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1,802.0536 | Radial transport and plasma heating in Jupiter's magnetodisc | The ion temperature of the magnetosphere of Jupiter derived from Galileo PLS
data was observed to increase by about an order of magnitude from 10 to 40
Jupiter radii. This suggests the presence of heating sources that counteract
the adiabatic cooling effect of expanding plasma. There have been different
attempts of explaining this phenomena, including a magnetohydrodynamic (MHD)
turbulent heating model which is based on flux tube diffusion [Saur, Astrophys.
J. Lett., 602, L137, 2004]. We explore an alternate turbulent heating model
based on advection, similar to models commonly used in solar wind heating.
Based on spectral analysis of Galileo magnetometer (MAG) data, we find that
observed MHD turbulence could potentially provide the required heating to
explain some of the increase in plasma temperature. This indicates that
advection is a more appropriate way to describe radial transport of plasma in
the jovian magnetosphere beyond 10 Jupiter radii.
| physics.space-ph | the ion temperature of the magnetosphere of jupiter derived from galileo pls data was observed to increase by about an order of magnitude from 10 to 40 jupiter radii this suggests the presence of heating sources that counteract the adiabatic cooling effect of expanding plasma there have been different attempts of explaining this phenomena including a magnetohydrodynamic mhd turbulent heating model which is based on flux tube diffusion saur astrophys j lett 602 l137 2004 we explore an alternate turbulent heating model based on advection similar to models commonly used in solar wind heating based on spectral analysis of galileo magnetometer mag data we find that observed mhd turbulence could potentially provide the required heating to explain some of the increase in plasma temperature this indicates that advection is a more appropriate way to describe radial transport of plasma in the jovian magnetosphere beyond 10 jupiter radii | [['the', 'ion', 'temperature', 'of', 'the', 'magnetosphere', 'of', 'jupiter', 'derived', 'from', 'galileo', 'pls', 'data', 'was', 'observed', 'to', 'increase', 'by', 'about', 'an', 'order', 'of', 'magnitude', 'from', '10', 'to', '40', 'jupiter', 'radii', 'this', 'suggests', 'the', 'presence', 'of', 'heating', 'sources', 'that', 'counteract', 'the', 'adiabatic', 'cooling', 'effect', 'of', 'expanding', 'plasma', 'there', 'have', 'been', 'different', 'attempts', 'of', 'explaining', 'this', 'phenomena', 'including', 'a', 'magnetohydrodynamic', 'mhd', 'turbulent', 'heating', 'model', 'which', 'is', 'based', 'on', 'flux', 'tube', 'diffusion', 'saur', 'astrophys', 'j', 'lett', '602', 'l137', '2004', 'we', 'explore', 'an', 'alternate', 'turbulent', 'heating', 'model', 'based', 'on', 'advection', 'similar', 'to', 'models', 'commonly', 'used', 'in', 'solar', 'wind', 'heating', 'based', 'on', 'spectral', 'analysis', 'of', 'galileo', 'magnetometer', 'mag', 'data', 'we', 'find', 'that', 'observed', 'mhd', 'turbulence', 'could', 'potentially', 'provide', 'the', 'required', 'heating', 'to', 'explain', 'some', 'of', 'the', 'increase', 'in', 'plasma', 'temperature', 'this', 'indicates', 'that', 'advection', 'is', 'a', 'more', 'appropriate', 'way', 'to', 'describe', 'radial', 'transport', 'of', 'plasma', 'in', 'the', 'jovian', 'magnetosphere', 'beyond', '10', 'jupiter', 'radii']] | [-0.09450397259668158, 0.17359655548613678, -0.06364349795922894, 0.06708265876092402, -0.06744285807784262, -0.05305911528733399, -0.0025291913927629078, 0.33651865102222256, -0.19030539931665208, -0.3742884730695394, 0.05247708690705998, -0.24491198051335483, -0.04509353781568593, 0.23668096752451925, -0.07960232753218312, 0.026634845889077104, 0.033090756275562634, -0.08376761931938055, -0.011786472521208484, -0.18976135000602568, 0.23058592600041422, 0.15551493122916796, 0.19304481009282332, 0.038941048426104005, 0.051821398693297445, -0.13690072379299792, -0.042772110403868656, -0.009185521988647765, -0.17934881956642695, 0.03105327048467408, 0.17813824869098707, 0.07737836246539292, 0.22990031583427356, -0.4923368354681237, -0.3194347772811507, 0.0238272018313151, 0.1586836343823836, 0.06319394441981849, -0.033820520517725404, -0.21147806180705284, 0.01302800170820335, -0.21278461158789438, -0.15220324596518586, -0.006443902429450175, 0.07121967657244412, 0.0021488338355617276, -0.30531014716734406, 0.13410551734108092, 0.08015192535481062, 0.12948096274430382, -0.1151246586718179, -0.09773859125722585, -0.04856731237137112, 0.025248338996657523, 0.08725712460040211, 0.03718560508227554, 0.21077848328277468, -0.06408021548955604, -0.0665890938803104, 0.4000217262783955, -0.09168705330471155, -0.04704977932932048, 0.23343308212492486, -0.21379747571552107, -0.07994211915045463, 0.1960215729436484, 0.1881991754446564, 0.06765038638921647, -0.18019984039510117, -0.018227185604387318, -0.062457483255400736, 0.16603580061318693, 0.09260832858593042, -0.020359433789994438, 0.2789414883045287, 0.16783178290031078, 0.027839573326616966, 0.08622317626632767, -0.20071576898756982, -0.04872012598488223, -0.22029490472813104, -0.10534796166908124, -0.11866700771136274, 0.08429313634024484, -0.07895622496846422, -0.15105470929511597, 0.36434959678043577, 0.27800101276872485, 0.17105179904349918, -0.075036166943128, 0.3134358086815939, 0.11315773741004924, 0.062186636782154955, 0.18967845230523883, 0.33056776445652036, 0.18353059979252004, 0.16200377225297793, -0.2869791216152752, 0.04573847597208003, 0.048442485684464714] |
1,802.05361 | Uniqueness of a Potential from Local Boundary Measurements | Let $(\Omega^3,g)$ be a compact smooth Riemannian manifold with smooth
boundary and suppose that $U$ is an open set in $\Omega$ such that $g|_U$ is
the Euclidean metric. Let $\Gamma= \overline{U} \cap \partial \Omega$ be
non-empty, connected, strictly convex and that $U$ is the convex hull of
$\Gamma$. We will study the uniqueness of an unknown potential for the
Schr\"{o}dinger operator $ -\triangle_g + q $ from the associated local
Dirichlet to Neumann map, $C_q^{\Gamma,\Gamma}$. Indeed, we will prove that if
the potential $q$ is a priori explicitly known in $U^c$, then one can uniquely
reconstruct $q$ from the knowledge of $C^{\Gamma,\Gamma}_q$.
| math.AP | let omega3g be a compact smooth riemannian manifold with smooth boundary and suppose that u is an open set in omega such that g_u is the euclidean metric let gamma overlineu cap partial omega be nonempty connected strictly convex and that u is the convex hull of gamma we will study the uniqueness of an unknown potential for the schrodinger operator triangle_g q from the associated local dirichlet to neumann map c_qgammagamma indeed we will prove that if the potential q is a priori explicitly known in uc then one can uniquely reconstruct q from the knowledge of cgammagamma_q | [['let', 'omega3g', 'be', 'a', 'compact', 'smooth', 'riemannian', 'manifold', 'with', 'smooth', 'boundary', 'and', 'suppose', 'that', 'u', 'is', 'an', 'open', 'set', 'in', 'omega', 'such', 'that', 'g_u', 'is', 'the', 'euclidean', 'metric', 'let', 'gamma', 'overlineu', 'cap', 'partial', 'omega', 'be', 'nonempty', 'connected', 'strictly', 'convex', 'and', 'that', 'u', 'is', 'the', 'convex', 'hull', 'of', 'gamma', 'we', 'will', 'study', 'the', 'uniqueness', 'of', 'an', 'unknown', 'potential', 'for', 'the', 'schrodinger', 'operator', 'triangle_g', 'q', 'from', 'the', 'associated', 'local', 'dirichlet', 'to', 'neumann', 'map', 'c_qgammagamma', 'indeed', 'we', 'will', 'prove', 'that', 'if', 'the', 'potential', 'q', 'is', 'a', 'priori', 'explicitly', 'known', 'in', 'uc', 'then', 'one', 'can', 'uniquely', 'reconstruct', 'q', 'from', 'the', 'knowledge', 'of', 'cgammagamma_q']] | [-0.14007575349872545, 0.09947454170742276, -0.07565254692841943, 0.0060358027258189395, -0.1343814333328434, -0.17031052880338393, -0.009318881910682345, 0.34843278039140085, -0.36986632528714836, -0.1326637795912878, 0.11412436681227216, -0.3281756992995118, -0.12038299421935032, 0.170582059804777, -0.13040357106850328, 0.004934378657101964, 0.06804353996994905, 0.15139054813577482, -0.07184744716020457, -0.1932382924569538, 0.43273954408747767, -0.12340258985447387, 0.1422493806652104, 0.09906860367239763, 0.08072015048431543, -0.0289240704914846, 0.05993457530955008, 0.04252012643943696, -0.17462101539088812, 0.0868551172449467, 0.26669872949908796, 0.15266855697215456, 0.28285953503412503, -0.37299918398881954, -0.19019096988874176, 0.24381727802028763, 0.14024385803107484, -0.07480637031646135, -0.008345289760958016, -0.2982348398848747, 0.16450979474757332, -0.06682667161415641, -0.19181850197007103, -0.0424995250456656, 0.10643464827929468, 0.003754366891371319, -0.35773944670412067, 0.024917842910023563, 0.0741691096033416, -0.014490530843128605, -0.1304875023973485, -0.11130720509390812, -0.08154551057911401, 0.07763682116637938, -0.04676048884478708, 0.21316851516652, 0.0533545596311645, -0.04278289932214344, -0.06412368704877736, 0.3428981981657368, -0.06598402963330348, -0.30851834484928986, 0.11375973514320019, -0.22875374853416966, -0.07548593923759957, 0.10049311774006735, 0.1434842428037276, 0.14783062970188135, -0.13706590698954338, 0.2618858234673098, -0.09690634428382812, 0.1326440831023016, 0.0652648818383265, -0.05128910765051842, 0.117563328773637, 0.07295136866741814, 0.18579642372302865, 0.11903313343888537, -0.028462593005315284, 0.0076740508957300335, -0.40787809264535707, -0.12722081669441346, -0.22379982225053632, 0.1785143833355202, -0.11931460433697794, -0.18563964172305228, 0.3154229785541247, 0.030140021878954332, 0.17890524941806993, 0.05194594832816316, 0.21996011963346973, 0.11276938341946637, -0.0073840160815355676, 0.14305684907594696, 0.10555297753307968, 0.13976673588331323, -0.03613318663822914, -0.20543265862215776, 0.011794764766818844, 0.10698938572022598] |
1,802.05362 | Holography, Fractals and the Weyl Anomaly | We study the large source asymptotics of the generating functional in quantum
field theory using the holographic renormalization group, and draw comparisons
with the asymptotics of the Hopf characteristic function in fractal geometry.
Based on the asymptotic behavior, we find a correspondence relating the Weyl
anomaly and the fractal dimension of the Euclidean path integral measure. We
are led to propose an equivalence between the logarithmic ultraviolet
divergence of the Shannon entropy of this measure and the integrated Weyl
anomaly, reminiscent of a known relation between logarithmic divergences of
entanglement entropy and a central charge. It follows that the information
dimension associated with the Euclidean path integral measure satisfies a
c-theorem.
| hep-th | we study the large source asymptotics of the generating functional in quantum field theory using the holographic renormalization group and draw comparisons with the asymptotics of the hopf characteristic function in fractal geometry based on the asymptotic behavior we find a correspondence relating the weyl anomaly and the fractal dimension of the euclidean path integral measure we are led to propose an equivalence between the logarithmic ultraviolet divergence of the shannon entropy of this measure and the integrated weyl anomaly reminiscent of a known relation between logarithmic divergences of entanglement entropy and a central charge it follows that the information dimension associated with the euclidean path integral measure satisfies a ctheorem | [['we', 'study', 'the', 'large', 'source', 'asymptotics', 'of', 'the', 'generating', 'functional', 'in', 'quantum', 'field', 'theory', 'using', 'the', 'holographic', 'renormalization', 'group', 'and', 'draw', 'comparisons', 'with', 'the', 'asymptotics', 'of', 'the', 'hopf', 'characteristic', 'function', 'in', 'fractal', 'geometry', 'based', 'on', 'the', 'asymptotic', 'behavior', 'we', 'find', 'a', 'correspondence', 'relating', 'the', 'weyl', 'anomaly', 'and', 'the', 'fractal', 'dimension', 'of', 'the', 'euclidean', 'path', 'integral', 'measure', 'we', 'are', 'led', 'to', 'propose', 'an', 'equivalence', 'between', 'the', 'logarithmic', 'ultraviolet', 'divergence', 'of', 'the', 'shannon', 'entropy', 'of', 'this', 'measure', 'and', 'the', 'integrated', 'weyl', 'anomaly', 'reminiscent', 'of', 'a', 'known', 'relation', 'between', 'logarithmic', 'divergences', 'of', 'entanglement', 'entropy', 'and', 'a', 'central', 'charge', 'it', 'follows', 'that', 'the', 'information', 'dimension', 'associated', 'with', 'the', 'euclidean', 'path', 'integral', 'measure', 'satisfies', 'a', 'ctheorem']] | [-0.15963807991949153, 0.09453858655517153, -0.1825239332466227, 0.1223599039855086, -0.0745155580828453, -0.11597583616605482, 0.03354511148954935, 0.29404408686693656, -0.2711306788480362, -0.22945014143211617, 0.06564814138628945, -0.3411020662400637, -0.19539289324133247, 0.13897377521907156, -0.07046215411544114, 0.0798746174335614, -0.03819158517646494, 0.1146227258335178, -0.13081861391604818, -0.18133444852942415, 0.39362308566525644, 0.031851968501467966, 0.3312540229175005, 0.08587049748664638, 0.139530586693352, 0.026549605380005396, -0.07015881612729113, 0.055925372161541705, -0.19869478601525245, 0.17178378951015277, 0.20957652503532265, 0.06696570739920905, 0.19736745647314163, -0.36198574145165113, -0.19713618104589414, 0.10812363758167201, 0.10717329129090046, 0.04147584695234761, -0.044072354775601744, -0.25743412890949763, 0.03005369405287343, -0.1512662498028697, -0.1778619212627008, -0.045622318785906106, 0.01941669823104717, -0.034485215791039636, -0.2128981134334059, 0.11254466101023797, 0.02319008267593679, 0.09483345752363806, -0.03718738616779782, -0.013467152472921051, -0.0005720914131752006, 0.13453088952689893, 0.07281680388359336, 0.050547535430539296, 0.10084054863650922, -0.13724594818939967, -0.13159787899663522, 0.2961786994039341, -0.0778772142854192, -0.1761570269124465, 0.15760011421327755, -0.18327413272817392, -0.1117913236145165, 0.08098126893932826, 0.09111885213385429, 0.11103420712573973, -0.14326000576083725, 0.14609048079053225, -0.05071831018420624, 0.10138321085623256, 0.08166688453691366, 0.09254893733540902, 0.1673595968562835, 0.0675277592255608, 0.07178125295553121, 0.20782032294349895, -0.09126952707375241, -0.14414030138973719, -0.3980584616881904, -0.23681847093341588, -0.22124767936933953, 0.11072128766996635, -0.1951702459886259, -0.20559600075976378, 0.3512524014993294, 0.1249927089146867, 0.22090653705553295, 0.10029552423034434, 0.19709044332439835, 0.16553564204179957, 0.09776785660446999, 0.09298439139315674, 0.19194295475687329, 0.19438870865767738, 0.06259796337585326, -0.2788114974417866, -0.02046284739448278, 0.18385320723576992] |
1,802.05363 | The Ricci flow for circle bundles over surfaces | In this work, we study and solve the normalized Ricci flow equation for
circle bundles over surfaces. Moreover, we study the asymptotic behavior of the
solutions and their connections to some model geometries.
| math.DG | in this work we study and solve the normalized ricci flow equation for circle bundles over surfaces moreover we study the asymptotic behavior of the solutions and their connections to some model geometries | [['in', 'this', 'work', 'we', 'study', 'and', 'solve', 'the', 'normalized', 'ricci', 'flow', 'equation', 'for', 'circle', 'bundles', 'over', 'surfaces', 'moreover', 'we', 'study', 'the', 'asymptotic', 'behavior', 'of', 'the', 'solutions', 'and', 'their', 'connections', 'to', 'some', 'model', 'geometries']] | [-0.18747463638922482, 0.00045127829953068584, -0.12370432743971999, 0.06598546721083536, -0.07847336109614733, -0.12733364204001246, -0.00018420358273116025, 0.39156915032953926, -0.2698690584070529, -0.22782286098509125, 0.15965667370862016, -0.33401890561887715, -0.2316804906974236, 0.16681055057878522, -0.08278079703450203, 0.06130571711356893, 0.03323119290342385, 0.042472068248598865, -0.09992095406138987, -0.24284042283949075, 0.4008162264063051, 0.012521834465477503, 0.2834592763560288, 0.11765902492223893, 0.09874305904447807, -0.059750354580694075, 0.008314436894248833, 0.04771579045689467, -0.27613966240350046, 0.14990510461343962, 0.19184483165824504, 0.0476043289809516, 0.20530699543429143, -0.43014966877120914, -0.2470288071781397, 0.18713043635767518, 0.15987494435500016, 0.07005961961818463, 0.0022968500570365877, -0.22884406550138287, 0.08691429092802784, -0.12115752104331147, -0.1903620610740318, -0.08956448191946204, -0.018502745608037167, 0.06287999480087875, -0.1385397237697334, 0.02023005527867512, 0.0930482859187054, 0.05000445074542905, -0.16643385995518079, -0.021305001799412297, 0.013310655697502873, 0.09507941687479615, 0.15229423959019847, -0.05527832471963131, 0.05680179460482164, -0.18794147511520845, -0.06334561936444405, 0.3106614632362669, -0.0971949032197396, -0.2946001765402881, 0.1309361777296572, -0.1288828718842882, -0.10112873351935184, 0.053345399598280586, 0.2672259333882142, 0.24527483862457852, -0.11318988554095002, 0.0909712957183012, -0.07144126702438701, 0.05384133819660002, 0.08825411390738958, -0.041396823824580875, 0.12224125673034877, 0.10880545176791423, 0.0761476654813371, 0.1457269435155798, -0.025934797062566788, -0.17733964583638942, -0.3274364174541199, -0.2492148747498339, -0.07824470147942052, 0.09368865059999128, -0.11252761049333705, -0.14333411752754313, 0.4667828550392931, 0.11784582260544553, 0.2613725045865232, 0.1579065491642916, 0.2113493218114882, 0.07257020592012188, -0.004043516574187599, 0.11593825931689053, 0.2050957556886274, 0.22450538498885703, 0.10968819191100809, -0.21410191812637178, -0.06988435959669226, 0.13035539026851906] |
1,802.05364 | Long--Term Analysis of Positive Operator Semigroups via Asymptotic
Domination | We consider positive operator semigroups on ordered Banach spac\-es and study
the relation of their long time behaviour to two different domination
properties. First, we analyse under which conditions almost periodicity and
mean ergodicity of a semigroup $\mathcal{T}$ are inherited by other semigroups
which are asymptotically dominated by $\mathcal{T}$. Then, we consider
semigroups whose orbits asymptotically dominate a positive vector and show that
this assumption is often sufficient to conclude strong convergence of the
semigroup as time tends to infinity.
Our theorems are applicable to time-discrete as well as time-continuous
semigroups. They generalise several results from the literature to considerably
larger classes of ordered Banach spaces.
| math.FA | we consider positive operator semigroups on ordered banach spaces and study the relation of their long time behaviour to two different domination properties first we analyse under which conditions almost periodicity and mean ergodicity of a semigroup mathcalt are inherited by other semigroups which are asymptotically dominated by mathcalt then we consider semigroups whose orbits asymptotically dominate a positive vector and show that this assumption is often sufficient to conclude strong convergence of the semigroup as time tends to infinity our theorems are applicable to timediscrete as well as timecontinuous semigroups they generalise several results from the literature to considerably larger classes of ordered banach spaces | [['we', 'consider', 'positive', 'operator', 'semigroups', 'on', 'ordered', 'banach', 'spaces', 'and', 'study', 'the', 'relation', 'of', 'their', 'long', 'time', 'behaviour', 'to', 'two', 'different', 'domination', 'properties', 'first', 'we', 'analyse', 'under', 'which', 'conditions', 'almost', 'periodicity', 'and', 'mean', 'ergodicity', 'of', 'a', 'semigroup', 'mathcalt', 'are', 'inherited', 'by', 'other', 'semigroups', 'which', 'are', 'asymptotically', 'dominated', 'by', 'mathcalt', 'then', 'we', 'consider', 'semigroups', 'whose', 'orbits', 'asymptotically', 'dominate', 'a', 'positive', 'vector', 'and', 'show', 'that', 'this', 'assumption', 'is', 'often', 'sufficient', 'to', 'conclude', 'strong', 'convergence', 'of', 'the', 'semigroup', 'as', 'time', 'tends', 'to', 'infinity', 'our', 'theorems', 'are', 'applicable', 'to', 'timediscrete', 'as', 'well', 'as', 'timecontinuous', 'semigroups', 'they', 'generalise', 'several', 'results', 'from', 'the', 'literature', 'to', 'considerably', 'larger', 'classes', 'of', 'ordered', 'banach', 'spaces']] | [-0.1081303408509999, 0.18840358532385304, -0.0731515794869442, 0.1714116289579081, -0.07399679723916189, -0.11159169004421751, 0.010740081824687362, 0.39759742435208467, -0.34602308811022425, -0.1824883042641406, 0.19624962173138727, -0.28652486937858584, -0.12202559712887653, 0.19787801725719617, -0.08614746668982266, 0.056949477128789956, 0.0471850852218439, 0.057544568778709775, -0.1201916895416908, -0.2588205362454865, 0.39693206586470864, -0.0010786741589775906, 0.2314800324823426, 0.008913321232199142, 0.1003859297373499, -0.046633312649989746, -0.05085072801272684, 0.04542425269577301, -0.1906195591703713, 0.028604288212076393, 0.26606932881437595, 0.08371900305380377, 0.2821081528343471, -0.4038089956376561, -0.167719438140509, 0.210847687064055, 0.11969262044788953, 0.006384631867063636, -0.002221806085765151, -0.3141437743105135, 0.13599057043551133, -0.12164095196574223, -0.1272006180359244, -0.12522592937698993, 0.03942384571825453, 0.06891700799163994, -0.2764445304484019, 0.04737017029909558, 0.1974544248396513, 0.052054719173542735, -0.09497026816001689, -0.079411993655464, -0.06281816992548488, 0.09104104652860255, 0.0830781867261976, -0.029861659681389353, 0.09638936503105006, -0.018318631323845178, -0.13337825962676192, 0.3466774447170912, -0.06974279140737259, -0.19988801275854404, 0.21804904155345317, -0.18747225526790573, -0.12425850906391751, 0.10983906695531365, 0.1254214883852258, 0.18192222750446987, -0.09991075481586861, 0.13624256331185847, -0.09057629610991703, 0.08162827989227085, 0.07641954260069947, 0.09338956635954948, 0.10545053411240005, 0.0876778614957694, 0.1426514198801498, 0.17595627479406759, 0.08109397815654652, -0.12190271799676647, -0.33088346007543634, -0.11234821979712062, -0.14829460380133241, 0.1227037424379784, -0.10726520835099641, -0.2147718112803732, 0.3288351794801442, 0.14959880686804372, 0.19128894351668796, 0.17743309359900863, 0.18694368112867452, 0.10862924318424566, -0.004194533863140024, 0.07630136661793825, 0.13908921824856046, 0.20591910671495464, 0.05322073778260569, -0.14816461043335707, 0.04184515279594739, 0.16642506287503495] |
1,802.05365 | Deep contextualized word representations | We introduce a new type of deep contextualized word representation that
models both (1) complex characteristics of word use (e.g., syntax and
semantics), and (2) how these uses vary across linguistic contexts (i.e., to
model polysemy). Our word vectors are learned functions of the internal states
of a deep bidirectional language model (biLM), which is pre-trained on a large
text corpus. We show that these representations can be easily added to existing
models and significantly improve the state of the art across six challenging
NLP problems, including question answering, textual entailment and sentiment
analysis. We also present an analysis showing that exposing the deep internals
of the pre-trained network is crucial, allowing downstream models to mix
different types of semi-supervision signals.
| cs.CL | we introduce a new type of deep contextualized word representation that models both 1 complex characteristics of word use eg syntax and semantics and 2 how these uses vary across linguistic contexts ie to model polysemy our word vectors are learned functions of the internal states of a deep bidirectional language model bilm which is pretrained on a large text corpus we show that these representations can be easily added to existing models and significantly improve the state of the art across six challenging nlp problems including question answering textual entailment and sentiment analysis we also present an analysis showing that exposing the deep internals of the pretrained network is crucial allowing downstream models to mix different types of semisupervision signals | [['we', 'introduce', 'a', 'new', 'type', 'of', 'deep', 'contextualized', 'word', 'representation', 'that', 'models', 'both', '1', 'complex', 'characteristics', 'of', 'word', 'use', 'eg', 'syntax', 'and', 'semantics', 'and', '2', 'how', 'these', 'uses', 'vary', 'across', 'linguistic', 'contexts', 'ie', 'to', 'model', 'polysemy', 'our', 'word', 'vectors', 'are', 'learned', 'functions', 'of', 'the', 'internal', 'states', 'of', 'a', 'deep', 'bidirectional', 'language', 'model', 'bilm', 'which', 'is', 'pretrained', 'on', 'a', 'large', 'text', 'corpus', 'we', 'show', 'that', 'these', 'representations', 'can', 'be', 'easily', 'added', 'to', 'existing', 'models', 'and', 'significantly', 'improve', 'the', 'state', 'of', 'the', 'art', 'across', 'six', 'challenging', 'nlp', 'problems', 'including', 'question', 'answering', 'textual', 'entailment', 'and', 'sentiment', 'analysis', 'we', 'also', 'present', 'an', 'analysis', 'showing', 'that', 'exposing', 'the', 'deep', 'internals', 'of', 'the', 'pretrained', 'network', 'is', 'crucial', 'allowing', 'downstream', 'models', 'to', 'mix', 'different', 'types', 'of', 'semisupervision', 'signals']] | [-0.022189536116524668, 0.04344690371074325, -0.04343908016368711, 0.11796706669082603, -0.17788775123594222, -0.16827176386646325, 0.04876474539647341, 0.4378510807787091, -0.3580705354467404, -0.33011441271413455, 0.0323834436183626, -0.2899528404251357, -0.1707732603723599, 0.1876254294607365, -0.0880728904071287, 0.031611687411392524, 0.1441829780516915, 0.05655112670164956, -0.05617857457538836, -0.27079161495828524, 0.3636739105140128, -0.013718333136442325, 0.3402011142427887, 0.028687986576052243, 0.12281482886091984, -0.05284583980259909, -0.0590047301408981, -0.04192018996219897, -0.04477743830906818, 0.20443069581702458, 0.34978293672548333, 0.25960524561468606, 0.2755460816162277, -0.413324350918343, -0.25049203027170613, 0.06622058525777706, 0.1469156000331912, 0.128576094803231, 0.028918493922976855, -0.3668342799848823, 0.09527819666411143, -0.2028935666403864, 0.07403300693528034, -0.16932891678904022, -0.012453129261054775, 0.00875214032813414, -0.22137659648385222, 0.0240966087485729, 0.15399451000223413, 0.08341807243991489, -0.06938165251150234, -0.13358216106021883, -0.0004072018094699491, 0.18663787542773055, 0.020101979074707208, 0.05204514784676846, 0.11035599210107129, -0.22965867943245888, -0.1559059779825505, 0.33815799768213645, -0.09144204430097391, -0.2520121136326189, 0.24628413625615686, -0.013606726291997374, -0.16611077640713615, 0.02457998325463292, 0.2428594608444813, 0.08514948038592134, -0.13812150922411484, 0.018732996720989995, -0.07545428419070056, 0.26045643693232556, 0.07774269991469654, 0.0013548349538310008, 0.19578323941985684, 0.24565065120662416, -0.06124772133752088, 0.13525021030309742, -0.08125875260457704, -0.044241869868325796, -0.21151355912760747, -0.10054657064870862, -0.10606035924408862, -0.03841703873674177, -0.14010066799925303, -0.16011751545901018, 0.4483518425282861, 0.253386211466075, 0.18636934940649336, 0.11925652143324164, 0.275959044340844, 0.03337606907464287, 0.12305447775562686, 0.0948595779708371, 0.09917940824361872, -0.003509550886459587, 0.1244655172263603, -0.11353914187821822, 0.10256504763518114, 0.05560131439493444] |
1,802.05366 | MARS-MD: rejection based image domain material decomposition | This paper outlines image domain material decomposition algorithms that have
been routinely used in MARS spectral CT systems. These algorithms (known
collectively as MARS-MD) are based on a pragmatic heuristic for solving the
under-determined problem where there are more materials than energy bins. This
heuristic contains three parts: (1) splitting the problem into a number of
possible sub-problems, each containing fewer materials; (2) solving each
sub-problem; and (3) applying rejection criteria to eliminate all but one
sub-problem's solution. An advantage of this process is that different
constraints can be applied to each sub-problem if necessary. In addition, the
result of this process is that solutions will be sparse in the material domain,
which reduces crossover of signal between material images. Two algorithms based
on this process are presented: the Segmentation variant, which uses segmented
material classes to define each sub-problem; and the Angular Rejection variant,
which defines the rejection criteria using the angle between reconstructed
attenuation vectors.
| physics.med-ph | this paper outlines image domain material decomposition algorithms that have been routinely used in mars spectral ct systems these algorithms known collectively as marsmd are based on a pragmatic heuristic for solving the underdetermined problem where there are more materials than energy bins this heuristic contains three parts 1 splitting the problem into a number of possible subproblems each containing fewer materials 2 solving each subproblem and 3 applying rejection criteria to eliminate all but one subproblems solution an advantage of this process is that different constraints can be applied to each subproblem if necessary in addition the result of this process is that solutions will be sparse in the material domain which reduces crossover of signal between material images two algorithms based on this process are presented the segmentation variant which uses segmented material classes to define each subproblem and the angular rejection variant which defines the rejection criteria using the angle between reconstructed attenuation vectors | [['this', 'paper', 'outlines', 'image', 'domain', 'material', 'decomposition', 'algorithms', 'that', 'have', 'been', 'routinely', 'used', 'in', 'mars', 'spectral', 'ct', 'systems', 'these', 'algorithms', 'known', 'collectively', 'as', 'marsmd', 'are', 'based', 'on', 'a', 'pragmatic', 'heuristic', 'for', 'solving', 'the', 'underdetermined', 'problem', 'where', 'there', 'are', 'more', 'materials', 'than', 'energy', 'bins', 'this', 'heuristic', 'contains', 'three', 'parts', '1', 'splitting', 'the', 'problem', 'into', 'a', 'number', 'of', 'possible', 'subproblems', 'each', 'containing', 'fewer', 'materials', '2', 'solving', 'each', 'subproblem', 'and', '3', 'applying', 'rejection', 'criteria', 'to', 'eliminate', 'all', 'but', 'one', 'subproblems', 'solution', 'an', 'advantage', 'of', 'this', 'process', 'is', 'that', 'different', 'constraints', 'can', 'be', 'applied', 'to', 'each', 'subproblem', 'if', 'necessary', 'in', 'addition', 'the', 'result', 'of', 'this', 'process', 'is', 'that', 'solutions', 'will', 'be', 'sparse', 'in', 'the', 'material', 'domain', 'which', 'reduces', 'crossover', 'of', 'signal', 'between', 'material', 'images', 'two', 'algorithms', 'based', 'on', 'this', 'process', 'are', 'presented', 'the', 'segmentation', 'variant', 'which', 'uses', 'segmented', 'material', 'classes', 'to', 'define', 'each', 'subproblem', 'and', 'the', 'angular', 'rejection', 'variant', 'which', 'defines', 'the', 'rejection', 'criteria', 'using', 'the', 'angle', 'between', 'reconstructed', 'attenuation', 'vectors']] | [-0.07637974414571475, 0.04093367867188555, -0.084931997800222, 0.040891751341580226, -0.1193396915302564, -0.18171540937626746, 0.02195849775811299, 0.40320196722705776, -0.3052405990952721, -0.3308902381776044, 0.13683241267333357, -0.2592608953276888, -0.13524823855513182, 0.18816757193491915, -0.06512776906721485, 0.047921855128012024, 0.08134652366742301, -0.018890318304049566, -0.06406853359247833, -0.25627274935518823, 0.28675440900648635, -0.005216024374255003, 0.2652951820502774, -0.002207206439776107, 0.13601817898691082, -0.011598106591592137, -0.005033281622812725, 0.039156845931966715, -0.08398959490552489, 0.09872970815586786, 0.3120839399187109, 0.18874526986082157, 0.3008951777920652, -0.39805391202800167, -0.1790502828468258, 0.12826480297744872, 0.19029141361049065, 0.08029727012184687, -0.04484924684528288, -0.23009149899903256, 0.10648881475954579, -0.0944251303608792, -0.015708680604942717, -0.050404939078128874, -0.018805496716268886, -0.01384599407752737, -0.2977857392994711, 0.04074068061816387, 0.04534263852035973, -0.004256619597212053, -0.09213692481623581, -0.17414573171229747, 0.04347834760884348, 0.11246355360326095, 0.03740929422593371, 0.04283959373536233, 0.13627906486642763, -0.10302346808972428, -0.11254875906384908, 0.3933177108231645, 0.041111423145477416, -0.27322637512444115, 0.16400015834817447, -0.06095217254896385, -0.14616946736649156, 0.19665502273710445, 0.1788884271055651, 0.15659964005821025, -0.1948333075389457, 0.014346774666755198, -0.040637262855829574, 0.18855303562938786, 0.08213956780039156, 0.012241158780689614, 0.16177162607439244, 0.1767891254126787, 0.10906555925948151, 0.16077140749891838, -0.09715832092572385, -0.03689855317479202, -0.26841233724441665, -0.1493108908777149, -0.19512970168337537, -0.042757641427063696, -0.10742795688547165, -0.13718648632773414, 0.39689855026797605, 0.17262709461068973, 0.18153556627937809, 0.012015676560949009, 0.330741425007522, 0.1171033527121509, 0.08096295864225585, 0.08342420704698619, 0.20460011219629684, 0.07274317188272014, 0.07635412560417675, -0.15789838725378594, 0.06918169919831249, 0.11339779403090525] |
1,802.05367 | Dade's ordinary conjecture implies the Alperin-McKay conjecture | We show that Dade's ordinary conjecture implies the Alperin-McKay conjecture.
We remark that some of the methods can be used to identify a canonical height
zero character in a nilpotent block.
| math.RT math.GR | we show that dades ordinary conjecture implies the alperinmckay conjecture we remark that some of the methods can be used to identify a canonical height zero character in a nilpotent block | [['we', 'show', 'that', 'dades', 'ordinary', 'conjecture', 'implies', 'the', 'alperinmckay', 'conjecture', 'we', 'remark', 'that', 'some', 'of', 'the', 'methods', 'can', 'be', 'used', 'to', 'identify', 'a', 'canonical', 'height', 'zero', 'character', 'in', 'a', 'nilpotent', 'block']] | [-0.1831322587545841, 0.08082973686677794, -0.18858160559208162, 0.1197991264953969, -0.0816957832899906, -0.17067781135800383, -0.015863697314935345, 0.3128570222085522, -0.32085529859027556, -0.1933042050128983, 0.07368192669650118, -0.19701308509214752, -0.20883296652426642, 0.21855347783815476, -0.14189898300795786, -0.030806729149433873, 0.06551497021029072, 0.08564008085898334, -0.1096783213077053, -0.3521008269200402, 0.31745386541250253, -0.048518320185042194, 0.22976993432929438, 0.11094619177522198, 0.06568979783614556, -0.00999598772895913, 0.03492903066498618, 0.021175504301584536, -0.14209644966843282, 0.11211865233077158, 0.2829770916892636, 0.12494932807561371, 0.23639811336573574, -0.3458157900360323, -0.14728554222552526, 0.1747475608342117, 0.14425359747462696, 0.13141844149739032, -0.02105415348083742, -0.2112270602355561, 0.196348184636595, -0.1830601718637251, -0.2619583477536517, -0.056826285537212126, -0.017248119918569442, -0.02329686028702605, -0.2541961444151257, 0.05419482873572457, 0.18536200545608036, 0.039935107733453476, -0.0344775638753368, -0.11369628468838205, -0.03649587785044024, 0.05114276795798252, 0.02406554032237299, -0.03751354781730521, 0.06776568395716528, -0.05448933681773563, -0.14716333942487836, 0.32231734988970623, -0.1278874059357951, -0.18135644531538408, 0.1306286424158081, -0.12961594339820645, -0.21790450715249585, 0.09301091882310086, 0.047506441091818195, 0.15427333717384645, 0.010911326464866438, 0.13126073849778022, -0.18651586461571917, 0.1436472679939001, 0.12151063134473178, -0.0595971995724305, 0.16163029936292478, 0.035139591583321174, 0.06493910534247276, 0.13031002692502713, -0.02184402383863926, 0.006537539824362724, -0.3255577149891084, -0.25813338388839074, -0.14138761667474622, 0.14554693706093297, -0.050100423125249725, -0.17347341884047754, 0.34346665676322674, 0.21378783967285866, 0.21719728308098932, 0.12409612416259704, 0.1668823006893358, 0.11998075460113826, 0.08923741141634603, 0.07811373141744445, 0.16218181878737656, 0.2019199208626824, -0.05079257368080078, -0.18408981552103446, 0.008409155367483054, 0.19599331599930603] |
1,802.05368 | Universal Neural Machine Translation for Extremely Low Resource
Languages | In this paper, we propose a new universal machine translation approach
focusing on languages with a limited amount of parallel data. Our proposed
approach utilizes a transfer-learning approach to share lexical and sentence
level representations across multiple source languages into one target
language. The lexical part is shared through a Universal Lexical Representation
to support multilingual word-level sharing. The sentence-level sharing is
represented by a model of experts from all source languages that share the
source encoders with all other languages. This enables the low-resource
language to utilize the lexical and sentence representations of the higher
resource languages. Our approach is able to achieve 23 BLEU on Romanian-English
WMT2016 using a tiny parallel corpus of 6k sentences, compared to the 18 BLEU
of strong baseline system which uses multilingual training and
back-translation. Furthermore, we show that the proposed approach can achieve
almost 20 BLEU on the same dataset through fine-tuning a pre-trained
multi-lingual system in a zero-shot setting.
| cs.CL | in this paper we propose a new universal machine translation approach focusing on languages with a limited amount of parallel data our proposed approach utilizes a transferlearning approach to share lexical and sentence level representations across multiple source languages into one target language the lexical part is shared through a universal lexical representation to support multilingual wordlevel sharing the sentencelevel sharing is represented by a model of experts from all source languages that share the source encoders with all other languages this enables the lowresource language to utilize the lexical and sentence representations of the higher resource languages our approach is able to achieve 23 bleu on romanianenglish wmt2016 using a tiny parallel corpus of 6k sentences compared to the 18 bleu of strong baseline system which uses multilingual training and backtranslation furthermore we show that the proposed approach can achieve almost 20 bleu on the same dataset through finetuning a pretrained multilingual system in a zeroshot setting | [['in', 'this', 'paper', 'we', 'propose', 'a', 'new', 'universal', 'machine', 'translation', 'approach', 'focusing', 'on', 'languages', 'with', 'a', 'limited', 'amount', 'of', 'parallel', 'data', 'our', 'proposed', 'approach', 'utilizes', 'a', 'transferlearning', 'approach', 'to', 'share', 'lexical', 'and', 'sentence', 'level', 'representations', 'across', 'multiple', 'source', 'languages', 'into', 'one', 'target', 'language', 'the', 'lexical', 'part', 'is', 'shared', 'through', 'a', 'universal', 'lexical', 'representation', 'to', 'support', 'multilingual', 'wordlevel', 'sharing', 'the', 'sentencelevel', 'sharing', 'is', 'represented', 'by', 'a', 'model', 'of', 'experts', 'from', 'all', 'source', 'languages', 'that', 'share', 'the', 'source', 'encoders', 'with', 'all', 'other', 'languages', 'this', 'enables', 'the', 'lowresource', 'language', 'to', 'utilize', 'the', 'lexical', 'and', 'sentence', 'representations', 'of', 'the', 'higher', 'resource', 'languages', 'our', 'approach', 'is', 'able', 'to', 'achieve', '23', 'bleu', 'on', 'romanianenglish', 'wmt2016', 'using', 'a', 'tiny', 'parallel', 'corpus', 'of', '6k', 'sentences', 'compared', 'to', 'the', '18', 'bleu', 'of', 'strong', 'baseline', 'system', 'which', 'uses', 'multilingual', 'training', 'and', 'backtranslation', 'furthermore', 'we', 'show', 'that', 'the', 'proposed', 'approach', 'can', 'achieve', 'almost', '20', 'bleu', 'on', 'the', 'same', 'dataset', 'through', 'finetuning', 'a', 'pretrained', 'multilingual', 'system', 'in', 'a', 'zeroshot', 'setting']] | [-0.0731225328673196, -0.008209653746902565, -0.05370257689134598, 0.07909858065341965, -0.17411809538997994, -0.18397067192222782, 0.11818739753054408, 0.4435419858775184, -0.2950971264383296, -0.3449372112090829, -0.0389458278895509, -0.30245557665541956, -0.0717469799638832, 0.2103297659475946, -0.12788926857771188, 0.04822421924776952, 0.16039245442378253, 0.09756944665266791, -0.05928119793062723, -0.3098737028135647, 0.32673463475583947, 0.007939543510209533, 0.38984099687430773, 0.01151143016494056, 0.15570403972148447, -0.08370848639597055, -0.024508340779361846, -0.08224600571195913, 0.011646441095107075, 0.2228133338695109, 0.3822931586042683, 0.24124245078574064, 0.30641261848644646, -0.3145079172033628, -0.1740136222922703, 0.01830061561062555, 0.11558917713575537, 0.12231876001077338, 0.023112265491133106, -0.3659501440139322, 0.09192354293522437, -0.23261001817528396, 0.11997562415803535, -0.14129753974620016, -0.05447952099633698, -0.040263885920248405, -0.25238907905266017, 0.022289426121978633, 0.16769311704696452, 0.13361563086745482, -0.04401795990629522, -0.10709959477970164, 0.022073175684442815, 0.1654150565744295, 0.026045572343488706, 0.12768331374711492, 0.08202538657579807, -0.12616240043087001, -0.19409391686211258, 0.39014464577872165, -0.11366645207398725, -0.252346880828278, 0.2258767348572706, -0.024912781276610455, -0.15745670483241306, 0.04155340014302608, 0.22919330921847833, 0.07307607564438559, -0.1839082799910839, 0.031430840835968456, -0.08627088663471816, 0.3056226556227672, 0.13669680317722355, -0.012920954488689386, 0.18701547222459525, 0.28359654845811333, -0.03038271738045342, 0.17307191495555216, -0.06973683140741638, -0.04148379622683588, -0.22309580860148137, -0.11280775871245732, -0.16650163068265147, -0.0420912883444866, -0.09378896408710302, -0.14386094091577997, 0.4115338695111767, 0.24050690245901565, 0.13459663098984503, 0.17546428196832886, 0.30664156061373177, -0.02233880916109332, 0.174498234094486, 0.15165984942253585, 0.08970806633525298, -0.06121021495613305, 0.1699588788360876, -0.13189693491410795, 0.07196234319435692, 0.04925310185261637] |
1,802.05369 | Vortices in stably-stratified rapidly rotating Boussinesq convection | We study the Boussinesq approximation for rapidly rotating stably-stratified
fluids in a three dimensional infinite layer with either stress-free or
periodic boundary conditions in the vertical direction. For initial conditions
satisfying a certain quasi-geostrophic smallness condition, we use dispersive
estimates and the large rotation limit to prove global-in-time existence of
solutions. We then use self-similar variable techniques to show that the
barotropic vorticity converges to an Oseen vortex, while other components decay
to zero. We finally use algebraically weighted spaces to determine leading
order asymptotics. In particular we show that the barotropic vorticity
approaches the Oseen vortex with algebraic rate while the barotropic vertical
velocity and thermal fluctuations go to zero as Gaussians whose amplitudes
oscillate in opposite phase of each other while decaying with an algebraic
rate.
| math.AP math-ph math.MP | we study the boussinesq approximation for rapidly rotating stablystratified fluids in a three dimensional infinite layer with either stressfree or periodic boundary conditions in the vertical direction for initial conditions satisfying a certain quasigeostrophic smallness condition we use dispersive estimates and the large rotation limit to prove globalintime existence of solutions we then use selfsimilar variable techniques to show that the barotropic vorticity converges to an oseen vortex while other components decay to zero we finally use algebraically weighted spaces to determine leading order asymptotics in particular we show that the barotropic vorticity approaches the oseen vortex with algebraic rate while the barotropic vertical velocity and thermal fluctuations go to zero as gaussians whose amplitudes oscillate in opposite phase of each other while decaying with an algebraic rate | [['we', 'study', 'the', 'boussinesq', 'approximation', 'for', 'rapidly', 'rotating', 'stablystratified', 'fluids', 'in', 'a', 'three', 'dimensional', 'infinite', 'layer', 'with', 'either', 'stressfree', 'or', 'periodic', 'boundary', 'conditions', 'in', 'the', 'vertical', 'direction', 'for', 'initial', 'conditions', 'satisfying', 'a', 'certain', 'quasigeostrophic', 'smallness', 'condition', 'we', 'use', 'dispersive', 'estimates', 'and', 'the', 'large', 'rotation', 'limit', 'to', 'prove', 'globalintime', 'existence', 'of', 'solutions', 'we', 'then', 'use', 'selfsimilar', 'variable', 'techniques', 'to', 'show', 'that', 'the', 'barotropic', 'vorticity', 'converges', 'to', 'an', 'oseen', 'vortex', 'while', 'other', 'components', 'decay', 'to', 'zero', 'we', 'finally', 'use', 'algebraically', 'weighted', 'spaces', 'to', 'determine', 'leading', 'order', 'asymptotics', 'in', 'particular', 'we', 'show', 'that', 'the', 'barotropic', 'vorticity', 'approaches', 'the', 'oseen', 'vortex', 'with', 'algebraic', 'rate', 'while', 'the', 'barotropic', 'vertical', 'velocity', 'and', 'thermal', 'fluctuations', 'go', 'to', 'zero', 'as', 'gaussians', 'whose', 'amplitudes', 'oscillate', 'in', 'opposite', 'phase', 'of', 'each', 'other', 'while', 'decaying', 'with', 'an', 'algebraic', 'rate']] | [-0.1764052388643904, 0.17946029242580153, -0.08786422977232178, 0.062325378188688774, -0.11012303149254876, -0.1498522171168588, -0.031245304883668723, 0.3033521660909173, -0.3041160216962453, -0.1830616165752872, 0.11886205196242372, -0.25116265242468216, -0.044636608252403676, 0.15201049764255004, -0.01063120915023319, 0.08939755987012177, 0.057143986865412444, 0.041025255144631956, -0.07771205048175034, -0.22000729178398615, 0.37502732077700784, -0.05222632690492901, 0.28953075167373754, -0.013017852335906355, 0.08296062801673543, -0.083547506837931, 0.022607187675021123, 0.02448866573831765, -0.2447337421077691, 0.0007613759844389278, 0.19409101488417946, -0.013098070843625464, 0.2648651823037653, -0.47893624577591254, -0.21012475347379223, 0.1279048200995021, 0.2131592297955649, 0.11345175012320396, -0.0032392157172580482, -0.23659364075865597, 0.0942380297856289, -0.130016973183956, -0.2537373391605797, -0.08450847271342354, 0.02586956083723635, 0.08464448809581882, -0.31792868029879173, 0.11533612538914895, 0.10299532770841324, 0.04891066451455117, -0.16639902773204085, -0.06917198404335068, -0.06759411309030838, 0.058389744264786714, 0.13181687271753617, 0.018486659153495566, 0.08856990095591755, -0.15870083610826669, -0.036099242886848515, 0.3275121817696345, -0.1683194650599944, -0.2964809985132888, 0.18292813590960577, -0.15895406376193932, -0.08092309554558597, 0.13933758850907907, 0.1496248790208483, 0.0966652404672459, -0.06389245868922444, 0.05022734669819329, -0.059593001110442856, 0.13135209234133072, 0.15607971050485503, -0.00634554755379213, 0.1738906822138233, 0.10472324117836251, 0.1344779384757544, 0.14119805478640046, -0.09786267342315114, -0.11636405975286834, -0.3175239956472069, -0.1491817926071235, -0.12262141934661486, 0.06968680261343252, -0.11790616864868753, -0.25266243314399617, 0.3471805693843635, 0.12293245713362921, 0.15584851354242346, 0.09830385570603539, 0.3021474321540154, 0.13484914705168194, -0.004652548046578886, 0.16265290654700948, 0.23413779309976235, 0.19939946763588523, 0.14790295438979228, -0.2231843095023578, -0.009124107289608219, 0.12818619070094428] |
1,802.0537 | Covariance Function Pre-Training with m-Kernels for Accelerated Bayesian
Optimisation | The paper presents a novel approach to direct covariance function learning
for Bayesian optimisation, with particular emphasis on experimental design
problems where an existing corpus of condensed knowledge is present. The method
presented borrows techniques from reproducing kernel Banach space theory
(specifically m-kernels) and leverages them to convert (or re-weight) existing
covariance functions into new, problem-specific covariance functions. The key
advantage of this approach is that rather than relying on the user to manually
select (with some hyperparameter tuning and experimentation) an appropriate
covariance function it constructs the covariance function to specifically match
the problem at hand. The technique is demonstrated on two real-world problems -
specifically alloy design and short-polymer fibre manufacturing - as well as a
selected test function.
| stat.ML | the paper presents a novel approach to direct covariance function learning for bayesian optimisation with particular emphasis on experimental design problems where an existing corpus of condensed knowledge is present the method presented borrows techniques from reproducing kernel banach space theory specifically mkernels and leverages them to convert or reweight existing covariance functions into new problemspecific covariance functions the key advantage of this approach is that rather than relying on the user to manually select with some hyperparameter tuning and experimentation an appropriate covariance function it constructs the covariance function to specifically match the problem at hand the technique is demonstrated on two realworld problems specifically alloy design and shortpolymer fibre manufacturing as well as a selected test function | [['the', 'paper', 'presents', 'a', 'novel', 'approach', 'to', 'direct', 'covariance', 'function', 'learning', 'for', 'bayesian', 'optimisation', 'with', 'particular', 'emphasis', 'on', 'experimental', 'design', 'problems', 'where', 'an', 'existing', 'corpus', 'of', 'condensed', 'knowledge', 'is', 'present', 'the', 'method', 'presented', 'borrows', 'techniques', 'from', 'reproducing', 'kernel', 'banach', 'space', 'theory', 'specifically', 'mkernels', 'and', 'leverages', 'them', 'to', 'convert', 'or', 'reweight', 'existing', 'covariance', 'functions', 'into', 'new', 'problemspecific', 'covariance', 'functions', 'the', 'key', 'advantage', 'of', 'this', 'approach', 'is', 'that', 'rather', 'than', 'relying', 'on', 'the', 'user', 'to', 'manually', 'select', 'with', 'some', 'hyperparameter', 'tuning', 'and', 'experimentation', 'an', 'appropriate', 'covariance', 'function', 'it', 'constructs', 'the', 'covariance', 'function', 'to', 'specifically', 'match', 'the', 'problem', 'at', 'hand', 'the', 'technique', 'is', 'demonstrated', 'on', 'two', 'realworld', 'problems', 'specifically', 'alloy', 'design', 'and', 'shortpolymer', 'fibre', 'manufacturing', 'as', 'well', 'as', 'a', 'selected', 'test', 'function']] | [0.012869230921427577, -0.019854377940153087, -0.09324007457264273, 0.07248440073305835, -0.1757677181079289, -0.16277699184335642, 0.027476202526871683, 0.44011895392500494, -0.27178158995427065, -0.32270321840325655, 0.09302360920424936, -0.23896959042808008, -0.2007155525596749, 0.21739400473178633, -0.07281728594769109, 0.11914593908320165, 0.074885446057355, -0.013104925626667879, -0.11047626102651012, -0.24297463489791093, 0.37426553022953035, 0.06642552949014609, 0.334102788198171, 0.00022205526711640216, 0.14347662291710533, 0.056571706135015366, -0.05560607643466506, -0.03688726596700983, -0.09967813994253293, 0.16055289024025424, 0.29223176925364186, 0.20788132028764714, 0.36426608763256313, -0.39333515148609877, -0.1979113679884349, 0.09322243427928789, 0.11427781652753889, 0.06413979495686116, -0.044311348613750155, -0.2924760301928904, 0.05744770604138404, -0.13864463630874277, -0.033482094449227895, -0.11913761597568706, -0.062384837378069, -0.01320273665143019, -0.34581311763722006, -0.008655382793838694, 0.028378549589325626, 0.054563616729988634, -0.03370536094624155, -0.18146381210520965, 0.06049531171200955, 0.10673680192577827, 0.03289033387890244, 0.05312182876908899, 0.16598522145832864, -0.09374144806534494, -0.11471854995095597, 0.347524903983004, 0.002039264856371042, -0.2556394752602377, 0.1612358392942381, -0.04951027468225713, -0.1487977160574963, 0.07262835528512122, 0.22413960618156373, 0.1186911489704024, -0.1784591471492234, 0.08772078015046368, -0.020720664759056043, 0.19137426798026694, -0.003385312361958421, 0.010743526062115207, 0.12723124709367056, 0.23153551707194203, 0.09384288076090358, 0.16131148151895505, -0.045462189376664, -0.08612311753381233, -0.2833773662351956, -0.12098807889585367, -0.2541942838421565, -0.01402464010200258, -0.11750456893861479, -0.20115021262633598, 0.38091062548791327, 0.17949080339364581, 0.189943864913973, 0.09634201242825237, 0.35304465100674304, 0.09086605639520527, 0.08756793049258828, 0.03982688306230989, 0.16948485896371154, 0.07388828725009462, 0.09232903847281458, -0.1563029129273195, 0.08151119499925082, 0.06621701157478205] |
1,802.05371 | Input-Aware Auto-Tuning of Compute-Bound HPC Kernels | Efficient implementations of HPC applications for parallel architectures
generally rely on external software packages (e.g., BLAS, LAPACK, CUDNN). While
these libraries provide highly optimized routines for certain characteristics
of inputs (e.g., square matrices), they generally do not retain optimal
performance across the wide range of problems encountered in practice. In this
paper, we present an input-aware auto-tuning framework for matrix
multiplications and convolutions, ISAAC, which uses predictive modeling
techniques to drive highly parameterized PTX code templates towards not only
hardware-, but also application-specific kernels. Numerical experiments on the
NVIDIA Maxwell and Pascal architectures show up to 3x performance gains over
both cuBLAS and cuDNN after only a few hours of auto-tuning.
