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1,802.0386 | Discovery of switchable weak topological insulator state in
quasi-one-dimensional bismuth iodide | The major breakthroughs in the understanding of topological materials over
the past decade were all triggered by the discovery of the Z$_2$ topological
insulator (TI). In three dimensions (3D), the TI is classified as either
"strong" or "weak", and experimental confirmations of the strong topological
insulator (STI) rapidly followed the theoretical predictions. In contrast, the
weak topological insulator has so far eluded experimental verification, since
the topological surface states emerge only on particular side surfaces which
are typically undetectable in real 3D crystals. Here we provide experimental
evidence for the WTI state in a bismuth iodide, $\beta$-Bi4I4. Significantly,
the crystal has naturally cleavable top and side planes both stacked via
van-der-Waals forces, which have long been desirable for the experimental
realization of the WTI state. As a definitive signature of it, we find quasi-1D
Dirac TSS at the side-surface (100) while the top-surface (001) is
topologically dark. Furthermore, a crystal transition from the $\beta$- to
$\alpha$-phase drives a topological phase transition from a nontrivial WTI to
the trivial insulator around room temperature. This topological phase, viewed
as quantum spin Hall (QSH) insulators stacked three-dimensionally, and
excellent functionality with on/off switching will lay a foundation for new
technology benefiting from highly directional spin-currents with large density
protected against backscattering.
| cond-mat.mtrl-sci | the major breakthroughs in the understanding of topological materials over the past decade were all triggered by the discovery of the z_2 topological insulator ti in three dimensions 3d the ti is classified as either strong or weak and experimental confirmations of the strong topological insulator sti rapidly followed the theoretical predictions in contrast the weak topological insulator has so far eluded experimental verification since the topological surface states emerge only on particular side surfaces which are typically undetectable in real 3d crystals here we provide experimental evidence for the wti state in a bismuth iodide betabi4i4 significantly the crystal has naturally cleavable top and side planes both stacked via vanderwaals forces which have long been desirable for the experimental realization of the wti state as a definitive signature of it we find quasi1d dirac tss at the sidesurface 100 while the topsurface 001 is topologically dark furthermore a crystal transition from the beta to alphaphase drives a topological phase transition from a nontrivial wti to the trivial insulator around room temperature this topological phase viewed as quantum spin hall qsh insulators stacked threedimensionally and excellent functionality with onoff switching will lay a foundation for new technology benefiting from highly directional spincurrents with large density protected against backscattering | [['the', 'major', 'breakthroughs', 'in', 'the', 'understanding', 'of', 'topological', 'materials', 'over', 'the', 'past', 'decade', 'were', 'all', 'triggered', 'by', 'the', 'discovery', 'of', 'the', 'z_2', 'topological', 'insulator', 'ti', 'in', 'three', 'dimensions', '3d', 'the', 'ti', 'is', 'classified', 'as', 'either', 'strong', 'or', 'weak', 'and', 'experimental', 'confirmations', 'of', 'the', 'strong', 'topological', 'insulator', 'sti', 'rapidly', 'followed', 'the', 'theoretical', 'predictions', 'in', 'contrast', 'the', 'weak', 'topological', 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1,802.03861 | Variational principle for quantum impurity systems in and out of
equilibrium: application to Kondo problems | We provide a detailed formulation of the recently proposed variational
approach [Y. Ashida et al., Phys. Rev. Lett. 121, 026805 (2018)] to study
ground-state properties and out-of-equilibrium dynamics for generic quantum
spin-impurity systems. Motivated by the original ideas by Tomonaga, Lee, Low,
and Pines, we construct a canonical transformation that completely decouples
the impurity from the bath degrees of freedom. By combining this transformation
with a Gaussian ansatz for the fermionic bath, we obtain a family of
variational many-body states that can efficiently encode the strong
entanglement between the impurity and fermions of the bath. We give a detailed
derivation of equations of motions in the imaginary- and real-time evolutions
on the variational manifold. We benchmark our approach by applying it to
investigate ground-state and dynamical properties of the anisotropic Kondo
model and compare results with those obtained using matrix-product state (MPS)
ansatz. We show that our approach can achieve an accuracy comparable to
MPS-based methods with several orders of magnitude fewer variational parameters
than the corresponding MPS ansatz. Comparisons to the Yosida ansatz and the
exact solution from the Bethe ansatz are also discussed. We use our approach to
investigate the two-lead Kondo model and analyze its long-time spatiotemporal
behavior and the conductance behavior at finite bias and magnetic fields. The
obtained results are consistent with the previous findings in the Anderson
model and the exact solutions at the Toulouse point.
| cond-mat.str-el cond-mat.quant-gas cond-mat.stat-mech quant-ph | we provide a detailed formulation of the recently proposed variational approach y ashida et al phys rev lett 121 026805 2018 to study groundstate properties and outofequilibrium dynamics for generic quantum spinimpurity systems motivated by the original ideas by tomonaga lee low and pines we construct a canonical transformation that completely decouples the impurity from the bath degrees of freedom by combining this transformation with a gaussian ansatz for the fermionic bath we obtain a family of variational manybody states that can efficiently encode the strong entanglement between the impurity and fermions of the bath we give a detailed derivation of equations of motions in the imaginary and realtime evolutions on the variational manifold we benchmark our approach by applying it to investigate groundstate and dynamical properties of the anisotropic kondo model and compare results with those obtained using matrixproduct state mps ansatz we show that our approach can achieve an accuracy comparable to mpsbased methods with several orders of magnitude fewer variational parameters than the corresponding mps ansatz comparisons to the yosida ansatz and the exact solution from the bethe ansatz are also discussed we use our approach to investigate the twolead kondo model and analyze its longtime spatiotemporal behavior and the conductance behavior at finite bias and magnetic fields the obtained results are consistent with the previous findings in the anderson model and the exact solutions at the toulouse point | [['we', 'provide', 'a', 'detailed', 'formulation', 'of', 'the', 'recently', 'proposed', 'variational', 'approach', 'y', 'ashida', 'et', 'al', 'phys', 'rev', 'lett', '121', '026805', '2018', 'to', 'study', 'groundstate', 'properties', 'and', 'outofequilibrium', 'dynamics', 'for', 'generic', 'quantum', 'spinimpurity', 'systems', 'motivated', 'by', 'the', 'original', 'ideas', 'by', 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1,802.03862 | Long spin coherence times in the ground state and an optically excited
state of $^{167}$Er$^{3+}$:Y$_2$SiO$_5$ at zero magnetic field | Spins in solids are an ideal candidate to act as a memory and interface with
superconducting qubits due to their long coherence times. We spectroscopically
investigate erbium-167-doped yttrium orthosilicate as a possible
microwave-addressed memory employing its microwave frequency transitions that
occur without applying an external magnetic field. We obtain coherence times of
380 $\mu$s in a ground state spin transition and 1.48 ms in an excited state
spin transition. This is 28 times longer compared to previous zero field
measurements, as well as 200 times longer than a previous microwave memory
demonstration in the same material. These long coherence times show that
erbium-167-doped yttrium orthosilicate has potential as a microwave-addressed
quantum memory.
| quant-ph | spins in solids are an ideal candidate to act as a memory and interface with superconducting qubits due to their long coherence times we spectroscopically investigate erbium167doped yttrium orthosilicate as a possible microwaveaddressed memory employing its microwave frequency transitions that occur without applying an external magnetic field we obtain coherence times of 380 mus in a ground state spin transition and 148 ms in an excited state spin transition this is 28 times longer compared to previous zero field measurements as well as 200 times longer than a previous microwave memory demonstration in the same material these long coherence times show that erbium167doped yttrium orthosilicate has potential as a microwaveaddressed quantum memory | [['spins', 'in', 'solids', 'are', 'an', 'ideal', 'candidate', 'to', 'act', 'as', 'a', 'memory', 'and', 'interface', 'with', 'superconducting', 'qubits', 'due', 'to', 'their', 'long', 'coherence', 'times', 'we', 'spectroscopically', 'investigate', 'erbium167doped', 'yttrium', 'orthosilicate', 'as', 'a', 'possible', 'microwaveaddressed', 'memory', 'employing', 'its', 'microwave', 'frequency', 'transitions', 'that', 'occur', 'without', 'applying', 'an', 'external', 'magnetic', 'field', 'we', 'obtain', 'coherence', 'times', 'of', '380', 'mus', 'in', 'a', 'ground', 'state', 'spin', 'transition', 'and', '148', 'ms', 'in', 'an', 'excited', 'state', 'spin', 'transition', 'this', 'is', '28', 'times', 'longer', 'compared', 'to', 'previous', 'zero', 'field', 'measurements', 'as', 'well', 'as', '200', 'times', 'longer', 'than', 'a', 'previous', 'microwave', 'memory', 'demonstration', 'in', 'the', 'same', 'material', 'these', 'long', 'coherence', 'times', 'show', 'that', 'erbium167doped', 'yttrium', 'orthosilicate', 'has', 'potential', 'as', 'a', 'microwaveaddressed', 'quantum', 'memory']] | [-0.16976251211707238, 0.24994501054081614, 0.012595506949798652, -0.0021940730264220663, -0.0016218920203822631, -0.13969006207426665, 0.06266355043475481, 0.4571275092937328, -0.21234432880387263, -0.34144753719145365, 0.11282196374937754, -0.2840778853623541, 0.002478272067727866, 0.19792773985062484, 0.02786485139384038, 0.028173414541352367, -0.014270359654565927, 0.054554729618959956, -0.08126277186427076, -0.1965308889544017, 0.1647876438174636, 0.04854871555451407, 0.2974459275676593, -0.0006765421842121416, 0.08586669140667827, -0.04124056129216182, 0.11414364228861544, -0.04750330256367171, -0.10240258584707151, -0.028375380163736367, 0.2528447349107376, -0.002758138847571832, 0.23485500322378897, -0.5014183615955213, -0.21643457081410344, 0.06641031551623235, 0.13342516277959846, 0.1773701306708433, -0.035033331226764455, -0.3165836893711929, 0.05618754489276196, -0.19850314096375196, -0.08816803881415615, -0.06902880000847357, 0.07590572849666286, -0.02343986748234817, -0.22442988656392251, 0.08550534604755403, 0.08618769471865596, 0.0776861662409458, -0.07762235089709672, -0.09377386405443151, 0.0031695667808148923, 0.09276945913572693, 0.012583208328578621, 0.1343423904704482, 0.15436334302648902, -0.084215455569965, -0.16600132160992534, 0.3355539600554578, -0.12665474809626984, -0.04671686633352052, 0.19072094066413464, -0.12501341005770006, -0.023827236917525255, 0.11418553688606524, 0.11512643622700125, 0.12034676581431457, -0.14987756582990255, 0.00857391287637357, 0.03952062822429946, 0.25878370832651854, 0.07984752649941516, 0.1809773842321226, 0.22148026074972693, 0.20164487262566885, 0.039665566455503856, 0.16587725948247006, -0.1362336736853683, -0.053868168573158244, -0.19768320139566506, -0.20301161355476965, -0.22051930684527313, 0.18233475238957908, -0.057586165116744165, -0.12528963215407674, 0.37271207495575287, 0.1605364334245678, 0.20216203175692093, 0.0024730188392654614, 0.2744471533137753, 0.09036886869041526, 0.13269083015405125, 0.054266095363008963, 0.23145417374110333, 0.18025431038242662, 0.11904338247108238, -0.2473464159131119, 0.017760104788639756, -0.06397440408666928] |
1,802.03863 | Particle-Hole Mirror Symmetries around the Half-Filled Shell: The
Quantum Numbers and Algebraic Structure of Composite Fermions | Composite fermions (CFs) of the fractional quantum Hall effect are described
as spherical products of electron and vortex spinors, built from underlying
L=1/2 ladder operators aligned so that the spinor angular momenta Le and Lv are
maximal. We identify the CF's quantum numbers as the angular momentum L in (L_e
L_v)L, its magnetic projection m_L, the electron number N, with L_v={N-1)/2,
and magnetic \nu-spin, m_\nu=L_e-L_v. Translationally invariant FQHE states are
formed by filling p subshells with their respective CFs, in order of ascending
L for fixed L_e and L_v, beginning with the lowest allowed value, L=|m_\nu|. We
show that this wave function has an exactly equivalent hierarchical form.
FQHE states can be grouped into \nu-spin multiplets mirror symmetric around
m_\nu=0, with N held constant. Electron particle-hole conjugation with respect
to this vacuum is identified as the mirror symmetry relating FQHE states of the
same N but distinct fillings \nu = p/(2p+1} and p/( 2p-1).
Alternatively, mirror symmetric \nu-spin multiplets can be constructed in
which the magnetic field strength is held fixed: the valence states are
electron particle-vortex hole excitations. Particle-hole symmetry -- relating
the N-particle FQHE state of filling \nu=p/(2p+1} to the $\bar{N}$-particle
state of filling {p+1)/(2p+1} -- is shown to be equivalent to electron-vortex
exchange.
In this construction $\bar{N}$-N CFs of the higher density state occupy an
extra zero-mode subshell. We link this structure, familiar from supersymmetric
quantum mechanics, to the CF Pauli Hamiltonian, which we show is isospectral,
quadratic in the \nu-spin raising and lowering operators, and four-fold
degenerate. On linearization, it takes a Dirac form similar to that found in
the integer quantum Hall effect (IQHE).
| cond-mat.str-el nucl-th physics.atom-ph | composite fermions cfs of the fractional quantum hall effect are described as spherical products of electron and vortex spinors built from underlying l12 ladder operators aligned so that the spinor angular momenta le and lv are maximal we identify the cfs quantum numbers as the angular momentum l in l_e l_vl its magnetic projection m_l the electron number n with l_vn12 and magnetic nuspin m_nul_el_v translationally invariant fqhe states are formed by filling p subshells with their respective cfs in order of ascending l for fixed l_e and l_v beginning with the lowest allowed value lm_nu we show that this wave function has an exactly equivalent hierarchical form fqhe states can be grouped into nuspin multiplets mirror symmetric around m_nu0 with n held constant electron particlehole conjugation with respect to this vacuum is identified as the mirror symmetry relating fqhe states of the same n but distinct fillings nu p2p1 and p 2p1 alternatively mirror symmetric nuspin multiplets can be constructed in which the magnetic field strength is held fixed the valence states are electron particlevortex hole excitations particlehole symmetry relating the nparticle fqhe state of filling nup2p1 to the barnparticle state of filling p12p1 is shown to be equivalent to electronvortex exchange in this construction barnn cfs of the higher density state occupy an extra zeromode subshell we link this structure familiar from supersymmetric quantum mechanics to the cf pauli hamiltonian which we show is isospectral quadratic in the nuspin raising and lowering operators and fourfold degenerate on linearization it takes a dirac form similar to that found in the integer quantum hall effect iqhe | [['composite', 'fermions', 'cfs', 'of', 'the', 'fractional', 'quantum', 'hall', 'effect', 'are', 'described', 'as', 'spherical', 'products', 'of', 'electron', 'and', 'vortex', 'spinors', 'built', 'from', 'underlying', 'l12', 'ladder', 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1,802.03864 | Projective Truncation Approximation for Equations of Motion of Two-Time
Green's Functions | In the equation of motion approach to the two-time Green's functions,
conventional Tyablikov-type truncation of the chain of equations is rather
arbitrary and apt to violate the analytical structure of Green's functions.
Here, we propose a practical way to truncate the equations of motion using
operator projection. The partial projection approximation is introduced to
evaluate the Liouville matrix. It guarantees the causality of Green's
functions, fulfills the time translation invariance and the particle-hole
symmetry, and is easy to implement in a computer. To benchmark this method, we
study the Anderson impurity model using the operator basis at the level of
Lacroix approximation. Improvement over conventional Lacroix approximation is
observed. The distribution of Kondo screening in the energy space is studied
using this method.
| cond-mat.str-el cond-mat.supr-con | in the equation of motion approach to the twotime greens functions conventional tyablikovtype truncation of the chain of equations is rather arbitrary and apt to violate the analytical structure of greens functions here we propose a practical way to truncate the equations of motion using operator projection the partial projection approximation is introduced to evaluate the liouville matrix it guarantees the causality of greens functions fulfills the time translation invariance and the particlehole symmetry and is easy to implement in a computer to benchmark this method we study the anderson impurity model using the operator basis at the level of lacroix approximation improvement over conventional lacroix approximation is observed the distribution of kondo screening in the energy space is studied using this method | [['in', 'the', 'equation', 'of', 'motion', 'approach', 'to', 'the', 'twotime', 'greens', 'functions', 'conventional', 'tyablikovtype', 'truncation', 'of', 'the', 'chain', 'of', 'equations', 'is', 'rather', 'arbitrary', 'and', 'apt', 'to', 'violate', 'the', 'analytical', 'structure', 'of', 'greens', 'functions', 'here', 'we', 'propose', 'a', 'practical', 'way', 'to', 'truncate', 'the', 'equations', 'of', 'motion', 'using', 'operator', 'projection', 'the', 'partial', 'projection', 'approximation', 'is', 'introduced', 'to', 'evaluate', 'the', 'liouville', 'matrix', 'it', 'guarantees', 'the', 'causality', 'of', 'greens', 'functions', 'fulfills', 'the', 'time', 'translation', 'invariance', 'and', 'the', 'particlehole', 'symmetry', 'and', 'is', 'easy', 'to', 'implement', 'in', 'a', 'computer', 'to', 'benchmark', 'this', 'method', 'we', 'study', 'the', 'anderson', 'impurity', 'model', 'using', 'the', 'operator', 'basis', 'at', 'the', 'level', 'of', 'lacroix', 'approximation', 'improvement', 'over', 'conventional', 'lacroix', 'approximation', 'is', 'observed', 'the', 'distribution', 'of', 'kondo', 'screening', 'in', 'the', 'energy', 'space', 'is', 'studied', 'using', 'this', 'method']] | [-0.11153775306426172, 0.037806045220111356, -0.15015703159743218, 0.10788839253440469, -0.06821957353471977, -0.09798946019765906, 0.04205699712343392, 0.3494768087145277, -0.26425169468819176, -0.2500053736434669, 0.04921534942314181, -0.2631496876920955, -0.14578713470264568, 0.12364225254249071, 0.005370432484253752, 0.120771802900756, 0.009751114089683186, -0.009459576390103483, -0.14832798317532803, -0.21986959721763297, 0.3011502054191698, 0.027919472176123592, 0.30647605730480226, 0.06922036269679666, 0.12627617075092723, 0.05615055804011091, 0.022057673897685818, -0.01141963950115004, -0.08533429937841767, 0.1033172284531575, 0.21403928177209847, 0.0783834434610593, 0.26357161512857946, -0.4289489930007057, -0.1780348810745159, 0.03022127323898441, 0.1272075365786059, 0.1350070035970602, 0.0047647950743309785, -0.2909748250443, 0.054687518091131856, -0.18259553186839722, -0.187760576892064, -0.12857036190832674, -0.013212265160514927, -0.01655593470738987, -0.3020452934371681, 0.11976145025434308, 0.03949969373338046, 0.022157063918592804, -0.07271170215757533, -0.08399654084007394, 0.034712177937941964, 0.07098207290696561, 0.017121374355171057, 0.06728589782213647, 0.10590863181373532, -0.09603284122979366, -0.09953634501205849, 0.3906537670046702, -0.06977744617300932, -0.28207359953829253, 0.14784725857650494, -0.16183534630223131, -0.08896209120933639, 0.09207344603472863, 0.13754488599532452, 0.14120830415717525, -0.19289820042933092, 0.16472805596622792, -0.047769824962788185, 0.1228994118740599, 0.042133235773377, 0.0021627603144552866, 0.06947789622134849, 0.15869178365606845, 0.06328274054666523, 0.14273951537754448, -0.06831713952124119, -0.1493928364752868, -0.3101621541332026, -0.14945551274405394, -0.22595587626770405, 0.03376862203671795, -0.05298736551287488, -0.20721127669189554, 0.43156197288485826, 0.19259542237741847, 0.13779658725729488, 0.06056080710287893, 0.3175564482407339, 0.23573839739270386, 0.09009152500829125, 0.05441020715019864, 0.17510522274132512, 0.16457218731768797, 0.05328003852532749, -0.28876366011691507, 0.02663593927444127, 0.17006210522553464] |
1,802.03865 | Spin-orbit torque and spin pumping in YIG/Pt with interfacial insertion
layers | We experimentally investigate spin-orbit torque and spin pumping in
Y$_3$Fe$_5$O$_{12}$(YIG)/Pt bilayers with ultrathin insertion layers at the
interface. An insertion layer of Cu suppresses both spin-orbit torque and spin
pumping, whereas an insertion layer of Ni$_{80}$Fe$_{20}$ (permalloy, Py)
enhances them, in a quantitatively consistent manner with the reciprocity of
the two spin transmission processes. However, we observe a large enhancement of
Gilbert damping with the insertion of Py that cannot be accounted for solely by
spin pumping, suggesting significant spin-memory loss due to the interfacial
magnetic layer. Our findings indicate that the magnetization at the YIG-metal
interface strongly influences the transmission and depolarization of pure spin
current.
| cond-mat.mtrl-sci | we experimentally investigate spinorbit torque and spin pumping in y_3fe_5o_12yigpt bilayers with ultrathin insertion layers at the interface an insertion layer of cu suppresses both spinorbit torque and spin pumping whereas an insertion layer of ni_80fe_20 permalloy py enhances them in a quantitatively consistent manner with the reciprocity of the two spin transmission processes however we observe a large enhancement of gilbert damping with the insertion of py that cannot be accounted for solely by spin pumping suggesting significant spinmemory loss due to the interfacial magnetic layer our findings indicate that the magnetization at the yigmetal interface strongly influences the transmission and depolarization of pure spin current | [['we', 'experimentally', 'investigate', 'spinorbit', 'torque', 'and', 'spin', 'pumping', 'in', 'y_3fe_5o_12yigpt', 'bilayers', 'with', 'ultrathin', 'insertion', 'layers', 'at', 'the', 'interface', 'an', 'insertion', 'layer', 'of', 'cu', 'suppresses', 'both', 'spinorbit', 'torque', 'and', 'spin', 'pumping', 'whereas', 'an', 'insertion', 'layer', 'of', 'ni_80fe_20', 'permalloy', 'py', 'enhances', 'them', 'in', 'a', 'quantitatively', 'consistent', 'manner', 'with', 'the', 'reciprocity', 'of', 'the', 'two', 'spin', 'transmission', 'processes', 'however', 'we', 'observe', 'a', 'large', 'enhancement', 'of', 'gilbert', 'damping', 'with', 'the', 'insertion', 'of', 'py', 'that', 'can', 'not', 'be', 'accounted', 'for', 'solely', 'by', 'spin', 'pumping', 'suggesting', 'significant', 'spinmemory', 'loss', 'due', 'to', 'the', 'interfacial', 'magnetic', 'layer', 'our', 'findings', 'indicate', 'that', 'the', 'magnetization', 'at', 'the', 'yigmetal', 'interface', 'strongly', 'influences', 'the', 'transmission', 'and', 'depolarization', 'of', 'pure', 'spin', 'current']] | [-0.20203796937569696, 0.2038158730733898, 0.04768617087447981, -0.04644909545884661, -0.033754549415360366, -0.15610314201001288, 0.05265231788782227, 0.4285622799663611, -0.31481448390503536, -0.32345466082668556, -0.010488591608832995, -0.27004817603507414, -0.12201943687813462, 0.18152514717734927, 0.0376273670216214, -0.022691589199722622, -0.013249519131726251, -0.11797699940232735, -0.04729373400310441, -0.15788558629376567, 0.2948013085625925, 0.02090045682787192, 0.3260477872934403, 0.15286070307857305, 0.06235210591804166, 0.04443119439946593, 0.10407037311096298, -0.0153683993563425, -0.1048807593482329, 0.05174636469169889, 0.19952842067786544, -0.15501330285069514, 0.1321025429258369, -0.5180672189827025, -0.20026519019398312, -0.07871412195019284, 0.14439250560442232, 0.19023635326708965, -0.0711776695372361, -0.22571292100254586, 0.0432874180271097, -0.1913766719286664, -0.055447031695501144, -0.04734760067285091, -0.012635256567934775, -0.018164290414512473, -0.3335093435840154, 0.08818304072186914, 0.17992931676431084, 0.08134064674684953, -0.08509874342993465, -0.11959685262222335, -0.16493023898373446, 0.052321903627225254, 0.05596176878426154, 0.06482931677656213, 0.21066274517655093, -0.1516524392590893, -0.15157893689279006, 0.2572665991226457, -0.10756967322602165, -0.1712858726990954, 0.15787804630949637, -0.21093100818524244, 0.0067485984346723924, 0.14522563128398275, 0.1058708333041308, 0.09575689981586107, -0.12107376015376088, 0.006567995077688774, 0.025794898577139905, 0.2130523961529417, 0.08065500518939686, 0.05460568883825304, 0.2727679825906273, 0.19100363476570906, 0.03184354624640689, 0.15500962209200292, -0.1826919839876146, -0.049952675078837376, -0.18491199121476626, -0.1878699881182808, -0.2140507104384871, 0.06375368378130074, -0.08918896251325693, -0.1387438134439641, 0.35015475861113166, 0.18080891260244655, 0.1780342513992987, -0.022042036306921323, 0.28692635750169604, 0.1520828653164296, 0.14697635995294406, 0.038139682706832045, 0.2997904704567396, 0.1871421083649796, 0.1132578530155544, -0.38312098816177753, 0.17383988052914096, -0.06882289645947376] |
1,802.03866 | Katyusha X: Practical Momentum Method for Stochastic Sum-of-Nonconvex
Optimization | The problem of minimizing sum-of-nonconvex functions (i.e., convex functions
that are average of non-convex ones) is becoming increasingly important in
machine learning, and is the core machinery for PCA, SVD, regularized Newton's
method, accelerated non-convex optimization, and more.
We show how to provably obtain an accelerated stochastic algorithm for
minimizing sum-of-nonconvex functions, by $\textit{adding one additional line}$
to the well-known SVRG method. This line corresponds to momentum, and shows how
to directly apply momentum to the finite-sum stochastic minimization of
sum-of-nonconvex functions. As a side result, our method enjoys linear parallel
speed-up using mini-batch.
| cs.LG cs.DS math.OC stat.ML | the problem of minimizing sumofnonconvex functions ie convex functions that are average of nonconvex ones is becoming increasingly important in machine learning and is the core machinery for pca svd regularized newtons method accelerated nonconvex optimization and more we show how to provably obtain an accelerated stochastic algorithm for minimizing sumofnonconvex functions by textitadding one additional line to the wellknown svrg method this line corresponds to momentum and shows how to directly apply momentum to the finitesum stochastic minimization of sumofnonconvex functions as a side result our method enjoys linear parallel speedup using minibatch | [['the', 'problem', 'of', 'minimizing', 'sumofnonconvex', 'functions', 'ie', 'convex', 'functions', 'that', 'are', 'average', 'of', 'nonconvex', 'ones', 'is', 'becoming', 'increasingly', 'important', 'in', 'machine', 'learning', 'and', 'is', 'the', 'core', 'machinery', 'for', 'pca', 'svd', 'regularized', 'newtons', 'method', 'accelerated', 'nonconvex', 'optimization', 'and', 'more', 'we', 'show', 'how', 'to', 'provably', 'obtain', 'an', 'accelerated', 'stochastic', 'algorithm', 'for', 'minimizing', 'sumofnonconvex', 'functions', 'by', 'textitadding', 'one', 'additional', 'line', 'to', 'the', 'wellknown', 'svrg', 'method', 'this', 'line', 'corresponds', 'to', 'momentum', 'and', 'shows', 'how', 'to', 'directly', 'apply', 'momentum', 'to', 'the', 'finitesum', 'stochastic', 'minimization', 'of', 'sumofnonconvex', 'functions', 'as', 'a', 'side', 'result', 'our', 'method', 'enjoys', 'linear', 'parallel', 'speedup', 'using', 'minibatch']] | [-0.04371170641388744, -0.03664601425329218, -0.12325762157198242, 0.12530459114725911, -0.10439902270383011, -0.19425008940179983, -0.005930171583488744, 0.42494307426355216, -0.3564608256102249, -0.27466087832906716, 0.10326061383645821, -0.25969179612534343, -0.17857559586084018, 0.2360259821107711, -0.10671432924041305, 0.12280747961373098, 0.0740597446859684, -0.04619437938315734, -0.11239903406941042, -0.285612689651629, 0.2734408000614294, 0.055034095801973854, 0.25136163564378855, 0.010879258239661814, 0.14380016093272516, 0.02447897186803241, 0.00930363104067823, 0.0251396913112702, -0.013145335787304513, 0.18114450083099184, 0.28839976785914795, 0.23354849536272307, 0.3896094856443264, -0.4020312927983781, -0.1528556552122257, 0.1480548576632094, 0.16533089684940855, 0.0772799514234066, -0.020958797358538485, -0.17143021037261333, 0.07074414386654333, -0.11051050201308743, -0.08849276641323682, -0.14618593829393547, -0.06281953215378747, 0.05113037838225042, -0.3056592478777372, 0.11088891595261552, 0.060863617407057875, -0.018091273143567066, -0.07348494722397737, -0.18592052225283878, 0.045188705209061826, 0.0025718840770423412, 0.1161346852488976, 0.11723803543055089, 0.1630543231042803, -0.08020981195663172, -0.1306806350557474, 0.35221341298392383, -0.048717978290252144, -0.24869412945593478, 0.1381797758081267, -0.022388031673667732, -0.1308820027187066, 0.1535041965644366, 0.23750110062700447, 0.19978226528012305, -0.15275448049488727, 0.055202141031748066, -0.024942230267990983, 0.12913645498619805, 0.010606573428958654, -0.03298494540735759, 0.06403354984602981, 0.12971465385228556, 0.21192838995647367, 0.2121808520638414, -0.03937177532803147, -0.16752494433494183, -0.24093559340158258, -0.1305102873919031, -0.2097812265267856, 0.0039211498668557535, -0.1425357577987128, -0.19055224633750617, 0.34238718219742337, 0.12306190605565745, 0.20390097115949918, 0.15009446851708877, 0.37053714596456094, 0.15621805418675805, 0.0719451510875676, 0.16101540519257568, 0.20709808958914652, 0.13456452240596614, 0.11107485845524778, -0.24663498784385382, 0.03598128733355352, 0.15619890298694372] |
1,802.03867 | High-Resolution Angle Tracking for Mobile Wideband Millimeter-Wave
Systems with Antenna Array Calibration | Millimeter-wave (mmWave) systems use directional beams to support high-rate
data communications. Small misalignment between the transmit and receive beams
(e.g., due to the mobility) can result in significant drop of the received
signal quality especially in line-of-sight communication channels. In this
paper, we propose and evaluate high-resolution angle tracking strategies for
wideband mmWave systems with mobility. We custom design pairs of auxiliary
beams as the tracking beams, and use them to capture the angle variations,
towards which the steering directions of the data beams are adjusted. Different
from conventional beam tracking designs, the proposed framework neither depends
on the angle variation model nor requires an on-grid assumption. For practical
implementation of the proposed methods, we examine the impact of the array
calibration errors on the auxiliary beam pair design. Numerical results reveal
that by employing the proposed methods, good angle tracking performance can be
achieved under various antenna array configurations, channel models, and
mobility conditions.
| eess.SP cs.IT math.IT | millimeterwave mmwave systems use directional beams to support highrate data communications small misalignment between the transmit and receive beams eg due to the mobility can result in significant drop of the received signal quality especially in lineofsight communication channels in this paper we propose and evaluate highresolution angle tracking strategies for wideband mmwave systems with mobility we custom design pairs of auxiliary beams as the tracking beams and use them to capture the angle variations towards which the steering directions of the data beams are adjusted different from conventional beam tracking designs the proposed framework neither depends on the angle variation model nor requires an ongrid assumption for practical implementation of the proposed methods we examine the impact of the array calibration errors on the auxiliary beam pair design numerical results reveal that by employing the proposed methods good angle tracking performance can be achieved under various antenna array configurations channel models and mobility conditions | [['millimeterwave', 'mmwave', 'systems', 'use', 'directional', 'beams', 'to', 'support', 'highrate', 'data', 'communications', 'small', 'misalignment', 'between', 'the', 'transmit', 'and', 'receive', 'beams', 'eg', 'due', 'to', 'the', 'mobility', 'can', 'result', 'in', 'significant', 'drop', 'of', 'the', 'received', 'signal', 'quality', 'especially', 'in', 'lineofsight', 'communication', 'channels', 'in', 'this', 'paper', 'we', 'propose', 'and', 'evaluate', 'highresolution', 'angle', 'tracking', 'strategies', 'for', 'wideband', 'mmwave', 'systems', 'with', 'mobility', 'we', 'custom', 'design', 'pairs', 'of', 'auxiliary', 'beams', 'as', 'the', 'tracking', 'beams', 'and', 'use', 'them', 'to', 'capture', 'the', 'angle', 'variations', 'towards', 'which', 'the', 'steering', 'directions', 'of', 'the', 'data', 'beams', 'are', 'adjusted', 'different', 'from', 'conventional', 'beam', 'tracking', 'designs', 'the', 'proposed', 'framework', 'neither', 'depends', 'on', 'the', 'angle', 'variation', 'model', 'nor', 'requires', 'an', 'ongrid', 'assumption', 'for', 'practical', 'implementation', 'of', 'the', 'proposed', 'methods', 'we', 'examine', 'the', 'impact', 'of', 'the', 'array', 'calibration', 'errors', 'on', 'the', 'auxiliary', 'beam', 'pair', 'design', 'numerical', 'results', 'reveal', 'that', 'by', 'employing', 'the', 'proposed', 'methods', 'good', 'angle', 'tracking', 'performance', 'can', 'be', 'achieved', 'under', 'various', 'antenna', 'array', 'configurations', 'channel', 'models', 'and', 'mobility', 'conditions']] | [-0.19972743032680404, 0.05139869874157387, -0.022741218780978553, 0.004311084809652981, -0.061325720618028316, -0.20699157288237927, 0.03325549493648953, 0.4859945141500042, -0.23225093626927945, -0.34409728148171015, 0.08190952781362519, -0.23862176008762853, -0.1402534931898117, 0.21056048416742876, -0.09738996352718987, 0.11230908670132199, 0.11913567514369083, -0.06684513027899928, -0.07404774001258756, -0.17346554642666184, 0.2670024094051651, 0.156550400259514, 0.39853599671154255, 0.03637192452799589, 0.13279458946969, 0.05775571829909759, -0.040999939782364714, -0.026557578278645393, -0.08927290367902324, 0.10527841103593669, 0.2666372967233568, 0.14809451141965485, 0.2149252373364664, -0.42931891199081174, -0.24386954436739605, 0.06622289522070318, 0.12192756539809653, 0.08373869307894981, -0.08155807355689185, -0.30545889678140803, 0.05283538931980729, -0.17648517407717243, -0.10871736095455896, -0.045341753417197914, -0.07267413328612043, 0.07340797446668149, -0.32437038793559037, -0.003032431649344583, -0.029503004685524973, 0.04868715385035161, -0.0037374892001671178, -0.13520766827428052, 0.039818957023653054, 0.1359365813073612, 0.05434737070388491, -0.02311510942696083, 0.13462841534806835, -0.11624651670774197, -0.11329290956218169, 0.36848025919208605, 0.0006902791379440216, -0.2627960040474371, 0.16860626054244235, -0.1211018452480916, -0.04923821459974973, 0.16153114457246698, 0.2886968243506647, 0.07074916562395951, -0.1417034206588963, -0.023366399747770158, 0.011060473321366214, 0.20509065884736277, 0.10902969535860804, 0.10215483829979935, 0.20800358274681194, 0.15858590026956892, 0.097444006884771, 0.10580284108873457, -0.2099437341651684, -0.05655452202584955, -0.2346535245437295, -0.09576741085958577, -0.1644537864072669, -0.034348507177914614, -0.057699347866864335, -0.06139094154200246, 0.37831375238395504, 0.19476406354937822, 0.12976833321454545, 0.03899504973237673, 0.39350032093784504, 0.056897923716103596, 0.085021878232158, 0.057236759550869466, 0.24948864571389653, 0.0984258987535272, 0.14887234455034618, -0.2289938179130155, 0.0677544164591499, -0.056182226140592845] |
1,802.03868 | Dynamics of a magnetic skyrmionium driven by spin waves | The magnetic skyrmionium is a skyrmion-like structure but carries a zero net
skyrmion number, which can be used as a building block for non-volatile
information processing devices. Here, we study the dynamics of a magnetic
skyrmionium driven by propagating spin waves. It is found that the skyrmionium
can be effectively driven into motion by spin waves showing tiny skyrmion Hall
effect, of which the mobility is much better than that of the skyrmion at the
same condition. We also show that the skyrmionium mobility depends on the
nanotrack width and damping coefficient, and can be controlled by an external
out-of-plane magnetic field. Besides, we demonstrate the skyrmionium motion
driven by spin waves is inertial. Our results indicate that the skyrmionium is
a promising building block for building spin-wave spintronic devices.
| cond-mat.mes-hall cond-mat.mtrl-sci | the magnetic skyrmionium is a skyrmionlike structure but carries a zero net skyrmion number which can be used as a building block for nonvolatile information processing devices here we study the dynamics of a magnetic skyrmionium driven by propagating spin waves it is found that the skyrmionium can be effectively driven into motion by spin waves showing tiny skyrmion hall effect of which the mobility is much better than that of the skyrmion at the same condition we also show that the skyrmionium mobility depends on the nanotrack width and damping coefficient and can be controlled by an external outofplane magnetic field besides we demonstrate the skyrmionium motion driven by spin waves is inertial our results indicate that the skyrmionium is a promising building block for building spinwave spintronic devices | [['the', 'magnetic', 'skyrmionium', 'is', 'a', 'skyrmionlike', 'structure', 'but', 'carries', 'a', 'zero', 'net', 'skyrmion', 'number', 'which', 'can', 'be', 'used', 'as', 'a', 'building', 'block', 'for', 'nonvolatile', 'information', 'processing', 'devices', 'here', 'we', 'study', 'the', 'dynamics', 'of', 'a', 'magnetic', 'skyrmionium', 'driven', 'by', 'propagating', 'spin', 'waves', 'it', 'is', 'found', 'that', 'the', 'skyrmionium', 'can', 'be', 'effectively', 'driven', 'into', 'motion', 'by', 'spin', 'waves', 'showing', 'tiny', 'skyrmion', 'hall', 'effect', 'of', 'which', 'the', 'mobility', 'is', 'much', 'better', 'than', 'that', 'of', 'the', 'skyrmion', 'at', 'the', 'same', 'condition', 'we', 'also', 'show', 'that', 'the', 'skyrmionium', 'mobility', 'depends', 'on', 'the', 'nanotrack', 'width', 'and', 'damping', 'coefficient', 'and', 'can', 'be', 'controlled', 'by', 'an', 'external', 'outofplane', 'magnetic', 'field', 'besides', 'we', 'demonstrate', 'the', 'skyrmionium', 'motion', 'driven', 'by', 'spin', 'waves', 'is', 'inertial', 'our', 'results', 'indicate', 'that', 'the', 'skyrmionium', 'is', 'a', 'promising', 'building', 'block', 'for', 'building', 'spinwave', 'spintronic', 'devices']] | [-0.2171401209036748, 0.25525599777655983, -0.0575521477462294, -0.012114335630142775, -0.12244288466082742, -0.11695875449177738, -0.01412417610677389, 0.3849444992123888, -0.3030780313966366, -0.27374924312417326, 0.07243777206391454, -0.23317090367468504, -0.17242250735378967, 0.26419707557473043, 0.02596436537300738, -0.011741558061196254, 0.0023136411981585507, 0.021514467543994005, 0.006620064542557184, -0.17067761956893193, 0.2351208410010888, 0.03683509595083216, 0.3288698957480777, 0.06541811899425319, 0.10845249165924123, -0.021509303156143196, 0.1048425770436342, 0.07840365827656709, -0.10692513336342437, 0.052736589334940964, 0.17195715481916873, -0.04999873849849861, 0.21407702800710326, -0.4807730648809901, -0.24579292573034764, 0.004302426012089619, 0.20145847940674194, 0.21576049112392445, -0.08923470343266113, -0.3183181205836053, 0.12781590388084835, -0.15620216331086478, -0.12712037294292644, -0.11552543345874605, 0.04423452523053409, 0.01632644469724395, -0.2651817977558284, 0.07852485226982166, 0.12125993961325059, 0.011456731234032375, -0.07071401719653155, -0.08688871318546053, -0.10090361096573851, 0.07458355413893095, 0.05704373928467528, 0.08155192981492011, 0.21241179671353447, -0.1328977266434007, -0.15987724447981097, 0.34472307338594244, -0.0662397864101177, -0.21333255795761943, 0.09602610998285505, -0.15981241202590843, -0.03094018603889988, 0.1272494052984537, 0.13477232778767267, 0.08362781234945242, -0.08871621821980136, 0.026188240711613058, -0.03447018682849235, 0.1833743831859185, 0.061340352842727534, 0.053359565708356406, 0.3087409364346128, 0.213092494632404, 0.08084899367979513, 0.1486717798527724, -0.11007151291616117, -0.04678398767629495, -0.21899407617747785, -0.19304166234707318, -0.24483117792182243, 0.07309123591448252, -0.07480403248090835, -0.1292861876427196, 0.3956948850626269, 0.18685788426978084, 0.15397499753520466, -0.03771751586240358, 0.2932269669209535, 0.14224440537464733, 0.11338425028997545, 0.1194723109392306, 0.2295005180228215, 0.14014768831861707, 0.12491869726934685, -0.2813923600667085, 0.07822780227646804, 0.0036432485221526943] |
1,802.03869 | Beam energy and system dependence of rapidity-even dipolar flow | New measurements of rapidity-even dipolar flow, v$^{even}_{1}$, are presented
for several transverse momenta, $p_T$, and centrality intervals in Au+Au
collisions at $\sqrt{s_{NN}}~=~200,~39$ and $19.6$ GeV, U+U collisions at
$\sqrt{s_{NN}}~=~193$ GeV, and Cu+Au, Cu+Cu, d+Au and p+Au collisions at
$\sqrt{s_{NN}}~=~200$~GeV. The v$^{even}_{1}$ shows characteristic dependencies
on $p_{T}$, centrality, collision system and $\sqrt{s_{_{NN}}}$, consistent
with the expectation from a hydrodynamic-like expansion to the dipolar
fluctuation in the initial state. These measurements could serve as constraints
to distinguish between different initial-state models, and aid a more reliable
extraction of the specific viscosity $\eta/s$.
| nucl-ex hep-ex | new measurements of rapidityeven dipolar flow veven_1 are presented for several transverse momenta p_t and centrality intervals in auau collisions at sqrts_nn20039 and 196 gev uu collisions at sqrts_nn193 gev and cuau cucu dau and pau collisions at sqrts_nn200gev the veven_1 shows characteristic dependencies on p_t centrality collision system and sqrts__nn consistent with the expectation from a hydrodynamiclike expansion to the dipolar fluctuation in the initial state these measurements could serve as constraints to distinguish between different initialstate models and aid a more reliable extraction of the specific viscosity etas | [['new', 'measurements', 'of', 'rapidityeven', 'dipolar', 'flow', 'veven_1', 'are', 'presented', 'for', 'several', 'transverse', 'momenta', 'p_t', 'and', 'centrality', 'intervals', 'in', 'auau', 'collisions', 'at', 'sqrts_nn20039', 'and', '196', 'gev', 'uu', 'collisions', 'at', 'sqrts_nn193', 'gev', 'and', 'cuau', 'cucu', 'dau', 'and', 'pau', 'collisions', 'at', 'sqrts_nn200gev', 'the', 'veven_1', 'shows', 'characteristic', 'dependencies', 'on', 'p_t', 'centrality', 'collision', 'system', 'and', 'sqrts__nn', 'consistent', 'with', 'the', 'expectation', 'from', 'a', 'hydrodynamiclike', 'expansion', 'to', 'the', 'dipolar', 'fluctuation', 'in', 'the', 'initial', 'state', 'these', 'measurements', 'could', 'serve', 'as', 'constraints', 'to', 'distinguish', 'between', 'different', 'initialstate', 'models', 'and', 'aid', 'a', 'more', 'reliable', 'extraction', 'of', 'the', 'specific', 'viscosity', 'etas']] | [-0.11715928147595119, 0.24490023194544616, -0.22702834093622093, 0.14670791324955978, 0.019363437233300043, -0.13495607406500426, -0.13974849957619503, 0.359675542547785, -0.22169072364173273, -0.3178913688351368, -0.11121787151439522, -0.4135490387849424, 0.17729380063113126, 0.1880963130789841, 0.09960862248065486, 0.12277613397173841, 0.17218121339101344, 0.018403003535543878, -0.03947391775276127, -0.12669675559308594, 0.2712194960612547, 0.13326395131733226, 0.22564927878222246, 0.21567922336017264, 0.058139816665186965, 0.08961001893066554, 0.01570867269631775, 0.04459828238977098, -0.1937854221122789, 0.054268095496140055, 0.3159716684850359, -0.0027073431765425138, 0.13648911554569743, -0.2844154802632743, -0.12357762294832146, 0.1036508212176462, 0.121359349918905, 0.06685840023745751, -0.03164628187636711, -0.2554854892793743, 0.10982678489138682, -0.28265989262439395, -0.08516216121785257, -0.06036264285573672, 0.033159712479374874, 0.06675863259568296, -0.35903135755623894, 0.2398519760543673, -0.019293259762377404, 0.14513441274844474, -0.031292615581475114, -0.21504198579283582, -0.1294486690526721, -0.021746693039848202, 0.07242340062498703, 0.11219750545402432, 0.18367734543936348, -0.13117308613988732, -0.15818197273477996, 0.3655194620890864, 0.03635233870022341, -0.10649813259898246, 0.2585491709218457, -0.2097612581718927, -0.14367858040007367, 0.12630031270713643, 0.31777789988474725, 0.07231662139810364, -0.21101263046650023, -0.06279766651261169, 0.026554104818523615, 0.17378831014636603, 0.13690675094445376, 0.05317470188595183, 0.18389709251022887, 0.13643969921930427, -0.0014820540131166063, 0.07485327750829787, -0.108661885343856, -0.07495987805773387, -0.4139091993371646, -0.03277818443275046, -0.09675861257342515, 0.020871254283635097, -0.15992252423124248, 0.010561103354497203, 0.35558563365932855, 0.13050586055584878, 0.34666293564161565, -0.017913053609730525, 0.24980548903581568, 0.0710615375970394, 0.014576925724830436, 0.15873058712987723, 0.2622765650994134, 0.14580671846780968, 0.3139923262923699, -0.2545536479768183, 0.04179989785372799, 0.028158636710669406] |
1,802.0387 | A New Combinatorial Design of Coded Distributed Computing | Coded distributed computing introduced by Li et al. in 2015 is an efficient
approach to trade computing power to reduce the communication load in general
distributed computing frameworks such as MapReduce. In particular, Li et al.
show that increasing the computation load in the Map phase by a factor of $r$
can create coded multicasting opportunities to reduce the communication load in
the Reduce phase by the same factor. However, there are two major limitations
in practice. First, it requires an exponentially large number of input files
(data batches) when the number of computing nodes gets large. Second, it forces
every $s$ computing nodes to compute one Map function, which leads to a large
number of Map functions required to achieve the promised gain. In this paper,
we make an attempt to overcome these two limitations by proposing a novel coded
distributed computing approach based on a combinatorial design. We demonstrate
that when the number of computing nodes becomes large, 1) the proposed approach
requires an exponentially less number of input files; 2) the required number of
Map functions is also reduced exponentially. Meanwhile, the resulting
computation-communication trade-off maintains the multiplicative gain compared
to conventional uncoded unicast and achieves the information theoretic lower
bound asymmetrically for some system parameters.
| cs.DC cs.IT math.IT | coded distributed computing introduced by li et al in 2015 is an efficient approach to trade computing power to reduce the communication load in general distributed computing frameworks such as mapreduce in particular li et al show that increasing the computation load in the map phase by a factor of r can create coded multicasting opportunities to reduce the communication load in the reduce phase by the same factor however there are two major limitations in practice first it requires an exponentially large number of input files data batches when the number of computing nodes gets large second it forces every s computing nodes to compute one map function which leads to a large number of map functions required to achieve the promised gain in this paper we make an attempt to overcome these two limitations by proposing a novel coded distributed computing approach based on a combinatorial design we demonstrate that when the number of computing nodes becomes large 1 the proposed approach requires an exponentially less number of input files 2 the required number of map functions is also reduced exponentially meanwhile the resulting computationcommunication tradeoff maintains the multiplicative gain compared to conventional uncoded unicast and achieves the information theoretic lower bound asymmetrically for some system parameters | [['coded', 'distributed', 'computing', 'introduced', 'by', 'li', 'et', 'al', 'in', '2015', 'is', 'an', 'efficient', 'approach', 'to', 'trade', 'computing', 'power', 'to', 'reduce', 'the', 'communication', 'load', 'in', 'general', 'distributed', 'computing', 'frameworks', 'such', 'as', 'mapreduce', 'in', 'particular', 'li', 'et', 'al', 'show', 'that', 'increasing', 'the', 'computation', 'load', 'in', 'the', 'map', 'phase', 'by', 'a', 'factor', 'of', 'r', 'can', 'create', 'coded', 'multicasting', 'opportunities', 'to', 'reduce', 'the', 'communication', 'load', 'in', 'the', 'reduce', 'phase', 'by', 'the', 'same', 'factor', 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1,802.03871 | Algebraic Intersection Spaces | We define a variant of intersection space theory that applies to many compact
complex and real analytic spaces $X$, including all complex projective
varieties; this is a significant extension to a theory which has so far only
been shown to apply to a particular subclass of spaces with smooth singular
sets. We verify existence of these so-called algebraic intersection spaces and
show that they are the (reduced) chain complexes of known topological
intersection spaces in the case that both exist. We next analyze "local duality
obstructions", which we can choose to vanish, and verify that algebraic
intersection spaces satisfy duality in the absence of these obstructions. We
conclude by defining an untwisted algebraic intersection space pairing, whose
signature is equal to the Novikov signature of the complement in $X$ of a
tubular neighborhood of the singular set.
| math.AT | we define a variant of intersection space theory that applies to many compact complex and real analytic spaces x including all complex projective varieties this is a significant extension to a theory which has so far only been shown to apply to a particular subclass of spaces with smooth singular sets we verify existence of these socalled algebraic intersection spaces and show that they are the reduced chain complexes of known topological intersection spaces in the case that both exist we next analyze local duality obstructions which we can choose to vanish and verify that algebraic intersection spaces satisfy duality in the absence of these obstructions we conclude by defining an untwisted algebraic intersection space pairing whose signature is equal to the novikov signature of the complement in x of a tubular neighborhood of the singular set | [['we', 'define', 'a', 'variant', 'of', 'intersection', 'space', 'theory', 'that', 'applies', 'to', 'many', 'compact', 'complex', 'and', 'real', 'analytic', 'spaces', 'x', 'including', 'all', 'complex', 'projective', 'varieties', 'this', 'is', 'a', 'significant', 'extension', 'to', 'a', 'theory', 'which', 'has', 'so', 'far', 'only', 'been', 'shown', 'to', 'apply', 'to', 'a', 'particular', 'subclass', 'of', 'spaces', 'with', 'smooth', 'singular', 'sets', 'we', 'verify', 'existence', 'of', 'these', 'socalled', 'algebraic', 'intersection', 'spaces', 'and', 'show', 'that', 'they', 'are', 'the', 'reduced', 'chain', 'complexes', 'of', 'known', 'topological', 'intersection', 'spaces', 'in', 'the', 'case', 'that', 'both', 'exist', 'we', 'next', 'analyze', 'local', 'duality', 'obstructions', 'which', 'we', 'can', 'choose', 'to', 'vanish', 'and', 'verify', 'that', 'algebraic', 'intersection', 'spaces', 'satisfy', 'duality', 'in', 'the', 'absence', 'of', 'these', 'obstructions', 'we', 'conclude', 'by', 'defining', 'an', 'untwisted', 'algebraic', 'intersection', 'space', 'pairing', 'whose', 'signature', 'is', 'equal', 'to', 'the', 'novikov', 'signature', 'of', 'the', 'complement', 'in', 'x', 'of', 'a', 'tubular', 'neighborhood', 'of', 'the', 'singular', 'set']] | [-0.18768314503211472, 0.05616474771002447, -0.08843046232011087, 0.14733581543801502, -0.11191469090684814, -0.11076545361497432, 0.0017790292639516458, 0.34566648409151246, -0.30674422564950304, -0.18724356996860816, 0.12897497155265134, -0.2355642460939223, -0.18867638937612302, 0.18588135230361763, -0.1327026289986297, -0.00256864716346464, 0.019250304837554803, 0.06879983353598491, -0.1185630936537577, -0.2967074905007221, 0.42985264257439515, -0.08498593388519583, 0.2379666339252552, 0.057418380476479985, 0.10292458723734275, 0.008745479703384594, 0.0042451474795213145, 0.07459876799818305, -0.14583454193164133, 0.15610174836767635, 0.31125673556523603, 0.09681776461504182, 0.1880093199221322, -0.3871235294321919, -0.18789427482903712, 0.22892300244233824, 0.15602749420404707, 0.04328876574537343, 0.015925847454485994, -0.2526778595260324, 0.14678519725609218, -0.14465134805340293, -0.1757890503670014, -0.14137804638517912, 0.040334516792918426, 0.032503414654383694, -0.2284130176315152, -0.07626823978294639, 0.08423046483216386, 0.08154277592543921, -0.06454853665841621, -0.04241211184922497, -0.06891004336202504, 0.07979690178508198, -0.010300483116132282, 0.05416605569911699, 0.07409985137790659, -0.05099318879928413, -0.13603000938688425, 0.3509424109263414, -0.020298631870303386, -0.22549517152416282, 0.2005350271725932, -0.16341462118014094, -0.17593249662636515, 0.13113245246320093, 0.10068558272055901, 0.139847487602111, -0.026584256457396014, 0.17655146500498506, -0.12677436801231037, 0.06871322448956814, 0.09539309372092142, 0.03614616321984434, 0.15236034844900026, 0.08670779825256199, 0.1140394731434096, 0.1305417294382886, -0.014008812171264286, -0.11462208603969333, -0.3610240161677238, -0.19169009295734066, -0.14184257781122858, 0.11017677141693387, -0.07936450331881863, -0.2207648895524126, 0.35406718859924885, 0.08713002654237582, 0.20148402501414292, 0.07647309087074097, 0.2334096785073923, 0.054196868707994374, 0.03813136546417092, 0.05498031439110093, 0.18581702940872985, 0.18141433930561546, -0.010627017410839126, -0.13045391195112105, 0.00023540250793860777, 0.1595680745875966] |
1,802.03872 | Twofold Cantor sets in R | We introduce a class of self-similar sets which we call {\em twofold Cantor
sets} $K_{pq}$ in $\mathbb R$ which are totally disconnected, do not have weak
separation property and at the same time have isomorphic self-similar
structures.
