id float64 706 1.8k | title stringlengths 1 343 | abstract stringlengths 6 6.09k | categories stringlengths 5 125 | processed_abstract stringlengths 2 5.96k | tokenized_abstract stringlengths 8 8.74k | centroid stringlengths 2.1k 2.17k |
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1,802.0346 | Electrochemical and mechanical behaviors of dissimilar friction stir
welding between 5086 and 6061 aluminum alloy | The electrochemical behavior and mechanical properties of friction stir
welded AA5086 and AA6061 Al alloys were investigated. Micro-hardness
measurements and tensile tests showed that the heat-affected zone (HAZ) in
AA6061 had minimum hardness value (i.e., 88 HV) and served as failure site in
the dissimilar weld. Corrosion testing revealed that the minimum value of Icorr
appeared in the HAZ 5086 (0.54 uA/cm2) and HAZ 5086 was most resistant to
corrosion. The AA 5086 side of the weld showed better corrosion resistance than
the AA 6061 side.
| cond-mat.mtrl-sci physics.chem-ph | the electrochemical behavior and mechanical properties of friction stir welded aa5086 and aa6061 al alloys were investigated microhardness measurements and tensile tests showed that the heataffected zone haz in aa6061 had minimum hardness value ie 88 hv and served as failure site in the dissimilar weld corrosion testing revealed that the minimum value of icorr appeared in the haz 5086 054 uacm2 and haz 5086 was most resistant to corrosion the aa 5086 side of the weld showed better corrosion resistance than the aa 6061 side | [['the', 'electrochemical', 'behavior', 'and', 'mechanical', 'properties', 'of', 'friction', 'stir', 'welded', 'aa5086', 'and', 'aa6061', 'al', 'alloys', 'were', 'investigated', 'microhardness', 'measurements', 'and', 'tensile', 'tests', 'showed', 'that', 'the', 'heataffected', 'zone', 'haz', 'in', 'aa6061', 'had', 'minimum', 'hardness', 'value', 'ie', '88', 'hv', 'and', 'served', 'as', 'failure', 'site', 'in', 'the', 'dissimilar', 'weld', 'corrosion', 'testing', 'revealed', 'that', 'the', 'minimum', 'value', 'of', 'icorr', 'appeared', 'in', 'the', 'haz', '5086', '054', 'uacm2', 'and', 'haz', '5086', 'was', 'most', 'resistant', 'to', 'corrosion', 'the', 'aa', '5086', 'side', 'of', 'the', 'weld', 'showed', 'better', 'corrosion', 'resistance', 'than', 'the', 'aa', '6061', 'side']] | [-0.06199888607267948, 0.10837467003832846, -0.060541129568660194, -0.03502880303079591, -0.009643999037935454, -0.21281242165714503, 0.0875894734142896, 0.3682877063477302, -0.19079974573242534, -0.31284814183326326, 0.07153775194687222, -0.3348210735794376, -0.12492307286490412, 0.1652348626185866, -0.12220742265173398, 0.07838050347944613, 0.03471396890852381, -0.04517116079873899, -0.02611729063312797, -0.24340566597632407, 0.10026426871635896, 0.12985324403818915, 0.33973836712539196, 0.10479574038592332, 0.0006120538558153546, -0.009633798026205863, 0.05634338200530585, 0.0797297820339308, -0.20129453318607857, -0.005916997330153689, 0.20897133472539922, 0.007163909146123949, 0.24749714666429687, -0.4180176587665782, -0.1995330904583063, 0.0025582116315490564, 0.011972417770063176, -0.014983467900139444, -0.0015002421158201555, -0.18671689817736692, 0.12650796779814888, -0.09883776375585619, -0.04165069885305403, 0.08012219587998355, 0.059436490294277014, 0.016719595360679223, -0.2280492032302872, 0.11596402942805606, 0.034289325864109044, 0.17454395494697725, -0.11865654860842316, -0.23889334756771433, -0.10511160592603333, 0.07141185914001921, 0.06413538379265982, 0.005988639358924154, 0.24444707852295217, -0.0927251856554957, -0.053932037412682, 0.363068667317138, 0.0006245100595098098, 0.014726627837209141, 0.20584008211598676, -0.12945812783916208, -0.05611916655838928, 0.1713731062488959, 0.07290346630136757, 0.053645195907858365, -0.13884037189623888, -0.055253130288602896, 0.049519634948057285, 0.14624504280440948, 0.21239097597187057, -0.04401147855336175, 0.09412391342441825, 0.12519432730315364, -0.04668874870985747, 0.16276473480769815, -0.1339623409022084, 0.03990311557555448, -0.17175517429542891, -0.22490576275569552, -0.1115031806861653, 0.0688288942554637, -0.09594316910318894, -0.17023669746978318, 0.30814096463077206, 0.08923940062454408, 0.1424082798828535, -0.020388818995150573, 0.14870221597306868, -0.014611070425085285, 0.0866621379247483, 0.06528793076217612, 0.32257147675160974, 0.1739416123559589, 0.15066535794723998, -0.26207144963828954, 0.24658748021842364, 0.0285394350929624] |
1,802.03461 | Suppression of bend loss in writing of three-dimensional optical
waveguides with femtosecond laser pulses | We provide a solution toward compact and low-loss three dimensional (3D)
photonic circuits by femtosecond laser direct writing of 3D waveguides in fused
silica. We suppress the bend loss by sandwiching the waveguide between a pair
of walls formed by internal modification of glass. Our method allows to reduce
the bend loss of a curved waveguide with a bending radius of 15 mm by more than
one order of magnitude.
| physics.app-ph | we provide a solution toward compact and lowloss three dimensional 3d photonic circuits by femtosecond laser direct writing of 3d waveguides in fused silica we suppress the bend loss by sandwiching the waveguide between a pair of walls formed by internal modification of glass our method allows to reduce the bend loss of a curved waveguide with a bending radius of 15 mm by more than one order of magnitude | [['we', 'provide', 'a', 'solution', 'toward', 'compact', 'and', 'lowloss', 'three', 'dimensional', '3d', 'photonic', 'circuits', 'by', 'femtosecond', 'laser', 'direct', 'writing', 'of', '3d', 'waveguides', 'in', 'fused', 'silica', 'we', 'suppress', 'the', 'bend', 'loss', 'by', 'sandwiching', 'the', 'waveguide', 'between', 'a', 'pair', 'of', 'walls', 'formed', 'by', 'internal', 'modification', 'of', 'glass', 'our', 'method', 'allows', 'to', 'reduce', 'the', 'bend', 'loss', 'of', 'a', 'curved', 'waveguide', 'with', 'a', 'bending', 'radius', 'of', '15', 'mm', 'by', 'more', 'than', 'one', 'order', 'of', 'magnitude']] | [-0.10100205750038316, 0.17300171907991171, -0.03335975254220622, -0.08100266771923219, -0.06379909463825502, -0.17474417701097472, 0.007143297658434936, 0.5269560555262225, -0.19690853599458932, -0.31221421230584384, 0.03002383030252531, -0.2764466209106364, -0.107348128767418, 0.19752489208642926, 0.006919848928893251, 0.07729685408994555, 0.036031924417641546, -0.09849422493176202, -0.08418342846312693, -0.14436058627907186, 0.2794561922816294, 0.020963411332507217, 0.285748299962974, 0.04685736883963857, 0.10594235870854131, 0.010987209806418312, 0.03997798919444904, -0.0032501509013984887, -0.12824592738678411, 0.2001512612814882, 0.19910757234320045, -0.04993407637146967, 0.2757591004084263, -0.5076397985219956, -0.24414801468540515, -0.013040780848158257, 0.1207868737800579, 0.12301150251379503, -0.06116235278916845, -0.26578896468771357, 0.08053665818380458, -0.1469008736744789, -0.16920129919225083, 0.008641829248517752, -0.036280274178300584, -0.025147810565041645, -0.21392320449064886, 0.036305213846000176, 0.09717819419955569, 0.000851366575807333, -0.008468278145716926, -0.027364917987558458, -0.05384411614587797, 0.025784851517528296, -0.06550661655408996, 0.04563908111304045, 0.17122086394312128, -0.11817382057332516, -0.12941516586579382, 0.3754157338823591, -0.0836399995299871, -0.18020862257108092, 0.14513687384980065, -0.14333458227504578, 0.07553606630702103, 0.22334322367927859, 0.19859468463276114, 0.1245010895388467, -0.0818348284012505, -0.046402183036220124, 0.02791622327474345, 0.23333226120032902, 0.22111519152032477, 0.03947777484676668, 0.2280330052599311, 0.25610938351601364, 0.07775285594231848, 0.24251017524594706, -0.11733090566870358, 0.009545343715165343, -0.2642112776370985, -0.20669859975709448, -0.18727598545540655, 0.053563744415129934, -0.1671102713404772, -0.1560836551444871, 0.3786985580144184, 0.07057613187186819, 0.1812016457851444, 0.004838372500879424, 0.3201944989817483, 0.04818262414441311, 0.1654680906528873, 0.01375832451241357, 0.3005428038537502, 0.17416727051604539, 0.05829334326886705, -0.22362202578889473, -0.05820998766326479, 0.04971077446160572] |
1,802.03462 | OAT: Attesting Operation Integrity of Embedded Devices | Due to the wide adoption of IoT/CPS systems, embedded devices(IoT frontends)
become increasingly connected and mission-critical, which in turn has attracted
advanced attacks (e.g., control-flow hijacks and data-only attacks).
Unfortunately, IoT backends are unable to detect if such attacks have happened
while receiving data, service requests, or operation status from IoT devices.
As a result, currently, IoT backends are forced to blindly trust the IoT
devices that they interact with.
To fill this void, we first formulate a new security property for embedded
devices, called "Operation Execution Integrity" or OEI. We then design and
build a system, OAT, that enables remote OEI attestation for ARM-based
bare-metal embedded devices. Our formulation of OEI captures the integrity of
both control flow and critical data involved in an operation execution.
Therefore, satisfying OEI entails that an operation execution is free of
unexpected control and data manipulations, which existing attestation methods
cannot check. Our design of OAT strikes a balance between prover's constraints
(embedded devices' limited computing power and storage) and verifier's
requirements(complete verifiability and forensic assistance). OAT uses a new
control-flow measurement scheme, which enables light-weight and space-efficient
collection of measurements (97% space reduction from the trace-based approach).
OAT performs the remote control-flow verification through abstract execution,
which is fast and deterministic. OAT also features lightweight integrity
checking for critical data (74% fewer instrumentation needed than previous
work). Our security analysis shows that OAT allows remote verifiers or IoT
backends to detect both control-flow hijacks and data-only attacks that affect
the execution of operations on IoT devices. In our evaluation using real
embedded programs, OAT incurs a runtime overhead of 2.7%.
| cs.CR | due to the wide adoption of iotcps systems embedded devicesiot frontends become increasingly connected and missioncritical which in turn has attracted advanced attacks eg controlflow hijacks and dataonly attacks unfortunately iot backends are unable to detect if such attacks have happened while receiving data service requests or operation status from iot devices as a result currently iot backends are forced to blindly trust the iot devices that they interact with to fill this void we first formulate a new security property for embedded devices called operation execution integrity or oei we then design and build a system oat that enables remote oei attestation for armbased baremetal embedded devices our formulation of oei captures the integrity of both control flow and critical data involved in an operation execution therefore satisfying oei entails that an operation execution is free of unexpected control and data manipulations which existing attestation methods cannot check our design of oat strikes a balance between provers constraints embedded devices limited computing power and storage and verifiers requirementscomplete verifiability and forensic assistance oat uses a new controlflow measurement scheme which enables lightweight and spaceefficient collection of measurements 97 space reduction from the tracebased approach oat performs the remote controlflow verification through abstract execution which is fast and deterministic oat also features lightweight integrity checking for critical data 74 fewer instrumentation needed than previous work our security analysis shows that oat allows remote verifiers or iot backends to detect both controlflow hijacks and dataonly attacks that affect the execution of operations on iot devices in our evaluation using real embedded programs oat incurs a runtime overhead of 27 | [['due', 'to', 'the', 'wide', 'adoption', 'of', 'iotcps', 'systems', 'embedded', 'devicesiot', 'frontends', 'become', 'increasingly', 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1,802.03463 | Using Intelligent Control to Improve PV Systems Efficiency | One of the important technologies in renewable energy is the Photovoltaic
(PV), which is the direct conversion of light into electricity at the atomic
level, and improving the efficiency of PV systems is one of the very important
factors in getting the most out of this invaluable renewable resource of
energy. While most research work we studied used conventional techniques to
control two parameters at most, like power and change in power, or voltage and
change in voltage, etc., we implemented unconventional techniques, namely
intelligent control to control more than two parameters at a time, including
change in temperature which had been ignored by many researchers for various
reasons, as well as the use of probability theory to predict the location of
power point and control how it would move before not after it did. Practically,
we utilized available PV systems devices to test our controlled systems and we
used simulations and compare our findings with previous work done by others in
this area and our techniques showed good improvement in efficiency and we
believe that it could open the door for other colleagues to add valuable work
in this important field.
