193792136, 0.027578747902652655, -0.0028133283490720004, 0.23710380220866722, 0.1536089778351395, 0.13055284669946718, 0.24111199004818565, 0.06828974344484184, 0.047808988343762314, 0.11140213280754245, -0.2090389095550484, -0.10855614192297926, -0.2764414527170036, -0.09854469430511413, -0.1634888753456914, 0.05162908125506795, -0.08352685397371913, -0.12819324398069115, 0.3715451734383469, 0.11823809019330403, 0.19769379645231705, 0.017929069158297194, 0.2939283853882681, 0.10471117840624293, 0.10494250415864846, 0.028111834427260833, 0.39939800086228744, 0.17924036610385646, 0.14434405340732115, -0.2808401407269032, 0.03705754291023249, -0.014222531727231716] |
1,802.00943 | Examples of nonalgebraic nilpotent Lie algebra | We describe some examples of non abelian nilpotent Lie algebras which are not
algebraic.
| math.AG math.RA | we describe some examples of non abelian nilpotent lie algebras which are not algebraic | [['we', 'describe', 'some', 'examples', 'of', 'non', 'abelian', 'nilpotent', 'lie', 'algebras', 'which', 'are', 'not', 'algebraic']] | [-0.21061004591839655, 0.12387347432585168, -0.06376629881560802, 0.15109222887882165, -0.3174510107242635, -0.23580131626554898, -0.11806402375389423, 0.41776600453470436, -0.33185078629425596, -0.14432043449154922, 0.22068848218103604, -0.2183363831468991, -0.1582727168819734, 0.2323897506243416, -0.22443300525524787, -0.1619278209150902, -0.008282750179725034, 0.20358435264123337, -0.14869719263099665, -0.37981479881065233, 0.49294572110686985, -0.15805162645327592, 0.1356914173811674, 0.004360358064462032, 0.11556915672762054, -0.06679846708928901, -0.05966997838446072, -0.014247918501496315, -0.14080591592937708, 0.057113788822399716, 0.4718836011098964, -0.005859326837318284, 0.13861132732459477, -0.3575421119374888, -0.06594007302607809, 0.3015291640268905, 0.19678073093694234, 0.0972841804032214, -0.08580369422478336, -0.2571058802173606, 0.09059231129607984, -0.26934695669582914, -0.18280903842034085, -0.20639661273786, 0.018913472603474344, 0.04033365632806506, -0.12005217180454306, -0.03904407577855246, 0.15242565742560796, 0.2528296052478254, -0.11374862808068949, -0.060895930315642284, -0.10220927149722618, -0.021847211057320237, -0.11433797858522407, -0.14946625621191093, 0.1922530243838472, 0.012950002415371793, -0.20189875776746444, 0.36219002173415255, 0.10531107389501163, -0.2919383448149477, 0.17023196896272047, -0.2232677558703082, -0.26431095650020453, 0.1399370873613017, 0.034874635083334785, 0.19895755223530745, -0.055975072884133885, 0.28384142209376606, -0.19521761313080788, -0.1333408717598234, 0.03493763794124659, 0.012940532660910062, 0.08648311340117029, 0.03824307317180293, -0.04891572759619781, 0.034020078634577136, 0.23107217717915773, -0.006419151489223752, -0.5138629653624126, -0.12266381846607796, -5.416297686419317e-05, 0.1683547757742677, -0.08154858954782997, -0.25792818223791464, 0.47914938096489224, 0.12367857839646083, 0.10953638356711183, 0.15586039837216958, 0.14251711718471988, 0.043937967491469214, 0.1259586936128991, 0.10897827494357314, 0.10869023720442783, 0.363928926443415, -0.17938260278398438, -0.08750811511916774, -0.14831515192054212, 0.24240315027002776] |
1,802.00944 | Right-Handed Neutrinos: DM and LFV $vs$ Collider | In a class of neutrino mass models with a lepton flavor violation (LFV)
Yukawa interaction term that involves a heavy right handed neutrino, a charged
scalar and a charged lepton, we investigate at the ILC@500 GeV the possibility
of observing news physics. These models can address neutrino mass and dark
matter without being in conflict with different LFV constraints. By imposing DM
relic density and LFV constraints, we recast the analysis done by L3
collaboration at LEP-II of monophoton searches on our space parameter and look
for new physics in such channels like monophoton and $S S(\gamma)$, where we
give different cuts and show the predicted distributions. We show also that
using polarized beams could improve the statistical significance.
| hep-ph hep-ex | in a class of neutrino mass models with a lepton flavor violation lfv yukawa interaction term that involves a heavy right handed neutrino a charged scalar and a charged lepton we investigate at the ilc500 gev the possibility of observing news physics these models can address neutrino mass and dark matter without being in conflict with different lfv constraints by imposing dm relic density and lfv constraints we recast the analysis done by l3 collaboration at lepii of monophoton searches on our space parameter and look for new physics in such channels like monophoton and s sgamma where we give different cuts and show the predicted distributions we show also that using polarized beams could improve the statistical significance | [['in', 'a', 'class', 'of', 'neutrino', 'mass', 'models', 'with', 'a', 'lepton', 'flavor', 'violation', 'lfv', 'yukawa', 'interaction', 'term', 'that', 'involves', 'a', 'heavy', 'right', 'handed', 'neutrino', 'a', 'charged', 'scalar', 'and', 'a', 'charged', 'lepton', 'we', 'investigate', 'at', 'the', 'ilc500', 'gev', 'the', 'possibility', 'of', 'observing', 'news', 'physics', 'these', 'models', 'can', 'address', 'neutrino', 'mass', 'and', 'dark', 'matter', 'without', 'being', 'in', 'conflict', 'with', 'different', 'lfv', 'constraints', 'by', 'imposing', 'dm', 'relic', 'density', 'and', 'lfv', 'constraints', 'we', 'recast', 'the', 'analysis', 'done', 'by', 'l3', 'collaboration', 'at', 'lepii', 'of', 'monophoton', 'searches', 'on', 'our', 'space', 'parameter', 'and', 'look', 'for', 'new', 'physics', 'in', 'such', 'channels', 'like', 'monophoton', 'and', 's', 'sgamma', 'where', 'we', 'give', 'different', 'cuts', 'and', 'show', 'the', 'predicted', 'distributions', 'we', 'show', 'also', 'that', 'using', 'polarized', 'beams', 'could', 'improve', 'the', 'statistical', 'significance']] | [-0.09958815587559293, 0.27787609853852197, -0.02940219674384644, 0.21784326305002474, -0.10469359751329013, -0.1939743345705088, 0.06323645713136476, 0.29812385960474236, -0.2123050371247555, -0.36174317243109855, 0.01151345984740261, -0.2988666924765381, -0.0034229432099631856, 0.12630578409684742, 0.08595698600353188, 0.056192382385547295, 0.07217001207388092, -0.032462172225869, -0.08822320760501658, -0.20170956946118979, 0.31289940545758027, 0.05852043331295502, 0.18506808016261383, 0.11764286587625242, 0.06102444217227274, -0.004298854637362783, -0.09719051940008576, -0.06778988727400698, -0.13140615401459377, 0.03525722500312479, 0.17066593410012149, 0.14492542849180579, 0.08295011625061703, -0.40237028003052, -0.16976618384472839, 0.22551603457780883, 0.12956305167113408, 0.06818519595681745, -0.12903839432411365, -0.3761083948574647, 0.06430058813523776, -0.2300323614647158, -0.07419599927648776, -0.07821077791613941, -0.05776521070476841, -0.06286919264889815, -0.35586604405538874, 0.08198045370410618, -0.053646579494371134, -0.03915154058173174, 0.008087433992000688, -0.20995185325438737, -0.02976524267353493, -0.022018133901341372, 0.18278735909858493, -0.02618222123700656, 0.1594525528073843, -0.2148153896095278, -0.17536733679946886, 0.402315678826275, -0.10084221555710528, -0.19072932671528592, 0.16277119302740634, -0.20141475416813828, -0.22050604351772732, 0.05964973719962755, 0.23593887219013757, 0.042525570051149784, -0.1771564080679257, 0.1800736238712351, -0.08188433519920663, 0.13532833855290027, 0.08210147264562234, 0.05573574074019654, 0.33000645044596255, 0.19911781038321993, 0.09591495905447156, 0.0015811141924697812, -0.11351869793162689, 0.013511461053168824, -0.4038386628923922, -0.11008067532372073, -0.07556506029541503, 0.051841455506979675, -0.05847406033118365, -0.019914519753964507, 0.4150903603461172, 0.10758498523720861, 0.241198702934472, 0.017902358912495003, 0.28257102501664716, 0.05844235643372722, 0.04210798344535738, 0.044071038844486495, 0.3005405021440081, 0.09731853146599058, 0.14412544711939052, -0.2269412526839311, 0.0019288318967368423, 0.044856371504778995] |
1,802.00945 | Type II Seesaw and tau lepton at the HL-LHC, HE-LHC and FCC-hh | The tau lepton plays important role in distinguishing neutrino mass patterns
and determining the chirality nature in heavy scalar mediated neutrino mass
models, in the light of the neutrino oscillation experiments and its
polarization measurement. We investigate the lepton flavor signatures with tau
lepton at LHC upgrades, i.e. HL-LHC, HE-LHC and FCC-hh, through leptonic
processes from doubly charged Higgs in the Type II Seesaw. We find that for the
channel with one tau lepton in final states, the accessible doubly charged
Higgs mass at HL-LHC can reach 655 GeV and 695 GeV for the neutrino mass
patterns of normal hierarchy (NH) and inverted hierarchy (IH) respectively,
with the luminosity of 3000 fb$^{-1}$. Higher masses, 975-1930 GeV for NH and
1035-2070 GeV for IH, can be achieved at HE-LHC and FCC-hh.