| math.MG math.DS | we introduce a class of selfsimilar sets which we call em twofold cantor sets k_pq in mathbb r which are totally disconnected do not have weak separation property and at the same time have isomorphic selfsimilar structures | [['we', 'introduce', 'a', 'class', 'of', 'selfsimilar', 'sets', 'which', 'we', 'call', 'em', 'twofold', 'cantor', 'sets', 'k_pq', 'in', 'mathbb', 'r', 'which', 'are', 'totally', 'disconnected', 'do', 'not', 'have', 'weak', 'separation', 'property', 'and', 'at', 'the', 'same', 'time', 'have', 'isomorphic', 'selfsimilar', 'structures']] | [-0.2010638879461063, 0.1684184861998703, -0.09939599149180828, 0.07865751380371433, -0.0928649379796273, -0.13250546382639455, -0.01157890331956583, 0.41240251205257467, -0.3199727537261473, -0.11982559251624185, 0.08050922035378076, -0.2981436870790817, -0.10451000943981312, 0.13196170111375585, -0.07223140063217363, -0.02407137565063061, 0.04205814143642783, 0.0672287420319343, -0.052099108721154766, -0.2592242447395079, 0.40664613897584984, -0.10211790770896383, 0.25561151301135887, 0.021316747533509862, 0.0680197016713587, -0.08256352142858747, -0.036414171871099923, 0.1132006257067661, -0.2113689013278565, 0.032864215544651486, 0.22561996369748502, 0.12508030527749578, 0.26381625154848537, -0.35867003892623894, -0.13854104625312863, 0.25198118383618623, 0.15348031725482764, 0.006160764250199537, -0.007084611828464108, -0.2634019469087188, 0.1681158119510557, -0.14005612521558194, -0.12364821859031312, -0.10526237805449479, 0.07407470732121854, 0.0243649548863539, -0.19950503331760094, 0.022440543146552267, 0.15784888639039285, 0.08480968809611089, -0.02516227596867326, -0.07583328082251388, -0.05869399176004368, 0.04531828770917412, -0.04526573905368914, 0.041177695604494295, 0.01063561550266034, 0.05503181451176469, -0.13853861249643504, 0.3735618979946987, -0.02431539911776781, -0.24406974188782074, 0.21204724063392025, -0.19204927628507484, -0.24711442113275062, 0.17573247255279204, 0.15404297663150607, 0.13108505706327991, -0.0974713428248022, 0.20561126720103612, -0.16675639253210378, 0.09908357896917575, 0.1441599594655673, 0.04348987567465048, 0.17431881082420414, 0.03322661999059287, 0.11800625158247312, 0.1604361285178645, -0.01671031424524011, -0.03602595607171188, -0.32725557868645805, -0.06822127043395429, -0.13569076127699903, 0.12077269421904537, -0.06184735731811678, -0.3115809616204855, 0.35174944989282536, 0.08725426779002757, 0.19360172056366462, 0.15145936377371685, 0.1838783334336571, 0.025966614459616108, 0.09643089262818968, 0.12720511214354555, 0.11438337396326903, 0.06750140643703777, -0.0050900985134413115, -0.05748680712202111, 0.042078645232863525, 0.1641323891896251] |
1,802.03873 | PRIL: Perceptron Ranking Using Interval Labeled Data | In this paper, we propose an online learning algorithm PRIL for learning
ranking classifiers using interval labeled data and show its correctness. We
show its convergence in finite number of steps if there exists an ideal
classifier such that the rank given by it for an example always lies in its
label interval. We then generalize this mistake bound result for the general
case. We also provide regret bound for the proposed algorithm. We propose a
multiplicative update algorithm for PRIL called M-PRIL. We provide its
correctness and convergence results. We show the effectiveness of PRIL by
showing its performance on various datasets.
| cs.LG | in this paper we propose an online learning algorithm pril for learning ranking classifiers using interval labeled data and show its correctness we show its convergence in finite number of steps if there exists an ideal classifier such that the rank given by it for an example always lies in its label interval we then generalize this mistake bound result for the general case we also provide regret bound for the proposed algorithm we propose a multiplicative update algorithm for pril called mpril we provide its correctness and convergence results we show the effectiveness of pril by showing its performance on various datasets | [['in', 'this', 'paper', 'we', 'propose', 'an', 'online', 'learning', 'algorithm', 'pril', 'for', 'learning', 'ranking', 'classifiers', 'using', 'interval', 'labeled', 'data', 'and', 'show', 'its', 'correctness', 'we', 'show', 'its', 'convergence', 'in', 'finite', 'number', 'of', 'steps', 'if', 'there', 'exists', 'an', 'ideal', 'classifier', 'such', 'that', 'the', 'rank', 'given', 'by', 'it', 'for', 'an', 'example', 'always', 'lies', 'in', 'its', 'label', 'interval', 'we', 'then', 'generalize', 'this', 'mistake', 'bound', 'result', 'for', 'the', 'general', 'case', 'we', 'also', 'provide', 'regret', 'bound', 'for', 'the', 'proposed', 'algorithm', 'we', 'propose', 'a', 'multiplicative', 'update', 'algorithm', 'for', 'pril', 'called', 'mpril', 'we', 'provide', 'its', 'correctness', 'and', 'convergence', 'results', 'we', 'show', 'the', 'effectiveness', 'of', 'pril', 'by', 'showing', 'its', 'performance', 'on', 'various', 'datasets']] | [-0.07209759685850027, -0.012495977270637192, -0.08049638083606374, 0.06441367459346485, -0.06899145063852855, -0.13254864388784648, 0.10676012797322233, 0.45981418469226826, -0.2588982134874837, -0.2832581635254125, 0.09187694333662626, -0.245971395816727, -0.22178507899276584, 0.208825495077369, -0.14790434847750208, 0.05646439461099148, 0.1248993701210209, 0.1085774079107853, -0.032623418148004395, -0.3698186809197068, 0.3132704540941061, 0.02913330081740723, 0.2520753646694014, 0.08474221776066092, 0.14557134138885885, 0.014968640750785377, 0.01354258987075631, 0.018107416026075098, -0.15703441778589644, 0.10935572221619534, 0.2791196758032539, 0.26921489970375073, 0.35015526578268585, -0.32973184361688646, -0.09920130168571703, 0.15094749783348366, 0.13254094144155948, 0.11392218786803664, -0.1144699865367775, -0.28356161913674727, 0.1425168090773856, -0.18585407575957624, -0.05393044611293019, -0.1680650862079917, -0.0060908425469225385, -0.018705545981670273, -0.30428412826914414, 0.011824518827637019, 0.1401155310883826, 0.06319430264257415, -0.04795779000771433, -0.11269598630800222, 0.07202773305071591, 0.09105150074716292, 0.03881052478819209, 0.013459922092984997, 0.06082675459009467, -0.09111282334425579, -0.18073584747902466, 0.32061124518148454, -0.06590351357362141, -0.22020903246148543, 0.15182322750379348, -0.03394651574575726, -0.17953123031731913, 0.06880468670187481, 0.1977653341623498, 0.13981466088244263, -0.0775992978734019, 0.09549821019925944, -0.11053580630972397, 0.14999915878562367, 0.042633169285003464, -0.013421953429226014, 0.06546062759954628, 0.22100973478518426, 0.1189039098692364, 0.19271197894305064, -0.07590465606235917, -0.018763239235233736, -0.3068774819520174, -0.16331998718669638, -0.22267113774832265, -0.021419630824204755, -0.1458571459844083, -0.1582674392517291, 0.3668671727691795, 0.2342371602894227, 0.1987512929858092, 0.19054521128133523, 0.3183238287673186, 0.10696150547843061, -0.01842292690850502, 0.19358474571787404, 0.19749451987445354, 0.04599878807807816, 0.05156491168628575, -0.18540752344472589, 0.10293580027406707, 0.10999941253377234] |
1,802.03874 | Canted ferrimagnetism and giant coercivity in the non-stoichiometric
double perovskite La2Ni1.19Os0.81O6 | The non-stoichiometric double perovskite oxide La2Ni1.19Os0.81O6 was
synthesized by solid state reaction and its crystal and magnetic structures
were investigated by powder x-ray and neutron diffraction. La2Ni1.19Os0.81O6
crystallizes in the monoclinic double perovskite structure (general formula
A2BB'O6) with space group P21/n, where the B site is fully occupied by Ni and
the B' site by 19 % Ni and 81 % Os atoms. Using x-ray absorption spectroscopy
an Os4.5+ oxidation state was established, suggesting presence of about 50 %
paramagnetic Os5+ (5d3, S = 3/2) and 50 % non-magnetic Os4+ (5d4, Jeff = 0)
ions at the B' sites. Magnetization and neutron diffraction measurements on
La2Ni1.19Os0.81O6 provide evidence for a ferrimagnetic transition at 125 K. The
analysis of the neutron data suggests a canted ferrimagnetic spin structure
with collinear Ni2+ spin chains extending along the c axis but a non-collinear
spin alignment within the ab plane. The magnetization curve of
La2Ni1.19Os0.81O6 features a hysteresis with a very high coercive field, HC =
41 kOe, at T = 5 K, which is explained in terms of large magnetocrystalline
anisotropy due to the presence of Os ions together with atomic disorder. Our
results are encouraging to search for rare earth free hard magnets in the class
of double perovskite oxides.
| cond-mat.str-el cond-mat.mtrl-sci | the nonstoichiometric double perovskite oxide la2ni119os081o6 was synthesized by solid state reaction and its crystal and magnetic structures were investigated by powder xray and neutron diffraction la2ni119os081o6 crystallizes in the monoclinic double perovskite structure general formula a2bbo6 with space group p21n where the b site is fully occupied by ni and the b site by 19 ni and 81 os atoms using xray absorption spectroscopy an os45 oxidation state was established suggesting presence of about 50 paramagnetic os5 5d3 s 32 and 50 nonmagnetic os4 5d4 jeff 0 ions at the b sites magnetization and neutron diffraction measurements on la2ni119os081o6 provide evidence for a ferrimagnetic transition at 125 k the analysis of the neutron data suggests a canted ferrimagnetic spin structure with collinear ni2 spin chains extending along the c axis but a noncollinear spin alignment within the ab plane the magnetization curve of la2ni119os081o6 features a hysteresis with a very high coercive field hc 41 koe at t 5 k which is explained in terms of large magnetocrystalline anisotropy due to the presence of os ions together with atomic disorder our results are encouraging to search for rare earth free hard magnets in the class of double perovskite oxides | [['the', 'nonstoichiometric', 'double', 'perovskite', 'oxide', 'la2ni119os081o6', 'was', 'synthesized', 'by', 'solid', 'state', 'reaction', 'and', 'its', 'crystal', 'and', 'magnetic', 'structures', 'were', 'investigated', 'by', 'powder', 'xray', 'and', 'neutron', 'diffraction', 'la2ni119os081o6', 'crystallizes', 'in', 'the', 'monoclinic', 'double', 'perovskite', 'structure', 'general', 'formula', 'a2bbo6', 'with', 'space', 'group', 'p21n', 'where', 'the', 'b', 'site', 'is', 'fully', 'occupied', 'by', 'ni', 'and', 'the', 'b', 'site', 'by', '19', 'ni', 'and', '81', 'os', 'atoms', 'using', 'xray', 'absorption', 'spectroscopy', 'an', 'os45', 'oxidation', 'state', 'was', 'established', 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1,802.03875 | Pseudo-Recursal: Solving the Catastrophic Forgetting Problem in Deep
Neural Networks | In general, neural networks are not currently capable of learning tasks in a
sequential fashion. When a novel, unrelated task is learnt by a neural network,
it substantially forgets how to solve previously learnt tasks. One of the
original solutions to this problem is pseudo-rehearsal, which involves learning
the new task while rehearsing generated items representative of the previous
task/s. This is very effective for simple tasks. However, pseudo-rehearsal has
not yet been successfully applied to very complex tasks because in these tasks
it is difficult to generate representative items. We accomplish
pseudo-rehearsal by using a Generative Adversarial Network to generate items so
that our deep network can learn to sequentially classify the CIFAR-10, SVHN and
MNIST datasets. After training on all tasks, our network loses only 1.67%
absolute accuracy on CIFAR-10 and gains 0.24% absolute accuracy on SVHN. Our
model's performance is a substantial improvement compared to the current state
of the art solution.
| cs.LG cs.AI stat.ML | in general neural networks are not currently capable of learning tasks in a sequential fashion when a novel unrelated task is learnt by a neural network it substantially forgets how to solve previously learnt tasks one of the original solutions to this problem is pseudorehearsal which involves learning the new task while rehearsing generated items representative of the previous tasks this is very effective for simple tasks however pseudorehearsal has not yet been successfully applied to very complex tasks because in these tasks it is difficult to generate representative items we accomplish pseudorehearsal by using a generative adversarial network to generate items so that our deep network can learn to sequentially classify the cifar10 svhn and mnist datasets after training on all tasks our network loses only 167 absolute accuracy on cifar10 and gains 024 absolute accuracy on svhn our models performance is a substantial improvement compared to the current state of the art solution | [['in', 'general', 'neural', 'networks', 'are', 'not', 'currently', 'capable', 'of', 'learning', 'tasks', 'in', 'a', 'sequential', 'fashion', 'when', 'a', 'novel', 'unrelated', 'task', 'is', 'learnt', 'by', 'a', 'neural', 'network', 'it', 'substantially', 'forgets', 'how', 'to', 'solve', 'previously', 'learnt', 'tasks', 'one', 'of', 'the', 'original', 'solutions', 'to', 'this', 'problem', 'is', 'pseudorehearsal', 'which', 'involves', 'learning', 'the', 'new', 'task', 'while', 'rehearsing', 'generated', 'items', 'representative', 'of', 'the', 'previous', 'tasks', 'this', 'is', 'very', 'effective', 'for', 'simple', 'tasks', 'however', 'pseudorehearsal', 'has', 'not', 'yet', 'been', 'successfully', 'applied', 'to', 'very', 'complex', 'tasks', 'because', 'in', 'these', 'tasks', 'it', 'is', 'difficult', 'to', 'generate', 'representative', 'items', 'we', 'accomplish', 'pseudorehearsal', 'by', 'using', 'a', 'generative', 'adversarial', 'network', 'to', 'generate', 'items', 'so', 'that', 'our', 'deep', 'network', 'can', 'learn', 'to', 'sequentially', 'classify', 'the', 'cifar10', 'svhn', 'and', 'mnist', 'datasets', 'after', 'training', 'on', 'all', 'tasks', 'our', 'network', 'loses', 'only', '167', 'absolute', 'accuracy', 'on', 'cifar10', 'and', 'gains', '024', 'absolute', 'accuracy', 'on', 'svhn', 'our', 'models', 'performance', 'is', 'a', 'substantial', 'improvement', 'compared', 'to', 'the', 'current', 'state', 'of', 'the', 'art', 'solution']] | [-0.012436632550651989, -0.011383841388047702, -0.05388422188078684, 0.08104329387311854, -0.15065808825555346, -0.20873153004494885, 0.06352189909967203, 0.4784948299819183, -0.260022323734818, -0.37010387872015277, 0.06360922970193168, -0.2548092354239235, -0.17579510615117128, 0.22994890872148738, -0.17797353143893904, 0.10316005604221455, 0.19304629767974538, 0.08191093069561306, -0.06664072562594928, -0.40762216757622455, 0.2743138495712511, 0.024672879270961387, 0.34945576625966257, 0.006955142757825313, 0.15863868660563904, -0.10240977933750518, 0.04643042748042893, -0.01856047832256844, -0.006381117279365313, 0.13666829902213068, 0.35477905359014494, 0.18488537219292936, 0.3501210181463149, -0.37439161463370246, -0.2195712368905304, 0.1260840824444688, 0.12764364747450718, 0.11414323472126853, 0.011050359876976619, -0.3377599326292834, 0.13487540649535554, -0.1607555683221548, 0.07021857254928159, -0.1878647796614396, 0.008379830690401215, -0.04672371094272802, -0.2948232615794686, 0.028961699659327766, 0.069307307873462, 0.04059886234181542, -0.018163404063952546, -0.14295207068939964, 0.030129737582718655, 0.18035155704128555, 0.03961441379881674, 0.10127129007671629, 0.15063984119183113, -0.2249663915928273, -0.1703876173288952, 0.37800707064809336, -0.0498664882075372, -0.2213181715518717, 0.21288783444962914, 0.009328179283728522, -0.16885336410674837, 0.10481914212146114, 0.22478933493050957, 0.1442327323189426, -0.16958027505362783, -0.0061255026766429506, -0.08578055187939636, 0.21242262105787954, 0.038133760220220976, -0.06006670311626588, 0.15488968355010355, 0.290023744622651, 0.026479973517838987, 0.13950626812875272, -0.08203276361098454, -0.07216663268364726, -0.1501972438527211, -0.05404400483014122, -0.21514451351512465, 0.018907377541613493, -0.07687687520820256, -0.09170710720063653, 0.41085263149542434, 0.2298683081933808, 0.2376648996926604, 0.12396380278026504, 0.3597028332582164, -0.003242654348122737, 0.1447870175504396, 0.12248016792094155, 0.21598446808394886, -0.006913847144272539, 0.14028035663518934, -0.14854141120243097, 0.10636848978578083, 0.01119876466184524] |
1,802.03876 | Brownian motion between two random trajectories | Consider the first exit time of one-dimensional Brownian motion
$\{B_s\}_{s\geq 0}$ from a random passageway. We discuss a Brownian motion with
two time-dependent random boundaries in quenched sense. Let $\{W_s\}_{s\geq 0}$
be an other one-dimensional Brownian motion independent of $\{B_s\}_{s\geq 0}$
and let $\bfP(\cdot|W)$ represent the conditional probability depending on the
realization of $\{W_s\}_{s\geq 0}$. We show that
$$-t^{-1}\ln\bfP^x(\forall_{s\in[0,t]}a+\beta W_s\leq B_s\leq b+\beta W_s|W)$$
converges to a finite positive constant $\gamma(\beta)(b-a)^{-2}$ almost surely
and in $L^p~ (p\geq 1)$ if $a<B_0=x<b$ and $W_0=0.$ When $\beta=1, a+b=2x,$ it
is equivalent to the random small ball probability problem in the sense of
equiditribution, which has been investigated in \cite{DL2005}. We also find
some properties of the function $\gamma(\beta)$. An important moment estimation
has also been obtained, which can be applied to discuss the small deviation of
random walk with random environment in time (see [12]).
| math.PR | consider the first exit time of onedimensional brownian motion b_s_sgeq 0 from a random passageway we discuss a brownian motion with two timedependent random boundaries in quenched sense let w_s_sgeq 0 be an other onedimensional brownian motion independent of b_s_sgeq 0 and let bfpcdotw represent the conditional probability depending on the realization of w_s_sgeq 0 we show that t1lnbfpxforall_sin0tabeta w_sleq b_sleq bbeta w_sw converges to a finite positive constant gammabetaba2 almost surely and in lp pgeq 1 if ab_0xb and w_00 when beta1 ab2x it is equivalent to the random small ball probability problem in the sense of equiditribution which has been investigated in citedl2005 we also find some properties of the function gammabeta an important moment estimation has also been obtained which can be applied to discuss the small deviation of random walk with random environment in time see 12 | [['consider', 'the', 'first', 'exit', 'time', 'of', 'onedimensional', 'brownian', 'motion', 'b_s_sgeq', '0', 'from', 'a', 'random', 'passageway', 'we', 'discuss', 'a', 'brownian', 'motion', 'with', 'two', 'timedependent', 'random', 'boundaries', 'in', 'quenched', 'sense', 'let', 'w_s_sgeq', '0', 'be', 'an', 'other', 'onedimensional', 'brownian', 'motion', 'independent', 'of', 'b_s_sgeq', '0', 'and', 'let', 'bfpcdotw', 'represent', 'the', 'conditional', 'probability', 'depending', 'on', 'the', 'realization', 'of', 'w_s_sgeq', '0', 'we', 'show', 'that', 't1lnbfpxforall_sin0tabeta', 'w_sleq', 'b_sleq', 'bbeta', 'w_sw', 'converges', 'to', 'a', 'finite', 'positive', 'constant', 'gammabetaba2', 'almost', 'surely', 'and', 'in', 'lp', 'pgeq', '1', 'if', 'ab_0xb', 'and', 'w_00', 'when', 'beta1', 'ab2x', 'it', 'is', 'equivalent', 'to', 'the', 'random', 'small', 'ball', 'probability', 'problem', 'in', 'the', 'sense', 'of', 'equiditribution', 'which', 'has', 'been', 'investigated', 'in', 'citedl2005', 'we', 'also', 'find', 'some', 'properties', 'of', 'the', 'function', 'gammabeta', 'an', 'important', 'moment', 'estimation', 'has', 'also', 'been', 'obtained', 'which', 'can', 'be', 'applied', 'to', 'discuss', 'the', 'small', 'deviation', 'of', 'random', 'walk', 'with', 'random', 'environment', 'in', 'time', 'see', '12']] | [-0.09825042473870939, 0.19707416793845747, -0.07499647953026876, 0.007855925766758504, -0.02129576085355338, -0.16910946960597645, 0.004476231242819711, 0.400651309507217, -0.30803331930014327, -0.20236542923962625, 0.10439261219805239, -0.2669057276862578, -0.133017253075675, 0.1452307095842832, -0.08192895180509081, 0.054092888760666445, 0.027827262497089042, 0.10979336961429184, -0.030508570906552155, -0.2508451446439836, 0.2537799538736503, -0.0364785675751412, 0.19456038168653494, 0.006291240464719966, 0.12585282166429804, 0.025835285344579087, 0.015132303540338211, 0.03850212018188881, -0.1837572787751881, 0.01669694841051049, 0.1779040269458329, 0.04108670130082155, 0.32985797011124807, -0.372648146293148, -0.1899581892767877, 0.18471319791970878, 0.16573354371962704, 0.04901044382933733, -0.032365730667567455, -0.30796897146249497, 0.1285442389184829, -0.10497341894610661, -0.17754411069661613, -0.029062795232776113, 0.09850674139438417, 0.07723909212866666, -0.3043272132347301, 0.07366660447730719, 0.09350171446154906, 0.007136059356633369, -0.04832890899265258, -0.13930418971177982, 0.011844738709234347, 0.13439409783330936, 0.04700808077097291, 0.06921292842809462, 0.10133977978321629, -0.07618746079145161, -0.1303039364458069, 0.38157415944408246, -0.12308448621651202, -0.265305150879591, 0.1383182337932934, -0.23199853291515055, -0.14247455690642746, 0.10622987858621095, 0.1492877642470082, 0.1267807375874341, -0.12661303025287646, 0.16573030259836039, -0.058180210919405416, 0.13223275437072932, 0.06522850555873762, -0.015116993290203528, 0.1417012452294566, 0.11348832409469573, 0.14123100754768242, 0.15951538852857808, -0.094494269201972, -0.11710661695903446, -0.3010188296261265, -0.1581934899399717, -0.23327410626995165, 0.16027756950884, -0.1404501528431722, -0.1926658350138331, 0.30608958324901464, 0.16320537769155005, 0.22642190396917736, 0.11134005046273604, 0.19232182738217196, 0.17161653534963903, -0.06144050666952403, 0.1013168809150793, 0.14210240781700467, 0.14488837359231113, 0.04444022926567929, -0.1496002133184032, 0.0713633939084839, 0.06322399486684951] |
1,802.03877 | Gaussian Process Classification with Privileged Information by
Soft-to-Hard Labeling Transfer | Learning using privileged information is an attractive problem setting that
helps many learning scenarios in the real world. A state-of-the-art method of
Gaussian process classification (GPC) with privileged information is GPC+,
which incorporates privileged information into a noise term of the likelihood.
A drawback of GPC+ is that it requires numerical quadrature to calculate the
posterior distribution of the latent function, which is extremely
time-consuming. To overcome this limitation, we propose a novel classification
method with privileged information based on Gaussian processes, called
"soft-label-transferred Gaussian process (SLT-GP)." Our basic idea is that we
construct another learning task of predicting soft labels (continuous values)
obtained from privileged information and we perform transfer learning from this
task to the target task of predicting hard labels. We derive a PAC-Bayesian
bound of our proposed method, which justifies optimizing hyperparameters by the
empirical Bayes method. We also experimentally show the usefulness of our
proposed method compared with GPC and GPC+.
| stat.ML | learning using privileged information is an attractive problem setting that helps many learning scenarios in the real world a stateoftheart method of gaussian process classification gpc with privileged information is gpc which incorporates privileged information into a noise term of the likelihood a drawback of gpc is that it requires numerical quadrature to calculate the posterior distribution of the latent function which is extremely timeconsuming to overcome this limitation we propose a novel classification method with privileged information based on gaussian processes called softlabeltransferred gaussian process sltgp our basic idea is that we construct another learning task of predicting soft labels continuous values obtained from privileged information and we perform transfer learning from this task to the target task of predicting hard labels we derive a pacbayesian bound of our proposed method which justifies optimizing hyperparameters by the empirical bayes method we also experimentally show the usefulness of our proposed method compared with gpc and gpc | [['learning', 'using', 'privileged', 'information', 'is', 'an', 'attractive', 'problem', 'setting', 'that', 'helps', 'many', 'learning', 'scenarios', 'in', 'the', 'real', 'world', 'a', 'stateoftheart', 'method', 'of', 'gaussian', 'process', 'classification', 'gpc', 'with', 'privileged', 'information', 'is', 'gpc', 'which', 'incorporates', 'privileged', 'information', 'into', 'a', 'noise', 'term', 'of', 'the', 'likelihood', 'a', 'drawback', 'of', 'gpc', 'is', 'that', 'it', 'requires', 'numerical', 'quadrature', 'to', 'calculate', 'the', 'posterior', 'distribution', 'of', 'the', 'latent', 'function', 'which', 'is', 'extremely', 'timeconsuming', 'to', 'overcome', 'this', 'limitation', 'we', 'propose', 'a', 'novel', 'classification', 'method', 'with', 'privileged', 'information', 'based', 'on', 'gaussian', 'processes', 'called', 'softlabeltransferred', 'gaussian', 'process', 'sltgp', 'our', 'basic', 'idea', 'is', 'that', 'we', 'construct', 'another', 'learning', 'task', 'of', 'predicting', 'soft', 'labels', 'continuous', 'values', 'obtained', 'from', 'privileged', 'information', 'and', 'we', 'perform', 'transfer', 'learning', 'from', 'this', 'task', 'to', 'the', 'target', 'task', 'of', 'predicting', 'hard', 'labels', 'we', 'derive', 'a', 'pacbayesian', 'bound', 'of', 'our', 'proposed', 'method', 'which', 'justifies', 'optimizing', 'hyperparameters', 'by', 'the', 'empirical', 'bayes', 'method', 'we', 'also', 'experimentally', 'show', 'the', 'usefulness', 'of', 'our', 'proposed', 'method', 'compared', 'with', 'gpc', 'and', 'gpc']] | [-0.024482053013627308, -0.013754480416253784, -0.13593817529776556, 0.07812632094512036, -0.1481030102571255, -0.16168629189094455, 0.08824299876396725, 0.4163884042532413, -0.30556502510557654, -0.3090942182832144, 0.05538592991258876, -0.23418094492335986, -0.19834629562381026, 0.19360661552463018, -0.09313637167578201, 0.10738462032139824, 0.10344012597479023, 0.05856117324951988, -0.07627832477567596, -0.2718161177132037, 0.3336627644943315, 0.07746447560486275, 0.3330012105961705, -0.009106992674362543, 0.2029314465705886, 0.016361121245502652, -0.05923356245159982, -0.0581282731901564, -0.08524387232522146, 0.1913308566443731, 0.2786805982166272, 0.21586163333230107, 0.3729046539149501, -0.324159819064299, -0.2578918402046933, 0.11060406949957712, 0.14179018966539145, 0.12178164825592547, -0.02680760054235262, -0.3128584730076035, 0.07088430691510439, -0.17440146313717153, -0.048163500109493926, -0.1609848688187247, -0.059003260307159126, -0.03412334223326247, -0.327619334658632, 0.09630689087649656, 0.09813752148357861, 0.011155541762467715, -0.012808022042434034, -0.10178604816818344, 0.07138067491159036, 0.10525350062200775, 0.04686041811122801, 0.04344437197500888, 0.12744640215759637, -0.12752657649167753, -0.13932721075441615, 0.3356345537566132, -0.04777924665824401, -0.24643470310642349, 0.15848268031342222, -0.04129239997233857, -0.1454580065555411, 0.1275841619187434, 0.20318318319252945, 0.13490876254879616, -0.18575487437343538, 0.02746477531641117, -0.03599656519991067, 0.20107966636960672, 0.01741592653168293, -0.030790706640434477, 0.15172058889678644, 0.24263883907009254, 0.019899320809469958, 0.16899810133308366, -0.17145770369248778, -0.10740250307921466, -0.2742107381209634, -0.13006075942327947, -0.27632159318218563, 0.002403528939073833, -0.14582477595831733, -0.17015876679064393, 0.345100258975955, 0.2462951830805993, 0.20663142063981527, 0.09099371271894326, 0.3577476440650689, 0.07439380637657309, 0.054433777111933215, 0.12195071610569191, 0.18183974924424737, 0.06348333677712392, 0.06032630375425056, -0.15479684656322631, 0.0935590492422366, 0.04826606360608833] |
1,802.03878 | Ultra-Reliable Communication in 5G mmWave Networks: A Risk-Sensitive
Approach | This letter investigates the problem of providing gigabit wireless access
with reliable communication in 5G millimeter-Wave (mmWave) massive
multiple-input multiple-output (MIMO) networks. In contrast to the classical
network design based on average metrics, a distributed risk-sensitive
reinforcement learning-based framework is proposed to jointly optimize the
beamwidth and transmit power, while taking into account the sensitivity of
mmWave links due to blockage. Numerical results show that our proposed
algorithm achieves more than 9 Gbps of user throughput with a guaranteed
probability of 90%, whereas the baselines guarantee less than 7.5 Gbps. More
importantly, there exists a rate-reliability-network density tradeoff, in which
as the user density increases from 16 to 96 per km2, the fraction of users that
achieve 4 Gbps are reduced by 11.61% and 39.11% in the proposed and the
baseline models, respectively.
| cs.NI | this letter investigates the problem of providing gigabit wireless access with reliable communication in 5g millimeterwave mmwave massive multipleinput multipleoutput mimo networks in contrast to the classical network design based on average metrics a distributed risksensitive reinforcement learningbased framework is proposed to jointly optimize the beamwidth and transmit power while taking into account the sensitivity of mmwave links due to blockage numerical results show that our proposed algorithm achieves more than 9 gbps of user throughput with a guaranteed probability of 90 whereas the baselines guarantee less than 75 gbps more importantly there exists a ratereliabilitynetwork density tradeoff in which as the user density increases from 16 to 96 per km2 the fraction of users that achieve 4 gbps are reduced by 1161 and 3911 in the proposed and the baseline models respectively | [['this', 'letter', 'investigates', 'the', 'problem', 'of', 'providing', 'gigabit', 'wireless', 'access', 'with', 'reliable', 'communication', 'in', '5g', 'millimeterwave', 'mmwave', 'massive', 'multipleinput', 'multipleoutput', 'mimo', 'networks', 'in', 'contrast', 'to', 'the', 'classical', 'network', 'design', 'based', 'on', 'average', 'metrics', 'a', 'distributed', 'risksensitive', 'reinforcement', 'learningbased', 'framework', 'is', 'proposed', 'to', 'jointly', 'optimize', 'the', 'beamwidth', 'and', 'transmit', 'power', 'while', 'taking', 'into', 'account', 'the', 'sensitivity', 'of', 'mmwave', 'links', 'due', 'to', 'blockage', 'numerical', 'results', 'show', 'that', 'our', 'proposed', 'algorithm', 'achieves', 'more', 'than', '9', 'gbps', 'of', 'user', 'throughput', 'with', 'a', 'guaranteed', 'probability', 'of', '90', 'whereas', 'the', 'baselines', 'guarantee', 'less', 'than', '75', 'gbps', 'more', 'importantly', 'there', 'exists', 'a', 'ratereliabilitynetwork', 'density', 'tradeoff', 'in', 'which', 'as', 'the', 'user', 'density', 'increases', 'from', '16', 'to', '96', 'per', 'km2', 'the', 'fraction', 'of', 'users', 'that', 'achieve', '4', 'gbps', 'are', 'reduced', 'by', '1161', 'and', '3911', 'in', 'the', 'proposed', 'and', 'the', 'baseline', 'models', 'respectively']] | [-0.2183128813419237, 0.009547038760501891, 0.05056963530792431, -0.01079166417405235, -0.036119120795223295, -0.22247476249255918, 0.11273824549138997, 0.3871317758351903, -0.16734366369024484, -0.3375088832496355, 0.05266382410561412, -0.2730163528696831, -0.17522892551002742, 0.1819695163444108, -0.11164433408747286, 0.06595427853365739, 0.06253448912210649, 0.014552327059914895, -0.053853834672323006, -0.28638872423449135, 0.20237382866572728, 0.15237748156527453, 0.38693712006771064, 0.03582033965749636, 0.10795722568979148, -0.011541377126846011, -0.034325104845320864, -0.06347497750671623, -0.07053064660287804, 0.12187720624162025, 0.35521104962999467, 0.20776468964829814, 0.3165210332919025, -0.3881884375375442, -0.2622739683526258, 0.08882323011516058, 0.18059393060464185, -0.015087476330915128, -0.01619847010045884, -0.2988438361648922, 0.20544424693269486, -0.2622579604979943, -0.014018566584017015, 0.04250692755379008, -0.06138454628120543, 0.03717307202389224, -0.3626228445033176, 0.046515447623801956, -0.021239095536822624, 0.04358982034803679, -0.0363123697426283, -0.11589282810349356, 0.02146006946515461, 0.11697277253155004, 0.02039122217243086, 0.06416668079327792, 0.08552494938626434, -0.1094452740556638, -0.11512366127718368, 0.3879141506851848, -0.006763568639474794, -0.2126827612025381, 0.15247944837337304, -0.09936267317794828, -0.08506880820641527, 0.20583044671917433, 0.2727589977933376, 0.04923542617523873, -0.15870494646051983, -0.02975007354865683, 0.0004950102566826073, 0.23868590937645145, 0.09746357814274785, 0.12508776400125388, 0.13934315156339752, 0.25279331213184353, 0.1704451591693655, 0.0906134035709935, -0.18092079728199734, -0.1246983307170343, -0.1700554150297786, -0.12946910942043882, -0.181487158565274, 0.054287071835433104, -0.15228655237572666, -0.016332363743320897, 0.3742982426263166, 0.2041949458066621, 0.11514801260835322, 0.2107719028603187, 0.3886795566006295, 0.07215864796131483, 0.09257222548997864, 0.18939276027606067, 0.23742808285169303, 0.08487277316762078, 0.15619818582755132, -0.1615882164703428, 0.03799603210945585, -0.08155839010071235] |
1,802.03879 | Dark-ages Reionization and Galaxy Formation Simulation - XIV. Gas
accretion, cooling and star formation in dwarf galaxies at high redshift | We study dwarf galaxy formation at high redshift ($z\ge5$) using a suite of
high- resolution, cosmological hydrodynamic simulations and a semi-analytic
model (SAM). We focus on gas accretion, cooling and star formation in this work
by isolating the relevant process from reionization and supernova feedback,
which will be further discussed in a companion paper. We apply the SAM to halo
merger trees constructed from a collisionless N-body simulation sharing
identical initial conditions to the hydrodynamic suite, and calibrate the free
parameters against the stellar mass function predicted by the hydrodynamic
simulations at z = 5. By making comparisons of the star formation history and
gas components calculated by the two modelling techniques, we find that
semi-analytic prescriptions that are commonly adopted in the literature of
low-redshift galaxy formation do not accurately represent dwarf galaxy
properties in the hydrodynamic simulation at earlier times. We propose 3
modifications to SAMs that will provide more accurate high-redshift
simulations. These include 1) the halo mass and baryon fraction which are
overestimated by collisionless N-body simulations; 2) the star formation
efficiency which follows a different cosmic evolutionary path from the
hydrodynamic simulation; and 3) the cooling rate which is not well defined for
dwarf galaxies at high redshift. Accurate semi-analytic modelling of dwarf
galaxy formation informed by detailed hydrodynamical modelling will facilitate
reliable semi-analytic predictions over the large volumes needed for the study
of reionization.
| astro-ph.CO astro-ph.GA | we study dwarf galaxy formation at high redshift zge5 using a suite of high resolution cosmological hydrodynamic simulations and a semianalytic model sam we focus on gas accretion cooling and star formation in this work by isolating the relevant process from reionization and supernova feedback which will be further discussed in a companion paper we apply the sam to halo merger trees constructed from a collisionless nbody simulation sharing identical initial conditions to the hydrodynamic suite and calibrate the free parameters against the stellar mass function predicted by the hydrodynamic simulations at z 5 by making comparisons of the star formation history and gas components calculated by the two modelling techniques we find that semianalytic prescriptions that are commonly adopted in the literature of lowredshift galaxy formation do not accurately represent dwarf galaxy properties in the hydrodynamic simulation at earlier times we propose 3 modifications to sams that will provide more accurate highredshift simulations these include 1 the halo mass and baryon fraction which are overestimated by collisionless nbody simulations 2 the star formation efficiency which follows a different cosmic evolutionary path from the hydrodynamic simulation and 3 the cooling rate which is not well defined for dwarf galaxies at high redshift accurate semianalytic modelling of dwarf galaxy formation informed by detailed hydrodynamical modelling will facilitate reliable semianalytic predictions over the large volumes needed for the study of reionization | [['we', 'study', 'dwarf', 'galaxy', 'formation', 'at', 'high', 'redshift', 'zge5', 'using', 'a', 'suite', 'of', 'high', 'resolution', 'cosmological', 'hydrodynamic', 'simulations', 'and', 'a', 'semianalytic', 'model', 'sam', 'we', 'focus', 'on', 'gas', 'accretion', 'cooling', 'and', 'star', 'formation', 'in', 'this', 'work', 'by', 'isolating', 'the', 'relevant', 'process', 'from', 'reionization', 'and', 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0.05674373277861576, 0.04319634660163398] |
1,802.0388 | Towards the Standardization of Non-orthogonal Multiple Access for Next
Generation Wireless Networks | Non-orthogonal multiple access (NoMA) as an efficient way of radio resource
sharing can root back to the network information theory. For generations of
wireless communication systems design, orthogonal multiple access (OMA) schemes
in time, frequency, or code domain have been the main choices due to the
limited processing capability in the transceiver hardware, as well as the
modest traffic demands in both latency and connectivity. However, for the next
generation radio systems, given its vision to connect everything and the much
evolved hardware capability, NoMA has been identified as a promising technology
to help achieve all the targets in system capacity, user connectivity, and
service latency. This article will provide a systematic overview of the
state-of-the-art design of the NoMA transmission based on a unified transceiver
design framework, the related standardization progress, and some promising use
cases in future cellular networks, based on which the interested researchers
can get a quick start in this area.
| cs.IT math.IT | nonorthogonal multiple access noma as an efficient way of radio resource sharing can root back to the network information theory for generations of wireless communication systems design orthogonal multiple access oma schemes in time frequency or code domain have been the main choices due to the limited processing capability in the transceiver hardware as well as the modest traffic demands in both latency and connectivity however for the next generation radio systems given its vision to connect everything and the much evolved hardware capability noma has been identified as a promising technology to help achieve all the targets in system capacity user connectivity and service latency this article will provide a systematic overview of the stateoftheart design of the noma transmission based on a unified transceiver design framework the related standardization progress and some promising use cases in future cellular networks based on which the interested researchers can get a quick start in this area | [['nonorthogonal', 'multiple', 'access', 'noma', 'as', 'an', 'efficient', 'way', 'of', 'radio', 'resource', 'sharing', 'can', 'root', 'back', 'to', 'the', 'network', 'information', 'theory', 'for', 'generations', 'of', 'wireless', 'communication', 'systems', 'design', 'orthogonal', 'multiple', 'access', 'oma', 'schemes', 'in', 'time', 'frequency', 'or', 'code', 'domain', 'have', 'been', 'the', 'main', 'choices', 'due', 'to', 'the', 'limited', 'processing', 'capability', 'in', 'the', 'transceiver', 'hardware', 'as', 'well', 'as', 'the', 'modest', 'traffic', 'demands', 'in', 'both', 'latency', 'and', 'connectivity', 'however', 'for', 'the', 'next', 'generation', 'radio', 'systems', 'given', 'its', 'vision', 'to', 'connect', 'everything', 'and', 'the', 'much', 'evolved', 'hardware', 'capability', 'noma', 'has', 'been', 'identified', 'as', 'a', 'promising', 'technology', 'to', 'help', 'achieve', 'all', 'the', 'targets', 'in', 'system', 'capacity', 'user', 'connectivity', 'and', 'service', 'latency', 'this', 'article', 'will', 'provide', 'a', 'systematic', 'overview', 'of', 'the', 'stateoftheart', 'design', 'of', 'the', 'noma', 'transmission', 'based', 'on', 'a', 'unified', 'transceiver', 'design', 'framework', 'the', 'related', 'standardization', 'progress', 'and', 'some', 'promising', 'use', 'cases', 'in', 'future', 'cellular', 'networks', 'based', 'on', 'which', 'the', 'interested', 'researchers', 'can', 'get', 'a', 'quick', 'start', 'in', 'this', 'area']] | [-0.22281832005528193, -0.014020469902560204, -0.01581273673612985, 0.030497469309146607, -0.11041249392794505, -0.22410363989972298, 0.09814735936646861, 0.406443401551505, -0.25710285666789257, -0.28222629899279245, 0.1204409786965698, -0.2041625473167627, -0.16899153602670036, 0.2084837884296693, -0.12802716347449009, 0.09555373081539367, 0.07395953206766036, 0.03968784982777922, -0.025606701739372746, -0.265151343317402, 0.24178185599044927, 0.15523088955350461, 0.3996327350111378, 0.05866382984264243, 0.02750730936475579, -0.005093450662529757, -0.04679636600457372, -0.06748726491192253, -0.05730686003413622, 0.14586529161690945, 0.3774880781018686, 0.2190489590558554, 0.3377543336261184, -0.4863169810584476, -0.2812030232241077, 0.06090547052182017, 0.2095230022250044, 0.05570824586020242, -0.07156835730683299, -0.2670990760948869, 0.07794296806189982, -0.2747143020673144, -0.07184165884410182, -0.034280089321996894, -0.04744378517592145, 0.06036210546435247, -0.26192958320701315, -0.06272685928570647, -0.03346346506713739, 0.04609243037900136, -0.022465049558799834, -0.1078521705585562, 0.07126897839498857, 0.21397822047884185, -0.00016739311958512953, 0.039769510366773654, 0.08756818604587968, -0.12145710415056636, -0.1828370416499374, 0.41401595588653317, 0.04857910018894942, -0.18971798201992868, 0.2014023293500706, -0.053568793014593184, -0.1581957624804589, 0.10080161512859406, 0.2637766902391318, 0.022313289870057377, -0.21564062027065384, 0.030294210738652655, 0.04079583055309711, 0.13601122415594516, 0.07700932980214636, 0.1658373031194412, 0.22550088437574525, 0.23950800800906313, 0.1367212842331238, 0.09510662636359132, -0.07282674237484893, -0.07798374461971463, -0.19312761313086677, -0.13946480608334946, -0.19097260477156527, 0.03431056721676742, -0.05635414452765197, -0.08466544229116651, 0.4134354017254326, 0.16129286799339518, 0.09014117855037893, 0.09147102462593466, 0.42113774292651684, 0.04419482532407968, 0.13494373093389214, 0.10164188019088244, 0.16233657688717357, 0.07302320135997668, 0.22542194719876973, -0.17241752553851375, 0.06336358867044892, -0.03596497681292315] |
1,802.03881 | Answerer in Questioner's Mind: Information Theoretic Approach to
Goal-Oriented Visual Dialog | Goal-oriented dialog has been given attention due to its numerous
applications in artificial intelligence. Goal-oriented dialogue tasks occur
when a questioner asks an action-oriented question and an answerer responds
with the intent of letting the questioner know a correct action to take. To ask
the adequate question, deep learning and reinforcement learning have been
recently applied. However, these approaches struggle to find a competent
recurrent neural questioner, owing to the complexity of learning a series of
sentences. Motivated by theory of mind, we propose "Answerer in Questioner's
Mind" (AQM), a novel information theoretic algorithm for goal-oriented dialog.
With AQM, a questioner asks and infers based on an approximated probabilistic
model of the answerer. The questioner figures out the answerer's intention via
selecting a plausible question by explicitly calculating the information gain
of the candidate intentions and possible answers to each question. We test our
framework on two goal-oriented visual dialog tasks: "MNIST Counting Dialog" and
"GuessWhat?!". In our experiments, AQM outperforms comparative algorithms by a
large margin.
| cs.CV cs.AI cs.CL cs.LG | goaloriented dialog has been given attention due to its numerous applications in artificial intelligence goaloriented dialogue tasks occur when a questioner asks an actionoriented question and an answerer responds with the intent of letting the questioner know a correct action to take to ask the adequate question deep learning and reinforcement learning have been recently applied however these approaches struggle to find a competent recurrent neural questioner owing to the complexity of learning a series of sentences motivated by theory of mind we propose answerer in questioners mind aqm a novel information theoretic algorithm for goaloriented dialog with aqm a questioner asks and infers based on an approximated probabilistic model of the answerer the questioner figures out the answerers intention via selecting a plausible question by explicitly calculating the information gain of the candidate intentions and possible answers to each question we test our framework on two goaloriented visual dialog tasks mnist counting dialog and guesswhat in our experiments aqm outperforms comparative algorithms by a large margin | [['goaloriented', 'dialog', 'has', 'been', 'given', 'attention', 'due', 'to', 'its', 'numerous', 'applications', 'in', 'artificial', 'intelligence', 'goaloriented', 'dialogue', 'tasks', 'occur', 'when', 'a', 'questioner', 'asks', 'an', 'actionoriented', 'question', 'and', 'an', 'answerer', 'responds', 'with', 'the', 'intent', 'of', 'letting', 'the', 'questioner', 'know', 'a', 'correct', 'action', 'to', 'take', 'to', 'ask', 'the', 'adequate', 'question', 'deep', 'learning', 'and', 'reinforcement', 'learning', 'have', 'been', 'recently', 'applied', 'however', 'these', 'approaches', 'struggle', 'to', 'find', 'a', 'competent', 'recurrent', 'neural', 'questioner', 'owing', 'to', 'the', 'complexity', 'of', 'learning', 'a', 'series', 'of', 'sentences', 'motivated', 'by', 'theory', 'of', 'mind', 'we', 'propose', 'answerer', 'in', 'questioners', 'mind', 'aqm', 'a', 'novel', 'information', 'theoretic', 'algorithm', 'for', 'goaloriented', 'dialog', 'with', 'aqm', 'a', 'questioner', 'asks', 'and', 'infers', 'based', 'on', 'an', 'approximated', 'probabilistic', 'model', 'of', 'the', 'answerer', 'the', 'questioner', 'figures', 'out', 'the', 'answerers', 'intention', 'via', 'selecting', 'a', 'plausible', 'question', 'by', 'explicitly', 'calculating', 'the', 'information', 'gain', 'of', 'the', 'candidate', 'intentions', 'and', 'possible', 'answers', 'to', 'each', 'question', 'we', 'test', 'our', 'framework', 'on', 'two', 'goaloriented', 'visual', 'dialog', 'tasks', 'mnist', 'counting', 'dialog', 'and', 'guesswhat', 'in', 'our', 'experiments', 'aqm', 'outperforms', 'comparative', 'algorithms', 'by', 'a', 'large', 'margin']] | [-0.049394549160960116, 0.004002696632092723, -0.08045791328361493, 0.09069966424939627, -0.17092484737611768, -0.21683883335699428, 0.09544815933570289, 0.4032585859882185, -0.2616520547981273, -0.35975748388074247, 0.03716090044651197, -0.2838115286494392, -0.20672159666666784, 0.18028684932014805, -0.20207867342754302, 0.1005967283247639, 0.07518712407488555, 0.12230124377578228, -0.0022329040967820056, -0.31724934954566497, 0.2834190861744517, 0.04309529912113128, 0.30656977907995725, 0.019277466792074673, 0.1738337891636394, -0.015040306067963815, -0.03242955741918567, -0.024902262228417647, -0.08540408576681581, 0.15449372482456614, 0.3745499118764223, 0.23986834334490081, 0.4379086744444198, -0.39428288770940556, -0.19093184313086053, 0.09819420965119119, 0.123480150373884, 0.0866428389864783, -0.03779945448630052, -0.3610255070681493, 0.06947363544457469, -0.19341904143185681, 0.025471801096040487, -0.1347978443513254, 0.011995941411748708, -0.048149200522758805, -0.27215525156156306, -0.05720381863297423, 0.1254760661493448, 0.08388757440365861, -0.045275607203127795, -0.07536745152761598, 0.07184589676227959, 0.2057446292371851, 0.04085794223980215, 0.07547973570938457, 0.11994026517719929, -0.20872536666567876, -0.20959462108215351, 0.37016271184905464, -0.016956655256708253, -0.20466270362846103, 0.17273167466060316, 0.031233844919556594, -0.15748257821927925, 0.051250371391918646, 0.22347766202060976, 0.11976025640930846, -0.16373476535770887, 0.036789743261655836, -0.07574262422879777, 0.20365168493386943, 0.034464492148394324, -0.05418093721606735, 0.2319480669684708, 0.24929994402638445, -0.0008215556392468602, 0.11363393130869302, -0.011234902964725372, -0.09757729097494242, -0.17203358578753758, -0.11288251144340239, -0.16469125051320677, 0.015625625046836886, -0.03348802394576929, -0.13834710813587509, 0.3687029386483447, 0.2466775339730757, 0.18936568054760503, 0.08220221100835107, 0.3059188445738281, 0.043568463082963894, 0.04443676198036017, 0.09495787563206382, 0.15969307772171437, 0.017205204092336435, 0.11617100721853504, -0.21640054017574673, 0.11056934747457818, 0.06408691912025483] |
1,802.03882 | Random Hinge Forest for Differentiable Learning | We propose random hinge forests, a simple, efficient, and novel variant of
decision forests. Importantly, random hinge forests can be readily incorporated
as a general component within arbitrary computation graphs that are optimized
end-to-end with stochastic gradient descent or variants thereof. We derive
random hinge forest and ferns, focusing on their sparse and efficient nature,
their min-max margin property, strategies to initialize them for arbitrary
network architectures, and the class of optimizers most suitable for optimizing
random hinge forest. The performance and versatility of random hinge forests
are demonstrated by experiments incorporating a variety of of small and large
UCI machine learning data sets and also ones involving the MNIST, Letter, and
USPS image datasets. We compare random hinge forests with random forests and
the more recent backpropagating deep neural decision forests.
| stat.ML cs.LG | we propose random hinge forests a simple efficient and novel variant of decision forests importantly random hinge forests can be readily incorporated as a general component within arbitrary computation graphs that are optimized endtoend with stochastic gradient descent or variants thereof we derive random hinge forest and ferns focusing on their sparse and efficient nature their minmax margin property strategies to initialize them for arbitrary network architectures and the class of optimizers most suitable for optimizing random hinge forest the performance and versatility of random hinge forests are demonstrated by experiments incorporating a variety of of small and large uci machine learning data sets and also ones involving the mnist letter and usps image datasets we compare random hinge forests with random forests and the more recent backpropagating deep neural decision forests | [['we', 'propose', 'random', 'hinge', 'forests', 'a', 'simple', 'efficient', 'and', 'novel', 'variant', 'of', 'decision', 'forests', 'importantly', 'random', 'hinge', 'forests', 'can', 'be', 'readily', 'incorporated', 'as', 'a', 'general', 'component', 'within', 'arbitrary', 'computation', 'graphs', 'that', 'are', 'optimized', 'endtoend', 'with', 'stochastic', 'gradient', 'descent', 'or', 'variants', 'thereof', 'we', 'derive', 'random', 'hinge', 'forest', 'and', 'ferns', 'focusing', 'on', 'their', 'sparse', 'and', 'efficient', 'nature', 'their', 'minmax', 'margin', 'property', 'strategies', 'to', 'initialize', 'them', 'for', 'arbitrary', 'network', 'architectures', 'and', 'the', 'class', 'of', 'optimizers', 'most', 'suitable', 'for', 'optimizing', 'random', 'hinge', 'forest', 'the', 'performance', 'and', 'versatility', 'of', 'random', 'hinge', 'forests', 'are', 'demonstrated', 'by', 'experiments', 'incorporating', 'a', 'variety', 'of', 'of', 'small', 'and', 'large', 'uci', 'machine', 'learning', 'data', 'sets', 'and', 'also', 'ones', 'involving', 'the', 'mnist', 'letter', 'and', 'usps', 'image', 'datasets', 'we', 'compare', 'random', 'hinge', 'forests', 'with', 'random', 'forests', 'and', 'the', 'more', 'recent', 'backpropagating', 'deep', 'neural', 'decision', 'forests']] | [-0.04800618039779224, 0.08736359737778196, -0.013897885865651804, 0.088764349760657, -0.12524976379195737, -0.21854285113725133, 0.11656593803799654, 0.5018867739673817, -0.29718679670071596, -0.27864052243460197, 0.1344556457056156, -0.2534710020955765, -0.2374016329466196, 0.19883525051232992, -0.13029051738042405, 0.16302519298782994, 0.15285429571557677, -0.0442146309228106, -0.032972906008856655, -0.34425054212580575, 0.27671139492568647, 0.044119897120143636, 0.3457706281421424, -0.01868766698824016, 0.13820843125259588, 0.07759687691489517, -0.027841483551486763, 0.07050731477669836, -0.02404809973378371, 0.19192788548146686, 0.24816979223749402, 0.22093465686846298, 0.34618154979861254, -0.4263188202272762, -0.22668834893336293, 0.16160240610638124, 0.09344430617652093, 0.12684803187882857, -0.024391478013664408, -0.2581230001241872, 0.08450681131128504, -0.12480358141615536, -0.008982138789371786, -0.19577030020511962, -0.06724198134686572, 0.09997607658927639, -0.32891996095118387, 0.026623357203568572, 0.10116534103343094, 0.06559721916952792, 0.023938959198086664, -0.20426156111749483, -0.020346224689009516, 0.0708472451219815, -0.0366049170340623, 0.06197698991639878, 0.15562287460180055, -0.16754131175041426, -0.2210421528019872, 0.31727232728294574, -0.0924892160815723, -0.20033828764589448, 0.22490335458000613, 0.027154890555803748, -0.1703610164325007, 0.05918694836101636, 0.30419101796702086, 0.14507064374276635, -0.14260591123478883, -0.0037332863523095675, -0.062128043130294165, 0.04926459757244271, 0.038358854948345456, -0.022884089257621035, 0.1581345655528518, 0.20377482395799068, 0.05812825046979759, 0.21167644257851256, -0.12231480358825524, -0.07390816469683847, -0.15630946213125504, -0.0600659623534849, -0.19139967290649304, -0.023731494442362226, -0.22905280028554084, -0.25299318392926856, 0.3641258087714739, 0.17346960181991258, 0.2483110682700168, 0.21652848924735957, 0.3204964441388394, 0.05525601466069929, 0.08138239374878167, 0.11683516879566014, 0.16062659431234794, 0.1471539498747778, 0.019968334433735545, -0.09214874600398947, 0.10314550072000588, 0.08091233212811251] |
1,802.03883 | Estimating Depth-Salient Edges And its Application To Stereoscopic Image
Quality Assessment | The human visual system pays attention to salient regions while perceiving an
image. When viewing a stereoscopic 3D (S3D) image, we hypothesize that while
most of the contribution to saliency is provided by the 2D image, a small but
significant contribution is provided by the depth component. Further, we claim
that only a subset of image edges contribute to depth perception while viewing
an S3D image. In this paper, we propose a systematic approach for depth
saliency estimation, called Salient Edges with respect to Depth perception
(SED) which localizes the depth-salient edges in an S3D image. We demonstrate
the utility of SED in full reference stereoscopic image quality assessment
(FRSIQA). We consider gradient magnitude and inter-gradient maps for predicting
structural similarity. A coarse quality estimate is derived first by comparing
the 2D saliency and gradient maps of reference and test stereo pairs. We refine
this quality using SED maps for evaluating depth quality. Finally, we combine
this luminance and depth quality to obtain an overall stereo image quality. We
perform a comprehensive evaluation of our metric on seven publicly available
S3D IQA databases. The proposed metric shows competitive performance on all
seven databases with state-of-the-art performance on three of them.
| eess.IV | the human visual system pays attention to salient regions while perceiving an image when viewing a stereoscopic 3d s3d image we hypothesize that while most of the contribution to saliency is provided by the 2d image a small but significant contribution is provided by the depth component further we claim that only a subset of image edges contribute to depth perception while viewing an s3d image in this paper we propose a systematic approach for depth saliency estimation called salient edges with respect to depth perception sed which localizes the depthsalient edges in an s3d image we demonstrate the utility of sed in full reference stereoscopic image quality assessment frsiqa we consider gradient magnitude and intergradient maps for predicting structural similarity a coarse quality estimate is derived first by comparing the 2d saliency and gradient maps of reference and test stereo pairs we refine this quality using sed maps for evaluating depth quality finally we combine this luminance and depth quality to obtain an overall stereo image quality we perform a comprehensive evaluation of our metric on seven publicly available s3d iqa databases the proposed metric shows competitive performance on all seven databases with stateoftheart performance on three of them | [['the', 'human', 'visual', 'system', 'pays', 'attention', 'to', 'salient', 'regions', 'while', 'perceiving', 'an', 'image', 'when', 'viewing', 'a', 'stereoscopic', '3d', 's3d', 'image', 'we', 'hypothesize', 'that', 'while', 'most', 'of', 'the', 'contribution', 'to', 'saliency', 'is', 'provided', 'by', 'the', '2d', 'image', 'a', 'small', 'but', 'significant', 'contribution', 'is', 'provided', 'by', 'the', 'depth', 'component', 'further', 'we', 'claim', 'that', 'only', 'a', 'subset', 'of', 'image', 'edges', 'contribute', 'to', 'depth', 'perception', 'while', 'viewing', 'an', 's3d', 'image', 'in', 'this', 'paper', 'we', 'propose', 'a', 'systematic', 'approach', 'for', 'depth', 'saliency', 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'databases', 'the', 'proposed', 'metric', 'shows', 'competitive', 'performance', 'on', 'all', 'seven', 'databases', 'with', 'stateoftheart', 'performance', 'on', 'three', 'of', 'them']] | [-0.0356391395529647, -0.05193190826522659, -0.058042245595550476, 0.0374748045289038, -0.0861661957227034, -0.10184706433151428, 0.005345344883247945, 0.5189639271099857, -0.2208626275193088, -0.3492804947985823, 0.0427026074754339, -0.31331247388654704, -0.15383488186640987, 0.15741559031518734, -0.18850230630889042, 0.08375312886047201, 0.1465453446953827, 0.02309802863723244, -0.06142362489880342, -0.27417285493657767, 0.30035368553693714, 0.06543204442687749, 0.3409844681923159, 0.02816572516381287, 0.12513725742281692, -0.015759378036975785, -0.08052146127967513, 0.05649705539424407, -0.10543981028360558, 0.19429554184307793, 0.2476357648345325, 0.19446953531439762, 0.2500088405015354, -0.3593082911769864, -0.21730466580330418, 0.014641016575172165, 0.11601710039767706, 0.054393208464536685, 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1,802.03884 | Simultaneous Rank Tests in Analysis of Covariance Based on Pairwise
Ranking | Nonparametric tests provide robust and powerful alternatives to the
corresponding least squares methods. There are two approaches to nonparametric
pairwise comparisons of treatment effects, the method based on pairwise
rankings and the method based on overall ranking. The former is generally
recommended in the literature because of its strong control of familywise error
rate. However, this method is developed only for one-way layouts and randomized
complete blocks. By combining the method of aligned ranks and pairwise ranking,
we extend the Steel-Dwass pairwise comparisons to the analysis of covariance
and factorial models for both one-sided and two-sided comparisons as well as
testing for treatment versus control. Unlike the traditional two-sample
standardization of test statistics, we propose a weighted estimate of the scale
parameter for ranks and show through simulation that it has superior small
sample performance by controlling the familywise error rate at nominal level.
