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1,803.00867 | Probing vorticity structure in heavy-ion collisions by local $\Lambda$
polarization | We study the local structure of the vorticity field and the $\Lambda$
polarization in Au+Au collisions in the energy range
$\sqrt{s_{\mathrm{NN}}}=7.7$--$200$ GeV and Pb+Pb collisions at
$\sqrt{s_{\mathrm{NN}}}=2760$ GeV using A Multi-Phase Transport (AMPT) model.
We focus on the vorticity field arising from the non-uniform expansion of the
fireball, which gives the circular structure of the transverse vorticity
$\boldsymbol{\omega}_{\perp}=(\omega_{x},\omega_{y})$ around the $z$ direction
as well as the quadrupole pattern of the longitudinal vorticity $\omega_{z}$ in
the transverse plane. As a consequence, the three components of the
polarization vector $\mathbf{P}=(P_{x},P_{y},P_{z})$ for $\Lambda$ hyperons
show harmonic behaviors as $\mathrm{sgn}(Y)\sin\phi_{p}$,
$-\mathrm{sgn}(Y)\cos\phi_{p}$, and $-\sin(2\phi_{p})$, where $\phi_{p}$ and
$Y$ are the azimuthal angle and rapidity in momentum space. These patterns of
the local $\Lambda$ polarization are expected to be tested in future
experiments.
| nucl-th hep-ph | we study the local structure of the vorticity field and the lambda polarization in auau collisions in the energy range sqrts_mathrmnn77200 gev and pbpb collisions at sqrts_mathrmnn2760 gev using a multiphase transport ampt model we focus on the vorticity field arising from the nonuniform expansion of the fireball which gives the circular structure of the transverse vorticity boldsymbolomega_perpomega_xomega_y around the z direction as well as the quadrupole pattern of the longitudinal vorticity omega_z in the transverse plane as a consequence the three components of the polarization vector mathbfpp_xp_yp_z for lambda hyperons show harmonic behaviors as mathrmsgnysinphi_p mathrmsgnycosphi_p and sin2phi_p where phi_p and y are the azimuthal angle and rapidity in momentum space these patterns of the local lambda polarization are expected to be tested in future experiments | [['we', 'study', 'the', 'local', 'structure', 'of', 'the', 'vorticity', 'field', 'and', 'the', 'lambda', 'polarization', 'in', 'auau', 'collisions', 'in', 'the', 'energy', 'range', 'sqrts_mathrmnn77200', 'gev', 'and', 'pbpb', 'collisions', 'at', 'sqrts_mathrmnn2760', 'gev', 'using', 'a', 'multiphase', 'transport', 'ampt', 'model', 'we', 'focus', 'on', 'the', 'vorticity', 'field', 'arising', 'from', 'the', 'nonuniform', 'expansion', 'of', 'the', 'fireball', 'which', 'gives', 'the', 'circular', 'structure', 'of', 'the', 'transverse', 'vorticity', 'boldsymbolomega_perpomega_xomega_y', 'around', 'the', 'z', 'direction', 'as', 'well', 'as', 'the', 'quadrupole', 'pattern', 'of', 'the', 'longitudinal', 'vorticity', 'omega_z', 'in', 'the', 'transverse', 'plane', 'as', 'a', 'consequence', 'the', 'three', 'components', 'of', 'the', 'polarization', 'vector', 'mathbfpp_xp_yp_z', 'for', 'lambda', 'hyperons', 'show', 'harmonic', 'behaviors', 'as', 'mathrmsgnysinphi_p', 'mathrmsgnycosphi_p', 'and', 'sin2phi_p', 'where', 'phi_p', 'and', 'y', 'are', 'the', 'azimuthal', 'angle', 'and', 'rapidity', 'in', 'momentum', 'space', 'these', 'patterns', 'of', 'the', 'local', 'lambda', 'polarization', 'are', 'expected', 'to', 'be', 'tested', 'in', 'future', 'experiments']] | [-0.15881430090715487, 0.2057490018584455, -0.10661842952637622, 0.08139353259466589, -0.021558475443938126, -0.025036101889175672, -0.10029035097298523, 0.37321368971218666, -0.2966724050114863, -0.24462379492276037, -0.006308829606859944, -0.27559748260149114, 0.033783326763659714, 0.17377705387577105, 0.10102943240199239, 0.05314693386899307, 0.031874040110657614, 0.03796820359614988, -0.02269869646212707, -0.1510033660995153, 0.31895676560234276, 0.06712020282478383, 0.2559919709339738, 0.09418730779240529, 0.09028270513129731, 0.0481242585403379, 0.0015351228687601785, 0.058575448063735776, -0.15059218266178503, 0.04354281119109752, 0.21503215969811815, 0.016937768083808653, 0.15542742031229864, -0.36099928775802254, -0.16350195804455628, 0.09550426855372886, 0.1380337909096852, 0.10427331325578658, -0.036160721450384396, -0.25987412963683404, 0.07603617129837706, -0.162064196668022, -0.16787594639075298, -0.03589645044024413, 0.015991790423868224, 0.0919417930029643, -0.28447137181356086, 0.17286250944986628, 0.053650594131128555, 0.10766437778559824, -0.03931375132330383, -0.15636077064555137, -0.13560012121646045, 0.02183038111155232, 0.11653682010461731, 0.1362429523510703, 0.15362131159054115, -0.17581468162873837, -0.11031733214234313, 0.4142666486712793, -0.06737621874684313, -0.18993846830368663, 0.10603299885988235, -0.248878710779051, -0.08086385854597514, 0.10998634198137248, 0.2505591040166716, 0.0997925405545781, -0.10518713808172227, 0.0782331679923421, -0.04441250823147129, 0.13458458734676243, 0.10941730921234315, 0.04834920843907942, 0.2405012165661901, 0.13371004857278118, 0.029381202135543086, 0.1075581188071131, -0.18298863084058514, -0.09044753093330656, -0.36801591306284537, -0.11885271747596562, -0.1208976707275724, 0.021935301763005555, -0.1220278014306435, -0.09331617367764314, 0.43134323360476023, 0.09074687313677714, 0.29526817876224715, -0.036735579808979915, 0.28686093966631837, 0.0772714813045847, 0.03648063746513799, 0.0836109319003299, 0.2825440790116166, 0.16867680430877954, 0.22458449248612547, -0.25122880964190697, 0.007728093420155346, 0.03296379131885866] |
1,803.00868 | Equation of state of the intergalactic medium in the early Universe | Spectroscopy of the Ly$\alpha$ forest in quasar spectra proved to be a useful
tool for probing the intergalactic gas. We developed the automatic program for
Voigt profile fitting of Ly$\alpha$ forest lines. We run this code on 9 high
resolution ($\sim 50000$) quasars spectra with a high signal-to-noise ratio
($\sim 50 -100$) from the Keck telescope archive and obtained the sample of
single well-fitted Ly$\alpha$ lines. Fitting the joint 2d distribution of
column density and Doppler parameter from this sample by physically reasonable
model we estimate a power law index $\gamma$ of the equation of state of the
intergalactic medium in the redshift range $z\sim 2-3$. We found that our
measurement is in an agreement with measurements by other groups obtained with
Voigt profile fitting technique.
| astro-ph.CO | spectroscopy of the lyalpha forest in quasar spectra proved to be a useful tool for probing the intergalactic gas we developed the automatic program for voigt profile fitting of lyalpha forest lines we run this code on 9 high resolution sim 50000 quasars spectra with a high signaltonoise ratio sim 50 100 from the keck telescope archive and obtained the sample of single wellfitted lyalpha lines fitting the joint 2d distribution of column density and doppler parameter from this sample by physically reasonable model we estimate a power law index gamma of the equation of state of the intergalactic medium in the redshift range zsim 23 we found that our measurement is in an agreement with measurements by other groups obtained with voigt profile fitting technique | [['spectroscopy', 'of', 'the', 'lyalpha', 'forest', 'in', 'quasar', 'spectra', 'proved', 'to', 'be', 'a', 'useful', 'tool', 'for', 'probing', 'the', 'intergalactic', 'gas', 'we', 'developed', 'the', 'automatic', 'program', 'for', 'voigt', 'profile', 'fitting', 'of', 'lyalpha', 'forest', 'lines', 'we', 'run', 'this', 'code', 'on', '9', 'high', 'resolution', 'sim', '50000', 'quasars', 'spectra', 'with', 'a', 'high', 'signaltonoise', 'ratio', 'sim', '50', '100', 'from', 'the', 'keck', 'telescope', 'archive', 'and', 'obtained', 'the', 'sample', 'of', 'single', 'wellfitted', 'lyalpha', 'lines', 'fitting', 'the', 'joint', '2d', 'distribution', 'of', 'column', 'density', 'and', 'doppler', 'parameter', 'from', 'this', 'sample', 'by', 'physically', 'reasonable', 'model', 'we', 'estimate', 'a', 'power', 'law', 'index', 'gamma', 'of', 'the', 'equation', 'of', 'state', 'of', 'the', 'intergalactic', 'medium', 'in', 'the', 'redshift', 'range', 'zsim', '23', 'we', 'found', 'that', 'our', 'measurement', 'is', 'in', 'an', 'agreement', 'with', 'measurements', 'by', 'other', 'groups', 'obtained', 'with', 'voigt', 'profile', 'fitting', 'technique']] | [-0.013891255169356655, 0.07145068554370756, -0.04286869170351161, 0.04285394834513436, -0.07067443644036613, -0.14031827926547044, 0.0644262574376568, 0.44775035391960827, -0.16110513681760205, -0.3856218337258768, 0.03020257063834588, -0.32134022073273266, 0.017900624672331597, 0.2230154104560377, 0.0493346718066032, 0.060931631334362524, 0.04695281744860704, -0.14662794055535444, 0.0013402486784755076, -0.24069270385163172, 0.27707222126246916, 0.15746934393361683, 0.2608245581181513, -0.04966154117336994, 0.09477952800299382, -0.019281973317635082, -0.132677262153713, 0.04368062276718399, -0.18229947960040474, 0.04458868572865391, 0.2546962897278487, 0.16579866490238124, 0.20304883354239994, -0.27849067882798695, -0.23445910876912493, 0.054285746067762375, 0.1851409676869119, 0.0690719327872752, -0.053983655699994415, -0.27620986395973773, 0.043490776126938205, -0.16754997628075735, -0.1727800482204036, 0.04482578342070892, -0.004865966729878906, 0.017666294346637433, -0.25373536106642514, 0.12424485443810386, -0.050960608573708065, 0.13500730420595833, -0.087691957119941, -0.09072281467166567, -0.04166070460933187, 0.04132948607753312, -0.04992595710699254, 0.04697448697074184, 0.15510738257777004, -0.1570797866055121, 0.016337622844037555, 0.37378525869950413, -0.15079781416774032, 0.00010065014459311016, 0.11865917681406865, -0.2119742811450528, -0.1703000027050693, 0.2011093233771149, 0.16084556254957402, 0.11530816419141797, -0.10811415357544782, 0.03277156078201231, -0.05848864728810325, 0.27581020503024023, 0.025374288519193964, -0.001768617920860711, 0.22138489573632206, 0.04308007291651198, 0.01543322517462666, 0.07349453751221373, -0.230340370500002, 0.0676240969619285, -0.2372225966260192, -0.12366076173918647, -0.17637707629252136, 0.12167438344904295, -0.17374102790107293, -0.11058772121193922, 0.38053648102112, 0.14103659865629328, 0.2601972913872155, 0.10071158199386286, 0.3350103587385208, 0.15877044906041451, 0.04736685664107744, 0.06453317761169894, 0.2661414833310696, 0.19242170068716247, 0.1147403330209532, -0.18749345657563518, 0.02330355204656602, 0.0040988748648761964] |
1,803.00869 | A perfect obstruction theory for moduli of coherent systems | Let $C$ be a curve of genus $g$. A coherent system on $C$ is a pair $(E,V)$,
where $E$ is a finite rank vector bundle on $C$ and $V$ is a linear subspace of
the space of global sections of $E$. The type of a coherent system $(E,V)$ is a
triple $(n,d,k)$, where $n$ is the rank of $E$, $d$ is the degree of $E$ and
$k$ is the dimension of $V$. The notion of stability for a coherent system
$(E,V)$ differs from the stability of the bundle $E$ and depends on the choice
of a real parameter $\alpha$. The moduli space of $\alpha$-stable coherent
systems of type $(n,d,k)$ has an expected dimension $\beta = \beta(n,d,k)$
which depends on the genus of the curve $C$ and on the type of the coherent
systems. We construct a perfect obstruction theory for the moduli spaces of
$\alpha$-stable coherent systems which has rank equal to the expected dimension
$\beta$. In our construction we do not fix one curve, but we work on families
of Gorenstein projective curves.
| math.AG | let c be a curve of genus g a coherent system on c is a pair ev where e is a finite rank vector bundle on c and v is a linear subspace of the space of global sections of e the type of a coherent system ev is a triple ndk where n is the rank of e d is the degree of e and k is the dimension of v the notion of stability for a coherent system ev differs from the stability of the bundle e and depends on the choice of a real parameter alpha the moduli space of alphastable coherent systems of type ndk has an expected dimension beta betandk which depends on the genus of the curve c and on the type of the coherent systems we construct a perfect obstruction theory for the moduli spaces of alphastable coherent systems which has rank equal to the expected dimension beta in our construction we do not fix one curve but we work on families of gorenstein projective curves | [['let', 'c', 'be', 'a', 'curve', 'of', 'genus', 'g', 'a', 'coherent', 'system', 'on', 'c', 'is', 'a', 'pair', 'ev', 'where', 'e', 'is', 'a', 'finite', 'rank', 'vector', 'bundle', 'on', 'c', 'and', 'v', 'is', 'a', 'linear', 'subspace', 'of', 'the', 'space', 'of', 'global', 'sections', 'of', 'e', 'the', 'type', 'of', 'a', 'coherent', 'system', 'ev', 'is', 'a', 'triple', 'ndk', 'where', 'n', 'is', 'the', 'rank', 'of', 'e', 'd', 'is', 'the', 'degree', 'of', 'e', 'and', 'k', 'is', 'the', 'dimension', 'of', 'v', 'the', 'notion', 'of', 'stability', 'for', 'a', 'coherent', 'system', 'ev', 'differs', 'from', 'the', 'stability', 'of', 'the', 'bundle', 'e', 'and', 'depends', 'on', 'the', 'choice', 'of', 'a', 'real', 'parameter', 'alpha', 'the', 'moduli', 'space', 'of', 'alphastable', 'coherent', 'systems', 'of', 'type', 'ndk', 'has', 'an', 'expected', 'dimension', 'beta', 'betandk', 'which', 'depends', 'on', 'the', 'genus', 'of', 'the', 'curve', 'c', 'and', 'on', 'the', 'type', 'of', 'the', 'coherent', 'systems', 'we', 'construct', 'a', 'perfect', 'obstruction', 'theory', 'for', 'the', 'moduli', 'spaces', 'of', 'alphastable', 'coherent', 'systems', 'which', 'has', 'rank', 'equal', 'to', 'the', 'expected', 'dimension', 'beta', 'in', 'our', 'construction', 'we', 'do', 'not', 'fix', 'one', 'curve', 'but', 'we', 'work', 'on', 'families', 'of', 'gorenstein', 'projective', 'curves']] | [-0.21429319117694748, 0.11765275290041287, -0.0827029024922224, 0.011443903308962796, -0.03686567430714752, -0.16378645669820524, 0.04692320219514523, 0.320165217150176, -0.2808373017686008, -0.19862206878010616, 0.0595973419603556, -0.26935073195653425, -0.1061588711901243, 0.22110571916726807, -0.08137889161467725, -0.013362229514737116, 0.020873944019238182, 0.1258137980543705, -0.0683756424357768, -0.2713667385586061, 0.4095382102207432, -0.012882998332294613, 0.2328863505783036, 0.021115152593052317, 0.12483018869804868, 0.023668219390877543, 0.010703073554607324, -0.0045494517276935325, -0.15071712217163727, 0.1554741150954045, 0.21305787403591323, 0.0984160441913382, 0.20259113975822232, -0.28337347176099237, -0.19350777928521828, 0.2174772532700106, 0.08384565025842802, 0.011472321551314793, 0.06307109131418212, -0.22518377562681602, 0.12243412143204274, -0.13292433101310583, -0.1679217160062128, -0.027786365618149556, 0.13070023969170463, 0.013916283694290838, -0.28334388020448387, 0.006223781952136304, 0.08902313058849337, 0.09253578364231818, -0.0583450042731995, -0.12151405185956486, -0.09308641910996981, 0.01974054321519446, -0.027311920636430988, 0.07222102520727487, 0.0952701746800178, -0.10393577717721116, -0.11249600920479658, 0.388391156607784, -0.08045319825286305, -0.19219242694766023, 0.1546732986800719, -0.1389424910519792, -0.06505951852954621, 0.1470717409988432, 0.17216620328944438, 0.1768985720123584, -0.004643182984964792, 0.2333457338512702, -0.08584007643299248, 0.17997063050144027, 0.034519814122684817, -0.010778024465083903, 0.14855988512660356, 0.13994368547330024, 0.11600442041797282, 0.06058671565044151, -0.08168615776602839, -0.012404005531543801, -0.3716535787964456, -0.20524035590625678, -0.15912894004154518, 0.15278805931632441, -0.10041776472170631, -0.1758178014275726, 0.41454144310054564, 0.003901768578649607, 0.22679329635900294, 0.03575387259208879, 0.22788440227660156, 0.10704285518583019, -0.002491119201295078, 0.04243979564337277, 0.17968815786162998, 0.17041904627627066, 0.00228164121400305, -0.17665382558723566, 0.030541788021489696, 0.13338327067635133] |
1,803.0087 | Interaction of a supersonic particle with a three-dimensional complex
plasma | The influence of a supersonic projectile on a three-dimensional complex
plasma is studied. Micron sized particles in a low-temperature plasma formed a
large undisturbed system in the new 'Zyflex' chamber during microgravity
conditions. A supersonic probe particle excited a Mach cone with Mach number M
$\approx$ 1.5 - 2 and double Mach cone structure in the large weakly damped
particle cloud. The speed of sound is measured with different methods and
particle charge estimations are compared to calculations from standard
theories. The high image resolution enables the study of Mach cones in
microgravity on the single particle level of a three-dimensional complex plasma
and gives insight to the dynamics. A heating of the microparticles is
discovered behind the supersonic projectile but not in the flanks of the Mach
cone.
| physics.plasm-ph | the influence of a supersonic projectile on a threedimensional complex plasma is studied micron sized particles in a lowtemperature plasma formed a large undisturbed system in the new zyflex chamber during microgravity conditions a supersonic probe particle excited a mach cone with mach number m approx 15 2 and double mach cone structure in the large weakly damped particle cloud the speed of sound is measured with different methods and particle charge estimations are compared to calculations from standard theories the high image resolution enables the study of mach cones in microgravity on the single particle level of a threedimensional complex plasma and gives insight to the dynamics a heating of the microparticles is discovered behind the supersonic projectile but not in the flanks of the mach cone | [['the', 'influence', 'of', 'a', 'supersonic', 'projectile', 'on', 'a', 'threedimensional', 'complex', 'plasma', 'is', 'studied', 'micron', 'sized', 'particles', 'in', 'a', 'lowtemperature', 'plasma', 'formed', 'a', 'large', 'undisturbed', 'system', 'in', 'the', 'new', 'zyflex', 'chamber', 'during', 'microgravity', 'conditions', 'a', 'supersonic', 'probe', 'particle', 'excited', 'a', 'mach', 'cone', 'with', 'mach', 'number', 'm', 'approx', '15', '2', 'and', 'double', 'mach', 'cone', 'structure', 'in', 'the', 'large', 'weakly', 'damped', 'particle', 'cloud', 'the', 'speed', 'of', 'sound', 'is', 'measured', 'with', 'different', 'methods', 'and', 'particle', 'charge', 'estimations', 'are', 'compared', 'to', 'calculations', 'from', 'standard', 'theories', 'the', 'high', 'image', 'resolution', 'enables', 'the', 'study', 'of', 'mach', 'cones', 'in', 'microgravity', 'on', 'the', 'single', 'particle', 'level', 'of', 'a', 'threedimensional', 'complex', 'plasma', 'and', 'gives', 'insight', 'to', 'the', 'dynamics', 'a', 'heating', 'of', 'the', 'microparticles', 'is', 'discovered', 'behind', 'the', 'supersonic', 'projectile', 'but', 'not', 'in', 'the', 'flanks', 'of', 'the', 'mach', 'cone']] | [-0.12571206609681834, 0.256078127478286, -0.09477056213837909, 0.03710037297074221, -0.019765866945560023, -0.08604955712817317, -0.012758159569985284, 0.3238907602769653, -0.21997902455087018, -0.3166564557789348, 0.0253332978926643, -0.24927376743083393, -0.00669922079436305, 0.19431192354541124, -0.00930666887590031, 0.04873903236520572, 0.06920589907571498, -0.03354134557101377, -0.04574591113533091, -0.1577759651360872, 0.2920628640980379, 0.10025224574350232, 0.26121431313396437, 0.06499552501614873, 0.15978934559384433, -0.08195362026128536, 0.004890059260284807, 0.06667073464065086, -0.1572671346688381, 0.018412765273164343, 0.1599016531597911, 0.04304470217926061, 0.2540066839896113, -0.45894605427335095, -0.20459669180705733, -0.035727423483755176, 0.14822147355788923, 0.06992299321261565, -0.07228698328735057, -0.2642311458365889, 0.03528300558508366, -0.13128840482581083, -0.20491733131783568, 0.037202141123024494, -0.002384733408689499, 0.054509712366651246, -0.25658931582295297, 0.08604356914018024, -0.00042985353296197306, 0.05984061080214199, -0.029901420032077537, -0.06119798726537888, -0.04847779996898465, 0.02540165110579215, 0.04773084130541488, 0.03350768963128922, 0.22607580941962443, -0.19020757258789042, -0.03154179637181008, 0.4675508821969779, -0.039675188952998264, -0.16099903523540637, 0.2748416432025512, -0.2705680502094622, -0.04981292990278306, 0.2584934932011084, 0.21348610533929363, 0.14879328547060255, -0.06380964927127819, 0.05334693776291436, -0.10882831727774213, 0.16159618838770887, 0.12402210012788693, -0.012957236788228271, 0.22597114033410398, 0.20530549108919754, 0.03170086349558642, 0.0928081575199947, -0.17184139821883732, -0.06473248451214841, -0.2547752006514746, -0.1925339104816699, -0.1638789824906766, 0.03668995961323692, -0.11502984067112093, -0.1675167728039458, 0.372782726904128, 0.08858511889514727, 0.1865942915168217, -0.029021810050907097, 0.32374976444109455, 0.07476756250207114, 0.020452801359993382, 0.09805523059294215, 0.2611789138250669, 0.1856062273076904, 0.1391025278643947, -0.2216139719271519, -0.004287484545612664, 0.05424888033463847] |
1,803.00871 | Nanodiamonds-induced effects on neuronal firing of mouse hippocampal
microcircuits | Fluorescent nanodiamonds (FND) are carbon-based nanomaterials that can
efficiently incorporate optically active photoluminescent centers such as the
nitrogen-vacancy complex, thus making them promising candidates as optical
biolabels and drug-delivery agents. FNDs exhibit bright fluorescence without
photobleaching combined with high uptake rate and low cytotoxicity. Focusing on
FNDs interference with neuronal function, here we examined their effect on
cultured hippocampal neurons, monitoring the whole network development as well
as the electrophysiological properties of single neurons. We observed that FNDs
drastically decreased the frequency of inhibitory (from 1.81 Hz to 0.86 Hz) and
excitatory (from 1.61 Hz to 0.68 Hz) miniature postsynaptic currents, and
consistently reduced action potential (AP) firing frequency (by 36%), as
measured by microelectrode arrays. On the contrary, bursts synchronization was
preserved, as well as the amplitude of spontaneous inhibitory and excitatory
events. Current-clamp recordings revealed that the ratio of neurons responding
with AP trains of high-frequency (fast-spiking) versus neurons responding with
trains of low-frequency (slow-spiking) was unaltered, suggesting that FNDs
exerted a comparable action on neuronal subpopulations. At the single cell
level, rapid onset of the somatic AP ("kink") was drastically reduced in
FND-treated neurons, suggesting a reduced contribution of axonal and dendritic
components while preserving neuronal excitability.
| q-bio.NC physics.bio-ph | fluorescent nanodiamonds fnd are carbonbased nanomaterials that can efficiently incorporate optically active photoluminescent centers such as the nitrogenvacancy complex thus making them promising candidates as optical biolabels and drugdelivery agents fnds exhibit bright fluorescence without photobleaching combined with high uptake rate and low cytotoxicity focusing on fnds interference with neuronal function here we examined their effect on cultured hippocampal neurons monitoring the whole network development as well as the electrophysiological properties of single neurons we observed that fnds drastically decreased the frequency of inhibitory from 181 hz to 086 hz and excitatory from 161 hz to 068 hz miniature postsynaptic currents and consistently reduced action potential ap firing frequency by 36 as measured by microelectrode arrays on the contrary bursts synchronization was preserved as well as the amplitude of spontaneous inhibitory and excitatory events currentclamp recordings revealed that the ratio of neurons responding with ap trains of highfrequency fastspiking versus neurons responding with trains of lowfrequency slowspiking was unaltered suggesting that fnds exerted a comparable action on neuronal subpopulations at the single cell level rapid onset of the somatic ap kink was drastically reduced in fndtreated neurons suggesting a reduced contribution of axonal and dendritic components while preserving neuronal excitability | [['fluorescent', 'nanodiamonds', 'fnd', 'are', 'carbonbased', 'nanomaterials', 'that', 'can', 'efficiently', 'incorporate', 'optically', 'active', 'photoluminescent', 'centers', 'such', 'as', 'the', 'nitrogenvacancy', 'complex', 'thus', 'making', 'them', 'promising', 'candidates', 'as', 'optical', 'biolabels', 'and', 'drugdelivery', 'agents', 'fnds', 'exhibit', 'bright', 'fluorescence', 'without', 'photobleaching', 'combined', 'with', 'high', 'uptake', 'rate', 'and', 'low', 'cytotoxicity', 'focusing', 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1,803.00872 | On Nelson-type Hamiltonians and abstract boundary conditions | We construct Hamiltonians for systems of nonrelativistic particles linearly
coupled to massive scalar bosons using abstract boundary conditions. The
construction yields an explicit characterisation of the domain of
self-adjointness in terms of boundary conditions that relate sectors with
different numbers of bosons. We treat both models in which the Hamiltonian may
be defined as a form perturbation of the free operator, such as Fr\"ohlich's
polaron, and renormalisable models, such as the massive Nelson model.
| math-ph math.MP | we construct hamiltonians for systems of nonrelativistic particles linearly coupled to massive scalar bosons using abstract boundary conditions the construction yields an explicit characterisation of the domain of selfadjointness in terms of boundary conditions that relate sectors with different numbers of bosons we treat both models in which the hamiltonian may be defined as a form perturbation of the free operator such as frohlichs polaron and renormalisable models such as the massive nelson model | [['we', 'construct', 'hamiltonians', 'for', 'systems', 'of', 'nonrelativistic', 'particles', 'linearly', 'coupled', 'to', 'massive', 'scalar', 'bosons', 'using', 'abstract', 'boundary', 'conditions', 'the', 'construction', 'yields', 'an', 'explicit', 'characterisation', 'of', 'the', 'domain', 'of', 'selfadjointness', 'in', 'terms', 'of', 'boundary', 'conditions', 'that', 'relate', 'sectors', 'with', 'different', 'numbers', 'of', 'bosons', 'we', 'treat', 'both', 'models', 'in', 'which', 'the', 'hamiltonian', 'may', 'be', 'defined', 'as', 'a', 'form', 'perturbation', 'of', 'the', 'free', 'operator', 'such', 'as', 'frohlichs', 'polaron', 'and', 'renormalisable', 'models', 'such', 'as', 'the', 'massive', 'nelson', 'model']] | [-0.1408813637555451, 0.19649378011003923, -0.05880869207022762, 0.10068650573108194, -0.04461707530945942, -0.15683537569979356, -0.05763071498556717, 0.2823681166219349, -0.22203484970472148, -0.2845490554259536, 0.08093797099285734, -0.24656869887021948, -0.11921753461560788, 0.1363054001420686, -0.013712070504757198, 0.04577416886348982, 0.06011807407894348, 0.05363611001647203, -0.07374380379474747, -0.2111045175886436, 0.3790487908644954, -0.007693104342104414, 0.173940936871175, 0.05039543955511338, 0.09146107002979496, 0.026777675784369175, 0.03985362618884726, -0.01910865434596466, -0.12830947802413484, 0.06682203325593995, 0.21128790859713903, 0.0630793911782471, 0.19340204239848094, -0.44481872998782107, -0.2216896072019842, 0.10730843894133293, 0.183030648798858, 0.12280585590240269, -0.003662489482038025, -0.29456397984176874, 0.011894476864285566, -0.2116319617080326, -0.19092415911271363, -0.11020789263814033, -0.021460709199812765, -0.0001029781242077415, -0.31952277019720626, 0.08535378088394331, 0.03721709858399589, 0.0397850337085893, -0.13221174690595552, -0.08266641044787862, -0.07873239556981905, 0.07813025428913534, 0.05037592922658282, -0.010118389766461946, 0.09367215824061753, -0.17891640158848385, -0.12552824339709823, 0.4116361951958892, -0.11764838677717725, -0.2832916389546684, 0.24792209454824696, -0.07501885920410624, -0.12347249470248416, 0.05371369267939716, 0.16020770232520393, 0.14497370208689087, -0.1895577143374327, 0.1844353263787107, -0.05942028015537339, 0.09997228867802266, 0.05752051901701536, 0.09732012381868411, 0.18226954018747485, 0.10245695286367491, 0.055318710608156144, 0.1472047920499274, 0.04105543414432857, -0.1529674482204624, -0.3652451908266222, -0.18108818867925014, -0.16093463051711787, 0.05167593971851307, -0.09153830501029891, -0.23369313868366787, 0.3815049805042863, 0.15342098554577427, 0.19221646236759182, 0.054535951577064055, 0.20335838013655833, 0.18407705279753064, 0.08559043080596304, 0.04224043375359395, 0.1746125198850358, 0.18058268148829607, 0.04153161431738251, -0.1910994541327897, -0.09114559909102281, 0.139020568804463] |
1,803.00873 | Using Spatial Correlation in Semi-Supervised Hyperspectral Unmixing
under Polynomial Post-nonlinear Mixing Model | This paper presents a semi-supervised hyperspectral unmixing solution that
integrate the spatial information in the abundance estimation procedure. The
proposed method is applied on a nonlinear model based on polynomial
postnonlinear mixing model where characterizes each pixel reflections composed
of nonlinear function of pure spectral signatures added by noise. We
partitioned the image to classes where contains similar materials so share the
same abundance vector. The spatial correlation between pixels belonging to each
class is modelled by Markov Random Field. A Bayesian framework is proposed to
estimate the classes and corresponding abundance vectors alternatively. We
proposed sparse Dirichlet prior for abundance vector that made it possible to
use this algorithm in semi-supervised scenario where the exact involved
materials are unknown. In this approach, we just need to have a large library
of pure spectral signatures including the desired materials. An MCMC algorithm
is used to estimate the abundance vector based on generated samples. The result
of implementation on simulated data shows the prominence of proposed approach.
| eess.SP | this paper presents a semisupervised hyperspectral unmixing solution that integrate the spatial information in the abundance estimation procedure the proposed method is applied on a nonlinear model based on polynomial postnonlinear mixing model where characterizes each pixel reflections composed of nonlinear function of pure spectral signatures added by noise we partitioned the image to classes where contains similar materials so share the same abundance vector the spatial correlation between pixels belonging to each class is modelled by markov random field a bayesian framework is proposed to estimate the classes and corresponding abundance vectors alternatively we proposed sparse dirichlet prior for abundance vector that made it possible to use this algorithm in semisupervised scenario where the exact involved materials are unknown in this approach we just need to have a large library of pure spectral signatures including the desired materials an mcmc algorithm is used to estimate the abundance vector based on generated samples the result of implementation on simulated data shows the prominence of proposed approach | [['this', 'paper', 'presents', 'a', 'semisupervised', 'hyperspectral', 'unmixing', 'solution', 'that', 'integrate', 'the', 'spatial', 'information', 'in', 'the', 'abundance', 'estimation', 'procedure', 'the', 'proposed', 'method', 'is', 'applied', 'on', 'a', 'nonlinear', 'model', 'based', 'on', 'polynomial', 'postnonlinear', 'mixing', 'model', 'where', 'characterizes', 'each', 'pixel', 'reflections', 'composed', 'of', 'nonlinear', 'function', 'of', 'pure', 'spectral', 'signatures', 'added', 'by', 'noise', 'we', 'partitioned', 'the', 'image', 'to', 'classes', 'where', 'contains', 'similar', 'materials', 'so', 'share', 'the', 'same', 'abundance', 'vector', 'the', 'spatial', 'correlation', 'between', 'pixels', 'belonging', 'to', 'each', 'class', 'is', 'modelled', 'by', 'markov', 'random', 'field', 'a', 'bayesian', 'framework', 'is', 'proposed', 'to', 'estimate', 'the', 'classes', 'and', 'corresponding', 'abundance', 'vectors', 'alternatively', 'we', 'proposed', 'sparse', 'dirichlet', 'prior', 'for', 'abundance', 'vector', 'that', 'made', 'it', 'possible', 'to', 'use', 'this', 'algorithm', 'in', 'semisupervised', 'scenario', 'where', 'the', 'exact', 'involved', 'materials', 'are', 'unknown', 'in', 'this', 'approach', 'we', 'just', 'need', 'to', 'have', 'a', 'large', 'library', 'of', 'pure', 'spectral', 'signatures', 'including', 'the', 'desired', 'materials', 'an', 'mcmc', 'algorithm', 'is', 'used', 'to', 'estimate', 'the', 'abundance', 'vector', 'based', 'on', 'generated', 'samples', 'the', 'result', 'of', 'implementation', 'on', 'simulated', 'data', 'shows', 'the', 'prominence', 'of', 'proposed', 'approach']] | [-0.03122449621939695, 0.04156588674468586, -0.06520676935029901, 0.04650458464180175, -0.09190481829426128, -0.14120364357774842, 0.012873699806535235, 0.425096215433385, -0.26783126621810066, -0.3314321727003812, 0.06304889516722613, -0.2395075593446662, -0.15269853279530352, 0.13643584733391292, -0.07054612532269254, 0.0816415381780545, 0.06966962842607355, 0.039028925442484666, -0.04866680357337323, -0.24699061167642408, 0.3104019703354731, 0.02023995612994824, 0.31296502410557614, -0.05047589990433799, 0.1286879807173454, -0.017793408265854203, -0.06646164964200053, 0.002806827282892114, -0.09005017103166221, 0.13446318741932692, 0.2660577785089073, 0.16488393689421602, 0.2578143996820527, -0.36805622337711413, -0.22726035432845174, 0.14717454540386168, 0.13477727818493562, 0.08722396865374892, -0.06379463789768584, -0.29580532317891356, 0.08828598339074145, -0.12550989822596761, -0.061569548639081734, -0.06974671709811292, -0.039398462727949224, -0.023112571129928827, -0.3345566285852657, 0.07243653092847531, 0.04014473717592387, 0.025324527011533458, -0.07467332544893655, -0.14539249210775526, -0.005270258989185095, 0.08117519086226821, 0.023103346867228758, 0.007710413252710399, 0.10510629802067058, -0.08171116985385558, -0.09109922508528198, 0.35512438040024724, -0.10676415342310197, -0.2252701102322562, 0.16694529817498524, -0.06319319804939609, -0.16999436150506947, 0.1341474904771333, 0.21639323134693395, 0.13314023641140077, -0.1568743937637612, 0.06863415906877617, -0.06569227972909449, 0.1993934108149148, 0.02593639998764069, -0.009200267246868237, 0.16655577477404332, 0.18660743872029145, 0.055475617361995556, 0.15338889320369764, -0.13824906942423387, -0.07980006591294869, -0.265312067162195, -0.13549093100598575, -0.2356969485976677, -0.02042935859322189, -0.11289820086808121, -0.19372135393478604, 0.4257910634189036, 0.21650024626056202, 0.21844009649154383, 0.017654553165165596, 0.3255086565580982, 0.09508777958206294, 0.0841779252542295, 0.07735378352970064, 0.16513383566929274, 0.14282919105467756, 0.08301252586744635, -0.18121796618888986, 0.09811133570711028, 0.07316230391909603] |
1,803.00874 | Estimating Total Search Space Size for Specific Piece Sets in Chess | Automatic chess problem or puzzle composition typically involves generating
and testing various different positions, sometimes using particular piece sets.
Once a position has been generated, it is then usually tested for positional
legality based on the game rules. However, it is useful to be able to estimate
what the search space size for particular piece combinations is to begin with.
So if a desirable chess problem was successfully generated by examining
'merely' 100,000 or so positions in a theoretical search space of about 100
billion, this would imply the composing approach used was quite viable and
perhaps even impressive. In this article, I explain a method of calculating the
size of this search space using a combinatorics and permutations approach.
While the mathematics itself may already be established, a precise method and
justification of applying it with regard to the chessboard and chess pieces has
not been documented, to the best of our knowledge. Additionally, the method
could serve as a useful starting point for further estimations of search space
size which filter out positions for legality and rotation, depending on how the
automatic composer is allowed to place pieces on the board (because this
affects its total search space size).
| cs.AI | automatic chess problem or puzzle composition typically involves generating and testing various different positions sometimes using particular piece sets once a position has been generated it is then usually tested for positional legality based on the game rules however it is useful to be able to estimate what the search space size for particular piece combinations is to begin with so if a desirable chess problem was successfully generated by examining merely 100000 or so positions in a theoretical search space of about 100 billion this would imply the composing approach used was quite viable and perhaps even impressive in this article i explain a method of calculating the size of this search space using a combinatorics and permutations approach while the mathematics itself may already be established a precise method and justification of applying it with regard to the chessboard and chess pieces has not been documented to the best of our knowledge additionally the method could serve as a useful starting point for further estimations of search space size which filter out positions for legality and rotation depending on how the automatic composer is allowed to place pieces on the board because this affects its total search space size | [['automatic', 'chess', 'problem', 'or', 'puzzle', 'composition', 'typically', 'involves', 'generating', 'and', 'testing', 'various', 'different', 'positions', 'sometimes', 'using', 'particular', 'piece', 'sets', 'once', 'a', 'position', 'has', 'been', 'generated', 'it', 'is', 'then', 'usually', 'tested', 'for', 'positional', 'legality', 'based', 'on', 'the', 'game', 'rules', 'however', 'it', 'is', 'useful', 'to', 'be', 'able', 'to', 'estimate', 'what', 'the', 'search', 'space', 'size', 'for', 'particular', 'piece', 'combinations', 'is', 'to', 'begin', 'with', 'so', 'if', 'a', 'desirable', 'chess', 'problem', 'was', 'successfully', 'generated', 'by', 'examining', 'merely', '100000', 'or', 'so', 'positions', 'in', 'a', 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1,803.00875 | Supercritical Poincar\'e-Andronov-Hopf bifurcation in a mean field
quantum laser equation | We deal with the dynamical system properties of a
Gorini-Kossakowski-Sudarshan-Lindblad (GKSL) equation with mean-field
Hamiltonian that models a simple laser by applying a mean field approximation
to a quantum system describing a single-mode optical cavity and a set of two
level atoms, each coupled to a reservoir. We prove that the mean field quantum
master equation has a unique regular stationary solution. In case a relevant
parameter $C_\mathfrak{b} $, i.e., the cavity cooperative parameter, is less
than $1$, we prove that any regular solution converges exponentially fast to
the equilibrium, and so the regular stationary state is a globally
asymptotically stable equilibrium solution. We obtain that a locally
exponential stable limit cycle is born at the regular stationary state as
$C_\mathfrak{b} $ passes through the critical value $1$. Then, the mean-field
laser equation has a Poincar\'e-Andronov-Hopf bifurcation at $C_\mathfrak{b} =1
$ of supercritical-like type. Namely, we derive rigorously, at the level of
density matrices --for the first time--, the transition from a global attractor
quantum state, where the light is not emitted, to a locally stable set of
coherent quantum states producing coherent light. Moreover, we establish the
local exponential stability of the limit cycle in case a relevant parameter is
between the first and second laser thresholds appearing in the semiclassical
laser theory. Thus, we get that the coherent laser light persists over time
under this condition. In order to prove the exponential convergence of the
quantum state, we develop a new technique for proving the exponential
convergence in open quantum systems that is based in a new variation of
constant formula. Applying our main results we find the long-time behavior of
the von Neumann entropy, the photon-number statistics, and the quantum variance
of the quadratures.
| math-ph math.FA math.MP math.PR nlin.CD quant-ph | we deal with the dynamical system properties of a gorinikossakowskisudarshanlindblad gksl equation with meanfield hamiltonian that models a simple laser by applying a mean field approximation to a quantum system describing a singlemode optical cavity and a set of two level atoms each coupled to a reservoir we prove that the mean field quantum master equation has a unique regular stationary solution in case a relevant parameter c_mathfrakb ie the cavity cooperative parameter is less than 1 we prove that any regular solution converges exponentially fast to the equilibrium and so the regular stationary state is a globally asymptotically stable equilibrium solution we obtain that a locally exponential stable limit cycle is born at the regular stationary state as c_mathfrakb passes through the critical value 1 then the meanfield laser equation has a poincareandronovhopf bifurcation at c_mathfrakb 1 of supercriticallike type namely we derive rigorously at the level of density matrices for the first time the transition from a global attractor quantum state where the light is not emitted to a locally stable set of coherent quantum states producing coherent light moreover we establish the local exponential stability of the limit cycle in case a relevant parameter is between the first and second laser thresholds appearing in the semiclassical laser theory thus we get that the coherent laser light persists over time under this condition in order to prove the exponential convergence of the quantum state we develop a new technique for proving the exponential convergence in open quantum systems that is based in a new variation of constant formula applying our main results we find the longtime behavior of the von neumann entropy the photonnumber statistics and the quantum variance of the quadratures | [['we', 'deal', 'with', 'the', 'dynamical', 'system', 'properties', 'of', 'a', 'gorinikossakowskisudarshanlindblad', 'gksl', 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1,803.00876 | fCDN: A Flexible and Efficient CDN Infrastructure without DNS
Redirection or Content Reflection | Flexible and efficient CDNs are critical to facilitate content distribution
in 5G+ architectures. Current CDNs suffer from inefficient request mapping
based on DNS redirection, and inefficient content distribution from origin to
edge servers, through content reflection. We proposes a novel, flexible CDN
architecture that removes the need for DNS-based mapping and content
reflection. Instead, requests to/from the CDN are treated as service
transactions in the network, which utilises a routing function embraced from
emerging research in Information-Centric Networks (ICN) to route edge-to-edge
transactions to the true nearest service point. The same function is utilized
to establish path-based flows over a fast forwarding substrate; thereby,
eliminating the need for IP routing between service points within a single
domain, and potentially at peering points with other domains. We model our
architecture and formulate the resource placement problem as a variance of the
K-center problem. To address the problem, we propose a greedy algorithm, Swing,
that balances the placement of service points between highly and poorly
connected nodes. We evaluate the efficiency of our architecture in utilising
the CDN and network resources through Monte Carlo simulations that explore a
range of K values. Moreover, we compare the goodness of the placement solutions
provided by Swing with those provided by Largest First and Closest First
Algorithms. Evaluation results show the superiority of our fCDN solution in
reducing the edge-to-edge path length and the required network resources.
| cs.NI | flexible and efficient cdns are critical to facilitate content distribution in 5g architectures current cdns suffer from inefficient request mapping based on dns redirection and inefficient content distribution from origin to edge servers through content reflection we proposes a novel flexible cdn architecture that removes the need for dnsbased mapping and content reflection instead requests tofrom the cdn are treated as service transactions in the network which utilises a routing function embraced from emerging research in informationcentric networks icn to route edgetoedge transactions to the true nearest service point the same function is utilized to establish pathbased flows over a fast forwarding substrate thereby eliminating the need for ip routing between service points within a single domain and potentially at peering points with other domains we model our architecture and formulate the resource placement problem as a variance of the kcenter problem to address the problem we propose a greedy algorithm swing that balances the placement of service points between highly and poorly connected nodes we evaluate the efficiency of our architecture in utilising the cdn and network resources through monte carlo simulations that explore a range of k values moreover we compare the goodness of the placement solutions provided by swing with those provided by largest first and closest first algorithms evaluation results show the superiority of our fcdn solution in reducing the edgetoedge path length and the required network resources | [['flexible', 'and', 'efficient', 'cdns', 'are', 'critical', 'to', 'facilitate', 'content', 'distribution', 'in', '5g', 'architectures', 'current', 'cdns', 'suffer', 'from', 'inefficient', 'request', 'mapping', 'based', 'on', 'dns', 'redirection', 'and', 'inefficient', 'content', 'distribution', 'from', 'origin', 'to', 'edge', 'servers', 'through', 'content', 'reflection', 'we', 'proposes', 'a', 'novel', 'flexible', 'cdn', 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1,803.00877 | The last zero crossing of an iterated Brownian motion with drift | In this paper we consider the iterated Brownian motion $
^{\mu_1}_{\mu_2}\!I(t) = B_1^{\mu_1} ( | B_{2}^{\mu_2} (t)|) $ where
$B_j^{\mu_j} , j=1,2$ are two independent Brownian motions with drift $\mu_j$.
Here we study the last zero crossing of $ ^{\mu_1}_{\mu_2}\!I(t) $ and for this
purpose we derive the last zero-crossing distribution of the drifted Brownian
motion. We derive also the joint distribution of the last zero crossing before
$ t $ and of the first passage time through the zero level of a Brownian motion
with drift $ \mu $ after $ t $. All these results permit us to derive explicit
formulas for ${^I_\mu T_0} = \sup \{ s < \max_{0\leq z\leq t} |B_2(z)| :
B_1^\mu (s) = 0 \}$. Also the iterated zero-crossing $ {^{\mu_1} T}_{0,
{^{\mu_2} T}_{0,t}} $ is analyzed and extended to the case where the level of
nesting is arbitrary.
| math.PR | in this paper we consider the iterated brownian motion mu_1_mu_2it b_1mu_1 b_2mu_2 t where b_jmu_j j12 are two independent brownian motions with drift mu_j here we study the last zero crossing of mu_1_mu_2it and for this purpose we derive the last zerocrossing distribution of the drifted brownian motion we derive also the joint distribution of the last zero crossing before t and of the first passage time through the zero level of a brownian motion with drift mu after t all these results permit us to derive explicit formulas for i_mu t_0 sup s max_0leq zleq t b_2z b_1mu s 0 also the iterated zerocrossing mu_1 t_0 mu_2 t_0t is analyzed and extended to the case where the level of nesting is arbitrary | [['in', 'this', 'paper', 'we', 'consider', 'the', 'iterated', 'brownian', 'motion', 'mu_1_mu_2it', 'b_1mu_1', 'b_2mu_2', 't', 'where', 'b_jmu_j', 'j12', 'are', 'two', 'independent', 'brownian', 'motions', 'with', 'drift', 'mu_j', 'here', 'we', 'study', 'the', 'last', 'zero', 'crossing', 'of', 'mu_1_mu_2it', 'and', 'for', 'this', 'purpose', 'we', 'derive', 'the', 'last', 'zerocrossing', 'distribution', 'of', 'the', 'drifted', 'brownian', 'motion', 'we', 'derive', 'also', 'the', 'joint', 'distribution', 'of', 'the', 'last', 'zero', 'crossing', 'before', 't', 'and', 'of', 'the', 'first', 'passage', 'time', 'through', 'the', 'zero', 'level', 'of', 'a', 'brownian', 'motion', 'with', 'drift', 'mu', 'after', 't', 'all', 'these', 'results', 'permit', 'us', 'to', 'derive', 'explicit', 'formulas', 'for', 'i_mu', 't_0', 'sup', 's', 'max_0leq', 'zleq', 't', 'b_2z', 'b_1mu', 's', '0', 'also', 'the', 'iterated', 'zerocrossing', 'mu_1', 't_0', 'mu_2', 't_0t', 'is', 'analyzed', 'and', 'extended', 'to', 'the', 'case', 'where', 'the', 'level', 'of', 'nesting', 'is', 'arbitrary']] | [-0.14825707768764476, 0.19105803559871454, -0.08650714059456668, 0.010160334689285734, -0.022694100629827327, -0.12842470780014992, 0.05664754362379458, 0.3671315123471592, -0.29244932084312214, -0.21473888597932367, 0.08644545785099236, -0.2741425120279771, -0.08219697650778911, 0.12122572817365992, -0.03619175695687239, 0.04608122011293368, 0.01465913143153463, 0.09887486187628759, -0.0506614676981779, -0.2319324484901856, 0.26239452944618874, -0.0193328153871899, 0.15398287446767991, 0.003212003896398277, 0.13665871880848185, 0.035275439399591615, -0.031616162540840695, -0.03059767722955038, -0.26585908048657764, 0.04440758859032187, 0.17435600656759123, 0.028771234851303923, 0.2692639306669348, -0.35586602093073827, -0.12591357874796436, 0.14087314246190263, 0.15355790255928475, 0.01776421382413085, 0.03459118284959475, -0.2838412767561185, 0.06991502778616669, -0.1397513441645123, -0.17646276334236408, -0.021178159573725586, 0.1341801498181604, 0.07282797529980348, -0.2789435102534079, 0.13535850888854775, 0.1303656301059728, 0.046744367380722844, -0.05439338779703168, -0.14746945527992372, 0.0006245670334339656, 0.13611214740025201, 0.07270105174995542, 0.029387916081508716, 0.1055757219678369, -0.04873353262544321, -0.0905439921008455, 0.31729289154714807, -0.13486470498449715, -0.19421310594369626, 0.12830822657520785, -0.2593262047599198, -0.14845842053583855, 0.12721981619613182, 0.11400346381686113, 0.1454904702901519, -0.16570800346129672, 0.16681129991825558, -0.007202825685641889, 0.0577241029000263, 0.13161581322342028, -0.02160491905945899, 0.14273089774209877, 0.10332726294190847, 0.10652929222500272, 0.1361999333081446, -0.16208166464087392, -0.10387538021815748, -0.405233310246519, -0.18490496047286734, -0.16566017867921018, 0.11384254171721765, -0.07176614492182752, -0.12218630660710664, 0.3401762708732537, 0.16420971812150473, 0.2555543481661328, 0.12359425364511795, 0.2345422478352577, 0.19708882709001674, -0.056550412422752584, 0.08495631031009593, 0.11716737369364448, 0.13599629501073524, 0.09891420504447587, -0.21896190003989713, 0.010097653986404425, 0.07964683912598111] |
1,803.00878 | Sunspot Equilibrium in General Quitting Games | We prove that positive recursive general quitting games, which are quitting
games in which each player may have more than one continue action, admit a
sunspot $\ep$-equilibrium, for every $\ep > 0$. To this end we show that the
equilibrium set of strategic-form games can be uniformly approximated by a
smooth manifold, and develop a new fixed-point theorem for smooth manifolds.
| math.PR | we prove that positive recursive general quitting games which are quitting games in which each player may have more than one continue action admit a sunspot epequilibrium for every ep 0 to this end we show that the equilibrium set of strategicform games can be uniformly approximated by a smooth manifold and develop a new fixedpoint theorem for smooth manifolds | [['we', 'prove', 'that', 'positive', 'recursive', 'general', 'quitting', 'games', 'which', 'are', 'quitting', 'games', 'in', 'which', 'each', 'player', 'may', 'have', 'more', 'than', 'one', 'continue', 'action', 'admit', 'a', 'sunspot', 'epequilibrium', 'for', 'every', 'ep', '0', 'to', 'this', 'end', 'we', 'show', 'that', 'the', 'equilibrium', 'set', 'of', 'strategicform', 'games', 'can', 'be', 'uniformly', 'approximated', 'by', 'a', 'smooth', 'manifold', 'and', 'develop', 'a', 'new', 'fixedpoint', 'theorem', 'for', 'smooth', 'manifolds']] | [-0.10097654384056416, 0.09877467051458856, -0.17820115921398003, 0.12041730997734704, -0.07697670203633607, -0.24626341397718837, 0.03796449720781917, 0.3988339725881815, -0.2524174629400174, -0.19821896245703102, 0.10766181649135736, -0.2415798322763294, -0.18102846040079992, 0.14758180013256303, -0.15766520001925527, -0.007188316488948961, 0.12724463409977033, 0.07289744252339006, -0.008547395071946084, -0.2729924163005004, 0.3654728970179955, -0.10187017263378947, 0.13305836419264475, 0.03196896412409842, 0.12785277774867912, -0.036072310192200044, 0.0550559368954661, 0.11170134348018716, -0.11757296073216518, 0.08434035951892535, 0.32177138443415365, 0.14963103568103786, 0.394285481950889, -0.40724412811832733, -0.18779095757830266, 0.20786151255791385, 0.1265285478051131, 0.08777165682598327, -0.02966680162862758, -0.2660734567286757, 0.17758729333678883, -0.15390804580723247, -0.1154501737561077, -0.10184908633430799, 0.006991737118611733, 0.02640848169103265, -0.31101329162095986, -0.0557721736918514, 0.09391263503736506, 0.05923899016149032, -0.06613966266741045, -0.1078998971672263, -0.03701757788270091, 0.12383132172593227, -0.030334901064634323, 0.06787016060940611, 0.09542328830866609, -0.06841196384824191, -0.1961416023162504, 0.3424996388455232, -0.09375720170913458, -0.2166134896998604, 0.11689225851247707, -0.12433817979569237, -0.17946439699735492, 0.12303064690592388, 0.16238786034906905, 0.21633664574474096, -0.09008464724756778, 0.08184394409472588, -0.15819510015038152, 0.14741708628522854, 0.10092269314918667, -0.03571185951101749, 0.15230678228350977, 0.07965462220211823, 0.23131074369108925, 0.0976602757621246, 0.04239508101406197, -0.13907015656586735, -0.30751866282274326, -0.13914578799158334, -0.0953363152531286, 0.13718042448163031, -0.11104995820181406, -0.1947530260309577, 0.3476987625860299, 0.06246201945662809, 0.1330454462595905, 0.205258509334332, 0.2129481009556912, 0.13497645879615447, 0.0028402372263371944, 0.149690427359504, 0.1805699944224519, 0.07274659471974397, 0.07994715929962695, -0.10829902841554334, 0.057323197515991825, 0.12586396801440666] |
1,803.00879 | Probabilistic design of a molybdenum-base alloy using a neural network | An artificial intelligence tool is exploited to discover and characterize a
new molybdenum-base alloy that is the most likely to simultaneously satisfy
targets of cost, phase stability, precipitate content, yield stress, and
hardness. Experimental testing demonstrates that the proposed alloy fulfils the
computational predictions, and furthermore the physical properties exceed those
of other commercially available Mo-base alloys for forging-die applications.
| cond-mat.mtrl-sci cs.LG physics.comp-ph | an artificial intelligence tool is exploited to discover and characterize a new molybdenumbase alloy that is the most likely to simultaneously satisfy targets of cost phase stability precipitate content yield stress and hardness experimental testing demonstrates that the proposed alloy fulfils the computational predictions and furthermore the physical properties exceed those of other commercially available mobase alloys for forgingdie applications | [['an', 'artificial', 'intelligence', 'tool', 'is', 'exploited', 'to', 'discover', 'and', 'characterize', 'a', 'new', 'molybdenumbase', 'alloy', 'that', 'is', 'the', 'most', 'likely', 'to', 'simultaneously', 'satisfy', 'targets', 'of', 'cost', 'phase', 'stability', 'precipitate', 'content', 'yield', 'stress', 'and', 'hardness', 'experimental', 'testing', 'demonstrates', 'that', 'the', 'proposed', 'alloy', 'fulfils', 'the', 'computational', 'predictions', 'and', 'furthermore', 'the', 'physical', 'properties', 'exceed', 'those', 'of', 'other', 'commercially', 'available', 'mobase', 'alloys', 'for', 'forgingdie', 'applications']] | [-0.047837052159338145, 0.1054167260210083, -0.1022050356851858, 0.03958124772384109, -0.08401588994290746, -0.16980159067826575, 0.04606820590663375, 0.3882706707768273, -0.2698203019671175, -0.3371530541529258, 0.0638666404942214, -0.2815834114311688, -0.15876312660903, 0.23950190631378637, -0.06546670991698639, 0.1039124263487266, 0.06486297911849984, -0.01653570489010267, -0.029065620118400835, -0.2770820719173603, 0.22327638955239468, 0.08691448867811184, 0.3653248087094541, 0.07317196370282195, 0.033456928750271335, -0.0394740126569543, 0.041617937937476916, 0.03033800182962104, -0.13543965387808593, 0.11842972509105477, 0.2952131419197509, 0.1310922574300907, 0.19232155348321325, -0.4522233714529297, -0.22827548411135612, 0.07361756202666775, 0.051124666817486286, 0.05784117636319838, -0.09102134195793617, -0.20012319970287776, 0.14320521503523514, -0.12247435318044665, -0.132974824002176, -0.13714380283317154, 0.010950580590584298, 0.025147594451258908, -0.27840035752671066, 0.014329291929147746, 0.02978281991294863, 0.06164087329811433, -0.13466757270017346, -0.18873858968155427, -0.02334612456003302, 0.11999785748980286, 0.019809063270753414, -0.011053196111644962, 0.1917595303568401, -0.13847875277707844, -0.1021054039725609, 0.4227008384309317, 0.03091382359873438, -0.12196513156308547, 0.2433332035628458, -0.05221196376767598, -0.16837006325326992, 0.13408063981159216, 0.15346763307522787, 0.05499221154145504, -0.19260738121770454, 0.02243322937590021, 0.07192578295801293, 0.2432710292602056, 0.027440101055330353, 0.07599609852523397, 0.17288878318249135, 0.21695582003316335, 0.01817377964688236, 0.16628668881777117, -0.06692554759221118, -0.04092878763351524, -0.22038696136064173, -0.22451886917840233, -0.20712492021470608, -0.013934733215392682, -0.10325800784247756, -0.1510099498718454, 0.32301558299284233, 0.22195589068456015, 0.09563262074401505, 0.04147960562585739, 0.25574676705557003, 0.04554769388612425, 0.09312120534218195, 0.024427163071538274, 0.2912681337789093, 0.12771160983921667, 0.12575393840017027, -0.2228134022862242, 0.18269235508418397, -0.029305923591253526] |
1,803.0088 | A comparative study of stochastic resonance for a model with two
pathways by escape times, linear response, invariant measures and the
conditional Kolmogorov-Smirnov Test | We consider stochastic resonance for a diffusion with drift given by a
potential, which has two metastable states and two pathways between them.