| physics.app-ph | one of the important technologies in renewable energy is the photovoltaic pv which is the direct conversion of light into electricity at the atomic level and improving the efficiency of pv systems is one of the very important factors in getting the most out of this invaluable renewable resource of energy while most research work we studied used conventional techniques to control two parameters at most like power and change in power or voltage and change in voltage etc we implemented unconventional techniques namely intelligent control to control more than two parameters at a time including change in temperature which had been ignored by many researchers for various reasons as well as the use of probability theory to predict the location of power point and control how it would move before not after it did practically we utilized available pv systems devices to test our controlled systems and we used simulations and compare our findings with previous work done by others in this area and our techniques showed good improvement in efficiency and we believe that it could open the door for other colleagues to add valuable work in this important field | [['one', 'of', 'the', 'important', 'technologies', 'in', 'renewable', 'energy', 'is', 'the', 'photovoltaic', 'pv', 'which', 'is', 'the', 'direct', 'conversion', 'of', 'light', 'into', 'electricity', 'at', 'the', 'atomic', 'level', 'and', 'improving', 'the', 'efficiency', 'of', 'pv', 'systems', 'is', 'one', 'of', 'the', 'very', 'important', 'factors', 'in', 'getting', 'the', 'most', 'out', 'of', 'this', 'invaluable', 'renewable', 'resource', 'of', 'energy', 'while', 'most', 'research', 'work', 'we', 'studied', 'used', 'conventional', 'techniques', 'to', 'control', 'two', 'parameters', 'at', 'most', 'like', 'power', 'and', 'change', 'in', 'power', 'or', 'voltage', 'and', 'change', 'in', 'voltage', 'etc', 'we', 'implemented', 'unconventional', 'techniques', 'namely', 'intelligent', 'control', 'to', 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1,802.03464 | Metric Learning via Maximizing the Lipschitz Margin Ratio | In this paper, we propose the Lipschitz margin ratio and a new metric
learning framework for classification through maximizing the ratio. This
framework enables the integration of both the inter-class margin and the
intra-class dispersion, as well as the enhancement of the generalization
ability of a classifier. To introduce the Lipschitz margin ratio and its
associated learning bound, we elaborate the relationship between metric
learning and Lipschitz functions, as well as the representability and
learnability of the Lipschitz functions. After proposing the new metric
learning framework based on the introduced Lipschitz margin ratio, we also
prove that some well known metric learning algorithms can be shown as special
cases of the proposed framework. In addition, we illustrate the framework by
implementing it for learning the squared Mahalanobis metric, and by
demonstrating its encouraging results on eight popular datasets of machine
learning.
| cs.LG | in this paper we propose the lipschitz margin ratio and a new metric learning framework for classification through maximizing the ratio this framework enables the integration of both the interclass margin and the intraclass dispersion as well as the enhancement of the generalization ability of a classifier to introduce the lipschitz margin ratio and its associated learning bound we elaborate the relationship between metric learning and lipschitz functions as well as the representability and learnability of the lipschitz functions after proposing the new metric learning framework based on the introduced lipschitz margin ratio we also prove that some well known metric learning algorithms can be shown as special cases of the proposed framework in addition we illustrate the framework by implementing it for learning the squared mahalanobis metric and by demonstrating its encouraging results on eight popular datasets of machine learning | [['in', 'this', 'paper', 'we', 'propose', 'the', 'lipschitz', 'margin', 'ratio', 'and', 'a', 'new', 'metric', 'learning', 'framework', 'for', 'classification', 'through', 'maximizing', 'the', 'ratio', 'this', 'framework', 'enables', 'the', 'integration', 'of', 'both', 'the', 'interclass', 'margin', 'and', 'the', 'intraclass', 'dispersion', 'as', 'well', 'as', 'the', 'enhancement', 'of', 'the', 'generalization', 'ability', 'of', 'a', 'classifier', 'to', 'introduce', 'the', 'lipschitz', 'margin', 'ratio', 'and', 'its', 'associated', 'learning', 'bound', 'we', 'elaborate', 'the', 'relationship', 'between', 'metric', 'learning', 'and', 'lipschitz', 'functions', 'as', 'well', 'as', 'the', 'representability', 'and', 'learnability', 'of', 'the', 'lipschitz', 'functions', 'after', 'proposing', 'the', 'new', 'metric', 'learning', 'framework', 'based', 'on', 'the', 'introduced', 'lipschitz', 'margin', 'ratio', 'we', 'also', 'prove', 'that', 'some', 'well', 'known', 'metric', 'learning', 'algorithms', 'can', 'be', 'shown', 'as', 'special', 'cases', 'of', 'the', 'proposed', 'framework', 'in', 'addition', 'we', 'illustrate', 'the', 'framework', 'by', 'implementing', 'it', 'for', 'learning', 'the', 'squared', 'mahalanobis', 'metric', 'and', 'by', 'demonstrating', 'its', 'encouraging', 'results', 'on', 'eight', 'popular', 'datasets', 'of', 'machine', 'learning']] | [-0.03830999900679375, -0.01911309179816897, -0.05812641807191127, 0.1006801935583221, -0.09637990454211831, -0.14689139222580913, 0.055703739355754185, 0.4145299165595508, -0.29404450834444756, -0.3134243343131128, 0.07615689488610002, -0.23084144686771466, -0.2417871397226415, 0.1917223983178211, -0.1561643795196152, 0.13636215593821058, 0.07002677946807222, 0.04043475523135288, -0.11003476021157459, -0.27655190298439447, 0.3816804072707363, 0.060003896435418876, 0.3348894943483174, 0.12272158754883823, 0.16520294332414442, 0.0024395671157919348, -0.02612291847633079, 0.054605426490628335, -0.1014412658849788, 0.18217449867767646, 0.26399806849922697, 0.21860242213439593, 0.35480504403723045, -0.2924545191670933, -0.1900532581968933, 0.14300054708142362, 0.09696326739689771, 0.024110427006673735, -0.03374502056522678, -0.3471818352976801, 0.05019986978071537, -0.18083788905662637, -0.029124675901500998, -0.17233861238755127, -0.0470333379092263, 0.02437057706745381, -0.29213660284430976, 0.05177942497787026, 0.1173459628711666, 0.03897952647099077, -0.1010383580107876, -0.1295259035562317, 0.044758406878904776, 0.10493569257041664, 0.09280879150230623, 0.07584632212850959, 0.10784897391175759, -0.1338886466349561, -0.12249439930648985, 0.3468165415345777, -0.1073185792787278, -0.24587350422582516, 0.18091405455373122, -0.034560936629848146, -0.09534459117245167, -0.0034561776151510737, 0.2498923518153317, 0.1611522176023903, -0.1462906749773744, 0.07233586478466499, -0.03242963781012288, 0.09847528990259037, 0.06893846898535055, 0.02704154039858609, 0.11229926602029842, 0.21674536311906492, 0.0834734717521037, 0.1899762158323383, -0.1105268045352501, -0.08084513721261041, -0.2984661336142755, -0.13435534104343227, -0.177026300601899, -0.02502947656358493, -0.172034462087611, -0.1553816540513092, 0.37688525523065675, 0.13342041438709654, 0.2183084053793222, 0.19720204006748418, 0.30390153153707, 0.06691128482748193, 0.07965764337291954, 0.12582915085070628, 0.2630095332722652, 0.11654689813244111, 0.09721851474044065, -0.19016973696341935, 0.1124331012784325, 0.10456702993614665] |
1,802.03465 | Monopoles of Twelve Types in 3-Body Problems | We consider twelve different ways of modelling the 3-body problem in
dimension $\geq 2$. These can be viewed as models of classical and quantum
background independence. We show that a different type of monopole is realized
in each's relational space: a type of reduced configuration space. 8 cases
occur in 2-$d$, and 4 distinct ones in 3-$d$; these reflect counts of
non-equivalent subgroup actions of $S_3 \times C_2$ and $S_3$ respectively. The
$S_3$ acts on particle labels; the extra $C_2$ corresponds to the purely 2-$d$
option of whether or not to identify mirror images. The non-equivalent
realization is due to a suite of subgroup, orbit space and stratification
features.
Our 2-$d$ monopoles include 4 known ones: a realization of Dirac's monopole
in relational space rather than its more habitual setting of space, the 2-$d$
version of Iwai's monopole, and indistinguishable particle monopoles with and
without mirror image identification. The 4 new ones are indistinguishable under
a 2-particle label switch or under even permutations, in each case with
optional mirror image identification. Our 4 3-$d$ monopoles are 2 known ones:
the actual Iwai monopole and its already-announced indistinguishable-particles
counterpart, and 2 new ones: the two-particle label switch and even permutation
cases. All 4 3-$d$ cases are stratified. The three even-permutation cases are
orbifolds, two with boundary, the 3-$d$ case's boundary constituting a separate
stratum, giving a stratified orbifold. We document each of the 12 cases'
underlying shape space and relational space, and each monopole's Hopf
mathematics, global-section versus topological quantization dichotomy, Dirac
string positioning, and Chern integral concordance with topological
contributions form of Gauss--Bonnet Theorem.
| gr-qc | we consider twelve different ways of modelling the 3body problem in dimension geq 2 these can be viewed as models of classical and quantum background independence we show that a different type of monopole is realized in eachs relational space a type of reduced configuration space 8 cases occur in 2d and 4 distinct ones in 3d these reflect counts of nonequivalent subgroup actions of s_3 times c_2 and s_3 respectively the s_3 acts on particle labels the extra c_2 corresponds to the purely 2d option of whether or not to identify mirror images the nonequivalent realization is due to a suite of subgroup orbit space and stratification features our 2d monopoles include 4 known ones a realization of diracs monopole in relational space rather than its more habitual setting of space the 2d version of iwais monopole and indistinguishable particle monopoles with and without mirror image identification the 4 new ones are indistinguishable under a 2particle label switch or under even permutations in each case with optional mirror image identification our 4 3d monopoles are 2 known ones the actual iwai monopole and its alreadyannounced indistinguishableparticles counterpart and 2 new ones the twoparticle label switch and even permutation cases all 4 3d cases are stratified the three evenpermutation cases are orbifolds two with boundary the 3d cases boundary constituting a separate stratum giving a stratified orbifold we document each of the 12 cases underlying shape space and relational space and each monopoles hopf mathematics globalsection versus topological quantization dichotomy dirac string positioning and chern integral concordance with topological contributions form of gaussbonnet theorem | [['we', 'consider', 'twelve', 'different', 'ways', 'of', 'modelling', 'the', '3body', 'problem', 'in', 'dimension', 'geq', '2', 'these', 'can', 'be', 'viewed', 'as', 'models', 'of', 'classical', 'and', 'quantum', 'background', 'independence', 'we', 'show', 'that', 'a', 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1,802.03466 | Microscopic modeling of gas-surface scattering. I. A combined molecular
dynamics-rate equation approach | A combination of first principle molecular dynamics (MD) simulations with a
rate equation model (MD-RE approach) is presented to study the trapping and the
scattering of rare gas atoms from metal surfaces. The temporal evolution of the
atom fractions that are either adsorbed or scattered into the continuum is
investigated in detail. We demonstrate that for this description one has to
consider trapped, quasi-trapped and scattering states, and present an energetic
definition of these states. The rate equations contain the transition
probabilities between the states. We demonstrate how these rate equations can
be derived from kinetic theory. Moreover, we present a rigorous way to
determine the transition probabilities from a microscopic analysis of the
particle trajectories generated by MD simulations. Once the system reaches
quasi-equilibrium, the rates converge to stationary values, and the subsequent
thermal adsorption/desorption dynamics is completely described by the rate
equations without the need to perform further time-consuming MD simulations.