| hep-ph | the tau lepton plays important role in distinguishing neutrino mass patterns and determining the chirality nature in heavy scalar mediated neutrino mass models in the light of the neutrino oscillation experiments and its polarization measurement we investigate the lepton flavor signatures with tau lepton at lhc upgrades ie hllhc helhc and fcchh through leptonic processes from doubly charged higgs in the type ii seesaw we find that for the channel with one tau lepton in final states the accessible doubly charged higgs mass at hllhc can reach 655 gev and 695 gev for the neutrino mass patterns of normal hierarchy nh and inverted hierarchy ih respectively with the luminosity of 3000 fb1 higher masses 9751930 gev for nh and 10352070 gev for ih can be achieved at helhc and fcchh | [['the', 'tau', 'lepton', 'plays', 'important', 'role', 'in', 'distinguishing', 'neutrino', 'mass', 'patterns', 'and', 'determining', 'the', 'chirality', 'nature', 'in', 'heavy', 'scalar', 'mediated', 'neutrino', 'mass', 'models', 'in', 'the', 'light', 'of', 'the', 'neutrino', 'oscillation', 'experiments', 'and', 'its', 'polarization', 'measurement', 'we', 'investigate', 'the', 'lepton', 'flavor', 'signatures', 'with', 'tau', 'lepton', 'at', 'lhc', 'upgrades', 'ie', 'hllhc', 'helhc', 'and', 'fcchh', 'through', 'leptonic', 'processes', 'from', 'doubly', 'charged', 'higgs', 'in', 'the', 'type', 'ii', 'seesaw', 'we', 'find', 'that', 'for', 'the', 'channel', 'with', 'one', 'tau', 'lepton', 'in', 'final', 'states', 'the', 'accessible', 'doubly', 'charged', 'higgs', 'mass', 'at', 'hllhc', 'can', 'reach', '655', 'gev', 'and', '695', 'gev', 'for', 'the', 'neutrino', 'mass', 'patterns', 'of', 'normal', 'hierarchy', 'nh', 'and', 'inverted', 'hierarchy', 'ih', 'respectively', 'with', 'the', 'luminosity', 'of', '3000', 'fb1', 'higher', 'masses', '9751930', 'gev', 'for', 'nh', 'and', '10352070', 'gev', 'for', 'ih', 'can', 'be', 'achieved', 'at', 'helhc', 'and', 'fcchh']] | [-0.03044120625781943, 0.3492542949352355, 0.061682020918851777, 0.21683702859445475, -0.031226427021465497, -0.20853023697418394, 0.03423360399756348, 0.345325372836669, -0.18298208924534265, -0.3407652734022122, 0.01669671682520857, -0.31167681376246037, 0.09917584295544657, 0.13615487866627518, 0.1306583176346976, 0.04939622702613633, 0.12203331121347105, -0.0562178728505387, -0.11009486087823461, -0.20380302165722242, 0.27285092209967843, 0.09986018588097068, 0.1908480314832559, 0.11841830071534787, 0.07437771969125606, -0.030865577407894307, -0.038019936917407904, -0.1556530285597546, -0.08969838005788233, 0.008673210307733825, 0.22398029558954136, 0.11073386331190704, 0.04900270672806073, -0.31442063342547044, -0.03881121429003542, 0.22052504942621454, 0.15941152347932075, 0.017091489640733926, -0.08865283216437092, -0.32797039131401107, 0.11223706397140631, -0.22872321625618497, -0.16334119407838443, 0.026249391285091406, -0.028618487995117903, -0.13164269702974707, -0.3525476979339146, 0.137162724038717, -0.08600433755418635, 0.017383541395247448, 0.014920574080861115, -0.23607748500216985, -0.11780891744638211, -0.0411361808401125, 0.15539417041964043, -0.046262846793979406, 0.1623510641845769, -0.2088960810269782, -0.18937867506701878, 0.3985476745147025, -0.07313193430672982, -0.12228118369966978, 0.1467489393839969, -0.2321272565623076, -0.15565881861948583, 0.1297826467853156, 0.2593949114670977, 0.029468270779034356, -0.1655903194223356, 0.1284626301389835, -0.06449582311324775, 0.1576918983610085, 0.08266984261172183, 0.07137821016112866, 0.3440342987596523, 0.28767877620703075, 0.08555465855897637, -0.03828311227789527, -0.17187786467911792, 0.0222882233611017, -0.43091783751151524, -0.13550120241598052, -0.011487149909953587, 0.0922527909569908, -0.06102915577787371, 0.0003421464061830193, 0.44633944216548116, 0.07540832545782905, 0.2664524498832179, -0.00920361912358203, 0.2365315267443293, 0.10056822849219316, 0.07465473936827038, 0.046429744270426454, 0.34418953307613265, 0.15380673573963577, 0.2108587141665339, -0.30101595138512494, -0.008511964623721724, 0.054311859435983934] |
1,802.00946 | Content based Weighted Consensus Summarization | Multi-document summarization has received a great deal of attention in the
past couple of decades. Several approaches have been proposed, many of which
perform equally well and it is becoming in- creasingly difficult to choose one
particular system over another. An ensemble of such systems that is able to
leverage the strengths of each individual systems can build a better and more
robust summary. Despite this, few attempts have been made in this direction. In
this paper, we describe a category of ensemble systems which use consensus
between the candidate systems to build a better meta-summary. We highlight two
major shortcomings of such systems: the inability to take into account relative
performance of individual systems and overlooking content of candidate
summaries in favour of the sentence rankings. We propose an alternate method,
content-based weighted consensus summarization, which address these concerns.
We use pseudo-relevant summaries to estimate the performance of individual
candidate systems, and then use this information to generate a better aggregate
ranking. Experiments on DUC 2003 and DUC 2004 datasets show that the proposed
system outperforms existing consensus-based techniques by a large margin.
| cs.IR cs.CL | multidocument summarization has received a great deal of attention in the past couple of decades several approaches have been proposed many of which perform equally well and it is becoming in creasingly difficult to choose one particular system over another an ensemble of such systems that is able to leverage the strengths of each individual systems can build a better and more robust summary despite this few attempts have been made in this direction in this paper we describe a category of ensemble systems which use consensus between the candidate systems to build a better metasummary we highlight two major shortcomings of such systems the inability to take into account relative performance of individual systems and overlooking content of candidate summaries in favour of the sentence rankings we propose an alternate method contentbased weighted consensus summarization which address these concerns we use pseudorelevant summaries to estimate the performance of individual candidate systems and then use this information to generate a better aggregate ranking experiments on duc 2003 and duc 2004 datasets show that the proposed system outperforms existing consensusbased techniques by a large margin | [['multidocument', 'summarization', 'has', 'received', 'a', 'great', 'deal', 'of', 'attention', 'in', 'the', 'past', 'couple', 'of', 'decades', 'several', 'approaches', 'have', 'been', 'proposed', 'many', 'of', 'which', 'perform', 'equally', 'well', 'and', 'it', 'is', 'becoming', 'in', 'creasingly', 'difficult', 'to', 'choose', 'one', 'particular', 'system', 'over', 'another', 'an', 'ensemble', 'of', 'such', 'systems', 'that', 'is', 'able', 'to', 'leverage', 'the', 'strengths', 'of', 'each', 'individual', 'systems', 'can', 'build', 'a', 'better', 'and', 'more', 'robust', 'summary', 'despite', 'this', 'few', 'attempts', 'have', 'been', 'made', 'in', 'this', 'direction', 'in', 'this', 'paper', 'we', 'describe', 'a', 'category', 'of', 'ensemble', 'systems', 'which', 'use', 'consensus', 'between', 'the', 'candidate', 'systems', 'to', 'build', 'a', 'better', 'metasummary', 'we', 'highlight', 'two', 'major', 'shortcomings', 'of', 'such', 'systems', 'the', 'inability', 'to', 'take', 'into', 'account', 'relative', 'performance', 'of', 'individual', 'systems', 'and', 'overlooking', 'content', 'of', 'candidate', 'summaries', 'in', 'favour', 'of', 'the', 'sentence', 'rankings', 'we', 'propose', 'an', 'alternate', 'method', 'contentbased', 'weighted', 'consensus', 'summarization', 'which', 'address', 'these', 'concerns', 'we', 'use', 'pseudorelevant', 'summaries', 'to', 'estimate', 'the', 'performance', 'of', 'individual', 'candidate', 'systems', 'and', 'then', 'use', 'this', 'information', 'to', 'generate', 'a', 'better', 'aggregate', 'ranking', 'experiments', 'on', 'duc', '2003', 'and', 'duc', '2004', 'datasets', 'show', 'that', 'the', 'proposed', 'system', 'outperforms', 'existing', 'consensusbased', 'techniques', 'by', 'a', 'large', 'margin']] | [-0.07695606841410224, -0.027568182937262303, -0.0832693045319232, 0.07612039309358326, -0.09483782184351194, -0.14140278383434474, 0.04115284038873922, 0.4354706155443257, -0.23758354265201878, -0.33622065129700107, 0.07953208657416545, -0.2985680300964984, -0.1610210094780656, 0.18872664895440852, -0.12991851512700892, 0.06693374367870882, 0.10668627469695512, 0.051102689281873566, -0.04972380610658777, -0.3343901234873376, 0.29394842190847087, 0.04867017606611026, 0.3299737976004298, 0.01027398096118634, 0.11328440519807102, -0.009868608494698592, -0.04624196159164677, 0.023327477584633934, -0.0781843832712064, 0.18501189576492613, 0.3001896266078392, 0.2004123021756391, 0.3753005604388878, -0.3950030713823143, -0.22281069978611073, 0.12627630140171847, 0.16475328026022149, 0.12814252013041558, -0.04551372298394179, -0.28459625724948695, 0.08215763288739414, -0.24592675468219177, -0.008336717349855782, -0.15359142249332394, 0.026786275150428352, 0.02915554797145088, -0.23699287480705386, 0.016263211453026467, 0.07787937684750451, 0.03202828649267718, -0.019616646566786446, -0.12935992831474294, 0.051932929683691606, 0.1798903495022147, 0.06255466192621728, 0.030659803326860847, 0.08642919347263299, -0.12114353393873578, -0.17602529525142777, 0.3858436020807578, -0.031815585356006135, -0.18830543591616583, 0.23112720748215176, -0.035764396704917586, -0.1739793384324882, 0.08278774718272981, 0.23116882964661653, 0.11003528165779394, -0.1916076351049264, -0.019689552820157157, -0.05692248323492215, 0.20917615618628377, 0.027505476822165753, 0.027027623446474035, 0.23842399972900338, 0.22517597813870513, 0.045191872535864465, 0.11706174376093179, -0.07154638334407706, -0.09297965829270882, -0.1632370883087208, -0.13928736281897208, -0.15015345217869872, -0.011369311882395318, -0.02447966062219156, -0.14931181003595448, 0.40720573100542834, 0.29001813653119646, 0.21984786828877506, 0.028012166954295526, 0.31833107973152136, 0.03005012876487204, 0.08374573429406794, 0.05944171900461827, 0.22252327587217563, 0.04870066331356641, 0.11250479504719356, -0.14300899258955474, 0.07914469756856356, 0.01576971560831765] |
1,802.00947 | Ensembling Neural Networks for Digital Pathology Images Classification
and Segmentation | In the last years, neural networks have proven to be a powerful framework for
various image analysis problems. However, some application domains have
specific limitations. Notably, digital pathology is an example of such fields
due to tremendous image sizes and quite limited number of training examples
available. In this paper, we adopt state-of-the-art convolutional neural
networks (CNN) architectures for digital pathology images analysis. We propose
to classify image patches to increase effective sample size and then to apply
an ensembling technique to build prediction for the original images. To
validate the developed approaches, we conducted experiments with \textit{Breast
Cancer Histology Challenge} dataset and obtained 90\% accuracy for the 4-class
tissue classification task.