This method provides an improvement for large sample approximation of
Steel-Dwass method for one-way layouts. The marginal and joint asymptotic
distributions are derived and power comparisons are made with the method of
aligned rank transformation and the least squares method.
| stat.ME | nonparametric tests provide robust and powerful alternatives to the corresponding least squares methods there are two approaches to nonparametric pairwise comparisons of treatment effects the method based on pairwise rankings and the method based on overall ranking the former is generally recommended in the literature because of its strong control of familywise error rate however this method is developed only for oneway layouts and randomized complete blocks by combining the method of aligned ranks and pairwise ranking we extend the steeldwass pairwise comparisons to the analysis of covariance and factorial models for both onesided and twosided comparisons as well as testing for treatment versus control unlike the traditional twosample standardization of test statistics we propose a weighted estimate of the scale parameter for ranks and show through simulation that it has superior small sample performance by controlling the familywise error rate at nominal level this method provides an improvement for large sample approximation of steeldwass method for oneway layouts the marginal and joint asymptotic distributions are derived and power comparisons are made with the method of aligned rank transformation and the least squares method | [['nonparametric', 'tests', 'provide', 'robust', 'and', 'powerful', 'alternatives', 'to', 'the', 'corresponding', 'least', 'squares', 'methods', 'there', 'are', 'two', 'approaches', 'to', 'nonparametric', 'pairwise', 'comparisons', 'of', 'treatment', 'effects', 'the', 'method', 'based', 'on', 'pairwise', 'rankings', 'and', 'the', 'method', 'based', 'on', 'overall', 'ranking', 'the', 'former', 'is', 'generally', 'recommended', 'in', 'the', 'literature', 'because', 'of', 'its', 'strong', 'control', 'of', 'familywise', 'error', 'rate', 'however', 'this', 'method', 'is', 'developed', 'only', 'for', 'oneway', 'layouts', 'and', 'randomized', 'complete', 'blocks', 'by', 'combining', 'the', 'method', 'of', 'aligned', 'ranks', 'and', 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1,802.03885 | ClosNets: a Priori Sparse Topologies for Faster DNN Training | Fully-connected layers in deep neural networks (DNN) are often the throughput
and power bottleneck during training. This is due to their large size and low
data reuse. Pruning dense layers can significantly reduce the size of these
networks, but this approach can only be applied after training. In this work we
propose a novel fully-connected layer that reduces the memory requirements of
DNNs without sacrificing accuracy. We replace a dense matrix with products of
sparse matrices whose topologies we pick in advance. This allows us to: (1)
train significantly smaller networks without a loss in accuracy, and (2) store
the network weights without having to store connection indices. We therefore
achieve significant training speedups due to the smaller network size, and a
reduced amount of computation per epoch. We tested several sparse layer
topologies and found that Clos networks perform well due to their high path
diversity, shallowness, and high model accuracy. With the ClosNets, we are able
to reduce dense layer sizes by as much as an order of magnitude without hurting
model accuracy.
| cs.LG cs.AR | fullyconnected layers in deep neural networks dnn are often the throughput and power bottleneck during training this is due to their large size and low data reuse pruning dense layers can significantly reduce the size of these networks but this approach can only be applied after training in this work we propose a novel fullyconnected layer that reduces the memory requirements of dnns without sacrificing accuracy we replace a dense matrix with products of sparse matrices whose topologies we pick in advance this allows us to 1 train significantly smaller networks without a loss in accuracy and 2 store the network weights without having to store connection indices we therefore achieve significant training speedups due to the smaller network size and a reduced amount of computation per epoch we tested several sparse layer topologies and found that clos networks perform well due to their high path diversity shallowness and high model accuracy with the closnets we are able to reduce dense layer sizes by as much as an order of magnitude without hurting model accuracy | [['fullyconnected', 'layers', 'in', 'deep', 'neural', 'networks', 'dnn', 'are', 'often', 'the', 'throughput', 'and', 'power', 'bottleneck', 'during', 'training', 'this', 'is', 'due', 'to', 'their', 'large', 'size', 'and', 'low', 'data', 'reuse', 'pruning', 'dense', 'layers', 'can', 'significantly', 'reduce', 'the', 'size', 'of', 'these', 'networks', 'but', 'this', 'approach', 'can', 'only', 'be', 'applied', 'after', 'training', 'in', 'this', 'work', 'we', 'propose', 'a', 'novel', 'fullyconnected', 'layer', 'that', 'reduces', 'the', 'memory', 'requirements', 'of', 'dnns', 'without', 'sacrificing', 'accuracy', 'we', 'replace', 'a', 'dense', 'matrix', 'with', 'products', 'of', 'sparse', 'matrices', 'whose', 'topologies', 'we', 'pick', 'in', 'advance', 'this', 'allows', 'us', 'to', '1', 'train', 'significantly', 'smaller', 'networks', 'without', 'a', 'loss', 'in', 'accuracy', 'and', '2', 'store', 'the', 'network', 'weights', 'without', 'having', 'to', 'store', 'connection', 'indices', 'we', 'therefore', 'achieve', 'significant', 'training', 'speedups', 'due', 'to', 'the', 'smaller', 'network', 'size', 'and', 'a', 'reduced', 'amount', 'of', 'computation', 'per', 'epoch', 'we', 'tested', 'several', 'sparse', 'layer', 'topologies', 'and', 'found', 'that', 'clos', 'networks', 'perform', 'well', 'due', 'to', 'their', 'high', 'path', 'diversity', 'shallowness', 'and', 'high', 'model', 'accuracy', 'with', 'the', 'closnets', 'we', 'are', 'able', 'to', 'reduce', 'dense', 'layer', 'sizes', 'by', 'as', 'much', 'as', 'an', 'order', 'of', 'magnitude', 'without', 'hurting', 'model', 'accuracy']] | [-0.07841924499925149, 0.07238030433462432, 0.019609011666870665, 0.03598402086016454, -0.08074692044348253, -0.14785780885856298, 0.10032306453440575, 0.4578633141200775, -0.29410223683995335, -0.3788481101249184, 0.09209065915128195, -0.2259847246253115, -0.15603110920129365, 0.12432178752945104, -0.12537398063775082, 0.08749121687128797, 0.14118399656439284, 0.02207191135391764, -0.09962417120006921, -0.33022366515788965, 0.27036693191353833, 0.11096145074407104, 0.3219879349029955, 0.026410552651632106, 0.08746778910451745, -0.0597300769027922, -0.0014231775488554575, 0.0009518085708627584, -0.02505136036955358, 0.16015206000783705, 0.24960906881954115, 0.13953209577406617, 0.31248508790379453, -0.5012150021750298, -0.24306478544978702, 0.15014672314683938, 0.1603953406701785, 0.11481559706648448, 0.02766814933094228, -0.24040977412904346, 0.17541458885902647, -0.2197601853966199, -0.03526292016847466, -0.14014414248415322, -0.042154501949054936, 0.0039630174789385035, -0.2929399858460355, 0.031457283711074265, 0.06508211344520658, -0.004279904914538151, 0.0064100108397776665, -0.11442281329073012, -0.01251737797102923, 0.15999765449803022, -0.005323978338990061, 0.048313388550097404, 0.10738963146169467, -0.2008055583386543, -0.08414675262167878, 0.3434258750104613, -0.04131287401046968, -0.20653558066971173, 0.20370468019170623, -0.052361629255672636, -0.1061412278621661, 0.16463083782147658, 0.2771450759944688, 0.07563435963900952, -0.11468838716739382, 0.014421247601028715, 0.029412141770938004, 0.23618093965007741, 0.09405455167201915, 0.051836294362899556, 0.1128465735403123, 0.2486793344034867, 0.07221939627231024, 0.1677153433489763, -0.1252286142492988, -0.05404830477989932, -0.17156510941895667, -0.10387481383219276, -0.2017358276128619, 0.024100402166702967, -0.16965225281079443, -0.1157829732869634, 0.38360548688462753, 0.2129159866612747, 0.27536906516072396, 0.14734122346572834, 0.33927319951398277, 0.058775312041103756, 0.20799895722001535, 0.13739680852500144, 0.18926511797936896, 0.07028347756526294, 0.1443284550452894, -0.15442345558997544, 0.10369826756663963, 0.012026712503123644] |
1,802.03886 | Local and Global Existence of Solutions to Scalar Equations on Spatially
Flat Universe as a Background with Non-minimal Coupling | We prove the wellposedness of scalar wave equations on spatially flat
universe as a background with nonminimal coupling with the scalar potential
turned on by introducing the $k$-order linear energy and the corresponding
energy norm. In the local case, we show that both the $k$-order linear energy
and the energy norm are bounded for finite time with initial data in
$H^{k+1}\times H^{k}$. Whereas in the global case, we have to add three
assumptions related to the nonminimal coupling constant, the scale factor of
spacetimes, and the form of the scalar that has to be a polynomial with a small
positive parameter. Then, we show that the solution does globally exist with a
particular decay estimate that depends on the scale factor of the spacetimes.
Finally, we provide some physical models that support our general setup.
| math-ph gr-qc math.AP math.MP | we prove the wellposedness of scalar wave equations on spatially flat universe as a background with nonminimal coupling with the scalar potential turned on by introducing the korder linear energy and the corresponding energy norm in the local case we show that both the korder linear energy and the energy norm are bounded for finite time with initial data in hk1times hk whereas in the global case we have to add three assumptions related to the nonminimal coupling constant the scale factor of spacetimes and the form of the scalar that has to be a polynomial with a small positive parameter then we show that the solution does globally exist with a particular decay estimate that depends on the scale factor of the spacetimes finally we provide some physical models that support our general setup | [['we', 'prove', 'the', 'wellposedness', 'of', 'scalar', 'wave', 'equations', 'on', 'spatially', 'flat', 'universe', 'as', 'a', 'background', 'with', 'nonminimal', 'coupling', 'with', 'the', 'scalar', 'potential', 'turned', 'on', 'by', 'introducing', 'the', 'korder', 'linear', 'energy', 'and', 'the', 'corresponding', 'energy', 'norm', 'in', 'the', 'local', 'case', 'we', 'show', 'that', 'both', 'the', 'korder', 'linear', 'energy', 'and', 'the', 'energy', 'norm', 'are', 'bounded', 'for', 'finite', 'time', 'with', 'initial', 'data', 'in', 'hk1times', 'hk', 'whereas', 'in', 'the', 'global', 'case', 'we', 'have', 'to', 'add', 'three', 'assumptions', 'related', 'to', 'the', 'nonminimal', 'coupling', 'constant', 'the', 'scale', 'factor', 'of', 'spacetimes', 'and', 'the', 'form', 'of', 'the', 'scalar', 'that', 'has', 'to', 'be', 'a', 'polynomial', 'with', 'a', 'small', 'positive', 'parameter', 'then', 'we', 'show', 'that', 'the', 'solution', 'does', 'globally', 'exist', 'with', 'a', 'particular', 'decay', 'estimate', 'that', 'depends', 'on', 'the', 'scale', 'factor', 'of', 'the', 'spacetimes', 'finally', 'we', 'provide', 'some', 'physical', 'models', 'that', 'support', 'our', 'general', 'setup']] | [-0.13606137752449557, 0.12482094930036605, -0.059375203468701315, 0.06613622730208644, -0.08223666491529057, -0.13740413730838963, -0.024728608439301154, 0.3347475837165518, -0.24777534705545032, -0.26214552300186383, 0.11908946771073892, -0.25342325457899983, -0.11745916042735786, 0.1576384023707638, 0.013013925276515978, 0.029483144241037654, 0.026925224673572872, 0.08335418949696868, -0.0733566708261592, -0.2587898001561303, 0.4195172914456743, 0.039161324911097535, 0.22408616221718378, 0.0837141996763869, 0.11734868174700289, -0.04529690509314524, -0.0025842495109719126, 0.050089486641809344, -0.15699299307738357, 0.07991105294438886, 0.1745636576569499, 0.09829925383100592, 0.2617352923531252, -0.4140780090672353, -0.2196443490753534, 0.16778970671706459, 0.10306231679009106, 0.10571893133264758, -0.0660797729697287, -0.2524691834983263, 0.09473854291892207, -0.12219406843338329, -0.14719902759821002, -0.09187782476412883, -0.003703377960221981, 0.033622624880787155, -0.30867543033977496, 0.11168046023196249, 0.04901634556338636, -0.01087789650339244, -0.1373733533893837, -0.07349687731744889, -0.03159670409948023, 0.05418853520818833, 0.1010191975727872, 0.031419062847097806, 0.07901861982892698, -0.11883818359572941, -0.059809834841437245, 0.35569112351748033, -0.15753126721490354, -0.2801734778018835, 0.1483808091959791, -0.1562627763296269, -0.11051479479256295, 0.06558775656279732, 0.16353030750917188, 0.11195270231327237, -0.09018192571629562, 0.1790129627546803, -0.045535524045495115, 0.19505641981264327, 0.07312112954556386, 0.04924179214172746, 0.13641098020737297, 0.10590752611569226, 0.1166647546733521, 0.10480847396564656, -0.04789031885864922, -0.10965892626580769, -0.3823211567559794, -0.14290455225229598, -0.15188435408801063, 0.08087220592734859, -0.1565770123067265, -0.17808669936884916, 0.4053764453474015, 0.10286673951421434, 0.20503956169140206, 0.10143229964800846, 0.23853059331260956, 0.14785500473704244, 0.06274446969636396, 0.1242980537797088, 0.292824534513621, 0.10524834412212636, 0.10373401269664181, -0.22147814261530693, -0.006237017946428994, 0.05498499243636181] |
1,802.03887 | A Systems Theory Approach to the Synthesis of Minimum Noise
Phase-Insensitive Quantum Amplifiers | We present a systems theory approach to the proof of a result bounding the
required level of added quantum noise in a phase-insensitive quantum amplifier.
We also present a synthesis procedure for constructing a quantum optical
phase-insensitive quantum amplifier which adds the minimum level of quantum
noise and achieves a required gain and bandwidth. This synthesis procedure is
based on a singularly perturbed quantum system and leads to an amplifier
involving two squeezers and two beamsplitters.
| cs.SY | we present a systems theory approach to the proof of a result bounding the required level of added quantum noise in a phaseinsensitive quantum amplifier we also present a synthesis procedure for constructing a quantum optical phaseinsensitive quantum amplifier which adds the minimum level of quantum noise and achieves a required gain and bandwidth this synthesis procedure is based on a singularly perturbed quantum system and leads to an amplifier involving two squeezers and two beamsplitters | [['we', 'present', 'a', 'systems', 'theory', 'approach', 'to', 'the', 'proof', 'of', 'a', 'result', 'bounding', 'the', 'required', 'level', 'of', 'added', 'quantum', 'noise', 'in', 'a', 'phaseinsensitive', 'quantum', 'amplifier', 'we', 'also', 'present', 'a', 'synthesis', 'procedure', 'for', 'constructing', 'a', 'quantum', 'optical', 'phaseinsensitive', 'quantum', 'amplifier', 'which', 'adds', 'the', 'minimum', 'level', 'of', 'quantum', 'noise', 'and', 'achieves', 'a', 'required', 'gain', 'and', 'bandwidth', 'this', 'synthesis', 'procedure', 'is', 'based', 'on', 'a', 'singularly', 'perturbed', 'quantum', 'system', 'and', 'leads', 'to', 'an', 'amplifier', 'involving', 'two', 'squeezers', 'and', 'two', 'beamsplitters']] | [-0.1650924284292973, 0.10743098659000488, -0.09437083713955392, -0.00595582936473779, -0.006083866103405231, -0.2491112828414005, 0.11528577051419568, 0.34558233544904443, -0.22167975106889284, -0.2835981151377047, 0.07743363596014924, -0.26222356457851437, -0.1957742842404466, 0.26168861267107885, -0.11361948231627282, 0.15019527836212596, 0.06089894554430717, 0.0011924485228710661, -0.0390791962793293, -0.22862309077754617, 0.27487260897913457, 0.062077907162203794, 0.2663259048407015, -0.007134384843275735, 0.20739047677795353, -0.0007900117902624372, 0.04153744214655537, -0.04599216809545301, -0.08121066791691671, 0.1556985186731541, 0.24605069137913615, 0.09213553564500455, 0.3047882635514007, -0.3904014400598642, -0.2039482297412561, 0.06220083607140144, 0.07483043131957713, 0.2307651199838514, -0.06613752322645221, -0.2566223635191196, 0.05644185520022323, -0.21319075695876227, -0.0654334880068506, -0.07073325450581155, -0.10628131397166535, -0.05542570120949102, -0.31541554605294214, 0.013821868547223093, 0.1358609486885957, 0.053653999342043936, 0.035103945490463, -0.04889017372165414, 0.07162408656318132, 0.0837977865533168, -0.15431325224926695, -0.03370127675206229, 0.1408998704895279, -0.135386065013192, -0.1810280686661013, 0.34140467381496964, -0.08340890394021316, -0.20688332445723445, 0.12952626109625653, -0.04872694465723869, -0.08336490564244359, 0.09555089531319313, 0.16181109826031484, 0.06858528091719276, -0.14670731769384524, 0.06727993762500487, 0.04974183167245141, 0.25826412635414225, 0.07009055010827356, 0.15048631109101207, 0.17071155008345254, 0.19250387003596284, 0.12320477638001505, 0.25139066263249044, -0.10370701384473298, -0.09220914555541974, -0.3530930034865282, -0.14564879188669452, -0.1864158856608954, 0.10893628556330345, -0.03894530688213356, -0.206084236282071, 0.4091105744683821, 0.16876619242094948, 0.13938409945388375, 0.04319252192477794, 0.37073103274757924, 0.2070832252134814, 0.022490885776565654, 0.03277316577364936, 0.22073897693649328, 0.20706608313086786, 0.06308799119448706, -0.2319914003356213, -0.036806466842168255, 0.052154627331459015] |
1,802.03888 | Consistent Individualized Feature Attribution for Tree Ensembles | Interpreting predictions from tree ensemble methods such as gradient boosting
machines and random forests is important, yet feature attribution for trees is
often heuristic and not individualized for each prediction. Here we show that
popular feature attribution methods are inconsistent, meaning they can lower a
feature's assigned importance when the true impact of that feature actually
increases. This is a fundamental problem that casts doubt on any comparison
between features. To address it we turn to recent applications of game theory
and develop fast exact tree solutions for SHAP (SHapley Additive exPlanation)
values, which are the unique consistent and locally accurate attribution
values. We then extend SHAP values to interaction effects and define SHAP
interaction values. We propose a rich visualization of individualized feature
attributions that improves over classic attribution summaries and partial
dependence plots, and a unique "supervised" clustering (clustering based on
feature attributions). We demonstrate better agreement with human intuition
through a user study, exponential improvements in run time, improved clustering
performance, and better identification of influential features. An
implementation of our algorithm has also been merged into XGBoost and LightGBM,
see http://github.com/slundberg/shap for details.
| cs.LG stat.ML | interpreting predictions from tree ensemble methods such as gradient boosting machines and random forests is important yet feature attribution for trees is often heuristic and not individualized for each prediction here we show that popular feature attribution methods are inconsistent meaning they can lower a features assigned importance when the true impact of that feature actually increases this is a fundamental problem that casts doubt on any comparison between features to address it we turn to recent applications of game theory and develop fast exact tree solutions for shap shapley additive explanation values which are the unique consistent and locally accurate attribution values we then extend shap values to interaction effects and define shap interaction values we propose a rich visualization of individualized feature attributions that improves over classic attribution summaries and partial dependence plots and a unique supervised clustering clustering based on feature attributions we demonstrate better agreement with human intuition through a user study exponential improvements in run time improved clustering performance and better identification of influential features an implementation of our algorithm has also been merged into xgboost and lightgbm see httpgithubcomslundbergshap for details | [['interpreting', 'predictions', 'from', 'tree', 'ensemble', 'methods', 'such', 'as', 'gradient', 'boosting', 'machines', 'and', 'random', 'forests', 'is', 'important', 'yet', 'feature', 'attribution', 'for', 'trees', 'is', 'often', 'heuristic', 'and', 'not', 'individualized', 'for', 'each', 'prediction', 'here', 'we', 'show', 'that', 'popular', 'feature', 'attribution', 'methods', 'are', 'inconsistent', 'meaning', 'they', 'can', 'lower', 'a', 'features', 'assigned', 'importance', 'when', 'the', 'true', 'impact', 'of', 'that', 'feature', 'actually', 'increases', 'this', 'is', 'a', 'fundamental', 'problem', 'that', 'casts', 'doubt', 'on', 'any', 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1,802.03889 | Convergence Analysis of Alternating Projection Method for Nonconvex Sets | Alternating projection method has been used in a wide range of engineering
applications since it is a gradient-free method (without requiring tuning the
step size) and usually has fast speed of convergence. In this paper, we
formalize two properties of proper, lower semi-continuous and semi-algebraic
sets: the three-point property for all possible iterates and the local
contraction property that serves as the non-expensiveness property of the
projector but only for the iterates that are close enough to each other. Then
by exploiting the geometric properties of the objective function around its
critical point, i.e. the Kurdyka-\L{ojasiewicz} (KL) property, we establish a
new convergence analysis framework to show that if one set satisfies the
three-point property and the other one obeys the local contraction property,
the iterates generated by alternating projection method is a convergent
sequence and converges to a critical point. We complete this study by providing
convergence rate which depends on the explicit expression of the KL exponent.
As a byproduct, we use our new analysis framework to recover the linear
convergence rate of alternating projection method onto closed convex sets. To
illustrate the power of our new framework, we provide new convergence result
for a class of concrete applications: alternating projection method for
designing structured tight frames that are widely used in sparse
representation, compressed sensing and communication.
| math.OC cs.IT math.IT | alternating projection method has been used in a wide range of engineering applications since it is a gradientfree method without requiring tuning the step size and usually has fast speed of convergence in this paper we formalize two properties of proper lower semicontinuous and semialgebraic sets the threepoint property for all possible iterates and the local contraction property that serves as the nonexpensiveness property of the projector but only for the iterates that are close enough to each other then by exploiting the geometric properties of the objective function around its critical point ie the kurdykalojasiewicz kl property we establish a new convergence analysis framework to show that if one set satisfies the threepoint property and the other one obeys the local contraction property the iterates generated by alternating projection method is a convergent sequence and converges to a critical point we complete this study by providing convergence rate which depends on the explicit expression of the kl exponent as a byproduct we use our new analysis framework to recover the linear convergence rate of alternating projection method onto closed convex sets to illustrate the power of our new framework we provide new convergence result for a class of concrete applications alternating projection method for designing structured tight frames that are widely used in sparse representation compressed sensing and communication | [['alternating', 'projection', 'method', 'has', 'been', 'used', 'in', 'a', 'wide', 'range', 'of', 'engineering', 'applications', 'since', 'it', 'is', 'a', 'gradientfree', 'method', 'without', 'requiring', 'tuning', 'the', 'step', 'size', 'and', 'usually', 'has', 'fast', 'speed', 'of', 'convergence', 'in', 'this', 'paper', 'we', 'formalize', 'two', 'properties', 'of', 'proper', 'lower', 'semicontinuous', 'and', 'semialgebraic', 'sets', 'the', 'threepoint', 'property', 'for', 'all', 'possible', 'iterates', 'and', 'the', 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1,802.0389 | Unveiling the Origin of the Fermi Bubbles | The Fermi bubbles, two giant structures above and below the Galactic center
(GC), are among the most important discoveries of the Fermi Gamma-ray Space
Telescope. Studying their physical origin has been providing valuable insights
into cosmic-ray transport, the Galactic magnetic field, and past activity at
the GC in the Milky Way galaxy. Despite their importance, the formation
mechanism of the bubbles is still elusive. Over the past few years there have
been numerous efforts, both observational and theoretical, to uncover the
nature of the bubbles. In this article, we present an overview of the current
status of our understanding of the bubbles' origin, and discuss possible future
directions that will help to distinguish different scenarios of bubble
formation.
| astro-ph.HE astro-ph.GA | the fermi bubbles two giant structures above and below the galactic center gc are among the most important discoveries of the fermi gammaray space telescope studying their physical origin has been providing valuable insights into cosmicray transport the galactic magnetic field and past activity at the gc in the milky way galaxy despite their importance the formation mechanism of the bubbles is still elusive over the past few years there have been numerous efforts both observational and theoretical to uncover the nature of the bubbles in this article we present an overview of the current status of our understanding of the bubbles origin and discuss possible future directions that will help to distinguish different scenarios of bubble formation | [['the', 'fermi', 'bubbles', 'two', 'giant', 'structures', 'above', 'and', 'below', 'the', 'galactic', 'center', 'gc', 'are', 'among', 'the', 'most', 'important', 'discoveries', 'of', 'the', 'fermi', 'gammaray', 'space', 'telescope', 'studying', 'their', 'physical', 'origin', 'has', 'been', 'providing', 'valuable', 'insights', 'into', 'cosmicray', 'transport', 'the', 'galactic', 'magnetic', 'field', 'and', 'past', 'activity', 'at', 'the', 'gc', 'in', 'the', 'milky', 'way', 'galaxy', 'despite', 'their', 'importance', 'the', 'formation', 'mechanism', 'of', 'the', 'bubbles', 'is', 'still', 'elusive', 'over', 'the', 'past', 'few', 'years', 'there', 'have', 'been', 'numerous', 'efforts', 'both', 'observational', 'and', 'theoretical', 'to', 'uncover', 'the', 'nature', 'of', 'the', 'bubbles', 'in', 'this', 'article', 'we', 'present', 'an', 'overview', 'of', 'the', 'current', 'status', 'of', 'our', 'understanding', 'of', 'the', 'bubbles', 'origin', 'and', 'discuss', 'possible', 'future', 'directions', 'that', 'will', 'help', 'to', 'distinguish', 'different', 'scenarios', 'of', 'bubble', 'formation']] | [-0.12023497372865677, 0.11524804876991489, -0.08162769426441735, 0.11629690199900987, -0.13869440452969176, -0.01823752844248409, 0.04600554039125662, 0.39923130664027345, -0.2351327678080159, -0.3468129647524743, 0.07910484594285046, -0.28535573030979833, -0.09404523297355084, 0.238860552550404, 0.01991619078158322, -0.03366539012162228, 0.025446067366875328, -0.04428180960654202, -0.012865368180643861, -0.27166628430329137, 0.3062400877420491, 0.14294677547581683, 0.2431629310481416, 0.08633166131902865, 0.05981577904618708, -0.10940268096687683, -0.07901232464680985, -0.0275221081271519, -0.16333539151963708, 0.11626800207936173, 0.25628803367331876, 0.1364632244261329, 0.26857293933882553, -0.47643805895063834, -0.27215633266684364, 0.11437086353346831, 0.2000825649977788, 0.07392091859700316, -0.12768054571223714, -0.28170819915199685, 0.04208243138825726, -0.15607196145485772, -0.21874548631554502, -0.012123893686941178, 0.04807854665273611, 0.011448902590989561, -0.06935494020383931, 0.04594903231708933, 0.03511244987563814, 0.05508753809026221, -0.10973693231728433, -0.13023749200461465, -0.0005381460473241316, 0.15699662272502685, 0.12799665660269843, 0.05191917549739829, 0.1614912203039532, -0.17370237981884787, -0.11787958061164719, 0.3843625801757483, 0.0199687014239193, -0.003528779309432385, 0.2624762831966124, -0.23945278730401295, -0.17711175087507877, 0.11959359228563637, 0.17448368459403263, 0.03820297617654679, -0.19002684173805606, 0.059643548441719325, -0.025481179956409892, 0.08044764631964518, 0.024470193272109253, 0.09098737174065456, 0.38311144818504483, 0.21609817488687272, 0.04012279595066872, 0.07054729975949404, -0.19263750221580267, -0.09609722686286273, -0.26346588328133447, -0.12379588059621263, -0.09895437780641399, 0.045332443694213125, -0.05685104009788089, -0.09611275254638266, 0.4230645372792763, 0.15953275176767512, 0.2147052079533874, -0.05875181054925325, 0.27539327147148424, -0.0008781796579754327, 0.05197861288689961, 0.09325958035838933, 0.33065949942348366, 0.1326210685153255, 0.09393184058964095, -0.21974366250195368, 0.12154903531891405, -0.03652478437633964] |
1,802.03891 | Multifunctionality in embodied agents: Three levels of neural reuse | The brain in conjunction with the body is able to adapt to new environments
and perform multiple behaviors through reuse of neural resources and transfer
of existing behavioral traits. Although mechanisms that underlie this ability
are not well understood, they are largely attributed to neuromodulation. In
this work, we demonstrate that an agent can be multifunctional using the same
sensory and motor systems across behaviors, in the absence of modulatory
mechanisms. Further, we lay out the different levels at which neural reuse can
occur through a dynamical filtering of the brain-body-environment system's
operation: structural network, autonomous dynamics, and transient dynamics.
Notably, transient dynamics reuse could only be explained by studying the
brain-body-environment system as a whole and not just the brain. The
multifunctional agent we present here demonstrates neural reuse at all three
levels.
| cs.NE cs.LG q-bio.NC | the brain in conjunction with the body is able to adapt to new environments and perform multiple behaviors through reuse of neural resources and transfer of existing behavioral traits although mechanisms that underlie this ability are not well understood they are largely attributed to neuromodulation in this work we demonstrate that an agent can be multifunctional using the same sensory and motor systems across behaviors in the absence of modulatory mechanisms further we lay out the different levels at which neural reuse can occur through a dynamical filtering of the brainbodyenvironment systems operation structural network autonomous dynamics and transient dynamics notably transient dynamics reuse could only be explained by studying the brainbodyenvironment system as a whole and not just the brain the multifunctional agent we present here demonstrates neural reuse at all three levels | [['the', 'brain', 'in', 'conjunction', 'with', 'the', 'body', 'is', 'able', 'to', 'adapt', 'to', 'new', 'environments', 'and', 'perform', 'multiple', 'behaviors', 'through', 'reuse', 'of', 'neural', 'resources', 'and', 'transfer', 'of', 'existing', 'behavioral', 'traits', 'although', 'mechanisms', 'that', 'underlie', 'this', 'ability', 'are', 'not', 'well', 'understood', 'they', 'are', 'largely', 'attributed', 'to', 'neuromodulation', 'in', 'this', 'work', 'we', 'demonstrate', 'that', 'an', 'agent', 'can', 'be', 'multifunctional', 'using', 'the', 'same', 'sensory', 'and', 'motor', 'systems', 'across', 'behaviors', 'in', 'the', 'absence', 'of', 'modulatory', 'mechanisms', 'further', 'we', 'lay', 'out', 'the', 'different', 'levels', 'at', 'which', 'neural', 'reuse', 'can', 'occur', 'through', 'a', 'dynamical', 'filtering', 'of', 'the', 'brainbodyenvironment', 'systems', 'operation', 'structural', 'network', 'autonomous', 'dynamics', 'and', 'transient', 'dynamics', 'notably', 'transient', 'dynamics', 'reuse', 'could', 'only', 'be', 'explained', 'by', 'studying', 'the', 'brainbodyenvironment', 'system', 'as', 'a', 'whole', 'and', 'not', 'just', 'the', 'brain', 'the', 'multifunctional', 'agent', 'we', 'present', 'here', 'demonstrates', 'neural', 'reuse', 'at', 'all', 'three', 'levels']] | [-0.12148458527491442, 0.10260224203788067, -0.07755729851639594, 0.05639711883068502, -0.06971507309240971, -0.15591105742731007, 0.050854888384981055, 0.4344620950130829, -0.3149186575528123, -0.2951789700335809, 0.08245964865148095, -0.20493908578168546, -0.2750155934646948, 0.18643568905457897, -0.08404002968680614, 0.020017425981419746, 0.04066172040435972, -0.004508127623460075, 0.02403106573367936, -0.2095167571609256, 0.2796260146736932, 0.05398872590262387, 0.2929472697433084, 0.019327392763416492, 0.097329772126152, -0.03334557550868937, 0.013331936830446235, -0.008566049727743297, -0.042626957402676656, 0.11674267381304569, 0.33248304378818166, 0.1695295247777518, 0.3106993908139029, -0.48620783815285495, -0.28828675352009153, 0.06837788814028947, 0.18387751492025303, 0.12214494309016267, 0.004927377279069441, -0.29682739656557566, 0.0974572371802668, -0.18148963883477473, -0.07287265081306113, -0.13905897286873478, -0.05864181980462884, 0.045459930835389045, -0.21209496815529055, 0.03393876618955896, 0.06632524856983914, 0.09867319505689527, -0.08828355008458245, -0.058209924144607815, -0.020567393502039805, 0.2303506547807535, 0.01668696004355243, -0.016172713655700433, 0.20849951732653513, -0.15099352106465888, -0.1698963526017797, 0.34917153908050996, -0.007349527448784116, -0.19770253600497076, 0.2776022841545767, -0.12226762927794106, -0.14472190027625592, 0.10021356014194273, 0.1987638998526468, 0.0804591706012195, -0.22276533362386164, -0.008777070646747641, 0.046661414157376804, 0.2046252012933924, 0.03874215098626133, 0.055788438016576555, 0.2325522423228388, 0.2252055083022717, 0.017707963137011697, 0.1263117288732762, -0.04904497496839335, -0.0923008034040512, -0.21028896957095503, -0.09689629945466156, -0.14068988931049078, 0.03595688125685921, -0.02258065909982586, -0.09987039048287238, 0.4147606574541041, 0.18102775329598852, 0.18974753713874676, 0.05211565180559149, 0.28304416335921095, 0.06186182612688767, 0.14463212200676773, 0.06590539327850427, 0.21177567162120076, 0.008432806568539966, 0.15624731493446586, -0.257353552589332, 0.14739640465061835, -0.03678380783626448] |
1,802.03892 | Escape of Resonantly Scattered Ly$\beta$ and H$\alpha$ from Hot and
Optically Thick Media | We investigate the escape of Ly$\beta$ from emission nebulae with a
significant population of excited hydrogen atoms in the level $n=2$, rendering
them optically thick in H$\alpha$. The transfer of Ly$\beta$ line photons in
these optically thick regions is complicated by the presence of another
scattering channel leading to re-emission of H$\alpha$, alternating their
identities between Ly$\beta$ and H$\alpha$. In this work, we develop a Monte
Carlo code to simulate the transfer of Ly$\beta$ line photons incorporating the
scattering channel into H$\alpha$. Both H$\alpha$ and Ly$\beta$ lines are
formed through diffusion in frequency space, where a line photon enters the
wing regime after a fairly large number of resonance scatterings with hydrogen
atoms. Various line profiles of H$\alpha$ and Ly$\beta$ emergent from our model
nebulae are presented. It is argued that the electron temperature is a critical
parameter which controls the flux ratio of emergent Ly$\beta$ and H$\alpha$.
Specifically for $T=3 \times 10^4{\rm\ K}$ and H$\alpha$ line center optical
depth $\tau_\alpha=10$, the number flux ratio of emergent Ly$\beta$ and
H$\alpha$ is $\sim 49$ percent, which is quite significant. We propose that the
leaking Ly$\beta$ can be an interesting source for the formation of H$\alpha$
wings observed in many symbiotic stars and active galactic nuclei. Similar
broad H$\alpha$ wings are also expected in Ly$\alpha$ emitting halos found in
the early universe, which can be potentially probed by the {\it James Webb
Telescope} in the future.
| astro-ph.HE | we investigate the escape of lybeta from emission nebulae with a significant population of excited hydrogen atoms in the level n2 rendering them optically thick in halpha the transfer of lybeta line photons in these optically thick regions is complicated by the presence of another scattering channel leading to reemission of halpha alternating their identities between lybeta and halpha in this work we develop a monte carlo code to simulate the transfer of lybeta line photons incorporating the scattering channel into halpha both halpha and lybeta lines are formed through diffusion in frequency space where a line photon enters the wing regime after a fairly large number of resonance scatterings with hydrogen atoms various line profiles of halpha and lybeta emergent from our model nebulae are presented it is argued that the electron temperature is a critical parameter which controls the flux ratio of emergent lybeta and halpha specifically for t3 times 104rm k and halpha line center optical depth tau_alpha10 the number flux ratio of emergent lybeta and halpha is sim 49 percent which is quite significant we propose that the leaking lybeta can be an interesting source for the formation of halpha wings observed in many symbiotic stars and active galactic nuclei similar broad halpha wings are also expected in lyalpha emitting halos found in the early universe which can be potentially probed by the it james webb telescope in the future | [['we', 'investigate', 'the', 'escape', 'of', 'lybeta', 'from', 'emission', 'nebulae', 'with', 'a', 'significant', 'population', 'of', 'excited', 'hydrogen', 'atoms', 'in', 'the', 'level', 'n2', 'rendering', 'them', 'optically', 'thick', 'in', 'halpha', 'the', 'transfer', 'of', 'lybeta', 'line', 'photons', 'in', 'these', 'optically', 'thick', 'regions', 'is', 'complicated', 'by', 'the', 'presence', 'of', 'another', 'scattering', 'channel', 'leading', 'to', 'reemission', 'of', 'halpha', 'alternating', 'their', 'identities', 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1,802.03893 | Dynamics of a bilayer membrane with membrane-solvent partial slip
boundary conditions | We discuss the dynamics of a bilayer membrane with partial slip boundary
conditions between the monolayers and the bulk fluid. Using Onsager's
variational principle to account for the associated dissipations, we derive the
coupled dynamic equations for the membrane height and the excess lipid density.
The newly introduced friction coefficients appear in the renormalized fluid
viscosities. For ordinary lipid bilayer membranes, we find that it is generally
justified to ignore the effects of permeation and parallel slip at the membrane
surface.
| cond-mat.soft | we discuss the dynamics of a bilayer membrane with partial slip boundary conditions between the monolayers and the bulk fluid using onsagers variational principle to account for the associated dissipations we derive the coupled dynamic equations for the membrane height and the excess lipid density the newly introduced friction coefficients appear in the renormalized fluid viscosities for ordinary lipid bilayer membranes we find that it is generally justified to ignore the effects of permeation and parallel slip at the membrane surface | [['we', 'discuss', 'the', 'dynamics', 'of', 'a', 'bilayer', 'membrane', 'with', 'partial', 'slip', 'boundary', 'conditions', 'between', 'the', 'monolayers', 'and', 'the', 'bulk', 'fluid', 'using', 'onsagers', 'variational', 'principle', 'to', 'account', 'for', 'the', 'associated', 'dissipations', 'we', 'derive', 'the', 'coupled', 'dynamic', 'equations', 'for', 'the', 'membrane', 'height', 'and', 'the', 'excess', 'lipid', 'density', 'the', 'newly', 'introduced', 'friction', 'coefficients', 'appear', 'in', 'the', 'renormalized', 'fluid', 'viscosities', 'for', 'ordinary', 'lipid', 'bilayer', 'membranes', 'we', 'find', 'that', 'it', 'is', 'generally', 'justified', 'to', 'ignore', 'the', 'effects', 'of', 'permeation', 'and', 'parallel', 'slip', 'at', 'the', 'membrane', 'surface']] | [-0.16671524761405623, 0.17213312514632204, -0.06848771134851339, 0.013255276322008375, -0.03748657825448907, -0.14532101176748122, -0.012117036882741952, 0.3237585545758958, -0.29426345802602116, -0.2546446547602062, 0.016444484333875648, -0.24094137302197424, -0.18228104638430165, 0.11681050835407258, -0.028847399593135457, 0.07722070359015538, 0.0027335385653983663, -0.04588209197247102, -0.024556186357743027, -0.16183892386838977, 0.2651651289406014, -0.007473952033453518, 0.3084375311471062, 0.13190430870141695, 0.12281968376748724, -0.013911444831777501, 0.03884560135940527, 0.09762044210722785, -0.2848753082587701, 0.08272017572466055, 0.20617456042874652, -0.12193127646812318, 0.19084521203681273, -0.5357133905391818, -0.27108961303522927, -0.007857546457491907, 0.08938548914092179, 0.14994477275988938, 0.011952359838710154, -0.24557874411528494, 0.0376309310718451, -0.13517814686084972, -0.11484779462356258, -0.03061967256858393, 0.007912564424820889, 0.020874871274479376, -0.2421467634446827, 0.2314072456670764, 0.014083418104061742, 0.04526271314337206, -0.1603579005272484, -0.09759352138311958, -0.10264315717870852, 0.0843843723371349, 0.09505273504642608, -0.0631107183828674, 0.2502929129275597, -0.14832039770704727, -0.004440255687330608, 0.3833882056268645, -0.07070310006814974, -0.28491960821190365, 0.20178057009607187, -0.12074634047616044, -0.0694666252023092, 0.15731169917709825, 0.12538264579151148, 0.07053732159145085, -0.18051835697397223, 0.035628193320367305, -0.01040735387208837, 0.1521011261344186, 0.1406265179063251, -0.03750428970167299, 0.23673917863655974, 0.15915044126917183, 0.02371062279138484, 0.14944470529502005, -0.1120630803002033, -0.11575341959955332, -0.31200205026493394, -0.22181164588273308, -0.13232321554312, 0.01956067080207077, -0.10500523831855568, -0.2271258385024137, 0.30081057737087025, 0.11706869416283788, 0.12693024692298085, 0.07265015811096003, 0.22342927883563127, 0.1256298421088744, 0.06525387564375076, 0.05172688721529679, 0.26113931751913494, 0.2169506566392051, 0.13658649987184707, -0.31977268348355997, 0.03924812003035949, 0.08045405153196628] |
1,802.03894 | Fundamental noise dynamics in cascaded-order Brillouin lasers | The dynamics of cascaded-order Brillouin lasers make them ideal for
applications such as rotation sensing, highly coherent optical communications,
and low-noise microwave signal synthesis. Remark- ably, when implemented at the
chip-scale, recent experimental studies have revealed that Brillouin lasers can
operate in the fundamental linewidth regime where optomechanical and quantum
noise sources dominate. To explore new opportunities for enhanced performance,
we formulate a simple model to describe the physics of cascaded Brillouin
lasers based on the coupled mode dynamics governed by electrostriction and the
fluctuation-dissipation theorem. From this model, we obtain analytical formulas
describing the steady state power evolution and accompanying noise properties,
including expressions for phase noise, relative intensity noise and power
spectra for beat notes of cascaded laser orders. Our analysis reveals that
cascading modifies the dynamics of intermediate laser orders, yielding noise
properties that differ from single-mode Brillouin lasers. These modifications
lead to a Stokes order linewidth dependency on the coupled order dynamics and a
broader linewidth than that predicted with previous single order theories. We
also derive a simple analytical expression for the higher order beat notes that
enables calculation of the Stokes linewidth based on only the relative measured
powers between orders instead of absolute parameters, yielding a method to
measure cascaded order linewidth as well as a prediction for sub-Hz operation.
We validate our results using stochastic numerical simulations of the cascaded
laser dynamics.
| physics.optics | the dynamics of cascadedorder brillouin lasers make them ideal for applications such as rotation sensing highly coherent optical communications and lownoise microwave signal synthesis remark ably when implemented at the chipscale recent experimental studies have revealed that brillouin lasers can operate in the fundamental linewidth regime where optomechanical and quantum noise sources dominate to explore new opportunities for enhanced performance we formulate a simple model to describe the physics of cascaded brillouin lasers based on the coupled mode dynamics governed by electrostriction and the fluctuationdissipation theorem from this model we obtain analytical formulas describing the steady state power evolution and accompanying noise properties including expressions for phase noise relative intensity noise and power spectra for beat notes of cascaded laser orders our analysis reveals that cascading modifies the dynamics of intermediate laser orders yielding noise properties that differ from singlemode brillouin lasers these modifications lead to a stokes order linewidth dependency on the coupled order dynamics and a broader linewidth than that predicted with previous single order theories we also derive a simple analytical expression for the higher order beat notes that enables calculation of the stokes linewidth based on only the relative measured powers between orders instead of absolute parameters yielding a method to measure cascaded order linewidth as well as a prediction for subhz operation we validate our results using stochastic numerical simulations of the cascaded laser dynamics | [['the', 'dynamics', 'of', 'cascadedorder', 'brillouin', 'lasers', 'make', 'them', 'ideal', 'for', 'applications', 'such', 'as', 'rotation', 'sensing', 'highly', 'coherent', 'optical', 'communications', 'and', 'lownoise', 'microwave', 'signal', 'synthesis', 'remark', 'ably', 'when', 'implemented', 'at', 'the', 'chipscale', 'recent', 'experimental', 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1,802.03895 | Isospin Conservation in Neutron Rich Systems of Heavy Nuclei | It is generally believed that isospin would diminish in its importance as we
go towards heavy mass region due to isospin mixing caused by the growing
Coulomb forces. However, it was realized quite early that isospin could become
an important and useful quantum number for all nuclei including heavy nuclei
due to neutron richness of the systems~\cite{robson}. Lane and
Soper~\cite{lane} also showed in a theoretical calculation that isospin indeed
remains quite good in heavy mass neutron rich systems. In this paper, we
present isospin based calculations~\cite{jain,swati} for the fission fragment
distributions obtained from heavy-ion fusion fission reactions. We discuss in
detail the procedure adopted to assign the isospin values and the role of
neutron multiplicity data in obtaining the total fission fragment
distributions. We show that the observed fragment distributions can be
reproduced rather reasonably well by the calculations based on the idea of
conservation of isospin. This is probably the very first direct experimental
evidence of the validity of isospin in heavy nuclei, which arises largely due
to the neutron-rich nature of heavy nuclei and their fragments. This result may
eventually become useful for the theories of nuclear fission and also in other
practical applications.
| nucl-th | it is generally believed that isospin would diminish in its importance as we go towards heavy mass region due to isospin mixing caused by the growing coulomb forces however it was realized quite early that isospin could become an important and useful quantum number for all nuclei including heavy nuclei due to neutron richness of the systemsciterobson lane and sopercitelane also showed in a theoretical calculation that isospin indeed remains quite good in heavy mass neutron rich systems in this paper we present isospin based calculationscitejainswati for the fission fragment distributions obtained from heavyion fusion fission reactions we discuss in detail the procedure adopted to assign the isospin values and the role of neutron multiplicity data in obtaining the total fission fragment distributions we show that the observed fragment distributions can be reproduced rather reasonably well by the calculations based on the idea of conservation of isospin this is probably the very first direct experimental evidence of the validity of isospin in heavy nuclei which arises largely due to the neutronrich nature of heavy nuclei and their fragments this result may eventually become useful for the theories of nuclear fission and also in other practical applications | [['it', 'is', 'generally', 'believed', 'that', 'isospin', 'would', 'diminish', 'in', 'its', 'importance', 'as', 'we', 'go', 'towards', 'heavy', 'mass', 'region', 'due', 'to', 'isospin', 'mixing', 'caused', 'by', 'the', 'growing', 'coulomb', 'forces', 'however', 'it', 'was', 'realized', 'quite', 'early', 'that', 'isospin', 'could', 'become', 'an', 'important', 'and', 'useful', 'quantum', 'number', 'for', 'all', 'nuclei', 'including', 'heavy', 'nuclei', 'due', 'to', 'neutron', 'richness', 'of', 'the', 'systemsciterobson', 'lane', 'and', 'sopercitelane', 'also', 'showed', 'in', 'a', 'theoretical', 'calculation', 'that', 'isospin', 'indeed', 'remains', 'quite', 'good', 'in', 'heavy', 'mass', 'neutron', 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1,802.03896 | Weighted first moments of some special quadratic Dirichlet $L$-functions | In this paper, we obtain asymptotic formulas for weighted first moments of
central values of families of primitive quadratic Dirichlet $L$-functions whose
conductors comprise only primes that split in a given quadratic number field.
We then deduce a non-vanishing result of these $L$-functions at the point
$s=1/2$.