As a proof of concept of our approach, MD simulations for argon atoms
interacting with a platinum (111) surface are presented. A detailed
deterministic trajectory analysis is performed, and the transition rates are
constructed. The dependence of the rates on the incidence conditions and the
lattice temperature is analyzed. Based on this example, we analyze the time
scale of the gas-surface system to approach the quasi-stationary state. The
MD-rate equation model has great relevance for the plasma-surface modeling as
it makes an extension of accurate simulations to long, experimentally relevant
time scales possible. Its application to the computation of atomic sticking
probabilities is given in the second part (paper II).
| cond-mat.stat-mech physics.plasm-ph | a combination of first principle molecular dynamics md simulations with a rate equation model mdre approach is presented to study the trapping and the scattering of rare gas atoms from metal surfaces the temporal evolution of the atom fractions that are either adsorbed or scattered into the continuum is investigated in detail we demonstrate that for this description one has to consider trapped quasitrapped and scattering states and present an energetic definition of these states the rate equations contain the transition probabilities between the states we demonstrate how these rate equations can be derived from kinetic theory moreover we present a rigorous way to determine the transition probabilities from a microscopic analysis of the particle trajectories generated by md simulations once the system reaches quasiequilibrium the rates converge to stationary values and the subsequent thermal adsorptiondesorption dynamics is completely described by the rate equations without the need to perform further timeconsuming md simulations as a proof of concept of our approach md simulations for argon atoms interacting with a platinum 111 surface are presented a detailed deterministic trajectory analysis is performed and the transition rates are constructed the dependence of the rates on the incidence conditions and the lattice temperature is analyzed based on this example we analyze the time scale of the gassurface system to approach the quasistationary state the mdrate equation model has great relevance for the plasmasurface modeling as it makes an extension of accurate simulations to long experimentally relevant time scales possible its application to the computation of atomic sticking probabilities is given in the second part paper ii | [['a', 'combination', 'of', 'first', 'principle', 'molecular', 'dynamics', 'md', 'simulations', 'with', 'a', 'rate', 'equation', 'model', 'mdre', 'approach', 'is', 'presented', 'to', 'study', 'the', 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1,802.03467 | The role of defects in the etching of graphene by intercalated oxygen | Graphene is one of the most promising 2D materials for various applications
due to its unique electronic properties and high thermal stability. In previous
studies, it was shown that when graphene is deposited onto some transition
metal substrates, small molecules, such as O$_2$, intercalate between the
graphene and the substrate and react to partially etch the graphene film when
heated to desorb the intercalates. Here, carbon vacancy defects are
intentionally formed on Gr/Ru(0001) and their effect on the intercalation of
oxygen and etching of the graphene layer are investigated. 50 eV Ar$^+$
sputtering with a low fluence is used to create isolated single vacancy defects
in the graphene overlayer and helium low energy ion scattering (LEIS) is
employed for surface analysis. It is found that the defects both ease the
intercalation of the oxygen molecules and improve the etching efficiency of the
graphene during annealing.
| cond-mat.mtrl-sci | graphene is one of the most promising 2d materials for various applications due to its unique electronic properties and high thermal stability in previous studies it was shown that when graphene is deposited onto some transition metal substrates small molecules such as o_2 intercalate between the graphene and the substrate and react to partially etch the graphene film when heated to desorb the intercalates here carbon vacancy defects are intentionally formed on grru0001 and their effect on the intercalation of oxygen and etching of the graphene layer are investigated 50 ev ar sputtering with a low fluence is used to create isolated single vacancy defects in the graphene overlayer and helium low energy ion scattering leis is employed for surface analysis it is found that the defects both ease the intercalation of the oxygen molecules and improve the etching efficiency of the graphene during annealing | [['graphene', 'is', 'one', 'of', 'the', 'most', 'promising', '2d', 'materials', 'for', 'various', 'applications', 'due', 'to', 'its', 'unique', 'electronic', 'properties', 'and', 'high', 'thermal', 'stability', 'in', 'previous', 'studies', 'it', 'was', 'shown', 'that', 'when', 'graphene', 'is', 'deposited', 'onto', 'some', 'transition', 'metal', 'substrates', 'small', 'molecules', 'such', 'as', 'o_2', 'intercalate', 'between', 'the', 'graphene', 'and', 'the', 'substrate', 'and', 'react', 'to', 'partially', 'etch', 'the', 'graphene', 'film', 'when', 'heated', 'to', 'desorb', 'the', 'intercalates', 'here', 'carbon', 'vacancy', 'defects', 'are', 'intentionally', 'formed', 'on', 'grru0001', 'and', 'their', 'effect', 'on', 'the', 'intercalation', 'of', 'oxygen', 'and', 'etching', 'of', 'the', 'graphene', 'layer', 'are', 'investigated', '50', 'ev', 'ar', 'sputtering', 'with', 'a', 'low', 'fluence', 'is', 'used', 'to', 'create', 'isolated', 'single', 'vacancy', 'defects', 'in', 'the', 'graphene', 'overlayer', 'and', 'helium', 'low', 'energy', 'ion', 'scattering', 'leis', 'is', 'employed', 'for', 'surface', 'analysis', 'it', 'is', 'found', 'that', 'the', 'defects', 'both', 'ease', 'the', 'intercalation', 'of', 'the', 'oxygen', 'molecules', 'and', 'improve', 'the', 'etching', 'efficiency', 'of', 'the', 'graphene', 'during', 'annealing']] | [-0.04266419299990581, 0.19473079210468408, 0.01278835702687502, -0.03171438763745065, 0.04995832006318559, -0.17163315162961854, 0.0936501832678914, 0.48317004519289936, -0.267868464643232, -0.308378796382197, 0.018622608198222288, -0.365094517762291, -0.09805013996484721, 0.1477952934769464, -0.002432893036768354, 0.03452658292492209, 0.02747261754783063, -0.13096112087529538, -0.04848059009889077, -0.2668128770069572, 0.23672711524373755, 0.12336129915804185, 0.3602878730535379, 0.18266314522138444, 0.004838310809934448, -0.055087934439231094, 0.13592338738481288, -0.02201213789140356, -0.1415456440850715, 0.09416656438903562, 0.23263241402836965, -0.11248638725614753, 0.19943972577437244, -0.5529972321385968, -0.2507934522429674, -0.026664155494855266, 0.10963342687243531, 0.16204250876607115, -0.1472692919172475, -0.21320833059605854, 0.08042091021378492, -0.09024724180906497, -0.07966230822534397, -0.011079160525495636, -0.0051667956828043374, 0.011678008654893473, -0.20301147177815437, 0.02690904304256727, 0.02465873667649154, 0.05485889250091438, -0.117296472621343, -0.13769800497604343, -0.19756349486636063, 0.05322032519335184, 0.03331622059842379, 0.018146043261191968, 0.3283598429427065, -0.07566663038370938, -0.014704498683969522, 0.40699215276038336, -0.04020112881372715, -0.09735287917328292, 0.252261034848875, -0.1357405170139953, -0.06720345024950802, 0.20755583916244835, 0.08660753974851607, 0.14326303601264953, -0.1637561152827653, 0.03130282229214008, 0.046900770100283214, 0.1634183252628959, 0.1679947605806178, 0.04556602173452747, 0.22146800405121056, 0.24631895057150516, 0.04705199883158865, 0.14233370158863495, -0.14090089720577514, 0.05807302111824011, -0.13035533092916013, -0.25342144429330427, -0.2066006044359428, 0.06356400027148554, -0.05371026560184466, -0.22840172292339486, 0.35148484901492966, 0.12333059106022119, 0.1571254719578629, -0.13115054821788236, 0.24744178805949874, 0.07256133292946194, 0.08259597222384965, -0.02991908080233582, 0.23996567869787747, 0.1816873943914884, 0.11613655787218234, -0.25334370793003974, 0.1307445102242817, -0.031591878598555924] |
1,802.03468 | Three-dimensional modeling of chiral nematic texture evolution under
electric switching | Chiral nematic liquid crystals exhibit both a helical planar ground state
with uniform twist and a metastable defect-rich focal conic texture, and can be
switched between the two microstructures via application of transient voltage
pulses. In this work, we model these electrically-induced texture transitions
using finite difference methods to examine resulting microstructural evolution,
the first time this transition has been modeled in three dimensions. We analyze
the planar to focal conic, focal conic to planar, and planar to planar
transitions depending on voltage pulse magnitude. We consider first the special
case of chiral nematics with matched twist and bend elastic constants. Results
show a variety of defect-rich morphologies in the disordered focal conic
texture and demonstrate a fast recovery of the planar ground state on switching
without formation of a transient planar state. We evaluate both texture
microstructural evolution as well as cell capacitance. Beyond the single
elastic constant approximation, we evaluate the planar to transient-planar as
well as the planar to Helfrich-deformed transitions in simulations of a liquid
crystal compound with different elastic constants. Our methods represent the
evolving microstructure as a uniaxial director field, with relaxation dynamics
calculated from a tensor representation so that half charge disclination
defects are not suppressed. We discuss potential application of these
computationally efficient three-dimensional modeling approaches for design and
optimization of chiral nematic devices.
| cond-mat.mtrl-sci | chiral nematic liquid crystals exhibit both a helical planar ground state with uniform twist and a metastable defectrich focal conic texture and can be switched between the two microstructures via application of transient voltage pulses in this work we model these electricallyinduced texture transitions using finite difference methods to examine resulting microstructural evolution the first time this transition has been modeled in three dimensions we analyze the planar to focal conic focal conic to planar and planar to planar transitions depending on voltage pulse magnitude we consider first the special case of chiral nematics with matched twist and bend elastic constants results show a variety of defectrich morphologies in the disordered focal conic texture and demonstrate a fast recovery of the planar ground state on switching without formation of a transient planar state we evaluate both texture microstructural evolution as well as cell capacitance beyond the single elastic constant approximation we evaluate the planar to transientplanar as well as the planar to helfrichdeformed transitions in simulations of a liquid crystal compound with different elastic constants our methods represent the evolving microstructure as a uniaxial director field with relaxation dynamics calculated from a tensor representation so that half charge disclination defects are not suppressed we discuss potential application of these computationally efficient threedimensional modeling approaches for design and optimization of chiral nematic devices | [['chiral', 'nematic', 'liquid', 'crystals', 'exhibit', 'both', 'a', 'helical', 'planar', 'ground', 'state', 'with', 'uniform', 'twist', 'and', 'a', 'metastable', 'defectrich', 'focal', 'conic', 'texture', 'and', 'can', 'be', 'switched', 'between', 'the', 'two', 'microstructures', 'via', 'application', 'of', 'transient', 'voltage', 'pulses', 'in', 'this', 'work', 'we', 'model', 'these', 'electricallyinduced', 'texture', 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1,802.03469 | Cosmological Lithium Problems | We briefly describe the cosmological lithium problems followed by a summary
of our recent theoretical work on the magnitude of the effects of electron
screening, the possible existence of dark matter parallel universes and the use
of non-extensive (Tsallis) statistics during big bang nucleosynthesis.