| cs.DC | efficient implementations of hpc applications for parallel architectures generally rely on external software packages eg blas lapack cudnn while these libraries provide highly optimized routines for certain characteristics of inputs eg square matrices they generally do not retain optimal performance across the wide range of problems encountered in practice in this paper we present an inputaware autotuning framework for matrix multiplications and convolutions isaac which uses predictive modeling techniques to drive highly parameterized ptx code templates towards not only hardware but also applicationspecific kernels numerical experiments on the nvidia maxwell and pascal architectures show up to 3x performance gains over both cublas and cudnn after only a few hours of autotuning | [['efficient', 'implementations', 'of', 'hpc', 'applications', 'for', 'parallel', 'architectures', 'generally', 'rely', 'on', 'external', 'software', 'packages', 'eg', 'blas', 'lapack', 'cudnn', 'while', 'these', 'libraries', 'provide', 'highly', 'optimized', 'routines', 'for', 'certain', 'characteristics', 'of', 'inputs', 'eg', 'square', 'matrices', 'they', 'generally', 'do', 'not', 'retain', 'optimal', 'performance', 'across', 'the', 'wide', 'range', 'of', 'problems', 'encountered', 'in', 'practice', 'in', 'this', 'paper', 'we', 'present', 'an', 'inputaware', 'autotuning', 'framework', 'for', 'matrix', 'multiplications', 'and', 'convolutions', 'isaac', 'which', 'uses', 'predictive', 'modeling', 'techniques', 'to', 'drive', 'highly', 'parameterized', 'ptx', 'code', 'templates', 'towards', 'not', 'only', 'hardware', 'but', 'also', 'applicationspecific', 'kernels', 'numerical', 'experiments', 'on', 'the', 'nvidia', 'maxwell', 'and', 'pascal', 'architectures', 'show', 'up', 'to', '3x', 'performance', 'gains', 'over', 'both', 'cublas', 'and', 'cudnn', 'after', 'only', 'a', 'few', 'hours', 'of', 'autotuning']] | [-0.08295548822823073, -0.02338450421620609, -0.0005151194725856856, 0.02613600045295751, -0.09098144967176088, -0.2243605059621906, 0.017880732423273493, 0.5114499576859646, -0.25868295864747454, -0.3831877118135962, 0.14806024631475379, -0.20220195894708504, -0.15186558342496823, 0.33107841595155735, -0.06949075527834564, 0.13568915038260523, 0.18338046935261101, -0.051156277543611337, -0.1579797403091934, -0.332622866448317, 0.18821447259570295, 0.09376213984010187, 0.28907630388254746, 0.026934933590677543, 0.09778290631685842, -0.05058643734502094, -0.026815514747254753, -0.05119800015545643, -0.023667931124601548, 0.12539732610798432, 0.29700386541820056, 0.19572292083689757, 0.3021510022457454, -0.47346138689029327, -0.1513681882711312, 0.04827833331299124, 0.1520585252005116, 0.026079208163234096, -0.02177389158850519, -0.2173478393506066, 0.10989772244398524, -0.233165980157283, -0.011149765932009564, -0.16533966190055818, -0.02891422775448174, 0.05552759435109643, -0.27606516340348936, -0.026261111486730427, 0.04560411624664961, 0.0690163246162974, 0.010274735468162878, -0.21005752480060264, 0.06954657936863064, 0.09100187892882942, -0.06622552390101256, 0.00017723785440630472, 0.19365103414433227, -0.15988636458384897, -0.13685710270897494, 0.3742406044054676, 0.0007448293109264996, -0.22722939800403946, 0.2077183988541029, -0.012934284578371156, -0.1653145273962805, 0.12148542239473344, 0.2635033337214777, 0.10557066468387707, -0.14642221439154693, 0.10296048447416323, 0.034684624136970925, 0.23569868753353754, 0.07104510648452954, 0.028493960163564428, 0.1516294563398906, 0.14597584151134296, 0.033115463425732544, 0.09488405729434243, -0.03183281720775339, -0.11149827778960268, -0.20553015422512283, -0.12864876162629943, -0.12090396192679936, -0.026274615140193403, -0.11004834926673022, -0.23157576925062515, 0.3838356703075136, 0.21133783724796665, 0.10384102718619344, 0.13102107576743766, 0.3745988552282388, 0.009274403581412526, 0.20146461416807798, 0.19458845957090054, 0.12245841943218398, 0.02267987555446657, 0.16902009304298238, -0.1256199217986668, 0.02969134974016531, -0.007096358793074483] |
1,802.05372 | Direct Measurement of Coating Thermal Noise in Optical Resonators | The best measurements of space and time currently possible (e.g.
gravitational wave detectors and optical reference cavities) rely on optical
resonators, and are ultimately limited by thermally induced fluctuations in the
reflective coatings which form the resonator. We present measurements of
coating thermal noise in the audio band and show that for a standard ion beam
sputtered coating, the power spectrum of the noise does not have the expected
power-law behavior.
| physics.ins-det | the best measurements of space and time currently possible eg gravitational wave detectors and optical reference cavities rely on optical resonators and are ultimately limited by thermally induced fluctuations in the reflective coatings which form the resonator we present measurements of coating thermal noise in the audio band and show that for a standard ion beam sputtered coating the power spectrum of the noise does not have the expected powerlaw behavior | [['the', 'best', 'measurements', 'of', 'space', 'and', 'time', 'currently', 'possible', 'eg', 'gravitational', 'wave', 'detectors', 'and', 'optical', 'reference', 'cavities', 'rely', 'on', 'optical', 'resonators', 'and', 'are', 'ultimately', 'limited', 'by', 'thermally', 'induced', 'fluctuations', 'in', 'the', 'reflective', 'coatings', 'which', 'form', 'the', 'resonator', 'we', 'present', 'measurements', 'of', 'coating', 'thermal', 'noise', 'in', 'the', 'audio', 'band', 'and', 'show', 'that', 'for', 'a', 'standard', 'ion', 'beam', 'sputtered', 'coating', 'the', 'power', 'spectrum', 'of', 'the', 'noise', 'does', 'not', 'have', 'the', 'expected', 'powerlaw', 'behavior']] | [-0.10546229548201384, 0.18061775458373233, -0.04844699731804955, -0.026538646386437853, -0.03715043119810731, -0.15669799624806152, 0.03069726496764367, 0.43274969654813616, -0.21204273561409243, -0.31382738502407576, 0.09796547263034318, -0.3265442482759835, -0.0863064029057261, 0.23410758681096872, -0.04143879406685403, 0.11017369907874038, 0.046173783789821704, -0.04715339579737522, -0.011749304138274836, -0.1381641949959834, 0.2940218868619487, 0.13066166958076433, 0.3300089879800946, 0.0335109180206774, 0.09106088644811805, -0.01985115579552424, -0.01652016280822351, -0.02579222374938389, -0.11054750969094425, 0.047400282689330865, 0.22367582362617405, 0.0019860764716187833, 0.1779907569682724, -0.5018073894398313, -0.25760214939765946, 0.09432313261999631, 0.11497201203613733, 0.09397481941178598, -0.0473513013279905, -0.2535766823755079, 0.002102791397294528, -0.1252420233221541, -0.08333471045553895, -0.04081349229623734, -0.022940335578849197, 0.07795206332434966, -0.24264097664321602, 0.05122466679190246, 0.0890503766434923, 0.022870221542535533, -0.05981034747141243, -0.09049475756408253, -0.012290761806070805, 0.039303791256700187, -0.02638992875761969, -0.025352353138298218, 0.2701897642011164, -0.14160944837552142, -0.06715848735323779, 0.36721972725026203, -0.11282681676388627, -0.12904624889840857, 0.1461664244637523, -0.20624088504495966, -0.0070068891861961346, 0.18137473696973963, 0.14970343102763314, 0.06177108367236043, -0.16853170229358153, 0.019476951418024763, 0.044778534814815404, 0.24458932085234292, 0.12810005245625344, 0.14361082658197888, 0.22164952743168867, 0.1810778275289586, 0.027981987357480635, 0.12070184756700822, -0.14214792972574877, 0.0661674105409782, -0.2693255181804719, -0.12759582022808388, -0.23009049249681787, 0.04339709558317595, -0.08607119136132484, -0.1932740347381209, 0.3853853383908232, 0.130766350828426, 0.10834093702773392, -0.012911918538648792, 0.33905568944526393, 0.07555217215706082, 0.08336762950139147, 0.03764357269597306, 0.3519333684948129, 0.10408468185399304, 0.12986815083299724, -0.24807913227557715, 0.0520194895814737, -0.07293108446707189] |
1,802.05373 | Improving Retrieval Modeling Using Cross Convolution Networks And Multi
Frequency Word Embedding | To build a satisfying chatbot that has the ability of managing a
goal-oriented multi-turn dialogue, accurate modeling of human conversation is
crucial. In this paper we concentrate on the task of response selection for
multi-turn human-computer conversation with a given context. Previous
approaches show weakness in capturing information of rare keywords that appear
in either or both context and correct response, and struggle with long input
sequences. We propose Cross Convolution Network (CCN) and Multi Frequency word
embedding to address both problems. We train several models using the Ubuntu
Dialogue dataset which is the largest freely available multi-turn based
dialogue corpus. We further build an ensemble model by averaging predictions of
multiple models. We achieve a new state-of-the-art on this dataset with
considerable improvements compared to previous best results.
| cs.CL | to build a satisfying chatbot that has the ability of managing a goaloriented multiturn dialogue accurate modeling of human conversation is crucial in this paper we concentrate on the task of response selection for multiturn humancomputer conversation with a given context previous approaches show weakness in capturing information of rare keywords that appear in either or both context and correct response and struggle with long input sequences we propose cross convolution network ccn and multi frequency word embedding to address both problems we train several models using the ubuntu dialogue dataset which is the largest freely available multiturn based dialogue corpus we further build an ensemble model by averaging predictions of multiple models we achieve a new stateoftheart on this dataset with considerable improvements compared to previous best results | [['to', 'build', 'a', 'satisfying', 'chatbot', 'that', 'has', 'the', 'ability', 'of', 'managing', 'a', 'goaloriented', 'multiturn', 'dialogue', 'accurate', 'modeling', 'of', 'human', 'conversation', 'is', 'crucial', 'in', 'this', 'paper', 'we', 'concentrate', 'on', 'the', 'task', 'of', 'response', 'selection', 'for', 'multiturn', 'humancomputer', 'conversation', 'with', 'a', 'given', 'context', 'previous', 'approaches', 'show', 'weakness', 'in', 'capturing', 'information', 'of', 'rare', 'keywords', 'that', 'appear', 'in', 'either', 'or', 'both', 'context', 'and', 'correct', 'response', 'and', 'struggle', 'with', 'long', 'input', 'sequences', 'we', 'propose', 'cross', 'convolution', 'network', 'ccn', 'and', 'multi', 'frequency', 'word', 'embedding', 'to', 'address', 'both', 'problems', 'we', 'train', 'several', 'models', 'using', 'the', 'ubuntu', 'dialogue', 'dataset', 'which', 'is', 'the', 'largest', 'freely', 'available', 'multiturn', 'based', 'dialogue', 'corpus', 'we', 'further', 'build', 'an', 'ensemble', 'model', 'by', 'averaging', 'predictions', 'of', 'multiple', 'models', 'we', 'achieve', 'a', 'new', 'stateoftheart', 'on', 'this', 'dataset', 'with', 'considerable', 'improvements', 'compared', 'to', 'previous', 'best', 'results']] | [-0.05940268293579015, 0.013656511665579083, -0.040691726353261334, 0.06613598417765064, -0.14617025934506295, -0.14669057844177813, 0.0235306620895646, 0.4472334797010403, -0.21348688283992945, -0.3271759364956109, 0.06544590850140496, -0.30549100484670133, -0.1430113086848729, 0.20575867098748915, -0.12931127011840945, 0.08048394723407755, 0.14481989470267193, 0.06820336813034937, -0.02218483209108226, -0.25589380882564905, 0.3155207941254471, 0.0536200655696466, 0.3645364379207062, 0.019740194836703612, 0.11970397005355808, -0.006365416149153959, -0.07702804006304043, -0.040457492932504, -0.06919495191828026, 0.194872409249692, 0.3352575061575788, 0.1820437565315948, 0.37413076914182697, -0.4318066544310991, -0.21940160529731317, 0.0892346077021067, 0.12494087746807252, 0.13093183815114692, -0.04789837301607684, -0.33302911991152423, 0.0657765944091271, -0.23128724718748866, 0.029072905865375153, -0.12361915277447118, -0.010153789665232333, 0.009440228144472191, -0.286555291215308, 0.008826189552607057, 0.10417718454733375, 0.059479724189858564, -0.06680109905793744, -0.09215041097657856, 0.061038958080214006, 0.1982431864030226, 0.04161069457249557, 0.10146868513633547, 0.09637837065022814, -0.17765804466091153, -0.17851680097469785, 0.38929159422368964, -0.11081229416787075, -0.22155422038694686, 0.19620441187844548, -0.05098389251908475, -0.14349731365254856, 0.030378673301446578, 0.2624917938373983, 0.10349841302174122, -0.1864063975738737, -0.0010294492059574976, -0.054891529536351215, 0.25136170734817437, 0.027255952823907137, -0.00957384075637358, 0.20104119590981756, 0.3074391346800235, -0.009890754988720251, 0.10841856656646602, -0.06587784473293339, -0.0793998857180393, -0.20695087500322654, -0.08465644152726266, -0.1358399651836344, -0.03012453481670498, -0.036714655881077085, -0.17194168227760828, 0.38579561777932697, 0.2800711459270859, 0.2069335409346246, 0.10341608944590847, 0.3191347706109978, 0.01956592171720063, 0.07442347092514266, 0.08449173147578962, 0.1314862204332973, -0.03737942474287783, 0.14136244236065668, -0.168165688171925, 0.07860521503946159, 0.03687627165571086] |
1,802.05374 | A Progressive Batching L-BFGS Method for Machine Learning | The standard L-BFGS method relies on gradient approximations that are not
dominated by noise, so that search directions are descent directions, the line
search is reliable, and quasi-Newton updating yields useful quadratic models of
the objective function. All of this appears to call for a full batch approach,
but since small batch sizes give rise to faster algorithms with better
generalization properties, L-BFGS is currently not considered an algorithm of
choice for large-scale machine learning applications. One need not, however,
choose between the two extremes represented by the full batch or highly
stochastic regimes, and may instead follow a progressive batching approach in
which the sample size increases during the course of the optimization. In this
paper, we present a new version of the L-BFGS algorithm that combines three
basic components - progressive batching, a stochastic line search, and stable
quasi-Newton updating - and that performs well on training logistic regression
and deep neural networks. We provide supporting convergence theory for the
method.
| math.OC cs.LG stat.ML | the standard lbfgs method relies on gradient approximations that are not dominated by noise so that search directions are descent directions the line search is reliable and quasinewton updating yields useful quadratic models of the objective function all of this appears to call for a full batch approach but since small batch sizes give rise to faster algorithms with better generalization properties lbfgs is currently not considered an algorithm of choice for largescale machine learning applications one need not however choose between the two extremes represented by the full batch or highly stochastic regimes and may instead follow a progressive batching approach in which the sample size increases during the course of the optimization in this paper we present a new version of the lbfgs algorithm that combines three basic components progressive batching a stochastic line search and stable quasinewton updating and that performs well on training logistic regression and deep neural networks we provide supporting convergence theory for the method | [['the', 'standard', 'lbfgs', 'method', 'relies', 'on', 'gradient', 'approximations', 'that', 'are', 'not', 'dominated', 'by', 'noise', 'so', 'that', 'search', 'directions', 'are', 'descent', 'directions', 'the', 'line', 'search', 'is', 'reliable', 'and', 'quasinewton', 'updating', 'yields', 'useful', 'quadratic', 'models', 'of', 'the', 'objective', 'function', 'all', 'of', 'this', 'appears', 'to', 'call', 'for', 'a', 'full', 'batch', 'approach', 'but', 'since', 'small', 'batch', 'sizes', 'give', 'rise', 'to', 'faster', 'algorithms', 'with', 'better', 'generalization', 'properties', 'lbfgs', 'is', 'currently', 'not', 'considered', 'an', 'algorithm', 'of', 'choice', 'for', 'largescale', 'machine', 'learning', 'applications', 'one', 'need', 'not', 'however', 'choose', 'between', 'the', 'two', 'extremes', 'represented', 'by', 'the', 'full', 'batch', 'or', 'highly', 'stochastic', 'regimes', 'and', 'may', 'instead', 'follow', 'a', 'progressive', 'batching', 'approach', 'in', 'which', 'the', 'sample', 'size', 'increases', 'during', 'the', 'course', 'of', 'the', 'optimization', 'in', 'this', 'paper', 'we', 'present', 'a', 'new', 'version', 'of', 'the', 'lbfgs', 'algorithm', 'that', 'combines', 'three', 'basic', 'components', 'progressive', 'batching', 'a', 'stochastic', 'line', 'search', 'and', 'stable', 'quasinewton', 'updating', 'and', 'that', 'performs', 'well', 'on', 'training', 'logistic', 'regression', 'and', 'deep', 'neural', 'networks', 'we', 'provide', 'supporting', 'convergence', 'theory', 'for', 'the', 'method']] | [-0.05286803592997169, 0.03525560849025536, -0.0865304522008074, 0.07729412799249895, -0.1176670967624378, -0.2083991510701013, 0.05837755202986763, 0.43958827417043056, -0.2983899258470498, -0.2949428764773498, 0.10290549371408889, -0.20573141333981135, -0.1831067150519432, 0.21869542195189814, -0.08678476355379876, 0.06033985151891938, 0.1002707962533864, -0.02562482862264028, -0.08789759161679642, -0.3049806592818234, 0.26029366434274115, 0.07223159511358859, 0.30502483711768846, -0.04518732480612231, 0.11211540706105738, 0.013644692036814942, -0.05094488888620312, 0.027935627602063907, -0.05062441756426044, 0.1311685687338829, 0.2411955244956375, 0.20413244611971218, 0.3630297220144305, -0.40594719896961245, -0.18722009524321243, 0.13465499011141693, 0.17817174975712557, 0.11832250783965426, -0.05062099646990945, -0.2230652069625219, 0.08257444784904235, -0.11297054845053031, -0.06069979302133925, -0.15259741969460475, -0.048787229614526105, 0.054401186889222596, -0.3063713979548688, 0.05611820101610714, 0.10087434439059428, 0.035093202048595215, -0.03082609901881505, -0.15827442171711545, 0.0584031964857594, 0.0535829675706358, 0.04610315720429239, 0.04872970177606905, 0.1396560636204407, -0.1007502005053742, -0.13961812472028762, 0.35143115184044244, -0.0643037931685573, -0.1989587416542659, 0.18697827500334555, -0.0034620848293445126, -0.15524629900403328, 0.1387900063444091, 0.22384616264260682, 0.1679275886384157, -0.1557508460853411, 0.056582311354505234, -0.006515075964202399, 0.14948188813738755, 0.007716811450071031, -0.034719423292586524, 0.13349166172498733, 0.20458074724475234, 0.14295222425520795, 0.1120667926683698, -0.07799781790373396, -0.1514079042664087, -0.26423326339167746, -0.1257876219945202, -0.17154280646626044, -0.025460130017433715, -0.12963712781691933, -0.20355623087597005, 0.3772377247418861, 0.17238200125385725, 0.21338275357898648, 0.1150076196489925, 0.34398616900702256, 0.09069695638967042, 0.09466472472301367, 0.1397223313749762, 0.201747617422551, 0.06626351034667875, 0.13647543856156816, -0.19330190785261023, 0.1253395940246558, 0.10458199245427151] |
1,802.05375 | Ultrafast demagnetization at high temperatures | Time-resolved pump-probe measurements were made at variable heat accumulation
in Co/Pd superlattices. Heat accumulation increases the baseline temperature
and decreases the equilibrium magnetization. Transient ultrafast
demagnetization first develops with higher fluence in parallel with strong
equilibrium thermal spin fluctuations. The ultrafast demagnetization is then
gradually removed as the equilibrium temperature approaches the Curie
temperature. The transient magnetization time-dependence is fit well with the
spin-flip scattering model.
| cond-mat.mes-hall | timeresolved pumpprobe measurements were made at variable heat accumulation in copd superlattices heat accumulation increases the baseline temperature and decreases the equilibrium magnetization transient ultrafast demagnetization first develops with higher fluence in parallel with strong equilibrium thermal spin fluctuations the ultrafast demagnetization is then gradually removed as the equilibrium temperature approaches the curie temperature the transient magnetization timedependence is fit well with the spinflip scattering model | [['timeresolved', 'pumpprobe', 'measurements', 'were', 'made', 'at', 'variable', 'heat', 'accumulation', 'in', 'copd', 'superlattices', 'heat', 'accumulation', 'increases', 'the', 'baseline', 'temperature', 'and', 'decreases', 'the', 'equilibrium', 'magnetization', 'transient', 'ultrafast', 'demagnetization', 'first', 'develops', 'with', 'higher', 'fluence', 'in', 'parallel', 'with', 'strong', 'equilibrium', 'thermal', 'spin', 'fluctuations', 'the', 'ultrafast', 'demagnetization', 'is', 'then', 'gradually', 'removed', 'as', 'the', 'equilibrium', 'temperature', 'approaches', 'the', 'curie', 'temperature', 'the', 'transient', 'magnetization', 'timedependence', 'is', 'fit', 'well', 'with', 'the', 'spinflip', 'scattering', 'model']] | [-0.11317010615444319, 0.2779851915653456, -0.04844428699774047, 0.022439739204745627, -0.03185489821895648, -0.14771287292806487, 0.06827882951069059, 0.45474142660245753, -0.31717683623234433, -0.2711057794407349, 0.044917668932061075, -0.3852984224530784, -0.0025429660313282952, 0.20442106365578983, 0.0956290583902349, 0.05321099059162379, -0.05336456540195892, -0.06292592199293501, -0.09935992999023503, -0.1884113147988859, 0.16134974871282326, 0.10470354027435833, 0.3537355418401686, 0.07718516810035164, 0.05958846553976676, 0.03140319723796424, 0.10553605610391859, 0.017400664774785666, -0.13978723528315173, -0.10061272582265013, 0.23252781036408673, -0.14031475109302186, 0.18914756645928277, -0.47122226925355126, -0.26515521982924617, 0.009616336742924019, 0.09834341560913758, 0.1776373533348581, -0.0404000353688995, -0.18190395173080492, -0.06572283429065437, -0.1132862374158294, -0.15238413483497093, -0.13443659027953717, 0.0074010270675926495, 0.039497937583787876, -0.26275884647938336, 0.17828933990588694, 0.08281787317140367, 0.131487456002188, -0.14632076536531025, -0.09722625807097013, -0.10642396512963442, 0.02481789302758195, 0.08860897266651702, 0.10782322902796847, 0.2872737086039375, -0.06578199953461687, -0.1127268922388215, 0.21131262799837824, -0.14059827437229228, 0.004583859322310397, 0.10946131471516282, -0.2335419852117246, -0.03373939581576975, 0.23141821637523896, 0.07098001808944074, 0.13661508518271148, -0.18725923018417123, 0.0033953337582660783, 0.09301820892671292, 0.20293884650648883, 0.0833387357662573, 0.008399168861973467, 0.2618469729045914, 0.2674596114507453, 0.038752842574577895, 0.1980946563240705, -0.1960461040761209, -0.07348288695864154, -0.20136105887253175, -0.09724614733942982, -0.20164749960443287, 0.1856559057099124, -0.11476660585432194, -0.1469963495055157, 0.36733540530655195, 0.18792907683404558, 0.1403744577035082, -0.031971195536298735, 0.34017143748474843, 0.18717667254068973, 0.02491695996881886, 0.08679997130071349, 0.2521702261393798, 0.18382411961316725, 0.24771833803617593, -0.41915105092057, 0.16610865061749902, -0.057453346414011765] |
1,802.05376 | Extensive numerical study and circuitry implementation of the Watt
governor model | In this work we carry out extensive numerical study of a
Watt-centrifugal-governor system model, and we also implement an electronic
circuit by analog computation to experimentally solve the model. Our numerical
results show the existence of self-organized stable periodic structures (SPSs)
on parameter-space of the largest Lyapunov exponent and isospikes of time
series of the Watt governor system model. A peculiar hierarchical organization
and period-adding bifurcation cascade of the SPSs are observed, and this
self-organized cascade accumulates on a periodic boundary. It is also shown
that the periods of these structures organize themselves obeying the solutions
of Diophantine equations. In addition, an experimental setup is implemented by
a circuitry analogy of mechanical systems using analog computing technique to
characterize the robustness of our numerical results. After applying an active
control of chaos in the experiment, the effect of intrinsic experimental noise
was minimized such that, the experimental results are in astonishing well
agreement with our numerical findings. We can also mention as another
remarkable result, the application of analog computing technique to perform an
experimental circuitry analysis in real mechanical problems.
| nlin.CD | in this work we carry out extensive numerical study of a wattcentrifugalgovernor system model and we also implement an electronic circuit by analog computation to experimentally solve the model our numerical results show the existence of selforganized stable periodic structures spss on parameterspace of the largest lyapunov exponent and isospikes of time series of the watt governor system model a peculiar hierarchical organization and periodadding bifurcation cascade of the spss are observed and this selforganized cascade accumulates on a periodic boundary it is also shown that the periods of these structures organize themselves obeying the solutions of diophantine equations in addition an experimental setup is implemented by a circuitry analogy of mechanical systems using analog computing technique to characterize the robustness of our numerical results after applying an active control of chaos in the experiment the effect of intrinsic experimental noise was minimized such that the experimental results are in astonishing well agreement with our numerical findings we can also mention as another remarkable result the application of analog computing technique to perform an experimental circuitry analysis in real mechanical problems | [['in', 'this', 'work', 'we', 'carry', 'out', 'extensive', 'numerical', 'study', 'of', 'a', 'wattcentrifugalgovernor', 'system', 'model', 'and', 'we', 'also', 'implement', 'an', 'electronic', 'circuit', 'by', 'analog', 'computation', 'to', 'experimentally', 'solve', 'the', 'model', 'our', 'numerical', 'results', 'show', 'the', 'existence', 'of', 'selforganized', 'stable', 'periodic', 'structures', 'spss', 'on', 'parameterspace', 'of', 'the', 'largest', 'lyapunov', 'exponent', 'and', 'isospikes', 'of', 'time', 'series', 'of', 'the', 'watt', 'governor', 'system', 'model', 'a', 'peculiar', 'hierarchical', 'organization', 'and', 'periodadding', 'bifurcation', 'cascade', 'of', 'the', 'spss', 'are', 'observed', 'and', 'this', 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1,802.05377 | A novel superhard tungsten nitride predicted by machine-learning
accelerated crystal structure searching | Transition metal nitrides have been suggested to have both high hardness and
good thermal stability with large potential application value, but so far
stable superhard transition metal nitrides have not been synthesized. Here,
with our newly developed machine-learning accelerated crystal structure
searching method, we designed a superhard tungsten nitride, h-WN6, which can be
synthesized at pressure around 65 GPa and quenchable to ambient pressure. This
h-WN6 is constructed with single-bonded N6 rings and presents ionic-like
features, which can be formulated as W2.4+N62.4-. It has a band gap of 1.6 eV
at 0 GPa and exhibits an abnormal gap broadening behavior under pressure.
Excitingly, this h-WN6 is found to be the hardest among transition metal
nitrides known so far (Vickers hardness around 57 GPa) and also has a very high
melting temperature (around 1900 K). These predictions support the designing
rules and may stimulate future experiments to synthesize superhard material.
| cond-mat.mtrl-sci physics.comp-ph | transition metal nitrides have been suggested to have both high hardness and good thermal stability with large potential application value but so far stable superhard transition metal nitrides have not been synthesized here with our newly developed machinelearning accelerated crystal structure searching method we designed a superhard tungsten nitride hwn6 which can be synthesized at pressure around 65 gpa and quenchable to ambient pressure this hwn6 is constructed with singlebonded n6 rings and presents ioniclike features which can be formulated as w24n624 it has a band gap of 16 ev at 0 gpa and exhibits an abnormal gap broadening behavior under pressure excitingly this hwn6 is found to be the hardest among transition metal nitrides known so far vickers hardness around 57 gpa and also has a very high melting temperature around 1900 k these predictions support the designing rules and may stimulate future experiments to synthesize superhard material | [['transition', 'metal', 'nitrides', 'have', 'been', 'suggested', 'to', 'have', 'both', 'high', 'hardness', 'and', 'good', 'thermal', 'stability', 'with', 'large', 'potential', 'application', 'value', 'but', 'so', 'far', 'stable', 'superhard', 'transition', 'metal', 'nitrides', 'have', 'not', 'been', 'synthesized', 'here', 'with', 'our', 'newly', 'developed', 'machinelearning', 'accelerated', 'crystal', 'structure', 'searching', 'method', 'we', 'designed', 'a', 'superhard', 'tungsten', 'nitride', 'hwn6', 'which', 'can', 'be', 'synthesized', 'at', 'pressure', 'around', '65', 'gpa', 'and', 'quenchable', 'to', 'ambient', 'pressure', 'this', 'hwn6', 'is', 'constructed', 'with', 'singlebonded', 'n6', 'rings', 'and', 'presents', 'ioniclike', 'features', 'which', 'can', 'be', 'formulated', 'as', 'w24n624', 'it', 'has', 'a', 'band', 'gap', 'of', '16', 'ev', 'at', '0', 'gpa', 'and', 'exhibits', 'an', 'abnormal', 'gap', 'broadening', 'behavior', 'under', 'pressure', 'excitingly', 'this', 'hwn6', 'is', 'found', 'to', 'be', 'the', 'hardest', 'among', 'transition', 'metal', 'nitrides', 'known', 'so', 'far', 'vickers', 'hardness', 'around', '57', 'gpa', 'and', 'also', 'has', 'a', 'very', 'high', 'melting', 'temperature', 'around', '1900', 'k', 'these', 'predictions', 'support', 'the', 'designing', 'rules', 'and', 'may', 'stimulate', 'future', 'experiments', 'to', 'synthesize', 'superhard', 'material']] | [-0.04749912650246794, 0.162645452050116, -0.08463113806939045, -0.020451073373230745, -0.06687699742995355, -0.19532639488615855, 0.11879177373789605, 0.4965919439940631, -0.25531819017388585, -0.35280613023724383, 0.06225834153972718, -0.32362882929797077, -0.12035358140916647, 0.13559000817171873, 0.026517170566913426, 0.07283484137800782, -0.018578435601603216, -0.039281204689712246, -0.10961694792023904, -0.20518307508045466, 0.1965842854713096, 0.11466826650561118, 0.31186599993672803, 0.10386317698277064, 0.0037332638578654147, -0.1569356922959896, 0.16113464150349704, 0.04979400493229936, -0.144187567593826, 0.017532752791964482, 0.32667407425133144, -0.011210185773417253, 0.2343541646176683, -0.3616686994526662, -0.24597482434568965, 0.059117885917657986, 0.09849227407985196, 0.08183065309746787, -0.14406611513169038, -0.2484220335779547, 0.163302251661662, -0.13508952345198566, -0.13332830536730436, -0.13915939299947247, 0.013879775864762717, -0.030537181551295783, -0.19286837303562748, 0.04183490128318944, 0.005067269994123351, 0.08661212747659991, -0.11328207288368218, -0.22257086577616175, -0.04027454788023669, 0.027023955663273327, 0.025401855600351582, 0.05816413321354914, 0.1754657489502625, -0.0552940829847084, -0.06410193767994135, 0.411794678947958, -0.035882027176696626, 0.013742439801247196, 0.21758966309613972, -0.12320837945214846, -0.11697731410837447, 0.25593942002754433, 0.10421998895741176, 0.12076290811215636, -0.13349744839139194, 0.037446715369294746, 0.004998234466516546, 0.19343718131655688, 0.10839045653436459, 0.015986397916286373, 0.24662964100011808, 0.20887330644383875, -0.03286363081776911, 0.12986160261432964, -0.08461417992865401, 0.013735491249054594, -0.15357292659555366, -0.18294595952342158, -0.14868950460749192, 0.07093360611614945, -0.04046311195512923, -0.18573497606403366, 0.3054978217482947, 0.12341979465076738, 0.16356346712841296, -0.044703396087905176, 0.173873649767543, 0.08505292930862024, 0.11405101360841877, 0.07769677737968511, 0.32781631913238846, 0.14632650500252134, 0.1386033233229806, -0.17332882054196036, 0.1182482740049865, -0.01697978956092681] |
1,802.05378 | The properties of Planck Galactic cold clumps in the L1495 dark cloud | Planck Galactic Cold Clumps (PGCCs) possibly represent the early stages of
star formation. To understand better the properties of PGCCs, we studied 16
PGCCs in the L1495 cloud with molecular lines and continuum data from Herschel,
JCMT/SCUBA-2 and the PMO 13.7 m telescope. Thirty dense cores were identified
in 16 PGCCs from 2-D Gaussian fitting. The dense cores have dust temperatures
of $T_{\rm d}$ = 11-14 K, and H$_{2}$ column densities of $N_{\rm H_{2}}$ =
0.36-2.5$\times10^{22}$ cm$^{-2}$. We found that not all PGCCs contain
prestellar objects. In general, the dense cores in PGCCs are usually at their
earliest evolutionary stages. All the dense cores have non-thermal velocity
dispersions larger than the thermal velocity dispersions from molecular line
data, suggesting that the dense cores may be turbulence-dominated. We have
calculated the virial parameter $\alpha$ and found that 14 of the dense cores
have $\alpha$ $<$ 2, while 16 of the dense cores have $\alpha$ $>$ 2. This
suggests that some of the dense cores are not bound in the absence of external
pressure and magnetic fields. The column density profiles of dense cores were
fitted. The sizes of the flat regions and core radii decrease with the
evolution of dense cores. CO depletion was found to occur in all the dense
cores, but is more significant in prestellar core candidates than in
protostellar or starless cores. The protostellar cores inside the PGCCs are
still at a very early evolutionary stage, sharing similar physical and chemical
properties with the prestellar core candidates.
| astro-ph.GA | planck galactic cold clumps pgccs possibly represent the early stages of star formation to understand better the properties of pgccs we studied 16 pgccs in the l1495 cloud with molecular lines and continuum data from herschel jcmtscuba2 and the pmo 137 m telescope thirty dense cores were identified in 16 pgccs from 2d gaussian fitting the dense cores have dust temperatures of t_rm d 1114 k and h_2 column densities of n_rm h_2 03625times1022 cm2 we found that not all pgccs contain prestellar objects in general the dense cores in pgccs are usually at their earliest evolutionary stages all the dense cores have nonthermal velocity dispersions larger than the thermal velocity dispersions from molecular line data suggesting that the dense cores may be turbulencedominated we have calculated the virial parameter alpha and found that 14 of the dense cores have alpha 2 while 16 of the dense cores have alpha 2 this suggests that some of the dense cores are not bound in the absence of external pressure and magnetic fields the column density profiles of dense cores were fitted the sizes of the flat regions and core radii decrease with the evolution of dense cores co depletion was found to occur in all the dense cores but is more significant in prestellar core candidates than in protostellar or starless cores the protostellar cores inside the pgccs are still at a very early evolutionary stage sharing similar physical and chemical properties with the prestellar core candidates | [['planck', 'galactic', 'cold', 'clumps', 'pgccs', 'possibly', 'represent', 'the', 'early', 'stages', 'of', 'star', 'formation', 'to', 'understand', 'better', 'the', 'properties', 'of', 'pgccs', 'we', 'studied', '16', 'pgccs', 'in', 'the', 'l1495', 'cloud', 'with', 'molecular', 'lines', 'and', 'continuum', 'data', 'from', 'herschel', 'jcmtscuba2', 'and', 'the', 'pmo', '137', 'm', 'telescope', 'thirty', 'dense', 'cores', 'were', 'identified', 'in', '16', 'pgccs', 'from', '2d', 'gaussian', 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1,802.05379 | A Theory of Growing Crystalline Nanorods - Mode I | Nanorods grow in two modes through physical vapor deposition (PVD). In mode
I, monolayer surface steps dictate the diameter of nanorods. In mode II,
multiple-layer surface steps dictate the diameter, which is the smallest
possible under physical vapor deposition [X. B. Niu et al., Phys. Rev. Lett
110, 136102 (2013) and F. Du & H. C. Huang, Phys. Rev. Materials. 1, 033401
(2017)]. This paper reports closed-form theories of terrace lengths and nanorod
diameter during the growth in mode I, as a function of deposition conditions.
The accompanying lattice kinetic Monte Carlo simulations verify the theories.
This study reveals that (1) quasi-steady growth exists for each set of nanorod
growth condition, (2) the characteristic length scales, including terrace
lengths and nanorod diameter at the quasi-steady state, depend on the
deposition conditions - deposition rate F, substrate temperature T, and
incidence angle .
| cond-mat.mtrl-sci | nanorods grow in two modes through physical vapor deposition pvd in mode i monolayer surface steps dictate the diameter of nanorods in mode ii multiplelayer surface steps dictate the diameter which is the smallest possible under physical vapor deposition x b niu et al phys rev lett 110 136102 2013 and f du h c huang phys rev materials 1 033401 2017 this paper reports closedform theories of terrace lengths and nanorod diameter during the growth in mode i as a function of deposition conditions the accompanying lattice kinetic monte carlo simulations verify the theories this study reveals that 1 quasisteady growth exists for each set of nanorod growth condition 2 the characteristic length scales including terrace lengths and nanorod diameter at the quasisteady state depend on the deposition conditions deposition rate f substrate temperature t and incidence angle | [['nanorods', 'grow', 'in', 'two', 'modes', 'through', 'physical', 'vapor', 'deposition', 'pvd', 'in', 'mode', 'i', 'monolayer', 'surface', 'steps', 'dictate', 'the', 'diameter', 'of', 'nanorods', 'in', 'mode', 'ii', 'multiplelayer', 'surface', 'steps', 'dictate', 'the', 'diameter', 'which', 'is', 'the', 'smallest', 'possible', 'under', 'physical', 'vapor', 'deposition', 'x', 'b', 'niu', 'et', 'al', 'phys', 'rev', 'lett', '110', '136102', '2013', 'and', 'f', 'du', 'h', 'c', 'huang', 'phys', 'rev', 'materials', '1', '033401', '2017', 'this', 'paper', 'reports', 'closedform', 'theories', 'of', 'terrace', 'lengths', 'and', 'nanorod', 'diameter', 'during', 'the', 'growth', 'in', 'mode', 'i', 'as', 'a', 'function', 'of', 'deposition', 'conditions', 'the', 'accompanying', 'lattice', 'kinetic', 'monte', 'carlo', 'simulations', 'verify', 'the', 'theories', 'this', 'study', 'reveals', 'that', '1', 'quasisteady', 'growth', 'exists', 'for', 'each', 'set', 'of', 'nanorod', 'growth', 'condition', '2', 'the', 'characteristic', 'length', 'scales', 'including', 'terrace', 'lengths', 'and', 'nanorod', 'diameter', 'at', 'the', 'quasisteady', 'state', 'depend', 'on', 'the', 'deposition', 'conditions', 'deposition', 'rate', 'f', 'substrate', 'temperature', 't', 'and', 'incidence', 'angle']] | [-0.1311868521874349, 0.20606283828561758, -0.03134615567383649, -0.11655626232977141, -0.01900051713302514, -0.13846137074169015, 0.053568559747196084, 0.39525446150601456, -0.2169657703013207, -0.3054374391960837, -0.0007103574258295725, -0.25943855466796967, -0.123549212203667, 0.15891248063374663, -0.03498407256157294, 0.04588432010453548, 0.029221555125903013, -0.1291243679631148, -0.03283098227060638, -0.26361726712602035, 0.190498992963864, 0.11440561218595091, 0.34562908450862134, 0.08295462275200938, 0.05290557522272324, 0.010452864373809774, 0.06483758394232522, -0.02522993262476512, -0.3643414680457807, -0.04070786423437352, 0.13669016444715706, 0.02677470121369527, 0.2273581689291627, -0.46009854945850415, -0.21322696681821, 0.004191799680736378, 0.08580389295289986, 0.08390191127971006, -0.0062401398606802315, -0.20389313558728372, 0.04480343372187149, -0.09070128303558232, -0.14761428402424076, 0.04925749793879851, 0.12984157102454427, 0.008682434077894683, -0.30163999002454056, 0.10907016351417959, 0.03764414167827009, 0.10528623370494503, -0.008218291929385958, -0.15030102701653747, -0.12078823612316301, -0.016748874853569063, 0.008910897688389532, 0.039828957406885544, 0.22532432082102355, -0.017815556897217557, -0.08442398589648252, 0.2863855541812895, -0.04658257484225298, -0.11138387746262589, 0.22460607342748312, -0.17853560920441047, -0.06316191709879106, 0.1971543626112007, 0.15586893060958407, 0.17895507742820757, -0.12012302531201365, 0.12256122493171508, -0.018200013229120387, 0.20121148002442707, 0.22914209562170246, 0.011381857365668907, 0.1664091065988271, 0.19584316566559304, 0.0055254268719998265, 0.09559156238543291, -0.12612113651962284, -0.015167912018798075, -0.29502324617882497, -0.2281545052563187, -0.2179786590228357, 0.09120408490123431, -0.0763856050975614, -0.16408174822147745, 0.35940199250178617, 0.0869090588075911, 0.20397368923210316, 0.0033868408981153238, 0.17053234583552057, 0.02582872990549824, 0.023065884615869066, 0.09910300875721621, 0.21416221065514715, 0.1696127633535283, 0.09851001729903212, -0.2680328022178779, 0.0726140135131015, 0.07294612446541551] |
1,802.0538 | Active Feature Acquisition with Supervised Matrix Completion | Feature missing is a serious problem in many applications, which may lead to
low quality of training data and further significantly degrade the learning
performance. While feature acquisition usually involves special devices or
complex process, it is expensive to acquire all feature values for the whole
dataset. On the other hand, features may be correlated with each other, and
some values may be recovered from the others. It is thus important to decide
which features are most informative for recovering the other features as well
as improving the learning performance. In this paper, we try to train an
effective classification model with least acquisition cost by jointly
performing active feature querying and supervised matrix completion. When
completing the feature matrix, a novel target function is proposed to
simultaneously minimize the reconstruction error on observed entries and the
supervised loss on training data. When querying the feature value, the most
uncertain entry is actively selected based on the variance of previous
iterations. In addition, a bi-objective optimization method is presented for
cost-aware active selection when features bear different acquisition costs. The
effectiveness of the proposed approach is well validated by both theoretical
analysis and experimental study.
| cs.LG stat.ML | feature missing is a serious problem in many applications which may lead to low quality of training data and further significantly degrade the learning performance while feature acquisition usually involves special devices or complex process it is expensive to acquire all feature values for the whole dataset on the other hand features may be correlated with each other and some values may be recovered from the others it is thus important to decide which features are most informative for recovering the other features as well as improving the learning performance in this paper we try to train an effective classification model with least acquisition cost by jointly performing active feature querying and supervised matrix completion when completing the feature matrix a novel target function is proposed to simultaneously minimize the reconstruction error on observed entries and the supervised loss on training data when querying the feature value the most uncertain entry is actively selected based on the variance of previous iterations in addition a biobjective optimization method is presented for costaware active selection when features bear different acquisition costs the effectiveness of the proposed approach is well validated by both theoretical analysis and experimental study | [['feature', 'missing', 'is', 'a', 'serious', 'problem', 'in', 'many', 'applications', 'which', 'may', 'lead', 'to', 'low', 'quality', 'of', 'training', 'data', 'and', 'further', 'significantly', 'degrade', 'the', 'learning', 'performance', 'while', 'feature', 'acquisition', 'usually', 'involves', 'special', 'devices', 'or', 'complex', 'process', 'it', 'is', 'expensive', 'to', 'acquire', 'all', 'feature', 'values', 'for', 'the', 'whole', 'dataset', 'on', 'the', 'other', 'hand', 'features', 'may', 'be', 'correlated', 'with', 'each', 'other', 'and', 'some', 'values', 'may', 'be', 'recovered', 'from', 'the', 'others', 'it', 'is', 'thus', 'important', 'to', 'decide', 'which', 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1,802.05381 | Injection orbit matching for CSNS/RCS | The Rapid Cycling Synchrotron(RCS) of China Spallation Neutron Source(CSNS)
employs painting injection to achieve uniform beam distribution and suppress
space-charge effects. In the painting injection, the mismatch between the
injection beam orbit and circulating orbit could make the non-uniformity of
beam distribution and emittance growth, which may lead to beam loss. To match
the injection beam orbit and the circulating beam orbit, it is necessary to
identify the orbit of injection beam relative to the circulating orbit in the
injection point. However, it's hard to measure this relative injection orbit
directly. Theoretically, the relative injection beam orbit can be deduced by
measuring the turn-by-turn beam position of a single turn injection beam.
However, the intensity of single turn injected beam is too low to be measured
with sufficient signal-noise ratio by using beam position monitor(BPM). In this
paper, two effective methods based on multi-turn injection and turn-by-turn BPM
are given to perform the match between the injection and the circulating beam
orbit. The simulation results and application in the beam commissioning of
CSNS/RCS show the validity of the methods.
| physics.acc-ph | the rapid cycling synchrotronrcs of china spallation neutron sourcecsns employs painting injection to achieve uniform beam distribution and suppress spacecharge effects in the painting injection the mismatch between the injection beam orbit and circulating orbit could make the nonuniformity of beam distribution and emittance growth which may lead to beam loss to match the injection beam orbit and the circulating beam orbit it is necessary to identify the orbit of injection beam relative to the circulating orbit in the injection point however its hard to measure this relative injection orbit directly theoretically the relative injection beam orbit can be deduced by measuring the turnbyturn beam position of a single turn injection beam however the intensity of single turn injected beam is too low to be measured with sufficient signalnoise ratio by using beam position monitorbpm in this paper two effective methods based on multiturn injection and turnbyturn bpm are given to perform the match between the injection and the circulating beam orbit the simulation results and application in the beam commissioning of csnsrcs show the validity of the methods | [['the', 'rapid', 'cycling', 'synchrotronrcs', 'of', 'china', 'spallation', 'neutron', 'sourcecsns', 'employs', 'painting', 'injection', 'to', 'achieve', 'uniform', 'beam', 'distribution', 'and', 'suppress', 'spacecharge', 'effects', 'in', 'the', 'painting', 'injection', 'the', 'mismatch', 'between', 'the', 'injection', 'beam', 'orbit', 'and', 'circulating', 'orbit', 'could', 'make', 'the', 'nonuniformity', 'of', 'beam', 'distribution', 'and', 'emittance', 'growth', 'which', 'may', 'lead', 'to', 'beam', 'loss', 'to', 'match', 'the', 'injection', 'beam', 'orbit', 'and', 'the', 'circulating', 'beam', 'orbit', 'it', 'is', 'necessary', 'to', 'identify', 'the', 'orbit', 'of', 'injection', 'beam', 'relative', 'to', 'the', 'circulating', 'orbit', 'in', 'the', 'injection', 'point', 'however', 'its', 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-0.3070190200069396, 0.10360862494572563, -0.002839480554214103] |
1,802.05382 | Popularity-Aware Item Weighting for Long-Tail Recommendation | Many recommender systems suffer from the popularity bias problem: popular
items are being recommended frequently while less popular, niche products, are
recommended rarely if not at all. However, those ignored products are exactly
the products that businesses need to find customers for and their
recommendations would be more beneficial. In this paper, we examine an item
weighting approach to improve long-tail recommendation. Our approach works as a
simple yet powerful add-on to existing recommendation algorithms for making a
tunable trade-off between accuracy and long-tail coverage.
| cs.IR cs.AI | many recommender systems suffer from the popularity bias problem popular items are being recommended frequently while less popular niche products are recommended rarely if not at all however those ignored products are exactly the products that businesses need to find customers for and their recommendations would be more beneficial in this paper we examine an item weighting approach to improve longtail recommendation our approach works as a simple yet powerful addon to existing recommendation algorithms for making a tunable tradeoff between accuracy and longtail coverage | [['many', 'recommender', 'systems', 'suffer', 'from', 'the', 'popularity', 'bias', 'problem', 'popular', 'items', 'are', 'being', 'recommended', 'frequently', 'while', 'less', 'popular', 'niche', 'products', 'are', 'recommended', 'rarely', 'if', 'not', 'at', 'all', 'however', 'those', 'ignored', 'products', 'are', 'exactly', 'the', 'products', 'that', 'businesses', 'need', 'to', 'find', 'customers', 'for', 'and', 'their', 'recommendations', 'would', 'be', 'more', 'beneficial', 'in', 'this', 'paper', 'we', 'examine', 'an', 'item', 'weighting', 'approach', 'to', 'improve', 'longtail', 'recommendation', 'our', 'approach', 'works', 'as', 'a', 'simple', 'yet', 'powerful', 'addon', 'to', 'existing', 'recommendation', 'algorithms', 'for', 'making', 'a', 'tunable', 'tradeoff', 'between', 'accuracy', 'and', 'longtail', 'coverage']] | [-0.020486495609614342, 0.0251440304237322, -0.017159663003814572, 0.14774317620390587, -0.1875426387633471, -0.23162364767973914, 0.0739200162660221, 0.4982832817470326, -0.25784350957940605, -0.3476566099945237, 0.09426732307102237, -0.3539498454781578, -0.14938636876861838, 0.21865718022865407, -0.1366099025177605, 0.03417044812265564, 0.11256283074617386, 0.05278966739116346, -0.04526311189538854, -0.3667235803516472, 0.2772225088220747, 0.09704930943382137, 0.31419535968452694, 0.05152480062097311, 0.017458551288034547, 0.008960226106950466, -0.08035310043419218, 0.0214296216966913, -0.07875216660296942, 0.12728524963946683, 0.4064737541929764, 0.18920109455528505, 0.38175463873673887, -0.34774144708233723, -0.16778982077889582, 0.14192091029356507, 0.2070213193895624, 0.07908851068521686, -0.05069065087505698, -0.2555608200457166, 0.07838840426000603, -0.28641191381577624, -0.019987960685701932, -0.14620271609438693, 0.016708228956250584, 0.03671454939895364, -0.29124025778625817, 0.01782658766297733, 0.010484257510260624, 0.019585508749108103, 0.013758302493678296, -0.17819688712411066, 0.038910744270748074, 0.1967176894465571, 0.09633714705705643, -0.04257874053574222, 0.14063390837982298, -0.15250240914140115, -0.11777571214719072, 0.44067653329933393, 0.028620597961194375, -0.20346416331389372, 0.21834340619662887, -0.038833841571913046, -0.16742155733792222, 0.10323081328807508, 0.19361683271606178, 0.09371498676345628, -0.22833179717554766, -0.018022808850304606, -0.019688396703671007, 0.15629740459981428, 0.06379751403651693, 0.04639235140536638, 0.20586012240280124, 0.17414334077826318, 0.08946584295960801, 0.010844936034203891, 0.04871656317126882, -0.05347580372883171, -0.10944193314541789, -0.10417697863543735, -0.16176903797724448, 0.0031853558565832344, -0.10074791459003737, -0.16810324408990496, 0.32987072266200007, 0.24319988931803144, 0.13691208818379572, 0.07751050522167455, 0.3572837832329028, 0.0668609091628562, 0.10530947587891099, 0.099416478342541, 0.17818836948615224, -0.05360495709978482, 0.14920074763598248, -0.08018628842854762, 0.17592624388087322, -0.016442323402118156] |
1,802.05383 | Deep Learning Based Speech Beamforming | Multi-channel speech enhancement with ad-hoc sensors has been a challenging
task. Speech model guided beamforming algorithms are able to recover natural
sounding speech, but the speech models tend to be oversimplified or the
inference would otherwise be too complicated. On the other hand, deep learning
based enhancement approaches are able to learn complicated speech distributions
and perform efficient inference, but they are unable to deal with variable
number of input channels. Also, deep learning approaches introduce a lot of
errors, particularly in the presence of unseen noise types and settings. We
have therefore proposed an enhancement framework called DEEPBEAM, which
combines the two complementary classes of algorithms. DEEPBEAM introduces a
beamforming filter to produce natural sounding speech, but the filter
coefficients are determined with the help of a monaural speech enhancement
neural network. Experiments on synthetic and real-world data show that DEEPBEAM
is able to produce clean, dry and natural sounding speech, and is robust
against unseen noise.
| cs.CL cs.AI cs.SD eess.AS eess.SP | multichannel speech enhancement with adhoc sensors has been a challenging task speech model guided beamforming algorithms are able to recover natural sounding speech but the speech models tend to be oversimplified or the inference would otherwise be too complicated on the other hand deep learning based enhancement approaches are able to learn complicated speech distributions and perform efficient inference but they are unable to deal with variable number of input channels also deep learning approaches introduce a lot of errors particularly in the presence of unseen noise types and settings we have therefore proposed an enhancement framework called deepbeam which combines the two complementary classes of algorithms deepbeam introduces a beamforming filter to produce natural sounding speech but the filter coefficients are determined with the help of a monaural speech enhancement neural network experiments on synthetic and realworld data show that deepbeam is able to produce clean dry and natural sounding speech and is robust against unseen noise | [['multichannel', 'speech', 'enhancement', 'with', 'adhoc', 'sensors', 'has', 'been', 'a', 'challenging', 'task', 'speech', 'model', 'guided', 'beamforming', 'algorithms', 'are', 'able', 'to', 'recover', 'natural', 'sounding', 'speech', 'but', 'the', 'speech', 'models', 'tend', 'to', 'be', 'oversimplified', 'or', 'the', 'inference', 'would', 'otherwise', 'be', 'too', 'complicated', 'on', 'the', 'other', 'hand', 'deep', 'learning', 'based', 'enhancement', 'approaches', 'are', 'able', 'to', 'learn', 'complicated', 'speech', 'distributions', 'and', 'perform', 'efficient', 'inference', 'but', 'they', 'are', 'unable', 'to', 'deal', 'with', 'variable', 'number', 'of', 'input', 'channels', 'also', 'deep', 'learning', 'approaches', 'introduce', 'a', 'lot', 'of', 'errors', 'particularly', 'in', 'the', 'presence', 'of', 'unseen', 'noise', 'types', 'and', 'settings', 'we', 'have', 'therefore', 'proposed', 'an', 'enhancement', 'framework', 'called', 'deepbeam', 'which', 'combines', 'the', 'two', 'complementary', 'classes', 'of', 'algorithms', 'deepbeam', 'introduces', 'a', 'beamforming', 'filter', 'to', 'produce', 'natural', 'sounding', 'speech', 'but', 'the', 'filter', 'coefficients', 'are', 'determined', 'with', 'the', 'help', 'of', 'a', 'monaural', 'speech', 'enhancement', 'neural', 'network', 'experiments', 'on', 'synthetic', 'and', 'realworld', 'data', 'show', 'that', 'deepbeam', 'is', 'able', 'to', 'produce', 'clean', 'dry', 'and', 'natural', 'sounding', 'speech', 'and', 'is', 'robust', 'against', 'unseen', 'noise']] | [-0.05663522075466765, 0.045586109147469744, -0.0847259425098383, 0.0941206198789863, -0.17213117273795547, -0.2601582056878111, -0.020051604044723878, 0.4739821368380438, -0.2405933132896176, -0.3378128764791343, 0.08291651447755992, -0.2545318827298293, -0.22218306163544, 0.21601681624055852, -0.16956899229649314, 0.08832357892224306, 0.13025125524668682, 0.04049131411858657, -0.031962504270751785, -0.2530528131792841, 0.24528480194143573, 0.058029850139916295, 0.34663163391943974, -0.03607206949315776, 0.11652356402173379, -0.06401822153814844, -0.0666629991811314, -0.054279309527247056, -0.011471478467671969, 0.13649425455781666, 0.3679904716152779, 0.1906241286572517, 0.26986718772250357, -0.4249061962922658, -0.24339374961313803, 0.1448445815616582, 0.14838220381260483, 0.12732647831884766, -0.05536717294768395, -0.36239664078560435, 0.09697061853742675, -0.14130610220990428, 0.07191899287826795, -0.14359348635719735, -0.04233656066788148, -0.025914849333252898, -0.3412564648374255, 0.027542975085567908, 0.10380146351548596, 0.06188124269718611, -0.032834292996580465, -0.12206788467762025, 0.05036458925084694, 0.14864089053943516, 0.05744970745121164, 0.023307135109819143, 0.15077847766371655, -0.15423409693839052, -0.11015716017388939, 0.3803438952758531, -0.07360314609058484, -0.2574710025887898, 0.27110893057240765, -0.01194052775744113, -0.12906040561008209, 0.15166661105578458, 0.2638642021404768, 0.09001266419934699, -0.18139143238032732, -0.0020417019012354642, -0.0066317091666492104, 0.2097768603729768, 0.0608329901035545, 0.029832813675851336, 0.17726339823035878, 0.18876129074635195, 0.03148026567370973, 0.13932364342179404, -0.16238447972219675, -0.029368093954929727, -0.1420497134610822, -0.04297100465385172, -0.16748299276680226, -0.034080533686541965, -0.03455712011957444, -0.17399689011230454, 0.34718576622350455, 0.25367694484995373, 0.19366104409240092, 0.09398392878570064, 0.3689120576740537, 0.06709069295939576, 0.11764569800270462, 0.0543500212241477, 0.19684242556914924, 0.060589808882273076, 0.12177757350904748, -0.14994057823750603, 0.11139410100081534, -0.037899499159318076] |
1,802.05384 | AtlasNet: A Papier-M\^ach\'e Approach to Learning 3D Surface Generation | We introduce a method for learning to generate the surface of 3D shapes. Our
approach represents a 3D shape as a collection of parametric surface elements
and, in contrast to methods generating voxel grids or point clouds, naturally
infers a surface representation of the shape. Beyond its novelty, our new shape
generation framework, AtlasNet, comes with significant advantages, such as
improved precision and generalization capabilities, and the possibility to
generate a shape of arbitrary resolution without memory issues. We demonstrate
these benefits and compare to strong baselines on the ShapeNet benchmark for
two applications: (i) auto-encoding shapes, and (ii) single-view reconstruction
from a still image. We also provide results showing its potential for other
applications, such as morphing, parametrization, super-resolution, matching,
and co-segmentation.
| cs.CV | we introduce a method for learning to generate the surface of 3d shapes our approach represents a 3d shape as a collection of parametric surface elements and in contrast to methods generating voxel grids or point clouds naturally infers a surface representation of the shape beyond its novelty our new shape generation framework atlasnet comes with significant advantages such as improved precision and generalization capabilities and the possibility to generate a shape of arbitrary resolution without memory issues we demonstrate these benefits and compare to strong baselines on the shapenet benchmark for two applications i autoencoding shapes and ii singleview reconstruction from a still image we also provide results showing its potential for other applications such as morphing parametrization superresolution matching and cosegmentation | [['we', 'introduce', 'a', 'method', 'for', 'learning', 'to', 'generate', 'the', 'surface', 'of', '3d', 'shapes', 'our', 'approach', 'represents', 'a', '3d', 'shape', 'as', 'a', 'collection', 'of', 'parametric', 'surface', 'elements', 'and', 'in', 'contrast', 'to', 'methods', 'generating', 'voxel', 'grids', 'or', 'point', 'clouds', 'naturally', 'infers', 'a', 'surface', 'representation', 'of', 'the', 'shape', 'beyond', 'its', 'novelty', 'our', 'new', 'shape', 'generation', 'framework', 'atlasnet', 'comes', 'with', 'significant', 'advantages', 'such', 'as', 'improved', 'precision', 'and', 'generalization', 'capabilities', 'and', 'the', 'possibility', 'to', 'generate', 'a', 'shape', 'of', 'arbitrary', 'resolution', 'without', 'memory', 'issues', 'we', 'demonstrate', 'these', 'benefits', 'and', 'compare', 'to', 'strong', 'baselines', 'on', 'the', 'shapenet', 'benchmark', 'for', 'two', 'applications', 'i', 'autoencoding', 'shapes', 'and', 'ii', 'singleview', 'reconstruction', 'from', 'a', 'still', 'image', 'we', 'also', 'provide', 'results', 'showing', 'its', 'potential', 'for', 'other', 'applications', 'such', 'as', 'morphing', 'parametrization', 'superresolution', 'matching', 'and', 'cosegmentation']] | [0.004520135565248669, -0.025875951629132032, -0.08105339129554627, 0.05296427825633742, -0.08653919036179537, -0.12354445935143005, 0.012201651182697445, 0.4453172779538989, -0.2823355268442728, -0.3531924454480043, 0.0660581174449705, -0.2640011795857524, -0.18616910584339658, 0.23009752833902775, -0.12862065342086992, 0.08173195255412644, 0.11616501075856876, -0.018667927326502843, -0.11238500989255969, -0.18471725633344996, 0.2880458811862913, 0.0037914980088406412, 0.31916103833813037, 0.06481360362796876, 0.1395337205937468, -0.015097580193618282, -0.038135161361901364, 0.03773793221649821, -0.06461815303000698, 0.20221218601352017, 0.21532005752574224, 0.19391021092391772, 0.22591394544426413, -0.4100399434215343, -0.26482031834989284, 0.061077985114830195, 0.13189315976055918, 0.1453352947509466, -0.11744577943091662, -0.29993685922722835, 0.08546586256458989, -0.15986127672778044, -0.11920562017228088, -0.14864592143853547, -0.018925558642072023, 0.012231229017221476, -0.30596177570796645, 0.01355696366656167, 0.09378591447412113, 0.045448148638972834, -0.04907579678225285, -0.12279214392813137, 0.026929966845839728, 0.16675620938490954, -0.021902344343691423, 0.041477882925451535, 0.12639323324604404, -0.2185705026915129, -0.13179096045186284, 0.4150071870149151, -0.0683354085185336, -0.2144560426565223, 0.2200664488644507, -0.07700647538161424, -0.12307446537020265, 0.10839241194981532, 0.21086918224855403, 0.11390859469939328, -0.06744393591051463, 0.04603160798003836, -0.00331854463180863, 0.1662865721052379, 0.06178745727405929, 0.029662564297618925, 0.209168615254772, 0.22262224680209747, 0.06825230674451736, 0.1318460298357646, -0.19358400239219858, -0.05995618673919349, -0.27028167381554413, -0.14493183894693606, -0.18198157128587852, -0.028255763349169103, -0.11064899309283527, -0.1916605960482304, 0.43054816524162276, 0.19340274923435244, 0.2439627920994993, 0.0818821003614757, 0.35786630196466307, 0.01569885674188463, 0.07706215624224211, 0.02403959837268855, 0.15621694586178686, 0.03881153634550874, 0.06763824513830917, -0.17225228239816506, 0.014223809672718043, 0.06284302713914362] |
1,802.05385 | Fooling OCR Systems with Adversarial Text Images | We demonstrate that state-of-the-art optical character recognition (OCR)
based on deep learning is vulnerable to adversarial images. Minor modifications
to images of printed text, which do not change the meaning of the text to a
human reader, cause the OCR system to "recognize" a different text where
certain words chosen by the adversary are replaced by their semantic opposites.