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1,802.03897 | A Novel Sub-Nyquist Multiband Signal Detection Algorithm for Cognitive
Radio | Wideband spectrum sensing (WSS) is an essential technology for cognitive
radio. However, the sampling rate is still a bottleneck of WSS. Several
sub-Nyquist sensing methods have been proposed. These technologies deteriorate
in the low signal to noise ratio (SNR) regime or suffer high computational
complexity. In this paper, we propose a novel sub-Nyquist WSS method based on
Multi-coset (MC) sampling. We design a simple SNR-robust and low-complexity
multiband signal detection algorithm. In particular, the proposed method
differs the commonly used detection algorithms which are based on energy
detection (ED), matched filter (MF) or cyclostationary detection (CD). We
exploit the linear recurrent relation between the locations of nonzero
frequencies and the DFT of the arithmetic-shifted subsampled signals. These
relations can be uniquely expressed by a series of the so-called frequency
locator polynomials (FLPs). The scalar of the relations is related to the
bandwidths of the subsignals. Basing on this, we propose a detector for sparse
multiband signals along with the method estimating carrier frequency and
bandwidth. The detector does not require priori knowledge about the frequency
locations of the signals of interest. Moreover, it has lower complexity of both
samples and computation compared to CD in sparse case. Experimental results
show the detector outperforms ED in the sub-Nyquist regime especially in low
SNRs.
| eess.SP | wideband spectrum sensing wss is an essential technology for cognitive radio however the sampling rate is still a bottleneck of wss several subnyquist sensing methods have been proposed these technologies deteriorate in the low signal to noise ratio snr regime or suffer high computational complexity in this paper we propose a novel subnyquist wss method based on multicoset mc sampling we design a simple snrrobust and lowcomplexity multiband signal detection algorithm in particular the proposed method differs the commonly used detection algorithms which are based on energy detection ed matched filter mf or cyclostationary detection cd we exploit the linear recurrent relation between the locations of nonzero frequencies and the dft of the arithmeticshifted subsampled signals these relations can be uniquely expressed by a series of the socalled frequency locator polynomials flps the scalar of the relations is related to the bandwidths of the subsignals basing on this we propose a detector for sparse multiband signals along with the method estimating carrier frequency and bandwidth the detector does not require priori knowledge about the frequency locations of the signals of interest moreover it has lower complexity of both samples and computation compared to cd in sparse case experimental results show the detector outperforms ed in the subnyquist regime especially in low snrs | [['wideband', 'spectrum', 'sensing', 'wss', 'is', 'an', 'essential', 'technology', 'for', 'cognitive', 'radio', 'however', 'the', 'sampling', 'rate', 'is', 'still', 'a', 'bottleneck', 'of', 'wss', 'several', 'subnyquist', 'sensing', 'methods', 'have', 'been', 'proposed', 'these', 'technologies', 'deteriorate', 'in', 'the', 'low', 'signal', 'to', 'noise', 'ratio', 'snr', 'regime', 'or', 'suffer', 'high', 'computational', 'complexity', 'in', 'this', 'paper', 'we', 'propose', 'a', 'novel', 'subnyquist', 'wss', 'method', 'based', 'on', 'multicoset', 'mc', 'sampling', 'we', 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1,802.03898 | Scalable Downward Routing for Wireless Sensor Networks and Internet of
Things Actuation | In this paper, we study the downward routing for network control/actuation in
large-scale and heterogeneous wireless sensor networks (WSNs) and Internet of
Things (IoT). We propose the Opportunistic Source Routing (OSR), a scalable and
reliable downward routing protocol for WSNs/IoT. OSR introduces opportunistic
routing into traditional source routing based on the parent set of a node's
upward routing in data collection, significantly addressing the drastic link
dynamics in low-power and lossy WSNs. We devise a novel adaptive Bloom filter
mechanism to effectively and efficiently encode a downward source-route in OSR,
which enables a significant reduction of the length of source-route field in
packet header. OSR is scalable to very large-size WSN/IoT deployments, since
each resource-constrained node in the network only stores the set of its direct
children. The probabilistic nature of the Bloom filter passively explores
opportunistic routing. Upon a delivery failure at any hop along the downward
path, OSR actively performs opportunistic routing to bypass the obsolete/bad
link. We demonstrate the desirable scalability of OSR against the standard RPL
downward routing. We evaluate the performance of OSR via both simulations and
real-world testbed experiments, in comparison with the standard RPL (both
storing mode and non-storing mode), ORPL, and the representative dissemination
protocol Drip. Our results show that OSR significantly outperforms RPL and ORPL
in scalability and reliability. OSR also achieves significantly better energy
efficiency compared to TinyRPL and Drip which are based on the same TinyOS
platform as OSR implementation.
| cs.NI | in this paper we study the downward routing for network controlactuation in largescale and heterogeneous wireless sensor networks wsns and internet of things iot we propose the opportunistic source routing osr a scalable and reliable downward routing protocol for wsnsiot osr introduces opportunistic routing into traditional source routing based on the parent set of a nodes upward routing in data collection significantly addressing the drastic link dynamics in lowpower and lossy wsns we devise a novel adaptive bloom filter mechanism to effectively and efficiently encode a downward sourceroute in osr which enables a significant reduction of the length of sourceroute field in packet header osr is scalable to very largesize wsniot deployments since each resourceconstrained node in the network only stores the set of its direct children the probabilistic nature of the bloom filter passively explores opportunistic routing upon a delivery failure at any hop along the downward path osr actively performs opportunistic routing to bypass the obsoletebad link we demonstrate the desirable scalability of osr against the standard rpl downward routing we evaluate the performance of osr via both simulations and realworld testbed experiments in comparison with the standard rpl both storing mode and nonstoring mode orpl and the representative dissemination protocol drip our results show that osr significantly outperforms rpl and orpl in scalability and reliability osr also achieves significantly better energy efficiency compared to tinyrpl and drip which are based on the same tinyos platform as osr implementation | [['in', 'this', 'paper', 'we', 'study', 'the', 'downward', 'routing', 'for', 'network', 'controlactuation', 'in', 'largescale', 'and', 'heterogeneous', 'wireless', 'sensor', 'networks', 'wsns', 'and', 'internet', 'of', 'things', 'iot', 'we', 'propose', 'the', 'opportunistic', 'source', 'routing', 'osr', 'a', 'scalable', 'and', 'reliable', 'downward', 'routing', 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1,802.03899 | Measurement of the intensity ratio of Auger and conversion electrons for
the electron capture decay of $^{125}$I | Auger electrons emitted after nuclear decay have potential application in
targeted cancer therapy. For this purpose it is important to know the Auger
electron yield per nuclear decay. In this work we describe a measurement of the
ratio of the number of conversion electrons (emitted as part of the nuclear
decay process) to the number of Auger electrons (emitted as part of the atomic
relaxation process after the nuclear decay) for the case of $^{125}$I. Results
are compared with Monte-Carlo type simulations of the relaxation cascade using
the BrIccEmis code. Our results indicate that for $^{125}$I the calculations
based on rates from the Evaluated Atomic Data Library (EADL) underestimate the
K Auger yields by 20\%.
| nucl-ex physics.med-ph | auger electrons emitted after nuclear decay have potential application in targeted cancer therapy for this purpose it is important to know the auger electron yield per nuclear decay in this work we describe a measurement of the ratio of the number of conversion electrons emitted as part of the nuclear decay process to the number of auger electrons emitted as part of the atomic relaxation process after the nuclear decay for the case of 125i results are compared with montecarlo type simulations of the relaxation cascade using the briccemis code our results indicate that for 125i the calculations based on rates from the evaluated atomic data library eadl underestimate the k auger yields by 20 | [['auger', 'electrons', 'emitted', 'after', 'nuclear', 'decay', 'have', 'potential', 'application', 'in', 'targeted', 'cancer', 'therapy', 'for', 'this', 'purpose', 'it', 'is', 'important', 'to', 'know', 'the', 'auger', 'electron', 'yield', 'per', 'nuclear', 'decay', 'in', 'this', 'work', 'we', 'describe', 'a', 'measurement', 'of', 'the', 'ratio', 'of', 'the', 'number', 'of', 'conversion', 'electrons', 'emitted', 'as', 'part', 'of', 'the', 'nuclear', 'decay', 'process', 'to', 'the', 'number', 'of', 'auger', 'electrons', 'emitted', 'as', 'part', 'of', 'the', 'atomic', 'relaxation', 'process', 'after', 'the', 'nuclear', 'decay', 'for', 'the', 'case', 'of', '125i', 'results', 'are', 'compared', 'with', 'montecarlo', 'type', 'simulations', 'of', 'the', 'relaxation', 'cascade', 'using', 'the', 'briccemis', 'code', 'our', 'results', 'indicate', 'that', 'for', '125i', 'the', 'calculations', 'based', 'on', 'rates', 'from', 'the', 'evaluated', 'atomic', 'data', 'library', 'eadl', 'underestimate', 'the', 'k', 'auger', 'yields', 'by', '20']] | [0.009055995202639647, 0.15938835257762357, -0.028698870944872237, 0.09554548025695558, 0.049483611783535594, -0.07216070020130198, 0.018581822743279894, 0.3559505453766242, -0.2232746833142939, -0.31322872976919536, 0.036381155311685516, -0.32932344962140186, -0.027546704040121352, 0.247438378066331, 0.09193343737391396, 0.028530041728038014, 0.1099936002130179, 0.0003145893263679586, -0.015736769890951876, -0.21156036391396793, 0.2856140393077543, 0.1709649467252587, 0.2502277827159943, 0.09721588570085403, 0.022278418034323278, 0.06388280856864233, -0.05132125829460851, -0.06936724448837993, -0.11331200390358981, 0.08952854452613872, 0.24053787613867603, 0.13623521987986015, 0.22163988328375445, -0.4656731724379617, -0.17115936577810267, 0.06776926304589499, 0.15154468053706774, 0.12358796133316662, -0.12553422410437315, -0.2396045229887884, 0.04015017032884715, -0.18348022053639093, -0.10564686320803798, -0.019015671914083918, -0.005246646914696484, 0.05478174517159922, -0.2715964060983408, 0.09339142767630779, 0.007826800923794508, 0.016007483501246162, -0.08934786985219832, -0.1755145198926983, 0.037760560803131706, 0.11480761504948564, 0.10049776078289653, 0.03190767485239919, 0.23796969046407754, -0.07660528634707525, -0.12831025144081204, 0.355659224107257, -0.06551856259823564, -0.10229060266232282, 0.13424224645234317, -0.18691232239035072, -0.10009674311272408, 0.24300949484632725, 0.15506709116437523, 0.1212689695377393, -0.13459774170463024, 0.049328525395443044, 0.011609886813778104, 0.16767257597836616, 0.041866740528412424, 0.046467165270689545, 0.13225315485288502, 0.20431725698800987, -0.015564504420597172, 0.11375316449751456, -0.15032960400761416, -0.042745088662118895, -0.29264385059573933, -0.1647263431876669, -0.20232608993721538, 0.11820901080500335, -0.008524579853799782, -0.10611496528757638, 0.3998776607153112, 0.09580022378481533, 0.15323730299183935, -0.008357623351018941, 0.2951247402336122, 0.08814064992424264, 0.07722026565553326, 0.03990120486879166, 0.27535507645511087, 0.1546939591562637, 0.09832633530574017, -0.31706931389124837, 0.09063084376158945, 0.03047104612025514] |
1,802.039 | Q-learning with Nearest Neighbors | We consider model-free reinforcement learning for infinite-horizon discounted
Markov Decision Processes (MDPs) with a continuous state space and unknown
transition kernel, when only a single sample path under an arbitrary policy of
the system is available. We consider the Nearest Neighbor Q-Learning (NNQL)
algorithm to learn the optimal Q function using nearest neighbor regression
method. As the main contribution, we provide tight finite sample analysis of
the convergence rate. In particular, for MDPs with a $d$-dimensional state
space and the discounted factor $\gamma \in (0,1)$, given an arbitrary sample
path with "covering time" $ L $, we establish that the algorithm is guaranteed
to output an $\varepsilon$-accurate estimate of the optimal Q-function using
$\tilde{O}\big(L/(\varepsilon^3(1-\gamma)^7)\big)$ samples. For instance, for a
well-behaved MDP, the covering time of the sample path under the purely random
policy scales as $ \tilde{O}\big(1/\varepsilon^d\big),$ so the sample
complexity scales as $\tilde{O}\big(1/\varepsilon^{d+3}\big).$ Indeed, we
establish a lower bound that argues that the dependence of $
\tilde{\Omega}\big(1/\varepsilon^{d+2}\big)$ is necessary.
| cs.LG math.OC stat.ML | we consider modelfree reinforcement learning for infinitehorizon discounted markov decision processes mdps with a continuous state space and unknown transition kernel when only a single sample path under an arbitrary policy of the system is available we consider the nearest neighbor qlearning nnql algorithm to learn the optimal q function using nearest neighbor regression method as the main contribution we provide tight finite sample analysis of the convergence rate in particular for mdps with a ddimensional state space and the discounted factor gamma in 01 given an arbitrary sample path with covering time l we establish that the algorithm is guaranteed to output an varepsilonaccurate estimate of the optimal qfunction using tildeobiglvarepsilon31gamma7big samples for instance for a wellbehaved mdp the covering time of the sample path under the purely random policy scales as tildeobig1varepsilondbig so the sample complexity scales as tildeobig1varepsilond3big indeed we establish a lower bound that argues that the dependence of tildeomegabig1varepsilond2big is necessary | [['we', 'consider', 'modelfree', 'reinforcement', 'learning', 'for', 'infinitehorizon', 'discounted', 'markov', 'decision', 'processes', 'mdps', 'with', 'a', 'continuous', 'state', 'space', 'and', 'unknown', 'transition', 'kernel', 'when', 'only', 'a', 'single', 'sample', 'path', 'under', 'an', 'arbitrary', 'policy', 'of', 'the', 'system', 'is', 'available', 'we', 'consider', 'the', 'nearest', 'neighbor', 'qlearning', 'nnql', 'algorithm', 'to', 'learn', 'the', 'optimal', 'q', 'function', 'using', 'nearest', 'neighbor', 'regression', 'method', 'as', 'the', 'main', 'contribution', 'we', 'provide', 'tight', 'finite', 'sample', 'analysis', 'of', 'the', 'convergence', 'rate', 'in', 'particular', 'for', 'mdps', 'with', 'a', 'ddimensional', 'state', 'space', 'and', 'the', 'discounted', 'factor', 'gamma', 'in', '01', 'given', 'an', 'arbitrary', 'sample', 'path', 'with', 'covering', 'time', 'l', 'we', 'establish', 'that', 'the', 'algorithm', 'is', 'guaranteed', 'to', 'output', 'an', 'varepsilonaccurate', 'estimate', 'of', 'the', 'optimal', 'qfunction', 'using', 'tildeobiglvarepsilon31gamma7big', 'samples', 'for', 'instance', 'for', 'a', 'wellbehaved', 'mdp', 'the', 'covering', 'time', 'of', 'the', 'sample', 'path', 'under', 'the', 'purely', 'random', 'policy', 'scales', 'as', 'tildeobig1varepsilondbig', 'so', 'the', 'sample', 'complexity', 'scales', 'as', 'tildeobig1varepsilond3big', 'indeed', 'we', 'establish', 'a', 'lower', 'bound', 'that', 'argues', 'that', 'the', 'dependence', 'of', 'tildeomegabig1varepsilond2big', 'is', 'necessary']] | [-0.08323542955486725, 0.08675054028581751, -0.07977049891216059, 0.09819410925653453, -0.0899617432958136, -0.14631036745384335, 0.14578114119979244, 0.4604363448700557, -0.30147540957977376, -0.2717123971010248, 0.11415810839117815, -0.22868633967203397, -0.09784456210715385, 0.17214661624282598, -0.046143692502131066, 0.10084324826486409, 0.04860580447440346, 0.07174646131092838, -0.043480883279504876, -0.268210380775854, 0.31928684723873935, 0.06366205332179864, 0.2501226413436234, -0.055174747079145166, 0.17864190015941858, 0.06543982966337353, 0.026180601293841997, 0.012445501318434254, -0.1499689003258148, 0.07099804449205598, 0.28834258751012387, 0.15830582634545862, 0.35331056682392953, -0.33400225500765374, -0.15989315210996816, 0.16901572420727462, 0.11382781354400019, 0.07424555653706193, -0.020210300787196805, -0.252554611278077, 0.057006217337523896, -0.14449066050350667, -0.07266539323066051, -0.04198136168376853, 0.01563478936906904, 0.011972625146930416, -0.36297176441798606, 0.02235588933651646, 0.07019154190085829, 0.012125596931825081, -0.09099297961841027, -0.13659964628129576, 0.045769522776827214, 0.13121250927390066, 0.027181609906256197, 0.07877661398301522, 0.1162211322481744, -0.10011534716933966, -0.17523520493259032, 0.32162663803746305, -0.09363722653904309, -0.18043362055594722, 0.14985148867436995, -0.12210522181664904, -0.13287341309711337, 0.157509172456339, 0.23477250155430132, 0.17368593430146576, -0.1647777497509378, 0.11457458430513119, -0.07243564588328202, 0.17632720855996012, -0.01768620595956842, 0.03890613553424676, 0.07209497027521138, 0.21668429795575017, 0.1848116357034693, 0.19618080606063207, -0.06994311494888583, -0.12111087011716638, -0.31193806228538357, -0.14918330319885476, -0.22845920773843925, 0.039484135459642855, -0.17807733145911092, -0.17741062962576204, 0.281669515579318, 0.15148365111555903, 0.20922747193525235, 0.20850382758459698, 0.2698806571618964, 0.17001237953430973, -0.003424361447493235, 0.15417614314472303, 0.1774439144693315, 0.07666516235563904, 0.03440194700844586, -0.22444962777818242, 0.140384566122666, 0.08694793110092482] |
1,802.03901 | Network Overload due to Massive Attacks | We study the cascading failure of networks due to overload, using the
betweenness centrality of a node as the measure of its load following the
Motter and Lai model. We study the fraction of survived nodes at the end of the
cascade $p_f$ as function of the strength of the initial attack, measured by
the fraction of nodes $p$, which survive the initial attack for different
values of tolerance $\alpha$ in random regular and Erd\"os-Renyi graphs. We
find the existence of first order phase transition line $p_t(\alpha)$ on a
$p-\alpha$ plane, such that if $p <p_t$ the cascade of failures lead to a very
small fraction of survived nodes $p_f$ and the giant component of the network
disappears, while for $p>p_t$, $p_f$ is large and the giant component of the
network is still present. Exactly at $p_t$ the function $p_f(p)$ undergoes a
first order discontinuity. We find that the line $p_t(\alpha)$ ends at critical
point $(p_c,\alpha_c)$ ,in which the cascading failures are replaced by a
second order percolation transition. We analytically find the average
betweenness of nodes with different degrees before and after the initial
attack, investigate their roles in the cascading failures, and find a lower
bound for $p_t(\alpha)$. We also study the difference between a localized and
random attacks.
| physics.soc-ph cs.SI | we study the cascading failure of networks due to overload using the betweenness centrality of a node as the measure of its load following the motter and lai model we study the fraction of survived nodes at the end of the cascade p_f as function of the strength of the initial attack measured by the fraction of nodes p which survive the initial attack for different values of tolerance alpha in random regular and erdosrenyi graphs we find the existence of first order phase transition line p_talpha on a palpha plane such that if p p_t the cascade of failures lead to a very small fraction of survived nodes p_f and the giant component of the network disappears while for pp_t p_f is large and the giant component of the network is still present exactly at p_t the function p_fp undergoes a first order discontinuity we find that the line p_talpha ends at critical point p_calpha_c in which the cascading failures are replaced by a second order percolation transition we analytically find the average betweenness of nodes with different degrees before and after the initial attack investigate their roles in the cascading failures and find a lower bound for p_talpha we also study the difference between a localized and random attacks | [['we', 'study', 'the', 'cascading', 'failure', 'of', 'networks', 'due', 'to', 'overload', 'using', 'the', 'betweenness', 'centrality', 'of', 'a', 'node', 'as', 'the', 'measure', 'of', 'its', 'load', 'following', 'the', 'motter', 'and', 'lai', 'model', 'we', 'study', 'the', 'fraction', 'of', 'survived', 'nodes', 'at', 'the', 'end', 'of', 'the', 'cascade', 'p_f', 'as', 'function', 'of', 'the', 'strength', 'of', 'the', 'initial', 'attack', 'measured', 'by', 'the', 'fraction', 'of', 'nodes', 'p', 'which', 'survive', 'the', 'initial', 'attack', 'for', 'different', 'values', 'of', 'tolerance', 'alpha', 'in', 'random', 'regular', 'and', 'erdosrenyi', 'graphs', 'we', 'find', 'the', 'existence', 'of', 'first', 'order', 'phase', 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'a', 'lower', 'bound', 'for', 'p_talpha', 'we', 'also', 'study', 'the', 'difference', 'between', 'a', 'localized', 'and', 'random', 'attacks']] | [-0.19431938607530874, 0.14833167284887516, -0.053649496169021114, 0.03936187514442612, 0.014211826913758364, -0.11271039020876138, 0.11268966169871888, 0.3245547746896352, -0.231633125118127, -0.28442684181355404, 0.0633821248318599, -0.31026669372592797, -0.1464412430319133, 0.08577584965782838, 0.005322374035849383, 0.04204662173239204, 0.04238483831553796, 0.0877401488510063, -0.029377569452275713, -0.22255896143481563, 0.35337271248592, 0.05881918769811044, 0.2458687842072714, 0.07952153398298868, 0.05913019814343597, -0.01112626708390745, 0.019292203249670476, 0.032252497185672226, -0.13525383691806134, 0.03296671251785322, 0.21325032801910984, 0.10332716649184315, 0.28588441905507633, -0.3852744872356502, -0.17825379242683853, 0.1581312022856342, 0.12382587603222062, 0.09210206871923576, 0.006980370798674034, -0.2556140493984784, 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1,802.03902 | On a curvature flow model for embryonic epidermal wound healing | The paper studies a curvature flow linked to the physical phenomenon of wound
closure. Under the flow we show that a closed, initially convex or
close-to-convex curve shrinks to a round point in finite time. We also study
the singularity, showing that the singularity profile after continuous
rescaling is that of a circle. We additionally give a maximal time estimate,
with an application to the classification of blowups.
| math.DG | the paper studies a curvature flow linked to the physical phenomenon of wound closure under the flow we show that a closed initially convex or closetoconvex curve shrinks to a round point in finite time we also study the singularity showing that the singularity profile after continuous rescaling is that of a circle we additionally give a maximal time estimate with an application to the classification of blowups | [['the', 'paper', 'studies', 'a', 'curvature', 'flow', 'linked', 'to', 'the', 'physical', 'phenomenon', 'of', 'wound', 'closure', 'under', 'the', 'flow', 'we', 'show', 'that', 'a', 'closed', 'initially', 'convex', 'or', 'closetoconvex', 'curve', 'shrinks', 'to', 'a', 'round', 'point', 'in', 'finite', 'time', 'we', 'also', 'study', 'the', 'singularity', 'showing', 'that', 'the', 'singularity', 'profile', 'after', 'continuous', 'rescaling', 'is', 'that', 'of', 'a', 'circle', 'we', 'additionally', 'give', 'a', 'maximal', 'time', 'estimate', 'with', 'an', 'application', 'to', 'the', 'classification', 'of', 'blowups']] | [-0.1758576383102028, 0.0707274457144034, -0.16110326066914507, 0.034131533214616976, -0.06885143898098785, -0.12538165996974224, 0.05385864733012996, 0.3745135617278078, -0.295282613239525, -0.1506207294707351, 0.16010636456057376, -0.2785185767502031, -0.16953680487366748, 0.1879458952907418, -0.11760657230008613, 0.033043315743698794, 0.06933657393571646, 0.07798553651938324, -0.10894762447466799, -0.2671559673651834, 0.3678869471940048, 0.002407503741029787, 0.23648263455149443, 0.10568987642781234, 0.10287359499317758, -0.018254128572366694, 0.014478082357741454, 0.08692349907120361, -0.19675878867982485, 0.06474430208532687, 0.19426768877105238, 0.11883701077278923, 0.2699591087670449, -0.406618838883279, -0.21870554614510826, 0.15267366929637158, 0.15314699822406777, 0.06962015236015706, -0.06179113922339371, -0.21443917013376074, 0.100761610083282, -0.12225156539010212, -0.236791814554154, -0.025356503860915407, 0.04538475097198149, 0.0015436059432378149, -0.22255962420261793, 0.04025656732290586, 0.12613141806522274, 0.07583233493599384, -0.09535681352238445, 0.030920577057472923, -0.021039503133472276, 0.0770148259745089, 0.08043284102356718, 0.08173067855087164, 0.13295438296079418, -0.09967528716387118, -0.07009484473725452, 0.3511059231172754, -0.09041973625254981, -0.19506405121437104, 0.12732072853866747, -0.16512564571081279, -0.10775254333994407, 0.13261640449876294, 0.1546867623076062, 0.14464689808322445, -0.10272893226732883, 0.05172024141339695, -0.07751219015678062, 0.12996841060649947, 0.09766509108628858, -0.0724353294559371, 0.15333296591073603, 0.14723478806862497, 0.1396848634844098, 0.1827932036591365, -0.08346225926652551, -0.1092667402151753, -0.38051234152140645, -0.1705896693199654, -0.13898996362353072, 0.12276827694629044, -0.11761973739382536, -0.22445860933786368, 0.4229943602181533, 0.06331840594408705, 0.2785341303576441, 0.09109987958115251, 0.2706347324145848, 0.09754299173501375, 0.040627568152130526, 0.10024340525748865, 0.1761827306027579, 0.0839328879956156, 0.02991337570882238, -0.20885592123137459, 0.0002492007813142503, 0.098406888284337] |
1,802.03903 | Unsupervised Anomaly Detection via Variational Auto-Encoder for Seasonal
KPIs in Web Applications | To ensure undisrupted business, large Internet companies need to closely
monitor various KPIs (e.g., Page Views, number of online users, and number of
orders) of its Web applications, to accurately detect anomalies and trigger
timely troubleshooting/mitigation. However, anomaly detection for these
seasonal KPIs with various patterns and data quality has been a great
challenge, especially without labels. In this paper, we proposed Donut, an
unsupervised anomaly detection algorithm based on VAE. Thanks to a few of our
key techniques, Donut greatly outperforms a state-of-arts supervised ensemble
approach and a baseline VAE approach, and its best F-scores range from 0.75 to
0.9 for the studied KPIs from a top global Internet company. We come up with a
novel KDE interpretation of reconstruction for Donut, making it the first
VAE-based anomaly detection algorithm with solid theoretical explanation.
| cs.LG stat.ML | to ensure undisrupted business large internet companies need to closely monitor various kpis eg page views number of online users and number of orders of its web applications to accurately detect anomalies and trigger timely troubleshootingmitigation however anomaly detection for these seasonal kpis with various patterns and data quality has been a great challenge especially without labels in this paper we proposed donut an unsupervised anomaly detection algorithm based on vae thanks to a few of our key techniques donut greatly outperforms a stateofarts supervised ensemble approach and a baseline vae approach and its best fscores range from 075 to 09 for the studied kpis from a top global internet company we come up with a novel kde interpretation of reconstruction for donut making it the first vaebased anomaly detection algorithm with solid theoretical explanation | [['to', 'ensure', 'undisrupted', 'business', 'large', 'internet', 'companies', 'need', 'to', 'closely', 'monitor', 'various', 'kpis', 'eg', 'page', 'views', 'number', 'of', 'online', 'users', 'and', 'number', 'of', 'orders', 'of', 'its', 'web', 'applications', 'to', 'accurately', 'detect', 'anomalies', 'and', 'trigger', 'timely', 'troubleshootingmitigation', 'however', 'anomaly', 'detection', 'for', 'these', 'seasonal', 'kpis', 'with', 'various', 'patterns', 'and', 'data', 'quality', 'has', 'been', 'a', 'great', 'challenge', 'especially', 'without', 'labels', 'in', 'this', 'paper', 'we', 'proposed', 'donut', 'an', 'unsupervised', 'anomaly', 'detection', 'algorithm', 'based', 'on', 'vae', 'thanks', 'to', 'a', 'few', 'of', 'our', 'key', 'techniques', 'donut', 'greatly', 'outperforms', 'a', 'stateofarts', 'supervised', 'ensemble', 'approach', 'and', 'a', 'baseline', 'vae', 'approach', 'and', 'its', 'best', 'fscores', 'range', 'from', '075', 'to', '09', 'for', 'the', 'studied', 'kpis', 'from', 'a', 'top', 'global', 'internet', 'company', 'we', 'come', 'up', 'with', 'a', 'novel', 'kde', 'interpretation', 'of', 'reconstruction', 'for', 'donut', 'making', 'it', 'the', 'first', 'vaebased', 'anomaly', 'detection', 'algorithm', 'with', 'solid', 'theoretical', 'explanation']] | [-0.05735266501098739, 0.004118911168862085, -0.054771948879764505, 0.057719861840961305, -0.13581932605932287, -0.16302826346607127, 0.09137331233655255, 0.4057249173334341, -0.2313705971273385, -0.40214071243500976, 0.11879371519638364, -0.30987218861926846, -0.1619076792692849, 0.2078140953750308, -0.12976694112268292, 0.11624880757707574, 0.0917242295039234, 0.04695131633180513, -0.04772513922053709, -0.2785876529315598, 0.23728970622942686, 0.08812012765973584, 0.3625039787886581, 0.08335660661591578, 0.1083434301041025, -0.019254092074089697, -0.08186345726317629, -0.023805292353638683, -0.0574813964950449, 0.16185630512173607, 0.3278825558068703, 0.1992916554801597, 0.2826735678532008, -0.3707182976180938, -0.20303242714074787, 0.11335589056278565, 0.13315537080562712, 0.0697656793662333, -0.056267328543845675, -0.34555314498633816, 0.11531709201474076, -0.23904073370426004, -0.06602576555733729, -0.12060312155995574, -0.017773385381207926, -0.010926545750516565, -0.270534769374766, 0.02581503580379953, -0.002186224679028226, 0.06578139588931825, -0.03887702407030417, -0.10482386569145011, 0.044411694240033515, 0.1505661475048192, 0.09851442764725175, 0.05545503461260849, 0.15169564333619245, -0.17068999949251928, -0.14914800426679484, 0.35580528136898776, -0.06002260321653934, -0.11158889779688985, 0.2020588649893805, -0.055895576286546664, -0.16928989541675174, 0.14283010588764256, 0.208160450803672, 0.08739248661623239, -0.18727375610745442, -0.02383520545953982, -0.0009477048931615566, 0.17809853546404794, 0.06382821277200953, 0.009309698942242496, 0.2085627426932837, 0.2538134680032285, 0.07926175128251536, 0.07174583123701472, -0.14105651415615175, -0.04862657140370514, -0.1890563082467737, -0.12543799558571025, -0.15651288959753715, -0.01210447127145451, -0.10237933799514544, -0.14327919308735587, 0.3983532015526139, 0.23121448345968862, 0.18903111062992586, 0.044135423922744485, 0.34605139096392623, -0.0024173238354651674, 0.11008434640174147, 0.09080407344527654, 0.18312444770274294, 0.023765281982934483, 0.1686959429470294, -0.14650803425179015, 0.10260209827614364, 0.026844613552927526] |
1,802.03904 | An Improved and More Accurate Expression for a PDF Related to
Eigenvalue-Based Spectrum Sensing | Cooperative spectrum sensing based on the limiting eigenvalue ratio of the
covariance matrix offers superior detection performance and overcomes the noise
uncertainty problem. While an exact expression exists, it is complex and
multiple useful approximate expressions have been published in the literature.
An improved, more accurate, integral solution for the probability density
function of the ratio is derived using order statistical analysis to remove the
simplifying, but incorrect, independence assumption. Thereby, the letter makes
an advance in the rigorous theory of eigenvalue-based spectrum sensing.
| eess.SP | cooperative spectrum sensing based on the limiting eigenvalue ratio of the covariance matrix offers superior detection performance and overcomes the noise uncertainty problem while an exact expression exists it is complex and multiple useful approximate expressions have been published in the literature an improved more accurate integral solution for the probability density function of the ratio is derived using order statistical analysis to remove the simplifying but incorrect independence assumption thereby the letter makes an advance in the rigorous theory of eigenvaluebased spectrum sensing | [['cooperative', 'spectrum', 'sensing', 'based', 'on', 'the', 'limiting', 'eigenvalue', 'ratio', 'of', 'the', 'covariance', 'matrix', 'offers', 'superior', 'detection', 'performance', 'and', 'overcomes', 'the', 'noise', 'uncertainty', 'problem', 'while', 'an', 'exact', 'expression', 'exists', 'it', 'is', 'complex', 'and', 'multiple', 'useful', 'approximate', 'expressions', 'have', 'been', 'published', 'in', 'the', 'literature', 'an', 'improved', 'more', 'accurate', 'integral', 'solution', 'for', 'the', 'probability', 'density', 'function', 'of', 'the', 'ratio', 'is', 'derived', 'using', 'order', 'statistical', 'analysis', 'to', 'remove', 'the', 'simplifying', 'but', 'incorrect', 'independence', 'assumption', 'thereby', 'the', 'letter', 'makes', 'an', 'advance', 'in', 'the', 'rigorous', 'theory', 'of', 'eigenvaluebased', 'spectrum', 'sensing']] | [-0.11397628536741693, -0.040815950344473266, -0.10697673118141081, 0.08368738834092039, -0.08949884537252642, -0.12566368559020616, 0.05336298542083352, 0.3621723269821987, -0.21401566758729695, -0.3155918996871194, 0.09677715703479148, -0.2679224516531186, -0.17863592916789153, 0.17329376423731446, -0.0904203199814739, 0.1344272097173546, 0.06705217319817859, 0.05381862530928282, -0.09138490437042146, -0.18138860365641968, 0.2579968094969878, 0.15925557556606473, 0.3718014400053237, 0.04253012211882465, 0.10076796396502427, 0.053114646774095796, -0.07589216038052525, -0.019726761960468832, -0.11512197059900722, 0.15066639137859011, 0.24385094829735213, 0.16876973580136628, 0.27337881816285, -0.4062498743601498, -0.2331247835730513, 0.10967980977147818, 0.1770418288208367, 0.10392788713631619, -0.027286940580822937, -0.25915780539313954, 0.07896691939372215, -0.1713290137150103, -0.12938858498819172, -0.08980871920773227, 0.02531598632534345, -0.045562975048337, -0.3208962312589089, 0.12648016331617587, 0.03492803890964881, 0.03712447034749424, -0.0452391363824496, -0.18844001577235758, 0.10248727156015645, 0.16340323627394224, 0.0489819144020744, -0.023186158000802, 0.11892608711717739, -0.12503662388328285, -0.0535341734288349, 0.33863483989123433, -0.05459901086370727, -0.2354182726454123, 0.14005529678182765, -0.06901661812194756, -0.14633292872791312, 0.1973241733558964, 0.10311418064382105, 0.09999257502966516, -0.17918193393519946, 0.12209303383133374, -0.020286252584940354, 0.18887233986918414, 0.06927448044353653, 0.05430411064576003, 0.14491575269909976, 0.13030628964077637, 0.11621765591414823, 0.10996806296142972, -0.07783643725893594, -0.09882756020496822, -0.22406931886784842, -0.1143968488220361, -0.2340538916870996, 0.03876506621322949, -0.1321235784447968, -0.1578102006876309, 0.33500386736843557, 0.18867674800622763, 0.13813925646744402, 0.08370596627216964, 0.3656686024582346, 0.2116359565296166, -0.009584313286246643, 0.03516881417072866, 0.2563164874778262, 0.17197949288501627, 0.05108169546361924, -0.2113659287237429, 0.12713460097022886, 0.03016275595707287] |
1,802.03905 | How to Match when All Vertices Arrive Online | We introduce a fully online model of maximum cardinality matching in which
all vertices arrive online. On the arrival of a vertex, its incident edges to
previously-arrived vertices are revealed. Each vertex has a deadline that is
after all its neighbors' arrivals. If a vertex remains unmatched until its
deadline, the algorithm must then irrevocably either match it to an unmatched
neighbor, or leave it unmatched. The model generalizes the existing one-sided
online model and is motivated by applications including ride-sharing platforms,
real-estate agency, etc.
We show that the Ranking algorithm by Karp et al. (STOC 1990) is
$0.5211$-competitive in our fully online model for general graphs. Our analysis
brings a novel charging mechanic into the randomized primal dual technique by
Devanur et al. (SODA 2013), allowing a vertex other than the two endpoints of a
matched edge to share the gain. To our knowledge, this is the first analysis of
Ranking that beats $0.5$ on general graphs in an online matching problem, a
first step towards solving the open problem by Karp et al. (STOC 1990) about
the optimality of Ranking on general graphs. If the graph is bipartite, we show
that the competitive ratio of Ranking is between $0.5541$ and $0.5671$.
Finally, we prove that the fully online model is strictly harder than the
previous model as no online algorithm can be $0.6317 <
1-\frac{1}{e}$-competitive in our model even for bipartite graphs.
| cs.DS | we introduce a fully online model of maximum cardinality matching in which all vertices arrive online on the arrival of a vertex its incident edges to previouslyarrived vertices are revealed each vertex has a deadline that is after all its neighbors arrivals if a vertex remains unmatched until its deadline the algorithm must then irrevocably either match it to an unmatched neighbor or leave it unmatched the model generalizes the existing onesided online model and is motivated by applications including ridesharing platforms realestate agency etc we show that the ranking algorithm by karp et al stoc 1990 is 05211competitive in our fully online model for general graphs our analysis brings a novel charging mechanic into the randomized primal dual technique by devanur et al soda 2013 allowing a vertex other than the two endpoints of a matched edge to share the gain to our knowledge this is the first analysis of ranking that beats 05 on general graphs in an online matching problem a first step towards solving the open problem by karp et al stoc 1990 about the optimality of ranking on general graphs if the graph is bipartite we show that the competitive ratio of ranking is between 05541 and 05671 finally we prove that the fully online model is strictly harder than the previous model as no online algorithm can be 06317 1frac1ecompetitive in our model even for bipartite graphs | [['we', 'introduce', 'a', 'fully', 'online', 'model', 'of', 'maximum', 'cardinality', 'matching', 'in', 'which', 'all', 'vertices', 'arrive', 'online', 'on', 'the', 'arrival', 'of', 'a', 'vertex', 'its', 'incident', 'edges', 'to', 'previouslyarrived', 'vertices', 'are', 'revealed', 'each', 'vertex', 'has', 'a', 'deadline', 'that', 'is', 'after', 'all', 'its', 'neighbors', 'arrivals', 'if', 'a', 'vertex', 'remains', 'unmatched', 'until', 'its', 'deadline', 'the', 'algorithm', 'must', 'then', 'irrevocably', 'either', 'match', 'it', 'to', 'an', 'unmatched', 'neighbor', 'or', 'leave', 'it', 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1,802.03906 | UAV-Enabled Mobile Edge Computing: Offloading Optimization and
Trajectory Design | With the emergence of diverse mobile applications (such as augmented
reality), the quality of experience of mobile users is greatly limited by their
computation capacity and finite battery lifetime. Mobile edge computing (MEC)
and wireless power transfer are promising to address this issue. However, these
two techniques are susceptible to propagation delay and loss. Motivated by the
chance of short-distance line-of-sight achieved by leveraging unmanned aerial
vehicle (UAV) communications, an UAV-enabled wireless powered MEC system is
studied. A power minimization problem is formulated subject to the constraints
on the number of the computation bits and energy harvesting causality. The
problem is non-convex and challenging to tackle. An alternative optimization
algorithm is proposed based on sequential convex optimization. Simulation
results show that our proposed design is superior to other benchmark schemes
and the proposed algorithm is efficient in terms of the convergence.
| eess.SP cs.IT math.IT | with the emergence of diverse mobile applications such as augmented reality the quality of experience of mobile users is greatly limited by their computation capacity and finite battery lifetime mobile edge computing mec and wireless power transfer are promising to address this issue however these two techniques are susceptible to propagation delay and loss motivated by the chance of shortdistance lineofsight achieved by leveraging unmanned aerial vehicle uav communications an uavenabled wireless powered mec system is studied a power minimization problem is formulated subject to the constraints on the number of the computation bits and energy harvesting causality the problem is nonconvex and challenging to tackle an alternative optimization algorithm is proposed based on sequential convex optimization simulation results show that our proposed design is superior to other benchmark schemes and the proposed algorithm is efficient in terms of the convergence | [['with', 'the', 'emergence', 'of', 'diverse', 'mobile', 'applications', 'such', 'as', 'augmented', 'reality', 'the', 'quality', 'of', 'experience', 'of', 'mobile', 'users', 'is', 'greatly', 'limited', 'by', 'their', 'computation', 'capacity', 'and', 'finite', 'battery', 'lifetime', 'mobile', 'edge', 'computing', 'mec', 'and', 'wireless', 'power', 'transfer', 'are', 'promising', 'to', 'address', 'this', 'issue', 'however', 'these', 'two', 'techniques', 'are', 'susceptible', 'to', 'propagation', 'delay', 'and', 'loss', 'motivated', 'by', 'the', 'chance', 'of', 'shortdistance', 'lineofsight', 'achieved', 'by', 'leveraging', 'unmanned', 'aerial', 'vehicle', 'uav', 'communications', 'an', 'uavenabled', 'wireless', 'powered', 'mec', 'system', 'is', 'studied', 'a', 'power', 'minimization', 'problem', 'is', 'formulated', 'subject', 'to', 'the', 'constraints', 'on', 'the', 'number', 'of', 'the', 'computation', 'bits', 'and', 'energy', 'harvesting', 'causality', 'the', 'problem', 'is', 'nonconvex', 'and', 'challenging', 'to', 'tackle', 'an', 'alternative', 'optimization', 'algorithm', 'is', 'proposed', 'based', 'on', 'sequential', 'convex', 'optimization', 'simulation', 'results', 'show', 'that', 'our', 'proposed', 'design', 'is', 'superior', 'to', 'other', 'benchmark', 'schemes', 'and', 'the', 'proposed', 'algorithm', 'is', 'efficient', 'in', 'terms', 'of', 'the', 'convergence']] | [-0.22153689625593734, 0.009726286540674839, 0.011323675552898265, 0.01593696498906517, -0.09636078432123078, -0.20353466975469645, 0.06547864643751201, 0.3986161404342146, -0.29557953599893244, -0.35804951431251175, 0.09836515433779185, -0.24404240595110765, -0.19141744486228002, 0.21046046260744333, -0.1764677240168973, 0.14766359953407912, 0.09986025682491158, 0.014100137633991474, 0.004078110438897043, -0.27256452847034374, 0.253596759795918, 0.10433477509093095, 0.3680210729387212, 0.10773830796370666, 0.07835303601670138, 0.011413843228963884, -0.007446312956538395, 0.004737471905702078, -0.05602817914675936, 0.19033475848515882, 0.3163842320415779, 0.21138698405896625, 0.3396710972202585, -0.47005765508622566, -0.2685234776833458, 0.10003333468095842, 0.1619708836673105, 0.010750586905100562, -0.07933678323277503, -0.30019243246486, 0.14384955526015833, -0.243064996119298, -0.04087580522812956, -0.036651300045113404, -0.03389289475818898, 0.037702504851762866, -0.30736781704298993, -0.005845595072750134, -0.04401881010634891, 0.017874897880387222, -0.06784792339548151, -0.09011822957000597, 0.044428052489665595, 0.1361770997722204, 0.06946730076794427, 0.0077472292293434475, 0.13495455308510187, -0.14350037653071818, -0.15546303379501328, 0.417134536608578, 0.0634049695160187, -0.2107597483310259, 0.17284878462401107, 0.038916342286764584, -0.07615729460842802, 0.14638489377123456, 0.2408506595492462, 0.12517394648257194, -0.1787213197189337, 0.05474397680533931, 0.013344376410707726, 0.0987750638989692, 0.019041329986788993, 0.0783812155887643, 0.154156697923912, 0.24050258582206904, 0.17745435447266972, 0.11760412261149258, -0.08254357141852114, -0.14897017869834148, -0.1666552111876349, -0.10572869862357141, -0.2765266735563699, 0.023869175140263765, -0.0625082208101674, -0.07606423536483992, 0.34574611430672114, 0.16545950233645684, 0.12674347768993455, 0.07156592069841022, 0.45924077404940383, 0.13005412631529442, 0.03741709920511654, 0.13039984523008266, 0.2041888064737861, 0.07431224343723598, 0.16187741930418192, -0.254130957691917, 0.06839583527151469, 0.03341319086690638] |
1,802.03907 | Superconducting transition temperatures in the electronic and magnetic
phase diagrams of Sr2VFeAsO3-delta, a superconductor | We elucidate the magnetic phases and superconducting transition temperatures
(Tc) in Sr2VFeAsO3-delta (21113V), an iron-based superconductor with a
thick-blocking layer fabricated from a perovskite-related transition metal
oxide. At low temperatures (T < 37.1 K), 21113V exhibited a superconducting
phase in the range 0.031 =< delta =< 0.145 and an antiferromagnetic (AFM) iron
sublattice in the range 0.267 =< delta =< 0.664. Mixed-valent vanadium
exhibited a dominant AFM phase in 0.031 =< delta =< 0.088, and a partial
ferrimagnetic (Ferri.) phase in the range 0.124 =< delta =< 0.664. The Ferri.
phase was the most dominant at a delta value of 0.267, showing an AFM phase of
Fe at T < 20 K. Increasing the spontaneous magnetic moments reduced the
magnetic shielding volume fraction due to the superconducting phase. This
result was attributed to the magnetic phase of vanadium, which dominates the
superconductivity of Fe in 21113V. The Tc-delta curve showed two maxima. The
smaller and larger of Tc maxima occurred at delta = 0.073 and delta = 0.145,
respectively; the latter resides on the phase boundary between AFM and the
partial Ferri. phases of vanadium. 21113V is a useful platform for verifing new
mechanisms of Tc enhancement in iron-based superconductors.
| cond-mat.supr-con | we elucidate the magnetic phases and superconducting transition temperatures tc in sr2vfeaso3delta 21113v an ironbased superconductor with a thickblocking layer fabricated from a perovskiterelated transition metal oxide at low temperatures t 371 k 21113v exhibited a superconducting phase in the range 0031 delta 0145 and an antiferromagnetic afm iron sublattice in the range 0267 delta 0664 mixedvalent vanadium exhibited a dominant afm phase in 0031 delta 0088 and a partial ferrimagnetic ferri phase in the range 0124 delta 0664 the ferri phase was the most dominant at a delta value of 0267 showing an afm phase of fe at t 20 k increasing the spontaneous magnetic moments reduced the magnetic shielding volume fraction due to the superconducting phase this result was attributed to the magnetic phase of vanadium which dominates the superconductivity of fe in 21113v the tcdelta curve showed two maxima the smaller and larger of tc maxima occurred at delta 0073 and delta 0145 respectively the latter resides on the phase boundary between afm and the partial ferri phases of vanadium 21113v is a useful platform for verifing new mechanisms of tc enhancement in ironbased superconductors | [['we', 'elucidate', 'the', 'magnetic', 'phases', 'and', 'superconducting', 'transition', 'temperatures', 'tc', 'in', 'sr2vfeaso3delta', '21113v', 'an', 'ironbased', 'superconductor', 'with', 'a', 'thickblocking', 'layer', 'fabricated', 'from', 'a', 'perovskiterelated', 'transition', 'metal', 'oxide', 'at', 'low', 'temperatures', 't', '371', 'k', '21113v', 'exhibited', 'a', 'superconducting', 'phase', 'in', 'the', 'range', '0031', 'delta', '0145', 'and', 'an', 'antiferromagnetic', 'afm', 'iron', 'sublattice', 'in', 'the', 'range', '0267', 'delta', '0664', 'mixedvalent', 'vanadium', 'exhibited', 'a', 'dominant', 'afm', 'phase', 'in', '0031', 'delta', '0088', 'and', 'a', 'partial', 'ferrimagnetic', 'ferri', 'phase', 'in', 'the', 'range', '0124', 'delta', '0664', 'the', 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1,802.03908 | Resource Allocation for Secure MISO-NOMA Cognitive Radios Relying on
SWIPT | Cognitive radio (CR) and non-orthogonal multiple access (NOMA) are two
promising technologies in the next generation wireless communication systems.
The security of a NOMA CR network (CRN) is important but lacks of study. In
this paper, a multiple-input single-output NOMA CRN relying on simultaneous
wireless information and power transfer is studied. In order to improve the
security of both the primary and secondary network, an artificial noise-aided
cooperative jamming scheme is proposed. Different from the most existing works,
a power minimization problem is formulated under a practical non-linear energy
harvesting model. A suboptimal scheme is proposed to solve this problem based
on semidefinite relaxation and successive convex approximation. Simulation
results show that the proposed cooperative jamming scheme is efficient to
achieve secure communication and NOMA outperforms the conventional orthogonal
multiple access in terms of the power consumption.
| cs.IT eess.SP math.IT | cognitive radio cr and nonorthogonal multiple access noma are two promising technologies in the next generation wireless communication systems the security of a noma cr network crn is important but lacks of study in this paper a multipleinput singleoutput noma crn relying on simultaneous wireless information and power transfer is studied in order to improve the security of both the primary and secondary network an artificial noiseaided cooperative jamming scheme is proposed different from the most existing works a power minimization problem is formulated under a practical nonlinear energy harvesting model a suboptimal scheme is proposed to solve this problem based on semidefinite relaxation and successive convex approximation simulation results show that the proposed cooperative jamming scheme is efficient to achieve secure communication and noma outperforms the conventional orthogonal multiple access in terms of the power consumption | [['cognitive', 'radio', 'cr', 'and', 'nonorthogonal', 'multiple', 'access', 'noma', 'are', 'two', 'promising', 'technologies', 'in', 'the', 'next', 'generation', 'wireless', 'communication', 'systems', 'the', 'security', 'of', 'a', 'noma', 'cr', 'network', 'crn', 'is', 'important', 'but', 'lacks', 'of', 'study', 'in', 'this', 'paper', 'a', 'multipleinput', 'singleoutput', 'noma', 'crn', 'relying', 'on', 'simultaneous', 'wireless', 'information', 'and', 'power', 'transfer', 'is', 'studied', 'in', 'order', 'to', 'improve', 'the', 'security', 'of', 'both', 'the', 'primary', 'and', 'secondary', 'network', 'an', 'artificial', 'noiseaided', 'cooperative', 'jamming', 'scheme', 'is', 'proposed', 'different', 'from', 'the', 'most', 'existing', 'works', 'a', 'power', 'minimization', 'problem', 'is', 'formulated', 'under', 'a', 'practical', 'nonlinear', 'energy', 'harvesting', 'model', 'a', 'suboptimal', 'scheme', 'is', 'proposed', 'to', 'solve', 'this', 'problem', 'based', 'on', 'semidefinite', 'relaxation', 'and', 'successive', 'convex', 'approximation', 'simulation', 'results', 'show', 'that', 'the', 'proposed', 'cooperative', 'jamming', 'scheme', 'is', 'efficient', 'to', 'achieve', 'secure', 'communication', 'and', 'noma', 'outperforms', 'the', 'conventional', 'orthogonal', 'multiple', 'access', 'in', 'terms', 'of', 'the', 'power', 'consumption']] | [-0.28923425631747196, -0.04245991970445998, -0.005139385815709829, 0.011109326000369568, -0.07029909389025539, -0.2775152340279824, 0.07170491222458736, 0.38312177799886815, -0.2930655061466157, -0.2208892912230026, 0.0550716195789159, -0.22655261214142733, -0.23324526264842083, 0.158096077476363, -0.10228494288957249, 0.10068798156703285, 0.023517301924744227, 0.0036263262791844616, -0.023896588911608296, -0.2559363019183604, 0.29648103475924176, 0.1523765724791336, 0.4474435749388959, 0.0812456545765838, 0.07230498049625733, 0.0236027886985672, -0.007112803846006248, -0.06680923960185235, -0.036727071296430894, 0.13877179821694854, 0.38413205645455006, 0.21875519830951073, 0.3338042280915445, -0.42009011073459457, -0.33545162024343533, 0.08696196375102022, 0.1880326368913979, 0.04101130139103064, -0.06831246216944589, -0.23203058387866637, 0.1499395025282228, -0.2756048936165706, 0.010092253470453469, -0.001730628134886714, -0.13845079568942098, 0.048744807681357694, -0.37740669757753176, 0.007962576841024588, 0.016498718529482488, 0.020819758321668436, -0.10167625579085664, -0.10368463492197712, 0.08645784337902918, 0.12998941457782784, 0.008609885505373406, -0.0008595796738730838, 0.08638964193052252, -0.06229934183690344, -0.19419366174826166, 0.41414125573678606, 0.055754712603503606, -0.22524224084387295, 0.19183686279256684, -0.009348489514069401, -0.14003281144894333, 0.15637181888259674, 0.2605113115528748, 0.09452971193368417, -0.19909152318978424, 0.04750219764940808, -0.022896430098254532, 0.15293394069928323, 0.05279095520190623, 0.13733406071245235, 0.11758537401689938, 0.22436526328508816, 0.1833082966805592, 0.11017793846948848, -0.09099952131339832, -0.15787527498102535, -0.130172878830952, -0.109041656027582, -0.2690995747400244, 0.03950725407441602, -0.08022586744307136, -0.043931182113605256, 0.38697454710361945, 0.12499795030177074, 0.04981175499091292, 0.09266620009532538, 0.48509146917584167, 0.09937786268423834, 0.018054778074563557, 0.13168762375797777, 0.2191041878077888, 0.09034270356429645, 0.1755886197593199, -0.2501586001369776, 0.04312162664650946, 0.0038219257563787655] |
1,802.03909 | RAPPER: Ransomware Prevention via Performance Counters | Ransomware can produce direct and controllable economic loss, which makes it
one of the most prominent threats in cyber security. As per the latest
statistics, more than half of malwares reported in Q1 of 2017 are ransomware
and there is a potent threat of a novice cybercriminals accessing
rasomware-as-a-service. The concept of public-key based data kidnapping and
subsequent extortion was introduced in 1996. Since then, variants of ransomware
emerged with different cryptosystems and larger key sizes though, the
underlying techniques remained same. Though there are works in literature which
proposes a generic framework to detect the crypto ransomwares, we present a two
step unsupervised detection tool which when suspects a process activity to be
malicious, issues an alarm for further analysis to be carried in the second
step and detects it with minimal traces. The two step detection framework-
RAPPER uses Artificial Neural Network and Fast Fourier Transformation to
develop a highly accurate, fast and reliable solution to ransomware detection
using minimal trace points.
| cs.CR | ransomware can produce direct and controllable economic loss which makes it one of the most prominent threats in cyber security as per the latest statistics more than half of malwares reported in q1 of 2017 are ransomware and there is a potent threat of a novice cybercriminals accessing rasomwareasaservice the concept of publickey based data kidnapping and subsequent extortion was introduced in 1996 since then variants of ransomware emerged with different cryptosystems and larger key sizes though the underlying techniques remained same though there are works in literature which proposes a generic framework to detect the crypto ransomwares we present a two step unsupervised detection tool which when suspects a process activity to be malicious issues an alarm for further analysis to be carried in the second step and detects it with minimal traces the two step detection framework rapper uses artificial neural network and fast fourier transformation to develop a highly accurate fast and reliable solution to ransomware detection using minimal trace points | [['ransomware', 'can', 'produce', 'direct', 'and', 'controllable', 'economic', 'loss', 'which', 'makes', 'it', 'one', 'of', 'the', 'most', 'prominent', 'threats', 'in', 'cyber', 'security', 'as', 'per', 'the', 'latest', 'statistics', 'more', 'than', 'half', 'of', 'malwares', 'reported', 'in', 'q1', 'of', '2017', 'are', 'ransomware', 'and', 'there', 'is', 'a', 'potent', 'threat', 'of', 'a', 'novice', 'cybercriminals', 'accessing', 'rasomwareasaservice', 'the', 'concept', 'of', 'publickey', 'based', 'data', 'kidnapping', 'and', 'subsequent', 'extortion', 'was', 'introduced', 'in', '1996', 'since', 'then', 'variants', 'of', 'ransomware', 'emerged', 'with', 'different', 'cryptosystems', 'and', 'larger', 'key', 'sizes', 'though', 'the', 'underlying', 'techniques', 'remained', 'same', 'though', 'there', 'are', 'works', 'in', 'literature', 'which', 'proposes', 'a', 'generic', 'framework', 'to', 'detect', 'the', 'crypto', 'ransomwares', 'we', 'present', 'a', 'two', 'step', 'unsupervised', 'detection', 'tool', 'which', 'when', 'suspects', 'a', 'process', 'activity', 'to', 'be', 'malicious', 'issues', 'an', 'alarm', 'for', 'further', 'analysis', 'to', 'be', 'carried', 'in', 'the', 'second', 'step', 'and', 'detects', 'it', 'with', 'minimal', 'traces', 'the', 'two', 'step', 'detection', 'framework', 'rapper', 'uses', 'artificial', 'neural', 'network', 'and', 'fast', 'fourier', 'transformation', 'to', 'develop', 'a', 'highly', 'accurate', 'fast', 'and', 'reliable', 'solution', 'to', 'ransomware', 'detection', 'using', 'minimal', 'trace', 'points']] | [-0.10978243026856944, 0.03323580185906394, -0.08463201271079016, 0.0801794675059341, -0.10390926566178678, -0.23117763974296396, 0.05615495924575953, 0.3478338753004209, -0.2373863698892819, -0.3226379870844539, 0.1399511012270523, -0.29193845197150947, -0.18122468498331729, 0.22493787728308234, -0.11501924929580128, 0.08973271348586423, 0.03669383667875081, 0.014165003426774092, -0.0032096503302454947, -0.2845451662666164, 0.2715259472781327, 0.0911968307918869, 0.2987771709857043, 0.032017332001123576, 0.0691972639040614, -0.00268456745397998, -0.07749418969178805, -0.044473330955952406, -0.05405503215588396, 0.1461176590208197, 0.3145975081104552, 0.21154618609289172, 0.34042950606672095, -0.42184782166732476, -0.19909091997469658, 0.1155446514254436, 0.13347313415724785, 0.13353694320321666, -0.08081182015057493, -0.29932397935772315, 0.12940535990055652, -0.2148834016203182, -0.09626166053494671, -0.09505871511209989, 0.034473893265385414, -0.03773562822425447, -0.24418589267588686, 0.023390089522240486, 0.035453389942995274, 0.07689390895247925, 0.0025733211063197815, -0.08127131896553692, 0.0023046219212119468, 0.15644469550388748, 0.05650919760646502, 0.012478957243365585, 0.13874078212538735, -0.11021184885757976, -0.15327180758758913, 0.34778834821190685, -0.024135760557419415, -0.12896603792178213, 0.2071800806457759, -0.03205961998028215, -0.1899015147355385, 0.12569973873760318, 0.1792665086919442, 0.13085811012424528, -0.1717194867019316, -0.03314471636949747, 0.009054700488923118, 0.1980173238844145, 0.04889659072396171, 0.014440996592020383, 0.17231112470071822, 0.20032851262221812, 0.09466027222224511, 0.11808380785078043, -0.11559169428946917, -0.0739773727953434, -0.21226444301064476, -0.16944094741838853, -0.1464299804007169, 0.013099048799631419, -0.02727008566716904, -0.176464479311835, 0.4083043899445329, 0.18865887981792184, 0.17690810499407233, 0.013365156499639852, 0.350508900762361, 0.03928435833004187, 0.12286500034970231, 0.11669137398712337, 0.20733543862370424, 0.029765919889177893, 0.10711920889443718, -0.1077580480452525, 0.14260974937060383, 0.03771134891139809] |
1,802.0391 | Discrete spacetime, quantum walks and relativistic wave equations | It has been observed that quantum walks on regular lattices can give rise to
wave equations for relativistic particles in the continuum limit. In this paper
we define the 3D walk as a product of three coined one-dimensional walks. The
factor corresponding to each one-dimensional walk involves two projection
operators that act on an internal coin space, each projector is associated with
either the "forward" or "backward" direction in that physical dimension. We
show that the simple requirement that there is no preferred axis or direction
along an axis---that is, that the walk be symmetric under parity
transformations and rotations that swap the axes of the cubic lattice---leads
to the requirement that the continuum limit of the walk is fully Lorentz
invariant. We show further that, in the case of a massive particle, this simple
symmetry requirement necessitates that inclusion of antimatter---the use of a
four-dimensional internal space---and that the "coin flip" operation is
generated by the parity transformation on the internal coin space, while the
differences of the projection operators associated to each dimension must all
anticommute. Finally, we discuss the leading correction to the continuum limit,
and the possibility of distinguishing through experiment between the discrete
random walk and the continuum-based Dirac equation as a description of fermion
dynamics.