Depending on the direction of the forcing, the height of the two barriers, one
for each path, will either oscillate alternating or in synchronisation. We
consider a simplified model given by a continuous time Markov Chains with two
states. This was done for alternating and synchronised wells. The invariant
measures are derived for both cases and shown to be constant for the
synchronised case. A PDF for the escape time from an oscillatory potential is
studied. Methods of detecting stochastic resonance are presented, which are
linear response, signal-noise ratio, energy, out-of-phase measures, relative
entropy and entropy. A new statistical test called the conditional
Kolmogorov-Smirnov test is developed, which can be used to analyse stochastic
resonance. An explicit two dimensional potential is introduced, the critical
point structure derived and the dynamics, the invariant state and escape time
studied numerically. The six measures are unable to detect the stochastic
resonance in the case of synchronised saddles. The distribution of escape times
however not only shows a clear sign of stochastic resonance, but changing the
direction of the forcing from alternating to synchronised saddles an additional
resonance at double the forcing frequency starts to appear. The conditional KS
test reliably detects the stochastic resonance. This paper is mainly based on
the thesis "Stochastic Resonance for a Model with Two Pathways"
| math.PR math-ph math.MP physics.data-an physics.geo-ph | we consider stochastic resonance for a diffusion with drift given by a potential which has two metastable states and two pathways between them depending on the direction of the forcing the height of the two barriers one for each path will either oscillate alternating or in synchronisation we consider a simplified model given by a continuous time markov chains with two states this was done for alternating and synchronised wells the invariant measures are derived for both cases and shown to be constant for the synchronised case a pdf for the escape time from an oscillatory potential is studied methods of detecting stochastic resonance are presented which are linear response signalnoise ratio energy outofphase measures relative entropy and entropy a new statistical test called the conditional kolmogorovsmirnov test is developed which can be used to analyse stochastic resonance an explicit two dimensional potential is introduced the critical point structure derived and the dynamics the invariant state and escape time studied numerically the six measures are unable to detect the stochastic resonance in the case of synchronised saddles the distribution of escape times however not only shows a clear sign of stochastic resonance but changing the direction of the forcing from alternating to synchronised saddles an additional resonance at double the forcing frequency starts to appear the conditional ks test reliably detects the stochastic resonance this paper is mainly based on the thesis stochastic resonance for a model with two pathways | [['we', 'consider', 'stochastic', 'resonance', 'for', 'a', 'diffusion', 'with', 'drift', 'given', 'by', 'a', 'potential', 'which', 'has', 'two', 'metastable', 'states', 'and', 'two', 'pathways', 'between', 'them', 'depending', 'on', 'the', 'direction', 'of', 'the', 'forcing', 'the', 'height', 'of', 'the', 'two', 'barriers', 'one', 'for', 'each', 'path', 'will', 'either', 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1,803.00881 | Identifying Driver Behaviors using Trajectory Features for Vehicle
Navigation | We present a novel approach to automatically identify driver behaviors from
vehicle trajectories and use them for safe navigation of autonomous vehicles.
We propose a novel set of features that can be easily extracted from car
trajectories. We derive a data-driven mapping between these features and six
driver behaviors using an elaborate web-based user study. We also compute a
summarized score indicating a level of awareness that is needed while driving
next to other vehicles. We also incorporate our algorithm into a vehicle
navigation simulation system and demonstrate its benefits in terms of safer
real-time navigation, while driving next to aggressive or dangerous drivers.
| cs.RO | we present a novel approach to automatically identify driver behaviors from vehicle trajectories and use them for safe navigation of autonomous vehicles we propose a novel set of features that can be easily extracted from car trajectories we derive a datadriven mapping between these features and six driver behaviors using an elaborate webbased user study we also compute a summarized score indicating a level of awareness that is needed while driving next to other vehicles we also incorporate our algorithm into a vehicle navigation simulation system and demonstrate its benefits in terms of safer realtime navigation while driving next to aggressive or dangerous drivers | [['we', 'present', 'a', 'novel', 'approach', 'to', 'automatically', 'identify', 'driver', 'behaviors', 'from', 'vehicle', 'trajectories', 'and', 'use', 'them', 'for', 'safe', 'navigation', 'of', 'autonomous', 'vehicles', 'we', 'propose', 'a', 'novel', 'set', 'of', 'features', 'that', 'can', 'be', 'easily', 'extracted', 'from', 'car', 'trajectories', 'we', 'derive', 'a', 'datadriven', 'mapping', 'between', 'these', 'features', 'and', 'six', 'driver', 'behaviors', 'using', 'an', 'elaborate', 'webbased', 'user', 'study', 'we', 'also', 'compute', 'a', 'summarized', 'score', 'indicating', 'a', 'level', 'of', 'awareness', 'that', 'is', 'needed', 'while', 'driving', 'next', 'to', 'other', 'vehicles', 'we', 'also', 'incorporate', 'our', 'algorithm', 'into', 'a', 'vehicle', 'navigation', 'simulation', 'system', 'and', 'demonstrate', 'its', 'benefits', 'in', 'terms', 'of', 'safer', 'realtime', 'navigation', 'while', 'driving', 'next', 'to', 'aggressive', 'or', 'dangerous', 'drivers']] | [-0.11001363741413045, 0.05146005763708672, -0.10890165330895868, 0.06185724211144798, -0.12915277693313187, -0.18304288627741, 0.09958817241288041, 0.4269142894504162, -0.2528920546688963, -0.33832006974933815, 0.0849021101368215, -0.2676566856769988, -0.2211749857009496, 0.23349329986824438, -0.12899513899062115, 0.040313068997280564, 0.09139767079614103, 0.04012985688705857, -0.006139503321789492, -0.16516420017712966, 0.2642777381518569, -0.0075261872619963605, 0.2709862454906285, 0.03195008268812671, 0.16024061011222118, 0.008171668206789317, -0.018317345216368828, 0.005190982313181918, -0.07190254412195757, 0.1638232565713294, 0.3006100110203708, 0.21661917704085892, 0.3235615810307746, -0.44823245748949164, -0.19938610883456512, 0.07631091002482347, 0.1535542127228557, 0.09497860672462803, -0.059001680539670184, -0.38260051830170244, 0.0963524731765364, -0.24031700148211363, -0.1354490259456305, -0.1510644990779698, -0.004188414989934804, 0.05107348564174922, -0.2991404671562262, -0.05020228555804351, -0.012976968454985091, 0.06953320653365853, -0.06919052261218894, -0.011434558043569827, -0.005531028325024705, 0.26712571075432395, 0.031558859473219715, 0.013892158471068027, 0.20129836976187876, -0.16240089022232077, -0.15406481632425523, 0.4054440649029297, -0.018194358489171673, -0.1864263275056146, 0.2071555704684355, -0.04350053866857287, -0.14824367778895137, 0.07657878357881251, 0.2631539511315238, 0.11408326576034036, -0.23120408379047427, -0.07753437162598577, 0.02837838787728777, 0.16002879643257564, -0.008861332272772415, -0.020282292447518557, 0.21014936106243673, 0.22811978505566144, 0.15387527252404162, 0.12136327644503371, -0.11007955513000175, -0.06891795664733553, -0.26508743832640064, -0.15321506393733075, -0.08805691140094915, -0.01846619649306656, -0.048941043171558146, -0.08241366505479583, 0.4138987200477949, 0.29584584236055467, 0.17209392508420235, 0.08318402434591778, 0.3764837429894564, 0.09188338992862555, 0.06131586143657422, 0.11623061468932204, 0.16519076623640452, -0.09329569911978279, 0.13098385510518432, -0.1986009944230318, 0.08597044567292099, 0.06030597230598617] |
1,803.00882 | Temporal Graph Classes: A View Through Temporal Separators | We investigate the computational complexity of separating two distinct
vertices s and z by vertex deletion in a temporal graph. In a temporal graph,
the vertex set is fixed but the edges have (discrete) time labels. Since the
corresponding Temporal (s, z)-Separation problem is NP-hard, it is natural to
investigate whether relevant special cases exist that are computationally
tractable. To this end, we study restrictions of the underlying (static)
graph---there we observe polynomial-time solvability in the case of bounded
treewidth---as well as restrictions concerning the "temporal evolution" along
the time steps. Systematically studying partially novel concepts in this
direction, we identify sharp borders between tractable and intractable cases.
| cs.CC | we investigate the computational complexity of separating two distinct vertices s and z by vertex deletion in a temporal graph in a temporal graph the vertex set is fixed but the edges have discrete time labels since the corresponding temporal s zseparation problem is nphard it is natural to investigate whether relevant special cases exist that are computationally tractable to this end we study restrictions of the underlying static graphthere we observe polynomialtime solvability in the case of bounded treewidthas well as restrictions concerning the temporal evolution along the time steps systematically studying partially novel concepts in this direction we identify sharp borders between tractable and intractable cases | [['we', 'investigate', 'the', 'computational', 'complexity', 'of', 'separating', 'two', 'distinct', 'vertices', 's', 'and', 'z', 'by', 'vertex', 'deletion', 'in', 'a', 'temporal', 'graph', 'in', 'a', 'temporal', 'graph', 'the', 'vertex', 'set', 'is', 'fixed', 'but', 'the', 'edges', 'have', 'discrete', 'time', 'labels', 'since', 'the', 'corresponding', 'temporal', 's', 'zseparation', 'problem', 'is', 'nphard', 'it', 'is', 'natural', 'to', 'investigate', 'whether', 'relevant', 'special', 'cases', 'exist', 'that', 'are', 'computationally', 'tractable', 'to', 'this', 'end', 'we', 'study', 'restrictions', 'of', 'the', 'underlying', 'static', 'graphthere', 'we', 'observe', 'polynomialtime', 'solvability', 'in', 'the', 'case', 'of', 'bounded', 'treewidthas', 'well', 'as', 'restrictions', 'concerning', 'the', 'temporal', 'evolution', 'along', 'the', 'time', 'steps', 'systematically', 'studying', 'partially', 'novel', 'concepts', 'in', 'this', 'direction', 'we', 'identify', 'sharp', 'borders', 'between', 'tractable', 'and', 'intractable', 'cases']] | [-0.1429580970889046, 0.09527914233927039, -0.01660740225620213, 0.11420402404120458, -0.13441148753987536, -0.170216646772765, 0.0806874102003695, 0.43844589363960995, -0.3348425497700061, -0.28504224298965364, 0.09171008037491923, -0.2424033807346686, -0.15566221950464837, 0.08180416932418233, -0.08080912530865697, 0.053975797344797426, 0.07238437124927129, 0.04730113553149359, -0.04659891150049156, -0.2454179079432617, 0.30839836723392916, -0.031059105250807035, 0.2168871355996955, 0.06967587563697071, 0.06937208526013862, 0.02437868190574504, -0.04208721825409503, 0.07549580913283058, -0.17519160951744112, 0.04934189038994234, 0.2949113890528679, 0.15998101277897755, 0.29719500747021466, -0.4439056003554946, -0.17503969232950892, 0.1668158559904744, 0.12499417494095508, 0.10650211299902626, 0.025399126981695493, -0.23194708401736405, 0.10001259870561106, -0.06881220542958805, -0.08895880868214937, -0.02908567056680719, 0.08491818050055631, -0.022252024277778608, -0.21700308079653907, 0.048150022208158456, 0.0918537067066479, 0.03206547259663542, 0.01136183456650802, -0.08806161330791101, 0.0006258200765365647, 0.1333839530036563, 0.025312546424711833, 0.014511743211187422, 0.028180217051080297, -0.13827640149587145, -0.14892463976783413, 0.35821836181871947, 0.03132263129865307, -0.20316393825535972, 0.19267361699825242, -0.13379941391315134, -0.20931350822959627, 0.10644304844040778, 0.1637905829275648, 0.16445531378544512, -0.15775894914487643, 0.14950574438553305, -0.07954811647623068, 0.13463021154249352, 0.12436949371670683, 0.03923039901779876, 0.12927585230874164, 0.1696692597032303, 0.12246185113631544, 0.21252560713106677, -0.023577926029628587, -0.08605821390769311, -0.3050730123406365, -0.09617087874738943, -0.17422833589925651, -0.005742563669835883, -0.12551625906683814, -0.17948115600184317, 0.44359549659171277, 0.14648611092319092, 0.22257041434890457, 0.08890443038017977, 0.2598675993581613, 0.11845913708356896, -0.0015850512754349482, 0.12747594078647947, 0.14959004034421275, 0.10264093530630426, 0.04371757214622838, -0.21648870698520026, 0.10099835885644314, 0.08968545929792093] |
1,803.00883 | The Shape of Alerts: Detecting Malware Using Distributed Detectors by
Robustly Amplifying Transient Correlations | We introduce a new malware detector - Shape-GD - that aggregates per-machine
detectors into a robust global detector. Shape-GD is based on two insights: 1.
Structural: actions such as visiting a website (waterhole attack) by nodes
correlate well with malware spread, and create dynamic neighborhoods of nodes
that were exposed to the same attack vector. However, neighborhood sizes vary
unpredictably and require aggregating an unpredictable number of local
detectors' outputs into a global alert. 2. Statistical: feature vectors
corresponding to true and false positives of local detectors have markedly
different conditional distributions - i.e. their shapes differ. The shape of
neighborhoods can identify infected neighborhoods without having to estimate
neighborhood sizes - on 5 years of Symantec detectors' logs, Shape-GD reduces
false positives from ~1M down to ~110K and raises alerts 345 days (on average)
before commercial anti-virus products; in a waterhole attack simulated using
Yahoo web-service logs, Shape-GD detects infected machines when only ~100 of
~550K are compromised.
| cs.CR | we introduce a new malware detector shapegd that aggregates permachine detectors into a robust global detector shapegd is based on two insights 1 structural actions such as visiting a website waterhole attack by nodes correlate well with malware spread and create dynamic neighborhoods of nodes that were exposed to the same attack vector however neighborhood sizes vary unpredictably and require aggregating an unpredictable number of local detectors outputs into a global alert 2 statistical feature vectors corresponding to true and false positives of local detectors have markedly different conditional distributions ie their shapes differ the shape of neighborhoods can identify infected neighborhoods without having to estimate neighborhood sizes on 5 years of symantec detectors logs shapegd reduces false positives from 1m down to 110k and raises alerts 345 days on average before commercial antivirus products in a waterhole attack simulated using yahoo webservice logs shapegd detects infected machines when only 100 of 550k are compromised | [['we', 'introduce', 'a', 'new', 'malware', 'detector', 'shapegd', 'that', 'aggregates', 'permachine', 'detectors', 'into', 'a', 'robust', 'global', 'detector', 'shapegd', 'is', 'based', 'on', 'two', 'insights', '1', 'structural', 'actions', 'such', 'as', 'visiting', 'a', 'website', 'waterhole', 'attack', 'by', 'nodes', 'correlate', 'well', 'with', 'malware', 'spread', 'and', 'create', 'dynamic', 'neighborhoods', 'of', 'nodes', 'that', 'were', 'exposed', 'to', 'the', 'same', 'attack', 'vector', 'however', 'neighborhood', 'sizes', 'vary', 'unpredictably', 'and', 'require', 'aggregating', 'an', 'unpredictable', 'number', 'of', 'local', 'detectors', 'outputs', 'into', 'a', 'global', 'alert', '2', 'statistical', 'feature', 'vectors', 'corresponding', 'to', 'true', 'and', 'false', 'positives', 'of', 'local', 'detectors', 'have', 'markedly', 'different', 'conditional', 'distributions', 'ie', 'their', 'shapes', 'differ', 'the', 'shape', 'of', 'neighborhoods', 'can', 'identify', 'infected', 'neighborhoods', 'without', 'having', 'to', 'estimate', 'neighborhood', 'sizes', 'on', '5', 'years', 'of', 'symantec', 'detectors', 'logs', 'shapegd', 'reduces', 'false', 'positives', 'from', '1m', 'down', 'to', '110k', 'and', 'raises', 'alerts', '345', 'days', 'on', 'average', 'before', 'commercial', 'antivirus', 'products', 'in', 'a', 'waterhole', 'attack', 'simulated', 'using', 'yahoo', 'webservice', 'logs', 'shapegd', 'detects', 'infected', 'machines', 'when', 'only', '100', 'of', '550k', 'are', 'compromised']] | [-0.09786921919533803, 0.1083839053108785, -0.04462742429797448, 0.09253219159905829, -0.06680813029168113, -0.2257273042758536, 0.1280680332212679, 0.35155995280271574, -0.22130449399231902, -0.37697843232702827, 0.10968292212909869, -0.40537121414897903, -0.09335255878414178, 0.16653622619717592, -0.12244680034402278, 0.04399971254169941, 0.0836492240128498, 0.05586586450376818, -0.03188516889593654, -0.3209587962036171, 0.2757914843878919, 0.05310990101988277, 0.2763156333157132, -0.03475958969173652, 0.10928949756142232, 0.020802063542988993, -0.05726925805962134, 0.01879934434809031, -0.044008864528938915, 0.048001444847473214, 0.2887321179086763, 0.23209902158667964, 0.29247559078578506, -0.4156937312094435, -0.15489346734304432, 0.12198536090868255, 0.13731109415811876, 0.07347562898160709, -0.007655996212645644, -0.3641158239465327, 0.15660701265498514, -0.18335671045126453, -0.08905529745013242, -0.050367139505162355, 0.03264839917181, 0.07173386609647422, -0.21291982630688336, 0.015907588147468146, -0.012217501279777818, 0.08923526027750585, -0.031063186323210118, -0.1140402103203439, -0.034583064127382976, 0.18094125269825811, 0.03748215874206395, -0.014719999830929502, 0.26035781697400157, -0.124243576057075, -0.1186818051181974, 0.2703815607173789, 0.0021315674485278225, -0.10773889000767901, 0.19456111336457393, -0.06353417224956737, -0.11533440051885742, 0.20692435032387654, 0.2647285827886193, 0.08991149647627025, -0.15893551412746384, -0.03502362227037309, 0.008721708999045433, 0.22184220813815633, 0.13033668346958416, 0.01682317809111649, 0.18713222971606638, 0.1653068949934095, 0.1022874903159156, 0.08794224322322876, -0.15788638461005663, -0.010656095210522893, -0.19680532789638927, -0.11428241790722936, -0.15881907156698646, 0.06407888835984199, -0.10859166362375441, -0.17057980636074657, 0.3941351426645152, 0.18105406586771772, 0.2180658505688752, 0.09847706930050927, 0.2763421115487994, -0.04523812582370855, 0.12087787792777582, 0.10643803235293636, 0.18605132336397806, -0.04503566121922866, 0.07302812623581098, -0.0998024366991294, 0.16924401856282906, 0.0022661892817385735] |
1,803.00884 | Physical Layer Security for RF Satellite Channels in the Finite-length
Regime | Secure communications is becoming increasingly relevant in the development of
space technology. Well established cryptographic technology is already in place
and is expected to continue to be so. On the other hand, information
theoretical security emerges as a post-quantum versatile candidate to
complement overall security strength. In order to prove such potential,
performance analysis methods are needed that consider realistic legitimate and
eavesdropper system assumptions and non-asymptotic coding lengths. In this
paper we propose the design of secure radio frequency (RF) satellite links with
realistic system assumptions. Our contribution is three-fold. First, we propose
a wiretap channel model for the finite-length regime. The model includes an
stochastic wiretap encoding method using existing practical linear error
correcting codes and hash codes. Secrecy is provided with privacy
amplification, for which the finite-length secrecy metric is given that upper
bounds semantic secrecy. Second, we derive a novel RF (broadcast) satellite
wiretap channel model that parameterizes the stochastic degraded channel around
the legitimate channel, a necessary condition to enable secure communication.
Finally, we show the design of a secure satellite physical layer and
finite-length performance evaluation. In doing so, we define as sacrifice rate
the fixed fraction of the overall coding rate budget for reliability that needs
to be allocated to secrecy. Our methodology does not make use of channel side
information of the eavesdropper, only assumes worst case system assumptions. We
illustrate our proposed design method with numerical results using practical
error correcting codes in current standards of satellite communication.
| cs.IT math.IT | secure communications is becoming increasingly relevant in the development of space technology well established cryptographic technology is already in place and is expected to continue to be so on the other hand information theoretical security emerges as a postquantum versatile candidate to complement overall security strength in order to prove such potential performance analysis methods are needed that consider realistic legitimate and eavesdropper system assumptions and nonasymptotic coding lengths in this paper we propose the design of secure radio frequency rf satellite links with realistic system assumptions our contribution is threefold first we propose a wiretap channel model for the finitelength regime the model includes an stochastic wiretap encoding method using existing practical linear error correcting codes and hash codes secrecy is provided with privacy amplification for which the finitelength secrecy metric is given that upper bounds semantic secrecy second we derive a novel rf broadcast satellite wiretap channel model that parameterizes the stochastic degraded channel around the legitimate channel a necessary condition to enable secure communication finally we show the design of a secure satellite physical layer and finitelength performance evaluation in doing so we define as sacrifice rate the fixed fraction of the overall coding rate budget for reliability that needs to be allocated to secrecy our methodology does not make use of channel side information of the eavesdropper only assumes worst case system assumptions we illustrate our proposed design method with numerical results using practical error correcting codes in current standards of satellite communication | [['secure', 'communications', 'is', 'becoming', 'increasingly', 'relevant', 'in', 'the', 'development', 'of', 'space', 'technology', 'well', 'established', 'cryptographic', 'technology', 'is', 'already', 'in', 'place', 'and', 'is', 'expected', 'to', 'continue', 'to', 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1,803.00885 | Essentially No Barriers in Neural Network Energy Landscape | Training neural networks involves finding minima of a high-dimensional
non-convex loss function. Knowledge of the structure of this energy landscape
is sparse. Relaxing from linear interpolations, we construct continuous paths
between minima of recent neural network architectures on CIFAR10 and CIFAR100.
Surprisingly, the paths are essentially flat in both the training and test
landscapes. This implies that neural networks have enough capacity for
structural changes, or that these changes are small between minima. Also, each
minimum has at least one vanishing Hessian eigenvalue in addition to those
resulting from trivial invariance.
| stat.ML cs.AI cs.LG | training neural networks involves finding minima of a highdimensional nonconvex loss function knowledge of the structure of this energy landscape is sparse relaxing from linear interpolations we construct continuous paths between minima of recent neural network architectures on cifar10 and cifar100 surprisingly the paths are essentially flat in both the training and test landscapes this implies that neural networks have enough capacity for structural changes or that these changes are small between minima also each minimum has at least one vanishing hessian eigenvalue in addition to those resulting from trivial invariance | [['training', 'neural', 'networks', 'involves', 'finding', 'minima', 'of', 'a', 'highdimensional', 'nonconvex', 'loss', 'function', 'knowledge', 'of', 'the', 'structure', 'of', 'this', 'energy', 'landscape', 'is', 'sparse', 'relaxing', 'from', 'linear', 'interpolations', 'we', 'construct', 'continuous', 'paths', 'between', 'minima', 'of', 'recent', 'neural', 'network', 'architectures', 'on', 'cifar10', 'and', 'cifar100', 'surprisingly', 'the', 'paths', 'are', 'essentially', 'flat', 'in', 'both', 'the', 'training', 'and', 'test', 'landscapes', 'this', 'implies', 'that', 'neural', 'networks', 'have', 'enough', 'capacity', 'for', 'structural', 'changes', 'or', 'that', 'these', 'changes', 'are', 'small', 'between', 'minima', 'also', 'each', 'minimum', 'has', 'at', 'least', 'one', 'vanishing', 'hessian', 'eigenvalue', 'in', 'addition', 'to', 'those', 'resulting', 'from', 'trivial', 'invariance']] | [-0.12663250056269404, 0.07051441387611103, -0.06549447370631682, 0.09381349180482206, -0.06693031703336881, -0.18361864669330336, 0.04351553373591422, 0.43098453736024817, -0.30694818910170385, -0.31124333677951577, 0.06363464671872802, -0.28521979970161565, -0.2537342201212196, 0.13627419624078488, -0.09715584142563435, 0.10310453080807577, 0.15077099161079296, 0.0011903193349448533, -0.12855073613636606, -0.2932025540471691, 0.29602211479057305, 0.02751088074848547, 0.29804160455651174, 0.02357469095367488, 0.10674742252596624, -0.04851981427694497, 0.06373661099472544, -0.013737271090836398, -0.06706511851347047, 0.17337672867198797, 0.2671221108431672, 0.1454082824658234, 0.3218902652023954, -0.4423337307257148, -0.23730570307144752, 0.19868917305759348, 0.0851088299103859, 0.11127781232941773, 0.011764128169218836, -0.22516405197617773, 0.09740362624422862, -0.07341992536784855, -0.009158510110944837, -0.1377445628794913, -0.009831602101797586, 0.05516957266865155, -0.26720999321800526, 0.10329837835320926, 0.05505228172578327, 0.07896305174454228, -0.06942891528072594, -0.13901491850041425, -0.07903395333214776, 0.1261991150677204, 0.06044880792774915, 0.048119036076529016, 0.09072463969178088, -0.1699107888789213, -0.10534673700477559, 0.30123945788204015, -0.03492852356182514, -0.18828785481893426, 0.19611254424000016, -0.05805717895810421, -0.14490532240789425, 0.1449583112057503, 0.2129678350699308, 0.09368967504373618, -0.13293851000208223, 0.09493202037449189, -0.031338963903241104, 0.14072446956984944, 0.12373432129171195, 0.026001338862332996, 0.1728514958183271, 0.1851896860580482, 0.15024105667464108, 0.14844892345941985, -0.09929012472147715, -0.13412598970827166, -0.2745758672193675, -0.045756328881687514, -0.2592148799039833, 0.05284456401592591, -0.1483944971213542, -0.19387081881555226, 0.40598967708237876, 0.10543061697927232, 0.26542856548349936, 0.16527679635232295, 0.27365646996294857, 0.05279028103533354, 0.12287436293151516, 0.14385935386315316, 0.2229926612248624, 0.07928946098711874, 0.10036804075966907, -0.17686220178262915, 0.09096167314986975, 0.051337802721248875] |
1,803.00886 | Deep factorization for speech signal | Various informative factors mixed in speech signals, leading to great
difficulty when decoding any of the factors. An intuitive idea is to factorize
each speech frame into individual informative factors, though it turns out to
be highly difficult. Recently, we found that speaker traits, which were assumed
to be long-term distributional properties, are actually short-time patterns,
and can be learned by a carefully designed deep neural network (DNN). This
discovery motivated a cascade deep factorization (CDF) framework that will be
presented in this paper. The proposed framework infers speech factors in a
sequential way, where factors previously inferred are used as conditional
variables when inferring other factors. We will show that this approach can
effectively factorize speech signals, and using these factors, the original
speech spectrum can be recovered with a high accuracy. This factorization and
reconstruction approach provides potential values for many speech processing
tasks, e.g., speaker recognition and emotion recognition, as will be
demonstrated in the paper.
| eess.AS cs.CL cs.LG cs.SD | various informative factors mixed in speech signals leading to great difficulty when decoding any of the factors an intuitive idea is to factorize each speech frame into individual informative factors though it turns out to be highly difficult recently we found that speaker traits which were assumed to be longterm distributional properties are actually shorttime patterns and can be learned by a carefully designed deep neural network dnn this discovery motivated a cascade deep factorization cdf framework that will be presented in this paper the proposed framework infers speech factors in a sequential way where factors previously inferred are used as conditional variables when inferring other factors we will show that this approach can effectively factorize speech signals and using these factors the original speech spectrum can be recovered with a high accuracy this factorization and reconstruction approach provides potential values for many speech processing tasks eg speaker recognition and emotion recognition as will be demonstrated in the paper | [['various', 'informative', 'factors', 'mixed', 'in', 'speech', 'signals', 'leading', 'to', 'great', 'difficulty', 'when', 'decoding', 'any', 'of', 'the', 'factors', 'an', 'intuitive', 'idea', 'is', 'to', 'factorize', 'each', 'speech', 'frame', 'into', 'individual', 'informative', 'factors', 'though', 'it', 'turns', 'out', 'to', 'be', 'highly', 'difficult', 'recently', 'we', 'found', 'that', 'speaker', 'traits', 'which', 'were', 'assumed', 'to', 'be', 'longterm', 'distributional', 'properties', 'are', 'actually', 'shorttime', 'patterns', 'and', 'can', 'be', 'learned', 'by', 'a', 'carefully', 'designed', 'deep', 'neural', 'network', 'dnn', 'this', 'discovery', 'motivated', 'a', 'cascade', 'deep', 'factorization', 'cdf', 'framework', 'that', 'will', 'be', 'presented', 'in', 'this', 'paper', 'the', 'proposed', 'framework', 'infers', 'speech', 'factors', 'in', 'a', 'sequential', 'way', 'where', 'factors', 'previously', 'inferred', 'are', 'used', 'as', 'conditional', 'variables', 'when', 'inferring', 'other', 'factors', 'we', 'will', 'show', 'that', 'this', 'approach', 'can', 'effectively', 'factorize', 'speech', 'signals', 'and', 'using', 'these', 'factors', 'the', 'original', 'speech', 'spectrum', 'can', 'be', 'recovered', 'with', 'a', 'high', 'accuracy', 'this', 'factorization', 'and', 'reconstruction', 'approach', 'provides', 'potential', 'values', 'for', 'many', 'speech', 'processing', 'tasks', 'eg', 'speaker', 'recognition', 'and', 'emotion', 'recognition', 'as', 'will', 'be', 'demonstrated', 'in', 'the', 'paper']] | [-0.032621410413229336, 0.07755229235914135, -0.1283378281448265, 0.08520070548424111, -0.12871703069727375, -0.17960232895734551, -0.009624048067350526, 0.455852335318923, -0.2993219546481686, -0.29460401589903135, 0.06926274455940269, -0.2377897448744039, -0.24417796790154664, 0.18046446282522016, -0.1272377220902053, 0.09850866018009954, 0.12245453952419215, 0.06523859312863282, -0.04161068157776777, -0.2857216971533947, 0.2607919751848065, 0.05725416940828762, 0.3256186972190655, 0.013486628531941078, 0.09530956078827404, -0.04122342864408761, -0.03528069337220781, -0.02785478373652664, -0.010851439966411263, 0.14419680305774202, 0.3998316500889454, 0.22247420597101017, 0.284605628725891, -0.385605695077552, -0.2338338924027136, 0.11737031945103923, 0.19349732101664646, 0.11707429404724858, -0.0074054413568814224, -0.38504184243626566, 0.11598449653166833, -0.17580653276036745, 0.01975199653007055, -0.16718513072825641, -0.010090683340885729, -0.039816168425370405, -0.32373066312697885, 0.040688089222453955, 0.09955158565301776, 0.03143804880011382, -0.016867537528815323, -0.142741067209076, 0.05878986909022004, 0.19134990570945196, 0.09787090030006783, 0.048631044107624005, 0.1678546619624977, -0.17573235048209582, -0.11570448420192085, 0.3699634164021169, -0.05824035857862693, -0.25196030943174175, 0.1782503723204956, -0.06852769939105888, -0.17780800569515512, 0.10094482069776493, 0.2409167606383562, 0.06687125527122754, -0.2626555516926715, -0.011275792536882203, -0.015877016190345743, 0.22946666133057023, 0.08030171772031663, 0.020911246898390773, 0.19094785113684223, 0.1753658081954726, -0.03998292890910364, 0.13574247252534338, -0.09336006271494728, 0.00011950184198670417, -0.20379328621923337, -0.09003143270650832, -0.16115968819684204, 0.0038864807981365134, -0.08334268784441885, -0.0972954432462465, 0.3892964225484984, 0.18597430135738455, 0.20803714963554773, 0.07127814382569478, 0.31807334617998617, 0.13444850765133523, 0.1134816116236777, 0.06433871553027593, 0.19914766951206392, 0.02035723433613121, 0.08172132576809926, -0.14296183324237383, 0.13544458634975665, 0.021620126405976853] |
1,803.00887 | Power series solution of the inhomogeneous exclusion process | We develop a power series method for the nonequilibrium steady state of the
inhomogeneous one-dimensional totally asymmetric simple exclusion process
(TASEP) in contact with two particle reservoirs and with site-dependent hopping
rates in the bulk. The power series is performed in the entrance or exit rates
governing particle exchange with the reservoirs, and the corresponding particle
current is computed analytically up to the cubic term in the entry or exit
rate, respectively. We also show how to compute higher-order terms using
combinatorial objects known as Young tableaux. Our results address the long
outstanding problem of finding the exact nonequilibrium steady state of the
inhomogeneous TASEP. The findings are particularly relevant to the modelling of
mRNA translation in which the rate of translation initiation, corresponding to
the entrance rate in the TASEP, is typically small.
| cond-mat.stat-mech math-ph math.MP | we develop a power series method for the nonequilibrium steady state of the inhomogeneous onedimensional totally asymmetric simple exclusion process tasep in contact with two particle reservoirs and with sitedependent hopping rates in the bulk the power series is performed in the entrance or exit rates governing particle exchange with the reservoirs and the corresponding particle current is computed analytically up to the cubic term in the entry or exit rate respectively we also show how to compute higherorder terms using combinatorial objects known as young tableaux our results address the long outstanding problem of finding the exact nonequilibrium steady state of the inhomogeneous tasep the findings are particularly relevant to the modelling of mrna translation in which the rate of translation initiation corresponding to the entrance rate in the tasep is typically small | [['we', 'develop', 'a', 'power', 'series', 'method', 'for', 'the', 'nonequilibrium', 'steady', 'state', 'of', 'the', 'inhomogeneous', 'onedimensional', 'totally', 'asymmetric', 'simple', 'exclusion', 'process', 'tasep', 'in', 'contact', 'with', 'two', 'particle', 'reservoirs', 'and', 'with', 'sitedependent', 'hopping', 'rates', 'in', 'the', 'bulk', 'the', 'power', 'series', 'is', 'performed', 'in', 'the', 'entrance', 'or', 'exit', 'rates', 'governing', 'particle', 'exchange', 'with', 'the', 'reservoirs', 'and', 'the', 'corresponding', 'particle', 'current', 'is', 'computed', 'analytically', 'up', 'to', 'the', 'cubic', 'term', 'in', 'the', 'entry', 'or', 'exit', 'rate', 'respectively', 'we', 'also', 'show', 'how', 'to', 'compute', 'higherorder', 'terms', 'using', 'combinatorial', 'objects', 'known', 'as', 'young', 'tableaux', 'our', 'results', 'address', 'the', 'long', 'outstanding', 'problem', 'of', 'finding', 'the', 'exact', 'nonequilibrium', 'steady', 'state', 'of', 'the', 'inhomogeneous', 'tasep', 'the', 'findings', 'are', 'particularly', 'relevant', 'to', 'the', 'modelling', 'of', 'mrna', 'translation', 'in', 'which', 'the', 'rate', 'of', 'translation', 'initiation', 'corresponding', 'to', 'the', 'entrance', 'rate', 'in', 'the', 'tasep', 'is', 'typically', 'small']] | [-0.10540588933335089, 0.16294112654382, -0.04912956973621205, 0.05440115008237192, -0.013127428854801762, -0.15325859972665004, 0.04662911905933158, 0.3605778986073808, -0.30987590021773503, -0.25280438690905027, 0.10054104226648307, -0.23491537736975518, -0.08170448763316858, 0.18898866228488231, 0.0008915986782356874, 0.039760485722391464, 0.06236649765077033, 0.06035296733616226, -0.015355787720344873, -0.2497482616836622, 0.28281544384358925, 0.06678198395755643, 0.2908783402335621, 0.07129519780986567, 0.10543407328816047, -0.003936445274487582, -0.022994111121785062, -0.038144805840452885, -0.18046687820182641, 0.09064866439352479, 0.21753818223534374, 0.02219050155549463, 0.20726589004356247, -0.4448317052629679, -0.1549964001777568, 0.10082170869129946, 0.16165018663567893, 0.1523905092765188, -0.045379321549588174, -0.263562845778582, 0.017252281235197363, -0.18147141705336634, -0.1584500696079166, -0.012583109824709705, 0.03407518768252166, 0.07110605145146055, -0.2615327576328236, 0.1419819707001834, 0.06761184588848759, 0.011739542026684355, -0.06940375087059923, -0.08642849763299325, -0.007297600214647602, 0.15409874517832006, 0.06770105072572014, -0.01784988222886989, 0.15898276785207885, -0.15493467083173013, -0.1262506929976838, 0.3562042772936732, -0.08461282661170769, -0.22823100155497442, 0.20928083136399736, -0.16284585442852728, -0.09189704465401817, 0.16485404064739817, 0.15932896052862502, 0.09867742830720633, -0.19334417213199298, 0.057524682543025035, -0.017922913172601987, 0.09315292529545467, 0.07255817030612101, -0.05061609818210909, 0.2124608309501643, 0.16282315716258625, 0.035088205654452094, 0.1962150501090783, -0.10588188853059242, -0.208774766836093, -0.28854546608375525, -0.13093805282963997, -0.1674556289330375, 0.06457890708993341, -0.10577670200150147, -0.17113416860185898, 0.38639657145866485, 0.11337810421862932, 0.20075640036488202, 0.08561117274933425, 0.24730177399521666, 0.16423742586413204, -0.005137246516498445, 0.05890790086739989, 0.1641990354896259, 0.1361897636496865, 0.10627178017070878, -0.28489577607251704, 0.08955224263377544, 0.06981131282231923] |
1,803.00888 | Tetragonal-orthorhombic phase coexistence under magnetic fields in
BaFe$_2$As$_2$ and Sr(Fe$_{1-x}$Co$_{x}$)$_2$As$_2$: evidence of magnetically
driven structural transition | Synchrotron x-ray diffraction experiments were performed on BaFe$_2$As$_2$
and Sr(Fe$_{1-x}$Co$_{x}$)$_2$As$_2$ single crystals as a function of
temperature and applied magnetic field along the tetragonal $[1 \bar{1} 0]$
direction, complemented by electrical resistivity and specific heat
experiments. For a BaFe$_2$As$_2$ crystal with spin-density-wave
antiferromagnetic ordering temperature $T_{AF}=132.5$ K and onset of the
orthorhombic phase at $T_{o}=137$ K, the magnetic field favors the growth of
tetragonal domains that compete with orthorhombic ones for $T \gtrsim T_{AF}$.
For a Sr(Fe$_{1-x}$Co$_{x}$)$_2$As$_2$ crystal with more separated transitions
($T_{AF} = 132$ K and $T_{o} = 152$ K), the crystal structure also shows
significant field-dependence in a narrow temperature interval close to
$T_{AF}$. These results favor magnetism as the driver of the structural and
nematic transitions in 122 Fe pnictides.
| cond-mat.supr-con cond-mat.str-el | synchrotron xray diffraction experiments were performed on bafe_2as_2 and srfe_1xco_x_2as_2 single crystals as a function of temperature and applied magnetic field along the tetragonal 1 bar1 0 direction complemented by electrical resistivity and specific heat experiments for a bafe_2as_2 crystal with spindensitywave antiferromagnetic ordering temperature t_af1325 k and onset of the orthorhombic phase at t_o137 k the magnetic field favors the growth of tetragonal domains that compete with orthorhombic ones for t gtrsim t_af for a srfe_1xco_x_2as_2 crystal with more separated transitions t_af 132 k and t_o 152 k the crystal structure also shows significant fielddependence in a narrow temperature interval close to t_af these results favor magnetism as the driver of the structural and nematic transitions in 122 fe pnictides | [['synchrotron', 'xray', 'diffraction', 'experiments', 'were', 'performed', 'on', 'bafe_2as_2', 'and', 'srfe_1xco_x_2as_2', 'single', 'crystals', 'as', 'a', 'function', 'of', 'temperature', 'and', 'applied', 'magnetic', 'field', 'along', 'the', 'tetragonal', '1', 'bar1', '0', 'direction', 'complemented', 'by', 'electrical', 'resistivity', 'and', 'specific', 'heat', 'experiments', 'for', 'a', 'bafe_2as_2', 'crystal', 'with', 'spindensitywave', 'antiferromagnetic', 'ordering', 'temperature', 't_af1325', 'k', 'and', 'onset', 'of', 'the', 'orthorhombic', 'phase', 'at', 't_o137', 'k', 'the', 'magnetic', 'field', 'favors', 'the', 'growth', 'of', 'tetragonal', 'domains', 'that', 'compete', 'with', 'orthorhombic', 'ones', 'for', 't', 'gtrsim', 't_af', 'for', 'a', 'srfe_1xco_x_2as_2', 'crystal', 'with', 'more', 'separated', 'transitions', 't_af', '132', 'k', 'and', 't_o', '152', 'k', 'the', 'crystal', 'structure', 'also', 'shows', 'significant', 'fielddependence', 'in', 'a', 'narrow', 'temperature', 'interval', 'close', 'to', 't_af', 'these', 'results', 'favor', 'magnetism', 'as', 'the', 'driver', 'of', 'the', 'structural', 'and', 'nematic', 'transitions', 'in', '122', 'fe', 'pnictides']] | [-0.16757524673956126, 0.2725299181721854, 0.012656373273142996, -0.03610693639097344, -0.08677622463804584, -0.14628057562973068, 0.1552017016117187, 0.4579043271873115, -0.2378722793508728, -0.2715873833620498, 0.0005188917118993376, -0.3750394522955939, -0.02956986940708481, 0.1612251568937815, 0.14604963558655576, -0.007854423900487042, -0.0793144168848751, 0.0002618250163162456, -0.1539406387248867, -0.1993070270752675, 0.2277810330475297, 0.01295205218200924, 0.30038406549632046, 0.03032926413692346, 0.020938498703684628, -0.0259837883665469, 0.17835116382891766, 0.05836457516528478, -0.20427439923960952, -0.034726804316028204, 0.32016685734964717, -0.11324682214665187, 0.1486702292089785, -0.36482056991017164, -0.23647224751228718, -0.024567585767192, 0.09230758016537718, 0.04546566228499683, -0.07469663946650151, -0.25114173774349213, 0.07859719817784905, -0.06117041693499484, -0.08913606883881676, -0.11317092396647614, -0.05052025624386528, -0.016680458104218348, -0.25922107459251387, 0.16505150656786183, 0.09488979313315182, 0.20261142243232047, -0.17119372560165258, -0.1934702163346957, -0.08948600903826476, -0.03518421889259284, 0.06723038558730808, 0.17402297737734282, 0.15967877929200644, -0.053058953420145945, -0.11521532659677147, 0.36915963736339275, -0.04244033744492169, 0.07001684039846814, 0.1276613309462907, -0.2545409246754809, -0.11828259885029382, 0.2573389103597014, 0.06172220324246338, 0.09093829619447787, -0.07645446460101236, 0.056414822594873106, 0.025280078857385813, 0.24512413198895314, 0.07711200450132248, 0.013592222034383346, 0.2245755208726628, 0.17438652922943265, 0.01609415073239268, 0.1418963973234337, -0.1529623179534106, -0.0005388033200650155, -0.20284551260758096, -0.16660010980289555, -0.1600045427394358, 0.034926090924120155, -0.14182035370554752, -0.17317213784774155, 0.34039713873942734, 0.09297230445886004, 0.2037339142891539, -0.10206313616744861, 0.1600167021975156, 0.018025833790527045, 0.06608396484281774, 0.05257913904624576, 0.1971696524865174, 0.22013957508323134, 0.19518803636457363, -0.32094905986118, 0.0762712387172427, -0.02891272630448602] |
1,803.00889 | Scalable Bayesian uncertainty quantification in imaging inverse problems
via convex optimization | We propose a Bayesian uncertainty quantification method for large-scale
imaging inverse problems. Our method applies to all Bayesian models that are
log-concave, where maximum-a-posteriori (MAP) estimation is a convex
optimization problem. The method is a framework to analyse the confidence in
specific structures observed in MAP estimates (e.g., lesions in medical
imaging, celestial sources in astronomical imaging), to enable using them as
evidence to inform decisions and conclusions. Precisely, following Bayesian
decision theory, we seek to assert the structures under scrutiny by performing
a Bayesian hypothesis test that proceeds as follows: firstly, it postulates
that the structures are not present in the true image, and then seeks to use
the data and prior knowledge to reject this null hypothesis with high
probability. Computing such tests for imaging problems is generally very
difficult because of the high dimensionality involved. A main feature of this
work is to leverage probability concentration phenomena and the underlying
convex geometry to formulate the Bayesian hypothesis test as a convex problem,
that we then efficiently solve by using scalable optimization algorithms. This
allows scaling to high-resolution and high-sensitivity imaging problems that
are computationally unaffordable for other Bayesian computation approaches. We
illustrate our methodology, dubbed BUQO (Bayesian Uncertainty Quantification by
Optimization), on a range of challenging Fourier imaging problems arising in
astronomy and medicine.
| stat.ME astro-ph.IM | we propose a bayesian uncertainty quantification method for largescale imaging inverse problems our method applies to all bayesian models that are logconcave where maximumaposteriori map estimation is a convex optimization problem the method is a framework to analyse the confidence in specific structures observed in map estimates eg lesions in medical imaging celestial sources in astronomical imaging to enable using them as evidence to inform decisions and conclusions precisely following bayesian decision theory we seek to assert the structures under scrutiny by performing a bayesian hypothesis test that proceeds as follows firstly it postulates that the structures are not present in the true image and then seeks to use the data and prior knowledge to reject this null hypothesis with high probability computing such tests for imaging problems is generally very difficult because of the high dimensionality involved a main feature of this work is to leverage probability concentration phenomena and the underlying convex geometry to formulate the bayesian hypothesis test as a convex problem that we then efficiently solve by using scalable optimization algorithms this allows scaling to highresolution and highsensitivity imaging problems that are computationally unaffordable for other bayesian computation approaches we illustrate our methodology dubbed buqo bayesian uncertainty quantification by optimization on a range of challenging fourier imaging problems arising in astronomy and medicine | [['we', 'propose', 'a', 'bayesian', 'uncertainty', 'quantification', 'method', 'for', 'largescale', 'imaging', 'inverse', 'problems', 'our', 'method', 'applies', 'to', 'all', 'bayesian', 'models', 'that', 'are', 'logconcave', 'where', 'maximumaposteriori', 'map', 'estimation', 'is', 'a', 'convex', 'optimization', 'problem', 'the', 'method', 'is', 'a', 'framework', 'to', 'analyse', 'the', 'confidence', 'in', 'specific', 'structures', 'observed', 'in', 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1,803.0089 | The no information at a distance principle and local mathematics: some
effects on physics and geometry | Local mathematics assumes the existence of number structures of different
types, vector spaces, etc. localized at each space time point. Relations
between number structures at different locations are based on two aspects:
distinction between two so far conflated concepts, number and number value and
the "No information at a distance" principle. This principle forbids the choice
of the value of a number at one location to determine the value of the same
number at another location. Value changing connections, related to a real
valued field, $g,$ move numbers between structures at different locations. The
effect of the $g$ field, or its exponential equivalent, $g(y)=e^{\alpha(y)},$
on numbers extends to other mathematical structures, vector spaces, etc.
The presence of $\alpha$ affects theoretical descriptions of quantities in
physics and geometry. Two examples are described, the effect on the Dirac
Lagrangian in gauge theory, and the effect on path lengths and distances in
geometry. The gradient field of $\alpha$, $\vec{A},$ appears in the Lagrangian
as a spin $0$, real scalar field that couples to the fermion field. Any value
for the mass of $\vec{A}$ is possible. The lack of direct experimental evidence
for the presence of the $g$ or $\alpha$ field means that the field must be
essentially constant within a local region of the cosmological universe.
Outside the local region there are no restrictions on the field. Possible
physical candidates, (inflaton, dark matter, dark energy) for $\alpha$ are
noted.
| quant-ph gr-qc hep-th math-ph math.MP | local mathematics assumes the existence of number structures of different types vector spaces etc localized at each space time point relations between number structures at different locations are based on two aspects distinction between two so far conflated concepts number and number value and the no information at a distance principle this principle forbids the choice of the value of a number at one location to determine the value of the same number at another location value changing connections related to a real valued field g move numbers between structures at different locations the effect of the g field or its exponential equivalent gyealphay on numbers extends to other mathematical structures vector spaces etc the presence of alpha affects theoretical descriptions of quantities in physics and geometry two examples are described the effect on the dirac lagrangian in gauge theory and the effect on path lengths and distances in geometry the gradient field of alpha veca appears in the lagrangian as a spin 0 real scalar field that couples to the fermion field any value for the mass of veca is possible the lack of direct experimental evidence for the presence of the g or alpha field means that the field must be essentially constant within a local region of the cosmological universe outside the local region there are no restrictions on the field possible physical candidates inflaton dark matter dark energy for alpha are noted | [['local', 'mathematics', 'assumes', 'the', 'existence', 'of', 'number', 'structures', 'of', 'different', 'types', 'vector', 'spaces', 'etc', 'localized', 'at', 'each', 'space', 'time', 'point', 'relations', 'between', 'number', 'structures', 'at', 'different', 'locations', 'are', 'based', 'on', 'two', 'aspects', 'distinction', 'between', 'two', 'so', 'far', 'conflated', 'concepts', 'number', 'and', 'number', 'value', 'and', 'the', 'no', 'information', 'at', 'a', 'distance', 'principle', 'this', 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1,803.00891 | Monocular Depth Estimation using Multi-Scale Continuous CRFs as
Sequential Deep Networks | Depth cues have been proved very useful in various computer vision and
robotic tasks. This paper addresses the problem of monocular depth estimation
from a single still image. Inspired by the effectiveness of recent works on
multi-scale convolutional neural networks (CNN), we propose a deep model which
fuses complementary information derived from multiple CNN side outputs.