This completely changes the meaning of the output produced by the OCR system
and by the NLP applications that use OCR for preprocessing their inputs.
| cs.LG cs.AI cs.CR cs.CV | we demonstrate that stateoftheart optical character recognition ocr based on deep learning is vulnerable to adversarial images minor modifications to images of printed text which do not change the meaning of the text to a human reader cause the ocr system to recognize a different text where certain words chosen by the adversary are replaced by their semantic opposites this completely changes the meaning of the output produced by the ocr system and by the nlp applications that use ocr for preprocessing their inputs | [['we', 'demonstrate', 'that', 'stateoftheart', 'optical', 'character', 'recognition', 'ocr', 'based', 'on', 'deep', 'learning', 'is', 'vulnerable', 'to', 'adversarial', 'images', 'minor', 'modifications', 'to', 'images', 'of', 'printed', 'text', 'which', 'do', 'not', 'change', 'the', 'meaning', 'of', 'the', 'text', 'to', 'a', 'human', 'reader', 'cause', 'the', 'ocr', 'system', 'to', 'recognize', 'a', 'different', 'text', 'where', 'certain', 'words', 'chosen', 'by', 'the', 'adversary', 'are', 'replaced', 'by', 'their', 'semantic', 'opposites', 'this', 'completely', 'changes', 'the', 'meaning', 'of', 'the', 'output', 'produced', 'by', 'the', 'ocr', 'system', 'and', 'by', 'the', 'nlp', 'applications', 'that', 'use', 'ocr', 'for', 'preprocessing', 'their', 'inputs']] | [-0.04142234399027768, 0.03231828408094034, -0.03343176090025476, 0.07053081207309983, -0.16316147890895427, -0.20106674151472925, 0.02375463570933789, 0.43361229165678933, -0.32516261496182, -0.3106274876155935, 0.08108541791305124, -0.31064780049824287, -0.16819844348910487, 0.19990478626970712, -0.19325381266840158, 0.06851020514122433, 0.14238974888853373, 0.10691252868855372, -0.032235376459235944, -0.3354662510433367, 0.3157409573544837, 0.01131839038758439, 0.29238586439827313, 6.843291755233492e-06, 0.09664499919883729, -0.04844769062696114, -0.0310340171591157, -0.07099196267968398, -0.014046932726722887, 0.16691298574325975, 0.3587044715615256, 0.18489990779753065, 0.2887276517271641, -0.420019765334603, -0.18350110642079795, 0.07935020814294971, 0.14586566356454223, 0.13901872528644854, -0.042501322499620506, -0.40591654218483847, 0.1371569378586303, -0.17159525138725126, 0.08281717039201231, -0.10700530049923275, 0.04479236168221438, 0.0059947554033160915, -0.19178023604798086, -0.05344920666954879, 0.19308016710662437, 0.12435156157395492, -0.022481350000903365, -0.053164061782549, 0.03843452361096362, 0.22206273148878522, 0.03169433243982937, 0.07286941685846873, 0.19678034324065916, -0.256472773154244, -0.09527264964500708, 0.4220967898145318, -0.04017334926452133, -0.23413768970584942, 0.15936519484689815, -0.023370976659602354, -0.11508982354176364, 0.12705757116921068, 0.20578325125167057, 0.06324016119885657, -0.15369330383171992, -0.01583048540106531, -0.007159615988798794, 0.2574339264538139, 0.11251740852215637, 0.010660744399674946, 0.2066020415541494, 0.13584604786176765, -0.09414895833192748, 0.1564781589189633, -0.08919183486328361, 0.010357076192684915, -0.2246580886477161, -0.0890578219224976, -0.21534941564979299, 0.017233865481276076, -0.06649424004838995, -0.21281573940290227, 0.41327033655363177, 0.2810396391876219, 0.19369303461696422, 0.06054733047356075, 0.34478060081822887, 0.03579687559977174, 0.14912219513817468, 0.07011895413078102, 0.15877452808559783, -0.04381861746133793, 0.1484778298056751, -0.17594867471572279, 0.15509627441809112, 0.06497373178301911] |
1,802.05386 | Shamap: Shape-based Manifold Learning | For manifold learning, it is assumed that high-dimensional sample/data points
are embedded on a low-dimensional manifold. Usually, distances among samples
are computed to capture an underlying data structure. Here we propose a metric
according to angular changes along a geodesic line, thereby reflecting the
underlying shape-oriented information or a topological similarity between high-
and low-dimensional representations of a data cloud. Our results demonstrate
the feasibility and merits of the proposed dimensionality reduction scheme.
| cs.LG stat.ML | for manifold learning it is assumed that highdimensional sampledata points are embedded on a lowdimensional manifold usually distances among samples are computed to capture an underlying data structure here we propose a metric according to angular changes along a geodesic line thereby reflecting the underlying shapeoriented information or a topological similarity between high and lowdimensional representations of a data cloud our results demonstrate the feasibility and merits of the proposed dimensionality reduction scheme | [['for', 'manifold', 'learning', 'it', 'is', 'assumed', 'that', 'highdimensional', 'sampledata', 'points', 'are', 'embedded', 'on', 'a', 'lowdimensional', 'manifold', 'usually', 'distances', 'among', 'samples', 'are', 'computed', 'to', 'capture', 'an', 'underlying', 'data', 'structure', 'here', 'we', 'propose', 'a', 'metric', 'according', 'to', 'angular', 'changes', 'along', 'a', 'geodesic', 'line', 'thereby', 'reflecting', 'the', 'underlying', 'shapeoriented', 'information', 'or', 'a', 'topological', 'similarity', 'between', 'high', 'and', 'lowdimensional', 'representations', 'of', 'a', 'data', 'cloud', 'our', 'results', 'demonstrate', 'the', 'feasibility', 'and', 'merits', 'of', 'the', 'proposed', 'dimensionality', 'reduction', 'scheme']] | [-0.11453293228987604, 0.035638271739015986, -0.09888832581539948, 0.08068831937109483, -0.1244359722154008, -0.10650287866075006, 0.06012214419186219, 0.4267606671071715, -0.3086676827062749, -0.2974295134901897, 0.09309231525468123, -0.2973095841589384, -0.16954151869867928, 0.17091699858105536, -0.09263500820250353, 0.05170544875889189, 0.0867405133499738, 0.037287504483376525, -0.11626215172711657, -0.22375089250595515, 0.3836447761083643, 0.03344804286542866, 0.36198920983588323, -0.014199578701259775, 0.1586095420093948, -0.03146056719641718, -0.0123075537978568, 0.047639469443816376, -0.07063584616005553, 0.20171717614084628, 0.2791799459502929, 0.13513334853471154, 0.25250646723240305, -0.3815408725446711, -0.2508106125363459, 0.09046422504858735, 0.11514739251126432, 0.0865289232460782, -0.047591429434962466, -0.32229172634995645, 0.09439189960378119, -0.06001574901812193, -0.09202825745645289, -0.16631785825464046, 0.009360425892130783, -0.036430821540610246, -0.25312440917170737, 0.013917175011657592, 0.06722941252196, 0.04585423556596248, -0.06605584900050114, -0.05648220603406015, -0.048664307076251134, 0.1206018240546756, 0.013256566091311268, 0.0436977442861664, 0.13005617142577344, -0.07067743953990026, -0.111010537993732, 0.3890036156711479, -0.004885413150380676, -0.2746127941581007, 0.21322672715824512, -0.07113680378339875, -0.10619763137462239, 0.14542824441256622, 0.23177074600890693, 0.09439611848857668, -0.12925804178181957, 0.07959605311852, -0.011515658400538895, 0.16518463140851558, -0.0041451528668403625, 0.04860929845340757, 0.18727984081488103, 0.17864037193875346, 0.05997980702927129, 0.09605097830515458, -0.14813821770884614, -0.11719394284429857, -0.24694972798331744, -0.16329967989844996, -0.2484759906171045, 0.02974290972472065, -0.15834952160003013, -0.14081433661178583, 0.3644517531681433, 0.12149158142791647, 0.32240044142559376, 0.036546486507480345, 0.32633214640534586, 0.034884467250473485, 0.0447401535930112, 0.10690252615475199, 0.15151976352919722, 0.1165593076730147, 0.03946627283908634, -0.19371259254532763, 0.07597234929885922, 0.03812717670290214] |
1,802.05387 | Strongly connected components-Algorithm for finding the strongly
connected components of a graph | A directed graph G (V, E) is strongly connected if and only if, for a pair of
vertices X and Y from V, there exists a path from X to Y and a path from Y to
X. In Computer Science, the partition of a graph in strongly connected
components is represented by the partition of all vertices from the graph, so
that for any two vertices, X and Y, from the same partition, there exists a
path from X to Y and a path from Y to X and for any two vertices, U and V, from
different partition, the property is not met. The algorithm presented below is
meant to find the partition of a given graph in strongly connected components
in O (numberOfNodes + numberOfEdges * log* (numberOfNodes)), where log*
function stands for iterated logarithm.
| cs.DS | a directed graph g v e is strongly connected if and only if for a pair of vertices x and y from v there exists a path from x to y and a path from y to x in computer science the partition of a graph in strongly connected components is represented by the partition of all vertices from the graph so that for any two vertices x and y from the same partition there exists a path from x to y and a path from y to x and for any two vertices u and v from different partition the property is not met the algorithm presented below is meant to find the partition of a given graph in strongly connected components in o numberofnodes numberofedges log numberofnodes where log function stands for iterated logarithm | [['a', 'directed', 'graph', 'g', 'v', 'e', 'is', 'strongly', 'connected', 'if', 'and', 'only', 'if', 'for', 'a', 'pair', 'of', 'vertices', 'x', 'and', 'y', 'from', 'v', 'there', 'exists', 'a', 'path', 'from', 'x', 'to', 'y', 'and', 'a', 'path', 'from', 'y', 'to', 'x', 'in', 'computer', 'science', 'the', 'partition', 'of', 'a', 'graph', 'in', 'strongly', 'connected', 'components', 'is', 'represented', 'by', 'the', 'partition', 'of', 'all', 'vertices', 'from', 'the', 'graph', 'so', 'that', 'for', 'any', 'two', 'vertices', 'x', 'and', 'y', 'from', 'the', 'same', 'partition', 'there', 'exists', 'a', 'path', 'from', 'x', 'to', 'y', 'and', 'a', 'path', 'from', 'y', 'to', 'x', 'and', 'for', 'any', 'two', 'vertices', 'u', 'and', 'v', 'from', 'different', 'partition', 'the', 'property', 'is', 'not', 'met', 'the', 'algorithm', 'presented', 'below', 'is', 'meant', 'to', 'find', 'the', 'partition', 'of', 'a', 'given', 'graph', 'in', 'strongly', 'connected', 'components', 'in', 'o', 'numberofnodes', 'numberofedges', 'log', 'numberofnodes', 'where', 'log', 'function', 'stands', 'for', 'iterated', 'logarithm']] | [-0.1420997731915828, 0.11835957112993155, -0.07100781116008223, -0.011272671483040078, -0.04520159171122724, -0.17094183032081198, 0.09906400052079958, 0.41254534194273734, -0.32269187951321476, -0.24606912872239725, 0.0029580793239231875, -0.3533235020490725, -0.12401065621316544, 0.128867402811771, -0.06003797655132836, -0.04302038730525259, 0.08191895301787377, 0.12544366599185697, -0.03251545210264679, -0.22591178229777142, 0.31172938026321023, -0.13123715774559264, 0.1674137534434671, 0.04483371619969161, 0.16200622094711706, 0.0680785333266857, 0.00573530137789116, 0.10972686547007579, -0.13040167216495863, 0.07864840829453028, 0.28551309738558395, 0.1604311253055374, 0.2335196434895494, -0.34260060622898947, -0.15060147994072803, 0.1990581104608336, 0.10389067007423337, -0.06893291375982061, 0.012581893249845771, -0.2004764680542163, 0.13673563528138755, -0.08449804174032674, -0.06010553622340311, 0.0637933827012277, 0.16634513448346963, 0.03935121314308203, -0.3234505003992357, -0.044468340199829925, 0.07309986379626554, -0.009728353649530726, 0.08010557146200827, -0.1623567812798072, -0.14986779435134645, 0.11261971278199509, -0.029662166847221887, 0.21784994612732875, 0.042499284868114696, -0.1376608086944516, -0.0938073741628288, 0.3783475034696453, -0.05457246648590775, -0.17968188468783872, 0.15959260388757032, -0.16968229122863, -0.14465264836326241, 0.14192314136689946, 0.08664864073020977, 0.1606712966128739, -0.12596960401913124, 0.20179904119412193, -0.0499644903898319, 0.14774281039500414, 0.04321067703109401, -0.014192166613109077, 0.16284230372233408, 0.10801659908263819, 0.14699594712016475, 0.09050614285672, -0.01136057456927513, 0.028298712199998657, -0.3604473017448031, -0.15164666884203457, -0.23446733497670477, 0.14184634307055793, -0.10938286065715173, -0.16416349904194696, 0.34379593412211135, 0.08158444460662109, 0.2856089343567059, 0.029634525446888448, 0.2075368730514658, 0.06643696421924145, 0.021045902907264543, 0.1609999701630936, 0.07125153728941483, 0.14882879772001126, -0.008904242915893668, -0.12676695800636575, 0.05316552268897197, 0.14179043119092152] |
1,802.05388 | Single-particle potential of the $\Lambda$ hyperon in nuclear matter
with chiral effective field theory NLO interactions including effects of YNN
three-baryon interactions | Adopting hyperon-nucleon and hyperon-nucleon-nucleon interactions
parametrized in chiral effective field theory, single-particle potentials of
the $\Lambda$ and $\Sigma$ hyperons are evaluated in symmetric nuclear matter
and in pure neutron matter within the framework of lowest order Bruckner
theory. The chiral NLO interaction bears strong $\Lambda$N-$\Sigma$N coupling.
Although the $\Lambda$ potential is repulsive if the coupling is switched off,
the $\Lambda$N-$\Sigma$N correlation brings about the attraction consistent
with empirical data. The $\Sigma$ potential is repulsive, which is also
consistent with empirical information. The interesting result is that the
$\Lambda$ potential becomes shallower beyond normal density. This provides the
possibility to solve the hyperon puzzle without introducing ad hoc assumptions.
The effects of the $\Lambda$NN-$\Lambda$NN and $\Lambda$NN-$\Sigma$NN
three-baryon forces are considered. These three-baryon forces are first reduced
to normal-ordered effective two-baryon interactions in nuclear matter and then
incorporated in the $G$-matrix equation. The repulsion from the
$\Lambda$NN-$\Lambda$NN interaction is of the order of 5 MeV at the normal
density, and becomes larger with increasing the density. The effects of the
$\Lambda$NN-$\Sigma$NN coupling compensate the repulsion at normal density. The
net effect of the three-baryon interactions to the $\Lambda$ single-particle
potential is repulsive at higher densities.
| nucl-th | adopting hyperonnucleon and hyperonnucleonnucleon interactions parametrized in chiral effective field theory singleparticle potentials of the lambda and sigma hyperons are evaluated in symmetric nuclear matter and in pure neutron matter within the framework of lowest order bruckner theory the chiral nlo interaction bears strong lambdansigman coupling although the lambda potential is repulsive if the coupling is switched off the lambdansigman correlation brings about the attraction consistent with empirical data the sigma potential is repulsive which is also consistent with empirical information the interesting result is that the lambda potential becomes shallower beyond normal density this provides the possibility to solve the hyperon puzzle without introducing ad hoc assumptions the effects of the lambdannlambdann and lambdannsigmann threebaryon forces are considered these threebaryon forces are first reduced to normalordered effective twobaryon interactions in nuclear matter and then incorporated in the gmatrix equation the repulsion from the lambdannlambdann interaction is of the order of 5 mev at the normal density and becomes larger with increasing the density the effects of the lambdannsigmann coupling compensate the repulsion at normal density the net effect of the threebaryon interactions to the lambda singleparticle potential is repulsive at higher densities | [['adopting', 'hyperonnucleon', 'and', 'hyperonnucleonnucleon', 'interactions', 'parametrized', 'in', 'chiral', 'effective', 'field', 'theory', 'singleparticle', 'potentials', 'of', 'the', 'lambda', 'and', 'sigma', 'hyperons', 'are', 'evaluated', 'in', 'symmetric', 'nuclear', 'matter', 'and', 'in', 'pure', 'neutron', 'matter', 'within', 'the', 'framework', 'of', 'lowest', 'order', 'bruckner', 'theory', 'the', 'chiral', 'nlo', 'interaction', 'bears', 'strong', 'lambdansigman', 'coupling', 'although', 'the', 'lambda', 'potential', 'is', 'repulsive', 'if', 'the', 'coupling', 'is', 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1,802.05389 | Dynamical Galam model | We introduce a model of temporal evolution of political opinions which
amounts to a dynamical extension of Galam model in which the proportions of
inflexibles are treated as dynamical variables. We find that the critical value
of inflexibles in the original Galam model now turns into a fixed point of the
system whose stability controls the phase trajectory of the political opinions.
The appearance of two phases, in which majority-preserving and regime-changing
limit cycles are respectively dominant, is found, and also the transition
between them is observed.
| physics.soc-ph nlin.AO | we introduce a model of temporal evolution of political opinions which amounts to a dynamical extension of galam model in which the proportions of inflexibles are treated as dynamical variables we find that the critical value of inflexibles in the original galam model now turns into a fixed point of the system whose stability controls the phase trajectory of the political opinions the appearance of two phases in which majoritypreserving and regimechanging limit cycles are respectively dominant is found and also the transition between them is observed | [['we', 'introduce', 'a', 'model', 'of', 'temporal', 'evolution', 'of', 'political', 'opinions', 'which', 'amounts', 'to', 'a', 'dynamical', 'extension', 'of', 'galam', 'model', 'in', 'which', 'the', 'proportions', 'of', 'inflexibles', 'are', 'treated', 'as', 'dynamical', 'variables', 'we', 'find', 'that', 'the', 'critical', 'value', 'of', 'inflexibles', 'in', 'the', 'original', 'galam', 'model', 'now', 'turns', 'into', 'a', 'fixed', 'point', 'of', 'the', 'system', 'whose', 'stability', 'controls', 'the', 'phase', 'trajectory', 'of', 'the', 'political', 'opinions', 'the', 'appearance', 'of', 'two', 'phases', 'in', 'which', 'majoritypreserving', 'and', 'regimechanging', 'limit', 'cycles', 'are', 'respectively', 'dominant', 'is', 'found', 'and', 'also', 'the', 'transition', 'between', 'them', 'is', 'observed']] | [-0.14209493989234462, 0.15727825874791426, -0.12443703911922592, 0.04704247127177522, -0.016213523511610487, -0.10075268231277519, 0.09409862952443826, 0.33576825423275725, -0.26702975961653624, -0.2884709067642689, 0.10488968808340896, -0.27758600733735983, -0.15886710653191102, 0.10651527802350329, -0.02282598438563154, -0.0012924020836020218, -0.0013972412794828414, 0.07689573917373577, -0.028771999250987872, -0.23344554657609584, 0.3225126878851477, -0.01723206226220902, 0.2460080106249627, -0.007625767862533822, 0.1048286254517734, -0.027120481100042955, -0.011361843522857218, 0.03624831518048749, -0.09596938784277026, 0.06737908186281429, 0.22690681957037134, 0.11997243700538734, 0.29115398556870575, -0.36410531312884653, -0.1999255281599129, 0.12160010809243163, 0.1179405443918179, 0.09187592128702604, 0.020720957482562346, -0.2891507328761851, 0.04554782143300947, -0.16844364775289947, -0.11466217499281116, -0.04577673274804564, 0.03640677332878113, 0.030539370240534054, -0.2638666779788978, 0.09904417261611397, 0.08071021300891196, 0.05384788133806604, -0.08640817889176747, -0.08758439099936581, -0.0932715076734038, 0.16799498087014345, 0.09963949876700473, -0.012683122966657667, 0.11458235589568229, -0.15288214396466226, -0.1084200102589367, 0.42357902307720746, -0.04252830225536052, -0.16184324854437043, 0.16260656693073758, -0.13730141741569454, -0.12220619522473392, 0.08945628413382699, 0.1659672736409394, 0.08715824136400924, -0.15785075889571626, 0.019512398758738794, -0.0420967840753934, 0.1888437399329097, 0.024022396117010538, 0.0030671522295212046, 0.24140500960981146, 0.17558518813799737, 0.04581188634585808, 0.14827467100146938, -0.05926638082087533, -0.2152267323587747, -0.27461463265559255, -0.10980886051102597, -0.10529814540025066, 0.027559185395126834, -0.11088566898388844, -0.17799607250182067, 0.41692495649570926, 0.16374745618497186, 0.2334405996124534, 0.02529751604888588, 0.2245962351834511, 0.10234280603198224, 0.034510834254187474, 0.04579596755040043, 0.23824111520148375, 0.09270782727090751, 0.0949851348886595, -0.19329720653374405, 0.1008704770137282, 0.06719828019028201] |
1,802.0539 | Asymptotic Stability of Solutions for 1-D Compressible
Navier-Stokes-Cahn-Hilliard system | This paper is concerned with the evolution of the periodic boundary value
problem and the mixed boundary value problem for a compressible mixture of
binary fluids modeled by the Navier-Stokes-Cahn-Hilliard system in one
dimensional space. The global existence and the large time behavior of the
strong solutions for these two systems are studied. The solutions are proved to
be asymptotically stable even for the large initial disturbance of the density
and the large velocity data. We show that the average concentration difference
for the two components of the initial state determines the long time behavior
of the diffusive interface for the two-phase flow.
| math.AP | this paper is concerned with the evolution of the periodic boundary value problem and the mixed boundary value problem for a compressible mixture of binary fluids modeled by the navierstokescahnhilliard system in one dimensional space the global existence and the large time behavior of the strong solutions for these two systems are studied the solutions are proved to be asymptotically stable even for the large initial disturbance of the density and the large velocity data we show that the average concentration difference for the two components of the initial state determines the long time behavior of the diffusive interface for the twophase flow | [['this', 'paper', 'is', 'concerned', 'with', 'the', 'evolution', 'of', 'the', 'periodic', 'boundary', 'value', 'problem', 'and', 'the', 'mixed', 'boundary', 'value', 'problem', 'for', 'a', 'compressible', 'mixture', 'of', 'binary', 'fluids', 'modeled', 'by', 'the', 'navierstokescahnhilliard', 'system', 'in', 'one', 'dimensional', 'space', 'the', 'global', 'existence', 'and', 'the', 'large', 'time', 'behavior', 'of', 'the', 'strong', 'solutions', 'for', 'these', 'two', 'systems', 'are', 'studied', 'the', 'solutions', 'are', 'proved', 'to', 'be', 'asymptotically', 'stable', 'even', 'for', 'the', 'large', 'initial', 'disturbance', 'of', 'the', 'density', 'and', 'the', 'large', 'velocity', 'data', 'we', 'show', 'that', 'the', 'average', 'concentration', 'difference', 'for', 'the', 'two', 'components', 'of', 'the', 'initial', 'state', 'determines', 'the', 'long', 'time', 'behavior', 'of', 'the', 'diffusive', 'interface', 'for', 'the', 'twophase', 'flow']] | [-0.19706010581581918, 0.13639097214558893, -0.06820966180377794, 0.04786565853734907, 0.009108865073004973, -0.10410318400222555, -0.03267468200675528, 0.2985748924785158, -0.30497935587015834, -0.27759220371547255, 0.18795147707608972, -0.24507690057411646, -0.07387011032551527, 0.15660220021110716, -0.005510340408575766, 0.11554125601480018, 0.0769882333889893, 0.032010357734268004, -0.0530159183443316, -0.2102028721315081, 0.39055029335386543, -0.021872422685032908, 0.28552101764068444, 0.00551402417816294, 0.11900730155412903, -0.0143169513157596, 0.021760204576353713, 0.07902039969976436, -0.14104475316771553, 0.07627663002530276, 0.21545640152915227, 0.053181971531831525, 0.2633850716452286, -0.41670344296155626, -0.23687821611291865, 0.10572792340558275, 0.0960276213393194, 0.10973207433753222, -0.04329147328536741, -0.2600396091482394, 0.0937815876225534, -0.11901042588254057, -0.18061235046818924, -0.0019903646203354723, 0.04592777487767625, 0.052228160613912694, -0.28232898928442046, 0.13271293704040818, 0.05594182881815992, 0.01927583757787943, -0.15512467653137965, -0.09312228477012945, -0.02139490702216631, 0.14775973361763126, 0.07445292403351841, -0.028925127548717178, 0.04790896038497681, -0.1746632218691074, -0.02097427960738395, 0.35538067976103244, -0.0904957299206623, -0.22251147945022698, 0.2332383468747139, -0.13723127499168503, -0.07417254593874355, 0.15516534038973087, 0.18680761834082094, 0.14627966683954724, -0.13416593618582465, 0.060891222306504594, -0.05898416504711455, 0.17022046112247463, 0.038806886373774954, -0.02139725527970247, 0.18383871299413584, 0.17706244214645867, 0.11548398769378958, 0.14131426153754253, -0.07698973496184566, -0.13286833817283938, -0.2972446196956687, -0.18823573306130553, -0.18258014318162522, 0.015518912701930815, -0.12174091242139061, -0.1975983432177635, 0.38335669347202606, 0.11437358927078889, 0.20048423400781687, 0.06374917800755249, 0.24347981815542846, 0.16025735071670372, -0.03228440359506069, 0.11776626672035137, 0.2560565823404281, 0.10326245452449159, 0.14439330505008258, -0.27493039546624837, 0.08811641386845737, 0.07572451142766015] |
1,802.05391 | A Fast Lax-Hopf formula to solve the Lighthill-Whitham-Richards traffic
flow model on networks | Efficient and exact algorithms are important for performing fast and accurate
traffic network simulations with macroscopic traffic models. In this paper, we
extend the semi-analytical Lax-Hopf algorithm in order to compute link inflows
and outflows with the LWR model. Our proposed Fax Lax-Hopf algorithm has a very
low computational complexity. We demonstrate that some of the original
algorithm's operations (associated with the initial conditions) can be
discarded, leading to a faster computation of boundary demand/supplies in
network simulation problems, for general concave fundamental diagrams.
Moreover, the computational cost can be further reduced for triangular
Fundamental Diagrams and specific space-time discretizations. The resulting
formulation has a performance comparable to the Link Transmission Model and,
since it solves the original LWR model for a wide range of FD shapes, with any
initial configuration, it is suitable to solve a broad range of traffic
operations problems. As part of the analysis, we compare the performance of the
proposed scheme to other well-known computational methods.
| math.AP | efficient and exact algorithms are important for performing fast and accurate traffic network simulations with macroscopic traffic models in this paper we extend the semianalytical laxhopf algorithm in order to compute link inflows and outflows with the lwr model our proposed fax laxhopf algorithm has a very low computational complexity we demonstrate that some of the original algorithms operations associated with the initial conditions can be discarded leading to a faster computation of boundary demandsupplies in network simulation problems for general concave fundamental diagrams moreover the computational cost can be further reduced for triangular fundamental diagrams and specific spacetime discretizations the resulting formulation has a performance comparable to the link transmission model and since it solves the original lwr model for a wide range of fd shapes with any initial configuration it is suitable to solve a broad range of traffic operations problems as part of the analysis we compare the performance of the proposed scheme to other wellknown computational methods | [['efficient', 'and', 'exact', 'algorithms', 'are', 'important', 'for', 'performing', 'fast', 'and', 'accurate', 'traffic', 'network', 'simulations', 'with', 'macroscopic', 'traffic', 'models', 'in', 'this', 'paper', 'we', 'extend', 'the', 'semianalytical', 'laxhopf', 'algorithm', 'in', 'order', 'to', 'compute', 'link', 'inflows', 'and', 'outflows', 'with', 'the', 'lwr', 'model', 'our', 'proposed', 'fax', 'laxhopf', 'algorithm', 'has', 'a', 'very', 'low', 'computational', 'complexity', 'we', 'demonstrate', 'that', 'some', 'of', 'the', 'original', 'algorithms', 'operations', 'associated', 'with', 'the', 'initial', 'conditions', 'can', 'be', 'discarded', 'leading', 'to', 'a', 'faster', 'computation', 'of', 'boundary', 'demandsupplies', 'in', 'network', 'simulation', 'problems', 'for', 'general', 'concave', 'fundamental', 'diagrams', 'moreover', 'the', 'computational', 'cost', 'can', 'be', 'further', 'reduced', 'for', 'triangular', 'fundamental', 'diagrams', 'and', 'specific', 'spacetime', 'discretizations', 'the', 'resulting', 'formulation', 'has', 'a', 'performance', 'comparable', 'to', 'the', 'link', 'transmission', 'model', 'and', 'since', 'it', 'solves', 'the', 'original', 'lwr', 'model', 'for', 'a', 'wide', 'range', 'of', 'fd', 'shapes', 'with', 'any', 'initial', 'configuration', 'it', 'is', 'suitable', 'to', 'solve', 'a', 'broad', 'range', 'of', 'traffic', 'operations', 'problems', 'as', 'part', 'of', 'the', 'analysis', 'we', 'compare', 'the', 'performance', 'of', 'the', 'proposed', 'scheme', 'to', 'other', 'wellknown', 'computational', 'methods']] | [-0.10171924891183153, 0.00796999158919789, -0.08290003129513934, 0.07568956422037446, -0.0683639170601964, -0.17514634092513007, 0.041802586882113246, 0.39734566080151124, -0.267613664639066, -0.3305028167320415, 0.12952161869397968, -0.20373851351905614, -0.16800856910122092, 0.23016586637386355, -0.09378190410479874, 0.10902890238794498, 0.11026971770043019, -0.005150801304262131, -0.10226920215682185, -0.25528059461576047, 0.2547947043996828, 0.06603282985743135, 0.278442165076558, 0.04318105213169474, 0.10528631495835725, -0.04439891455986071, -0.015944721881533043, 0.04534155378205469, -0.11633341558213033, 0.1267297408383456, 0.25869626375599636, 0.14778847263660283, 0.2605190076283179, -0.4195871420786716, -0.23999123379089723, 0.10845405700238189, 0.1552525277569657, 0.12209972166456282, -0.01336441453313455, -0.233631602342939, 0.10990582852973603, -0.18820691075961804, -0.10540361886960455, -0.10470059103245148, -0.017270148301031442, 0.023737902846187355, -0.30349015503306875, 0.05518138386833016, -0.0030345357896294444, -0.0046232687891460955, -0.042575914194458164, -0.09394722607321455, 0.007587660547869746, 0.12358049528629636, 0.017640200410824036, 0.020019900603074348, 0.09417687416716944, -0.1312963504678919, -0.1274622187793284, 0.4147763776010834, -0.013828385560918833, -0.2086178908299189, 0.21793562991078944, -0.06319793467991985, -0.1386769517092034, 0.15815139160222316, 0.20767295193945756, 0.10558207583053444, -0.12048593522631564, 0.05943842588148982, -0.03594040396346827, 0.1468936962308362, 0.014864802041847725, -0.009853227009807597, 0.11256734053313266, 0.20926007876187214, 0.09812963185831905, 0.1472964637512632, -0.05519267978706921, -0.13334903083596145, -0.2492815360383247, -0.1420680943439038, -0.14720617592392954, -0.024770333700143966, -0.1368671355386141, -0.1486765130830463, 0.3970586974400021, 0.17956559800304603, 0.15035452509291644, 0.10519066986162215, 0.3666301406919956, 0.12779851236264222, 0.05063195331599672, 0.11986293519439642, 0.17319792880534807, 0.1128773624484893, 0.1328511113242712, -0.23289524858410005, 0.05912145023903577, 0.07996371503104456] |
1,802.05392 | Reducing over-clustering via the powered Chinese restaurant process | Dirichlet process mixture (DPM) models tend to produce many small clusters
regardless of whether they are needed to accurately characterize the data -
this is particularly true for large data sets. However, interpretability,
parsimony, data storage and communication costs all are hampered by having
overly many clusters. We propose a powered Chinese restaurant process to limit
this kind of problem and penalize over clustering. The method is illustrated
using some simulation examples and data with large and small sample size
including MNIST and the Old Faithful Geyser data.
| cs.LG stat.ML | dirichlet process mixture dpm models tend to produce many small clusters regardless of whether they are needed to accurately characterize the data this is particularly true for large data sets however interpretability parsimony data storage and communication costs all are hampered by having overly many clusters we propose a powered chinese restaurant process to limit this kind of problem and penalize over clustering the method is illustrated using some simulation examples and data with large and small sample size including mnist and the old faithful geyser data | [['dirichlet', 'process', 'mixture', 'dpm', 'models', 'tend', 'to', 'produce', 'many', 'small', 'clusters', 'regardless', 'of', 'whether', 'they', 'are', 'needed', 'to', 'accurately', 'characterize', 'the', 'data', 'this', 'is', 'particularly', 'true', 'for', 'large', 'data', 'sets', 'however', 'interpretability', 'parsimony', 'data', 'storage', 'and', 'communication', 'costs', 'all', 'are', 'hampered', 'by', 'having', 'overly', 'many', 'clusters', 'we', 'propose', 'a', 'powered', 'chinese', 'restaurant', 'process', 'to', 'limit', 'this', 'kind', 'of', 'problem', 'and', 'penalize', 'over', 'clustering', 'the', 'method', 'is', 'illustrated', 'using', 'some', 'simulation', 'examples', 'and', 'data', 'with', 'large', 'and', 'small', 'sample', 'size', 'including', 'mnist', 'and', 'the', 'old', 'faithful', 'geyser', 'data']] | [-0.04658552885440917, 0.07287426111716563, -0.03235698506321715, 0.13489949105504997, -0.10926141937787848, -0.13833463097248097, 0.08085157182709925, 0.3996160674369198, -0.30008889251955967, -0.36762135715127503, 0.16346966473642607, -0.29588633051661284, -0.07369397349398711, 0.19984139330503156, -0.13048058013356795, 0.07786746530782902, 0.14536701946154013, -0.0007813973726983043, 0.032802708943417275, -0.29791232530461176, 0.3162201931245154, 0.06274113200347999, 0.30879731766408547, -0.020405663372734667, 0.07676314716307639, -0.04249945687044455, -0.05899747383737958, -0.0037367710115752003, -0.07897819125296912, 0.14059634016955208, 0.3112296689315194, 0.18846843087638931, 0.2931834226795312, -0.428270923936771, -0.18451114837883104, 0.21300924970800506, 0.13812400543548423, 0.09375750379444196, -0.04163719602357382, -0.2677427193219506, 0.11880407426051325, -0.172719679949484, -0.07377477324214475, -0.1461616741351505, 0.014105395301534185, 0.03375377012523769, -0.2936534271629035, 0.10280738507622275, 0.038536217304524674, 0.06522856814380007, -0.03824424005017198, -0.14021629592853374, 0.020115249474457968, 0.1277035200197635, 0.0644397690360991, -0.02222272062716984, 0.09991386513849292, -0.15066620502246267, -0.07722941411380795, 0.3923978663167392, 0.020203632048341906, -0.20551208220957512, 0.2634601471785071, -0.11306334782563064, -0.14759770692352892, 0.11726720695351732, 0.20021122682173254, 0.09042195205566013, -0.17607857257090295, 0.05234882328860012, -0.022769434045700507, 0.17154875886894164, 0.046366510207593525, -0.013038571923971176, 0.16949411172649942, 0.22524218683013286, -0.0053805382293381394, 0.13957050461027687, -0.12082697716862734, -0.08688448235692306, -0.2095489733713283, -0.07710253188504312, -0.23017445447635249, -0.0055484123014170545, -0.1369378145684785, -0.17255286989069876, 0.29851415802866166, 0.17539246771591663, 0.2292639408748993, 0.10107392676431558, 0.28725625694750795, 0.032641386945100354, 0.09544467738155148, 0.1187049084752477, 0.11100653904368406, 0.060066621132387685, 0.06372385858906027, -0.14193553719990726, 0.12747880106460002, -0.04931698690286313] |
1,802.05393 | One-dimensional Superdiffusive Heat Propagation Induced by Optical
Phonon-Phonon Interactions | It is known that one-dimensional anomalous heat propagation is usually
characterized by a L\'{e}vy walk superdiffusive spreading function with two
side peaks located on the fronts due to the finite velocity of acoustic
phonons, and in the case when the acoustic phonons vanish, e.g., due to the
phonon-lattice interactions such that the system's momentum is not conserved,
the side peaks will disappear and a normal Gaussian diffusive heat propagating
behavior will be observed. Here we show that there exists another type of
superdiffusive, non-Gaussian heat propagation but without side peaks in a
typical nonacoustic, momentum-nonconserving system. It implies that thermal
transport in this system disobeys the Fourier law, in clear contrast with the
existing theoretical predictions. The underlying mechanism is related to a
novel effect of optical phonon-phonon interactions. These findings may open a
new avenue for further exploring thermal transport in low dimensions.
| cond-mat.stat-mech nlin.CD | it is known that onedimensional anomalous heat propagation is usually characterized by a levy walk superdiffusive spreading function with two side peaks located on the fronts due to the finite velocity of acoustic phonons and in the case when the acoustic phonons vanish eg due to the phononlattice interactions such that the systems momentum is not conserved the side peaks will disappear and a normal gaussian diffusive heat propagating behavior will be observed here we show that there exists another type of superdiffusive nongaussian heat propagation but without side peaks in a typical nonacoustic momentumnonconserving system it implies that thermal transport in this system disobeys the fourier law in clear contrast with the existing theoretical predictions the underlying mechanism is related to a novel effect of optical phononphonon interactions these findings may open a new avenue for further exploring thermal transport in low dimensions | [['it', 'is', 'known', 'that', 'onedimensional', 'anomalous', 'heat', 'propagation', 'is', 'usually', 'characterized', 'by', 'a', 'levy', 'walk', 'superdiffusive', 'spreading', 'function', 'with', 'two', 'side', 'peaks', 'located', 'on', 'the', 'fronts', 'due', 'to', 'the', 'finite', 'velocity', 'of', 'acoustic', 'phonons', 'and', 'in', 'the', 'case', 'when', 'the', 'acoustic', 'phonons', 'vanish', 'eg', 'due', 'to', 'the', 'phononlattice', 'interactions', 'such', 'that', 'the', 'systems', 'momentum', 'is', 'not', 'conserved', 'the', 'side', 'peaks', 'will', 'disappear', 'and', 'a', 'normal', 'gaussian', 'diffusive', 'heat', 'propagating', 'behavior', 'will', 'be', 'observed', 'here', 'we', 'show', 'that', 'there', 'exists', 'another', 'type', 'of', 'superdiffusive', 'nongaussian', 'heat', 'propagation', 'but', 'without', 'side', 'peaks', 'in', 'a', 'typical', 'nonacoustic', 'momentumnonconserving', 'system', 'it', 'implies', 'that', 'thermal', 'transport', 'in', 'this', 'system', 'disobeys', 'the', 'fourier', 'law', 'in', 'clear', 'contrast', 'with', 'the', 'existing', 'theoretical', 'predictions', 'the', 'underlying', 'mechanism', 'is', 'related', 'to', 'a', 'novel', 'effect', 'of', 'optical', 'phononphonon', 'interactions', 'these', 'findings', 'may', 'open', 'a', 'new', 'avenue', 'for', 'further', 'exploring', 'thermal', 'transport', 'in', 'low', 'dimensions']] | [-0.1579800816562183, 0.22891530848492483, -0.09552466279055298, 0.06664816787632481, -0.10444054697392756, -0.18045561178750197, 0.03550799898119396, 0.3701064864863883, -0.3179051482973057, -0.1983850355800632, 0.07117047114867613, -0.3300618379722004, -0.16709641351566448, 0.2190104395906617, -0.0044671115237516126, 0.009161133254012029, 0.020632381039310144, 0.006024218304408389, -0.00943621615244245, -0.1593651702917357, 0.2856980740268539, 0.035957523543465805, 0.3177733678286048, 0.09529897099738563, 0.06614453608652095, -0.022318733878876573, 0.002590382194888967, 0.030093435828176723, -0.12005072192657719, 0.03771721654395749, 0.21772899158898143, -0.039148820893044445, 0.2357024533569917, -0.4261520863152467, -0.28900870522060856, 0.0992888239761033, 0.19635656684622008, 0.127914122240683, -0.0671279247259148, -0.24702424962249409, 0.006139955287994621, -0.10485368499441164, -0.16612831187935975, -0.0319641761421112, 0.01892331544264265, 0.017397275987859503, -0.21874189666322583, 0.1749702434876113, 0.11241025046504044, 0.008671172152084487, -0.04048711281608452, -0.07588351250079076, -0.030112077127100078, 0.09493793924180367, 0.05108178522310813, 0.0068907994513198005, 0.14382703801013783, -0.1366665218419467, -0.09582987149128808, 0.35909874974326655, -0.08474537912573847, -0.17962310047025656, 0.23600876595746803, -0.19839463660198792, -0.07597138922192626, 0.15376429328160895, 0.14341502872432446, 0.057153367501990036, -0.16482625891492372, 0.030032663418255237, -0.012620493092320183, 0.1406147917702068, 0.0458101272986798, 0.07071381449451918, 0.2228601666043636, 0.16283110308402277, 0.052742780231825125, 0.12813245280323407, -0.10225595313879837, -0.07620613054873851, -0.2978705058095755, -0.13224889754303046, -0.18805286188290773, 0.09245959645753052, -0.07275297231235463, -0.1832483880469328, 0.3600821899627889, 0.173216146861457, 0.18269024566597455, 0.01285245362017955, 0.28222618383189363, 0.1655418433426469, 0.05642347947622721, 0.11571186798025522, 0.2274270381491918, 0.1282388281852163, 0.1372345866541007, -0.25693315726060134, 0.0853682590772274, -0.0019096702785423299] |
1,802.05394 | Cost-Effective Training of Deep CNNs with Active Model Adaptation | Deep convolutional neural networks have achieved great success in various
applications. However, training an effective DNN model for a specific task is
rather challenging because it requires a prior knowledge or experience to
design the network architecture, repeated trial-and-error process to tune the
parameters, and a large set of labeled data to train the model. In this paper,
we propose to overcome these challenges by actively adapting a pre-trained
model to a new task with less labeled examples. Specifically, the pre-trained
model is iteratively fine tuned based on the most useful examples. The examples
are actively selected based on a novel criterion, which jointly estimates the
potential contribution of an instance on optimizing the feature representation
as well as improving the classification model for the target task. On one hand,
the pre-trained model brings plentiful information from its original task,
avoiding redesign of the network architecture or training from scratch; and on
the other hand, the labeling cost can be significantly reduced by active label
querying. Experiments on multiple datasets and different pre-trained models
demonstrate that the proposed approach can achieve cost-effective training of
DNNs.
| cs.LG stat.ML | deep convolutional neural networks have achieved great success in various applications however training an effective dnn model for a specific task is rather challenging because it requires a prior knowledge or experience to design the network architecture repeated trialanderror process to tune the parameters and a large set of labeled data to train the model in this paper we propose to overcome these challenges by actively adapting a pretrained model to a new task with less labeled examples specifically the pretrained model is iteratively fine tuned based on the most useful examples the examples are actively selected based on a novel criterion which jointly estimates the potential contribution of an instance on optimizing the feature representation as well as improving the classification model for the target task on one hand the pretrained model brings plentiful information from its original task avoiding redesign of the network architecture or training from scratch and on the other hand the labeling cost can be significantly reduced by active label querying experiments on multiple datasets and different pretrained models demonstrate that the proposed approach can achieve costeffective training of dnns | [['deep', 'convolutional', 'neural', 'networks', 'have', 'achieved', 'great', 'success', 'in', 'various', 'applications', 'however', 'training', 'an', 'effective', 'dnn', 'model', 'for', 'a', 'specific', 'task', 'is', 'rather', 'challenging', 'because', 'it', 'requires', 'a', 'prior', 'knowledge', 'or', 'experience', 'to', 'design', 'the', 'network', 'architecture', 'repeated', 'trialanderror', 'process', 'to', 'tune', 'the', 'parameters', 'and', 'a', 'large', 'set', 'of', 'labeled', 'data', 'to', 'train', 'the', 'model', 'in', 'this', 'paper', 'we', 'propose', 'to', 'overcome', 'these', 'challenges', 'by', 'actively', 'adapting', 'a', 'pretrained', 'model', 'to', 'a', 'new', 'task', 'with', 'less', 'labeled', 'examples', 'specifically', 'the', 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1,802.05395 | An Adaptive Markov Random Field for Structured Compressive Sensing | Exploiting intrinsic structures in sparse signals underpins the recent
progress in compressive sensing (CS). The key for exploiting such structures is
to achieve two desirable properties: generality (\ie, the ability to fit a wide
range of signals with diverse structures) and adaptability (\ie, being adaptive
to a specific signal). Most existing approaches, however, often only achieve
one of these two properties. In this study, we propose a novel adaptive Markov
random field sparsity prior for CS, which not only is able to capture a broad
range of sparsity structures, but also can adapt to each sparse signal through
refining the parameters of the sparsity prior with respect to the compressed
measurements. To maximize the adaptability, we also propose a new sparse signal
estimation where the sparse signals, support, noise and signal parameter
estimation are unified into a variational optimization problem, which can be
effectively solved with an alternative minimization scheme. Extensive
experiments on three real-world datasets demonstrate the effectiveness of the
proposed method in recovery accuracy, noise tolerance, and runtime.
| eess.SP eess.IV | exploiting intrinsic structures in sparse signals underpins the recent progress in compressive sensing cs the key for exploiting such structures is to achieve two desirable properties generality ie the ability to fit a wide range of signals with diverse structures and adaptability ie being adaptive to a specific signal most existing approaches however often only achieve one of these two properties in this study we propose a novel adaptive markov random field sparsity prior for cs which not only is able to capture a broad range of sparsity structures but also can adapt to each sparse signal through refining the parameters of the sparsity prior with respect to the compressed measurements to maximize the adaptability we also propose a new sparse signal estimation where the sparse signals support noise and signal parameter estimation are unified into a variational optimization problem which can be effectively solved with an alternative minimization scheme extensive experiments on three realworld datasets demonstrate the effectiveness of the proposed method in recovery accuracy noise tolerance and runtime | [['exploiting', 'intrinsic', 'structures', 'in', 'sparse', 'signals', 'underpins', 'the', 'recent', 'progress', 'in', 'compressive', 'sensing', 'cs', 'the', 'key', 'for', 'exploiting', 'such', 'structures', 'is', 'to', 'achieve', 'two', 'desirable', 'properties', 'generality', 'ie', 'the', 'ability', 'to', 'fit', 'a', 'wide', 'range', 'of', 'signals', 'with', 'diverse', 'structures', 'and', 'adaptability', 'ie', 'being', 'adaptive', 'to', 'a', 'specific', 'signal', 'most', 'existing', 'approaches', 'however', 'often', 'only', 'achieve', 'one', 'of', 'these', 'two', 'properties', 'in', 'this', 'study', 'we', 'propose', 'a', 'novel', 'adaptive', 'markov', 'random', 'field', 'sparsity', 'prior', 'for', 'cs', 'which', 'not', 'only', 'is', 'able', 'to', 'capture', 'a', 'broad', 'range', 'of', 'sparsity', 'structures', 'but', 'also', 'can', 'adapt', 'to', 'each', 'sparse', 'signal', 'through', 'refining', 'the', 'parameters', 'of', 'the', 'sparsity', 'prior', 'with', 'respect', 'to', 'the', 'compressed', 'measurements', 'to', 'maximize', 'the', 'adaptability', 'we', 'also', 'propose', 'a', 'new', 'sparse', 'signal', 'estimation', 'where', 'the', 'sparse', 'signals', 'support', 'noise', 'and', 'signal', 'parameter', 'estimation', 'are', 'unified', 'into', 'a', 'variational', 'optimization', 'problem', 'which', 'can', 'be', 'effectively', 'solved', 'with', 'an', 'alternative', 'minimization', 'scheme', 'extensive', 'experiments', 'on', 'three', 'realworld', 'datasets', 'demonstrate', 'the', 'effectiveness', 'of', 'the', 'proposed', 'method', 'in', 'recovery', 'accuracy', 'noise', 'tolerance', 'and', 'runtime']] | [-0.06626433539615177, 0.029855185126122223, -0.05463782970738761, 0.02025503325755434, -0.13830358162522316, -0.1551784397157676, 0.017578023266704643, 0.4395418934743194, -0.33222048742600774, -0.3310120143095607, 0.14253317366135032, -0.19106252676095156, -0.20215415307046736, 0.1520785053151057, -0.11845041310332496, 0.11783422587562681, 0.06013467762386426, 0.024930887289263088, -0.10032726781552329, -0.24001863147406016, 0.2475046390955172, 0.07986254929838811, 0.3320268308634267, -0.0036746523945647125, 0.1241535018567982, 0.0021935532170840925, -0.016768763133886217, 0.01940919406890102, -0.0579448059461463, 0.16199746048420338, 0.31657281948719174, 0.1973773504829403, 0.31599727222586377, -0.40224998047684923, -0.2880209157736424, 0.11842036711408153, 0.13515781516702297, 0.09999692404089729, -0.07966570412972943, -0.30808056657586025, 0.1252961083979565, -0.1255851248772267, 0.002923140729613164, -0.14836554182693362, -0.07847785670241779, 0.028864991851835786, -0.36173755997956236, 0.054672750407103995, 0.04825156577862799, 0.01443871254210963, -0.048006179813733875, -0.11882371819742463, 0.07410022875170826, 0.11138144227237824, 0.032453602133318785, 0.001110354669200366, 0.12707947984830859, -0.13696836842470528, -0.13499937967725975, 0.3679402935154298, -0.048207623160937255, -0.23838472584466217, 0.23447841339854195, -0.08495866517700693, -0.16152665518799467, 0.1724694159539307, 0.2352889841532006, 0.0892493810997728, -0.1610390396295663, 0.05019826859078261, -0.005479682245127419, 0.21024232325623468, 0.03559738998164368, 0.07333952505141497, 0.14393325193155118, 0.20240827611483195, 0.10705178110476803, 0.11980867602191318, -0.12280021491049624, -0.054504259686697934, -0.19375177744865035, -0.0992340074153617, -0.21229485261308798, -0.05228392426326857, -0.12815469838927077, -0.1381236592759652, 0.4257671979961956, 0.22724745839103194, 0.24098493735141613, 0.05786360513336737, 0.3735749024021275, 0.059102608761339284, 0.060942085761138624, 0.06037424102370791, 0.21764768648016103, 0.12822465648128212, 0.060871641792576106, -0.18457048630054274, 0.08914938531473608, -0.02638497961383751] |
1,802.05396 | High-efficiency cold-atom transport into a waveguide trap | We have developed and characterized an atom-guiding technique that loads
$3\times10^6$ cold rubidium atoms into hollow-core optical fibre, an
order-of-magnitude larger than previously reported results. This result was
possible because it was guided by a physically realistic simulation that could
provide the specifications for loading efficiencies of 3% and a peak optical
depth of 600. The simulation further showed that the demonstrated loading
efficiency is limited solely by the geometric overlap of the atom cloud and the
optical guide beam, and is thus open to further improvement with experimental
modification. The experimental arrangement allows observation of the real-time
effects of light-assisted cold atom collisions and background gas collisions by
tracking the dynamics of the cold atom cloud as it falls into the fibre. The
combination of these observations, and physical understanding from the
simulation, allows estimation of the limits to loading cold atoms into
hollow-core fibres.