| cs.CV | in the last years neural networks have proven to be a powerful framework for various image analysis problems however some application domains have specific limitations notably digital pathology is an example of such fields due to tremendous image sizes and quite limited number of training examples available in this paper we adopt stateoftheart convolutional neural networks cnn architectures for digital pathology images analysis we propose to classify image patches to increase effective sample size and then to apply an ensembling technique to build prediction for the original images to validate the developed approaches we conducted experiments with textitbreast cancer histology challenge dataset and obtained 90 accuracy for the 4class tissue classification task | [['in', 'the', 'last', 'years', 'neural', 'networks', 'have', 'proven', 'to', 'be', 'a', 'powerful', 'framework', 'for', 'various', 'image', 'analysis', 'problems', 'however', 'some', 'application', 'domains', 'have', 'specific', 'limitations', 'notably', 'digital', 'pathology', 'is', 'an', 'example', 'of', 'such', 'fields', 'due', 'to', 'tremendous', 'image', 'sizes', 'and', 'quite', 'limited', 'number', 'of', 'training', 'examples', 'available', 'in', 'this', 'paper', 'we', 'adopt', 'stateoftheart', 'convolutional', 'neural', 'networks', 'cnn', 'architectures', 'for', 'digital', 'pathology', 'images', 'analysis', 'we', 'propose', 'to', 'classify', 'image', 'patches', 'to', 'increase', 'effective', 'sample', 'size', 'and', 'then', 'to', 'apply', 'an', 'ensembling', 'technique', 'to', 'build', 'prediction', 'for', 'the', 'original', 'images', 'to', 'validate', 'the', 'developed', 'approaches', 'we', 'conducted', 'experiments', 'with', 'textitbreast', 'cancer', 'histology', 'challenge', 'dataset', 'and', 'obtained', '90', 'accuracy', 'for', 'the', '4class', 'tissue', 'classification', 'task']] | [0.0015087307070021157, -0.07800623240669861, -0.027488500083301653, 0.07519108223988935, -0.10206206256171337, -0.1633099919260555, 0.010373661572507976, 0.46211584877675854, -0.23839212771974974, -0.35511259078090124, 0.13341789461697476, -0.26369963023341725, -0.2166043319445741, 0.2332256426615221, -0.17247953616200132, 0.10932757469263298, 0.16079546605983563, 0.0026715384452259757, -0.038558073102460906, -0.36561161758344585, 0.2783883110859205, 0.027351904331144313, 0.4025562917729756, 0.025171878932228497, 0.13113454789728732, -0.07397707143949496, -0.04401638979713122, 0.00673848820162249, -0.08057069996065619, 0.17289046243646997, 0.3628058048711905, 0.20135442159969258, 0.3198583175109381, -0.45043057947479104, -0.2521928042039141, 0.12585987982147182, 0.18240645878763684, 0.15899572331407988, -0.06452067027606284, -0.3204954800443506, 0.13378350929092817, -0.1565512125086677, -0.019479385661045172, -0.17310982065900388, 0.021642039939006034, -0.051765872686807705, -0.2624383914108212, 0.04139180980734988, 0.024729776081864024, 0.1237726661603193, -0.05321608754588073, -0.11172602622274745, 0.06885655392443778, 0.19199297189091644, 0.04097426369542944, 0.08290103129069279, 0.1404353326799029, -0.21280785367471744, -0.14552560330553166, 0.3306406501865199, -0.012148805633858518, -0.18333467763957675, 0.22005245522470088, -0.01361301167960371, -0.1598693577841193, 0.11037792154768196, 0.2653689643253807, 0.09657620424236935, -0.1869357557933744, -0.01297162088454357, -0.006640202034399047, 0.1877933244581695, 0.0687034951652996, -0.031916668768636546, 0.1283293697114631, 0.2885543058918965, -0.024906002089951758, 0.15775602632768554, -0.1866409305885837, -0.0017337046630747682, -0.16237277810268844, -0.09885898552969233, -0.1780418005943936, -0.006697250679051419, -0.07124204379005614, -0.15500981058203825, 0.40360699620030754, 0.24874307528814105, 0.19822210649569594, 0.10802938954837553, 0.3380675424762943, -0.017698977843161906, 0.16840116143685993, 0.01634217235878496, 0.17327978071543756, 0.06851164579626408, 0.14421253117757873, -0.10920995908374018, 0.010716433299134846, 0.039874306510764734] |
1,802.00948 | Resset: A Recurrent Model for Sequence of Sets with Applications to
Electronic Medical Records | Modern healthcare is ripe for disruption by AI. A game changer would be
automatic understanding the latent processes from electronic medical records,
which are being collected for billions of people worldwide. However, these
healthcare processes are complicated by the interaction between at least three
dynamic components: the illness which involves multiple diseases, the care
which involves multiple treatments, and the recording practice which is biased
and erroneous. Existing methods are inadequate in capturing the dynamic
structure of care. We propose Resset, an end-to-end recurrent model that reads
medical record and predicts future risk. The model adopts the algebraic view in
that discrete medical objects are embedded into continuous vectors lying in the
same space. We formulate the problem as modeling sequences of sets, a novel
setting that have rarely, if not, been addressed. Within Resset, the bag of
diseases recorded at each clinic visit is modeled as function of sets. The same
hold for the bag of treatments. The interaction between the disease bag and the
treatment bag at a visit is modeled in several, one of which as residual of
diseases minus the treatments. Finally, the health trajectory, which is a
sequence of visits, is modeled using a recurrent neural network. We report
results on over a hundred thousand hospital visits by patients suffered from
two costly chronic diseases -- diabetes and mental health. Resset shows
promises in multiple predictive tasks such as readmission prediction,
treatments recommendation and diseases progression.
| cs.NE | modern healthcare is ripe for disruption by ai a game changer would be automatic understanding the latent processes from electronic medical records which are being collected for billions of people worldwide however these healthcare processes are complicated by the interaction between at least three dynamic components the illness which involves multiple diseases the care which involves multiple treatments and the recording practice which is biased and erroneous existing methods are inadequate in capturing the dynamic structure of care we propose resset an endtoend recurrent model that reads medical record and predicts future risk the model adopts the algebraic view in that discrete medical objects are embedded into continuous vectors lying in the same space we formulate the problem as modeling sequences of sets a novel setting that have rarely if not been addressed within resset the bag of diseases recorded at each clinic visit is modeled as function of sets the same hold for the bag of treatments the interaction between the disease bag and the treatment bag at a visit is modeled in several one of which as residual of diseases minus the treatments finally the health trajectory which is a sequence of visits is modeled using a recurrent neural network we report results on over a hundred thousand hospital visits by patients suffered from two costly chronic diseases diabetes and mental health resset shows promises in multiple predictive tasks such as readmission prediction treatments recommendation and diseases progression | [['modern', 'healthcare', 'is', 'ripe', 'for', 'disruption', 'by', 'ai', 'a', 'game', 'changer', 'would', 'be', 'automatic', 'understanding', 'the', 'latent', 'processes', 'from', 'electronic', 'medical', 'records', 'which', 'are', 'being', 'collected', 'for', 'billions', 'of', 'people', 'worldwide', 'however', 'these', 'healthcare', 'processes', 'are', 'complicated', 'by', 'the', 'interaction', 'between', 'at', 'least', 'three', 'dynamic', 'components', 'the', 'illness', 'which', 'involves', 'multiple', 'diseases', 'the', 'care', 'which', 'involves', 'multiple', 'treatments', 'and', 'the', 'recording', 'practice', 'which', 'is', 'biased', 'and', 'erroneous', 'existing', 'methods', 'are', 'inadequate', 'in', 'capturing', 'the', 'dynamic', 'structure', 'of', 'care', 'we', 'propose', 'resset', 'an', 'endtoend', 'recurrent', 'model', 'that', 'reads', 'medical', 'record', 'and', 'predicts', 'future', 'risk', 'the', 'model', 'adopts', 'the', 'algebraic', 'view', 'in', 'that', 'discrete', 'medical', 'objects', 'are', 'embedded', 'into', 'continuous', 'vectors', 'lying', 'in', 'the', 'same', 'space', 'we', 'formulate', 'the', 'problem', 'as', 'modeling', 'sequences', 'of', 'sets', 'a', 'novel', 'setting', 'that', 'have', 'rarely', 'if', 'not', 'been', 'addressed', 'within', 'resset', 'the', 'bag', 'of', 'diseases', 'recorded', 'at', 'each', 'clinic', 'visit', 'is', 'modeled', 'as', 'function', 'of', 'sets', 'the', 'same', 'hold', 'for', 'the', 'bag', 'of', 'treatments', 'the', 'interaction', 'between', 'the', 'disease', 'bag', 'and', 'the', 'treatment', 'bag', 'at', 'a', 'visit', 'is', 'modeled', 'in', 'several', 'one', 'of', 'which', 'as', 'residual', 'of', 'diseases', 'minus', 'the', 'treatments', 'finally', 'the', 'health', 'trajectory', 'which', 'is', 'a', 'sequence', 'of', 'visits', 'is', 'modeled', 'using', 'a', 'recurrent', 'neural', 'network', 'we', 'report', 'results', 'on', 'over', 'a', 'hundred', 'thousand', 'hospital', 'visits', 'by', 'patients', 'suffered', 'from', 'two', 'costly', 'chronic', 'diseases', 'diabetes', 'and', 'mental', 'health', 'resset', 'shows', 'promises', 'in', 'multiple', 'predictive', 'tasks', 'such', 'as', 'readmission', 'prediction', 'treatments', 'recommendation', 'and', 'diseases', 'progression']] | [-0.07808780108074037, 0.09194571419751203, -0.04702199939735389, 0.104802801659874, -0.07836104835344788, -0.15828477017372886, 0.05959257630262679, 0.40541675427618123, -0.2399421416737217, -0.2857238181789095, 0.12763050410606713, -0.3331343517890976, -0.16831873486662516, 0.18414260907641922, -0.127736335004253, 0.03364750587400825, 0.12162740772861677, 0.09310700562394535, 0.01813656274753157, -0.27013792986496504, 0.27559172028898804, -0.012633413724446048, 0.2985366988539075, 0.02902059500144484, 0.10314262593492458, 0.049358581590543814, -0.03415462088378263, -0.010175839179404041, -0.03691756866595218, 0.1160343954936252, 0.3653991667160881, 0.19785519339687502, 0.414363239412584, -0.4577060869623286, -0.2870897433798139, 0.08667293408652768, 0.11679674658031823, 0.07780450451828073, 0.011373572452672913, -0.30143546043351915, 0.05177461458636875, -0.17736539953378572, -0.06924471855600131, -0.047209920984460044, -0.017631762584399743, -0.002653820444538724, -0.2850183786875277, 0.09339534681445609, -0.028567802135754997, 0.12920147254675005, -0.09214431021149115, -0.1365002999455707, -0.008930801465855136, 0.23311458334404353, 0.07608570081744498, 0.047284492103305334, 0.15382527321150216, -0.15983232958242297, -0.14029175278483308, 0.37172190909817193, 0.03286270089398992, -0.1536305911373347, 0.18984807272815185, -0.07606368172176493, -0.1518423348285675, 0.10815391716896557, 0.20697991634757879, 0.08060065579096166, -0.23138869141136334, -0.033928526427189354, -0.009207676782777223, 0.12939292579588557, 0.07192324402761491, -0.02965832710712372, 0.21584290764294564, 0.23761701810484132, -0.007682986796407931, 0.08384135462353394, -0.08716820781555726, -0.07902946609538049, -0.2292921383222468, -0.1120672683774804, -0.13188891754931925, 0.03529493161192174, -0.07421160169336266, -0.19963666513795034, 0.3883880962365462, 0.18174439082698277, 0.15585678757634014, 0.025778951130147713, 0.29411439752148, 0.0236508472065907, 0.11443414125096751, 0.040108817307433736, 0.09947117092087865, -0.0046268153424231665, 0.11248066125505526, -0.16913338969170583, 0.176676356359773, 0.016011003938425954] |
1,802.00949 | A parallel-in-time fixed-stress splitting method for Biot's
consolidation model | In this work, we study the parallel-in-time iterative solution of coupled
flow and geomechanics in porous media, modelled by a two-field formulation of
the Biot's equations. In particular, we propose a new version of the fixed
stress splitting method, which has been widely used as solution method of these
problems. This new approach forgets about the sequential nature of the temporal
variable and considers the time direction as a further direction for
parallelization. We present a rigorous convergence analysis of the method and a
numerical experiment to demonstrate the robust behaviour of the algorithm.