Different from previous methods using concatenation or weighted average
schemes, the integration is obtained by means of continuous Conditional Random
Fields (CRFs). In particular, we propose two different variations, one based on
a cascade of multiple CRFs, the other on a unified graphical model. By
designing a novel CNN implementation of mean-field updates for continuous CRFs,
we show that both proposed models can be regarded as sequential deep networks
and that training can be performed end-to-end. Through an extensive
experimental evaluation, we demonstrate the effectiveness of the proposed
approach and establish new state of the art results for the monocular depth
estimation task on three publicly available datasets, i.e. NYUD-V2, Make3D and
KITTI.
| cs.CV | depth cues have been proved very useful in various computer vision and robotic tasks this paper addresses the problem of monocular depth estimation from a single still image inspired by the effectiveness of recent works on multiscale convolutional neural networks cnn we propose a deep model which fuses complementary information derived from multiple cnn side outputs different from previous methods using concatenation or weighted average schemes the integration is obtained by means of continuous conditional random fields crfs in particular we propose two different variations one based on a cascade of multiple crfs the other on a unified graphical model by designing a novel cnn implementation of meanfield updates for continuous crfs we show that both proposed models can be regarded as sequential deep networks and that training can be performed endtoend through an extensive experimental evaluation we demonstrate the effectiveness of the proposed approach and establish new state of the art results for the monocular depth estimation task on three publicly available datasets ie nyudv2 make3d and kitti | [['depth', 'cues', 'have', 'been', 'proved', 'very', 'useful', 'in', 'various', 'computer', 'vision', 'and', 'robotic', 'tasks', 'this', 'paper', 'addresses', 'the', 'problem', 'of', 'monocular', 'depth', 'estimation', 'from', 'a', 'single', 'still', 'image', 'inspired', 'by', 'the', 'effectiveness', 'of', 'recent', 'works', 'on', 'multiscale', 'convolutional', 'neural', 'networks', 'cnn', 'we', 'propose', 'a', 'deep', 'model', 'which', 'fuses', 'complementary', 'information', 'derived', 'from', 'multiple', 'cnn', 'side', 'outputs', 'different', 'from', 'previous', 'methods', 'using', 'concatenation', 'or', 'weighted', 'average', 'schemes', 'the', 'integration', 'is', 'obtained', 'by', 'means', 'of', 'continuous', 'conditional', 'random', 'fields', 'crfs', 'in', 'particular', 'we', 'propose', 'two', 'different', 'variations', 'one', 'based', 'on', 'a', 'cascade', 'of', 'multiple', 'crfs', 'the', 'other', 'on', 'a', 'unified', 'graphical', 'model', 'by', 'designing', 'a', 'novel', 'cnn', 'implementation', 'of', 'meanfield', 'updates', 'for', 'continuous', 'crfs', 'we', 'show', 'that', 'both', 'proposed', 'models', 'can', 'be', 'regarded', 'as', 'sequential', 'deep', 'networks', 'and', 'that', 'training', 'can', 'be', 'performed', 'endtoend', 'through', 'an', 'extensive', 'experimental', 'evaluation', 'we', 'demonstrate', 'the', 'effectiveness', 'of', 'the', 'proposed', 'approach', 'and', 'establish', 'new', 'state', 'of', 'the', 'art', 'results', 'for', 'the', 'monocular', 'depth', 'estimation', 'task', 'on', 'three', 'publicly', 'available', 'datasets', 'ie', 'nyudv2', 'make3d', 'and', 'kitti']] | [-0.051171479275010624, 0.0018407690660654672, -0.06619863293461568, 0.028597739647754285, -0.08058889741739313, -0.18344970328248375, -0.007028996350640464, 0.4744076476096225, -0.27160242077007274, -0.3147990126658738, 0.07980601035593045, -0.21625644817242962, -0.22594648669749365, 0.22923153481782366, -0.13251920946468682, 0.14552998247407598, 0.17593955611341672, 0.028071105108904988, -0.07007621346281279, -0.28558968656734957, 0.29898204122251176, 0.001887106578783325, 0.3708591670756774, 0.02453879417421726, 0.17443585321692937, 0.01784049437351018, -0.05730362273284273, 0.015799024054152372, -0.0657898561314152, 0.1886308883801075, 0.2673298206104604, 0.19652909816093964, 0.32554977287995746, -0.4628413672167698, -0.29907635347256384, 0.049947661270536264, 0.16019860394547017, 0.10840566295136067, -0.04194453103294232, -0.396850922999121, 0.06559832186090964, -0.17764047652306642, 0.07674156992349046, -0.1342049235789403, -0.08134519401111105, 0.0029871096288972706, -0.31653241020180944, 0.0321738779029572, 0.03993287515728707, 0.0955756163092319, -0.05356902047473739, -0.16131884768638297, 0.03804898134380381, 0.16762436779364723, -0.008266601102569928, 0.05794244967938329, 0.11657660098239427, -0.2020367408824324, -0.1894860960179444, 0.31010735358425673, -0.0921369730618909, -0.20525151571622613, 0.20290391343045075, 0.029455581616566202, -0.17706758778586368, 0.0732595054680735, 0.22745308809233114, 0.15094780276623174, -0.16898696297592314, 0.013377336747999463, -0.0902677180339202, 0.1623375800633858, 0.022703302555756102, -0.01866995352292819, 0.18109751832203047, 0.27041650865882766, 0.007822734159114357, 0.16269798781316364, -0.17080217063944986, -0.08517810475692902, -0.21391485688381984, -0.06260879206768727, -0.22694760491553495, -0.04970794355706174, -0.10432344513219236, -0.12464453962207193, 0.399056332196886, 0.2631610524868207, 0.2151422025057206, 0.13812843260641855, 0.4036644952421314, 0.026039508643240485, 0.09083411111441281, 0.08431625500915176, 0.18262643456326785, 0.029069653815540247, 0.1099466062757082, -0.13572175075512202, 0.08999766536214053, 0.10516372380434458] |
1,803.00892 | A Framework for Blockchain-Based Applications | Blockchains have recently generated explosive interest from both academia and
industry, with many proposed applications. But descriptions of many these
proposals are more visionary projections than realizable proposals, and even
basic definitions are often missing. We define "blockchain" and "blockchain
network", and then discuss two very different, well known classes of blockchain
networks: cryptocurrencies and Git repositories. We identify common primitive
elements of both and use them to construct a framework for explicitly
articulating what characterizes blockchain networks. The framework consists of
a set of questions that every blockchain initiative should address at the very
outset. It is intended to help one decide whether or not blockchain is an
appropriate approach to a particular application, and if it is, to assist in
its initial design stage.
| cs.CY cs.CR | blockchains have recently generated explosive interest from both academia and industry with many proposed applications but descriptions of many these proposals are more visionary projections than realizable proposals and even basic definitions are often missing we define blockchain and blockchain network and then discuss two very different well known classes of blockchain networks cryptocurrencies and git repositories we identify common primitive elements of both and use them to construct a framework for explicitly articulating what characterizes blockchain networks the framework consists of a set of questions that every blockchain initiative should address at the very outset it is intended to help one decide whether or not blockchain is an appropriate approach to a particular application and if it is to assist in its initial design stage | [['blockchains', 'have', 'recently', 'generated', 'explosive', 'interest', 'from', 'both', 'academia', 'and', 'industry', 'with', 'many', 'proposed', 'applications', 'but', 'descriptions', 'of', 'many', 'these', 'proposals', 'are', 'more', 'visionary', 'projections', 'than', 'realizable', 'proposals', 'and', 'even', 'basic', 'definitions', 'are', 'often', 'missing', 'we', 'define', 'blockchain', 'and', 'blockchain', 'network', 'and', 'then', 'discuss', 'two', 'very', 'different', 'well', 'known', 'classes', 'of', 'blockchain', 'networks', 'cryptocurrencies', 'and', 'git', 'repositories', 'we', 'identify', 'common', 'primitive', 'elements', 'of', 'both', 'and', 'use', 'them', 'to', 'construct', 'a', 'framework', 'for', 'explicitly', 'articulating', 'what', 'characterizes', 'blockchain', 'networks', 'the', 'framework', 'consists', 'of', 'a', 'set', 'of', 'questions', 'that', 'every', 'blockchain', 'initiative', 'should', 'address', 'at', 'the', 'very', 'outset', 'it', 'is', 'intended', 'to', 'help', 'one', 'decide', 'whether', 'or', 'not', 'blockchain', 'is', 'an', 'appropriate', 'approach', 'to', 'a', 'particular', 'application', 'and', 'if', 'it', 'is', 'to', 'assist', 'in', 'its', 'initial', 'design', 'stage']] | [-0.0918634969164573, 0.044473825049487735, -0.06273311761296577, 0.11068219141129197, -0.12428149723610471, -0.2152014809424087, 0.022529676161144697, 0.4051981328557881, -0.30888417697260306, -0.2968742402694521, 0.1637387305326272, -0.28812702995769324, -0.16678272987649376, 0.18915751593978336, -0.12619714825488035, 0.047627165576808954, 0.06763841614260205, 0.03639875114747575, -0.006828796136976471, -0.318919740352107, 0.3351880048904272, 0.0010525566185750658, 0.32372462460660334, 0.06258977065971565, 0.07237180767004334, -0.0489531043308994, -0.04115858967489903, -0.010966983624726593, -0.09468684967225907, 0.16857823746330622, 0.34792585259983466, 0.2546238102543626, 0.3544486617375653, -0.43335106574430404, -0.14370443356696458, 0.12487664513589282, 0.10971290961883608, 0.11471511415156928, -0.04122860490823523, -0.283393514612394, 0.13274801294929126, -0.2500686253761015, -0.107678333966298, -0.12963426444265577, 0.028697936529559747, -0.0005099690253181117, -0.2302776917497376, -0.0932235003833378, 0.034014080023755215, 0.029759023737694536, -0.0011960784119317337, -0.08191874707930737, -0.017863176153263167, 0.1673623360342361, 0.04968487003648151, 0.010803023648876992, 0.13304083464280997, -0.11479636619517965, -0.15571532370790558, 0.39999703876674175, 0.0658702630606722, -0.14554631125019302, 0.21110703876160736, -0.04068498733630847, -0.1862178507209238, 0.031103821079586706, 0.1634996094933105, 0.07157770869514299, -0.20678453058713958, 0.03607357285652561, -0.0328919353362705, 0.14867017379710598, 0.06375068325331316, 0.05098484780654193, 0.23402033178764026, 0.1840738577025366, 0.0514740257067532, 0.0904768362072193, 0.02104768972074251, -0.11138634829382811, -0.24806487608316635, -0.13889532299741866, -0.15948064409987261, 0.015186996082890786, -0.03505156401257005, -0.1645865551018644, 0.37808972369465565, 0.2189646598010782, 0.14543792882948994, -0.002859654196996301, 0.32360961074416067, -0.0019141256832101914, 0.10693581392323333, 0.12506530381413916, 0.15375329418447875, 0.05474951321495667, 0.13958466496311187, -0.057043477639718544, 0.13093968872941794, 0.033341139774873026] |
1,803.00893 | Dose Delivery Concept and Instrumentation | Radiation therapy aims to deliver the prescribed amount of dose to a tumour
at the same time as sparing the surrounding tissues as much as possible. In
charged particle therapy, delivering the prescribed dose is equivalent to
delivering the prescribed number of ions of a given energy at each position of
the irradiation field. The accurate delivery is committed to a dose delivery
(DD) system that shapes, guides and controls the beam before the patient
entrance. Most of the early DD systems provided uniform lateral dose profiles
by using different devices, mainly patient-specific, placed in the beam line to
shape the three-dimensional final target dose. More recently, systems that
provide highly conformal dose distributions using thousands of narrow beams at
well-defined energy were developed which feature advanced scanning magnets and
real-time beam monitors, without patient-specific hardware. This lecture will
cover the general dose delivery concept as well as the different DD
instrumentations depending mainly on the beam delivery technique and on the
particle and accelerator types. Some characteristic worldwide DD and beam
monitor systems will be mentioned.
| physics.med-ph physics.acc-ph | radiation therapy aims to deliver the prescribed amount of dose to a tumour at the same time as sparing the surrounding tissues as much as possible in charged particle therapy delivering the prescribed dose is equivalent to delivering the prescribed number of ions of a given energy at each position of the irradiation field the accurate delivery is committed to a dose delivery dd system that shapes guides and controls the beam before the patient entrance most of the early dd systems provided uniform lateral dose profiles by using different devices mainly patientspecific placed in the beam line to shape the threedimensional final target dose more recently systems that provide highly conformal dose distributions using thousands of narrow beams at welldefined energy were developed which feature advanced scanning magnets and realtime beam monitors without patientspecific hardware this lecture will cover the general dose delivery concept as well as the different dd instrumentations depending mainly on the beam delivery technique and on the particle and accelerator types some characteristic worldwide dd and beam monitor systems will be mentioned | [['radiation', 'therapy', 'aims', 'to', 'deliver', 'the', 'prescribed', 'amount', 'of', 'dose', 'to', 'a', 'tumour', 'at', 'the', 'same', 'time', 'as', 'sparing', 'the', 'surrounding', 'tissues', 'as', 'much', 'as', 'possible', 'in', 'charged', 'particle', 'therapy', 'delivering', 'the', 'prescribed', 'dose', 'is', 'equivalent', 'to', 'delivering', 'the', 'prescribed', 'number', 'of', 'ions', 'of', 'a', 'given', 'energy', 'at', 'each', 'position', 'of', 'the', 'irradiation', 'field', 'the', 'accurate', 'delivery', 'is', 'committed', 'to', 'a', 'dose', 'delivery', 'dd', 'system', 'that', 'shapes', 'guides', 'and', 'controls', 'the', 'beam', 'before', 'the', 'patient', 'entrance', 'most', 'of', 'the', 'early', 'dd', 'systems', 'provided', 'uniform', 'lateral', 'dose', 'profiles', 'by', 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1,803.00894 | Sparse Identification of Nonlinear Dynamics for Rapid Model Recovery | Big data has become a critically enabling component of emerging mathematical
methods aimed at the automated discovery of dynamical systems, where first
principles modeling may be intractable. However, in many engineering systems,
abrupt changes must be rapidly characterized based on limited, incomplete, and
noisy data. Many leading automated learning techniques rely on unrealistically
large data sets and it is unclear how to leverage prior knowledge effectively
to re-identify a model after an abrupt change. In this work, we propose a
conceptual framework to recover parsimonious models of a system in response to
abrupt changes in the low-data limit. First, the abrupt change is detected by
comparing the estimated Lyapunov time of the data with the model prediction.
Next, we apply the sparse identification of nonlinear dynamics (SINDy)
regression to update a previously identified model with the fewest changes,
either by addition, deletion, or modification of existing model terms. We
demonstrate this sparse model recovery on several examples for abrupt system
change detection in periodic and chaotic dynamical systems. Our examples show
that sparse updates to a previously identified model perform better with less
data, have lower runtime complexity, and are less sensitive to noise than
identifying an entirely new model. The proposed abrupt-SINDy architecture
provides a new paradigm for the rapid and efficient recovery of a system model
after abrupt changes.
| physics.data-an nlin.AO | big data has become a critically enabling component of emerging mathematical methods aimed at the automated discovery of dynamical systems where first principles modeling may be intractable however in many engineering systems abrupt changes must be rapidly characterized based on limited incomplete and noisy data many leading automated learning techniques rely on unrealistically large data sets and it is unclear how to leverage prior knowledge effectively to reidentify a model after an abrupt change in this work we propose a conceptual framework to recover parsimonious models of a system in response to abrupt changes in the lowdata limit first the abrupt change is detected by comparing the estimated lyapunov time of the data with the model prediction next we apply the sparse identification of nonlinear dynamics sindy regression to update a previously identified model with the fewest changes either by addition deletion or modification of existing model terms we demonstrate this sparse model recovery on several examples for abrupt system change detection in periodic and chaotic dynamical systems our examples show that sparse updates to a previously identified model perform better with less data have lower runtime complexity and are less sensitive to noise than identifying an entirely new model the proposed abruptsindy architecture provides a new paradigm for the rapid and efficient recovery of a system model after abrupt changes | [['big', 'data', 'has', 'become', 'a', 'critically', 'enabling', 'component', 'of', 'emerging', 'mathematical', 'methods', 'aimed', 'at', 'the', 'automated', 'discovery', 'of', 'dynamical', 'systems', 'where', 'first', 'principles', 'modeling', 'may', 'be', 'intractable', 'however', 'in', 'many', 'engineering', 'systems', 'abrupt', 'changes', 'must', 'be', 'rapidly', 'characterized', 'based', 'on', 'limited', 'incomplete', 'and', 'noisy', 'data', 'many', 'leading', 'automated', 'learning', 'techniques', 'rely', 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1,803.00895 | Observational constraints on Gauss-Bonnet cosmology | We analyze a fully geometric approach to dark energy in the framework of
$F(R,{\cal G})$ theories of gravity, where $R$ is the Ricci curvature scalar
and ${\cal G}$ is the Gauss-Bonnet topological invariant. The latter invariant
naturally exhausts, together with $R$, the whole curvature content related to
curvature invariants coming from the Riemann tensor. In particular, we study a
class of $F(R, {\cal G})$ models with power law solutions and find that,
depending on the value of the geometrical parameter, a shift in the anisotropy
peaks position of the temperature power spectrum is produced, as well as an
increasing in the matter power spectrum amplitude. This fact could be extremely
relevant to fix the form of the $F(R, {\cal G})$ model. We also perform a MCMC
analysis using both Cosmic Microwave Background data by the Planck (2015)
release and the Joint Light-Curve Analysis of the SNLS-SDSS collaborative
effort, combined with the current local measurements of the Hubble value,
$H_0$, and galaxy data from the Sloan Digital Sky Survey (BOSS CMASS DR11). We
show that such a model can describe the CMB data with slightly high $H_0$
values, and the prediction on the amplitude matter spectrum value is proved to
be in accordance with the observed matter distribution of the universe. At the
same time, the value constrained for the geometric parameter implies a density
evolution of such a components that is growing with time.
| gr-qc astro-ph.CO hep-th | we analyze a fully geometric approach to dark energy in the framework of frcal g theories of gravity where r is the ricci curvature scalar and cal g is the gaussbonnet topological invariant the latter invariant naturally exhausts together with r the whole curvature content related to curvature invariants coming from the riemann tensor in particular we study a class of fr cal g models with power law solutions and find that depending on the value of the geometrical parameter a shift in the anisotropy peaks position of the temperature power spectrum is produced as well as an increasing in the matter power spectrum amplitude this fact could be extremely relevant to fix the form of the fr cal g model we also perform a mcmc analysis using both cosmic microwave background data by the planck 2015 release and the joint lightcurve analysis of the snlssdss collaborative effort combined with the current local measurements of the hubble value h_0 and galaxy data from the sloan digital sky survey boss cmass dr11 we show that such a model can describe the cmb data with slightly high h_0 values and the prediction on the amplitude matter spectrum value is proved to be in accordance with the observed matter distribution of the universe at the same time the value constrained for the geometric parameter implies a density evolution of such a components that is growing with time | [['we', 'analyze', 'a', 'fully', 'geometric', 'approach', 'to', 'dark', 'energy', 'in', 'the', 'framework', 'of', 'frcal', 'g', 'theories', 'of', 'gravity', 'where', 'r', 'is', 'the', 'ricci', 'curvature', 'scalar', 'and', 'cal', 'g', 'is', 'the', 'gaussbonnet', 'topological', 'invariant', 'the', 'latter', 'invariant', 'naturally', 'exhausts', 'together', 'with', 'r', 'the', 'whole', 'curvature', 'content', 'related', 'to', 'curvature', 'invariants', 'coming', 'from', 'the', 'riemann', 'tensor', 'in', 'particular', 'we', 'study', 'a', 'class', 'of', 'fr', 'cal', 'g', 'models', 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1,803.00896 | Hausdorff Morita Equivalence of singular foliations | We introduce a notion of equivalence for singular foliations - understood as
suitable families of vector fields - that preserves their transverse geometry.
Associated to every singular foliation there is a holonomy groupoid, by the
work of Androulidakis-Skandalis. We show that our notion of equivalence is
compatible with this assignment, and as a consequence we obtain several
invariants. Further, we show that it unifies some of the notions of transverse
equivalence for regular foliations that appeared in the 1980's.
| math.DG | we introduce a notion of equivalence for singular foliations understood as suitable families of vector fields that preserves their transverse geometry associated to every singular foliation there is a holonomy groupoid by the work of androulidakisskandalis we show that our notion of equivalence is compatible with this assignment and as a consequence we obtain several invariants further we show that it unifies some of the notions of transverse equivalence for regular foliations that appeared in the 1980s | [['we', 'introduce', 'a', 'notion', 'of', 'equivalence', 'for', 'singular', 'foliations', 'understood', 'as', 'suitable', 'families', 'of', 'vector', 'fields', 'that', 'preserves', 'their', 'transverse', 'geometry', 'associated', 'to', 'every', 'singular', 'foliation', 'there', 'is', 'a', 'holonomy', 'groupoid', 'by', 'the', 'work', 'of', 'androulidakisskandalis', 'we', 'show', 'that', 'our', 'notion', 'of', 'equivalence', 'is', 'compatible', 'with', 'this', 'assignment', 'and', 'as', 'a', 'consequence', 'we', 'obtain', 'several', 'invariants', 'further', 'we', 'show', 'that', 'it', 'unifies', 'some', 'of', 'the', 'notions', 'of', 'transverse', 'equivalence', 'for', 'regular', 'foliations', 'that', 'appeared', 'in', 'the', '1980s']] | [-0.2088987502073379, 0.11528383800759912, -0.12249584546263673, 0.11584161335792344, -0.10086710415290374, -0.11238574281359386, -0.04683808511196587, 0.37995455070937934, -0.3039822495468941, -0.20189929121222935, 0.06823898607246137, -0.23226990442919104, -0.2080874325658538, 0.19956919074741708, -0.1598038894222363, 0.01569803448786077, 0.08677894690711248, 0.0774167686406719, -0.1294108989562741, -0.19504957889684624, 0.4674537148277618, -0.025102498107834867, 0.23277531343659288, 0.074648225698375, 0.17537423352539344, 0.011945346836000681, -0.004903505347963227, 0.09585215724808605, -0.16284061594763588, 0.11630694766675956, 0.25313929201250795, 0.13470080891918196, 0.22948036675562003, -0.32442958217143314, -0.17767064164294616, 0.14790005187387578, 0.10042340793083177, 0.059220897925306895, -0.029531326426781322, -0.2650627288967371, 0.13754691706823283, -0.15541264870645186, -0.18303834347630313, -0.13172546424290263, 0.030095350769299426, -0.007504343912985764, -0.17157168275872736, 0.026789569075366382, 0.17174905644711352, 0.10419412127597943, -0.06307149909461211, -0.028427379800153797, -0.06402585860403058, 0.057388298078685215, 0.07448835127165933, 0.05531360165179266, 0.0837340877984197, -0.062249526599005456, -0.17611998932338074, 0.3533780694216196, -0.06766976907506193, -0.2457719473015076, 0.15548686055760635, -0.11218234356860385, -0.22819863471790755, 0.0663804209448005, 0.08190829205130667, 0.144144213807426, -0.072224507071568, 0.14510922771066698, -0.12251401759414493, 0.031029297412667228, 0.12600474679646523, 0.0433427203863271, 0.12301091302651912, 0.09828856163413117, 0.1344908435191763, 0.15802897894632464, 0.015882314690603187, -0.06892272912112898, -0.38145911088213325, -0.21722569891349658, -0.08520697359926999, 0.13479044241217994, -0.07487288704367911, -0.19837201445510513, 0.42155755604127126, 0.14581539336052773, 0.23833126185069742, 0.13014564072517187, 0.2413017975264474, 0.05411608375786608, 0.0718645298528779, 0.06137811812866283, 0.22362140858872168, 0.2218685490232402, 0.026613578629238827, -0.10541840140266638, -0.00018092606133952933, 0.1568495101712056] |
1,803.00897 | Impact of Biases in Big Data | The underlying paradigm of big data-driven machine learning reflects the
desire of deriving better conclusions from simply analyzing more data, without
the necessity of looking at theory and models. Is having simply more data
always helpful? In 1936, The Literary Digest collected 2.3M filled in
questionnaires to predict the outcome of that year's US presidential election.
The outcome of this big data prediction proved to be entirely wrong, whereas
George Gallup only needed 3K handpicked people to make an accurate prediction.
Generally, biases occur in machine learning whenever the distributions of
training set and test set are different. In this work, we provide a review of
different sorts of biases in (big) data sets in machine learning. We provide
definitions and discussions of the most commonly appearing biases in machine
learning: class imbalance and covariate shift. We also show how these biases
can be quantified and corrected. This work is an introductory text for both
researchers and practitioners to become more aware of this topic and thus to
derive more reliable models for their learning problems.
| cs.LG | the underlying paradigm of big datadriven machine learning reflects the desire of deriving better conclusions from simply analyzing more data without the necessity of looking at theory and models is having simply more data always helpful in 1936 the literary digest collected 23m filled in questionnaires to predict the outcome of that years us presidential election the outcome of this big data prediction proved to be entirely wrong whereas george gallup only needed 3k handpicked people to make an accurate prediction generally biases occur in machine learning whenever the distributions of training set and test set are different in this work we provide a review of different sorts of biases in big data sets in machine learning we provide definitions and discussions of the most commonly appearing biases in machine learning class imbalance and covariate shift we also show how these biases can be quantified and corrected this work is an introductory text for both researchers and practitioners to become more aware of this topic and thus to derive more reliable models for their learning problems | [['the', 'underlying', 'paradigm', 'of', 'big', 'datadriven', 'machine', 'learning', 'reflects', 'the', 'desire', 'of', 'deriving', 'better', 'conclusions', 'from', 'simply', 'analyzing', 'more', 'data', 'without', 'the', 'necessity', 'of', 'looking', 'at', 'theory', 'and', 'models', 'is', 'having', 'simply', 'more', 'data', 'always', 'helpful', 'in', '1936', 'the', 'literary', 'digest', 'collected', '23m', 'filled', 'in', 'questionnaires', 'to', 'predict', 'the', 'outcome', 'of', 'that', 'years', 'us', 'presidential', 'election', 'the', 'outcome', 'of', 'this', 'big', 'data', 'prediction', 'proved', 'to', 'be', 'entirely', 'wrong', 'whereas', 'george', 'gallup', 'only', 'needed', '3k', 'handpicked', 'people', 'to', 'make', 'an', 'accurate', 'prediction', 'generally', 'biases', 'occur', 'in', 'machine', 'learning', 'whenever', 'the', 'distributions', 'of', 'training', 'set', 'and', 'test', 'set', 'are', 'different', 'in', 'this', 'work', 'we', 'provide', 'a', 'review', 'of', 'different', 'sorts', 'of', 'biases', 'in', 'big', 'data', 'sets', 'in', 'machine', 'learning', 'we', 'provide', 'definitions', 'and', 'discussions', 'of', 'the', 'most', 'commonly', 'appearing', 'biases', 'in', 'machine', 'learning', 'class', 'imbalance', 'and', 'covariate', 'shift', 'we', 'also', 'show', 'how', 'these', 'biases', 'can', 'be', 'quantified', 'and', 'corrected', 'this', 'work', 'is', 'an', 'introductory', 'text', 'for', 'both', 'researchers', 'and', 'practitioners', 'to', 'become', 'more', 'aware', 'of', 'this', 'topic', 'and', 'thus', 'to', 'derive', 'more', 'reliable', 'models', 'for', 'their', 'learning', 'problems']] | [-0.02218545830030714, 0.07856979857569968, -0.12319062970957682, 0.13897723890642572, -0.16887148534244095, -0.17348026905727404, 0.07988177384554133, 0.3787367793636143, -0.2518741273068537, -0.3586289370088542, 0.07630808453210523, -0.30075216869590804, -0.12741849645019762, 0.202888963560161, -0.14875724719075317, 0.031510631099412094, 0.10747214399849657, 0.025678363538397454, -0.028437400023738683, -0.32436639560645825, 0.32599354519334156, 0.05592983191176741, 0.310167336799416, 0.015623473644320091, 0.039411635076579514, 0.0010931161205834624, -0.09179927443355237, 0.005655244262677364, -0.09626263067734313, 0.18177069457967512, 0.37878578245053257, 0.2273399633969265, 0.36491769613415026, -0.41932305007834325, -0.1839197788080624, 0.14259585991649973, 0.11720854834658728, 0.1353975426715227, -0.013918459047338481, -0.30973555492131377, 0.07989798110264185, -0.17168046706700063, -0.05754157070292753, -0.1217081592800572, 0.033342510051707824, -0.0440354405568955, -0.2398888248505748, 0.04071241613878969, 0.0627600617272864, 0.1344084153216417, -0.02875411359061466, -0.14221704803706167, 0.044312698853900656, 0.16591353324706273, 0.08827796782134101, 0.03411437548162543, 0.10724682376497764, -0.160791884097059, -0.11572588151267899, 0.3763425546772355, 0.01422546754871622, -0.1551378206085329, 0.16546522168937372, -0.09554662903246935, -0.17354064341105352, 0.04509116302837025, 0.21647783716484395, 0.08212502178734708, -0.1899698736782408, -0.0018943629081398037, 0.01301585429543841, 0.15614433055584828, 0.06249778299984014, -0.03376266549722376, 0.19298661059160208, 0.18722135362225922, -0.0003044091810641641, 0.05122876361102416, -0.04120918816294183, -0.07844547572180587, -0.2505033422600139, -0.11925700936915912, -0.14887817263529127, 0.03416539523327215, -0.06839450754416082, -0.15370239215752174, 0.3511537390559996, 0.23675694813904224, 0.19882366183645156, 0.04203992371119305, 0.2737126479121137, 0.05179028010372184, 0.0671631061479936, 0.08374971205326305, 0.19957407904439606, 0.03925372281942559, 0.13999881141419543, -0.11559833243716805, 0.11272604679900476, -0.03267315546558662] |
1,803.00898 | The split in the ancient cold front in the Perseus cluster | Sloshing cold fronts in clusters, produced as the dense cluster core moves
around in the cluster potential in response to in-falling subgroups, provide a
powerful probe of the physics of the intracluster medium (ICM), and the
magnetic fields permeating it. These sharp discontinuities in density and
temperature rise gradually outwards with age in a characteristic spiral
pattern, embedding into the intracluster medium a record of the minor merging
activity of clusters: the further from the cluster centre a cold front is, the
older it is. Recently it has been discovered that these cold fronts can survive
out to extremely large radii in the Perseus cluster. Here we report on high
spatial resolution Chandra observations of the large scale cold front in
Perseus. We find that rather than broadening through diffusion, the cold front
remains extremely sharp (consistent with abrupt jumps in density) but instead
is split into two sharp edges. These results show that magnetic draping can
suppress diffusion for vast periods of time, around ~5 Gyr, even as the cold
front expands out to nearly half the cluster virial radius.
| astro-ph.HE astro-ph.GA | sloshing cold fronts in clusters produced as the dense cluster core moves around in the cluster potential in response to infalling subgroups provide a powerful probe of the physics of the intracluster medium icm and the magnetic fields permeating it these sharp discontinuities in density and temperature rise gradually outwards with age in a characteristic spiral pattern embedding into the intracluster medium a record of the minor merging activity of clusters the further from the cluster centre a cold front is the older it is recently it has been discovered that these cold fronts can survive out to extremely large radii in the perseus cluster here we report on high spatial resolution chandra observations of the large scale cold front in perseus we find that rather than broadening through diffusion the cold front remains extremely sharp consistent with abrupt jumps in density but instead is split into two sharp edges these results show that magnetic draping can suppress diffusion for vast periods of time around 5 gyr even as the cold front expands out to nearly half the cluster virial radius | [['sloshing', 'cold', 'fronts', 'in', 'clusters', 'produced', 'as', 'the', 'dense', 'cluster', 'core', 'moves', 'around', 'in', 'the', 'cluster', 'potential', 'in', 'response', 'to', 'infalling', 'subgroups', 'provide', 'a', 'powerful', 'probe', 'of', 'the', 'physics', 'of', 'the', 'intracluster', 'medium', 'icm', 'and', 'the', 'magnetic', 'fields', 'permeating', 'it', 'these', 'sharp', 'discontinuities', 'in', 'density', 'and', 'temperature', 'rise', 'gradually', 'outwards', 'with', 'age', 'in', 'a', 'characteristic', 'spiral', 'pattern', 'embedding', 'into', 'the', 'intracluster', 'medium', 'a', 'record', 'of', 'the', 'minor', 'merging', 'activity', 'of', 'clusters', 'the', 'further', 'from', 'the', 'cluster', 'centre', 'a', 'cold', 'front', 'is', 'the', 'older', 'it', 'is', 'recently', 'it', 'has', 'been', 'discovered', 'that', 'these', 'cold', 'fronts', 'can', 'survive', 'out', 'to', 'extremely', 'large', 'radii', 'in', 'the', 'perseus', 'cluster', 'here', 'we', 'report', 'on', 'high', 'spatial', 'resolution', 'chandra', 'observations', 'of', 'the', 'large', 'scale', 'cold', 'front', 'in', 'perseus', 'we', 'find', 'that', 'rather', 'than', 'broadening', 'through', 'diffusion', 'the', 'cold', 'front', 'remains', 'extremely', 'sharp', 'consistent', 'with', 'abrupt', 'jumps', 'in', 'density', 'but', 'instead', 'is', 'split', 'into', 'two', 'sharp', 'edges', 'these', 'results', 'show', 'that', 'magnetic', 'draping', 'can', 'suppress', 'diffusion', 'for', 'vast', 'periods', 'of', 'time', 'around', '5', 'gyr', 'even', 'as', 'the', 'cold', 'front', 'expands', 'out', 'to', 'nearly', 'half', 'the', 'cluster', 'virial', 'radius']] | [-0.10369974998397063, 0.19275367689365264, -0.10841139969647506, 0.06259506197005654, -0.07703723656383636, -0.028728027008628237, 0.038818323620703095, 0.42212735165620624, -0.27096844677947135, -0.32272500499506684, 0.06791627687499832, -0.27148485320150073, -0.030820671312037588, 0.16381607697865005, 0.023475585739483325, -0.04779688405221616, 0.04096796760304692, 0.005431024092847728, -0.020346468831644038, -0.24710384321923867, 0.27405323758618444, 0.08208932150772758, 0.1991195094781974, 0.022680663567059545, 0.042756716100735725, -0.09271750552965667, -0.027370851620702453, 0.03985462719523116, -0.1137246617504112, 0.023756218964146522, 0.21152870698178505, 0.06284251820890346, 0.2876783124047707, -0.4599915470063522, -0.2462008066902462, 0.04426994204690978, 0.23557695496353984, 0.09085063771370448, -0.11045002807201869, -0.291099999233751, 0.041024986348367036, -0.14345863878209844, -0.22392207656688576, 0.029863285525954065, 0.055601823557868024, 0.024282886898813985, -0.16880325992586534, 0.19671706460642716, 0.026350155029926985, 0.018029640302658904, -0.08347551810853594, -0.08744862062460357, -0.02359270130783237, 0.06287531576181109, -0.004913520058282543, 0.07368020365948567, 0.22093175782292093, -0.1635823691037396, 0.01304651728126308, 0.3727106981041188, -0.07234907524703, 0.007293442850778116, 0.2692671871347898, -0.23293767004783228, -0.14440394376995663, 0.19653709983911097, 0.16562389162303467, 0.05635876747527422, -0.10862687753400256, -0.0004528797706280377, -0.07811813708201812, 0.18573289074953028, 0.11970342773923558, 0.008087968934862489, 0.3020981714407301, 0.13826545188975267, 0.09386750841281732, 0.13674060019757084, -0.1718489791457001, -0.09045632385252589, -0.2293116161998526, -0.12127594137178395, -0.12129358717662318, 0.013829007993699231, -0.12551572752163942, -0.1577190310380406, 0.3083228453355764, 0.12812710644996297, 0.24623198985785905, -0.02588404920106517, 0.2988378046831703, 0.06458621044149263, 0.12840525288705956, 0.16142980459799672, 0.2533198554809746, 0.1978973821577364, 0.08858474032973963, -0.21856698260169208, 0.06622116677128602, -0.017989496408462608] |
1,803.00899 | Service-based Fog architecture without DNS redirection | The heterogeneous and distributed nature of the Internet of Things (IoT) is
driving the need for extremely fast and fine-grained service provisioning in
5/5+G architectures and beyond. To meet these needs, it is critical to enable
efficient and flexible computation and networking fabrics, that can be rapidly
reconfigured to meet the computation and communication tasks at hand. In this
article, we propose a novel Fog Computing architecture that translates IoT
communications into service transactions, provisioned over a fast and efficient
networking fabric. Service matching is provided by a network function designed
using principles from Information-Centric Networks (ICN) research, routing edge
requests directly to the nearest service points without expensive and slow DNS
redirects. The proposed Fog substrate reduces the networking complexity and
overhead while being architecturally simple. We evaluate the architecture
through a comparison with how Fog might be established over existing networking
fabrics and quantify the performance benefits. Evaluation results illustrate
the superiority of the proposed architecture in reducing the required backhaul
capacity and the path length.
| cs.NI | the heterogeneous and distributed nature of the internet of things iot is driving the need for extremely fast and finegrained service provisioning in 55g architectures and beyond to meet these needs it is critical to enable efficient and flexible computation and networking fabrics that can be rapidly reconfigured to meet the computation and communication tasks at hand in this article we propose a novel fog computing architecture that translates iot communications into service transactions provisioned over a fast and efficient networking fabric service matching is provided by a network function designed using principles from informationcentric networks icn research routing edge requests directly to the nearest service points without expensive and slow dns redirects the proposed fog substrate reduces the networking complexity and overhead while being architecturally simple we evaluate the architecture through a comparison with how fog might be established over existing networking fabrics and quantify the performance benefits evaluation results illustrate the superiority of the proposed architecture in reducing the required backhaul capacity and the path length | [['the', 'heterogeneous', 'and', 'distributed', 'nature', 'of', 'the', 'internet', 'of', 'things', 'iot', 'is', 'driving', 'the', 'need', 'for', 'extremely', 'fast', 'and', 'finegrained', 'service', 'provisioning', 'in', '55g', 'architectures', 'and', 'beyond', 'to', 'meet', 'these', 'needs', 'it', 'is', 'critical', 'to', 'enable', 'efficient', 'and', 'flexible', 'computation', 'and', 'networking', 'fabrics', 'that', 'can', 'be', 'rapidly', 'reconfigured', 'to', 'meet', 'the', 'computation', 'and', 'communication', 'tasks', 'at', 'hand', 'in', 'this', 'article', 'we', 'propose', 'a', 'novel', 'fog', 'computing', 'architecture', 'that', 'translates', 'iot', 'communications', 'into', 'service', 'transactions', 'provisioned', 'over', 'a', 'fast', 'and', 'efficient', 'networking', 'fabric', 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1,803.009 | Slotted CSMA/CA Based Energy Efficient MAC Protocol Design in
Nanonetworks | Devices in a beacon-enabled network use slotted CSMA/CA to contend for
channel usage. Each node in the network competes for the channels when ready to
transmit data. The slotted CSMA/CA mechanism based on the super-frame structure
fairly provides communication chance for each node and makes a reasonable usage
of the available energy in beacon-enabled Zigbee networks. When wireless
nano-sensor nodes are implanted into the target human body area for detecting
disease symptoms or virus existence, each node also requires a similar
characteristic in channel sharing and in the transmission of event-driven data
with a short length. In this paper, we suggest a wireless network model with
nano-sensor nodes for the in-body application. We propose a novel MAC protocol
derived from an existing Zigbee MAC protocol scheme and analyze the performance
of energy usage with variable super-frame durations and packet sizes.
| cs.NI cs.IT math.IT | devices in a beaconenabled network use slotted csmaca to contend for channel usage each node in the network competes for the channels when ready to transmit data the slotted csmaca mechanism based on the superframe structure fairly provides communication chance for each node and makes a reasonable usage of the available energy in beaconenabled zigbee networks when wireless nanosensor nodes are implanted into the target human body area for detecting disease symptoms or virus existence each node also requires a similar characteristic in channel sharing and in the transmission of eventdriven data with a short length in this paper we suggest a wireless network model with nanosensor nodes for the inbody application we propose a novel mac protocol derived from an existing zigbee mac protocol scheme and analyze the performance of energy usage with variable superframe durations and packet sizes | [['devices', 'in', 'a', 'beaconenabled', 'network', 'use', 'slotted', 'csmaca', 'to', 'contend', 'for', 'channel', 'usage', 'each', 'node', 'in', 'the', 'network', 'competes', 'for', 'the', 'channels', 'when', 'ready', 'to', 'transmit', 'data', 'the', 'slotted', 'csmaca', 'mechanism', 'based', 'on', 'the', 'superframe', 'structure', 'fairly', 'provides', 'communication', 'chance', 'for', 'each', 'node', 'and', 'makes', 'a', 'reasonable', 'usage', 'of', 'the', 'available', 'energy', 'in', 'beaconenabled', 'zigbee', 'networks', 'when', 'wireless', 'nanosensor', 'nodes', 'are', 'implanted', 'into', 'the', 'target', 'human', 'body', 'area', 'for', 'detecting', 'disease', 'symptoms', 'or', 'virus', 'existence', 'each', 'node', 'also', 'requires', 'a', 'similar', 'characteristic', 'in', 'channel', 'sharing', 'and', 'in', 'the', 'transmission', 'of', 'eventdriven', 'data', 'with', 'a', 'short', 'length', 'in', 'this', 'paper', 'we', 'suggest', 'a', 'wireless', 'network', 'model', 'with', 'nanosensor', 'nodes', 'for', 'the', 'inbody', 'application', 'we', 'propose', 'a', 'novel', 'mac', 'protocol', 'derived', 'from', 'an', 'existing', 'zigbee', 'mac', 'protocol', 'scheme', 'and', 'analyze', 'the', 'performance', 'of', 'energy', 'usage', 'with', 'variable', 'superframe', 'durations', 'and', 'packet', 'sizes']] | [-0.2517402765208057, 0.014222263756944032, -0.029637484019622205, 0.011238037058917273, -0.06567765787642982, -0.29385499522489095, 0.13306644004520163, 0.4434479632414877, -0.2561120038319911, -0.24763300358983023, 0.04339340228886743, -0.2577262948400208, -0.15721199414193895, 0.13506533043858196, -0.12730948801950684, 0.06729231470796679, 0.08984310151842823, 0.08299165384045669, 0.04856144313733759, -0.2057259300494999, 0.27148338707296976, 0.11881963056844792, 0.3768730322364718, 0.046998267785446454, 0.08075583619897121, 0.06361277868439044, -0.009756016665986474, -0.08964940159099309, -0.09165274507332859, 0.10416518929580759, 0.30492641884567484, 0.16785445699102378, 0.30102316267023393, -0.47364546862331086, -0.2848436755667665, 0.06334349941462278, 0.147792694616198, 0.09138880020805767, -0.04202587799185754, -0.28348725783372564, 0.1460191830140372, -0.2655551122767585, -0.045318157676436904, 0.05712670268757003, -0.03382083110045642, 0.07264549083275987, -0.31332020093562146, 0.0016739081032158408, -0.07078780764880191, 0.05021994843014649, -0.06445231792053424, -0.05966450076417199, 0.01749029148902212, 0.1583784256799845, -0.009754755179164932, -0.03460528953499826, 0.13119546851209765, -0.11332449892569067, -0.14057458998357625, 0.3567651839128562, 0.022205625068662423, -0.17781073974245892, 0.15046384349116124, -0.017830690841323563, -0.10549927917142798, 0.12936797226513072, 0.2453330524398812, 0.041004941098591575, -0.19688462544311602, -0.029607182187360845, 0.0033205830741540663, 0.1919124613782125, 0.06807207589902516, 0.11903167180384376, 0.11166693697949605, 0.26393580835512176, 0.10789789370527225, 0.12343374088938747, -0.15818884817251402, -0.10095799054418292, -0.2224381339802806, -0.13851595347015452, -0.1981461928425623, 0.003704289293714932, -0.10700329163602354, -0.09901700702362827, 0.41876702978133834, 0.150331436689677, 0.17742673428063946, 0.11852454677490251, 0.39746508530474134, -0.00564547838377101, 0.10591450171279056, 0.17268822039477527, 0.11975382504918214, 0.03575588774422483, 0.17283229450029985, -0.1576386946702509, 0.13498791887624456, -0.03863945125901539] |
1,803.00901 | The fundamental group of an algebra with strongly simply connected
Galois covering | In this work, we prove that if a triangular algebra $A$ admits a strongly
simply connected universal Galois covering for a given presentation then the
fundamental group associated to this presentation is free.
| math.RT | in this work we prove that if a triangular algebra a admits a strongly simply connected universal galois covering for a given presentation then the fundamental group associated to this presentation is free | [['in', 'this', 'work', 'we', 'prove', 'that', 'if', 'a', 'triangular', 'algebra', 'a', 'admits', 'a', 'strongly', 'simply', 'connected', 'universal', 'galois', 'covering', 'for', 'a', 'given', 'presentation', 'then', 'the', 'fundamental', 'group', 'associated', 'to', 'this', 'presentation', 'is', 'free']] | [-0.17938197703298295, 0.13939579838717525, -0.13801525173370133, 0.013263973728471407, -0.1857302297104263, -0.13694990273903718, 0.025953611020337452, 0.35486787320538, -0.37176628477137647, -0.15632267801486183, 0.11623589654636542, -0.21074901498628384, -0.17940705006405938, 0.22063665969692398, -0.17186940303354553, -0.11570225318780902, 0.06608863451489896, 0.19171324169093912, -0.11233136117825228, -0.22674264792691579, 0.37849462393558386, -0.05167268256120609, 0.2069843618029898, 0.07050799307498065, 0.14634066408104968, 0.05119591743939302, -0.023662768586566955, 0.08871620588681915, -0.19317767911023553, 0.16212592573102677, 0.32053762804152386, 0.04637784529900686, 0.23405833186751063, -0.31636588303654484, -0.1286864330371221, 0.1922451059305758, 0.1554045881229368, 0.027395221912725407, -0.10362745277498933, -0.19534595206267003, 0.126966454060466, -0.23328852551904591, -0.14456128467325913, 0.0008330097647778915, 0.07635367187586697, -0.03601122994653203, -0.20889239323635897, -0.03357957436166929, 0.09922799276131572, 0.10277158775451509, -0.02069019831039689, -0.0033460519784553485, -0.004892967034582839, 0.06340407603420317, -0.09183018285583591, 0.09126559163754185, 0.07726254140619528, -0.11101804090889567, -0.091424415523017, 0.40829292745470547, -0.015815494582057, -0.21197822983517792, 0.13024097209739866, -0.15118413827748914, -0.23828936198895628, 0.10265852906035655, 0.08701418649941457, 0.08883896558263311, -0.07992554060889012, 0.17544892404200227, -0.23007116873155942, 0.1607958897383827, 0.03313635300957796, -0.06877655585324674, 0.15168806969780813, 0.15839452334594997, 0.13018598566960657, 0.13499049845179825, 0.11503321716780633, 0.0008571101352572441, -0.39439238822369865, -0.18831808752182758, -0.14083617146719585, 0.14455273812354513, -0.039716451301832094, -0.21159429803039087, 0.43756747245788574, 0.07381423630497673, 0.17307535649249048, 0.11156467018140988, 0.2552718555159641, 0.1164694365791299, 0.06653896527308406, 0.1066250446196081, 0.09689449736227591, 0.20610077593080472, -0.06809923820423358, -0.13525391189437924, -0.045006165450269524, 0.19332656700333412] |
1,803.00902 | DEMorphy, German Language Morphological Analyzer | DEMorphy is a morphological analyzer for German. It is built onto large,
compactified lexicons from German Morphological Dictionary. A guesser based on
German declension suffixed is also provided. For German, we provided a
state-of-art morphological analyzer. DEMorphy is implemented in Python with
ease of usability and accompanying documentation. The package is suitable for
both academic and commercial purposes wit a permissive licence.
| cs.CL | demorphy is a morphological analyzer for german it is built onto large compactified lexicons from german morphological dictionary a guesser based on german declension suffixed is also provided for german we provided a stateofart morphological analyzer demorphy is implemented in python with ease of usability and accompanying documentation the package is suitable for both academic and commercial purposes wit a permissive licence | [['demorphy', 'is', 'a', 'morphological', 'analyzer', 'for', 'german', 'it', 'is', 'built', 'onto', 'large', 'compactified', 'lexicons', 'from', 'german', 'morphological', 'dictionary', 'a', 'guesser', 'based', 'on', 'german', 'declension', 'suffixed', 'is', 'also', 'provided', 'for', 'german', 'we', 'provided', 'a', 'stateofart', 'morphological', 'analyzer', 'demorphy', 'is', 'implemented', 'in', 'python', 'with', 'ease', 'of', 'usability', 'and', 'accompanying', 'documentation', 'the', 'package', 'is', 'suitable', 'for', 'both', 'academic', 'and', 'commercial', 'purposes', 'wit', 'a', 'permissive', 'licence']] | [-0.042967634859605364, 0.006737188325595047, -0.06843239954530687, 0.05646719600634365, -0.1843484733964048, -0.204852082214113, -0.0010404727200709156, 0.45464254214459937, -0.18114502753241588, -0.2801518260611821, 0.13426969602696157, -0.2796966886873973, -0.055152081947584274, 0.30796368208603336, -0.08454787557534242, 0.023954184410178055, 0.13685498954886097, -0.015682433037308312, -0.03370269994750635, -0.21794960679391684, 0.26820011878922834, 0.11144155364925579, 0.41558153712648455, 0.05718931900653041, 0.14331187838412254, -0.032078742570543696, -0.09714158858030529, -0.015605175394122884, -0.03185240379376811, 0.1355453386776528, 0.3058467578711146, 0.2981077250708842, 0.2886415135304807, -0.3263710123186899, -0.08876784755138017, -0.07424073882590411, 0.0259924067058048, 0.05001455184735232, -0.09047201718795665, -0.3400953801001533, 0.0711879514086739, -0.2652296440959987, -0.019170056649688946, -0.06026686198409584, 0.02868220738087923, 0.0029538466310980965, -0.2371932052833549, -0.042057604391336975, 0.003522636609593943, 0.20385650711102507, -0.03602717636879218, -0.11415351700763833, -0.03975276721609851, 0.1882506612198964, 0.004486394884317356, 0.06415500963832867, 0.14000836555388266, -0.08614488182810404, -0.08185430631925494, 0.4200188729722621, -0.11025545559823513, -0.18664208188321504, 0.1806004503149918, -0.003890528832956896, -0.1612508524351327, 0.011046566195407156, 0.1676557609906136, 0.07855354369444362, -0.179539477357925, 0.15970145891049592, -0.01682155564332665, 0.3050209873876834, 0.11287533996974007, -0.08792932843000202, 0.16696805106002396, 0.2242671950893887, -0.07500687236818722, 0.23900151824168228, -0.03236261160523331, -0.02164996249632815, -0.23593099169054274, -0.18672916144764018, -0.13875143492783784, -0.014929116632536812, -0.04079548923754512, -0.17880892516824148, 0.41119138921721504, 0.13297667576126376, 0.015978424683592077, -0.0040865722033431974, 0.36584804996342984, -0.06259808610430208, 0.12558939020638749, 0.10585776373144176, 0.150994894931377, -0.04148377444973941, 0.20537355328497914, -0.10751911927156656, 0.05760978869462417, 0.08502960139569843] |
1,803.00903 | Characterization of nuclear pseudo-multipliers associated to the
harmonic oscillator | In this paper we study pseudo-multipliers associated to the harmonic
oscillator (also called Hermite multipliers) belonging to the ideal of
$r$-nuclear operators on Lebesgue spaces.
| math.FA | in this paper we study pseudomultipliers associated to the harmonic oscillator also called hermite multipliers belonging to the ideal of rnuclear operators on lebesgue spaces | [['in', 'this', 'paper', 'we', 'study', 'pseudomultipliers', 'associated', 'to', 'the', 'harmonic', 'oscillator', 'also', 'called', 'hermite', 'multipliers', 'belonging', 'to', 'the', 'ideal', 'of', 'rnuclear', 'operators', 'on', 'lebesgue', 'spaces']] | [-0.09862062558531762, 0.09954113733023405, -0.0029663751274347306, 0.08353729201015085, -0.08469555296935141, -0.07849020354449748, -0.0053944560466334225, 0.3807818639278412, -0.30663628071546556, -0.0440308103710413, 0.11689690149854869, -0.28861993059515956, -0.15292515426874162, 0.20021776650100948, -0.15271665811538696, 0.07271524405106902, -0.006314313262701035, 0.0746638225018978, -0.10751987624913455, -0.2090772868692875, 0.45664879724383356, 0.019367875903844832, 0.20102738961577415, -0.05871671184897423, 0.08459027457982302, -0.061154789309948686, -0.026034811437129976, -0.09049156829714775, -0.16925893682986498, 0.21530071951448917, 0.20734442196786404, 0.010593287404626608, 0.3229689899086952, -0.3343065981566906, -0.0748970128968358, 0.25645281359553335, 0.16179517310112715, -0.09029744744300842, 0.0015015392005443572, -0.28196171164512634, 0.01622500404715538, -0.15967172712087632, -0.20063739128410815, -0.1378795836865902, -0.00029006369411945345, 0.11719149268232286, -0.3043607517331839, 0.015522883766097948, 0.10469403836876154, 0.06233151901513338, -0.16322510726749898, -0.0677774471975863, 0.0169968743622303, 0.008635032214224339, -0.03356302827596665, 0.053930587796494366, 0.037659679651260373, 0.01319969774223864, -0.13106640197336675, 0.38454568713903425, -0.0825850724428892, -0.3154707534611225, 0.1525990031939, -0.2265570906549692, -0.19579071417450905, 0.013960321880877018, 0.20763318860903382, 0.11171535536646843, -0.12988667942583562, 0.1401528115198016, -0.05954388562589884, 0.06723038032650948, 0.12223305471241475, 0.07673680927604437, 0.04125703029800207, 0.027586252577602863, 0.16450384702533483, 0.21588942452915943, -0.007945562035311014, -0.08279874459607527, -0.3060534545779228, -0.2523555760085583, -0.21492301189573482, 0.04507225126028061, -0.011727618547156453, -0.24180177591741084, 0.48857084542512896, 0.1686974345520139, 0.11733634980395437, 0.04877747323364019, 0.2067426972463727, 0.14171533610671758, 0.04571874231100082, -0.005871741659939289, 0.16425243601202966, 0.2262974737584591, 0.10497551904991269, -0.18543551350012422, -0.10706700121983886, 0.26360915150493386] |
1,803.00904 | Hardness of Approximate Nearest Neighbor Search | We prove conditional near-quadratic running time lower bounds for approximate
Bichromatic Closest Pair with Euclidean, Manhattan, Hamming, or edit distance.
Specifically, unless the Strong Exponential Time Hypothesis (SETH) is false,
for every $\delta>0$ there exists a constant $\epsilon>0$ such that computing a
$(1+\epsilon)$-approximation to the Bichromatic Closest Pair requires
$n^{2-\delta}$ time. In particular, this implies a near-linear query time for
Approximate Nearest Neighbor search with polynomial preprocessing time.
Our reduction uses the Distributed PCP framework of [ARW'17], but obtains
improved efficiency using Algebraic Geometry (AG) codes. Efficient PCPs from AG
codes have been constructed in other settings before [BKKMS'16, BCGRS'17], but
our construction is the first to yield new hardness results.
| cs.CC cs.DS | we prove conditional nearquadratic running time lower bounds for approximate bichromatic closest pair with euclidean manhattan hamming or edit distance specifically unless the strong exponential time hypothesis seth is false for every delta0 there exists a constant epsilon0 such that computing a 1epsilonapproximation to the bichromatic closest pair requires n2delta time in particular this implies a nearlinear query time for approximate nearest neighbor search with polynomial preprocessing time our reduction uses the distributed pcp framework of arw17 but obtains improved efficiency using algebraic geometry ag codes efficient pcps from ag codes have been constructed in other settings before bkkms16 bcgrs17 but our construction is the first to yield new hardness results | [['we', 'prove', 'conditional', 'nearquadratic', 'running', 'time', 'lower', 'bounds', 'for', 'approximate', 'bichromatic', 'closest', 'pair', 'with', 'euclidean', 'manhattan', 'hamming', 'or', 'edit', 'distance', 'specifically', 'unless', 'the', 'strong', 'exponential', 'time', 'hypothesis', 'seth', 'is', 'false', 'for', 'every', 'delta0', 'there', 'exists', 'a', 'constant', 'epsilon0', 'such', 'that', 'computing', 'a', '1epsilonapproximation', 'to', 'the', 'bichromatic', 'closest', 'pair', 'requires', 'n2delta', 'time', 'in', 'particular', 'this', 'implies', 'a', 'nearlinear', 'query', 'time', 'for', 'approximate', 'nearest', 'neighbor', 'search', 'with', 'polynomial', 'preprocessing', 'time', 'our', 'reduction', 'uses', 'the', 'distributed', 'pcp', 'framework', 'of', 'arw17', 'but', 'obtains', 'improved', 'efficiency', 'using', 'algebraic', 'geometry', 'ag', 'codes', 'efficient', 'pcps', 'from', 'ag', 'codes', 'have', 'been', 'constructed', 'in', 'other', 'settings', 'before', 'bkkms16', 'bcgrs17', 'but', 'our', 'construction', 'is', 'the', 'first', 'to', 'yield', 'new', 'hardness', 'results']] | [-0.16151919289323916, 0.040506330905137236, -0.09006186247872258, 0.09897628089055177, -0.06137112256449958, -0.2596086137798718, 0.1221217650325141, 0.4244785440257854, -0.2944339906313905, -0.3230092419544235, 0.05350249390661096, -0.2393543404802956, -0.1018536524778163, 0.19405566088001555, -0.05340122863115674, 0.09817933602500017, 0.11349357678382485, 0.05064551285640509, -0.10861647412120537, -0.35411325559080403, 0.2204934694832277, 0.0655730697784478, 0.22716716272515003, 0.036620490140329046, 0.07367820983334375, 0.03940170191021429, -0.016181260011055403, -0.008387952404855578, -0.11519678740545782, 0.07712581787361866, 0.26046396245429704, 0.1857243491448807, 0.27246035065467433, -0.38287425034596473, -0.15053673518613558, 0.20356502637042906, 0.12762402492161426, 0.1181940058272125, -0.11178269439120346, -0.21235229757924876, 0.11010792880767474, -0.08186465049166819, -0.06783832893793092, -0.01433590660841825, 0.11239357024465722, -0.0008040258954106657, -0.32533473956088227, 0.02826719321914155, 0.09701668155483073, -0.008352745602476515, 0.0009219685420652644, -0.12091103769489564, 0.08609196516529967, 0.07295279960035933, -0.018346825877583964, 0.14097702319203462, 0.04328038045841148, -0.0030462945071997602, -0.1987829481745225, 0.3568776442186424, -0.08713367000799971, -0.14217783662024885, 0.16597491310460977, -0.0736057481866468, -0.16156237531901785, 0.13892230100032907, 0.1638110688439122, 0.14066656618120355, -0.07741536460471926, 0.17551905426403713, -0.08619803919336172, 0.19874593258524934, 0.1477382752302758, 0.04015184430991886, 0.1011461409270177, 0.1418085277869573, 0.16183916038048832, 0.1565889436606085, -0.05679480543498088, -0.10242655376593272, -0.2653051996603608, -0.14923031387782734, -0.2548697281551237, 0.024582813627569488, -0.2209546332100958, -0.18373516896907757, 0.30047574752286355, 0.10462048316279564, 0.16471145703124218, 0.19114867690088297, 0.29927247224043607, 0.0896792652158722, 0.051138496502837236, 0.22544990228918863, 0.1979020296992665, 0.049915631671427506, 0.012966001634830955, -0.17928715359637323, 0.13449664078049223, 0.17177052149135205] |
1,803.00905 | Inferring correlations associated to causal interactions in brain
signals using autoregressive models | The specific connectivity of a neuronal network is reflected in the dynamics
of the signals recorded on its nodes. The analysis of how the activity in one
node predicts the behaviour of another gives the directionality in their
relationship. However, each node is composed of many different elements which
define the properties of the links. For instance, excitatory and inhibitory
neuronal subtypes determine the functionality of the connection. Classic
indexes such as the Granger causality (GC) quantifies these interactions, but
they do not infer into the mechanism behind them. Here, we introduce an
extension of the well-known GC that analyses the correlation associated to the
specific influence that a transmitter node has over the receiver. This way, the
G-causal link has a positive or negative effect if the predicted activity
follows directly or inversely, respectively, the dynamics of the sender. The
method is validated in a neuronal population model, testing the paradigm that
excitatory and inhibitory neurons have a differential effect in the
connectivity. Our approach correctly infers the positive or negative coupling
produced by different types of neurons. Our results suggest that the proposed
approach provides additional information on the characterization of G-causal
connections, which is potentially relevant when it comes to understanding
interactions in the brain circuits.
| q-bio.QM q-bio.NC | the specific connectivity of a neuronal network is reflected in the dynamics of the signals recorded on its nodes the analysis of how the activity in one node predicts the behaviour of another gives the directionality in their relationship however each node is composed of many different elements which define the properties of the links for instance excitatory and inhibitory neuronal subtypes determine the functionality of the connection classic indexes such as the granger causality gc quantifies these interactions but they do not infer into the mechanism behind them here we introduce an extension of the wellknown gc that analyses the correlation associated to the specific influence that a transmitter node has over the receiver this way the gcausal link has a positive or negative effect if the predicted activity follows directly or inversely respectively the dynamics of the sender the method is validated in a neuronal population model testing the paradigm that excitatory and inhibitory neurons have a differential effect in the connectivity our approach correctly infers the positive or negative coupling produced by different types of neurons our results suggest that the proposed approach provides additional information on the characterization of gcausal connections which is potentially relevant when it comes to understanding interactions in the brain circuits | [['the', 'specific', 'connectivity', 'of', 'a', 'neuronal', 'network', 'is', 'reflected', 'in', 'the', 'dynamics', 'of', 'the', 'signals', 'recorded', 'on', 'its', 'nodes', 'the', 'analysis', 'of', 'how', 'the', 'activity', 'in', 'one', 'node', 'predicts', 'the', 'behaviour', 'of', 'another', 'gives', 'the', 'directionality', 'in', 'their', 'relationship', 'however', 'each', 'node', 'is', 'composed', 'of', 'many', 'different', 'elements', 'which', 'define', 'the', 'properties', 'of', 'the', 'links', 'for', 'instance', 'excitatory', 'and', 'inhibitory', 'neuronal', 'subtypes', 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1,803.00906 | Enhancing Cooperative Coevolution for Large Scale Optimization by
Adaptively Constructing Surrogate Models | It has been shown that cooperative coevolution (CC) can effectively deal with
large scale optimization problems (LSOPs) through a divide-and-conquer
strategy. However, its performance is severely restricted by the current
context-vector-based sub-solution evaluation method since this method needs to
access the original high dimensional simulation model when evaluating each
sub-solution and thus requires many computation resources. To alleviate this
issue, this study proposes an adaptive surrogate model assisted CC framework.