| physics.atom-ph | we have developed and characterized an atomguiding technique that loads 3times106 cold rubidium atoms into hollowcore optical fibre an orderofmagnitude larger than previously reported results this result was possible because it was guided by a physically realistic simulation that could provide the specifications for loading efficiencies of 3 and a peak optical depth of 600 the simulation further showed that the demonstrated loading efficiency is limited solely by the geometric overlap of the atom cloud and the optical guide beam and is thus open to further improvement with experimental modification the experimental arrangement allows observation of the realtime effects of lightassisted cold atom collisions and background gas collisions by tracking the dynamics of the cold atom cloud as it falls into the fibre the combination of these observations and physical understanding from the simulation allows estimation of the limits to loading cold atoms into hollowcore fibres | [['we', 'have', 'developed', 'and', 'characterized', 'an', 'atomguiding', 'technique', 'that', 'loads', '3times106', 'cold', 'rubidium', 'atoms', 'into', 'hollowcore', 'optical', 'fibre', 'an', 'orderofmagnitude', 'larger', 'than', 'previously', 'reported', 'results', 'this', 'result', 'was', 'possible', 'because', 'it', 'was', 'guided', 'by', 'a', 'physically', 'realistic', 'simulation', 'that', 'could', 'provide', 'the', 'specifications', 'for', 'loading', 'efficiencies', 'of', '3', 'and', 'a', 'peak', 'optical', 'depth', 'of', '600', 'the', 'simulation', 'further', 'showed', 'that', 'the', 'demonstrated', 'loading', 'efficiency', 'is', 'limited', 'solely', 'by', 'the', 'geometric', 'overlap', 'of', 'the', 'atom', 'cloud', 'and', 'the', 'optical', 'guide', 'beam', 'and', 'is', 'thus', 'open', 'to', 'further', 'improvement', 'with', 'experimental', 'modification', 'the', 'experimental', 'arrangement', 'allows', 'observation', 'of', 'the', 'realtime', 'effects', 'of', 'lightassisted', 'cold', 'atom', 'collisions', 'and', 'background', 'gas', 'collisions', 'by', 'tracking', 'the', 'dynamics', 'of', 'the', 'cold', 'atom', 'cloud', 'as', 'it', 'falls', 'into', 'the', 'fibre', 'the', 'combination', 'of', 'these', 'observations', 'and', 'physical', 'understanding', 'from', 'the', 'simulation', 'allows', 'estimation', 'of', 'the', 'limits', 'to', 'loading', 'cold', 'atoms', 'into', 'hollowcore', 'fibres']] | [-0.08655392462146821, 0.15142517961252577, -0.06692731734365225, -0.03144073789168535, -0.01442505200232925, -0.11559971830454366, 0.05894570042828805, 0.42750634486819133, -0.21491510615651976, -0.3299487237529508, 0.06699044604239793, -0.24800842671578846, -0.042295081516856264, 0.23462354229313545, -0.0038835335763749376, 0.08092554658169633, 0.09303777540857293, -0.056056354928496775, 0.00940839186158224, -0.22869891657590352, 0.246145997894125, 0.1467604055294189, 0.30519666414836355, 0.09003438992698773, 0.1061256911188107, -0.010467419240238338, -0.011496214202508844, -0.013656580435304806, -0.1257162817970123, 0.14013953912593746, 0.2086934213414146, 0.09574233854173458, 0.2284923238358621, -0.4711489327251911, -0.25185043174082994, 0.059973190284880074, 0.15776821655574544, 0.13349838074625886, -0.08514153791655754, -0.29687578301748324, 0.0026178873810467533, -0.16922474918276842, -0.13811441823390538, -0.047502949902916265, 0.01999796898072136, 0.03568439911087525, -0.26750016219528583, 0.013827098292651874, 0.037948527310750095, 0.06310587111732055, -0.07438943620032534, -0.07171316040551354, 0.0009354853713563804, 0.03711108785636466, -0.05833701739601534, 0.02373718259021126, 0.21907770467022883, -0.12471538021646697, -0.051778504387314975, 0.4473009590252206, -0.0559892508993126, -0.09144869015776906, 0.19955725765722837, -0.12438928420366398, -0.038960118318811576, 0.18390049613562637, 0.13698376631083223, 0.023789095518917874, -0.12456272461226787, -0.023872738507383598, -0.06385926418648712, 0.19189479195207743, 0.10551690868391045, 0.05500695628659993, 0.21603286689717388, 0.20866845474610554, 0.010983623341047044, 0.17796244404342926, -0.13535582591329925, -0.0651061136327716, -0.24793663236055652, -0.14965013790723128, -0.14655250423619973, 0.026431082429942385, -0.08477823387064715, -0.057865069686948996, 0.34125543134608144, 0.14777516479250685, 0.19620426124090265, -0.03034743853876817, 0.3606933966407488, 0.0900127208077124, 0.09295789689981732, 0.004423890706023266, 0.29307755319851225, 0.13395212224427738, 0.0791856547544614, -0.2609992357990544, 0.031292063840825494, -0.01435895163790676] |
1,802.05397 | Investigating Continuous Power Flow Solutions of IEEE-14 Bus System | This letter focuses on the multiplicity of power flow (PF) equations and
presents two continuous solutions for widely studied IEEE-14 bus system. The
continuous solutions are located by a method combining the semidefinite program
(SDP) relaxation and reformulation linearization technique (RLT). Although the
observation is non-trivial, it is of interest to researchers investigating the
geometry or multiplicity nature of PF equations.
| math.OC | this letter focuses on the multiplicity of power flow pf equations and presents two continuous solutions for widely studied ieee14 bus system the continuous solutions are located by a method combining the semidefinite program sdp relaxation and reformulation linearization technique rlt although the observation is nontrivial it is of interest to researchers investigating the geometry or multiplicity nature of pf equations | [['this', 'letter', 'focuses', 'on', 'the', 'multiplicity', 'of', 'power', 'flow', 'pf', 'equations', 'and', 'presents', 'two', 'continuous', 'solutions', 'for', 'widely', 'studied', 'ieee14', 'bus', 'system', 'the', 'continuous', 'solutions', 'are', 'located', 'by', 'a', 'method', 'combining', 'the', 'semidefinite', 'program', 'sdp', 'relaxation', 'and', 'reformulation', 'linearization', 'technique', 'rlt', 'although', 'the', 'observation', 'is', 'nontrivial', 'it', 'is', 'of', 'interest', 'to', 'researchers', 'investigating', 'the', 'geometry', 'or', 'multiplicity', 'nature', 'of', 'pf', 'equations']] | [-0.18642894450391903, -0.0059342255021949284, -0.12677825291137226, 0.04434623704439785, -0.09198167312462799, -0.1792254108156948, -0.01581855128961997, 0.2890459772077252, -0.29103928428815035, -0.3021554690862044, 0.14540275171796074, -0.28010049950881083, -0.13705737209787255, 0.2191195742601193, -0.034084029846870506, 0.11858559585344351, 0.053528576998658414, -0.016158911433895348, -0.08816337373229813, -0.20652354778874604, 0.3002437845036601, 0.01080488779994308, 0.2913769378677988, 0.028319300812684366, 0.12111975019034303, 0.028366949184049594, -0.03453276840374484, 0.05536014725622095, -0.11865836374278439, 0.11167541769004931, 0.2803496298013774, 0.15340089334411638, 0.2976815740226722, -0.37353140176808247, -0.17386510817822617, 0.05742355092566033, 0.10015552917106046, 0.0725212849058272, -0.054248946728413835, -0.24600035601035983, 0.09536695475766405, -0.12990162649848422, -0.13244284513849217, -0.05312386098638421, -0.00394599339695739, 0.0350382274711413, -0.2379592079096703, 0.07271177017084154, 0.09064412123111427, 0.03908993645769651, -0.07803897986371743, -0.12920914876435075, 0.023431890369316595, 0.024644861660981704, 0.08467174621230206, -0.00064294679151451, 0.10714175320062481, -0.06476301665906413, -0.11632212437689304, 0.3256833286055165, 0.005982776863317265, -0.23665854796484784, 0.18731223742218048, -0.09738766192076881, -0.12578122500238603, 0.15333677567236248, 0.1898620288483188, 0.19635776230363083, -0.19669161153743503, 0.13668938646604475, -0.03892857418777269, 0.1802446013994011, 0.05094165960326791, -0.0218379798299465, 0.19715392888813724, 0.19162675014842057, 0.10565947471033843, 0.13499185746749404, -0.025462615483089306, -0.17508553273853708, -0.2374500320369347, -0.13927688161350146, -0.2103799305260792, 0.053505073687000596, -0.017174938593926214, -0.12602156907945994, 0.42591594782520514, 0.08919562539849125, 0.0987628019017885, 0.03281434564316859, 0.3137463819174493, 0.18669256948117838, -0.002069683115715619, 0.08612911152790804, 0.2176438503532258, 0.1612116918349486, 0.1525052298303144, -0.26153770609774063, 0.04488620806874738, 0.15280454769180934] |
1,802.05398 | Pre-Calabi-Yau structures and moduli of representations | We establish a system of formal noncommutative calculus for differential
forms and polyvector fields, which forms the foundations for the study of
pre-Calabi-Yau categories. Using an explicit trace map, we show that any
$n$-Calabi-Yau structure on a non-positively graded dg algebra $A$ induces a
$(2-n)$-shifted symplectic structure on its derived moduli stack of
representations; while any $n$-pre-Calabi-Yau structure on $A$ induces a
$(2-n)$-shifted Poisson structure on this derived moduli stack.
| math.AG math.KT math.RT math.SG | we establish a system of formal noncommutative calculus for differential forms and polyvector fields which forms the foundations for the study of precalabiyau categories using an explicit trace map we show that any ncalabiyau structure on a nonpositively graded dg algebra a induces a 2nshifted symplectic structure on its derived moduli stack of representations while any nprecalabiyau structure on a induces a 2nshifted poisson structure on this derived moduli stack | [['we', 'establish', 'a', 'system', 'of', 'formal', 'noncommutative', 'calculus', 'for', 'differential', 'forms', 'and', 'polyvector', 'fields', 'which', 'forms', 'the', 'foundations', 'for', 'the', 'study', 'of', 'precalabiyau', 'categories', 'using', 'an', 'explicit', 'trace', 'map', 'we', 'show', 'that', 'any', 'ncalabiyau', 'structure', 'on', 'a', 'nonpositively', 'graded', 'dg', 'algebra', 'a', 'induces', 'a', '2nshifted', 'symplectic', 'structure', 'on', 'its', 'derived', 'moduli', 'stack', 'of', 'representations', 'while', 'any', 'nprecalabiyau', 'structure', 'on', 'a', 'induces', 'a', '2nshifted', 'poisson', 'structure', 'on', 'this', 'derived', 'moduli', 'stack']] | [-0.21665156200866809, -0.030633518844269653, -0.14384560367431154, 0.07691056904150173, -0.12696537451649254, -0.056493745128991024, -0.0088989817904252, 0.35945403711362317, -0.3856238570685188, -0.19207054054872555, 0.08632449059267387, -0.17013625064723645, -0.2101553910778779, 0.21207804364067587, -0.11008764244616032, -0.11510674716908287, 0.06417829851676343, 0.0976797604023225, -0.15504914813914872, -0.21170574270019477, 0.4808185546038051, -0.007322327255017378, 0.26827269387219776, -0.008704762228510597, 0.22621884222396396, 0.015288474131728324, -0.03414441234516827, 0.011560997784589275, -0.1552355174001908, 0.20787181623392936, 0.23451402736474958, 0.014987905403937806, 0.16644556880716913, -0.41589606192075845, -0.1634211829778823, 0.09371407614371767, 0.06367629440501332, 0.04201361810202908, -0.05033505746784309, -0.3020613149768024, 0.11846211606241537, -0.19276508056756222, -0.08925504192258372, -0.10783017148743525, 0.0561147458696117, -0.010604575802889567, -0.2158033594270378, -0.06863460643922514, 0.09113644569526624, 0.14036159423377478, -0.13872450909217482, -0.06769771461029339, -0.08092218352601666, 0.0674613145617486, -0.10726273838769305, 0.008502330973208176, 0.1379835028977444, -0.1376987470377406, -0.12010731721635569, 0.3575043744322928, -0.0893492057291565, -0.26997556365236186, 0.11434599941342392, -0.10855168686248362, -0.17563537289529588, 0.0891452134219986, 0.11627074274601358, 0.1582209626494935, -0.027551708563061602, 0.24444087209430468, -0.09941264131868427, 0.16181851053349391, 0.09059960771860047, -0.012446277330375531, 0.16750029296390218, 0.12976536124055696, 0.0846686671473876, 0.11676124412086651, 0.0020371059830667396, -0.09580626680205266, -0.37570196404262923, -0.24845524174584585, -0.024353587601305633, 0.17427717538719828, -0.12136113618930033, -0.24959986419840294, 0.3812709032755458, 0.04635530886607188, 0.23123052556095927, 0.11468639553729162, 0.23479354429538501, 0.09739448076509165, 0.10652360168398556, 0.019736760691034073, 0.10137516559299195, 0.26354053032330493, 0.025496697099173838, -0.09821088099852204, 0.008875463715274676, 0.19476070218352656] |
1,802.05399 | Competitive caching with machine learned advice | Traditional online algorithms encapsulate decision making under uncertainty,
and give ways to hedge against all possible future events, while guaranteeing a
nearly optimal solution as compared to an offline optimum. On the other hand,
machine learning algorithms are in the business of extrapolating patterns found
in the data to predict the future, and usually come with strong guarantees on
the expected generalization error.
In this work we develop a framework for augmenting online algorithms with a
machine learned oracle to achieve competitive ratios that provably improve upon
unconditional worst case lower bounds when the oracle has low error. Our
approach treats the oracle as a complete black box, and is not dependent on its
inner workings, or the exact distribution of its errors.
We apply this framework to the traditional caching problem -- creating an
eviction strategy for a cache of size $k$. We demonstrate that naively
following the oracle's recommendations may lead to very poor performance, even
when the average error is quite low. Instead we show how to modify the Marker
algorithm to take into account the oracle's predictions, and prove that this
combined approach achieves a competitive ratio that both (i) decreases as the
oracle's error decreases, and (ii) is always capped by $O(\log k)$, which can
be achieved without any oracle input. We complement our results with an
empirical evaluation of our algorithm on real world datasets, and show that it
performs well empirically even using simple off-the-shelf predictions.
| cs.DS cs.LG | traditional online algorithms encapsulate decision making under uncertainty and give ways to hedge against all possible future events while guaranteeing a nearly optimal solution as compared to an offline optimum on the other hand machine learning algorithms are in the business of extrapolating patterns found in the data to predict the future and usually come with strong guarantees on the expected generalization error in this work we develop a framework for augmenting online algorithms with a machine learned oracle to achieve competitive ratios that provably improve upon unconditional worst case lower bounds when the oracle has low error our approach treats the oracle as a complete black box and is not dependent on its inner workings or the exact distribution of its errors we apply this framework to the traditional caching problem creating an eviction strategy for a cache of size k we demonstrate that naively following the oracles recommendations may lead to very poor performance even when the average error is quite low instead we show how to modify the marker algorithm to take into account the oracles predictions and prove that this combined approach achieves a competitive ratio that both i decreases as the oracles error decreases and ii is always capped by olog k which can be achieved without any oracle input we complement our results with an empirical evaluation of our algorithm on real world datasets and show that it performs well empirically even using simple offtheshelf predictions | [['traditional', 'online', 'algorithms', 'encapsulate', 'decision', 'making', 'under', 'uncertainty', 'and', 'give', 'ways', 'to', 'hedge', 'against', 'all', 'possible', 'future', 'events', 'while', 'guaranteeing', 'a', 'nearly', 'optimal', 'solution', 'as', 'compared', 'to', 'an', 'offline', 'optimum', 'on', 'the', 'other', 'hand', 'machine', 'learning', 'algorithms', 'are', 'in', 'the', 'business', 'of', 'extrapolating', 'patterns', 'found', 'in', 'the', 'data', 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1,802.054 | High Dimensional Bayesian Optimization Using Dropout | Scaling Bayesian optimization to high dimensions is challenging task as the
global optimization of high-dimensional acquisition function can be expensive
and often infeasible. Existing methods depend either on limited active
variables or the additive form of the objective function. We propose a new
method for high-dimensional Bayesian optimization, that uses a dropout strategy
to optimize only a subset of variables at each iteration. We derive theoretical
bounds for the regret and show how it can inform the derivation of our
algorithm. We demonstrate the efficacy of our algorithms for optimization on
two benchmark functions and two real-world applications- training cascade
classifiers and optimizing alloy composition.
| stat.ML | scaling bayesian optimization to high dimensions is challenging task as the global optimization of highdimensional acquisition function can be expensive and often infeasible existing methods depend either on limited active variables or the additive form of the objective function we propose a new method for highdimensional bayesian optimization that uses a dropout strategy to optimize only a subset of variables at each iteration we derive theoretical bounds for the regret and show how it can inform the derivation of our algorithm we demonstrate the efficacy of our algorithms for optimization on two benchmark functions and two realworld applications training cascade classifiers and optimizing alloy composition | [['scaling', 'bayesian', 'optimization', 'to', 'high', 'dimensions', 'is', 'challenging', 'task', 'as', 'the', 'global', 'optimization', 'of', 'highdimensional', 'acquisition', 'function', 'can', 'be', 'expensive', 'and', 'often', 'infeasible', 'existing', 'methods', 'depend', 'either', 'on', 'limited', 'active', 'variables', 'or', 'the', 'additive', 'form', 'of', 'the', 'objective', 'function', 'we', 'propose', 'a', 'new', 'method', 'for', 'highdimensional', 'bayesian', 'optimization', 'that', 'uses', 'a', 'dropout', 'strategy', 'to', 'optimize', 'only', 'a', 'subset', 'of', 'variables', 'at', 'each', 'iteration', 'we', 'derive', 'theoretical', 'bounds', 'for', 'the', 'regret', 'and', 'show', 'how', 'it', 'can', 'inform', 'the', 'derivation', 'of', 'our', 'algorithm', 'we', 'demonstrate', 'the', 'efficacy', 'of', 'our', 'algorithms', 'for', 'optimization', 'on', 'two', 'benchmark', 'functions', 'and', 'two', 'realworld', 'applications', 'training', 'cascade', 'classifiers', 'and', 'optimizing', 'alloy', 'composition']] | [-0.056254803327222665, -0.025242902879558857, -0.08723577846152088, 0.10165083208453975, -0.12776121878553004, -0.18358840009729777, 0.09944326978154658, 0.4361368425722633, -0.28979468847447565, -0.35044100860222466, 0.12807695352516713, -0.2130022209004632, -0.21139329957687075, 0.22087117669483025, -0.10213498391565823, 0.1402046569756099, 0.10623413120130343, -0.023610422458677064, -0.08744047170815368, -0.324555673875979, 0.27647498102548224, 0.023422668692434118, 0.29576216431423313, -0.01565001095157294, 0.14629924215731166, 0.030096312521380328, -0.009929880002025692, 0.007996543230754988, -0.08900514723146834, 0.1492552781255827, 0.29574685001723644, 0.24344201772695495, 0.37629284834755317, -0.38907074451091744, -0.19674507903955168, 0.12863601866133867, 0.16124279386408272, 0.07038456059637524, -0.010193287791861665, -0.23383116185487735, 0.05946769199910618, -0.12308356894978455, 0.012938696518540382, -0.17415131221392324, -0.05492425223457671, 0.04052058066285792, -0.3852683633477205, 0.04714915473457603, 0.00891582651529461, 0.018984938346381697, -0.059058916384709025, -0.18271492181257123, 0.03628587160880367, 0.09760301609612292, 0.04046372017889683, 0.022117403937902833, 0.18320436494957124, -0.1044686436963578, -0.1569895518211914, 0.32686392834321376, -0.018177286983423288, -0.2637848332435602, 0.2071092155717668, -0.03378844372928143, -0.18449651696497485, 0.1120446542455327, 0.28216938850043033, 0.17993066490051293, -0.14502701150874298, 0.05718831575386936, -0.024025009253195354, 0.17701624920148226, -0.0016781600369583992, 0.008672560919963178, 0.11956206945968526, 0.25111463325364247, 0.12072577201206947, 0.1605788350759429, -0.08194681609831085, -0.08630670670952116, -0.2732665612140582, -0.11688236926815339, -0.24931073407864288, -0.008441061056440784, -0.14166291284629898, -0.1712400624384394, 0.3562483490910381, 0.19883010926700773, 0.1906222612641397, 0.10466865252792126, 0.36305100092930453, 0.10162513504536556, 0.04502638152667454, 0.09661412072711668, 0.19050052198581396, 0.04136299719767911, 0.06724917117861055, -0.20851161318520706, 0.10764155119347076, 0.052405819141616426] |
1,802.05401 | Establishing a microscopic model for nonfullerene organic solar cells:
Self-accumulation effect of charges | A one-dimensional many-body model is established to mimic the charge
distribution and dynamics in nonfullerene organic solar cells. Two essential
issues are taken into account in the model: The alternating donor and acceptor
structure and the local imbalance of the intrinsic electrons and holes. The
alternating structure is beneficial for the direct generation of charge
transfer state which enhances the local imbalance of intrinsic charges. The
most remarkable outcome of the model is that, due to the strong Coulomb
attractive potential energy, the intrinsic charges in the cells are
self-accumulated in a small spatial region. Outside the self-accumulation
region, the charge density vanishes so that the recombination is regarded to be
largely suppressed. The photogenerated electrons are subsequently observed to
spread freely outside the self-accumulation region implying the Coulomb
attraction does not matter in the ultrafast charge separation dynamics. These
findings enable an appealing understanding of the high performance of emerging
nonfullerene cells, and the designing rules of molecules and devices are then
comprehensively discussed.
| cond-mat.mtrl-sci | a onedimensional manybody model is established to mimic the charge distribution and dynamics in nonfullerene organic solar cells two essential issues are taken into account in the model the alternating donor and acceptor structure and the local imbalance of the intrinsic electrons and holes the alternating structure is beneficial for the direct generation of charge transfer state which enhances the local imbalance of intrinsic charges the most remarkable outcome of the model is that due to the strong coulomb attractive potential energy the intrinsic charges in the cells are selfaccumulated in a small spatial region outside the selfaccumulation region the charge density vanishes so that the recombination is regarded to be largely suppressed the photogenerated electrons are subsequently observed to spread freely outside the selfaccumulation region implying the coulomb attraction does not matter in the ultrafast charge separation dynamics these findings enable an appealing understanding of the high performance of emerging nonfullerene cells and the designing rules of molecules and devices are then comprehensively discussed | [['a', 'onedimensional', 'manybody', 'model', 'is', 'established', 'to', 'mimic', 'the', 'charge', 'distribution', 'and', 'dynamics', 'in', 'nonfullerene', 'organic', 'solar', 'cells', 'two', 'essential', 'issues', 'are', 'taken', 'into', 'account', 'in', 'the', 'model', 'the', 'alternating', 'donor', 'and', 'acceptor', 'structure', 'and', 'the', 'local', 'imbalance', 'of', 'the', 'intrinsic', 'electrons', 'and', 'holes', 'the', 'alternating', 'structure', 'is', 'beneficial', 'for', 'the', 'direct', 'generation', 'of', 'charge', 'transfer', 'state', 'which', 'enhances', 'the', 'local', 'imbalance', 'of', 'intrinsic', 'charges', 'the', 'most', 'remarkable', 'outcome', 'of', 'the', 'model', 'is', 'that', 'due', 'to', 'the', 'strong', 'coulomb', 'attractive', 'potential', 'energy', 'the', 'intrinsic', 'charges', 'in', 'the', 'cells', 'are', 'selfaccumulated', 'in', 'a', 'small', 'spatial', 'region', 'outside', 'the', 'selfaccumulation', 'region', 'the', 'charge', 'density', 'vanishes', 'so', 'that', 'the', 'recombination', 'is', 'regarded', 'to', 'be', 'largely', 'suppressed', 'the', 'photogenerated', 'electrons', 'are', 'subsequently', 'observed', 'to', 'spread', 'freely', 'outside', 'the', 'selfaccumulation', 'region', 'implying', 'the', 'coulomb', 'attraction', 'does', 'not', 'matter', 'in', 'the', 'ultrafast', 'charge', 'separation', 'dynamics', 'these', 'findings', 'enable', 'an', 'appealing', 'understanding', 'of', 'the', 'high', 'performance', 'of', 'emerging', 'nonfullerene', 'cells', 'and', 'the', 'designing', 'rules', 'of', 'molecules', 'and', 'devices', 'are', 'then', 'comprehensively', 'discussed']] | [-0.1320036006703352, 0.1763338363179858, -0.025068459540812507, 0.11498721960334131, -0.004169648225752659, -0.1277936442674678, 0.0496649205437459, 0.3652808557977371, -0.2753735773393275, -0.28621634232156257, 0.015286968310254362, -0.2866152061500161, -0.11060525574376462, 0.12761413905464894, -0.018543405198732127, -0.031123131347236647, 0.0047830236661765314, -0.011484741342518433, -0.02630374171587144, -0.17302121335631926, 0.26378977750025595, 0.0706519099693662, 0.33169748401844207, 0.13608265362599473, 0.07126666162519452, -0.005948266176863309, 0.01665257780674707, 0.020517015341025444, -0.06743076801083386, 0.12408797267858905, 0.22108874930482772, 0.006793051284743634, 0.2604147059825697, -0.47738122002991995, -0.23183421345131563, 0.07958484416896547, 0.17830234291218797, 0.12447776544456841, -0.10166756941844927, -0.26337689634435524, 0.06839008829383939, -0.1552612869511646, -0.14276461060765058, -0.04107586593930552, 0.02288350942372172, 0.06095255912535962, -0.2549957754270082, 0.10268248854360233, 0.0774016167787421, -0.03090603137963716, -0.11156154099112034, -0.10895774811851205, -0.08507362569906507, 0.16357918226240595, 0.05663469554870792, 0.007578759195490016, 0.19535563108446594, -0.16365051712771808, -0.05863142366820003, 0.3626787803339898, 0.001399188482110607, -0.1886394762140871, 0.19999804353071263, -0.1786204005805718, -0.04123206618299455, 0.167292785393879, 0.13182134448010244, 0.11019796396053776, -0.15186673440800888, 0.0827322033166391, 0.013192527748164318, 0.15764651314070144, -0.003231875529443776, 0.09922411192760423, 0.29380534998756364, 0.18328736952712965, 0.0400174126247473, 0.09894989246580128, -0.12396215758412893, -0.13210590999217037, -0.2398626449989316, -0.1570368422091835, -0.18880677151344258, 0.03613120188836345, -0.05717479755072912, -0.15052742127582064, 0.40259641819571745, 0.12894011594866758, 0.1967455121384212, -0.08155072445731128, 0.2917001317152123, 0.10694283018762499, 0.1132159921007208, 0.02290144993889111, 0.27190178786806857, 0.1278290672423203, 0.09737364652445885, -0.28394545514113556, 0.0709301298311739, 0.039066850745268625] |
1,802.05402 | Controlling fracture cascades through twisting and quenching | Fracture limits the structural stability of macroscopic and microscopic
materials, from beams and bones to microtubules and nanotubes. Despite recent
progress, fracture control continues to present profound practical and
theoretical challenges. A famous longstanding problem posed by Feynman asks why
brittle elastic rods appear almost always to fragment into at least three
pieces when placed under large bending stresses. Feynman's observation raises
fundamental questions about the existence of protocols that can robustly induce
binary fracture in brittle materials. Using experiments, simulations and
analytical scaling arguments, we demonstrate controlled binary fracture of
brittle elastic rods for two distinct protocols based on twisting and
nonadiabatic quenching. Our experimental data for twist-controlled fracture
agree quantitatively with a theoretically predicted phase diagram. Furthermore,
we establish novel asymptotic scaling relations for quenched fracture. Due to
their general character, these results are expected to apply to torsional and
kinetic fracture processes in a wide range of systems.
| cond-mat.soft | fracture limits the structural stability of macroscopic and microscopic materials from beams and bones to microtubules and nanotubes despite recent progress fracture control continues to present profound practical and theoretical challenges a famous longstanding problem posed by feynman asks why brittle elastic rods appear almost always to fragment into at least three pieces when placed under large bending stresses feynmans observation raises fundamental questions about the existence of protocols that can robustly induce binary fracture in brittle materials using experiments simulations and analytical scaling arguments we demonstrate controlled binary fracture of brittle elastic rods for two distinct protocols based on twisting and nonadiabatic quenching our experimental data for twistcontrolled fracture agree quantitatively with a theoretically predicted phase diagram furthermore we establish novel asymptotic scaling relations for quenched fracture due to their general character these results are expected to apply to torsional and kinetic fracture processes in a wide range of systems | [['fracture', 'limits', 'the', 'structural', 'stability', 'of', 'macroscopic', 'and', 'microscopic', 'materials', 'from', 'beams', 'and', 'bones', 'to', 'microtubules', 'and', 'nanotubes', 'despite', 'recent', 'progress', 'fracture', 'control', 'continues', 'to', 'present', 'profound', 'practical', 'and', 'theoretical', 'challenges', 'a', 'famous', 'longstanding', 'problem', 'posed', 'by', 'feynman', 'asks', 'why', 'brittle', 'elastic', 'rods', 'appear', 'almost', 'always', 'to', 'fragment', 'into', 'at', 'least', 'three', 'pieces', 'when', 'placed', 'under', 'large', 'bending', 'stresses', 'feynmans', 'observation', 'raises', 'fundamental', 'questions', 'about', 'the', 'existence', 'of', 'protocols', 'that', 'can', 'robustly', 'induce', 'binary', 'fracture', 'in', 'brittle', 'materials', 'using', 'experiments', 'simulations', 'and', 'analytical', 'scaling', 'arguments', 'we', 'demonstrate', 'controlled', 'binary', 'fracture', 'of', 'brittle', 'elastic', 'rods', 'for', 'two', 'distinct', 'protocols', 'based', 'on', 'twisting', 'and', 'nonadiabatic', 'quenching', 'our', 'experimental', 'data', 'for', 'twistcontrolled', 'fracture', 'agree', 'quantitatively', 'with', 'a', 'theoretically', 'predicted', 'phase', 'diagram', 'furthermore', 'we', 'establish', 'novel', 'asymptotic', 'scaling', 'relations', 'for', 'quenched', 'fracture', 'due', 'to', 'their', 'general', 'character', 'these', 'results', 'are', 'expected', 'to', 'apply', 'to', 'torsional', 'and', 'kinetic', 'fracture', 'processes', 'in', 'a', 'wide', 'range', 'of', 'systems']] | [-0.11490378336360058, 0.18032536956171194, -0.10358677670670052, 0.049330250478039184, -0.09235954598213235, -0.1506617079519977, 0.0683760207441325, 0.40282342084993916, -0.2905053998157382, -0.27406128755770626, 0.0448648825318863, -0.25858457224133113, -0.18587431638191143, 0.21254447934217752, -0.059282927569001914, 0.15645870417356492, 0.07902209928104033, -0.08525751045284172, -0.04132122841818879, -0.18809307496567879, 0.2686597254569642, 0.02037045775602261, 0.34010149852683147, 0.12222812698339112, 0.0661631257358628, 0.0036768663626814183, 0.04763355021675428, 0.0678077545715496, -0.21856071995905949, 0.10253174638220419, 0.2860662381382038, 0.012361586258436242, 0.22587402556091546, -0.521568810986355, -0.22954615868628025, 0.03311959026847035, 0.1370198094410201, 0.11635535627603531, -0.0023091514594852924, -0.24795536002299437, 0.07322170581668615, -0.11080248640850186, -0.14720353383881352, -0.09874951743210356, 0.04890850119603177, 0.03279059720536073, -0.19445171589031815, 0.11020302957079063, 0.06731613455961148, 0.08540235748824974, -0.09146204151174364, -0.09949958094007647, 0.023053754076051217, 0.10364836156698098, 0.10383300040770943, -0.038525672232887394, 0.18250020924334726, -0.12686751229067644, -0.12704269778138647, 0.4382976630764703, 0.05390682922055324, -0.12282383169513196, 0.22395243235087642, -0.11343188052725357, -0.12789312548935414, 0.15486958963563666, 0.18536293022645017, 0.04747118119460841, -0.14210963109168612, -0.036092334447506196, -0.004544968333405753, 0.14472149131276335, 0.09380650292926779, -0.033077315900397176, 0.2349895701557398, 0.22517170246535292, -0.02904419519007206, 0.12800032335993214, -0.032260238570161165, -0.1096489632090864, -0.28723780216649175, -0.11049339693815757, -0.16460982131461302, 0.07111432482866803, -0.10481872249637188, -0.18960439277191957, 0.30886641803818443, 0.14044136261567475, 0.15914902937908967, 0.0979736653668806, 0.2146247761688816, 0.032874760451571394, 0.03153740071846793, 0.023083963034053644, 0.2985724144987762, 0.187549646939151, 0.08117636613237361, -0.2105941665902113, 0.09055945700694186, 0.025500791656474273] |
1,802.05403 | Advanced Diagnostics for the Study of Linearly Polarized Emission. II:
Application to Diffuse Interstellar Radio Synchrotron Emission | Diagnostics of polarized emission provide us with valuable information on the
Galactic magnetic field and the state of turbulence in the interstellar medium,
which cannot be obtained from synchrotron intensity alone. In Paper I (Herron
et al. 2017b), we derived polarization diagnostics that are rotationally and
translationally invariant in the $Q$-$U$ plane, similar to the polarization
gradient. In this paper, we apply these diagnostics to simulations of ideal
magnetohydrodynamic turbulence that have a range of sonic and Alfv\'enic Mach
numbers. We generate synthetic images of Stokes $Q$ and $U$ for these
simulations, for the cases where the turbulence is illuminated from behind by
uniform polarized emission, and where the polarized emission originates from
within the turbulent volume. From these simulated images we calculate the
polarization diagnostics derived in Paper I, for different lines of sight
relative to the mean magnetic field, and for a range of frequencies. For all of
our simulations, we find that the polarization gradient is very similar to the
generalized polarization gradient, and that both trace spatial variations in
the magnetoionic medium for the case where emission originates within the
turbulent volume, provided that the medium is not supersonic. We propose a
method for distinguishing the cases of emission coming from behind or within a
turbulent, Faraday rotating medium, and a method to partly map the rotation
measure of the observed region. We also speculate on statistics of these
diagnostics that may allow us to constrain the physical properties of an
observed turbulent region.
| astro-ph.GA astro-ph.IM | diagnostics of polarized emission provide us with valuable information on the galactic magnetic field and the state of turbulence in the interstellar medium which cannot be obtained from synchrotron intensity alone in paper i herron et al 2017b we derived polarization diagnostics that are rotationally and translationally invariant in the qu plane similar to the polarization gradient in this paper we apply these diagnostics to simulations of ideal magnetohydrodynamic turbulence that have a range of sonic and alfvenic mach numbers we generate synthetic images of stokes q and u for these simulations for the cases where the turbulence is illuminated from behind by uniform polarized emission and where the polarized emission originates from within the turbulent volume from these simulated images we calculate the polarization diagnostics derived in paper i for different lines of sight relative to the mean magnetic field and for a range of frequencies for all of our simulations we find that the polarization gradient is very similar to the generalized polarization gradient and that both trace spatial variations in the magnetoionic medium for the case where emission originates within the turbulent volume provided that the medium is not supersonic we propose a method for distinguishing the cases of emission coming from behind or within a turbulent faraday rotating medium and a method to partly map the rotation measure of the observed region we also speculate on statistics of these diagnostics that may allow us to constrain the physical properties of an observed turbulent region | [['diagnostics', 'of', 'polarized', 'emission', 'provide', 'us', 'with', 'valuable', 'information', 'on', 'the', 'galactic', 'magnetic', 'field', 'and', 'the', 'state', 'of', 'turbulence', 'in', 'the', 'interstellar', 'medium', 'which', 'can', 'not', 'be', 'obtained', 'from', 'synchrotron', 'intensity', 'alone', 'in', 'paper', 'i', 'herron', 'et', 'al', '2017b', 'we', 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1,802.05404 | A modification of the factorization method for scatterers with different
physical properties | We study an inverse acoustic scattering problem by the Factorization Method
when the unknown scatterer consists of two objects with different physical
properties. Especially, we consider the following two cases: One is the case
when each object has the different boundary condition, and the other one is
when different penetrability. Our idea here is to modify the far field operator
depending on the cases to avoid unnecessary a priori assumptions.
| math.AP | we study an inverse acoustic scattering problem by the factorization method when the unknown scatterer consists of two objects with different physical properties especially we consider the following two cases one is the case when each object has the different boundary condition and the other one is when different penetrability our idea here is to modify the far field operator depending on the cases to avoid unnecessary a priori assumptions | [['we', 'study', 'an', 'inverse', 'acoustic', 'scattering', 'problem', 'by', 'the', 'factorization', 'method', 'when', 'the', 'unknown', 'scatterer', 'consists', 'of', 'two', 'objects', 'with', 'different', 'physical', 'properties', 'especially', 'we', 'consider', 'the', 'following', 'two', 'cases', 'one', 'is', 'the', 'case', 'when', 'each', 'object', 'has', 'the', 'different', 'boundary', 'condition', 'and', 'the', 'other', 'one', 'is', 'when', 'different', 'penetrability', 'our', 'idea', 'here', 'is', 'to', 'modify', 'the', 'far', 'field', 'operator', 'depending', 'on', 'the', 'cases', 'to', 'avoid', 'unnecessary', 'a', 'priori', 'assumptions']] | [-0.09496245642325708, 0.10638165506721374, -0.07181639673986605, 0.06497893866949848, -0.12492479389599924, -0.16904138145036995, -0.009455281196694289, 0.37221646958163807, -0.2887464788476271, -0.2771726933574038, 0.13849854001642337, -0.27980241358413227, -0.13984977337531745, 0.1809270002347018, -0.051837897906079886, 0.03875336144119501, 0.05415936979864325, 0.0663439511454531, -0.06913240802075182, -0.22720585333549284, 0.45415046957454513, -0.01844615604329322, 0.260833880125678, 0.04241074827898826, 0.09762162446194063, 0.048608812130987646, 0.01290647752716073, -0.022249535978075333, -0.09126787399540522, 0.06091191921482927, 0.1950986892517124, 0.08115338099721287, 0.28779959561236734, -0.44286430752170935, -0.21845997884603482, 0.11603661859408021, 0.11743490339389868, 0.1366731770336628, 0.0037594530448716666, -0.2385912191156032, 0.11410173412212836, -0.08558946544570582, -0.1436654813126162, 0.02388213341390448, 0.014891487680974284, -0.01816093600687704, -0.2688446751995278, 0.015153909847140313, 0.05848250238757048, 0.002209726132319442, -0.07888616796117276, -0.11788621406470026, 0.055787633815115055, 0.14718534758472482, 0.10437701801669651, -0.031151772440145057, 0.13984927413985132, -0.1487010609225503, -0.05554436498454639, 0.3768311851790973, 0.0086491281632334, -0.2841968918485301, 0.23012253119211112, -0.11832001666272325, -0.11413545552641154, 0.08324477944988758, 0.10090268582571298, 0.15731477063548352, -0.16479712779213773, 0.0892541544703168, -0.07496072885281008, 0.1416437107404428, 0.07253967993227499, 0.0015060387418738433, 0.1343553103373519, 0.1457410851260647, 0.08119355161740843, 0.1715552198043692, -0.11483274182038648, -0.05150813697172063, -0.3033857540892703, -0.10977773228660226, -0.1895532910179879, 0.013638067484966347, -0.10894776161606257, -0.15232008106208272, 0.39255295149715885, 0.1816773110601519, 0.22568946108222007, -0.01714456577652267, 0.35455568933061193, 0.1594525377349263, 0.02559968548427735, 0.0407516323096518, 0.24932208753057888, 0.08209729958658239, 0.06264227640016802, -0.22228988303270722, 0.07021080986596644, 0.07646560283111674] |
1,802.05405 | Putting a bug in ML: The moth olfactory network learns to read MNIST | We seek to (i) characterize the learning architectures exploited in
biological neural networks for training on very few samples, and (ii) port
these algorithmic structures to a machine learning context. The Moth Olfactory
Network is among the simplest biological neural systems that can learn, and its
architecture includes key structural elements and mechanisms widespread in
biological neural nets, such as cascaded networks, competitive inhibition, high
intrinsic noise, sparsity, reward mechanisms, and Hebbian plasticity. These
structural biological elements, in combination, enable rapid learning.
MothNet is a computational model of the Moth Olfactory Network, closely
aligned with the moth's known biophysics and with in vivo electrode data
collected from moths learning new odors. We assign this model the task of
learning to read the MNIST digits. We show that MothNet successfully learns to
read given very few training samples (1 to 10 samples per class). In this
few-samples regime, it outperforms standard machine learning methods such as
nearest-neighbors, support-vector machines, and neural networks (NNs), and
matches specialized one-shot transfer-learning methods but without the need for
pre-training. The MothNet architecture illustrates how algorithmic structures
derived from biological brains can be used to build alternative NNs that may
avoid some of the learning rate limitations of current engineered NNs.
| cs.LG cs.NE q-bio.NC | we seek to i characterize the learning architectures exploited in biological neural networks for training on very few samples and ii port these algorithmic structures to a machine learning context the moth olfactory network is among the simplest biological neural systems that can learn and its architecture includes key structural elements and mechanisms widespread in biological neural nets such as cascaded networks competitive inhibition high intrinsic noise sparsity reward mechanisms and hebbian plasticity these structural biological elements in combination enable rapid learning mothnet is a computational model of the moth olfactory network closely aligned with the moths known biophysics and with in vivo electrode data collected from moths learning new odors we assign this model the task of learning to read the mnist digits we show that mothnet successfully learns to read given very few training samples 1 to 10 samples per class in this fewsamples regime it outperforms standard machine learning methods such as nearestneighbors supportvector machines and neural networks nns and matches specialized oneshot transferlearning methods but without the need for pretraining the mothnet architecture illustrates how algorithmic structures derived from biological brains can be used to build alternative nns that may avoid some of the learning rate limitations of current engineered nns | [['we', 'seek', 'to', 'i', 'characterize', 'the', 'learning', 'architectures', 'exploited', 'in', 'biological', 'neural', 'networks', 'for', 'training', 'on', 'very', 'few', 'samples', 'and', 'ii', 'port', 'these', 'algorithmic', 'structures', 'to', 'a', 'machine', 'learning', 'context', 'the', 'moth', 'olfactory', 'network', 'is', 'among', 'the', 'simplest', 'biological', 'neural', 'systems', 'that', 'can', 'learn', 'and', 'its', 'architecture', 'includes', 'key', 'structural', 'elements', 'and', 'mechanisms', 'widespread', 'in', 'biological', 'neural', 'nets', 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1,802.05406 | An Analysis of Stochastic Jovian Oscillation Excitation by Moist
Convection | Recent observations of Jupiter have suggested the existence of global
oscillatory modes at millihertz frequencies, yet the source mechanism
responsible for driving these modes is still unknown. However, the energies
necessary to produce observable surface oscillations have been predicted. Here
we investigate if moist convection in Jupiter's upper atmosphere can be
responsible for driving the global oscillations and what moist convective
energy requirements are necessary to achieve these theoretical mode energies
and surface amplitudes. We begin by creating a one-dimensional moist convective
cloud model and find that the available kinetic energy of the rising cloud
column falls below theoretical estimates of oscillations energies. That is,
mode excitation cannot occur with a single storm eruption. We then explore
stochastic excitation scenarios of the oscillations by moist convective storms.
We find that mode energies and amplitudes can reach theoretical estimates if
the storm energy available to the modes is more than just kinetic. In order for
the modes to be excited, we find that they require $5 \times 10^{27}$ to
10$^{28}$ erg per day. However, even for a large storm eruption each day, the
available kinetic energy from the storms falls two orders of magnitude short of
the required driving energy. Although our models may oversimplify the true
complexity of the coupling between Jovian storms and global oscillations, our
findings reveal that enough thermal energy is associated with moist convection
to drive the modes, should it be available to them.
| astro-ph.EP | recent observations of jupiter have suggested the existence of global oscillatory modes at millihertz frequencies yet the source mechanism responsible for driving these modes is still unknown however the energies necessary to produce observable surface oscillations have been predicted here we investigate if moist convection in jupiters upper atmosphere can be responsible for driving the global oscillations and what moist convective energy requirements are necessary to achieve these theoretical mode energies and surface amplitudes we begin by creating a onedimensional moist convective cloud model and find that the available kinetic energy of the rising cloud column falls below theoretical estimates of oscillations energies that is mode excitation cannot occur with a single storm eruption we then explore stochastic excitation scenarios of the oscillations by moist convective storms we find that mode energies and amplitudes can reach theoretical estimates if the storm energy available to the modes is more than just kinetic in order for the modes to be excited we find that they require 5 times 1027 to 1028 erg per day however even for a large storm eruption each day the available kinetic energy from the storms falls two orders of magnitude short of the required driving energy although our models may oversimplify the true complexity of the coupling between jovian storms and global oscillations our findings reveal that enough thermal energy is associated with moist convection to drive the modes should it be available to them | [['recent', 'observations', 'of', 'jupiter', 'have', 'suggested', 'the', 'existence', 'of', 'global', 'oscillatory', 'modes', 'at', 'millihertz', 'frequencies', 'yet', 'the', 'source', 'mechanism', 'responsible', 'for', 'driving', 'these', 'modes', 'is', 'still', 'unknown', 'however', 'the', 'energies', 'necessary', 'to', 'produce', 'observable', 'surface', 'oscillations', 'have', 'been', 'predicted', 'here', 'we', 'investigate', 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1,802.05407 | New high-spin structure and possible chirality in $^{109}$In | High-spin structure of $^{109}$In has been investigated with the
$^{100}$Mo($^{14}$N, 5$n$)$^{109}$In reaction at a beam energy of 78 MeV using
the in-beam $\gamma$ spectroscopic method. The level scheme of $^{109}$In has
been modified considerably and extended by 46 new $\gamma$-rays to the highest
excited state at 8.979 MeV and $J^{\pi}$=(45/2$^{+}$). The new level scheme
consists of eight bands, six of which are identified as dipole bands. The
configurations have been tentatively assigned with the help of the systematics
of neighboring odd-$A$ indium isotopes and the experimental aligned angular
momenta. The dipole bands are then compared with the titled axis cranking
calculation in the framework of covariant density function theory (TAC-CDFT).
The results of theoretical calculation based on the configurations, which
involve one proton hole at the $g_{9/2}$ orbital and two or four unpaired
neutrons at $g_{7/2}$, $d_{5/2}$ and $h_{11/2}$ orbitals, show that the shape
of $^{109}$In undergoes an evolution on both $\beta$ and $\gamma$ deformations
and possible chirality is suggested in $^{109}$In.
| nucl-ex nucl-th | highspin structure of 109in has been investigated with the 100mo14n 5n109in reaction at a beam energy of 78 mev using the inbeam gamma spectroscopic method the level scheme of 109in has been modified considerably and extended by 46 new gammarays to the highest excited state at 8979 mev and jpi452 the new level scheme consists of eight bands six of which are identified as dipole bands the configurations have been tentatively assigned with the help of the systematics of neighboring odda indium isotopes and the experimental aligned angular momenta the dipole bands are then compared with the titled axis cranking calculation in the framework of covariant density function theory taccdft the results of theoretical calculation based on the configurations which involve one proton hole at the g_92 orbital and two or four unpaired neutrons at g_72 d_52 and h_112 orbitals show that the shape of 109in undergoes an evolution on both beta and gamma deformations and possible chirality is suggested in 109in | [['highspin', 'structure', 'of', '109in', 'has', 'been', 'investigated', 'with', 'the', '100mo14n', '5n109in', 'reaction', 'at', 'a', 'beam', 'energy', 'of', '78', 'mev', 'using', 'the', 'inbeam', 'gamma', 'spectroscopic', 'method', 'the', 'level', 'scheme', 'of', '109in', 'has', 'been', 'modified', 'considerably', 'and', 'extended', 'by', '46', 'new', 'gammarays', 'to', 'the', 'highest', 'excited', 'state', 'at', '8979', 'mev', 'and', 'jpi452', 'the', 'new', 'level', 'scheme', 'consists', 'of', 'eight', 'bands', 'six', 'of', 'which', 'are', 'identified', 'as', 'dipole', 'bands', 'the', 'configurations', 'have', 'been', 'tentatively', 'assigned', 'with', 'the', 'help', 'of', 'the', 'systematics', 'of', 'neighboring', 'odda', 'indium', 'isotopes', 'and', 'the', 'experimental', 'aligned', 'angular', 'momenta', 'the', 'dipole', 'bands', 'are', 'then', 'compared', 'with', 'the', 'titled', 'axis', 'cranking', 'calculation', 'in', 'the', 'framework', 'of', 'covariant', 'density', 'function', 'theory', 'taccdft', 'the', 'results', 'of', 'theoretical', 'calculation', 'based', 'on', 'the', 'configurations', 'which', 'involve', 'one', 'proton', 'hole', 'at', 'the', 'g_92', 'orbital', 'and', 'two', 'or', 'four', 'unpaired', 'neutrons', 'at', 'g_72', 'd_52', 'and', 'h_112', 'orbitals', 'show', 'that', 'the', 'shape', 'of', '109in', 'undergoes', 'an', 'evolution', 'on', 'both', 'beta', 'and', 'gamma', 'deformations', 'and', 'possible', 'chirality', 'is', 'suggested', 'in', '109in']] | [-0.07509662105648476, 0.17515577479707933, -0.05002105103640307, 0.049265027844797894, 0.011087873144962106, -0.10274051251386701, 0.04981967066132885, 0.3948306268092933, -0.1721738176420331, -0.3265018821056268, -0.02230088883316868, -0.31915449218788916, -0.025066559151064153, 0.13912978857773456, 0.08301193335473206, 0.025800523404506552, 0.037412020176127075, 0.026471674928714516, -0.08912975956711051, -0.1795114843822885, 0.3046820966473478, 0.10233074177803982, 0.28103248445186646, 0.05831577047589977, 0.08721003701886648, -0.011020318389461289, 0.01878878770538481, -0.056949615597155445, -0.10752710189832898, 0.09985517816333349, 0.20239738281667943, 0.015867112125833607, 0.1797242707759735, -0.39208052411770367, -0.16260876519320783, 0.011157257538763392, 0.1405538777660602, 0.09956544325953931, -0.030983453291723397, -0.29437963216369795, 0.05332514410690185, -0.20641620572846217, -0.16249564149161908, -0.08224939081186108, 0.026107442218240847, 0.03559841890374472, -0.22408038783398795, 0.07286692123005914, -0.014665022277374082, 0.04673503695803273, -0.12980948546441615, -0.24007410114144634, -0.08523178945894644, 0.04057391567428591, 0.0815648601025604, 0.0758641699837316, 0.12066936179710801, -0.038535976818033085, -0.13031727850294797, 0.3919763741599526, -0.012718716731211941, -0.1464990466539481, 0.14602790081986244, -0.18155237437844288, -0.1586349654133034, 0.21797156799584627, 0.10940042300636221, 0.12367619909820662, -0.12440677812941384, 0.07420302858720029, 0.020399748593135765, 0.1619536172135905, 0.09739461536875149, 0.036897898058460395, 0.2259254283894589, 0.14238852255675158, -0.004936266955949223, 0.0820683440754107, -0.2157312131480901, -0.06231210327738374, -0.25425330004921765, -0.10442386789078331, -0.16926481333934956, 0.00839791159889999, -0.0005700072759479154, -0.09388551603302739, 0.4180211105257785, 0.018185712930977725, 0.18090881546366083, -0.06209566783023535, 0.24871112107059024, 0.09608685746299589, 0.07735358050781166, 0.03751817238843365, 0.31011936877065216, 0.1605538636680313, 0.0398068014335089, -0.26973918797804197, 0.020546160261650945, 0.024877051067161135] |
1,802.05408 | "Dependency Bottleneck" in Auto-encoding Architectures: an Empirical
Study | Recent works investigated the generalization properties in deep neural
networks (DNNs) by studying the Information Bottleneck in DNNs. However, the
mea- surement of the mutual information (MI) is often inaccurate due to the
density estimation. To address this issue, we propose to measure the dependency
instead of MI between layers in DNNs. Specifically, we propose to use
Hilbert-Schmidt Independence Criterion (HSIC) as the dependency measure, which
can measure the dependence of two random variables without estimating
probability densities. Moreover, HSIC is a special case of the Squared-loss
Mutual Information (SMI). In the experiment, we empirically evaluate the
generalization property using HSIC in both the reconstruction and prediction
auto-encoding (AE) architectures.
| cs.IT cs.LG math.IT stat.ML | recent works investigated the generalization properties in deep neural networks dnns by studying the information bottleneck in dnns however the mea surement of the mutual information mi is often inaccurate due to the density estimation to address this issue we propose to measure the dependency instead of mi between layers in dnns specifically we propose to use hilbertschmidt independence criterion hsic as the dependency measure which can measure the dependence of two random variables without estimating probability densities moreover hsic is a special case of the squaredloss mutual information smi in the experiment we empirically evaluate the generalization property using hsic in both the reconstruction and prediction autoencoding ae architectures | [['recent', 'works', 'investigated', 'the', 'generalization', 'properties', 'in', 'deep', 'neural', 'networks', 'dnns', 'by', 'studying', 'the', 'information', 'bottleneck', 'in', 'dnns', 'however', 'the', 'mea', 'surement', 'of', 'the', 'mutual', 'information', 'mi', 'is', 'often', 'inaccurate', 'due', 'to', 'the', 'density', 'estimation', 'to', 'address', 'this', 'issue', 'we', 'propose', 'to', 'measure', 'the', 'dependency', 'instead', 'of', 'mi', 'between', 'layers', 'in', 'dnns', 'specifically', 'we', 'propose', 'to', 'use', 'hilbertschmidt', 'independence', 'criterion', 'hsic', 'as', 'the', 'dependency', 'measure', 'which', 'can', 'measure', 'the', 'dependence', 'of', 'two', 'random', 'variables', 'without', 'estimating', 'probability', 'densities', 'moreover', 'hsic', 'is', 'a', 'special', 'case', 'of', 'the', 'squaredloss', 'mutual', 'information', 'smi', 'in', 'the', 'experiment', 'we', 'empirically', 'evaluate', 'the', 'generalization', 'property', 'using', 'hsic', 'in', 'both', 'the', 'reconstruction', 'and', 'prediction', 'autoencoding', 'ae', 'architectures']] | [-0.08597321432422508, 0.008372955058108677, -0.09397918384056539, 0.14638657624917953, -0.06276622584766962, -0.13807593281592495, 0.05540577987981537, 0.3988045283677903, -0.2869580721059306, -0.304101797032424, 0.08597306380873884, -0.27458875307347624, -0.21456071221777662, 0.10662388691509311, -0.14132295566695657, 0.11947363370775499, 0.06384714156897231, 0.03232205764136531, -0.12009440820918164, -0.24994234807831658, 0.325458846766163, 0.10036621594732754, 0.3880983682687987, 0.05105548930269751, 0.11551769335154942, 0.02707032191313126, -0.041642640806226566, -0.0008219329492104324, -0.1355716502166946, 0.154202408246188, 0.25851031640671535, 0.18226877879351377, 0.3456314966082573, -0.39011272968174043, -0.2270889900624752, 0.16250432466410777, 0.10548556644300168, 0.06313551624030382, 0.020153873587365857, -0.30080468866085125, 0.07620956297455864, -0.14204820377582852, -0.00466483480095948, -0.12249279224144465, -0.010938291349024935, 0.021817531101193957, -0.3026061030845581, 0.08020853332921185, 0.10780766602520915, 0.0631690524341221, 0.00449354936347597, -0.09714903335340998, 0.022140709852630443, 0.11408214037391272, 0.02395559308669445, 0.01866340778281235, 0.09730296685275706, -0.1627710902041475, -0.09233897611530582, 0.2630247631025585, -0.07948052564170212, -0.26582841270349244, 0.16805356442504985, -0.09458437089977617, -0.12769483556039632, 0.0007185567519627512, 0.20783671987327662, 0.10480712514624677, -0.19035465453582054, -0.004020046231612055, -0.02680729208196598, 0.15904161884364756, 0.06828386882658709, 0.08484173810786821, 0.15176007611761716, 0.17950541111905213, 0.049122227406637235, 0.19322360240409828, -0.1750115097246387, -0.10511110271750526, -0.19031021584841337, -0.12409000842149412, -0.24815670180727134, 0.00329135610002347, -0.11689108664749338, -0.14829678347926925, 0.358115677239204, 0.22573953538032418, 0.23824691476977683, 0.09778541398594495, 0.3458833745257421, 0.11135397228645161, 0.07703740416750819, 0.07030422789764336, 0.2378348814238879, 0.17427841902668165, 0.06541833106259054, -0.18070840555539525, 0.16171521419680424, 0.06999810129742731] |
1,802.05409 | Optimizing Precision for Open-World Website Fingerprinting | Traffic analysis attacks to identify which web page a client is browsing,
using only her packet metadata --- known as website fingerprinting --- has been
proven effective in closed-world experiments against privacy technologies like
Tor. However, due to the base rate fallacy, these attacks have failed in large
open-world settings against clients that visit sensitive pages with a low base
rate. We find that this is because they have poor precision as they were
designed to maximize recall.