| math.NA | in this work we study the parallelintime iterative solution of coupled flow and geomechanics in porous media modelled by a twofield formulation of the biots equations in particular we propose a new version of the fixed stress splitting method which has been widely used as solution method of these problems this new approach forgets about the sequential nature of the temporal variable and considers the time direction as a further direction for parallelization we present a rigorous convergence analysis of the method and a numerical experiment to demonstrate the robust behaviour of the algorithm | [['in', 'this', 'work', 'we', 'study', 'the', 'parallelintime', 'iterative', 'solution', 'of', 'coupled', 'flow', 'and', 'geomechanics', 'in', 'porous', 'media', 'modelled', 'by', 'a', 'twofield', 'formulation', 'of', 'the', 'biots', 'equations', 'in', 'particular', 'we', 'propose', 'a', 'new', 'version', 'of', 'the', 'fixed', 'stress', 'splitting', 'method', 'which', 'has', 'been', 'widely', 'used', 'as', 'solution', 'method', 'of', 'these', 'problems', 'this', 'new', 'approach', 'forgets', 'about', 'the', 'sequential', 'nature', 'of', 'the', 'temporal', 'variable', 'and', 'considers', 'the', 'time', 'direction', 'as', 'a', 'further', 'direction', 'for', 'parallelization', 'we', 'present', 'a', 'rigorous', 'convergence', 'analysis', 'of', 'the', 'method', 'and', 'a', 'numerical', 'experiment', 'to', 'demonstrate', 'the', 'robust', 'behaviour', 'of', 'the', 'algorithm']] | [-0.09661694302005654, 0.007246435917121299, -0.14192765489458403, 0.017618721058750723, -0.06446568378831874, -0.11537915781794235, 0.027928474842214047, 0.3720489663983438, -0.3025667816747297, -0.28999437058364297, 0.12070768067558751, -0.19562040435823988, -0.20037544678470634, 0.17459217851307798, -0.04287057593365774, 0.11072848168538606, 0.07980944750275701, -0.023606166917275875, -0.056330403290129206, -0.20942144537161284, 0.28405733097423896, 0.032951460774750155, 0.3042038799699475, 0.03947959912871506, 0.14621828383508514, 0.0031128247098828805, -0.045854141271891115, 0.07177568891858484, -0.1337003918721321, 0.14278938399350388, 0.20227684952972574, 0.11192417904447288, 0.34305237717133885, -0.4174572616899782, -0.23481357442908624, 0.05303755702767918, 0.16240849185046402, 0.15230408919593716, -0.07654448567909446, -0.25933547359594006, 0.08258060969788819, -0.16462108138156065, -0.1419456781908632, -0.09695207099232109, -0.04311434812150262, 0.00966327129912741, -0.2687570101066314, 0.09955201150869276, 0.06617237660201623, 0.023470954949709962, -0.06936860203921319, -0.06320681860125525, 0.06686814541016962, 0.05622631368385826, 0.07399582769350208, 0.001605870511798941, 0.0634161216761679, -0.09084653813253216, -0.12103536323287543, 0.38985373254152056, -0.09677121899477108, -0.22673151436995, 0.16267686133629622, -0.04503693805631012, -0.13086498402891325, 0.10119550055180221, 0.2550069872250265, 0.22086734319244453, -0.1848546280426548, 0.06395945465501814, -0.06743626131061861, 0.16271677107470942, 0.025380067804709396, -0.04456845609015448, 0.11849847731200304, 0.24099070036615383, 0.0863540479814277, 0.18020687597070603, -0.06800623305954356, -0.12115655088440534, -0.3030374747839697, -0.19732765351085269, -0.14855806138416, 0.0018805947982671453, -0.09472086656039229, -0.1785306300611255, 0.4315569320138782, 0.19389113637202599, 0.15248569809494816, 0.03963866021528721, 0.32153045666463514, 0.13097392467217164, 0.014195496335308602, 0.080974709569536, 0.21749439632440817, 0.13735316375498363, 0.17493542568124038, -0.2384388219723676, 0.05511963363003699, 0.12532658655037907] |
1,802.0095 | Temperature-dependent magnetic properties of a magnetoactive elastomer:
immobilization of the soft-magnetic filler | Magnetic properties of a magnetoactive elastomer (MAE) filled with
{\mu}m-sized soft-magnetic iron particles have been experimentally studied in
the temperature range between 150 K and 310 K. By changing the temperature, the
elastic modulus of the elastomer matrix was modified and it was possible to
obtain magnetization curves for an invariable arrangement of particles in the
sample as well as in the case when the particles were able to change their
position within the MAE under the influence of magnetic forces. At low (less
than 220 K) temperatures, when the matrix becomes rigid, the magnetization of
the MAE does not show a hysteresis behavior and it is characterized by a
negative value of the Rayleigh constant. At room temperature, when the polymer
matrix is compliant, a magnetic hysteresis exists and exhibits local maxima of
the field dependence of the differential magnetic susceptibility. The
appearance of these maxima is explained by the elastic resistance of the matrix
to the displacement of particles under the action of magnetic forces.
| cond-mat.mtrl-sci | magnetic properties of a magnetoactive elastomer mae filled with mumsized softmagnetic iron particles have been experimentally studied in the temperature range between 150 k and 310 k by changing the temperature the elastic modulus of the elastomer matrix was modified and it was possible to obtain magnetization curves for an invariable arrangement of particles in the sample as well as in the case when the particles were able to change their position within the mae under the influence of magnetic forces at low less than 220 k temperatures when the matrix becomes rigid the magnetization of the mae does not show a hysteresis behavior and it is characterized by a negative value of the rayleigh constant at room temperature when the polymer matrix is compliant a magnetic hysteresis exists and exhibits local maxima of the field dependence of the differential magnetic susceptibility the appearance of these maxima is explained by the elastic resistance of the matrix to the displacement of particles under the action of magnetic forces | [['magnetic', 'properties', 'of', 'a', 'magnetoactive', 'elastomer', 'mae', 'filled', 'with', 'mumsized', 'softmagnetic', 'iron', 'particles', 'have', 'been', 'experimentally', 'studied', 'in', 'the', 'temperature', 'range', 'between', '150', 'k', 'and', '310', 'k', 'by', 'changing', 'the', 'temperature', 'the', 'elastic', 'modulus', 'of', 'the', 'elastomer', 'matrix', 'was', 'modified', 'and', 'it', 'was', 'possible', 'to', 'obtain', 'magnetization', 'curves', 'for', 'an', 'invariable', 'arrangement', 'of', 'particles', 'in', 'the', 'sample', 'as', 'well', 'as', 'in', 'the', 'case', 'when', 'the', 'particles', 'were', 'able', 'to', 'change', 'their', 'position', 'within', 'the', 'mae', 'under', 'the', 'influence', 'of', 'magnetic', 'forces', 'at', 'low', 'less', 'than', '220', 'k', 'temperatures', 'when', 'the', 'matrix', 'becomes', 'rigid', 'the', 'magnetization', 'of', 'the', 'mae', 'does', 'not', 'show', 'a', 'hysteresis', 'behavior', 'and', 'it', 'is', 'characterized', 'by', 'a', 'negative', 'value', 'of', 'the', 'rayleigh', 'constant', 'at', 'room', 'temperature', 'when', 'the', 'polymer', 'matrix', 'is', 'compliant', 'a', 'magnetic', 'hysteresis', 'exists', 'and', 'exhibits', 'local', 'maxima', 'of', 'the', 'field', 'dependence', 'of', 'the', 'differential', 'magnetic', 'susceptibility', 'the', 'appearance', 'of', 'these', 'maxima', 'is', 'explained', 'by', 'the', 'elastic', 'resistance', 'of', 'the', 'matrix', 'to', 'the', 'displacement', 'of', 'particles', 'under', 'the', 'action', 'of', 'magnetic', 'forces']] | [-0.14787792334678823, 0.24187546108877578, -0.05034880846665282, -0.014347545652717219, -0.030689646496889208, -0.08716206678919895, 0.026871861743567857, 0.3947149088305092, -0.2656236703613561, -0.3446687974459575, 0.04927895754550634, -0.30074672959187254, -0.10549047705856804, 0.1599107435374649, 0.02242471289843276, 0.0169044624495426, -0.051531463920718765, 0.08463135820745162, -0.07767362356176154, -0.211966441983279, 0.2531691105411706, 0.06958076085694535, 0.2873800825327635, 0.08607659817597979, 0.1024432559943797, 0.006160746197478322, 0.11201289733927586, 0.0932672323134845, -0.12284270395517131, 0.017385975958135163, 0.18582858044018377, -0.0565167488773391, 0.1907657594712797, -0.41187272894782295, -0.1968790218010991, 0.08064262317995469, 0.0828561499954653, 0.06489539748158105, 0.0026856244668576443, -0.22463795076358595, 0.10057096909675858, -0.08636037911522665, -0.16944535670754596, -0.044542589372659695, 0.03672889375035835, 0.01942951110481636, -0.2552657798741258, 0.12867937074182276, 0.05685647056803308, 0.13904702240143885, -0.11745296226825543, -0.15230050540813608, -0.04228461120963587, 0.07201538878074056, 0.1042615193831818, 0.015265418584653717, 0.22758864435160944, -0.11802111529259982, -0.027612370873862754, 0.39053369324074355, -0.0775532671505715, -0.12703315397406634, 0.1277255871483755, -0.17602695266487534, -0.00978704732368717, 0.2136355919378648, 0.12547820338053617, 0.09700125240592543, -0.14258392836923311, 0.07686128914596731, -0.012660586151076916, 0.1755504871266866, 0.08323476260840268, -0.03100080767362016, 0.21422434486671835, 0.1551596384375596, 0.045302714172382644, 0.1586367719225351, -0.14083413287305724, -0.03731684699064834, -0.24070204580816146, -0.14512031016213273, -0.20302041404403767, 0.053770662864065774, -0.12756845849380732, -0.20759140918154023, 0.35349759495744626, 0.08419841425326056, 0.21882852947640563, 0.00616453530010349, 0.24982057241541697, 0.0944605201362872, 0.0948629975235257, 0.06704688023965427, 0.3033440112788788, 0.2077385888094763, 0.14056905733789513, -0.27846640377154874, 0.12236119646155191, -0.005244176558770998] |
1,802.00951 | Scheduling and Checkpointing optimization algorithm for Byzantine fault
tolerance in Cloud Clusters | Among those faults Byzantine faults offers serious challenge to fault
tolerance mechanism, because it often go undetected at the initial stage and it
can easily propagate to other VMs before a detection is made. Consequently some
of the mission critical application such as air traffic control, online baking
etc still staying away from the cloud for such reasons. However if a Byzantine
faults is not detected and tolerated at initial stage then applications such as
big data analytics can go completely wrong in spite of hours of computations
performed by the entire cloud. Therefore in the previous work a fool-proof
Byzantine fault detection has been proposed, as a continuation this work
designs a scheduling algorithm (WSSS) and checkpoint optimization algorithm
(TCC) to tolerate and eliminate the Byzantine faults before it makes any
impact. The WSSS algorithm keeps track of server performance which is part of
Virtual Clusters to help allocate best performing server to mission critical
application. WSSS therefore ranks the servers based on a counter which monitors
every Virtual Nodes (VN) for time and performance failures. The TCC algorithm
works to generalize the possible Byzantine error prone region through
monitoring delay variation to start new VNs with previous checkpointing.