| quant-ph | it has been observed that quantum walks on regular lattices can give rise to wave equations for relativistic particles in the continuum limit in this paper we define the 3d walk as a product of three coined onedimensional walks the factor corresponding to each onedimensional walk involves two projection operators that act on an internal coin space each projector is associated with either the forward or backward direction in that physical dimension we show that the simple requirement that there is no preferred axis or direction along an axisthat is that the walk be symmetric under parity transformations and rotations that swap the axes of the cubic latticeleads to the requirement that the continuum limit of the walk is fully lorentz invariant we show further that in the case of a massive particle this simple symmetry requirement necessitates that inclusion of antimatterthe use of a fourdimensional internal spaceand that the coin flip operation is generated by the parity transformation on the internal coin space while the differences of the projection operators associated to each dimension must all anticommute finally we discuss the leading correction to the continuum limit and the possibility of distinguishing through experiment between the discrete random walk and the continuumbased dirac equation as a description of fermion dynamics | [['it', 'has', 'been', 'observed', 'that', 'quantum', 'walks', 'on', 'regular', 'lattices', 'can', 'give', 'rise', 'to', 'wave', 'equations', 'for', 'relativistic', 'particles', 'in', 'the', 'continuum', 'limit', 'in', 'this', 'paper', 'we', 'define', 'the', '3d', 'walk', 'as', 'a', 'product', 'of', 'three', 'coined', 'onedimensional', 'walks', 'the', 'factor', 'corresponding', 'to', 'each', 'onedimensional', 'walk', 'involves', 'two', 'projection', 'operators', 'that', 'act', 'on', 'an', 'internal', 'coin', 'space', 'each', 'projector', 'is', 'associated', 'with', 'either', 'the', 'forward', 'or', 'backward', 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1,802.03911 | Detection of discrete spacetime by matter interferometry | If the structure of spacetime is discrete, then Lorentz symmetry should only
be an approximation, valid at long length scales. At finite lattice spacings
there will be small corrections to the Dirac evolution that could in principle
be experimentally detected. In particular, the lattice structure should be
reflected in a modification of the free-particle dispersion relation. We show
that these can produce a surprisingly large phase shift between the two arms of
an asymmetrical interferometer. This method could be employed to test any model
that predicts a direction-dependent dispersion relation. Here, we calculate the
size of this phase shift for a particular model, the 3D quantum walk on the
body-centered cubic lattice, which has been shown to give rise to the Dirac
equation in the continuum limit. Though the details of this model will affect
the size of the shift, its magnitude is set largely by dimensional analysis, so
there is reason to believe that other models would yield similar results. We
find that, with current technology, a modest-sized neutron interferometer could
put strong bounds on the size of the lattice spacing. This discreteness could
possibly be detected even for lattice spacings at the Planck scale by a
suitably scaled-up experiment.
| quant-ph | if the structure of spacetime is discrete then lorentz symmetry should only be an approximation valid at long length scales at finite lattice spacings there will be small corrections to the dirac evolution that could in principle be experimentally detected in particular the lattice structure should be reflected in a modification of the freeparticle dispersion relation we show that these can produce a surprisingly large phase shift between the two arms of an asymmetrical interferometer this method could be employed to test any model that predicts a directiondependent dispersion relation here we calculate the size of this phase shift for a particular model the 3d quantum walk on the bodycentered cubic lattice which has been shown to give rise to the dirac equation in the continuum limit though the details of this model will affect the size of the shift its magnitude is set largely by dimensional analysis so there is reason to believe that other models would yield similar results we find that with current technology a modestsized neutron interferometer could put strong bounds on the size of the lattice spacing this discreteness could possibly be detected even for lattice spacings at the planck scale by a suitably scaledup experiment | [['if', 'the', 'structure', 'of', 'spacetime', 'is', 'discrete', 'then', 'lorentz', 'symmetry', 'should', 'only', 'be', 'an', 'approximation', 'valid', 'at', 'long', 'length', 'scales', 'at', 'finite', 'lattice', 'spacings', 'there', 'will', 'be', 'small', 'corrections', 'to', 'the', 'dirac', 'evolution', 'that', 'could', 'in', 'principle', 'be', 'experimentally', 'detected', 'in', 'particular', 'the', 'lattice', 'structure', 'should', 'be', 'reflected', 'in', 'a', 'modification', 'of', 'the', 'freeparticle', 'dispersion', 'relation', 'we', 'show', 'that', 'these', 'can', 'produce', 'a', 'surprisingly', 'large', 'phase', 'shift', 'between', 'the', 'two', 'arms', 'of', 'an', 'asymmetrical', 'interferometer', 'this', 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1,802.03912 | Hochschild cohomology and orbifold Jacobian algebras associated to
invertible polynomials | Let $f$ be an invertible polynomial and $G$ a group of diagonal symmetries of
$f$. This note shows that the orbifold Jacobian algebra $\mathrm{Jac}(f,G)$ of
$(f,G)$ defined by the authors and Elisabeth Werner in arXiv:1608.08962 is
isomorphic as a $\mathbb{ZZ}/2\mathbb{ZZ}$-graded algebra to the Hochschild
cohomology $\mathsf{HH}^*(\mathrm{MF}_G(f))$ of the dg-category
$\mathrm{MF}_G(f)$ of $G$-equivariant matrix factorizations of $f$ by
calculating the product formula of $\mathsf{HH}^*(\mathrm{MF}_G(f))$ given by
Shklyarov in arXiv:1708.06030. We also discuss the relation of our previous
results to the categorical equivalence.
| math.AG | let f be an invertible polynomial and g a group of diagonal symmetries of f this note shows that the orbifold jacobian algebra mathrmjacfg of fg defined by the authors and elisabeth werner in arxiv160808962 is isomorphic as a mathbbzz2mathbbzzgraded algebra to the hochschild cohomology mathsfhhmathrmmf_gf of the dgcategory mathrmmf_gf of gequivariant matrix factorizations of f by calculating the product formula of mathsfhhmathrmmf_gf given by shklyarov in arxiv170806030 we also discuss the relation of our previous results to the categorical equivalence | [['let', 'f', 'be', 'an', 'invertible', 'polynomial', 'and', 'g', 'a', 'group', 'of', 'diagonal', 'symmetries', 'of', 'f', 'this', 'note', 'shows', 'that', 'the', 'orbifold', 'jacobian', 'algebra', 'mathrmjacfg', 'of', 'fg', 'defined', 'by', 'the', 'authors', 'and', 'elisabeth', 'werner', 'in', 'arxiv160808962', 'is', 'isomorphic', 'as', 'a', 'mathbbzz2mathbbzzgraded', 'algebra', 'to', 'the', 'hochschild', 'cohomology', 'mathsfhhmathrmmf_gf', 'of', 'the', 'dgcategory', 'mathrmmf_gf', 'of', 'gequivariant', 'matrix', 'factorizations', 'of', 'f', 'by', 'calculating', 'the', 'product', 'formula', 'of', 'mathsfhhmathrmmf_gf', 'given', 'by', 'shklyarov', 'in', 'arxiv170806030', 'we', 'also', 'discuss', 'the', 'relation', 'of', 'our', 'previous', 'results', 'to', 'the', 'categorical', 'equivalence']] | [-0.1923891475071778, 0.04411159036858039, -0.09547886629966465, 0.003654849901952705, -0.06533253100398626, -0.11844150048705775, -0.014441083251448298, 0.30712456724329573, -0.3633716284372919, -0.21657056486661974, 0.06901709690400576, -0.21543405275706146, -0.16339121117080385, 0.1437810308432106, -0.15880154669788238, -0.027912953994362742, 0.027785067544067028, 0.09209411365382776, -0.16178435575204422, -0.2819173670550053, 0.43745428973154443, -0.008080210687743651, 0.1896215977046538, 0.05213194999944519, 0.09019942502994593, -0.010467704932985676, -0.04649922455585486, -0.042230116370184395, -0.13803707901752205, 0.11792174656403477, 0.3271323296971418, 0.09840155504266354, 0.18332456379052806, -0.3540522257570882, -0.0724654237714571, 0.20065856505990834, 0.11983787575484933, -0.04777816788142396, 0.009427306456239643, -0.30081585079834267, 0.10296823718975223, -0.247249614610966, -0.10868016958261865, -0.055045916079040115, 0.13455583213048206, -0.0015599883564219282, -0.26233923022408745, 0.007655592682076039, 0.11201489666426503, 0.12994864481073376, -0.04418962899708768, -0.09964412723894457, -0.11754688937405779, 0.05825930309632944, -0.026554952825520287, 0.08007066323723946, 0.09305425969304869, -0.06364732233467638, -0.12979724260701522, 0.3699958518248152, -0.1050364314861646, -0.20709383483296512, 0.04250462041110606, -0.17486019760395424, -0.19307184674953287, 0.06732667354564811, -0.003266852078455928, 0.156320051308973, -0.007000465273253016, 0.23547614288887614, -0.15872886970739913, 0.0564395572700714, 0.060435085065389124, -0.04518971290206143, 0.09574228258035775, 0.024901703103898547, 0.03306072542594897, 0.16515272232936695, 0.07651224969005263, 0.027419587200809573, -0.3634053245186806, -0.22114010394953593, -0.17939141870010644, 0.16974693531724247, -0.12643636778159054, -0.15586195356288068, 0.4410734083503485, 0.10980200163415961, 0.18441425102788048, 0.127224419052033, 0.2037628060364089, 0.11293406603618751, 0.07130501246255999, 0.01668445795547922, 0.10900865913670812, 0.30471352513917294, -0.05233146381136533, -0.1547723078433223, 0.001980443308885033, 0.268505729860752] |
1,802.03913 | Assessing the Utility of Weather Data for Photovoltaic Power Prediction | Photovoltaic systems have been widely deployed in recent times to meet the
increased electricity demand as an environmental-friendly energy source. The
major challenge for integrating photovoltaic systems in power systems is the
unpredictability of the solar power generated. In this paper, we analyze the
impact of having access to weather information for solar power generation
prediction and find weather information that can help best predict photovoltaic
power.
| stat.ML cs.LG | photovoltaic systems have been widely deployed in recent times to meet the increased electricity demand as an environmentalfriendly energy source the major challenge for integrating photovoltaic systems in power systems is the unpredictability of the solar power generated in this paper we analyze the impact of having access to weather information for solar power generation prediction and find weather information that can help best predict photovoltaic power | [['photovoltaic', 'systems', 'have', 'been', 'widely', 'deployed', 'in', 'recent', 'times', 'to', 'meet', 'the', 'increased', 'electricity', 'demand', 'as', 'an', 'environmentalfriendly', 'energy', 'source', 'the', 'major', 'challenge', 'for', 'integrating', 'photovoltaic', 'systems', 'in', 'power', 'systems', 'is', 'the', 'unpredictability', 'of', 'the', 'solar', 'power', 'generated', 'in', 'this', 'paper', 'we', 'analyze', 'the', 'impact', 'of', 'having', 'access', 'to', 'weather', 'information', 'for', 'solar', 'power', 'generation', 'prediction', 'and', 'find', 'weather', 'information', 'that', 'can', 'help', 'best', 'predict', 'photovoltaic', 'power']] | [-0.13786609778462938, 0.08348725660846068, 0.019523468154313214, 0.04518261657689105, -0.022080230865288864, -0.09625227622349154, 0.012339656278279355, 0.3500296209346164, -0.2718812563654148, -0.3689553207915389, 0.12352243755330247, -0.31695154077853216, -0.1283793165419025, 0.30099063152165123, -0.17303762353505148, 0.10869244008437928, 0.027570019929429913, 0.0191862864673815, 0.025695311219544346, -0.23039585216478867, 0.22284899357790974, 0.17527118505853595, 0.3556037385186011, 0.07576544707020123, 0.0771721836151273, -0.10013039924105573, -0.02822275037496266, -0.024877173769654648, -0.03577797015774801, 0.15320863769474355, 0.3290670713071119, 0.14822883432321815, 0.3038643757040104, -0.48666845151985233, -0.320091646630317, 0.14485507655296137, 0.09366591139978757, -0.006990402972212795, -0.10534301436873096, -0.15663303158274203, 0.10323994560167193, -0.27168281263474264, -0.07907292097242492, -0.05250480073983922, 0.008032062994034, 0.07166718977334147, -0.2982547477621472, -0.005255888004533269, -0.02076951331092101, 0.076500226828185, -0.07438983306091165, -0.08503515321486206, -0.052555266146858536, 0.21359350495604854, 0.058742071258496835, -0.031164174303271328, 0.13408404307450505, -0.1466543657782798, -0.13277288402120271, 0.441033831035549, -0.006718248361721635, -0.10021867146927187, 0.121045814431978, -0.13088393945839594, -0.14494439705528997, 0.07300081068762776, 0.3019270080028836, 0.00855610053986311, -0.22154465978824053, 0.019278665446274153, 0.025924477740332033, 0.19911529919640583, 0.041774079482296875, 0.10154563626697795, 0.29334316706059105, 0.24328228503917204, 0.09326237187876056, 0.08204257128421556, -0.10845154680481012, -0.09471758239140565, -0.1367902529115478, -0.1679074739486995, -0.15415132878997334, 0.08674545649608428, -0.03748271994581129, -0.1266128026005445, 0.44512858010376943, 0.2582075111779638, 0.10735778016687343, -0.003265797889043549, 0.37704975575660216, 0.1719807774008421, 0.0745298692067577, 0.08939367151028957, 0.26073625869872613, 0.003565337364279637, 0.22178771259319602, -0.2004246502677261, 0.10502010919894515, -0.01831655472404126] |
1,802.03914 | BagMinHash - Minwise Hashing Algorithm for Weighted Sets | Minwise hashing has become a standard tool to calculate signatures which
allow direct estimation of Jaccard similarities. While very efficient
algorithms already exist for the unweighted case, the calculation of signatures
for weighted sets is still a time consuming task. BagMinHash is a new algorithm
that can be orders of magnitude faster than current state of the art without
any particular restrictions or assumptions on weights or data dimensionality.
Applied to the special case of unweighted sets, it represents the first
efficient algorithm producing independent signature components. A series of
tests finally verifies the new algorithm and also reveals limitations of other
approaches published in the recent past.
| cs.DS | minwise hashing has become a standard tool to calculate signatures which allow direct estimation of jaccard similarities while very efficient algorithms already exist for the unweighted case the calculation of signatures for weighted sets is still a time consuming task bagminhash is a new algorithm that can be orders of magnitude faster than current state of the art without any particular restrictions or assumptions on weights or data dimensionality applied to the special case of unweighted sets it represents the first efficient algorithm producing independent signature components a series of tests finally verifies the new algorithm and also reveals limitations of other approaches published in the recent past | [['minwise', 'hashing', 'has', 'become', 'a', 'standard', 'tool', 'to', 'calculate', 'signatures', 'which', 'allow', 'direct', 'estimation', 'of', 'jaccard', 'similarities', 'while', 'very', 'efficient', 'algorithms', 'already', 'exist', 'for', 'the', 'unweighted', 'case', 'the', 'calculation', 'of', 'signatures', 'for', 'weighted', 'sets', 'is', 'still', 'a', 'time', 'consuming', 'task', 'bagminhash', 'is', 'a', 'new', 'algorithm', 'that', 'can', 'be', 'orders', 'of', 'magnitude', 'faster', 'than', 'current', 'state', 'of', 'the', 'art', 'without', 'any', 'particular', 'restrictions', 'or', 'assumptions', 'on', 'weights', 'or', 'data', 'dimensionality', 'applied', 'to', 'the', 'special', 'case', 'of', 'unweighted', 'sets', 'it', 'represents', 'the', 'first', 'efficient', 'algorithm', 'producing', 'independent', 'signature', 'components', 'a', 'series', 'of', 'tests', 'finally', 'verifies', 'the', 'new', 'algorithm', 'and', 'also', 'reveals', 'limitations', 'of', 'other', 'approaches', 'published', 'in', 'the', 'recent', 'past']] | [-0.08688619226286902, 0.028172764744365884, -0.0992763984043186, 0.08956729208236718, -0.11579934696484113, -0.16670333443505464, 0.0639351712209042, 0.3964996235880339, -0.22658143813121262, -0.32410817944105264, 0.12822925938602328, -0.27028509606955414, -0.11110619162905648, 0.2758799602677959, -0.04399043797933122, 0.0747089622037433, 0.14218577507134772, 0.04517877208521572, -0.10376144993275613, -0.3076036827503834, 0.2751787663655025, 0.04011009687505593, 0.3007325344571026, 0.02785180671419078, 0.06896448814365622, -0.005879594757282567, -0.07772519195748266, 0.032739108776517006, -0.08136725026025343, 0.1142640516284862, 0.23394240522496054, 0.16650592240357498, 0.2833333960516709, -0.4052374755145512, -0.20526490736726635, 0.18558852286412594, 0.14175724944482757, 0.13953684402790756, -0.02814038459181124, -0.23918069768087746, 0.08556292324205077, -0.14833610447963577, -0.07484931160182318, -0.12874666847523616, 0.046188216343104285, -0.028303466411416255, -0.3041050455524264, 0.08053643113871488, 0.06114909185366444, 0.016468498290072534, -0.014824756162206286, -0.1588737718982918, 0.07019586729486342, 0.093815528361179, 0.04050626044779598, 0.05011074987858116, 0.07789763222909837, -0.12858888568687382, -0.19301587034225742, 0.3696531423619974, -0.0616977581201898, -0.1816145500068109, 0.20048016128696014, -0.07463413802689273, -0.1720937092866853, 0.11637571114376535, 0.14810769751158795, 0.14963249942270396, -0.0985122314767442, 0.06449551954724017, -0.029416650172462252, 0.170249146365813, 0.05941922799901706, 0.047623345214943186, 0.12584981508553028, 0.19039578491900222, 0.11751577485439522, 0.12675787513438072, -0.07331589433347162, -0.06883251028588024, -0.22418498555981667, -0.16030571574919691, -0.20745364223197799, -0.018722461451663175, -0.11962233059759421, -0.18252654627365905, 0.40037046452966807, 0.17645928928210775, 0.19144321624385455, 0.04884934690498059, 0.3705529163843978, 0.07823801867524598, 0.08276823463318782, 0.11061373316393469, 0.20756546828556352, 0.07391715081948265, 0.06668710245676861, -0.1379113673862444, 0.141676177865843, 0.06865328439281793] |
1,802.03915 | Topological pseudodefects of a supersymmetric $SO(10)$ model and
cosmology | Obtaining realistic supersymmetry preserving vacua in the minimal
renormalizable supersymmetric $Spin(10)$ GUT model introduces considerations of
the non-trivial topology of the vacuum manifold. The $D$-parity of low energy
unification schemes gets lifted to a one-parameter subgroup $U(1)_D$ of
$Spin(10)$. Yet, the choice of the fields signaling spontaneous symmetry
breaking leads to disconnected subsets in the vacuum manifold related by the
$D$-parity. The resulting domain walls, existing due to topological reasons but
not stable, are identified as topological pseudodefects. We obtain a class of
one-parameter paths connecting $D$-parity flipped vacua and compute the energy
barrier height along the same. We consider the various patterns of symmetry
breaking which can result in either intermediate scale gauge groups or a
supersymmetric extension of the Standard Model. If the onset of inflation is
subsequent to GUT breaking, as could happen also if inflation is naturally
explained by the same GUT, the existence of such pseudodefects can leave
signatures in the CMB. Specifically, this could have an impact on the scale
invariance of the CMB fluctuations and LSS data at the largest scale.
| hep-ph hep-th | obtaining realistic supersymmetry preserving vacua in the minimal renormalizable supersymmetric spin10 gut model introduces considerations of the nontrivial topology of the vacuum manifold the dparity of low energy unification schemes gets lifted to a oneparameter subgroup u1_d of spin10 yet the choice of the fields signaling spontaneous symmetry breaking leads to disconnected subsets in the vacuum manifold related by the dparity the resulting domain walls existing due to topological reasons but not stable are identified as topological pseudodefects we obtain a class of oneparameter paths connecting dparity flipped vacua and compute the energy barrier height along the same we consider the various patterns of symmetry breaking which can result in either intermediate scale gauge groups or a supersymmetric extension of the standard model if the onset of inflation is subsequent to gut breaking as could happen also if inflation is naturally explained by the same gut the existence of such pseudodefects can leave signatures in the cmb specifically this could have an impact on the scale invariance of the cmb fluctuations and lss data at the largest scale | [['obtaining', 'realistic', 'supersymmetry', 'preserving', 'vacua', 'in', 'the', 'minimal', 'renormalizable', 'supersymmetric', 'spin10', 'gut', 'model', 'introduces', 'considerations', 'of', 'the', 'nontrivial', 'topology', 'of', 'the', 'vacuum', 'manifold', 'the', 'dparity', 'of', 'low', 'energy', 'unification', 'schemes', 'gets', 'lifted', 'to', 'a', 'oneparameter', 'subgroup', 'u1_d', 'of', 'spin10', 'yet', 'the', 'choice', 'of', 'the', 'fields', 'signaling', 'spontaneous', 'symmetry', 'breaking', 'leads', 'to', 'disconnected', 'subsets', 'in', 'the', 'vacuum', 'manifold', 'related', 'by', 'the', 'dparity', 'the', 'resulting', 'domain', 'walls', 'existing', 'due', 'to', 'topological', 'reasons', 'but', 'not', 'stable', 'are', 'identified', 'as', 'topological', 'pseudodefects', 'we', 'obtain', 'a', 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1,802.03916 | Detecting and Correcting for Label Shift with Black Box Predictors | Faced with distribution shift between training and test set, we wish to
detect and quantify the shift, and to correct our classifiers without test set
labels. Motivated by medical diagnosis, where diseases (targets) cause symptoms
(observations), we focus on label shift, where the label marginal $p(y)$
changes but the conditional $p(x| y)$ does not. We propose Black Box Shift
Estimation (BBSE) to estimate the test distribution $p(y)$. BBSE exploits
arbitrary black box predictors to reduce dimensionality prior to shift
correction. While better predictors give tighter estimates, BBSE works even
when predictors are biased, inaccurate, or uncalibrated, so long as their
confusion matrices are invertible. We prove BBSE's consistency, bound its
error, and introduce a statistical test that uses BBSE to detect shift. We also
leverage BBSE to correct classifiers. Experiments demonstrate accurate
estimates and improved prediction, even on high-dimensional datasets of natural
images.
| cs.LG cs.AI cs.NE stat.ML | faced with distribution shift between training and test set we wish to detect and quantify the shift and to correct our classifiers without test set labels motivated by medical diagnosis where diseases targets cause symptoms observations we focus on label shift where the label marginal py changes but the conditional px y does not we propose black box shift estimation bbse to estimate the test distribution py bbse exploits arbitrary black box predictors to reduce dimensionality prior to shift correction while better predictors give tighter estimates bbse works even when predictors are biased inaccurate or uncalibrated so long as their confusion matrices are invertible we prove bbses consistency bound its error and introduce a statistical test that uses bbse to detect shift we also leverage bbse to correct classifiers experiments demonstrate accurate estimates and improved prediction even on highdimensional datasets of natural images | [['faced', 'with', 'distribution', 'shift', 'between', 'training', 'and', 'test', 'set', 'we', 'wish', 'to', 'detect', 'and', 'quantify', 'the', 'shift', 'and', 'to', 'correct', 'our', 'classifiers', 'without', 'test', 'set', 'labels', 'motivated', 'by', 'medical', 'diagnosis', 'where', 'diseases', 'targets', 'cause', 'symptoms', 'observations', 'we', 'focus', 'on', 'label', 'shift', 'where', 'the', 'label', 'marginal', 'py', 'changes', 'but', 'the', 'conditional', 'px', 'y', 'does', 'not', 'we', 'propose', 'black', 'box', 'shift', 'estimation', 'bbse', 'to', 'estimate', 'the', 'test', 'distribution', 'py', 'bbse', 'exploits', 'arbitrary', 'black', 'box', 'predictors', 'to', 'reduce', 'dimensionality', 'prior', 'to', 'shift', 'correction', 'while', 'better', 'predictors', 'give', 'tighter', 'estimates', 'bbse', 'works', 'even', 'when', 'predictors', 'are', 'biased', 'inaccurate', 'or', 'uncalibrated', 'so', 'long', 'as', 'their', 'confusion', 'matrices', 'are', 'invertible', 'we', 'prove', 'bbses', 'consistency', 'bound', 'its', 'error', 'and', 'introduce', 'a', 'statistical', 'test', 'that', 'uses', 'bbse', 'to', 'detect', 'shift', 'we', 'also', 'leverage', 'bbse', 'to', 'correct', 'classifiers', 'experiments', 'demonstrate', 'accurate', 'estimates', 'and', 'improved', 'prediction', 'even', 'on', 'highdimensional', 'datasets', 'of', 'natural', 'images']] | [-0.030483381431238254, 0.022823948754181325, -0.028918270541446835, 0.17019449145300314, -0.1519200777287372, -0.22919876285185906, 0.11300506727264958, 0.4210726611945831, -0.21755693944721993, -0.336958649765018, 0.0838699359856677, -0.314659268323096, -0.12163962979684584, 0.14188915477003114, -0.18617518129758537, 0.08668139852332542, 0.12461542805105867, 0.02568720940264388, -0.09502731838663646, -0.2834749658080474, 0.2774086000896256, 0.05597108568746964, 0.3002187308911103, -0.024639610683178187, 0.10260387242797919, 0.015358826473460232, -0.06280582538038544, 0.003382299574170734, -0.09588072427023164, 0.11586200493404479, 0.2926694519266906, 0.18150581630819124, 0.303100687412309, -0.3932318786539557, -0.20099562078937364, 0.15054748266358192, 0.12203257612664331, 0.13109758968596613, 0.004456197600287031, -0.3360951965073006, 0.06544604819779559, -0.1361046137130806, -0.03723446021641893, -0.14989141777197135, 0.018900821196131776, -0.01312351740796683, -0.36235884441600613, 0.10118513440440284, 0.07803530091441131, 0.049162281345917096, -0.033666727615972905, -0.1361108945984341, 0.014590101144407732, 0.15150503920900313, 0.07619064797247223, 0.05371552940890927, 0.15382936021352422, -0.11703778776458242, -0.0905382421497308, 0.29833390969704365, -0.049389648031522064, -0.2576665391548681, 0.15822028171133354, -0.1474662966350697, -0.1265372535554876, 0.06843377729799126, 0.22808527504421874, 0.08213595945296258, -0.10304155525586217, 0.007958632531087242, 0.007268541420377056, 0.23203298361630928, 0.08482160134008154, 0.008118762064915361, 0.16465164816767816, 0.11316690544604363, 0.07667410348437097, 0.12583603842747987, -0.16633559202998352, -0.00826129692436104, -0.24126081815986355, -0.0911629314096374, -0.1994547083363934, 0.01741748592419907, -0.10991670399472613, -0.2114970073182608, 0.30845666635276514, 0.2724891447062066, 0.23816591151662067, 0.11546699621085026, 0.29663608258616336, 0.05785137421347391, 0.06334838083646797, 0.06686935810090214, 0.18408313523453307, 0.07352176455708719, 0.00599762215443008, -0.17891325408996175, 0.1615901276515141, 0.018368551037011956] |
1,802.03917 | The Axially Symmetric Displacement Problem in the Transversely Isotropic
Elasticity | In the assumption of hexagonal symmetry of an elastic material the axially
symmetric displacement problem in a bounded axially symmetric solid with a
Lyapunov boundary is reduced to a system of regular (Fredholm) integral
equations.
| math.AP | in the assumption of hexagonal symmetry of an elastic material the axially symmetric displacement problem in a bounded axially symmetric solid with a lyapunov boundary is reduced to a system of regular fredholm integral equations | [['in', 'the', 'assumption', 'of', 'hexagonal', 'symmetry', 'of', 'an', 'elastic', 'material', 'the', 'axially', 'symmetric', 'displacement', 'problem', 'in', 'a', 'bounded', 'axially', 'symmetric', 'solid', 'with', 'a', 'lyapunov', 'boundary', 'is', 'reduced', 'to', 'a', 'system', 'of', 'regular', 'fredholm', 'integral', 'equations']] | [-0.22010607825858253, 0.11331727870981143, -0.08542583936027118, -0.020359861168877353, -0.07939909494348935, -0.13649665491123283, -0.08863007016479969, 0.34339597682867734, -0.3374283905540194, -0.15733161685722216, 0.15233461374549995, -0.25956053164388454, -0.12833586908610803, 0.0879017757784043, -0.043531390918152675, 0.13724660591355392, -0.00559255877243621, 0.06502564764315528, -0.18691913911274502, -0.1432883839255997, 0.356996565418584, -0.038560597039759156, 0.25409013908356426, 0.004640353471040725, 0.11871307061186859, 0.011330497032031418, 0.0778617898268359, 0.02585431012724127, -0.12500632907530027, 0.13128279642800667, 0.22310200060850807, -0.05414483972958156, 0.21227883526257108, -0.4709343208266156, -0.1637146581230419, 0.11651208504502263, 0.11354196932432907, 0.10323434696931924, -0.07864390366843768, -0.3142378620271172, 0.06652484922004598, -0.18928359347794738, -0.2980360844837768, 0.03313744178574, 0.07509807396147933, 0.024761317084942544, -0.31446680490459716, 0.12150250995265587, 0.09523217111293758, 0.07411526134570262, -0.22781553968255008, -0.04272386621617313, -0.09207367328261691, -0.010090598064873899, 0.025641291003142084, -0.020595702449125904, 0.11416938491498253, -0.07941619705940996, -0.021759798909936633, 0.4038239963851603, -0.027011395318965827, -0.3383097796567849, 0.05081169267318079, -0.14490836329225984, -0.03320750075259379, 0.23540334781365735, 0.17525498281632151, 0.17026502080261707, -0.15474779696835736, 0.16715429574251175, -0.13668070551274078, 0.15537297211454382, 0.0969236699332084, -0.07412291499120849, 0.16350583657622336, 0.1557602009070771, 0.14784136611436094, 0.22002238099064145, 0.002708286000415683, -0.1407647306033011, -0.33003429946090496, -0.15789140375064953, -0.21207414003355163, 0.10939682895051582, -0.1373517725084509, -0.3188653443008661, 0.33765740266868044, -0.04282153977879456, 0.07894320961620127, 0.029928912754569735, 0.22404053722109113, 0.10726745212450624, 0.021579821301358087, 0.06048182120014514, 0.23532406756920474, 0.21820653767458031, 0.11491040342620441, -0.2315538125511791, -0.007760155187653644, 0.12712605144687197] |
1,802.03918 | Improved bounds for rainbow numbers of matchings in plane triangulations | Given two graphs $G$ and $H$, the {\it rainbow number} $rb(G,H)$ for $H$ with
respect to $G$ is defined as the minimum number $k$ such that any
$k$-edge-coloring of $G$ contains a rainbow $H$, i.e., a copy of $H$, all of
whose edges have different colors. Denote by $kK_2$ a matching of size $k$ and
$\mathcal {T}_n$ the class of all plane triangulations of order $n$,
respectively. In [S. Jendrol$'$, I. Schiermeyer and J. Tu, Rainbow numbers for
matchings in plane triangulations, Discrete Math. 331(2014), 158--164], the
authors determined the exact values of $rb(\mathcal {T}_n, kK_2)$ for $2\leq k
\le 4$ and proved that $2n+2k-9 \le rb(\mathcal {T}_n, kK_2) \le
2n+2k-7+2\binom{2k-2}{3}$ for $k \ge 5$. In this paper, we improve the upper
bounds and prove that $rb(\mathcal {T}_n, kK_2)\le 2n+6k-16$ for $n \ge 2k$ and
$k\ge 5$. Especially, we show that $rb(\mathcal {T}_n, 5K_2)=2n+1$ for $n \ge
11$.
| math.CO | given two graphs g and h the it rainbow number rbgh for h with respect to g is defined as the minimum number k such that any kedgecoloring of g contains a rainbow h ie a copy of h all of whose edges have different colors denote by kk_2 a matching of size k and mathcal t_n the class of all plane triangulations of order n respectively in s jendrol i schiermeyer and j tu rainbow numbers for matchings in plane triangulations discrete math 3312014 158164 the authors determined the exact values of rbmathcal t_n kk_2 for 2leq k le 4 and proved that 2n2k9 le rbmathcal t_n kk_2 le 2n2k72binom2k23 for k ge 5 in this paper we improve the upper bounds and prove that rbmathcal t_n kk_2le 2n6k16 for n ge 2k and kge 5 especially we show that rbmathcal t_n 5k_22n1 for n ge 11 | [['given', 'two', 'graphs', 'g', 'and', 'h', 'the', 'it', 'rainbow', 'number', 'rbgh', 'for', 'h', 'with', 'respect', 'to', 'g', 'is', 'defined', 'as', 'the', 'minimum', 'number', 'k', 'such', 'that', 'any', 'kedgecoloring', 'of', 'g', 'contains', 'a', 'rainbow', 'h', 'ie', 'a', 'copy', 'of', 'h', 'all', 'of', 'whose', 'edges', 'have', 'different', 'colors', 'denote', 'by', 'kk_2', 'a', 'matching', 'of', 'size', 'k', 'and', 'mathcal', 't_n', 'the', 'class', 'of', 'all', 'plane', 'triangulations', 'of', 'order', 'n', 'respectively', 'in', 's', 'jendrol', 'i', 'schiermeyer', 'and', 'j', 'tu', 'rainbow', 'numbers', 'for', 'matchings', 'in', 'plane', 'triangulations', 'discrete', 'math', '3312014', '158164', 'the', 'authors', 'determined', 'the', 'exact', 'values', 'of', 'rbmathcal', 't_n', 'kk_2', 'for', '2leq', 'k', 'le', '4', 'and', 'proved', 'that', '2n2k9', 'le', 'rbmathcal', 't_n', 'kk_2', 'le', '2n2k72binom2k23', 'for', 'k', 'ge', '5', 'in', 'this', 'paper', 'we', 'improve', 'the', 'upper', 'bounds', 'and', 'prove', 'that', 'rbmathcal', 't_n', 'kk_2le', '2n6k16', 'for', 'n', 'ge', '2k', 'and', 'kge', '5', 'especially', 'we', 'show', 'that', 'rbmathcal', 't_n', '5k_22n1', 'for', 'n', 'ge', '11']] | [-0.2079139072381964, 0.21733036279432305, 0.024996655053584605, -0.021066101338938936, -0.03490400160596779, -0.14135076286409923, 0.06197452111787275, 0.35875549710170757, -0.204975641292936, -0.3648230588672095, 0.04080391207095632, -0.3653979721274359, -0.09597134920570285, 0.10522073290991128, -0.031723323967538984, -0.03013313953764324, 0.010917030925472788, 0.12456525521392518, -0.002681392608276496, -0.31888524865639123, 0.21975313168157765, -0.0971426443190228, 0.1114465850952382, 0.05288057173569257, 0.05610072344511827, 0.058591565360391104, 0.04378672257554552, 0.07503343370729885, -0.2930446570445635, 0.03682840401174646, 0.28517752811510827, 0.13047236875888515, 0.2070482660626583, -0.28291693826021086, -0.149028300859219, 0.19759109170103434, 0.13309840911814402, -0.0624316379498915, 0.03623948063939176, -0.1766096245210968, 0.2240421359517268, -0.06601924099133792, -0.08738831396017335, 0.01830477904237754, 0.19378217775044396, -0.008027317085805355, -0.33182485659230265, 6.372487859417361e-05, 0.1768070165437798, 0.1018959301214662, 0.07528194957511856, -0.27713791380392006, -0.09434957770921343, 0.058583334050346034, -0.07963228860625614, 0.1094653199580399, -0.04429306618535746, -0.07674105514186857, -0.10728675735031143, 0.31301209911809746, -0.08357115224390192, -0.07284085835715762, 0.0840958696657937, -0.19175519461657667, -0.1947730694639873, 0.1462183776608807, 0.0637710537457614, 0.22155649995048207, -0.03382580766143898, 0.24461244289508974, -0.10441011556998846, 0.12341511977426972, 0.19469172009498092, 0.006980622847769277, 0.07981662511667038, 0.052860945738944495, 0.12814320185996184, 0.11221658980394615, -0.02096184914855696, 0.08582533410492729, -0.32498753530547975, -0.1618564654140013, -0.24689961958953993, 0.12094848939871534, -0.20590083969444042, -0.12120444044150439, 0.2752494135646955, 0.101072275389402, 0.2162489747390785, 0.11273206102641974, 0.1066833307226816, 0.04535202647258812, -3.5413279997330187e-05, 0.19733081747123854, 0.06316589440073792, 0.21708896414852363, -0.031925144268477215, -0.1982308301566076, -0.03671703219796855, 0.13773052373253708] |
1,802.03919 | First-principles investigation of graphene/MoS2 bilayer heterostructures
using Tkatchenko-Scheffler van der Waals method | Graphene/MoS$_2$ van der Waals (vdW) heterostructures have promising
technological applications due to their unique properties and functionalities.
Many experimental and theoretical research groups across the globe have made
outstanding contributions to benchmark the properties of graphene/MoS$_2$
heterostructures. Even though some research groups have already made an attempt
to model the graphene/MoS$_2$ heterostructures using {\it first-principles}
calculations, there exists several discrepancies in the results from different
theoretical research groups and the experimental findings. In the present work,
we revisit this problem by first principles approach and address the existing
discrepancies about the interlayer spacing between graphene and MoS$_2$
monolayers in graphene/MoS$_2$ heterostructures, and the location of Dirac
points near Fermi-level. We find that the Tkatchenko--Scheffler method
efficiently evaluates the long-range vdW interactions and accurately predicts
interlayer spacing between graphene and MoS$_2$ sheets. We further investigate
the electronic, mechanical and vibrational properties of the optimized
graphene/MoS$_2$ heterostructures created using 5$\times$5/4$\times$4 and
4$\times$4/3$\times$3 supercell geometries having different magnitudes of
lattice mismatch. The effect of the varying interlayer spacing on the
electronic properties of heterostructures is discussed. Our phonon calculations
reveal that the interlayer shear and breathing phonon modes, which are very
sensitive to the weak vdW interactions, play vital role in describing the
thermal properties of the studied systems. The thermodynamic and elastic
properties of heterostructures are further discussed. A comparison between our
results and the results reported from other research groups is presented.
| cond-mat.mtrl-sci | graphenemos_2 van der waals vdw heterostructures have promising technological applications due to their unique properties and functionalities many experimental and theoretical research groups across the globe have made outstanding contributions to benchmark the properties of graphenemos_2 heterostructures even though some research groups have already made an attempt to model the graphenemos_2 heterostructures using it firstprinciples calculations there exists several discrepancies in the results from different theoretical research groups and the experimental findings in the present work we revisit this problem by first principles approach and address the existing discrepancies about the interlayer spacing between graphene and mos_2 monolayers in graphenemos_2 heterostructures and the location of dirac points near fermilevel we find that the tkatchenkoscheffler method efficiently evaluates the longrange vdw interactions and accurately predicts interlayer spacing between graphene and mos_2 sheets we further investigate the electronic mechanical and vibrational properties of the optimized graphenemos_2 heterostructures created using 5times54times4 and 4times43times3 supercell geometries having different magnitudes of lattice mismatch the effect of the varying interlayer spacing on the electronic properties of heterostructures is discussed our phonon calculations reveal that the interlayer shear and breathing phonon modes which are very sensitive to the weak vdw interactions play vital role in describing the thermal properties of the studied systems the thermodynamic and elastic properties of heterostructures are further discussed a comparison between our results and the results reported from other research groups is presented | [['graphenemos_2', 'van', 'der', 'waals', 'vdw', 'heterostructures', 'have', 'promising', 'technological', 'applications', 'due', 'to', 'their', 'unique', 'properties', 'and', 'functionalities', 'many', 'experimental', 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1,802.0392 | On Minrank and the Lov\'asz Theta Function | Two classical upper bounds on the Shannon capacity of graphs are the
$\vartheta$-function due to Lov\'asz and the minrank parameter due to Haemers.
We provide several explicit constructions of $n$-vertex graphs with a constant
$\vartheta$-function and minrank at least $n^\delta$ for a constant $\delta>0$
(over various prime order fields). This implies a limitation on the
$\vartheta$-function-based algorithmic approach to approximating the minrank
parameter of graphs. The proofs involve linear spaces of multivariate
polynomials and the method of higher incidence matrices.
| cs.DS cs.IT math.CO math.IT | two classical upper bounds on the shannon capacity of graphs are the varthetafunction due to lovasz and the minrank parameter due to haemers we provide several explicit constructions of nvertex graphs with a constant varthetafunction and minrank at least ndelta for a constant delta0 over various prime order fields this implies a limitation on the varthetafunctionbased algorithmic approach to approximating the minrank parameter of graphs the proofs involve linear spaces of multivariate polynomials and the method of higher incidence matrices | [['two', 'classical', 'upper', 'bounds', 'on', 'the', 'shannon', 'capacity', 'of', 'graphs', 'are', 'the', 'varthetafunction', 'due', 'to', 'lovasz', 'and', 'the', 'minrank', 'parameter', 'due', 'to', 'haemers', 'we', 'provide', 'several', 'explicit', 'constructions', 'of', 'nvertex', 'graphs', 'with', 'a', 'constant', 'varthetafunction', 'and', 'minrank', 'at', 'least', 'ndelta', 'for', 'a', 'constant', 'delta0', 'over', 'various', 'prime', 'order', 'fields', 'this', 'implies', 'a', 'limitation', 'on', 'the', 'varthetafunctionbased', 'algorithmic', 'approach', 'to', 'approximating', 'the', 'minrank', 'parameter', 'of', 'graphs', 'the', 'proofs', 'involve', 'linear', 'spaces', 'of', 'multivariate', 'polynomials', 'and', 'the', 'method', 'of', 'higher', 'incidence', 'matrices']] | [-0.184379302032286, 0.09085654634157125, -0.045725026674851586, 0.10431801560305388, -0.10776536090040131, -0.15435975506500918, 0.0992568489665142, 0.30798587391648113, -0.2774504503305954, -0.3382914282581946, 0.14395724176989186, -0.271330083667336, -0.1258717791407216, 0.22115462561008298, -0.10496794438296103, 0.10360012940555528, 0.007749793449817579, 0.07489398844824527, -0.04039703131818413, -0.31403854080251875, 0.3109281768144073, 0.009741413996472388, 0.18679360775394907, 0.12491990449024906, 0.10431052121345567, 0.005772590737412625, -0.008929233526504493, -0.004260386517153511, -0.22260275698868157, 0.157990615455363, 0.26936653193935184, 0.12522186109607544, 0.26751803567845234, -0.3490524578933852, -0.16048037779482105, 0.200973277451777, 0.0942784480343844, 0.07038438170440967, 0.030344441129319086, -0.21255004413048678, 0.07155410897081034, -0.1251810115078299, -0.08400370107220981, -0.0341675189689179, 0.021939796071405275, 0.02741550846331859, -0.3293335114051647, 0.03484604402763557, 0.13695870757934084, 0.06781238950221634, 0.031244926761741502, -0.22795420721809886, 0.07091306956761953, 0.04735557690757928, 0.004462010380399378, 0.03215472193293368, 0.011121616132934636, -0.11017817173660084, -0.1773042954162612, 0.30100023828128564, -0.032325253986953933, -0.19713099996406067, 0.10683233024481731, -0.1433981912729295, -0.16485772640562227, 0.1253278937509071, 0.17010522619644297, 0.15786140368473303, -0.04099324820091641, 0.18466647780762654, -0.08030727298199376, 0.11192934427858342, 0.15707596882446845, 0.05531424878730993, 0.04817637213494016, 0.05809309516290698, 0.17141114207149682, 0.18093022780162812, 0.006315262487181757, -0.08377703861849194, -0.2628906836285244, -0.10876984680896695, -0.22706701805744367, 0.038034795759882355, -0.2671160328054957, -0.20387478684601257, 0.3780188378465327, 0.10649033340763985, 0.1723304117287073, 0.162883877471278, 0.25021890616574904, 0.08702925029151802, 0.03602781755661097, 0.13927572139220523, 0.15917617039861087, 0.27414525967515724, 0.011362928217254827, -0.15472916030336784, 0.10020491133644423, 0.16855224958750642] |
1,802.03921 | Test Agents: Adaptive, Autonomous and Intelligent Test Cases | Growth of software size, lack of resources to perform regression testing, and
failure to detect bugs faster have seen increased reliance on continuous
integration and test automation. Even with greater hardware and software
resources dedicated to test automation, software testing is faced with enormous
challenges, resulting in increased dependence on complex mechanisms for
automated test case selection and prioritization as part of a continuous
integration framework. These mechanisms are currently using simple entities
called test cases that are concretely realized as executable scripts. Our key
idea is to provide test cases with more reasoning, adaptive behavior and
learning capabilities by using the concepts of intelligent software agents. We
refer to such test cases as test agents. The model that underlie a test agent
is capable of flexible and autonomous actions in order to meet overall testing
objectives. Our goal is to increase the decentralization of regression testing
by letting test agents to know for themselves when they should be executing,
how they should update their purpose, and when they should interact with each
other. In this paper, we envision software test agents that display such
adaptive autonomous behavior. Emerging developments and challenges regarding
the use of test agents are explored-in particular, new research that seeks to
use adaptive autonomous agents in software testing.
| cs.SE | growth of software size lack of resources to perform regression testing and failure to detect bugs faster have seen increased reliance on continuous integration and test automation even with greater hardware and software resources dedicated to test automation software testing is faced with enormous challenges resulting in increased dependence on complex mechanisms for automated test case selection and prioritization as part of a continuous integration framework these mechanisms are currently using simple entities called test cases that are concretely realized as executable scripts our key idea is to provide test cases with more reasoning adaptive behavior and learning capabilities by using the concepts of intelligent software agents we refer to such test cases as test agents the model that underlie a test agent is capable of flexible and autonomous actions in order to meet overall testing objectives our goal is to increase the decentralization of regression testing by letting test agents to know for themselves when they should be executing how they should update their purpose and when they should interact with each other in this paper we envision software test agents that display such adaptive autonomous behavior emerging developments and challenges regarding the use of test agents are exploredin particular new research that seeks to use adaptive autonomous agents in software testing | [['growth', 'of', 'software', 'size', 'lack', 'of', 'resources', 'to', 'perform', 'regression', 'testing', 'and', 'failure', 'to', 'detect', 'bugs', 'faster', 'have', 'seen', 'increased', 'reliance', 'on', 'continuous', 'integration', 'and', 'test', 'automation', 'even', 'with', 'greater', 'hardware', 'and', 'software', 'resources', 'dedicated', 'to', 'test', 'automation', 'software', 'testing', 'is', 'faced', 'with', 'enormous', 'challenges', 'resulting', 'in', 'increased', 'dependence', 'on', 'complex', 'mechanisms', 'for', 'automated', 'test', 'case', 'selection', 'and', 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1,802.03922 | Entangled Scent of a Charge | We argue that the ground state of a field theory, in the presence of charged
particles, becomes an entangled state involving an infinity of soft photons.
The quantum field vacuum is altered by the passage of a uniformly moving
charge, leaving in its wake a different dressed ground state. In this sense a
charged particle leaves its electromagnetic scent even after passing by. Unlike
in classical electrodynamics the effect of the charge remains even at infinite
time. The calculation is done in detail for the ground state of a spacetime
wedge, although the results are more general. This agrees in spirit with recent
results over the infrared aspects of field theory, although the technical
details are different. These considerations open the possibility that the
information carried by quantum fields, being nonlocal, does not disappear
beyond the horizon of black holes.
| hep-th gr-qc math-ph math.MP | we argue that the ground state of a field theory in the presence of charged particles becomes an entangled state involving an infinity of soft photons the quantum field vacuum is altered by the passage of a uniformly moving charge leaving in its wake a different dressed ground state in this sense a charged particle leaves its electromagnetic scent even after passing by unlike in classical electrodynamics the effect of the charge remains even at infinite time the calculation is done in detail for the ground state of a spacetime wedge although the results are more general this agrees in spirit with recent results over the infrared aspects of field theory although the technical details are different these considerations open the possibility that the information carried by quantum fields being nonlocal does not disappear beyond the horizon of black holes | [['we', 'argue', 'that', 'the', 'ground', 'state', 'of', 'a', 'field', 'theory', 'in', 'the', 'presence', 'of', 'charged', 'particles', 'becomes', 'an', 'entangled', 'state', 'involving', 'an', 'infinity', 'of', 'soft', 'photons', 'the', 'quantum', 'field', 'vacuum', 'is', 'altered', 'by', 'the', 'passage', 'of', 'a', 'uniformly', 'moving', 'charge', 'leaving', 'in', 'its', 'wake', 'a', 'different', 'dressed', 'ground', 'state', 'in', 'this', 'sense', 'a', 'charged', 'particle', 'leaves', 'its', 'electromagnetic', 'scent', 'even', 'after', 'passing', 'by', 'unlike', 'in', 'classical', 'electrodynamics', 'the', 'effect', 'of', 'the', 'charge', 'remains', 'even', 'at', 'infinite', 'time', 'the', 'calculation', 'is', 'done', 'in', 'detail', 'for', 'the', 'ground', 'state', 'of', 'a', 'spacetime', 'wedge', 'although', 'the', 'results', 'are', 'more', 'general', 'this', 'agrees', 'in', 'spirit', 'with', 'recent', 'results', 'over', 'the', 'infrared', 'aspects', 'of', 'field', 'theory', 'although', 'the', 'technical', 'details', 'are', 'different', 'these', 'considerations', 'open', 'the', 'possibility', 'that', 'the', 'information', 'carried', 'by', 'quantum', 'fields', 'being', 'nonlocal', 'does', 'not', 'disappear', 'beyond', 'the', 'horizon', 'of', 'black', 'holes']] | [-0.14942785190734348, 0.23329095837315045, -0.08377974516645606, 0.06309507182865803, -0.00896526964248291, -0.10944960650646993, 0.02040632968731058, 0.3246897824640785, -0.2136194497014263, -0.262489387639133, 0.08266078025917523, -0.29192582149324675, -0.07873325238802603, 0.15997243006630535, -0.034224987176379985, 0.013685389939928428, 0.039934052566864664, 0.09082658061358545, -0.06952178031372439, -0.21208427581670028, 0.3537717756382855, 0.06608925731686344, 0.25789055875169914, 0.05202878650743514, 0.0854056569369277, 0.04874421368752207, 0.008072769904226463, 0.054165482720626254, -0.07901596674814106, 0.062195723493849595, 0.20441877939405717, 0.07165370528860616, 0.2685898397774768, -0.4726011772573526, -0.2195145038505351, 0.0651450850202569, 0.13655320784476185, 0.18584704755007156, -0.07552645116032052, -0.3045107112000031, 0.038679532041507106, -0.1479012186007042, -0.20978417018361922, -0.03302038618296917, 0.005032820004271343, -0.05000028483693443, -0.1855411710059603, 0.08589349803265317, 0.07569652697586987, 0.001500370100672756, -0.06710473843351272, -0.06069572273159533, -0.007483126808490072, 0.12742337586823851, 0.08147957895042575, 0.04811759596673905, 0.1733556431751432, -0.20231394928414376, -0.13519057912303, 0.3463790905927973, -0.04918006341405479, -0.18954182058639293, 0.1701087651308626, -0.17997651402505913, -0.08833232780369664, 0.16476645943226426, 0.07447613900959758, 0.16474750541217092, -0.1275231931624668, 0.16163186016492545, -0.04351665213970202, 0.15177604158941124, 0.06196552870262947, 0.07910242799677819, 0.26325704018984525, 0.10660100770681831, 0.02489534418397982, 0.14793870227544437, -0.05542739029707653, -0.16711829859164676, -0.34141305995040705, -0.16359825073242454, -0.18481354552454182, 0.07555138027871311, -0.05591258265017781, -0.17264942486446153, 0.3753968087956309, 0.1303032782606481, 0.16059832636466517, -0.015987400387945983, 0.29342389739522334, 0.09909271495749376, 0.01897972786599504, 0.08261256674554066, 0.3152777077547008, 0.12202230151576389, 0.13636585009932917, -0.21732294550165535, 0.011017606969523643, 0.026361818751320244] |
1,802.03923 | Safe Triplet Screening for Distance Metric Learning | We study safe screening for metric learning. Distance metric learning can
optimize a metric over a set of triplets, each one of which is defined by a
pair of same class instances and an instance in a different class. However, the
number of possible triplets is quite huge even for a small dataset. Our safe
triplet screening identifies triplets which can be safely removed from the
optimization problem without losing the optimality. Compared with existing safe
screening studies, triplet screening is particularly significant because of (1)
the huge number of possible triplets, and (2) the semi-definite constraint in
the optimization. We derive several variants of screening rules, and analyze
their relationships. Numerical experiments on benchmark datasets demonstrate
the effectiveness of safe triplet screening.
| stat.ML | we study safe screening for metric learning distance metric learning can optimize a metric over a set of triplets each one of which is defined by a pair of same class instances and an instance in a different class however the number of possible triplets is quite huge even for a small dataset our safe triplet screening identifies triplets which can be safely removed from the optimization problem without losing the optimality compared with existing safe screening studies triplet screening is particularly significant because of 1 the huge number of possible triplets and 2 the semidefinite constraint in the optimization we derive several variants of screening rules and analyze their relationships numerical experiments on benchmark datasets demonstrate the effectiveness of safe triplet screening | [['we', 'study', 'safe', 'screening', 'for', 'metric', 'learning', 'distance', 'metric', 'learning', 'can', 'optimize', 'a', 'metric', 'over', 'a', 'set', 'of', 'triplets', 'each', 'one', 'of', 'which', 'is', 'defined', 'by', 'a', 'pair', 'of', 'same', 'class', 'instances', 'and', 'an', 'instance', 'in', 'a', 'different', 'class', 'however', 'the', 'number', 'of', 'possible', 'triplets', 'is', 'quite', 'huge', 'even', 'for', 'a', 'small', 'dataset', 'our', 'safe', 'triplet', 'screening', 'identifies', 'triplets', 'which', 'can', 'be', 'safely', 'removed', 'from', 'the', 'optimization', 'problem', 'without', 'losing', 'the', 'optimality', 'compared', 'with', 'existing', 'safe', 'screening', 'studies', 'triplet', 'screening', 'is', 'particularly', 'significant', 'because', 'of', '1', 'the', 'huge', 'number', 'of', 'possible', 'triplets', 'and', '2', 'the', 'semidefinite', 'constraint', 'in', 'the', 'optimization', 'we', 'derive', 'several', 'variants', 'of', 'screening', 'rules', 'and', 'analyze', 'their', 'relationships', 'numerical', 'experiments', 'on', 'benchmark', 'datasets', 'demonstrate', 'the', 'effectiveness', 'of', 'safe', 'triplet', 'screening']] | [-0.10765668894618569, 0.06873829618800996, -0.02393182772363165, 0.13292411990308514, -0.08065373308737044, -0.17229786665534708, 0.07089482691698344, 0.36274253475532786, -0.25360460357354364, -0.3567830096491105, 0.10711247292044383, -0.28433183040394167, -0.11076726222304793, 0.16152769615078663, -0.06173684998468413, 0.021667369482357327, 0.08675715086103333, 0.04914898896680736, -0.07656716205921721, -0.2771020759430873, 0.33350760356439807, -0.0003879725804355571, 0.29719837247659037, 0.08976747986777284, 0.09204207981815486, 0.007777508268723401, -0.008466679265919498, 0.08022015146169115, -0.0037334916651309506, 0.12645303827191787, 0.2640138341840811, 0.2135274161705823, 0.3384570816183478, -0.35855140826263565, -0.17272201984575608, 0.1583637181911769, 0.12657060528105898, 0.11747913256325906, -0.06599108120180454, -0.28017566106622904, 0.11439686453696794, -0.14531262622525296, -0.03976121712173146, -0.1293752835987787, 0.0012840215510469143, -0.02816157801572718, -0.33966148172683136, -0.003077633207573033, 0.011745006576695336, 0.021573533519829918, -0.06885891956299907, -0.1444372165100119, -0.012035429473905786, 0.12493761047168173, 0.08997606953352356, 0.010345209986198603, 0.11318127392995649, -0.16795444750128602, -0.12460649077500391, 0.3890900158179485, -0.06132003384816453, -0.18661300793320426, 0.2108571319348686, -0.037809080788789966, -0.09170470221301283, 0.10684804925581486, 0.1812661542884657, 0.22416525546128188, -0.20009202683511063, 0.0674543374362856, -0.07140213391190864, 0.1569654054379439, 0.04994221138095907, 0.040816416244620714, 0.19465375155952525, 0.21504349988408206, 0.07469224333157384, 0.15695358347053057, -0.10176591997648157, -0.10142874673891025, -0.30365741343002733, -0.11696498785702686, -0.17240111745621373, -0.01054101815538072, -0.14414552470058206, -0.1687561409941655, 0.3980807620271041, 0.19168836935764041, 0.20307253092755487, 0.07454824073777754, 0.2779787407233948, 0.04998450462407777, 0.14698413911851016, 0.03894111115723939, 0.2061795111351866, 0.03767349570421729, -0.005242428304135375, -0.2503911517427977, 0.07162247990386758, 0.033874894636191005] |
1,802.03924 | Holomorphic approximation: the legacy of Weierstrass, Runge, Oka-Weil,
and Mergelyan | In this paper we survey the theory of holomorphic approximation, from the
classical 19th century results of Runge and Weierstrass, continuing with the
20th century work of Oka and Weil, Mergelyan, Vitushkin and others, to the most
recent ones on higher dimensional manifolds. The paper includes some new
results and applications of this theory, especially to manifold-valued maps.