In this work, we argue that precision is more important than recall for
open-world website fingerprinting. For this reason, we develop three classes of
{\em precision optimizers}, based on confidence, distance, and ensemble
learning, that can be applied to any classifier to increase precision. We test
them on known website fingerprinting attacks and show significant improvements
in precision. Against a difficult scenario, where the attacker wants to monitor
and distinguish 100 sensitive pages each with a low mean base rate of 0.00001,
our best optimized classifier can achieve a precision of 0.78; the highest
precision of any known attack before optimization was 0.014. We use precise
classifiers to tackle realistic objectives in website fingerprinting, including
selection, identification, and defeating website fingerprinting defenses.
| cs.CR | traffic analysis attacks to identify which web page a client is browsing using only her packet metadata known as website fingerprinting has been proven effective in closedworld experiments against privacy technologies like tor however due to the base rate fallacy these attacks have failed in large openworld settings against clients that visit sensitive pages with a low base rate we find that this is because they have poor precision as they were designed to maximize recall in this work we argue that precision is more important than recall for openworld website fingerprinting for this reason we develop three classes of em precision optimizers based on confidence distance and ensemble learning that can be applied to any classifier to increase precision we test them on known website fingerprinting attacks and show significant improvements in precision against a difficult scenario where the attacker wants to monitor and distinguish 100 sensitive pages each with a low mean base rate of 000001 our best optimized classifier can achieve a precision of 078 the highest precision of any known attack before optimization was 0014 we use precise classifiers to tackle realistic objectives in website fingerprinting including selection identification and defeating website fingerprinting defenses | [['traffic', 'analysis', 'attacks', 'to', 'identify', 'which', 'web', 'page', 'a', 'client', 'is', 'browsing', 'using', 'only', 'her', 'packet', 'metadata', 'known', 'as', 'website', 'fingerprinting', 'has', 'been', 'proven', 'effective', 'in', 'closedworld', 'experiments', 'against', 'privacy', 'technologies', 'like', 'tor', 'however', 'due', 'to', 'the', 'base', 'rate', 'fallacy', 'these', 'attacks', 'have', 'failed', 'in', 'large', 'openworld', 'settings', 'against', 'clients', 'that', 'visit', 'sensitive', 'pages', 'with', 'a', 'low', 'base', 'rate', 'we', 'find', 'that', 'this', 'is', 'because', 'they', 'have', 'poor', 'precision', 'as', 'they', 'were', 'designed', 'to', 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1,802.0541 | Collision of eigenvalues for matrix-valued processes | We examine the probability that at least two eigenvalues of an Hermitian
matrix-valued Gaussian process, collide. In particular, we determine sharp
conditions under which such probability is zero. As an application, we show
that the eigenvalues of a real symmetric matrix-valued fractional Brownian
motion of Hurst parameter $H$, collide when $H<1/2$ and don't collide when
$H>\frac{1}{2}$, while those of a complex Hermitian fractional Brownian motion
collide when $H<\frac{1}{3}$ and don't collide when $H>\frac{1}{3}$. Our
approach is based on the relation between hitting probabilities for Gaussian
processes with the capacity and Hausdorff dimension of measurable sets.
| math.PR | we examine the probability that at least two eigenvalues of an hermitian matrixvalued gaussian process collide in particular we determine sharp conditions under which such probability is zero as an application we show that the eigenvalues of a real symmetric matrixvalued fractional brownian motion of hurst parameter h collide when h12 and dont collide when hfrac12 while those of a complex hermitian fractional brownian motion collide when hfrac13 and dont collide when hfrac13 our approach is based on the relation between hitting probabilities for gaussian processes with the capacity and hausdorff dimension of measurable sets | [['we', 'examine', 'the', 'probability', 'that', 'at', 'least', 'two', 'eigenvalues', 'of', 'an', 'hermitian', 'matrixvalued', 'gaussian', 'process', 'collide', 'in', 'particular', 'we', 'determine', 'sharp', 'conditions', 'under', 'which', 'such', 'probability', 'is', 'zero', 'as', 'an', 'application', 'we', 'show', 'that', 'the', 'eigenvalues', 'of', 'a', 'real', 'symmetric', 'matrixvalued', 'fractional', 'brownian', 'motion', 'of', 'hurst', 'parameter', 'h', 'collide', 'when', 'h12', 'and', 'dont', 'collide', 'when', 'hfrac12', 'while', 'those', 'of', 'a', 'complex', 'hermitian', 'fractional', 'brownian', 'motion', 'collide', 'when', 'hfrac13', 'and', 'dont', 'collide', 'when', 'hfrac13', 'our', 'approach', 'is', 'based', 'on', 'the', 'relation', 'between', 'hitting', 'probabilities', 'for', 'gaussian', 'processes', 'with', 'the', 'capacity', 'and', 'hausdorff', 'dimension', 'of', 'measurable', 'sets']] | [-0.09605714001594798, 0.18239788911336002, -0.06666380581200908, 0.06806481564978496, -0.06924048985942806, -0.17242628391832113, -0.0005542046537524776, 0.4082292953604146, -0.2729155059609758, -0.17544566246337798, 0.07956626525908513, -0.342144244203442, -0.17080156050475412, 0.16574715342568724, -0.0873859830445757, 0.0848802120473824, 0.059724808180410614, 0.12705765538603853, -0.05405820310728526, -0.21918478112079595, 0.3572250800305291, -0.027653779461979867, 0.18366991596688564, -0.014510717618548753, 0.11667983853993447, 0.005129954054657566, 0.020034390589908548, -0.03422001506152906, -0.17884170117521486, 0.05221501996152495, 0.18072253959008344, 0.04983059753369736, 0.28315492130442227, -0.42198524631951984, -0.16419001421646068, 0.20077523175921094, 0.1507001090206598, -0.011337706413561185, 0.012632186508639471, -0.32258616973106796, 0.033418116551872934, -0.12298830665255847, -0.19991316067447004, -0.03925607921065469, 0.09399540338077043, 0.06908718906281712, -0.3301599349630506, 0.09728772894871471, 0.10875390796480995, 0.003182871002507837, -0.058583901013786854, -0.12512283601405982, -0.026322635213574884, 0.09454986654025943, 0.02895495968497064, -0.045943294846648844, 0.11706526159848038, -0.09325737257144953, -0.11916139509136739, 0.32413850474220357, -0.06592998769820521, -0.2859453384421374, 0.188942397574551, -0.2512524193250819, -0.11378925216237182, 0.1467296026745125, 0.18156386595219373, 0.1293947429837365, -0.08797468851185648, 0.1319985596150601, -0.031787609921670276, 0.08922454300459082, 0.15426904456199783, 0.005650199371341028, 0.1358607599585268, 0.06081565478081374, 0.16359640990236873, 0.10334238650669393, -0.09003412534458269, -0.14560590106013574, -0.3347401554059041, -0.18877936730063274, -0.2408291273211178, 0.10784912540969488, -0.1525829789273159, -0.16937350687619887, 0.2616160378399256, 0.13071609187479083, 0.2626061770868929, 0.10727787975736551, 0.24579886411757845, 0.16792530802225597, -0.05921244309528878, 0.10254205989798433, 0.129812582895944, 0.11296429006186755, 0.04045316318883316, -0.15446581080751984, 0.026702527040125507, 0.06736562919900997] |
1,802.05411 | Selecting the Best in GANs Family: a Post Selection Inference Framework | "Which Generative Adversarial Networks (GANs) generates the most plausible
images?" has been a frequently asked question among researchers. To address
this problem, we first propose an \emph{incomplete} U-statistics estimate of
maximum mean discrepancy $\mathrm{MMD}_{inc}$ to measure the distribution
discrepancy between generated and real images. $\mathrm{MMD}_{inc}$ enjoys the
advantages of asymptotic normality, computation efficiency, and model
agnosticity. We then propose a GANs analysis framework to select and test the
"best" member in GANs family using the Post Selection Inference (PSI) with
$\mathrm{MMD}_{inc}$. In the experiments, we adopt the proposed framework on 7
GANs variants and compare their $\mathrm{MMD}_{inc}$ scores.
| cs.LG stat.ML | which generative adversarial networks gans generates the most plausible images has been a frequently asked question among researchers to address this problem we first propose an emphincomplete ustatistics estimate of maximum mean discrepancy mathrmmmd_inc to measure the distribution discrepancy between generated and real images mathrmmmd_inc enjoys the advantages of asymptotic normality computation efficiency and model agnosticity we then propose a gans analysis framework to select and test the best member in gans family using the post selection inference psi with mathrmmmd_inc in the experiments we adopt the proposed framework on 7 gans variants and compare their mathrmmmd_inc scores | [['which', 'generative', 'adversarial', 'networks', 'gans', 'generates', 'the', 'most', 'plausible', 'images', 'has', 'been', 'a', 'frequently', 'asked', 'question', 'among', 'researchers', 'to', 'address', 'this', 'problem', 'we', 'first', 'propose', 'an', 'emphincomplete', 'ustatistics', 'estimate', 'of', 'maximum', 'mean', 'discrepancy', 'mathrmmmd_inc', 'to', 'measure', 'the', 'distribution', 'discrepancy', 'between', 'generated', 'and', 'real', 'images', 'mathrmmmd_inc', 'enjoys', 'the', 'advantages', 'of', 'asymptotic', 'normality', 'computation', 'efficiency', 'and', 'model', 'agnosticity', 'we', 'then', 'propose', 'a', 'gans', 'analysis', 'framework', 'to', 'select', 'and', 'test', 'the', 'best', 'member', 'in', 'gans', 'family', 'using', 'the', 'post', 'selection', 'inference', 'psi', 'with', 'mathrmmmd_inc', 'in', 'the', 'experiments', 'we', 'adopt', 'the', 'proposed', 'framework', 'on', '7', 'gans', 'variants', 'and', 'compare', 'their', 'mathrmmmd_inc', 'scores']] | [0.012199500254019327, -0.054985831235865486, -0.10430173272478212, 0.15078385612452114, -0.08833138469952284, -0.14232637729380548, 0.04670351635083825, 0.4515353872527167, -0.24275226112978399, -0.3481590078913213, 0.03370624877867702, -0.28944098726683054, -0.18658333145545258, 0.12309095948771334, -0.1627794169198563, 0.09816662437575341, 0.08967066236733238, 0.009761273656904544, -0.04006967044521853, -0.29817228743172797, 0.2951003885923971, 0.0419866462458962, 0.3572233749803194, -0.024475670286415854, 0.1448729032885821, -0.054154698712962496, -0.0013504232155139913, -0.026595015424428527, -0.138641715508204, 0.17760478015196962, 0.2370809756637042, 0.24699158393585882, 0.390552255603456, -0.3616893178562528, -0.19733524814094464, 0.173904111685673, 0.10855551187270657, 0.05965957600868201, -0.06079446016579804, -0.29786596703583124, 0.12983363706464926, -0.14995619745657435, -0.01993260025670848, -0.1302469141142846, -0.03752513595415068, -0.025726645013567098, -0.32244678727859993, 0.03516640434924936, 0.06051676450585274, 0.06211982610318618, -0.028002124728596547, -0.12399931266404611, 0.03153190957670359, 0.11507453949949176, 0.08369156416654394, 0.021819706422939282, 0.0519499027064627, -0.13519840946027376, -0.16779543877550468, 0.32398424249024793, -0.07162737058110766, -0.18293399931430893, 0.15339774130507536, -0.08076192025988142, -0.1464033465947687, 0.023857184100089614, 0.22519991288595287, 0.1627416387872444, -0.17108334067845285, 0.018815845114265366, -0.05337311510601532, 0.11574453442866348, 0.07569415658741191, -0.033017164345869085, 0.15275491131757646, 0.1929233898264692, 0.009213802999980057, 0.15553674042301693, -0.1662550795050434, -0.07658414078933984, -0.22337741819546394, -0.10568665028501724, -0.2011934296640846, 0.011976370867581788, -0.09728160310121368, -0.14392670728842316, 0.40420455389569715, 0.24824590621919362, 0.22104176024896735, 0.11376529496724803, 0.2908952424360305, 0.0562503830404933, 0.054975019245811725, 0.10897242367152393, 0.18455241621980809, 0.12605970583318435, 0.04506113914504033, -0.14370898432434373, 0.11795967048725363, 0.06916693527942773] |
1,802.05412 | NtMalDetect: A Machine Learning Approach to Malware Detection Using
Native API System Calls | As computing systems become increasingly advanced and as users increasingly
engage themselves in technology, security has never been a greater concern. In
malware detection, static analysis, the method of analyzing potentially
malicious files, has been the prominent approach. This approach, however,
quickly falls short as malicious programs become more advanced and adopt the
capabilities of obfuscating its binaries to execute the same malicious
functions, making static analysis extremely difficult for newer variants. The
approach assessed in this paper is a novel dynamic malware analysis method,
which may generalize better than static analysis to newer variants. Inspired by
recent successes in Natural Language Processing (NLP), widely used document
classification techniques were assessed in detecting malware by doing such
analysis on system calls, which contain useful information about the operation
of a program as requests that the program makes of the kernel. Features
considered are extracted from system call traces of benign and malicious
programs, and the task to classify these traces is treated as a binary document
classification task of system call traces. The system call traces were
processed to remove the parameters to only leave the system call function
names. The features were grouped into various n-grams and weighted with Term
Frequency-Inverse Document Frequency. This paper shows that Linear Support
Vector Machines (SVM) optimized by Stochastic Gradient Descent and the
traditional Coordinate Descent on the Wolfe Dual form of the SVM are effective
in this approach, achieving a highest of 96% accuracy with 95% recall score.
Additional contributions include the identification of significant system call
sequences that could be avenues for further research.
| cs.CR cs.CL | as computing systems become increasingly advanced and as users increasingly engage themselves in technology security has never been a greater concern in malware detection static analysis the method of analyzing potentially malicious files has been the prominent approach this approach however quickly falls short as malicious programs become more advanced and adopt the capabilities of obfuscating its binaries to execute the same malicious functions making static analysis extremely difficult for newer variants the approach assessed in this paper is a novel dynamic malware analysis method which may generalize better than static analysis to newer variants inspired by recent successes in natural language processing nlp widely used document classification techniques were assessed in detecting malware by doing such analysis on system calls which contain useful information about the operation of a program as requests that the program makes of the kernel features considered are extracted from system call traces of benign and malicious programs and the task to classify these traces is treated as a binary document classification task of system call traces the system call traces were processed to remove the parameters to only leave the system call function names the features were grouped into various ngrams and weighted with term frequencyinverse document frequency this paper shows that linear support vector machines svm optimized by stochastic gradient descent and the traditional coordinate descent on the wolfe dual form of the svm are effective in this approach achieving a highest of 96 accuracy with 95 recall score additional contributions include the identification of significant system call sequences that could be avenues for further research | [['as', 'computing', 'systems', 'become', 'increasingly', 'advanced', 'and', 'as', 'users', 'increasingly', 'engage', 'themselves', 'in', 'technology', 'security', 'has', 'never', 'been', 'a', 'greater', 'concern', 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1,802.05413 | Inverse Gauss curvature flows with free boundaries in a cone | We consider strictly convex hypersurfaces with the boundary which meets a
strictly convex cone perpendicularly. We prove that if these hypersurfaces
expand inside this cone, driven by the power of the Gauss curvature, then the
evolution exists for all the time and the evolving hypersurfaces converge
smoothly to a piece of round sphere after rescaling.
| math.DG math.AP | we consider strictly convex hypersurfaces with the boundary which meets a strictly convex cone perpendicularly we prove that if these hypersurfaces expand inside this cone driven by the power of the gauss curvature then the evolution exists for all the time and the evolving hypersurfaces converge smoothly to a piece of round sphere after rescaling | [['we', 'consider', 'strictly', 'convex', 'hypersurfaces', 'with', 'the', 'boundary', 'which', 'meets', 'a', 'strictly', 'convex', 'cone', 'perpendicularly', 'we', 'prove', 'that', 'if', 'these', 'hypersurfaces', 'expand', 'inside', 'this', 'cone', 'driven', 'by', 'the', 'power', 'of', 'the', 'gauss', 'curvature', 'then', 'the', 'evolution', 'exists', 'for', 'all', 'the', 'time', 'and', 'the', 'evolving', 'hypersurfaces', 'converge', 'smoothly', 'to', 'a', 'piece', 'of', 'round', 'sphere', 'after', 'rescaling']] | [-0.1813767329535701, 0.1271786763925444, -0.09761671032756566, -0.026643335795961322, -0.07080379970879717, -0.16285568655214527, -0.007317258248274976, 0.3668256204913963, -0.31065349646589974, -0.14057584059509365, 0.14504767765545032, -0.305633490329439, -0.10204988443716005, 0.14249882951209492, -0.08975570613349027, 0.02188869451426647, 0.06903527279130438, 0.05004608344964006, -0.11101064661263742, -0.302230714739893, 0.434532599455931, -0.06543618943542243, 0.18440383034334942, 0.08420652860606259, 0.13723425363105807, 0.010786185447465289, 0.05707504756071351, 0.10868518660691652, -0.18807382695866345, 0.08913036074320024, 0.18780756125395948, 0.13292951080524787, 0.2841845278712836, -0.43869484134695746, -0.21686327509920705, 0.17711342632431876, 0.1254698093790053, -0.027324408557350664, -0.045512436097487806, -0.22402036748826504, 0.07373588832349262, -0.020794785666194828, -0.25125834329731084, -0.015539818726988001, -0.007984415136955001, 0.04294951599599286, -0.1942311135734516, 0.031324243668297475, 0.13492932096123694, -0.007914064333520153, -0.07418862210417336, -0.032307914187285035, -0.07911776795305989, 0.03573557632890614, 0.05496154147073288, 0.13246569865467875, 0.13339288961988957, -0.030691100398755885, -0.03729581168260087, 0.36437204243107274, -0.05429744524034587, -0.29292639356254685, 0.07204860428517515, -0.20597943180156025, -0.07145703470503742, 0.16093032878230917, 0.16156449136747555, 0.18211501428688115, -0.07644548814066432, 0.1417852835719135, -0.03193433606277474, 0.0936771060763435, 0.1407547020607374, -0.08072764572941445, 0.21128690747798168, 0.07416262582621791, 0.1944174136966467, 0.16123918531970544, -0.02450543359683996, -0.12243946916847066, -0.3871697177433155, -0.21414203456687655, -0.16040538268544796, 0.13489353951405395, -0.1406093200327384, -0.21925848139957949, 0.38209862458434973, 0.011143227179788731, 0.21859917782924393, 0.2059435411631553, 0.30469222008462316, 0.06053404167971828, 0.026457344300367617, 0.2179041767027229, 0.23227447081695904, 0.10480230870995332, 0.050204765424132346, -0.15571160700002853, -0.019722040776502, 0.11021985144100406] |
1,802.05414 | Efficiency of Centrifugal Mechanism in Producing PeV Neutrinos From
Active Galactic Nuclei | A several-step theoretical model is constructed to trace the origin of ultra
high energy (UHE) $[1-2]$PeV neutrinos detected, recently, by the IceCube
collaboration. Protons in the AGN magnetosphere, experiencing different
gravitational centrifugal force, provide free energy for the parametric
excitation of Langmuir waves via a generalized two-stream instability. Landau
damping of these waves, outside the AGN magnetosphere, can accelerate protons
to ultra high energies. The ultimate source for this mechanism, the
Langmuir-Landau-Centrifugal-Drive (LLCD), is the gravitational energy of the
compact object. The LLCD generated UHE protons provide the essential ingredient
in the creation of UHE neutrinos via appropriate hadronic reactions; protons of
energy~ $10^{17}$eV can be generated in the plasmas surrounding AGN with
bolometric luminosities of the order of $10^{43}$ergs s$^{-1}$. By estimating
the diffusive energy flux of extragalactic neutrinos in the energy interval
$[1-2]$PeV, we find that an acceptably small fraction $0.003\%$ of the total
bolometric luminosity will suffice to create the observed fluxes of
extragalactic ultra-high energy neutrinos.
| astro-ph.HE astro-ph.GA | a severalstep theoretical model is constructed to trace the origin of ultra high energy uhe 12pev neutrinos detected recently by the icecube collaboration protons in the agn magnetosphere experiencing different gravitational centrifugal force provide free energy for the parametric excitation of langmuir waves via a generalized twostream instability landau damping of these waves outside the agn magnetosphere can accelerate protons to ultra high energies the ultimate source for this mechanism the langmuirlandaucentrifugaldrive llcd is the gravitational energy of the compact object the llcd generated uhe protons provide the essential ingredient in the creation of uhe neutrinos via appropriate hadronic reactions protons of energy 1017ev can be generated in the plasmas surrounding agn with bolometric luminosities of the order of 1043ergs s1 by estimating the diffusive energy flux of extragalactic neutrinos in the energy interval 12pev we find that an acceptably small fraction 0003 of the total bolometric luminosity will suffice to create the observed fluxes of extragalactic ultrahigh energy neutrinos | [['a', 'severalstep', 'theoretical', 'model', 'is', 'constructed', 'to', 'trace', 'the', 'origin', 'of', 'ultra', 'high', 'energy', 'uhe', '12pev', 'neutrinos', 'detected', 'recently', 'by', 'the', 'icecube', 'collaboration', 'protons', 'in', 'the', 'agn', 'magnetosphere', 'experiencing', 'different', 'gravitational', 'centrifugal', 'force', 'provide', 'free', 'energy', 'for', 'the', 'parametric', 'excitation', 'of', 'langmuir', 'waves', 'via', 'a', 'generalized', 'twostream', 'instability', 'landau', 'damping', 'of', 'these', 'waves', 'outside', 'the', 'agn', 'magnetosphere', 'can', 'accelerate', 'protons', 'to', 'ultra', 'high', 'energies', 'the', 'ultimate', 'source', 'for', 'this', 'mechanism', 'the', 'langmuirlandaucentrifugaldrive', 'llcd', 'is', 'the', 'gravitational', 'energy', 'of', 'the', 'compact', 'object', 'the', 'llcd', 'generated', 'uhe', 'protons', 'provide', 'the', 'essential', 'ingredient', 'in', 'the', 'creation', 'of', 'uhe', 'neutrinos', 'via', 'appropriate', 'hadronic', 'reactions', 'protons', 'of', 'energy', '1017ev', 'can', 'be', 'generated', 'in', 'the', 'plasmas', 'surrounding', 'agn', 'with', 'bolometric', 'luminosities', 'of', 'the', 'order', 'of', '1043ergs', 's1', 'by', 'estimating', 'the', 'diffusive', 'energy', 'flux', 'of', 'extragalactic', 'neutrinos', 'in', 'the', 'energy', 'interval', '12pev', 'we', 'find', 'that', 'an', 'acceptably', 'small', 'fraction', '0003', 'of', 'the', 'total', 'bolometric', 'luminosity', 'will', 'suffice', 'to', 'create', 'the', 'observed', 'fluxes', 'of', 'extragalactic', 'ultrahigh', 'energy', 'neutrinos']] | [-0.10103914823818516, 0.2865882140877029, -0.02864960304388849, 0.21007174468605308, -0.0658553413073109, -0.020137481866162512, 0.006180276722320682, 0.36471274590676095, -0.1798924615912067, -0.3453621568714643, -0.0616217783826869, -0.28433334632692014, 0.07494663488869736, 0.2017436239206196, 0.051456413334598404, -0.012099291252383551, 0.06565096146009813, -0.04596619788434828, 0.024261683215048502, -0.17194176950588724, 0.28780407136209407, 0.2312294604875111, 0.22068640358697672, 0.09121494324176342, 0.13361974704297042, -0.08315471487343554, -0.022586123602065657, -0.09942330086192527, -0.10958424146014437, 0.07118536142160656, 0.2550126459727717, 0.08833597502036189, 0.18263164111534014, -0.462640451731511, -0.2643074459258721, 0.14996142927617706, 0.15300401455160328, 0.03156357908367433, -0.08659588787462805, -0.2671951137622214, 0.0339990036604418, -0.22642908279191365, -0.18747548302847214, 0.03931460071161583, -0.02330874399941221, 0.08145282325613035, -0.24486394698259892, 0.11088130490660861, 0.010589242784318501, -0.028835307204999126, -0.13880915555183765, -0.0871902705508877, -0.04895422098456652, 0.02801176081351065, 0.12410893134527852, 0.05790155320488787, 0.18680033620217112, -0.15890230763062543, -0.0690954608620483, 0.3971928020681454, -0.061060328977696546, -0.07822684245486441, 0.1646378207940445, -0.19371001955572298, -0.09462447259907744, 0.26652525589111953, 0.18895306410993656, 0.06493231740344751, -0.18162327896283528, 0.04483714647887444, 0.008577551768318592, 0.12525050156309825, 0.05562324472551851, 0.051159173665967374, 0.3003647220623377, 0.15082187400888894, 0.03818190421957474, 0.06938014226450052, -0.2066169425659184, 0.05495170399232151, -0.3286746607945216, -0.0987855217951749, -0.1621299529563055, 0.13038810184406865, -0.08480546040635846, -0.1163492611815016, 0.37699058553046705, 0.11224139444288109, 0.13943025327133401, -0.021089577521257973, 0.32399426501655537, 0.12799056551295224, 0.025762531195779318, 0.13192603138106113, 0.3922421245037445, 0.15342489360716774, 0.10988299346108643, -0.2602739498881845, -0.0026184544662715167, 0.08087102722304014] |
1,802.05415 | Teaching Machines to Code: Neural Markup Generation with Visual
Attention | We present a neural transducer model with visual attention that learns to
generate LaTeX markup of a real-world math formula given its image. Applying
sequence modeling and transduction techniques that have been very successful
across modalities such as natural language, image, handwriting, speech and
audio; we construct an image-to-markup model that learns to produce
syntactically and semantically correct LaTeX markup code over 150 words long
and achieves a BLEU score of 89%; improving upon the previous state-of-art for
the Im2Latex problem. We also demonstrate with heat-map visualization how
attention helps in interpreting the model and can pinpoint (detect and
localize) symbols on the image accurately despite having been trained without
any bounding box data.
| cs.LG cs.CL cs.CV cs.NE | we present a neural transducer model with visual attention that learns to generate latex markup of a realworld math formula given its image applying sequence modeling and transduction techniques that have been very successful across modalities such as natural language image handwriting speech and audio we construct an imagetomarkup model that learns to produce syntactically and semantically correct latex markup code over 150 words long and achieves a bleu score of 89 improving upon the previous stateofart for the im2latex problem we also demonstrate with heatmap visualization how attention helps in interpreting the model and can pinpoint detect and localize symbols on the image accurately despite having been trained without any bounding box data | [['we', 'present', 'a', 'neural', 'transducer', 'model', 'with', 'visual', 'attention', 'that', 'learns', 'to', 'generate', 'latex', 'markup', 'of', 'a', 'realworld', 'math', 'formula', 'given', 'its', 'image', 'applying', 'sequence', 'modeling', 'and', 'transduction', 'techniques', 'that', 'have', 'been', 'very', 'successful', 'across', 'modalities', 'such', 'as', 'natural', 'language', 'image', 'handwriting', 'speech', 'and', 'audio', 'we', 'construct', 'an', 'imagetomarkup', 'model', 'that', 'learns', 'to', 'produce', 'syntactically', 'and', 'semantically', 'correct', 'latex', 'markup', 'code', 'over', '150', 'words', 'long', 'and', 'achieves', 'a', 'bleu', 'score', 'of', '89', 'improving', 'upon', 'the', 'previous', 'stateofart', 'for', 'the', 'im2latex', 'problem', 'we', 'also', 'demonstrate', 'with', 'heatmap', 'visualization', 'how', 'attention', 'helps', 'in', 'interpreting', 'the', 'model', 'and', 'can', 'pinpoint', 'detect', 'and', 'localize', 'symbols', 'on', 'the', 'image', 'accurately', 'despite', 'having', 'been', 'trained', 'without', 'any', 'bounding', 'box', 'data']] | [0.0072508052645779185, -0.015049987840419428, -0.06395133217197976, 0.08053122815181268, -0.16575604022779902, -0.21443662790781154, 0.015571522641818904, 0.4947551835378233, -0.24796018922851154, -0.3833241327608578, 0.024036605457825187, -0.3149565944331698, -0.16989281831826833, 0.21542397398661706, -0.16172077163771195, 0.08233158282434618, 0.13344684460857284, 0.08551466302014887, -0.036891460778341365, -0.2610599513552318, 0.229823785759176, 0.03535268549603643, 0.3130279273630419, 0.026455606402513304, 0.16946331079608562, -0.022646244486427998, -0.04756394896165667, -0.05921150545847403, -0.040025896341765996, 0.16394594998057851, 0.3681973700877279, 0.2545257971339327, 0.267107303258464, -0.40542295676589546, -0.22791623747408657, 0.07769256954114619, 0.12841437230651667, 0.1455296321905085, -0.047335766481410246, -0.36399290414660107, 0.13092162123367806, -0.17880586208255927, 0.06558023329125717, -0.1697251387023633, 0.027602678842543225, -0.024440872409676167, -0.2630734255955239, 0.009079814244874538, 0.14053609390975907, 0.10503340342048821, -0.05714450263206215, -0.06243307644035667, -0.01632295037935754, 0.2174845252926129, 0.0007439472215732426, 0.12347602959822065, 0.13575929993281274, -0.16280492785309825, -0.13576971156414533, 0.3740018764344443, -0.07016947262621086, -0.24741239013383165, 0.2094914350808332, -0.0013525295861264957, -0.11839036331678342, 0.0999757395683056, 0.209386753342447, 0.08486659793873384, -0.15023241973748164, 0.026535028097506647, -0.07287632434495858, 0.2804914986897659, 0.15432571366545744, -0.023709406092945886, 0.2200968447858551, 0.22133400731750694, -0.06996441176202747, 0.2052727121294343, -0.13483079797463557, -0.01427380705717951, -0.1739377639938279, -0.1107829045707019, -0.16775862300502403, -0.056039280173925884, -0.07851782663497683, -0.17707345956296194, 0.42371524691614987, 0.2811137840014583, 0.2059107323044113, 0.16366528902368113, 0.31720637262333184, 0.007016082359768916, 0.12903364598501607, 0.056056199000782465, 0.09933276202251104, -0.011311259158121954, 0.13356208708501072, -0.12207086354672876, 0.10973059484136424, 0.06821236413504396] |
1,802.05416 | Magnetic moments of the lowest-lying singly heavy baryons | A light baryon is viewed as $N_c$ valence quarks bound by meson mean fields
in the large $N_c$ limit. In much the same way a singly heavy baryon is
regarded as $N_c-1$ valence quarks bound by the same mean fields, which makes
it possible to use the properties of light baryons to investigate those of the
heavy baryons. A heavy quark being regarded as a static color source in the
limit of the infinitely heavy quark mass, the magnetic moments of the heavy
baryon are determined entirely by the chiral soliton consisting of a
light-quark pair. The magnetic moments of the baryon sextet are obtained by
using the parameters fixed in the light-baryon sector. In this mean-field
approach, the numerical results of the magnetic moments of the baryon sextet
with spin $3/2$ are just 3/2 larger than those with spin $1/2$. The magnetic
moments of the bottom baryons are the same as those of the corresponding
charmed baryons.
| hep-ph hep-ex nucl-th | a light baryon is viewed as n_c valence quarks bound by meson mean fields in the large n_c limit in much the same way a singly heavy baryon is regarded as n_c1 valence quarks bound by the same mean fields which makes it possible to use the properties of light baryons to investigate those of the heavy baryons a heavy quark being regarded as a static color source in the limit of the infinitely heavy quark mass the magnetic moments of the heavy baryon are determined entirely by the chiral soliton consisting of a lightquark pair the magnetic moments of the baryon sextet are obtained by using the parameters fixed in the lightbaryon sector in this meanfield approach the numerical results of the magnetic moments of the baryon sextet with spin 32 are just 32 larger than those with spin 12 the magnetic moments of the bottom baryons are the same as those of the corresponding charmed baryons | [['a', 'light', 'baryon', 'is', 'viewed', 'as', 'n_c', 'valence', 'quarks', 'bound', 'by', 'meson', 'mean', 'fields', 'in', 'the', 'large', 'n_c', 'limit', 'in', 'much', 'the', 'same', 'way', 'a', 'singly', 'heavy', 'baryon', 'is', 'regarded', 'as', 'n_c1', 'valence', 'quarks', 'bound', 'by', 'the', 'same', 'mean', 'fields', 'which', 'makes', 'it', 'possible', 'to', 'use', 'the', 'properties', 'of', 'light', 'baryons', 'to', 'investigate', 'those', 'of', 'the', 'heavy', 'baryons', 'a', 'heavy', 'quark', 'being', 'regarded', 'as', 'a', 'static', 'color', 'source', 'in', 'the', 'limit', 'of', 'the', 'infinitely', 'heavy', 'quark', 'mass', 'the', 'magnetic', 'moments', 'of', 'the', 'heavy', 'baryon', 'are', 'determined', 'entirely', 'by', 'the', 'chiral', 'soliton', 'consisting', 'of', 'a', 'lightquark', 'pair', 'the', 'magnetic', 'moments', 'of', 'the', 'baryon', 'sextet', 'are', 'obtained', 'by', 'using', 'the', 'parameters', 'fixed', 'in', 'the', 'lightbaryon', 'sector', 'in', 'this', 'meanfield', 'approach', 'the', 'numerical', 'results', 'of', 'the', 'magnetic', 'moments', 'of', 'the', 'baryon', 'sextet', 'with', 'spin', '32', 'are', 'just', '32', 'larger', 'than', 'those', 'with', 'spin', '12', 'the', 'magnetic', 'moments', 'of', 'the', 'bottom', 'baryons', 'are', 'the', 'same', 'as', 'those', 'of', 'the', 'corresponding', 'charmed', 'baryons']] | [-0.09723909547599623, 0.328701865292726, -0.05340207106716742, 0.1544219709975539, -0.023441823532427603, -0.0617576006452164, 0.07817240509122045, 0.27871693563470734, -0.17084657481600402, -0.3027722602828017, -0.016145484605244113, -0.32666821776641697, 0.05738746315164232, 0.07122096088066508, 0.08415617454278318, 0.04199129500709285, -0.008411946128818053, 0.11104154277038819, -0.04574712508548157, -0.23036619226294983, 0.31716693735009505, -0.05062315172126776, 0.2247420596607218, 0.08662021288477309, 0.010877167375734713, -0.010242963439631688, 0.006173749981504052, -0.01936776122031144, -0.03133907767684682, 0.07576971839600584, 0.15267702956798876, 0.00681665928185552, 0.16613178640969475, -0.37941930749559705, -0.1653557023169072, 0.10312807783956014, 0.183099920834898, 0.19501640770327935, -0.06402900397756926, -0.3021500608235409, 0.1227884709339919, -0.16500956179644866, -0.23163273387081637, -0.09240509972530359, -0.034409322811267046, 0.04644773193470002, -0.29616900971100396, 0.12969357180821744, -0.008458193885221418, 0.04208543553323591, -0.017142956967383056, -0.2565902097480772, -0.12048526687689032, 0.05715973821728007, 0.12730858499579178, 0.08879787875195712, 0.13125244672763856, -0.19251117978651736, -0.13395857066505507, 0.4461272922875006, -0.11158837204037567, -0.16097743578363488, 0.09517314657568932, -0.18519207487425096, -0.10748359735442113, 0.10296333602723913, 0.1626501766540393, 0.13117369708510798, -0.17305196588269517, 0.09248727767527858, -0.1038862747063601, 0.15509714385423856, 0.06601062020923518, 0.10200914291700325, 0.2871269569850138, 0.16912304902967962, -0.01535861878184295, 0.09257645891607987, -0.07002901579135487, -0.07208384347444435, -0.32037855985374014, -0.10462876946644716, -0.1937728660251898, 0.050955552097905095, -0.10980687820340825, -0.13728773613354261, 0.3979480891853948, 0.05446004352968516, 0.25394664938809186, -0.031487591726015784, 0.3150152725556606, 0.0947428883931635, 0.10291699426398979, 0.10831545316319488, 0.2870994050385831, 0.2636895648371782, 0.13723981479047268, -0.2726319983728301, -0.043749033219970854, 0.10185496184291153] |
1,802.05417 | Extraction of $|V_{cb}|$ from two-body hadronic B decays | We propose a method of extracting the Cabibbo-Kobayashi-Maskawa matrix
element $|V_{cb}|$ from two-body hadronic decay processes of $B\to DK$ with
precisely determined form factors of $B$ meson semi-leptonic decays. The
amplitude $\mathcal{M}(\bar{B}^0 \to D^+ K^-)$ which does not include the
effect of hadronic final state interactions can be theoretically evaluated by
using factorization and form factors of semi-leptonic B decays. We can obtain
all the amplitudes in an isospin relation $\mathcal{A}(B^-\to D^0K^-)
=\mathcal{A}(\bar{B}^0\to D^+K^{-})+\mathcal{A}(\bar{B}^0\to D^{0}\bar{K}^0)$
including the effect of hadronic final state interactions as well as $|V_{cb}|$
using the experimental data of branching fractions of these three processes
with a truncation of the states which contribute to the hadronic final state
interactions. The extracted value of $|V_{cb}|$ is $(37\pm 6)\times 10^{-3}$.
The decay processes of $B\to DK^{*}$ and $B\to D^{*}K$ can also be used in the
same way and the extracted values of $|V_{cb}|$ are $(41\pm 7)\times 10^{-3}$
and $(42\pm 9)\times 10^{-3}$, respectively. This method becomes possible by
virtue of recent precise determinations of the form factors of semi-leptonic B
decays. The uncertainties of $|V_{cb}|$ by this method are expected to be
reduced by the results of future B-factory experiments and lattice
calculations.
| hep-ph | we propose a method of extracting the cabibbokobayashimaskawa matrix element v_cb from twobody hadronic decay processes of bto dk with precisely determined form factors of b meson semileptonic decays the amplitude mathcalmbarb0 to d k which does not include the effect of hadronic final state interactions can be theoretically evaluated by using factorization and form factors of semileptonic b decays we can obtain all the amplitudes in an isospin relation mathcalabto d0k mathcalabarb0to dkmathcalabarb0to d0bark0 including the effect of hadronic final state interactions as well as v_cb using the experimental data of branching fractions of these three processes with a truncation of the states which contribute to the hadronic final state interactions the extracted value of v_cb is 37pm 6times 103 the decay processes of bto dk and bto dk can also be used in the same way and the extracted values of v_cb are 41pm 7times 103 and 42pm 9times 103 respectively this method becomes possible by virtue of recent precise determinations of the form factors of semileptonic b decays the uncertainties of v_cb by this method are expected to be reduced by the results of future bfactory experiments and lattice calculations | [['we', 'propose', 'a', 'method', 'of', 'extracting', 'the', 'cabibbokobayashimaskawa', 'matrix', 'element', 'v_cb', 'from', 'twobody', 'hadronic', 'decay', 'processes', 'of', 'bto', 'dk', 'with', 'precisely', 'determined', 'form', 'factors', 'of', 'b', 'meson', 'semileptonic', 'decays', 'the', 'amplitude', 'mathcalmbarb0', 'to', 'd', 'k', 'which', 'does', 'not', 'include', 'the', 'effect', 'of', 'hadronic', 'final', 'state', 'interactions', 'can', 'be', 'theoretically', 'evaluated', 'by', 'using', 'factorization', 'and', 'form', 'factors', 'of', 'semileptonic', 'b', 'decays', 'we', 'can', 'obtain', 'all', 'the', 'amplitudes', 'in', 'an', 'isospin', 'relation', 'mathcalabto', 'd0k', 'mathcalabarb0to', 'dkmathcalabarb0to', 'd0bark0', 'including', 'the', 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1,802.05418 | Dual Garside structures and Coxeter sortable elements | In Artin-Tits groups attached to Coxeter groups of spherical type, we give a
combinatorial formula to express the simple elements of the dual braid monoids
in the classical Artin generators. Every simple dual braid is obtained by
lifting an $S$-reduced expression of its image in the Coxeter group, in a way
which involves Reading's $c$-sortable elements. It has as an immediate
consequence that simple dual braids are Mikado braids (the known proofs of this
result either require topological realizations of the Artin groups or
categorification techniques), and hence that their images in the Iwahori-Hecke
algebras have positivity properties. In the classical types, this requires to
give an explicit description of the inverse of Reading's bijection from
$c$-sortable elements to noncrossing partitions of a Coxeter element $c$, which
might be of independent interest. The bijections are described in terms of the
noncrossing partition models in these types. While the proof of the formula is
case-by-case, it is entirely combinatorial and we develop an approach which
reduces a uniform proof to uniformly proving a lemma about inversion sets of
$c$-sortable elements.
| math.GR math.CO math.RT | in artintits groups attached to coxeter groups of spherical type we give a combinatorial formula to express the simple elements of the dual braid monoids in the classical artin generators every simple dual braid is obtained by lifting an sreduced expression of its image in the coxeter group in a way which involves readings csortable elements it has as an immediate consequence that simple dual braids are mikado braids the known proofs of this result either require topological realizations of the artin groups or categorification techniques and hence that their images in the iwahorihecke algebras have positivity properties in the classical types this requires to give an explicit description of the inverse of readings bijection from csortable elements to noncrossing partitions of a coxeter element c which might be of independent interest the bijections are described in terms of the noncrossing partition models in these types while the proof of the formula is casebycase it is entirely combinatorial and we develop an approach which reduces a uniform proof to uniformly proving a lemma about inversion sets of csortable elements | [['in', 'artintits', 'groups', 'attached', 'to', 'coxeter', 'groups', 'of', 'spherical', 'type', 'we', 'give', 'a', 'combinatorial', 'formula', 'to', 'express', 'the', 'simple', 'elements', 'of', 'the', 'dual', 'braid', 'monoids', 'in', 'the', 'classical', 'artin', 'generators', 'every', 'simple', 'dual', 'braid', 'is', 'obtained', 'by', 'lifting', 'an', 'sreduced', 'expression', 'of', 'its', 'image', 'in', 'the', 'coxeter', 'group', 'in', 'a', 'way', 'which', 'involves', 'readings', 'csortable', 'elements', 'it', 'has', 'as', 'an', 'immediate', 'consequence', 'that', 'simple', 'dual', 'braids', 'are', 'mikado', 'braids', 'the', 'known', 'proofs', 'of', 'this', 'result', 'either', 'require', 'topological', 'realizations', 'of', 'the', 'artin', 'groups', 'or', 'categorification', 'techniques', 'and', 'hence', 'that', 'their', 'images', 'in', 'the', 'iwahorihecke', 'algebras', 'have', 'positivity', 'properties', 'in', 'the', 'classical', 'types', 'this', 'requires', 'to', 'give', 'an', 'explicit', 'description', 'of', 'the', 'inverse', 'of', 'readings', 'bijection', 'from', 'csortable', 'elements', 'to', 'noncrossing', 'partitions', 'of', 'a', 'coxeter', 'element', 'c', 'which', 'might', 'be', 'of', 'independent', 'interest', 'the', 'bijections', 'are', 'described', 'in', 'terms', 'of', 'the', 'noncrossing', 'partition', 'models', 'in', 'these', 'types', 'while', 'the', 'proof', 'of', 'the', 'formula', 'is', 'casebycase', 'it', 'is', 'entirely', 'combinatorial', 'and', 'we', 'develop', 'an', 'approach', 'which', 'reduces', 'a', 'uniform', 'proof', 'to', 'uniformly', 'proving', 'a', 'lemma', 'about', 'inversion', 'sets', 'of', 'csortable', 'elements']] | [-0.1307552148247258, 0.10331489959453086, -0.11339612500699567, 0.06235051531347268, -0.12312119622097424, -0.12559097579005435, 0.04585943028090124, 0.3303344371843706, -0.35357565710875677, -0.25542883719239134, 0.07115325626911952, -0.24522274230957408, -0.1398366738118629, 0.19039612854019938, -0.18989411008765325, -0.06363517025471246, 0.05259661892824021, 0.08577682809731557, -0.09992269528472177, -0.25372190660472665, 0.3203089810813662, -0.018517510898756594, 0.24365676856427088, 0.006929444714208667, 0.07894825070227883, 0.025518812205161105, -0.058773130364192835, -0.024385010530607085, -0.12949884738133804, 0.14447827880098119, 0.32676816148987303, 0.09071222665418675, 0.16974952921344574, -0.4134687530758118, -0.08093838962422831, 0.15024736632522342, 0.1544889300705844, 0.0935961157914115, -0.07578503601828569, -0.265696285355292, 0.07321986187709852, -0.18832562535247777, -0.16545677198662181, -0.03497737697116361, 0.07857078974320961, 0.018196525794828017, -0.2457664036087357, -0.013445772753840082, 0.14920347077468557, 0.13033868436795776, -0.022894944214089406, -0.12303709324313265, -0.006745032088443888, 0.14706675157479399, -0.016661681652100486, -0.02738035738918135, 0.08743445979126499, -0.09391977485358255, -0.1677175712604285, 0.4032716845538928, 0.048541595212319925, -0.232979721287684, 0.16809363842266872, -0.1140127739832433, -0.16221591545363073, 0.13630816808331422, 0.059248526094041847, 0.12672383443902382, -0.09085362337204612, 0.15102880723775905, -0.17681115860213642, 0.08250801055561333, 0.103662929910999, 0.009071583399193341, 0.14192921735785818, 0.04729412820156182, 0.06718415830917447, 0.182461318131031, 0.06869973034043325, -0.01976433095490832, -0.33825057180954166, -0.2125210652305755, -0.15360780852438694, 0.09521530702019508, -0.14208259067535425, -0.2475526741394522, 0.3812625386563831, 0.08741909390762763, 0.144089758617207, 0.1016738109121863, 0.22602768356420017, 0.08219573681773827, 0.12809145597288427, -0.006382943601923042, 0.09632221133163031, 0.24289110557861585, -0.03526832991452323, -0.1410030575006091, 0.04249052234580008, 0.2943331787122016] |
1,802.05419 | Seismic response in modern cities | The proposed homogeneous flat-faced layer-like model of a city (termed
overlayer), covering what is generally considered to be a dangerous site (from
the point of view of seismic hazard) lends itself to an explicit theoretical
analysis of its response to a seismic body wave radiated by distant sources.
This study is carried out for: ground response of the complete site/overlayer
configuration which is compared to the response of the configuration in which
the overlayer is absent, response at the top of the layer for various layer
thicknesses, and determination, as a function of frequency, of the fraction of
incident flux that is dissipated in the overlayer, the underlying layer and, by
radiation damping, in the hard half space. It is shown that all of these
entities are highly frequency-dependent and even large in certain frequency
intervals, without any resonant (in the sense of mode excitation) phenomena
coming into play. The results of this study also show that transfer functions
do not necessarily reflect the global response in the built component of a city
and that more-appropriate energy-related functions, termed spectral absorptance
(in the blocks of the city or their layer-like surrogate at the characteristic
frequency of the seismic pulse) and absorptance (integral over frequency of the
spectral absorptance), can increase with increasing city density or increasing
city height. In fact, it is shown that more than a third of the incident
seismic energy can be sent into, and therefore cause serious damage to, the
built component. On the basis of these findings, it appears that the probable
evolution of the morphology and constitutive properties of cities with time
will make the latter more vulnerable to damage and destruction when submitted
to seismic waves.
| physics.geo-ph | the proposed homogeneous flatfaced layerlike model of a city termed overlayer covering what is generally considered to be a dangerous site from the point of view of seismic hazard lends itself to an explicit theoretical analysis of its response to a seismic body wave radiated by distant sources this study is carried out for ground response of the complete siteoverlayer configuration which is compared to the response of the configuration in which the overlayer is absent response at the top of the layer for various layer thicknesses and determination as a function of frequency of the fraction of incident flux that is dissipated in the overlayer the underlying layer and by radiation damping in the hard half space it is shown that all of these entities are highly frequencydependent and even large in certain frequency intervals without any resonant in the sense of mode excitation phenomena coming into play the results of this study also show that transfer functions do not necessarily reflect the global response in the built component of a city and that moreappropriate energyrelated functions termed spectral absorptance in the blocks of the city or their layerlike surrogate at the characteristic frequency of the seismic pulse and absorptance integral over frequency of the spectral absorptance can increase with increasing city density or increasing city height in fact it is shown that more than a third of the incident seismic energy can be sent into and therefore cause serious damage to the built component on the basis of these findings it appears that the probable evolution of the morphology and constitutive properties of cities with time will make the latter more vulnerable to damage and destruction when submitted to seismic waves | [['the', 'proposed', 'homogeneous', 'flatfaced', 'layerlike', 'model', 'of', 'a', 'city', 'termed', 'overlayer', 'covering', 'what', 'is', 'generally', 'considered', 'to', 'be', 'a', 'dangerous', 'site', 'from', 'the', 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1,802.0542 | On the Power-of-d-choices with Least Loaded Server Selection | Motivated by distributed schedulers that combine the power-of-d-choices with
late binding and systems that use replication with cancellation-on-start, we
study the performance of the LL(d) policy which assigns a job to a server that
currently has the least workload among d randomly selected servers in
large-scale homogeneous clusters. We consider general service time
distributions and propose a partial integro-differential equation to describe
the evolution of the system. This equation relies on the earlier proven ansatz
for LL(d) which asserts that the workload distribution of any finite set of
queues becomes independent of one another as the number of servers tends to
infinity. Based on this equation we propose a fixed point iteration for the
limiting workload distribution and study its convergence. For exponential job
sizes we present a simple closed form expression for the limiting workload
distribution that is valid for any work-conserving service discipline as well
as for the limiting response time distribution in case of
first-come-first-served scheduling. We further show that for phase-type
distributed job sizes the limiting workload and response time distribution can
be expressed via the unique solution of a simple set of ordinary differential
equations. Numerical and analytical results that compare response time of the
classic power-of-d-choices algorithm and the LL(d) policy are also presented
and the accuracy of the limiting response time distribution for finite systems
is illustrated using simulation.
| cs.PF | motivated by distributed schedulers that combine the powerofdchoices with late binding and systems that use replication with cancellationonstart we study the performance of the lld policy which assigns a job to a server that currently has the least workload among d randomly selected servers in largescale homogeneous clusters we consider general service time distributions and propose a partial integrodifferential equation to describe the evolution of the system this equation relies on the earlier proven ansatz for lld which asserts that the workload distribution of any finite set of queues becomes independent of one another as the number of servers tends to infinity based on this equation we propose a fixed point iteration for the limiting workload distribution and study its convergence for exponential job sizes we present a simple closed form expression for the limiting workload distribution that is valid for any workconserving service discipline as well as for the limiting response time distribution in case of firstcomefirstserved scheduling we further show that for phasetype distributed job sizes the limiting workload and response time distribution can be expressed via the unique solution of a simple set of ordinary differential equations numerical and analytical results that compare response time of the classic powerofdchoices algorithm and the lld policy are also presented and the accuracy of the limiting response time distribution for finite systems is illustrated using simulation | [['motivated', 'by', 'distributed', 'schedulers', 'that', 'combine', 'the', 'powerofdchoices', 'with', 'late', 'binding', 'and', 'systems', 'that', 'use', 'replication', 'with', 'cancellationonstart', 'we', 'study', 'the', 'performance', 'of', 'the', 'lld', 'policy', 'which', 'assigns', 'a', 'job', 'to', 'a', 'server', 'that', 'currently', 'has', 'the', 'least', 'workload', 'among', 'd', 'randomly', 'selected', 'servers', 'in', 'largescale', 'homogeneous', 'clusters', 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1,802.05421 | CADDeLaG: Framework for distributed anomaly detection in large dense
graph sequences | Random walk based distance measures for graphs such as commute-time distance
are useful in a variety of graph algorithms, such as clustering, anomaly
detection, and creating low dimensional embeddings. Since such measures hinge
on the spectral decomposition of the graph, the computation becomes a
bottleneck for large graphs and do not scale easily to graphs that cannot be
loaded in memory. Most existing graph mining libraries for large graphs either
resort to sampling or exploit the sparsity structure of such graphs for
spectral analysis. However, such methods do not work for dense graphs
constructed for studying pairwise relationships among entities in a data set.
Examples of such studies include analyzing pairwise locations in gridded
climate data for discovering long distance climate phenomena. These graphs
representations are fully connected by construction and cannot be sparsified
without loss of meaningful information. In this paper we describe CADDeLaG, a
framework for scalable computation of commute-time distance based anomaly
detection in large dense graphs without the need to load the entire graph in
memory. The framework relies on Apache Spark's memory-centric cluster-computing
infrastructure and consists of two building blocks: a decomposable algorithm
for commute time distance computation and a distributed linear system solver.