Moreover it can stretch the state interval for performing and error free VNs in
an effect to minimize the space, time and cost overheads caused by
checkpointing. The analysis is performed with plotting state transition and
CloudSim based simulation. The result shows TCC reduces fault tolerance
overhead exponentially and the WSSS allots virtual resources effectively
| cs.DC | among those faults byzantine faults offers serious challenge to fault tolerance mechanism because it often go undetected at the initial stage and it can easily propagate to other vms before a detection is made consequently some of the mission critical application such as air traffic control online baking etc still staying away from the cloud for such reasons however if a byzantine faults is not detected and tolerated at initial stage then applications such as big data analytics can go completely wrong in spite of hours of computations performed by the entire cloud therefore in the previous work a foolproof byzantine fault detection has been proposed as a continuation this work designs a scheduling algorithm wsss and checkpoint optimization algorithm tcc to tolerate and eliminate the byzantine faults before it makes any impact the wsss algorithm keeps track of server performance which is part of virtual clusters to help allocate best performing server to mission critical application wsss therefore ranks the servers based on a counter which monitors every virtual nodes vn for time and performance failures the tcc algorithm works to generalize the possible byzantine error prone region through monitoring delay variation to start new vns with previous checkpointing moreover it can stretch the state interval for performing and error free vns in an effect to minimize the space time and cost overheads caused by checkpointing the analysis is performed with plotting state transition and cloudsim based simulation the result shows tcc reduces fault tolerance overhead exponentially and the wsss allots virtual resources effectively | [['among', 'those', 'faults', 'byzantine', 'faults', 'offers', 'serious', 'challenge', 'to', 'fault', 'tolerance', 'mechanism', 'because', 'it', 'often', 'go', 'undetected', 'at', 'the', 'initial', 'stage', 'and', 'it', 'can', 'easily', 'propagate', 'to', 'other', 'vms', 'before', 'a', 'detection', 'is', 'made', 'consequently', 'some', 'of', 'the', 'mission', 'critical', 'application', 'such', 'as', 'air', 'traffic', 'control', 'online', 'baking', 'etc', 'still', 'staying', 'away', 'from', 'the', 'cloud', 'for', 'such', 'reasons', 'however', 'if', 'a', 'byzantine', 'faults', 'is', 'not', 'detected', 'and', 'tolerated', 'at', 'initial', 'stage', 'then', 'applications', 'such', 'as', 'big', 'data', 'analytics', 'can', 'go', 'completely', 'wrong', 'in', 'spite', 'of', 'hours', 'of', 'computations', 'performed', 'by', 'the', 'entire', 'cloud', 'therefore', 'in', 'the', 'previous', 'work', 'a', 'foolproof', 'byzantine', 'fault', 'detection', 'has', 'been', 'proposed', 'as', 'a', 'continuation', 'this', 'work', 'designs', 'a', 'scheduling', 'algorithm', 'wsss', 'and', 'checkpoint', 'optimization', 'algorithm', 'tcc', 'to', 'tolerate', 'and', 'eliminate', 'the', 'byzantine', 'faults', 'before', 'it', 'makes', 'any', 'impact', 'the', 'wsss', 'algorithm', 'keeps', 'track', 'of', 'server', 'performance', 'which', 'is', 'part', 'of', 'virtual', 'clusters', 'to', 'help', 'allocate', 'best', 'performing', 'server', 'to', 'mission', 'critical', 'application', 'wsss', 'therefore', 'ranks', 'the', 'servers', 'based', 'on', 'a', 'counter', 'which', 'monitors', 'every', 'virtual', 'nodes', 'vn', 'for', 'time', 'and', 'performance', 'failures', 'the', 'tcc', 'algorithm', 'works', 'to', 'generalize', 'the', 'possible', 'byzantine', 'error', 'prone', 'region', 'through', 'monitoring', 'delay', 'variation', 'to', 'start', 'new', 'vns', 'with', 'previous', 'checkpointing', 'moreover', 'it', 'can', 'stretch', 'the', 'state', 'interval', 'for', 'performing', 'and', 'error', 'free', 'vns', 'in', 'an', 'effect', 'to', 'minimize', 'the', 'space', 'time', 'and', 'cost', 'overheads', 'caused', 'by', 'checkpointing', 'the', 'analysis', 'is', 'performed', 'with', 'plotting', 'state', 'transition', 'and', 'cloudsim', 'based', 'simulation', 'the', 'result', 'shows', 'tcc', 'reduces', 'fault', 'tolerance', 'overhead', 'exponentially', 'and', 'the', 'wsss', 'allots', 'virtual', 'resources', 'effectively']] | [-0.17395024503736448, 0.0492583778089337, -0.05245276318735206, 0.01913962083476066, -0.0789026456620252, -0.20406920849619542, 0.17092045232945796, 0.3742644562185103, -0.2770679896934798, -0.36243779929902625, 0.14540193866374537, -0.25988790784396376, -0.12690534051690325, 0.16633341132923413, -0.13909786615039077, 0.12864049840946773, 0.1146056612837699, 0.03864783142127243, 0.019843453979667497, -0.31254542256869816, 0.22531063723533104, 0.124703400684338, 0.27761242517690154, 0.05175461932176761, 0.03868852695170562, 0.010460162486004478, -0.0010680074584396447, 0.005377351733577419, -0.042193068655700806, 0.0157923436582125, 0.31416587960614145, 0.1930161032761794, 0.31563614733224993, -0.45218866931801827, -0.19118979237243242, 0.12332438370212913, 0.15529821634306298, 0.11378359054397864, -0.022213700924561743, -0.3007339879267794, 0.13895627790605466, -0.21880337908571842, -0.1034186880524252, -0.05046856629778631, 0.018613584686125464, -0.004982239786334628, -0.24883989535052986, 0.002195988140781136, 0.010405748887170179, 0.029961083131824055, -0.039671959172861246, -0.05092609783853678, 0.009758673717413902, 0.14422175334766507, 0.04305310349123365, 0.0734258439446635, 0.20298675166913216, -0.07050988693064188, -0.14530962159784108, 0.3664023633233692, 0.028863716107227055, -0.13759310347023512, 0.1674536567007867, 0.004828469097322109, -0.1772366342956529, 0.16261662072045546, 0.17688620879734848, 0.08205644989713762, -0.13727597569243288, 0.029669029587724556, 0.07570206696879776, 0.1930416021888674, 0.07617418379248941, -0.002672779561002173, 0.11961527354193523, 0.17916711471500057, 0.11949094131442846, 0.14863045709595704, -0.08404594867252836, -0.0883684206044064, -0.22785178573035142, -0.15231736789128797, -0.16857447041582097, -0.005732675523933151, -0.05560164607863864, -0.16424142020590735, 0.354854128041816, 0.2063404236591476, 0.15696038626426576, 0.08986656518707818, 0.40248746295441307, 0.03651841032391816, 0.10419066738264234, 0.1828622567266518, 0.20082445394341358, -0.019119637731152276, 0.12937861062613698, -0.19097799570025767, 0.17744684677808453, 0.0026537438818052704] |
1,802.00952 | A note on the folklore of free independence | It is shown that a Wishart matrix of standard complex normal random variables
is asymptotically freely independent of an independent random matrix, under
minimal conditions, in two different sense of asymptotic free independence.
| math.PR | it is shown that a wishart matrix of standard complex normal random variables is asymptotically freely independent of an independent random matrix under minimal conditions in two different sense of asymptotic free independence | [['it', 'is', 'shown', 'that', 'a', 'wishart', 'matrix', 'of', 'standard', 'complex', 'normal', 'random', 'variables', 'is', 'asymptotically', 'freely', 'independent', 'of', 'an', 'independent', 'random', 'matrix', 'under', 'minimal', 'conditions', 'in', 'two', 'different', 'sense', 'of', 'asymptotic', 'free', 'independence']] | [-0.1091517436593263, 0.2626701975986538, -0.09765772127800366, 0.05258828461537081, -0.038270678194804175, -0.17873082130752277, -0.020454698275760606, 0.3628919764252549, -0.2686944588115721, -0.14806210424638155, 0.09881650812740465, -0.22798990401806254, -0.19099194838693648, 0.1596381218370163, -0.11734575340806534, 0.09250292232768102, 0.05422549354702686, 0.10937601598826321, -0.08080476235287885, -0.31641335412859917, 0.30965593694285914, 0.006187264497081439, 0.3693573689370444, -0.09981137375297928, 0.17614791533825072, 0.08627536084333604, -0.047860143734424404, 0.01210669507131432, -0.06547414306238131, 0.028406477053508614, 0.22309127619320696, 0.13013030119040966, 0.24925492184631753, -0.3890886006763939, -0.16942459164243756, 0.18387431022005551, 0.11470614610747858, 0.006639159397419655, 0.029951839623126118, -0.20887451037538773, 0.1276474270800298, -0.15992935919061754, -0.17984267744715465, -0.02492039849643003, 0.014392908436782433, 0.0017279194047053654, -0.41305197464923066, 0.03668043443538023, 0.08963880074125799, 0.05845257944681428, 0.042425227210377205, -0.15229634926513289, 0.035712009352264984, 0.0844418745572594, 0.11382420969223886, -0.0747510687250531, 0.12820667261522348, -0.02967594468006582, -0.03203157347161323, 0.30538295734335075, -0.06287165141354005, -0.33919977340282814, 0.20999407565051859, -0.13332653356095156, -0.1265195540406487, 0.0957929880047838, 0.07633715585777254, 0.11007301650489822, -0.2579622663783305, 0.19443685852104065, -0.15545992843919632, 0.1383765098836386, 0.05805003075775775, -0.009497024951446238, 0.10687272253474503, 0.059998231065092666, 0.14623076721292103, 0.126226990003929, 0.06478259641902917, -0.16764498445572276, -0.35035296202157484, -0.15137831415455422, -0.2774427312454491, 0.12055522924514883, -0.2916620849264842, -0.31845503196271957, 0.372971618717367, 0.08973473298476276, 0.18109647924024047, 0.05433334590810718, 0.2006045106911298, 0.10833667306173028, -0.05447880353665713, 0.08519975742268743, 0.1016336320617208, 0.2491062912158668, -0.04383097487417134, -0.10146737443000982, 0.12134185062269821, 0.04350453919985078] |
1,802.00953 | Spatial Distribution of Gamma-Ray Burst Sources | The spatial distribution of sources of gamma-ray bursts (GRB) with known red
shifts is analyzed by the conditional density and pairwise distance methods.
The sample of GRB is based on data from the Swift mission and contains fluxes,
coordinates, and red shifts for 384 GRB sources. Selection effects that distort
the true source distribution are taken into account by comparing the observed
distribution with fractal and uniform model catalogs. The Malmqvist effect is
modeled using an approximation for the visible luminosity function of the GRB.
The case of absorption in the galactic plane is also examined.This approach
makes it possible to study the spatial structure of the entire sample at one
time without artificial truncations. The estimated fractal dimensionality is
$D=2.55\pm0.06$ on scales of $2\div6$ Gpc.