This framework adaptively constructs surrogate models for different
sub-problems by fully considering their characteristics. For the single
dimensional sub-problems obtained through decomposition, accurate enough
surrogate models can be obtained and used to find out the optimal solutions of
the corresponding sub-problems directly. As for the nonseparable sub-problems,
the surrogate models are employed to evaluate the corresponding sub-solutions,
and the original simulation model is only adopted to reevaluate some good
sub-solutions selected by surrogate models. By these means, the computation
cost could be greatly reduced without significantly sacrificing evaluation
quality. Empirical studies on IEEE CEC 2010 benchmark functions show that the
concrete algorithm based on this framework is able to find much better
solutions than the conventional CC algorithms and a non-CC algorithm even with
much fewer computation resources.
| cs.NE | it has been shown that cooperative coevolution cc can effectively deal with large scale optimization problems lsops through a divideandconquer strategy however its performance is severely restricted by the current contextvectorbased subsolution evaluation method since this method needs to access the original high dimensional simulation model when evaluating each subsolution and thus requires many computation resources to alleviate this issue this study proposes an adaptive surrogate model assisted cc framework this framework adaptively constructs surrogate models for different subproblems by fully considering their characteristics for the single dimensional subproblems obtained through decomposition accurate enough surrogate models can be obtained and used to find out the optimal solutions of the corresponding subproblems directly as for the nonseparable subproblems the surrogate models are employed to evaluate the corresponding subsolutions and the original simulation model is only adopted to reevaluate some good subsolutions selected by surrogate models by these means the computation cost could be greatly reduced without significantly sacrificing evaluation quality empirical studies on ieee cec 2010 benchmark functions show that the concrete algorithm based on this framework is able to find much better solutions than the conventional cc algorithms and a noncc algorithm even with much fewer computation resources | [['it', 'has', 'been', 'shown', 'that', 'cooperative', 'coevolution', 'cc', 'can', 'effectively', 'deal', 'with', 'large', 'scale', 'optimization', 'problems', 'lsops', 'through', 'a', 'divideandconquer', 'strategy', 'however', 'its', 'performance', 'is', 'severely', 'restricted', 'by', 'the', 'current', 'contextvectorbased', 'subsolution', 'evaluation', 'method', 'since', 'this', 'method', 'needs', 'to', 'access', 'the', 'original', 'high', 'dimensional', 'simulation', 'model', 'when', 'evaluating', 'each', 'subsolution', 'and', 'thus', 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1,803.00907 | Multi-Instance Dynamic Ordinal Random Fields for Weakly-supervised
Facial Behavior Analysis | We propose a Multi-Instance-Learning (MIL) approach for weakly-supervised
learning problems, where a training set is formed by bags (sets of feature
vectors or instances) and only labels at bag-level are provided. Specifically,
we consider the Multi-Instance Dynamic-Ordinal-Regression (MI-DOR) setting,
where the instance labels are naturally represented as ordinal variables and
bags are structured as temporal sequences. To this end, we propose
Multi-Instance Dynamic Ordinal Random Fields (MI-DORF). In this framework, we
treat instance-labels as temporally-dependent latent variables in an Undirected
Graphical Model. Different MIL assumptions are modelled via newly introduced
high-order potentials relating bag and instance-labels within the energy
function of the model. We also extend our framework to address the
Partially-Observed MI-DOR problems, where a subset of instance labels are
available during training. We show on the tasks of weakly-supervised facial
behavior analysis, Facial Action Unit (DISFA dataset) and Pain (UNBC dataset)
Intensity estimation, that the proposed framework outperforms alternative
learning approaches. Furthermore, we show that MIDORF can be employed to reduce
the data annotation efforts in this context by large-scale.
| cs.CV cs.AI | we propose a multiinstancelearning mil approach for weaklysupervised learning problems where a training set is formed by bags sets of feature vectors or instances and only labels at baglevel are provided specifically we consider the multiinstance dynamicordinalregression midor setting where the instance labels are naturally represented as ordinal variables and bags are structured as temporal sequences to this end we propose multiinstance dynamic ordinal random fields midorf in this framework we treat instancelabels as temporallydependent latent variables in an undirected graphical model different mil assumptions are modelled via newly introduced highorder potentials relating bag and instancelabels within the energy function of the model we also extend our framework to address the partiallyobserved midor problems where a subset of instance labels are available during training we show on the tasks of weaklysupervised facial behavior analysis facial action unit disfa dataset and pain unbc dataset intensity estimation that the proposed framework outperforms alternative learning approaches furthermore we show that midorf can be employed to reduce the data annotation efforts in this context by largescale | [['we', 'propose', 'a', 'multiinstancelearning', 'mil', 'approach', 'for', 'weaklysupervised', 'learning', 'problems', 'where', 'a', 'training', 'set', 'is', 'formed', 'by', 'bags', 'sets', 'of', 'feature', 'vectors', 'or', 'instances', 'and', 'only', 'labels', 'at', 'baglevel', 'are', 'provided', 'specifically', 'we', 'consider', 'the', 'multiinstance', 'dynamicordinalregression', 'midor', 'setting', 'where', 'the', 'instance', 'labels', 'are', 'naturally', 'represented', 'as', 'ordinal', 'variables', 'and', 'bags', 'are', 'structured', 'as', 'temporal', 'sequences', 'to', 'this', 'end', 'we', 'propose', 'multiinstance', 'dynamic', 'ordinal', 'random', 'fields', 'midorf', 'in', 'this', 'framework', 'we', 'treat', 'instancelabels', 'as', 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1,803.00908 | Goldberg's Conjecture is true for random multigraphs | In the 70s, Goldberg, and independently Seymour, conjectured that for any
multigraph $G$, the chromatic index $\chi'(G)$ satisfies $\chi'(G)\leq \max
\{\Delta(G)+1, \lceil\rho(G)\rceil\}$, where $\rho(G)=\max \{\frac
{e(G[S])}{\lfloor
|S|/2\rfloor} \mid S\subseteq V \}$. We show that their conjecture (in a
stronger form) is true for random multigraphs. Let $M(n,m)$ be the probability
space consisting of all loopless multigraphs with $n$ vertices and $m$ edges,
in which $m$ pairs from $[n]$ are chosen independently at random with
repetitions. Our result states that, for a given $m:=m(n)$, $M\sim M(n,m)$
typically satisfies $\chi'(G)=\max\{\Delta(G),\lceil\rho(G)\rceil\}$. In
particular, we show that if $n$ is even and $m:=m(n)$, then
$\chi'(M)=\Delta(M)$ for a typical $M\sim M(n,m)$. Furthermore, for a fixed
$\varepsilon>0$, if $n$ is odd, then a typical $M\sim M(n,m)$ has
$\chi'(M)=\Delta(M)$ for $m\leq (1-\varepsilon)n^3\log n$, and
$\chi'(M)=\lceil\rho(M)\rceil$ for $m\geq (1+\varepsilon)n^3\log n$.
| math.CO math.PR | in the 70s goldberg and independently seymour conjectured that for any multigraph g the chromatic index chig satisfies chigleq max deltag1 lceilrhogrceil where rhogmax frac egslfloor s2rfloor mid ssubseteq v we show that their conjecture in a stronger form is true for random multigraphs let mnm be the probability space consisting of all loopless multigraphs with n vertices and m edges in which m pairs from n are chosen independently at random with repetitions our result states that for a given mmn msim mnm typically satisfies chigmaxdeltaglceilrhogrceil in particular we show that if n is even and mmn then chimdeltam for a typical msim mnm furthermore for a fixed varepsilon0 if n is odd then a typical msim mnm has chimdeltam for mleq 1varepsilonn3log n and chimlceilrhomrceil for mgeq 1varepsilonn3log n | [['in', 'the', '70s', 'goldberg', 'and', 'independently', 'seymour', 'conjectured', 'that', 'for', 'any', 'multigraph', 'g', 'the', 'chromatic', 'index', 'chig', 'satisfies', 'chigleq', 'max', 'deltag1', 'lceilrhogrceil', 'where', 'rhogmax', 'frac', 'egslfloor', 's2rfloor', 'mid', 'ssubseteq', 'v', 'we', 'show', 'that', 'their', 'conjecture', 'in', 'a', 'stronger', 'form', 'is', 'true', 'for', 'random', 'multigraphs', 'let', 'mnm', 'be', 'the', 'probability', 'space', 'consisting', 'of', 'all', 'loopless', 'multigraphs', 'with', 'n', 'vertices', 'and', 'm', 'edges', 'in', 'which', 'm', 'pairs', 'from', 'n', 'are', 'chosen', 'independently', 'at', 'random', 'with', 'repetitions', 'our', 'result', 'states', 'that', 'for', 'a', 'given', 'mmn', 'msim', 'mnm', 'typically', 'satisfies', 'chigmaxdeltaglceilrhogrceil', 'in', 'particular', 'we', 'show', 'that', 'if', 'n', 'is', 'even', 'and', 'mmn', 'then', 'chimdeltam', 'for', 'a', 'typical', 'msim', 'mnm', 'furthermore', 'for', 'a', 'fixed', 'varepsilon0', 'if', 'n', 'is', 'odd', 'then', 'a', 'typical', 'msim', 'mnm', 'has', 'chimdeltam', 'for', 'mleq', '1varepsilonn3log', 'n', 'and', 'chimlceilrhomrceil', 'for', 'mgeq', '1varepsilonn3log', 'n']] | [-0.1970956531567041, 0.22461859861266661, -0.017849704321305893, 0.010424317850265652, 0.00047193092607022795, -0.24051374289756794, 0.03365773847326636, 0.3513003458826207, -0.20562089356731195, -0.3099882502459967, 0.01265951517547603, -0.34789607654226423, -0.11471506985683055, 0.117345560651242, -0.07084582371973112, -0.02376680634653417, 0.07113866587207637, 0.12518668573227573, 0.020306319752173712, -0.3116132222100726, 0.25657920337847023, -0.08930781530598025, 0.14108410005488234, 0.03510686517006061, 0.09026569382862004, 0.05179692888403403, 0.12445487038110245, 0.04739720362298702, -0.19512346493901844, -0.0033959717605240095, 0.2545693634902356, 0.1383810456475762, 0.24738844437906365, -0.35719882316704166, -0.13654883966407144, 0.23913486740460282, 0.14626531268576862, -0.029742622958900802, 0.004210642119106211, -0.17112295077655648, 0.24307805702823107, -0.0996567158517046, -0.11812335853922928, 0.04681806157144611, 0.22334045776333966, -0.0006774568159255336, -0.3902287509322899, 0.006220551803097373, 0.11685506950636379, 0.006196366731825545, 0.06021893317963867, -0.1976485842815982, -0.07764073327130287, 0.06073569380067533, -0.08577305270687173, 0.1147688462773002, -0.033207706664307196, -0.09359401282205505, -0.09748653975910827, 0.33913349141137766, -0.06578882356278301, -0.16197049823498016, 0.06451672157959738, -0.18499134394309683, -0.2317958026656835, 0.08473131260796465, 0.06169541274029456, 0.171043762540231, -0.0063125500336220705, 0.19537273105653766, -0.16412046215054196, 0.16408499807012497, 0.16389184773109516, -0.012782627205196464, 0.10397497148894262, 0.07716855361721799, 0.1415796256891345, 0.11707707517303252, -0.009084411927300398, 0.0636102509929142, -0.31139572180013675, -0.1497337411340998, -0.28106552265206597, 0.17474229222346768, -0.1900883166336065, -0.0911575577290515, 0.2571796857633582, 0.06882002868033091, 0.22020857140481984, 0.18549858654379753, 0.17809617494091728, 0.06543805709081991, -0.0048770573978755075, 0.22771490930215088, 0.08093627938451092, 0.15961601383235977, -0.03160472582170709, -0.13503603360684374, 0.05201042445040629, 0.11932164835209241] |
1,803.00909 | Understanding the Loss Surface of Neural Networks for Binary
Classification | It is widely conjectured that the reason that training algorithms for neural
networks are successful because all local minima lead to similar performance,
for example, see (LeCun et al., 2015, Choromanska et al., 2015, Dauphin et al.,
2014). Performance is typically measured in terms of two metrics: training
performance and generalization performance. Here we focus on the training
performance of single-layered neural networks for binary classification, and
provide conditions under which the training error is zero at all local minima
of a smooth hinge loss function. Our conditions are roughly in the following
form: the neurons have to be strictly convex and the surrogate loss function
should be a smooth version of hinge loss. We also provide counterexamples to
show that when the loss function is replaced with quadratic loss or logistic
loss, the result may not hold.
| cs.LG cs.AI stat.ML | it is widely conjectured that the reason that training algorithms for neural networks are successful because all local minima lead to similar performance for example see lecun et al 2015 choromanska et al 2015 dauphin et al 2014 performance is typically measured in terms of two metrics training performance and generalization performance here we focus on the training performance of singlelayered neural networks for binary classification and provide conditions under which the training error is zero at all local minima of a smooth hinge loss function our conditions are roughly in the following form the neurons have to be strictly convex and the surrogate loss function should be a smooth version of hinge loss we also provide counterexamples to show that when the loss function is replaced with quadratic loss or logistic loss the result may not hold | [['it', 'is', 'widely', 'conjectured', 'that', 'the', 'reason', 'that', 'training', 'algorithms', 'for', 'neural', 'networks', 'are', 'successful', 'because', 'all', 'local', 'minima', 'lead', 'to', 'similar', 'performance', 'for', 'example', 'see', 'lecun', 'et', 'al', '2015', 'choromanska', 'et', 'al', '2015', 'dauphin', 'et', 'al', '2014', 'performance', 'is', 'typically', 'measured', 'in', 'terms', 'of', 'two', 'metrics', 'training', 'performance', 'and', 'generalization', 'performance', 'here', 'we', 'focus', 'on', 'the', 'training', 'performance', 'of', 'singlelayered', 'neural', 'networks', 'for', 'binary', 'classification', 'and', 'provide', 'conditions', 'under', 'which', 'the', 'training', 'error', 'is', 'zero', 'at', 'all', 'local', 'minima', 'of', 'a', 'smooth', 'hinge', 'loss', 'function', 'our', 'conditions', 'are', 'roughly', 'in', 'the', 'following', 'form', 'the', 'neurons', 'have', 'to', 'be', 'strictly', 'convex', 'and', 'the', 'surrogate', 'loss', 'function', 'should', 'be', 'a', 'smooth', 'version', 'of', 'hinge', 'loss', 'we', 'also', 'provide', 'counterexamples', 'to', 'show', 'that', 'when', 'the', 'loss', 'function', 'is', 'replaced', 'with', 'quadratic', 'loss', 'or', 'logistic', 'loss', 'the', 'result', 'may', 'not', 'hold']] | [-0.04817403397475281, 0.020317823349933282, -0.0643203052229853, 0.08441012982537385, -0.08050953612125812, -0.15787526084123737, 0.017339335508087146, 0.41981515391682184, -0.23223577708305015, -0.3338995998874851, 0.07141480102190191, -0.25202211119696827, -0.2648317294864895, 0.22720045250174284, -0.18386148807531508, 0.1049226408554105, 0.1269154074877147, -0.012288611480391782, -0.08928893905823003, -0.4242719724964704, 0.29360626046171917, 0.06629549531116538, 0.3011704649008049, 0.06104750862051087, 0.07633175496283891, -0.030007200856033686, 0.06892661857044827, 0.0056726454627992464, -0.08691847544535442, 0.08219797465521979, 0.2287428758249883, 0.16903731259636337, 0.32234914980855955, -0.3821745563475211, -0.25054086226099814, 0.15200541975031043, 0.061716509525779714, 0.07627934537212752, -0.024336076114687008, -0.2324823479339766, 0.10476490143214073, -0.14900762464062575, -0.03855421879485141, -0.10394168825289846, 0.00916972486166297, 0.05465625035134654, -0.3597991168261064, 0.10381951902795032, 0.12934295262420809, 0.053502505638889024, -0.05270593786394618, -0.1291274923634083, -0.042906122154685374, 0.06885449188344188, 0.011154058416557573, 0.0939682812300803, 0.07571087759599959, -0.10928193998971723, -0.1236871078943521, 0.2963294583257206, -0.03848596555894635, -0.214513919367217, 0.2107604393913125, -0.05091840690885582, -0.10142470665112899, 0.08935496721579864, 0.21987720495548752, 0.09589291668163925, -0.15667790395981399, 0.035903895083886236, -0.047335725349041013, 0.1283831840217875, 0.11019609303614736, 0.033611931617161, 0.13844473214554906, 0.1535850842525829, 0.07644052557365962, 0.12356569087489705, -0.0739834640195498, -0.06482639699370471, -0.26289730044557663, -0.125411820472609, -0.21734308109645914, 0.030394093756479838, -0.04184286628756757, -0.16337156040172507, 0.3560551727363962, 0.11775872973728599, 0.2625881881133592, 0.13366055996480133, 0.26457327438423234, 0.10611164746347163, 0.08266743507752888, 0.1497921931122287, 0.2807161152434202, 0.05224338601968748, 0.07092666966215211, -0.1612065154727698, 0.11588652747390914, 0.07829776292082168] |
1,803.0091 | A survey of non-uniqueness results for the anisotropic Calder{\'o}n
problem with disjoint data | After giving a general introduction to the main known results on the
anisotropic Calder{\'o}n problem on n-dimensional compact Riemannian manifolds
with boundary, we give a motivated review of some recent non-uniqueness results
obtained in [5, 6] for the anisotropic Calder{\'o}n problem at fixed frequency,
in dimension n $\ge$ 3, when the Dirichlet and Neumann data are measured on
disjoint subsets of the boundary. These non-uniqueness results are of the
following nature: given a smooth compact connected Riemannian manifold with
boundary (M, g) of dimension n $\ge$ 3, we first show that there exist in the
conformal class of g an infinite number of Riemannian metrics gmetrics metrics
g such that their corresponding Dirichlet-to-Neumann maps at a fixed frequency
coincide when the Dirichlet data $\Gamma$D and Neumann data $\Gamma$N are
measured on disjoint sets and satisfy $\Gamma$D $\cup$ $\Gamma$N = $\partial$M.
The corresponding conformal factors satisfy a nonlinear elliptic PDE of Yamabe
type on (M, g) and arise from a natural but subtle gauge invariance of the
Calder{\'o}n when the data are given on disjoint sets. We then present
counterexamples to uniqueness in dimension n $\ge$ 3 to the anisotropic
Calder{\'o}n problem at fixed frequency with data on disjoint sets, which do
not arise from this gauge invariance. They are given by cylindrical Riemannian
manifolds with boundary having two ends, equipped with a suitably chosen warped
product metric. This survey concludes with some remarks on the case of
manifolds with corners.
| math.AP math-ph math.DG math.MP | after giving a general introduction to the main known results on the anisotropic calderon problem on ndimensional compact riemannian manifolds with boundary we give a motivated review of some recent nonuniqueness results obtained in 5 6 for the anisotropic calderon problem at fixed frequency in dimension n ge 3 when the dirichlet and neumann data are measured on disjoint subsets of the boundary these nonuniqueness results are of the following nature given a smooth compact connected riemannian manifold with boundary m g of dimension n ge 3 we first show that there exist in the conformal class of g an infinite number of riemannian metrics gmetrics metrics g such that their corresponding dirichlettoneumann maps at a fixed frequency coincide when the dirichlet data gammad and neumann data gamman are measured on disjoint sets and satisfy gammad cup gamman partialm the corresponding conformal factors satisfy a nonlinear elliptic pde of yamabe type on m g and arise from a natural but subtle gauge invariance of the calderon when the data are given on disjoint sets we then present counterexamples to uniqueness in dimension n ge 3 to the anisotropic calderon problem at fixed frequency with data on disjoint sets which do not arise from this gauge invariance they are given by cylindrical riemannian manifolds with boundary having two ends equipped with a suitably chosen warped product metric this survey concludes with some remarks on the case of manifolds with corners | [['after', 'giving', 'a', 'general', 'introduction', 'to', 'the', 'main', 'known', 'results', 'on', 'the', 'anisotropic', 'calderon', 'problem', 'on', 'ndimensional', 'compact', 'riemannian', 'manifolds', 'with', 'boundary', 'we', 'give', 'a', 'motivated', 'review', 'of', 'some', 'recent', 'nonuniqueness', 'results', 'obtained', 'in', '5', '6', 'for', 'the', 'anisotropic', 'calderon', 'problem', 'at', 'fixed', 'frequency', 'in', 'dimension', 'n', 'ge', '3', 'when', 'the', 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1,803.00911 | Dual spaces of cadlag processes | This article characterizes topological duals of spaces of cadlag processes.
We obtain extensions of functional analytic results of Dellacherie and Meyer
that underlie many fundamental results in stochastic analysis. In particular,
we obtain a characterization of the dual of cadlag processes of class $(D)$ in
terms of optional measures of essentially bounded variation. When specialized
to regular processes, we find extensions of the main result of Bismut
\cite{bis78} on projections of continuous processes. The dual characterizations
yield existence results and optimality conditions for many fundamental problems
from optimal stopping to singular stochastic control well beyond classical
formulations.
| math.PR | this article characterizes topological duals of spaces of cadlag processes we obtain extensions of functional analytic results of dellacherie and meyer that underlie many fundamental results in stochastic analysis in particular we obtain a characterization of the dual of cadlag processes of class d in terms of optional measures of essentially bounded variation when specialized to regular processes we find extensions of the main result of bismut citebis78 on projections of continuous processes the dual characterizations yield existence results and optimality conditions for many fundamental problems from optimal stopping to singular stochastic control well beyond classical formulations | [['this', 'article', 'characterizes', 'topological', 'duals', 'of', 'spaces', 'of', 'cadlag', 'processes', 'we', 'obtain', 'extensions', 'of', 'functional', 'analytic', 'results', 'of', 'dellacherie', 'and', 'meyer', 'that', 'underlie', 'many', 'fundamental', 'results', 'in', 'stochastic', 'analysis', 'in', 'particular', 'we', 'obtain', 'a', 'characterization', 'of', 'the', 'dual', 'of', 'cadlag', 'processes', 'of', 'class', 'd', 'in', 'terms', 'of', 'optional', 'measures', 'of', 'essentially', 'bounded', 'variation', 'when', 'specialized', 'to', 'regular', 'processes', 'we', 'find', 'extensions', 'of', 'the', 'main', 'result', 'of', 'bismut', 'citebis78', 'on', 'projections', 'of', 'continuous', 'processes', 'the', 'dual', 'characterizations', 'yield', 'existence', 'results', 'and', 'optimality', 'conditions', 'for', 'many', 'fundamental', 'problems', 'from', 'optimal', 'stopping', 'to', 'singular', 'stochastic', 'control', 'well', 'beyond', 'classical', 'formulations']] | [-0.09050121541561869, 0.06131064387348791, -0.08107457834557863, 0.12174491572053132, -0.0652726056650863, -0.08111302903368293, 0.046019481829716824, 0.33121493022190407, -0.31233619250512373, -0.20057564678912362, 0.1442933739745058, -0.22284304143492287, -0.20112074478917444, 0.24286471761297435, -0.14386692038776042, 0.0846671238153552, 0.036348158112862926, -0.002397160104010254, -0.0978316381181988, -0.23081564208647856, 0.34327052721346263, -0.027917212253669277, 0.2391816369490698, 0.06583499304542784, 0.1140320849372074, 0.04770818302737704, -0.07795327268346834, 0.02289666434323105, -0.20472279335846602, 0.14689630261273123, 0.2851655351696536, 0.14617923093222393, 0.27516721373346326, -0.40636554220691323, -0.1969869967821675, 0.14295802992516352, 0.0906058586594251, 0.025314434863200102, -0.026013427808114404, -0.2538913261669222, 0.07988441416091518, -0.09156547061381086, -0.15328283161701015, -0.09450179573226099, 0.015340878160107726, 0.10449504293501377, -0.31282021693186834, 0.06247506400662436, 0.15571446494262395, 0.04027033085973623, -0.134468524629483, -0.11296586031191207, -0.00029239507906216505, 0.08484017849605152, 0.06456069594363119, -0.027256633586754713, 0.1034658444938638, -0.0890962778236523, -0.22703494469169527, 0.3296031866460301, -0.08143890259574012, -0.2173591511673294, 0.2343664816095649, -0.17672456099050274, -0.20591624666606853, 0.10855929466197267, 0.18723533956411606, 0.17100517356205577, -0.1405534400723809, 0.16300347401192994, -0.06140984154869026, 0.04400025581708178, 0.10733201374144603, 0.12354563262488227, 0.07972744712606072, 0.13340046468025926, 0.14744512582425764, 0.15963456241540067, 0.060636031623289455, -0.18069294517711873, -0.37862724436369416, -0.165190392464865, -0.10760047110185648, 0.07448327672439821, -0.12232882747260494, -0.2169953598640859, 0.36888580315280706, 0.12046123997364096, 0.15789429844395877, 0.10378371550662753, 0.19763302814681083, 0.11922807215402524, -0.016430533374659717, 0.027011518012538243, 0.16299344323730716, 0.21438218121753985, 0.057715597463053804, -0.14596683211614922, 0.0427971635654103, 0.1493285227678219] |
1,803.00912 | Cleaning Interfaces in Layered Materials Heterostructures | Heterostructures formed by stacking layered materials require atomically
clean interfaces. However, contaminants are usually trapped between the layers,
aggregating into blisters. We report a process to remove such blisters,
resulting in clean interfaces. We fabricate blister-free regions of graphene
encapsulated in hexagonal boron nitride of$\sim$5000$\mu $m$^{2}$, limited only
by the size of the exfoliated flakes. These have mobilities up
to$\sim$180000cm$^2$V$^{-1}$s$^{-1}$ at room temperature,
and$\sim$1.8$\times$10$^6$cm$^2$V$^{-1}$s$^{-1}$ at 9K. We further demonstrate
the effectiveness of our approach by cleaning heterostructures assembled using
graphene intentionally exposed to polymers and solvents. After cleaning, these
samples reach similar high mobilities. We also showcase the general
applicability of our approach to layered materials by cleaning blisters in
other heterostructures based on MoS$_{2}$. This demonstrates that exposure of
graphene to processing-related contaminants is compatible with the realization
of high mobility samples, paving the way to the development of fab-based
processes for the integration of layered materials in (opto)-electronic
devices.
| cond-mat.mes-hall | heterostructures formed by stacking layered materials require atomically clean interfaces however contaminants are usually trapped between the layers aggregating into blisters we report a process to remove such blisters resulting in clean interfaces we fabricate blisterfree regions of graphene encapsulated in hexagonal boron nitride ofsim5000mu m2 limited only by the size of the exfoliated flakes these have mobilities up tosim180000cm2v1s1 at room temperature andsim18times106cm2v1s1 at 9k we further demonstrate the effectiveness of our approach by cleaning heterostructures assembled using graphene intentionally exposed to polymers and solvents after cleaning these samples reach similar high mobilities we also showcase the general applicability of our approach to layered materials by cleaning blisters in other heterostructures based on mos_2 this demonstrates that exposure of graphene to processingrelated contaminants is compatible with the realization of high mobility samples paving the way to the development of fabbased processes for the integration of layered materials in optoelectronic devices | [['heterostructures', 'formed', 'by', 'stacking', 'layered', 'materials', 'require', 'atomically', 'clean', 'interfaces', 'however', 'contaminants', 'are', 'usually', 'trapped', 'between', 'the', 'layers', 'aggregating', 'into', 'blisters', 'we', 'report', 'a', 'process', 'to', 'remove', 'such', 'blisters', 'resulting', 'in', 'clean', 'interfaces', 'we', 'fabricate', 'blisterfree', 'regions', 'of', 'graphene', 'encapsulated', 'in', 'hexagonal', 'boron', 'nitride', 'ofsim5000mu', 'm2', 'limited', 'only', 'by', 'the', 'size', 'of', 'the', 'exfoliated', 'flakes', 'these', 'have', 'mobilities', 'up', 'tosim180000cm2v1s1', 'at', 'room', 'temperature', 'andsim18times106cm2v1s1', 'at', '9k', 'we', 'further', 'demonstrate', 'the', 'effectiveness', 'of', 'our', 'approach', 'by', 'cleaning', 'heterostructures', 'assembled', 'using', 'graphene', 'intentionally', 'exposed', 'to', 'polymers', 'and', 'solvents', 'after', 'cleaning', 'these', 'samples', 'reach', 'similar', 'high', 'mobilities', 'we', 'also', 'showcase', 'the', 'general', 'applicability', 'of', 'our', 'approach', 'to', 'layered', 'materials', 'by', 'cleaning', 'blisters', 'in', 'other', 'heterostructures', 'based', 'on', 'mos_2', 'this', 'demonstrates', 'that', 'exposure', 'of', 'graphene', 'to', 'processingrelated', 'contaminants', 'is', 'compatible', 'with', 'the', 'realization', 'of', 'high', 'mobility', 'samples', 'paving', 'the', 'way', 'to', 'the', 'development', 'of', 'fabbased', 'processes', 'for', 'the', 'integration', 'of', 'layered', 'materials', 'in', 'optoelectronic', 'devices']] | [-0.08675350078766973, 0.11395982060709904, 0.004037664162701574, -0.08551860372168323, -0.005669523300281886, -0.16377416052373833, 0.11786887100094866, 0.48630538140905316, -0.25351485495038073, -0.3220638261144531, 0.004373387186306304, -0.34587684353363923, -0.13880986815302407, 0.22059287328016142, -0.030965379610304433, 0.06955480324425574, 0.03266951951985087, -0.1992611975890809, -0.06946976923961834, -0.27519620813204554, 0.22880950385768867, 0.05744313848815088, 0.3630574087387529, 0.07776137794978145, 0.07131381875931703, -0.049003050993357236, 0.10477978546003779, 0.01851134140301367, -0.15524405376159722, 0.125538399770599, 0.2905069710799204, -0.14047506904101065, 0.2335890676183947, -0.5328883508413003, -0.24803718097251037, -0.046511947068160975, 0.13903765640871857, 0.15015033885258539, -0.15021388901092497, -0.2704897328071168, 0.13642952835893837, -0.1080855863072492, -0.06577825190906879, -0.10203164734010553, -0.04782804032659222, -0.0030815096162580724, -0.17805593243141754, 0.0586942163422495, 0.01685741153898938, 0.042016761022587786, -0.06891343799261135, -0.14981529946435956, -0.07565674430787049, 0.06783626926099431, -0.006634864461776833, -0.04742211473880914, 0.25606193249595577, -0.12233461587456987, -0.06994982434814025, 0.391242647659162, -0.015605848812466037, -0.10402653786385882, 0.2392728685947328, -0.14353867690881777, -0.08286230787875709, 0.14557341422949885, 0.16542698882544285, 0.13504275022203038, -0.205273875999987, 0.04993054829818871, 0.036696866419615934, 0.16028384159415446, 0.13590520437614156, 0.06687544585277873, 0.26583590237905497, 0.2690834259758864, 0.031851644314632846, 0.17252039556142648, -0.08176980704101253, 0.08333444128391043, -0.16205652210347612, -0.22883037114837046, -0.21441840883986704, 0.07736565766310127, -0.07875640260623688, -0.238612835398265, 0.363515650265818, 0.22179463439428343, 0.1763860749401923, -0.04026263694054094, 0.27582153264866693, 0.003130381151327285, 0.15446579335417984, -0.030534821416347706, 0.23406943218654086, 0.09804514834721541, 0.0903759775972315, -0.14515756934495835, 0.11642999184671148, -0.04345868947305556] |
1,803.00913 | Fixed points for $G$-cyclic $(\phi -\psi)$-Kannan and $G$-cyclic $(\phi
-\psi)$-Chatterjea contractions in $G$-metric spaces | Definitions of what are called $G$-cyclic $\left( \phi -\psi \right)$-Kannan
contraction and $G$-cyclic $\left( \phi -\psi\right)$-Chatterjea contraction
are introduced in this paper. We use these new concepts to establish new fixed
point results in the context of complete generalized metric spaces. These
results are new generalizations and extensions of the Kannan and Chatterjea
fixed point theorems and are generalized versions of some fixed point results
proved in the literature. The analysis and theory are illustrated by some
examples.
| math.GM | definitions of what are called gcyclic left phi psi rightkannan contraction and gcyclic left phi psirightchatterjea contraction are introduced in this paper we use these new concepts to establish new fixed point results in the context of complete generalized metric spaces these results are new generalizations and extensions of the kannan and chatterjea fixed point theorems and are generalized versions of some fixed point results proved in the literature the analysis and theory are illustrated by some examples | [['definitions', 'of', 'what', 'are', 'called', 'gcyclic', 'left', 'phi', 'psi', 'rightkannan', 'contraction', 'and', 'gcyclic', 'left', 'phi', 'psirightchatterjea', 'contraction', 'are', 'introduced', 'in', 'this', 'paper', 'we', 'use', 'these', 'new', 'concepts', 'to', 'establish', 'new', 'fixed', 'point', 'results', 'in', 'the', 'context', 'of', 'complete', 'generalized', 'metric', 'spaces', 'these', 'results', 'are', 'new', 'generalizations', 'and', 'extensions', 'of', 'the', 'kannan', 'and', 'chatterjea', 'fixed', 'point', 'theorems', 'and', 'are', 'generalized', 'versions', 'of', 'some', 'fixed', 'point', 'results', 'proved', 'in', 'the', 'literature', 'the', 'analysis', 'and', 'theory', 'are', 'illustrated', 'by', 'some', 'examples']] | [-0.08170412180383932, 0.11129377827316217, -0.07328039655232266, 0.10175003382945051, -0.07530586621468913, -0.12981661946244843, 0.016488685546927666, 0.3406536111174381, -0.3006198468988072, -0.21338078596514382, 0.15048193454595715, -0.3124457420908833, -0.2145342860651547, 0.22900282543101538, -0.13558094507108812, 0.05716187995539544, 0.018715207586192512, 0.035091190484084496, -0.13165593559994068, -0.2668875345118242, 0.3882342992375975, -0.023777241056325706, 0.20874825242447526, 0.07405787543712618, 0.05636572331343204, -0.016855132804341512, -0.115511004851289, 0.03869651378553412, -0.22745010236354724, 0.12118400074541569, 0.2194726724330693, 0.1568514274509802, 0.2843075988635625, -0.373275635585393, -0.1544018121793458, 0.11394714395681473, 0.06757219756031664, 0.03441891582421193, -0.08152954253584963, -0.3170463738274084, 0.16725909692228913, -0.11791968224442577, -0.15087056964355178, -0.13231973332187083, -0.01691492588724941, 0.07527906237144584, -0.23742504820406232, 0.002998318245685468, 0.13189467840049773, 0.07408285820984269, -0.05502700061243895, -0.1541548527853742, 0.0246542784232289, 0.08180906406718574, 0.0464796821318873, 0.032270453972359225, 0.07609358450321302, -0.07378163411639223, -0.15800354070323583, 0.3286997482997097, -0.019986546279428755, -0.23980241950142056, 0.17822182671587966, -0.0925305623956637, -0.2047363364214256, 0.010260914465131825, 0.09961275461652916, 0.13732735637881577, -0.14406962271728743, 0.13721751439152285, -0.07634042601471078, 0.024209091700980286, 0.14191099667717536, 0.04056478821200459, 0.13908022540391818, 0.07265017255630395, 0.060134326020089834, 0.15268733539006174, 0.012441355403359623, -0.16662195084013734, -0.4047688131207881, -0.14783132036714114, -0.09864367123961143, -0.002433999133781109, -0.10924102472398935, -0.11993617421551926, 0.3505262100227075, 0.12885849213559333, 0.18741604333666906, 0.0768357209338207, 0.21116610750721845, 0.12319564634347208, -0.018101793803172568, 0.08017738537276037, 0.17987917482052382, 0.1900222019056394, 0.06431656670820428, -0.0652324400255329, -0.037889012837246674, 0.14973173929021172] |
1,803.00914 | Realizability Interpretation and Normalization of Typed Call-by-Need
$$\lambda$$-calculus With Control | We define a variant of realizability where realizers are pairs of a term and
a substitution. This variant allows us to prove the normalization of a
simply-typed call-by-need $$\lambda$-$calculus with control due to Ariola et
al. Indeed, in such call-by-need calculus, substitutions have to be delayed
until knowing if an argument is really needed. In a second step, we extend the
proof to a call-by-need $$\lambda$$-calculus equipped with a type system
equivalent to classical second-order predicate logic, representing one step
towards proving the normalization of the call-by-need classical second-order
arithmetic introduced by the second author to provide a proof-as-program
interpretation of the axiom of dependent choice.
| cs.LO | we define a variant of realizability where realizers are pairs of a term and a substitution this variant allows us to prove the normalization of a simplytyped callbyneed lambdacalculus with control due to ariola et al indeed in such callbyneed calculus substitutions have to be delayed until knowing if an argument is really needed in a second step we extend the proof to a callbyneed lambdacalculus equipped with a type system equivalent to classical secondorder predicate logic representing one step towards proving the normalization of the callbyneed classical secondorder arithmetic introduced by the second author to provide a proofasprogram interpretation of the axiom of dependent choice | [['we', 'define', 'a', 'variant', 'of', 'realizability', 'where', 'realizers', 'are', 'pairs', 'of', 'a', 'term', 'and', 'a', 'substitution', 'this', 'variant', 'allows', 'us', 'to', 'prove', 'the', 'normalization', 'of', 'a', 'simplytyped', 'callbyneed', 'lambdacalculus', 'with', 'control', 'due', 'to', 'ariola', 'et', 'al', 'indeed', 'in', 'such', 'callbyneed', 'calculus', 'substitutions', 'have', 'to', 'be', 'delayed', 'until', 'knowing', 'if', 'an', 'argument', 'is', 'really', 'needed', 'in', 'a', 'second', 'step', 'we', 'extend', 'the', 'proof', 'to', 'a', 'callbyneed', 'lambdacalculus', 'equipped', 'with', 'a', 'type', 'system', 'equivalent', 'to', 'classical', 'secondorder', 'predicate', 'logic', 'representing', 'one', 'step', 'towards', 'proving', 'the', 'normalization', 'of', 'the', 'callbyneed', 'classical', 'secondorder', 'arithmetic', 'introduced', 'by', 'the', 'second', 'author', 'to', 'provide', 'a', 'proofasprogram', 'interpretation', 'of', 'the', 'axiom', 'of', 'dependent', 'choice']] | [-0.09720306790300778, 0.02694901218762555, -0.12282123982108065, 0.07521827868407681, -0.18930449273792052, -0.1938632338174752, 0.10065237247340736, 0.2926952590012834, -0.3478949385296021, -0.28348211319230143, 0.04180575853568457, -0.2268662665639394, -0.10309952199459076, 0.1439355092389243, -0.1784144217984985, 0.0337621377426244, 0.004881592622647683, 0.05599386516010522, -0.03657193159950631, -0.2460191418150706, 0.3359364201758234, 0.015203883680736734, 0.1799155903536649, 0.009593354529213338, 0.12480749168898911, 0.05828209921407203, -0.03704643857532314, 0.022709358616599014, -0.10711262703518427, 0.14331515324080274, 0.26368249344329037, 0.15209218338131905, 0.3345021843555428, -0.39955943225040325, -0.12547594741696402, 0.10207631132077603, 0.07915676937305502, 0.13496800436682643, 0.025448208025080108, -0.2881613219777743, 0.10437458279415122, -0.18418983248550266, -0.11157281872860733, -0.08259422330274468, 0.044262420102244335, 0.02166327406164436, -0.2828790111644637, -0.03125031818857505, 0.20735642222598905, 0.08237639717048123, -0.0252328637682478, -0.033381574311559754, 0.013263674898605261, 0.036582044050252685, -0.04068272054040183, 0.06484029409253882, 0.058804423689088295, -0.039246457098938876, -0.1685170782330845, 0.3493050142945278, -0.0665976993096549, -0.2088303190256868, 0.13096065139397978, -0.041125882204089845, -0.20946489932192933, 0.06366085312273796, 0.046208971259849414, 0.13426596863372695, -0.09513833553951588, 0.1058722463741322, -0.01948403763485008, 0.2434272099995897, 0.14772134452969546, 0.034685715070615214, 0.10948100176063322, 0.16674363888090565, 0.06775786843971304, 0.17264397981953586, 0.0352349426929972, -0.13491653651719734, -0.34605698414324293, -0.22071646683450255, -0.07368792563765532, 0.039445565365964455, -0.03631624165975068, -0.21474412434423965, 0.3192615285870575, 0.13460938370276598, 0.1647311805969193, 0.15354753231541032, 0.26387419028296355, 0.16664693250010412, 0.08912129616364836, 0.005188027596367257, 0.14990302157543955, 0.18627569977681907, 0.12363760382646606, -0.16228253556736966, 0.12507134113638174, 0.1984731270288605] |
1,803.00915 | Radial basis function methods for optimal control of the
convection-diffusion equation | PDE-constrained optimization problems have been barely solved by radial basis
functions (RBFs) methods [Pearson, 2013]. It is well known that RBF methods can
attain an exponential rate of convergence when $C^{\infty}$ kernels are used,
also, these techniques, which are truly scattered, are known to be flexible to
discretize complex boundaries in several dimensions. On the other hand,
exponential convergence implies an exponential growth of the condition number
of the Gram matrix associated with these meshfree methods and global
collocation techniques are known to be computationally expensive. In this
paper, and in the context of optimal constrained optimization problems, we aim
to explore a possible answer to both problems. Specifically, we introduce two
local RBF methods: LAM-DQ based in the combination of an asymmetric local
method (LAM), inspired in local Hermite interpolation (LHI), with the
differential quadrature method (DQ), and LAM-LAM which use two times the local
asymmetric method. The efficiency of these local methods against global
collocation by solving several synthetic convection-diffusion control problems
is analyzed. In this article, we also propose a preconditioning technique and
treat the ill-conditioning problem by using extended arithmetic precision. We
think that these local methods, which are highly parallelizable, shows a
possible way to solve massive optimization control problems in an efficient
way.
| math.NA | pdeconstrained optimization problems have been barely solved by radial basis functions rbfs methods pearson 2013 it is well known that rbf methods can attain an exponential rate of convergence when cinfty kernels are used also these techniques which are truly scattered are known to be flexible to discretize complex boundaries in several dimensions on the other hand exponential convergence implies an exponential growth of the condition number of the gram matrix associated with these meshfree methods and global collocation techniques are known to be computationally expensive in this paper and in the context of optimal constrained optimization problems we aim to explore a possible answer to both problems specifically we introduce two local rbf methods lamdq based in the combination of an asymmetric local method lam inspired in local hermite interpolation lhi with the differential quadrature method dq and lamlam which use two times the local asymmetric method the efficiency of these local methods against global collocation by solving several synthetic convectiondiffusion control problems is analyzed in this article we also propose a preconditioning technique and treat the illconditioning problem by using extended arithmetic precision we think that these local methods which are highly parallelizable shows a possible way to solve massive optimization control problems in an efficient way | [['pdeconstrained', 'optimization', 'problems', 'have', 'been', 'barely', 'solved', 'by', 'radial', 'basis', 'functions', 'rbfs', 'methods', 'pearson', '2013', 'it', 'is', 'well', 'known', 'that', 'rbf', 'methods', 'can', 'attain', 'an', 'exponential', 'rate', 'of', 'convergence', 'when', 'cinfty', 'kernels', 'are', 'used', 'also', 'these', 'techniques', 'which', 'are', 'truly', 'scattered', 'are', 'known', 'to', 'be', 'flexible', 'to', 'discretize', 'complex', 'boundaries', 'in', 'several', 'dimensions', 'on', 'the', 'other', 'hand', 'exponential', 'convergence', 'implies', 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1,803.00916 | Deep Learning for Signal Authentication and Security in Massive Internet
of Things Systems | Secure signal authentication is arguably one of the most challenging problems
in the Internet of Things (IoT) environment, due to the large-scale nature of
the system and its susceptibility to man-in-the-middle and eavesdropping
attacks. In this paper, a novel deep learning method is proposed for dynamic
authentication of IoT signals to detect cyber attacks. The proposed learning
framework, based on a long short-term memory (LSTM) structure, enables the IoT
devices (IoTDs) to extract a set of stochastic features from their generated
signal and dynamically watermark these features into the signal. This method
enables the cloud, which collects signals from the IoT devices, to effectively
authenticate the reliability of the signals. Moreover, in massive IoT
scenarios, since the cloud cannot authenticate all the IoTDs simultaneously due
to computational limitations, a game-theoretic framework is proposed to improve
the cloud's decision making process by predicting vulnerable IoTDs. The
mixed-strategy Nash equilibrium (MSNE) for this game is derived and the
uniqueness of the expected utility at the equilibrium is proven. In the massive
IoT system, due to a large set of available actions for the cloud, it is shown
that analytically deriving the MSNE is challenging and, thus, a learning
algorithm proposed that converges to the MSNE. Moreover, in order to cope with
the incomplete information case in which the cloud cannot access the state of
the unauthenticated IoTDs, a deep reinforcement learning algorithm is proposed
to dynamically predict the state of unauthenticated IoTDs and allow the cloud
to decide on which IoTDs to authenticate. Simulation results show that, with an
attack detection delay of under 1 second the messages can be transmitted from
IoT devices with an almost 100% reliability.
| cs.CR cs.GT cs.LG | secure signal authentication is arguably one of the most challenging problems in the internet of things iot environment due to the largescale nature of the system and its susceptibility to maninthemiddle and eavesdropping attacks in this paper a novel deep learning method is proposed for dynamic authentication of iot signals to detect cyber attacks the proposed learning framework based on a long shortterm memory lstm structure enables the iot devices iotds to extract a set of stochastic features from their generated signal and dynamically watermark these features into the signal this method enables the cloud which collects signals from the iot devices to effectively authenticate the reliability of the signals moreover in massive iot scenarios since the cloud cannot authenticate all the iotds simultaneously due to computational limitations a gametheoretic framework is proposed to improve the clouds decision making process by predicting vulnerable iotds the mixedstrategy nash equilibrium msne for this game is derived and the uniqueness of the expected utility at the equilibrium is proven in the massive iot system due to a large set of available actions for the cloud it is shown that analytically deriving the msne is challenging and thus a learning algorithm proposed that converges to the msne moreover in order to cope with the incomplete information case in which the cloud cannot access the state of the unauthenticated iotds a deep reinforcement learning algorithm is proposed to dynamically predict the state of unauthenticated iotds and allow the cloud to decide on which iotds to authenticate simulation results show that with an attack detection delay of under 1 second the messages can be transmitted from iot devices with an almost 100 reliability | [['secure', 'signal', 'authentication', 'is', 'arguably', 'one', 'of', 'the', 'most', 'challenging', 'problems', 'in', 'the', 'internet', 'of', 'things', 'iot', 'environment', 'due', 'to', 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1,803.00917 | On the geometry of period doubling invariant sets for area-preserving maps | The geometry of the period doubling Cantor sets of strongly dissipative infinitely renormalizable H\'enon-like maps has been shown to be unbounded by M. Lyubich, M. Martens and A. de Carvalho, although the measure of unbounded "spots" in the Cantor set has been demonstrated to be zero.
We show that an even more extreme situation takes places for infinitely renormalizable area-preserving H\'enon-like maps: both bounded and unbounded geometries exist on subsets of positive measure. | math.DS | the geometry of the period doubling cantor sets of strongly dissipative infinitely renormalizable henonlike maps has been shown to be unbounded by m lyubich m martens and a de carvalho although the measure of unbounded spots in the cantor set has been demonstrated to be zero we show that an even more extreme situation takes places for infinitely renormalizable areapreserving henonlike maps both bounded and unbounded geometries exist on subsets of positive measure | [['the', 'geometry', 'of', 'the', 'period', 'doubling', 'cantor', 'sets', 'of', 'strongly', 'dissipative', 'infinitely', 'renormalizable', 'henonlike', 'maps', 'has', 'been', 'shown', 'to', 'be', 'unbounded', 'by', 'm', 'lyubich', 'm', 'martens', 'and', 'a', 'de', 'carvalho', 'although', 'the', 'measure', 'of', 'unbounded', 'spots', 'in', 'the', 'cantor', 'set', 'has', 'been', 'demonstrated', 'to', 'be', 'zero', 'we', 'show', 'that', 'an', 'even', 'more', 'extreme', 'situation', 'takes', 'places', 'for', 'infinitely', 'renormalizable', 'areapreserving', 'henonlike', 'maps', 'both', 'bounded', 'and', 'unbounded', 'geometries', 'exist', 'on', 'subsets', 'of', 'positive', 'measure']] | [-0.2108881542082774, 0.16734350496470093, -0.1196551752493602, 0.11663183532272504, -0.03525254385520334, -0.17580252714225486, -0.022951835115784653, 0.310296901846177, -0.2110586449478383, -0.17972880667585828, 0.11268234307628941, -0.31894586296522454, -0.13412673352924112, 0.24535414346251383, -0.13255875033313688, 0.08132594726877669, 0.016607605673576872, 0.03239256461240249, 0.027408543049217495, -0.2947194787182475, 0.3529319958378599, -0.05097942401285041, 0.20023225365870007, 0.06622191076485874, 0.1661652524362331, -0.09019411015898397, -0.010369501809018652, 0.04904629950003367, -0.13340279129893942, 0.06298145406866727, 0.23452059598001715, 0.09536688022111377, 0.2649376942482713, -0.35669882901727334, -0.2789412882204538, 0.22505276374620933, 0.1287132041426758, 0.01794395899425631, -0.005307750648233644, -0.32056101349067606, 0.07596612863293657, -0.14915562455210682, -0.15178188914433122, -0.10977584960525982, 0.13832091062954843, -0.024413305481462037, -0.25748402752262883, -0.017012260356688336, 0.15419743899000835, 0.10339440266308311, -0.03986151734596654, -0.061866153570606486, -0.11353574908491582, 0.07771244346585175, 0.018764497495371187, 0.0626469520542592, 0.06562258214498423, 0.005579854910300203, -0.1025562738865767, 0.31220056763403625, -0.14802411198616028, -0.25632785873053826, 0.24511738356253873, -0.23145389614614006, -0.1491476133950565, 0.16376475318756006, 0.10084738066955788, 0.12533650605952087, -0.1005885968597172, 0.2803192450617096, -0.1360957977770228, 0.12540456805735417, 0.20434133524764075, -0.01824931029153809, 0.19222844728868302, 0.056237237907512345, 0.20016507740285605, 0.14181563470429703, 0.006898891770803969, -0.1009695995152507, -0.24820860524578914, -0.05849815893931034, -0.17257621023191572, 0.11586217575846877, -0.06526785214353442, -0.24561506716577552, 0.3176082046957661, 0.08021089005641231, 0.23133722989985797, 0.041615531881888435, 0.1776410769479834, 0.09631590524625372, 0.04690152496256553, 0.09335261078117645, 0.16502975946139187, 0.12500590572376058, 0.012790645121864667, -0.13720219067460537, -0.011685275568384421, 0.12094034023055877] |
1,803.00918 | An analogue of a result of Tits for linear and symplectic transvection
groups | In [9] Bogdan Nica presented an elementary proof of a result which says that
the relative elementary linear group with respect to a square of an ideal of
the ring is a subset of the true relative elementary linear group. The original
result was proved by J. Tits in [16] in the much general context of Chevalley
groups. In this paper we prove analogues of the result of Tits for linear
transvection group and symplectic transvection group. We also obtain an
elementary proof of a special case of the result by Tits, namely the case of
elementary symplectic group, using commutator identities for generators of this
group.
| math.AC | in 9 bogdan nica presented an elementary proof of a result which says that the relative elementary linear group with respect to a square of an ideal of the ring is a subset of the true relative elementary linear group the original result was proved by j tits in 16 in the much general context of chevalley groups in this paper we prove analogues of the result of tits for linear transvection group and symplectic transvection group we also obtain an elementary proof of a special case of the result by tits namely the case of elementary symplectic group using commutator identities for generators of this group | [['in', '9', 'bogdan', 'nica', 'presented', 'an', 'elementary', 'proof', 'of', 'a', 'result', 'which', 'says', 'that', 'the', 'relative', 'elementary', 'linear', 'group', 'with', 'respect', 'to', 'a', 'square', 'of', 'an', 'ideal', 'of', 'the', 'ring', 'is', 'a', 'subset', 'of', 'the', 'true', 'relative', 'elementary', 'linear', 'group', 'the', 'original', 'result', 'was', 'proved', 'by', 'j', 'tits', 'in', '16', 'in', 'the', 'much', 'general', 'context', 'of', 'chevalley', 'groups', 'in', 'this', 'paper', 'we', 'prove', 'analogues', 'of', 'the', 'result', 'of', 'tits', 'for', 'linear', 'transvection', 'group', 'and', 'symplectic', 'transvection', 'group', 'we', 'also', 'obtain', 'an', 'elementary', 'proof', 'of', 'a', 'special', 'case', 'of', 'the', 'result', 'by', 'tits', 'namely', 'the', 'case', 'of', 'elementary', 'symplectic', 'group', 'using', 'commutator', 'identities', 'for', 'generators', 'of', 'this', 'group']] | [-0.15453050273718644, 0.07661990621102709, -0.13301697697177112, 0.04443166491458036, -0.1031974492699047, -0.09391416859090607, 0.009837531416119885, 0.2899772448848202, -0.3058683160762085, -0.2456601872049037, 0.10266718087352325, -0.21227732868473453, -0.13064147903699272, 0.2204549007866288, -0.1661158396435954, -0.053059226511237775, 0.011750661776723148, 0.11664780861236781, -0.08187639220426225, -0.2838767268738457, 0.3526741485067896, 0.02024253900815672, 0.22311910797072634, 0.010321528034403846, 0.10444675346131428, 0.09216739361122181, -0.0804273327029197, -0.03541666018193431, -0.1078393928879773, 0.12697080990177728, 0.27460137611716356, 0.021610276629412808, 0.2361023758111574, -0.3526678190993093, -0.093772991995597, 0.12966913101916142, 0.12106728176890968, 0.063951315039499, -0.06562297924630622, -0.27541285964320894, 0.11567739248449836, -0.23379630598927212, -0.21739311477987566, -0.005402150559543728, 0.07534314977509954, 0.005588949266298909, -0.2312331817800475, 0.02770914547074647, 0.19032007189117758, 0.16278935272124745, -0.04320237614109973, -0.130572907429702, 0.013020079017172908, 0.056837473412819, 0.007432419839358636, 0.019510737578406337, 0.061461736818026996, -0.04740264315036274, -0.15018148348823399, 0.4362843647320694, -0.05097233956444695, -0.18785685292123078, 0.14696573690624437, -0.1438359966431962, -0.1831692260761841, 0.0858163824707496, 0.07811989826715995, 0.13537447602340133, -0.07758308218565778, 0.16906393428983724, -0.20404743032843292, 0.0815865681898371, 0.08522535421097843, -0.01093910001903755, 0.0571658901139119, 0.08404421206361344, 0.12043971979634828, 0.12512407532814784, 0.07981613029891224, -0.003241478056411877, -0.3782815582980619, -0.26165135459817734, -0.1774473981322529, 0.11697426678343006, -0.09982237787076781, -0.13645479099381122, 0.3993308107220681, 0.042310213591799, 0.1167027196394297, 0.114680779620213, 0.18027623800264897, 0.06510662775765234, 0.0403467164879853, 0.050351447847924224, 0.1739690776252858, 0.26130223789939955, -0.055248270551108314, -0.13118307609720348, -0.05119422443263302, 0.23680541015047335] |
1,803.00919 | House Price Modeling over Heterogeneous Regions with Hierarchical
Spatial Functional Analysis | Online real-estate information systems such as Zillow and Trulia have gained
increasing popularity in recent years. One important feature offered by these
systems is the online home price estimate through automated data-intensive
computation based on housing information and comparative market value analysis.