Solutions within nuclear physics are also discussed and recent measurements of
cross-sections based on indirect experimental techniques are summarized.
| nucl-th | we briefly describe the cosmological lithium problems followed by a summary of our recent theoretical work on the magnitude of the effects of electron screening the possible existence of dark matter parallel universes and the use of nonextensive tsallis statistics during big bang nucleosynthesis solutions within nuclear physics are also discussed and recent measurements of crosssections based on indirect experimental techniques are summarized | [['we', 'briefly', 'describe', 'the', 'cosmological', 'lithium', 'problems', 'followed', 'by', 'a', 'summary', 'of', 'our', 'recent', 'theoretical', 'work', 'on', 'the', 'magnitude', 'of', 'the', 'effects', 'of', 'electron', 'screening', 'the', 'possible', 'existence', 'of', 'dark', 'matter', 'parallel', 'universes', 'and', 'the', 'use', 'of', 'nonextensive', 'tsallis', 'statistics', 'during', 'big', 'bang', 'nucleosynthesis', 'solutions', 'within', 'nuclear', 'physics', 'are', 'also', 'discussed', 'and', 'recent', 'measurements', 'of', 'crosssections', 'based', 'on', 'indirect', 'experimental', 'techniques', 'are', 'summarized']] | [-0.04075405164252198, 0.1594165390401724, -0.11040936762260066, 0.12071706880889242, -0.061183903913294514, -0.07849610998250899, 0.009112596689235596, 0.2987783916501535, -0.22153057263691037, -0.30914370260304874, 0.06627299182767434, -0.31908655298193767, -0.04855255556425878, 0.22885844767803237, -0.0044645028787532025, 0.0913250335447845, 0.08013260435490381, 0.0028691343697054045, -0.08272496675277898, -0.3128799477976466, 0.3569390577308479, 0.11389912001138168, 0.24589174811447423, 0.11418633423154316, 0.048083200673794464, -0.029991475399583578, -0.11855923924742946, 0.018798792677458435, -0.25173687281471396, 0.12364662431239609, 0.21756102857253853, 0.1596366592224628, 0.21524008443682557, -0.49340275261137223, -0.2436926467196336, 0.07303089487351595, 0.1180730809324554, 0.1422052143792075, -0.14983789748807866, -0.2943827086350038, 0.01050518945391689, -0.19207044381145683, -0.07772529718746977, -0.08218288825908472, 0.011549741155394012, 0.05466606064019577, -0.18215775540808127, 0.15811003727186101, -0.018589285224498736, 0.00720160495164612, -0.06679218177986701, -0.18705322090832013, 0.07376571597960142, -0.031245863913292333, 0.10322975016860587, -0.04667136789713469, 0.18686781913071634, -0.12469739625821748, -0.16917631978036038, 0.37995264238663135, -0.04559279653051543, -0.05736669852945303, 0.147162249241586, -0.11565572331615147, -0.15864984092435666, 0.03640052992554884, 0.12949341887955568, 0.10535177603984873, -0.13803469172368446, 0.12120635629934629, 0.019448762042595755, 0.118283400903382, 0.042696532941172045, 0.04296397276607061, 0.2804478521538263, 0.2375060266977738, -0.04937843215607461, 0.02236828420843397, -0.07968880373999358, -0.1299921928928973, -0.3832824985895838, -0.10596150033442038, -0.14789622578222955, 0.013294432999654895, -0.07673593494433185, -0.13864403266814493, 0.3895012321808035, 0.1730667403086074, 0.19532446528504055, -0.06561987110901446, 0.3243052698228331, 0.025368169519222446, -0.03265885633253862, 0.0014014675888040708, 0.3097261881015988, 0.1914533315825143, 0.1356818779475159, -0.24167323535186283, 0.06507678589061094, 0.029609352110012893] |
1,802.0347 | Non-Relativistic Fermion-Fermion Scattering in Higher Derivative Gravity | In this note, we examine the scattering of two identical fermions in theories
where fermionic fields minimally coupled to higher derivative gravity. In
particular, we consider the extension of general relativity with $R^2$
corrections or non-local terms. We expand the action of fermions around the
flat space background and obtain two fermion-one graviton vertex. Then, by
considering the scattering amplitude of two fermions, we calculate the
non-relativistic limit and that obtain the potential for two fermion-fermion
interaction which would be the usual Newtonian potential corrected with a
Yukawa-like term. At the end, we briefly discuss the astronomical effects of
such Yukawa-like potential by computing the gravitational pressure of a
spherical star and use it for a white dwarf to obtain quantum corrections of
Chandrasekhar radius.
| gr-qc | in this note we examine the scattering of two identical fermions in theories where fermionic fields minimally coupled to higher derivative gravity in particular we consider the extension of general relativity with r2 corrections or nonlocal terms we expand the action of fermions around the flat space background and obtain two fermionone graviton vertex then by considering the scattering amplitude of two fermions we calculate the nonrelativistic limit and that obtain the potential for two fermionfermion interaction which would be the usual newtonian potential corrected with a yukawalike term at the end we briefly discuss the astronomical effects of such yukawalike potential by computing the gravitational pressure of a spherical star and use it for a white dwarf to obtain quantum corrections of chandrasekhar radius | [['in', 'this', 'note', 'we', 'examine', 'the', 'scattering', 'of', 'two', 'identical', 'fermions', 'in', 'theories', 'where', 'fermionic', 'fields', 'minimally', 'coupled', 'to', 'higher', 'derivative', 'gravity', 'in', 'particular', 'we', 'consider', 'the', 'extension', 'of', 'general', 'relativity', 'with', 'r2', 'corrections', 'or', 'nonlocal', 'terms', 'we', 'expand', 'the', 'action', 'of', 'fermions', 'around', 'the', 'flat', 'space', 'background', 'and', 'obtain', 'two', 'fermionone', 'graviton', 'vertex', 'then', 'by', 'considering', 'the', 'scattering', 'amplitude', 'of', 'two', 'fermions', 'we', 'calculate', 'the', 'nonrelativistic', 'limit', 'and', 'that', 'obtain', 'the', 'potential', 'for', 'two', 'fermionfermion', 'interaction', 'which', 'would', 'be', 'the', 'usual', 'newtonian', 'potential', 'corrected', 'with', 'a', 'yukawalike', 'term', 'at', 'the', 'end', 'we', 'briefly', 'discuss', 'the', 'astronomical', 'effects', 'of', 'such', 'yukawalike', 'potential', 'by', 'computing', 'the', 'gravitational', 'pressure', 'of', 'a', 'spherical', 'star', 'and', 'use', 'it', 'for', 'a', 'white', 'dwarf', 'to', 'obtain', 'quantum', 'corrections', 'of', 'chandrasekhar', 'radius']] | [-0.133295732572855, 0.18321016121415362, -0.07640590577953375, 0.12084599019820622, -0.07143030648735622, -0.13888131897340739, -0.005689576131126453, 0.28290842161438756, -0.19133860662940047, -0.2640699683716746, 0.03785570866462114, -0.3041209672998276, -0.14275738236216468, 0.12331360131464598, -0.011180247933577536, -0.005755626093295794, 0.0024686476155634848, 0.07414972623519134, -0.10770186929655592, -0.26677872036253253, 0.38733272963473875, 0.017897346589316222, 0.15536308692648046, 0.06414078803913247, 0.06742632419111268, 0.03556602134654719, -0.027355880375140376, 0.01972292588940153, -0.15475355531306112, 0.06814619126699624, 0.16202692711216984, 0.005249886923739987, 0.2026434862355311, -0.44034206824407224, -0.23639283924844237, 0.10012141442407042, 0.13536878343250963, 0.14602265672311338, -0.049287636535272246, -0.27892930413626377, 0.012245397656149562, -0.2470882569830264, -0.19614115300317925, -0.03536078289729513, 0.026505756976773903, -0.028272679571302667, -0.2585734220131511, 0.08485809832458355, 0.02763272179084109, -0.01751026484904991, -0.06978199939335501, -0.08862145250895992, 0.019827624690914227, 0.05970002843570264, 0.08041749289540964, 0.04622380391904904, 0.115738068466946, -0.18185598838646266, -0.07207775582379151, 0.43819831363948797, -0.17262053880990752, -0.21422924274567426, 0.17438999548809783, -0.17695267083904437, -0.10373723459610294, 0.04579306897526734, 0.14462810886212654, 0.11127119923701449, -0.17554115518672211, 0.16320755418189126, 0.045908605787835476, 0.08973426816444244, 0.11900909942003988, 0.06451164230548462, 0.2919574290431375, 0.08589773319639085, 0.02834257555960287, 0.13995448730777077, -0.09598155705832816, -0.09005106569853641, -0.35187185157631196, -0.14768116288788377, -0.13535355494147347, 0.06354758532665249, -0.12385164257836051, -0.1822762211011122, 0.35741423259324, 0.16534015982441844, 0.13510338548052636, 0.0792762969288179, 0.267075901805994, 0.14757988004918932, 0.060946759884245694, 0.05367589426872831, 0.32214970671526727, 0.1534539855182952, 0.05690532397655319, -0.2555359038336563, -0.1196394696270281, 0.09377616008503302] |
1,802.03471 | Certified Robustness to Adversarial Examples with Differential Privacy | Adversarial examples that fool machine learning models, particularly deep
neural networks, have been a topic of intense research interest, with attacks
and defenses being developed in a tight back-and-forth. Most past defenses are
best effort and have been shown to be vulnerable to sophisticated attacks.
Recently a set of certified defenses have been introduced, which provide
guarantees of robustness to norm-bounded attacks, but they either do not scale
to large datasets or are limited in the types of models they can support. This
paper presents the first certified defense that both scales to large networks
and datasets (such as Google's Inception network for ImageNet) and applies
broadly to arbitrary model types. Our defense, called PixelDP, is based on a
novel connection between robustness against adversarial examples and
differential privacy, a cryptographically-inspired formalism, that provides a
rigorous, generic, and flexible foundation for defense.