We illustrate the scalability of CADDeLaG and its dependency on various factors
using both synthetic and real world data sets. We demonstrate the usefulness of
CADDeLaG in identifying anomalies in a climate graph sequence, that have been
historically missed due to ad hoc graph sparsification and on an election
donation data set.
| cs.DC | random walk based distance measures for graphs such as commutetime distance are useful in a variety of graph algorithms such as clustering anomaly detection and creating low dimensional embeddings since such measures hinge on the spectral decomposition of the graph the computation becomes a bottleneck for large graphs and do not scale easily to graphs that cannot be loaded in memory most existing graph mining libraries for large graphs either resort to sampling or exploit the sparsity structure of such graphs for spectral analysis however such methods do not work for dense graphs constructed for studying pairwise relationships among entities in a data set examples of such studies include analyzing pairwise locations in gridded climate data for discovering long distance climate phenomena these graphs representations are fully connected by construction and cannot be sparsified without loss of meaningful information in this paper we describe caddelag a framework for scalable computation of commutetime distance based anomaly detection in large dense graphs without the need to load the entire graph in memory the framework relies on apache sparks memorycentric clustercomputing infrastructure and consists of two building blocks a decomposable algorithm for commute time distance computation and a distributed linear system solver we illustrate the scalability of caddelag and its dependency on various factors using both synthetic and real world data sets we demonstrate the usefulness of caddelag in identifying anomalies in a climate graph sequence that have been historically missed due to ad hoc graph sparsification and on an election donation data set | [['random', 'walk', 'based', 'distance', 'measures', 'for', 'graphs', 'such', 'as', 'commutetime', 'distance', 'are', 'useful', 'in', 'a', 'variety', 'of', 'graph', 'algorithms', 'such', 'as', 'clustering', 'anomaly', 'detection', 'and', 'creating', 'low', 'dimensional', 'embeddings', 'since', 'such', 'measures', 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1,802.05422 | Photoionization of the bound systems at high energies | We consider photoionization of a system bound by the central potential
$V(r)$. We demonstrate that the high energy nonrelativistic asymptotics of the
photoionization cross section can be obtained without solving the wave
equation. The asymptotics can be expressed in terms of the Fourier transform of
the potential by employing the Lippmann--Schwinger equation. We find the
asymptotics for the screened Coulomb field. We demonstrate that the leading
corrections to this asymptotics are described by the universal factor. The high
energy nonrelativistic asymptotics is found to be determined by the analytic
properties of the potential $V(r)$. We show that the energy dependence of the
asymptotics of photoionization cross sections of fullerenes is to large extent
model dependent. We demonstrate that if the fullerene field $V(r)$ is
approximated by the function with singularities in the complex plane, the power
drop of the asymptotics is reached at the energies which as so high that the
cross section becomes unobservably small. The preasymptotic behavior with a
faster drop of the cross sections becomes important in these cases.
| physics.atom-ph quant-ph | we consider photoionization of a system bound by the central potential vr we demonstrate that the high energy nonrelativistic asymptotics of the photoionization cross section can be obtained without solving the wave equation the asymptotics can be expressed in terms of the fourier transform of the potential by employing the lippmannschwinger equation we find the asymptotics for the screened coulomb field we demonstrate that the leading corrections to this asymptotics are described by the universal factor the high energy nonrelativistic asymptotics is found to be determined by the analytic properties of the potential vr we show that the energy dependence of the asymptotics of photoionization cross sections of fullerenes is to large extent model dependent we demonstrate that if the fullerene field vr is approximated by the function with singularities in the complex plane the power drop of the asymptotics is reached at the energies which as so high that the cross section becomes unobservably small the preasymptotic behavior with a faster drop of the cross sections becomes important in these cases | [['we', 'consider', 'photoionization', 'of', 'a', 'system', 'bound', 'by', 'the', 'central', 'potential', 'vr', 'we', 'demonstrate', 'that', 'the', 'high', 'energy', 'nonrelativistic', 'asymptotics', 'of', 'the', 'photoionization', 'cross', 'section', 'can', 'be', 'obtained', 'without', 'solving', 'the', 'wave', 'equation', 'the', 'asymptotics', 'can', 'be', 'expressed', 'in', 'terms', 'of', 'the', 'fourier', 'transform', 'of', 'the', 'potential', 'by', 'employing', 'the', 'lippmannschwinger', 'equation', 'we', 'find', 'the', 'asymptotics', 'for', 'the', 'screened', 'coulomb', 'field', 'we', 'demonstrate', 'that', 'the', 'leading', 'corrections', 'to', 'this', 'asymptotics', 'are', 'described', 'by', 'the', 'universal', 'factor', 'the', 'high', 'energy', 'nonrelativistic', 'asymptotics', 'is', 'found', 'to', 'be', 'determined', 'by', 'the', 'analytic', 'properties', 'of', 'the', 'potential', 'vr', 'we', 'show', 'that', 'the', 'energy', 'dependence', 'of', 'the', 'asymptotics', 'of', 'photoionization', 'cross', 'sections', 'of', 'fullerenes', 'is', 'to', 'large', 'extent', 'model', 'dependent', 'we', 'demonstrate', 'that', 'if', 'the', 'fullerene', 'field', 'vr', 'is', 'approximated', 'by', 'the', 'function', 'with', 'singularities', 'in', 'the', 'complex', 'plane', 'the', 'power', 'drop', 'of', 'the', 'asymptotics', 'is', 'reached', 'at', 'the', 'energies', 'which', 'as', 'so', 'high', 'that', 'the', 'cross', 'section', 'becomes', 'unobservably', 'small', 'the', 'preasymptotic', 'behavior', 'with', 'a', 'faster', 'drop', 'of', 'the', 'cross', 'sections', 'becomes', 'important', 'in', 'these', 'cases']] | [-0.10405361000989932, 0.08979811423544794, -0.09689765172295792, 0.10535613882157757, -0.0012448266971596452, -0.07171040264683841, -0.0063891392878018495, 0.33790568937547505, -0.23736616444427433, -0.2802895073870977, 0.03648309953589274, -0.3007792964642651, -0.11068825919024083, 0.17638557187286963, 0.0232100086611544, 0.048196033358054106, 0.04849371569756375, 0.04101593686987884, -0.06872920551591791, -0.22212239388690524, 0.3341786556844708, 0.07411667220511062, 0.23252319216424988, 0.16026774054219903, 0.06778669544105786, 0.018442123207848434, 0.027046225694289733, 0.033769201456919314, -0.13676165855327402, 0.10556046685019811, 0.23367317739419294, 0.04083028874295049, 0.20945829120572917, -0.409131663786464, -0.1845985512454929, 0.08463529464626295, 0.18347576871205684, 0.11136657157899314, -0.03171173438191587, -0.2588646847234909, 0.07105950455500766, -0.17829323079177115, -0.20467066656199803, -0.06980451147700119, 0.03779488659685824, 0.08160959021052952, -0.25896620933984427, 0.09062636143332997, 0.004084450811375106, -0.017117516798248817, -0.062239942254585234, -0.10119570487620157, -0.03158895916381288, 0.08297427484276976, 0.07387352267653834, -0.0007630222000528213, 0.13131206403053258, -0.18274056455688983, -0.04841731043889859, 0.3954083064549364, -0.11151760404924103, -0.19406581866915498, 0.09577084773323001, -0.21305941549118954, -0.055736579078108854, 0.1873467042067543, 0.15371672248029214, 0.14609745972712665, -0.12354342978404359, 0.14337498169439894, 0.00889779418054765, 0.12739730564008336, 0.09061700665814326, 0.010779944029203507, 0.143183647194704, 0.1311840153201808, 0.011453311248048924, 0.1248048410465302, -0.11061162282550317, -0.09100278568464916, -0.34321024283485185, -0.13109302865564662, -0.1853676618024068, 0.08358624819312502, -0.10912535026084719, -0.1665386358644207, 0.3577317368683505, 0.10262132039412769, 0.2506632536316702, 0.04754534550011158, 0.290623281067129, 0.264610414471082, 0.07334123176338454, 0.0767668227524345, 0.2908604539562623, 0.11080878655437033, 0.0923533305812757, -0.2617513824600813, 0.052094937743467476, 0.07611392315498791] |
1,802.05423 | The Meissner Effect for axially symmetric charged black holes | In our previous work [N. G\"urlebeck, M. Scholtz, Phys. Rev. D 95 064010
(2017)], we have shown that electric and magnetic fields are expelled from the
horizons of extremal, stationary and axially symmetric uncharged black holes;
this is called the Meissner effect for black holes. Here, we generalize this
result in several directions. First, we allow that the black hole carries
charge, which requires a generalization of the definition of the Meissner
effect.
Next, we introduce the notion of almost isolated horizons, which is weaker
than the usual notion of isolated horizons, since the geometry of the former is
not necessarily completely time-independent. Moreover, we allow the horizon to
be pierced by strings, thereby violating the usual assumption on the spherical
topology made in the definition of the weakly isolated horizon. Finally, we
spell out in detail all assumptions entering the proof and show that the
Meissner effect is an inherent property of black holes even in full non-linear
theory.
| gr-qc | in our previous work n gurlebeck m scholtz phys rev d 95 064010 2017 we have shown that electric and magnetic fields are expelled from the horizons of extremal stationary and axially symmetric uncharged black holes this is called the meissner effect for black holes here we generalize this result in several directions first we allow that the black hole carries charge which requires a generalization of the definition of the meissner effect next we introduce the notion of almost isolated horizons which is weaker than the usual notion of isolated horizons since the geometry of the former is not necessarily completely timeindependent moreover we allow the horizon to be pierced by strings thereby violating the usual assumption on the spherical topology made in the definition of the weakly isolated horizon finally we spell out in detail all assumptions entering the proof and show that the meissner effect is an inherent property of black holes even in full nonlinear theory | [['in', 'our', 'previous', 'work', 'n', 'gurlebeck', 'm', 'scholtz', 'phys', 'rev', 'd', '95', '064010', '2017', 'we', 'have', 'shown', 'that', 'electric', 'and', 'magnetic', 'fields', 'are', 'expelled', 'from', 'the', 'horizons', 'of', 'extremal', 'stationary', 'and', 'axially', 'symmetric', 'uncharged', 'black', 'holes', 'this', 'is', 'called', 'the', 'meissner', 'effect', 'for', 'black', 'holes', 'here', 'we', 'generalize', 'this', 'result', 'in', 'several', 'directions', 'first', 'we', 'allow', 'that', 'the', 'black', 'hole', 'carries', 'charge', 'which', 'requires', 'a', 'generalization', 'of', 'the', 'definition', 'of', 'the', 'meissner', 'effect', 'next', 'we', 'introduce', 'the', 'notion', 'of', 'almost', 'isolated', 'horizons', 'which', 'is', 'weaker', 'than', 'the', 'usual', 'notion', 'of', 'isolated', 'horizons', 'since', 'the', 'geometry', 'of', 'the', 'former', 'is', 'not', 'necessarily', 'completely', 'timeindependent', 'moreover', 'we', 'allow', 'the', 'horizon', 'to', 'be', 'pierced', 'by', 'strings', 'thereby', 'violating', 'the', 'usual', 'assumption', 'on', 'the', 'spherical', 'topology', 'made', 'in', 'the', 'definition', 'of', 'the', 'weakly', 'isolated', 'horizon', 'finally', 'we', 'spell', 'out', 'in', 'detail', 'all', 'assumptions', 'entering', 'the', 'proof', 'and', 'show', 'that', 'the', 'meissner', 'effect', 'is', 'an', 'inherent', 'property', 'of', 'black', 'holes', 'even', 'in', 'full', 'nonlinear', 'theory']] | [-0.15917768389726342, 0.1322367430961716, -0.08408667735425354, 0.08464120713039007, -0.0953345441117977, -0.11219759617602051, 0.02415625214255474, 0.30326758787814007, -0.1711962295112589, -0.2790179567270075, 0.06851081379698118, -0.2885657614804333, -0.1385808549320349, 0.1694866575674991, -0.07788106541722553, -0.013099299407788093, -0.00929657297350372, 0.044699398077187376, -0.09744229108460625, -0.2714090433864725, 0.35784846159063655, 0.07113581088289053, 0.272029466826846, 0.04147939843897814, 0.07627581491700927, 0.039933840549680626, 0.0057217952790905875, 0.09781898129024083, -0.16811911052580492, 0.04586566536380803, 0.1683452178656324, 0.09991657126641867, 0.21295590673667628, -0.44970555485320524, -0.1876493919658463, 0.1257556313029903, 0.1222805641856799, 0.16604282327963016, -0.05844190423568428, -0.2762693070771196, 0.1015320827704088, -0.20456864874031913, -0.16919337731750705, -0.03910216584778096, 0.07448767415908035, -0.05245316514002654, -0.2127343382055671, 0.1054814102192987, 0.1813926755287095, -0.03501296774850778, -0.09315392975047042, -0.03712540460143333, -0.024994321908695695, 0.04627233717437315, 0.08864102093208157, 0.011139429222992799, 0.14391244066985254, -0.0655956407360544, -0.12492962419040667, 0.29955474123405884, -0.014378264762370559, -0.20750695053205082, 0.15867540959852358, -0.2388908802737965, -0.10726976380506649, 0.11049716237378365, 0.0999141648836151, 0.21201518647994586, -0.13884321165188582, 0.16364874946479568, -0.07457341408781425, 0.11611397591304576, 0.12157704791678942, 0.0615294864872777, 0.3041865045640948, 0.10691778172622435, 0.054250336212365406, 0.1720094609846467, -0.05280422911937997, -0.094475092369285, -0.33525087209300525, -0.15973679987194983, -0.160138869971255, 0.1319249980123102, -0.05768592317584575, -0.19301860994726414, 0.32103836806373126, 0.15483442277211365, 0.171312256468625, 0.006439319241473664, 0.24514590080344775, 0.05159497515466418, 0.04675560652962119, 0.12250092243952558, 0.3195933923611559, 0.12889660820748092, 0.12027057874770954, -0.2025009199381582, -0.008336115327864132, 0.09999690079012344] |
1,802.05424 | Threshold response and bistability in gene regulation by small noncoding
RNA | In this paper, we study through mathematical modelling the combined effect of
transcriptional and translational regulation by proteins and small noncoding
RNAs (sRNA) in a genetic feedback motif that has an important role in the
survival of E.coli under stress associated with oxygen and energy availability.
We show that subtle changes in this motif can bring in drastically different
effects on the gene expression. In particular, we show that a threshold
response in the gene expression changes to a bistable response as the
regulation on sRNA synthesis or degradation is altered. These results are
obtained under deterministic conditions. Next, we study how the gene expression
is altered by additive and multiplicative noise which might arise due to
probabilistic occurrences of different biochemical events. Using the
Fokker-Planck formulation, we obtain steady state probability distributions for
sRNA concentration for the network motifs displaying bistability. The
probability distributions are found to be bimodal with two peaks at low and
high concentrations of sRNAs. We further study the variations in the
probability distributions under different values of noise strength and
correlations. The results presented here might be of interest for designing
synthetic network for artificial control.
| q-bio.MN q-bio.CB | in this paper we study through mathematical modelling the combined effect of transcriptional and translational regulation by proteins and small noncoding rnas srna in a genetic feedback motif that has an important role in the survival of ecoli under stress associated with oxygen and energy availability we show that subtle changes in this motif can bring in drastically different effects on the gene expression in particular we show that a threshold response in the gene expression changes to a bistable response as the regulation on srna synthesis or degradation is altered these results are obtained under deterministic conditions next we study how the gene expression is altered by additive and multiplicative noise which might arise due to probabilistic occurrences of different biochemical events using the fokkerplanck formulation we obtain steady state probability distributions for srna concentration for the network motifs displaying bistability the probability distributions are found to be bimodal with two peaks at low and high concentrations of srnas we further study the variations in the probability distributions under different values of noise strength and correlations the results presented here might be of interest for designing synthetic network for artificial control | [['in', 'this', 'paper', 'we', 'study', 'through', 'mathematical', 'modelling', 'the', 'combined', 'effect', 'of', 'transcriptional', 'and', 'translational', 'regulation', 'by', 'proteins', 'and', 'small', 'noncoding', 'rnas', 'srna', 'in', 'a', 'genetic', 'feedback', 'motif', 'that', 'has', 'an', 'important', 'role', 'in', 'the', 'survival', 'of', 'ecoli', 'under', 'stress', 'associated', 'with', 'oxygen', 'and', 'energy', 'availability', 'we', 'show', 'that', 'subtle', 'changes', 'in', 'this', 'motif', 'can', 'bring', 'in', 'drastically', 'different', 'effects', 'on', 'the', 'gene', 'expression', 'in', 'particular', 'we', 'show', 'that', 'a', 'threshold', 'response', 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1,802.05425 | Miyaoka-Yau inequality for compact K\"ahler manifolds with semi-positive
canonical bundle | In this paper, we prove the Miyaoka-Yau inequality for compact K\"ahler
manifolds with semi-positive canonical bundle. The key point of the proof is
the estimate for the $L^2$-norm of the scalar curvature along the
K\"ahler-Ricci flow.
| math.DG math.AG math.CV | in this paper we prove the miyaokayau inequality for compact kahler manifolds with semipositive canonical bundle the key point of the proof is the estimate for the l2norm of the scalar curvature along the kahlerricci flow | [['in', 'this', 'paper', 'we', 'prove', 'the', 'miyaokayau', 'inequality', 'for', 'compact', 'kahler', 'manifolds', 'with', 'semipositive', 'canonical', 'bundle', 'the', 'key', 'point', 'of', 'the', 'proof', 'is', 'the', 'estimate', 'for', 'the', 'l2norm', 'of', 'the', 'scalar', 'curvature', 'along', 'the', 'kahlerricci', 'flow']] | [-0.22442712500277492, 0.0033393475071837506, -0.095910443769147, 0.08701372513961461, -0.0994824134848184, -0.1634709421131346, -0.09825979154751015, 0.32095624728956157, -0.271648067360123, -0.1417408326588985, 0.13225646779756062, -0.2654406157218748, -0.15480608407233376, 0.17397156238762868, -0.17768616125815445, 0.04946219745195574, 0.09015136051716076, 0.10711813534403013, -0.10935138288186863, -0.2399787318330103, 0.5626846853022774, -0.017124480619612668, 0.2418436788332959, 0.17471168402375448, 0.1379247302862091, -0.0501618127260978, 0.008346541474262873, -0.05461654474493116, -0.19010787550359964, 0.1738536131257812, 0.2068407265081381, 0.06717616342292684, 0.23778456351202396, -0.34690128375465673, -0.2011065154025952, 0.24521195210723412, 0.10845714429807332, 0.006632697289913065, -0.043472581213184945, -0.2916551717143092, 0.11589419420690522, -0.029200850054621696, -0.2563325812936657, -0.08234986727539864, -0.04112903555182533, 0.031894383719190955, -0.2263403492850355, 0.052719438112237386, 0.1267801356087956, 0.049446243301240936, -0.10797466490314239, -0.08008771979560454, -0.0815054382999531, 0.0069443053669399684, 0.11852088723874961, 0.10880812719309081, 0.13375980806692192, -0.05519798221454645, -0.03811122204448717, 0.32182527240365744, -0.17689956248634392, -0.27075148404886323, -0.04461419044269456, -0.10250738620137174, -0.17536611826572981, 0.10163963071277572, 0.14659386672752184, 0.2250498493667692, -0.025906536378897727, 0.1325996647744129, -0.0941389286890626, 0.05514313306856719, 0.08219371327302522, -0.017439860333171155, 0.12128007132560015, 0.122734767333087, 0.21651299727252787, 0.11162399482499394, -0.043045750301745206, -0.12254821064157619, -0.4466341564224826, -0.3418156257054458, -0.1618514408926583, 0.211897133047589, -0.21554095533469486, -0.16563939256593585, 0.3992385066424807, -0.009483034127495356, 0.19244003771907753, 0.19078068230818543, 0.28860816990749705, 0.045444220298021615, 0.0051289091190685416, 0.13133406736111888, 0.2873784305734767, 0.2584998654201627, 0.12654094841693425, -0.12916113512538788, -0.07229424658645359, 0.20297407431321013] |
1,802.05426 | Accelerating Adaptive Cubic Regularization of Newton's Method via Random
Sampling | In this paper, we consider an unconstrained optimization model where the
objective is a sum of a large number of possibly nonconvex functions, though
overall the objective is assumed to be smooth and convex. Our bid to solving
such model uses the framework of cubic regularization of Newton's method. As
well known, the crux in cubic regularization is its utilization of the Hessian
information, which may be computationally expensive for large-scale problems.
To tackle this, we resort to approximating the Hessian matrix via sub-sampling.
In particular, we propose to compute an approximated Hessian matrix by either
\textit{uniformly}\/ or \textit{non-uniformly}\/ sub-sampling the components of
the objective. Based upon such sampling strategy, we develop accelerated
adaptive cubic regularization approaches and provide theoretical guarantees on
global iteration complexity of $O(\epsilon^{-1/3})$ with high probability,
which matches that of the original accelerated cubic regularization methods
\cite{Jiang-2017-Unified} using the \textit{full}\/ Hessian information.
Interestingly, we show that in the worst case scenario our algorithm still
achieves an $O\left(\log(\epsilon^{-1})\epsilon^{-5/6}\right)$ iteration
complexity bound. The performances of the proposed methods on the regularized
logistic regression problems show a clear effect of acceleration in terms of
the epoch counts on several real data sets.
| math.OC | in this paper we consider an unconstrained optimization model where the objective is a sum of a large number of possibly nonconvex functions though overall the objective is assumed to be smooth and convex our bid to solving such model uses the framework of cubic regularization of newtons method as well known the crux in cubic regularization is its utilization of the hessian information which may be computationally expensive for largescale problems to tackle this we resort to approximating the hessian matrix via subsampling in particular we propose to compute an approximated hessian matrix by either textituniformly or textitnonuniformly subsampling the components of the objective based upon such sampling strategy we develop accelerated adaptive cubic regularization approaches and provide theoretical guarantees on global iteration complexity of oepsilon13 with high probability which matches that of the original accelerated cubic regularization methods citejiang2017unified using the textitfull hessian information interestingly we show that in the worst case scenario our algorithm still achieves an oleftlogepsilon1epsilon56right iteration complexity bound the performances of the proposed methods on the regularized logistic regression problems show a clear effect of acceleration in terms of the epoch counts on several real data sets | [['in', 'this', 'paper', 'we', 'consider', 'an', 'unconstrained', 'optimization', 'model', 'where', 'the', 'objective', 'is', 'a', 'sum', 'of', 'a', 'large', 'number', 'of', 'possibly', 'nonconvex', 'functions', 'though', 'overall', 'the', 'objective', 'is', 'assumed', 'to', 'be', 'smooth', 'and', 'convex', 'our', 'bid', 'to', 'solving', 'such', 'model', 'uses', 'the', 'framework', 'of', 'cubic', 'regularization', 'of', 'newtons', 'method', 'as', 'well', 'known', 'the', 'crux', 'in', 'cubic', 'regularization', 'is', 'its', 'utilization', 'of', 'the', 'hessian', 'information', 'which', 'may', 'be', 'computationally', 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1,802.05427 | Implementation of Massive MIMO Uplink Receiver on RaPro Prototyping
Platform | The updated physical layer standard of the fifth generation wireless
communication suggests the necessity of a rapid prototyping platform. To this
end, we develop RaPro, a multi-core general purpose processor-based massive
multiple-input-multiple-output (MIMO) prototyping platform. To enhance RaPro,
high performance detection and beamforming are needed, whereas both of them
request for accurate channel state information (CSI). In this paper, linear
minimum mean square error (LMMSE)-based channel estimator is adopted and
encapsulated inside RaPro to gain more accurate CSI. Considering the high
comlexity and unknown of channel statistics, we design low-complexity LMMSE
channel estimator to alleviate the rising complexity along with increasing
antenna number and set more computational resource aside for massive MIMO
uplink detection and downlink beamforming. Simulation results indicate the high
mean square error performance and robustness of designed low-complexity method.
Indoor and corridor scenario tests show prominent improvement in bit error rate
performance. Time cost analysis proves the practical use and real-time
transmission ability of the implemented uplink receiver on RaPro.
| eess.SP cs.IT math.IT | the updated physical layer standard of the fifth generation wireless communication suggests the necessity of a rapid prototyping platform to this end we develop rapro a multicore general purpose processorbased massive multipleinputmultipleoutput mimo prototyping platform to enhance rapro high performance detection and beamforming are needed whereas both of them request for accurate channel state information csi in this paper linear minimum mean square error lmmsebased channel estimator is adopted and encapsulated inside rapro to gain more accurate csi considering the high comlexity and unknown of channel statistics we design lowcomplexity lmmse channel estimator to alleviate the rising complexity along with increasing antenna number and set more computational resource aside for massive mimo uplink detection and downlink beamforming simulation results indicate the high mean square error performance and robustness of designed lowcomplexity method indoor and corridor scenario tests show prominent improvement in bit error rate performance time cost analysis proves the practical use and realtime transmission ability of the implemented uplink receiver on rapro | [['the', 'updated', 'physical', 'layer', 'standard', 'of', 'the', 'fifth', 'generation', 'wireless', 'communication', 'suggests', 'the', 'necessity', 'of', 'a', 'rapid', 'prototyping', 'platform', 'to', 'this', 'end', 'we', 'develop', 'rapro', 'a', 'multicore', 'general', 'purpose', 'processorbased', 'massive', 'multipleinputmultipleoutput', 'mimo', 'prototyping', 'platform', 'to', 'enhance', 'rapro', 'high', 'performance', 'detection', 'and', 'beamforming', 'are', 'needed', 'whereas', 'both', 'of', 'them', 'request', 'for', 'accurate', 'channel', 'state', 'information', 'csi', 'in', 'this', 'paper', 'linear', 'minimum', 'mean', 'square', 'error', 'lmmsebased', 'channel', 'estimator', 'is', 'adopted', 'and', 'encapsulated', 'inside', 'rapro', 'to', 'gain', 'more', 'accurate', 'csi', 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1,802.05428 | Quantum Circuit Design for Training Perceptron Models | Perceptron model is a fundamental linear classifier in machine learning and
also the building block of artificial neural networks. Recently, Wiebe et al.
(arXiv:1602.04799) proposed that the training of a perceptron can be
quadratically speeded using Grover search with a quantum computer, which has
potentially important big-data applications. In this paper, we design a quantum
circuit for implementing this algorithm. The Grover oracle, the central part of
the circuit, is realized by Quantum-Fourier-Transform based arithmetics that
specifies whether an input weight vector can correctly classify all training
data samples. We also analyze the required number of qubits and universal gates
for the algorithm, as well as the success probability using uniform sampling,
showing that it has higher possibility than spherical Gaussian distribution
$N(0,1)$. The feasibility of the circuit is demonstrated by a testing example
using the IBM-Q cloud quantum computer, where 16 qubits are used to classify
four data samples.
| quant-ph | perceptron model is a fundamental linear classifier in machine learning and also the building block of artificial neural networks recently wiebe et al arxiv160204799 proposed that the training of a perceptron can be quadratically speeded using grover search with a quantum computer which has potentially important bigdata applications in this paper we design a quantum circuit for implementing this algorithm the grover oracle the central part of the circuit is realized by quantumfouriertransform based arithmetics that specifies whether an input weight vector can correctly classify all training data samples we also analyze the required number of qubits and universal gates for the algorithm as well as the success probability using uniform sampling showing that it has higher possibility than spherical gaussian distribution n01 the feasibility of the circuit is demonstrated by a testing example using the ibmq cloud quantum computer where 16 qubits are used to classify four data samples | [['perceptron', 'model', 'is', 'a', 'fundamental', 'linear', 'classifier', 'in', 'machine', 'learning', 'and', 'also', 'the', 'building', 'block', 'of', 'artificial', 'neural', 'networks', 'recently', 'wiebe', 'et', 'al', 'arxiv160204799', 'proposed', 'that', 'the', 'training', 'of', 'a', 'perceptron', 'can', 'be', 'quadratically', 'speeded', 'using', 'grover', 'search', 'with', 'a', 'quantum', 'computer', 'which', 'has', 'potentially', 'important', 'bigdata', 'applications', 'in', 'this', 'paper', 'we', 'design', 'a', 'quantum', 'circuit', 'for', 'implementing', 'this', 'algorithm', 'the', 'grover', 'oracle', 'the', 'central', 'part', 'of', 'the', 'circuit', 'is', 'realized', 'by', 'quantumfouriertransform', 'based', 'arithmetics', 'that', 'specifies', 'whether', 'an', 'input', 'weight', 'vector', 'can', 'correctly', 'classify', 'all', 'training', 'data', 'samples', 'we', 'also', 'analyze', 'the', 'required', 'number', 'of', 'qubits', 'and', 'universal', 'gates', 'for', 'the', 'algorithm', 'as', 'well', 'as', 'the', 'success', 'probability', 'using', 'uniform', 'sampling', 'showing', 'that', 'it', 'has', 'higher', 'possibility', 'than', 'spherical', 'gaussian', 'distribution', 'n01', 'the', 'feasibility', 'of', 'the', 'circuit', 'is', 'demonstrated', 'by', 'a', 'testing', 'example', 'using', 'the', 'ibmq', 'cloud', 'quantum', 'computer', 'where', '16', 'qubits', 'are', 'used', 'to', 'classify', 'four', 'data', 'samples']] | [-0.07408882109951731, 0.08452647968246114, -0.05338762806904678, 0.040288749727502314, -0.05472910059206943, -0.21262403267649682, 0.0698172812408302, 0.37937149217484417, -0.2602605230074275, -0.33807865830572525, 0.10202970767843637, -0.22065350687685045, -0.2089767457187377, 0.23980362150875925, -0.08144499998692285, 0.16807358766704597, 0.05667108179044884, 0.03861348526520856, -0.03343412402790744, -0.341942669144152, 0.27020523902752464, 0.07619986534700729, 0.310772640570822, -0.050380392059859995, 0.10293455936511778, 0.00801276180868012, 0.03219486344951784, -0.00891850412999456, -0.04628357456557923, 0.11825240874423872, 0.2802146285004612, 0.19138657997639194, 0.3051503713217539, -0.4095054059037687, -0.19316674236560594, 0.12670787104178924, 0.1522614159338126, 0.1511284220118919, -0.057838067387205525, -0.2843074802548398, 0.09897332698754915, -0.15960003151180777, -0.04446536731100767, -0.11779736655109839, 0.02014575841422885, -0.007390708069087358, -0.27740712818765156, -0.024505134750978794, 0.08553883917330815, 0.049774085058251746, 0.032894903592563966, -0.10194471964260211, 0.04966741095366258, 0.10085683023695506, -0.07960580354100219, 0.06520516237218839, 0.15550681197940297, -0.11987327526455645, -0.2101549722908719, 0.3453026795130525, -0.034588103096580565, -0.17868425352876452, 0.1426692964443056, -0.02925404634584698, -0.14079125165291181, 0.05595266981667059, 0.19875676611696394, 0.07522877756304838, -0.1591086378761542, 0.10353016110712888, -0.06698445641322413, 0.18842542569807455, 0.04159109300549922, -0.00828416650561062, 0.1489884716339057, 0.20492355785382962, 0.03239869047585573, 0.19202230973216403, -0.10698342315280004, -0.09246779878732334, -0.2744233760466749, -0.18083409350868818, -0.2745312967509145, 0.032340766924961996, -0.09375365644774393, -0.15863850882050354, 0.3922440391033888, 0.1732219210464029, 0.19646360200980828, 0.07602195259627283, 0.31208524157409556, 0.1013759513325813, 0.1180154941220944, 0.13039371914330972, 0.1855978297472403, 0.12562728143687285, 0.06710025734217787, -0.17732595052960254, 0.07325403889695441, 0.04767138411202845] |
1,802.05429 | Blind Source Separation with Optimal Transport Non-negative Matrix
Factorization | Optimal transport as a loss for machine learning optimization problems has
recently gained a lot of attention. Building upon recent advances in
computational optimal transport, we develop an optimal transport non-negative
matrix factorization (NMF) algorithm for supervised speech blind source
separation (BSS). Optimal transport allows us to design and leverage a cost
between short-time Fourier transform (STFT) spectrogram frequencies, which
takes into account how humans perceive sound. We give empirical evidence that
using our proposed optimal transport NMF leads to perceptually better results
than Euclidean NMF, for both isolated voice reconstruction and BSS tasks.
Finally, we demonstrate how to use optimal transport for cross domain sound
processing tasks, where frequencies represented in the input spectrograms may
be different from one spectrogram to another.
| cs.SD eess.AS stat.ML | optimal transport as a loss for machine learning optimization problems has recently gained a lot of attention building upon recent advances in computational optimal transport we develop an optimal transport nonnegative matrix factorization nmf algorithm for supervised speech blind source separation bss optimal transport allows us to design and leverage a cost between shorttime fourier transform stft spectrogram frequencies which takes into account how humans perceive sound we give empirical evidence that using our proposed optimal transport nmf leads to perceptually better results than euclidean nmf for both isolated voice reconstruction and bss tasks finally we demonstrate how to use optimal transport for cross domain sound processing tasks where frequencies represented in the input spectrograms may be different from one spectrogram to another | [['optimal', 'transport', 'as', 'a', 'loss', 'for', 'machine', 'learning', 'optimization', 'problems', 'has', 'recently', 'gained', 'a', 'lot', 'of', 'attention', 'building', 'upon', 'recent', 'advances', 'in', 'computational', 'optimal', 'transport', 'we', 'develop', 'an', 'optimal', 'transport', 'nonnegative', 'matrix', 'factorization', 'nmf', 'algorithm', 'for', 'supervised', 'speech', 'blind', 'source', 'separation', 'bss', 'optimal', 'transport', 'allows', 'us', 'to', 'design', 'and', 'leverage', 'a', 'cost', 'between', 'shorttime', 'fourier', 'transform', 'stft', 'spectrogram', 'frequencies', 'which', 'takes', 'into', 'account', 'how', 'humans', 'perceive', 'sound', 'we', 'give', 'empirical', 'evidence', 'that', 'using', 'our', 'proposed', 'optimal', 'transport', 'nmf', 'leads', 'to', 'perceptually', 'better', 'results', 'than', 'euclidean', 'nmf', 'for', 'both', 'isolated', 'voice', 'reconstruction', 'and', 'bss', 'tasks', 'finally', 'we', 'demonstrate', 'how', 'to', 'use', 'optimal', 'transport', 'for', 'cross', 'domain', 'sound', 'processing', 'tasks', 'where', 'frequencies', 'represented', 'in', 'the', 'input', 'spectrograms', 'may', 'be', 'different', 'from', 'one', 'spectrogram', 'to', 'another']] | [-0.0509782331215778, -0.002459886672076527, -0.17165967537377907, 0.08702726961999405, -0.186521012411733, -0.1917228441026698, 0.03682403791623902, 0.47077460393188447, -0.32207600128783925, -0.28357612190065845, 0.06672931734540659, -0.2593387402614202, -0.19463478598168227, 0.21228623570783473, -0.11727544893813509, 0.15227530847352785, 0.11389092882958854, 0.006432068647771347, -0.0900751832248908, -0.2302458062502972, 0.2292229799275491, 0.025878392917111637, 0.3651195372628972, 0.018926674507679493, 0.14678292665479145, 0.03488273671222472, -0.012819148997617205, -0.04488507785448214, -0.07394536379924682, 0.1464557820984079, 0.3851393487255233, 0.21333148678749558, 0.314339283499791, -0.4353972089181586, -0.2587112906971962, 0.10150112924771762, 0.2137657772424078, 0.08348571723686697, -0.07992302109316991, -0.2764873526596684, 0.05376009015192285, -0.13375431127331364, 0.06235327040852328, -0.14375192398674846, -0.0015175837637266007, -0.03131508827304119, -0.3267032900755104, 0.07278251947789657, 0.05210397307329425, 0.00607326916017668, -0.05850117749059421, -0.18721464235237156, 0.09321931028185881, 0.1845118256921812, 0.047068662222141656, 0.02343015936089725, 0.15101915073762762, -0.10510254090621583, -0.14401685886796775, 0.33224269771206427, -0.03834848097685017, -0.2436906723532735, 0.1773655414770592, -0.056058652507807546, -0.11892322958087383, 0.1488120224160271, 0.269955813385001, 0.07225782245140129, -0.17371166751121844, -0.0034882464584311454, -0.027951466131622228, 0.20093052048705579, 0.10102116486731523, 0.04539888344205371, 0.15670703494792607, 0.21686566062271595, 0.10694164815320958, 0.16279026806521102, -0.08619204555766855, -0.05717675390130863, -0.18218001948926993, -0.10315981820013707, -0.2254737204781211, 0.02789820000014412, -0.11032283325784659, -0.12782367033414482, 0.390305382184442, 0.21786070776047048, 0.19086491819319687, 0.10716851807589363, 0.37129260966084837, 0.10283846407750152, 0.04979815118476688, 0.12656449050286678, 0.1666080566819727, 0.10093556315168678, 0.17589312350392583, -0.22202314171235918, 0.049911351534689403, 0.06459859763915704] |
1,802.0543 | Virtual walks in spin space: a study in a family of two-parameter models | We investigate the dynamics of classical spins mapped as walkers in a virtual
"spin" space using a generalised two-parameter family of spin models
characterized by parameters $y$ and $z$ [M. J. de Oliveira, J. F. F. Mendes and
M. A. Santos, J. Phys. A Math. Gen. \textbf{26}, 2317 (1993)]. The behavior of
$S(x,t)$, the probability that the walker is at position $x$ at time $t$ is
studied in detail. In general $S(x,t) \sim t^{-\alpha}f(x/t^{\alpha})$ with
$\alpha \simeq 1$ or $0.5$ at large times depending on the parameters. In
particular, $S(x,t)$ for the point $y=1, z=0.5$ corresponding to the voter
model shows a crossover in time; associated with this crossover, two timescales
can be defined which vary with the system size $L$ as $L^2\log L$. We also show
that as the voter model point is approached from the disordered regions along
different directions, the width of the Gaussian distribution $S(x,t)$ diverges
in a power law manner with different exponents. For the majority voter case,
the results indicate that the the virtual walk can detect the phase transition
perhaps more efficiently compared to other non-equilibrium methods.
| cond-mat.stat-mech | we investigate the dynamics of classical spins mapped as walkers in a virtual spin space using a generalised twoparameter family of spin models characterized by parameters y and z m j de oliveira j f f mendes and m a santos j phys a math gen textbf26 2317 1993 the behavior of sxt the probability that the walker is at position x at time t is studied in detail in general sxt sim talphafxtalpha with alpha simeq 1 or 05 at large times depending on the parameters in particular sxt for the point y1 z05 corresponding to the voter model shows a crossover in time associated with this crossover two timescales can be defined which vary with the system size l as l2log l we also show that as the voter model point is approached from the disordered regions along different directions the width of the gaussian distribution sxt diverges in a power law manner with different exponents for the majority voter case the results indicate that the the virtual walk can detect the phase transition perhaps more efficiently compared to other nonequilibrium methods | [['we', 'investigate', 'the', 'dynamics', 'of', 'classical', 'spins', 'mapped', 'as', 'walkers', 'in', 'a', 'virtual', 'spin', 'space', 'using', 'a', 'generalised', 'twoparameter', 'family', 'of', 'spin', 'models', 'characterized', 'by', 'parameters', 'y', 'and', 'z', 'm', 'j', 'de', 'oliveira', 'j', 'f', 'f', 'mendes', 'and', 'm', 'a', 'santos', 'j', 'phys', 'a', 'math', 'gen', 'textbf26', '2317', '1993', 'the', 'behavior', 'of', 'sxt', 'the', 'probability', 'that', 'the', 'walker', 'is', 'at', 'position', 'x', 'at', 'time', 't', 'is', 'studied', 'in', 'detail', 'in', 'general', 'sxt', 'sim', 'talphafxtalpha', 'with', 'alpha', 'simeq', '1', 'or', '05', 'at', 'large', 'times', 'depending', 'on', 'the', 'parameters', 'in', 'particular', 'sxt', 'for', 'the', 'point', 'y1', 'z05', 'corresponding', 'to', 'the', 'voter', 'model', 'shows', 'a', 'crossover', 'in', 'time', 'associated', 'with', 'this', 'crossover', 'two', 'timescales', 'can', 'be', 'defined', 'which', 'vary', 'with', 'the', 'system', 'size', 'l', 'as', 'l2log', 'l', 'we', 'also', 'show', 'that', 'as', 'the', 'voter', 'model', 'point', 'is', 'approached', 'from', 'the', 'disordered', 'regions', 'along', 'different', 'directions', 'the', 'width', 'of', 'the', 'gaussian', 'distribution', 'sxt', 'diverges', 'in', 'a', 'power', 'law', 'manner', 'with', 'different', 'exponents', 'for', 'the', 'majority', 'voter', 'case', 'the', 'results', 'indicate', 'that', 'the', 'the', 'virtual', 'walk', 'can', 'detect', 'the', 'phase', 'transition', 'perhaps', 'more', 'efficiently', 'compared', 'to', 'other', 'nonequilibrium', 'methods']] | [-0.12301151665729786, 0.16392885490455486, -0.06364196445920144, 0.02837375320539978, -0.0032963440600843703, -0.189422555822533, 0.05864331383599081, 0.35558255515528886, -0.23796410365997117, -0.2792089372578133, 0.049121137325463644, -0.29353949962733467, -0.10135361154136233, 0.17054070402165783, -0.031169198504318305, -0.0009614634620219143, -0.021704865138611345, 0.008468356768503948, -0.06892583328248332, -0.22494798800984367, 0.26286080615749885, 0.03235896509217068, 0.23211001107438028, -0.03340911236464407, 0.08576244495674215, 0.01846573233314665, 0.0032042170398616705, 0.01649584462392256, -0.17534779111120458, -0.016668350347368646, 0.22118404203265427, 0.030085831246964755, 0.24144645371662626, -0.3073605489688086, -0.1973070612932998, 0.1190952559961057, 0.11519952943736861, 0.038359380107693616, 0.0496713440139909, -0.30571202737090936, 0.06006601508013405, -0.16208154311698805, -0.17069770995517328, 0.003767410758882761, 0.09303891723924647, 0.03269035659623561, -0.2612341707317881, 0.11197920237992931, 0.06220707086632486, 0.04497349031442583, 0.0031026542247807394, -0.13378213591430885, -0.0676301593938079, 0.07545731819865849, 0.015566112074105303, 0.11120404028511845, 0.1307090017359317, -0.08736073069116661, -0.12805947488176386, 0.3436992178461769, -0.08553844688923347, -0.15162196440388107, 0.1752648515565718, -0.18468841945477685, -0.16170619528669397, 0.12781703757259566, 0.13025186605156078, 0.12876016912108593, -0.10213205989582536, 0.13615222840475763, -0.05270338170942876, 0.159896081910517, 0.06577303085496516, -0.01883566311956256, 0.17882725050866277, 0.11666126577488956, 0.020812707469681575, 0.10534499953672687, -0.12458364625971591, -0.12036300762785507, -0.26681340340005816, -0.14322801799477775, -0.2122515689294093, 0.09997258613730878, -0.10198868199775143, -0.10271677039716332, 0.36010846087729426, 0.12301819952544527, 0.27031679343829146, 0.07529663681650606, 0.16476813257707518, 0.11491688563490969, 0.0026916349739284137, 0.1253960042647366, 0.1788206522968786, 0.09144473525878714, 0.12829425997390856, -0.2043051221242551, 0.03228055234347048, 0.06686886762438375] |
1,802.05431 | On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo | We provide convergence guarantees in Wasserstein distance for a variety of
variance-reduction methods: SAGA Langevin diffusion, SVRG Langevin diffusion
and control-variate underdamped Langevin diffusion. We analyze these methods
under a uniform set of assumptions on the log-posterior distribution, assuming
it to be smooth, strongly convex and Hessian Lipschitz. This is achieved by a
new proof technique combining ideas from finite-sum optimization and the
analysis of sampling methods. Our sharp theoretical bounds allow us to identify
regimes of interest where each method performs better than the others. Our
theory is verified with experiments on real-world and synthetic datasets.
| stat.ML cs.LG | we provide convergence guarantees in wasserstein distance for a variety of variancereduction methods saga langevin diffusion svrg langevin diffusion and controlvariate underdamped langevin diffusion we analyze these methods under a uniform set of assumptions on the logposterior distribution assuming it to be smooth strongly convex and hessian lipschitz this is achieved by a new proof technique combining ideas from finitesum optimization and the analysis of sampling methods our sharp theoretical bounds allow us to identify regimes of interest where each method performs better than the others our theory is verified with experiments on realworld and synthetic datasets | [['we', 'provide', 'convergence', 'guarantees', 'in', 'wasserstein', 'distance', 'for', 'a', 'variety', 'of', 'variancereduction', 'methods', 'saga', 'langevin', 'diffusion', 'svrg', 'langevin', 'diffusion', 'and', 'controlvariate', 'underdamped', 'langevin', 'diffusion', 'we', 'analyze', 'these', 'methods', 'under', 'a', 'uniform', 'set', 'of', 'assumptions', 'on', 'the', 'logposterior', 'distribution', 'assuming', 'it', 'to', 'be', 'smooth', 'strongly', 'convex', 'and', 'hessian', 'lipschitz', 'this', 'is', 'achieved', 'by', 'a', 'new', 'proof', 'technique', 'combining', 'ideas', 'from', 'finitesum', 'optimization', 'and', 'the', 'analysis', 'of', 'sampling', 'methods', 'our', 'sharp', 'theoretical', 'bounds', 'allow', 'us', 'to', 'identify', 'regimes', 'of', 'interest', 'where', 'each', 'method', 'performs', 'better', 'than', 'the', 'others', 'our', 'theory', 'is', 'verified', 'with', 'experiments', 'on', 'realworld', 'and', 'synthetic', 'datasets']] | [-0.0272307289361032, -0.01726544780906328, -0.17133715327291452, 0.08031207183736001, -0.08448213289885484, -0.18378046909155152, 0.05457517279902492, 0.41265506475933433, -0.2919530555145028, -0.2718851420591518, 0.0904247806390709, -0.24676258888902122, -0.15974553193556165, 0.2745450076253451, -0.10905997052827139, 0.09557465050062261, 0.11450308680125709, -0.04575261012765041, -0.07785433357984749, -0.29866753085084335, 0.25923653583032724, 0.008041861015804036, 0.28468052580587794, 0.04273621104453623, 0.1434303223702711, -0.043198055318873566, -0.024416645386342688, 0.037226133999059496, -0.18399857082548216, 0.16281172476670483, 0.20899509648629225, 0.14714754389157308, 0.3146611254988718, -0.39625562523904534, -0.23484410968076283, 0.09970644270982017, 0.12501108996838944, 0.0948989269764405, -0.06005473111602526, -0.31094086400626864, 0.05393189437610587, -0.08406495776257872, -0.09163868869894866, -0.18066335866016517, -0.07312668455265385, 0.0877826258194508, -0.349070647462588, 0.10702392387146265, 0.0684138510193791, 0.059576634456854814, -0.02712709130719304, -0.1644053984428612, 0.05954348268803478, 0.04819283678109959, 0.0708093955181539, 0.015293803945731026, 0.1579140304136522, -0.06099809523547049, -0.1405373932121648, 0.3270584166136369, -0.10774599241417444, -0.2648437856744552, 0.2292097199356577, -0.09746670801811803, -0.13689420334322705, 0.1351878374008481, 0.20542885685704418, 0.2093734674194117, -0.17337253680164666, 0.0954809126626585, -0.03018960177168711, 0.10337370309556268, 0.025372568995586223, -0.04081124335661838, 0.04575409813419215, 0.2123475908493796, 0.1629440243039088, 0.09450682146791561, -0.08186168956675942, -0.1662211524725882, -0.26219133359677704, -0.10702103880454891, -0.21543466743110612, 0.04247550418739658, -0.18546938338765837, -0.13862638575260133, 0.34495955736366746, 0.19486095795657524, 0.16727056240029248, 0.13260777153197156, 0.32338560381712217, 0.09362666057941825, -0.008747571836868973, 0.11974802578693812, 0.18049089316177883, 0.16277009345946314, 0.09056046709763943, -0.2126081902581776, 0.08042425707405068, 0.10838716951938174] |
1,802.05432 | On the timing behavior of PSR B1259-63 under the propeller torque from a
transient accretion disc | The $\gamma$-ray pulsar binary system PSR B1259-63 flares in GeV after each
periastron. The origin of those flares is still under debate. Recently,
[2017ApJ...844..114Y] proposed a mechanism that might explain the GeV flares.
In that model, a transient accretion disc is expected to be formed from the
matter which was gravity-captured by the neutron star from the main sequence
companion's circumstellar disc. The transient accretion disc exerts a spin down
torque on the neutron star (propeller effect), which might be traceable via
pulsar timing observation of PSR B1259-63. In this paper, we phenomenologically
consider the propeller effect with a parameter $\chi$, which describes the
coupling between the disc matter and the neutron star. Comparing the expected
timing residuals against the recent observation in [2014MNRAS.437.3255S], we
conclude that the angular momentum transfer is very weak (with the coupling
parameter $\chi\le10^{-4}$).
| astro-ph.HE | the gammaray pulsar binary system psr b125963 flares in gev after each periastron the origin of those flares is still under debate recently 2017apj844114y proposed a mechanism that might explain the gev flares in that model a transient accretion disc is expected to be formed from the matter which was gravitycaptured by the neutron star from the main sequence companions circumstellar disc the transient accretion disc exerts a spin down torque on the neutron star propeller effect which might be traceable via pulsar timing observation of psr b125963 in this paper we phenomenologically consider the propeller effect with a parameter chi which describes the coupling between the disc matter and the neutron star comparing the expected timing residuals against the recent observation in 2014mnras4373255s we conclude that the angular momentum transfer is very weak with the coupling parameter chile104 | [['the', 'gammaray', 'pulsar', 'binary', 'system', 'psr', 'b125963', 'flares', 'in', 'gev', 'after', 'each', 'periastron', 'the', 'origin', 'of', 'those', 'flares', 'is', 'still', 'under', 'debate', 'recently', '2017apj844114y', 'proposed', 'a', 'mechanism', 'that', 'might', 'explain', 'the', 'gev', 'flares', 'in', 'that', 'model', 'a', 'transient', 'accretion', 'disc', 'is', 'expected', 'to', 'be', 'formed', 'from', 'the', 'matter', 'which', 'was', 'gravitycaptured', 'by', 'the', 'neutron', 'star', 'from', 'the', 'main', 'sequence', 'companions', 'circumstellar', 'disc', 'the', 'transient', 'accretion', 'disc', 'exerts', 'a', 'spin', 'down', 'torque', 'on', 'the', 'neutron', 'star', 'propeller', 'effect', 'which', 'might', 'be', 'traceable', 'via', 'pulsar', 'timing', 'observation', 'of', 'psr', 'b125963', 'in', 'this', 'paper', 'we', 'phenomenologically', 'consider', 'the', 'propeller', 'effect', 'with', 'a', 'parameter', 'chi', 'which', 'describes', 'the', 'coupling', 'between', 'the', 'disc', 'matter', 'and', 'the', 'neutron', 'star', 'comparing', 'the', 'expected', 'timing', 'residuals', 'against', 'the', 'recent', 'observation', 'in', '2014mnras4373255s', 'we', 'conclude', 'that', 'the', 'angular', 'momentum', 'transfer', 'is', 'very', 'weak', 'with', 'the', 'coupling', 'parameter', 'chile104']] | [-0.14281198755079122, 0.19095182107002648, -0.09793218417714039, 0.12528483386289466, -0.17965310955489122, -0.08212347562873253, 0.04602367209477557, 0.38035903784135977, -0.23049181637664637, -0.32187973078409277, 0.05799745070506577, -0.2578726505515752, -0.04673125223705062, 0.23717417144216596, -0.05189784595535861, -0.011211786238925048, 0.13558665187167074, -0.026981852308812518, -0.03501700565943287, -0.19406863271185473, 0.29518611327447514, 0.10424151940754166, 0.08185257136269852, -0.006673050408372311, 0.07851324525351326, -0.0669911515329861, 0.03292753254580829, -0.09616919948409001, -0.13441470187341717, -0.03013147932296205, 0.1969963019368825, 0.06939815458565675, 0.1696417620509035, -0.39305437501106, -0.24273432904328393, 0.06619146965720035, 0.11791636663040629, 0.006701100332214049, -0.07120512651917697, -0.28551205951737724, 0.061300755673329585, -0.3121291084861797, -0.12322991107487016, 0.06186895871672917, 0.07486662340729877, 0.033936645104377355, -0.23120174829554915, 0.09292123905486531, 0.12215314700647636, -0.016107012217657434, -0.14113962352620782, -0.034163168016648676, -0.009879211108717654, -0.003519473600856684, 0.15886790750168817, 0.10828545847562727, 0.1747916392809539, -0.134853677848285, -0.09662185650732782, 0.3716204859868244, -0.05702524571420832, -0.02967317142325488, 0.1655139109223253, -0.2777027089873122, -0.17345496625294565, 0.1534336251034229, 0.1165390294459131, 0.10837498147868448, -0.17818414648796466, 0.009520255047741726, -0.04332894298627421, 0.22960533737722372, 0.04865248008217249, 0.015099808283978038, 0.4315605506990795, 0.18126016413810214, 0.004473825923546597, 0.14880752937212863, -0.2606850501029166, -0.05736497023694769, -0.21185992884070234, -0.018850665336854203, -0.16471391078774575, 0.12205690454381207, -0.10663420722498644, -0.07682296872069991, 0.37028124671843315, 0.11669422407048168, 0.19579040747549797, -0.0235886095193025, 0.3148724257980508, 0.13530944883133525, 0.04200278732115058, 0.14361942121241655, 0.38542309412129083, 0.1736171001844384, 0.13503466441330533, -0.3218476831499073, 0.14514221115193018, 0.00436340447653223] |
1,802.05433 | Characterising the observational properties of {\delta} Sct stars in the
era of space photometry from the Kepler mission | The {\delta} Sct stars are a diverse group of intermediate-mass pulsating
stars located on and near the main sequence within the classical instability
strip in the Hertzsprung-Russell diagram. Many of these stars are hybrid stars
pulsating simultaneously with pressure and gravity modes that probe the physics
at different depths within a star's interior. Using two large ensembles of
{\delta} Sct stars observed by the Kepler Space Telescope, the instrumental
biases inherent to Kepler mission data and the statistical properties of these
stars are investigated. An important focus of this work is an analysis of the
relationships between the pulsational and stellar parameters, and their
distribution within the classical instability strip. It is found that a
non-negligible fraction of main sequence {\delta} Sct stars exist outside
theoretical predictions of the classical instability boundaries, which
indicates the necessity of a mass-dependent mixing length parameter to
simultaneously explain low- and high-radial order pressure modes in {\delta}
Sct stars within the Hertzsprung-Russell diagram. Furthermore, a search for
regularities in the amplitude spectra of these stars is also presented,
specifically the frequency difference between pressure modes of consecutive
radial order. In this work, it is demonstrated that an ensemble-based approach
using space photometry from the Kepler mission is not only plausible for
{\delta} Sct stars, but that it is a valuable method for identifying the most
promising stars for mode identification and asteroseismic modelling. The full
scientific potential of studying {\delta} Sct stars is as yet unrealised. The
ensembles discussed in this paper represent a high-quality data set for future
studies of rotation and angular momentum transport inside A and F stars using
asteroseismology.
| astro-ph.SR | the delta sct stars are a diverse group of intermediatemass pulsating stars located on and near the main sequence within the classical instability strip in the hertzsprungrussell diagram many of these stars are hybrid stars pulsating simultaneously with pressure and gravity modes that probe the physics at different depths within a stars interior using two large ensembles of delta sct stars observed by the kepler space telescope the instrumental biases inherent to kepler mission data and the statistical properties of these stars are investigated an important focus of this work is an analysis of the relationships between the pulsational and stellar parameters and their distribution within the classical instability strip it is found that a nonnegligible fraction of main sequence delta sct stars exist outside theoretical predictions of the classical instability boundaries which indicates the necessity of a massdependent mixing length parameter to simultaneously explain low and highradial order pressure modes in delta sct stars within the hertzsprungrussell diagram furthermore a search for regularities in the amplitude spectra of these stars is also presented specifically the frequency difference between pressure modes of consecutive radial order in this work it is demonstrated that an ensemblebased approach using space photometry from the kepler mission is not only plausible for delta sct stars but that it is a valuable method for identifying the most promising stars for mode identification and asteroseismic modelling the full scientific potential of studying delta sct stars is as yet unrealised the ensembles discussed in this paper represent a highquality data set for future studies of rotation and angular momentum transport inside a and f stars using asteroseismology | [['the', 'delta', 'sct', 'stars', 'are', 'a', 'diverse', 'group', 'of', 'intermediatemass', 'pulsating', 'stars', 'located', 'on', 'and', 'near', 'the', 'main', 'sequence', 'within', 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1,802.05434 | Modeling Venus-Like Worlds Through Time | We explore the atmospheric and surface history of a hypothetical paleo-Venus
climate using a 3-D General Circulation Model. We constrain our model with the
in-situ and remote sensing Venus data available today. Given that Venus and
Earth are believed to be similar geochemically some aspects of Earth's history
are also utilized. We demonstrate that it is possible for ancient Venus and
Venus-like exoplanetary worlds to exist within the liquid water habitable zone
with insolations up to nearly 2 times that of modern Earth.