State-of-the-art approaches model house prices as a combination of a latent
land desirability surface and a regression from house features. However, by
using uniformly damping kernels, they are unable to handle irregularly shaped
regions or capture land value discontinuities within the same region due to the
existence of implicit sub-communities, which are common in real-world
scenarios. In this paper, we explore the novel application of recent advances
in spatial functional analysis to house price modeling and propose the
Hierarchical Spatial Functional Model (HSFM), which decomposes house values
into land desirability at both the global scale and hidden local scales as well
as the feature regression component. We propose statistical learning algorithms
based on finite-element spatial functional analysis and spatial constrained
clustering to train our model. Extensive evaluations based on housing data in a
major Canadian city show that our proposed approach can reduce the mean
relative house price estimation error down to 6.60%.
| cs.CE | online realestate information systems such as zillow and trulia have gained increasing popularity in recent years one important feature offered by these systems is the online home price estimate through automated dataintensive computation based on housing information and comparative market value analysis stateoftheart approaches model house prices as a combination of a latent land desirability surface and a regression from house features however by using uniformly damping kernels they are unable to handle irregularly shaped regions or capture land value discontinuities within the same region due to the existence of implicit subcommunities which are common in realworld scenarios in this paper we explore the novel application of recent advances in spatial functional analysis to house price modeling and propose the hierarchical spatial functional model hsfm which decomposes house values into land desirability at both the global scale and hidden local scales as well as the feature regression component we propose statistical learning algorithms based on finiteelement spatial functional analysis and spatial constrained clustering to train our model extensive evaluations based on housing data in a major canadian city show that our proposed approach can reduce the mean relative house price estimation error down to 660 | [['online', 'realestate', 'information', 'systems', 'such', 'as', 'zillow', 'and', 'trulia', 'have', 'gained', 'increasing', 'popularity', 'in', 'recent', 'years', 'one', 'important', 'feature', 'offered', 'by', 'these', 'systems', 'is', 'the', 'online', 'home', 'price', 'estimate', 'through', 'automated', 'dataintensive', 'computation', 'based', 'on', 'housing', 'information', 'and', 'comparative', 'market', 'value', 'analysis', 'stateoftheart', 'approaches', 'model', 'house', 'prices', 'as', 'a', 'combination', 'of', 'a', 'latent', 'land', 'desirability', 'surface', 'and', 'a', 'regression', 'from', 'house', 'features', 'however', 'by', 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1,803.0092 | Distributed Robust Output Regulation of Heterogeneous Uncertain Linear
Agents by Adaptive Internal Model Principle | We study a multi-agent output regulation problem, where not all agents have
access to the exosystem's dynamics. We propose a fully distributed controller
that solves the problem for linear, heterogeneous, and uncertain agent dynamics
as well as time-varying directed networks. The distributed controller consists
of two parts: (1) an exosystem generator that locally estimates the exosystem
dynamics, and (2) a dynamic compensator that, by locally approaching the
internal model of the exosystem, achieves perfect output regulation. Moreover,
we extend this distributed controller to solve an output synchronization
problem where not all agents initially have the same internal model dynamics.
Our approach leverages methods from internal model based controller synthesis
and multi-agent consensus over time-varying directed networks; the derived
result is a generalization of the (centralized) internal model principle to the
distributed, networked setting.
| cs.SY | we study a multiagent output regulation problem where not all agents have access to the exosystems dynamics we propose a fully distributed controller that solves the problem for linear heterogeneous and uncertain agent dynamics as well as timevarying directed networks the distributed controller consists of two parts 1 an exosystem generator that locally estimates the exosystem dynamics and 2 a dynamic compensator that by locally approaching the internal model of the exosystem achieves perfect output regulation moreover we extend this distributed controller to solve an output synchronization problem where not all agents initially have the same internal model dynamics our approach leverages methods from internal model based controller synthesis and multiagent consensus over timevarying directed networks the derived result is a generalization of the centralized internal model principle to the distributed networked setting | [['we', 'study', 'a', 'multiagent', 'output', 'regulation', 'problem', 'where', 'not', 'all', 'agents', 'have', 'access', 'to', 'the', 'exosystems', 'dynamics', 'we', 'propose', 'a', 'fully', 'distributed', 'controller', 'that', 'solves', 'the', 'problem', 'for', 'linear', 'heterogeneous', 'and', 'uncertain', 'agent', 'dynamics', 'as', 'well', 'as', 'timevarying', 'directed', 'networks', 'the', 'distributed', 'controller', 'consists', 'of', 'two', 'parts', '1', 'an', 'exosystem', 'generator', 'that', 'locally', 'estimates', 'the', 'exosystem', 'dynamics', 'and', '2', 'a', 'dynamic', 'compensator', 'that', 'by', 'locally', 'approaching', 'the', 'internal', 'model', 'of', 'the', 'exosystem', 'achieves', 'perfect', 'output', 'regulation', 'moreover', 'we', 'extend', 'this', 'distributed', 'controller', 'to', 'solve', 'an', 'output', 'synchronization', 'problem', 'where', 'not', 'all', 'agents', 'initially', 'have', 'the', 'same', 'internal', 'model', 'dynamics', 'our', 'approach', 'leverages', 'methods', 'from', 'internal', 'model', 'based', 'controller', 'synthesis', 'and', 'multiagent', 'consensus', 'over', 'timevarying', 'directed', 'networks', 'the', 'derived', 'result', 'is', 'a', 'generalization', 'of', 'the', 'centralized', 'internal', 'model', 'principle', 'to', 'the', 'distributed', 'networked', 'setting']] | [-0.16627920509309024, 0.04675620724468954, -0.042451052268252784, -0.018464570462395597, -0.08612063106261474, -0.19952891505134285, 0.023140266819513942, 0.4028063600784854, -0.3550344334593169, -0.2919421529579431, 0.09655439163579192, -0.19939478379639244, -0.19553545544001, 0.1295969937122742, -0.11043252736414061, 0.08608222159704096, 0.05904267243585808, 0.05042711323253194, 0.06286377330744793, -0.21367415297107728, 0.28643383687586155, 0.04583812926552797, 0.2871146083606365, -0.08046189240439046, 0.208202199929891, 0.026906519771126427, 0.04206693939920655, 0.032062719681097154, -0.05555208797711674, 0.11216071380487874, 0.28155847318134364, 0.16964960847526117, 0.3523444269321635, -0.4470634021397148, -0.23887400607109294, 0.13517556862233387, 0.13149797979434183, 0.12130165219138887, 0.005611799814899389, -0.27514989604767326, 0.11581656347988244, -0.22208016234120928, -0.06185994384096081, -0.017577543478333076, -0.03777025706883996, 0.044759248370213674, -0.3257553928524704, 0.01832960678187472, 0.09952449091426552, 0.03396837527505245, -0.15162609998815993, -0.048406178898465144, -0.015172459241142846, 0.1785343051467903, -0.06269267652204778, 0.0015184940556694467, 0.16683549090827765, -0.10128431527917378, -0.19881119493647179, 0.34107309989316065, -0.005238990219751079, -0.26632095886944945, 0.13666544185909338, -0.023384354682989363, -0.15415833933678055, 0.08001682328417394, 0.24394852406681425, 0.1311176195495615, -0.20871585136265458, 0.06759005698377155, -0.07261770203205428, 0.2226690289501409, -0.028571267983407006, -0.009997716957801267, 0.13523037773662044, 0.22832060048944855, 0.18234402627537125, 0.14254444170939295, 0.014695537650101656, -0.21349746012741602, -0.25582300500744687, -0.03291716800540462, -0.14799872241543144, -0.002070789167410659, -0.10110101536499717, -0.14911410627060367, 0.3680947122577214, 0.1592092816245259, 0.15802657903921336, 0.14633001903198042, 0.35374111208812636, 0.08483491806117327, 0.01444048766142827, 0.1722786893640974, 0.1873780250920445, 0.1162043858154964, 0.12797924334881827, -0.24929669564082182, 0.13140890317009693, 0.0178533595027332] |
1,803.00921 | Evaluation of weighted Fibonacci sums of a certain type | We derive a formula for the evaluation of weighted generalized Fibonacci sums
of the type $S_k^n (w,r) = \sum_{j = 0}^k {w^j j^r G_j{}^n }$. Several explicit
evaluations are presented as examples.
| math.GM | we derive a formula for the evaluation of weighted generalized fibonacci sums of the type s_kn wr sum_j 0k wj jr g_jn several explicit evaluations are presented as examples | [['we', 'derive', 'a', 'formula', 'for', 'the', 'evaluation', 'of', 'weighted', 'generalized', 'fibonacci', 'sums', 'of', 'the', 'type', 's_kn', 'wr', 'sum_j', '0k', 'wj', 'jr', 'g_jn', 'several', 'explicit', 'evaluations', 'are', 'presented', 'as', 'examples']] | [-0.20672175188415817, 0.05069424973667732, -0.003902385476976633, 0.13997448461928538, -0.1045650082773396, -0.12961071708040045, 0.10020829060314489, 0.3190469148435763, -0.2109486374787853, -0.19793217296579055, 0.0918199792338003, -0.32536309091041665, -0.15995019421513593, 0.255361475457903, -0.022691706378412033, -0.017770126428721205, 0.07188135823733839, 0.06973047654277512, -0.116654047326717, -0.2994240004170154, 0.24383652520704605, -0.03092615935435918, 0.14542901189997792, 0.043322434033533294, 0.08350238882537399, 0.005999953120148608, -0.06763528440413731, -0.07200823337448778, -0.27826661713022205, 0.09937734260789252, 0.20692041349996412, 0.17576718609780073, 0.22041416752784113, -0.31221002539885895, -0.10922898751284395, 0.1571141440487866, 0.17659617030793534, -0.036673865713445206, -0.006318744138947555, -0.23302653012797236, 0.09595151993978236, -0.3109190816591893, -0.15194030671513506, -0.12946318140685825, 0.060376674169674516, 0.2080173533675926, -0.3738169655469911, 0.06532544489683849, 0.07735793764836021, 0.08992741898899632, -0.02908475386045341, -0.3044798691158316, 0.06752075697295368, 0.015988610718133196, 0.017594324790739586, -0.003940469618620617, 0.003808301025336342, -0.0576904039231262, -0.13659054805923784, 0.3357620299710626, -0.044949111981882846, -0.2031238762927907, 0.037379484523886015, -0.051856700996203084, -0.189202972538104, 0.06550217446471963, 0.0518625787725406, 0.16248756301190173, -0.13138956663065723, 0.1250358794350177, -0.17089476276721274, 0.009412754692935519, 0.14346324464505805, 0.03996820115883436, 0.05939320548038397, -0.01971588297081845, -0.06577900896913239, 0.24189060334382312, -0.04527228392128434, -0.08972281723150186, -0.4214460576352264, -0.21927967930345663, -0.24724729675134377, 0.12607565222840225, -0.17012682465636836, -0.20108992137414003, 0.32182425791896613, 0.04308162349376029, 0.1340855760499835, 0.15145637703660345, 0.18788723195237772, 0.19706367717507028, 0.00878084244738732, 0.05776159797928163, 0.013805019336619548, 0.2306402187927493, 0.037054199791912525, -0.16865492434174353, -0.019185400567948818, 0.2719536144951625] |
1,803.00922 | Online Scheduling of Spark Workloads with Mesos using Different Fair
Allocation Algorithms | In the following, we present example illustrative and experimental results
comparing fair schedulers allocating resources from multiple servers to
distributed application frameworks. Resources are allocated so that at least
one resource is exhausted in every server. Schedulers considered include DRF
(DRFH) and Best-Fit DRF (BF-DRF), TSF, and PS-DSF. We also consider server
selection under Randomized Round Robin (RRR) and based on their residual
(unreserved) resources. In the following, we consider cases with frameworks of
equal priority and without server-preference constraints. We first give typical
results of a illustrative numerical study and then give typical results of a
study involving Spark workloads on Mesos which we have modified and
open-sourced to prototype different schedulers.
| cs.PF | in the following we present example illustrative and experimental results comparing fair schedulers allocating resources from multiple servers to distributed application frameworks resources are allocated so that at least one resource is exhausted in every server schedulers considered include drf drfh and bestfit drf bfdrf tsf and psdsf we also consider server selection under randomized round robin rrr and based on their residual unreserved resources in the following we consider cases with frameworks of equal priority and without serverpreference constraints we first give typical results of a illustrative numerical study and then give typical results of a study involving spark workloads on mesos which we have modified and opensourced to prototype different schedulers | [['in', 'the', 'following', 'we', 'present', 'example', 'illustrative', 'and', 'experimental', 'results', 'comparing', 'fair', 'schedulers', 'allocating', 'resources', 'from', 'multiple', 'servers', 'to', 'distributed', 'application', 'frameworks', 'resources', 'are', 'allocated', 'so', 'that', 'at', 'least', 'one', 'resource', 'is', 'exhausted', 'in', 'every', 'server', 'schedulers', 'considered', 'include', 'drf', 'drfh', 'and', 'bestfit', 'drf', 'bfdrf', 'tsf', 'and', 'psdsf', 'we', 'also', 'consider', 'server', 'selection', 'under', 'randomized', 'round', 'robin', 'rrr', 'and', 'based', 'on', 'their', 'residual', 'unreserved', 'resources', 'in', 'the', 'following', 'we', 'consider', 'cases', 'with', 'frameworks', 'of', 'equal', 'priority', 'and', 'without', 'serverpreference', 'constraints', 'we', 'first', 'give', 'typical', 'results', 'of', 'a', 'illustrative', 'numerical', 'study', 'and', 'then', 'give', 'typical', 'results', 'of', 'a', 'study', 'involving', 'spark', 'workloads', 'on', 'mesos', 'which', 'we', 'have', 'modified', 'and', 'opensourced', 'to', 'prototype', 'different', 'schedulers']] | [-0.14923687695356255, 0.016161434636027976, -0.06315110175531696, 0.04247400438603522, -0.05754379800055176, -0.24659951232077384, 0.1376681401521306, 0.44418359342962505, -0.21102397441440685, -0.29915775569494474, 0.13594219033204188, -0.2581091822294349, -0.07572945350654084, 0.2121962239288471, -0.1010303272781047, 0.04889487645758146, 0.08912303405390544, 0.008195198361169208, -0.021753903721797873, -0.31679714607201853, 0.3197864174599421, 0.04581668006933548, 0.28870271143757487, 0.04947050075544129, 0.03005929019471461, -0.014453426445834338, -0.051739071885293184, 0.028006140158669784, -0.15751701363353113, 0.03746006235808388, 0.26742966324937617, 0.19356073946692048, 0.29036782157438046, -0.4914161492337536, -0.1232887935062701, 0.08088116064583036, 0.09984619311395694, 0.0225635717485354, -0.07780603930904445, -0.2254312863573432, 0.1255542039574886, -0.23130042828561273, -0.03152634949338707, -0.06617640057546934, -0.03467789218676361, 0.06535284336740997, -0.2991768055510792, 0.0005980126112593676, -0.018492578304457392, 0.022805613215843384, -0.10401847146197477, -0.15173715582456102, 0.043825645368038256, 0.11088136128488589, 0.046275203878229317, -0.05563409441066059, 0.12374754432874563, -0.06265501217408613, -0.1875571155581962, 0.4159759061749686, -0.008947623771382496, -0.16501684203288858, 0.1799098486207764, -0.02348479057069529, -0.22740922911642966, 0.007520664999769493, 0.20107966382479803, 0.10685049955844245, -0.15857922869988464, 0.024053072301268748, -0.0657331108792939, 0.1395015279116871, 0.07479609219272705, 0.04902423996562985, 0.09929245130624623, 0.19753640712323514, 0.08842092940308661, 0.2367063841973008, -0.011893099339001558, -0.12743732898750088, -0.29447188678631475, -0.13745494980619036, -0.15983245558190076, 0.011361952009610831, -0.07826048846009971, -0.08143780051772906, 0.36058662049049006, 0.15316964151676404, 0.1386996768457307, 0.13060718341836366, 0.36179485554722224, 0.06631618012589487, 0.07406514723658222, 0.19098559599369763, 0.10386880956251512, -0.003288636314259334, 0.11612247906794602, -0.183097499943423, 0.03453917315187441, -0.02385853777046908] |
1,803.00923 | Fokker-Planck equation driven by asymmetric L\'evy motion | Non-Gaussian L\'evy noises are present in many models for understanding
underlining principles of physics, finance, biology and more. In this work, we
consider the Fokker-Planck equation(FPE) due to one-dimensional asymmetric
L\'evy motion, which is a nonlocal partial differential equation. We present an
accurate numerical quadrature for the singular integrals in the nonlocal FPE
and develop a fast summation method to reduce the order of the complexity from
$O(J^2)$ to $O(J\log J)$ in one time-step, where $J$ is the number of unknowns.
We also provide conditions under which the numerical schemes satisfy maximum
principle. Our numerical method is validated by comparing with exact solutions
for special cases. We also discuss the properties of the probability density
functions and the effects of various factors on the solutions, including the
stability index, the skewness parameter, the drift term, the Gaussian and
non-Gaussian noises and the domain size.
| math.DS math.NA | nongaussian levy noises are present in many models for understanding underlining principles of physics finance biology and more in this work we consider the fokkerplanck equationfpe due to onedimensional asymmetric levy motion which is a nonlocal partial differential equation we present an accurate numerical quadrature for the singular integrals in the nonlocal fpe and develop a fast summation method to reduce the order of the complexity from oj2 to ojlog j in one timestep where j is the number of unknowns we also provide conditions under which the numerical schemes satisfy maximum principle our numerical method is validated by comparing with exact solutions for special cases we also discuss the properties of the probability density functions and the effects of various factors on the solutions including the stability index the skewness parameter the drift term the gaussian and nongaussian noises and the domain size | [['nongaussian', 'levy', 'noises', 'are', 'present', 'in', 'many', 'models', 'for', 'understanding', 'underlining', 'principles', 'of', 'physics', 'finance', 'biology', 'and', 'more', 'in', 'this', 'work', 'we', 'consider', 'the', 'fokkerplanck', 'equationfpe', 'due', 'to', 'onedimensional', 'asymmetric', 'levy', 'motion', 'which', 'is', 'a', 'nonlocal', 'partial', 'differential', 'equation', 'we', 'present', 'an', 'accurate', 'numerical', 'quadrature', 'for', 'the', 'singular', 'integrals', 'in', 'the', 'nonlocal', 'fpe', 'and', 'develop', 'a', 'fast', 'summation', 'method', 'to', 'reduce', 'the', 'order', 'of', 'the', 'complexity', 'from', 'oj2', 'to', 'ojlog', 'j', 'in', 'one', 'timestep', 'where', 'j', 'is', 'the', 'number', 'of', 'unknowns', 'we', 'also', 'provide', 'conditions', 'under', 'which', 'the', 'numerical', 'schemes', 'satisfy', 'maximum', 'principle', 'our', 'numerical', 'method', 'is', 'validated', 'by', 'comparing', 'with', 'exact', 'solutions', 'for', 'special', 'cases', 'we', 'also', 'discuss', 'the', 'properties', 'of', 'the', 'probability', 'density', 'functions', 'and', 'the', 'effects', 'of', 'various', 'factors', 'on', 'the', 'solutions', 'including', 'the', 'stability', 'index', 'the', 'skewness', 'parameter', 'the', 'drift', 'term', 'the', 'gaussian', 'and', 'nongaussian', 'noises', 'and', 'the', 'domain', 'size']] | [-0.08646820641720522, 0.05369617133196988, -0.07169915142374986, 0.06861412166683863, -0.0708446411254109, -0.10898267819896235, 0.016798795136484378, 0.3231857528923251, -0.2690127309979805, -0.251714300899271, 0.12845675940361165, -0.2586730992213446, -0.1652187412300854, 0.21869259795292895, -0.0694704085086053, 0.11043065429354708, 0.04064284682815485, -0.01588071107427616, -0.05064214362782684, -0.23597202612440504, 0.3286093390721775, 0.026740899421803074, 0.24773496883989027, 0.022240891997745354, 0.12021776716099351, -0.026551884998640057, -0.07802716451069565, 0.010330472107743328, -0.179157000378514, 0.09795512761986742, 0.21998777083428894, 0.05919008857590404, 0.3057522975066875, -0.42907787287436056, -0.23699502891312677, 0.10484768376613023, 0.08554295581147894, 0.13756661117572258, -0.038064282003262555, -0.2937289438761956, 0.05561840976918684, -0.16151395239966346, -0.1628946530077844, -0.12116049959964997, 0.0434323031858191, 0.09006444088031426, -0.32491225105254573, 0.14135026395055036, 0.049767768968240166, 0.015635540294427926, -0.03782769375157061, -0.14416535082438314, 0.025564344745981408, 0.07731839252556576, 0.05889931165675983, -0.06271331693237343, 0.07055925910116721, -0.15120669933042574, -0.10961542219082092, 0.36124642701416626, -0.053048824496791824, -0.302241609802103, 0.1661144728270353, -0.1426095278497706, -0.13009126830502604, 0.12614251624756487, 0.17002239415144665, 0.1307178525128633, -0.1563768986487061, 0.1093910847627888, 0.008138074206043008, 0.13582181409542146, 0.052996433845305065, 0.026477133300393186, 0.10360406857010321, 0.12230515758777764, 0.10327059825005486, 0.14113414709304348, -0.08644451341981478, -0.1561821184372067, -0.34089669734503786, -0.14942051181625426, -0.15849033111197783, 0.018582229036837816, -0.12852973149354976, -0.1692699221894145, 0.40012628217855245, 0.18723992304546191, 0.15129872022117388, 0.05858079924129267, 0.28592607011714727, 0.2088459436327602, -0.024594473474203273, 0.06925843873206552, 0.17726634241019687, 0.17046130436195178, 0.10975187690433194, -0.24214032021721046, 0.061142305207788836, 0.07612800040176627] |
1,803.00924 | All-Electron, Real-Space Perturbation Theory for Homogeneous Electric
Fields: Theory, Implementation, and Application within DFT | Within density-functional theory, perturbation theory~(PT) is the
state-of-the-art formalism for assessing the response to homogeneous electric
fields and the associated material properties, e.g., polarizabilities,
dielectric constants, and Raman intensities. Here we derive a real-space
formulation of PT and present an implementation within the all-electron,
numeric atom-centered orbitals electronic structure code FHI-aims that allows
for massively-parallel calculations. As demonstrated by extensive validation,
this allows the rapid computation of accurate response properties of molecules
and solids. As an application showcase, we present harmonic and anharmonic
Raman spectra, the latter obtained by combining hundreds of thousands of PT
calculations with \textit{ab initio} molecular dynamics. By using the PBE
exchange-correlation functional with many-body van der Waals corrections, we
obtain spectra in good agreement with experiment especially with respect to
lineshapes for the isolated paracetamol molecule and two polymorphs of the
paracetamol crystal.
| cond-mat.mtrl-sci physics.comp-ph | within densityfunctional theory perturbation theorypt is the stateoftheart formalism for assessing the response to homogeneous electric fields and the associated material properties eg polarizabilities dielectric constants and raman intensities here we derive a realspace formulation of pt and present an implementation within the allelectron numeric atomcentered orbitals electronic structure code fhiaims that allows for massivelyparallel calculations as demonstrated by extensive validation this allows the rapid computation of accurate response properties of molecules and solids as an application showcase we present harmonic and anharmonic raman spectra the latter obtained by combining hundreds of thousands of pt calculations with textitab initio molecular dynamics by using the pbe exchangecorrelation functional with manybody van der waals corrections we obtain spectra in good agreement with experiment especially with respect to lineshapes for the isolated paracetamol molecule and two polymorphs of the paracetamol crystal | [['within', 'densityfunctional', 'theory', 'perturbation', 'theorypt', 'is', 'the', 'stateoftheart', 'formalism', 'for', 'assessing', 'the', 'response', 'to', 'homogeneous', 'electric', 'fields', 'and', 'the', 'associated', 'material', 'properties', 'eg', 'polarizabilities', 'dielectric', 'constants', 'and', 'raman', 'intensities', 'here', 'we', 'derive', 'a', 'realspace', 'formulation', 'of', 'pt', 'and', 'present', 'an', 'implementation', 'within', 'the', 'allelectron', 'numeric', 'atomcentered', 'orbitals', 'electronic', 'structure', 'code', 'fhiaims', 'that', 'allows', 'for', 'massivelyparallel', 'calculations', 'as', 'demonstrated', 'by', 'extensive', 'validation', 'this', 'allows', 'the', 'rapid', 'computation', 'of', 'accurate', 'response', 'properties', 'of', 'molecules', 'and', 'solids', 'as', 'an', 'application', 'showcase', 'we', 'present', 'harmonic', 'and', 'anharmonic', 'raman', 'spectra', 'the', 'latter', 'obtained', 'by', 'combining', 'hundreds', 'of', 'thousands', 'of', 'pt', 'calculations', 'with', 'textitab', 'initio', 'molecular', 'dynamics', 'by', 'using', 'the', 'pbe', 'exchangecorrelation', 'functional', 'with', 'manybody', 'van', 'der', 'waals', 'corrections', 'we', 'obtain', 'spectra', 'in', 'good', 'agreement', 'with', 'experiment', 'especially', 'with', 'respect', 'to', 'lineshapes', 'for', 'the', 'isolated', 'paracetamol', 'molecule', 'and', 'two', 'polymorphs', 'of', 'the', 'paracetamol', 'crystal']] | [-0.08350708330369616, 0.050119001863831926, -0.06797894905491249, 0.013860207223537358, 0.015565292513759358, -0.10195611198165073, 0.040087334532120746, 0.43744387695851333, -0.20732355405182246, -0.30929645460760086, -0.05385189402736316, -0.3347433516179997, -0.15858790243085283, 0.18332666906963246, 0.10449988212471277, 0.10753264246902762, 0.06101071082028377, -0.08191768783841445, -0.11487401408537624, -0.15929049800190884, 0.2448761527423142, 0.11598209309031385, 0.23583862931216068, 0.08312775673222368, 0.02542632279567502, 0.054133825976211226, 0.05218309914033833, 0.018065949443075126, -0.17437750337600588, 0.19296427601803923, 0.29265806718430326, -0.048274589858840415, 0.24593425708156705, -0.5139916072171318, -0.19967988054319047, -0.06727617163292683, 0.08671847110209276, 0.1782801146585032, -0.0823820547367927, -0.2627644759303734, 0.019976288039427605, -0.18341886061141743, -0.13259841240351905, -0.2503917016000589, -0.006529295861204393, 0.08370480987066607, -0.2509803020715693, 0.09975256891513284, -0.06485747446962055, 0.10945211905621954, -0.1379161433794325, -0.14670909078703365, -0.025478822158744734, 0.04770417136554844, -0.02891143713397973, 0.016606097840398116, 0.19068766742550436, -0.07417962168017063, -0.11293384061368304, 0.45794417815500477, -0.10334964403644693, -0.11655611983316876, 0.16087283587497897, -0.10060775332003288, -0.11190742155686564, 0.15088359271957927, 0.07456319988492781, 0.12226995987729271, -0.1590594056417807, 0.10622493997233219, 0.04747417042657298, 0.21456379329201078, 0.07585972751640327, 0.05140077491603362, 0.15375699781826324, 0.14282994485038747, -0.04514223433237346, 0.12683492881606204, -0.08696621362447915, -0.1005451344905326, -0.23701165647782985, -0.1495714585997479, -0.21962630467312613, 0.03891616279979909, -0.08215594114536032, -0.22257203117937502, 0.3966143280131756, 0.1174961348832415, 0.10386610527380105, 0.01684694686551055, 0.2857197618951518, 0.08371515994735171, 0.03910372288469361, 0.0035512231708415888, 0.24595614251328876, 0.207262706208675, 0.047544113237981814, -0.2907034926367992, 0.03286582899082751, 0.04929592915178433] |
1,803.00925 | Experimental Evaluation of Parameterized Algorithms for Feedback Vertex
Set | Feedback Vertex Set is a classic combinatorial optimization problem that asks
for a minimum set of vertices in a given graph whose deletion makes the graph
acyclic. From the point of view of parameterized algorithms and fixed-parameter
tractability, Feedback Vertex Set is one of the landmark problems: a long line
of study resulted in multiple algorithmic approaches and deep understanding of
the combinatorics of the problem. Because of its central role in parameterized
complexity, the first edition of the Parameterized Algorithms and Computational
Experiments Challenge (PACE) in 2016 featured Feedback Vertex Set as the
problem of choice in one of its tracks. The results of PACE 2016 on one hand
showed large discrepancy between performance of different classic approaches to
the problem, and on the other hand indicated a new approach based on
half-integral relaxations of the problem as probably the most efficient
approach to the problem. In this paper we provide an exhaustive experimental
evaluation of fixed-parameter and branching algorithms for Feedback Vertex Set.
| cs.DS | feedback vertex set is a classic combinatorial optimization problem that asks for a minimum set of vertices in a given graph whose deletion makes the graph acyclic from the point of view of parameterized algorithms and fixedparameter tractability feedback vertex set is one of the landmark problems a long line of study resulted in multiple algorithmic approaches and deep understanding of the combinatorics of the problem because of its central role in parameterized complexity the first edition of the parameterized algorithms and computational experiments challenge pace in 2016 featured feedback vertex set as the problem of choice in one of its tracks the results of pace 2016 on one hand showed large discrepancy between performance of different classic approaches to the problem and on the other hand indicated a new approach based on halfintegral relaxations of the problem as probably the most efficient approach to the problem in this paper we provide an exhaustive experimental evaluation of fixedparameter and branching algorithms for feedback vertex set | [['feedback', 'vertex', 'set', 'is', 'a', 'classic', 'combinatorial', 'optimization', 'problem', 'that', 'asks', 'for', 'a', 'minimum', 'set', 'of', 'vertices', 'in', 'a', 'given', 'graph', 'whose', 'deletion', 'makes', 'the', 'graph', 'acyclic', 'from', 'the', 'point', 'of', 'view', 'of', 'parameterized', 'algorithms', 'and', 'fixedparameter', 'tractability', 'feedback', 'vertex', 'set', 'is', 'one', 'of', 'the', 'landmark', 'problems', 'a', 'long', 'line', 'of', 'study', 'resulted', 'in', 'multiple', 'algorithmic', 'approaches', 'and', 'deep', 'understanding', 'of', 'the', 'combinatorics', 'of', 'the', 'problem', 'because', 'of', 'its', 'central', 'role', 'in', 'parameterized', 'complexity', 'the', 'first', 'edition', 'of', 'the', 'parameterized', 'algorithms', 'and', 'computational', 'experiments', 'challenge', 'pace', 'in', '2016', 'featured', 'feedback', 'vertex', 'set', 'as', 'the', 'problem', 'of', 'choice', 'in', 'one', 'of', 'its', 'tracks', 'the', 'results', 'of', 'pace', '2016', 'on', 'one', 'hand', 'showed', 'large', 'discrepancy', 'between', 'performance', 'of', 'different', 'classic', 'approaches', 'to', 'the', 'problem', 'and', 'on', 'the', 'other', 'hand', 'indicated', 'a', 'new', 'approach', 'based', 'on', 'halfintegral', 'relaxations', 'of', 'the', 'problem', 'as', 'probably', 'the', 'most', 'efficient', 'approach', 'to', 'the', 'problem', 'in', 'this', 'paper', 'we', 'provide', 'an', 'exhaustive', 'experimental', 'evaluation', 'of', 'fixedparameter', 'and', 'branching', 'algorithms', 'for', 'feedback', 'vertex', 'set']] | [-0.10240545393934361, -0.012645585100416969, -0.042965421571650286, 0.032859955189721374, -0.14487231197964512, -0.14001888895943534, 0.11581068675485298, 0.34059726776922067, -0.3126210127117564, -0.3594769784334031, 0.09807134018223168, -0.2780806578893327, -0.1678431128123493, 0.2076856619367997, -0.1073581856703668, 0.09378458148320064, 0.13711131699947696, 0.05071811751818821, -0.022643097258608604, -0.29320758068263814, 0.3186553357987467, 0.03598142699603076, 0.21929526147467904, 0.08533126683270728, 0.131163260837396, 0.02475307456528147, -0.06666193228553642, 0.07401898847046223, -0.12171874694463578, 0.12621782299582707, 0.27556368925354696, 0.22480528380891138, 0.3318294180754685, -0.41514058381770597, -0.13382973839955714, 0.13199007154837478, 0.11850368284027685, 0.10081977848894894, -0.028089204898358068, -0.21079328327693722, 0.063785401813573, -0.11916623455895618, -0.04190371598991932, 0.03476765321736986, 0.05451222915541042, -0.024388883538874022, -0.25631296747561655, -0.008822739053037808, 0.08687687972419415, 0.03236015147783539, -0.02747832355230595, -0.16836102710980358, 0.04036713250997392, 0.12034472350849572, 0.009413979085388057, 0.08951211714072886, 0.07833671799268235, -0.19008794966379575, -0.23680747051469303, 0.3929911662626899, 0.03399897665914261, -0.1535955783163169, 0.18629539371185908, -0.0754900469769244, -0.2047005456702953, 0.1264100187712095, 0.19505341032054274, 0.15060003969084584, -0.125582409748864, 0.11123628516956656, -0.07512238756859337, 0.11413484349314151, 0.05220042578170471, 0.001638222240250219, 0.14826916819438338, 0.23466937859175782, 0.12551865214764168, 0.17040702100229366, -0.003079657404768196, -0.09379991205888942, -0.2540249851140702, -0.06923872357460134, -0.18609574666867654, -0.01653545545484645, -0.12454014831940488, -0.20726292322418002, 0.4235686618721846, 0.15256320744132001, 0.2170209803533825, 0.07960581918613929, 0.28725916216093483, 0.08091940896305129, 0.01484596791366736, 0.10070627232392629, 0.1849527115855987, 0.11867970996959643, 0.057471398077905175, -0.24144123618095886, 0.09923420777883042, 0.14089929062703793] |
1,803.00926 | Semi-Supervised Algorithms for Approximately Optimal and Accurate
Clustering | We study $k$-means clustering in a semi-supervised setting. Given an oracle
that returns whether two given points belong to the same cluster in a fixed
optimal clustering, we investigate the following question: how many oracle
queries are sufficient to efficiently recover a clustering that, with
probability at least $(1 - \delta)$, simultaneously has a cost of at most $(1 +
\epsilon)$ times the optimal cost and an accuracy of at least $(1 - \epsilon)$?
We show how to achieve such a clustering on $n$ points with $O{((k^2 \log n)
\cdot m{(Q, \epsilon^4, \delta / (k\log n))})}$ oracle queries, when the $k$
clusters can be learned with an $\epsilon'$ error and a failure probability
$\delta'$ using $m(Q, \epsilon',\delta')$ labeled samples in the supervised
setting, where $Q$ is the set of candidate cluster centers. We show that $m(Q,
\epsilon', \delta')$ is small both for $k$-means instances in Euclidean space
and for those in finite metric spaces. We further show that, for the Euclidean
$k$-means instances, we can avoid the dependency on $n$ in the query complexity
at the expense of an increased dependency on $k$: specifically, we give a
slightly more involved algorithm that uses $O(k^4/(\epsilon^2 \delta) +
(k^{9}/\epsilon^4) \log(1/\delta) + k \cdot m(\mathbb{R}^r, \epsilon^4/k,
\delta))$ oracle queries.
We also show that the number of queries needed for $(1 - \epsilon)$-accuracy
in Euclidean $k$-means must linearly depend on the dimension of the underlying
Euclidean space, and for finite metric space $k$-means, we show that it must at
least be logarithmic in the number of candidate centers. This shows that our
query complexities capture the right dependencies on the respective parameters.
| cs.DS | we study kmeans clustering in a semisupervised setting given an oracle that returns whether two given points belong to the same cluster in a fixed optimal clustering we investigate the following question how many oracle queries are sufficient to efficiently recover a clustering that with probability at least 1 delta simultaneously has a cost of at most 1 epsilon times the optimal cost and an accuracy of at least 1 epsilon we show how to achieve such a clustering on n points with ok2 log n cdot mq epsilon4 delta klog n oracle queries when the k clusters can be learned with an epsilon error and a failure probability delta using mq epsilondelta labeled samples in the supervised setting where q is the set of candidate cluster centers we show that mq epsilon delta is small both for kmeans instances in euclidean space and for those in finite metric spaces we further show that for the euclidean kmeans instances we can avoid the dependency on n in the query complexity at the expense of an increased dependency on k specifically we give a slightly more involved algorithm that uses ok4epsilon2 delta k9epsilon4 log1delta k cdot mmathbbrr epsilon4k delta oracle queries we also show that the number of queries needed for 1 epsilonaccuracy in euclidean kmeans must linearly depend on the dimension of the underlying euclidean space and for finite metric space kmeans we show that it must at least be logarithmic in the number of candidate centers this shows that our query complexities capture the right dependencies on the respective parameters | [['we', 'study', 'kmeans', 'clustering', 'in', 'a', 'semisupervised', 'setting', 'given', 'an', 'oracle', 'that', 'returns', 'whether', 'two', 'given', 'points', 'belong', 'to', 'the', 'same', 'cluster', 'in', 'a', 'fixed', 'optimal', 'clustering', 'we', 'investigate', 'the', 'following', 'question', 'how', 'many', 'oracle', 'queries', 'are', 'sufficient', 'to', 'efficiently', 'recover', 'a', 'clustering', 'that', 'with', 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1,803.00927 | Finding Hamiltonian Cycle in Graphs of Bounded Treewidth: Experimental
Evaluation | The notion of treewidth, introduced by Robertson and Seymour in their seminal
Graph Minors series, turned out to have tremendous impact on graph
algorithmics. Many hard computational problems on graphs turn out to be
efficiently solvable in graphs of bounded treewidth: graphs that can be sweeped
with separators of bounded size. These efficient algorithms usually follow the
dynamic programming paradigm.
In the recent years, we have seen a rapid and quite unexpected development of
involved techniques for solving various computational problems in graphs of
bounded treewidth. One of the most surprising directions is the development of
algorithms for connectivity problems that have only single-exponential
dependency (i.e., $2^{O(t)}$) on the treewidth in the running time bound, as
opposed to slightly superexponential (i.e., $2^{O(t \log t)}$) stemming from
more naive approaches. In this work, we perform a thorough experimental
evaluation of these approaches in the context of one of the most classic
connectivity problem, namely Hamiltonian Cycle.
| cs.DS | the notion of treewidth introduced by robertson and seymour in their seminal graph minors series turned out to have tremendous impact on graph algorithmics many hard computational problems on graphs turn out to be efficiently solvable in graphs of bounded treewidth graphs that can be sweeped with separators of bounded size these efficient algorithms usually follow the dynamic programming paradigm in the recent years we have seen a rapid and quite unexpected development of involved techniques for solving various computational problems in graphs of bounded treewidth one of the most surprising directions is the development of algorithms for connectivity problems that have only singleexponential dependency ie 2ot on the treewidth in the running time bound as opposed to slightly superexponential ie 2ot log t stemming from more naive approaches in this work we perform a thorough experimental evaluation of these approaches in the context of one of the most classic connectivity problem namely hamiltonian cycle | [['the', 'notion', 'of', 'treewidth', 'introduced', 'by', 'robertson', 'and', 'seymour', 'in', 'their', 'seminal', 'graph', 'minors', 'series', 'turned', 'out', 'to', 'have', 'tremendous', 'impact', 'on', 'graph', 'algorithmics', 'many', 'hard', 'computational', 'problems', 'on', 'graphs', 'turn', 'out', 'to', 'be', 'efficiently', 'solvable', 'in', 'graphs', 'of', 'bounded', 'treewidth', 'graphs', 'that', 'can', 'be', 'sweeped', 'with', 'separators', 'of', 'bounded', 'size', 'these', 'efficient', 'algorithms', 'usually', 'follow', 'the', 'dynamic', 'programming', 'paradigm', 'in', 'the', 'recent', 'years', 'we', 'have', 'seen', 'a', 'rapid', 'and', 'quite', 'unexpected', 'development', 'of', 'involved', 'techniques', 'for', 'solving', 'various', 'computational', 'problems', 'in', 'graphs', 'of', 'bounded', 'treewidth', 'one', 'of', 'the', 'most', 'surprising', 'directions', 'is', 'the', 'development', 'of', 'algorithms', 'for', 'connectivity', 'problems', 'that', 'have', 'only', 'singleexponential', 'dependency', 'ie', '2ot', 'on', 'the', 'treewidth', 'in', 'the', 'running', 'time', 'bound', 'as', 'opposed', 'to', 'slightly', 'superexponential', 'ie', '2ot', 'log', 't', 'stemming', 'from', 'more', 'naive', 'approaches', 'in', 'this', 'work', 'we', 'perform', 'a', 'thorough', 'experimental', 'evaluation', 'of', 'these', 'approaches', 'in', 'the', 'context', 'of', 'one', 'of', 'the', 'most', 'classic', 'connectivity', 'problem', 'namely', 'hamiltonian', 'cycle']] | [-0.1207486534724012, 0.0672297699797538, -0.0490510557612945, 0.06003374778214962, -0.13380214827315462, -0.1532455087669434, 0.04877329172506448, 0.37711384740447806, -0.29848078927776267, -0.36668515844691185, 0.12531876414788948, -0.25034535939865293, -0.15189776262568852, 0.22102623072963568, -0.09558602117993419, 0.09646454986725603, 0.11117498191118601, 0.01743997167195043, -0.021259507428734534, -0.2938244831737041, 0.2771263476866748, 0.02600652376009572, 0.19911095473104187, 0.09638108592480421, 0.024003867693846263, -0.014647289452653738, -0.03018538426728018, 0.10721704309746143, -0.11960158385458447, 0.11640825087069384, 0.29960162287668113, 0.1810196513640544, 0.30799378029881946, -0.46193548201793627, -0.20422357881231415, 0.15674388378167586, 0.15033281138016572, 0.08213396458736351, 0.010351544084128804, -0.2237264868608045, 0.0624045055269474, -0.10966770872774144, -0.05185990635936539, -0.051742892833276384, 0.0632341236525756, -0.02193148491243201, -0.1829805009547741, 0.017326086789609924, 0.11733964095793424, 0.05152783302424547, 0.05397963350518577, -0.17167411501849852, 0.05181509504590424, 0.09161027048023478, 0.023656638523924255, 0.04111176491715014, 0.07867749327973973, -0.13884824373564053, -0.21717775363114572, 0.3472653791428574, -0.002728819587237893, -0.14043767103745092, 0.18032735856550355, -0.07965372010104117, -0.247462531374467, 0.11253438274406137, 0.19977211039212922, 0.17128813283240063, -0.0995031405180212, 0.15028418956790118, -0.045459917956782926, 0.12094669718625806, 0.13035984723077668, 0.007730382184437927, 0.09300712586958862, 0.17702137266225632, 0.11356364175820002, 0.1570694159641261, 0.04900500479815227, -0.1100460822033065, -0.20692646441410387, -0.06693410839652643, -0.19614961694835895, 0.0193506361315808, -0.17125002270725917, -0.2028262603547304, 0.4116218759866071, 0.14048911153053445, 0.19040523080095167, 0.11524175874768726, 0.2752320306270473, 0.08562356730427352, 0.08712097655065477, 0.15281602455633542, 0.20493374316757845, 0.14697391510220065, 0.11837443189635392, -0.1771642554010595, 0.13166820709818913, 0.09287593879280687] |
1,803.00928 | Estimating model bias over the complete nuclide chart with sparse
Gaussian processes at the example of INCL/ABLA and double-differential
neutron spectra | Predictions of nuclear models guide the design of nuclear facilities to
ensure their safe and efficient operation. Because nuclear models often do not
perfectly reproduce available experimental data, decisions based on their
predictions may not be optimal. Awareness about systematic deviations between
models and experimental data helps to alleviate this problem. This paper shows
how a sparse approximation to Gaussian processes can be used to estimate the
model bias over the complete nuclide chart at the example of inclusive
double-differential neutron spectra for incident protons above 100\,MeV. A
powerful feature of the presented approach is the ability to predict the model
bias for energies, angles, and isotopes where data are missing. The number of
experimental data points that can be taken into account is at least in the
order of magnitude of~$10^4$ thanks to the sparse approximation. The approach
is applied to the Li\`ege Intranuclear Cascade Model (INCL) coupled to the
evaporation code ABLA. The results suggest that sparse Gaussian process
regression is a viable candidate to perform global and quantitative assessments
of models. Limitations of a philosophical nature of this (and any other)
approach are also discussed.
| nucl-th physics.data-an | predictions of nuclear models guide the design of nuclear facilities to ensure their safe and efficient operation because nuclear models often do not perfectly reproduce available experimental data decisions based on their predictions may not be optimal awareness about systematic deviations between models and experimental data helps to alleviate this problem this paper shows how a sparse approximation to gaussian processes can be used to estimate the model bias over the complete nuclide chart at the example of inclusive doubledifferential neutron spectra for incident protons above 100mev a powerful feature of the presented approach is the ability to predict the model bias for energies angles and isotopes where data are missing the number of experimental data points that can be taken into account is at least in the order of magnitude of104 thanks to the sparse approximation the approach is applied to the liege intranuclear cascade model incl coupled to the evaporation code abla the results suggest that sparse gaussian process regression is a viable candidate to perform global and quantitative assessments of models limitations of a philosophical nature of this and any other approach are also discussed | [['predictions', 'of', 'nuclear', 'models', 'guide', 'the', 'design', 'of', 'nuclear', 'facilities', 'to', 'ensure', 'their', 'safe', 'and', 'efficient', 'operation', 'because', 'nuclear', 'models', 'often', 'do', 'not', 'perfectly', 'reproduce', 'available', 'experimental', 'data', 'decisions', 'based', 'on', 'their', 'predictions', 'may', 'not', 'be', 'optimal', 'awareness', 'about', 'systematic', 'deviations', 'between', 'models', 'and', 'experimental', 'data', 'helps', 'to', 'alleviate', 'this', 'problem', 'this', 'paper', 'shows', 'how', 'a', 'sparse', 'approximation', 'to', 'gaussian', 'processes', 'can', 'be', 'used', 'to', 'estimate', 'the', 'model', 'bias', 'over', 'the', 'complete', 'nuclide', 'chart', 'at', 'the', 'example', 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1,803.00929 | God plays coins or superposition principle for classical probabilities
in quantum suprematism representation of qubit states | For three given probability distributions describing positions of three
classical coins the quantum density matrix of spin-$1/2$ state is constructed
and its matrix elements are associated with triada of Malevich's squares. The
superposition principle of spin-$1/2$ states is presented in the form of
nonlinear addition rule for these classical coin probabilities. We illustrate
the formulas by the statement"God does not play dice - God plays coins".
| quant-ph | for three given probability distributions describing positions of three classical coins the quantum density matrix of spin12 state is constructed and its matrix elements are associated with triada of malevichs squares the superposition principle of spin12 states is presented in the form of nonlinear addition rule for these classical coin probabilities we illustrate the formulas by the statementgod does not play dice god plays coins | [['for', 'three', 'given', 'probability', 'distributions', 'describing', 'positions', 'of', 'three', 'classical', 'coins', 'the', 'quantum', 'density', 'matrix', 'of', 'spin12', 'state', 'is', 'constructed', 'and', 'its', 'matrix', 'elements', 'are', 'associated', 'with', 'triada', 'of', 'malevichs', 'squares', 'the', 'superposition', 'principle', 'of', 'spin12', 'states', 'is', 'presented', 'in', 'the', 'form', 'of', 'nonlinear', 'addition', 'rule', 'for', 'these', 'classical', 'coin', 'probabilities', 'we', 'illustrate', 'the', 'formulas', 'by', 'the', 'statementgod', 'does', 'not', 'play', 'dice', 'god', 'plays', 'coins']] | [-0.12191262147098314, 0.18707910168450326, -0.06221311938861618, 0.05922848780028289, -0.010876031155930832, -0.19121209120203275, 0.016234240330959437, 0.34418762123095803, -0.22696494232513942, -0.2531228459774866, 0.07647217936937523, -0.32899862551130354, -0.1267398394702468, 0.13500507269782247, -0.042925611042619494, 0.07407081192559417, 0.051623959425342036, 0.08582076765742386, -0.05480602609895868, -0.25518552683934104, 0.3403621315956116, 0.007777799697578303, 0.26248619672333007, -0.016088559917989187, 0.14184083171130624, 0.062344209851289634, -0.011476470092020463, -0.017664796861936338, -0.08055638139194343, 0.09238013841513748, 0.21908330516453134, 0.1363049056235468, 0.2502963220977108, -0.42263496160740033, -0.11029339859669562, 0.13263261023530504, 0.1117837141136988, 0.1281652963371016, -0.03342275050817989, -0.26585206351592205, 0.01170778112282278, -0.15902783077035565, -0.16605486083608412, -0.06723410468111979, -0.01953537907320424, 0.020040279981913045, -0.25177714444726007, 0.13100610052060802, 0.08341739528168546, 0.026363633436176315, -0.007625869076946401, -0.21466294751007808, 0.023278437955013942, 0.16857230410096236, -0.04263829442061251, -0.06301077744137729, 0.09052300963230664, -0.1222520246374188, -0.1881105711509008, 0.3778278459503781, -0.011920469733013306, -0.2361828007560689, 0.06411410997679923, -0.13125138871691888, -0.09287178639351623, 0.06241752332425676, 0.09677839273354039, 0.09545962732227053, -0.12960809956712183, 0.04785099418859318, -0.0970913038636354, 0.1329514826356899, 0.05848516667174408, 0.047826931113377213, 0.2102529704279732, 0.030571655010135146, 0.024979684167192318, 0.14145143469795585, -0.07954334700571053, -0.24153230099909706, -0.3226249148574425, -0.1931291673827218, -0.258356659564015, 0.05911245350284844, -0.1137959870100076, -0.19869832202675752, 0.37310749067910365, 0.07204957051180827, 0.15952555717376526, 0.03622530138818547, 0.1862416309886612, 0.16013369731081184, 0.010452229391376022, 0.034204676616354845, 0.19672838024416706, 0.2003898343864421, -0.0034614708347362466, -0.23123076385036256, 0.12084612355374702, 0.15599068343362887] |
1,803.0093 | Beyond black-boxes in Bayesian inverse problems and model validation:
applications in solid mechanics of elastography | The present paper is motivated by one of the most fundamental challenges in
inverse problems, that of quantifying model discrepancies and errors. While
significant strides have been made in calibrating model parameters, the
overwhelming majority of pertinent methods is based on the assumption of a
perfect model. Motivated by problems in solid mechanics which, as all problems
in continuum thermodynamics, are described by conservation laws and
phenomenological constitutive closures, we argue that in order to quantify
model uncertainty in a physically meaningful manner, one should break open the
black-box forward model. In particular we propose formulating an undirected
probabilistic model that explicitly accounts for the governing equations and
their validity. This recasts the solution of both forward and inverse problems
as probabilistic inference tasks where the problem's state variables should not
only be compatible with the data but also with the governing equations as well.