| stat.ML cs.AI cs.CR cs.LG | adversarial examples that fool machine learning models particularly deep neural networks have been a topic of intense research interest with attacks and defenses being developed in a tight backandforth most past defenses are best effort and have been shown to be vulnerable to sophisticated attacks recently a set of certified defenses have been introduced which provide guarantees of robustness to normbounded attacks but they either do not scale to large datasets or are limited in the types of models they can support this paper presents the first certified defense that both scales to large networks and datasets such as googles inception network for imagenet and applies broadly to arbitrary model types our defense called pixeldp is based on a novel connection between robustness against adversarial examples and differential privacy a cryptographicallyinspired formalism that provides a rigorous generic and flexible foundation for defense | [['adversarial', 'examples', 'that', 'fool', 'machine', 'learning', 'models', 'particularly', 'deep', 'neural', 'networks', 'have', 'been', 'a', 'topic', 'of', 'intense', 'research', 'interest', 'with', 'attacks', 'and', 'defenses', 'being', 'developed', 'in', 'a', 'tight', 'backandforth', 'most', 'past', 'defenses', 'are', 'best', 'effort', 'and', 'have', 'been', 'shown', 'to', 'be', 'vulnerable', 'to', 'sophisticated', 'attacks', 'recently', 'a', 'set', 'of', 'certified', 'defenses', 'have', 'been', 'introduced', 'which', 'provide', 'guarantees', 'of', 'robustness', 'to', 'normbounded', 'attacks', 'but', 'they', 'either', 'do', 'not', 'scale', 'to', 'large', 'datasets', 'or', 'are', 'limited', 'in', 'the', 'types', 'of', 'models', 'they', 'can', 'support', 'this', 'paper', 'presents', 'the', 'first', 'certified', 'defense', 'that', 'both', 'scales', 'to', 'large', 'networks', 'and', 'datasets', 'such', 'as', 'googles', 'inception', 'network', 'for', 'imagenet', 'and', 'applies', 'broadly', 'to', 'arbitrary', 'model', 'types', 'our', 'defense', 'called', 'pixeldp', 'is', 'based', 'on', 'a', 'novel', 'connection', 'between', 'robustness', 'against', 'adversarial', 'examples', 'and', 'differential', 'privacy', 'a', 'cryptographicallyinspired', 'formalism', 'that', 'provides', 'a', 'rigorous', 'generic', 'and', 'flexible', 'foundation', 'for', 'defense']] | [-0.08794033871714159, 0.0143730363342911, -0.05725640663211899, 0.11217882071754762, -0.12857051909502062, -0.2635172027479192, 0.01979521157835344, 0.40683573309797794, -0.19276457976541547, -0.3354365682495492, 0.10798464624177931, -0.28064251119836364, -0.22517286734572345, 0.24463234592112712, -0.17088436410828892, 0.1396371566389072, 0.046183170647626476, -0.012817045425721776, 0.004706187178713402, -0.33060845455287824, 0.32031905413771583, 0.06689391394091737, 0.33426779795305006, 0.0744551962474361, 0.0868799262081406, -0.10334266963348325, 0.0008746404163373102, 0.05201179797378635, -0.05655180499800606, 0.13027420902092543, 0.32428687026159814, 0.22130734895056645, 0.381504301440769, -0.4537825349319194, -0.26175620702228375, 0.1376731923182628, 0.11818090776713298, 0.18212122472572706, -0.07920244738010557, -0.36010509870414226, 0.13453671268486816, -0.25488028016739656, -0.019282544367680593, -0.2211694251346801, 0.043661188989478565, 0.02141779354424216, -0.2524310781354351, -0.061161112492637976, 0.10383650468096935, 0.08632364927325398, 0.013368365950217204, -0.08815951444474714, 0.006980843469500541, 0.12890836550754362, 0.05462643753022089, 0.012180393591656217, 0.14372283544923578, -0.12380123737301411, -0.19428480047333455, 0.31925902260201316, -0.025055240265958543, -0.20269622867344878, 0.24549229650791468, 0.045734865896935976, -0.19256724063972278, 0.08864927307835647, 0.2736678726900469, 0.11658918224275112, -0.1665781071276537, 0.026388282984927562, -0.022695428768305905, 0.16544248786771537, 0.026845732264752897, 0.035607679514214396, 0.15469122091973467, 0.22530324730489934, 0.06906923601886124, 0.11521612157895496, -0.0807593123571548, -0.09584434230013618, -0.19281133143231272, -0.06477671348072493, -0.16791613503093167, 0.005463110542560961, -0.00921651056373126, -0.166094813888984, 0.37749145647643934, 0.2217662880163906, 0.18075085157262427, 0.11402365248733466, 0.37807795291799784, -0.02046089937451013, 0.14662417469413153, 0.14645989186995265, 0.26083904408317593, 0.05695959109746452, 0.08696486192888447, -0.05592541516691978, 0.17511918906322016, -0.004621380890187409] |
1,802.03472 | Modeling of Teager Energy Operated Perceptual Wavelet Packet
Coefficients with an Erlang-2 PDF for Real Time Enhancement of Noisy Speech | In this paper, for real time enhancement of noisy speech, a method of
threshold determination based on modeling of Teager energy (TE) operated
perceptual wavelet packet (PWP) coefficients of the noisy speech and noise by
an Erlang-2 PDF is presented. The proposed method is computationally much
faster than the existing wavelet packet based thresholding methods. A custom
thresholding function based on a combination of mu-law and semisoft
thresholding functions is designed and exploited to apply the statistically
derived threshold upon the PWP coefficients. The proposed custom thresholding
function works as a mu-law or a semisoft thresholding function or their
combination based on the probability of speech presence and absence in a
subband of the PWP transformed noisy speech. By using the speech files
available in NOIZEUS database, a number of simulations are performed to
evaluate the performance of the proposed method for speech signals in the
presence of Gaussian white and street noises. The proposed method outperforms
some of the state-of-the-art speech enhancement methods both at high and low
levels of SNRs in terms of standard objective measures and subjective
evaluations including formal listening tests.
| eess.AS cs.SD | in this paper for real time enhancement of noisy speech a method of threshold determination based on modeling of teager energy te operated perceptual wavelet packet pwp coefficients of the noisy speech and noise by an erlang2 pdf is presented the proposed method is computationally much faster than the existing wavelet packet based thresholding methods a custom thresholding function based on a combination of mulaw and semisoft thresholding functions is designed and exploited to apply the statistically derived threshold upon the pwp coefficients the proposed custom thresholding function works as a mulaw or a semisoft thresholding function or their combination based on the probability of speech presence and absence in a subband of the pwp transformed noisy speech by using the speech files available in noizeus database a number of simulations are performed to evaluate the performance of the proposed method for speech signals in the presence of gaussian white and street noises the proposed method outperforms some of the stateoftheart speech enhancement methods both at high and low levels of snrs in terms of standard objective measures and subjective evaluations including formal listening tests | [['in', 'this', 'paper', 'for', 'real', 'time', 'enhancement', 'of', 'noisy', 'speech', 'a', 'method', 'of', 'threshold', 'determination', 'based', 'on', 'modeling', 'of', 'teager', 'energy', 'te', 'operated', 'perceptual', 'wavelet', 'packet', 'pwp', 'coefficients', 'of', 'the', 'noisy', 'speech', 'and', 'noise', 'by', 'an', 'erlang2', 'pdf', 'is', 'presented', 'the', 'proposed', 'method', 'is', 'computationally', 'much', 'faster', 'than', 'the', 'existing', 'wavelet', 'packet', 'based', 'thresholding', 'methods', 'a', 'custom', 'thresholding', 'function', 'based', 'on', 'a', 'combination', 'of', 'mulaw', 'and', 'semisoft', 'thresholding', 'functions', 'is', 'designed', 'and', 'exploited', 'to', 'apply', 'the', 'statistically', 'derived', 'threshold', 'upon', 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1,802.03473 | Low-frequency pulse profile variation in PSR B2217+47: evidence for
echoes from the interstellar medium | We have observed a complex and continuous change in the integrated pulse
profile of PSR B2217+47, manifested as additional components trailing the main
peak. These transient components are detected over 6 years at $150$ MHz using
the LOw Frequency ARray (LOFAR), but they are not seen in contemporaneous
Lovell observations at $1.5$ GHz. We argue that propagation effects in the
ionized interstellar medium (IISM) are the most likely cause. The putative
structures in the IISM causing the profile variation are roughly half-way
between the pulsar and the Earth and have transverse radii $R \sim 30$ AU. We
consider different models for the structures. Under the assumption of spherical
symmetry, their implied average electron density is $\overline{n}_e \sim 100$
cm$^{-3}$. Since PSR B2217+47 is more than an order of magnitude brighter than
the average pulsar population visible to LOFAR, similar profile variations
would not have been identified in most pulsars, suggesting that subtle profile
variations in low-frequency profiles might be more common than we have observed
to date. Systematic studies of these variations at low frequencies can provide
a new tool to investigate the proprieties of the IISM and the limits to the
precision of pulsar timing.
| astro-ph.HE | we have observed a complex and continuous change in the integrated pulse profile of psr b221747 manifested as additional components trailing the main peak these transient components are detected over 6 years at 150 mhz using the low frequency array lofar but they are not seen in contemporaneous lovell observations at 15 ghz we argue that propagation effects in the ionized interstellar medium iism are the most likely cause the putative structures in the iism causing the profile variation are roughly halfway between the pulsar and the earth and have transverse radii r sim 30 au we consider different models for the structures under the assumption of spherical symmetry their implied average electron density is overlinen_e sim 100 cm3 since psr b221747 is more than an order of magnitude brighter than the average pulsar population visible to lofar similar profile variations would not have been identified in most pulsars suggesting that subtle profile variations in lowfrequency profiles might be more common than we have observed to date systematic studies of these variations at low frequencies can provide a new tool to investigate the proprieties of the iism and the limits to the precision of pulsar timing | [['we', 'have', 'observed', 'a', 'complex', 'and', 'continuous', 'change', 'in', 'the', 'integrated', 'pulse', 'profile', 'of', 'psr', 'b221747', 'manifested', 'as', 'additional', 'components', 'trailing', 'the', 'main', 'peak', 'these', 'transient', 'components', 'are', 'detected', 'over', '6', 'years', 'at', '150', 'mhz', 'using', 'the', 'low', 'frequency', 'array', 'lofar', 'but', 'they', 'are', 'not', 'seen', 'in', 'contemporaneous', 'lovell', 'observations', 'at', '15', 'ghz', 'we', 'argue', 'that', 'propagation', 'effects', 'in', 'the', 'ionized', 'interstellar', 'medium', 'iism', 'are', 'the', 'most', 'likely', 'cause', 'the', 'putative', 'structures', 'in', 'the', 'iism', 'causing', 'the', 'profile', 'variation', 'are', 'roughly', 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1,802.03474 | Skyglow Changes Over Tucson, Arizona, Resulting From A Municipal LED
Street Lighting Conversion | The transition from earlier lighting technologies to white light-emitting
diodes (LEDs) is a significant change in the use of artificial light at night.
LEDs emit considerably more short-wavelength light into the environment than
earlier technologies on a per-lumen basis. Radiative transfer models predict
increased skyglow over cities transitioning to LED unless the total lumen
output of new lighting systems is reduced. The City of Tucson, Arizona (U.S.),
recently converted its municipal street lighting system from a mixture of fully
shielded high- and low-pressure sodium (HPS/LPS) luminaires to fully shielded
3000 K white LED luminaires. The lighting design intended to minimize increases
to skyglow in order to protect the sites of nearby astronomical observatories
without compromising public safety. This involved the migration of over 445
million fully shielded HPS/LPS lumens to roughly 142 million fully shielded
3000 K white LED lumens and an expected concomitant reduction in the amount of
visual skyglow over Tucson. SkyGlow Simulator models predict skyglow decreases
on the order of 10-20% depending on whether fully shielded or partly shielded
lights are in use. We tested this prediction using visual night sky brightness
estimates and luminance-calibrated, panchromatic all-sky imagery at 15
locations in and near the city. Data were obtained in 2014, before the LED
conversion began, and in mid-2017 after approximately 95% of $\sim$18,000
luminaires was converted. Skyglow differed marginally, and in all cases with
valid data changed by $<{\pm}$20%. Over the same period, the city's
upward-directed optical radiance detected from Earth orbit decreased by
approximately 7%. While these results are not conclusive, they suggest that LED
conversions paired with dimming can reduce skyglow over cities.
| astro-ph.IM | the transition from earlier lighting technologies to white lightemitting diodes leds is a significant change in the use of artificial light at night leds emit considerably more shortwavelength light into the environment than earlier technologies on a perlumen basis radiative transfer models predict increased skyglow over cities transitioning to led unless the total lumen output of new lighting systems is reduced the city of tucson arizona us recently converted its municipal street lighting system from a mixture of fully shielded high and lowpressure sodium hpslps luminaires to fully shielded 3000 k white led luminaires the lighting design intended to minimize increases to skyglow in order to protect the sites of nearby astronomical observatories without compromising public safety this involved the migration of over 445 million fully shielded hpslps lumens to roughly 142 million fully shielded 3000 k white led lumens and an expected concomitant reduction in the amount of visual skyglow over tucson skyglow simulator models predict skyglow decreases on the order of 1020 depending on whether fully shielded or partly shielded lights are in use we tested this prediction using visual night sky brightness estimates and luminancecalibrated panchromatic allsky imagery at 15 locations in and near the city data were obtained in 2014 before the led conversion began and in mid2017 after approximately 95 of sim18000 luminaires was converted skyglow differed marginally and in all cases with valid data changed by pm20 over the same period the citys upwarddirected optical radiance detected from earth orbit decreased by approximately 7 while these results are not conclusive they suggest that led conversions paired with dimming can reduce skyglow over cities | [['the', 'transition', 'from', 'earlier', 'lighting', 'technologies', 'to', 'white', 'lightemitting', 'diodes', 'leds', 'is', 'a', 'significant', 'change', 'in', 'the', 'use', 'of', 'artificial', 'light', 'at', 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1,802.03475 | Communication-Computation Efficient Gradient Coding | This paper develops coding techniques to reduce the running time of
distributed learning tasks. It characterizes the fundamental tradeoff to
compute gradients (and more generally vector summations) in terms of three
parameters: computation load, straggler tolerance and communication cost. It
further gives an explicit coding scheme that achieves the optimal tradeoff
based on recursive polynomial constructions, coding both across data subsets
and vector components. As a result, the proposed scheme allows to minimize the
running time for gradient computations. Implementations are made on Amazon EC2
clusters using Python with mpi4py package. Results show that the proposed
scheme maintains the same generalization error while reducing the running time
by $32\%$ compared to uncoded schemes and $23\%$ compared to prior coded
schemes focusing only on stragglers (Tandon et al., ICML 2017).