| math.CV | in this paper we survey the theory of holomorphic approximation from the classical 19th century results of runge and weierstrass continuing with the 20th century work of oka and weil mergelyan vitushkin and others to the most recent ones on higher dimensional manifolds the paper includes some new results and applications of this theory especially to manifoldvalued maps | [['in', 'this', 'paper', 'we', 'survey', 'the', 'theory', 'of', 'holomorphic', 'approximation', 'from', 'the', 'classical', '19th', 'century', 'results', 'of', 'runge', 'and', 'weierstrass', 'continuing', 'with', 'the', '20th', 'century', 'work', 'of', 'oka', 'and', 'weil', 'mergelyan', 'vitushkin', 'and', 'others', 'to', 'the', 'most', 'recent', 'ones', 'on', 'higher', 'dimensional', 'manifolds', 'the', 'paper', 'includes', 'some', 'new', 'results', 'and', 'applications', 'of', 'this', 'theory', 'especially', 'to', 'manifoldvalued', 'maps']] | [-0.059390122544598474, -0.011527270624606773, -0.09541969081579611, 0.076209962187367, -0.13155203245596253, -0.05387675274035026, 0.005193816698638016, 0.2845503017948619, -0.2535645212388019, -0.2532146071437104, 0.12559640265455277, -0.3251468905022946, -0.23926549912269773, 0.253435375136805, -0.1981171834571608, 0.060868340193149476, 0.05688834865577519, -0.0019523867181149021, -0.09679791889683312, -0.36986334822020206, 0.3867100008506456, 0.053438567845471976, 0.21914098288157377, 0.07195520445961377, 0.05660111863358781, 0.01351838257838169, -0.0855976914312562, -0.038766460758152196, -0.16340899106179332, 0.2412955901055629, 0.27457522430681974, 0.08943302064716559, 0.2875890872152201, -0.4269856655135237, -0.2028654256899809, 0.11304056524010055, 0.08842369629439481, 0.09590544638320289, -0.002468486797217087, -0.27200248363781077, 0.02060407523772326, -0.13442066653053567, -0.15904056234335415, -0.08462483527395746, 0.004696994361564003, 0.0042441169538631526, -0.13219418819865275, 0.06337646456587867, 0.13848160424044934, 0.15691708728414158, -0.05150595236296669, -0.16275892920535187, 0.019124683731331908, 0.0790914929359509, 0.08329977425907192, 0.1038863075302978, 0.0015355843231724254, -0.15299391586914085, -0.13502501720434118, 0.3739773756342715, -0.07054354769081392, -0.07977910033138148, 0.19288304528414174, -0.20297682821622182, -0.19819009361049994, 0.08519010713267751, 0.15582823991004763, 0.17225979252493587, -0.11615286666321857, 0.1421127334389643, -0.0539408122863749, 0.03849964872291633, 0.13273269528972692, -0.03155661400022178, 0.09007669934713892, 0.12150355449331732, 0.022937185725132966, 0.059836672370097245, -0.04025829926630932, -0.16439604624335108, -0.3097173475242894, -0.20085326705834475, -0.10100067034363747, 0.09233281801551066, -0.017283171542783686, -0.13962818014009953, 0.42670609692818134, 0.14264817416852596, 0.15073066315983005, 0.08782328274562666, 0.26547885511134717, 0.08288823578763625, 0.009574629591199858, 0.04828583733339248, 0.22074809769451506, 0.23362507598859997, 0.16383946026225799, -0.05278223497263188, -0.06500600138478431, 0.1745617900621788] |
1,802.03925 | Co-evolution of supermassive black holes with galaxies from
semi-analytic model: stochastic gravitational wave background and black hole
clustering | We study the co-evolution of supermassive black holes (SMBHs) with galaxies
by means of semi-analytic model (SAM) of galaxy formation based on sub-halo
merger trees built from Millennium and Millennium-II simulation. We utilize the
simulation results from Guo 2013 and Henriques 2015 to study two aspects of the
co-evolution, \emph{i.e.} the stochastic gravitational wave (GW) background
generated by SMBH merger and the SMBH/galaxy clustering. The characteristic
strain amplitude of GW background predicted by Guo 2013 and Henriques 2015
models are $A_{yr^{-1}}=5.00\times10^{-16}$ and
$A_{yr^{-1}}=9.42\times10^{-17}$, respectively. We find the GW amplitude is
very sensitive to the galaxy merger rate. The difference in the galaxy merger
rate between Guo 2013 and Henriques 2015, results in a factor $5$ deviation in
the GW strain amplitude. For clusterings, we calculate the spatially isotropic
two point auto- and cross-correlation functions (2PCFs) for both SMBHs and
galaxies by using the mock catalogs generated from Guo 2013 model. We find that
all 2PCFs have positive dependence on both SMBH and galaxy mass. And there
exist a significant time evolution in 2PCFs, namely, the clustering effect is
enhanced at lower redshifts. Interestingly, this result is not reported in the
active galactic nuclei samples in SDSS. Our analysis also shows that, roughly,
SMBHs and galaxies, with galaxy mass $10^2\sim10^3$ larger than SMBH mass, have
similar pattern of clustering, which is a reflection of the co-evolution of
SMBH and galaxy. Finally, we calculate the first ten multiples of the angular
power spectrum of the energy density of GW background. We find the amplitude of
angular power spectrum of the first ten multiples is about $10\%$ to $60\%$ of
the monopole component in the whole frequency range.
| astro-ph.CO astro-ph.GA astro-ph.HE | we study the coevolution of supermassive black holes smbhs with galaxies by means of semianalytic model sam of galaxy formation based on subhalo merger trees built from millennium and millenniumii simulation we utilize the simulation results from guo 2013 and henriques 2015 to study two aspects of the coevolution emphie the stochastic gravitational wave gw background generated by smbh merger and the smbhgalaxy clustering the characteristic strain amplitude of gw background predicted by guo 2013 and henriques 2015 models are a_yr1500times1016 and a_yr1942times1017 respectively we find the gw amplitude is very sensitive to the galaxy merger rate the difference in the galaxy merger rate between guo 2013 and henriques 2015 results in a factor 5 deviation in the gw strain amplitude for clusterings we calculate the spatially isotropic two point auto and crosscorrelation functions 2pcfs for both smbhs and galaxies by using the mock catalogs generated from guo 2013 model we find that all 2pcfs have positive dependence on both smbh and galaxy mass and there exist a significant time evolution in 2pcfs namely the clustering effect is enhanced at lower redshifts interestingly this result is not reported in the active galactic nuclei samples in sdss our analysis also shows that roughly smbhs and galaxies with galaxy mass 102sim103 larger than smbh mass have similar pattern of clustering which is a reflection of the coevolution of smbh and galaxy finally we calculate the first ten multiples of the angular power spectrum of the energy density of gw background we find the amplitude of angular power spectrum of the first ten multiples is about 10 to 60 of the monopole component in the whole frequency range | [['we', 'study', 'the', 'coevolution', 'of', 'supermassive', 'black', 'holes', 'smbhs', 'with', 'galaxies', 'by', 'means', 'of', 'semianalytic', 'model', 'sam', 'of', 'galaxy', 'formation', 'based', 'on', 'subhalo', 'merger', 'trees', 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1,802.03926 | Sarkisov program for generalized pairs | In this paper we show that any two birational Mori fiber spaces of
$\Qq$-factorial gklt g-pairs are connected by a finite sequence of Sarkisov
links.
| math.AG | in this paper we show that any two birational mori fiber spaces of qqfactorial gklt gpairs are connected by a finite sequence of sarkisov links | [['in', 'this', 'paper', 'we', 'show', 'that', 'any', 'two', 'birational', 'mori', 'fiber', 'spaces', 'of', 'qqfactorial', 'gklt', 'gpairs', 'are', 'connected', 'by', 'a', 'finite', 'sequence', 'of', 'sarkisov', 'links']] | [-0.22775654207604626, 0.12513871021413556, -0.08224586389648418, 0.05788243062852416, -0.05895095869588355, -0.13961409891877943, 0.006032650213455781, 0.44420349101225537, -0.3025558233105888, -0.10302626341581345, 0.08682275089571097, -0.22019191877916455, -0.23070890215846399, 0.20622401090804487, -0.23325966046346971, -0.039718983088581204, 0.06683889759976107, -0.011104142758995295, -0.07437629655275184, -0.3573323756766816, 0.49097243944803876, -0.18005086361275366, 0.19675353988228986, 0.046500847985347114, 0.1651100032419587, 0.014932447210109482, -0.04570712397495905, 0.02741536175987373, -0.18443500379756492, 0.15041176293743774, 0.35167085596670705, 0.06393322402921815, 0.22723952742914358, -0.3613301928465565, -0.17124660755507648, 0.23874886333942413, 0.13982060596269244, -0.0006230374953399102, -0.014787427770594755, -0.19821359504324695, 0.15672705000421652, -0.19126320977132613, -0.08859620792403196, -0.048397331032902, -0.02245623307923476, 0.13666948967147619, -0.1639940447251623, -0.11602242190080385, 0.1360739409768333, 0.1326513581055527, 0.03763259545667097, 0.020877984003163874, -0.07244510484936957, 0.08508178970078006, -0.05791628446119527, 0.08738315741841991, 0.03675080589406813, -0.035798591745939724, -0.1237457071741422, 0.3204977091518231, -0.08254229696467519, -0.20852581907820422, 0.12314078398048878, -0.12732800668648755, -0.17174572231791294, 0.1551612876743699, 0.07578641618601978, 0.12979559414088726, -0.06268596431861322, 0.12220090080518275, -0.12251299491617829, 0.10215900690915684, 0.1406884685275145, -0.04836016014451161, 0.10128849539129685, 0.14162937338308743, 0.09187088798110683, 0.12050838659827907, 0.012778792685518662, -0.00032559414345693466, -0.38810542194793624, -0.2505785084019105, -0.1559565340479215, 0.1312046340511491, -0.12293938489165157, -0.11523171498750646, 0.37777452714120346, 0.008377961892013749, 0.19758800005850694, 0.16303623921703547, 0.2608281047626709, -0.07252199932311972, -0.002624011210476359, 0.07731343091775973, 0.14999718219041824, 0.1613263830950018, -0.04237693742228051, -0.0898781733121723, -0.048889040326078735, 0.19883753372899568] |
1,802.03927 | Conductance scaling of junctions of Luttinger-liquid wires out of
equilibrium | We develop the renormalization group theory of the conductances of N-lead
junctions of spinless Luttinger-liquid wires as functions of bias voltages
applied to N independent Fermi-liquid reservoirs. Based on the perturbative
results up to second order in the interaction we demonstrate that the
conductances obey scaling. The corresponding renormalization group $\beta$
functions are derived up to second order.
| cond-mat.str-el | we develop the renormalization group theory of the conductances of nlead junctions of spinless luttingerliquid wires as functions of bias voltages applied to n independent fermiliquid reservoirs based on the perturbative results up to second order in the interaction we demonstrate that the conductances obey scaling the corresponding renormalization group beta functions are derived up to second order | [['we', 'develop', 'the', 'renormalization', 'group', 'theory', 'of', 'the', 'conductances', 'of', 'nlead', 'junctions', 'of', 'spinless', 'luttingerliquid', 'wires', 'as', 'functions', 'of', 'bias', 'voltages', 'applied', 'to', 'n', 'independent', 'fermiliquid', 'reservoirs', 'based', 'on', 'the', 'perturbative', 'results', 'up', 'to', 'second', 'order', 'in', 'the', 'interaction', 'we', 'demonstrate', 'that', 'the', 'conductances', 'obey', 'scaling', 'the', 'corresponding', 'renormalization', 'group', 'beta', 'functions', 'are', 'derived', 'up', 'to', 'second', 'order']] | [-0.1893404948380615, 0.16164987871286876, -0.10278370048336942, 0.0150000970401191, -0.007986429031006992, -0.16080616104641351, 0.08931498123726261, 0.3442185203833827, -0.23012335804002038, -0.2659259283401329, -0.029313294451828276, -0.336893738857631, -0.10067473917573305, 0.16342867037345624, 0.05663849368434528, 0.06011435495496824, -0.08549696219490906, 0.02074786028343028, -0.12732822791239695, -0.2522511891538984, 0.3217659597847097, -0.017863036454494656, 0.28268480496802206, 0.05781040019516287, 0.05570470683019737, -0.011365738059072915, 0.03854150678320178, 0.02338470212697726, -0.15013183730893773, 0.04608894701533277, 0.2649146827762738, -0.1617697377736602, 0.20968291956674437, -0.4608180059184288, -0.1446148438122252, 0.006912260926489173, 0.13266457295334289, 0.09618646444367437, 0.005491812125346142, -0.28827512617511997, 0.07347450993055928, -0.19571351515257668, -0.1413905211114164, -0.11122021425900788, -0.02562769422215281, 0.04787020371616658, -0.28232625179827725, 0.1400599106784974, -0.013263041374337828, -0.005276451754415858, 0.010109302051895266, -0.1125324200136298, -0.004334873407436856, 0.16467637041230396, 0.05680581025281471, -0.0008858186353383394, 0.19358288922250785, -0.1183194174463379, -0.10978790851117208, 0.32311948208973323, -0.0975444341471804, -0.15382352341488326, 0.15329938303229623, -0.2099730733707788, -0.13437503693496872, 0.07544912948626382, 0.14447173215288284, 0.0877909139601578, -0.17179696279545797, 0.12325968216012778, -0.016720111867220236, 0.14374560218882457, 0.013188266548617133, -0.011160896280138144, 0.1124561125059323, 0.11839644178940818, 0.06400381936691701, 0.108091203307187, -0.04081996860673073, -0.09624758584360624, -0.38611391835428516, -0.09687494509050558, -0.19593755711383862, 0.08827033732889285, -0.13446198116409883, -0.18273070433723002, 0.41765950584848377, 0.23010014768304496, 0.16958591472036366, 0.11594662912478991, 0.19548040499222255, 0.2242629770490598, 0.11926413969746952, 0.04760640083209025, 0.15924158377637124, 0.24029602225998353, 0.004840377083947432, -0.29111316532061177, -0.015203351090694296, 0.12657305297987728] |
1,802.03928 | Algorithms for robust production scheduling with energy consumption
limits | In this work, we consider a scheduling problem faced by production companies
with large electricity consumption. Due to the contract with the electric
utility, the production companies are obligated to comply with the total energy
consumption limits in the specified time intervals (usually 15-minutes long),
otherwise, the companies pay substantial penalty fees. Although it is possible
to design production schedules that consider these limits as hard constraints,
uncertainties occurring during the execution of the schedules are usually not
taken into account. This may lead to situations in which the unexpected delays
of the operations cause the violations of the energy consumption limits. Our
goal is to design robust production schedules pro-actively guaranteeing that
the energy consumption limits are not violated for the given set of uncertainty
scenarios. We consider scheduling on one machine with release times of the
operations and total tardiness as the objective function. To tackle this
problem, we first propose a pseudo-polynomial algorithm for finding the optimal
robust schedule for the given permutation of the operations. This algorithm is
then utilised in three different algorithms for finding the optimal
permutation: two exact (Branch-and-Bound and logic-based Benders decomposition)
and one heuristic algorithm (tabu search). All the algorithms were
experimentally evaluated on random instances with different sizes of the
uncertainty scenarios set. Using the tabu search algorithm, we are able to
solve large instances within one minute.
| cs.DS | in this work we consider a scheduling problem faced by production companies with large electricity consumption due to the contract with the electric utility the production companies are obligated to comply with the total energy consumption limits in the specified time intervals usually 15minutes long otherwise the companies pay substantial penalty fees although it is possible to design production schedules that consider these limits as hard constraints uncertainties occurring during the execution of the schedules are usually not taken into account this may lead to situations in which the unexpected delays of the operations cause the violations of the energy consumption limits our goal is to design robust production schedules proactively guaranteeing that the energy consumption limits are not violated for the given set of uncertainty scenarios we consider scheduling on one machine with release times of the operations and total tardiness as the objective function to tackle this problem we first propose a pseudopolynomial algorithm for finding the optimal robust schedule for the given permutation of the operations this algorithm is then utilised in three different algorithms for finding the optimal permutation two exact branchandbound and logicbased benders decomposition and one heuristic algorithm tabu search all the algorithms were experimentally evaluated on random instances with different sizes of the uncertainty scenarios set using the tabu search algorithm we are able to solve large instances within one minute | [['in', 'this', 'work', 'we', 'consider', 'a', 'scheduling', 'problem', 'faced', 'by', 'production', 'companies', 'with', 'large', 'electricity', 'consumption', 'due', 'to', 'the', 'contract', 'with', 'the', 'electric', 'utility', 'the', 'production', 'companies', 'are', 'obligated', 'to', 'comply', 'with', 'the', 'total', 'energy', 'consumption', 'limits', 'in', 'the', 'specified', 'time', 'intervals', 'usually', '15minutes', 'long', 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1,802.03929 | Gender Performance in Physics Practicals | An analysis of longitudinal student Physics practicals data over a period of
three semesters is conducted. In particular, we study the association of the
gender of students and their Physics practicals marks in mechanics,
thermodynamics, optics, electricity, and overall marks (OVM). Together with
gender, all variables are binarized, i.e. distinction mark is coded 1, and zero
otherwise. To visualize performance of students, the qualitative method of
plotting a two-dimensional orbit is used to represent binary multivariate
longitudinal data of each student. Analysis of orbits reveals information of
patterns in the data. This study gives a good indication of which Physics
practical each gender group perform better in. Our visual analysis indicate
that male students tend not to get distinctions in both mechanics and OVM, but
that females tend to get distinctions in OVM. We have also observed that
students' marks is most stable (i.e. least changing) in optics and OVM, and
most frequently changing in electricity. A comparison to the GEE statistical
model indicates that visual results present initial insights to help and
complement statistical analysis.
| physics.ed-ph | an analysis of longitudinal student physics practicals data over a period of three semesters is conducted in particular we study the association of the gender of students and their physics practicals marks in mechanics thermodynamics optics electricity and overall marks ovm together with gender all variables are binarized ie distinction mark is coded 1 and zero otherwise to visualize performance of students the qualitative method of plotting a twodimensional orbit is used to represent binary multivariate longitudinal data of each student analysis of orbits reveals information of patterns in the data this study gives a good indication of which physics practical each gender group perform better in our visual analysis indicate that male students tend not to get distinctions in both mechanics and ovm but that females tend to get distinctions in ovm we have also observed that students marks is most stable ie least changing in optics and ovm and most frequently changing in electricity a comparison to the gee statistical model indicates that visual results present initial insights to help and complement statistical analysis | [['an', 'analysis', 'of', 'longitudinal', 'student', 'physics', 'practicals', 'data', 'over', 'a', 'period', 'of', 'three', 'semesters', 'is', 'conducted', 'in', 'particular', 'we', 'study', 'the', 'association', 'of', 'the', 'gender', 'of', 'students', 'and', 'their', 'physics', 'practicals', 'marks', 'in', 'mechanics', 'thermodynamics', 'optics', 'electricity', 'and', 'overall', 'marks', 'ovm', 'together', 'with', 'gender', 'all', 'variables', 'are', 'binarized', 'ie', 'distinction', 'mark', 'is', 'coded', '1', 'and', 'zero', 'otherwise', 'to', 'visualize', 'performance', 'of', 'students', 'the', 'qualitative', 'method', 'of', 'plotting', 'a', 'twodimensional', 'orbit', 'is', 'used', 'to', 'represent', 'binary', 'multivariate', 'longitudinal', 'data', 'of', 'each', 'student', 'analysis', 'of', 'orbits', 'reveals', 'information', 'of', 'patterns', 'in', 'the', 'data', 'this', 'study', 'gives', 'a', 'good', 'indication', 'of', 'which', 'physics', 'practical', 'each', 'gender', 'group', 'perform', 'better', 'in', 'our', 'visual', 'analysis', 'indicate', 'that', 'male', 'students', 'tend', 'not', 'to', 'get', 'distinctions', 'in', 'both', 'mechanics', 'and', 'ovm', 'but', 'that', 'females', 'tend', 'to', 'get', 'distinctions', 'in', 'ovm', 'we', 'have', 'also', 'observed', 'that', 'students', 'marks', 'is', 'most', 'stable', 'ie', 'least', 'changing', 'in', 'optics', 'and', 'ovm', 'and', 'most', 'frequently', 'changing', 'in', 'electricity', 'a', 'comparison', 'to', 'the', 'gee', 'statistical', 'model', 'indicates', 'that', 'visual', 'results', 'present', 'initial', 'insights', 'to', 'help', 'and', 'complement', 'statistical', 'analysis']] | [-0.043193826990318485, 0.06593996153647326, -0.18528813747847875, 0.11516853942025178, -0.11756446213257732, -0.18054888452223333, 0.0739258497792021, 0.3929550485389138, -0.21638414985367985, -0.3489689189750193, 0.061416310198679144, -0.3273150934308598, -0.15087899782387962, 0.1971537381005791, -0.11823552747475995, -0.002725863822640597, 0.1035015240936032, 0.047236851943704423, -0.056111611253899435, -0.27187225965529005, 0.26071081951853226, 0.06814410206226801, 0.2840903367085213, -0.002145143654409119, 0.04975503555231791, -0.00017882898729029958, -0.107633680524073, 0.005051002001643858, -0.08517362387266819, 0.11452997860033065, 0.35579587663894263, 0.15789049429374494, 0.34172434367990884, -0.387820355059706, -0.1574273526017681, 0.06675821395500944, 0.12180575737412172, 0.07721937863045075, -0.03711623423044908, -0.2771267121345524, 0.048364346265522974, -0.13944026663904713, -0.08421807474753057, -0.07836268187913281, 0.010278391628079671, 0.003225732893042732, -0.2293675716209691, 0.08899757841523255, 0.06614735448172061, 0.16659018613228743, -0.05582672339964616, -0.14094695730918003, -0.014519227596710234, 0.20227732003232549, 0.07613681278929686, 0.0176107565619697, 0.11181614198589655, -0.14805605007991704, -0.13804846194794995, 0.37916214674690063, -0.0009172831646124409, -0.16664481125983663, 0.21516608265598983, -0.20948430948721414, -0.1509119399733291, 0.06752874928695912, 0.21772343302886424, 0.027754050129177896, -0.13980966999886071, -0.03033644873192481, -0.040131420360094955, 0.2136634343478363, 0.05513063945744017, -0.02190997379710263, 0.22152497960831335, 0.16917473985681267, -0.006234227260310118, 0.0810846099484479, -0.06905957660240926, -0.11391061865883371, -0.23774821019138803, -0.18351774594471368, -0.11710491616990608, -0.006447720718328624, -0.08440392647166432, -0.14359987900983964, 0.4077620424372567, 0.18564574066551193, 0.1587371072144544, 0.017550601420887026, 0.22539992034646936, 0.048913253223872744, 0.032796034802148905, 0.06272506172006781, 0.20432650575689462, 0.09032267103479667, 0.1419229361603026, -0.18832553446356376, 0.09002789210684238, -0.013481754099219952] |
1,802.0393 | Visualizing Neural Network Developing Perturbation Theory | In this letter, motivated by the question that whether the empirical fitting
of data by neural network can yield the same structure of physical laws, we
apply the neural network to a simple quantum mechanical two-body scattering
problem with short-range potentials, which by itself also plays an important
role in many branches of physics. We train a neural network to accurately
predict $ s $-wave scattering length, which governs the low-energy scattering
physics, directly from the scattering potential without solving Schr\"odinger
equation or obtaining the wavefunction. After analyzing the neural network, it
is shown that the neural network develops perturbation theory order by order
when the potential increases. This provides an important benchmark to the
machine-assisted physics research or even automated machine learning physics
laws.
| physics.comp-ph cond-mat.dis-nn cond-mat.quant-gas cs.AI cs.LG | in this letter motivated by the question that whether the empirical fitting of data by neural network can yield the same structure of physical laws we apply the neural network to a simple quantum mechanical twobody scattering problem with shortrange potentials which by itself also plays an important role in many branches of physics we train a neural network to accurately predict s wave scattering length which governs the lowenergy scattering physics directly from the scattering potential without solving schrodinger equation or obtaining the wavefunction after analyzing the neural network it is shown that the neural network develops perturbation theory order by order when the potential increases this provides an important benchmark to the machineassisted physics research or even automated machine learning physics laws | [['in', 'this', 'letter', 'motivated', 'by', 'the', 'question', 'that', 'whether', 'the', 'empirical', 'fitting', 'of', 'data', 'by', 'neural', 'network', 'can', 'yield', 'the', 'same', 'structure', 'of', 'physical', 'laws', 'we', 'apply', 'the', 'neural', 'network', 'to', 'a', 'simple', 'quantum', 'mechanical', 'twobody', 'scattering', 'problem', 'with', 'shortrange', 'potentials', 'which', 'by', 'itself', 'also', 'plays', 'an', 'important', 'role', 'in', 'many', 'branches', 'of', 'physics', 'we', 'train', 'a', 'neural', 'network', 'to', 'accurately', 'predict', 's', 'wave', 'scattering', 'length', 'which', 'governs', 'the', 'lowenergy', 'scattering', 'physics', 'directly', 'from', 'the', 'scattering', 'potential', 'without', 'solving', 'schrodinger', 'equation', 'or', 'obtaining', 'the', 'wavefunction', 'after', 'analyzing', 'the', 'neural', 'network', 'it', 'is', 'shown', 'that', 'the', 'neural', 'network', 'develops', 'perturbation', 'theory', 'order', 'by', 'order', 'when', 'the', 'potential', 'increases', 'this', 'provides', 'an', 'important', 'benchmark', 'to', 'the', 'machineassisted', 'physics', 'research', 'or', 'even', 'automated', 'machine', 'learning', 'physics', 'laws']] | [-0.07318024313539237, 0.10384369621287108, -0.09864049106498339, 0.09965264379148252, -0.08812051017453233, -0.16810205486047292, 0.000994690338097092, 0.3325097133150144, -0.33347954428304105, -0.30902159158816384, 0.019084422220100438, -0.2710081594245088, -0.2510096799176667, 0.17483821536053815, 0.011672249326722757, 0.12375119560578417, 0.07280023193039421, 0.04759046177364765, -0.025500676619084253, -0.23669834465028777, 0.34828735426473884, 0.08221843776634834, 0.295685573240682, 0.0733977709145796, 0.075359260607999, 0.05248737212030157, 0.01892741194798521, -0.03194468271554327, -0.08560539055824554, 0.15233566442921367, 0.28007378092321056, 0.13032871276711025, 0.30173794349708083, -0.4787284348370327, -0.27993052339133423, 0.09464029253661377, 0.16647404492569848, 0.16317021809875093, -0.034678982893726035, -0.28918801742275396, 0.012830578806554718, -0.150996852129127, -0.13481340800682384, -0.11727381542873298, 0.03449571580671135, -0.0296463449344775, -0.25486696389787306, 0.0553329869869612, 0.08007120817238765, 0.017588484221168103, -0.04920154287209434, -0.0705865053544497, 0.0207238748496879, 0.13323220467845526, 0.03687799383882403, 0.07621143706473371, 0.12719271164537677, -0.20348774693200306, -0.12225037848299009, 0.38171959308637005, -0.03808396039230208, -0.17333416317048814, 0.14837933613753487, -0.06078351682592784, -0.12180908733810816, 0.1060721150903602, 0.19907794280856428, 0.04519590978921887, -0.20121774215154037, 0.08534314407546434, -0.024870488837604492, 0.18369800953810372, 0.03739236565583739, -0.018222822882835905, 0.21358408793176134, 0.2521881866334532, 0.0017469625149275207, 0.10159974990998424, -0.08886642421730945, -0.09823022896964703, -0.2668895743216478, -0.06399187019785806, -0.19475970965539735, 0.07929152005289647, -0.06984180180335252, -0.15277472271601814, 0.4054135592774518, 0.18631401712510495, 0.17971155171342676, 0.04575362259469506, 0.28225118247792125, 0.13725728043655475, 0.09741341819115464, 0.08174289521693642, 0.24892932590052125, 0.11976814225424952, 0.1262145097848148, -0.23845401606259628, 0.06753303286724634, 0.08423038628402978] |
1,802.03931 | Deep feature compression for collaborative object detection | Recent studies have shown that the efficiency of deep neural networks in
mobile applications can be significantly improved by distributing the
computational workload between the mobile device and the cloud. This paradigm,
termed collaborative intelligence, involves communicating feature data between
the mobile and the cloud. The efficiency of such approach can be further
improved by lossy compression of feature data, which has not been examined to
date. In this work we focus on collaborative object detection and study the
impact of both near-lossless and lossy compression of feature data on its
accuracy. We also propose a strategy for improving the accuracy under lossy
feature compression. Experiments indicate that using this strategy, the
communication overhead can be reduced by up to 70% without sacrificing
accuracy.
| cs.CV | recent studies have shown that the efficiency of deep neural networks in mobile applications can be significantly improved by distributing the computational workload between the mobile device and the cloud this paradigm termed collaborative intelligence involves communicating feature data between the mobile and the cloud the efficiency of such approach can be further improved by lossy compression of feature data which has not been examined to date in this work we focus on collaborative object detection and study the impact of both nearlossless and lossy compression of feature data on its accuracy we also propose a strategy for improving the accuracy under lossy feature compression experiments indicate that using this strategy the communication overhead can be reduced by up to 70 without sacrificing accuracy | [['recent', 'studies', 'have', 'shown', 'that', 'the', 'efficiency', 'of', 'deep', 'neural', 'networks', 'in', 'mobile', 'applications', 'can', 'be', 'significantly', 'improved', 'by', 'distributing', 'the', 'computational', 'workload', 'between', 'the', 'mobile', 'device', 'and', 'the', 'cloud', 'this', 'paradigm', 'termed', 'collaborative', 'intelligence', 'involves', 'communicating', 'feature', 'data', 'between', 'the', 'mobile', 'and', 'the', 'cloud', 'the', 'efficiency', 'of', 'such', 'approach', 'can', 'be', 'further', 'improved', 'by', 'lossy', 'compression', 'of', 'feature', 'data', 'which', 'has', 'not', 'been', 'examined', 'to', 'date', 'in', 'this', 'work', 'we', 'focus', 'on', 'collaborative', 'object', 'detection', 'and', 'study', 'the', 'impact', 'of', 'both', 'nearlossless', 'and', 'lossy', 'compression', 'of', 'feature', 'data', 'on', 'its', 'accuracy', 'we', 'also', 'propose', 'a', 'strategy', 'for', 'improving', 'the', 'accuracy', 'under', 'lossy', 'feature', 'compression', 'experiments', 'indicate', 'that', 'using', 'this', 'strategy', 'the', 'communication', 'overhead', 'can', 'be', 'reduced', 'by', 'up', 'to', '70', 'without', 'sacrificing', 'accuracy']] | [-0.11189200544275434, 0.016221282641298226, -0.061968322760695894, -0.03289489165481721, -0.05794921024672447, -0.15321901254355907, 0.10550091737100194, 0.45017545351818683, -0.2672373132044149, -0.36517192231821677, 0.09314295614040607, -0.22715084238969271, -0.16635634393558926, 0.21411023433170012, -0.11147017811139624, 0.11597319693452934, 0.12005166876910915, 0.020222254998741612, -0.03422250115733233, -0.30831892276370537, 0.28284938962409095, 0.1280367942167833, 0.4032367631706649, 0.10958179780526177, 0.059410788418327067, -0.02420756276396494, -0.052915536937484096, -0.008228743043277533, -0.019094028818677368, 0.18386183743695578, 0.2892319417179322, 0.20886034267436293, 0.30744977951830915, -0.4481369800144626, -0.29572429148960977, 0.0762370225519032, 0.17803317885702727, 0.08211264627396068, -0.08532618343522172, -0.32564497415179144, 0.13030859137765102, -0.21518146251929143, -0.01785651659930966, -0.09898210010273502, -0.02702092095428417, 0.00485897529682505, -0.2583537929909184, 0.03857202121528849, 0.060960177550911, 0.053680659983412274, -0.011083139436161746, -0.08200874204011334, 0.05122491714334296, 0.12806475518136134, 0.00813783961160469, 0.025039916506953416, 0.15044177471164374, -0.18911420937154383, -0.14057869108904514, 0.384138525523726, -0.0587676816409634, -0.19270782421282942, 0.19611772526085616, -0.008560785734575362, -0.12018947815522552, 0.11884907234249817, 0.25577207357292214, 0.06716221330204844, -0.162784383737392, 0.047991831394498266, 0.002895285375416279, 0.17808622385059752, 0.06527808600578518, 0.05003578553066378, 0.15545505196637205, 0.25203092044009073, 0.058276046429865905, 0.16379299215810944, -0.1368377533883998, -0.06407232792680001, -0.15322663862964198, -0.15632886735482082, -0.2080121150150174, -0.02067246832009386, -0.11274123476816326, -0.04899881160212493, 0.35899310411825297, 0.2180489984513711, 0.19054754639622726, 0.044780655828822825, 0.36817757786822414, 0.07298826903945976, 0.1330227049841215, 0.11041185998868558, 0.2505560203665687, -0.012945179033258388, 0.1391377905008173, -0.21804342921877246, 0.09091119650064126, -0.007671434300831489] |
1,802.03932 | Frobenius Additive Fast Fourier Transform | In ISSAC 2017, van der Hoeven and Larrieu showed that evaluating a polynomial
P in GF(q)[x] of degree <n at all n-th roots of unity in GF($q^d$) can
essentially be computed d-time faster than evaluating Q in GF($q^d$)[x] at all
these roots, assuming GF($q^d$) contains a primitive n-th root of unity. Termed
the Frobenius FFT, this discovery has a profound impact on polynomial
multiplication, especially for multiplying binary polynomials, which finds
ample application in coding theory and cryptography. In this paper, we show
that the theory of Frobenius FFT beautifully generalizes to a class of additive
FFT developed by Cantor and Gao-Mateer. Furthermore, we demonstrate the power
of Frobenius additive FFT for q=2: to multiply two binary polynomials whose
product is of degree <256, the new technique requires only 29,005 bit
operations, while the best result previously reported was 33,397. To the best
of our knowledge, this is the first time that FFT-based multiplication
outperforms Karatsuba and the like at such a low degree in terms of
bit-operation count.
| cs.SC cs.CC | in issac 2017 van der hoeven and larrieu showed that evaluating a polynomial p in gfqx of degree n at all nth roots of unity in gfqd can essentially be computed dtime faster than evaluating q in gfqdx at all these roots assuming gfqd contains a primitive nth root of unity termed the frobenius fft this discovery has a profound impact on polynomial multiplication especially for multiplying binary polynomials which finds ample application in coding theory and cryptography in this paper we show that the theory of frobenius fft beautifully generalizes to a class of additive fft developed by cantor and gaomateer furthermore we demonstrate the power of frobenius additive fft for q2 to multiply two binary polynomials whose product is of degree 256 the new technique requires only 29005 bit operations while the best result previously reported was 33397 to the best of our knowledge this is the first time that fftbased multiplication outperforms karatsuba and the like at such a low degree in terms of bitoperation count | [['in', 'issac', '2017', 'van', 'der', 'hoeven', 'and', 'larrieu', 'showed', 'that', 'evaluating', 'a', 'polynomial', 'p', 'in', 'gfqx', 'of', 'degree', 'n', 'at', 'all', 'nth', 'roots', 'of', 'unity', 'in', 'gfqd', 'can', 'essentially', 'be', 'computed', 'dtime', 'faster', 'than', 'evaluating', 'q', 'in', 'gfqdx', 'at', 'all', 'these', 'roots', 'assuming', 'gfqd', 'contains', 'a', 'primitive', 'nth', 'root', 'of', 'unity', 'termed', 'the', 'frobenius', 'fft', 'this', 'discovery', 'has', 'a', 'profound', 'impact', 'on', 'polynomial', 'multiplication', 'especially', 'for', 'multiplying', 'binary', 'polynomials', 'which', 'finds', 'ample', 'application', 'in', 'coding', 'theory', 'and', 'cryptography', 'in', 'this', 'paper', 'we', 'show', 'that', 'the', 'theory', 'of', 'frobenius', 'fft', 'beautifully', 'generalizes', 'to', 'a', 'class', 'of', 'additive', 'fft', 'developed', 'by', 'cantor', 'and', 'gaomateer', 'furthermore', 'we', 'demonstrate', 'the', 'power', 'of', 'frobenius', 'additive', 'fft', 'for', 'q2', 'to', 'multiply', 'two', 'binary', 'polynomials', 'whose', 'product', 'is', 'of', 'degree', '256', 'the', 'new', 'technique', 'requires', 'only', '29005', 'bit', 'operations', 'while', 'the', 'best', 'result', 'previously', 'reported', 'was', '33397', 'to', 'the', 'best', 'of', 'our', 'knowledge', 'this', 'is', 'the', 'first', 'time', 'that', 'fftbased', 'multiplication', 'outperforms', 'karatsuba', 'and', 'the', 'like', 'at', 'such', 'a', 'low', 'degree', 'in', 'terms', 'of', 'bitoperation', 'count']] | [-0.16288677601281693, 0.0606132121652474, -0.06966517615867894, 0.014638379718504709, -0.04897835300258004, -0.15070358294430247, 0.0260628148579947, 0.2939745837845552, -0.28423632595998544, -0.2719025601356946, 0.07715216597159864, -0.24655113275501342, -0.20872360414589675, 0.24493598200834965, -0.09372227333405596, 0.05904206854900644, 0.02086967585125455, 0.08746994444583026, -0.08515314556181108, -0.33705047278860467, 0.2814968326991355, 0.06535612010069329, 0.19918119157721967, 0.017750126240706002, 0.12072599364191662, 0.00243936940441253, -0.05458912482689261, -0.04607830067268676, -0.07681919321525421, 0.12012085036581589, 0.29025106270579093, 0.11711733249292054, 0.2573977703850912, -0.3561999500947602, -0.11989996135579767, 0.13595941780066048, 0.16705492888232348, 0.045475254134982136, -0.02144603464352887, -0.19200277412281497, 0.14730078914996098, -0.19740991503168057, -0.09228414819122402, -0.08032769159367883, 0.04687766972526816, 0.04024367707021201, -0.2909623651497963, 0.03960710408648959, 0.09531405283983245, 0.08993227575779145, 0.01436886004708351, -0.19747566783593762, 0.05491762635740739, 0.0494455603543485, -0.03675360749705614, 0.051901134716226136, 0.05385048606667961, -0.09699860786107213, -0.1320517540357455, 0.3638735284258462, -0.048021882683786786, -0.16386141227615744, 0.11273012969953318, -0.19389008302643987, -0.1646546335476968, 0.1470737742938469, 0.12724222853940761, 0.1056636932548883, -0.04708242214595278, 0.1481399253633428, -0.06726096924049435, 0.1860822352802059, 0.1786295533536669, -0.002693901904916138, 0.0910460629808015, 0.08180823232791742, 0.030638141077540725, 0.14881372437625548, -0.042409165405932765, -0.06736758256756505, -0.24199876622124403, -0.18853564017429303, -0.24494068802443597, 0.07375438140136087, -0.13514315622411727, -0.1810497197345543, 0.40968515665017435, 0.10636474562803122, 0.1515980735881093, 0.11358522449369991, 0.30010027917142224, 0.10889030143368507, 0.09475919081582285, 0.1065577998454916, 0.16290059774635463, 0.12596929762196254, 0.03572425231004111, -0.16076117874709545, 0.06716032579460722, 0.15178131753360324] |
1,802.03933 | Unravelling the origin of piezo/ferro-electric properties of
metal-organic frameworks (MOFs) nanocrystals | Metal-organic framework (MOF) UiO-66 nanocrystals were previously believed to
be piezo/ferro-electrically inactive because of their centrosymmetric lattice
symmetries (Fm-3m (225)) revealed by Powder X-ray diffraction. However, via
delicate dual AC resonance tracking piezoresponse force microscopy and
piezoresponse force spectroscopy characterizations, our nanoscale probing for
the first time demonstrate that UiO-66 nanocrystals show piezo/ferro-electric
response. Our compelling experimental and theoretically analyses disclose that
the structure of UiO-66 should not be the highly centrosymmetric Fm-3m (225)
but a reduced symmetry form instead. UiO-66(Hf)-type MOFs possess stronger
piezoresponse and better ferroelectric switching behaviours than their
counterparts UiO-66 (Zr)-type MOFs. Our study not only enriches the structural
understanding of UiO-66 MOF, but also suggests possible modification of
electronic property of the MOFs by judicious selection of metal ions and
functional ligands.
| cond-mat.mtrl-sci | metalorganic framework mof uio66 nanocrystals were previously believed to be piezoferroelectrically inactive because of their centrosymmetric lattice symmetries fm3m 225 revealed by powder xray diffraction however via delicate dual ac resonance tracking piezoresponse force microscopy and piezoresponse force spectroscopy characterizations our nanoscale probing for the first time demonstrate that uio66 nanocrystals show piezoferroelectric response our compelling experimental and theoretically analyses disclose that the structure of uio66 should not be the highly centrosymmetric fm3m 225 but a reduced symmetry form instead uio66hftype mofs possess stronger piezoresponse and better ferroelectric switching behaviours than their counterparts uio66 zrtype mofs our study not only enriches the structural understanding of uio66 mof but also suggests possible modification of electronic property of the mofs by judicious selection of metal ions and functional ligands | [['metalorganic', 'framework', 'mof', 'uio66', 'nanocrystals', 'were', 'previously', 'believed', 'to', 'be', 'piezoferroelectrically', 'inactive', 'because', 'of', 'their', 'centrosymmetric', 'lattice', 'symmetries', 'fm3m', '225', 'revealed', 'by', 'powder', 'xray', 'diffraction', 'however', 'via', 'delicate', 'dual', 'ac', 'resonance', 'tracking', 'piezoresponse', 'force', 'microscopy', 'and', 'piezoresponse', 'force', 'spectroscopy', 'characterizations', 'our', 'nanoscale', 'probing', 'for', 'the', 'first', 'time', 'demonstrate', 'that', 'uio66', 'nanocrystals', 'show', 'piezoferroelectric', 'response', 'our', 'compelling', 'experimental', 'and', 'theoretically', 'analyses', 'disclose', 'that', 'the', 'structure', 'of', 'uio66', 'should', 'not', 'be', 'the', 'highly', 'centrosymmetric', 'fm3m', '225', 'but', 'a', 'reduced', 'symmetry', 'form', 'instead', 'uio66hftype', 'mofs', 'possess', 'stronger', 'piezoresponse', 'and', 'better', 'ferroelectric', 'switching', 'behaviours', 'than', 'their', 'counterparts', 'uio66', 'zrtype', 'mofs', 'our', 'study', 'not', 'only', 'enriches', 'the', 'structural', 'understanding', 'of', 'uio66', 'mof', 'but', 'also', 'suggests', 'possible', 'modification', 'of', 'electronic', 'property', 'of', 'the', 'mofs', 'by', 'judicious', 'selection', 'of', 'metal', 'ions', 'and', 'functional', 'ligands']] | [-0.07612551950917738, 0.1682868383056295, -0.06502337403958891, 0.019657615716076965, -0.12573185854220414, -0.1714144795640879, 0.10243736083829412, 0.49044227715187927, -0.24550659430584287, -0.2759217018246408, 0.019120382463059773, -0.29160663937559217, -0.20258250728067828, 0.15611768708889925, 0.03598700896117503, 0.014823708334952836, -0.035705031323572244, -0.11419355707258229, -0.06780830509459024, -0.17553297682415422, 0.18236966294092619, 0.05068217615579914, 0.3826786149747488, 0.08751891952999906, 0.02280893099420454, 0.007321295589662906, 0.09364147268129679, 0.042122634276331256, -0.1337389414814136, 0.12366153922204565, 0.2613960534780915, -0.05616895602334563, 0.1803637400924463, -0.5091445949988637, -0.2707448514042104, -0.013098500942945296, 0.1318163108705293, 0.08082831613113725, -0.15071019946362793, -0.2683241932645319, 0.08483537692932518, -0.07916942829426143, -0.10379487162501347, -0.20930680559145484, -0.048710989277446416, 0.004206831968291019, -0.19993468513570317, 0.08718194248276853, 0.07602961138704777, 0.1554931989469664, -0.1479835608837808, -0.1278415298982849, -0.09234114192479206, 0.018277867117942107, 0.02137438425173362, 0.012527098525239805, 0.24581919471275182, -0.07066097275194962, -0.10088512675961615, 0.4207548588453754, 0.04626746382789092, -0.05843410687899687, 0.2007805194054556, -0.1957762728358157, -0.13750857687593113, 0.1948883302615789, 0.0651543958735372, 0.17993281234805358, -0.22174106308351824, 0.03579965667284238, -0.004767823812922811, 0.2529341953738434, 0.10761019944145185, 0.12738038016468045, 0.184168178697942, 0.205895238759458, 0.005881669130403458, 0.1250622656056279, -0.04743786230001871, -0.03314070525338752, -0.15320138723354756, -0.1971035624316674, -0.19401280357070813, 0.04713071308432836, -0.0990190205114091, -0.1902040173261752, 0.31070101219683705, 0.06025328858551092, 0.08993362278532714, -0.061254446493174006, 0.23416370138189022, -0.029827356559027563, 0.17212866930805934, -0.04682382421068302, 0.31189477790480225, 0.15691562129252748, 0.08974099120624908, -0.3111740058227404, 0.16564275734732306, -0.017315082978911517] |
1,802.03934 | Object Detection with Mask-based Feature Encoding | Region-based Convolutional Neural Networks (R-CNNs) have achieved great
success in the field of object detection. The existing R-CNNs usually divide a
Region-of-Interest (ROI) into grids, and then localize objects by utilizing the
spatial information reflected by the relative position of each grid in the ROI.
In this paper, we propose a novel feature-encoding approach, where spatial
information is represented through the spatial distributions of visual
patterns. In particular, we design a Mask Weight Network (MWN) to learn a set
of masks and then apply channel-wise masking operations to ROI feature map,
followed by a global pooling and a cheap fully-connected layer. We integrate
the newly designed feature encoder into the Faster R-CNN architecture. The
resulting new Faster R-CNNs can preserve the object-detection accuracy of the
standard Faster R-CNNs by using substantially fewer parameters. Compared to
R-FCNs using state-of-art PS ROI pooling and deformable PS ROI pooling, the new
Faster R-CNNs can produce higher object-detection accuracy with good run-time
efficiency. We also show that a specifically designed and learned MWN can
capture global contextual information and further improve the object-detection
accuracy. Validation experiments are conducted on both PASCAL VOC and MS COCO
datasets.
| cs.CV | regionbased convolutional neural networks rcnns have achieved great success in the field of object detection the existing rcnns usually divide a regionofinterest roi into grids and then localize objects by utilizing the spatial information reflected by the relative position of each grid in the roi in this paper we propose a novel featureencoding approach where spatial information is represented through the spatial distributions of visual patterns in particular we design a mask weight network mwn to learn a set of masks and then apply channelwise masking operations to roi feature map followed by a global pooling and a cheap fullyconnected layer we integrate the newly designed feature encoder into the faster rcnn architecture the resulting new faster rcnns can preserve the objectdetection accuracy of the standard faster rcnns by using substantially fewer parameters compared to rfcns using stateofart ps roi pooling and deformable ps roi pooling the new faster rcnns can produce higher objectdetection accuracy with good runtime efficiency we also show that a specifically designed and learned mwn can capture global contextual information and further improve the objectdetection accuracy validation experiments are conducted on both pascal voc and ms coco datasets | [['regionbased', 'convolutional', 'neural', 'networks', 'rcnns', 'have', 'achieved', 'great', 'success', 'in', 'the', 'field', 'of', 'object', 'detection', 'the', 'existing', 'rcnns', 'usually', 'divide', 'a', 'regionofinterest', 'roi', 'into', 'grids', 'and', 'then', 'localize', 'objects', 'by', 'utilizing', 'the', 'spatial', 'information', 'reflected', 'by', 'the', 'relative', 'position', 'of', 'each', 'grid', 'in', 'the', 'roi', 'in', 'this', 'paper', 'we', 'propose', 'a', 'novel', 'featureencoding', 'approach', 'where', 'spatial', 'information', 'is', 'represented', 'through', 'the', 'spatial', 'distributions', 'of', 'visual', 'patterns', 'in', 'particular', 'we', 'design', 'a', 'mask', 'weight', 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1,802.03935 | Dynamic monopolies for interval graphs with bounded thresholds | For a graph $G$ and an integer-valued threshold function $\tau$ on its vertex
set, a dynamic monopoly is a set of vertices of $G$ such that iteratively
adding to it vertices $u$ of $G$ that have at least $\tau(u)$ neighbors in it
eventually yields the vertex set of $G$. We show that the problem of finding a
dynamic monopoly of minimum order can be solved in polynomial time for interval
graphs with bounded threshold functions, but is NP-hard for chordal graphs
allowing unbounded threshold functions.
| cs.DM math.CO | for a graph g and an integervalued threshold function tau on its vertex set a dynamic monopoly is a set of vertices of g such that iteratively adding to it vertices u of g that have at least tauu neighbors in it eventually yields the vertex set of g we show that the problem of finding a dynamic monopoly of minimum order can be solved in polynomial time for interval graphs with bounded threshold functions but is nphard for chordal graphs allowing unbounded threshold functions | [['for', 'a', 'graph', 'g', 'and', 'an', 'integervalued', 'threshold', 'function', 'tau', 'on', 'its', 'vertex', 'set', 'a', 'dynamic', 'monopoly', 'is', 'a', 'set', 'of', 'vertices', 'of', 'g', 'such', 'that', 'iteratively', 'adding', 'to', 'it', 'vertices', 'u', 'of', 'g', 'that', 'have', 'at', 'least', 'tauu', 'neighbors', 'in', 'it', 'eventually', 'yields', 'the', 'vertex', 'set', 'of', 'g', 'we', 'show', 'that', 'the', 'problem', 'of', 'finding', 'a', 'dynamic', 'monopoly', 'of', 'minimum', 'order', 'can', 'be', 'solved', 'in', 'polynomial', 'time', 'for', 'interval', 'graphs', 'with', 'bounded', 'threshold', 'functions', 'but', 'is', 'nphard', 'for', 'chordal', 'graphs', 'allowing', 'unbounded', 'threshold', 'functions']] | [-0.17426339693312698, 0.11127107465821182, -0.04618478139643283, 0.01865592334036003, -0.13234012777752735, -0.16065088396015412, 0.10377279406084734, 0.4166742757483221, -0.3414705978596912, -0.3105703696457888, 0.0806732092573143, -0.32846687177524847, -0.1363013009157251, 0.10389473175301271, -0.02038925390361863, 0.060984063958612215, 0.12970625298535998, 0.14448701097246477, 0.03747401037801276, -0.28129171405487474, 0.2917124571497826, -0.051476041846634714, 0.09777002343634034, 0.10911660709363573, 0.13798443898558616, 0.02790992240257123, 0.05441591477788547, 0.12302018417702879, -0.1375610669813742, 0.006274394777274745, 0.28710230325951297, 0.1823930492898559, 0.30751645779784986, -0.3736942437641761, -0.145183978525593, 0.2378348705194452, 0.10308498862911673, 0.007495872144970823, 0.05973655121017467, -0.1810401652556132, 0.17217034079572735, -0.13570819643928725, -0.10174230665297193, 0.014468989696572808, 0.14973309172646088, -0.003903266173951766, -0.3683124519446317, -0.008185329469030394, 0.08083781799000195, -0.0032369963715181633, 0.05811415078145835, -0.16468404968652656, -0.06996040353825425, 0.08481538909870912, -0.07547954909387045, 0.13751214318634833, 0.05893298177705968, -0.1459724281421479, -0.15100812806802638, 0.3548157975296764, -0.04048294801511528, -0.18709543805350276, 0.10377573847551556, -0.15147717890384443, -0.1416967252269387, 0.12608856253106804, 0.15466930762152462, 0.19709190248566516, -0.13589209079852, 0.13672838127719897, -0.1062770646503743, 0.10678302952471902, 0.10302951576955179, -0.005445936319473035, 0.11066595314837555, 0.1623314029920627, 0.24784537579261642, 0.18358255226904635, 0.05400173171313808, 0.024480311934124022, -0.3203492832096184, -0.010641088391489842, -0.23315126930966096, 0.04429913490654572, -0.201565548032351, -0.21699319433640032, 0.43338074096862006, 0.09551581066677019, 0.18962249668205486, 0.15506596342824838, 0.17941910769133007, 0.17381580662280186, 0.05006969090660705, 0.1865287813193658, 0.12190882576739087, 0.10765106948914335, -0.037726647620472835, -0.19258477345985525, 0.11097077786210267, 0.16155675723652274] |
1,802.03936 | On the Needs for Rotations in Hypercubic Quantization Hashing | The aim of this paper is to endow the well-known family of hypercubic
quantization hashing methods with theoretical guarantees. In hypercubic
quantization, applying a suitable (random or learned) rotation after
dimensionality reduction has been experimentally shown to improve the results
accuracy in the nearest neighbors search problem. We prove in this paper that
the use of these rotations is optimal under some mild assumptions: getting
optimal binary sketches is equivalent to applying a rotation uniformizing the
diagonal of the covariance matrix between data points. Moreover, for two closed
points, the probability to have dissimilar binary sketches is upper bounded by
a factor of the initial distance between the data points. Relaxing these
assumptions, we obtain a general concentration result for random matrices. We
also provide some experiments illustrating these theoretical points and compare
a set of algorithms in both the batch and online settings.