| astro-ph.EP | we explore the atmospheric and surface history of a hypothetical paleovenus climate using a 3d general circulation model we constrain our model with the insitu and remote sensing venus data available today given that venus and earth are believed to be similar geochemically some aspects of earths history are also utilized we demonstrate that it is possible for ancient venus and venuslike exoplanetary worlds to exist within the liquid water habitable zone with insolations up to nearly 2 times that of modern earth | [['we', 'explore', 'the', 'atmospheric', 'and', 'surface', 'history', 'of', 'a', 'hypothetical', 'paleovenus', 'climate', 'using', 'a', '3d', 'general', 'circulation', 'model', 'we', 'constrain', 'our', 'model', 'with', 'the', 'insitu', 'and', 'remote', 'sensing', 'venus', 'data', 'available', 'today', 'given', 'that', 'venus', 'and', 'earth', 'are', 'believed', 'to', 'be', 'similar', 'geochemically', 'some', 'aspects', 'of', 'earths', 'history', 'are', 'also', 'utilized', 'we', 'demonstrate', 'that', 'it', 'is', 'possible', 'for', 'ancient', 'venus', 'and', 'venuslike', 'exoplanetary', 'worlds', 'to', 'exist', 'within', 'the', 'liquid', 'water', 'habitable', 'zone', 'with', 'insolations', 'up', 'to', 'nearly', '2', 'times', 'that', 'of', 'modern', 'earth']] | [-0.0411275893236589, 0.19581364735916076, -0.06795400402828468, 0.09524653228813311, -0.08020324823839298, -0.057807985558567275, 0.0428278611928045, 0.3583958261163838, -0.23133798762707106, -0.3649589088128274, 0.16010971375774727, -0.27886995454374924, -0.18567732224487757, 0.23288863848513328, -0.08865766538621252, 0.05592463545344142, 0.11584219534556012, 0.0032442889501710973, -0.024923981748751354, -0.2457478426800794, 0.21867983870724417, 0.10411793611809073, 0.1180808082200497, 0.034526681768741595, 0.07135349720210139, -0.1036930815485048, -0.0015024848966803178, -0.07601385393056524, -0.17400274174812136, 0.11550786761365023, 0.27314293211471874, 0.19303635632372404, 0.1390084291819528, -0.5212409870856138, -0.2603109453479389, 0.062051964477714466, 0.07254000689767032, 0.05258320206888469, -0.058046171789517605, -0.25225364751216156, 0.046140912273289994, -0.19053972061409288, -0.16990981378917952, -0.03603626143591231, 0.05929845535611532, -0.057968291016758405, -0.2380139234290649, 0.04365183349876912, 0.007713711997269687, 0.12340812523097518, -0.14341467289613133, -0.13339099235108953, -0.0709353395518903, 0.16884424914998641, 0.04392567100788815, 0.013760661251887858, 0.18029416799657497, -0.04517910947058215, -0.02240961489368634, 0.47206393220877074, -0.12118010230766363, -0.08851079871676054, 0.2744334654164422, -0.23028167520357992, -0.08836204175983207, 0.08385926356584013, 0.17232939439365663, 0.12358780328678079, -0.16616210069911308, 0.017633685884234238, -0.07301843104076135, 0.13427798865161028, 0.0840668559820582, -0.00840967332632898, 0.35525690808510746, 0.20346385901457215, 0.07256339025313416, 0.021025666729995913, -0.18593026985167468, -0.09015815185610182, -0.1532630037743576, -0.17878994236823664, -0.13309577815459075, -0.0016404181629060262, -0.04713564239798607, -0.1248164002121572, 0.3551979546760579, 0.2329344682350575, 0.10968174254633935, -0.023339154602712894, 0.3092364868799965, -0.017979948190938545, 0.0670652060039448, 0.11815872946080852, 0.28950069902684955, 0.06946535543286163, 0.10686287195814481, -0.18096461486988644, 0.11038281155346208, -0.029091991035335035] |
1,802.05435 | Analysis of the Web Graph Aggregated by Host and Pay-Level Domain | In this paper the web is analyzed as a graph aggregated by host and pay-level
domain (PLD). The web graph datasets, publicly available, have been released by
the Common Crawl Foundation and are based on a web crawl performed during the
period May-June-July 2017. The host graph has $\sim$1.3 billion nodes and
$\sim$5.3 billion arcs. The PLD graph has $\sim$91 million nodes and $\sim$1.1
billion arcs. We study the distributions of degree and sizes of strongly/weakly
connected components (SCC/WCC) focusing on power laws detection using
statistical methods. The statistical plausibility of the power law model is
compared with that of several alternative distributions. While there is no
evidence of power law tails on host level, they emerge on PLD aggregation for
indegree, SCC and WCC size distributions. Finally, we analyze distance-related
features by studying the cumulative distributions of the shortest path lengths,
and give an estimation of the diameters of the graphs.
| cs.SI | in this paper the web is analyzed as a graph aggregated by host and paylevel domain pld the web graph datasets publicly available have been released by the common crawl foundation and are based on a web crawl performed during the period mayjunejuly 2017 the host graph has sim13 billion nodes and sim53 billion arcs the pld graph has sim91 million nodes and sim11 billion arcs we study the distributions of degree and sizes of stronglyweakly connected components sccwcc focusing on power laws detection using statistical methods the statistical plausibility of the power law model is compared with that of several alternative distributions while there is no evidence of power law tails on host level they emerge on pld aggregation for indegree scc and wcc size distributions finally we analyze distancerelated features by studying the cumulative distributions of the shortest path lengths and give an estimation of the diameters of the graphs | [['in', 'this', 'paper', 'the', 'web', 'is', 'analyzed', 'as', 'a', 'graph', 'aggregated', 'by', 'host', 'and', 'paylevel', 'domain', 'pld', 'the', 'web', 'graph', 'datasets', 'publicly', 'available', 'have', 'been', 'released', 'by', 'the', 'common', 'crawl', 'foundation', 'and', 'are', 'based', 'on', 'a', 'web', 'crawl', 'performed', 'during', 'the', 'period', 'mayjunejuly', '2017', 'the', 'host', 'graph', 'has', 'sim13', 'billion', 'nodes', 'and', 'sim53', 'billion', 'arcs', 'the', 'pld', 'graph', 'has', 'sim91', 'million', 'nodes', 'and', 'sim11', 'billion', 'arcs', 'we', 'study', 'the', 'distributions', 'of', 'degree', 'and', 'sizes', 'of', 'stronglyweakly', 'connected', 'components', 'sccwcc', 'focusing', 'on', 'power', 'laws', 'detection', 'using', 'statistical', 'methods', 'the', 'statistical', 'plausibility', 'of', 'the', 'power', 'law', 'model', 'is', 'compared', 'with', 'that', 'of', 'several', 'alternative', 'distributions', 'while', 'there', 'is', 'no', 'evidence', 'of', 'power', 'law', 'tails', 'on', 'host', 'level', 'they', 'emerge', 'on', 'pld', 'aggregation', 'for', 'indegree', 'scc', 'and', 'wcc', 'size', 'distributions', 'finally', 'we', 'analyze', 'distancerelated', 'features', 'by', 'studying', 'the', 'cumulative', 'distributions', 'of', 'the', 'shortest', 'path', 'lengths', 'and', 'give', 'an', 'estimation', 'of', 'the', 'diameters', 'of', 'the', 'graphs']] | [-0.11865622542123466, 0.07543837951057537, -0.0865812794252255, 0.04286190289644612, -0.0663141332513609, -0.08934525917315443, 0.07504101655830397, 0.41932560573488276, -0.22828408006591186, -0.37268827091152434, 0.10479396411245877, -0.35762864505034564, -0.08516375429697035, 0.1705623851017089, -0.027501373718986474, 0.03467622969296429, 0.08860835047116841, 0.03592040942944336, 0.01730856002892849, -0.28281307030834835, 0.29118508885102035, 0.0794357653260356, 0.2937228677625574, 0.038082483194490285, 0.09814639853926113, -0.009580291585759229, -0.08586234058001103, 0.03397147684989359, -0.16759099416777956, 0.12048272007213742, 0.19475956492894797, 0.1545311500327724, 0.25177277233951645, -0.40663407957056324, -0.2104170877227072, 0.10594406245501349, 0.10832829414413704, 0.026342690334090455, -0.05508670415932425, -0.27343875004897433, 0.13218673383942087, -0.15991474726933114, -0.072724689720136, -0.012800691034426074, 0.09127488716711733, 0.05935695999100392, -0.16869845913079312, 0.08649362775969051, 0.035242468559326945, 0.10231655547332844, -0.009221359246704202, -0.13812454399690283, -0.04608264431707411, 0.14873778278433136, 0.01692684672088456, 0.004933099333710981, 0.12823091944267087, -0.1106744861154467, -0.16097468322130457, 0.364164695142137, -0.00038830455878574474, -0.08716516325800311, 0.17296441397770315, -0.09686512095328645, -0.1462099376234582, 0.11241159459651016, 0.2175811364771736, 0.10736961202782301, -0.18868333949463678, 0.05263422544609643, -0.0365027800052358, 0.20184464861745102, 0.09633706215981569, 0.0010905759799725457, 0.2125644101712938, 0.2030366545405564, 0.04378658324442358, 0.13997642256494536, -0.12914421446695643, -0.0626012582438154, -0.2173906696472792, -0.10272461587864742, -0.2282633811404191, 0.015617332139971272, -0.15680248745267464, -0.13737839672264437, 0.44326137413189515, 0.12308388746237295, 0.20577498812122183, 0.09717993961487766, 0.2735090207483544, 0.0555962659567032, 0.09419281960548223, 0.1450837781401226, 0.16819245272297287, 0.0945678688570132, 0.08283132982390289, -0.11477363700572177, 0.12527163496753513, 0.030254594595702802] |
1,802.05436 | Detection of fractional solitons in quantum spin Hall systems | We propose two experimental setups that allow for the implementation and the
detection of fractional solitons of the Goldstone-Wilczek type. The first setup
is based on two magnetic barriers at the edge of a quantum spin Hall system for
generating the fractional soliton. If then a quantum point contact is created
with the other edge, the linear conductance shows evidence of the fractional
soliton. The second setup consists of a single magnetic barrier covering both
edges and implementing a long quantum point contact. In this case, the
fractional soliton can unambiguously be detected as a dip in the conductance
without the need to control the magnetization of the barrier.
| cond-mat.mes-hall | we propose two experimental setups that allow for the implementation and the detection of fractional solitons of the goldstonewilczek type the first setup is based on two magnetic barriers at the edge of a quantum spin hall system for generating the fractional soliton if then a quantum point contact is created with the other edge the linear conductance shows evidence of the fractional soliton the second setup consists of a single magnetic barrier covering both edges and implementing a long quantum point contact in this case the fractional soliton can unambiguously be detected as a dip in the conductance without the need to control the magnetization of the barrier | [['we', 'propose', 'two', 'experimental', 'setups', 'that', 'allow', 'for', 'the', 'implementation', 'and', 'the', 'detection', 'of', 'fractional', 'solitons', 'of', 'the', 'goldstonewilczek', 'type', 'the', 'first', 'setup', 'is', 'based', 'on', 'two', 'magnetic', 'barriers', 'at', 'the', 'edge', 'of', 'a', 'quantum', 'spin', 'hall', 'system', 'for', 'generating', 'the', 'fractional', 'soliton', 'if', 'then', 'a', 'quantum', 'point', 'contact', 'is', 'created', 'with', 'the', 'other', 'edge', 'the', 'linear', 'conductance', 'shows', 'evidence', 'of', 'the', 'fractional', 'soliton', 'the', 'second', 'setup', 'consists', 'of', 'a', 'single', 'magnetic', 'barrier', 'covering', 'both', 'edges', 'and', 'implementing', 'a', 'long', 'quantum', 'point', 'contact', 'in', 'this', 'case', 'the', 'fractional', 'soliton', 'can', 'unambiguously', 'be', 'detected', 'as', 'a', 'dip', 'in', 'the', 'conductance', 'without', 'the', 'need', 'to', 'control', 'the', 'magnetization', 'of', 'the', 'barrier']] | [-0.18697695248256171, 0.12383689476741017, -0.08239407650805976, 0.002209238358773291, -0.05793454462593985, -0.18190370877316103, 0.04362891774428068, 0.3564794086552131, -0.2583724625254853, -0.2902437121000722, 0.07958156586658859, -0.2681538539665213, -0.13385627760526236, 0.22006420993861323, 0.012240662337750743, 0.06754823526743492, 0.028200922226276966, 0.04814604294289826, -0.04482637413931207, -0.18430832030418234, 0.3312473673364916, -0.0354055149743436, 0.25251671867530556, 0.09612360564188673, 0.08589294460705436, 0.0031024462205930313, 0.04011322977448549, 0.028662919511147047, -0.11560105839393095, 0.07487817967541242, 0.2072027473701933, -0.04408267290339968, 0.23780242481841407, -0.4511220453460829, -0.19865719929610917, 0.07498272207625416, 0.11355580523644292, 0.1651255016753433, -0.06626061129345294, -0.29644363571958054, 0.07216407156575026, -0.13442404812560715, -0.13604132555117573, -0.010872415416455323, 0.008020560803968426, 0.013349396753372676, -0.22979555605406607, 0.074788734777147, 0.08399609328512478, 0.009323256021929443, -0.042122731019460825, -0.06376279571302954, -0.03749729117481124, 0.09522015575550223, -0.04318930554724888, -0.0012239215921166294, 0.12613415065298386, -0.16052792743467853, -0.20640120708699758, 0.34336495381532184, -0.0904396202312697, -0.16634579141167155, 0.1224005516402748, -0.14154707721579785, -0.07814679711570091, 0.12294451680478699, 0.09206574623806214, 0.10986641205958339, -0.12327909232074515, 0.05226933574111739, -0.020777942399161125, 0.15063587790023997, 0.07642333240273895, 0.03779580316788286, 0.25786796227575987, 0.19724574979018727, 0.12352880970104982, 0.1742438250984954, -0.19547197854933634, -0.10632932162202827, -0.3384714140257704, -0.2058518533658988, -0.21150451009759869, 0.06638471900545266, -0.06246185485127279, -0.1748637763159606, 0.4304063956962403, 0.11382455368944948, 0.19775061833313368, -0.009967288549203392, 0.29772461723502075, 0.1729735538797077, 0.05665524147980667, 0.05055299532010194, 0.20737390482687143, 0.11657178440396118, 0.1325583536130613, -0.2822460827458791, 0.030068722451020272, 0.02712493050211203] |
1,802.05437 | Optimization of the light intensity for Photodetectors calibration | In this article we present an evaluation of the uncertainty in the average
number of photoelectrons, which is important for the calibration of
photodetectors. We show that the statistical uncertainty depends on light
intensity, and on the method of evaluation. For some cases there is optimal
light intensity where the accuracy reaches its optimal value with fixed
statistics. A method of photoelectron evaluation based on the extraction of
pedestal's (zero) probability gives the best accuracy at approximately 1.6
photoelectrons for a noiseless photodetector and shifts out to higher values
with the presence of noise. In the general case, estimation of the average
number of photoelectrons is biased and might need special consideration.
| physics.ins-det hep-ex | in this article we present an evaluation of the uncertainty in the average number of photoelectrons which is important for the calibration of photodetectors we show that the statistical uncertainty depends on light intensity and on the method of evaluation for some cases there is optimal light intensity where the accuracy reaches its optimal value with fixed statistics a method of photoelectron evaluation based on the extraction of pedestals zero probability gives the best accuracy at approximately 16 photoelectrons for a noiseless photodetector and shifts out to higher values with the presence of noise in the general case estimation of the average number of photoelectrons is biased and might need special consideration | [['in', 'this', 'article', 'we', 'present', 'an', 'evaluation', 'of', 'the', 'uncertainty', 'in', 'the', 'average', 'number', 'of', 'photoelectrons', 'which', 'is', 'important', 'for', 'the', 'calibration', 'of', 'photodetectors', 'we', 'show', 'that', 'the', 'statistical', 'uncertainty', 'depends', 'on', 'light', 'intensity', 'and', 'on', 'the', 'method', 'of', 'evaluation', 'for', 'some', 'cases', 'there', 'is', 'optimal', 'light', 'intensity', 'where', 'the', 'accuracy', 'reaches', 'its', 'optimal', 'value', 'with', 'fixed', 'statistics', 'a', 'method', 'of', 'photoelectron', 'evaluation', 'based', 'on', 'the', 'extraction', 'of', 'pedestals', 'zero', 'probability', 'gives', 'the', 'best', 'accuracy', 'at', 'approximately', '16', 'photoelectrons', 'for', 'a', 'noiseless', 'photodetector', 'and', 'shifts', 'out', 'to', 'higher', 'values', 'with', 'the', 'presence', 'of', 'noise', 'in', 'the', 'general', 'case', 'estimation', 'of', 'the', 'average', 'number', 'of', 'photoelectrons', 'is', 'biased', 'and', 'might', 'need', 'special', 'consideration']] | [-0.08378821952179091, 0.07558437601164769, -0.05623784816582754, 0.021528278856879166, -0.0053917812786364394, -0.10421251522244088, 0.10243507077809356, 0.3923461795784533, -0.17746743733213016, -0.34435322540645885, 0.07188864952123757, -0.30108996160535234, -0.09494268722898726, 0.24694700143299997, -0.10562124953139573, 0.06784699383531136, 0.07634781818965816, 0.05701515994067969, -0.07975364485381371, -0.25002747752504156, 0.2786936891207006, 0.1127738191280514, 0.31507118222777664, 0.06735540456637475, 0.14910426644408808, 0.04106776482408999, -0.03346170718681866, -0.0012500983927858605, -0.09360140040923527, 0.12284917951702871, 0.213097594986071, 0.08953801502190929, 0.277936618001799, -0.3310806888122378, -0.1694799630121062, 0.10594842998710062, 0.11176882387787503, 0.10030080272762072, -0.028162250186142046, -0.22867718359962705, 0.06225594201324774, -0.10239179257145484, -0.08518036577152088, -0.017801919557054395, 0.017691807538670088, 0.05126351367549172, -0.29766716818890665, 0.08465059188893065, 0.02801445987175352, 0.060300339005022706, -0.025280960794978973, -0.16307873727782862, 0.011736717736182203, 0.12615027092397213, 0.055312286374308836, 0.013845473473858354, 0.15431230744330346, -0.13954942154357144, -0.06816696876610097, 0.3682902587627593, -0.08271990853661139, -0.2047252311209117, 0.0977247709722308, -0.1777878803051343, -0.11347520968411118, 0.18319686114825476, 0.1675755378507477, 0.11924362610027726, -0.09221268303060372, 0.01755665661195443, -0.02338222796346859, 0.21741403700850373, 0.0717168284962619, 0.06593673742203723, 0.17076055411598645, 0.18811649684461632, 0.09875758768404401, 0.14339348719555087, -0.1667588903115497, -0.060911919796905876, -0.33706693311354946, -0.15314663616507979, -0.17994205186343087, 0.06268697043248851, -0.10340990096990156, -0.1366905928922019, 0.41611432985934826, 0.20548311179404014, 0.1653139214156129, 0.06565543043896989, 0.3340791812765279, 0.16227912307035045, -0.0007345780572255275, 0.06480129475780164, 0.24885987904235662, 0.09727584346650733, 0.061525550882964, -0.23656149675870047, 0.08999065084831923, -0.007156645758576425] |
1,802.05438 | Mean Field Multi-Agent Reinforcement Learning | Existing multi-agent reinforcement learning methods are limited typically to
a small number of agents. When the agent number increases largely, the learning
becomes intractable due to the curse of the dimensionality and the exponential
growth of agent interactions. In this paper, we present \emph{Mean Field
Reinforcement Learning} where the interactions within the population of agents
are approximated by those between a single agent and the average effect from
the overall population or neighboring agents; the interplay between the two
entities is mutually reinforced: the learning of the individual agent's optimal
policy depends on the dynamics of the population, while the dynamics of the
population change according to the collective patterns of the individual
policies. We develop practical mean field Q-learning and mean field
Actor-Critic algorithms and analyze the convergence of the solution to Nash
equilibrium. Experiments on Gaussian squeeze, Ising model, and battle games
justify the learning effectiveness of our mean field approaches. In addition,
we report the first result to solve the Ising model via model-free
reinforcement learning methods.
| cs.MA cs.AI cs.LG | existing multiagent reinforcement learning methods are limited typically to a small number of agents when the agent number increases largely the learning becomes intractable due to the curse of the dimensionality and the exponential growth of agent interactions in this paper we present emphmean field reinforcement learning where the interactions within the population of agents are approximated by those between a single agent and the average effect from the overall population or neighboring agents the interplay between the two entities is mutually reinforced the learning of the individual agents optimal policy depends on the dynamics of the population while the dynamics of the population change according to the collective patterns of the individual policies we develop practical mean field qlearning and mean field actorcritic algorithms and analyze the convergence of the solution to nash equilibrium experiments on gaussian squeeze ising model and battle games justify the learning effectiveness of our mean field approaches in addition we report the first result to solve the ising model via modelfree reinforcement learning methods | [['existing', 'multiagent', 'reinforcement', 'learning', 'methods', 'are', 'limited', 'typically', 'to', 'a', 'small', 'number', 'of', 'agents', 'when', 'the', 'agent', 'number', 'increases', 'largely', 'the', 'learning', 'becomes', 'intractable', 'due', 'to', 'the', 'curse', 'of', 'the', 'dimensionality', 'and', 'the', 'exponential', 'growth', 'of', 'agent', 'interactions', 'in', 'this', 'paper', 'we', 'present', 'emphmean', 'field', 'reinforcement', 'learning', 'where', 'the', 'interactions', 'within', 'the', 'population', 'of', 'agents', 'are', 'approximated', 'by', 'those', 'between', 'a', 'single', 'agent', 'and', 'the', 'average', 'effect', 'from', 'the', 'overall', 'population', 'or', 'neighboring', 'agents', 'the', 'interplay', 'between', 'the', 'two', 'entities', 'is', 'mutually', 'reinforced', 'the', 'learning', 'of', 'the', 'individual', 'agents', 'optimal', 'policy', 'depends', 'on', 'the', 'dynamics', 'of', 'the', 'population', 'while', 'the', 'dynamics', 'of', 'the', 'population', 'change', 'according', 'to', 'the', 'collective', 'patterns', 'of', 'the', 'individual', 'policies', 'we', 'develop', 'practical', 'mean', 'field', 'qlearning', 'and', 'mean', 'field', 'actorcritic', 'algorithms', 'and', 'analyze', 'the', 'convergence', 'of', 'the', 'solution', 'to', 'nash', 'equilibrium', 'experiments', 'on', 'gaussian', 'squeeze', 'ising', 'model', 'and', 'battle', 'games', 'justify', 'the', 'learning', 'effectiveness', 'of', 'our', 'mean', 'field', 'approaches', 'in', 'addition', 'we', 'report', 'the', 'first', 'result', 'to', 'solve', 'the', 'ising', 'model', 'via', 'modelfree', 'reinforcement', 'learning', 'methods']] | [-0.07949533417875713, 0.08139825871433405, -0.08312358997885466, 0.05864749149255016, -0.08904734731049221, -0.1520777872434872, 0.0917790069429697, 0.41952146153403996, -0.3108625570325838, -0.32761553718763237, 0.04920040387249387, -0.2645488453316776, -0.1685228535809609, 0.10005873776835335, -0.08827182299539665, 0.039341140322058515, 0.04572181715082158, 0.05890211045248982, 0.019472893754787304, -0.2908976886339267, 0.3299490580102429, 0.027777445349184905, 0.3154601137208588, -0.027429764015901397, 0.1476497228093007, 0.020961585495730534, 0.03704037272700054, 0.026349590338983996, -0.0960446352906087, 0.15063205796348697, 0.27428910550387464, 0.15756765496369232, 0.4166612178854206, -0.4280136232319124, -0.17048278393306057, 0.15119296447921762, 0.16943370855057283, 0.13456760676002458, -0.009659902635030448, -0.3131898944430492, 0.010521957239903072, -0.17589570841039806, -0.05605656900990973, -0.06586224206120653, -0.04312664325321641, 0.06694770378800219, -0.28853813892449526, 0.035395824880448776, 0.06540195677867706, 0.08516415398403564, -0.09269188211354262, -0.11319031260414597, 0.03904892845654531, 0.16677663416468183, 0.10162693024886882, 0.006267798760467592, 0.20488343246329083, -0.20044342813164215, -0.18033063125193996, 0.32834655283128517, -0.03784155834411435, -0.1812481218839393, 0.20637528167366434, -0.07790250821751268, -0.08800649438892244, 0.10507608988349709, 0.25484788166598743, 0.1487091655459474, -0.1397837955858182, 0.059541990126907716, -0.023593427175108123, 0.15853642340311233, -0.04139420607072466, -0.028237455584766234, 0.14324573812568012, 0.2206398951149929, 0.08954444675541975, 0.12341564038823194, -0.052288763742784364, -0.22745754966128837, -0.2041377579793334, -0.09817594028598464, -0.19056769250070346, 0.014013173599617885, -0.14426677266695634, -0.14551454727702282, 0.3594304250525858, 0.2022097218014738, 0.17547090998576845, 0.13726123465115533, 0.3241828497060958, 0.08594665594522238, 0.0282883034351364, 0.11537805727682152, 0.2680648068801555, 0.0883340552175308, 0.09731730553732418, -0.2904786831439089, 0.15019549226580078, 0.026580762382432382] |
1,802.05439 | Mitigation of dynamical instabilities in laser arrays via non-Hermitian
coupling | Arrays of coupled semiconductor lasers are systems possessing complex
dynamical behavior that are of major interest in photonics and laser science.
Dynamical instabilities, arising from supermode competition and slow carrier
dynamics, are known to prevent stable phase locking in a wide range of
parameter space, requiring special methods to realize stable laser operation.
Inspired by recent concepts of parity-time ($\mathcal{PT}$) and non-Hermitian
photonics, in this work we consider non-Hermitian coupling engineering in laser
arrays in a ring geometry and show, both analytically and numerically, that
non-Hermitian coupling can help to mitigate the onset of dynamical laser
instabilities. In particular, we consider in details two kinds of
nearest-neighbor non-Hermitian couplings: symmetric but complex mode coupling
(type-I non-Hermitian coupling) and asymmetric mode coupling (type-II
non-Hermitian coupling). Suppression of dynamical instabilities can be realized
in both coupling schemes, resulting in stable phase-locking laser emission with
the lasers emitting in phase (for type-I coupling) or with $\pi/2$ phase
gradient (for type-II coupling), resulting in a vortex far-field beam. In
type-II non-Hermitian coupling, chirality induced by asymmetric mode coupling
enables laser phase locking even in presence of moderate disorder in the
resonance frequencies of the lasers.
| physics.optics quant-ph | arrays of coupled semiconductor lasers are systems possessing complex dynamical behavior that are of major interest in photonics and laser science dynamical instabilities arising from supermode competition and slow carrier dynamics are known to prevent stable phase locking in a wide range of parameter space requiring special methods to realize stable laser operation inspired by recent concepts of paritytime mathcalpt and nonhermitian photonics in this work we consider nonhermitian coupling engineering in laser arrays in a ring geometry and show both analytically and numerically that nonhermitian coupling can help to mitigate the onset of dynamical laser instabilities in particular we consider in details two kinds of nearestneighbor nonhermitian couplings symmetric but complex mode coupling typei nonhermitian coupling and asymmetric mode coupling typeii nonhermitian coupling suppression of dynamical instabilities can be realized in both coupling schemes resulting in stable phaselocking laser emission with the lasers emitting in phase for typei coupling or with pi2 phase gradient for typeii coupling resulting in a vortex farfield beam in typeii nonhermitian coupling chirality induced by asymmetric mode coupling enables laser phase locking even in presence of moderate disorder in the resonance frequencies of the lasers | [['arrays', 'of', 'coupled', 'semiconductor', 'lasers', 'are', 'systems', 'possessing', 'complex', 'dynamical', 'behavior', 'that', 'are', 'of', 'major', 'interest', 'in', 'photonics', 'and', 'laser', 'science', 'dynamical', 'instabilities', 'arising', 'from', 'supermode', 'competition', 'and', 'slow', 'carrier', 'dynamics', 'are', 'known', 'to', 'prevent', 'stable', 'phase', 'locking', 'in', 'a', 'wide', 'range', 'of', 'parameter', 'space', 'requiring', 'special', 'methods', 'to', 'realize', 'stable', 'laser', 'operation', 'inspired', 'by', 'recent', 'concepts', 'of', 'paritytime', 'mathcalpt', 'and', 'nonhermitian', 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1,802.0544 | Improving the Decoding Threshold of Tailbiting Spatially Coupled LDPC
Codes by Energy Shaping | We show how the iterative decoding threshold of tailbiting spatially coupled
(SC) low-density parity-check (LDPC) code ensembles can be improved over the
binary input additive white Gaussian noise channel by allowing the use of
different transmission energies for the codeword bits. We refer to the proposed
approach as energy shaping. We focus on the special case where the transmission
energy of a bit is selected among two values, and where a contiguous portion of
the codeword is transmitted with the largest one. Given these constraints, an
optimal energy boosting policy is derived by means of protograph extrinsic
information transfer analysis. We show that the threshold of tailbiting SC-LDPC
code ensembles can be made close to that of terminated code ensembles while
avoiding the rate loss (due to termination). The analysis is complemented by
Monte Carlo simulations, which confirm the viability of the approach.
| cs.IT math.IT | we show how the iterative decoding threshold of tailbiting spatially coupled sc lowdensity paritycheck ldpc code ensembles can be improved over the binary input additive white gaussian noise channel by allowing the use of different transmission energies for the codeword bits we refer to the proposed approach as energy shaping we focus on the special case where the transmission energy of a bit is selected among two values and where a contiguous portion of the codeword is transmitted with the largest one given these constraints an optimal energy boosting policy is derived by means of protograph extrinsic information transfer analysis we show that the threshold of tailbiting scldpc code ensembles can be made close to that of terminated code ensembles while avoiding the rate loss due to termination the analysis is complemented by monte carlo simulations which confirm the viability of the approach | [['we', 'show', 'how', 'the', 'iterative', 'decoding', 'threshold', 'of', 'tailbiting', 'spatially', 'coupled', 'sc', 'lowdensity', 'paritycheck', 'ldpc', 'code', 'ensembles', 'can', 'be', 'improved', 'over', 'the', 'binary', 'input', 'additive', 'white', 'gaussian', 'noise', 'channel', 'by', 'allowing', 'the', 'use', 'of', 'different', 'transmission', 'energies', 'for', 'the', 'codeword', 'bits', 'we', 'refer', 'to', 'the', 'proposed', 'approach', 'as', 'energy', 'shaping', 'we', 'focus', 'on', 'the', 'special', 'case', 'where', 'the', 'transmission', 'energy', 'of', 'a', 'bit', 'is', 'selected', 'among', 'two', 'values', 'and', 'where', 'a', 'contiguous', 'portion', 'of', 'the', 'codeword', 'is', 'transmitted', 'with', 'the', 'largest', 'one', 'given', 'these', 'constraints', 'an', 'optimal', 'energy', 'boosting', 'policy', 'is', 'derived', 'by', 'means', 'of', 'protograph', 'extrinsic', 'information', 'transfer', 'analysis', 'we', 'show', 'that', 'the', 'threshold', 'of', 'tailbiting', 'scldpc', 'code', 'ensembles', 'can', 'be', 'made', 'close', 'to', 'that', 'of', 'terminated', 'code', 'ensembles', 'while', 'avoiding', 'the', 'rate', 'loss', 'due', 'to', 'termination', 'the', 'analysis', 'is', 'complemented', 'by', 'monte', 'carlo', 'simulations', 'which', 'confirm', 'the', 'viability', 'of', 'the', 'approach']] | [-0.15363872498766376, 0.08869956537742914, -0.05187273870541395, 0.06282983225863101, 0.01556072428731339, -0.2121324595975709, 0.09281158778764359, 0.4014925374356392, -0.29621225931206263, -0.2887639037522298, 0.10780311588090487, -0.249147404449295, -0.13369683178884167, 0.17915495736784973, -0.05460653320228408, 0.09451908792651557, 0.10906423203210783, 0.030441664813421795, -0.09764184779650369, -0.30534506472840356, 0.3189767303752837, 0.20535962224345317, 0.27990453500769563, -0.017805516485359284, 0.07777660323870271, 0.027905280377807005, -0.032443830658766354, -0.04096633563847183, -0.13400586068003772, 0.07731943457042212, 0.24874191310165542, 0.15911195017853297, 0.2467964082904212, -0.389355436514516, -0.2642392326550154, 0.07876654385289476, 0.15252880472689867, 0.16778353570605925, -0.04025728636261701, -0.22565133750057695, 0.12436461978481261, -0.18716711763917773, -0.01468485690835681, 0.0036462108878145267, -0.10304713879640286, 0.06159259103054598, -0.3155235502465101, 0.017214990735470833, 0.036231791209820265, -0.001971981934291291, -0.005354339219434383, -0.1463151789641568, -0.009771617624413717, 0.11516385402455535, -0.014029509523863923, 0.01794762640191776, 0.10627418279426394, -0.061344593382355844, -0.09330290873450311, 0.3208233581671094, -0.057351603701880034, -0.1973357035760553, 0.11077450311757377, -0.05664807756372786, -0.06926324640211838, 0.18574783398519054, 0.1975096385365098, 0.10948274549344307, -0.1337483036699192, 0.039568921449143195, -0.023848605967432886, 0.2253289970510698, 0.09196098759067017, 0.06959664050190077, 0.19206319158974958, 0.125492905878781, 0.013314286732095313, 0.21089799873127074, -0.13136386394279298, -0.1257693858986551, -0.2567818747064867, -0.09134685283305345, -0.2189556596494586, 0.03631559281220816, -0.11955964986478725, -0.13924700692556538, 0.36640700411338073, 0.16084320072110717, 0.1577922826945469, 0.08718471921462617, 0.3162360034257799, 0.11042437000913298, 0.06798213895840141, 0.15473664778616877, 0.1858219099133373, 0.15732235001836467, -0.018775078799360648, -0.2530963509538947, 0.0753976360836125, 0.049162772342471496] |
1,802.05441 | Niels Henrik Abel and the birth of fractional calculus | This is the paper "Niels Henrik Abel and the birth of fractional calculus",
Podlubny, I., Magin, R. L., Trymorush I., Fractional Calculus and Applied
Analysis, vol.20, no.5, pp.1068-1075, 2017
(https://doi.org/10.1515/fca-2017-0057) with the supplements to it.
Hereby translation to English of the first part of the important Abel's paper
(Abel, N. H., Oplosning af et par opgaver ved hjelp af bestemte integraler,
Magazin for Naturvidenskaberne, Aargang I, Bind 2, Christiania, 1823) and
another important Abel's paper (Abel, N. H., Auflosung einer mechanischen
ausgabe. Journal fur die Reine und Angewandte Mathematik, Bind I, 153-157,
Berlin, 1826) are provided.
To preserve the original flavor and the historical development of
mathematical notation, the formulas are typeset in the original manner.
| math.HO | this is the paper niels henrik abel and the birth of fractional calculus podlubny i magin r l trymorush i fractional calculus and applied analysis vol20 no5 pp10681075 2017 httpsdoiorg101515fca20170057 with the supplements to it hereby translation to english of the first part of the important abels paper abel n h oplosning af et par opgaver ved hjelp af bestemte integraler magazin for naturvidenskaberne aargang i bind 2 christiania 1823 and another important abels paper abel n h auflosung einer mechanischen ausgabe journal fur die reine und angewandte mathematik bind i 153157 berlin 1826 are provided to preserve the original flavor and the historical development of mathematical notation the formulas are typeset in the original manner | [['this', 'is', 'the', 'paper', 'niels', 'henrik', 'abel', 'and', 'the', 'birth', 'of', 'fractional', 'calculus', 'podlubny', 'i', 'magin', 'r', 'l', 'trymorush', 'i', 'fractional', 'calculus', 'and', 'applied', 'analysis', 'vol20', 'no5', 'pp10681075', '2017', 'httpsdoiorg101515fca20170057', 'with', 'the', 'supplements', 'to', 'it', 'hereby', 'translation', 'to', 'english', 'of', 'the', 'first', 'part', 'of', 'the', 'important', 'abels', 'paper', 'abel', 'n', 'h', 'oplosning', 'af', 'et', 'par', 'opgaver', 'ved', 'hjelp', 'af', 'bestemte', 'integraler', 'magazin', 'for', 'naturvidenskaberne', 'aargang', 'i', 'bind', '2', 'christiania', '1823', 'and', 'another', 'important', 'abels', 'paper', 'abel', 'n', 'h', 'auflosung', 'einer', 'mechanischen', 'ausgabe', 'journal', 'fur', 'die', 'reine', 'und', 'angewandte', 'mathematik', 'bind', 'i', '153157', 'berlin', '1826', 'are', 'provided', 'to', 'preserve', 'the', 'original', 'flavor', 'and', 'the', 'historical', 'development', 'of', 'mathematical', 'notation', 'the', 'formulas', 'are', 'typeset', 'in', 'the', 'original', 'manner']] | [-0.08510714643207208, 0.03267127951863697, -0.06389589449960967, 0.05725908828709198, -0.133977508975124, -0.20400848089508256, -0.0022299646848945747, 0.24025717215156372, -0.2349829036001192, -0.2736301970268999, 0.10036129108273747, -0.33888138868199774, -0.1527404008404713, 0.12269007113325048, -0.16106994324099458, -0.037438124261454354, 0.024298507297335535, -0.016029110228243684, 0.031906941848597964, -0.30696835148395324, 0.22392820423630502, 0.05218641220458916, 0.19083020270669035, 0.007136377634252517, 0.05533895307049459, 0.002127791351011517, -0.10852255812063291, -0.10265964132790663, -0.20150482656947355, 0.1417833955160209, 0.2848310937109517, 0.1294095044651506, 0.29579658217119925, -0.3644122082112851, -0.03485424673821473, 0.02280142026886876, 0.130788192152977, 0.032570074288630665, 0.059129578080408425, -0.36219323211710674, 0.03763230620617313, -0.22659608201903045, -0.07158516543176101, -0.022206458362883756, 0.14453973018621302, 0.02019047087630998, -0.22454040428048608, 0.1011932144768308, 0.16152429383020012, 0.11737461700769407, -0.03875770881696015, -0.2133310814257426, -0.03847280629834502, 0.05888537671036866, -0.047526703636656155, 0.09797858221133296, 0.05648693189319527, -0.037891750290457694, -0.0656596332163626, 0.35479459493440024, -0.03153119629191957, -0.11065041712884392, 0.12301133579232407, -0.09858197287409282, -0.2145842302932727, 0.043225722786571295, 0.08713680265319286, 0.07845873402773726, -0.10848544819318519, 0.1634378506379126, -0.038845711137341546, 0.10954485996626318, 0.16988069709504441, -0.08469648896811569, 0.060946802059853714, 0.08449622738526716, -0.0648549026015217, 0.04034106102677024, -0.027417855199464426, -0.0696439719568862, -0.3028021431242933, -0.2580730860235588, -0.09158816634753376, 0.08811383255477044, 0.03307105997180547, -0.14086065296919978, 0.30344491838287485, 0.13956183439823894, 0.06434502641489843, 0.0339190517552197, 0.17528375276193328, 0.06361555030607448, -0.014895510390562442, 0.09632430770624505, 0.12991359711111505, 0.1814216737616427, 0.2306070865835158, -0.18069370405995572, -0.009946830988842614, 0.2027547238489651] |
1,802.05442 | Strain-assisted optomechanical coupling of polariton condensate spin to
a micromechanical resonator | We report spin and intensity coupling of an exciton-polariton condensate to
the mechanical vibrations of a circular membrane microcavity. We optically
drive the microcavity resonator at the lowest mechanical resonance frequency
while creating an optically-trapped spin-polarized polariton condensate in
different locations on the microcavity, and observe spin and intensity
oscillations of the condensate at the vibration frequency of the resonator.
Spin oscillations are induced by vibrational strain driving, whilst the
modulation of the optical trap due to the displacement of the membrane causes
intensity oscillations in the condensate emission. Our results demonstrate
spin-phonon coupling in a macroscopically coherent condensate.
| cond-mat.mes-hall | we report spin and intensity coupling of an excitonpolariton condensate to the mechanical vibrations of a circular membrane microcavity we optically drive the microcavity resonator at the lowest mechanical resonance frequency while creating an opticallytrapped spinpolarized polariton condensate in different locations on the microcavity and observe spin and intensity oscillations of the condensate at the vibration frequency of the resonator spin oscillations are induced by vibrational strain driving whilst the modulation of the optical trap due to the displacement of the membrane causes intensity oscillations in the condensate emission our results demonstrate spinphonon coupling in a macroscopically coherent condensate | [['we', 'report', 'spin', 'and', 'intensity', 'coupling', 'of', 'an', 'excitonpolariton', 'condensate', 'to', 'the', 'mechanical', 'vibrations', 'of', 'a', 'circular', 'membrane', 'microcavity', 'we', 'optically', 'drive', 'the', 'microcavity', 'resonator', 'at', 'the', 'lowest', 'mechanical', 'resonance', 'frequency', 'while', 'creating', 'an', 'opticallytrapped', 'spinpolarized', 'polariton', 'condensate', 'in', 'different', 'locations', 'on', 'the', 'microcavity', 'and', 'observe', 'spin', 'and', 'intensity', 'oscillations', 'of', 'the', 'condensate', 'at', 'the', 'vibration', 'frequency', 'of', 'the', 'resonator', 'spin', 'oscillations', 'are', 'induced', 'by', 'vibrational', 'strain', 'driving', 'whilst', 'the', 'modulation', 'of', 'the', 'optical', 'trap', 'due', 'to', 'the', 'displacement', 'of', 'the', 'membrane', 'causes', 'intensity', 'oscillations', 'in', 'the', 'condensate', 'emission', 'our', 'results', 'demonstrate', 'spinphonon', 'coupling', 'in', 'a', 'macroscopically', 'coherent', 'condensate']] | [-0.2148893964335774, 0.29257881428577204, -0.013995705393491068, -0.06376852491277275, 0.018363457455328018, -0.11458884099630093, 0.0380723713450322, 0.44168545125108777, -0.22770824592156017, -0.21646040016220827, -0.0438415180466072, -0.26380296444727314, -0.07440061123590126, 0.19703472618277024, 0.04914618542446106, 0.0388363359608885, -0.004587099370029237, -0.0434563092398222, 0.06432383398203805, -0.10257798541958134, 0.2774759979749268, 0.00796151289662271, 0.38153853961689904, 0.09801499632121337, 0.12160913047916962, -0.029942810855279067, 0.15098139173304193, -0.09332507416003882, -0.11108639813895511, 0.054849955260358525, 0.20900604881421483, -0.1267732384031394, 0.23254898036218652, -0.47815660150213674, -0.1753114266321063, 0.03518754206691878, 0.19757586979157893, 0.26716237492398404, -0.021365472295490854, -0.33812744898552244, -0.11261388864556346, -0.10987844481838471, -0.16112212640600224, -0.08157803198514328, 0.005332453740817128, 0.01837869990332466, -0.23878225752839236, 0.12407841351861605, 0.06430528610074573, 0.09136705997992646, -0.1278520011169027, -0.0068428911870778205, -0.0717921886437883, 0.004745659992249325, -0.00799929661055406, 0.01915099863151107, 0.28800437178914295, -0.13967743424835116, -0.09592714260398137, 0.34906154524798344, -0.1565907665462506, -0.08969881543607423, 0.11988314337595696, -0.24122482209438176, 0.035429224273133456, 0.1840196880464903, 0.15728919568349314, 0.022053158254071017, -0.0964012823636159, -0.02511565414706546, 0.027837595695422754, 0.22677205064606787, 0.17427998276502646, 0.14347464280882663, 0.3408929132715319, 0.21269753459615237, 0.01794453493949741, 0.2079637475203572, -0.1555729798967903, -0.030697794196035976, -0.22764291377700488, -0.07588935637790145, -0.22376249787971528, 0.04474871420781269, -0.0546034090357628, -0.1716332679233429, 0.4650848327261029, 0.09808945219324093, 0.155630559933306, -0.04518540939222081, 0.3273870920705976, 0.14650574999254648, 0.06974813310109605, -0.02457272233159253, 0.384250101068932, 0.22571942660571875, 0.102527792586691, -0.4637709745779784, -0.08374214963752287, -0.07130824596940945] |
1,802.05443 | Genomics as a Service: a Joint Computing and Networking Perspective | This paper provides a global picture about the deployment of networked
processing services for genomic data sets. Many current research make an
extensive use genomic data, which are massive and rapidly increasing over time.
They are typically stored in remote databases, accessible by using Internet.
For this reason, a significant issue for effectively handling genomic data
through data networks consists of the available network services. A first
contribution of this paper consists of identifying the still unexploited
features of genomic data that could allow optimizing their networked
management. The second and main contribution of this survey consists of a
methodological classification of computing and networking alternatives which
can be used to offer what we call the Genomic-as-a-Service (GaaS) paradigm. In
more detail, we analyze the main genomic processing applications, and classify
not only the main computing alternatives to run genomics workflows in either a
local machine or a distributed cloud environment, but also the main software
technologies available to develop genomic processing services. Since an
analysis encompassing only the computing aspects would provide only a partial
view of the issues for deploying GaaS system, we present also the main
networking technologies that are available to efficiently support a GaaS
solution. We first focus on existing service platforms, and analyze them in
terms of service features, such as scalability, flexibility, and efficiency.
Then, we present a taxonomy for both wide area and datacenter network
technologies that may fit the GaaS requirements. It emerges that
virtualization, both in computing and networking, is the key for a successful
large-scale exploitation of genomic data, by pushing ahead the adoption of the
GaaS paradigm. Finally, the paper illustrates a short and long-term vision on
future research challenges in the field.
| cs.DC q-bio.GN | this paper provides a global picture about the deployment of networked processing services for genomic data sets many current research make an extensive use genomic data which are massive and rapidly increasing over time they are typically stored in remote databases accessible by using internet for this reason a significant issue for effectively handling genomic data through data networks consists of the available network services a first contribution of this paper consists of identifying the still unexploited features of genomic data that could allow optimizing their networked management the second and main contribution of this survey consists of a methodological classification of computing and networking alternatives which can be used to offer what we call the genomicasaservice gaas paradigm in more detail we analyze the main genomic processing applications and classify not only the main computing alternatives to run genomics workflows in either a local machine or a distributed cloud environment but also the main software technologies available to develop genomic processing services since an analysis encompassing only the computing aspects would provide only a partial view of the issues for deploying gaas system we present also the main networking technologies that are available to efficiently support a gaas solution we first focus on existing service platforms and analyze them in terms of service features such as scalability flexibility and efficiency then we present a taxonomy for both wide area and datacenter network technologies that may fit the gaas requirements it emerges that virtualization both in computing and networking is the key for a successful largescale exploitation of genomic data by pushing ahead the adoption of the gaas paradigm finally the paper illustrates a short and longterm vision on future research challenges in the field | [['this', 'paper', 'provides', 'a', 'global', 'picture', 'about', 'the', 'deployment', 'of', 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1,802.05444 | A Weighted Likelihood Approach Based on Statistical Data Depths | We propose a general approach to construct weighted likelihood estimating
equations with the aim of obtain robust estimates. The weight, attached to each
score contribution, is evaluated by comparing the statistical data depth at the
model with that of the sample in a given point. Observations are considered
regular when the ratio of these two depths is close to one, whereas, when the
ratio is large the corresponding score contribution may be downweigthed.
Details and examples are provided for the robust estimation of the parameters
in the multivariate normal model. Because of the form of the weights, we expect
that, there will be no downweighting under the true model leading to highly
efficient estimators. Robustness is illustrated using two real data sets.
| stat.ME | we propose a general approach to construct weighted likelihood estimating equations with the aim of obtain robust estimates the weight attached to each score contribution is evaluated by comparing the statistical data depth at the model with that of the sample in a given point observations are considered regular when the ratio of these two depths is close to one whereas when the ratio is large the corresponding score contribution may be downweigthed details and examples are provided for the robust estimation of the parameters in the multivariate normal model because of the form of the weights we expect that there will be no downweighting under the true model leading to highly efficient estimators robustness is illustrated using two real data sets | [['we', 'propose', 'a', 'general', 'approach', 'to', 'construct', 'weighted', 'likelihood', 'estimating', 'equations', 'with', 'the', 'aim', 'of', 'obtain', 'robust', 'estimates', 'the', 'weight', 'attached', 'to', 'each', 'score', 'contribution', 'is', 'evaluated', 'by', 'comparing', 'the', 'statistical', 'data', 'depth', 'at', 'the', 'model', 'with', 'that', 'of', 'the', 'sample', 'in', 'a', 'given', 'point', 'observations', 'are', 'considered', 'regular', 'when', 'the', 'ratio', 'of', 'these', 'two', 'depths', 'is', 'close', 'to', 'one', 'whereas', 'when', 'the', 'ratio', 'is', 'large', 'the', 'corresponding', 'score', 'contribution', 'may', 'be', 'downweigthed', 'details', 'and', 'examples', 'are', 'provided', 'for', 'the', 'robust', 'estimation', 'of', 'the', 'parameters', 'in', 'the', 'multivariate', 'normal', 'model', 'because', 'of', 'the', 'form', 'of', 'the', 'weights', 'we', 'expect', 'that', 'there', 'will', 'be', 'no', 'downweighting', 'under', 'the', 'true', 'model', 'leading', 'to', 'highly', 'efficient', 'estimators', 'robustness', 'is', 'illustrated', 'using', 'two', 'real', 'data', 'sets']] | [-0.06126708349921117, 0.05714017491737642, -0.06805832487384765, 0.08075204819021362, -0.03979027724232186, -0.15180159350308736, 0.025973008620391085, 0.3756288087287964, -0.24628219197789006, -0.29088316814327414, 0.13453079059466042, -0.29232476548530345, -0.13759054014197558, 0.20380956116363344, -0.0783400218395039, 0.03232461765162216, 0.08934878814419007, 0.06323664631571405, -0.06854789423629302, -0.2776687449029039, 0.32907484161228806, 0.06228637563503417, 0.289942770380496, -0.001750748283672339, 0.078166372844832, -0.027737302832644094, -0.05894312714726841, 0.039113183348047094, -0.11007404783108052, 0.14495772831449824, 0.2504436503229894, 0.12604106020197764, 0.2778291545446562, -0.3592243342184003, -0.14299678390707113, 0.13789656214908627, 0.08198665323751894, 0.0909063296838979, -0.00539181553104446, -0.2509434970157329, 0.11051828813862272, -0.1136051990007135, -0.12446247992559897, -0.08468145714911117, -0.00959021950617802, 0.01568255370478082, -0.33889031469483266, 0.08697308871456352, 0.014878124433515732, 0.02202537190169096, -0.06707040759280694, -0.14991602787559405, -0.014824177130810485, 0.11879171899660329, 0.057679065209736445, 0.019423264413610225, 0.0743442225346461, -0.131707837220189, -0.06797673575549333, 0.3673299950170369, -0.05911301668109911, -0.2530291611487469, 0.16948974840314135, -0.13243115621749774, -0.11200732604527369, 0.12382026093680996, 0.17870433170306074, 0.09771016620345845, -0.16744175941364894, 0.019269762462108927, -0.031428458807943774, 0.16030104910259227, 0.01055838817380338, -0.02215572635013964, 0.2011705345574243, 0.16240169725588655, 0.0539978537054286, 0.16514847420871812, -0.15251793445295786, -0.07765350261056596, -0.29717821630934055, -0.10181089859332673, -0.19870244085688668, -0.00665444681374741, -0.13463068548731857, -0.1685200940383683, 0.3978992826247603, 0.1993832367074037, 0.24216413132322967, 0.09769055529974771, 0.30405184011598513, 0.16608260214340312, 0.05286330052397468, 0.07920007078431177, 0.23686822072779837, 0.10176729769561409, 2.2036390001924077e-05, -0.15617883740443245, 0.12431950064319232, 0.02872918903527489] |
1,802.05445 | Flat-space Holography and Correlators of Robinson-Trautman Stress tensor | We propose a quasi-local stress tensor for the four-dimensional
asymptotically flat Robinson-Trautman geometries by taking the flat-space limit
from the corresponding asymptotically AdS solutions. This stress tensor results
in the correct charges of the generators of BMS symmetry if we define conformal
infinity by an anisotropic scaling of the metric components. Using flat-space
holography this stress tensor is related to expectation values of the stress
tensor in a dual field theory called BMS-invariant field theory (BMSFT). We
also calculate the two and three point functions of the proposed stress tensor.
| hep-th | we propose a quasilocal stress tensor for the fourdimensional asymptotically flat robinsontrautman geometries by taking the flatspace limit from the corresponding asymptotically ads solutions this stress tensor results in the correct charges of the generators of bms symmetry if we define conformal infinity by an anisotropic scaling of the metric components using flatspace holography this stress tensor is related to expectation values of the stress tensor in a dual field theory called bmsinvariant field theory bmsft we also calculate the two and three point functions of the proposed stress tensor | [['we', 'propose', 'a', 'quasilocal', 'stress', 'tensor', 'for', 'the', 'fourdimensional', 'asymptotically', 'flat', 'robinsontrautman', 'geometries', 'by', 'taking', 'the', 'flatspace', 'limit', 'from', 'the', 'corresponding', 'asymptotically', 'ads', 'solutions', 'this', 'stress', 'tensor', 'results', 'in', 'the', 'correct', 'charges', 'of', 'the', 'generators', 'of', 'bms', 'symmetry', 'if', 'we', 'define', 'conformal', 'infinity', 'by', 'an', 'anisotropic', 'scaling', 'of', 'the', 'metric', 'components', 'using', 'flatspace', 'holography', 'this', 'stress', 'tensor', 'is', 'related', 'to', 'expectation', 'values', 'of', 'the', 'stress', 'tensor', 'in', 'a', 'dual', 'field', 'theory', 'called', 'bmsinvariant', 'field', 'theory', 'bmsft', 'we', 'also', 'calculate', 'the', 'two', 'and', 'three', 'point', 'functions', 'of', 'the', 'proposed', 'stress', 'tensor']] | [-0.13709865674229987, 0.17682893336976202, -0.12731272357338097, 0.04829654881725443, -0.06802410374426943, -0.07539162900968549, -0.088000778100987, 0.30492880871372946, -0.20566767693910581, -0.1681428405317593, 0.0850510045263414, -0.27223423535653046, -0.19532633052741208, 0.07329704842708085, -0.033284224428529484, 0.047236586652932885, -0.03214981395939595, 0.07291675016566525, -0.13699495624436925, -0.20060653729217776, 0.39048018218855257, 0.06856240068426293, 0.39923738000726094, 0.03084798019086377, 0.15489664509057935, 0.022214647162747517, -0.011667731508424276, 0.10500678386057863, -0.15936369606387918, 0.10112284323765655, 0.23644655843636836, 0.08418348168933325, 0.16208778444175306, -0.44469253139130854, -0.16793157440725337, 0.0859184840136846, 0.08499796761806762, 0.14018309640624885, 0.0030339140199082955, -0.25285116855180667, 0.09312370263584209, -0.20375552915790107, -0.17979539701652325, -0.10245389902566591, 0.002750822774584541, -0.09926228176048968, -0.24638317027286197, 0.1039636940927522, 0.025048668234703246, -0.005113488513181049, -0.1741815069167132, -0.07694151200448278, -0.05078140992849144, 0.07786997285242496, 0.1653025819307842, 0.018112519744841193, 0.13396700709310014, -0.16421474379832657, -0.09619358271423183, 0.3163121825324769, -0.07925412644830983, -0.3097175679305631, 0.08165049792549918, -0.12561700212719504, -0.10213339772975344, 0.05511115335472179, 0.13176019946008585, 0.1814104351476672, -0.15810064819714661, 0.18285889875371364, -0.011544725365853041, 0.05952041661216396, 0.08881294464671545, -0.01598863676386582, 0.23065343396633528, 0.01769389687210656, 0.042782508291279914, 0.20638456884405335, -0.014650779661167873, -0.08609678633929638, -0.42878126941119016, -0.192758138825217, -0.19222907694789132, 0.13774281892967358, -0.2478426026408966, -0.21784595169880416, 0.381862784264965, 0.14126332994112928, 0.1552026399621855, 0.11769989629935347, 0.23067737494992993, 0.12889339770661312, 0.1022194023034797, 0.12415540049354849, 0.31621516582273534, 0.20312819378241226, 0.09715610987302753, -0.2054878842331511, -0.11915347455697281, 0.16790673602372408] |
1,802.05446 | A 2x2 quantum dot array with controllable inter-dot tunnel couplings | The interaction between electrons in arrays of electrostatically defined
quantum dots is naturally described by a Fermi-Hubbard Hamiltonian. Moreover,
the high degree of tunability of these systems make them a powerful platform to
simulate different regimes of the Hubbard model. However, most quantum dot
array implementations have been limited to one-dimensional linear arrays. In
this letter, we present a square lattice unit cell of 2$\times$2 quantum dots
defined electrostatically in a AlGaAs/GaAs heterostructure using a double-layer
gate technique. We probe the properties of the array using nearby quantum dots
operated as charge sensors. We show that we can deterministically and
dynamically control the charge occupation in each quantum dot in the single- to
few-electron regime. Additionally, we achieve simultaneous individual control
of the nearest-neighbor tunnel couplings over a range 0-40~$\mu$eV. Finally, we
demonstrate fast ($\sim 1$~$\mu$s) single-shot readout of the spin state of
electrons in the dots, through spin-to-charge conversion via Pauli spin
blockade. These advances pave the way to analog quantum simulations in two
dimensions, not previously accessible in quantum dot systems.
| cond-mat.mes-hall | the interaction between electrons in arrays of electrostatically defined quantum dots is naturally described by a fermihubbard hamiltonian moreover the high degree of tunability of these systems make them a powerful platform to simulate different regimes of the hubbard model however most quantum dot array implementations have been limited to onedimensional linear arrays in this letter we present a square lattice unit cell of 2times2 quantum dots defined electrostatically in a algaasgaas heterostructure using a doublelayer gate technique we probe the properties of the array using nearby quantum dots operated as charge sensors we show that we can deterministically and dynamically control the charge occupation in each quantum dot in the single to fewelectron regime additionally we achieve simultaneous individual control of the nearestneighbor tunnel couplings over a range 040muev finally we demonstrate fast sim 1mus singleshot readout of the spin state of electrons in the dots through spintocharge conversion via pauli spin blockade these advances pave the way to analog quantum simulations in two dimensions not previously accessible in quantum dot systems | [['the', 'interaction', 'between', 'electrons', 'in', 'arrays', 'of', 'electrostatically', 'defined', 'quantum', 'dots', 'is', 'naturally', 'described', 'by', 'a', 'fermihubbard', 'hamiltonian', 'moreover', 'the', 'high', 'degree', 'of', 'tunability', 'of', 'these', 'systems', 'make', 'them', 'a', 'powerful', 'platform', 'to', 'simulate', 'different', 'regimes', 'of', 'the', 'hubbard', 'model', 'however', 'most', 'quantum', 'dot', 'array', 'implementations', 'have', 'been', 'limited', 'to', 'onedimensional', 'linear', 'arrays', 'in', 'this', 'letter', 'we', 'present', 'a', 'square', 'lattice', 'unit', 'cell', 'of', '2times2', 'quantum', 'dots', 'defined', 'electrostatically', 'in', 'a', 'algaasgaas', 'heterostructure', 'using', 'a', 'doublelayer', 'gate', 'technique', 'we', 'probe', 'the', 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1,802.05447 | History PCA: A New Algorithm for Streaming PCA | In this paper we propose a new algorithm for streaming principal component
analysis. With limited memory, small devices cannot store all the samples in
the high-dimensional regime. Streaming principal component analysis aims to
find the $k$-dimensional subspace which can explain the most variation of the
$d$-dimensional data points that come into memory sequentially. In order to
deal with large $d$ and large $N$ (number of samples), most streaming PCA
algorithms update the current model using only the incoming sample and then
dump the information right away to save memory. However the information
contained in previously streamed data could be useful. Motivated by this idea,
we develop a new streaming PCA algorithm called History PCA that achieves this
goal. By using $O(Bd)$ memory with $B\approx 10$ being the block size, our
algorithm converges much faster than existing streaming PCA algorithms. By
changing the number of inner iterations, the memory usage can be further
reduced to $O(d)$ while maintaining a comparable convergence speed. We provide
theoretical guarantees for the convergence of our algorithm along with the rate
of convergence. We also demonstrate on synthetic and real world data sets that
our algorithm compares favorably with other state-of-the-art streaming PCA
methods in terms of the convergence speed and performance.
| stat.ML | in this paper we propose a new algorithm for streaming principal component analysis with limited memory small devices cannot store all the samples in the highdimensional regime streaming principal component analysis aims to find the kdimensional subspace which can explain the most variation of the ddimensional data points that come into memory sequentially in order to deal with large d and large n number of samples most streaming pca algorithms update the current model using only the incoming sample and then dump the information right away to save memory however the information contained in previously streamed data could be useful motivated by this idea we develop a new streaming pca algorithm called history pca that achieves this goal by using obd memory with bapprox 10 being the block size our algorithm converges much faster than existing streaming pca algorithms by changing the number of inner iterations the memory usage can be further reduced to od while maintaining a comparable convergence speed we provide theoretical guarantees for the convergence of our algorithm along with the rate of convergence we also demonstrate on synthetic and real world data sets that our algorithm compares favorably with other stateoftheart streaming pca methods in terms of the convergence speed and performance | [['in', 'this', 'paper', 'we', 'propose', 'a', 'new', 'algorithm', 'for', 'streaming', 'principal', 'component', 'analysis', 'with', 'limited', 'memory', 'small', 'devices', 'can', 'not', 'store', 'all', 'the', 'samples', 'in', 'the', 'highdimensional', 'regime', 'streaming', 'principal', 'component', 'analysis', 'aims', 'to', 'find', 'the', 'kdimensional', 'subspace', 'which', 'can', 'explain', 'the', 'most', 'variation', 'of', 'the', 'ddimensional', 'data', 'points', 'that', 'come', 'into', 'memory', 'sequentially', 'in', 'order', 'to', 'deal', 'with', 'large', 'd', 'and', 'large', 'n', 'number', 'of', 'samples', 'most', 'streaming', 'pca', 'algorithms', 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1,802.05448 | Discrepancy-based Evolutionary Diversity Optimization | Diversity plays a crucial role in evolutionary computation. While diversity
has been mainly used to prevent the population of an evolutionary algorithm
from premature convergence, the use of evolutionary algorithms to obtain a
diverse set of solutions has gained increasing attention in recent years.