| astro-ph.CO | the spatial distribution of sources of gammaray bursts grb with known red shifts is analyzed by the conditional density and pairwise distance methods the sample of grb is based on data from the swift mission and contains fluxes coordinates and red shifts for 384 grb sources selection effects that distort the true source distribution are taken into account by comparing the observed distribution with fractal and uniform model catalogs the malmqvist effect is modeled using an approximation for the visible luminosity function of the grb the case of absorption in the galactic plane is also examinedthis approach makes it possible to study the spatial structure of the entire sample at one time without artificial truncations the estimated fractal dimensionality is d255pm006 on scales of 2div6 gpc | [['the', 'spatial', 'distribution', 'of', 'sources', 'of', 'gammaray', 'bursts', 'grb', 'with', 'known', 'red', 'shifts', 'is', 'analyzed', 'by', 'the', 'conditional', 'density', 'and', 'pairwise', 'distance', 'methods', 'the', 'sample', 'of', 'grb', 'is', 'based', 'on', 'data', 'from', 'the', 'swift', 'mission', 'and', 'contains', 'fluxes', 'coordinates', 'and', 'red', 'shifts', 'for', '384', 'grb', 'sources', 'selection', 'effects', 'that', 'distort', 'the', 'true', 'source', 'distribution', 'are', 'taken', 'into', 'account', 'by', 'comparing', 'the', 'observed', 'distribution', 'with', 'fractal', 'and', 'uniform', 'model', 'catalogs', 'the', 'malmqvist', 'effect', 'is', 'modeled', 'using', 'an', 'approximation', 'for', 'the', 'visible', 'luminosity', 'function', 'of', 'the', 'grb', 'the', 'case', 'of', 'absorption', 'in', 'the', 'galactic', 'plane', 'is', 'also', 'examinedthis', 'approach', 'makes', 'it', 'possible', 'to', 'study', 'the', 'spatial', 'structure', 'of', 'the', 'entire', 'sample', 'at', 'one', 'time', 'without', 'artificial', 'truncations', 'the', 'estimated', 'fractal', 'dimensionality', 'is', 'd255pm006', 'on', 'scales', 'of', '2div6', 'gpc']] | [-0.04111779691101831, 0.10662402786931394, -0.09078364837036819, 0.13862425293945174, -0.08733374171233813, -0.04792524468856024, 0.0454132365898733, 0.448730260255884, -0.20874391414499918, -0.32617502322527353, 0.033579690642387715, -0.341499031849633, -0.026563225054472197, 0.20707394157108835, 0.005305076231721972, 0.007022841552035791, 0.04181082411592857, -0.043060495579218275, -0.03457277945145491, -0.25101675836705284, 0.31171690401063895, 0.11185865314891104, 0.28290719168974454, -0.048438156294621164, 0.11589099612892376, 0.00021308537984847045, -0.09907997455104392, -0.004174335058167821, -0.09723162563494073, 0.05701321461161629, 0.17381089327849264, 0.14783461319213947, 0.2148837499477, -0.3460864000871432, -0.2627181236739041, 0.09577470211422864, 0.13500680345049526, 0.04370953070885334, -0.0006035059083589032, -0.31263302563361395, 0.029072125526297777, -0.11964521045270796, -0.18149770455831876, 0.04274261856573771, 0.05705746407925961, 0.07369900823496359, -0.21970297652535278, 0.12658583055814293, 0.0215975637356827, 0.05840060224787134, -0.09465743328795814, -0.07254807487870643, -0.0535754531766975, 0.10661850212767843, 0.044908371739677294, 0.041387586525190985, 0.11275960565888185, -0.09703125177713715, -0.055246214389984234, 0.4087590693274788, -0.04099745885850709, -0.09978478595654511, 0.1383722272461097, -0.1757351780829371, -0.12441977328662837, 0.1663830775164495, 0.17226166743785143, 0.0871762363087447, -0.17268890452802135, 0.05708708392349301, 0.013970204624232882, 0.20091992477718434, 0.020660606721515356, 0.06105506356393338, 0.21513734867826836, 0.15790408531387076, 0.0375118032555844, 0.14574893823534738, -0.25660597529552387, -0.03252822271840204, -0.30329330499497714, -0.0501039454743999, -0.23311315366681512, 0.05883627112756376, -0.15803881759753405, -0.14929650558567162, 0.37834898131273564, 0.12638475794772633, 0.22084521268662372, 0.056049016584855976, 0.30778770607545, 0.11409711664481485, 0.10901519304263543, 0.08722684690041742, 0.2561356860945826, 0.11078708795983283, 0.03527286894558394, -0.19702597992822957, 0.12163129224838903, 0.034591898039655115] |
1,802.00954 | On logarithmic bounds of maximal sparse operators | Given sparse collections of measurable sets $\mathcal S_k$, $k=1,2,\ldots
,N$, in a general measure space $(X,\mathfrak M,\mu)$, let $ \Lambda_{\mathcal
S_k}$ be the sparse operator, corresponding to $\mathcal S_k$. We show that the
maximal sparse function $ \Lambda f = \max _{1\le k\le N} \Lambda_{\mathcal
S_k} f $ satisfies \begin{align*}
&\| \Lambda \| _{L^p(X) \mapsto L^{p,\infty}(X)} \lesssim \log N\cdot
\|M_{\mathcal S}\|_{L^p(X) \mapsto L^{p,\infty}(X)},\,1\le p<\infty, \\ &\lVert
\Lambda \rVert _{L^p(X) \mapsto L^p(X)} \lesssim (\log
N)^{\max\{1,1/(p-1)\}}\cdot \|M_{\mathcal S}\|_{L^p(X) \mapsto L^p(X)},\,
1<p<\infty, \end{align*} where $M_{\mathcal S}$ is the maximal function
corresponding to the collection of sets $\mathcal S=\cup_k\mathcal S_k$. As a
consequence, one can derive norm bounds for maximal functions formed from
taking measurable selections of one-dimensional Calder\'on-Zygmund operators in
the plane. Prior results of this type had a fixed choice of Calder\'on-Zygmund
operator for each direction.
| math.CA | given sparse collections of measurable sets mathcal s_k k12ldots n in a general measure space xmathfrak mmu let lambda_mathcal s_k be the sparse operator corresponding to mathcal s_k we show that the maximal sparse function lambda f max _1le kle n lambda_mathcal s_k f satisfies beginalign lambda _lpx mapsto lpinftyx lesssim log ncdot m_mathcal s_lpx mapsto lpinftyx1le pinfty lvert lambda rvert _lpx mapsto lpx lesssim log nmax11p1cdot m_mathcal s_lpx mapsto lpx 1pinfty endalign where m_mathcal s is the maximal function corresponding to the collection of sets mathcal scup_kmathcal s_k as a consequence one can derive norm bounds for maximal functions formed from taking measurable selections of onedimensional calderonzygmund operators in the plane prior results of this type had a fixed choice of calderonzygmund operator for each direction | [['given', 'sparse', 'collections', 'of', 'measurable', 'sets', 'mathcal', 's_k', 'k12ldots', 'n', 'in', 'a', 'general', 'measure', 'space', 'xmathfrak', 'mmu', 'let', 'lambda_mathcal', 's_k', 'be', 'the', 'sparse', 'operator', 'corresponding', 'to', 'mathcal', 's_k', 'we', 'show', 'that', 'the', 'maximal', 'sparse', 'function', 'lambda', 'f', 'max', '_1le', 'kle', 'n', 'lambda_mathcal', 's_k', 'f', 'satisfies', 'beginalign', 'lambda', '_lpx', 'mapsto', 'lpinftyx', 'lesssim', 'log', 'ncdot', 'm_mathcal', 's_lpx', 'mapsto', 'lpinftyx1le', 'pinfty', 'lvert', 'lambda', 'rvert', '_lpx', 'mapsto', 'lpx', 'lesssim', 'log', 'nmax11p1cdot', 'm_mathcal', 's_lpx', 'mapsto', 'lpx', '1pinfty', 'endalign', 'where', 'm_mathcal', 's', 'is', 'the', 'maximal', 'function', 'corresponding', 'to', 'the', 'collection', 'of', 'sets', 'mathcal', 'scup_kmathcal', 's_k', 'as', 'a', 'consequence', 'one', 'can', 'derive', 'norm', 'bounds', 'for', 'maximal', 'functions', 'formed', 'from', 'taking', 'measurable', 'selections', 'of', 'onedimensional', 'calderonzygmund', 'operators', 'in', 'the', 'plane', 'prior', 'results', 'of', 'this', 'type', 'had', 'a', 'fixed', 'choice', 'of', 'calderonzygmund', 'operator', 'for', 'each', 'direction']] | [-0.18546606426542045, 0.15845990056727677, 0.01839132436542349, 0.04257747643635053, -0.05333076870194128, -0.1811645234261929, -0.005633031284170491, 0.31709995514228, -0.36555206742599416, -0.10049728362061897, 0.057733632888284925, -0.37548849790380534, -0.028038411091403162, 0.16772064050667362, -0.09107745686905976, 0.0373678680659984, -0.01631757471820011, 0.07267154562247902, -0.12571009582871212, -0.2170858347138875, 0.28502886634351327, -0.12843098988787843, 0.13757159846576572, 5.6074016547503594e-05, 0.07637314453814961, 0.03254415168871559, 0.07734732539979976, -0.09064468082276314, -0.2792339658958592, 0.017595709326477454, 0.2826122625441361, 0.19693756277258156, 0.28082818909843665, -0.2924451350132708, -0.06701251740778695, 0.32747568886633177, 0.18712704404092886, -0.20132749362559127, 0.06183354079746837, -0.28967318913954143, 0.14536110445575304, -0.08478423575635906, -0.1218679684151684, -0.055570794743908365, 0.15515855292617461, 0.03408692429280456, -0.5005823616407999, 0.07420242723750592, 0.10132186178963225, 0.030479191810268313, -0.004327160638890096, -0.23969631998099292, -0.09957780010857377, -0.015716790491636812, -0.06753073257253263, 0.2540669031128162, 0.0713749617067197, 0.009972574786056116, -0.015901389778317773, 0.32454583752399485, -0.11768227297041815, -0.2138156176646467, -0.01956836286953752, -0.24073883944324084, -0.18856337849645555, 0.03472812712082968, 0.06059703320076987, 0.21947439508747404, -0.02935296081069137, 0.3208507783945744, -0.15119747762977215, 0.14014046529416552, 0.13415139284329253, 0.09357259700616759, 0.03237536750311235, 0.05726793250303809, 0.1629928213156791, 0.07905294884391473, 0.05572968429862326, 0.07162177676976729, -0.4253342321001682, -0.11348162715698928, -0.2004731200457386, 0.1905552383527911, -0.22192090234774, -0.11934245388637915, 0.24783326656862842, 0.04558721219557298, 0.2457306608557701, 0.16195929827395544, 0.13026881511440547, 0.14525140167562245, 0.017593296378764847, 0.1039787639566392, -0.030712195309152937, 0.16490218222939543, -0.010032318804428732, -0.17864939646732783, -0.00902618293673927, 0.23041019938443982] |
1,802.00955 | Engineering Kondo state in two-dimensional semiconducting phosphorene | Correlated interaction between dilute localized impurity electrons with the
itinerant host conduction electrons in metals gives rise to the conventional
many-body Kondo effect below sufficiently low temperature. In sharp contrast to
these conventional Kondo systems, we report an intrinsic, robust and
high-temperature Kondo state in two-dimensional semiconducting phosphorene.
While absorbed at a thermodynamically stable lattice defect, Cr impurity
triggers an electronic phase transition in phosphorene to provide conduction
electrons, which strongly interact with the localized moment generated at the
Cr site. These manifests into intrinsic Kondo state, where the impurity moment
is quenched at multi-stage and at temperatures in the 40-200 K range. Further,
along with a much smaller extension of Kondo cloud, the predicted Kondo state
is shown to be robust under uniaxial strain and layer thickness, which greatly
simplifies its future experimental realization. We predict the present study
will open up new avenues in Kondo physics and trigger further theoretical and
experimental studies.