| cs.LG | the aim of this paper is to endow the wellknown family of hypercubic quantization hashing methods with theoretical guarantees in hypercubic quantization applying a suitable random or learned rotation after dimensionality reduction has been experimentally shown to improve the results accuracy in the nearest neighbors search problem we prove in this paper that the use of these rotations is optimal under some mild assumptions getting optimal binary sketches is equivalent to applying a rotation uniformizing the diagonal of the covariance matrix between data points moreover for two closed points the probability to have dissimilar binary sketches is upper bounded by a factor of the initial distance between the data points relaxing these assumptions we obtain a general concentration result for random matrices we also provide some experiments illustrating these theoretical points and compare a set of algorithms in both the batch and online settings | [['the', 'aim', 'of', 'this', 'paper', 'is', 'to', 'endow', 'the', 'wellknown', 'family', 'of', 'hypercubic', 'quantization', 'hashing', 'methods', 'with', 'theoretical', 'guarantees', 'in', 'hypercubic', 'quantization', 'applying', 'a', 'suitable', 'random', 'or', 'learned', 'rotation', 'after', 'dimensionality', 'reduction', 'has', 'been', 'experimentally', 'shown', 'to', 'improve', 'the', 'results', 'accuracy', 'in', 'the', 'nearest', 'neighbors', 'search', 'problem', 'we', 'prove', 'in', 'this', 'paper', 'that', 'the', 'use', 'of', 'these', 'rotations', 'is', 'optimal', 'under', 'some', 'mild', 'assumptions', 'getting', 'optimal', 'binary', 'sketches', 'is', 'equivalent', 'to', 'applying', 'a', 'rotation', 'uniformizing', 'the', 'diagonal', 'of', 'the', 'covariance', 'matrix', 'between', 'data', 'points', 'moreover', 'for', 'two', 'closed', 'points', 'the', 'probability', 'to', 'have', 'dissimilar', 'binary', 'sketches', 'is', 'upper', 'bounded', 'by', 'a', 'factor', 'of', 'the', 'initial', 'distance', 'between', 'the', 'data', 'points', 'relaxing', 'these', 'assumptions', 'we', 'obtain', 'a', 'general', 'concentration', 'result', 'for', 'random', 'matrices', 'we', 'also', 'provide', 'some', 'experiments', 'illustrating', 'these', 'theoretical', 'points', 'and', 'compare', 'a', 'set', 'of', 'algorithms', 'in', 'both', 'the', 'batch', 'and', 'online', 'settings']] | [-0.11130390735909007, 0.06588052636571876, -0.07731409807133281, 0.07294249118745534, -0.08085720510280225, -0.1564780449100201, 0.10604737845682798, 0.4129278312256146, -0.27228813513647765, -0.2562444652689414, 0.12599970183994932, -0.27068874832346207, -0.169578570037427, 0.17586309890612029, -0.12333061055202658, 0.10752852968612893, 0.08983693115361449, 0.04126672199850469, -0.15468228423560504, -0.3327940973944755, 0.34115897783906096, 0.036307605885667726, 0.28399231310080114, 0.005057164249996681, 0.10562759751499268, 0.0017274412772773455, -0.009918207089261463, 0.02381602773675695, -0.12392203584856058, 0.13720834841579846, 0.2433595417209694, 0.1374218966908908, 0.27062680655055577, -0.38874773774296045, -0.1692681908203263, 0.12850735716508804, 0.11018013715511188, 0.12627762464237297, -0.0753876921436232, -0.274137085466969, 0.12160421220727989, -0.10283543938324631, -0.11258564035395263, -0.10096462802337353, 0.003542005524246229, 0.0289732345554512, -0.3242614460056454, 0.023170024958340543, 0.10787326205819328, 0.07108075107175359, -0.05331549283841418, -0.12856773585452982, 0.04570296327357129, 0.1140087582470086, 0.06874929176087284, 0.033791963859888106, 0.0797804300123567, -0.07136988226880527, -0.12404784685673399, 0.3535854979106969, -0.022761794926029526, -0.24350555721967895, 0.16896024477642235, -0.10108189981353159, -0.1503732302549502, 0.09292093732963419, 0.19043245524840635, 0.09040323958551097, -0.1513828341331747, 0.11624371192899868, -0.08599365686435097, 0.13628371691771057, 0.08272671401209664, 0.01858632864322216, 0.11648481706571248, 0.1441094835592796, 0.10950968991609342, 0.167538923892102, -0.0772442875281235, -0.10017508873392621, -0.25998342898916843, -0.10721774538655558, -0.20509871463420698, 0.04763651402309835, -0.17255248313510188, -0.13938152706375856, 0.366041279798891, 0.17530490388162434, 0.26322420668374336, 0.0941986834902006, 0.27669562222824123, 0.1021542890999222, 0.016593175396944087, 0.10701717116009807, 0.21162826704069934, 0.13476761088855305, 0.029087412332753755, -0.16963855973315528, 0.07661321715568192, 0.09787698371120415] |
1,802.03937 | Compression for Multiple Reconstructions | In this work we propose a method for optimizing the lossy compression for a
network of diverse reconstruction systems. We focus on adapting a standard
image compression method to a set of candidate displays, presenting the
decompressed signals to viewers. Each display is modeled as a linear operator
applied after decompression, and its probability to serve a network user. We
formulate a complicated operational rate-distortion optimization trading-off
the network's expected mean-squared reconstruction error and the compression
bit-cost. Using the alternating direction method of multipliers (ADMM) we
develop an iterative procedure where the network structure is separated from
the compression method, enabling the reliance on standard compression
techniques. We present experimental results showing our method to be the best
approach for adjusting high bit-rate image compression (using the
state-of-the-art HEVC standard) to a set of displays modeled as blur
degradations.
| cs.MM | in this work we propose a method for optimizing the lossy compression for a network of diverse reconstruction systems we focus on adapting a standard image compression method to a set of candidate displays presenting the decompressed signals to viewers each display is modeled as a linear operator applied after decompression and its probability to serve a network user we formulate a complicated operational ratedistortion optimization tradingoff the networks expected meansquared reconstruction error and the compression bitcost using the alternating direction method of multipliers admm we develop an iterative procedure where the network structure is separated from the compression method enabling the reliance on standard compression techniques we present experimental results showing our method to be the best approach for adjusting high bitrate image compression using the stateoftheart hevc standard to a set of displays modeled as blur degradations | [['in', 'this', 'work', 'we', 'propose', 'a', 'method', 'for', 'optimizing', 'the', 'lossy', 'compression', 'for', 'a', 'network', 'of', 'diverse', 'reconstruction', 'systems', 'we', 'focus', 'on', 'adapting', 'a', 'standard', 'image', 'compression', 'method', 'to', 'a', 'set', 'of', 'candidate', 'displays', 'presenting', 'the', 'decompressed', 'signals', 'to', 'viewers', 'each', 'display', 'is', 'modeled', 'as', 'a', 'linear', 'operator', 'applied', 'after', 'decompression', 'and', 'its', 'probability', 'to', 'serve', 'a', 'network', 'user', 'we', 'formulate', 'a', 'complicated', 'operational', 'ratedistortion', 'optimization', 'tradingoff', 'the', 'networks', 'expected', 'meansquared', 'reconstruction', 'error', 'and', 'the', 'compression', 'bitcost', 'using', 'the', 'alternating', 'direction', 'method', 'of', 'multipliers', 'admm', 'we', 'develop', 'an', 'iterative', 'procedure', 'where', 'the', 'network', 'structure', 'is', 'separated', 'from', 'the', 'compression', 'method', 'enabling', 'the', 'reliance', 'on', 'standard', 'compression', 'techniques', 'we', 'present', 'experimental', 'results', 'showing', 'our', 'method', 'to', 'be', 'the', 'best', 'approach', 'for', 'adjusting', 'high', 'bitrate', 'image', 'compression', 'using', 'the', 'stateoftheart', 'hevc', 'standard', 'to', 'a', 'set', 'of', 'displays', 'modeled', 'as', 'blur', 'degradations']] | [-0.10288534646773402, -0.03414462394114138, -0.10145472682863407, 0.03500964937533969, -0.06459311185582213, -0.1806416967381194, 0.08077560763692201, 0.4543063562068579, -0.3211210624573638, -0.2942030886657161, 0.09597829958045413, -0.2165410818277503, -0.18230669300922817, 0.20703084652962014, -0.12217915322216606, 0.1112898244257018, 0.08462737401337664, 0.030606058650656784, -0.12408820342058115, -0.251689077041735, 0.2263045878896637, 0.08855698930381013, 0.34587419896868976, -0.0020120755213198904, 0.12898480826184797, 0.018947921823978208, -0.02746338169793967, 0.02971903771151983, -0.060515071691697286, 0.16639113463729405, 0.2533420109082844, 0.19905841128110027, 0.2857152733553147, -0.4008501682551109, -0.24175983593001854, 0.058686639912587275, 0.1377845372595965, 0.12937116495956727, -0.08650056175760282, -0.29803475828693926, 0.10886219491689673, -0.1726045079140676, -0.0047641591497248025, -0.08953224556108522, -0.09626957836976315, 0.020066941431690036, -0.3514019391250716, 0.06639269818367584, 0.044166695516683806, 0.036288120412006436, -0.06405125361224033, -0.10219184561002759, 0.08735156886735194, 0.12118146228838525, 0.012585596045773986, 0.08318621475624738, 0.17142460687528197, -0.12349069109789544, -0.1143779862444285, 0.4002244179974571, -0.06494412528226996, -0.22810389070632187, 0.1602815093720506, -0.011315101496339273, -0.09418679789720412, 0.13851515891988553, 0.22981029913070605, 0.10759949025517102, -0.12391505177047359, 0.005101475260796682, -0.010812119011291497, 0.1826174264462169, 0.08497759164813504, 0.0016079411419818728, 0.12282977216515234, 0.26294099862466186, 0.09759987130419438, 0.21786563188066244, -0.15681940298807, -0.03003082037802866, -0.2423416457583679, -0.11924583762013119, -0.21497636845857976, -0.030230342008076314, -0.11257208612552348, -0.17676211142470297, 0.41311210537289117, 0.1861189931041474, 0.21429009240442257, 0.08781180270317701, 0.3863535430818367, 0.09984813055811657, 0.06402216227256137, 0.09533870853349674, 0.1703649952634164, 0.06717693973198641, 0.11913532671257318, -0.17405770994679293, 0.059559044167177465, 0.1057258756938491] |
1,802.03938 | Revisiting the Vector Space Model: Sparse Weighted Nearest-Neighbor
Method for Extreme Multi-Label Classification | Machine learning has played an important role in information retrieval (IR)
in recent times. In search engines, for example, query keywords are accepted
and documents are returned in order of relevance to the given query; this can
be cast as a multi-label ranking problem in machine learning. Generally, the
number of candidate documents is extremely large (from several thousand to
several million); thus, the classifier must handle many labels. This problem is
referred to as extreme multi-label classification (XMLC). In this paper, we
propose a novel approach to XMLC termed the Sparse Weighted Nearest-Neighbor
Method. This technique can be derived as a fast implementation of
state-of-the-art (SOTA) one-versus-rest linear classifiers for very sparse
datasets. In addition, we show that the classifier can be written as a sparse
generalization of a representer theorem with a linear kernel. Furthermore, our
method can be viewed as the vector space model used in IR. Finally, we show
that the Sparse Weighted Nearest-Neighbor Method can process data points in
real time on XMLC datasets with equivalent performance to SOTA models, with a
single thread and smaller storage footprint. In particular, our method exhibits
superior performance to the SOTA models on a dataset with 3 million labels.
| stat.ML cs.LG | machine learning has played an important role in information retrieval ir in recent times in search engines for example query keywords are accepted and documents are returned in order of relevance to the given query this can be cast as a multilabel ranking problem in machine learning generally the number of candidate documents is extremely large from several thousand to several million thus the classifier must handle many labels this problem is referred to as extreme multilabel classification xmlc in this paper we propose a novel approach to xmlc termed the sparse weighted nearestneighbor method this technique can be derived as a fast implementation of stateoftheart sota oneversusrest linear classifiers for very sparse datasets in addition we show that the classifier can be written as a sparse generalization of a representer theorem with a linear kernel furthermore our method can be viewed as the vector space model used in ir finally we show that the sparse weighted nearestneighbor method can process data points in real time on xmlc datasets with equivalent performance to sota models with a single thread and smaller storage footprint in particular our method exhibits superior performance to the sota models on a dataset with 3 million labels | [['machine', 'learning', 'has', 'played', 'an', 'important', 'role', 'in', 'information', 'retrieval', 'ir', 'in', 'recent', 'times', 'in', 'search', 'engines', 'for', 'example', 'query', 'keywords', 'are', 'accepted', 'and', 'documents', 'are', 'returned', 'in', 'order', 'of', 'relevance', 'to', 'the', 'given', 'query', 'this', 'can', 'be', 'cast', 'as', 'a', 'multilabel', 'ranking', 'problem', 'in', 'machine', 'learning', 'generally', 'the', 'number', 'of', 'candidate', 'documents', 'is', 'extremely', 'large', 'from', 'several', 'thousand', 'to', 'several', 'million', 'thus', 'the', 'classifier', 'must', 'handle', 'many', 'labels', 'this', 'problem', 'is', 'referred', 'to', 'as', 'extreme', 'multilabel', 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1,802.03939 | On the convergence of FK-Ising Percolation to SLE$(16/3, 16/3-6)$ | We give a simplified and complete proof of the convergence of the chordal
exploration process in critical FK-Ising percolation to chordal SLE$_\kappa(
\kappa-6)$ with $\kappa=16/3$. Our proof follows the classical
excursion-construction of SLE$_\kappa(\kappa-6)$ processes in the continuum and
we are thus led to introduce suitable cut-off stopping times in order to
analyse the behaviour of the driving function of the discrete system when
Dobrushin boundary condition collapses to a single point. Our proof is very
different from [KS15, KS16] as it only relies on the convergence to the chordal
SLE$_{\kappa}$ process in Dobrushin boundary condition and does not require the
introduction of a new observable. Still, it relies crucially on several
ingredients:
a) the powerful topological framework developed in [KS17] as well as its
follow-up paper [CDCH$^+$14],
b) the strong RSW Theorem from [CDCH16],
c) the proof is inspired from the appendix A in [BH16].
One important emphasis of this paper is to carefully write down some
properties which are often considered {\em folklore} in the literature but
which are only justified so far by hand-waving arguments. The main examples of
these are:
1) the convergence of natural discrete stopping times to their continuous
analogues. (The usual hand-waving argument destroys the spatial Markov
property).
2) the fact that the discrete spatial Markov property is preserved in the the
scaling limit. (The enemy being that $\mathbb{E}[X_n |\, Y_n]$ does not
necessarily converge to $\mathbb{E}[X|\, Y]$ when $(X_n,Y_n)\to (X,Y)$).
We end the paper with a detailed sketch of the convergence to radial
SLE$_\kappa( \kappa-6)$ when $\kappa=16/3$ as well as the derivation of
Onsager's one-arm exponent $1/8$.
| math.PR | we give a simplified and complete proof of the convergence of the chordal exploration process in critical fkising percolation to chordal sle_kappa kappa6 with kappa163 our proof follows the classical excursionconstruction of sle_kappakappa6 processes in the continuum and we are thus led to introduce suitable cutoff stopping times in order to analyse the behaviour of the driving function of the discrete system when dobrushin boundary condition collapses to a single point our proof is very different from ks15 ks16 as it only relies on the convergence to the chordal sle_kappa process in dobrushin boundary condition and does not require the introduction of a new observable still it relies crucially on several ingredients a the powerful topological framework developed in ks17 as well as its followup paper cdch14 b the strong rsw theorem from cdch16 c the proof is inspired from the appendix a in bh16 one important emphasis of this paper is to carefully write down some properties which are often considered em folklore in the literature but which are only justified so far by handwaving arguments the main examples of these are 1 the convergence of natural discrete stopping times to their continuous analogues the usual handwaving argument destroys the spatial markov property 2 the fact that the discrete spatial markov property is preserved in the the scaling limit the enemy being that mathbbex_n y_n does not necessarily converge to mathbbex y when x_ny_nto xy we end the paper with a detailed sketch of the convergence to radial sle_kappa kappa6 when kappa163 as well as the derivation of onsagers onearm exponent 18 | [['we', 'give', 'a', 'simplified', 'and', 'complete', 'proof', 'of', 'the', 'convergence', 'of', 'the', 'chordal', 'exploration', 'process', 'in', 'critical', 'fkising', 'percolation', 'to', 'chordal', 'sle_kappa', 'kappa6', 'with', 'kappa163', 'our', 'proof', 'follows', 'the', 'classical', 'excursionconstruction', 'of', 'sle_kappakappa6', 'processes', 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1,802.0394 | Lorentz-boost eigenmodes | Plane waves and cylindrical or spherical vortex modes are important sets of
solutions of quantum and classical wave equations. These are eigenmodes of the
energy-momentum and angular-momentum operators, i.e., generators of spacetime
translations and spatial rotations, respectively. Here we describe another set
of wave modes: eigenmodes of the "boost momentum" operator, i.e., a generator
of Lorentz boosts (spatio-temporal rotations). Akin to the angular momentum,
only one (say, z) component of the boost momentum can have a well-defined
quantum number. The boost eigenmodes exhibit invariance with respect to the
Lorentz transformations along the z-axis, leading to scale-invariant wave forms
and step-like singularities moving with the speed of light. We describe basic
properties of the Lorentz-boost eigenmodes and argue that these can serve as a
convenient basis for problems involving causal propagation of signals.
| physics.optics quant-ph | plane waves and cylindrical or spherical vortex modes are important sets of solutions of quantum and classical wave equations these are eigenmodes of the energymomentum and angularmomentum operators ie generators of spacetime translations and spatial rotations respectively here we describe another set of wave modes eigenmodes of the boost momentum operator ie a generator of lorentz boosts spatiotemporal rotations akin to the angular momentum only one say z component of the boost momentum can have a welldefined quantum number the boost eigenmodes exhibit invariance with respect to the lorentz transformations along the zaxis leading to scaleinvariant wave forms and steplike singularities moving with the speed of light we describe basic properties of the lorentzboost eigenmodes and argue that these can serve as a convenient basis for problems involving causal propagation of signals | [['plane', 'waves', 'and', 'cylindrical', 'or', 'spherical', 'vortex', 'modes', 'are', 'important', 'sets', 'of', 'solutions', 'of', 'quantum', 'and', 'classical', 'wave', 'equations', 'these', 'are', 'eigenmodes', 'of', 'the', 'energymomentum', 'and', 'angularmomentum', 'operators', 'ie', 'generators', 'of', 'spacetime', 'translations', 'and', 'spatial', 'rotations', 'respectively', 'here', 'we', 'describe', 'another', 'set', 'of', 'wave', 'modes', 'eigenmodes', 'of', 'the', 'boost', 'momentum', 'operator', 'ie', 'a', 'generator', 'of', 'lorentz', 'boosts', 'spatiotemporal', 'rotations', 'akin', 'to', 'the', 'angular', 'momentum', 'only', 'one', 'say', 'z', 'component', 'of', 'the', 'boost', 'momentum', 'can', 'have', 'a', 'welldefined', 'quantum', 'number', 'the', 'boost', 'eigenmodes', 'exhibit', 'invariance', 'with', 'respect', 'to', 'the', 'lorentz', 'transformations', 'along', 'the', 'zaxis', 'leading', 'to', 'scaleinvariant', 'wave', 'forms', 'and', 'steplike', 'singularities', 'moving', 'with', 'the', 'speed', 'of', 'light', 'we', 'describe', 'basic', 'properties', 'of', 'the', 'lorentzboost', 'eigenmodes', 'and', 'argue', 'that', 'these', 'can', 'serve', 'as', 'a', 'convenient', 'basis', 'for', 'problems', 'involving', 'causal', 'propagation', 'of', 'signals']] | [-0.19978780056364043, 0.20036322144248211, -0.06666484135061954, 0.04604541924852652, -0.11745228921945179, -0.0777109768911914, -0.04884171453732327, 0.36064511759093765, -0.25695085275043367, -0.22100427458490507, 0.04909211515262017, -0.2791022599152656, -0.11332551840953811, 0.1762541928593154, 0.025461526214988255, 0.09997756729320496, 0.0353648601995894, 0.04694689129951946, -0.12845026233381676, -0.11858910793494998, 0.3525254877573444, 0.04785809029690978, 0.30086024003597256, -0.039509895130653276, 0.16559271545327536, -0.008175410552542995, -0.021877255957488986, 0.0023464164414414854, -0.05454964828211153, 0.08090120338778378, 0.19082563201134856, 0.08423370684084314, 0.1983148892177269, -0.4677071035698508, -0.18254725087039828, 0.06772317559542981, 0.1702482001926524, 0.13078631650525052, -0.011207890053893525, -0.2827066738391295, 0.021640046612557137, -0.12301961617247964, -0.2235764832391093, -0.13407554870090363, 0.028610238636081867, 0.04633838428311388, -0.24790197440024436, 0.1301031592240289, 0.09762148578143255, 0.04218854649331082, -0.024867449908263305, -0.037356146343165274, -0.09771290625918261, 0.07522508222080598, 0.09567132647763091, 0.029481850250511234, 0.13313734638645794, -0.1112507031472033, -0.1447008523479753, 0.4249797680443435, -0.003137599930844524, -0.28383259285086143, 0.15964459589943572, -0.16968302531445675, -0.051437144165957405, 0.12338022074563372, 0.22085887536223076, 0.08489787211654017, -0.09193878742569889, 0.0753117216033085, -0.023154601052242055, 0.12586580232406655, 0.15174139436038042, 0.12246191630732607, 0.22589501163527143, 0.03758171268484809, 0.046465764720006984, 0.0996103082839964, -0.08555816607832005, -0.07434838421635726, -0.33703937575663795, -0.1842921326557795, -0.13266592788965104, 0.05791328436349762, -0.1136565531710265, -0.18359534004279834, 0.4492289701363806, 0.10250954989081065, 0.18649846943122667, 0.016610645050108855, 0.24201735318638384, 0.14994179696794052, 0.11557320911775935, 0.11504865374952329, 0.25861822647240124, 0.16425896671981635, 0.061381892442251694, -0.23292948430841506, -0.059234889206532956, 0.0708200418157503] |
1,802.03941 | Consequences of strong stability of minimal submanifolds | In this note we show that the recent dynamical stability result for small
$C^1$-perturbations of strongly stable minimal submanifolds of C.-J. Tsai and
M.-T. Wang directly extends to the enhanced Brakke flows of Ilmanen. We
illustrate applications of this result, including a local uniqueness statement
for strongly stable minimal submanifolds amongst stationary varifolds, and a
mechanism to flow through some singularities of Lagrangian mean curvature flow
which are proved to occur by Neves.
| math.DG math.AP | in this note we show that the recent dynamical stability result for small c1perturbations of strongly stable minimal submanifolds of cj tsai and mt wang directly extends to the enhanced brakke flows of ilmanen we illustrate applications of this result including a local uniqueness statement for strongly stable minimal submanifolds amongst stationary varifolds and a mechanism to flow through some singularities of lagrangian mean curvature flow which are proved to occur by neves | [['in', 'this', 'note', 'we', 'show', 'that', 'the', 'recent', 'dynamical', 'stability', 'result', 'for', 'small', 'c1perturbations', 'of', 'strongly', 'stable', 'minimal', 'submanifolds', 'of', 'cj', 'tsai', 'and', 'mt', 'wang', 'directly', 'extends', 'to', 'the', 'enhanced', 'brakke', 'flows', 'of', 'ilmanen', 'we', 'illustrate', 'applications', 'of', 'this', 'result', 'including', 'a', 'local', 'uniqueness', 'statement', 'for', 'strongly', 'stable', 'minimal', 'submanifolds', 'amongst', 'stationary', 'varifolds', 'and', 'a', 'mechanism', 'to', 'flow', 'through', 'some', 'singularities', 'of', 'lagrangian', 'mean', 'curvature', 'flow', 'which', 'are', 'proved', 'to', 'occur', 'by', 'neves']] | [-0.20311045269035313, 0.10326444255570842, -0.08490974103955373, 0.07728711633994052, -0.04595935673848407, -0.13118737654868923, 0.00997768797312085, 0.2933614229630322, -0.2934677285997018, -0.22759435763455008, 0.11415616424007928, -0.23511473319097742, -0.19274182293615114, 0.2091457970338325, -0.17828706177334264, 0.06084597408006044, 0.07411102368815305, -0.0049047097053429856, -0.03248114618217598, -0.2743890487209396, 0.3497336902336715, 0.0036810004369240918, 0.23480416924935088, 0.12735339105033558, 0.0878080240441231, -0.0009707231010782393, -0.02119224264815909, 0.0636856473794156, -0.22309391269803586, 0.14293701951200627, 0.20389506377739042, 0.06610622434328271, 0.22490407878609553, -0.35405431399504617, -0.2776008676689705, 0.131759531189981, 0.10745441854280764, 0.06409306666488465, -0.04314297117764921, -0.27331457797386877, 0.18022992168489385, -0.12197801988087084, -0.22224811090743296, -0.09245528473378453, 0.012097329140500459, 0.06626334287423946, -0.24491939282886785, 0.10321946271172125, 0.1834250163292506, 0.07306257367440283, -0.06086687135752546, -0.022526481673631766, -0.07968784799824839, 0.038982768141192524, 0.09048983716877969, 0.03930040340462368, 0.10201122426134471, -0.08136311841312133, -0.07658087301437985, 0.31947000692151994, -0.11617359486312848, -0.2082332944503845, 0.19430364955420773, -0.1147533048212222, -0.15522123182077624, 0.1408939848985676, 0.12866693493361547, 0.19221326768755503, -0.12303104847733391, 0.09636125252390765, -0.081646725841581, 0.03909423786904408, 0.11526899984463641, -0.015253430251542428, 0.1109166786023607, 0.10027970839291811, 0.14283028123134825, 0.113664652533472, 0.0025976504620215664, -0.13539080193731934, -0.3189102882800037, -0.1942561663374662, -0.10226415315872595, 0.1091862498297777, -0.0680535663655251, -0.15001434278802003, 0.3651611256869255, 0.09949347920307558, 0.19673897551806413, 0.09671175883196244, 0.22265055544087536, 0.03694602283840514, -0.022234641576874745, 0.1310589013697758, 0.2730575449185044, 0.19566976437536515, 0.08830080680829817, -0.163249993743056, -0.0190814220177469, 0.1628331905294669] |
1,802.03942 | On long time behavior of periodic entropy solutions of a degenerate
non-linear parabolic equation | We prove the asymptotic convergence of a space-periodic entropy solution of a
one-dimensional degenerate parabolic equation to a traveling wave. It is also
shown that on a segment containing the essential range of the limit profile the
flux function is linear (with the slope equaled to the speed of the traveling
wave) and the diffusion function is constant.
| math.AP | we prove the asymptotic convergence of a spaceperiodic entropy solution of a onedimensional degenerate parabolic equation to a traveling wave it is also shown that on a segment containing the essential range of the limit profile the flux function is linear with the slope equaled to the speed of the traveling wave and the diffusion function is constant | [['we', 'prove', 'the', 'asymptotic', 'convergence', 'of', 'a', 'spaceperiodic', 'entropy', 'solution', 'of', 'a', 'onedimensional', 'degenerate', 'parabolic', 'equation', 'to', 'a', 'traveling', 'wave', 'it', 'is', 'also', 'shown', 'that', 'on', 'a', 'segment', 'containing', 'the', 'essential', 'range', 'of', 'the', 'limit', 'profile', 'the', 'flux', 'function', 'is', 'linear', 'with', 'the', 'slope', 'equaled', 'to', 'the', 'speed', 'of', 'the', 'traveling', 'wave', 'and', 'the', 'diffusion', 'function', 'is', 'constant']] | [-0.18103065059704962, 0.08576407164720626, -0.09683637963286762, 0.04113435669167866, -0.07554219048550545, -0.08591447216619191, 0.0090982631041572, 0.3157001279679866, -0.3087012080861063, -0.2103977326046804, 0.12312798169120376, -0.3125375046651682, -0.11948560168645506, 0.1886693969497393, 0.03047516465925708, 0.13324304557694444, 0.04816975244642075, 0.0855507941691783, -0.05843202195909067, -0.17953417063209004, 0.3082897798639947, -0.012142382176785633, 0.28847813837487124, 0.06696718063301824, 0.1605651574530478, -0.01774358436272576, 0.06587969445912488, 0.01694183287628252, -0.16544996601479742, 0.07025976387526968, 0.1660663207013417, 0.06308839971536835, 0.27983872049712927, -0.35895456344788446, -0.25166021573461655, 0.09872707987910714, 0.15255919593418465, 0.11391488165626752, -0.028338090121216172, -0.24351004976779222, 0.05324505518415365, -0.11281743780147799, -0.25165985620998105, 0.030555118662144602, 0.06025955487235353, 0.11835065433466486, -0.2873191101283863, 0.11800022898914277, 0.05671708472073078, -0.04121904930999053, -0.08651777490138493, -0.03195693139950263, -0.05716179409224925, 0.0481007778495229, 0.08392390666995198, 0.0639010730263745, 0.060420375975683845, -0.1490853650110035, -0.00031200300195607646, 0.356072006939814, -0.1597107749689242, -0.2599034355953336, 0.15838171927065686, -0.15888106876960154, -0.0280037495540455, 0.17198587863320677, 0.1571860977223721, 0.13855493476550126, -0.10711795061120186, 0.08986458715336429, -0.0827278239840184, 0.18129060985390016, 0.09349364618738663, -0.029375133746913796, 0.14030435210864606, 0.16795929574311294, 0.15970896860456157, 0.15241801385478726, -0.08811640014053033, -0.10101871258290164, -0.3314199534983471, -0.19218508136490808, -0.16358845664894786, 0.08781971909449002, -0.09098496739245783, -0.25422860592240387, 0.43035705962443144, 0.0706576127077228, 0.1792206167391951, 0.0952726333273639, 0.24813822805399782, 0.25339142697188877, 0.000766289023008069, 0.11038329838403774, 0.24892121309350276, 0.17446851321287324, 0.10748677707983759, -0.26408223418422555, 0.042535627903095605, 0.09584986090917012] |
1,802.03943 | Temporal and volumetric denoising via quantile sparse image prior | This paper introduces an universal and structure-preserving regularization
term, called quantile sparse image (QuaSI) prior. The prior is suitable for
denoising images from various medical imaging modalities. We demonstrate its
effectiveness on volumetric optical coherence tomography (OCT) and computed
tomography (CT) data, which show different noise and image characteristics. OCT
offers high-resolution scans of the human retina but is inherently impaired by
speckle noise. CT on the other hand has a lower resolution and shows
high-frequency noise. For the purpose of denoising, we propose a variational
framework based on the QuaSI prior and a Huber data fidelity model that can
handle 3-D and 3-D+t data. Efficient optimization is facilitated through the
use of an alternating direction method of multipliers (ADMM) scheme and the
linearization of the quantile filter. Experiments on multiple datasets
emphasize the excellent performance of the proposed method.
| cs.CV | this paper introduces an universal and structurepreserving regularization term called quantile sparse image quasi prior the prior is suitable for denoising images from various medical imaging modalities we demonstrate its effectiveness on volumetric optical coherence tomography oct and computed tomography ct data which show different noise and image characteristics oct offers highresolution scans of the human retina but is inherently impaired by speckle noise ct on the other hand has a lower resolution and shows highfrequency noise for the purpose of denoising we propose a variational framework based on the quasi prior and a huber data fidelity model that can handle 3d and 3dt data efficient optimization is facilitated through the use of an alternating direction method of multipliers admm scheme and the linearization of the quantile filter experiments on multiple datasets emphasize the excellent performance of the proposed method | [['this', 'paper', 'introduces', 'an', 'universal', 'and', 'structurepreserving', 'regularization', 'term', 'called', 'quantile', 'sparse', 'image', 'quasi', 'prior', 'the', 'prior', 'is', 'suitable', 'for', 'denoising', 'images', 'from', 'various', 'medical', 'imaging', 'modalities', 'we', 'demonstrate', 'its', 'effectiveness', 'on', 'volumetric', 'optical', 'coherence', 'tomography', 'oct', 'and', 'computed', 'tomography', 'ct', 'data', 'which', 'show', 'different', 'noise', 'and', 'image', 'characteristics', 'oct', 'offers', 'highresolution', 'scans', 'of', 'the', 'human', 'retina', 'but', 'is', 'inherently', 'impaired', 'by', 'speckle', 'noise', 'ct', 'on', 'the', 'other', 'hand', 'has', 'a', 'lower', 'resolution', 'and', 'shows', 'highfrequency', 'noise', 'for', 'the', 'purpose', 'of', 'denoising', 'we', 'propose', 'a', 'variational', 'framework', 'based', 'on', 'the', 'quasi', 'prior', 'and', 'a', 'huber', 'data', 'fidelity', 'model', 'that', 'can', 'handle', '3d', 'and', '3dt', 'data', 'efficient', 'optimization', 'is', 'facilitated', 'through', 'the', 'use', 'of', 'an', 'alternating', 'direction', 'method', 'of', 'multipliers', 'admm', 'scheme', 'and', 'the', 'linearization', 'of', 'the', 'quantile', 'filter', 'experiments', 'on', 'multiple', 'datasets', 'emphasize', 'the', 'excellent', 'performance', 'of', 'the', 'proposed', 'method']] | [-0.033904897705984434, -0.029971664347145374, -0.0995342207107959, 0.06659164454821231, -0.07742345936463348, -0.16550830097070762, -0.008991597535454535, 0.4782501607867224, -0.28996751266531645, -0.28332422881586744, 0.14260444011805312, -0.24541344424443587, -0.20288095686451665, 0.2359079841840347, -0.1695955888908689, 0.11608652633440215, 0.13178022703421968, -0.007820128985414548, -0.08978777862918963, -0.22861940788570792, 0.269025003610711, 0.06434196822478302, 0.3832388491785553, 0.0037700813862362077, 0.1934668320172932, 0.040096252531345404, -0.09066944993599983, -0.01318345381213086, -0.05331238876954428, 0.14173352819218832, 0.25023998133838177, 0.18114121527178212, 0.30916758510284126, -0.4103010603093675, -0.24486168867110142, 0.0583547725142645, 0.13878734665423897, 0.0793288123894724, -0.07366378575430385, -0.328169188947816, 0.03314253385178745, -0.10225152023402707, 0.028346139368867235, -0.16348690576186137, -0.07480636370517979, -0.02491511733803366, -0.3432663294287132, 0.12520084061543457, 0.04891899314409654, 0.09206448927787798, -0.08860243537845755, -0.10278004852921835, 0.041954215817219975, 0.08485016658835645, 0.0034611690797776515, 0.05239422523383317, 0.12973900186563178, -0.16334313435737777, -0.11936415304496352, 0.3147412452880027, -0.03009447673851225, -0.21655549584954445, 0.1731275225607013, -0.07314297038163724, -0.0836108401085117, 0.14942070730363152, 0.17755475006332355, 0.14450737151450344, -0.16417392985895277, 0.043824276025822784, -0.026748582816383402, 0.2015678802671443, 0.04621690932628034, 0.006734802869842887, 0.0942432868493987, 0.2207878263999841, 0.06615405610895582, 0.165749327947976, -0.25689239015004467, -0.0027807805878442846, -0.19860196642818795, -0.14407339153611767, -0.2355314195738174, -0.03259805503766984, -0.1204671913983475, -0.16402391472116246, 0.43231125725025255, 0.23417210958765022, 0.17307323459535837, 0.032131327410128765, 0.4139898815059236, 0.06572465408992555, 0.06749252273168947, 0.017742623165915055, 0.16793309404404552, 0.08757353389935035, 0.1244578950705805, -0.23732896611160997, 0.040921623040256754, 0.06379324018323262] |
1,802.03944 | A 1Mbps Real-time NLOS UV Scattering Communication System with Receiver
Diversity over 1km | In the non-line of sight (NLOS) ultraviolet (UV) scattering communication,
the received signals exhibit the characteristics of discrete photoelectrons due
to the extremely large path loss. We design and demonstrate an NLOS UV
scattering communication system in this work, where the receiver-side signal
detection is designed based on a discrete-time Poisson channel model. In our
system, a laser and multiple photomultiplier tubes are employed as the optical
transmitter and detector, respectively. Furthermore, we design algorithms for
pulse-counting, synchronization, channel estimation and $LLR$ computation for
hardware realization in FPGA board. Simulation results are provided to evaluate
the proposed system design and specify the system key parameters. We perform
field tests for real-time communication with the transmission range over $1$km,
where the system throughput reaches $1$Mbps.
| eess.SP | in the nonline of sight nlos ultraviolet uv scattering communication the received signals exhibit the characteristics of discrete photoelectrons due to the extremely large path loss we design and demonstrate an nlos uv scattering communication system in this work where the receiverside signal detection is designed based on a discretetime poisson channel model in our system a laser and multiple photomultiplier tubes are employed as the optical transmitter and detector respectively furthermore we design algorithms for pulsecounting synchronization channel estimation and llr computation for hardware realization in fpga board simulation results are provided to evaluate the proposed system design and specify the system key parameters we perform field tests for realtime communication with the transmission range over 1km where the system throughput reaches 1mbps | [['in', 'the', 'nonline', 'of', 'sight', 'nlos', 'ultraviolet', 'uv', 'scattering', 'communication', 'the', 'received', 'signals', 'exhibit', 'the', 'characteristics', 'of', 'discrete', 'photoelectrons', 'due', 'to', 'the', 'extremely', 'large', 'path', 'loss', 'we', 'design', 'and', 'demonstrate', 'an', 'nlos', 'uv', 'scattering', 'communication', 'system', 'in', 'this', 'work', 'where', 'the', 'receiverside', 'signal', 'detection', 'is', 'designed', 'based', 'on', 'a', 'discretetime', 'poisson', 'channel', 'model', 'in', 'our', 'system', 'a', 'laser', 'and', 'multiple', 'photomultiplier', 'tubes', 'are', 'employed', 'as', 'the', 'optical', 'transmitter', 'and', 'detector', 'respectively', 'furthermore', 'we', 'design', 'algorithms', 'for', 'pulsecounting', 'synchronization', 'channel', 'estimation', 'and', 'llr', 'computation', 'for', 'hardware', 'realization', 'in', 'fpga', 'board', 'simulation', 'results', 'are', 'provided', 'to', 'evaluate', 'the', 'proposed', 'system', 'design', 'and', 'specify', 'the', 'system', 'key', 'parameters', 'we', 'perform', 'field', 'tests', 'for', 'realtime', 'communication', 'with', 'the', 'transmission', 'range', 'over', '1km', 'where', 'the', 'system', 'throughput', 'reaches', '1mbps']] | [-0.1955665993349006, 0.03158372765649735, -0.033853198785895135, -0.020191802348681523, -0.02944931834633273, -0.22014365319117177, 0.0439907199953024, 0.4520252608065683, -0.21201453538899256, -0.3215194332254369, 0.11116030834600087, -0.2385633691797048, -0.16995733085928894, 0.23022972654581556, -0.1016346294188217, 0.11655509673301281, 0.07659680256998636, -0.009426834406255464, -0.020772613762930884, -0.22542764508066987, 0.21687882069528588, 0.12281420328692208, 0.3162096481679416, 0.017998194905013087, 0.13128115728542936, 0.05939048365311227, -0.02749429486040783, -0.07376413818115626, -0.09142294440539629, 0.05260126536917434, 0.2703911945587251, 0.14714393207076482, 0.20825789460715483, -0.42758785061750226, -0.2326808638370982, 0.07699569871429567, 0.13759577446295387, 0.04523727755872457, -0.03887148034982232, -0.3067922600103936, 0.06080795593968615, -0.17751186939970992, -0.02733986578291146, 0.022629083584751783, -0.0609475692489934, 0.05094970805131323, -0.3161909941477868, -0.03534324169991826, -0.03986822807691931, 0.06285178001091732, -0.03942653028165725, -0.07581880785490773, 0.04017845269224447, 0.13547993294243527, -0.025416814070005667, -0.017905964815186533, 0.16330781238668454, -0.11535865846377508, -0.11134123512945039, 0.36557408601681635, -0.028051475445811856, -0.17033431967493237, 0.1709802823180969, -0.1095189987908958, -0.07684255038516793, 0.1948063183288143, 0.26773777139198973, 0.053581660503627566, -0.18187515982010258, 0.023843688611603697, 0.022706358822259475, 0.20655762068397268, 0.05385390364913255, 0.12077600759552504, 0.16529034498902237, 0.22853890985129324, 0.09121457608539717, 0.12986844182355192, -0.19950983239332123, -0.07900955328884406, -0.2732529583958742, -0.12506130004559107, -0.17825563968068397, 0.004186177361241686, -0.07386278623383306, -0.10101306126490842, 0.36358372870136085, 0.21654340992795257, 0.11842621865538441, 0.10215744435620623, 0.4093735854073269, 0.10712324755978052, 0.05116833189154059, 0.09712053490499777, 0.24366991087317286, 0.12989736519511638, 0.13895397052467928, -0.24385551681620923, 0.023910036273641376, -0.03786332009060164] |
1,802.03945 | Estimating Diffusion With Compound Poisson Jumps Based On
Self-normalized Residuals | We consider parametric estimation of the continuous part of a class of
ergodic diffusions with jumps based on high-frequency samples. Various papers
previously proposed threshold based methods, which enable us to distinguish
whether observed increments have jumps or not at each small-time interval,
hence to estimate the unknown parameters separately. However, a data-adapted
and quantitative choice of the threshold parameter is known to be a subtle and
sensitive problem. In this paper, we present a simple alternative based on the
Jarque-Bera normality test for the Euler residuals. Different from the
threshold based method, the proposed method does not require any sensitive fine
tuning, hence is of practical value. It is shown that under suitable conditions
the proposed estimator is asymptotically equivalent to an estimator constructed
by the unobserved fluctuation of the continuous part of the solution process,
hence is asymptotically efficient. Some numerical experiments are conducted to
observe finite-sample performance of the proposed method.
| stat.ME | we consider parametric estimation of the continuous part of a class of ergodic diffusions with jumps based on highfrequency samples various papers previously proposed threshold based methods which enable us to distinguish whether observed increments have jumps or not at each smalltime interval hence to estimate the unknown parameters separately however a dataadapted and quantitative choice of the threshold parameter is known to be a subtle and sensitive problem in this paper we present a simple alternative based on the jarquebera normality test for the euler residuals different from the threshold based method the proposed method does not require any sensitive fine tuning hence is of practical value it is shown that under suitable conditions the proposed estimator is asymptotically equivalent to an estimator constructed by the unobserved fluctuation of the continuous part of the solution process hence is asymptotically efficient some numerical experiments are conducted to observe finitesample performance of the proposed method | [['we', 'consider', 'parametric', 'estimation', 'of', 'the', 'continuous', 'part', 'of', 'a', 'class', 'of', 'ergodic', 'diffusions', 'with', 'jumps', 'based', 'on', 'highfrequency', 'samples', 'various', 'papers', 'previously', 'proposed', 'threshold', 'based', 'methods', 'which', 'enable', 'us', 'to', 'distinguish', 'whether', 'observed', 'increments', 'have', 'jumps', 'or', 'not', 'at', 'each', 'smalltime', 'interval', 'hence', 'to', 'estimate', 'the', 'unknown', 'parameters', 'separately', 'however', 'a', 'dataadapted', 'and', 'quantitative', 'choice', 'of', 'the', 'threshold', 'parameter', 'is', 'known', 'to', 'be', 'a', 'subtle', 'and', 'sensitive', 'problem', 'in', 'this', 'paper', 'we', 'present', 'a', 'simple', 'alternative', 'based', 'on', 'the', 'jarquebera', 'normality', 'test', 'for', 'the', 'euler', 'residuals', 'different', 'from', 'the', 'threshold', 'based', 'method', 'the', 'proposed', 'method', 'does', 'not', 'require', 'any', 'sensitive', 'fine', 'tuning', 'hence', 'is', 'of', 'practical', 'value', 'it', 'is', 'shown', 'that', 'under', 'suitable', 'conditions', 'the', 'proposed', 'estimator', 'is', 'asymptotically', 'equivalent', 'to', 'an', 'estimator', 'constructed', 'by', 'the', 'unobserved', 'fluctuation', 'of', 'the', 'continuous', 'part', 'of', 'the', 'solution', 'process', 'hence', 'is', 'asymptotically', 'efficient', 'some', 'numerical', 'experiments', 'are', 'conducted', 'to', 'observe', 'finitesample', 'performance', 'of', 'the', 'proposed', 'method']] | [-0.0500939460947517, 0.045097114370970276, -0.1217616239544488, 0.0837025592212291, -0.11245435292306416, -0.16528776065029155, 0.07275046437431441, 0.39561184648085723, -0.22335649319234993, -0.2763940995323242, 0.15163266807477074, -0.20928885163467184, -0.16007256846574086, 0.22528626680428573, -0.0831014308178882, 0.11313825772845416, 0.05685396788508764, 0.040970255245829555, -0.056546640568775695, -0.2570731714534174, 0.3059195518514819, 0.07081658423987108, 0.33641066265522274, 0.01937498211333008, 0.1451672689562857, -0.02743122317562146, -0.04514607967953984, 0.03095967851559535, -0.15387072896475446, 0.0782756670022855, 0.2341246748064923, 0.09403848368069762, 0.31190388834757077, -0.36021135505530744, -0.19993231485162366, 0.14763828736575707, 0.11209554904913106, 0.07098148643060333, -0.024508505640853825, -0.2717222085815262, 0.10190118526791936, -0.12740910390857607, -0.11428043249897755, -0.09173566688989084, -0.016704979023061596, 0.0306149845082591, -0.35514900477167655, 0.08504350410090977, 0.0662117139375829, 0.02422893261264816, -0.01820032233553757, -0.1147419503714599, 0.034271226321232154, 0.10368742883150986, 0.06906718768647346, -0.027658822893334382, 0.12207704491598846, -0.058383450177407446, -0.10156014488024226, 0.3204947479924205, -0.06544297533230735, -0.25467240765642424, 0.18568344139977805, -0.11927707228724929, -0.1320323820192633, 0.1510794365796708, 0.14772466243571275, 0.15080849211300154, -0.1777056122641684, 0.06911496606325827, -0.029164590893612643, 0.18867331326564218, 0.02826841677653954, 0.0049157376694432525, 0.13396534780584662, 0.14772399090980362, 0.12030467279588006, 0.13084701371648313, -0.1030693022268159, -0.08263333961851412, -0.30367926779118454, -0.10808103223476413, -0.21181843351185708, 0.01162952333541327, -0.07993120748784799, -0.21112883933755575, 0.39388755130787173, 0.21419521051688822, 0.182605220597576, 0.06957277578722032, 0.2845958732673294, 0.13887604645773913, 0.01647105695703974, 0.07551596206370983, 0.23381794035807404, 0.09721371753431567, 0.04714203053819282, -0.20542699708973736, 0.1478755825891631, 0.05897158484863625] |
1,802.03946 | Metrical irrationality results related to values of the Riemann
$\zeta$-function | We introduce a one-parameter family of series associated to the Riemann
$\zeta$-function and prove that the values of the elements of this family at
integers are linearly independent over the rationals for almost all values of
the parameter, where almost all is with respect to any sufficiently nice
measure. We also give similar results for the Euler--Mascheroni constant, for
$\sum_{n=1}^\infty \frac{1}{n^n}$ and for $\sum_{n=1}^\infty \frac{1}{n! +1}$.
Finally, specialising the criteria used, we give some new criteria for the
irrationality of $\zeta(k)$, the Euler--Mascheroni constant and the latter two
series.
| math.NT | we introduce a oneparameter family of series associated to the riemann zetafunction and prove that the values of the elements of this family at integers are linearly independent over the rationals for almost all values of the parameter where almost all is with respect to any sufficiently nice measure we also give similar results for the eulermascheroni constant for sum_n1infty frac1nn and for sum_n1infty frac1n 1 finally specialising the criteria used we give some new criteria for the irrationality of zetak the eulermascheroni constant and the latter two series | [['we', 'introduce', 'a', 'oneparameter', 'family', 'of', 'series', 'associated', 'to', 'the', 'riemann', 'zetafunction', 'and', 'prove', 'that', 'the', 'values', 'of', 'the', 'elements', 'of', 'this', 'family', 'at', 'integers', 'are', 'linearly', 'independent', 'over', 'the', 'rationals', 'for', 'almost', 'all', 'values', 'of', 'the', 'parameter', 'where', 'almost', 'all', 'is', 'with', 'respect', 'to', 'any', 'sufficiently', 'nice', 'measure', 'we', 'also', 'give', 'similar', 'results', 'for', 'the', 'eulermascheroni', 'constant', 'for', 'sum_n1infty', 'frac1nn', 'and', 'for', 'sum_n1infty', 'frac1n', '1', 'finally', 'specialising', 'the', 'criteria', 'used', 'we', 'give', 'some', 'new', 'criteria', 'for', 'the', 'irrationality', 'of', 'zetak', 'the', 'eulermascheroni', 'constant', 'and', 'the', 'latter', 'two', 'series']] | [-0.16216631031155967, 0.10382620720933615, -0.06257067568896507, 0.05657005066644739, -0.03918143799951808, -0.1349149643051946, 0.021650022096847268, 0.33838575176725333, -0.2814062593123791, -0.19117452331556706, 0.10316617077280005, -0.29194040915635094, -0.12016406928358431, 0.27385577725627547, -0.04235183560046029, 0.05112600621578167, -0.008117125671229918, 0.09126345430162143, -0.06398518751534125, -0.3312268813685726, 0.38027718769047747, -0.04666452729989859, 0.18276427342789248, 0.04353421234356409, 0.10084780007971196, -0.0215085430377671, -0.0032020769695836034, -0.028362512778998775, -0.2328794254708555, 0.08816149261441421, 0.23856604942755605, 0.08742816345718231, 0.2752133331007578, -0.31371941036989237, -0.09875097171408759, 0.18727176908445967, 0.1350688387756236, -0.017259478984421796, 0.003516037162626162, -0.2006888914779252, 0.15883055943281346, -0.13584642835088412, -0.1642638211143838, -0.11534630722069944, 0.0712142094747502, 0.08684391244737939, -0.341925887232223, 0.033930474733179755, 0.05960387010550634, 0.06864530185703188, -0.0815144165112129, -0.20277858195582998, 0.06111594988562336, 0.09712783130287955, 0.09392663792823441, 0.025425811287608336, -0.008837277090854266, -0.05865719335270114, -0.07586424974512986, 0.33693365839876194, -0.11290556550441332, -0.219047387807884, 0.11401567457307299, -0.18141822172566952, -0.1757172414641404, 0.09400809895966879, 0.08158196335319769, 0.15779181025837632, -0.05405326823661612, 0.12796661308873974, -0.08932036842981522, 0.12492329388094897, 0.11905771609797905, 0.02318838535575196, 0.10898445325437933, 0.0035407722885297103, 0.08892536903096532, 0.12460763908298263, -0.01533814384244827, -0.05127342912072146, -0.3954629196616059, -0.2251711649587378, -0.1551667115920943, 0.10016173614316028, -0.17144518763052474, -0.21303602595898238, 0.401945213702592, 0.11697565329218791, 0.233041803966361, 0.1916840686780316, 0.18160259687680413, 0.12467778704806486, 0.01521684277320839, 0.05453163721408187, 0.13576515680026865, 0.12003905526679856, -0.0009651233677604151, -0.13764384704303334, 0.04675704210636799, 0.11389776724073189] |
1,802.03947 | Optimizing Bivariate Partial Information Decomposition | None of the BROJA information decomposition measures $\mbox{SI}, \mbox{CI},
\mbox{UIy}, \mbox{UIz}$ are convex or concave over the probability simplex. In
this paper, we provide formulas for the sub-gradient and super-gradients of any
of the information decomposition measures. Then we apply these results to
obtain an optimum of some of these information decomposition measures when
optimized over a constrained set of probability distributions.
| math.OC | none of the broja information decomposition measures mboxsi mboxci mboxuiy mboxuiz are convex or concave over the probability simplex in this paper we provide formulas for the subgradient and supergradients of any of the information decomposition measures then we apply these results to obtain an optimum of some of these information decomposition measures when optimized over a constrained set of probability distributions | [['none', 'of', 'the', 'broja', 'information', 'decomposition', 'measures', 'mboxsi', 'mboxci', 'mboxuiy', 'mboxuiz', 'are', 'convex', 'or', 'concave', 'over', 'the', 'probability', 'simplex', 'in', 'this', 'paper', 'we', 'provide', 'formulas', 'for', 'the', 'subgradient', 'and', 'supergradients', 'of', 'any', 'of', 'the', 'information', 'decomposition', 'measures', 'then', 'we', 'apply', 'these', 'results', 'to', 'obtain', 'an', 'optimum', 'of', 'some', 'of', 'these', 'information', 'decomposition', 'measures', 'when', 'optimized', 'over', 'a', 'constrained', 'set', 'of', 'probability', 'distributions']] | [-0.06668368470052193, 0.013839136522337554, -0.15982302273463073, 0.07582811582913815, -0.04098147261438185, -0.10611516544755933, 0.08716052990800155, 0.38891411431390666, -0.30823467694351386, -0.22088004924067905, 0.1652478549031316, -0.28183493848312, -0.14203371841797668, 0.1864794635270112, -0.12076929571174269, 0.0839223638738923, 0.05103979831368759, 0.06913309540762165, -0.14630662168151345, -0.25679484960334054, 0.33280637487769127, -0.02417034938417632, 0.23783783498621577, 0.0766707986175757, 0.13185685063744412, 0.028757678441189486, -0.06796819157095561, 0.05982691452613678, -0.20454721646543977, 0.1793251489449678, 0.2510941508627529, 0.2889366075075392, 0.29723258174409894, -0.3718270294625184, -0.15722526885681495, 0.1849987144935234, 0.12189869416460138, 0.06272736008696515, 0.005485664912627946, -0.24092988785484742, 0.10138408741753163, -0.12875618411484976, -0.07307599076262579, -0.12126533887830787, -0.035798323726088835, 0.07675257199925595, -0.3142844532327405, 0.009923151646066329, 0.06612793614702492, 0.03894747132114295, -0.11484921259548643, -0.17523452398720488, 0.010572669292992816, 0.10278195132559631, 0.009973016117924246, 0.02503937654847536, 0.10759297679660135, -0.07331975050479302, -0.14358496605940485, 0.30789445597549964, -0.03446026035215192, -0.3129405688237527, 0.12194367037315307, -0.1509646588759818, -0.15352248990555004, 0.1344793675756403, 0.18261876739240412, 0.15631249758158008, -0.16251232221864106, 0.029937935245408002, -0.09004599093620119, 0.09388372171991344, 0.07322775035422167, 0.09726150316218364, 0.13345955206474674, 0.08301118035110291, 0.1796995343094499, 0.1582871007708961, -0.08600937421754773, -0.1027695039479897, -0.29381592591004124, -0.18244711563376517, -0.20186837374007907, 0.02390772820418251, -0.16306313748269718, -0.18417164029810448, 0.39138463152379827, 0.12532554620250674, 0.18404425010797648, 0.1408820068463683, 0.2880206318764851, 0.1185258881428283, 0.006379438130634612, 0.09878076929277901, 0.13850038384081362, 0.1301713602232008, -0.007642694833238834, -0.144562415192546, 0.08340686797324953, 0.0619854341774922] |
1,802.03948 | Fast and rigorous arbitrary-precision computation of Gauss-Legendre
quadrature nodes and weights | We describe a strategy for rigorous arbitrary-precision evaluation of
Legendre polynomials on the unit interval and its application in the generation
of Gauss-Legendre quadrature rules. Our focus is on making the evaluation
practical for a wide range of realistic parameters, corresponding to the
requirements of numerical integration to an accuracy of about 100 to 100 000
bits. Our algorithm combines the summation by rectangular splitting of several
types of expansions in terms of hypergeometric series with a fixed-point
implementation of Bonnet's three-term recurrence relation. We then compute
rigorous enclosures of the Gauss-Legendre nodes and weights using the interval
Newton method. We provide rigorous error bounds for all steps of the algorithm.