Diversity optimization in terms of features on the underlying problem allows to
obtain a better understanding of possible solutions to the problem at hand and
can be used for algorithm selection when dealing with combinatorial
optimization problems such as the Traveling Salesperson Problem. We explore the
use of the star-discrepancy measure to guide the diversity optimization process
of an evolutionary algorithm.
In our experimental investigations, we consider our discrepancy-based
diversity optimization approaches for evolving diverse sets of images as well
as instances of the Traveling Salesperson problem where a local search is not
able to find near optimal solutions. Our experimental investigations comparing
three diversity optimization approaches show that a discrepancy-based diversity
optimization approach using a tie-breaking rule based on weighted differences
to surrounding feature points provides the best results in terms of the star
discrepancy measure.
| cs.NE | diversity plays a crucial role in evolutionary computation while diversity has been mainly used to prevent the population of an evolutionary algorithm from premature convergence the use of evolutionary algorithms to obtain a diverse set of solutions has gained increasing attention in recent years diversity optimization in terms of features on the underlying problem allows to obtain a better understanding of possible solutions to the problem at hand and can be used for algorithm selection when dealing with combinatorial optimization problems such as the traveling salesperson problem we explore the use of the stardiscrepancy measure to guide the diversity optimization process of an evolutionary algorithm in our experimental investigations we consider our discrepancybased diversity optimization approaches for evolving diverse sets of images as well as instances of the traveling salesperson problem where a local search is not able to find near optimal solutions our experimental investigations comparing three diversity optimization approaches show that a discrepancybased diversity optimization approach using a tiebreaking rule based on weighted differences to surrounding feature points provides the best results in terms of the star discrepancy measure | [['diversity', 'plays', 'a', 'crucial', 'role', 'in', 'evolutionary', 'computation', 'while', 'diversity', 'has', 'been', 'mainly', 'used', 'to', 'prevent', 'the', 'population', 'of', 'an', 'evolutionary', 'algorithm', 'from', 'premature', 'convergence', 'the', 'use', 'of', 'evolutionary', 'algorithms', 'to', 'obtain', 'a', 'diverse', 'set', 'of', 'solutions', 'has', 'gained', 'increasing', 'attention', 'in', 'recent', 'years', 'diversity', 'optimization', 'in', 'terms', 'of', 'features', 'on', 'the', 'underlying', 'problem', 'allows', 'to', 'obtain', 'a', 'better', 'understanding', 'of', 'possible', 'solutions', 'to', 'the', 'problem', 'at', 'hand', 'and', 'can', 'be', 'used', 'for', 'algorithm', 'selection', 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1,802.05449 | A range condition for polyconvex variational regularization | In the context of convex variational regularization it is a known result
that, under suitable differentiability assumptions, source conditions in the
form of variational inequalities imply range conditions, while the converse
implication only holds under an additional restriction on the operator. In this
article we prove the analogous result for polyconvex regularization. More
precisely, we show that the variational inequality derived by Kirisits,
Scherzer (2017) implies that the derivative of the regularization functional
must lie in the range of the dual-adjoint of the derivative of the operator. In
addition, we show how to adapt the restriction on the operator in order to
obtain the converse implication.
| math.OC | in the context of convex variational regularization it is a known result that under suitable differentiability assumptions source conditions in the form of variational inequalities imply range conditions while the converse implication only holds under an additional restriction on the operator in this article we prove the analogous result for polyconvex regularization more precisely we show that the variational inequality derived by kirisits scherzer 2017 implies that the derivative of the regularization functional must lie in the range of the dualadjoint of the derivative of the operator in addition we show how to adapt the restriction on the operator in order to obtain the converse implication | [['in', 'the', 'context', 'of', 'convex', 'variational', 'regularization', 'it', 'is', 'a', 'known', 'result', 'that', 'under', 'suitable', 'differentiability', 'assumptions', 'source', 'conditions', 'in', 'the', 'form', 'of', 'variational', 'inequalities', 'imply', 'range', 'conditions', 'while', 'the', 'converse', 'implication', 'only', 'holds', 'under', 'an', 'additional', 'restriction', 'on', 'the', 'operator', 'in', 'this', 'article', 'we', 'prove', 'the', 'analogous', 'result', 'for', 'polyconvex', 'regularization', 'more', 'precisely', 'we', 'show', 'that', 'the', 'variational', 'inequality', 'derived', 'by', 'kirisits', 'scherzer', '2017', 'implies', 'that', 'the', 'derivative', 'of', 'the', 'regularization', 'functional', 'must', 'lie', 'in', 'the', 'range', 'of', 'the', 'dualadjoint', 'of', 'the', 'derivative', 'of', 'the', 'operator', 'in', 'addition', 'we', 'show', 'how', 'to', 'adapt', 'the', 'restriction', 'on', 'the', 'operator', 'in', 'order', 'to', 'obtain', 'the', 'converse', 'implication']] | [-0.10547878445257225, 0.02846602056284539, -0.09471207099415145, 0.11397090489135205, -0.09513153661566887, -0.08050491443374058, 0.03698131200197397, 0.3441750453875482, -0.30720983373308647, -0.24886374528185257, 0.14752266457241064, -0.2052500681427208, -0.17831130141338097, 0.19382788684418045, -0.12976232171058655, 0.041148309541631095, 0.06838095105858971, 0.08475924838442826, -0.10469209537977009, -0.24856180873093675, 0.39986288605384457, -0.035644486022082346, 0.24439992381766149, 0.13368935665472872, 0.11574213152516236, 0.03828506368366115, 0.037697679119058025, -0.019324013701124677, -0.15792137999148323, 0.13539992620347935, 0.2018182034993056, 0.11524893001436247, 0.3017529085531686, -0.41083008286009715, -0.21698545288403057, 0.11495720138162989, 0.08145229458736564, 0.039308925303947, -0.03164511550858151, -0.2601737295847061, 0.09409687975511967, -0.12968553278993056, -0.15877019264509232, -0.08292707680607016, -0.03190764232800858, -0.006502856048913582, -0.33858286031494733, 0.11803332564999684, 0.15270523393291582, 0.021636276317136452, -0.14027001768422936, -0.07881559878226044, 0.004494618458302638, 0.038470402319273615, 0.0728783025135042, 0.041514085241801384, 0.060755698702035885, -0.10412588095330877, -0.05069587121545834, 0.33854767141266934, -0.0850381221389398, -0.2566208945388354, 0.1417974748454301, -0.17222862056393212, -0.20697740471279882, 0.03142463265040418, 0.1068169798304155, 0.15838419643312113, -0.15734948858040052, 0.1625472315580322, -0.07926821135418508, 0.13050142456822603, 0.09753028473373755, 0.06042557043810059, 0.02750136131800494, 0.07586550368990713, 0.19987988938550347, 0.17516577408368728, -0.01784338161023786, -0.08791678406533251, -0.3942409408215469, -0.1752203994281409, -0.18973875027692433, 0.05115141003455931, -0.10858502727707178, -0.11697256002005516, 0.32804034007215893, 0.1927084866594704, 0.16257824829228004, 0.10088374448524227, 0.21520812313967538, 0.1763831067884576, 0.08360551708169932, 0.06968279047733517, 0.26130080005624196, 0.15561104154901453, 0.06661787060613844, -0.18424196030212187, 0.08341903035140154, 0.12923882391701624] |
1,802.0545 | Hierarchical multi-stage MCMC follow-up of continuous gravitational wave
candidates | Leveraging Markov chain Monte Carlo (MCMC) optimization of the F-statistic,
we introduce a method for the hierarchical follow-up of continuous
gravitational wave candidates identified by wide-parameter space semi-coherent
searches. We demonstrate parameter estimation for continuous wave sources and
develop a framework and tools to understand and control the effective size of
the parameter space, critical to the success of the method. Monte Carlo tests
of simulated signals in noise demonstrate that this method is close to the
theoretical optimal performance.
| astro-ph.IM astro-ph.HE | leveraging markov chain monte carlo mcmc optimization of the fstatistic we introduce a method for the hierarchical followup of continuous gravitational wave candidates identified by wideparameter space semicoherent searches we demonstrate parameter estimation for continuous wave sources and develop a framework and tools to understand and control the effective size of the parameter space critical to the success of the method monte carlo tests of simulated signals in noise demonstrate that this method is close to the theoretical optimal performance | [['leveraging', 'markov', 'chain', 'monte', 'carlo', 'mcmc', 'optimization', 'of', 'the', 'fstatistic', 'we', 'introduce', 'a', 'method', 'for', 'the', 'hierarchical', 'followup', 'of', 'continuous', 'gravitational', 'wave', 'candidates', 'identified', 'by', 'wideparameter', 'space', 'semicoherent', 'searches', 'we', 'demonstrate', 'parameter', 'estimation', 'for', 'continuous', 'wave', 'sources', 'and', 'develop', 'a', 'framework', 'and', 'tools', 'to', 'understand', 'and', 'control', 'the', 'effective', 'size', 'of', 'the', 'parameter', 'space', 'critical', 'to', 'the', 'success', 'of', 'the', 'method', 'monte', 'carlo', 'tests', 'of', 'simulated', 'signals', 'in', 'noise', 'demonstrate', 'that', 'this', 'method', 'is', 'close', 'to', 'the', 'theoretical', 'optimal', 'performance']] | [-0.07857626469340176, 0.07625943018356338, -0.09271648824214936, 0.12246593351446791, -0.08329317522002384, -0.09097237651003524, 0.09910881322575733, 0.422080570878461, -0.26321235492359846, -0.3328407989814878, 0.115059828372614, -0.2273656732228119, -0.14184964768355712, 0.23591133133741096, 0.021076983422972262, 0.150787445215974, 0.09926571967080236, -0.0551829231902957, -0.09990936972026247, -0.20871251701319124, 0.24616357914928813, 0.1206426988937892, 0.275927703268826, -0.06612724091392011, 0.1517588841903489, 0.060528052382869645, -0.06671092813485302, -0.024313681032072054, -0.18798435400576635, 0.11371866607805714, 0.2697149218292907, 0.16821235885145142, 0.31198942094342785, -0.33309578045737, -0.25836728058056907, 0.13824756270041688, 0.13539714744547382, 0.1141149613977177, -0.06474079789768439, -0.35722448366286697, 0.06416577599011361, -0.17654691728530453, -0.10810959442460444, -0.12880710982135496, -0.0634422872972209, 0.04852480091503821, -0.32341922156629155, 0.04240983493327803, -0.03226365501759574, 0.008528209547512234, -0.021894405107013883, -0.0943674191716127, 0.025986198615282775, 0.055499674438033254, 0.04044723408878781, 0.053305913071380925, 0.13712583227315917, -0.09162539108365308, -0.1925675613223575, 0.311965431808494, -0.05262879130677902, -0.21836108558927664, 0.17119267201051117, -0.1056618853326654, -0.1707326745148748, 0.18721150492783636, 0.22507339052390307, 0.15284026194131, -0.1621336780837737, 0.08390400931602926, 0.05913951561124122, 0.17208586605265735, -0.06811528718098998, -0.021262109922827222, 0.16481716229463927, 0.2413240087334998, 0.08129228705074638, 0.17598784544388762, -0.18018520602490753, -0.1250633644289337, -0.2695610006870993, -0.16653599166311323, -0.23201118726283312, -0.042380417567983386, -0.11316561000367073, -0.18962789544530095, 0.36510662036016583, 0.26655384987935804, 0.11139954698737711, 0.0986453547142446, 0.3273726306855679, 0.09010357441293308, -0.008989670872688293, 0.05461738050798885, 0.23322658685501665, 0.10488437253225129, 0.02750689403619617, -0.23994656270951964, 0.05575782041996717, 0.054599741648416966] |
1,802.05451 | Mapping Images to Scene Graphs with Permutation-Invariant Structured
Prediction | Machine understanding of complex images is a key goal of artificial
intelligence. One challenge underlying this task is that visual scenes contain
multiple inter-related objects, and that global context plays an important role
in interpreting the scene. A natural modeling framework for capturing such
effects is structured prediction, which optimizes over complex labels, while
modeling within-label interactions. However, it is unclear what principles
should guide the design of a structured prediction model that utilizes the
power of deep learning components. Here we propose a design principle for such
architectures that follows from a natural requirement of permutation
invariance. We prove a necessary and sufficient characterization for
architectures that follow this invariance, and discuss its implication on model
design. Finally, we show that the resulting model achieves new state of the art
results on the Visual Genome scene graph labeling benchmark, outperforming all
recent approaches.
| stat.ML cs.CV cs.LG | machine understanding of complex images is a key goal of artificial intelligence one challenge underlying this task is that visual scenes contain multiple interrelated objects and that global context plays an important role in interpreting the scene a natural modeling framework for capturing such effects is structured prediction which optimizes over complex labels while modeling withinlabel interactions however it is unclear what principles should guide the design of a structured prediction model that utilizes the power of deep learning components here we propose a design principle for such architectures that follows from a natural requirement of permutation invariance we prove a necessary and sufficient characterization for architectures that follow this invariance and discuss its implication on model design finally we show that the resulting model achieves new state of the art results on the visual genome scene graph labeling benchmark outperforming all recent approaches | [['machine', 'understanding', 'of', 'complex', 'images', 'is', 'a', 'key', 'goal', 'of', 'artificial', 'intelligence', 'one', 'challenge', 'underlying', 'this', 'task', 'is', 'that', 'visual', 'scenes', 'contain', 'multiple', 'interrelated', 'objects', 'and', 'that', 'global', 'context', 'plays', 'an', 'important', 'role', 'in', 'interpreting', 'the', 'scene', 'a', 'natural', 'modeling', 'framework', 'for', 'capturing', 'such', 'effects', 'is', 'structured', 'prediction', 'which', 'optimizes', 'over', 'complex', 'labels', 'while', 'modeling', 'withinlabel', 'interactions', 'however', 'it', 'is', 'unclear', 'what', 'principles', 'should', 'guide', 'the', 'design', 'of', 'a', 'structured', 'prediction', 'model', 'that', 'utilizes', 'the', 'power', 'of', 'deep', 'learning', 'components', 'here', 'we', 'propose', 'a', 'design', 'principle', 'for', 'such', 'architectures', 'that', 'follows', 'from', 'a', 'natural', 'requirement', 'of', 'permutation', 'invariance', 'we', 'prove', 'a', 'necessary', 'and', 'sufficient', 'characterization', 'for', 'architectures', 'that', 'follow', 'this', 'invariance', 'and', 'discuss', 'its', 'implication', 'on', 'model', 'design', 'finally', 'we', 'show', 'that', 'the', 'resulting', 'model', 'achieves', 'new', 'state', 'of', 'the', 'art', 'results', 'on', 'the', 'visual', 'genome', 'scene', 'graph', 'labeling', 'benchmark', 'outperforming', 'all', 'recent', 'approaches']] | [-0.08726755115757143, 0.034135174690977786, -0.08050444327869576, 0.08291205170831376, -0.12723976767367937, -0.1437923731217226, -0.0033649738431888654, 0.42138208706791586, -0.28050828954999474, -0.3325314091085424, 0.061552713814816744, -0.21532190189260494, -0.2572422090114316, 0.193740087575963, -0.13565228809765378, 0.05629454616504441, 0.14072369352973008, 0.0404856085998716, -0.030929551538001823, -0.20948392134326693, 0.31664558148037614, 0.041574020790045275, 0.3452072963541882, 0.03617276208006315, 0.16060537930294688, 0.010148178479940324, -0.029538654311156303, -0.01162823429982458, -0.06696524694747165, 0.18961837230953615, 0.29531278426211877, 0.2452169367310858, 0.31262875193326206, -0.41527498753129183, -0.25971394557818456, 0.08824253462294078, 0.11247021764503805, 0.09896996208072568, -0.06699319824029307, -0.2646278260419002, 0.09901439179291857, -0.1169242967747256, -0.04681248597839808, -0.1349860627686123, 0.013591833709791251, -0.05346043091967467, -0.2854841862954981, 0.01898612554829854, 0.14377325742995575, 0.07831501227419277, -0.07646908955339186, -0.10813770237235495, 0.03512822851331879, 0.19399370414602174, 0.0020700049117676443, 0.047138259339087704, 0.13170988293416747, -0.21846086009880983, -0.15056130429078546, 0.434711297934914, -0.013702534193293536, -0.1897249640701534, 0.20255091569021708, -0.027302284045242608, -0.21395425189455802, 0.054877192182531516, 0.2142899221890456, 0.11108590416948903, -0.1441999594208791, 0.04307091505131586, -0.08831509693128455, 0.191687655241157, 0.01092969361038136, 0.03737749184735797, 0.23554836130225576, 0.2981889811556109, 0.04733361683290743, 0.1086274666623766, -0.08234587730880553, -0.10208489998224211, -0.2722595920171621, -0.13405344009373363, -0.16278750766508307, 0.017131505081417052, -0.09503646077756311, -0.13049270167061083, 0.40329378978772595, 0.25133590844876647, 0.18856696907342965, 0.07429921586747067, 0.3372982373083388, 0.029433537321409433, 0.0791757498913399, 0.05383194537493042, 0.1746403896314958, 0.045503610699483145, 0.0869765578800897, -0.1702590831685681, 0.11585611470085992, 0.04720822790693785] |
1,802.05452 | A dynamical approach in exploring the unknown mass in the Solar system
using pulsar timing arrays | The error in the Solar system ephemeris will lead to dipolar correlations in
the residuals of pulsar timing array for widely separated pulsars. In this
paper, we utilize such correlated signals, and construct a Bayesian
data-analysis framework to detect the unknown mass in the Solar system and to
measure the orbital parameters. The algorithm is designed to calculate the
waveform of the induced pulsar-timing residuals due to the unmodelled objects
following the Keplerian orbits in the Solar system. The algorithm incorporates
a Bayesian-analysis suit used to simultaneously analyse the pulsar-timing data
of multiple pulsars to search for coherent waveforms, evaluate the detection
significance of unknown objects, and to measure their parameters. When the
object is not detectable, our algorithm can be used to place upper limits on
the mass. The algorithm is verified using simulated data sets, and
cross-checked with analytical calculations. We also investigate the capability
of future pulsar-timing-array experiments in detecting the unknown objects. We
expect that the future pulsar timing data can limit the unknown massive objects
in the Solar system to be lighter than $10^{-11}$ to $10^{-12}$ $M_{\odot}$, or
measure the mass of Jovian system to fractional precision of $10^{-8}$ to
$10^{-9}$.
| astro-ph.IM astro-ph.EP | the error in the solar system ephemeris will lead to dipolar correlations in the residuals of pulsar timing array for widely separated pulsars in this paper we utilize such correlated signals and construct a bayesian dataanalysis framework to detect the unknown mass in the solar system and to measure the orbital parameters the algorithm is designed to calculate the waveform of the induced pulsartiming residuals due to the unmodelled objects following the keplerian orbits in the solar system the algorithm incorporates a bayesiananalysis suit used to simultaneously analyse the pulsartiming data of multiple pulsars to search for coherent waveforms evaluate the detection significance of unknown objects and to measure their parameters when the object is not detectable our algorithm can be used to place upper limits on the mass the algorithm is verified using simulated data sets and crosschecked with analytical calculations we also investigate the capability of future pulsartimingarray experiments in detecting the unknown objects we expect that the future pulsar timing data can limit the unknown massive objects in the solar system to be lighter than 1011 to 1012 m_odot or measure the mass of jovian system to fractional precision of 108 to 109 | [['the', 'error', 'in', 'the', 'solar', 'system', 'ephemeris', 'will', 'lead', 'to', 'dipolar', 'correlations', 'in', 'the', 'residuals', 'of', 'pulsar', 'timing', 'array', 'for', 'widely', 'separated', 'pulsars', 'in', 'this', 'paper', 'we', 'utilize', 'such', 'correlated', 'signals', 'and', 'construct', 'a', 'bayesian', 'dataanalysis', 'framework', 'to', 'detect', 'the', 'unknown', 'mass', 'in', 'the', 'solar', 'system', 'and', 'to', 'measure', 'the', 'orbital', 'parameters', 'the', 'algorithm', 'is', 'designed', 'to', 'calculate', 'the', 'waveform', 'of', 'the', 'induced', 'pulsartiming', 'residuals', 'due', 'to', 'the', 'unmodelled', 'objects', 'following', 'the', 'keplerian', 'orbits', 'in', 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1,802.05453 | Black Hole Metric: Overcoming the PageRank Normalization Problem | In network science, there is often the need to sort the graph nodes. While
the sorting strategy may be different, in general sorting is performed by
exploiting the network structure. In particular, the metric PageRank has been
used in the past decade in different ways to produce a ranking based on how
many neighbors point to a specific node. PageRank is simple, easy to compute
and effective in many applications, however it comes with a price: as PageRank
is an application of the random walker, the arc weights need to be normalized.
This normalization, while necessary, introduces a series of unwanted
side-effects. In this paper, we propose a generalization of PageRank named
Black Hole Metric which mitigates the problem. We devise a scenario in which
the side-effects are particularily impactful on the ranking, test the new
metric in both real and synthetic networks, and show the results.
| cs.SI cs.IR physics.soc-ph | in network science there is often the need to sort the graph nodes while the sorting strategy may be different in general sorting is performed by exploiting the network structure in particular the metric pagerank has been used in the past decade in different ways to produce a ranking based on how many neighbors point to a specific node pagerank is simple easy to compute and effective in many applications however it comes with a price as pagerank is an application of the random walker the arc weights need to be normalized this normalization while necessary introduces a series of unwanted sideeffects in this paper we propose a generalization of pagerank named black hole metric which mitigates the problem we devise a scenario in which the sideeffects are particularily impactful on the ranking test the new metric in both real and synthetic networks and show the results | [['in', 'network', 'science', 'there', 'is', 'often', 'the', 'need', 'to', 'sort', 'the', 'graph', 'nodes', 'while', 'the', 'sorting', 'strategy', 'may', 'be', 'different', 'in', 'general', 'sorting', 'is', 'performed', 'by', 'exploiting', 'the', 'network', 'structure', 'in', 'particular', 'the', 'metric', 'pagerank', 'has', 'been', 'used', 'in', 'the', 'past', 'decade', 'in', 'different', 'ways', 'to', 'produce', 'a', 'ranking', 'based', 'on', 'how', 'many', 'neighbors', 'point', 'to', 'a', 'specific', 'node', 'pagerank', 'is', 'simple', 'easy', 'to', 'compute', 'and', 'effective', 'in', 'many', 'applications', 'however', 'it', 'comes', 'with', 'a', 'price', 'as', 'pagerank', 'is', 'an', 'application', 'of', 'the', 'random', 'walker', 'the', 'arc', 'weights', 'need', 'to', 'be', 'normalized', 'this', 'normalization', 'while', 'necessary', 'introduces', 'a', 'series', 'of', 'unwanted', 'sideeffects', 'in', 'this', 'paper', 'we', 'propose', 'a', 'generalization', 'of', 'pagerank', 'named', 'black', 'hole', 'metric', 'which', 'mitigates', 'the', 'problem', 'we', 'devise', 'a', 'scenario', 'in', 'which', 'the', 'sideeffects', 'are', 'particularily', 'impactful', 'on', 'the', 'ranking', 'test', 'the', 'new', 'metric', 'in', 'both', 'real', 'and', 'synthetic', 'networks', 'and', 'show', 'the', 'results']] | [-0.09986037499315682, 0.04152911972837779, -0.08226560680892597, 0.0759293021916152, -0.132354726495386, -0.1574208217603313, 0.06772461587002462, 0.4203647764234924, -0.2793959980817879, -0.3146437312647396, 0.09152003062404312, -0.27039329643316923, -0.21673876496956868, 0.15668874659708568, -0.11460204826047023, 0.06109888663081465, 0.06309418850990177, 0.07927790135271898, -0.015538719349673815, -0.28697791954975255, 0.33505270821929334, 0.08207502138490479, 0.30229275449350174, 0.0542890674867794, 0.07893787698521672, -0.0012227625833178054, -0.046659200363570734, 0.056710675765829936, -0.058808262767206854, 0.12296972377502936, 0.26707111848328186, 0.18321710522920145, 0.3363145152439496, -0.3987545400452452, -0.20901051461443204, 0.14888886600846843, 0.1245349604338661, 0.1396650885862178, -0.05940771011515286, -0.26015582419044914, 0.11627054998815871, -0.17586232824757897, -0.03721537857576191, -0.10218845017026273, 0.02571968556869598, 0.006978595916017079, -0.2509268383946124, 0.00677630637172882, 0.04047521459394876, -0.008841357801602931, 0.006144192148963002, -0.08524708639510407, 0.0320558364243329, 0.15188012701351525, 0.046254038832904326, 0.04917509253548623, 0.1173940571199241, -0.11904917334263422, -0.1833931667331074, 0.41146048447307276, -0.025864488654294793, -0.21936332039395665, 0.16589692875356446, -0.0648974279150823, -0.16352475587544696, 0.05490469831187709, 0.2112458703792369, 0.12435769300819152, -0.1726926890091787, 0.049808998781075815, -0.020993998109483394, 0.12055343772731342, 0.05894760241681317, -0.014736164489136946, 0.18371722108342362, 0.17372414921917223, 0.10676939741052612, 0.175682749000511, -0.06405306416468656, -0.09416311921640522, -0.22249016476174197, -0.16667100128267898, -0.21593284233612622, 0.02807954955231525, -0.1395046769663712, -0.19624092620100547, 0.4367808943905798, 0.18322129277949978, 0.2307166867084852, 0.025760875642616428, 0.30552233426774644, 0.08861662423931899, 0.08271016899122521, 0.11231511881334239, 0.20227470538154446, 0.05336740297949588, 0.12778639683237009, -0.13138359021015314, 0.12141058931513658, 0.05497906870306881] |
1,802.05454 | Bifurcations, robustness and shape theory of attractors of discrete
dynamical systems | We study in this paper global properties, mainly of topological nature, of
attractors of discrete dynamical systems. We consider the Andronov-Hopf
bifurcation for homeomorphisms of the plane and establish some robustness
properties for attractors of such homeomorphisms. We also give relations
between attractors of flows and quasi-attractors of homeomorphisms in Rn.
Finally, we give a result on the shape (in the sense of Borsuk) of invariant
sets of IFSs on the plane, and make some remarks about the recent theory of
Conley attractors for IFS.
| math.DS | we study in this paper global properties mainly of topological nature of attractors of discrete dynamical systems we consider the andronovhopf bifurcation for homeomorphisms of the plane and establish some robustness properties for attractors of such homeomorphisms we also give relations between attractors of flows and quasiattractors of homeomorphisms in rn finally we give a result on the shape in the sense of borsuk of invariant sets of ifss on the plane and make some remarks about the recent theory of conley attractors for ifs | [['we', 'study', 'in', 'this', 'paper', 'global', 'properties', 'mainly', 'of', 'topological', 'nature', 'of', 'attractors', 'of', 'discrete', 'dynamical', 'systems', 'we', 'consider', 'the', 'andronovhopf', 'bifurcation', 'for', 'homeomorphisms', 'of', 'the', 'plane', 'and', 'establish', 'some', 'robustness', 'properties', 'for', 'attractors', 'of', 'such', 'homeomorphisms', 'we', 'also', 'give', 'relations', 'between', 'attractors', 'of', 'flows', 'and', 'quasiattractors', 'of', 'homeomorphisms', 'in', 'rn', 'finally', 'we', 'give', 'a', 'result', 'on', 'the', 'shape', 'in', 'the', 'sense', 'of', 'borsuk', 'of', 'invariant', 'sets', 'of', 'ifss', 'on', 'the', 'plane', 'and', 'make', 'some', 'remarks', 'about', 'the', 'recent', 'theory', 'of', 'conley', 'attractors', 'for', 'ifs']] | [-0.2327937741229749, 0.06975207504104165, -0.10486660662161953, 0.09548029184533173, -0.013791013070765664, -0.05886842351105502, 0.07265369316891712, 0.29152644129798694, -0.2828538777197109, -0.212124712321469, 0.1515013754929361, -0.3124667045276831, -0.23963126185185768, 0.21333563116324297, -0.14012421965325142, 0.10049932995701537, 0.014987585030715254, 0.009381346119677319, -0.07162640792701175, -0.22684898359207983, 0.4394077515393934, -0.07829599119722844, 0.19539057963034687, 0.04460741244530415, 0.062113902421996874, -0.02222971064312493, -0.052491756583399635, 0.029688624073477353, -0.2552002421963741, 0.11480224892935333, 0.18967751229510588, 0.1280989782542319, 0.19044385780306422, -0.404489550356041, -0.20654515933026285, 0.15340482317020787, 0.12410105467527448, 0.03956711106002331, -0.0583054658052895, -0.3198010656215689, 0.10607878217911895, -0.08605749734184322, -0.20309584737371872, -0.12513350394061384, 0.07388373700463596, 0.1171646518057541, -0.1798504686092629, 0.02763397765640334, 0.17925039248588934, 0.17010079959736152, -0.09665029409396297, 0.01903500996037003, -0.0780978068271104, 0.13308561949884545, 0.06855492453529116, 0.005586964404210448, 0.08356973366702304, -0.08472693963250255, -0.13105791570646141, 0.36382715518531555, -0.04346736568396035, -0.2370686616502045, 0.22197523276212022, -0.17315582962816253, -0.24792047809152043, 0.0372495841988198, 0.19574999377021896, 0.10006920298671021, -0.0935951504856348, 0.13759220745756892, -0.09465749073116218, 0.1261888523640878, 0.08882364063140225, 0.09054921908194528, 0.14855517850202674, 0.1340011971345281, 0.15853185172685805, 0.19794211950810517, 0.005463467158294995, -0.095976828701575, -0.31386864417616056, -0.14293731227517129, -0.08139448133845101, 0.09899894327801817, -0.11589904737655375, -0.22468849133480998, 0.4429470748257111, 0.1354378351886921, 0.24037469995810706, 0.07951376243384883, 0.19527529915589292, 0.05201369253370692, -0.05714676988256328, 0.05158020598704324, 0.22137409611759187, 0.1784273601082318, 0.0527570384718916, -0.15798810214242515, -0.012516168268013965, 0.19342702733462347] |
1,802.05455 | Remarks on hypergeometric Cauchy numbers | Hypergeometric numbers can be recognized as one of the most natural
extensions of the classical Cauchy numbers in terms of determinants, though
many kinds of generalizations of the Cauchy numbers have been considered by
many authors. In addition, there are some relations between the hypergeometric
Cauchy numbers and the classical Cauchy numbers. In this paper, we give the
determinant expressions of hypergeometric Cauchy numbers and their
generalizations, and show some interesting expressions of hypergeometric Cauchy
numbers.
| math.NT | hypergeometric numbers can be recognized as one of the most natural extensions of the classical cauchy numbers in terms of determinants though many kinds of generalizations of the cauchy numbers have been considered by many authors in addition there are some relations between the hypergeometric cauchy numbers and the classical cauchy numbers in this paper we give the determinant expressions of hypergeometric cauchy numbers and their generalizations and show some interesting expressions of hypergeometric cauchy numbers | [['hypergeometric', 'numbers', 'can', 'be', 'recognized', 'as', 'one', 'of', 'the', 'most', 'natural', 'extensions', 'of', 'the', 'classical', 'cauchy', 'numbers', 'in', 'terms', 'of', 'determinants', 'though', 'many', 'kinds', 'of', 'generalizations', 'of', 'the', 'cauchy', 'numbers', 'have', 'been', 'considered', 'by', 'many', 'authors', 'in', 'addition', 'there', 'are', 'some', 'relations', 'between', 'the', 'hypergeometric', 'cauchy', 'numbers', 'and', 'the', 'classical', 'cauchy', 'numbers', 'in', 'this', 'paper', 'we', 'give', 'the', 'determinant', 'expressions', 'of', 'hypergeometric', 'cauchy', 'numbers', 'and', 'their', 'generalizations', 'and', 'show', 'some', 'interesting', 'expressions', 'of', 'hypergeometric', 'cauchy', 'numbers']] | [-0.20515133022624804, 0.09401116256700143, -0.06303083908891208, 0.172574827905536, -0.06754657809965704, -0.09025673899113347, -0.06338725567299039, 0.23427957193435808, -0.2928441809199285, -0.2669851488776897, 0.1466535843685147, -0.302801983651558, -0.22956141998599233, 0.2911718657154492, -0.06985423188215416, 0.09503334540855385, 0.017399927513906732, 0.07502313798881675, -0.12153136474080384, -0.29704724766902235, 0.358492412775951, -0.0928808765694205, 0.1777230980560968, 0.03218677762503687, 0.032156918032437955, -0.026259649698132354, -0.03680605704798118, -0.015192449877136633, -0.12282242003436152, 0.0983179794183295, 0.3358679727013958, 0.15413413746405, 0.2745641125488634, -0.4266593619494846, -0.13062612366200865, 0.1985484621193456, 0.1854655141084406, 0.016873413657058814, -0.028037548285761948, -0.2514376814330095, 0.036227700465947, -0.19404926799286745, -0.19939047547277847, -0.11305111215302818, 0.04992502172459162, 0.1884479017340039, -0.17546253407521076, 0.10761117212180245, 0.0480608551358608, 0.07902742901473846, -0.054598405482370015, -0.20883246741600728, 0.040565766479918046, 0.0683807055159521, 0.15950379064796785, -0.12111254977552514, -0.04800535475614628, -0.17407730477555705, -0.12551824971543332, 0.3671224480494857, 0.05294929841827405, -0.3089060113079062, 0.11600654371278851, -0.17089611048638625, -0.17980480085641734, 0.07296942648673921, 0.1073871156274292, 0.13753212211457522, -0.09638876650531433, 0.10042886125723414, -0.12872830100700652, 0.0669785201745598, 0.283700471627526, 0.06049906516349629, 0.20680591593937656, -0.014303070419517002, -0.06336413489158363, 0.19792564418994038, 0.00990381577101193, -0.15225215035637743, -0.3613753333001545, -0.2520818767370656, -0.16072220193133321, 0.12060016877397797, -0.10483495087771046, -0.2067724752092832, 0.3787443199636121, 0.13392955074423776, 0.1498691393797727, 0.1254179280275773, 0.19033828647317072, 0.1888386701144842, 0.0343667343473307, 0.0077064227325057515, 0.08146167426802046, 0.2444593906310681, 0.11557886232376884, -0.08487153000284084, 0.029899726799493164, 0.18137045310025937] |
1,802.05456 | Transport of polymer particles in a oil-water flow in porous media:
enhancing oil recovery | We study a heuristic, core-scale model for the transport of polymer particles
in a two phase (oil and water) porous medium. We are motivated by recent
experimental observations which report increased oil recovery when polymers are
injected after the initial waterflood. The recovery mechanism is believed to be
microscopic diversion of the flow, where injected particles can accumulate in
narrow pore throats and clog it, in a process known as a log-jamming effect.
The blockage of the narrow pore channels lead to a microscopic diversion of the
water flow, causing a redistribution of the local pressure, which again can
lead to the mobilization of trapped oil, enhancing its recovery. Our objective
herein is to develop a core-scale model that is consistent with the observed
production profiles. We show that previously obtained experimental results can
be qualitatively explained by a simple two-phase flow model with an additional
transport equation for the polymer particles. A key aspect of the formulation
is that the microscopic heterogeneity of the rock and a dynamic altering of the
permeability must be taken into account in the rate equations.
| physics.flu-dyn | we study a heuristic corescale model for the transport of polymer particles in a two phase oil and water porous medium we are motivated by recent experimental observations which report increased oil recovery when polymers are injected after the initial waterflood the recovery mechanism is believed to be microscopic diversion of the flow where injected particles can accumulate in narrow pore throats and clog it in a process known as a logjamming effect the blockage of the narrow pore channels lead to a microscopic diversion of the water flow causing a redistribution of the local pressure which again can lead to the mobilization of trapped oil enhancing its recovery our objective herein is to develop a corescale model that is consistent with the observed production profiles we show that previously obtained experimental results can be qualitatively explained by a simple twophase flow model with an additional transport equation for the polymer particles a key aspect of the formulation is that the microscopic heterogeneity of the rock and a dynamic altering of the permeability must be taken into account in the rate equations | [['we', 'study', 'a', 'heuristic', 'corescale', 'model', 'for', 'the', 'transport', 'of', 'polymer', 'particles', 'in', 'a', 'two', 'phase', 'oil', 'and', 'water', 'porous', 'medium', 'we', 'are', 'motivated', 'by', 'recent', 'experimental', 'observations', 'which', 'report', 'increased', 'oil', 'recovery', 'when', 'polymers', 'are', 'injected', 'after', 'the', 'initial', 'waterflood', 'the', 'recovery', 'mechanism', 'is', 'believed', 'to', 'be', 'microscopic', 'diversion', 'of', 'the', 'flow', 'where', 'injected', 'particles', 'can', 'accumulate', 'in', 'narrow', 'pore', 'throats', 'and', 'clog', 'it', 'in', 'a', 'process', 'known', 'as', 'a', 'logjamming', 'effect', 'the', 'blockage', 'of', 'the', 'narrow', 'pore', 'channels', 'lead', 'to', 'a', 'microscopic', 'diversion', 'of', 'the', 'water', 'flow', 'causing', 'a', 'redistribution', 'of', 'the', 'local', 'pressure', 'which', 'again', 'can', 'lead', 'to', 'the', 'mobilization', 'of', 'trapped', 'oil', 'enhancing', 'its', 'recovery', 'our', 'objective', 'herein', 'is', 'to', 'develop', 'a', 'corescale', 'model', 'that', 'is', 'consistent', 'with', 'the', 'observed', 'production', 'profiles', 'we', 'show', 'that', 'previously', 'obtained', 'experimental', 'results', 'can', 'be', 'qualitatively', 'explained', 'by', 'a', 'simple', 'twophase', 'flow', 'model', 'with', 'an', 'additional', 'transport', 'equation', 'for', 'the', 'polymer', 'particles', 'a', 'key', 'aspect', 'of', 'the', 'formulation', 'is', 'that', 'the', 'microscopic', 'heterogeneity', 'of', 'the', 'rock', 'and', 'a', 'dynamic', 'altering', 'of', 'the', 'permeability', 'must', 'be', 'taken', 'into', 'account', 'in', 'the', 'rate', 'equations']] | [-0.10808680892740893, 0.19745167827616872, -0.10790366545940439, 0.005975081295491691, -0.0191753612132743, -0.1335648073633719, 0.02287280126216097, 0.350245818330182, -0.28568826051325436, -0.28441494594121147, 0.10764283316416873, -0.2610069199491085, -0.12402136059245095, 0.14170729828765616, -0.040648804667742094, 0.03439386364350665, 0.042781338369887734, -0.023979326935174565, 0.003912394971121102, -0.19288760844954392, 0.2333212849425359, 0.06862886358414673, 0.27276717787220456, 0.13326738932325194, 0.09088801305365955, -0.046584440066686106, 0.0060742565239909, 0.07498354084220611, -0.15201841106362635, 0.08176143144050406, 0.2118367420276627, 0.05302968090141399, 0.22949082330904072, -0.4998690986591909, -0.2923937483173278, 0.07313764310092666, 0.15101381092228824, 0.12972260779513614, -0.08145569761885176, -0.2530433989388661, 0.05428038624425729, -0.16209273690862272, -0.12251291209443783, -0.018147950174493922, -0.005568821754099594, 0.027920182110069112, -0.2943142946570232, 0.11112081189639866, 0.05739856052419378, 0.0030863963649608193, -0.11630265562626947, -0.08478177675149506, -0.04011114322347566, 0.11577507349510496, 0.07662344388324224, -0.0066936836953067945, 0.21985110037753153, -0.16048626419892065, -0.04035913896643453, 0.41106940649656787, -0.05287278677181651, -0.1839413924052173, 0.17294140739832073, -0.1335561138526019, -0.049520735709100136, 0.20397425855044277, 0.18760019235002498, 0.07468755256850272, -0.17028953324034873, -0.02385609910286601, -0.07513586020148877, 0.1591276450173205, 0.02734186891466379, -0.03294374172692187, 0.2198296065468134, 0.23742536152583651, 0.026409233113129933, 0.16002649940459782, -0.0991392687946144, -0.11016658570927879, -0.27348266339136496, -0.1810587640127374, -0.155945020996862, 0.043757861377930064, -0.09763791398379706, -0.14492343732725002, 0.36324668292266626, 0.11884176758402544, 0.21447846474798604, 0.0174307729738454, 0.28012498114516754, 0.08825501337285258, 0.054168461020001106, 0.0568676156263488, 0.2612416144214674, 0.10017552066646103, 0.13353191145902707, -0.2323887652471765, 0.1469720208842773, 0.028060100281274775] |
1,802.05457 | Computational Image Enhancement for Frequency Modulated Continuous Wave
(FMCW) THz Image | In this paper, a novel method to enhance Frequency Modulated Continuous Wave
(FMCW) THz imaging resolution beyond its diffraction limit is proposed. Our
method comprises two stages. Firstly, we reconstruct the signal in
depth-direction using a sinc-envelope, yielding a significant improvement in
depth estimation and signal parameter extraction. The resulting high precision
depth estimate is used to deduce an accurate reflection intensity THz image.
This image is fed in the second stage of our method to a 2D blind deconvolution
procedure, adopted to enhance the lateral THz image resolution beyond the
diffraction limit. Experimental data acquired with a FMCW system operating at
577 GHz with a bandwidth of 126 GHz shows that the proposed method enhances the
lateral resolution by a factor of 2.29 to 346.2um with respect to the
diffraction limit. The depth accuracy is 91um. Interestingly, the lateral
resolution enhancement achieved with this blind deconvolution concept leads to
better results in comparison to conventional gaussian deconvolution.
Experimental data on a PCB resolution target is presented, in order to quantify
the resolution enhancement and to compare the performance with established
image enhancement approaches. The presented technique allows exposure of the
interwoven fibre reinforced embedded structures of the PCB test sample.
| eess.IV | in this paper a novel method to enhance frequency modulated continuous wave fmcw thz imaging resolution beyond its diffraction limit is proposed our method comprises two stages firstly we reconstruct the signal in depthdirection using a sincenvelope yielding a significant improvement in depth estimation and signal parameter extraction the resulting high precision depth estimate is used to deduce an accurate reflection intensity thz image this image is fed in the second stage of our method to a 2d blind deconvolution procedure adopted to enhance the lateral thz image resolution beyond the diffraction limit experimental data acquired with a fmcw system operating at 577 ghz with a bandwidth of 126 ghz shows that the proposed method enhances the lateral resolution by a factor of 229 to 3462um with respect to the diffraction limit the depth accuracy is 91um interestingly the lateral resolution enhancement achieved with this blind deconvolution concept leads to better results in comparison to conventional gaussian deconvolution experimental data on a pcb resolution target is presented in order to quantify the resolution enhancement and to compare the performance with established image enhancement approaches the presented technique allows exposure of the interwoven fibre reinforced embedded structures of the pcb test sample | [['in', 'this', 'paper', 'a', 'novel', 'method', 'to', 'enhance', 'frequency', 'modulated', 'continuous', 'wave', 'fmcw', 'thz', 'imaging', 'resolution', 'beyond', 'its', 'diffraction', 'limit', 'is', 'proposed', 'our', 'method', 'comprises', 'two', 'stages', 'firstly', 'we', 'reconstruct', 'the', 'signal', 'in', 'depthdirection', 'using', 'a', 'sincenvelope', 'yielding', 'a', 'significant', 'improvement', 'in', 'depth', 'estimation', 'and', 'signal', 'parameter', 'extraction', 'the', 'resulting', 'high', 'precision', 'depth', 'estimate', 'is', 'used', 'to', 'deduce', 'an', 'accurate', 'reflection', 'intensity', 'thz', 'image', 'this', 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1,802.05458 | Point systems in Games for Health: A bibliometric scoping study | Very few details about point systems used in games for health are reported in
scientific literature. To shed some light on this topic a bibliometric study,
analyzing the papers containing terms related to games for health and point
systems was performed and a mini taxonomy was derived. The search string game*
AND health AND (point* OR score) AND system* in a Scopus bibliographic database
was used to produce the corpus. We limited the search to articles, reviews and
conference papers written in English and to topics related to medical, health
and social subjects. The corpus papers abstracts and titles were analysed by
VOSviewer and a scientific landscape was generated. The search resulted in a
corpus consisting of 354 papers. The derived taxonomy contains three objects;
video games, serious games and educational games. The biblimetric mapping and
taxonomy revealed some interesting conclusions: (1) the video games have mostly
negative effects on health, (2) the serious games might have both a direct
positive health effects on users and also indirect effects by improved
competencies of health professionals, and (3) the research is concerned not
only to computer based educational games, but also to traditional table games
and sporting games. Based on the derived taxonomy we can conclude that point
systems should reward physical activity and healthy living style and punish
sedentary activities.
| cs.CY cs.DL | very few details about point systems used in games for health are reported in scientific literature to shed some light on this topic a bibliometric study analyzing the papers containing terms related to games for health and point systems was performed and a mini taxonomy was derived the search string game and health and point or score and system in a scopus bibliographic database was used to produce the corpus we limited the search to articles reviews and conference papers written in english and to topics related to medical health and social subjects the corpus papers abstracts and titles were analysed by vosviewer and a scientific landscape was generated the search resulted in a corpus consisting of 354 papers the derived taxonomy contains three objects video games serious games and educational games the biblimetric mapping and taxonomy revealed some interesting conclusions 1 the video games have mostly negative effects on health 2 the serious games might have both a direct positive health effects on users and also indirect effects by improved competencies of health professionals and 3 the research is concerned not only to computer based educational games but also to traditional table games and sporting games based on the derived taxonomy we can conclude that point systems should reward physical activity and healthy living style and punish sedentary activities | [['very', 'few', 'details', 'about', 'point', 'systems', 'used', 'in', 'games', 'for', 'health', 'are', 'reported', 'in', 'scientific', 'literature', 'to', 'shed', 'some', 'light', 'on', 'this', 'topic', 'a', 'bibliometric', 'study', 'analyzing', 'the', 'papers', 'containing', 'terms', 'related', 'to', 'games', 'for', 'health', 'and', 'point', 'systems', 'was', 'performed', 'and', 'a', 'mini', 'taxonomy', 'was', 'derived', 'the', 'search', 'string', 'game', 'and', 'health', 'and', 'point', 'or', 'score', 'and', 'system', 'in', 'a', 'scopus', 'bibliographic', 'database', 'was', 'used', 'to', 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1,802.05459 | Documentation of TauSpinner algorithms -- program for simulating spin
effects in tau-lepton production at LHC | The tau-lepton plays an important role in the physics program at the Large
Hadron Collider (LHC). It offers a probe in searches for New Physics and to
measure parameters of the Standard Model. Spin of tau lepton is an interesting
phenomenological quantity for separation of signal from background or in
measuring properties of particles. A proper treatment of tau spin effects in
the Monte Carlo simulations is important for understanding the detector
acceptance as well as for the measurements/use of tau polarization and tau spin
correlations. The TauSpinner is a tool to modify tau$spin effects in any
sample. Also production matrix elements can be modified with its help. The
general principle of TauSpinner algorithm rely on the kinematic of outgoing
particles (or jets) only, and to average over all possible initial states
accordingly to assumed parton distributions and cross-sections. Incoming on
mass-shell parton four-momenta are reconstructed in approximation, but
energy-momentum conservation is obeyed. Samples of events featuring tau leptons
with their decay products, can be used as an input. The information on the
polarization and spin correlations, and production/decay matrix elements is
reconstructed from the kinematics of the tau lepton(s) (also nu_tau in case of
W-mediated processes) and tau decay products (also jets). No other information
is required. By calculating spin (or production/decay matrix element) weights,
attributed to each event, it enables evaluation of modifications without
regenerating events.
Elements of program functionalities together with explanation of the use are
given in Appendices. Present publication collect program documentation,
previously scattered over several publiications.
| hep-ph | the taulepton plays an important role in the physics program at the large hadron collider lhc it offers a probe in searches for new physics and to measure parameters of the standard model spin of tau lepton is an interesting phenomenological quantity for separation of signal from background or in measuring properties of particles a proper treatment of tau spin effects in the monte carlo simulations is important for understanding the detector acceptance as well as for the measurementsuse of tau polarization and tau spin correlations the tauspinner is a tool to modify tauspin effects in any sample also production matrix elements can be modified with its help the general principle of tauspinner algorithm rely on the kinematic of outgoing particles or jets only and to average over all possible initial states accordingly to assumed parton distributions and crosssections incoming on massshell parton fourmomenta are reconstructed in approximation but energymomentum conservation is obeyed samples of events featuring tau leptons with their decay products can be used as an input the information on the polarization and spin correlations and productiondecay matrix elements is reconstructed from the kinematics of the tau leptons also nu_tau in case of wmediated processes and tau decay products also jets no other information is required by calculating spin or productiondecay matrix element weights attributed to each event it enables evaluation of modifications without regenerating events elements of program functionalities together with explanation of the use are given in appendices present publication collect program documentation previously scattered over several publiications | [['the', 'taulepton', 'plays', 'an', 'important', 'role', 'in', 'the', 'physics', 'program', 'at', 'the', 'large', 'hadron', 'collider', 'lhc', 'it', 'offers', 'a', 'probe', 'in', 'searches', 'for', 'new', 'physics', 'and', 'to', 'measure', 'parameters', 'of', 'the', 'standard', 'model', 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