| cond-mat.mes-hall | correlated interaction between dilute localized impurity electrons with the itinerant host conduction electrons in metals gives rise to the conventional manybody kondo effect below sufficiently low temperature in sharp contrast to these conventional kondo systems we report an intrinsic robust and hightemperature kondo state in twodimensional semiconducting phosphorene while absorbed at a thermodynamically stable lattice defect cr impurity triggers an electronic phase transition in phosphorene to provide conduction electrons which strongly interact with the localized moment generated at the cr site these manifests into intrinsic kondo state where the impurity moment is quenched at multistage and at temperatures in the 40200 k range further along with a much smaller extension of kondo cloud the predicted kondo state is shown to be robust under uniaxial strain and layer thickness which greatly simplifies its future experimental realization we predict the present study will open up new avenues in kondo physics and trigger further theoretical and experimental studies | [['correlated', 'interaction', 'between', 'dilute', 'localized', 'impurity', 'electrons', 'with', 'the', 'itinerant', 'host', 'conduction', 'electrons', 'in', 'metals', 'gives', 'rise', 'to', 'the', 'conventional', 'manybody', 'kondo', 'effect', 'below', 'sufficiently', 'low', 'temperature', 'in', 'sharp', 'contrast', 'to', 'these', 'conventional', 'kondo', 'systems', 'we', 'report', 'an', 'intrinsic', 'robust', 'and', 'hightemperature', 'kondo', 'state', 'in', 'twodimensional', 'semiconducting', 'phosphorene', 'while', 'absorbed', 'at', 'a', 'thermodynamically', 'stable', 'lattice', 'defect', 'cr', 'impurity', 'triggers', 'an', 'electronic', 'phase', 'transition', 'in', 'phosphorene', 'to', 'provide', 'conduction', 'electrons', 'which', 'strongly', 'interact', 'with', 'the', 'localized', 'moment', 'generated', 'at', 'the', 'cr', 'site', 'these', 'manifests', 'into', 'intrinsic', 'kondo', 'state', 'where', 'the', 'impurity', 'moment', 'is', 'quenched', 'at', 'multistage', 'and', 'at', 'temperatures', 'in', 'the', '40200', 'k', 'range', 'further', 'along', 'with', 'a', 'much', 'smaller', 'extension', 'of', 'kondo', 'cloud', 'the', 'predicted', 'kondo', 'state', 'is', 'shown', 'to', 'be', 'robust', 'under', 'uniaxial', 'strain', 'and', 'layer', 'thickness', 'which', 'greatly', 'simplifies', 'its', 'future', 'experimental', 'realization', 'we', 'predict', 'the', 'present', 'study', 'will', 'open', 'up', 'new', 'avenues', 'in', 'kondo', 'physics', 'and', 'trigger', 'further', 'theoretical', 'and', 'experimental', 'studies']] | [-0.11864904269290667, 0.2600015123001104, -0.008513753302395344, 0.05326104110422274, -0.009273196995678929, -0.23021690936518774, 0.09794191906243682, 0.3900418123635914, -0.2843780029024328, -0.2605551155757219, -0.024132694801195495, -0.33428674423766713, -0.061770851097460236, 0.14805996971985985, 0.04825322837268393, -0.021811978957961282, -0.011957253218297997, -0.035953730212772386, -0.11398953162434121, -0.21975512778416517, 0.24491933992342843, 0.0775469460043936, 0.31714193226859694, 0.16877703337832503, -0.02147227718885387, -0.0039137634478749765, 0.15255880585961765, 0.020315816603420724, -0.1371263713230409, 0.040191167653087646, 0.27221178054967293, -0.1475054737480898, 0.20627524342064957, -0.4788475692812954, -0.20733172842526748, -0.019594515278755175, 0.1560669965741615, 0.16124131213573198, -0.11083611806313838, -0.31206810253582173, 0.041190375791366904, -0.15274360922555769, -0.13716285572116893, -0.09280070989663082, -0.03244018834009166, -0.0681890636340775, -0.2651442016805372, 0.1363033114406731, 0.057381149584187136, 0.05788615558544294, -0.09779232769168072, -0.13453353757203948, -0.05063312938136439, 0.021574872618000354, 0.04781485851778979, 0.07632889359393308, 0.19729382803122844, -0.1107668082590305, -0.07879229759827497, 0.3694541855685173, -0.037422003266551804, -0.0456227180037287, 0.242681577774666, -0.19427773774030707, -0.08542477792549517, 0.20770715936537712, 0.131312768422668, 0.04246240319804318, -0.12980325924938604, 0.07605675552021562, 0.01023355921910655, 0.16908425793517382, -0.040977764664398085, 0.11866204146776468, 0.30795223235843644, 0.22292371047810922, 0.09350158044647786, 0.16179201552389008, -0.11262543567816817, -0.07890819910192681, -0.18444462708167492, -0.1415998387991661, -0.2276220104491879, 0.08552687394042169, -0.05127004868934877, -0.1926032215152161, 0.417455642760521, 0.19173843386853415, 0.18165631742186605, -0.07738566783016487, 0.21439803762421492, 0.12010159053268933, 0.03342842278521388, 0.06639463154452613, 0.23473624074531177, 0.14986527702440658, 0.10487399886121913, -0.3164789049735954, 0.04440236507329128, 0.01864853020937693] |
1,802.00956 | An exponential estimate for the square partial sums of multiple Fourier
series | We prove an exponential integral estimate for the quadratic partial sums of
multiple Fourier series on large sets that implies some new properties of
Fourier series.
| math.CA | we prove an exponential integral estimate for the quadratic partial sums of multiple fourier series on large sets that implies some new properties of fourier series | [['we', 'prove', 'an', 'exponential', 'integral', 'estimate', 'for', 'the', 'quadratic', 'partial', 'sums', 'of', 'multiple', 'fourier', 'series', 'on', 'large', 'sets', 'that', 'implies', 'some', 'new', 'properties', 'of', 'fourier', 'series']] | [-0.206478807502068, 0.011154696654837327, -0.1504148133099079, 0.0990511948744265, -0.12027167142010652, -0.0013734519195098144, -0.005983027916115064, 0.3053626644496734, -0.40080753742502284, -0.1583843031862321, 0.14380289927626458, -0.26805028777856094, -0.15568675741311522, 0.33923989047224706, -0.06185434540160573, 0.13637499425273675, 0.04916856627768049, 0.017118372309666414, -0.09061613674454677, -0.3248618899199825, 0.32227107509970665, -0.08543084791073433, 0.198157737747981, -0.0240573869445003, 0.12855888072114724, 0.08070986182428896, -0.13036048446352091, -0.07119835526324235, -0.13166199618409827, 0.16562362101215583, 0.17841419477302295, 0.08239254779003274, 0.32679131569770664, -0.48136990517377853, -0.14582063300678363, 0.11889721961835256, 0.16115706829497448, -0.0342221030105765, -0.023157717798872348, -0.21142313722521067, 0.08081050033125883, -0.11719783252248397, -0.12283802510669026, -0.20517541382175225, 0.019613038891783126, 0.108775148454767, -0.3509133713176617, 0.07886106440295967, 0.06831815502104852, 0.12276676679567362, -0.08811521308066753, -0.17974552042925587, 0.1256901454180479, 0.04263917358520512, 0.06530271735615455, -0.07773083208415371, -0.024260636580248292, -0.06056840560184075, -0.09029945756237094, 0.2440416385921148, -0.14884510857071012, -0.15767815264944846, 0.14146436446417981, -0.26422612329658407, -0.2554182347196799, 0.1677840836883451, 0.16514411532821563, 0.13983989098610786, -0.12328937277197838, 0.12766509775358897, -0.1272188536106394, 0.15122072011805499, 0.12497156084730075, 0.07987273249846812, 0.1257002062809009, -0.0183910673054365, 0.14979011991706032, 0.2015172102703498, -0.05330697342287749, -0.03329212863284808, -0.3678645888330021, -0.22662997159820336, -0.24784346483647823, 0.06827064712370674, -0.20111021748408137, -0.2864955047575327, 0.37172013320601904, 0.013024872903210612, 0.22410590416536882, 0.16197411630016106, 0.1872899467841937, 0.23341817383726055, 0.02601122411970909, 0.008807902005803324, 0.0659140263059481, 0.14507409564864177, 0.021329665234169133, -0.19917324722672886, -0.0035949455072673466, 0.17140588889686534] |
1,802.00957 | Structure-Aware Bayesian Compressive Sensing for Frequency-Hopping
Spectrum Estimation with Missing Observations | In this paper, we address the problem of spectrum estimation of multiple
frequency-hopping (FH) signals in the presence of random missing observations.
The signals are analyzed within the bilinear time-frequency (TF) representation
framework, where a TF kernel is designed by exploiting the inherent FH signal
structures. The designed kernel permits effective suppression of cross-terms
and artifacts due to missing observations while preserving the FH signal
auto-terms. The kernelled results are represented in the instantaneous
autocorrelation function domain, which are then processed using a re-designed
structure-aware Bayesian compressive sensing algorithm to accurately estimate
the FH signal TF spectrum. The proposed method achieves high-resolution FH
signal spectrum estimation even when a large portion of data observations is
missing. Simulation results verify the effectiveness of the proposed method and
its superiority over existing techniques.
| eess.SP | in this paper we address the problem of spectrum estimation of multiple frequencyhopping fh signals in the presence of random missing observations the signals are analyzed within the bilinear timefrequency tf representation framework where a tf kernel is designed by exploiting the inherent fh signal structures the designed kernel permits effective suppression of crossterms and artifacts due to missing observations while preserving the fh signal autoterms the kernelled results are represented in the instantaneous autocorrelation function domain which are then processed using a redesigned structureaware bayesian compressive sensing algorithm to accurately estimate the fh signal tf spectrum the proposed method achieves highresolution fh signal spectrum estimation even when a large portion of data observations is missing simulation results verify the effectiveness of the proposed method and its superiority over existing techniques | [['in', 'this', 'paper', 'we', 'address', 'the', 'problem', 'of', 'spectrum', 'estimation', 'of', 'multiple', 'frequencyhopping', 'fh', 'signals', 'in', 'the', 'presence', 'of', 'random', 'missing', 'observations', 'the', 'signals', 'are', 'analyzed', 'within', 'the', 'bilinear', 'timefrequency', 'tf', 'representation', 'framework', 'where', 'a', 'tf', 'kernel', 'is', 'designed', 'by', 'exploiting', 'the', 'inherent', 'fh', 'signal', 'structures', 'the', 'designed', 'kernel', 'permits', 'effective', 'suppression', 'of', 'crossterms', 'and', 'artifacts', 'due', 'to', 'missing', 'observations', 'while', 'preserving', 'the', 'fh', 'signal', 'autoterms', 'the', 'kernelled', 'results', 'are', 'represented', 'in', 'the', 'instantaneous', 'autocorrelation', 'function', 'domain', 'which', 'are', 'then', 'processed', 'using', 'a', 'redesigned', 'structureaware', 'bayesian', 'compressive', 'sensing', 'algorithm', 'to', 'accurately', 'estimate', 'the', 'fh', 'signal', 'tf', 'spectrum', 'the', 'proposed', 'method', 'achieves', 'highresolution', 'fh', 'signal', 'spectrum', 'estimation', 'even', 'when', 'a', 'large', 'portion', 'of', 'data', 'observations', 'is', 'missing', 'simulation', 'results', 'verify', 'the', 'effectiveness', 'of', 'the', 'proposed', 'method', 'and', 'its', 'superiority', 'over', 'existing', 'techniques']] | [-0.11452073237428871, 0.02377677082728881, -0.08609954392215094, 0.05695984226275379, -0.09443019169669312, -0.11082533256759723, 0.03133118568579308, 0.3858237066736015, -0.2924719747155905, -0.2930422008467408, 0.11797583598482352, -0.2716001434287486, -0.18627871376542876, 0.16642720335378097, -0.06598064003046603, 0.10133972589526541, 0.09726048320388565, 0.019602040265901732, -0.06538537777423, -0.21038162706133265, 0.23176994246359056, 0.09485317596476167, 0.34056600836786227, -0.015257605149124104, 0.10918983292425624, 0.03742056901638324, -0.10996207620303791, -0.01846883523170478, -0.06478598972675033, 0.13276967172278092, 0.2946670007515842, 0.15028824595987178, 0.2748661301886806, -0.3662343247435414, -0.2710149119303633, 0.14653728734209345, 0.1606896325121992, 0.0613227562343057, -0.06318296659827376, -0.3160739603237464, 0.14390755796160262, -0.14032605984158672, -0.02304685512700906, -0.08668243392155721, -0.07188377457742508, 0.012222485469493012, -0.34981059985808455, 0.12139355657653561, 0.03652569915239628, 0.023865981861644497, -0.0726689176607089, -0.11448195120319724, 0.04846467739687516, 0.1285577618725186, 0.0030293601439692654, 0.03703592207927543, 0.11551911493297666, -0.10111270896696414, -0.08477867544413759, 0.3453229478775309, -0.10247196659374122, -0.22367703135196979, 0.14052414874761152, -0.12114325701975479, -0.11401999938803223, 0.1773244822003807, 0.17368823542044712, 0.0659661319035177, -0.15233892538246716, 0.10138666055147322, -0.01361213861606442, 0.17247288258602986, 0.019627042965462003, 0.03855031760266194, 0.11308999123642795, 0.14942993909801142, 0.0484093623316417, 0.13450280262085682, -0.18887916911715785, -0.02962675982584747, -0.21348042547872934, -0.07004576192165797, -0.26580447009764613, -0.08552312785043166, -0.10090634490770753, -0.10967099179800313, 0.43928009659911577, 0.18729882658584618, 0.190754624370199, 0.06903267801613905, 0.38988454334008005, 0.10071262778141178, 0.09387912779974823, 0.06653570783897661, 0.16641120875988585, 0.153604889777489, 0.08461199630401098, -0.2357641814303441, 0.04786181236402347, 0.03525409574280135] |
1,802.00958 | Arbitrarily accurate twin composite $\pi$ pulse sequences | We present three classes of symmetric broadband composite pulse sequences.