Even though the probability densities involved do not contain any black-box
terms, they live in much higher-dimensional spaces. In combination with the
intractability of the normalization constant of the undirected model employed,
this poses significant challenges which we propose to address with a
linearly-scaling, double-layer of Stochastic Variational Inference. We
demonstrate the capabilities and efficacy of the proposed model in synthetic
forward and inverse problems (with and without model error) in elastography.
| physics.comp-ph stat.ML | the present paper is motivated by one of the most fundamental challenges in inverse problems that of quantifying model discrepancies and errors while significant strides have been made in calibrating model parameters the overwhelming majority of pertinent methods is based on the assumption of a perfect model motivated by problems in solid mechanics which as all problems in continuum thermodynamics are described by conservation laws and phenomenological constitutive closures we argue that in order to quantify model uncertainty in a physically meaningful manner one should break open the blackbox forward model in particular we propose formulating an undirected probabilistic model that explicitly accounts for the governing equations and their validity this recasts the solution of both forward and inverse problems as probabilistic inference tasks where the problems state variables should not only be compatible with the data but also with the governing equations as well even though the probability densities involved do not contain any blackbox terms they live in much higherdimensional spaces in combination with the intractability of the normalization constant of the undirected model employed this poses significant challenges which we propose to address with a linearlyscaling doublelayer of stochastic variational inference we demonstrate the capabilities and efficacy of the proposed model in synthetic forward and inverse problems with and without model error in elastography | [['the', 'present', 'paper', 'is', 'motivated', 'by', 'one', 'of', 'the', 'most', 'fundamental', 'challenges', 'in', 'inverse', 'problems', 'that', 'of', 'quantifying', 'model', 'discrepancies', 'and', 'errors', 'while', 'significant', 'strides', 'have', 'been', 'made', 'in', 'calibrating', 'model', 'parameters', 'the', 'overwhelming', 'majority', 'of', 'pertinent', 'methods', 'is', 'based', 'on', 'the', 'assumption', 'of', 'a', 'perfect', 'model', 'motivated', 'by', 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1,803.00931 | Magnetoresistance of semi-metals: the case of antimony | Large unsaturated magnetoresistance has been recently reported in numerous
semi-metals. Many of them have a topologically non-trivial band dispersion,
such as Weyl nodes or lines. Here, we show that elemental antimony displays the
largest high-field magnetoresistance among all known semi-metals. We present a
detailed study of the angle-dependent magnetoresistance and use a
semi-classical framework invoking an anisotropic mobility tensor to fit the
data. A slight deviation from perfect compensation and a modest variation with
magnetic field of the components of the mobility tensor are required to attain
perfect fits at arbitrary strength and orientation of magnetic field in the
entire temperature window of study. Our results demonstrate that large orbital
magnetoresistance is an unavoidable consequence of low carrier concentration
and the sub-quadratic magnetoresistance seen in many semi-metals can be
attributed to field-dependent mobility, expected whenever the disorder
length-scale exceeds the Fermi wavelength.
| cond-mat.mtrl-sci cond-mat.str-el | large unsaturated magnetoresistance has been recently reported in numerous semimetals many of them have a topologically nontrivial band dispersion such as weyl nodes or lines here we show that elemental antimony displays the largest highfield magnetoresistance among all known semimetals we present a detailed study of the angledependent magnetoresistance and use a semiclassical framework invoking an anisotropic mobility tensor to fit the data a slight deviation from perfect compensation and a modest variation with magnetic field of the components of the mobility tensor are required to attain perfect fits at arbitrary strength and orientation of magnetic field in the entire temperature window of study our results demonstrate that large orbital magnetoresistance is an unavoidable consequence of low carrier concentration and the subquadratic magnetoresistance seen in many semimetals can be attributed to fielddependent mobility expected whenever the disorder lengthscale exceeds the fermi wavelength | [['large', 'unsaturated', 'magnetoresistance', 'has', 'been', 'recently', 'reported', 'in', 'numerous', 'semimetals', 'many', 'of', 'them', 'have', 'a', 'topologically', 'nontrivial', 'band', 'dispersion', 'such', 'as', 'weyl', 'nodes', 'or', 'lines', 'here', 'we', 'show', 'that', 'elemental', 'antimony', 'displays', 'the', 'largest', 'highfield', 'magnetoresistance', 'among', 'all', 'known', 'semimetals', 'we', 'present', 'a', 'detailed', 'study', 'of', 'the', 'angledependent', 'magnetoresistance', 'and', 'use', 'a', 'semiclassical', 'framework', 'invoking', 'an', 'anisotropic', 'mobility', 'tensor', 'to', 'fit', 'the', 'data', 'a', 'slight', 'deviation', 'from', 'perfect', 'compensation', 'and', 'a', 'modest', 'variation', 'with', 'magnetic', 'field', 'of', 'the', 'components', 'of', 'the', 'mobility', 'tensor', 'are', 'required', 'to', 'attain', 'perfect', 'fits', 'at', 'arbitrary', 'strength', 'and', 'orientation', 'of', 'magnetic', 'field', 'in', 'the', 'entire', 'temperature', 'window', 'of', 'study', 'our', 'results', 'demonstrate', 'that', 'large', 'orbital', 'magnetoresistance', 'is', 'an', 'unavoidable', 'consequence', 'of', 'low', 'carrier', 'concentration', 'and', 'the', 'subquadratic', 'magnetoresistance', 'seen', 'in', 'many', 'semimetals', 'can', 'be', 'attributed', 'to', 'fielddependent', 'mobility', 'expected', 'whenever', 'the', 'disorder', 'lengthscale', 'exceeds', 'the', 'fermi', 'wavelength']] | [-0.21534791665846928, 0.15615184702045568, -0.02707291181481273, 0.016556005968390897, -0.09713245530015933, -0.14303947194203945, 0.03937800840350052, 0.38604902335718067, -0.24537683139279695, -0.3391575801094443, -0.006379234220858821, -0.3012306163428535, -0.1625088463475088, 0.2031382426117796, -0.009966335357757101, 0.023718329037979907, -0.011651820034234666, -0.00820463640667813, -0.07473189427195982, -0.2131092812354401, 0.27726828556878386, 0.04326292233456346, 0.31873515023666266, 0.10497568889757172, 0.05985780365676553, -0.014651990251313947, 0.07391163543961994, 0.12986465845204576, -0.12896988696706052, 0.01695708095581389, 0.25337142978867017, -0.07697106772725483, 0.18723985922693367, -0.3930387540661376, -0.23347148690766223, 0.05147319986291466, 0.148530648473624, 0.1670100648238153, -0.0893256155426391, -0.25290166571552697, 0.07873031291061304, -0.1527640184348213, -0.13940067690613508, -0.11635412750060631, 0.0024291419630712817, -0.025500676266744102, -0.2306324688891526, 0.11282046275823311, 0.033946712481671235, 0.132676248897759, -0.09010069171199575, -0.14455049903079553, -0.06038075890949547, 0.07615139510344819, 0.09866077085779848, -0.004214974512956039, 0.11438901491568122, -0.10555048490887586, -0.11235667429011788, 0.3583696288200961, -0.08522254420103322, -0.061388439407021225, 0.166576807137946, -0.21304324686422316, -0.08886569333721844, 0.18313230998145127, 0.15085539375235077, 0.09079436912909675, -0.12963238673966745, 0.10708269533970964, -0.052459393009792646, 0.1561973889384576, 0.04588547314543434, 0.08703217281415586, 0.2572581256792264, 0.12953964023339287, 0.06507782951446413, 0.09688618376770738, -0.14607353162198347, 0.018270708761558358, -0.23290826908056952, -0.182245878260058, -0.250108884622074, 0.09473329559493233, -0.11673251454985674, -0.24111296634622653, 0.4042985212897331, 0.15877409303941253, 0.23111910423227836, -0.0062921783972111804, 0.2423356007784605, 0.12076640526213171, 0.11574701367037922, 0.08492133512922471, 0.2527198640934625, 0.17308538499891654, 0.14525850513346597, -0.2652815926941374, 0.09164840854953607, -0.045702101285634955] |
1,803.00932 | An Experimental Study of Factor Analysis over Cellular Network Data | Mobile Network Operators (MNOs) are evolving towards becoming data-driven,
while delivering capacity to collect and analyze data. This can help in
enhancing user experiences while empowering the operation workforce and
building new business models. Mobile traffic demands of users can give insights
to MNOs to plan, decide and act depending on network conditions. In this paper,
we investigate the behaviour of Istanbul residents using the cellular network
traffic activity over spatial and temporal dimensions via exploratory factor
analysis (EFA) using a major MNO's cellular network traffic data in Turkey. Our
results reveal various time and spatial patterns for Istanbul residents such as
morning and evening commuting factors, business and residential factors as well
as nightlife and weekend afternoon factors as the most prominent cultural
behaviour. The analysis results also demonstrate interesting findings such as
tunnels and transportation paths selected by Istanbul residents may differ
during morning rush work hour compared to evening rush after-work hour.
| cs.NI | mobile network operators mnos are evolving towards becoming datadriven while delivering capacity to collect and analyze data this can help in enhancing user experiences while empowering the operation workforce and building new business models mobile traffic demands of users can give insights to mnos to plan decide and act depending on network conditions in this paper we investigate the behaviour of istanbul residents using the cellular network traffic activity over spatial and temporal dimensions via exploratory factor analysis efa using a major mnos cellular network traffic data in turkey our results reveal various time and spatial patterns for istanbul residents such as morning and evening commuting factors business and residential factors as well as nightlife and weekend afternoon factors as the most prominent cultural behaviour the analysis results also demonstrate interesting findings such as tunnels and transportation paths selected by istanbul residents may differ during morning rush work hour compared to evening rush afterwork hour | [['mobile', 'network', 'operators', 'mnos', 'are', 'evolving', 'towards', 'becoming', 'datadriven', 'while', 'delivering', 'capacity', 'to', 'collect', 'and', 'analyze', 'data', 'this', 'can', 'help', 'in', 'enhancing', 'user', 'experiences', 'while', 'empowering', 'the', 'operation', 'workforce', 'and', 'building', 'new', 'business', 'models', 'mobile', 'traffic', 'demands', 'of', 'users', 'can', 'give', 'insights', 'to', 'mnos', 'to', 'plan', 'decide', 'and', 'act', 'depending', 'on', 'network', 'conditions', 'in', 'this', 'paper', 'we', 'investigate', 'the', 'behaviour', 'of', 'istanbul', 'residents', 'using', 'the', 'cellular', 'network', 'traffic', 'activity', 'over', 'spatial', 'and', 'temporal', 'dimensions', 'via', 'exploratory', 'factor', 'analysis', 'efa', 'using', 'a', 'major', 'mnos', 'cellular', 'network', 'traffic', 'data', 'in', 'turkey', 'our', 'results', 'reveal', 'various', 'time', 'and', 'spatial', 'patterns', 'for', 'istanbul', 'residents', 'such', 'as', 'morning', 'and', 'evening', 'commuting', 'factors', 'business', 'and', 'residential', 'factors', 'as', 'well', 'as', 'nightlife', 'and', 'weekend', 'afternoon', 'factors', 'as', 'the', 'most', 'prominent', 'cultural', 'behaviour', 'the', 'analysis', 'results', 'also', 'demonstrate', 'interesting', 'findings', 'such', 'as', 'tunnels', 'and', 'transportation', 'paths', 'selected', 'by', 'istanbul', 'residents', 'may', 'differ', 'during', 'morning', 'rush', 'work', 'hour', 'compared', 'to', 'evening', 'rush', 'afterwork', 'hour']] | [-0.11533520678119137, 0.09442494861367676, -0.019043756362425127, 0.06895249068365718, -0.09549573065980879, -0.16823047961357956, 0.12998510701840538, 0.397680538590827, -0.2323833009040933, -0.34703660205773573, 0.15730894686048547, -0.3341628267168025, -0.1953976914040087, 0.1737851591086855, -0.13878233908493998, -0.009696436414397717, 0.07085220512380105, 0.02145503768606075, 0.053401851737445674, -0.26180610725795206, 0.2625065455540845, 0.07258232083822923, 0.3494173366016422, 0.08855338935174194, 0.03213904113691373, 0.0029202796384886977, -0.08857654383272127, -0.049490200863158924, -0.06794786515867861, 0.11764832301479247, 0.37159609849203024, 0.19833657864535995, 0.33093918896785457, -0.5171844978007226, -0.22030903365055185, 0.0739189682663814, 0.15813767716130303, -0.013297132523791269, 0.020256821549302757, -0.33803820190038164, 0.05014427163899919, -0.20202398891682474, -0.10707857698549096, -0.07423935517717421, -0.0010192213957411012, 0.04836462475504008, -0.27399450703371875, -0.008921580372592799, -0.030527006727460698, 0.12574346183367024, -0.0609616202612718, -0.11327891983091831, -0.0645217004367546, 0.2868755816579401, 0.10129386908526904, -0.031929402929438536, 0.18724879137776013, -0.1288891470004969, -0.1649830459854474, 0.37717948225783365, 0.005241675357169965, -0.059167904243652335, 0.17439651606129666, -0.08809872247047382, -0.1348962009963549, 0.053441379890393687, 0.2960202726078968, 0.016403343406767627, -0.20032526069972056, -0.06870464938030779, 0.0011544095247515893, 0.1355350652703516, 0.12688719644059054, 0.02339813102560203, 0.177144490925023, 0.24520720185991984, 0.12037443736677661, 0.07270225916465659, -0.05034429212770382, -0.12015186699816434, -0.16851250900357378, -0.13152386599031637, -0.07320049224463991, 0.03308181263798592, -0.11085936451683451, -0.06456625028635113, 0.42323626833511335, 0.14466263574994706, 0.15727511848158696, 0.06606148587071087, 0.28219656481900635, 0.03867216900544959, 0.09066985163448099, 0.1383533546232369, 0.08670631238545468, -0.05894574181943694, 0.2822299940242231, -0.14917305483805393, 0.1130313039232301, -0.0006600606614370751] |
1,803.00933 | Distributed Prioritized Experience Replay | We propose a distributed architecture for deep reinforcement learning at
scale, that enables agents to learn effectively from orders of magnitude more
data than previously possible. The algorithm decouples acting from learning:
the actors interact with their own instances of the environment by selecting
actions according to a shared neural network, and accumulate the resulting
experience in a shared experience replay memory; the learner replays samples of
experience and updates the neural network. The architecture relies on
prioritized experience replay to focus only on the most significant data
generated by the actors. Our architecture substantially improves the state of
the art on the Arcade Learning Environment, achieving better final performance
in a fraction of the wall-clock training time.
| cs.LG | we propose a distributed architecture for deep reinforcement learning at scale that enables agents to learn effectively from orders of magnitude more data than previously possible the algorithm decouples acting from learning the actors interact with their own instances of the environment by selecting actions according to a shared neural network and accumulate the resulting experience in a shared experience replay memory the learner replays samples of experience and updates the neural network the architecture relies on prioritized experience replay to focus only on the most significant data generated by the actors our architecture substantially improves the state of the art on the arcade learning environment achieving better final performance in a fraction of the wallclock training time | [['we', 'propose', 'a', 'distributed', 'architecture', 'for', 'deep', 'reinforcement', 'learning', 'at', 'scale', 'that', 'enables', 'agents', 'to', 'learn', 'effectively', 'from', 'orders', 'of', 'magnitude', 'more', 'data', 'than', 'previously', 'possible', 'the', 'algorithm', 'decouples', 'acting', 'from', 'learning', 'the', 'actors', 'interact', 'with', 'their', 'own', 'instances', 'of', 'the', 'environment', 'by', 'selecting', 'actions', 'according', 'to', 'a', 'shared', 'neural', 'network', 'and', 'accumulate', 'the', 'resulting', 'experience', 'in', 'a', 'shared', 'experience', 'replay', 'memory', 'the', 'learner', 'replays', 'samples', 'of', 'experience', 'and', 'updates', 'the', 'neural', 'network', 'the', 'architecture', 'relies', 'on', 'prioritized', 'experience', 'replay', 'to', 'focus', 'only', 'on', 'the', 'most', 'significant', 'data', 'generated', 'by', 'the', 'actors', 'our', 'architecture', 'substantially', 'improves', 'the', 'state', 'of', 'the', 'art', 'on', 'the', 'arcade', 'learning', 'environment', 'achieving', 'better', 'final', 'performance', 'in', 'a', 'fraction', 'of', 'the', 'wallclock', 'training', 'time']] | [-0.08717695574209063, 0.07463362979529015, -0.06532493513866754, 0.007287029940556041, -0.13800576795681807, -0.1577035203059125, 0.12137592509704626, 0.4469891522332268, -0.26375292467399325, -0.37769704246577823, 0.02507422357931796, -0.2647755585839751, -0.13686061844671682, 0.17342651131862807, -0.1200304519408777, 0.015771475254550135, 0.1507551766126969, 0.09672079669011725, -0.03412171398926826, -0.35178023040042083, 0.3311414869938251, 0.07743071527314033, 0.33652693282623414, -0.0681245756035639, 0.1493206061324168, -0.002590748516969004, -0.02611911607022255, -0.05987241246666753, 0.022527366394354167, 0.17334023580523367, 0.27908152367098854, 0.22663361582731417, 0.39055724039484385, -0.4757265410454099, -0.18031743201979641, 0.0721934146595077, 0.11978364511692928, 0.08967701453810274, -0.020978829533012656, -0.3726215757334889, 0.051512817171465414, -0.1980880360529458, 0.021516047662787013, -0.09358875149608416, -0.05747648307232786, -0.0015693823106476437, -0.2866970221196317, -0.02280056569725275, 0.09288700284254817, 0.018396109117636995, -0.06630241993214872, -0.10230873424117848, 0.00020964229597820569, 0.18415297229253388, 0.01769293804349125, 0.08086295118476501, 0.2331355569456996, -0.19734536960200108, -0.1511200649063971, 0.32052766408581856, -0.04447229206426389, -0.14673935794824008, 0.230839868794369, -0.03776832286394754, -0.09770078891572559, 0.12657831155426674, 0.29805331898584836, 0.11181121487742669, -0.1393264756790536, -0.008493090315792963, 0.00028900437499001873, 0.1974792437296424, 0.013264833986995963, 0.02444173575781475, 0.14527346435204244, 0.2823208134514832, 0.06796911233341542, 0.12584922242499255, -0.050154124619439244, -0.1371756808174245, -0.1779207681913434, -0.09005489503822715, -0.19387852264789202, -0.015253832686092641, -0.1126679558051904, -0.10475601188967229, 0.4029246631820323, 0.23738161381136752, 0.2332648067237904, 0.15393554197498044, 0.3666022274232769, -0.0111764145273147, 0.166164864894492, 0.17557033522338686, 0.1921201420048157, -0.026968325934199205, 0.18266761780349475, -0.20583650963854486, 0.17765219187979603, 0.014200270504708465] |
1,803.00934 | Quadratic 2-step Lie algebras: Computational algorithms and
classification | Taking into account the theoretical results and guidelines given inthis work,
we introduce a computational method to construct any 2 step nilpotent quadratic
algebra of d generators. Along the work we show that the key of the
classification of this class of metric algebras relies on certain families of
skewsymmetric matrices. Computational examples for d<=8 will be given.
| math.RA | taking into account the theoretical results and guidelines given inthis work we introduce a computational method to construct any 2 step nilpotent quadratic algebra of d generators along the work we show that the key of the classification of this class of metric algebras relies on certain families of skewsymmetric matrices computational examples for d8 will be given | [['taking', 'into', 'account', 'the', 'theoretical', 'results', 'and', 'guidelines', 'given', 'inthis', 'work', 'we', 'introduce', 'a', 'computational', 'method', 'to', 'construct', 'any', '2', 'step', 'nilpotent', 'quadratic', 'algebra', 'of', 'd', 'generators', 'along', 'the', 'work', 'we', 'show', 'that', 'the', 'key', 'of', 'the', 'classification', 'of', 'this', 'class', 'of', 'metric', 'algebras', 'relies', 'on', 'certain', 'families', 'of', 'skewsymmetric', 'matrices', 'computational', 'examples', 'for', 'd8', 'will', 'be', 'given']] | [-0.12638151109497592, 0.043685284824560175, -0.04183361396707337, 0.013449256972746989, -0.11165511062176063, -0.15139129333165957, 0.008969440799334953, 0.3496832010047189, -0.2690488230597613, -0.2636982263306734, 0.09244831696008171, -0.22645140304390726, -0.1903374729445204, 0.2087431040046544, -0.1344381516251376, 0.014219102160683993, 0.09432089897193785, 0.046277101569134615, -0.1329627216608938, -0.312526308140589, 0.39509716742934964, 0.0131656903058997, 0.23038950398692798, 0.06753779895571542, 0.13119390280917287, 0.0011960531681262214, -0.07169085736225905, 0.001915300891188712, -0.16840667982953542, 0.17593560570144448, 0.2745104770593602, 0.14984655453309673, 0.27380602164515133, -0.39573578395206355, -0.14775477129773334, 0.15755975690413396, 0.14271798098042351, 0.12243206760091387, -0.05033700122570234, -0.2534518717177983, 0.11380288620872808, -0.18794966432876115, -0.12946472564663994, -0.11215358545425637, 0.021574612795214713, -0.016565287389375013, -0.2767622491649898, -0.011779072325162846, 0.14394366259461847, 0.08378598959072782, -0.08331108584614663, -0.1499715491856854, -0.006886843848845054, 0.100637602997173, -0.005682254691833052, -0.002969171145352824, 0.08360295835480994, -0.03613691680245744, -0.16140208010369078, 0.35649963815150587, 0.008872889447957277, -0.25281271894044915, 0.10869825053719226, -0.11992502858027301, -0.20713713832584948, 0.06870610778348456, 0.20102791843036638, 0.1209462429557381, -0.09452764946036041, 0.14555511828251022, -0.06706305105110695, 0.0842710640120866, 0.028212360636299026, 0.0007678113299710997, 0.12153006804272018, 0.16263605839701306, 0.046872811391949654, 0.15464941150677422, 0.018098732934269154, -0.0324474623876399, -0.3966161549846032, -0.19010281425515382, -0.14300496249202768, 0.08263057945416598, -0.14052233570390824, -0.14968160448338966, 0.4374357534187107, 0.1538907311985205, 0.19270864199718524, 0.13855303148337608, 0.23375417714424687, 0.09742873703161704, 0.08656256218794091, 0.07129714119344435, 0.1281404843682359, 0.15358939773306765, -0.009558014001633072, -0.15003494423782004, -0.010027582168073297, 0.159142781546789] |
1,803.00935 | Breakdown of the Wiedemann-Franz law in a unitary Fermi gas | We report on coupled heat and particle transport measurements through a
quantum point contact (QPC) connecting two reservoirs of resonantly
interacting, finite temperature Fermi gases. After heating one of them, we
observe a particle current flowing from cold to hot. We monitor the temperature
evolution of the reservoirs and find that the system evolves after an initial
response into a non-equilibrium steady state with finite temperature and
chemical potential differences across the QPC. In this state any relaxation in
the form of heat and particle currents vanishes. From our measurements we
extract the transport coefficients of the QPC and deduce a Lorenz number
violating the Wiedemann-Franz law by one order of magnitude, a characteristic
persisting even for a wide contact. In contrast, the Seebeck coefficient takes
a value close to that expected for a non-interacting Fermi gas and shows a
smooth decrease as the atom density close to the QPC is increased beyond the
superfluid transition. Our work represents a fermionic analog of the fountain
effect observed with superfluid helium and poses new challenges for microscopic
modeling of the finite temperature dynamics of the unitary Fermi gas.
| cond-mat.quant-gas cond-mat.mes-hall | we report on coupled heat and particle transport measurements through a quantum point contact qpc connecting two reservoirs of resonantly interacting finite temperature fermi gases after heating one of them we observe a particle current flowing from cold to hot we monitor the temperature evolution of the reservoirs and find that the system evolves after an initial response into a nonequilibrium steady state with finite temperature and chemical potential differences across the qpc in this state any relaxation in the form of heat and particle currents vanishes from our measurements we extract the transport coefficients of the qpc and deduce a lorenz number violating the wiedemannfranz law by one order of magnitude a characteristic persisting even for a wide contact in contrast the seebeck coefficient takes a value close to that expected for a noninteracting fermi gas and shows a smooth decrease as the atom density close to the qpc is increased beyond the superfluid transition our work represents a fermionic analog of the fountain effect observed with superfluid helium and poses new challenges for microscopic modeling of the finite temperature dynamics of the unitary fermi gas | [['we', 'report', 'on', 'coupled', 'heat', 'and', 'particle', 'transport', 'measurements', 'through', 'a', 'quantum', 'point', 'contact', 'qpc', 'connecting', 'two', 'reservoirs', 'of', 'resonantly', 'interacting', 'finite', 'temperature', 'fermi', 'gases', 'after', 'heating', 'one', 'of', 'them', 'we', 'observe', 'a', 'particle', 'current', 'flowing', 'from', 'cold', 'to', 'hot', 'we', 'monitor', 'the', 'temperature', 'evolution', 'of', 'the', 'reservoirs', 'and', 'find', 'that', 'the', 'system', 'evolves', 'after', 'an', 'initial', 'response', 'into', 'a', 'nonequilibrium', 'steady', 'state', 'with', 'finite', 'temperature', 'and', 'chemical', 'potential', 'differences', 'across', 'the', 'qpc', 'in', 'this', 'state', 'any', 'relaxation', 'in', 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1,803.00936 | Universal Central Extensions and Non-abelian tensor product of
Hom-Lie-Rinehart Algebras | In this paper we study universal central extensions and non-abelian tensor
product of hom-Lie-Rinehart algebras. We discuss about universal $\alpha$-
central extensions, and, lifting of automorphisms and $\alpha$-derivations to
central extensions for hom-Lie-Rinehart algebras. This is in turn provide such
lifting of automorphisms and $\alpha^k$-derivations to the central extensions
for hom-Lie algebras.
| math.KT math.RA math.RT | in this paper we study universal central extensions and nonabelian tensor product of homlierinehart algebras we discuss about universal alpha central extensions and lifting of automorphisms and alphaderivations to central extensions for homlierinehart algebras this is in turn provide such lifting of automorphisms and alphakderivations to the central extensions for homlie algebras | [['in', 'this', 'paper', 'we', 'study', 'universal', 'central', 'extensions', 'and', 'nonabelian', 'tensor', 'product', 'of', 'homlierinehart', 'algebras', 'we', 'discuss', 'about', 'universal', 'alpha', 'central', 'extensions', 'and', 'lifting', 'of', 'automorphisms', 'and', 'alphaderivations', 'to', 'central', 'extensions', 'for', 'homlierinehart', 'algebras', 'this', 'is', 'in', 'turn', 'provide', 'such', 'lifting', 'of', 'automorphisms', 'and', 'alphakderivations', 'to', 'the', 'central', 'extensions', 'for', 'homlie', 'algebras']] | [-0.14342561975121498, 0.037123157787136735, -0.04543598439544439, 0.11308146851835772, -0.1224940325692296, -0.1034575979784131, -0.055790095997508615, 0.3385121192038059, -0.37865941271185877, -0.1776810740493238, 0.16351150191854685, -0.2299128355877474, -0.13557954954449086, 0.15853767465800048, -0.2009814268629998, -0.059990621283650396, -0.020129475593566894, 0.11035283661971335, -0.1509466129168868, -0.20509340099990367, 0.4459439438022673, 0.04092574302572757, 0.16156236447393893, 0.09559610746800899, 0.05576548464596272, 0.08031108695082366, -0.05033240838907659, -0.028500035405158997, -0.21273979306221008, 0.1420788905583322, 0.3384546655416489, 0.05010938560590148, 0.1667774223536253, -0.33166230741888286, -0.08224770959466696, 0.2104647294804454, 0.182351862732321, 0.049584657177329065, -0.09221358761191369, -0.2289839068427682, 0.13602895893156527, -0.33006155110895635, -0.13081466646865011, -0.13501151092350483, 0.11040601027198135, -0.022003607493825256, -0.2068879273533821, 0.0381588850542903, 0.1680979161709547, 0.13586705930531026, -0.10677898930385708, -0.08471762016415596, -0.021998436618596316, 0.08168314496055246, -0.05545387329999357, -0.03593551763799042, 0.17634005213156342, -0.11794861788861453, -0.2056362473219633, 0.3376399330049753, 0.004945882502943278, -0.16399839103221894, 0.16954870169982314, -0.18282814528793095, -0.29069002211093903, -0.0341115515679121, 0.10542511868290604, 0.09217482708394527, -0.05443286597728729, 0.2001588444923982, -0.11950442794710397, 0.009154433980584145, 0.10986993739381433, 0.02979443907737732, 0.1948791535338387, 0.07422865256667137, 0.07324340363033116, 0.21288035467267036, 0.11321026200428605, -0.08557158393785358, -0.4014669033885002, -0.22637452563270927, 0.034329390451312065, 0.09241087334230542, -0.09910930917016231, -0.14631218009628355, 0.4820388168096542, 0.19100174693390726, 0.1559766599733848, 0.09892737161368131, 0.17469001969322562, 0.07867673935368658, 0.19658109914511443, 0.054548443695530295, 0.1313144102692604, 0.36883893933147194, -0.04972622241824865, -0.10937300087884068, -0.12216358677484095, 0.19272485982626678] |
1,803.00937 | An improved FPT algorithm for Independent Feedback Vertex Set | We study the Independent Feedback Vertex Set problem - a variant of the
classic Feedback Vertex Set problem where, given a graph $G$ and an integer
$k$, the problem is to decide whether there exists a vertex set $S\subseteq
V(G)$ such that $G\setminus S$ is a forest and $S$ is an independent set of
size at most $k$. We present an $O^\ast((1+\varphi^{2})^{k})$-time FPT
algorithm for this problem, where $\varphi<1.619$ is the golden ratio,
improving the previous fastest $O^\ast(4.1481^{k})$-time algorithm given by
Agrawal et al [IPEC 2016]. The exponential factor in our time complexity bound
matches the fastest deterministic FPT algorithm for the classic Feedback Vertex
Set problem. On the technical side, the main novelty is a refined measure of an
input instance in a branching process, that allows for a simpler and more
concise description and analysis of the algorithm.
| cs.DS | we study the independent feedback vertex set problem a variant of the classic feedback vertex set problem where given a graph g and an integer k the problem is to decide whether there exists a vertex set ssubseteq vg such that gsetminus s is a forest and s is an independent set of size at most k we present an oast1varphi2ktime fpt algorithm for this problem where varphi1619 is the golden ratio improving the previous fastest oast41481ktime algorithm given by agrawal et al ipec 2016 the exponential factor in our time complexity bound matches the fastest deterministic fpt algorithm for the classic feedback vertex set problem on the technical side the main novelty is a refined measure of an input instance in a branching process that allows for a simpler and more concise description and analysis of the algorithm | [['we', 'study', 'the', 'independent', 'feedback', 'vertex', 'set', 'problem', 'a', 'variant', 'of', 'the', 'classic', 'feedback', 'vertex', 'set', 'problem', 'where', 'given', 'a', 'graph', 'g', 'and', 'an', 'integer', 'k', 'the', 'problem', 'is', 'to', 'decide', 'whether', 'there', 'exists', 'a', 'vertex', 'set', 'ssubseteq', 'vg', 'such', 'that', 'gsetminus', 's', 'is', 'a', 'forest', 'and', 's', 'is', 'an', 'independent', 'set', 'of', 'size', 'at', 'most', 'k', 'we', 'present', 'an', 'oast1varphi2ktime', 'fpt', 'algorithm', 'for', 'this', 'problem', 'where', 'varphi1619', 'is', 'the', 'golden', 'ratio', 'improving', 'the', 'previous', 'fastest', 'oast41481ktime', 'algorithm', 'given', 'by', 'agrawal', 'et', 'al', 'ipec', '2016', 'the', 'exponential', 'factor', 'in', 'our', 'time', 'complexity', 'bound', 'matches', 'the', 'fastest', 'deterministic', 'fpt', 'algorithm', 'for', 'the', 'classic', 'feedback', 'vertex', 'set', 'problem', 'on', 'the', 'technical', 'side', 'the', 'main', 'novelty', 'is', 'a', 'refined', 'measure', 'of', 'an', 'input', 'instance', 'in', 'a', 'branching', 'process', 'that', 'allows', 'for', 'a', 'simpler', 'and', 'more', 'concise', 'description', 'and', 'analysis', 'of', 'the', 'algorithm']] | [-0.11599785671869005, 0.050369332957563316, -0.04099354268948767, 0.020690568317537756, -0.1403382681789534, -0.16269447661676062, 0.1192933900798808, 0.32888314566191507, -0.29680199409674113, -0.30933324299643145, 0.060465225068208596, -0.28960080793213444, -0.1488817777546287, 0.18725024815991193, -0.08326918511610369, 0.059884788712438686, 0.08224841418867822, 0.06976145558120847, 0.053839803957517314, -0.2936251358625989, 0.27907117066035214, 0.04590708070078536, 0.20602509311592096, 0.03735285600893857, 0.11406044813681065, 0.038860528240226865, -0.031537462084088475, 0.054721193388960435, -0.17160660673674583, 0.026844411392470218, 0.23457453666967065, 0.24181020689299176, 0.3186215438113055, -0.3442136224715368, -0.12544150303480872, 0.16836826956954182, 0.11852972031920217, 0.09616783952001509, -0.02272748684834583, -0.19651101911200813, 0.1100300435689004, -0.10672592707937036, -0.06283924462845522, 0.05120969159692964, 0.14827409199343117, -0.07298255934942437, -0.34852827874162945, -0.014761071169392808, 0.11493396535789703, -0.013170038545093335, 0.0196625538463944, -0.174349701977061, 0.04305521611754289, 0.07868299040573594, -0.08122687597321654, 0.1593642262964482, 0.05517412111957503, -0.1044390660844168, -0.1851821924929562, 0.35647119733724086, -0.027342272716957855, -0.16428889934083118, 0.14123728620110984, -0.06639208137537078, -0.16225085556096233, 0.12826901065486976, 0.1486173023688881, 0.15065509396577803, -0.1355982662849676, 0.14073252256254337, -0.15335273353711648, 0.17656621597501831, 0.05683702434522703, -0.03250917738349766, 0.09668128866948844, 0.20875045842652018, 0.16301597956814529, 0.15417861771386335, 0.015214501119985738, -0.018325267221371178, -0.31332107263562436, -0.09829070780174601, -0.21265277538623195, 0.025629746631773954, -0.149392583691224, -0.17792459067833774, 0.38751333011040356, 0.1220450665894211, 0.22996361829984166, 0.09871583229259533, 0.2907985584110172, 0.14773033891159523, -0.034610887991969805, 0.19004459878418428, 0.11089029421975069, 0.11463789935121038, 0.00722978581838748, -0.24433316322906382, 0.10967898454948548, 0.1593590093886151] |
1,803.00938 | Multivariate Fine-Grained Complexity of Longest Common Subsequence | We revisit the classic combinatorial pattern matching problem of finding a
longest common subsequence (LCS). For strings $x$ and $y$ of length $n$, a
textbook algorithm solves LCS in time $O(n^2)$, but although much effort has
been spent, no $O(n^{2-\varepsilon})$-time algorithm is known. Recent work
indeed shows that such an algorithm would refute the Strong Exponential Time
Hypothesis (SETH) [Abboud, Backurs, Vassilevska Williams + Bringmann,
K\"unnemann FOCS'15].
Despite the quadratic-time barrier, for over 40 years an enduring scientific
interest continued to produce fast algorithms for LCS and its variations.
Particular attention was put into identifying and exploiting input parameters
that yield strongly subquadratic time algorithms for special cases of interest,
e.g., differential file comparison. This line of research was successfully
pursued until 1990, at which time significant improvements came to a halt. In
this paper, using the lens of fine-grained complexity, our goal is to (1)
justify the lack of further improvements and (2) determine whether some special
cases of LCS admit faster algorithms than currently known.
To this end, we provide a systematic study of the multivariate complexity of
LCS, taking into account all parameters previously discussed in the literature:
the input size $n:=\max\{|x|,|y|\}$, the length of the shorter string
$m:=\min\{|x|,|y|\}$, the length $L$ of an LCS of $x$ and $y$, the numbers of
deletions $\delta := m-L$ and $\Delta := n-L$, the alphabet size, as well as
the numbers of matching pairs $M$ and dominant pairs $d$. For any class of
instances defined by fixing each parameter individually to a polynomial in
terms of the input size, we prove a SETH-based lower bound matching one of
three known algorithms. Specifically, we determine the optimal running time for
LCS under SETH as $(n+\min\{d, \delta \Delta, \delta m\})^{1\pm o(1)}$.
[...]
| cs.CC cs.DS | we revisit the classic combinatorial pattern matching problem of finding a longest common subsequence lcs for strings x and y of length n a textbook algorithm solves lcs in time on2 but although much effort has been spent no on2varepsilontime algorithm is known recent work indeed shows that such an algorithm would refute the strong exponential time hypothesis seth abboud backurs vassilevska williams bringmann kunnemann focs15 despite the quadratictime barrier for over 40 years an enduring scientific interest continued to produce fast algorithms for lcs and its variations particular attention was put into identifying and exploiting input parameters that yield strongly subquadratic time algorithms for special cases of interest eg differential file comparison this line of research was successfully pursued until 1990 at which time significant improvements came to a halt in this paper using the lens of finegrained complexity our goal is to 1 justify the lack of further improvements and 2 determine whether some special cases of lcs admit faster algorithms than currently known to this end we provide a systematic study of the multivariate complexity of lcs taking into account all parameters previously discussed in the literature the input size nmaxxy the length of the shorter string mminxy the length l of an lcs of x and y the numbers of deletions delta ml and delta nl the alphabet size as well as the numbers of matching pairs m and dominant pairs d for any class of instances defined by fixing each parameter individually to a polynomial in terms of the input size we prove a sethbased lower bound matching one of three known algorithms specifically we determine the optimal running time for lcs under seth as nmind delta delta delta m1pm o1 | [['we', 'revisit', 'the', 'classic', 'combinatorial', 'pattern', 'matching', 'problem', 'of', 'finding', 'a', 'longest', 'common', 'subsequence', 'lcs', 'for', 'strings', 'x', 'and', 'y', 'of', 'length', 'n', 'a', 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1,803.00939 | Current and future constraints on Higgs couplings in the nonlinear
Effective Theory | We perform a Bayesian statistical analysis of the constraints on the
nonlinear Effective Theory given by the Higgs electroweak chiral Lagrangian. We
obtain bounds on the effective coefficients entering in Higgs observables at
the leading order, using all available Higgs-boson signal strengths from the
LHC runs 1 and 2. Using a prior dependence study of the solutions, we discuss
the results within the context of natural-sized Wilson coefficients. We further
study the expected sensitivities to the different Wilson coefficients at
various possible future colliders. Finally, we interpret our results in terms
of some minimal composite Higgs models.
| hep-ph hep-ex | we perform a bayesian statistical analysis of the constraints on the nonlinear effective theory given by the higgs electroweak chiral lagrangian we obtain bounds on the effective coefficients entering in higgs observables at the leading order using all available higgsboson signal strengths from the lhc runs 1 and 2 using a prior dependence study of the solutions we discuss the results within the context of naturalsized wilson coefficients we further study the expected sensitivities to the different wilson coefficients at various possible future colliders finally we interpret our results in terms of some minimal composite higgs models | [['we', 'perform', 'a', 'bayesian', 'statistical', 'analysis', 'of', 'the', 'constraints', 'on', 'the', 'nonlinear', 'effective', 'theory', 'given', 'by', 'the', 'higgs', 'electroweak', 'chiral', 'lagrangian', 'we', 'obtain', 'bounds', 'on', 'the', 'effective', 'coefficients', 'entering', 'in', 'higgs', 'observables', 'at', 'the', 'leading', 'order', 'using', 'all', 'available', 'higgsboson', 'signal', 'strengths', 'from', 'the', 'lhc', 'runs', '1', 'and', '2', 'using', 'a', 'prior', 'dependence', 'study', 'of', 'the', 'solutions', 'we', 'discuss', 'the', 'results', 'within', 'the', 'context', 'of', 'naturalsized', 'wilson', 'coefficients', 'we', 'further', 'study', 'the', 'expected', 'sensitivities', 'to', 'the', 'different', 'wilson', 'coefficients', 'at', 'various', 'possible', 'future', 'colliders', 'finally', 'we', 'interpret', 'our', 'results', 'in', 'terms', 'of', 'some', 'minimal', 'composite', 'higgs', 'models']] | [-0.08684240987274758, 0.1254885655818219, -0.07318187876575694, 0.11623129248859111, -0.0743841234757805, -0.09945501132685806, 0.030872683908511913, 0.32698062302775144, -0.20547231116341716, -0.27166377062208413, 0.09349285227669038, -0.2826074185425934, -0.1206627727591807, 0.17359050597573064, 0.04918059005315617, 0.1273163354898006, 0.05431631463819865, 0.03726613256581051, -0.12975428365326977, -0.28611793716620537, 0.30579835040610015, 0.05956539479276338, 0.20183668931773335, 0.10813283457987241, 0.08246569383947044, 0.03235201899096677, -0.07575066581598877, -0.03432080516418845, -0.21093976961361177, 0.15185153323523315, 0.20983457842472847, 0.08550258521374661, 0.17062840320784406, -0.39343102773670685, -0.1547322496867825, 0.1066885630710569, 0.11839459209760517, 0.13051397368772744, 0.0029262448012944042, -0.2776070112871371, 0.09619374438993077, -0.18941358228204483, -0.10171742066524961, -0.10327678217946254, -0.08675855416420501, -0.04802231573662961, -0.32944715236188826, 0.040754522177145924, -0.047753713391337195, 0.07581812275823244, -0.04005259686850549, -0.17704070640545452, -0.011847745707575423, 0.06604061554637305, 0.11965035442166876, -0.02153362956896578, 0.14040816612253637, -0.20828147743319728, -0.1756872988699638, 0.3843926115479973, -0.14405472322844795, -0.18563114015683146, 0.16017329867583574, -0.16107543476751632, -0.17550067013590454, 0.06350849219642882, 0.22720686347732685, 0.10429550944431445, -0.1297942022648976, 0.172230071334778, -0.03013029926427707, 0.12429015643621036, 0.04252575227232247, 0.03863600639568776, 0.2178442681726721, 0.1421331371331933, 0.02795727556744187, 0.09660569269728568, -0.0732113976732434, -0.07567670633692351, -0.448285458763082, -0.08816361509877996, -0.07359064986914896, -0.0029337816780490664, -0.14723237927930463, -0.07139905371592793, 0.4420031436248538, 0.20804728946850165, 0.2579257621516272, 0.06853601173737768, 0.28458932209183874, 0.14203787288393288, 0.04474400266160056, 0.024417669356310952, 0.29130947010588026, 0.12510582275890275, 0.10937547070195072, -0.22701599612860873, 0.004384859887518219, 0.09484033497559272] |
1,803.0094 | Protecting JPEG Images Against Adversarial Attacks | As deep neural networks (DNNs) have been integrated into critical systems,
several methods to attack these systems have been developed. These adversarial
attacks make imperceptible modifications to an image that fool DNN classifiers.
We present an adaptive JPEG encoder which defends against many of these
attacks. Experimentally, we show that our method produces images with high
visual quality while greatly reducing the potency of state-of-the-art attacks.
Our algorithm requires only a modest increase in encoding time, produces a
compressed image which can be decompressed by an off-the-shelf JPEG decoder,
and classified by an unmodified classifier
| cs.CV cs.GR | as deep neural networks dnns have been integrated into critical systems several methods to attack these systems have been developed these adversarial attacks make imperceptible modifications to an image that fool dnn classifiers we present an adaptive jpeg encoder which defends against many of these attacks experimentally we show that our method produces images with high visual quality while greatly reducing the potency of stateoftheart attacks our algorithm requires only a modest increase in encoding time produces a compressed image which can be decompressed by an offtheshelf jpeg decoder and classified by an unmodified classifier | [['as', 'deep', 'neural', 'networks', 'dnns', 'have', 'been', 'integrated', 'into', 'critical', 'systems', 'several', 'methods', 'to', 'attack', 'these', 'systems', 'have', 'been', 'developed', 'these', 'adversarial', 'attacks', 'make', 'imperceptible', 'modifications', 'to', 'an', 'image', 'that', 'fool', 'dnn', 'classifiers', 'we', 'present', 'an', 'adaptive', 'jpeg', 'encoder', 'which', 'defends', 'against', 'many', 'of', 'these', 'attacks', 'experimentally', 'we', 'show', 'that', 'our', 'method', 'produces', 'images', 'with', 'high', 'visual', 'quality', 'while', 'greatly', 'reducing', 'the', 'potency', 'of', 'stateoftheart', 'attacks', 'our', 'algorithm', 'requires', 'only', 'a', 'modest', 'increase', 'in', 'encoding', 'time', 'produces', 'a', 'compressed', 'image', 'which', 'can', 'be', 'decompressed', 'by', 'an', 'offtheshelf', 'jpeg', 'decoder', 'and', 'classified', 'by', 'an', 'unmodified', 'classifier']] | [-0.07845938479233729, -0.024330499007881277, -0.09242957593186905, 0.05483834523903696, -0.04426988316209693, -0.27280919077365023, 0.0006533043679634208, 0.4884694501855656, -0.2571751969033166, -0.32870657028336275, 0.11154355777697147, -0.2652157050900553, -0.23875168957502435, 0.23158807565479564, -0.19687363655355417, 0.15858047900623398, 0.09853661296104914, 0.004717198751964851, -0.045343626819943125, -0.41066070472527494, 0.26642222345659605, 0.10190347131850247, 0.3239857082206168, -0.039441871211717, 0.12475708525039647, -0.07132319860944622, 0.018895753860277566, -0.0050109120281903365, 0.021531921261645805, 0.1163461632830532, 0.3338285501850279, 0.23019009962383855, 0.32760913027940614, -0.4492726095020771, -0.26368969509280044, 0.06579817315367492, 0.17925405630253646, 0.18045364659965824, -0.08597525149020122, -0.398766412809001, 0.1863325919585278, -0.2224319658683319, 0.06819952776361453, -0.20288673766741627, -0.04861817770564046, -0.00905852190427188, -0.23437461689310637, -0.04664427570410465, 0.12190595965489354, 0.04793155755063421, -0.011925188648073296, -0.09564327695652058, 0.006780542884218066, 0.12433331169580158, 0.02219130481152158, 0.07545971981078191, 0.19304272244243245, -0.17723473013917868, -0.15126473057622972, 0.3026176935846084, -0.051352679825044774, -0.1781657973255374, 0.18260104566028243, 0.07128165512296715, -0.12755870776937195, 0.19249662749077145, 0.26038477133077226, 0.08537645413981457, -0.1401699010380789, -0.03498723938460707, -0.012726389979453464, 0.2613523274267975, 0.08563184663653374, 0.021024615248959315, 0.13899791901440997, 0.2220791043811723, 0.02945781972099978, 0.186344931842024, -0.13420309575311348, 0.012266375673444647, -0.14522612355649472, -0.07117759766498287, -0.17238357771972293, -0.017272519545727654, -0.11708322131598833, -0.14831648899830485, 0.35400510656794437, 0.2738606005956076, 0.22538478168609896, 0.1099388230371436, 0.4233365372509549, 0.03086396676463712, 0.18630621984208884, 0.1365726369384088, 0.23457147916778923, -0.008398779726734288, 0.08169692211882457, -0.11159051660154211, 0.13245414088627225, 0.030602833383569592] |
1,803.00941 | Ignition of detonation in accreted helium | Sub-Chandrasekhar CO white dwarfs accreting helium have been considered as
candidates for Type Ia supernova(SNIa) progenitors since the early 1980s
(helium shell mass $> 0.1 M_\odot $). These models, once detonated did not fit
the observed spectra and light curve of typical SNIa observations. New
theoretical work examined detonations on much less massive ($< 0.05 M_\odot $)
envelopes. They find stable detonations that lead to light curves, spectra and
abundances that compare relatively well with the observational data. The exact
mechanism leading to the ignition of helium detonation is a key issue, since it
is a mandatory first step for the whole scenario. As the flow of the accreted
envelope is unstable to convection long before any hydrodynamic phenomena
develops, a multidimensional approach is needed in order to study the ignition
process. The complex convective reactive flow is challenging to any
hydrodynamical solver. To the best of our knowledge, all previous 2D studies
ignited the detonation artificially. We present here, for the first time, fully
consistent results from two hydrodynamical 2D solvers that adopt two
independent accurate schemes. For both solvers an effort was made to overcome
the problematics raised by the finite resolution and numerical diffusion by the
advective terms. Our best models lead to the ignition of a detonation in a
convective cell. Our results are robust and the agreement between the two
different numerical approaches is very good.
| astro-ph.HE | subchandrasekhar co white dwarfs accreting helium have been considered as candidates for type ia supernovasnia progenitors since the early 1980s helium shell mass 01 m_odot these models once detonated did not fit the observed spectra and light curve of typical snia observations new theoretical work examined detonations on much less massive 005 m_odot envelopes they find stable detonations that lead to light curves spectra and abundances that compare relatively well with the observational data the exact mechanism leading to the ignition of helium detonation is a key issue since it is a mandatory first step for the whole scenario as the flow of the accreted envelope is unstable to convection long before any hydrodynamic phenomena develops a multidimensional approach is needed in order to study the ignition process the complex convective reactive flow is challenging to any hydrodynamical solver to the best of our knowledge all previous 2d studies ignited the detonation artificially we present here for the first time fully consistent results from two hydrodynamical 2d solvers that adopt two independent accurate schemes for both solvers an effort was made to overcome the problematics raised by the finite resolution and numerical diffusion by the advective terms our best models lead to the ignition of a detonation in a convective cell our results are robust and the agreement between the two different numerical approaches is very good | [['subchandrasekhar', 'co', 'white', 'dwarfs', 'accreting', 'helium', 'have', 'been', 'considered', 'as', 'candidates', 'for', 'type', 'ia', 'supernovasnia', 'progenitors', 'since', 'the', 'early', '1980s', 'helium', 'shell', 'mass', '01', 'm_odot', 'these', 'models', 'once', 'detonated', 'did', 'not', 'fit', 'the', 'observed', 'spectra', 'and', 'light', 'curve', 'of', 'typical', 'snia', 'observations', 'new', 'theoretical', 'work', 'examined', 'detonations', 'on', 'much', 'less', 'massive', '005', 'm_odot', 'envelopes', 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1,803.00942 | Not All Samples Are Created Equal: Deep Learning with Importance
Sampling | Deep neural network training spends most of the computation on examples that
are properly handled, and could be ignored. We propose to mitigate this
phenomenon with a principled importance sampling scheme that focuses
computation on "informative" examples, and reduces the variance of the
stochastic gradients during training. Our contribution is twofold: first, we
derive a tractable upper bound to the per-sample gradient norm, and second we
derive an estimator of the variance reduction achieved with importance
sampling, which enables us to switch it on when it will result in an actual
speedup. The resulting scheme can be used by changing a few lines of code in a
standard SGD procedure, and we demonstrate experimentally, on image
classification, CNN fine-tuning, and RNN training, that for a fixed wall-clock
time budget, it provides a reduction of the train losses of up to an order of
magnitude and a relative improvement of test errors between 5% and 17%.
| cs.LG | deep neural network training spends most of the computation on examples that are properly handled and could be ignored we propose to mitigate this phenomenon with a principled importance sampling scheme that focuses computation on informative examples and reduces the variance of the stochastic gradients during training our contribution is twofold first we derive a tractable upper bound to the persample gradient norm and second we derive an estimator of the variance reduction achieved with importance sampling which enables us to switch it on when it will result in an actual speedup the resulting scheme can be used by changing a few lines of code in a standard sgd procedure and we demonstrate experimentally on image classification cnn finetuning and rnn training that for a fixed wallclock time budget it provides a reduction of the train losses of up to an order of magnitude and a relative improvement of test errors between 5 and 17 | [['deep', 'neural', 'network', 'training', 'spends', 'most', 'of', 'the', 'computation', 'on', 'examples', 'that', 'are', 'properly', 'handled', 'and', 'could', 'be', 'ignored', 'we', 'propose', 'to', 'mitigate', 'this', 'phenomenon', 'with', 'a', 'principled', 'importance', 'sampling', 'scheme', 'that', 'focuses', 'computation', 'on', 'informative', 'examples', 'and', 'reduces', 'the', 'variance', 'of', 'the', 'stochastic', 'gradients', 'during', 'training', 'our', 'contribution', 'is', 'twofold', 'first', 'we', 'derive', 'a', 'tractable', 'upper', 'bound', 'to', 'the', 'persample', 'gradient', 'norm', 'and', 'second', 'we', 'derive', 'an', 'estimator', 'of', 'the', 'variance', 'reduction', 'achieved', 'with', 'importance', 'sampling', 'which', 'enables', 'us', 'to', 'switch', 'it', 'on', 'when', 'it', 'will', 'result', 'in', 'an', 'actual', 'speedup', 'the', 'resulting', 'scheme', 'can', 'be', 'used', 'by', 'changing', 'a', 'few', 'lines', 'of', 'code', 'in', 'a', 'standard', 'sgd', 'procedure', 'and', 'we', 'demonstrate', 'experimentally', 'on', 'image', 'classification', 'cnn', 'finetuning', 'and', 'rnn', 'training', 'that', 'for', 'a', 'fixed', 'wallclock', 'time', 'budget', 'it', 'provides', 'a', 'reduction', 'of', 'the', 'train', 'losses', 'of', 'up', 'to', 'an', 'order', 'of', 'magnitude', 'and', 'a', 'relative', 'improvement', 'of', 'test', 'errors', 'between', '5', 'and', '17']] | [-0.07569790891702137, 0.04732855386504408, -0.06398489113405137, 0.08372694592201901, -0.07880464856993527, -0.14176905794189343, 0.10445277438979717, 0.43060469062578294, -0.26327746826028753, -0.36349304640485397, 0.09565731751912784, -0.2272031540877276, -0.1504284944595589, 0.22488743732833574, -0.1405200054084191, 0.046590429169666624, 0.11534262611906254, 0.006248828690619238, -0.08354509530528899, -0.3419070004906145, 0.2571586930463391, 0.08850876499162687, 0.29733701661349304, 0.02530843939842476, 0.14635693575706213, -0.04152638103151994, -0.026272357414446532, -0.00803916473054297, -0.07846252916511261, 0.1684268168834669, 0.2256860586664369, 0.1619560435894997, 0.34831764962404005, -0.39738164682301785, -0.1881743676539871, 0.12476259768910465, 0.14605262764079888, 0.15188421425785148, -0.016699521789597646, -0.24034254713765074, 0.09341817502337958, -0.17006498022725985, -0.03826530637640146, -0.1352180916755911, -0.02663729379269024, -0.033903588318536355, -0.31943220555061297, 0.05528516742370782, 0.08139424261683034, 0.03059077202323674, -0.000498249264614236, -0.09779479938348935, 0.02885078679800274, 0.11650687304659627, 0.04560314694930229, 0.0615324649749504, 0.12227601343165001, -0.1107189204228381, -0.10125394826454501, 0.33806934966555524, -0.09453504097632943, -0.2113750608160063, 0.13832424429034995, -0.05119479336745797, -0.11616451232844303, 0.1409635843739154, 0.2364024458483102, 0.10607650349397332, -0.13495090741921412, 0.013223352139410113, 0.018531066798154385, 0.1871051765016971, 0.04297746971189495, -0.010243565569661798, 0.11288986038416624, 0.22739452312309896, 0.1189800452621233, 0.17172776615404856, -0.150326305099072, -0.0793444890198448, -0.3048607881571497, -0.1345959456190617, -0.19706529929873443, 0.02128554550900815, -0.12249950153927784, -0.12575618156622495, 0.39225042586589437, 0.2151111906379341, 0.22558999256260934, 0.11568750127053429, 0.34351451676939765, 0.1175900039086569, 0.10282879073713576, 0.10666698864360731, 0.21976994135447087, 0.07760962926662497, 0.06620188798875579, -0.2056428695309742, 0.09802372363966799, 0.04865774928381847] |
1,803.00943 | Widespread HCN maser emission in carbon-rich evolved stars | Context. HCN is a major constituent of the circumstellar envelopes of
carbon-rich evolved stars, and rotational lines from within its vibrationally
excited states probe parts of these regions closest to the stellar surface. A
number of such lines are known to show maser action. Historically, in one of
them, the 177 GHz $J=2\rightarrow1$ line in the $l$-doubled bending mode has
been found to show relatively strong maser action, with results only published
for a single object, the archetypical high-mass loss asymptotic giant branch
(AGB) star IRC+10216. Aims. To examine how common 177 GHz HCN maser emission
is, we conducted an exploratory survey for this line toward a select sample of
carbon-rich asymptotic giant branch stars that are observable from the southern
hemisphere. Methods. We used the Atacama Pathfinder Experiment 12 meter
submillimeter Telescope (APEX) equipped with a new receiver to simultaneously
observe three $J=2\rightarrow1$ HCN rotational transitions, the $(0,1^{{1}_{\rm
c}},0)$ and $(0,1^{{1}_{\rm d}},0)$ $l$-doublet components, and the line from
the (0,0,0) ground state. Results. The $(0,1^{{1}_{\rm c}},0)$ maser line is
detected toward 11 of 13 observed sources, which all show emission in the
(0,0,0) transition. In most of the sources, the peak intensity of the
$(0,1^{{1}_{\rm c}},0)$ line rivals that of the (0,0,0) line; in two sources,
it is even stronger. Except for the object with the highest mass-loss rate,
IRC+10216, the $(0,1^{{1}_{\rm c}},0)$ line covers a smaller velocity range
than the (0,0,0) line. Conclusions. Maser emission in the 177 GHz
$J=2\rightarrow1$ $(0,1^{{1}_{\rm c}},0)$ line of HCN appears to be common in
carbon-rich AGB stars. (Abbreviated)
| astro-ph.SR | context hcn is a major constituent of the circumstellar envelopes of carbonrich evolved stars and rotational lines from within its vibrationally excited states probe parts of these regions closest to the stellar surface a number of such lines are known to show maser action historically in one of them the 177 ghz j2rightarrow1 line in the ldoubled bending mode has been found to show relatively strong maser action with results only published for a single object the archetypical highmass loss asymptotic giant branch agb star irc10216 aims to examine how common 177 ghz hcn maser emission is we conducted an exploratory survey for this line toward a select sample of carbonrich asymptotic giant branch stars that are observable from the southern hemisphere methods we used the atacama pathfinder experiment 12 meter submillimeter telescope apex equipped with a new receiver to simultaneously observe three j2rightarrow1 hcn rotational transitions the 011_rm c0 and 011_rm d0 ldoublet components and the line from the 000 ground state results the 011_rm c0 maser line is detected toward 11 of 13 observed sources which all show emission in the 000 transition in most of the sources the peak intensity of the 011_rm c0 line rivals that of the 000 line in two sources it is even stronger except for the object with the highest massloss rate irc10216 the 011_rm c0 line covers a smaller velocity range than the 000 line conclusions maser emission in the 177 ghz j2rightarrow1 011_rm c0 line of hcn appears to be common in carbonrich agb stars abbreviated | [['context', 'hcn', 'is', 'a', 'major', 'constituent', 'of', 'the', 'circumstellar', 'envelopes', 'of', 'carbonrich', 'evolved', 'stars', 'and', 'rotational', 'lines', 'from', 'within', 'its', 'vibrationally', 'excited', 'states', 'probe', 'parts', 'of', 'these', 'regions', 'closest', 'to', 'the', 'stellar', 'surface', 'a', 'number', 'of', 'such', 'lines', 'are', 'known', 'to', 'show', 'maser', 'action', 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1,803.00944 | Dolphin: a task orchestration language for autonomous vehicle networks | We present Dolphin, an extensible programming language for autonomous vehicle
networks. A Dolphin program expresses an orchestrated execution of tasks
defined compositionally for multiple vehicles. Building upon the base case of
elementary one-vehicle tasks, the built-in operators include support for
composing tasks in several forms, for instance according to concurrent,
sequential, or event-based task flow. The language is implemented as a Groovy
DSL, facilitating extension and integration with external software packages, in
particular robotic toolkits. The paper describes the Dolphin language, its
integration with an open-source toolchain for autonomous vehicles, and results
from field tests using unmanned underwater vehicles (UUVs) and unmanned aerial
vehicles (UAVs).