| stat.ML cs.DC cs.IT cs.LG math.IT | this paper develops coding techniques to reduce the running time of distributed learning tasks it characterizes the fundamental tradeoff to compute gradients and more generally vector summations in terms of three parameters computation load straggler tolerance and communication cost it further gives an explicit coding scheme that achieves the optimal tradeoff based on recursive polynomial constructions coding both across data subsets and vector components as a result the proposed scheme allows to minimize the running time for gradient computations implementations are made on amazon ec2 clusters using python with mpi4py package results show that the proposed scheme maintains the same generalization error while reducing the running time by 32 compared to uncoded schemes and 23 compared to prior coded schemes focusing only on stragglers tandon et al icml 2017 | [['this', 'paper', 'develops', 'coding', 'techniques', 'to', 'reduce', 'the', 'running', 'time', 'of', 'distributed', 'learning', 'tasks', 'it', 'characterizes', 'the', 'fundamental', 'tradeoff', 'to', 'compute', 'gradients', 'and', 'more', 'generally', 'vector', 'summations', 'in', 'terms', 'of', 'three', 'parameters', 'computation', 'load', 'straggler', 'tolerance', 'and', 'communication', 'cost', 'it', 'further', 'gives', 'an', 'explicit', 'coding', 'scheme', 'that', 'achieves', 'the', 'optimal', 'tradeoff', 'based', 'on', 'recursive', 'polynomial', 'constructions', 'coding', 'both', 'across', 'data', 'subsets', 'and', 'vector', 'components', 'as', 'a', 'result', 'the', 'proposed', 'scheme', 'allows', 'to', 'minimize', 'the', 'running', 'time', 'for', 'gradient', 'computations', 'implementations', 'are', 'made', 'on', 'amazon', 'ec2', 'clusters', 'using', 'python', 'with', 'mpi4py', 'package', 'results', 'show', 'that', 'the', 'proposed', 'scheme', 'maintains', 'the', 'same', 'generalization', 'error', 'while', 'reducing', 'the', 'running', 'time', 'by', '32', 'compared', 'to', 'uncoded', 'schemes', 'and', '23', 'compared', 'to', 'prior', 'coded', 'schemes', 'focusing', 'only', 'on', 'stragglers', 'tandon', 'et', 'al', 'icml', '2017']] | [-0.15726202443989298, -0.007474176486292602, -0.06799607534799003, 0.022475275401451965, -0.0642030788776185, -0.18882364941428798, 0.13115233747747027, 0.41291593679392985, -0.2496215646036056, -0.35925271919054924, 0.08762064889026444, -0.2181949048040682, -0.15661146143061477, 0.23903737955026386, -0.14798126676884235, 0.1018225585088827, 0.08027238028427196, -0.027934664593244247, -0.07421377215890011, -0.39167080220383726, 0.22813847361226358, 0.11510279500322748, 0.3583195418581482, 0.017270717017222628, 0.11842519135210779, -0.0021760427291945433, -0.0886437739029007, -0.05081539500555269, -0.0862424752731174, 0.1055161706176442, 0.2833182159865405, 0.19234933690420647, 0.28766265183744966, -0.3899001801357597, -0.18309792612007883, 0.06829916035134685, 0.17058416900347717, 0.08985783105947134, -0.018716335837759057, -0.23196353000956912, 0.11024620428017175, -0.19545271885112042, -0.014205207932711572, -0.10526491397118211, -0.0059093765827805496, 0.020746568657815198, -0.3055857184213088, 0.02915034626155745, 0.011454491375209114, 0.007229918570374139, -0.024940790850831587, -0.14683975728568008, 0.052666723179493766, 0.1088698245079596, 0.005205667276211108, 0.07006521358753937, 0.11460897015306608, -0.046734764924734946, -0.18817673086426978, 0.36371520289414844, -0.051172655920476415, -0.20319139750587734, 0.15174146843243883, 0.015674452597001034, -0.13018313535965634, 0.10395289374363803, 0.22083087399775206, 0.09319632290321034, -0.09788038082738486, 0.07426340675952504, -0.0022530859523965407, 0.22001419211340736, 0.11783730655605364, 0.065254435383592, 0.02768691952968406, 0.14835159293224298, 0.09236968073820426, 0.13737669455919907, -0.057952032846853484, -0.15480068417910356, -0.2289337037607681, -0.12551759039196222, -0.17222170410100227, -0.07475560850846444, -0.16520884313553846, -0.1135226918543263, 0.35466556014002293, 0.13755342412095647, 0.1569070371279363, 0.1506655194303436, 0.39588646202297634, 0.0730147407085151, 0.09322939396929718, 0.23178066045782247, 0.1548262670132613, 0.08834468239968889, 0.1102829554191314, -0.22913290291317095, 0.06539215885333774, 0.07444431607849723] |
1,802.03476 | The surface properties of neutron-rich exotic nuclei within relativistic
mean field formalisms | In this theoretical study, we establish a correlation between the neutron
skin thickness and the nuclear symmetry energy for the even$-$even isotopes of
Fe, Ni, Zn, Ge, Se and Kr within the framework of the axially deformed
self-consistent relativistic mean field for the non-linear NL3$^*$ and
density-dependent DD-ME1 interactions. The coherent density functional method
is used to formulate the symmetry energy, the neutron pressure and the
curvature of finite nuclei as a function of the nuclear radius. We have
performed broad studies for the mass dependence on the symmetry energy in terms
of the neutron-proton asymmetry for mass 70 $\leq$ A $\leq$ 96. From this
analysis, we found a notable signature of a shell closure at $N$ = 50 in the
isotopic chains of Fe, Ni, Zn, Ge, Se and Kr nuclei. The present study reveals
an interrelationship between the characteristics of infinite nuclear matter and
the neutron skin thickness of finite nuclei
| nucl-th | in this theoretical study we establish a correlation between the neutron skin thickness and the nuclear symmetry energy for the eveneven isotopes of fe ni zn ge se and kr within the framework of the axially deformed selfconsistent relativistic mean field for the nonlinear nl3 and densitydependent ddme1 interactions the coherent density functional method is used to formulate the symmetry energy the neutron pressure and the curvature of finite nuclei as a function of the nuclear radius we have performed broad studies for the mass dependence on the symmetry energy in terms of the neutronproton asymmetry for mass 70 leq a leq 96 from this analysis we found a notable signature of a shell closure at n 50 in the isotopic chains of fe ni zn ge se and kr nuclei the present study reveals an interrelationship between the characteristics of infinite nuclear matter and the neutron skin thickness of finite nuclei | [['in', 'this', 'theoretical', 'study', 'we', 'establish', 'a', 'correlation', 'between', 'the', 'neutron', 'skin', 'thickness', 'and', 'the', 'nuclear', 'symmetry', 'energy', 'for', 'the', 'eveneven', 'isotopes', 'of', 'fe', 'ni', 'zn', 'ge', 'se', 'and', 'kr', 'within', 'the', 'framework', 'of', 'the', 'axially', 'deformed', 'selfconsistent', 'relativistic', 'mean', 'field', 'for', 'the', 'nonlinear', 'nl3', 'and', 'densitydependent', 'ddme1', 'interactions', 'the', 'coherent', 'density', 'functional', 'method', 'is', 'used', 'to', 'formulate', 'the', 'symmetry', 'energy', 'the', 'neutron', 'pressure', 'and', 'the', 'curvature', 'of', 'finite', 'nuclei', 'as', 'a', 'function', 'of', 'the', 'nuclear', 'radius', 'we', 'have', 'performed', 'broad', 'studies', 'for', 'the', 'mass', 'dependence', 'on', 'the', 'symmetry', 'energy', 'in', 'terms', 'of', 'the', 'neutronproton', 'asymmetry', 'for', 'mass', '70', 'leq', 'a', 'leq', '96', 'from', 'this', 'analysis', 'we', 'found', 'a', 'notable', 'signature', 'of', 'a', 'shell', 'closure', 'at', 'n', '50', 'in', 'the', 'isotopic', 'chains', 'of', 'fe', 'ni', 'zn', 'ge', 'se', 'and', 'kr', 'nuclei', 'the', 'present', 'study', 'reveals', 'an', 'interrelationship', 'between', 'the', 'characteristics', 'of', 'infinite', 'nuclear', 'matter', 'and', 'the', 'neutron', 'skin', 'thickness', 'of', 'finite', 'nuclei']] | [-0.0703206379028247, 0.13615992664452412, -0.04325377801934331, 0.11305714210276399, 0.04550971777258527, -0.07226216420839658, 0.04302738337247133, 0.36152693948751646, -0.2127538439293472, -0.3106717359851306, -0.0534031151120076, -0.32858332923755346, -0.00521574434454226, 0.10857701712343472, 0.07404108299250832, -0.015347626528992558, -0.04150211444704343, 0.0566194767613482, -0.1676101045750871, -0.15138771181266186, 0.3286502056042756, 0.060005625421551384, 0.2623094193551023, 0.10997714360000949, 0.039813618243530094, 0.03399878864435615, 0.02652732413015421, 0.014713955037789234, -0.1744896731997026, 0.08593923379882973, 0.24155801051105094, 0.048629244354658374, 0.19902247390308916, -0.44495410404309926, -0.2003197006269093, 0.07266335447769094, 0.13694890000518575, 0.105728632129038, -0.114228025150473, -0.24783024017310498, 0.06226080339031898, -0.21619973265078685, -0.16939549632170023, -0.03626546057076841, 0.07391762251222493, 0.08063375992108358, -0.256065014225511, 0.10691956391200325, 0.051992562673492546, 0.10779057453627026, -0.14612043310351522, -0.20094377263788357, -0.04934041294738009, 0.034331962555671565, 0.07988542035092532, 0.004215284731757168, 0.15217132467717945, -0.0863512985698188, -0.004154103201743068, 0.36284184146253085, -0.03091569290965154, -0.10751826255674789, 0.10805109018669617, -0.17688956605702244, -0.12703654524987404, 0.16395408283172805, 0.1480110125795056, 0.16184900891011125, -0.11042556412431677, 0.12095039727440758, -0.0374711768282269, 0.2223437659269729, 0.026048197664454615, 0.015343298059302252, 0.18791344692384737, 0.20320408014096444, -0.0021998114646443272, 0.04702691621585003, -0.19647339978625394, -0.02774461700476163, -0.31362576648555945, -0.1734063133430846, -0.13221177798603348, 0.04478185925870171, -0.12366911508077416, -0.12734610917966424, 0.3642436032621375, 0.013858963667556939, 0.16666874987233604, -0.030506296922138194, 0.21663459494019185, 0.06377451691625405, 0.04686420897040393, 0.04674099431996116, 0.2913661552099696, 0.26570706765340113, 0.07320725677892664, -0.3489200298116228, 0.018823623580130323, 0.05018523726026398] |
1,802.03477 | N-break states in a chain of nonlinear oscillators | In the present work we explore a pre-stretched oscillator chain where the
nodes interact via a pairwise Lennard-Jones potential. In addition to a
homogeneous solution, we identify solutions with one or more (so-called)
`breaks', i.e., jumps. As a function of the canonical parameter of the system,
namely the precompression strain $d$, we find that the most fundamental one
break solution changes stability when the monotonicity of the Hamiltonian
changes with $d$. We provide a proof for this (motivated by numerical
computations) observation. This critical point separates stable and unstable
segments of the one break branch of solutions. We find similar branches for 2
through 5 break branches of solutions. Each of these higher `excited state'
solutions possesses an additional unstable pair of eigenvalues. We thus
conjecture that $k$ break solutions will possess at least $k-1$ (and at most
$k$) pairs of unstable eigenvalues. Our stability analysis is corroborated by
direct numerical computations of the evolutionary dynamics.