The composite phases are given by analytic formulas (rational fractions of
$\pi$) valid for any number of constituent pulses. The transition probability
is expressed by simple analytic formulas and the order of pulse area error
compensation grows linearly with the number of pulses. Therefore, any desired
compensation order can be produced by an appropriate composite sequence; in
this sense, they are arbitrarily accurate. These composite pulses perform
equally well or better than previously published ones. Moreover, the current
sequences are more flexible as they allow total pulse areas of arbitrary
integer multiples of $\pi$.
| quant-ph | we present three classes of symmetric broadband composite pulse sequences the composite phases are given by analytic formulas rational fractions of pi valid for any number of constituent pulses the transition probability is expressed by simple analytic formulas and the order of pulse area error compensation grows linearly with the number of pulses therefore any desired compensation order can be produced by an appropriate composite sequence in this sense they are arbitrarily accurate these composite pulses perform equally well or better than previously published ones moreover the current sequences are more flexible as they allow total pulse areas of arbitrary integer multiples of pi | [['we', 'present', 'three', 'classes', 'of', 'symmetric', 'broadband', 'composite', 'pulse', 'sequences', 'the', 'composite', 'phases', 'are', 'given', 'by', 'analytic', 'formulas', 'rational', 'fractions', 'of', 'pi', 'valid', 'for', 'any', 'number', 'of', 'constituent', 'pulses', 'the', 'transition', 'probability', 'is', 'expressed', 'by', 'simple', 'analytic', 'formulas', 'and', 'the', 'order', 'of', 'pulse', 'area', 'error', 'compensation', 'grows', 'linearly', 'with', 'the', 'number', 'of', 'pulses', 'therefore', 'any', 'desired', 'compensation', 'order', 'can', 'be', 'produced', 'by', 'an', 'appropriate', 'composite', 'sequence', 'in', 'this', 'sense', 'they', 'are', 'arbitrarily', 'accurate', 'these', 'composite', 'pulses', 'perform', 'equally', 'well', 'or', 'better', 'than', 'previously', 'published', 'ones', 'moreover', 'the', 'current', 'sequences', 'are', 'more', 'flexible', 'as', 'they', 'allow', 'total', 'pulse', 'areas', 'of', 'arbitrary', 'integer', 'multiples', 'of', 'pi']] | [-0.1628638126365751, 0.21218286079140222, -0.053784096541886144, 0.0985003797731434, -0.05783670370090896, -0.16145600923426592, 0.03071275151831707, 0.4391901643158725, -0.2176591356893858, -0.3436340127059688, 0.0795860260516817, -0.2514458046935033, -0.10550672844916474, 0.22106737005658664, -0.04965169100418839, 0.08601444967364212, 0.019441964404764943, 0.032355543984941997, -0.059647216490702704, -0.2535543710960505, 0.26166275583538945, -0.01648781310247544, 0.23136641080670345, -0.054014948156411544, 0.05391342072443177, 0.033361370300693895, -0.015379504139463488, -0.01596108331488302, -0.09321915839189807, 0.12031136047489081, 0.29428331972135663, 0.0728103420115076, 0.2082964331180287, -0.4303398256888613, -0.16850478669556862, 0.13079815872165804, 0.17974906293406653, 0.10407611081161751, -0.02310924085885255, -0.23881118228802314, 0.10749244732030022, -0.16813887606818192, -0.1476872911085733, -0.09894682397134602, 0.041374180322656266, 0.12484415093898021, -0.29480020721586275, 0.03478406099244379, 0.061085185671310264, 0.05633467715681316, -0.011561132595838549, -0.1567331777097514, 0.005865341206887164, 0.13534552122403581, 0.02253643237725975, 0.03828292868834419, 0.1159410694694648, -0.09722999412714181, -0.09033452060919864, 0.3344331140844868, -0.033837955000238326, -0.21871233991203973, 0.1268534419382027, -0.14044158900609405, -0.030482340975700375, 0.22241845116234168, 0.10434187902809264, 0.15566560273201993, -0.11220268732778585, 0.011162264786924845, -0.014706627026988337, 0.2230228086443206, 0.12285762063066404, 0.08462263324942726, 0.22199152587339854, 0.07465998873186226, 0.04480820913494636, 0.14663595470595353, -0.023004219674756035, -0.05180993663648573, -0.316194213464489, -0.11201201871027969, -0.1658775358586214, 0.04047061354392029, -0.09513071016394511, -0.15519549391711523, 0.38817534298421097, 0.043477764307261586, 0.1637975555450584, 0.11941643433573727, 0.2959396784381869, 0.17736391634389292, 0.021312451881105796, 0.025454170536249876, 0.17736860894812986, 0.10729210965039854, 0.03409937229186583, -0.1506459553179761, 0.08628755929217172, 0.041036077589244366] |
1,802.00959 | Combinatorial proofs for identities related to generalizations of the
mock theta functions $\omega(q)$ and $\nu(q)$ | The two partition functions $p_\omega(n)$ and $p_\nu(n)$ were introduced by
Andrews, Dixit and Yee, which are related to the third order mock theta
functions $\omega(q)$ and $\nu(q)$, respectively. Recently, Andrews and Yee
analytically studied two identities that connect the refinements of
$p_\omega(n)$ and $p_\nu(n)$ with the generalized bivariate mock theta
functions $\omega(z;q)$ and $\nu(z;q)$, respectively. However, they stated
these identities cried out for bijective proofs. In this paper, we first define
the generalized trivariate mock theta functions $\omega(y,z;q)$ and
$\nu(y,z;q)$. Then by utilizing odd Ferrers graph, we obtain certain identities
concerning to $\omega(y,z;q)$ and $\nu(y,z;q)$, which extend some early results
of Andrews that are related to $\omega(z;q)$ and $\nu(z;q)$. In virtue of the
combinatorial interpretations that arise from the identities involving
$\omega(y,z;q)$ and $\nu(y,z;q)$, we finally present bijective proofs for the
two identities of Andrews-Yee.
| math.CO | the two partition functions p_omegan and p_nun were introduced by andrews dixit and yee which are related to the third order mock theta functions omegaq and nuq respectively recently andrews and yee analytically studied two identities that connect the refinements of p_omegan and p_nun with the generalized bivariate mock theta functions omegazq and nuzq respectively however they stated these identities cried out for bijective proofs in this paper we first define the generalized trivariate mock theta functions omegayzq and nuyzq then by utilizing odd ferrers graph we obtain certain identities concerning to omegayzq and nuyzq which extend some early results of andrews that are related to omegazq and nuzq in virtue of the combinatorial interpretations that arise from the identities involving omegayzq and nuyzq we finally present bijective proofs for the two identities of andrewsyee | [['the', 'two', 'partition', 'functions', 'p_omegan', 'and', 'p_nun', 'were', 'introduced', 'by', 'andrews', 'dixit', 'and', 'yee', 'which', 'are', 'related', 'to', 'the', 'third', 'order', 'mock', 'theta', 'functions', 'omegaq', 'and', 'nuq', 'respectively', 'recently', 'andrews', 'and', 'yee', 'analytically', 'studied', 'two', 'identities', 'that', 'connect', 'the', 'refinements', 'of', 'p_omegan', 'and', 'p_nun', 'with', 'the', 'generalized', 'bivariate', 'mock', 'theta', 'functions', 'omegazq', 'and', 'nuzq', 'respectively', 'however', 'they', 'stated', 'these', 'identities', 'cried', 'out', 'for', 'bijective', 'proofs', 'in', 'this', 'paper', 'we', 'first', 'define', 'the', 'generalized', 'trivariate', 'mock', 'theta', 'functions', 'omegayzq', 'and', 'nuyzq', 'then', 'by', 'utilizing', 'odd', 'ferrers', 'graph', 'we', 'obtain', 'certain', 'identities', 'concerning', 'to', 'omegayzq', 'and', 'nuyzq', 'which', 'extend', 'some', 'early', 'results', 'of', 'andrews', 'that', 'are', 'related', 'to', 'omegazq', 'and', 'nuzq', 'in', 'virtue', 'of', 'the', 'combinatorial', 'interpretations', 'that', 'arise', 'from', 'the', 'identities', 'involving', 'omegayzq', 'and', 'nuyzq', 'we', 'finally', 'present', 'bijective', 'proofs', 'for', 'the', 'two', 'identities', 'of', 'andrewsyee']] | [-0.11644786654150412, 0.07291839168520772, -0.09929734196172643, 0.129967053878505, -0.11828343129453768, -0.11600309637382285, 0.01447719004172263, 0.30339665033406654, -0.2601563127725634, -0.29719599630712096, 0.10805971309620858, -0.2796679519797597, -0.24277324562195604, 0.2173963014804225, -0.05776392303954611, 0.04862032222511773, -0.0474269023833384, -0.07170170263337497, -0.11175963957255257, -0.310722302515033, 0.37603663412750993, -0.03571250208454448, 0.15693192049605018, 0.05789531479262612, 0.08522793004033334, 0.026748642365391304, -0.09824434927671806, -0.038917137068191554, -0.19661599829101373, 0.1162921003570766, 0.22409612025695902, 0.13126074930098447, 0.1821624480742421, -0.3827470624037371, -0.05297419794956009, 0.09020212679063432, 0.12064504924137856, -0.01819410196590799, -0.020185674604682523, -0.2914571914224679, 0.08549542203261197, -0.1691908134922442, -0.12128576679681322, -0.1213908506395608, 0.010257388591741474, 0.11864240939696924, -0.298630592185748, 0.07419018490410602, 0.05066647268262983, 0.03723081691503411, -0.004127243611933166, -0.2066809079762662, -0.00733630609065863, 0.08194127856426738, 0.041687618876723634, 0.03670334085850543, -0.015050309269425978, -0.1180042939185954, -0.1346790443515789, 0.27974219964319513, 0.06041604894584841, -0.19588960519145804, 0.11007875445981347, -0.1404237111155939, -0.23077421083705116, 0.015146095244807568, 0.07248288651089405, 0.1206724506195009, -0.12737581298068051, 0.06284933180978365, -0.12171893915204146, 0.05423887058353151, 0.2162963912417074, -0.037776777048005174, 0.11959993102026122, -0.08276335443263404, -0.03323100451095641, 0.2200083989873955, 0.010980534868471267, -0.06194156177878692, -0.30682504889448636, -0.14377018387424084, -0.15422011187428053, 0.048038004456756225, -0.1121925033034867, -0.129746088587487, 0.38838595001183396, 0.13172179945381998, 0.16739585500509116, 0.1471556831268551, 0.1815266256142436, 0.12259497468933847, 0.06983717032246121, 0.01664145076727381, 0.11466172559276572, 0.2063888011567778, 0.06472458181376675, -0.09741412017330925, 0.004809873059519944, 0.18516815021520353] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.