| cs.RO | we present dolphin an extensible programming language for autonomous vehicle networks a dolphin program expresses an orchestrated execution of tasks defined compositionally for multiple vehicles building upon the base case of elementary onevehicle tasks the builtin operators include support for composing tasks in several forms for instance according to concurrent sequential or eventbased task flow the language is implemented as a groovy dsl facilitating extension and integration with external software packages in particular robotic toolkits the paper describes the dolphin language its integration with an opensource toolchain for autonomous vehicles and results from field tests using unmanned underwater vehicles uuvs and unmanned aerial vehicles uavs | [['we', 'present', 'dolphin', 'an', 'extensible', 'programming', 'language', 'for', 'autonomous', 'vehicle', 'networks', 'a', 'dolphin', 'program', 'expresses', 'an', 'orchestrated', 'execution', 'of', 'tasks', 'defined', 'compositionally', 'for', 'multiple', 'vehicles', 'building', 'upon', 'the', 'base', 'case', 'of', 'elementary', 'onevehicle', 'tasks', 'the', 'builtin', 'operators', 'include', 'support', 'for', 'composing', 'tasks', 'in', 'several', 'forms', 'for', 'instance', 'according', 'to', 'concurrent', 'sequential', 'or', 'eventbased', 'task', 'flow', 'the', 'language', 'is', 'implemented', 'as', 'a', 'groovy', 'dsl', 'facilitating', 'extension', 'and', 'integration', 'with', 'external', 'software', 'packages', 'in', 'particular', 'robotic', 'toolkits', 'the', 'paper', 'describes', 'the', 'dolphin', 'language', 'its', 'integration', 'with', 'an', 'opensource', 'toolchain', 'for', 'autonomous', 'vehicles', 'and', 'results', 'from', 'field', 'tests', 'using', 'unmanned', 'underwater', 'vehicles', 'uuvs', 'and', 'unmanned', 'aerial', 'vehicles', 'uavs']] | [-0.22142014340768812, 0.0058640608245007315, -0.022910339911513537, 0.012878303602563887, -0.1509111339827656, -0.17547720575759423, 0.009326226962956338, 0.43004432957149247, -0.24613594757483423, -0.3602000399700646, 0.09355984189891194, -0.20989321589072063, -0.1637279900379741, 0.2830399470381186, -0.15907638892305345, 0.1159429810291328, 0.1417694626357949, 0.032888027943345235, 0.05133848726942692, -0.1728929189413569, 0.2457909883922237, -0.01506219356709603, 0.27356594386148875, 0.012570631179452231, 0.15812856371252282, 0.07076112693175673, 0.0027558707577872623, -0.04720499719448164, -0.021932549988263224, 0.1785680298960216, 0.43524179107835065, 0.23739626605371103, 0.29918992845341563, -0.5147034095925758, -0.18585286968731257, 0.025107631306118757, 0.12671146447797424, 0.026532751571350884, 0.0015460231196750137, -0.40760904576548024, 0.060611044260157836, -0.2989053906402686, -0.08066726762584402, -0.0836003000115596, 0.0188571769004977, 0.07544136126932588, -0.2821219455760461, -0.143118664848356, 0.023766467057267947, 0.21901068904265328, -0.08859753686803601, -0.050416318247619184, 0.02136962576904922, 0.21021656057508029, 0.028412388219470973, 0.031071334740472505, 0.21491559581758762, -0.13916555154634622, -0.22113575527751908, 0.417177390872311, 0.0027226947572362584, -0.20687992313836626, 0.2115916967057415, 0.10688004289184092, -0.18639122649068826, 0.038380194921046495, 0.24375344663677576, 0.09452739321780436, -0.21858938648681908, 0.07475508835666261, 0.02115267174752447, 0.16364453034927545, 0.05306927010742495, -0.08205066931443018, 0.20729857148801528, 0.29038887128948226, 0.11780112873744762, 0.11632057688271319, -0.012368337291934825, -0.11377478523396896, -0.21429372404857983, -0.2242471216431846, -0.12232218217556771, -0.07183985489881733, -0.032804734281120705, -0.14795155155707548, 0.3294773543009408, 0.17233453348315167, 0.04710967477085521, 0.1463244448696236, 0.4139911023158472, 0.008183148715563696, 0.1266961881339333, 0.15397274353172974, 0.04375414655982399, -0.01413079212844661, 0.23802753758517284, -0.16698200301520383, 0.08173945169326244, 0.06138674805429086] |
1,803.00945 | Low-energy Effects of Lepton Flavour Universality Violation | The persisting anomalous data in semileptonic B-decays point towards New
Physics models exhibiting large sources of Lepton Flavour Universality
Violation. In this work we generalise previous studies by considering
frameworks which include an enlarged set of semileptonic four-fermion operators
invariant under the SM gauge group, with New Physics affecting mainly the third
generation. We derive the low-energy effective Lagrangian including the leading
electroweak corrections, mandatory to obtain reliable predictions. As a
particularly interesting case, we analyse the scenario where the dominant New
Physics effects are encoded in the Wilson coefficient C_9, as favoured by
global fit analyses of b -> s data. We find that also in this case the
stringent experimental bounds on Z-pole observables and tau decays challenge a
simultaneous explanation of charged and neutral-current non-standard data.
| hep-ph | the persisting anomalous data in semileptonic bdecays point towards new physics models exhibiting large sources of lepton flavour universality violation in this work we generalise previous studies by considering frameworks which include an enlarged set of semileptonic fourfermion operators invariant under the sm gauge group with new physics affecting mainly the third generation we derive the lowenergy effective lagrangian including the leading electroweak corrections mandatory to obtain reliable predictions as a particularly interesting case we analyse the scenario where the dominant new physics effects are encoded in the wilson coefficient c_9 as favoured by global fit analyses of b s data we find that also in this case the stringent experimental bounds on zpole observables and tau decays challenge a simultaneous explanation of charged and neutralcurrent nonstandard data | [['the', 'persisting', 'anomalous', 'data', 'in', 'semileptonic', 'bdecays', 'point', 'towards', 'new', 'physics', 'models', 'exhibiting', 'large', 'sources', 'of', 'lepton', 'flavour', 'universality', 'violation', 'in', 'this', 'work', 'we', 'generalise', 'previous', 'studies', 'by', 'considering', 'frameworks', 'which', 'include', 'an', 'enlarged', 'set', 'of', 'semileptonic', 'fourfermion', 'operators', 'invariant', 'under', 'the', 'sm', 'gauge', 'group', 'with', 'new', 'physics', 'affecting', 'mainly', 'the', 'third', 'generation', 'we', 'derive', 'the', 'lowenergy', 'effective', 'lagrangian', 'including', 'the', 'leading', 'electroweak', 'corrections', 'mandatory', 'to', 'obtain', 'reliable', 'predictions', 'as', 'a', 'particularly', 'interesting', 'case', 'we', 'analyse', 'the', 'scenario', 'where', 'the', 'dominant', 'new', 'physics', 'effects', 'are', 'encoded', 'in', 'the', 'wilson', 'coefficient', 'c_9', 'as', 'favoured', 'by', 'global', 'fit', 'analyses', 'of', 'b', 's', 'data', 'we', 'find', 'that', 'also', 'in', 'this', 'case', 'the', 'stringent', 'experimental', 'bounds', 'on', 'zpole', 'observables', 'and', 'tau', 'decays', 'challenge', 'a', 'simultaneous', 'explanation', 'of', 'charged', 'and', 'neutralcurrent', 'nonstandard', 'data']] | [-0.0991851497474272, 0.19291243574582495, -0.01584718181493372, 0.15549238135099586, -0.08189851928727876, -0.1917763645742525, 0.08230764230484056, 0.23766932195394475, -0.21046530967032595, -0.2932153789297445, 0.028407895333657507, -0.31283695912838994, -0.060795065150159644, 0.14714302599713847, 0.027419009471984168, 0.08334898965767934, 0.06159325537009863, -0.03437497436971171, -0.08700601170312439, -0.19425698188661045, 0.2957372859309544, 0.05964244629285531, 0.24864425555460912, 0.10025267866649301, -0.005543155069062777, 0.0027854681811732007, -0.12394735644193133, -0.04242498603707645, -0.15549768055126378, 0.08774280987563543, 0.2051886200746651, 0.07659798945269358, 0.09630074232336483, -0.39409741530107567, -0.16013692354317755, 0.1459390260752116, 0.144041709278099, 0.12397217689522222, -0.07155176186006429, -0.3222533480038692, 0.04004885104222922, -0.17332756878386135, -0.12923998973656126, -0.14088608940073755, -0.005440770415589213, -0.09654820185824065, -0.3504527150253125, 0.10209858888674717, -0.010773206781095723, 0.07072034312295727, 0.00397919821079995, -0.17584306666321936, 0.01811516367160948, 0.07690883756367839, 0.1647126708230644, 0.03393644122115802, 0.1312701442288926, -0.1748776636486582, -0.18421515129011823, 0.4443284260632936, -0.10610210231379824, -0.16825107644035597, 0.15261557848134544, -0.20533967077062698, -0.2543395388264571, 0.08114296661295839, 0.21594704652306973, 0.07955445395737115, -0.20485566051866044, 0.17359546414354554, -0.0623886581233819, 0.09197828497167393, 0.008014264403755078, 0.08867907779858797, 0.20873194669547956, 0.18183830555790337, 0.025101002367591718, 0.06867314560440718, -0.06131228697313418, -0.06434035793790827, -0.4172111600491917, -0.07981821995463179, -0.054735953684939886, 0.06478085061826278, -0.08025150650405521, -0.10648570053535877, 0.39215599261842726, 0.15592305950485752, 0.23406625798179448, -0.0006923420614839415, 0.28048436577955727, 0.06463921088027291, 0.08749404850686915, 0.03829142353060888, 0.29582438616489526, 0.11957920236818609, 0.11810346413039952, -0.23636812499171356, 0.03604537438150146, 0.08152861670896527] |
1,803.00946 | LINER galaxy properties and the local environment | We analyse the properties of a sample of 5560 LINER galaxies selected from
SDSS-DR12 at low redshift, for a complete range of local density environments.
The host LINER galaxies were studied and compared with a well-defined control
sample of 5553 non-LINER galaxies matched in red shift, luminosity, morphology
and local density. By studying the distributions of galaxy colours and the
stellar age population, we find that LINERs are redder and older than the
control sample over a wide range of densities.In addition, LINERs are older
than the control sample, at a given galaxy colour, indicating that some
external process could have accelerated the evolution of the stellar
population. The analysis of the host properties shows that the control sample
exhibits a strong relation between colours, ages and the local density, while
more than 90 per cent of the LINERs are redder and older than the mean values,
independently of the neighbourhood density. Furthermore, a detailed study in
three local density ranges shows that, while control sample galaxies are redder
and older as a function of stellar mass and density, LINER galaxies mismatch
the known morphology-density relation of galaxies without low-ionization
features. The results support the contribution of hot and old stars to the
low-ionization emission although the contribution of nuclear activity is not
discarded.
| astro-ph.GA | we analyse the properties of a sample of 5560 liner galaxies selected from sdssdr12 at low redshift for a complete range of local density environments the host liner galaxies were studied and compared with a welldefined control sample of 5553 nonliner galaxies matched in red shift luminosity morphology and local density by studying the distributions of galaxy colours and the stellar age population we find that liners are redder and older than the control sample over a wide range of densitiesin addition liners are older than the control sample at a given galaxy colour indicating that some external process could have accelerated the evolution of the stellar population the analysis of the host properties shows that the control sample exhibits a strong relation between colours ages and the local density while more than 90 per cent of the liners are redder and older than the mean values independently of the neighbourhood density furthermore a detailed study in three local density ranges shows that while control sample galaxies are redder and older as a function of stellar mass and density liner galaxies mismatch the known morphologydensity relation of galaxies without lowionization features the results support the contribution of hot and old stars to the lowionization emission although the contribution of nuclear activity is not discarded | [['we', 'analyse', 'the', 'properties', 'of', 'a', 'sample', 'of', '5560', 'liner', 'galaxies', 'selected', 'from', 'sdssdr12', 'at', 'low', 'redshift', 'for', 'a', 'complete', 'range', 'of', 'local', 'density', 'environments', 'the', 'host', 'liner', 'galaxies', 'were', 'studied', 'and', 'compared', 'with', 'a', 'welldefined', 'control', 'sample', 'of', '5553', 'nonliner', 'galaxies', 'matched', 'in', 'red', 'shift', 'luminosity', 'morphology', 'and', 'local', 'density', 'by', 'studying', 'the', 'distributions', 'of', 'galaxy', 'colours', 'and', 'the', 'stellar', 'age', 'population', 'we', 'find', 'that', 'liners', 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1,803.00947 | A nonlinear Stokes-Biot model for the interaction of a non-Newtonian
fluid with poroelastic media | We develop and analyze a model for the interaction of a quasi-Newtonian free
fluid with a poroelastic medium. The flow in the fluid region is described by
the nonlinear Stokes equations and in the poroelastic medium by the nonlinear
quasi-static Biot model. Equilibrium and kinematic conditions are imposed on
the interface. We establish existence and uniqueness of a solution to the weak
formulation and its semidiscrete continuous-in-time finite element
approximation. We present error analysis, complemented by numerical
experiments.
| math.NA | we develop and analyze a model for the interaction of a quasinewtonian free fluid with a poroelastic medium the flow in the fluid region is described by the nonlinear stokes equations and in the poroelastic medium by the nonlinear quasistatic biot model equilibrium and kinematic conditions are imposed on the interface we establish existence and uniqueness of a solution to the weak formulation and its semidiscrete continuousintime finite element approximation we present error analysis complemented by numerical experiments | [['we', 'develop', 'and', 'analyze', 'a', 'model', 'for', 'the', 'interaction', 'of', 'a', 'quasinewtonian', 'free', 'fluid', 'with', 'a', 'poroelastic', 'medium', 'the', 'flow', 'in', 'the', 'fluid', 'region', 'is', 'described', 'by', 'the', 'nonlinear', 'stokes', 'equations', 'and', 'in', 'the', 'poroelastic', 'medium', 'by', 'the', 'nonlinear', 'quasistatic', 'biot', 'model', 'equilibrium', 'and', 'kinematic', 'conditions', 'are', 'imposed', 'on', 'the', 'interface', 'we', 'establish', 'existence', 'and', 'uniqueness', 'of', 'a', 'solution', 'to', 'the', 'weak', 'formulation', 'and', 'its', 'semidiscrete', 'continuousintime', 'finite', 'element', 'approximation', 'we', 'present', 'error', 'analysis', 'complemented', 'by', 'numerical', 'experiments']] | [-0.13866015357407144, 0.0745327070964357, -0.10500510721706235, 0.019799712712530237, -0.049251157323268645, -0.10730818727722344, 0.006493997434751155, 0.3253624492014448, -0.29115771638372767, -0.24459212651858345, 0.13912979748923904, -0.24670985515396565, -0.12443947626086764, 0.11109058155964774, 0.002206163238719082, 0.10390485953823185, 0.05342332079099157, -0.04684298361341158, -0.03957533525326886, -0.1264507339741939, 0.31535357425514704, 0.007443297917071061, 0.2476389118237421, 0.06505333719393, 0.1683732856853077, -0.014138643367168231, -0.053907062810582995, 0.09805502176571351, -0.22008353379817727, 0.06914410188507575, 0.2105285825919754, 0.02251323766242235, 0.2674874367001347, -0.4840471297860719, -0.27563711980548805, 0.004204886553522486, 0.10005514121327835, 0.11531199641430225, -0.07764955189299937, -0.28568148728794396, 0.0652929790592633, -0.1419056459592703, -0.1768487884889906, -0.07818670838307111, -0.04021176338816682, 0.06547902819390099, -0.30587103662009424, 0.14438738723775038, 0.04941517222696581, 0.061937853102930464, -0.1524726580067251, -0.03613326031093796, 0.000644667289004876, 0.053243022065800734, 0.02982547256247833, -0.053765215552770175, 0.08667680336377369, -0.15676181195064998, 0.009215986284498986, 0.4243423692070139, -0.12806900091564807, -0.27474613647716933, 0.19447464749025992, -0.09578754646011078, -0.006929067912726448, 0.13160257569203773, 0.21010749770376164, 0.12688718824527967, -0.1712928291481848, 0.11717687508634975, -0.09458674917904994, 0.138086166471625, 0.021445461784867056, -0.09224197531166749, 0.10418831351666878, 0.20350641100548017, 0.050467541763702266, 0.16240798994803277, -0.04296874252852435, -0.12127201581517091, -0.38078860890788907, -0.13787191859983766, -0.1334153398966942, 0.0027227389965003403, -0.13202959848235762, -0.1867785857966504, 0.3632894416865057, 0.13206343524302028, 0.1009560657832294, 0.05106867267153202, 0.29847103505371475, 0.1568976941291625, -0.06481537798968837, 0.12209816080613588, 0.29689847303029054, 0.22345305014497194, 0.14752289616407302, -0.2700174539290273, 0.026806543903568618, 0.17993629006788325] |
1,803.00948 | Alternatives for Generating a Reduced Basis to Solve the Hyperspectral
Diffuse Optical Tomography Model | The Reduced Basis Method (RBM) is a model reduction technique used to solve
parametric PDEs that relies upon a basis set of solutions to the PDE at
specific parameter values. To generate this reduced basis, the set of a small
number of parameter values must be strategically chosen. We apply a Metropolis
algorithm and a gradient algorithm to find the set of parameters and compare
them to the standard greedy algorithm most commonly used in the RBM. We test
our methods by using the RBM to solve a simplified version of the governing
partial differential equation for hyperspectral diffuse optical tomography
(hyDOT). The governing equation for hyDOT is an elliptic PDE parameterized by
the wavelength of the laser source. For this one-dimensional problem, we find
that both the Metropolis and gradient algorithms are potentially superior
alternatives to the greedy algorithm in that they generate a reduced basis
which produces solutions with a smaller relative error with respect to
solutions found using the finite element method and in less time.
| math.NA math.AP | the reduced basis method rbm is a model reduction technique used to solve parametric pdes that relies upon a basis set of solutions to the pde at specific parameter values to generate this reduced basis the set of a small number of parameter values must be strategically chosen we apply a metropolis algorithm and a gradient algorithm to find the set of parameters and compare them to the standard greedy algorithm most commonly used in the rbm we test our methods by using the rbm to solve a simplified version of the governing partial differential equation for hyperspectral diffuse optical tomography hydot the governing equation for hydot is an elliptic pde parameterized by the wavelength of the laser source for this onedimensional problem we find that both the metropolis and gradient algorithms are potentially superior alternatives to the greedy algorithm in that they generate a reduced basis which produces solutions with a smaller relative error with respect to solutions found using the finite element method and in less time | [['the', 'reduced', 'basis', 'method', 'rbm', 'is', 'a', 'model', 'reduction', 'technique', 'used', 'to', 'solve', 'parametric', 'pdes', 'that', 'relies', 'upon', 'a', 'basis', 'set', 'of', 'solutions', 'to', 'the', 'pde', 'at', 'specific', 'parameter', 'values', 'to', 'generate', 'this', 'reduced', 'basis', 'the', 'set', 'of', 'a', 'small', 'number', 'of', 'parameter', 'values', 'must', 'be', 'strategically', 'chosen', 'we', 'apply', 'a', 'metropolis', 'algorithm', 'and', 'a', 'gradient', 'algorithm', 'to', 'find', 'the', 'set', 'of', 'parameters', 'and', 'compare', 'them', 'to', 'the', 'standard', 'greedy', 'algorithm', 'most', 'commonly', 'used', 'in', 'the', 'rbm', 'we', 'test', 'our', 'methods', 'by', 'using', 'the', 'rbm', 'to', 'solve', 'a', 'simplified', 'version', 'of', 'the', 'governing', 'partial', 'differential', 'equation', 'for', 'hyperspectral', 'diffuse', 'optical', 'tomography', 'hydot', 'the', 'governing', 'equation', 'for', 'hydot', 'is', 'an', 'elliptic', 'pde', 'parameterized', 'by', 'the', 'wavelength', 'of', 'the', 'laser', 'source', 'for', 'this', 'onedimensional', 'problem', 'we', 'find', 'that', 'both', 'the', 'metropolis', 'and', 'gradient', 'algorithms', 'are', 'potentially', 'superior', 'alternatives', 'to', 'the', 'greedy', 'algorithm', 'in', 'that', 'they', 'generate', 'a', 'reduced', 'basis', 'which', 'produces', 'solutions', 'with', 'a', 'smaller', 'relative', 'error', 'with', 'respect', 'to', 'solutions', 'found', 'using', 'the', 'finite', 'element', 'method', 'and', 'in', 'less', 'time']] | [-0.038496911817797894, 0.03939294746393936, -0.11046850456051617, 0.04861557051076515, -0.09869593337795438, -0.14915702397764258, 0.05777423079924647, 0.37395905882406694, -0.3083809269461285, -0.29793689460645173, 0.10764752922251754, -0.24534681185352555, -0.11763496618420327, 0.23476487229930887, -0.049957276914356905, 0.10910658193521405, 0.09098416110004723, 0.0023919503430466682, -0.06100240097995627, -0.248483055943524, 0.27831420131556733, 0.03749670671332165, 0.27151545101897956, -0.05785261231788693, 0.14762666667453372, -0.03394575709664434, -0.0031666675604571253, 0.04554667580079221, -0.12053104367215818, 0.13351113488553257, 0.24766896186484652, 0.12545214488154507, 0.2959159132638759, -0.40343710909647174, -0.19319689911914184, 0.12966402579572867, 0.1384053126861997, 0.15164878106601645, -0.011293951051375834, -0.23044778816071312, 0.10054351682857385, -0.13903867510373336, -0.1332412400343538, -0.09948438322088907, -0.01654398071816599, 0.03391238513262116, -0.32504259676468616, 0.04328508555514396, -0.0007903513912515875, 0.0013843937556551405, -0.07510450580322663, -0.11482389473505097, 0.008818158011927583, 0.03133501208899818, 0.007922954437840928, 0.04423257902666882, 0.089903816437492, -0.1107023038809083, -0.11431639339670142, 0.39062844934844354, -0.0981234879337648, -0.28894669231931136, 0.14459002389401726, -0.06517971323571614, -0.0838290149781346, 0.15840880165824436, 0.18928686461890853, 0.16746755554368334, -0.15066595627105184, 0.06021532719643489, -0.040159790527306984, 0.2034134439799932, 0.0316718356358714, -0.037301146873058036, 0.07453075919531771, 0.17184063835212818, 0.10417176759939574, 0.13319327457898886, -0.07476502419852366, -0.09966906704443931, -0.26260752615259864, -0.13544723951704138, -0.18071194668575988, -0.011853608680260782, -0.12730435338666932, -0.19808655913324047, 0.3978860078203167, 0.19945132708014746, 0.19094814937671967, 0.058634546586069876, 0.2849889599040327, 0.1843211269105155, 0.06384550202872119, 0.08861616581468981, 0.20967763992020938, 0.12272620299065033, 0.06660955469399558, -0.2421831849345265, 0.05412424041630601, 0.13948577989069139] |
1,803.00949 | Tree Species Identification from Bark Images Using Convolutional Neural
Networks | Tree species identification using bark images is a challenging problem that
could prove useful for many forestry related tasks. However, while the recent
progress in deep learning showed impressive results on standard vision
problems, a lack of datasets prevented its use on tree bark species
classification. In this work, we present, and make publicly available, a novel
dataset called BarkNet 1.0 containing more than 23,000 high-resolution bark
images from 23 different tree species over a wide range of tree diameters. With
it, we demonstrate the feasibility of species recognition through bark images,
using deep learning. More specifically, we obtain an accuracy of 93.88% on
single crop, and an accuracy of 97.81% using a majority voting approach on all
of the images of a tree. We also empirically demonstrate that, for a fixed
number of images, it is better to maximize the number of tree individuals in
the training database, thus directing future data collection efforts.
| cs.CV | tree species identification using bark images is a challenging problem that could prove useful for many forestry related tasks however while the recent progress in deep learning showed impressive results on standard vision problems a lack of datasets prevented its use on tree bark species classification in this work we present and make publicly available a novel dataset called barknet 10 containing more than 23000 highresolution bark images from 23 different tree species over a wide range of tree diameters with it we demonstrate the feasibility of species recognition through bark images using deep learning more specifically we obtain an accuracy of 9388 on single crop and an accuracy of 9781 using a majority voting approach on all of the images of a tree we also empirically demonstrate that for a fixed number of images it is better to maximize the number of tree individuals in the training database thus directing future data collection efforts | [['tree', 'species', 'identification', 'using', 'bark', 'images', 'is', 'a', 'challenging', 'problem', 'that', 'could', 'prove', 'useful', 'for', 'many', 'forestry', 'related', 'tasks', 'however', 'while', 'the', 'recent', 'progress', 'in', 'deep', 'learning', 'showed', 'impressive', 'results', 'on', 'standard', 'vision', 'problems', 'a', 'lack', 'of', 'datasets', 'prevented', 'its', 'use', 'on', 'tree', 'bark', 'species', 'classification', 'in', 'this', 'work', 'we', 'present', 'and', 'make', 'publicly', 'available', 'a', 'novel', 'dataset', 'called', 'barknet', '10', 'containing', 'more', 'than', '23000', 'highresolution', 'bark', 'images', 'from', '23', 'different', 'tree', 'species', 'over', 'a', 'wide', 'range', 'of', 'tree', 'diameters', 'with', 'it', 'we', 'demonstrate', 'the', 'feasibility', 'of', 'species', 'recognition', 'through', 'bark', 'images', 'using', 'deep', 'learning', 'more', 'specifically', 'we', 'obtain', 'an', 'accuracy', 'of', '9388', 'on', 'single', 'crop', 'and', 'an', 'accuracy', 'of', '9781', 'using', 'a', 'majority', 'voting', 'approach', 'on', 'all', 'of', 'the', 'images', 'of', 'a', 'tree', 'we', 'also', 'empirically', 'demonstrate', 'that', 'for', 'a', 'fixed', 'number', 'of', 'images', 'it', 'is', 'better', 'to', 'maximize', 'the', 'number', 'of', 'tree', 'individuals', 'in', 'the', 'training', 'database', 'thus', 'directing', 'future', 'data', 'collection', 'efforts']] | [-0.04792601581338156, 0.041139336951011116, -0.03948158462452212, 0.03900175944574202, -0.08291513789261348, -0.10866953083467179, 0.08492466627687559, 0.42277716703124735, -0.23781364895617835, -0.3524226542029187, 0.07898333274505076, -0.3033845836210898, -0.15245817745668128, 0.2295373997430464, -0.09565456397218727, 0.06366943078395898, 0.2048485759013075, 0.06680372658822882, 0.005472505498849051, -0.30364847244192406, 0.28949711474246886, 0.025370875134141994, 0.3152414522100068, 0.0581418095236203, 0.12261958140190568, 0.005555785683866002, -0.03406817187696058, 0.0025011887230089328, -0.07805164859009413, 0.16899525672641558, 0.30980602322348494, 0.2423951795039772, 0.2929903104729754, -0.38732169588145454, -0.21223461423467502, 0.11228788452602825, 0.16086317903337707, 0.12123962284586971, -0.046891048905404444, -0.30517112425724535, 0.11389923574346253, -0.14557831459004725, -0.015329701439967673, -0.0986241604994958, 0.024174290520022623, -0.0465080837805688, -0.2479034356748391, 0.013382013977350556, -0.011151784406377518, 0.13379269053755133, -0.02972388287277059, -0.17635278002738855, 0.0030687884979048057, 0.1631402398343198, -0.003566144521372687, 0.05779871093890458, 0.11969357939424778, -0.21468494893021048, -0.1413136767847678, 0.3774913469280459, -0.07417459422494606, -0.16455908481331885, 0.2292591603135837, -0.0629712514775364, -0.190293497268086, 0.15301048846327162, 0.22479350276683507, 0.15879681728792525, -0.17117836581861698, 0.021206817965752345, -0.10061924804100081, 0.19180658556012387, 0.08185181372200052, -0.03235364354546427, 0.18353901447201343, 0.2879681499356306, 0.043218169697961696, 0.13321999524334005, -0.1324982408060398, -0.02525048280030636, -0.17218615522889244, -0.11578649009951685, -0.16327254598620225, 0.0031116512714345988, -0.11258524843766568, -0.12895970723567238, 0.39222550325365246, 0.2331462466846508, 0.23048814010843144, 0.12256837961046442, 0.3142965241629434, -0.012572327711447878, 0.11506747548073211, 0.060809736807928665, 0.1623290223552, 0.046959355298512445, 0.07507723773367041, -0.156068213853838, 0.07292023990042876, -0.006912755988244163] |
1,803.0095 | Multi-jet merged top-pair production including electroweak corrections | We present theoretical predictions for the production of top-quark pairs in
association with jets at the LHC including electroweak (EW) corrections. First,
we present and compare differential predictions at the fixed-order level for $t
\bar t$ and $t \bar t+$jet production at the LHC considering the dominant NLO
EW corrections of order $\mathcal{O}(\alpha_S^2 \alpha)$ and
$\mathcal{O}(\alpha_S^3 \alpha)$ respectively together with all additional
subleading Born and one-loop contributions. The NLO EW corrections are enhanced
at large energies and in particular alter the shape of the top transverse
momentum distribution, whose reliable modelling is crucial for many searches
for new physics at the energy frontier. Based on the fixed-order results we
motivate an approximation of the EW corrections valid at the percent level,
that allows us to readily incorporate the EW corrections in the MEPS@NLO
framework of Sherpa combined with OpenLoops. Subsequently, we present multi-jet
merged parton-level predictions for inclusive top-pair production incorporating
NLO QCD+EW corrections to $t \bar t$ and $t \bar t+$jet. Finally, we compare at
the particle-level against a recent 8 TeV measurement of the top transverse
momentum distribution performed by ATLAS in the lepton+jet channel. We find
very good agreement between the Monte Carlo prediction and the data when the EW
corrections are included.
| hep-ph hep-ex | we present theoretical predictions for the production of topquark pairs in association with jets at the lhc including electroweak ew corrections first we present and compare differential predictions at the fixedorder level for t bar t and t bar tjet production at the lhc considering the dominant nlo ew corrections of order mathcaloalpha_s2 alpha and mathcaloalpha_s3 alpha respectively together with all additional subleading born and oneloop contributions the nlo ew corrections are enhanced at large energies and in particular alter the shape of the top transverse momentum distribution whose reliable modelling is crucial for many searches for new physics at the energy frontier based on the fixedorder results we motivate an approximation of the ew corrections valid at the percent level that allows us to readily incorporate the ew corrections in the mepsnlo framework of sherpa combined with openloops subsequently we present multijet merged partonlevel predictions for inclusive toppair production incorporating nlo qcdew corrections to t bar t and t bar tjet finally we compare at the particlelevel against a recent 8 tev measurement of the top transverse momentum distribution performed by atlas in the leptonjet channel we find very good agreement between the monte carlo prediction and the data when the ew corrections are included | [['we', 'present', 'theoretical', 'predictions', 'for', 'the', 'production', 'of', 'topquark', 'pairs', 'in', 'association', 'with', 'jets', 'at', 'the', 'lhc', 'including', 'electroweak', 'ew', 'corrections', 'first', 'we', 'present', 'and', 'compare', 'differential', 'predictions', 'at', 'the', 'fixedorder', 'level', 'for', 't', 'bar', 't', 'and', 't', 'bar', 'tjet', 'production', 'at', 'the', 'lhc', 'considering', 'the', 'dominant', 'nlo', 'ew', 'corrections', 'of', 'order', 'mathcaloalpha_s2', 'alpha', 'and', 'mathcaloalpha_s3', 'alpha', 'respectively', 'together', 'with', 'all', 'additional', 'subleading', 'born', 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1,803.00951 | Multimodal Registration of Retinal Images Using Domain-Specific
Landmarks and Vessel Enhancement | The analysis of different image modalities is frequently performed in
ophthalmology as it provides complementary information for the diagnosis and
follow-up of relevant diseases, like hypertension or diabetes. This work
presents a hybrid method for the multimodal registration of color fundus
retinography and fluorescein angiography. The proposed method combines a
feature-based approach, using domain-specific landmarks, with an
intensity-based approach that employs a domain-adapted similarity metric. The
methodology is tested on a dataset of 59 image pairs containing both healthy
and pathological cases. The results show a satisfactory performance of the
proposed combined approach in this multimodal scenario, improving the
registration accuracy achieved by the feature-based and the intensity-based
approaches.
| cs.CV | the analysis of different image modalities is frequently performed in ophthalmology as it provides complementary information for the diagnosis and followup of relevant diseases like hypertension or diabetes this work presents a hybrid method for the multimodal registration of color fundus retinography and fluorescein angiography the proposed method combines a featurebased approach using domainspecific landmarks with an intensitybased approach that employs a domainadapted similarity metric the methodology is tested on a dataset of 59 image pairs containing both healthy and pathological cases the results show a satisfactory performance of the proposed combined approach in this multimodal scenario improving the registration accuracy achieved by the featurebased and the intensitybased approaches | [['the', 'analysis', 'of', 'different', 'image', 'modalities', 'is', 'frequently', 'performed', 'in', 'ophthalmology', 'as', 'it', 'provides', 'complementary', 'information', 'for', 'the', 'diagnosis', 'and', 'followup', 'of', 'relevant', 'diseases', 'like', 'hypertension', 'or', 'diabetes', 'this', 'work', 'presents', 'a', 'hybrid', 'method', 'for', 'the', 'multimodal', 'registration', 'of', 'color', 'fundus', 'retinography', 'and', 'fluorescein', 'angiography', 'the', 'proposed', 'method', 'combines', 'a', 'featurebased', 'approach', 'using', 'domainspecific', 'landmarks', 'with', 'an', 'intensitybased', 'approach', 'that', 'employs', 'a', 'domainadapted', 'similarity', 'metric', 'the', 'methodology', 'is', 'tested', 'on', 'a', 'dataset', 'of', '59', 'image', 'pairs', 'containing', 'both', 'healthy', 'and', 'pathological', 'cases', 'the', 'results', 'show', 'a', 'satisfactory', 'performance', 'of', 'the', 'proposed', 'combined', 'approach', 'in', 'this', 'multimodal', 'scenario', 'improving', 'the', 'registration', 'accuracy', 'achieved', 'by', 'the', 'featurebased', 'and', 'the', 'intensitybased', 'approaches']] | [-9.139081107563587e-05, -0.07646828836111519, -0.10595697083365206, 0.06367875782504823, -0.04560519550038382, -0.16683584752005584, 0.015211269547821048, 0.4343880481778755, -0.16689552510276848, -0.3246044183508792, 0.07706330636080456, -0.2873063297993546, -0.20572053807907217, 0.22841529215583492, -0.15889539553382254, 0.10316645857715688, 0.1616155895481416, 0.05124730975666177, -0.05256238964241032, -0.22968963824144198, 0.2623852988077037, 0.011865288847971946, 0.38020644322413644, 0.030893673637557195, 0.15515376787649793, 0.023196990648711766, -0.07419884543216557, 0.022816397120583767, -0.045194647640511586, 0.19005308690087852, 0.3171185016388569, 0.2393779441363973, 0.31106869943884263, -0.35293108599106654, -0.25359177539753086, 0.037614031180038775, 0.1160525458036076, 0.06903781041816953, -0.06342549663605254, -0.3820075906061251, 0.08327208768251702, -0.14658683231698538, -0.02536243366487666, -0.12119810838559898, -0.06621612205998886, -0.05038734082850294, -0.3271582950795189, 0.1286773091404263, 0.03797800637303143, 0.11864422564181167, -0.08627771713488966, -0.12057732480979703, 0.03622689249352851, 0.15540603918040496, 0.012827744160633568, 0.09512216148475065, 0.13335784943453932, -0.17492882643800672, -0.14861908047929953, 0.3624472646299032, -0.03203584979835112, -0.19953119621394252, 0.2096829075417524, -0.0065619526644570565, -0.1297261422807607, 0.1250437786717207, 0.16957324865667167, 0.17806640752257968, -0.20044759274759424, -0.02783380779694048, 0.003379387644316078, 0.17077853741260982, 0.04852092102033283, -0.045106809872518715, 0.13363682871060703, 0.2943099776898092, -0.02886642209454995, 0.13416114885471078, -0.20316815147936823, 0.0118555991489594, -0.21105788411346152, -0.154671171440854, -0.14837551104126995, -0.08255089750970551, -0.09991340148593769, -0.14789631921763813, 0.4189162583457767, 0.2108468301508815, 0.17132297706901878, 0.035687261944585436, 0.37738070662620415, -0.016129061649126267, 0.10061008602363664, -0.00044348546295264445, 0.17408368134670335, -0.004308215101087176, 0.11213178913015018, -0.21234142141194517, 0.07227450679910771, 0.07189821405549866] |
1,803.00952 | Mixed-Integer Convex Nonlinear Optimization with Gradient-Boosted Trees
Embedded | Decision trees usefully represent sparse, high dimensional and noisy data.
Having learned a function from this data, we may want to thereafter integrate
the function into a larger decision-making problem, e.g., for picking the best
chemical process catalyst. We study a large-scale, industrially-relevant
mixed-integer nonlinear nonconvex optimization problem involving both
gradient-boosted trees and penalty functions mitigating risk. This
mixed-integer optimization problem with convex penalty terms broadly applies to
optimizing pre-trained regression tree models. Decision makers may wish to
optimize discrete models to repurpose legacy predictive models, or they may
wish to optimize a discrete model that particularly well-represents a data set.
We develop several heuristic methods to find feasible solutions, and an exact,
branch-and-bound algorithm leveraging structural properties of the
gradient-boosted trees and penalty functions. We computationally test our
methods on concrete mixture design instance and a chemical catalysis industrial
instance.
| math.OC cs.AI | decision trees usefully represent sparse high dimensional and noisy data having learned a function from this data we may want to thereafter integrate the function into a larger decisionmaking problem eg for picking the best chemical process catalyst we study a largescale industriallyrelevant mixedinteger nonlinear nonconvex optimization problem involving both gradientboosted trees and penalty functions mitigating risk this mixedinteger optimization problem with convex penalty terms broadly applies to optimizing pretrained regression tree models decision makers may wish to optimize discrete models to repurpose legacy predictive models or they may wish to optimize a discrete model that particularly wellrepresents a data set we develop several heuristic methods to find feasible solutions and an exact branchandbound algorithm leveraging structural properties of the gradientboosted trees and penalty functions we computationally test our methods on concrete mixture design instance and a chemical catalysis industrial instance | [['decision', 'trees', 'usefully', 'represent', 'sparse', 'high', 'dimensional', 'and', 'noisy', 'data', 'having', 'learned', 'a', 'function', 'from', 'this', 'data', 'we', 'may', 'want', 'to', 'thereafter', 'integrate', 'the', 'function', 'into', 'a', 'larger', 'decisionmaking', 'problem', 'eg', 'for', 'picking', 'the', 'best', 'chemical', 'process', 'catalyst', 'we', 'study', 'a', 'largescale', 'industriallyrelevant', 'mixedinteger', 'nonlinear', 'nonconvex', 'optimization', 'problem', 'involving', 'both', 'gradientboosted', 'trees', 'and', 'penalty', 'functions', 'mitigating', 'risk', 'this', 'mixedinteger', 'optimization', 'problem', 'with', 'convex', 'penalty', 'terms', 'broadly', 'applies', 'to', 'optimizing', 'pretrained', 'regression', 'tree', 'models', 'decision', 'makers', 'may', 'wish', 'to', 'optimize', 'discrete', 'models', 'to', 'repurpose', 'legacy', 'predictive', 'models', 'or', 'they', 'may', 'wish', 'to', 'optimize', 'a', 'discrete', 'model', 'that', 'particularly', 'wellrepresents', 'a', 'data', 'set', 'we', 'develop', 'several', 'heuristic', 'methods', 'to', 'find', 'feasible', 'solutions', 'and', 'an', 'exact', 'branchandbound', 'algorithm', 'leveraging', 'structural', 'properties', 'of', 'the', 'gradientboosted', 'trees', 'and', 'penalty', 'functions', 'we', 'computationally', 'test', 'our', 'methods', 'on', 'concrete', 'mixture', 'design', 'instance', 'and', 'a', 'chemical', 'catalysis', 'industrial', 'instance']] | [-0.04075955969747156, -0.01730538213066341, -0.08906489316972771, 0.13649912110212037, -0.19793686158955098, -0.20680146337752894, 0.11593437681440263, 0.43919995365930453, -0.3498645759985915, -0.3132003029459156, 0.140300339643831, -0.24615471775635211, -0.19501679773841585, 0.1444763567631266, -0.11596965197739857, 0.14859053933157287, 0.08862454357690044, -0.058046927193312774, -0.06385927918683072, -0.28835656427379164, 0.2597099320331056, 0.04459362726192921, 0.25729957620081095, -0.007923088838080211, 0.11924726141067887, 0.015026762432950948, -0.013335680268106184, 0.020743647670107227, -0.11324164838822201, 0.12781542875537916, 0.38367772379941534, 0.241667622949795, 0.3772883889664497, -0.4342222527999963, -0.22317154622370644, 0.1978777935162985, 0.11642771630213247, 0.0895970801689795, -0.015518714608125655, -0.22838808780403008, 0.03665030607288437, -0.1421922314213589, -0.034377222491561304, -0.133969994986962, -0.06858433766090977, -0.01016261937994776, -0.3757960924146963, 0.004534110160810607, 0.006500236726632076, 0.002670895474563752, -0.10360745536828679, -0.19167297301215253, -0.004070318570094449, 0.07532252517828186, 0.025151352124313624, 0.04814904591101887, 0.15519160581752658, -0.12985402753843767, -0.1724512700174403, 0.3641881448482828, -0.0008580259878986648, -0.26201360080490954, 0.21531085749822004, -0.003933742949240176, -0.1813914264851649, 0.0966065465306331, 0.3196709023622264, 0.14646295675442422, -0.20982960682761456, 0.03720588615355414, -0.03624788380361029, 0.13728322639362886, 0.024762192669524147, -0.052526591129467955, 0.16794362534502788, 0.22280767909251153, 0.10827441100846044, 0.19031645455397667, -0.016333712391289217, -0.08924779309558549, -0.19974132660343977, -0.0737376551534648, -0.16164010792438474, -0.013767907181422094, -0.17393254807965214, -0.20848516785273594, 0.3329490800487942, 0.16870822195071794, 0.1603604888942625, 0.15540539636941894, 0.32721880768908057, 0.09854007216646486, 0.05212254540196487, 0.09005284455238975, 0.11963495856616646, 0.062161821230048576, 0.08295841795458858, -0.16766132971804057, 0.0994088594383876, 0.04886616479738482] |
1,803.00953 | A measure theoretic approach to traffic flow optimization on networks | We consider a class of optimal control problems for measure-valued nonlinear
transport equations describing traffic flow problems on networks. The objective
isto minimise/maximise macroscopic quantities, such as traffic volume or
average speed,controlling few agents, for example smart traffic lights and
automated cars. The measuretheoretic approach allows to study in a same setting
local and nonlocal drivers interactionsand to consider the control variables as
additional measures interacting with the driversdistribution. We also propose a
gradient descent adjoint-based optimization method, ob-tained by deriving
first-order optimality conditions for the control problem, and we providesome
numerical experiments in the case of smart traffic lights for a 2-1 junction.
| math.OC math.AP | we consider a class of optimal control problems for measurevalued nonlinear transport equations describing traffic flow problems on networks the objective isto minimisemaximise macroscopic quantities such as traffic volume or average speedcontrolling few agents for example smart traffic lights and automated cars the measuretheoretic approach allows to study in a same setting local and nonlocal drivers interactionsand to consider the control variables as additional measures interacting with the driversdistribution we also propose a gradient descent adjointbased optimization method obtained by deriving firstorder optimality conditions for the control problem and we providesome numerical experiments in the case of smart traffic lights for a 21 junction | [['we', 'consider', 'a', 'class', 'of', 'optimal', 'control', 'problems', 'for', 'measurevalued', 'nonlinear', 'transport', 'equations', 'describing', 'traffic', 'flow', 'problems', 'on', 'networks', 'the', 'objective', 'isto', 'minimisemaximise', 'macroscopic', 'quantities', 'such', 'as', 'traffic', 'volume', 'or', 'average', 'speedcontrolling', 'few', 'agents', 'for', 'example', 'smart', 'traffic', 'lights', 'and', 'automated', 'cars', 'the', 'measuretheoretic', 'approach', 'allows', 'to', 'study', 'in', 'a', 'same', 'setting', 'local', 'and', 'nonlocal', 'drivers', 'interactionsand', 'to', 'consider', 'the', 'control', 'variables', 'as', 'additional', 'measures', 'interacting', 'with', 'the', 'driversdistribution', 'we', 'also', 'propose', 'a', 'gradient', 'descent', 'adjointbased', 'optimization', 'method', 'obtained', 'by', 'deriving', 'firstorder', 'optimality', 'conditions', 'for', 'the', 'control', 'problem', 'and', 'we', 'providesome', 'numerical', 'experiments', 'in', 'the', 'case', 'of', 'smart', 'traffic', 'lights', 'for', 'a', '21', 'junction']] | [-0.16531214518754772, 0.0429888375460951, -0.0702090577100112, 0.08301622013918905, -0.07509109721932974, -0.1773135418994258, 0.08271334088299273, 0.35863602718319554, -0.2914220920641615, -0.3053075199466989, 0.1408559456932114, -0.27067634692627845, -0.1616479977024625, 0.21751885616333422, -0.1288913287495197, 0.16403957897552637, 0.053102073941680816, -0.015270472132610251, -0.03153305248630167, -0.21575525380445248, 0.29411871456117794, -0.061385288401836095, 0.3077274219349328, 0.038453359519028, 0.1857390299549966, 0.03338247285497309, -0.0052249111808986975, 0.07623900884216783, -0.10977782810229168, 0.11391326203598932, 0.2897240557952436, 0.12527521026574753, 0.328789152129732, -0.46453117744790184, -0.2840970843522386, 0.12646493765835962, 0.11525759744373235, 0.10190390332630186, -0.03979977775680259, -0.29218713305843524, 0.05054910456987493, -0.14600996185809073, -0.15274057798840182, -0.07805523668378216, -0.03804228986340641, 0.09045030495311801, -0.33186883048502486, 0.04158965972307458, 0.007919560605189715, 0.07576031504860947, -0.08342292786851017, -0.049938354560649115, 0.012653154744343325, 0.12860659977439978, 0.03735138980596505, -0.05948797132904557, 0.18127709023673275, -0.14919761556078387, -0.14269875971167678, 0.396748775672732, -0.04876111549409953, -0.23105364213133175, 0.15228679583342086, -0.015330288498050938, -0.13237944912315922, 0.06379756096026136, 0.27381168988373394, 0.171174937478182, -0.21464114836558248, -0.0003624120415564664, -0.0667499392716722, 0.1102335173072237, 0.024737340583926952, -0.006769921073238506, 0.12220391811040993, 0.21994196737862445, 0.23079675095475682, 0.14131104461895772, -0.03979474749895885, -0.16152569603626477, -0.29850992876471893, -0.13213584782329924, -0.11109003331512213, 0.042477671343908466, -0.12000662466523478, -0.12869495303943904, 0.3680239903430144, 0.1832131663186833, 0.13913654220864327, 0.1012781164726487, 0.3274103442978377, 0.13639780676528585, -0.01622961384404187, 0.09891881360531304, 0.1514404790494779, 0.09108954260944191, 0.2159850539950033, -0.2536412924339976, 0.06586209027392959, 0.11562140780322329] |
1,803.00954 | An Effective Multi-Cue Positioning System for Agricultural Robotics | The self-localization capability is a crucial component for Unmanned Ground
Vehicles (UGV) in farming applications. Approaches based solely on visual cues
or on low-cost GPS are easily prone to fail in such scenarios. In this paper,
we present a robust and accurate 3D global pose estimation framework, designed
to take full advantage of heterogeneous sensory data. By modeling the pose
estimation problem as a pose graph optimization, our approach simultaneously
mitigates the cumulative drift introduced by motion estimation systems (wheel
odometry, visual odometry, ...), and the noise introduced by raw GPS readings.