| nlin.PS cond-mat.mtrl-sci | in the present work we explore a prestretched oscillator chain where the nodes interact via a pairwise lennardjones potential in addition to a homogeneous solution we identify solutions with one or more socalled breaks ie jumps as a function of the canonical parameter of the system namely the precompression strain d we find that the most fundamental one break solution changes stability when the monotonicity of the hamiltonian changes with d we provide a proof for this motivated by numerical computations observation this critical point separates stable and unstable segments of the one break branch of solutions we find similar branches for 2 through 5 break branches of solutions each of these higher excited state solutions possesses an additional unstable pair of eigenvalues we thus conjecture that k break solutions will possess at least k1 and at most k pairs of unstable eigenvalues our stability analysis is corroborated by direct numerical computations of the evolutionary dynamics | [['in', 'the', 'present', 'work', 'we', 'explore', 'a', 'prestretched', 'oscillator', 'chain', 'where', 'the', 'nodes', 'interact', 'via', 'a', 'pairwise', 'lennardjones', 'potential', 'in', 'addition', 'to', 'a', 'homogeneous', 'solution', 'we', 'identify', 'solutions', 'with', 'one', 'or', 'more', 'socalled', 'breaks', 'ie', 'jumps', 'as', 'a', 'function', 'of', 'the', 'canonical', 'parameter', 'of', 'the', 'system', 'namely', 'the', 'precompression', 'strain', 'd', 'we', 'find', 'that', 'the', 'most', 'fundamental', 'one', 'break', 'solution', 'changes', 'stability', 'when', 'the', 'monotonicity', 'of', 'the', 'hamiltonian', 'changes', 'with', 'd', 'we', 'provide', 'a', 'proof', 'for', 'this', 'motivated', 'by', 'numerical', 'computations', 'observation', 'this', 'critical', 'point', 'separates', 'stable', 'and', 'unstable', 'segments', 'of', 'the', 'one', 'break', 'branch', 'of', 'solutions', 'we', 'find', 'similar', 'branches', 'for', '2', 'through', '5', 'break', 'branches', 'of', 'solutions', 'each', 'of', 'these', 'higher', 'excited', 'state', 'solutions', 'possesses', 'an', 'additional', 'unstable', 'pair', 'of', 'eigenvalues', 'we', 'thus', 'conjecture', 'that', 'k', 'break', 'solutions', 'will', 'possess', 'at', 'least', 'k1', 'and', 'at', 'most', 'k', 'pairs', 'of', 'unstable', 'eigenvalues', 'our', 'stability', 'analysis', 'is', 'corroborated', 'by', 'direct', 'numerical', 'computations', 'of', 'the', 'evolutionary', 'dynamics']] | [-0.19786845707224132, 0.09750450531833056, -0.09970138653611335, 0.02982846842944407, -0.033116335656720765, -0.18224906119315, 0.06859340374387848, 0.30956214831139034, -0.2607545590215476, -0.26943441586664474, 0.10557651017547752, -0.31105524935395434, -0.13377605399373585, 0.1325667583553765, 0.04947313561943025, 0.012490055427732818, 0.07475541413236314, 0.03759568640592848, -0.06824250561662783, -0.19763475550243106, 0.3279456322623166, -0.010894249748581877, 0.21740063450609645, 0.005858233455234231, 0.07349869346198364, -0.014375381797062567, 0.03862123100612408, -0.0011216435068024274, -0.17653722408907085, 0.09507328546719808, 0.2221497063560841, 0.08187419424976748, 0.2798495243381088, -0.4179359922925822, -0.18778139963680401, 0.12778115997878978, 0.19260285960701415, 0.1287728470753138, -0.03495084953101468, -0.22389688136844108, 0.12281305221264259, -0.11581374970951284, -0.2032844981727883, -0.08371207129078893, 0.031284345170626275, 0.030469551580981948, -0.24622444634755644, 0.08214802739735788, 0.07063631987264858, 0.05216854287144274, -0.0831312278810984, -0.08659249040945911, -0.07362452662066342, 0.09837743093484254, 0.08021859840617906, -0.016520407265768602, 0.08348109910431771, -0.12382332066324754, -0.09020073106213926, 0.349119795868412, -0.05607040246608309, -0.1809740331955254, 0.2152051269226015, -0.10957684101632391, -0.1503866110713436, 0.15069894411433965, 0.10616970961041844, 0.150849374754426, -0.06972063907009075, 0.06658716529282407, -0.00036330420810442703, 0.17694649708592364, 0.10181751336233738, -0.01501149970676917, 0.20134192653513777, 0.14987284623128241, 0.11565210879780352, 0.15883157009449908, -0.03800240417834944, -0.11125543713868143, -0.3167861256008156, -0.15405972162448167, -0.15647037661610505, 0.053827553833028875, -0.10590155398695526, -0.1773446752027107, 0.405049344465041, 0.11345240739469539, 0.22377222417614973, 0.04485409643698054, 0.2328438937293891, 0.145119064538262, 0.032667220764172576, 0.0994447794629452, 0.25708512138002193, 0.10548669262117563, 0.04208206378807051, -0.21998070571750689, 0.007792823798715686, 0.06557666347959103] |
1,802.03478 | Programming Requests/Responses with GreatFree in the Cloud Environment | Programming request with GreatFree is an efficient programming technique to
implement distributed polling in the cloud computing environment. GreatFree is
a distributed programming environment through which diverse distributed systems
can be established through programming rather than configuring or scripting.
GreatFree emphasizes the importance of programming since it offers developers
the opportunities to leverage their distributed knowledge and programming
skills. Additionally, programming is the unique way to construct creative,
adaptive and flexible systems to accommodate various distributed computing
environments. With the support of GreatFree code-level Distributed
Infrastructure Patterns, Distributed Operation Patterns and APIs, the difficult
procedure is accomplished in a programmable, rapid and highly-patterned manner,
i.e., the programming behaviors are simplified as the repeatable operation of
Copy-Paste-Replace. Since distributed polling is one of the fundamental
techniques to construct distributed systems, GreatFree provides developers with
relevant APIs and patterns to program requests/responses in the novel
programming environment.
| cs.PL cs.DC cs.SE | programming request with greatfree is an efficient programming technique to implement distributed polling in the cloud computing environment greatfree is a distributed programming environment through which diverse distributed systems can be established through programming rather than configuring or scripting greatfree emphasizes the importance of programming since it offers developers the opportunities to leverage their distributed knowledge and programming skills additionally programming is the unique way to construct creative adaptive and flexible systems to accommodate various distributed computing environments with the support of greatfree codelevel distributed infrastructure patterns distributed operation patterns and apis the difficult procedure is accomplished in a programmable rapid and highlypatterned manner ie the programming behaviors are simplified as the repeatable operation of copypastereplace since distributed polling is one of the fundamental techniques to construct distributed systems greatfree provides developers with relevant apis and patterns to program requestsresponses in the novel programming environment | [['programming', 'request', 'with', 'greatfree', 'is', 'an', 'efficient', 'programming', 'technique', 'to', 'implement', 'distributed', 'polling', 'in', 'the', 'cloud', 'computing', 'environment', 'greatfree', 'is', 'a', 'distributed', 'programming', 'environment', 'through', 'which', 'diverse', 'distributed', 'systems', 'can', 'be', 'established', 'through', 'programming', 'rather', 'than', 'configuring', 'or', 'scripting', 'greatfree', 'emphasizes', 'the', 'importance', 'of', 'programming', 'since', 'it', 'offers', 'developers', 'the', 'opportunities', 'to', 'leverage', 'their', 'distributed', 'knowledge', 'and', 'programming', 'skills', 'additionally', 'programming', 'is', 'the', 'unique', 'way', 'to', 'construct', 'creative', 'adaptive', 'and', 'flexible', 'systems', 'to', 'accommodate', 'various', 'distributed', 'computing', 'environments', 'with', 'the', 'support', 'of', 'greatfree', 'codelevel', 'distributed', 'infrastructure', 'patterns', 'distributed', 'operation', 'patterns', 'and', 'apis', 'the', 'difficult', 'procedure', 'is', 'accomplished', 'in', 'a', 'programmable', 'rapid', 'and', 'highlypatterned', 'manner', 'ie', 'the', 'programming', 'behaviors', 'are', 'simplified', 'as', 'the', 'repeatable', 'operation', 'of', 'copypastereplace', 'since', 'distributed', 'polling', 'is', 'one', 'of', 'the', 'fundamental', 'techniques', 'to', 'construct', 'distributed', 'systems', 'greatfree', 'provides', 'developers', 'with', 'relevant', 'apis', 'and', 'patterns', 'to', 'program', 'requestsresponses', 'in', 'the', 'novel', 'programming', 'environment']] | [-0.1883714430716256, 0.015212702252643845, -0.11772261975123219, 0.032590193836293906, -0.1725810325809453, -0.23696599826610215, 0.08118411374580063, 0.41545738205051336, -0.35395012574244133, -0.35996567275585, 0.12191713379054907, -0.2071926325795726, -0.1470859247328661, 0.2060690889853171, -0.1049005790461104, 0.08189681882668338, 0.02628251711155852, -0.05886025127379263, -0.01473297544182803, -0.2280478615471175, 0.228577446937725, 0.06901411878758333, 0.29131599425823546, -0.003587367308234245, 0.07083877651217643, 0.032684889438391575, -0.03052358751117193, -0.044623247757685225, -0.014073948454584035, 0.19571185086748866, 0.39964424151907557, 0.2751747760397028, 0.33517274222480997, -0.4659103183862819, -0.11262942390175353, 0.006240298187779918, 0.12984437715019506, 0.0747118216380149, -0.06626697652973235, -0.2672207262891699, 0.10017258581962817, -0.20705039063337402, -0.0907821860885851, -0.11032102536738024, 0.011179603428028944, 0.00410917549866291, -0.2881501540279304, -0.056604090068017095, 0.012225492378618096, 0.08593162318239067, 0.003220700235827498, -0.07249505652613203, 0.03112366617309638, 0.13388594547057078, -0.021875053960633214, 0.001647853485183974, 0.20921818482768262, -0.05285988474415589, -0.19026698442143192, 0.4018760048390799, 0.03126027186277804, -0.1919109636886352, 0.26939302442503543, 0.016615696849865497, -0.15431765332149053, 0.08326341968659363, 0.27339932705524944, 0.12157827671158167, -0.2661461676339763, 0.08988325794770147, 0.036733944087271236, 0.194402937708475, 0.006072558042868762, 0.017136245544771927, 0.2497453906304452, 0.2413715619436452, 0.12241857927776283, 0.1536589313854306, 0.06386392980709042, -0.15081687773328642, -0.16261381521807905, -0.15291468003554873, -0.14457053236309175, -0.0773810405139877, -0.07555829130740702, -0.1931509598761692, 0.33161106414627284, 0.19109683389722987, 0.056018653820911556, 0.11654433619450699, 0.3580874505872563, 0.07282056072874414, 0.1439565441023532, 0.1816883333597962, 0.08249077742384799, 0.03251903692037392, 0.24677026490877632, -0.16554830771047804, 0.13836526351941514, -0.06432113524228761] |
1,802.03479 | Gaussian Process Landmarking on Manifolds | As a means of improving analysis of biological shapes, we propose an
algorithm for sampling a Riemannian manifold by sequentially selecting points
with maximum uncertainty under a Gaussian process model. This greedy strategy
is known to be near-optimal in the experimental design literature, and appears
to outperform the use of user-placed landmarks in representing the geometry of
biological objects in our application. In the noiseless regime, we establish an
upper bound for the mean squared prediction error (MSPE) in terms of the number
of samples and geometric quantities of the manifold, demonstrating that the
MSPE for our proposed sequential design decays at a rate comparable to the
oracle rate achievable by any sequential or non-sequential optimal design; to
our knowledge this is the first result of this type for sequential experimental
design. The key is to link the greedy algorithm to reduced basis methods in the
context of model reduction for partial differential equations. We expect this
approach will find additional applications in other fields of research.