The approach is validated by an implementation in the Arb library, which
achieves order-of-magnitude speedups over previous code for computing
Gauss-Legendre rules with simultaneous high degree and precision.
| cs.NA cs.MS | we describe a strategy for rigorous arbitraryprecision evaluation of legendre polynomials on the unit interval and its application in the generation of gausslegendre quadrature rules our focus is on making the evaluation practical for a wide range of realistic parameters corresponding to the requirements of numerical integration to an accuracy of about 100 to 100 000 bits our algorithm combines the summation by rectangular splitting of several types of expansions in terms of hypergeometric series with a fixedpoint implementation of bonnets threeterm recurrence relation we then compute rigorous enclosures of the gausslegendre nodes and weights using the interval newton method we provide rigorous error bounds for all steps of the algorithm the approach is validated by an implementation in the arb library which achieves orderofmagnitude speedups over previous code for computing gausslegendre rules with simultaneous high degree and precision | [['we', 'describe', 'a', 'strategy', 'for', 'rigorous', 'arbitraryprecision', 'evaluation', 'of', 'legendre', 'polynomials', 'on', 'the', 'unit', 'interval', 'and', 'its', 'application', 'in', 'the', 'generation', 'of', 'gausslegendre', 'quadrature', 'rules', 'our', 'focus', 'is', 'on', 'making', 'the', 'evaluation', 'practical', 'for', 'a', 'wide', 'range', 'of', 'realistic', 'parameters', 'corresponding', 'to', 'the', 'requirements', 'of', 'numerical', 'integration', 'to', 'an', 'accuracy', 'of', 'about', '100', 'to', '100', '000', 'bits', 'our', 'algorithm', 'combines', 'the', 'summation', 'by', 'rectangular', 'splitting', 'of', 'several', 'types', 'of', 'expansions', 'in', 'terms', 'of', 'hypergeometric', 'series', 'with', 'a', 'fixedpoint', 'implementation', 'of', 'bonnets', 'threeterm', 'recurrence', 'relation', 'we', 'then', 'compute', 'rigorous', 'enclosures', 'of', 'the', 'gausslegendre', 'nodes', 'and', 'weights', 'using', 'the', 'interval', 'newton', 'method', 'we', 'provide', 'rigorous', 'error', 'bounds', 'for', 'all', 'steps', 'of', 'the', 'algorithm', 'the', 'approach', 'is', 'validated', 'by', 'an', 'implementation', 'in', 'the', 'arb', 'library', 'which', 'achieves', 'orderofmagnitude', 'speedups', 'over', 'previous', 'code', 'for', 'computing', 'gausslegendre', 'rules', 'with', 'simultaneous', 'high', 'degree', 'and', 'precision']] | [-0.1393102890671157, -0.01782302798518221, -0.060569878471194494, 0.01950807099627572, -0.07219943357966573, -0.09787533663400858, 0.12207531917625837, 0.3944864478096259, -0.23371900267155318, -0.32635580257867736, 0.11933487007585387, -0.236123197333445, -0.10625574855291345, 0.29850285686552525, -0.0448505600458748, 0.10799822307706027, 0.10716052325408933, 0.013739198655863259, -0.11873310351436087, -0.310671880151976, 0.24170878459866957, 0.0645297052901533, 0.25955931789898345, 0.03378986467928927, 0.1493765372955056, 0.0009967515868195098, -0.05795723445237808, -0.05538831747358115, -0.12211992139693537, 0.15603789740757976, 0.22506432627032932, 0.15070372478862246, 0.3000195865057034, -0.3774327508260985, -0.12126335339191899, 0.037889013328194186, 0.14436811895491622, 0.08123133117718313, -0.00325605386982022, -0.23539345586176613, 0.10324132351687165, -0.17753485839141, -0.11228477043223654, -0.15803996896666053, 0.008926455882920636, 0.07948381304171392, -0.3257362598598526, 0.03691587274853917, 0.016695069879244864, 0.12307509222476602, 0.011430355397776681, -0.17174856053336973, 0.08192121103010643, 0.1007132254199212, -0.03415003016577672, -0.0023153675393462717, 0.06361434886042738, -0.060869684519929634, -0.15310341517420875, 0.3621427965276747, -0.037311784725369494, -0.22144353218215831, 0.10194918955508754, -0.0773656825474698, -0.12793764230801905, 0.1548360812095858, 0.17010023910133504, 0.13412376788692723, -0.10699299709425851, 0.06222638829063504, 0.004865650758448747, 0.164742267114385, 0.1032830629105807, 0.020399308966124338, 0.11487177426704011, 0.162922732866195, 0.0332185402579552, 0.13884407250301095, -0.05847683776732829, -0.1377658267177159, -0.3333813654084643, -0.17219323897064268, -0.18895306162933023, -0.01713167345687318, -0.18444341307863168, -0.19344517991211427, 0.42280453980880245, 0.15929299775551228, 0.1489781591603231, 0.1637892511232736, 0.3386873283537386, 0.1509908320325286, 0.055215508342057254, 0.07660288728518988, 0.16015295784170036, 0.12267220529506533, 0.06525651082478584, -0.18500974037007784, 0.00019078031802054147, 0.1274984451948846] |
1,802.03949 | Spontaneous domain formation in disordered copolymers as a mechanism for
chromosome structuring | Motivated by the problem of domain formation in chromosomes, we studied a
co--polymer model where only a subset of the monomers feel attractive
interactions. These monomers are displaced randomly from a regularly-spaced
pattern, thus introducing some quenched disorder in the system. Previous work
has shown that in the case of regularly-spaced interacting monomers this chain
can fold into structures characterized by multiple distinct domains of
consecutive segments. In each domain, attractive interactions are balanced by
the entropy cost of forming loops. We show by advanced replica-exchange
simulations that adding disorder in the position of the interacting monomers
further stabilizes these domains. The model suggests that the partitioning of
the chain into well-defined domains of consecutive monomers is a spontaneous
property of heteropolymers. In the case of chromosomes, evolution could have
acted on the spacing of interacting monomers to modulate in a simple way the
underlying domains for functional reasons.
| q-bio.BM | motivated by the problem of domain formation in chromosomes we studied a copolymer model where only a subset of the monomers feel attractive interactions these monomers are displaced randomly from a regularlyspaced pattern thus introducing some quenched disorder in the system previous work has shown that in the case of regularlyspaced interacting monomers this chain can fold into structures characterized by multiple distinct domains of consecutive segments in each domain attractive interactions are balanced by the entropy cost of forming loops we show by advanced replicaexchange simulations that adding disorder in the position of the interacting monomers further stabilizes these domains the model suggests that the partitioning of the chain into welldefined domains of consecutive monomers is a spontaneous property of heteropolymers in the case of chromosomes evolution could have acted on the spacing of interacting monomers to modulate in a simple way the underlying domains for functional reasons | [['motivated', 'by', 'the', 'problem', 'of', 'domain', 'formation', 'in', 'chromosomes', 'we', 'studied', 'a', 'copolymer', 'model', 'where', 'only', 'a', 'subset', 'of', 'the', 'monomers', 'feel', 'attractive', 'interactions', 'these', 'monomers', 'are', 'displaced', 'randomly', 'from', 'a', 'regularlyspaced', 'pattern', 'thus', 'introducing', 'some', 'quenched', 'disorder', 'in', 'the', 'system', 'previous', 'work', 'has', 'shown', 'that', 'in', 'the', 'case', 'of', 'regularlyspaced', 'interacting', 'monomers', 'this', 'chain', 'can', 'fold', 'into', 'structures', 'characterized', 'by', 'multiple', 'distinct', 'domains', 'of', 'consecutive', 'segments', 'in', 'each', 'domain', 'attractive', 'interactions', 'are', 'balanced', 'by', 'the', 'entropy', 'cost', 'of', 'forming', 'loops', 'we', 'show', 'by', 'advanced', 'replicaexchange', 'simulations', 'that', 'adding', 'disorder', 'in', 'the', 'position', 'of', 'the', 'interacting', 'monomers', 'further', 'stabilizes', 'these', 'domains', 'the', 'model', 'suggests', 'that', 'the', 'partitioning', 'of', 'the', 'chain', 'into', 'welldefined', 'domains', 'of', 'consecutive', 'monomers', 'is', 'a', 'spontaneous', 'property', 'of', 'heteropolymers', 'in', 'the', 'case', 'of', 'chromosomes', 'evolution', 'could', 'have', 'acted', 'on', 'the', 'spacing', 'of', 'interacting', 'monomers', 'to', 'modulate', 'in', 'a', 'simple', 'way', 'the', 'underlying', 'domains', 'for', 'functional', 'reasons']] | [-0.1795558593768764, 0.20143376529098006, -0.049375185359350635, 0.022858957778787454, 0.019388332841105308, -0.148645351331481, 0.052233954196195774, 0.4195297843707888, -0.27940944975692056, -0.2518997817181501, 0.022344095248810248, -0.2890971832714925, -0.15340571967109837, 0.08126666063119363, 0.017821563282768998, -0.03146138554126424, 0.07524686074118761, 0.00416063696564794, 0.011024476503625187, -0.22957877860710824, 0.29773307201411925, -0.006271932380361445, 0.23755848305549898, 0.03578882112211529, 0.07024993067419769, 0.018841498482959882, 0.022533547040231326, 0.05653459967532814, -0.13193515056017405, 0.11099797261538197, 0.19584488589316607, 0.03187203898888406, 0.2764970084758563, -0.49358542862304505, -0.23737734385794215, 0.09705098977799984, 0.21681270691497415, 0.12589030682840605, -0.04107648673570471, -0.28212636935168367, 0.05660105705411242, -0.13526567630313538, -0.11427099022689281, -0.01394911993126191, 0.02909778246356037, 0.08888254740162933, -0.2433539601480401, 0.09088776703773929, 0.08771096476066183, 0.05307834654508921, -0.0538717336974563, -0.08508376056725322, -0.05670914967738142, 0.13809077970405428, 0.053752158942707386, 0.01077337628980511, 0.17489864616561057, -0.1136762900621749, -0.11642594660879206, 0.36258069995865727, 0.0003825734828867568, -0.20916794364234345, 0.2366946267351608, -0.117058174559634, -0.15100551193154998, 0.15645893559722393, 0.13051341048398074, 0.09889597704369597, -0.17206924178031266, 0.07047114860666479, -0.024296899976196305, 0.19260301230625318, 0.09802764727290245, -0.02520117379924995, 0.24367580449071108, 0.20843879454112893, 0.05394385412066625, 0.20952691935352832, -0.026991727944751014, -0.16150655812621367, -0.222113441098122, -0.13426386071181537, -0.23165454921756415, -0.00039458845276385546, -0.09692084761461456, -0.1764889278161205, 0.3938039136712983, 0.08406532150592888, 0.2164761190546439, 0.025407811865482428, 0.20771931677481673, 0.01865651385371117, 0.11057866897571507, -0.007180306679200406, 0.1641452057396871, 0.07328731734493135, 0.02087902542103657, -0.20086837989971942, 0.06842780866091383, 0.06493182839113194] |
1,802.0395 | Quasi-Optimal Partial Order Reduction | A dynamic partial order reduction (DPOR) algorithm is optimal when it always
explores at most one representative per Mazurkiewicz trace. Existing literature
suggests that the reduction obtained by the non-optimal, state-of-the-art
Source-DPOR (SDPOR) algorithm is comparable to optimal DPOR. We show the first
program with $\mathop{\mathcal{O}}(n)$ Mazurkiewicz traces where SDPOR explores
$\mathop{\mathcal{O}}(2^n)$ redundant schedules and identify the cause of the
blow-up as an NP-hard problem. Our main contribution is a new approach, called
Quasi-Optimal POR, that can arbitrarily approximate an optimal exploration
using a provided constant k. We present an implementation of our method in a
new tool called Dpu using specialised data structures. Experiments with Dpu,
including Debian packages, show that optimality is achieved with low values of
k, outperforming state-of-the-art tools.
| cs.PL | a dynamic partial order reduction dpor algorithm is optimal when it always explores at most one representative per mazurkiewicz trace existing literature suggests that the reduction obtained by the nonoptimal stateoftheart sourcedpor sdpor algorithm is comparable to optimal dpor we show the first program with mathopmathcalon mazurkiewicz traces where sdpor explores mathopmathcalo2n redundant schedules and identify the cause of the blowup as an nphard problem our main contribution is a new approach called quasioptimal por that can arbitrarily approximate an optimal exploration using a provided constant k we present an implementation of our method in a new tool called dpu using specialised data structures experiments with dpu including debian packages show that optimality is achieved with low values of k outperforming stateoftheart tools | [['a', 'dynamic', 'partial', 'order', 'reduction', 'dpor', 'algorithm', 'is', 'optimal', 'when', 'it', 'always', 'explores', 'at', 'most', 'one', 'representative', 'per', 'mazurkiewicz', 'trace', 'existing', 'literature', 'suggests', 'that', 'the', 'reduction', 'obtained', 'by', 'the', 'nonoptimal', 'stateoftheart', 'sourcedpor', 'sdpor', 'algorithm', 'is', 'comparable', 'to', 'optimal', 'dpor', 'we', 'show', 'the', 'first', 'program', 'with', 'mathopmathcalon', 'mazurkiewicz', 'traces', 'where', 'sdpor', 'explores', 'mathopmathcalo2n', 'redundant', 'schedules', 'and', 'identify', 'the', 'cause', 'of', 'the', 'blowup', 'as', 'an', 'nphard', 'problem', 'our', 'main', 'contribution', 'is', 'a', 'new', 'approach', 'called', 'quasioptimal', 'por', 'that', 'can', 'arbitrarily', 'approximate', 'an', 'optimal', 'exploration', 'using', 'a', 'provided', 'constant', 'k', 'we', 'present', 'an', 'implementation', 'of', 'our', 'method', 'in', 'a', 'new', 'tool', 'called', 'dpu', 'using', 'specialised', 'data', 'structures', 'experiments', 'with', 'dpu', 'including', 'debian', 'packages', 'show', 'that', 'optimality', 'is', 'achieved', 'with', 'low', 'values', 'of', 'k', 'outperforming', 'stateoftheart', 'tools']] | [-0.10843841250101893, -0.007592282832774799, -0.06942331873401368, 0.04977636656850198, -0.09291388348368454, -0.1569718187240789, 0.04314119317292599, 0.3674395186984438, -0.2786097006242864, -0.31926122609273355, 0.12883802414457393, -0.2579098936383439, -0.17327450952192736, 0.23904696889853072, -0.08263543482240432, 0.06801402226292184, 0.10471940605695974, 0.02077937976978088, -0.06671552122294334, -0.2871326855235828, 0.26601282547827876, 0.0657040496451496, 0.27671691896078193, 0.01943438044370357, 0.14577393021564772, -0.01765188920090638, -0.029171909051710517, 0.03743833690433826, -0.1219089745269014, 0.13600753587019487, 0.3075894444659984, 0.20429299475025323, 0.31724496102118394, -0.3741741881911027, -0.1542731734245258, 0.07677117411157716, 0.12617150664140106, 0.11665374547503572, -0.04333400830006921, -0.23254158553545032, 0.16193244352905176, -0.13886615120947865, -0.07987218802810599, -0.11128515031157156, -0.0316726656599065, -0.00845266701811451, -0.3087079416155436, 0.019347645075975965, 0.03416375548310452, 0.05001942688891254, -0.04572180090116937, -0.16966223536193434, 0.04487848063236324, 0.0970975174936091, 0.0077301277021849845, 0.06535302986927582, 0.08790276227203214, -0.07427458574312691, -0.17742600147928095, 0.3548660609176603, -0.10257255790411826, -0.1584595907795227, 0.16751772918939686, -0.0326786479139227, -0.1552539838170159, 0.17583111623870368, 0.1371025298169609, 0.17274739146548307, -0.11641172435930318, 0.06675957439450539, -0.04948581810803863, 0.18705988175756597, 0.040364943395400324, -0.006034277092420764, 0.08504997607383688, 0.21170027916106568, 0.1479938738600556, 0.17719240165493153, -0.04106212552179093, -0.07686674115011248, -0.27700120778221604, -0.138519532647047, -0.16290650143035515, -0.023027135580146716, -0.10494452532910115, -0.13072011949320844, 0.31524701445694947, 0.19366668012008956, 0.16677691946121848, 0.09784393994555951, 0.34402363918626966, 0.07260133592871618, 0.07390166323245266, 0.18307691971945056, 0.17050177384744067, 0.030943367142877463, 0.07848409566854647, -0.22186058806625425, 0.08519840737710059, 0.08460184885583566] |
1,802.03951 | From continuous to discontinuous transitions in social diffusion | Models of social diffusion reflect processes of how new products, ideas or
behaviors are adopted in a population. These models typically lead to a
continuous or a discontinuous phase transition of the number of adopters as a
function of a control parameter. We explore a simple model of social adoption
where the agents can be in two states, either adopters or non-adopters, and can
switch between these two states interacting with other agents through a
network. The probability of an agent to switch from non-adopter to adopter
depends on the number of adopters in her network neighborhood, the adoption
threshold $T$ and the adoption coefficient $a$, two parameters defining a Hill
function. In contrast, the transition from adopter to non-adopter is
spontaneous at a certain rate $\mu$. In a mean-field approach, we derive the
governing ordinary differential equations and show that the nature of the
transition between the global non-adoption and global adoption regimes depends
mostly on the balance between the probability to adopt with one and two
adopters. The transition changes from continuous, via a transcritical
bifurcation, to discontinuous, via a combination of a saddle-node and a
transcritical bifurcation, through a supercritical pitchfork bifurcation. We
characterize the full parameter space. Finally, we compare our analytical
results with Montecarlo simulations on annealed and quenched degree regular
networks, showing a better agreement for the annealed case. Our results show
how a simple model is able to capture two seemingly very different types of
transitions, i.e., continuous and discontinuous and thus unifies underlying
dynamics for different systems. Furthermore the form of the adoption
probability used here is based on empirical measurements.
| physics.soc-ph | models of social diffusion reflect processes of how new products ideas or behaviors are adopted in a population these models typically lead to a continuous or a discontinuous phase transition of the number of adopters as a function of a control parameter we explore a simple model of social adoption where the agents can be in two states either adopters or nonadopters and can switch between these two states interacting with other agents through a network the probability of an agent to switch from nonadopter to adopter depends on the number of adopters in her network neighborhood the adoption threshold t and the adoption coefficient a two parameters defining a hill function in contrast the transition from adopter to nonadopter is spontaneous at a certain rate mu in a meanfield approach we derive the governing ordinary differential equations and show that the nature of the transition between the global nonadoption and global adoption regimes depends mostly on the balance between the probability to adopt with one and two adopters the transition changes from continuous via a transcritical bifurcation to discontinuous via a combination of a saddlenode and a transcritical bifurcation through a supercritical pitchfork bifurcation we characterize the full parameter space finally we compare our analytical results with montecarlo simulations on annealed and quenched degree regular networks showing a better agreement for the annealed case our results show how a simple model is able to capture two seemingly very different types of transitions ie continuous and discontinuous and thus unifies underlying dynamics for different systems furthermore the form of the adoption probability used here is based on empirical measurements | [['models', 'of', 'social', 'diffusion', 'reflect', 'processes', 'of', 'how', 'new', 'products', 'ideas', 'or', 'behaviors', 'are', 'adopted', 'in', 'a', 'population', 'these', 'models', 'typically', 'lead', 'to', 'a', 'continuous', 'or', 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1,802.03952 | Quadrature formulae for the positive real axis in the setting of Mellin
analysis: Sharp error estimates in terms of the Mellin distance | The general Poisson summation formula of Mellin analysis can be considered as
a quadrature formula for the positive real axis with remainder. For Mellin
bandlimited functions it becomes an exact quadrature formula. Our main aim is
to study the speed of convergence to zero of the remainder for a function $f$
in terms of its distance from a space of Mellin bandlimited functions. The
resulting estimates turn out to be of best possible order. Moreover, we
characterize certain rates of convergence in terms of Mellin--Sobolev and
Mellin--Hardy type spaces that contain $f$. Some numerical experiments
illustrate and confirm these results.
| math.NA | the general poisson summation formula of mellin analysis can be considered as a quadrature formula for the positive real axis with remainder for mellin bandlimited functions it becomes an exact quadrature formula our main aim is to study the speed of convergence to zero of the remainder for a function f in terms of its distance from a space of mellin bandlimited functions the resulting estimates turn out to be of best possible order moreover we characterize certain rates of convergence in terms of mellinsobolev and mellinhardy type spaces that contain f some numerical experiments illustrate and confirm these results | [['the', 'general', 'poisson', 'summation', 'formula', 'of', 'mellin', 'analysis', 'can', 'be', 'considered', 'as', 'a', 'quadrature', 'formula', 'for', 'the', 'positive', 'real', 'axis', 'with', 'remainder', 'for', 'mellin', 'bandlimited', 'functions', 'it', 'becomes', 'an', 'exact', 'quadrature', 'formula', 'our', 'main', 'aim', 'is', 'to', 'study', 'the', 'speed', 'of', 'convergence', 'to', 'zero', 'of', 'the', 'remainder', 'for', 'a', 'function', 'f', 'in', 'terms', 'of', 'its', 'distance', 'from', 'a', 'space', 'of', 'mellin', 'bandlimited', 'functions', 'the', 'resulting', 'estimates', 'turn', 'out', 'to', 'be', 'of', 'best', 'possible', 'order', 'moreover', 'we', 'characterize', 'certain', 'rates', 'of', 'convergence', 'in', 'terms', 'of', 'mellinsobolev', 'and', 'mellinhardy', 'type', 'spaces', 'that', 'contain', 'f', 'some', 'numerical', 'experiments', 'illustrate', 'and', 'confirm', 'these', 'results']] | [-0.12633821266618642, 0.035892125794274796, -0.11507106747365359, 0.11994685466528278, -0.0837514525317032, -0.05696067700604673, 0.06544073755768212, 0.345695830956854, -0.25921846544275984, -0.2026022132486105, 0.14084360538041124, -0.2389906966103937, -0.17658738562404508, 0.27997161575941126, -0.055274392459353415, 0.06470951951529849, 0.044207349139726675, 0.05138978033768709, -0.13582560019109002, -0.28150388052818753, 0.31225399749654564, 0.024808860308669432, 0.2221486775340945, 0.042748142007945314, 0.07487244696727945, 0.00043504798051082725, -0.061636098566455674, -0.05298598510045041, -0.12668342878749522, 0.11748398088078682, 0.2568815177438235, 0.10626291411674836, 0.26054109681886856, -0.3566336121334873, -0.13922800911082464, 0.14375913264041748, 0.16115310793798981, 0.033822718556180145, 0.001151251964120552, -0.26795570006020214, 0.1100910252073046, -0.1476739025439578, -0.19200975715060425, -0.15740971512753854, 0.006611233153803782, 0.09902778555488571, -0.3273879287745615, 0.07517433849238613, 0.07413823604865959, 0.016148451358230426, -0.07307635009349936, -0.14314710643645753, 0.007828925470492304, 0.12400884036859026, 0.0822697416620536, 0.07543871856195795, 0.04597955965201812, -0.08261885523861932, -0.0926026534524304, 0.3197380533592418, -0.07035662654453342, -0.26996564884162083, 0.10990039968740835, -0.2163659344718914, -0.09680003067478538, 0.13884439713537994, 0.1588242887143008, 0.16597320799800483, -0.13235339050115358, 0.10805179290544016, -0.021505757545431454, 0.11999832483177836, 0.12941590492199692, 0.04849774392634498, 0.12519624153377884, 0.05533828919593508, 0.08458232945019398, 0.20383435475988068, -0.06217472789564518, -0.10115251824438497, -0.39472974706064873, -0.1826189013816548, -0.22996840415277867, 0.0476140519898065, -0.1810343160686163, -0.20954465495441296, 0.3673234997326602, 0.115892888839366, 0.18180864791602197, 0.13495638490551048, 0.28018262803629795, 0.22393125428752314, 0.046612709972330114, 0.01820150086881988, 0.16190411842509286, 0.17527073003925533, 0.045772071711418005, -0.17234764980490913, 0.03972122297067233, 0.14037232248893364] |
1,802.03953 | The Lattice of Idempotent States on a Locally Compact Quantum Group | We study lattice operations on the set of idempotent states on a locally
compact quantum group corresponding to the operations of intersection of
compact subgroups and forming the subgroup generated by two compact subgroups.
Normal ($\sigma$-weakly continuous) idempotent states are investigated and a
duality between normal idempotent states on a locally compact quantum group
$\mathbb{G}$ and on its dual $\widehat{\mathbb{G}}$ is established.
Additionally we analyze the question when a left coideal corresponding
canonically to an idempotent state is finite dimensional and give a
characterization of normal idempotent states on compact quantum groups.
| math.OA math.QA | we study lattice operations on the set of idempotent states on a locally compact quantum group corresponding to the operations of intersection of compact subgroups and forming the subgroup generated by two compact subgroups normal sigmaweakly continuous idempotent states are investigated and a duality between normal idempotent states on a locally compact quantum group mathbbg and on its dual widehatmathbbg is established additionally we analyze the question when a left coideal corresponding canonically to an idempotent state is finite dimensional and give a characterization of normal idempotent states on compact quantum groups | [['we', 'study', 'lattice', 'operations', 'on', 'the', 'set', 'of', 'idempotent', 'states', 'on', 'a', 'locally', 'compact', 'quantum', 'group', 'corresponding', 'to', 'the', 'operations', 'of', 'intersection', 'of', 'compact', 'subgroups', 'and', 'forming', 'the', 'subgroup', 'generated', 'by', 'two', 'compact', 'subgroups', 'normal', 'sigmaweakly', 'continuous', 'idempotent', 'states', 'are', 'investigated', 'and', 'a', 'duality', 'between', 'normal', 'idempotent', 'states', 'on', 'a', 'locally', 'compact', 'quantum', 'group', 'mathbbg', 'and', 'on', 'its', 'dual', 'widehatmathbbg', 'is', 'established', 'additionally', 'we', 'analyze', 'the', 'question', 'when', 'a', 'left', 'coideal', 'corresponding', 'canonically', 'to', 'an', 'idempotent', 'state', 'is', 'finite', 'dimensional', 'and', 'give', 'a', 'characterization', 'of', 'normal', 'idempotent', 'states', 'on', 'compact', 'quantum', 'groups']] | [-0.1647134303803677, 0.17226226061330122, -0.09285834189706846, 0.08257679533183007, -0.05043699230958262, -0.1222506547043019, 0.08231436805141604, 0.4201776016827511, -0.3365581295574489, -0.10615346603014547, 0.1536166324842033, -0.2672316205242406, -0.06263725843240062, 0.20833421029332702, -0.12341356159025885, -0.02777302206516691, 0.04996905846359289, 0.14444921331723098, -0.1279968605528626, -0.22353450796040503, 0.4177490771221726, -0.06927792820952954, 0.28885407212605374, -0.00623065953993279, 0.12248483684379607, 0.005023153503830342, -0.007516365660273511, -0.0009365712353230819, -0.0739407644647619, 0.1266071125738444, 0.3007904098814596, 0.06717195305669599, 0.21690827158644146, -0.41179628113446676, -0.08777018673793899, 0.16745255702256184, 0.042742509798541585, -0.011850098512418892, -0.07166643987511634, -0.3540821721291412, 0.11065590275330064, -0.19505586305066294, -0.11648128738464869, -0.10296070578751033, 0.10571175823797999, -0.06630093053931689, -0.2242938069343243, 0.0006970661368383014, 0.09209013455699239, 0.09279138402527441, -0.04831613024883984, -0.045284225068905434, -0.07657242788801617, 0.08804177421900575, -0.12856073458375328, 0.004096208056738681, 0.1203522328252565, -0.05339987505249155, -0.1698001131121798, 0.3670443937729072, -0.02459004615008345, -0.22811832127120832, 0.2002376361000959, -0.1734477488030477, -0.11328903360434038, 0.06613951502367854, 0.11463472614591212, 0.16142201064782136, -0.050263162376643813, 0.19799243863014018, -0.15812976584445848, 0.08456353599752259, 0.03708741496539796, 0.07692297240314276, 0.17134446334903655, 0.09422220791558451, 0.1263982314090042, 0.16105064201553393, 0.04040820759204824, -0.03579730722729279, -0.3495366139256436, -0.2047367786138278, -0.14904980323500122, 0.15084926495531006, -0.02256661955122937, -0.2175329295062946, 0.4341300419005363, -0.023131535376508153, 0.10882882411976386, 0.046806826134738716, 0.18186488274849302, 0.06344325754316346, 0.01787907034462399, 0.11363109822749444, 0.06468511533508402, 0.2879905402483216, -0.16584076211058899, -0.2083606254208185, -0.06015737368421548, 0.19042812674508794] |
1,802.03954 | On Dynamic Programming Principle for Stochastic Control under
Expectation Constraints | This paper studies the dynamic programming principle using the measurable
selection method for stochastic control of continuous processes. The novelty of
this work is to incorporate intermediate expectation constraints on the
canonical space at each time t. Motivated by some financial applications, we
show that several types of dynamic trading constraints can be reformulated into
expectation constraints on paths of controlled state processes. Our results can
therefore be employed to recover the dynamic programming principle for these
optimal investment problems under dynamic constraints, possibly path-dependent,
in a non-Markovian framework.
| math.OC | this paper studies the dynamic programming principle using the measurable selection method for stochastic control of continuous processes the novelty of this work is to incorporate intermediate expectation constraints on the canonical space at each time t motivated by some financial applications we show that several types of dynamic trading constraints can be reformulated into expectation constraints on paths of controlled state processes our results can therefore be employed to recover the dynamic programming principle for these optimal investment problems under dynamic constraints possibly pathdependent in a nonmarkovian framework | [['this', 'paper', 'studies', 'the', 'dynamic', 'programming', 'principle', 'using', 'the', 'measurable', 'selection', 'method', 'for', 'stochastic', 'control', 'of', 'continuous', 'processes', 'the', 'novelty', 'of', 'this', 'work', 'is', 'to', 'incorporate', 'intermediate', 'expectation', 'constraints', 'on', 'the', 'canonical', 'space', 'at', 'each', 'time', 't', 'motivated', 'by', 'some', 'financial', 'applications', 'we', 'show', 'that', 'several', 'types', 'of', 'dynamic', 'trading', 'constraints', 'can', 'be', 'reformulated', 'into', 'expectation', 'constraints', 'on', 'paths', 'of', 'controlled', 'state', 'processes', 'our', 'results', 'can', 'therefore', 'be', 'employed', 'to', 'recover', 'the', 'dynamic', 'programming', 'principle', 'for', 'these', 'optimal', 'investment', 'problems', 'under', 'dynamic', 'constraints', 'possibly', 'pathdependent', 'in', 'a', 'nonmarkovian', 'framework']] | [-0.10708905000951183, 0.07708125071223365, -0.11060151275707765, 0.09107868012768122, -0.13396535602168003, -0.11753547004832143, 0.0956709781109638, 0.3624697201205104, -0.36185518486603174, -0.28718514520716804, 0.14832582554827037, -0.1769625041581523, -0.15143691577812593, 0.2219539617470811, -0.09600012593515468, 0.08301648915106996, 0.0490687027589282, -0.03790831785868812, -0.058756278506009264, -0.2519830441144243, 0.29232104118330354, 0.005689908945252805, 0.24742805774668963, 0.042984805936438525, 0.15568548908425767, 0.04331729064036286, -0.021798550857628666, 0.0936559548838917, -0.13096168067273906, 0.13501767780673637, 0.31170366154826673, 0.17063829785191945, 0.3429277650093048, -0.45328042930347867, -0.24963276201252188, 0.1074565639614724, 0.04540150293398889, 0.09149788462902221, 0.018577406400019366, -0.2922505193620167, 0.05109990026335509, -0.13398581781467747, -0.07053700827710917, -0.09280538327698962, -0.03267297741507044, 0.003006268318826228, -0.3586869372075863, 0.03662757647715593, 0.05350591239435703, -0.03123706230747231, -0.10230103344656527, -0.09897228337175558, 0.01959152942722182, 0.08928163240799743, 0.06164184945970355, -0.037223921101174076, 0.1635356605931949, -0.06585215106920413, -0.2304430107659336, 0.3571912514129549, -0.060960421443320395, -0.23235991942497453, 0.16727267158625836, -0.08143806233060326, -0.21972049422708623, 0.09742622333840373, 0.246559199060868, 0.15514388820156455, -0.2445699144782645, 0.11310540275711022, -0.033339286359089816, 0.1288146083661763, 0.030234114089038935, 0.05354469727327147, 0.17640524720276046, 0.18263696288962042, 0.1323457584831487, 0.13888030486662736, 0.02115658571252997, -0.16379322368505128, -0.32735657231526427, -0.09886026106188807, -0.14949183956474024, 0.0093114890283748, -0.12760996100691596, -0.09978310678551873, 0.34404651833740013, 0.1902201616759883, 0.16452656037519486, 0.11060575409818524, 0.2909076466654124, 0.18610426789447876, 0.02178500930705432, 0.05620115782458628, 0.18140612410419368, 0.05292598995003389, 0.1436558589757828, -0.25328790772643484, 0.13619798991117585, 0.024294202174112368] |
1,802.03955 | Diffusion equations from kinetic models with non-conserved momentum | We derive diffusive macroscopic equations for the particle and energy density
of a system whose time evolution is described by a kinetic equation for the one
particle position and velocity function f(r,v,t) that consists of a part that
conserves energy and momentum such as the Boltzmann equation and an external
randomization of the particle velocity directions that breaks the momentum
conservation. Rescaling space and time by epsilon and epsilon square
respectively and carrying out a Hilbert expansion in epsilon around a local
equilibrium Maxwellian yields coupled diffusion equations with specified
Onsager coefficients for the particle and energy density. Our analysis includes
a system of hard disks at intermediate densities by using the Enskog equation
for the collision kernel.
| cond-mat.stat-mech | we derive diffusive macroscopic equations for the particle and energy density of a system whose time evolution is described by a kinetic equation for the one particle position and velocity function frvt that consists of a part that conserves energy and momentum such as the boltzmann equation and an external randomization of the particle velocity directions that breaks the momentum conservation rescaling space and time by epsilon and epsilon square respectively and carrying out a hilbert expansion in epsilon around a local equilibrium maxwellian yields coupled diffusion equations with specified onsager coefficients for the particle and energy density our analysis includes a system of hard disks at intermediate densities by using the enskog equation for the collision kernel | [['we', 'derive', 'diffusive', 'macroscopic', 'equations', 'for', 'the', 'particle', 'and', 'energy', 'density', 'of', 'a', 'system', 'whose', 'time', 'evolution', 'is', 'described', 'by', 'a', 'kinetic', 'equation', 'for', 'the', 'one', 'particle', 'position', 'and', 'velocity', 'function', 'frvt', 'that', 'consists', 'of', 'a', 'part', 'that', 'conserves', 'energy', 'and', 'momentum', 'such', 'as', 'the', 'boltzmann', 'equation', 'and', 'an', 'external', 'randomization', 'of', 'the', 'particle', 'velocity', 'directions', 'that', 'breaks', 'the', 'momentum', 'conservation', 'rescaling', 'space', 'and', 'time', 'by', 'epsilon', 'and', 'epsilon', 'square', 'respectively', 'and', 'carrying', 'out', 'a', 'hilbert', 'expansion', 'in', 'epsilon', 'around', 'a', 'local', 'equilibrium', 'maxwellian', 'yields', 'coupled', 'diffusion', 'equations', 'with', 'specified', 'onsager', 'coefficients', 'for', 'the', 'particle', 'and', 'energy', 'density', 'our', 'analysis', 'includes', 'a', 'system', 'of', 'hard', 'disks', 'at', 'intermediate', 'densities', 'by', 'using', 'the', 'enskog', 'equation', 'for', 'the', 'collision', 'kernel']] | [-0.13862149054821357, 0.1594801042697832, -0.11708075975059953, 0.03591905591878085, -0.014144856419064041, -0.11002132267110114, 0.0005659682817486489, 0.2823974353969734, -0.2757508609041922, -0.30336379333056956, 0.04012895926224211, -0.2733876830858425, -0.022452695263374563, 0.13264462366724053, 0.08489747553602116, 0.07244749242264746, 0.049262064361833356, 0.02569741639109631, -0.06340389317856768, -0.16016097766104448, 0.32866228133057934, 0.08868843727768996, 0.208472810824935, 0.004833017434112919, 0.23782579314648214, 0.04277086343703807, -0.017698787371667787, 0.043032934220306754, -0.15931316030416467, 0.02558365833555531, 0.15980218353872308, 0.01277039711100933, 0.27429040163978297, -0.41175332193414116, -0.22302419918342534, 0.04229130790147007, 0.15819134599027726, 0.07388338598255546, -0.03002135671921966, -0.22438327435197103, 0.010607809067154542, -0.15091792221029854, -0.2226892005508909, -0.08824235761665508, 0.06748163565778388, 0.08828790930824147, -0.2830982552124904, 0.1951159446890283, 0.06186081117226018, 0.002186615680718524, -0.1250704144143587, -0.1098360906725224, -0.060828282579413466, 0.039446638041358985, 0.03151894832213019, 0.022735745009257752, 0.15602296751995498, -0.1573940918653503, -0.043864614465552516, 0.3865285905189494, -0.10125059994588344, -0.27017185198835647, 0.15511712537974948, -0.13082425952610424, -0.07041511565255813, 0.1729560931507721, 0.13852227536929596, 0.11306441032017271, -0.16481712626086342, 0.12622254874770436, -0.02032704871450352, 0.1523141955367775, 0.06801741152333143, -0.022099273697210427, 0.1739184243015499, 0.12441322979573001, 0.08008804072080673, 0.08863590739948006, -0.10421175935950416, -0.17108221247824085, -0.36268719220454365, -0.1932871238563337, -0.2366805702416051, 0.08041960719881308, -0.1251794961788572, -0.13826034298469114, 0.3622155002374043, 0.08579491719237378, 0.1975274631817244, 0.06722011471676648, 0.2839512199712678, 0.23349841986185846, 0.020330857386032485, 0.14910407544662937, 0.20068796316577664, 0.13921858880243018, 0.16318348792787546, -0.27375637917803264, 0.015425489658219183, 0.11943399537211427] |
1,802.03956 | Moduli spaces of bundles and Hilbert schemes of scrolls over $\nu$-gonal
curves | The aim of this paper is two--fold. We first strongly improve our previous
main result Theorem 3.1 in Arxiv 1702.00918v3 12Feb2018 ("Brill-Noether loci of
rank two vector bundles on a general $\nu$-gonal curve"), concerning
classification of irreducible components of the Brill--Noether locus
parametrizing rank 2 semistable vector bundles of suitable degrees $d$, with at
least $d-2g+4$ independent global sections, on a general $\nu$--gonal curve $C$
of genus $g$. We then uses this classification to study several properties of
the Hilbert scheme of suitable surface scrolls in projective space, which turn
out to be special and stable.
| math.AG | the aim of this paper is twofold we first strongly improve our previous main result theorem 31 in arxiv 170200918v3 12feb2018 brillnoether loci of rank two vector bundles on a general nugonal curve concerning classification of irreducible components of the brillnoether locus parametrizing rank 2 semistable vector bundles of suitable degrees d with at least d2g4 independent global sections on a general nugonal curve c of genus g we then uses this classification to study several properties of the hilbert scheme of suitable surface scrolls in projective space which turn out to be special and stable | [['the', 'aim', 'of', 'this', 'paper', 'is', 'twofold', 'we', 'first', 'strongly', 'improve', 'our', 'previous', 'main', 'result', 'theorem', '31', 'in', 'arxiv', '170200918v3', '12feb2018', 'brillnoether', 'loci', 'of', 'rank', 'two', 'vector', 'bundles', 'on', 'a', 'general', 'nugonal', 'curve', 'concerning', 'classification', 'of', 'irreducible', 'components', 'of', 'the', 'brillnoether', 'locus', 'parametrizing', 'rank', '2', 'semistable', 'vector', 'bundles', 'of', 'suitable', 'degrees', 'd', 'with', 'at', 'least', 'd2g4', 'independent', 'global', 'sections', 'on', 'a', 'general', 'nugonal', 'curve', 'c', 'of', 'genus', 'g', 'we', 'then', 'uses', 'this', 'classification', 'to', 'study', 'several', 'properties', 'of', 'the', 'hilbert', 'scheme', 'of', 'suitable', 'surface', 'scrolls', 'in', 'projective', 'space', 'which', 'turn', 'out', 'to', 'be', 'special', 'and', 'stable']] | [-0.19773136687925666, 0.03590908082157013, -0.10271975569807554, 0.0207110169351161, -0.08392156750684784, -0.1728456852937578, 0.033077785721479326, 0.3178897368651564, -0.2604444801647176, -0.18536640758756348, 0.08820833942492402, -0.20681991197309027, -0.155049236060711, 0.19634991398541837, -0.15728288497375223, 0.011349112922025303, 0.054414979505142375, 0.06897724676196293, -0.08338706237163836, -0.3942185142227719, 0.46228683836037116, -0.03141696412637029, 0.2568501338102324, 0.06394894297925695, 0.09265336290662808, 0.05646151181821141, -0.044564164403866055, -0.0292249515565658, -0.13349930021751633, 0.1845162924183833, 0.33134413770930743, 0.0955982231765814, 0.1806834333486134, -0.3125949696367306, -0.16970387427136302, 0.23497024910544517, 0.1048445850610733, 0.041026188184567276, 0.07831699657253921, -0.2026055230839198, 0.09811530401930213, -0.1049187566424089, -0.20496764742038262, -0.08867829672050892, 0.05259198796314498, -0.0024936157153538797, -0.1749172641605299, -0.011078167175974256, 0.10146694593331827, 0.15994090462724367, -0.05596543031854816, -0.16218244801375575, -0.11793871167095839, 0.007296751488641065, 0.04397760043972202, 0.084810762017745, 0.07399016382393017, -0.08151213399645302, -0.0885487876642215, 0.33028103090742583, -0.09508187604144276, -0.1981574406907443, 0.16241463675396517, -0.138637319457547, -0.18644139400473045, 0.16692640082234697, 0.20888600703729418, 0.22172447139840393, -0.023995926473490012, 0.09566791863518677, -0.10772949877765871, 0.11481698905868877, 0.07377037854604823, -0.044828115996243735, 0.14343460545104036, 0.1368541317836692, 0.058258374939439195, 0.09687618409428665, -0.046288590102771955, -0.025474681163467065, -0.3929532264429395, -0.22681373619144002, -0.08153355840132923, 0.12227714903933067, -0.11728946804003264, -0.12511640845707828, 0.4731672035508851, 0.055480747488677824, 0.24355402478687865, 0.09360676216367152, 0.2618387088439958, 0.017784337409251238, 0.00284197796461603, 0.06011974041961053, 0.19220079303348578, 0.23477050433716468, -0.04657014746779716, -0.11066572012139424, -0.046022287509854765, 0.18574946564972722] |
1,802.03957 | Critical N=(1,1) General Massive Supergravity | In this paper we study the supermultiplet structure of $\mathcal{N}=(1,1)$
General Massive Supergravity at non-critical and critical points of its
parameter space. To do this, we first linearize the theory around its maximally
supersymmetric AdS$_3$ vacuum and obtain the full linearized Lagrangian
including fermionic terms. At generic values, linearized modes can be organized
as two massless and 2 massive multiplets where supersymmetry relates them in
the standard way. At critical points logarithmic modes appear and we find that
in three of such points some of the supersymmetry transformations are
non-invertible in logarithmic multiplets. However, in the fourth critical
point, there is a massive logarithmic multiplet with invertible supersymmetry
transformations.
| hep-th | in this paper we study the supermultiplet structure of mathcaln11 general massive supergravity at noncritical and critical points of its parameter space to do this we first linearize the theory around its maximally supersymmetric ads_3 vacuum and obtain the full linearized lagrangian including fermionic terms at generic values linearized modes can be organized as two massless and 2 massive multiplets where supersymmetry relates them in the standard way at critical points logarithmic modes appear and we find that in three of such points some of the supersymmetry transformations are noninvertible in logarithmic multiplets however in the fourth critical point there is a massive logarithmic multiplet with invertible supersymmetry transformations | [['in', 'this', 'paper', 'we', 'study', 'the', 'supermultiplet', 'structure', 'of', 'mathcaln11', 'general', 'massive', 'supergravity', 'at', 'noncritical', 'and', 'critical', 'points', 'of', 'its', 'parameter', 'space', 'to', 'do', 'this', 'we', 'first', 'linearize', 'the', 'theory', 'around', 'its', 'maximally', 'supersymmetric', 'ads_3', 'vacuum', 'and', 'obtain', 'the', 'full', 'linearized', 'lagrangian', 'including', 'fermionic', 'terms', 'at', 'generic', 'values', 'linearized', 'modes', 'can', 'be', 'organized', 'as', 'two', 'massless', 'and', '2', 'massive', 'multiplets', 'where', 'supersymmetry', 'relates', 'them', 'in', 'the', 'standard', 'way', 'at', 'critical', 'points', 'logarithmic', 'modes', 'appear', 'and', 'we', 'find', 'that', 'in', 'three', 'of', 'such', 'points', 'some', 'of', 'the', 'supersymmetry', 'transformations', 'are', 'noninvertible', 'in', 'logarithmic', 'multiplets', 'however', 'in', 'the', 'fourth', 'critical', 'point', 'there', 'is', 'a', 'massive', 'logarithmic', 'multiplet', 'with', 'invertible', 'supersymmetry', 'transformations']] | [-0.16042231159592305, 0.1714207740808162, -0.022030839608948027, 0.09359704789709847, -0.055190969476425045, -0.1506720780001936, -0.010172160667283972, 0.2863973815446942, -0.19482792148349481, -0.2472017280791604, 0.10050278328414665, -0.3103058484271852, -0.18823869273476646, 0.06918140793103861, -0.026744254535798907, 0.06970152408624208, -0.02687414805618448, 0.10044400962582285, -0.14616464801910206, -0.2781525012095023, 0.3818156583909663, -0.043851627412875856, 0.21448654454594496, -0.0035473615307053297, 0.10121166979501006, -0.01949373601910171, 0.020391663079312363, -0.04060340304501192, -0.10204597824679552, 0.09536618693227615, 0.2692686457981552, 0.06909804043163537, 0.1625067252311172, -0.4097132191506274, -0.15690218096752778, 0.13063378982809842, 0.20591757757537957, 0.14914623783354503, 0.016787634350769562, -0.2450140699094862, 0.11027406491851861, -0.13276740915458138, -0.2022723835602471, -0.1204035653382403, -0.019330054692445545, -0.1220101659098606, -0.2530886259708562, 0.10903094050158328, 0.022251750227200603, 0.05576943654840419, -0.05712893593430502, -0.0719001411915321, -0.11085334281360201, 0.04636483442533864, 0.1218365663384865, 0.055940354480023644, 0.12181001083942455, -0.18419288950032392, -0.11183645981876607, 0.3868873485133325, -0.07120870807370462, -0.21258630964973377, 0.17179317752309486, -0.1686340164512806, -0.2132051397529388, 0.09486030120778521, 0.14013855959451116, 0.15815433551377933, -0.12554184391874598, 0.21358397214912697, -0.028287325022366205, 0.11048705802526573, 0.1144302725715033, 0.07955855202653932, 0.27229038975415154, 0.08195071482022695, 0.06204064961665934, 0.11317840472628457, -0.014151736109468386, -0.12498918127849562, -0.43241075850134597, -0.16312516300400737, -0.08030846102351169, 0.04493838261573686, -0.1407723869448058, -0.14659010182741447, 0.4105387555580552, 0.10427518461087565, 0.18398476753452228, 0.06662206790076756, 0.19748858263323066, 0.13743372213996843, 0.10873736434300012, 0.105407496615219, 0.2811698314080703, 0.09129210010410176, 0.08510139189370045, -0.19893438917576173, -0.18772939515755077, 0.13367773243012504] |
1,802.03958 | Signal Processing for High Throughput Satellite Systems: Challenges in
New Interference-Limited Scenarios | The field of satellite communications is enjoying a renewed interest in the
global telecom market, and very high throughput satellites (V/HTS), with their
multiple spot-beams, are key for delivering the future rate demands. In this
article, the state-of-the-art and open research challenges of signal processing
techniques for V/HTS systems are presented for the first time, with focus on
novel approaches for efficient interference mitigation. The main signal
processing topics for the ground, satellite, and user segment are addressed.
Also, the critical components for the integration of satellite and terrestrial
networks are studied, such as cognitive satellite systems and
satellite-terrestrial backhaul for caching. All the reviewed techniques are
essential in empowering satellite systems to support the increasing demands of
the upcoming generation of communication networks.
| cs.IT math.IT | the field of satellite communications is enjoying a renewed interest in the global telecom market and very high throughput satellites vhts with their multiple spotbeams are key for delivering the future rate demands in this article the stateoftheart and open research challenges of signal processing techniques for vhts systems are presented for the first time with focus on novel approaches for efficient interference mitigation the main signal processing topics for the ground satellite and user segment are addressed also the critical components for the integration of satellite and terrestrial networks are studied such as cognitive satellite systems and satelliteterrestrial backhaul for caching all the reviewed techniques are essential in empowering satellite systems to support the increasing demands of the upcoming generation of communication networks | [['the', 'field', 'of', 'satellite', 'communications', 'is', 'enjoying', 'a', 'renewed', 'interest', 'in', 'the', 'global', 'telecom', 'market', 'and', 'very', 'high', 'throughput', 'satellites', 'vhts', 'with', 'their', 'multiple', 'spotbeams', 'are', 'key', 'for', 'delivering', 'the', 'future', 'rate', 'demands', 'in', 'this', 'article', 'the', 'stateoftheart', 'and', 'open', 'research', 'challenges', 'of', 'signal', 'processing', 'techniques', 'for', 'vhts', 'systems', 'are', 'presented', 'for', 'the', 'first', 'time', 'with', 'focus', 'on', 'novel', 'approaches', 'for', 'efficient', 'interference', 'mitigation', 'the', 'main', 'signal', 'processing', 'topics', 'for', 'the', 'ground', 'satellite', 'and', 'user', 'segment', 'are', 'addressed', 'also', 'the', 'critical', 'components', 'for', 'the', 'integration', 'of', 'satellite', 'and', 'terrestrial', 'networks', 'are', 'studied', 'such', 'as', 'cognitive', 'satellite', 'systems', 'and', 'satelliteterrestrial', 'backhaul', 'for', 'caching', 'all', 'the', 'reviewed', 'techniques', 'are', 'essential', 'in', 'empowering', 'satellite', 'systems', 'to', 'support', 'the', 'increasing', 'demands', 'of', 'the', 'upcoming', 'generation', 'of', 'communication', 'networks']] | [-0.22368718484261382, 0.06079525085321317, 0.014036584732821211, 0.058223997320358954, -0.0509225847471195, -0.1350928556988947, 0.02828409392774726, 0.3938433247928818, -0.24523965474994233, -0.30299137777571256, 0.16841263744766669, -0.29432340087369085, -0.15330784943847295, 0.27394964144720385, -0.09759484118161102, 0.1278031942512219, 0.0771994567515018, 0.018670483321572343, -0.007980020452426592, -0.274210549161459, 0.29946711056497105, 0.11764699581544846, 0.35065838519173365, 0.04853616985492408, 0.04556235926381002, 0.014216051906502495, -0.1016422960946026, -0.06072421883388112, -0.07730270209309917, 0.17977071440933892, 0.3587190155250331, 0.2050966887230364, 0.3017395107696454, -0.4610602361421722, -0.26456198285741267, 0.07494356612442062, 0.15071371380472556, 0.04024411650219311, -0.11190322026474557, -0.30408569530118257, 0.09576865955168615, -0.19373118199873715, -0.06589426422530474, -0.030421549252544842, 0.027254907271708362, 0.08629866062741105, -0.24133487248327584, -0.004845300761371618, 0.001983534071284036, 0.08071913545330366, -0.05811684230963389, -0.10108337382941196, 0.02385503165811921, 0.21404168471926824, 0.03228200591596154, 0.012943508410050224, 0.11584516204845083, -0.17817160515696742, -0.1449244933668524, 0.42945833134775363, 0.0009018227380389968, -0.11703048970084637, 0.2113655561581254, -0.09209012528978443, -0.20074504583220307, 0.08770819583830113, 0.2308773228743424, 0.018613593963285286, -0.17078990725955615, 0.04109638491014873, 0.06761429317217942, 0.11660641354489296, 0.04278421570779756, 0.1400999260658864, 0.26607272364975265, 0.246287012922888, 0.1439557816381542, 0.05952709896179537, -0.11068086935605, -0.0940697155650317, -0.19366488352388841, -0.15678915707976557, -0.1806862525923255, -0.06300276541927209, -0.04137056777969216, -0.059953574403577174, 0.35615698468560975, 0.17077724498230965, 0.13550372919319972, 0.06947385432334462, 0.40929586611067253, 0.10205404528242071, 0.07736289459474695, 0.07068796143770063, 0.21584122411635084, 0.057894647381423664, 0.19926445852033794, -0.15685520029510372, 0.04743278554912346, -0.05096425073376546] |
1,802.03959 | Three-dimensional Self-assembled Columnar Arrays of AlInP Quantum Wires
for Polarized Micron-sized Amber Light Emitting Diodes | A three-dimensional ordered and self-organized semiconductor system emitting
highly-polarized light in the yellow-orange visible range (580-650 nm) is
presented, comprising self-assembled in-plane AlInP wires vertically stacked in
regularly-spaced columns. More than 200 wires per column without detectable
defect formation could be stacked. Theoretical simulations and
temperature-dependent photoluminescence provided a benchmark to engineer
multilayered structures showing internal quantum efficiency at room temperature
larger than comparable quantum wells emitting at similar wavelengths. Finally,
proof-of-concept light emitting diodes (LED) showed a high degree of light
polarization and lower surface parasitic currents than comparable quantum well
LEDs, providing an interesting perspective for high-efficiency polarized
yellow-orange light emitting devices.
| physics.app-ph | a threedimensional ordered and selforganized semiconductor system emitting highlypolarized light in the yelloworange visible range 580650 nm is presented comprising selfassembled inplane alinp wires vertically stacked in regularlyspaced columns more than 200 wires per column without detectable defect formation could be stacked theoretical simulations and temperaturedependent photoluminescence provided a benchmark to engineer multilayered structures showing internal quantum efficiency at room temperature larger than comparable quantum wells emitting at similar wavelengths finally proofofconcept light emitting diodes led showed a high degree of light polarization and lower surface parasitic currents than comparable quantum well leds providing an interesting perspective for highefficiency polarized yelloworange light emitting devices | [['a', 'threedimensional', 'ordered', 'and', 'selforganized', 'semiconductor', 'system', 'emitting', 'highlypolarized', 'light', 'in', 'the', 'yelloworange', 'visible', 'range', '580650', 'nm', 'is', 'presented', 'comprising', 'selfassembled', 'inplane', 'alinp', 'wires', 'vertically', 'stacked', 'in', 'regularlyspaced', 'columns', 'more', 'than', '200', 'wires', 'per', 'column', 'without', 'detectable', 'defect', 'formation', 'could', 'be', 'stacked', 'theoretical', 'simulations', 'and', 'temperaturedependent', 'photoluminescence', 'provided', 'a', 'benchmark', 'to', 'engineer', 'multilayered', 'structures', 'showing', 'internal', 'quantum', 'efficiency', 'at', 'room', 'temperature', 'larger', 'than', 'comparable', 'quantum', 'wells', 'emitting', 'at', 'similar', 'wavelengths', 'finally', 'proofofconcept', 'light', 'emitting', 'diodes', 'led', 'showed', 'a', 'high', 'degree', 'of', 'light', 'polarization', 'and', 'lower', 'surface', 'parasitic', 'currents', 'than', 'comparable', 'quantum', 'well', 'leds', 'providing', 'an', 'interesting', 'perspective', 'for', 'highefficiency', 'polarized', 'yelloworange', 'light', 'emitting', 'devices']] | [-0.10029425872693254, 0.23356876048178624, 0.0013095165085210238, 0.026033919762358866, -0.020651445244845835, -0.2666530732508496, 0.04709243325028315, 0.497394580285526, -0.1847071299076225, -0.35136693030668115, -0.029818495820294524, -0.33797443867887106, -0.055797908351150824, 0.2918060379084742, 0.009082924156422608, 0.036644331959428865, -0.0034686854500233116, -0.15650214048473218, -0.01811861789342269, -0.1720035680664649, 0.1507495940274116, 0.08492976471044811, 0.3469738893717238, 0.08291385311054662, 0.06403348415420906, -0.03799045990442731, 0.08280545655972532, -0.003963908894909817, -0.08935658269091645, 0.09222708285325071, 0.21788288539646247, -0.08767675744026031, 0.15819891214877077, -0.48453431939977465, -0.25868057007042383, -0.002731136932462743, 0.18615928288657688, 0.09584300445157637, -0.10796078086281942, -0.25864241743297545, 0.08049977246843712, -0.08992870078657786, -0.13730020038498164, 0.037698459250906716, -0.010514269664304928, -0.028866097299400175, -0.18621010546690053, 0.02081365495676048, -0.017157105673601833, 0.09946736565890532, -0.024325732672524875, -0.13528168694120696, -0.07794255729158889, -0.0036180580963914113, -0.11662723935430742, 0.027134764623077748, 0.2757875960935088, -0.10545473121834503, -0.12206673428633259, 0.3274306459126325, -0.07543338994872507, -0.009118670423967804, 0.16558899644319222, -0.18937299633737725, -0.02641025298609606, 0.2231018938558194, 0.16479474137473887, 0.14765219388131812, -0.09252869916935134, -0.037546596572919776, -0.01993014193727698, 0.2751654920810201, 0.1302769932244877, 0.19013136042803594, 0.34203011368952907, 0.2143431575166387, 0.015109686694080823, 0.17962553929051528, -0.14160997333294414, -0.0359566515928713, -0.1845295474982088, -0.17566738265693116, -0.15815992283101366, 0.14553458945265094, -0.12224716708491877, -0.1440507950333571, 0.3773703667452758, 0.12302536577736796, 0.12010015419783812, -0.01584071519070314, 0.3095028489936613, 0.060124347809231975, 0.10646875728220109, 0.057502970018071455, 0.2527852568377569, 0.18951787819489113, 0.10821154846999685, -0.19050048646806442, 0.020945567746185563, -0.11238035542022401] |
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