Along with a suitable motion model, our system also integrates two additional
types of constraints: (i) a Digital Elevation Model and (ii) a Markov Random
Field assumption. We demonstrate how using these additional cues substantially
reduces the error along the altitude axis and, moreover, how this benefit
spreads to the other components of the state. We report exhaustive experiments
combining several sensor setups, showing accuracy improvements ranging from 37%
to 76% with respect to the exclusive use of a GPS sensor. We show that our
approach provides accurate results even if the GPS unexpectedly changes
positioning mode. The code of our system along with the acquired datasets are
released with this paper.
| cs.RO | the selflocalization capability is a crucial component for unmanned ground vehicles ugv in farming applications approaches based solely on visual cues or on lowcost gps are easily prone to fail in such scenarios in this paper we present a robust and accurate 3d global pose estimation framework designed to take full advantage of heterogeneous sensory data by modeling the pose estimation problem as a pose graph optimization our approach simultaneously mitigates the cumulative drift introduced by motion estimation systems wheel odometry visual odometry and the noise introduced by raw gps readings along with a suitable motion model our system also integrates two additional types of constraints i a digital elevation model and ii a markov random field assumption we demonstrate how using these additional cues substantially reduces the error along the altitude axis and moreover how this benefit spreads to the other components of the state we report exhaustive experiments combining several sensor setups showing accuracy improvements ranging from 37 to 76 with respect to the exclusive use of a gps sensor we show that our approach provides accurate results even if the gps unexpectedly changes positioning mode the code of our system along with the acquired datasets are released with this paper | [['the', 'selflocalization', 'capability', 'is', 'a', 'crucial', 'component', 'for', 'unmanned', 'ground', 'vehicles', 'ugv', 'in', 'farming', 'applications', 'approaches', 'based', 'solely', 'on', 'visual', 'cues', 'or', 'on', 'lowcost', 'gps', 'are', 'easily', 'prone', 'to', 'fail', 'in', 'such', 'scenarios', 'in', 'this', 'paper', 'we', 'present', 'a', 'robust', 'and', 'accurate', '3d', 'global', 'pose', 'estimation', 'framework', 'designed', 'to', 'take', 'full', 'advantage', 'of', 'heterogeneous', 'sensory', 'data', 'by', 'modeling', 'the', 'pose', 'estimation', 'problem', 'as', 'a', 'pose', 'graph', 'optimization', 'our', 'approach', 'simultaneously', 'mitigates', 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'provides', 'accurate', 'results', 'even', 'if', 'the', 'gps', 'unexpectedly', 'changes', 'positioning', 'mode', 'the', 'code', 'of', 'our', 'system', 'along', 'with', 'the', 'acquired', 'datasets', 'are', 'released', 'with', 'this', 'paper']] | [-0.108895036839224, 0.030752998495731373, -0.05074414986373829, 0.006801981255847942, -0.08868555025440719, -0.16260384747946607, 0.0392770590122991, 0.424750978193645, -0.25801045316147614, -0.35062978047736476, 0.12604699952796267, -0.2714850213252503, -0.19109735199883396, 0.20527602653844934, -0.1662447233409093, 0.0799444640523254, 0.15607658558493032, 0.014231876142121154, -0.03874270900799503, -0.19908729252325752, 0.26267865745051366, 0.03858048395882246, 0.3020741613306509, -0.009597687348906923, 0.1741144198498785, 0.029313915309363177, -0.05468619891139564, 0.01661470147959998, -0.07145306919560179, 0.17912334792481263, 0.24676824126534824, 0.15363229951119334, 0.26313072532470444, -0.42524321638322965, -0.23617407970194673, 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1,803.00955 | Global solution of the initial value problem for the focusing
Davey-Stewartson II system | We consider the two dimensional focusing Davey-Stewartson II system and
construct the global solution of the Cauchy problem for a dense in $L^2(\mathbb
C)$ set of initial data. We do not assume that the initial data is small. So,
the solutions may have singularities. We show that the blow-up may occur only
on a real analytic variety and the variety is bounded in each strip $t \leq T$.
| math-ph math.AP math.MP nlin.SI | we consider the two dimensional focusing daveystewartson ii system and construct the global solution of the cauchy problem for a dense in l2mathbb c set of initial data we do not assume that the initial data is small so the solutions may have singularities we show that the blowup may occur only on a real analytic variety and the variety is bounded in each strip t leq t | [['we', 'consider', 'the', 'two', 'dimensional', 'focusing', 'daveystewartson', 'ii', 'system', 'and', 'construct', 'the', 'global', 'solution', 'of', 'the', 'cauchy', 'problem', 'for', 'a', 'dense', 'in', 'l2mathbb', 'c', 'set', 'of', 'initial', 'data', 'we', 'do', 'not', 'assume', 'that', 'the', 'initial', 'data', 'is', 'small', 'so', 'the', 'solutions', 'may', 'have', 'singularities', 'we', 'show', 'that', 'the', 'blowup', 'may', 'occur', 'only', 'on', 'a', 'real', 'analytic', 'variety', 'and', 'the', 'variety', 'is', 'bounded', 'in', 'each', 'strip', 't', 'leq', 't']] | [-0.19147102871690602, 0.05903477820239084, -0.04193662405561875, 0.05288280677118832, -0.08041426747599069, -0.15374583724940963, -0.01980354849879137, 0.35463663105688553, -0.27609675142037515, -0.19900827416602304, 0.19155795818454968, -0.29276755701421814, -0.10541550638428067, 0.18005504011457293, -0.053945028373752445, 0.05588474191780038, 0.1036782862878788, 0.07149381495615029, -0.08380768647390034, -0.25091996518141757, 0.40778260951971307, -0.12431520653669448, 0.22519235790926306, 0.038612528608235365, 0.10111641670536141, -0.02879973330795217, 0.00021999417667222375, 0.031530587660039175, -0.17519588909999584, 0.019114619105547556, 0.22912728331763954, 0.17790221187340863, 0.26357146073132753, -0.42233592310153384, -0.21134054836104899, 0.19778771120507052, 0.17386477549135795, 0.11409411995726473, -0.047663544477579894, -0.23197772149818346, 0.13847233692307354, -0.11711656585719217, -0.14438007897286512, -0.06437662690330077, 0.06364512720200069, 0.0691802424570436, -0.29688657388803275, 0.027291267877444625, 0.07744124657748376, 0.024436433594126034, -0.11180675388587748, -0.08364382651052438, -0.05066263742457308, 0.06708427530247718, -0.012333039675414673, 0.03159035721054191, 0.055898610023124254, -0.1247464291190066, -0.022022643208722856, 0.3513217123554033, -0.07660685345078544, -0.24238595799269044, 0.22240393615998877, -0.2022575759808259, -0.12989395620260277, 0.09467994688790948, 0.18862511301824056, 0.1691268940716434, -0.09540922331678517, 0.20857936097137794, -0.10131832217688069, 0.1485266575781519, 0.06207497550777214, -0.01935342959511806, 0.13707470474764705, 0.1279191971920869, 0.09855923595536939, 0.08927509227000616, -0.09036616713702268, -0.06061999429948628, -0.38171186336480517, -0.14542892582454336, -0.17724495907040203, 0.10142809196793892, -0.08665167705304852, -0.1928530009189511, 0.34346900022972154, 0.11201891756311799, 0.2640658357892843, 0.030561057741151136, 0.19761168737621868, 0.12172784839661771, 0.023410700352328336, 0.10982808566597455, 0.17546375476248452, 0.018492375168374136, 0.12114668999771204, -0.19185938515618225, 0.01595204075634041, 0.07822868004094584] |
1,803.00956 | Diagnostics upgrades for investigations of HOM effects in Tesla-type
SCRF cavities | We describe the upgrades to diagnostic capabilities on the Fermilab
Accelerator Science and Technology (FAST) electron linear accelerator that will
allow investigations of the effects of high-order modes (HOMs) in SCRF cavities
on macropulse-average beam quality. We examine the dipole modes in the first
pass-band generally observed in the 1.6-1.9 GHz regime for TESLA-type SCRF
cavities due to uniform transverse beam offsets of the electron beam. Such
cavities are the basis of the accelerators such as the European XFEL and the
proposed MaRIE XFEL facility. Preliminary HOM detector data, prototype BPM test
data, and first framing camera OTR data with ~20 micron spatial resolution at
250 pC per bunch will be presented.
| physics.acc-ph | we describe the upgrades to diagnostic capabilities on the fermilab accelerator science and technology fast electron linear accelerator that will allow investigations of the effects of highorder modes homs in scrf cavities on macropulseaverage beam quality we examine the dipole modes in the first passband generally observed in the 1619 ghz regime for teslatype scrf cavities due to uniform transverse beam offsets of the electron beam such cavities are the basis of the accelerators such as the european xfel and the proposed marie xfel facility preliminary hom detector data prototype bpm test data and first framing camera otr data with 20 micron spatial resolution at 250 pc per bunch will be presented | [['we', 'describe', 'the', 'upgrades', 'to', 'diagnostic', 'capabilities', 'on', 'the', 'fermilab', 'accelerator', 'science', 'and', 'technology', 'fast', 'electron', 'linear', 'accelerator', 'that', 'will', 'allow', 'investigations', 'of', 'the', 'effects', 'of', 'highorder', 'modes', 'homs', 'in', 'scrf', 'cavities', 'on', 'macropulseaverage', 'beam', 'quality', 'we', 'examine', 'the', 'dipole', 'modes', 'in', 'the', 'first', 'passband', 'generally', 'observed', 'in', 'the', '1619', 'ghz', 'regime', 'for', 'teslatype', 'scrf', 'cavities', 'due', 'to', 'uniform', 'transverse', 'beam', 'offsets', 'of', 'the', 'electron', 'beam', 'such', 'cavities', 'are', 'the', 'basis', 'of', 'the', 'accelerators', 'such', 'as', 'the', 'european', 'xfel', 'and', 'the', 'proposed', 'marie', 'xfel', 'facility', 'preliminary', 'hom', 'detector', 'data', 'prototype', 'bpm', 'test', 'data', 'and', 'first', 'framing', 'camera', 'otr', 'data', 'with', '20', 'micron', 'spatial', 'resolution', 'at', '250', 'pc', 'per', 'bunch', 'will', 'be', 'presented']] | [-0.08654692039934096, 0.16159782056730274, -0.02835918764944549, 0.03179525204741203, -0.048119102780883376, -0.16810503929249337, -0.01839844983591287, 0.4786790850664581, -0.1846032775151921, -0.3213611424518833, 0.08549804472981172, -0.2897090389821175, 0.0428149993762139, 0.2656609069163752, 0.0007968369477034152, 0.1062329689734028, 0.10710298131547265, -0.07646327927658284, -0.011562706720134293, -0.15001958682461902, 0.22355206478554923, 0.2270651390767648, 0.3688439436357569, 0.05264448261450607, 0.13169660385140125, -0.02547280426999252, -0.03128069288491666, -0.07864380304844261, -0.101583167707439, 0.062050644367355655, 0.30581314386044806, 0.11839104435293356, 0.24450160459371972, -0.4584713456724867, -0.12213480831249743, -0.0013675831776397587, 0.09335355319267323, 0.04217214217862567, -0.03620617108436318, -0.2701639434582814, 0.013489629985210864, -0.18938675333183627, -0.17746628335937187, -0.003586566096658374, -0.04153372139275611, 0.08438668023325033, -0.2566383036675754, -0.04522629128349526, -0.016272284480667597, 0.0802434272938282, -0.011950162898010767, -0.12156297020647708, 0.04512592475978775, 0.025577679234514903, -0.045853238393393184, 0.06959338870050537, 0.16188277412826815, -0.10729154199361801, -0.13371898098282414, 0.3575441679447419, -0.031715982427101094, -0.07692928503158393, 0.14562287879688246, -0.2856469874580701, -0.06631562173542743, 0.12642727273742896, 0.2387838992416053, 0.01508497648037601, -0.1370223155454942, 0.00046107910315480987, 0.04992642530516998, 0.22826444607664337, 0.16612440506457812, 0.08401518184817522, 0.20730232824948994, 0.2003621290949685, 0.04379348893154849, 0.12782593175815, -0.2498995500264337, 0.04410341289523687, -0.343648273330014, -0.11616994383921092, -0.11968020815612027, -0.017013425854292198, -0.0068410079385340635, -0.06528023480718771, 0.42895583275638455, 0.13405046111962818, 0.07180383386979769, -0.07360309444426617, 0.36384114288297054, 0.03316234617582145, 0.1349613447432398, 0.020651349797844887, 0.2835059435115204, 0.10498384506282238, 0.17179763517636168, -0.233826433635644, -0.07925689441932214, -0.02667761445548889] |
1,803.00957 | New copulas based on general partitions-of-unity (part III) - the
continuous case (extended version) | In this paper we discuss a natural extension of infinite discrete
partition-of-unity copulas which were recently introduced in the literature to
continuous partition of copulas with possible applications in risk management
and other fields. We present a general simple algorithm to generate such
copulas on the basis of the empirical copula from high-dimensional data sets.
In particular, our constructions also allow for an implementation of positive
tail dependence which sometimes is a desirable property of copula modelling, in
particular for internal models under Solvency II.
| q-fin.RM | in this paper we discuss a natural extension of infinite discrete partitionofunity copulas which were recently introduced in the literature to continuous partition of copulas with possible applications in risk management and other fields we present a general simple algorithm to generate such copulas on the basis of the empirical copula from highdimensional data sets in particular our constructions also allow for an implementation of positive tail dependence which sometimes is a desirable property of copula modelling in particular for internal models under solvency ii | [['in', 'this', 'paper', 'we', 'discuss', 'a', 'natural', 'extension', 'of', 'infinite', 'discrete', 'partitionofunity', 'copulas', 'which', 'were', 'recently', 'introduced', 'in', 'the', 'literature', 'to', 'continuous', 'partition', 'of', 'copulas', 'with', 'possible', 'applications', 'in', 'risk', 'management', 'and', 'other', 'fields', 'we', 'present', 'a', 'general', 'simple', 'algorithm', 'to', 'generate', 'such', 'copulas', 'on', 'the', 'basis', 'of', 'the', 'empirical', 'copula', 'from', 'highdimensional', 'data', 'sets', 'in', 'particular', 'our', 'constructions', 'also', 'allow', 'for', 'an', 'implementation', 'of', 'positive', 'tail', 'dependence', 'which', 'sometimes', 'is', 'a', 'desirable', 'property', 'of', 'copula', 'modelling', 'in', 'particular', 'for', 'internal', 'models', 'under', 'solvency', 'ii']] | [-0.01723012792713502, 0.03025283195396803, -0.1106020532822346, 0.13081660494208336, -0.1132102032411186, -0.12180915698956918, 0.03380476802943603, 0.42768045909264507, -0.25164175693164853, -0.23350887811359236, 0.11161914810620467, -0.2193192001486964, -0.1569262665860793, 0.19377056045786423, -0.1223082255161203, 0.0980919705649071, -0.005471416310790707, 0.0033156486858120734, -0.036778671130784514, -0.25196087664979344, 0.3309371301027782, 0.07030385462019373, 0.309124559175004, 0.02550258169101332, 0.10986067036522881, 0.0002509600085699383, -0.04117761223631747, 0.016157127785332064, -0.13255960901870448, 0.17690443519283744, 0.2757879761121705, 0.1748844243312145, 0.31181869272361784, -0.4029375954257215, -0.21016902021844597, 0.14472187623819885, 0.09512978702345315, 0.06610611027161427, -0.060777261202661866, -0.25919321915134785, 0.01899371222757241, -0.22420181947595932, -0.12076584231519305, -0.12923853148103637, 0.04072654217974666, 0.03800883946652688, -0.35908605619607603, 0.05316775505506324, 0.09533880623194443, 0.09111874806420768, -0.03461732500616242, -0.1327443363103906, 0.016244291253935766, 0.021173857420902043, 0.08107252481174382, -0.04511569782899802, 0.05075482138816048, -0.0865332647712956, -0.13997462715761846, 0.3294554355280364, -0.03051439359619775, -0.23902110571370405, 0.20234419826746863, -0.11883276158067234, -0.23331338387968786, 0.04555895430419375, 0.2228409897974309, 0.09396716040175628, -0.18811338083708987, 0.13773240048698532, -0.08410744301646071, 0.09680009954335059, 0.06063517956610988, 0.029807358813773402, 0.13652498086585718, 0.14595986283110346, 0.08962625989909558, 0.18561765995383372, -0.03498926077668062, -0.09955073040704626, -0.32202192651217476, -0.15333505238351577, -0.15263142140910907, -0.010338666742010151, -0.11864574084808256, -0.2553913847433732, 0.40658194966175976, 0.18450809973018134, 0.18688425098491065, 0.08237762202453965, 0.25008260674281596, 0.10690898801720537, 0.044504388098550195, 0.06367651048280737, 0.11940770235241335, 0.11275923556976897, 0.03429373481365688, -0.10807491746073698, 0.11622037263587118, 0.016539484530906468] |
1,803.00958 | A study in $\mathbb{G}_{\mathbb{R}, \geq 0}$: from the geometric case
book of Wilson loop diagrams and SYM $N=4$ | We study the geometry underlying the Wilson loop diagram approach to
calculating scattering amplitudes in the gauge theory of Supersymmetric Yang
Mills (SYM) $N=4$. By applying the tools developed to study total positivity in
the real Grassmannian, we are able to systematically compute with all Wilson
loop diagrams of a given size and find unexpected patterns and relationships
between them. We focus on the smallest nontrivial multi-propagator case,
consisting of 2 propagators on 6 vertices, and compute the positroid cells
associated to each diagram and the homology of the subcomplex they generate in
$\mathbb{G}_{\mathbb{R}, \geq 0}$. We also verify in this case the conjecture
that the spurious singularities of the volume functional {\em do} all cancel on
the codimension 1 boundaries of these cells.
| math.CO math-ph math.MP | we study the geometry underlying the wilson loop diagram approach to calculating scattering amplitudes in the gauge theory of supersymmetric yang mills sym n4 by applying the tools developed to study total positivity in the real grassmannian we are able to systematically compute with all wilson loop diagrams of a given size and find unexpected patterns and relationships between them we focus on the smallest nontrivial multipropagator case consisting of 2 propagators on 6 vertices and compute the positroid cells associated to each diagram and the homology of the subcomplex they generate in mathbbg_mathbbr geq 0 we also verify in this case the conjecture that the spurious singularities of the volume functional em do all cancel on the codimension 1 boundaries of these cells | [['we', 'study', 'the', 'geometry', 'underlying', 'the', 'wilson', 'loop', 'diagram', 'approach', 'to', 'calculating', 'scattering', 'amplitudes', 'in', 'the', 'gauge', 'theory', 'of', 'supersymmetric', 'yang', 'mills', 'sym', 'n4', 'by', 'applying', 'the', 'tools', 'developed', 'to', 'study', 'total', 'positivity', 'in', 'the', 'real', 'grassmannian', 'we', 'are', 'able', 'to', 'systematically', 'compute', 'with', 'all', 'wilson', 'loop', 'diagrams', 'of', 'a', 'given', 'size', 'and', 'find', 'unexpected', 'patterns', 'and', 'relationships', 'between', 'them', 'we', 'focus', 'on', 'the', 'smallest', 'nontrivial', 'multipropagator', 'case', 'consisting', 'of', '2', 'propagators', 'on', '6', 'vertices', 'and', 'compute', 'the', 'positroid', 'cells', 'associated', 'to', 'each', 'diagram', 'and', 'the', 'homology', 'of', 'the', 'subcomplex', 'they', 'generate', 'in', 'mathbbg_mathbbr', 'geq', '0', 'we', 'also', 'verify', 'in', 'this', 'case', 'the', 'conjecture', 'that', 'the', 'spurious', 'singularities', 'of', 'the', 'volume', 'functional', 'em', 'do', 'all', 'cancel', 'on', 'the', 'codimension', '1', 'boundaries', 'of', 'these', 'cells']] | [-0.15105956941117823, 0.1401055857355966, -0.04590431095452094, 0.10396354574400962, -0.046412953381716714, -0.10868109501211247, 0.05440126210699224, 0.34770827766385726, -0.20136609762052043, -0.2652599603456796, 0.06220940969785156, -0.30134769614411855, -0.23104044007229024, 0.11078870500395929, -0.07357674147399357, 0.007391846420044904, 0.01558918910402591, 0.042656601431443675, -0.07919161331545958, -0.2886302842315836, 0.3446427362982459, -0.05049056017420209, 0.22747177912731517, 0.06739099296649582, 0.07225066116537715, 0.03961785890822314, -0.06820553176097388, 0.03357405794906567, -0.16514056028835675, 0.14830984522351903, 0.2257690049305207, 0.08067477318901019, 0.11858165945063849, -0.4375891309597942, -0.14622642445095554, 0.12273738868763578, 0.16040012852258248, 0.07329358446381254, 0.0503778952473515, -0.21540055125684585, 0.10869192864486306, -0.12784479941302512, -0.18537691739467965, -0.08177331377385703, 0.025657903143494833, -0.05281243573341778, -0.20681747325245659, 0.004374194750705444, -0.002762018223903829, 0.056723951743763004, -0.027146456429169925, -0.11589528345999109, -0.07249885749026033, 0.16434438398764392, 0.02970527286916116, 0.02329117859729001, 0.08716419643004898, -0.16560705711055532, -0.14300186075575527, 0.33992634223369483, -0.02148511126011488, -0.22254201777080898, 0.16685970359649815, -0.21424147711120178, -0.1883251009047123, 0.1310732162877923, 0.1276871311692063, 0.14855870674745958, -0.09762598931422976, 0.1629790140441848, -0.04885036491819459, 0.1088491039585368, 0.13963603761550955, -0.03705302876092066, 0.16717108476479522, 0.05430934734291351, 0.02960536078756034, 0.15313740878472806, -0.05606636213836237, -0.10006188805077652, -0.3630128159630494, -0.1572243721399945, -0.10616562039721146, 0.056794439572230224, -0.1384857498852681, -0.21084798835828655, 0.37295674854416216, 0.13575612325373976, 0.20244131707963267, 0.0753912120889567, 0.2411828470706451, 0.0846687524777944, 0.0883281303294858, 0.07207664596603909, 0.1715176366727616, 0.19067923378268042, 0.04813437699340284, -0.2605481189751967, -0.05692908285971975, 0.19318604404412087] |
1,803.00959 | Robustness of the Insulating Bulk in the Topological Kondo Insulator
SmB$_{6}$ | We used the inverted resistance method to extend the bulk resistivity of
SmB$_{6}$ to a regime where the surface conduction overwhelms the bulk.
Remarkably, the bulk resistivity shows an intrinsic thermally activated
behavior that changes ten orders of magnitude, suggesting that it is an ideal
insulator that is immune to disorder. Non-stoichiometrically-grown SmB$_{6}$
samples also show an almost identical thermally activated behavior. At low
temperatures, however, these samples show a mysterious high bulk resistivity
plateau, which may arise from extended defect conduction in a 3D TI.
| cond-mat.str-el | we used the inverted resistance method to extend the bulk resistivity of smb_6 to a regime where the surface conduction overwhelms the bulk remarkably the bulk resistivity shows an intrinsic thermally activated behavior that changes ten orders of magnitude suggesting that it is an ideal insulator that is immune to disorder nonstoichiometricallygrown smb_6 samples also show an almost identical thermally activated behavior at low temperatures however these samples show a mysterious high bulk resistivity plateau which may arise from extended defect conduction in a 3d ti | [['we', 'used', 'the', 'inverted', 'resistance', 'method', 'to', 'extend', 'the', 'bulk', 'resistivity', 'of', 'smb_6', 'to', 'a', 'regime', 'where', 'the', 'surface', 'conduction', 'overwhelms', 'the', 'bulk', 'remarkably', 'the', 'bulk', 'resistivity', 'shows', 'an', 'intrinsic', 'thermally', 'activated', 'behavior', 'that', 'changes', 'ten', 'orders', 'of', 'magnitude', 'suggesting', 'that', 'it', 'is', 'an', 'ideal', 'insulator', 'that', 'is', 'immune', 'to', 'disorder', 'nonstoichiometricallygrown', 'smb_6', 'samples', 'also', 'show', 'an', 'almost', 'identical', 'thermally', 'activated', 'behavior', 'at', 'low', 'temperatures', 'however', 'these', 'samples', 'show', 'a', 'mysterious', 'high', 'bulk', 'resistivity', 'plateau', 'which', 'may', 'arise', 'from', 'extended', 'defect', 'conduction', 'in', 'a', '3d', 'ti']] | [-0.11370256236918709, 0.23724838639797835, -0.06912184719763258, 0.03050736146979034, -0.02938572686877759, -0.19045741231016378, 0.08883664216024473, 0.34799653098863714, -0.30553155318450403, -0.29561685428899875, -0.0024947150021462756, -0.3507829862537191, -0.16162022299626294, 0.19227098538814222, -0.03281879048684941, -0.040069777135025055, -0.04238645522033467, -0.05008408514686915, -0.113239978322321, -0.23264811276732122, 0.25390795579125336, 0.025059288422412732, 0.37872467881397287, 0.05513867172001697, 0.022365742883480647, -0.09442532709525789, 0.1187279799953103, 0.10218714798855431, -0.12795363551387495, -0.021023709508367842, 0.28896584961125077, -0.19089191901114058, 0.16572912521660327, -0.4507790241993087, -0.24874361417091945, 0.00784049633452121, 0.14323783994597547, 0.1309837748282863, -0.0611291143221452, -0.20232695049661048, 0.06550526209997341, -0.1192416526377201, -0.12920755922575208, -0.07636400651570191, -0.006383080210755853, -0.13787932058604543, -0.22149903674555174, 0.14910706628682366, 0.07566191867158255, 0.06186360012849464, -0.11183054236804738, -0.11164212856880006, -0.12748398671255393, 0.062119323119302, 0.08495108196511865, 0.020983028461170547, 0.2250812402323765, -0.12527670050039888, -0.07066444473560242, 0.2974252385251662, -0.07298361101352116, -0.03781069612459225, 0.23174001890618134, -0.18663348244393574, -0.05972427105092827, 0.26645656167803444, 0.06072011308415848, 0.10391498849288944, -0.12867197524756194, 0.030508834806010673, -0.022680223207263386, 0.19676006646717295, -0.007258740341400399, 0.06578555434175273, 0.24338012526140493, 0.19096847954754007, 0.03510786125107723, 0.14325564773616326, -0.1275342851438943, 0.03251999022767824, -0.2319323788232663, -0.18686831800732762, -0.2253363788812695, 0.13203643009285718, -0.0804032159722684, -0.23670504987239838, 0.3675716531627318, 0.16851027720361292, 0.2115413165596478, -0.029516813256229985, 0.23157991259084904, 0.11111993270323557, 0.07730607567902874, 0.11166586478186004, 0.2611838552799514, 0.12958356825744405, 0.138058914989233, -0.3188045746455079, 0.1312031496042276, -0.02983500702197061] |
1,803.0096 | Fitting and Analysis Technique for Inconsistent Nuclear Data | Consistent experiment data are crucial to adjust parameters of physics models
and to determine best estimates of observables. However, often experiment data
are not consistent due to unrecognized systematic errors. Standard methods of
statistics such as $\chi^2$-fitting cannot deal with this case. Their
predictions become doubtful and associated uncertainties too small. A human has
then to figure out the problem, apply corrections to the data, and repeat the
fitting procedure. This takes time and potentially costs money. Therefore, a
Bayesian method is introduced to fit and analyze inconsistent experiment data.
It automatically detects and resolves inconsistencies. Furthermore, it allows
to extract consistent subsets from the data. Finally, it provides an overall
prediction with associated uncertainties and correlations less prone to the
common problem of too small uncertainties. The method is foreseen to function
with a large corpus of data and hence may be used in nuclear databases to deal
with inconsistencies in an automated fashion.
| nucl-th nucl-ex physics.data-an | consistent experiment data are crucial to adjust parameters of physics models and to determine best estimates of observables however often experiment data are not consistent due to unrecognized systematic errors standard methods of statistics such as chi2fitting cannot deal with this case their predictions become doubtful and associated uncertainties too small a human has then to figure out the problem apply corrections to the data and repeat the fitting procedure this takes time and potentially costs money therefore a bayesian method is introduced to fit and analyze inconsistent experiment data it automatically detects and resolves inconsistencies furthermore it allows to extract consistent subsets from the data finally it provides an overall prediction with associated uncertainties and correlations less prone to the common problem of too small uncertainties the method is foreseen to function with a large corpus of data and hence may be used in nuclear databases to deal with inconsistencies in an automated fashion | [['consistent', 'experiment', 'data', 'are', 'crucial', 'to', 'adjust', 'parameters', 'of', 'physics', 'models', 'and', 'to', 'determine', 'best', 'estimates', 'of', 'observables', 'however', 'often', 'experiment', 'data', 'are', 'not', 'consistent', 'due', 'to', 'unrecognized', 'systematic', 'errors', 'standard', 'methods', 'of', 'statistics', 'such', 'as', 'chi2fitting', 'can', 'not', 'deal', 'with', 'this', 'case', 'their', 'predictions', 'become', 'doubtful', 'and', 'associated', 'uncertainties', 'too', 'small', 'a', 'human', 'has', 'then', 'to', 'figure', 'out', 'the', 'problem', 'apply', 'corrections', 'to', 'the', 'data', 'and', 'repeat', 'the', 'fitting', 'procedure', 'this', 'takes', 'time', 'and', 'potentially', 'costs', 'money', 'therefore', 'a', 'bayesian', 'method', 'is', 'introduced', 'to', 'fit', 'and', 'analyze', 'inconsistent', 'experiment', 'data', 'it', 'automatically', 'detects', 'and', 'resolves', 'inconsistencies', 'furthermore', 'it', 'allows', 'to', 'extract', 'consistent', 'subsets', 'from', 'the', 'data', 'finally', 'it', 'provides', 'an', 'overall', 'prediction', 'with', 'associated', 'uncertainties', 'and', 'correlations', 'less', 'prone', 'to', 'the', 'common', 'problem', 'of', 'too', 'small', 'uncertainties', 'the', 'method', 'is', 'foreseen', 'to', 'function', 'with', 'a', 'large', 'corpus', 'of', 'data', 'and', 'hence', 'may', 'be', 'used', 'in', 'nuclear', 'databases', 'to', 'deal', 'with', 'inconsistencies', 'in', 'an', 'automated', 'fashion']] | [-0.027755351271480322, 0.04821723791675863, -0.10079666970817086, 0.15690827564909482, -0.1229171772988943, -0.152581308541509, 0.08336478578022276, 0.376973803447655, -0.26137128015621924, -0.37967917697731024, 0.12123332037262093, -0.31328489947386867, -0.06627803857032305, 0.21014830889628053, -0.15482530891387844, 0.08591725687102343, 0.12781657653454787, 8.807209535286977e-05, -0.04948336289425452, -0.21422125976114798, 0.28248915231923977, 0.13365815077812818, 0.2704610286805874, 0.026392084904588185, 0.0732433129242287, -0.04248650593217462, -0.12219767844101462, 0.039775742120139346, -0.08867904578801244, 0.10674389078765391, 0.30559591290150423, 0.13803588921515397, 0.2611216603515622, -0.4185883458226155, -0.1707317986083814, 0.1489245203944544, 0.13821543250770235, 0.14525930939513879, 0.003532116420758076, -0.26724464911818263, 0.07420481751875904, -0.1690354260671227, -0.12050528082853326, -0.14669371631223327, 0.019083787496082295, -0.03836846112001998, -0.3076559649097572, 0.08860212918788864, -0.010161675438165473, 0.01778010808928416, -0.03807440280163577, -0.10366182552071479, 0.013261450118779276, 0.14547425529095703, 0.10872035569329268, 0.06016808309449026, 0.10913505326084888, -0.09666187197077446, -0.09738176134235871, 0.41767078288233817, -0.016412102036739293, -0.19676447596597987, 0.1726160278522935, -0.10079242389362592, -0.15612396409508222, 0.13843803166244656, 0.16884818299410817, 0.059949732992278695, -0.19676092166143158, 0.01827500738764707, 0.031590946132126145, 0.21375961436961705, -0.019753447184577968, -0.0033890942935473644, 0.18439177714604646, 0.15020237451729676, 0.026606693380297378, 0.0819882945156095, -0.10148063779566795, -0.07772628720610952, -0.2701607162240319, -0.0689585515421454, -0.1530451005539642, -0.02808757477946645, -0.05597840462294885, -0.1505852781671983, 0.33223923588864124, 0.24669175359005563, 0.23194702152940386, 0.04559715464711189, 0.31019425326032946, 0.05969280832468007, 0.1382094423438852, 0.04657940584449814, 0.2212651668665692, 0.08728133237962492, 0.07854909186836523, -0.18821067293323815, 0.12768202502793896, -0.05671243875687464] |
1,803.00961 | Full and unbiased solution of the Dyson-Schwinger equation in the
functional integro-differential representation | We provide a full and unbiased solution to the Dyson-Schwinger equation
illustrated for $\phi^4$ theory in 2D. It is based on an exact treatment of the
functional derivative $\partial \Gamma / \partial G$ of the 4-point vertex
function $\Gamma$ with respect to the 2-point correlation function $G$ within
the framework of the homotopy analysis method (HAM) and the Monte Carlo
sampling of rooted tree diagrams. The resulting series solution in deformations
can be considered as an asymptotic series around $G=0$ in a HAM control
parameter $c_0G$, or even a convergent one up to the phase transition point if
shifts in $G$ can be performed (such as by summing up all ladder diagrams).
These considerations are equally applicable to fermionic quantum field theories
and offer a fresh approach to solving integro-differential equations.
| cond-mat.stat-mech hep-lat | we provide a full and unbiased solution to the dysonschwinger equation illustrated for phi4 theory in 2d it is based on an exact treatment of the functional derivative partial gamma partial g of the 4point vertex function gamma with respect to the 2point correlation function g within the framework of the homotopy analysis method ham and the monte carlo sampling of rooted tree diagrams the resulting series solution in deformations can be considered as an asymptotic series around g0 in a ham control parameter c_0g or even a convergent one up to the phase transition point if shifts in g can be performed such as by summing up all ladder diagrams these considerations are equally applicable to fermionic quantum field theories and offer a fresh approach to solving integrodifferential equations | [['we', 'provide', 'a', 'full', 'and', 'unbiased', 'solution', 'to', 'the', 'dysonschwinger', 'equation', 'illustrated', 'for', 'phi4', 'theory', 'in', '2d', 'it', 'is', 'based', 'on', 'an', 'exact', 'treatment', 'of', 'the', 'functional', 'derivative', 'partial', 'gamma', 'partial', 'g', 'of', 'the', '4point', 'vertex', 'function', 'gamma', 'with', 'respect', 'to', 'the', '2point', 'correlation', 'function', 'g', 'within', 'the', 'framework', 'of', 'the', 'homotopy', 'analysis', 'method', 'ham', 'and', 'the', 'monte', 'carlo', 'sampling', 'of', 'rooted', 'tree', 'diagrams', 'the', 'resulting', 'series', 'solution', 'in', 'deformations', 'can', 'be', 'considered', 'as', 'an', 'asymptotic', 'series', 'around', 'g0', 'in', 'a', 'ham', 'control', 'parameter', 'c_0g', 'or', 'even', 'a', 'convergent', 'one', 'up', 'to', 'the', 'phase', 'transition', 'point', 'if', 'shifts', 'in', 'g', 'can', 'be', 'performed', 'such', 'as', 'by', 'summing', 'up', 'all', 'ladder', 'diagrams', 'these', 'considerations', 'are', 'equally', 'applicable', 'to', 'fermionic', 'quantum', 'field', 'theories', 'and', 'offer', 'a', 'fresh', 'approach', 'to', 'solving', 'integrodifferential', 'equations']] | [-0.10966047291070796, 0.09549989557954877, -0.12851420818881776, 0.08245554948673368, -0.09229905016075533, -0.10794601766034388, 0.054967709338570085, 0.3708194182540935, -0.2760367811585848, -0.26941028633020603, 0.0860295305100198, -0.3037395498966082, -0.15591346030213082, 0.17751209632529374, 0.009552437573886262, 0.06467325020796405, 0.050163846330430646, 0.04281084130433066, -0.09484032107911144, -0.23481187713690677, 0.28855664207277676, -0.0005361216474109545, 0.21248958170754823, 0.009193948480802086, 0.0979700694935253, 0.03008204992287434, -0.014595415951827397, 0.04679547653605159, -0.13623556366118675, 0.06194961136565186, 0.28000638391822574, 0.06784796114437855, 0.23215587440734872, -0.39383867003310186, -0.19222403971048502, 0.07826824636520961, 0.16656203695859473, 0.08363761032143464, -0.009445705213763107, -0.2765108590802321, 0.0684519345442263, -0.19030467607080936, -0.15713469836359414, -0.11366619909038911, -0.00746781765214669, -0.006475836561562923, -0.2903583084161465, 0.073170491359913, 0.0035439452765366206, 0.026964276438005842, -0.01683204779177546, -0.1260810626205057, -0.03299914219846519, 0.10534129419113294, 0.006661651374228173, 0.08993021164161082, 0.07304303161083506, -0.13235404298354228, -0.12222771489625582, 0.3695168448970295, -0.09421337767719076, -0.23627762110330738, 0.12027986021533321, -0.1468368761993658, -0.14175237350953887, 0.1565894276304859, 0.10111626164199641, 0.1860618815649874, -0.16776505758842597, 0.152584448100802, 0.012044212248964379, 0.12511760873402147, 0.04486598741943733, -0.03818479793934295, 0.1573372567215791, 0.12187071233056486, 0.08971891131681892, 0.13575990113178985, 0.0031955940953384224, -0.11504317617688614, -0.3458719165637516, -0.13288868252599897, -0.14780162079349304, 0.05768416075734421, -0.13954324737166574, -0.2474276824424473, 0.35709521354964147, 0.13726187170877194, 0.15347661676664406, 0.060770246597866596, 0.2404656407674058, 0.20499197703678734, 0.0555537206819281, 0.040152481599495964, 0.15532629814070578, 0.2038319001565329, 6.427888113718767e-05, -0.2148082597512537, -0.020141805592440786, 0.1667154426435725] |
1,803.00962 | Broadband spectroscopy of thermodynamic magnetization fluctuations
through a ferromagnetic spin-reorientation transition | We use scanning optical magnetometry to study the broadband frequency spectra
of spontaneous magnetization fluctuations, or "magnetization noise", in an
archetypal ferromagnetic film that can be smoothly tuned through a spin
reorientation transition (SRT). The SRT is achieved by laterally varying the
magnetic anisotropy across an ultrathin Pt/Co/Pt trilayer, from the
perpendicular to in-plane direction, via graded Ar$^+$ irradiation. In regions
exhibiting perpendicular anisotropy, the power spectrum of the magnetization
noise, $S(\nu)$, exhibits a remarkably robust $\nu^{-3/2}$ power law over
frequencies $\nu$ from 1~kHz to 1~MHz. As the SRT region is traversed, however,
$S(\nu)$ spectra develop a steadily-increasing critical frequency, $\nu_0$,
below which the noise power is spectrally flat, indicating an evolving
low-frequency cutoff for magnetization fluctuations. The magnetization noise
depends strongly on applied in- and out-of-plane magnetic fields, revealing
local anisotropies and also a field-induced emergence of fluctuations in
otherwise stable ferromagnetic films. Finally, we demonstrate that higher-order
correlators can be computed from the noise. These results highlight broadband
spectroscopy of thermodynamic fluctuations as a powerful tool to characterize
the interplay between thermal and magnetic energy scales, and as a means of
characterizing phase transitions in ferromagnets.
| cond-mat.mtrl-sci cond-mat.mes-hall | we use scanning optical magnetometry to study the broadband frequency spectra of spontaneous magnetization fluctuations or magnetization noise in an archetypal ferromagnetic film that can be smoothly tuned through a spin reorientation transition srt the srt is achieved by laterally varying the magnetic anisotropy across an ultrathin ptcopt trilayer from the perpendicular to inplane direction via graded ar irradiation in regions exhibiting perpendicular anisotropy the power spectrum of the magnetization noise snu exhibits a remarkably robust nu32 power law over frequencies nu from 1khz to 1mhz as the srt region is traversed however snu spectra develop a steadilyincreasing critical frequency nu_0 below which the noise power is spectrally flat indicating an evolving lowfrequency cutoff for magnetization fluctuations the magnetization noise depends strongly on applied in and outofplane magnetic fields revealing local anisotropies and also a fieldinduced emergence of fluctuations in otherwise stable ferromagnetic films finally we demonstrate that higherorder correlators can be computed from the noise these results highlight broadband spectroscopy of thermodynamic fluctuations as a powerful tool to characterize the interplay between thermal and magnetic energy scales and as a means of characterizing phase transitions in ferromagnets | [['we', 'use', 'scanning', 'optical', 'magnetometry', 'to', 'study', 'the', 'broadband', 'frequency', 'spectra', 'of', 'spontaneous', 'magnetization', 'fluctuations', 'or', 'magnetization', 'noise', 'in', 'an', 'archetypal', 'ferromagnetic', 'film', 'that', 'can', 'be', 'smoothly', 'tuned', 'through', 'a', 'spin', 'reorientation', 'transition', 'srt', 'the', 'srt', 'is', 'achieved', 'by', 'laterally', 'varying', 'the', 'magnetic', 'anisotropy', 'across', 'an', 'ultrathin', 'ptcopt', 'trilayer', 'from', 'the', 'perpendicular', 'to', 'inplane', 'direction', 'via', 'graded', 'ar', 'irradiation', 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1,803.00963 | Entire functions arising from trees | Given any infinite tree in the plane satisfying certain topological
conditions, we construct an entire function $f$ with only two critical values
$\pm 1$ and no asymptotic values such that $f^{-1}([-1,1])$ is ambiently
homeomorphic to the given tree. This can be viewed as a generalization of a
result of Grothendieck to the case of infinite trees. Moreover, a similar idea
leads to a new proof of a result of Nevanlinna and Elfving.
| math.CV | given any infinite tree in the plane satisfying certain topological conditions we construct an entire function f with only two critical values pm 1 and no asymptotic values such that f111 is ambiently homeomorphic to the given tree this can be viewed as a generalization of a result of grothendieck to the case of infinite trees moreover a similar idea leads to a new proof of a result of nevanlinna and elfving | [['given', 'any', 'infinite', 'tree', 'in', 'the', 'plane', 'satisfying', 'certain', 'topological', 'conditions', 'we', 'construct', 'an', 'entire', 'function', 'f', 'with', 'only', 'two', 'critical', 'values', 'pm', '1', 'and', 'no', 'asymptotic', 'values', 'such', 'that', 'f111', 'is', 'ambiently', 'homeomorphic', 'to', 'the', 'given', 'tree', 'this', 'can', 'be', 'viewed', 'as', 'a', 'generalization', 'of', 'a', 'result', 'of', 'grothendieck', 'to', 'the', 'case', 'of', 'infinite', 'trees', 'moreover', 'a', 'similar', 'idea', 'leads', 'to', 'a', 'new', 'proof', 'of', 'a', 'result', 'of', 'nevanlinna', 'and', 'elfving']] | [-0.16229496730698478, 0.11463620610225513, -0.1136643384266386, 0.08197379334726268, -0.09251328905358808, -0.11025331444236347, 0.07624837737562808, 0.28651031396455234, -0.2706518584551911, -0.2548929817922827, 0.10383087696552845, -0.23403728367864257, -0.15241360441238308, 0.19591186390930992, -0.11780304142868975, 0.027385829154405253, 0.04182315526607757, 0.09465551784443152, -0.08665713620980063, -0.23751178911576667, 0.3320955098930022, -0.033973437666039295, 0.2123143752040859, 0.059962329415914915, 0.12228300168014054, 0.007062059228903511, 0.029792979948170897, 0.03585020819446072, -0.17780385631148318, 0.07614189993020976, 0.2725148315851887, 0.12178919882151401, 0.2465307456958625, -0.32764691624066067, -0.13929183087829086, 0.21507704072994077, 0.13935520396464401, 0.06450085281135721, -0.0007931575204970108, -0.23262341976644368, 0.14611232553660455, -0.16449816178323495, -0.18065673729870468, -0.0018355687207076699, 0.011388716307313492, 0.010754714409510294, -0.305498710147933, -0.014492346562393423, 0.12625013340988922, 0.04933671643023748, -0.06277861334900889, -0.10865948148097636, -0.06206808805129387, 0.09611971988084002, 0.00814649756001826, 0.13248590854022446, 0.029048751299544366, -0.06460182952448829, -0.12370091605731028, 0.35767256561666727, -0.09258621516508153, -0.2467243360200276, 0.17056511581823644, -0.13641719470292124, -0.15639232308603823, 0.11175047491107964, 0.0791374576640212, 0.14104185406015152, -0.0756700117748955, 0.11377719376307344, -0.1277717905662333, 0.1397128402475371, 0.13371069355505621, -0.002653561676399679, 0.15105723496203105, 0.10236666300463387, 0.12850808385863072, 0.18763789214911716, 0.021770700495431408, -0.04274804335242758, -0.35242036709354985, -0.17102476684117895, -0.17076357244028864, 0.10979620243435623, -0.16702828168207715, -0.26433196089540917, 0.3700132068350083, 0.10666344887188946, 0.27175205948555636, 0.11506402457598597, 0.20980623255794248, 0.13615971881760439, 0.05594216754171713, 0.04585488009939177, 0.11043614141332607, 0.1539779497058286, 0.006655544948039783, -0.08296416644427357, 0.05673919909814787, 0.14291454998440006] |
1,803.00964 | An influence of the spectator-nuclear motion on nonresonant formation of
the muonic hydrogen molecules | A model for description of nonresonant formation of the muonic molecules in
collisions of the muonic hydrogen atoms with the hydrogenic molecules has been
developed with taking into account the internal motion of all nuclei. It has
been shown that such a motion leads to a significant smearing of the calculated
energy-dependent formation rates at low collision energies. In particular, this
effect is strong in the $dd\mu$ and $dt\mu$ formation. An appreciable isotopic
effect in the case of nonresonant $dd\mu$ formation in $d\mu$ collisions with
the molecules D$_2$ and HD has been found. All these effects are of importance
for many experimental researches in low-energy muon physics.
| physics.atom-ph | a model for description of nonresonant formation of the muonic molecules in collisions of the muonic hydrogen atoms with the hydrogenic molecules has been developed with taking into account the internal motion of all nuclei it has been shown that such a motion leads to a significant smearing of the calculated energydependent formation rates at low collision energies in particular this effect is strong in the ddmu and dtmu formation an appreciable isotopic effect in the case of nonresonant ddmu formation in dmu collisions with the molecules d_2 and hd has been found all these effects are of importance for many experimental researches in lowenergy muon physics | [['a', 'model', 'for', 'description', 'of', 'nonresonant', 'formation', 'of', 'the', 'muonic', 'molecules', 'in', 'collisions', 'of', 'the', 'muonic', 'hydrogen', 'atoms', 'with', 'the', 'hydrogenic', 'molecules', 'has', 'been', 'developed', 'with', 'taking', 'into', 'account', 'the', 'internal', 'motion', 'of', 'all', 'nuclei', 'it', 'has', 'been', 'shown', 'that', 'such', 'a', 'motion', 'leads', 'to', 'a', 'significant', 'smearing', 'of', 'the', 'calculated', 'energydependent', 'formation', 'rates', 'at', 'low', 'collision', 'energies', 'in', 'particular', 'this', 'effect', 'is', 'strong', 'in', 'the', 'ddmu', 'and', 'dtmu', 'formation', 'an', 'appreciable', 'isotopic', 'effect', 'in', 'the', 'case', 'of', 'nonresonant', 'ddmu', 'formation', 'in', 'dmu', 'collisions', 'with', 'the', 'molecules', 'd_2', 'and', 'hd', 'has', 'been', 'found', 'all', 'these', 'effects', 'are', 'of', 'importance', 'for', 'many', 'experimental', 'researches', 'in', 'lowenergy', 'muon', 'physics']] | [-0.08477740897035453, 0.1743519282041642, -0.04702042527801453, 0.06326233216117476, 0.045265873735248346, -0.0769443690937848, -0.032910637621698186, 0.38711128610177575, -0.17457979099355012, -0.2767601297454578, -0.045916052920326036, -0.30474228290928024, -0.026231264965347598, 0.14589325479388446, 0.04539325994778877, 0.04366920926286099, 0.08580614996325468, 0.025535559316522606, -0.009305730620921354, -0.1868286967238359, 0.25130034914396915, 0.11030754473095185, 0.22172927739812393, 0.18203480863317012, 0.050325418152263234, -0.028795531290478866, 0.02761402409784844, -0.029056092132335513, -0.13990074785070758, 0.09145007891391152, 0.23776345139491223, 0.0307587238973024, 0.20918177189218384, -0.44520374796588286, -0.2401220355722531, 0.11903580908215318, 0.17379858709923993, 0.18711265453320242, -0.11853834600231358, -0.2661232558105176, 0.01847496607871813, -0.19229585983764344, -0.10542122739863312, -0.06473883675306896, 0.11064985163823188, 0.03483171947300434, -0.2646673702565239, 0.0597916312111002, 0.03781007052938863, 0.0671271937986331, -0.11301651209262069, -0.16950232043609928, -0.0528548322726807, 0.08759171701967716, 0.06809016015646091, 0.024554054300675046, 0.12891446775539178, -0.09380705256850641, -0.10383034136720767, 0.4762124724633922, -0.059306967533498164, -0.13513674381598134, 0.20875475613696, -0.21955606096781025, -0.17616627769649168, 0.23073143838408672, 0.15210010916882447, 0.0979962864298826, -0.16125404788181186, 0.08205855469068156, -0.0059456962604667535, 0.13297487012650294, 0.10622953636153976, 0.09604267615424557, 0.21226474577038784, 0.1945713326382825, -0.03127775423143877, 0.07987388587234734, -0.14870774187800415, -0.09036460088861879, -0.23281786764893575, -0.12963957269458432, -0.09334652921158786, 0.05287468551971436, -0.007219374488558289, -0.09295664369003526, 0.310022903706391, 0.05929173257911282, 0.20726656352352596, -0.10082210959849235, 0.30470733156549595, 0.12021457274209395, 0.08444131700165361, 0.015116052610677815, 0.3059429818999336, 0.17393854153168467, 0.0495078823677151, -0.31817417976959983, 0.1058056189408787, 0.013426424952867989] |
1,803.00965 | Type-Preserving Matrices and Security of Block Ciphers | We provide a new property, called Non-Type-Preserving, for a mixing layer
which guarantees protection against algebraic attacks based on the
imprimitivity of the group generated by the round functions. Our main result is
to present necessary and sufficient conditions on the structure of the binary
matrix associated to the mixing layer, so that it has this property. Then we
show how several families of linear maps are Non-Type-Preserving, including the
mixing layers of AES, GOST and PRESENT. Finally we prove that the group
generated by the round functions of an SPN cipher with addition modulo a power
of 2 as key mixing function is primitive if its mixing layer satisfies this
property. Moreover we generalise the definition of a GOST-like cipher using a
Non-Type-Preserving matrix as mixing layer and we show, under the only
assumption of invertibility of the S-Boxes, that the corresponding group is
primitive.
| math.GR cs.CR | we provide a new property called nontypepreserving for a mixing layer which guarantees protection against algebraic attacks based on the imprimitivity of the group generated by the round functions our main result is to present necessary and sufficient conditions on the structure of the binary matrix associated to the mixing layer so that it has this property then we show how several families of linear maps are nontypepreserving including the mixing layers of aes gost and present finally we prove that the group generated by the round functions of an spn cipher with addition modulo a power of 2 as key mixing function is primitive if its mixing layer satisfies this property moreover we generalise the definition of a gostlike cipher using a nontypepreserving matrix as mixing layer and we show under the only assumption of invertibility of the sboxes that the corresponding group is primitive | [['we', 'provide', 'a', 'new', 'property', 'called', 'nontypepreserving', 'for', 'a', 'mixing', 'layer', 'which', 'guarantees', 'protection', 'against', 'algebraic', 'attacks', 'based', 'on', 'the', 'imprimitivity', 'of', 'the', 'group', 'generated', 'by', 'the', 'round', 'functions', 'our', 'main', 'result', 'is', 'to', 'present', 'necessary', 'and', 'sufficient', 'conditions', 'on', 'the', 'structure', 'of', 'the', 'binary', 'matrix', 'associated', 'to', 'the', 'mixing', 'layer', 'so', 'that', 'it', 'has', 'this', 'property', 'then', 'we', 'show', 'how', 'several', 'families', 'of', 'linear', 'maps', 'are', 'nontypepreserving', 'including', 'the', 'mixing', 'layers', 'of', 'aes', 'gost', 'and', 'present', 'finally', 'we', 'prove', 'that', 'the', 'group', 'generated', 'by', 'the', 'round', 'functions', 'of', 'an', 'spn', 'cipher', 'with', 'addition', 'modulo', 'a', 'power', 'of', '2', 'as', 'key', 'mixing', 'function', 'is', 'primitive', 'if', 'its', 'mixing', 'layer', 'satisfies', 'this', 'property', 'moreover', 'we', 'generalise', 'the', 'definition', 'of', 'a', 'gostlike', 'cipher', 'using', 'a', 'nontypepreserving', 'matrix', 'as', 'mixing', 'layer', 'and', 'we', 'show', 'under', 'the', 'only', 'assumption', 'of', 'invertibility', 'of', 'the', 'sboxes', 'that', 'the', 'corresponding', 'group', 'is', 'primitive']] | [-0.1462816273215516, 0.10837326363895233, -0.09131213760902655, 0.018249572515648245, -0.03576191748685107, -0.12281730637468141, 0.0407576063407007, 0.33597848723835216, -0.34216053510277433, -0.22137416637791643, 0.15643086059275887, -0.24810725134508363, -0.20073667806011208, 0.16784468939656327, -0.07519840307308939, 0.06590597412091327, 0.04214176343882392, 0.047236074387007554, -0.08489632985618864, -0.24535272197862124, 0.39841266844807, 0.02848177332559536, 0.25203690435255655, 0.035672878237553586, 0.13513637282403893, -0.004867387302862159, 0.0035664138207533234, -0.03365688441273082, -0.1063020031537423, 0.09289974735985543, 0.1535726959512023, 0.14474472560405988, 0.22702066657807807, -0.41336531827932804, -0.17085686015157864, 0.08849772467631205, 0.09918333596132439, 0.05641116692507961, -0.10142415364690383, -0.2389042568786455, 0.18524453503176055, -0.18908708597822435, -0.10469925082930974, -0.07923115348314931, -0.020567918735845337, 0.005624091239838765, -0.31364382476393327, 0.003968163592548206, 0.13293604855586227, 0.05528192949673996, -0.006920490812927741, -0.09063805071717321, -0.04832527711505777, 0.13106351864106697, 0.037946668281701616, -0.019840101676125977, 0.09398100644285823, -0.09158938578169408, -0.06650857170321176, 0.37190853890416953, -0.07538809318553079, -0.22306243021940364, 0.1496166281177309, -0.08951164059148266, -0.15907786780936195, 0.053012987174864475, 0.14341125707382915, 0.10939598635545579, -0.09094071509998998, 0.08196125527530716, -0.15558743426768945, 0.1964936899535101, 0.06212739441222672, 0.04380443059389704, 0.08549746021243004, 0.13451423164264395, 0.11766487329812914, 0.17734335333686965, -0.05656718178852943, -0.03964472764075316, -0.33392132033176464, -0.19648425140707143, -0.1515451990543656, 0.053821957414841345, -0.08157886550249933, -0.17294254455924163, 0.42089052525316845, 0.13877755154951893, 0.18914686926672686, 0.10416642906353006, 0.2810586428326948, 0.08755355267343914, 0.11022820709722823, 0.0882697515992512, 0.19407308467117876, 0.18716978866703293, -0.006208522881156412, -0.15477467616056573, 0.11703262899255637, 0.12701917921417746] |
1,803.00966 | Stability and error analysis for the Helmholtz equation with variable
coefficients | We discuss the stability theory and numerical analysis of the Helmholtz
equation with variable and possibly non-smooth or oscillatory coefficients.
Using the unique continuation principle and the Fredholm alternative, we first
give an existence-uniqueness result for this problem, which holds under rather
general conditions on the coefficients and on the domain. Under additional
assumptions, we derive estimates for the stability constant (i.e., the norm of
the solution operator) in terms of the data (i.e. PDE coefficients and
frequency), and we apply these estimates to obtain a new finite element error
analysis for the Helmholtz equation which is valid at high frequency and with
variable wave speed. The central role played by the stability constant in this
theory leads us to investigate its behaviour with respect to coefficient
variation in detail. We give, via a 1D analysis, an a priori bound with
stability constant growing exponentially in the variance of the coefficients
(wave speed and/or diffusion coefficient). Then, by means a family of analytic
examples (supplemented by numerical experiments), we show that this estimate is
sharp
| math.NA | we discuss the stability theory and numerical analysis of the helmholtz equation with variable and possibly nonsmooth or oscillatory coefficients using the unique continuation principle and the fredholm alternative we first give an existenceuniqueness result for this problem which holds under rather general conditions on the coefficients and on the domain under additional assumptions we derive estimates for the stability constant ie the norm of the solution operator in terms of the data ie pde coefficients and frequency and we apply these estimates to obtain a new finite element error analysis for the helmholtz equation which is valid at high frequency and with variable wave speed the central role played by the stability constant in this theory leads us to investigate its behaviour with respect to coefficient variation in detail we give via a 1d analysis an a priori bound with stability constant growing exponentially in the variance of the coefficients wave speed andor diffusion coefficient then by means a family of analytic examples supplemented by numerical experiments we show that this estimate is sharp | [['we', 'discuss', 'the', 'stability', 'theory', 'and', 'numerical', 'analysis', 'of', 'the', 'helmholtz', 'equation', 'with', 'variable', 'and', 'possibly', 'nonsmooth', 'or', 'oscillatory', 'coefficients', 'using', 'the', 'unique', 'continuation', 'principle', 'and', 'the', 'fredholm', 'alternative', 'we', 'first', 'give', 'an', 'existenceuniqueness', 'result', 'for', 'this', 'problem', 'which', 'holds', 'under', 'rather', 'general', 'conditions', 'on', 'the', 'coefficients', 'and', 'on', 'the', 'domain', 'under', 'additional', 'assumptions', 'we', 'derive', 'estimates', 'for', 'the', 'stability', 'constant', 'ie', 'the', 'norm', 'of', 'the', 'solution', 'operator', 'in', 'terms', 'of', 'the', 'data', 'ie', 'pde', 'coefficients', 'and', 'frequency', 'and', 'we', 'apply', 'these', 'estimates', 'to', 'obtain', 'a', 'new', 'finite', 'element', 'error', 'analysis', 'for', 'the', 'helmholtz', 'equation', 'which', 'is', 'valid', 'at', 'high', 'frequency', 'and', 'with', 'variable', 'wave', 'speed', 'the', 'central', 'role', 'played', 'by', 'the', 'stability', 'constant', 'in', 'this', 'theory', 'leads', 'us', 'to', 'investigate', 'its', 'behaviour', 'with', 'respect', 'to', 'coefficient', 'variation', 'in', 'detail', 'we', 'give', 'via', 'a', '1d', 'analysis', 'an', 'a', 'priori', 'bound', 'with', 'stability', 'constant', 'growing', 'exponentially', 'in', 'the', 'variance', 'of', 'the', 'coefficients', 'wave', 'speed', 'andor', 'diffusion', 'coefficient', 'then', 'by', 'means', 'a', 'family', 'of', 'analytic', 'examples', 'supplemented', 'by', 'numerical', 'experiments', 'we', 'show', 'that', 'this', 'estimate', 'is', 'sharp']] | [-0.12383863201181937, 0.05656649720073412, -0.08760124911038604, 0.03977089156064072, -0.08954360886210842, -0.1008484924451581, 0.04864981808699667, 0.3252544509684334, -0.2838657488447747, -0.2423694711434655, 0.17686576253834313, -0.2510592895586576, -0.15185551762614133, 0.21655990120023488, -0.037665831756645014, 0.08211077055993624, 0.06300072127953171, 0.04943818617079939, -0.08319155929210995, -0.19594811316313487, 0.3499975459024842, 0.043870906084775924, 0.24327263514511288, 0.0679049043723249, 0.12617132467949496, 0.004115882797964982, -0.035228339822164605, 0.008279265356915338, -0.21025611598331514, 0.13473507966147735, 0.20553043543760266, 0.052164731768092935, 0.2838112626277975, -0.4219015381884362, -0.21839695306760923, 0.08523008680636329, 0.11027423208047236, 0.09305916960617261, -0.06912556315306574, -0.25143190252993786, 0.08826358032013688, -0.11798373740166426, -0.21286997141316533, -0.10717460322060755, 7.657224578516824e-05, 0.0448874758662922, -0.33947482279368807, 0.1474678750615567, 0.06388480638552989, 0.05457678918061512, -0.11847661111038178, -0.08587049063361649, 0.044405086854738846, 0.07947226823839758, 0.08356825485825539, -0.011677575179907893, 0.043872868118009396, -0.10629223025403917, -0.054966000786849434, 0.3375200115520108, -0.14300903416105679, -0.27745976045727727, 0.16538881133709635, -0.13556731908715197, -0.09013849759474396, 0.09861224129796028, 0.1637843397366149, 0.1309217617660761, -0.1212201262598059, 0.10720107695121052, -0.015330628555633927, 0.1713911792928619, 0.09460542485783142, 0.026230253062676637, 0.09400751542299986, 0.11285843135256853, 0.12129455232992768, 0.1481219778263143, -0.04016565533088786, -0.09285963246332747, -0.3490842401129859, -0.1575291736107985, -0.1580977757221886, 0.04996081665939917, -0.1471590152969085, -0.17678744513408415, 0.3921493116766214, 0.12378148296581848, 0.1955174205917865, 0.07407647952770016, 0.2557037429990513, 0.22307934861630202, -0.006735517922123628, 0.07071800150509391, 0.2266284249031118, 0.16221818972378968, 0.0967538637854159, -0.23808789235938874, 0.06198117880549814, 0.12692727880020227] |
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