| stat.ME cs.CV | as a means of improving analysis of biological shapes we propose an algorithm for sampling a riemannian manifold by sequentially selecting points with maximum uncertainty under a gaussian process model this greedy strategy is known to be nearoptimal in the experimental design literature and appears to outperform the use of userplaced landmarks in representing the geometry of biological objects in our application in the noiseless regime we establish an upper bound for the mean squared prediction error mspe in terms of the number of samples and geometric quantities of the manifold demonstrating that the mspe for our proposed sequential design decays at a rate comparable to the oracle rate achievable by any sequential or nonsequential optimal design to our knowledge this is the first result of this type for sequential experimental design the key is to link the greedy algorithm to reduced basis methods in the context of model reduction for partial differential equations we expect this approach will find additional applications in other fields of research | [['as', 'a', 'means', 'of', 'improving', 'analysis', 'of', 'biological', 'shapes', 'we', 'propose', 'an', 'algorithm', 'for', 'sampling', 'a', 'riemannian', 'manifold', 'by', 'sequentially', 'selecting', 'points', 'with', 'maximum', 'uncertainty', 'under', 'a', 'gaussian', 'process', 'model', 'this', 'greedy', 'strategy', 'is', 'known', 'to', 'be', 'nearoptimal', 'in', 'the', 'experimental', 'design', 'literature', 'and', 'appears', 'to', 'outperform', 'the', 'use', 'of', 'userplaced', 'landmarks', 'in', 'representing', 'the', 'geometry', 'of', 'biological', 'objects', 'in', 'our', 'application', 'in', 'the', 'noiseless', 'regime', 'we', 'establish', 'an', 'upper', 'bound', 'for', 'the', 'mean', 'squared', 'prediction', 'error', 'mspe', 'in', 'terms', 'of', 'the', 'number', 'of', 'samples', 'and', 'geometric', 'quantities', 'of', 'the', 'manifold', 'demonstrating', 'that', 'the', 'mspe', 'for', 'our', 'proposed', 'sequential', 'design', 'decays', 'at', 'a', 'rate', 'comparable', 'to', 'the', 'oracle', 'rate', 'achievable', 'by', 'any', 'sequential', 'or', 'nonsequential', 'optimal', 'design', 'to', 'our', 'knowledge', 'this', 'is', 'the', 'first', 'result', 'of', 'this', 'type', 'for', 'sequential', 'experimental', 'design', 'the', 'key', 'is', 'to', 'link', 'the', 'greedy', 'algorithm', 'to', 'reduced', 'basis', 'methods', 'in', 'the', 'context', 'of', 'model', 'reduction', 'for', 'partial', 'differential', 'equations', 'we', 'expect', 'this', 'approach', 'will', 'find', 'additional', 'applications', 'in', 'other', 'fields', 'of', 'research']] | [-0.08192887667062305, 0.01826602801367085, -0.07129419566810984, 0.06480994341018231, -0.06460013233843338, -0.11968681199969836, 0.09436592119479417, 0.38131191174735324, -0.2772190433152648, -0.31295867468488503, 0.09363309667706601, -0.22577181572924895, -0.17800654279045672, 0.22891328785237464, -0.11569497320262423, 0.08602405698543572, 0.07648697964488024, 0.04600494809684355, -0.07202045436379084, -0.2778955148645194, 0.2859543674367379, 0.08307017164910774, 0.29904561784084754, -0.0038415667736280083, 0.11392759162293051, 0.012332890112603823, -0.00931045429287366, -0.005019733895469143, -0.1352934434953239, 0.15466086220708733, 0.27755885321299084, 0.17000887728105468, 0.288366890276771, -0.3730375298007156, -0.2049923187606696, 0.11951014350507835, 0.14152974411438454, 0.13218853339631426, -0.04524496244687407, -0.26030223200403446, 0.10792799649973321, -0.11761699463465218, -0.08983496317628727, -0.07500781763756315, -0.029185371134003782, 0.006315975191901965, -0.3499090049124087, 0.05257189290384841, 0.06900244304722354, 0.03427856166952946, -0.049177095596959076, -0.13601749701739616, 0.058919535620216595, 0.114233766198551, 0.0349576991001994, 0.038516805683969556, 0.11720697072065289, -0.12317456328345137, -0.1781954985471016, 0.3371787237631808, -0.06372419455234545, -0.23013079611876283, 0.13966702269839057, -0.09135890853905444, -0.13370202937309283, 0.1382271094368322, 0.2103555955957756, 0.13354614219510458, -0.1554683116068278, 0.05314047312706215, -0.04329250053031348, 0.1527298851477274, 0.0226058428125254, 0.035951944009165265, 0.1151572972639879, 0.2242489916564201, 0.11650251809799347, 0.1527026754783618, -0.08359725232774684, -0.10416576157026666, -0.31788314349320995, -0.17838644632874673, -0.18218518744772635, 0.013256529794237295, -0.1308684335104246, -0.14649992413168034, 0.3550390526932855, 0.17484897403158697, 0.20206019750071116, 0.08339647262031774, 0.33116612890847474, 0.09695024688935738, 0.030518804962959993, 0.07385433699756143, 0.23564896936505944, 0.09696861798313997, 0.04871748997100506, -0.1990480512992131, 0.09300000163311341, 0.07127667211433761] |
1,802.0348 | GraphVAE: Towards Generation of Small Graphs Using Variational
Autoencoders | Deep learning on graphs has become a popular research topic with many
applications. However, past work has concentrated on learning graph embedding
tasks, which is in contrast with advances in generative models for images and
text. Is it possible to transfer this progress to the domain of graphs? We
propose to sidestep hurdles associated with linearization of such discrete
structures by having a decoder output a probabilistic fully-connected graph of
a predefined maximum size directly at once. Our method is formulated as a
variational autoencoder. We evaluate on the challenging task of molecule
generation.
| cs.LG cs.CV cs.NE | deep learning on graphs has become a popular research topic with many applications however past work has concentrated on learning graph embedding tasks which is in contrast with advances in generative models for images and text is it possible to transfer this progress to the domain of graphs we propose to sidestep hurdles associated with linearization of such discrete structures by having a decoder output a probabilistic fullyconnected graph of a predefined maximum size directly at once our method is formulated as a variational autoencoder we evaluate on the challenging task of molecule generation | [['deep', 'learning', 'on', 'graphs', 'has', 'become', 'a', 'popular', 'research', 'topic', 'with', 'many', 'applications', 'however', 'past', 'work', 'has', 'concentrated', 'on', 'learning', 'graph', 'embedding', 'tasks', 'which', 'is', 'in', 'contrast', 'with', 'advances', 'in', 'generative', 'models', 'for', 'images', 'and', 'text', 'is', 'it', 'possible', 'to', 'transfer', 'this', 'progress', 'to', 'the', 'domain', 'of', 'graphs', 'we', 'propose', 'to', 'sidestep', 'hurdles', 'associated', 'with', 'linearization', 'of', 'such', 'discrete', 'structures', 'by', 'having', 'a', 'decoder', 'output', 'a', 'probabilistic', 'fullyconnected', 'graph', 'of', 'a', 'predefined', 'maximum', 'size', 'directly', 'at', 'once', 'our', 'method', 'is', 'formulated', 'as', 'a', 'variational', 'autoencoder', 'we', 'evaluate', 'on', 'the', 'challenging', 'task', 'of', 'molecule', 'generation']] | [-0.028834307459777497, 0.0062529581915983495, -0.0495121256210544, 0.033777731458379076, -0.16526448106432848, -0.16404908562098888, 0.009301348024622557, 0.4870624141965775, -0.2776267294960215, -0.3279382321745791, 0.11272289560107078, -0.26376623953841233, -0.19381933042561597, 0.16605196587067653, -0.14022300441432348, 0.12113234914462775, 0.15469259177730915, 0.0873170330961968, -0.05423216234974345, -0.2694784311529378, 0.31390927485041714, 0.004691424984868834, 0.31776967979571286, 0.04115078533613222, 0.16106092310857345, -0.013489797706418533, 0.012273188895902577, -0.006510553201610976, -0.08198173094461574, 0.229193313929074, 0.32674490109735765, 0.1955892285381622, 0.3666952340863645, -0.41645120744137687, -0.30468172354356166, 0.11663051382539437, 0.13590745202221094, 0.13033928766108535, -0.0344739381093569, -0.2819708267529, 0.10250431358160332, -0.13793599692252564, 0.04357350713999427, -0.08851701345175822, 0.02908717552224729, -0.039329806358820975, -0.25754523965743115, -0.002117560968357832, 0.09302123725176492, 0.0580188622661034, 0.02888190222933809, -0.11129004747527553, 0.042453506288980626, 0.12165170139137735, 0.035081775366090875, 0.10249228323710725, 0.10455674077247944, -0.1939148877720575, -0.1704727966942765, 0.36540825137908156, -0.03492508789009236, -0.20444942139178277, 0.19664562146793654, -0.01363653178881616, -0.21647539795277285, 0.08776863232413505, 0.23710695959310582, 0.14788505163519305, -0.1366462092153094, 0.07029829192423619, -0.05332755705619112, 0.15480020131539315, 0.06432513567678472, -0.022299262803999034, 0.19583480181965104, 0.293472527937488, 0.07363961132161537, 0.1782463565666942, -0.08968505810387681, -0.09001448043025138, -0.19048802346545965, -0.09101652176902095, -0.2516877841293574, 0.014821208104431789, -0.04631559412039078, -0.18416020712329748, 0.40912715052353577, 0.17637092145060765, 0.221095462478301, 0.08326654097664744, 0.3456984401700344, 0.07785569676792527, 0.12318657518939134, 0.10477943998263614, 0.14374364586856633, 0.11665847739254302, 0.11467565308780746, -0.11392165250590745, 0.10382430566019042, 0.056495486357388026] |
1,802.03481 | Quantization of $A_{0}(K)$-Spaces | In this paper, we study $L^1$-matrix convex sets $\{K_{n}\}$ in $*$-locally
convex spaces and show that every C$^*$-ordered operator space is complete
isometrically, completely isomorphic to $\{A_{0}(K_{n}, M_{n}(V))\}$ for a
suitable $L^1$-matrix convex set $\{K_{n}\}$. Further, we generalize the notion
of regular embedding of a compact convex set to $L^{1}$-regular embedding of
$L^{1}$-matrix convex set. Using $L^{1}$-regular embedding of $L^{1}$-convex
set, we find conditions under which $A_{0}(K_{n}, M_{n}(V))$ is an abstract
operator system.
| math.OA math.FA | in this paper we study l1matrix convex sets k_n in locally convex spaces and show that every cordered operator space is complete isometrically completely isomorphic to a_0k_n m_nv for a suitable l1matrix convex set k_n further we generalize the notion of regular embedding of a compact convex set to l1regular embedding of l1matrix convex set using l1regular embedding of l1convex set we find conditions under which a_0k_n m_nv is an abstract operator system | [['in', 'this', 'paper', 'we', 'study', 'l1matrix', 'convex', 'sets', 'k_n', 'in', 'locally', 'convex', 'spaces', 'and', 'show', 'that', 'every', 'cordered', 'operator', 'space', 'is', 'complete', 'isometrically', 'completely', 'isomorphic', 'to', 'a_0k_n', 'm_nv', 'for', 'a', 'suitable', 'l1matrix', 'convex', 'set', 'k_n', 'further', 'we', 'generalize', 'the', 'notion', 'of', 'regular', 'embedding', 'of', 'a', 'compact', 'convex', 'set', 'to', 'l1regular', 'embedding', 'of', 'l1matrix', 'convex', 'set', 'using', 'l1regular', 'embedding', 'of', 'l1convex', 'set', 'we', 'find', 'conditions', 'under', 'which', 'a_0k_n', 'm_nv', 'is', 'an', 'abstract', 'operator', 'system']] | [-0.09577769642945991, 0.04185169311296785, -0.0794110342985325, 0.05395778177441584, -0.10459406384433717, -0.08593246172557571, 0.015307783519279267, 0.4161161386143805, -0.3446007412093789, -0.12396904375098312, 0.1264304909750875, -0.2976226114820856, -0.1734708549607712, 0.16669780399594733, -0.1984753741868841, 0.03300906142303303, 0.10849219510929464, 0.05267386534251273, -0.0871672771502731, -0.2864756136437628, 0.42674040861094176, -0.1364288083486148, 0.19028912048175264, 0.06055176445307321, 0.11841181301817631, 0.04727942992432682, 0.04649633097587459, 0.12941784615265275, -0.1741515010871808, 0.13697704047298254, 0.28318880452190653, 0.23258786602640774, 0.30844709271766635, -0.3717199418352354, -0.12783861853905135, 0.2382079935910653, 0.10342069928631631, -0.035435637314942904, -0.01428618928445364, -0.2651727158548449, 0.12713686704858027, -0.0990941640712432, -0.0870109079475167, -0.12627327553893147, 0.04084501480822688, -0.06387983896741782, -0.33742267200584286, -0.08070983683495823, 0.13421317640302785, 0.038450563361228846, -0.1328010347518903, -0.05712901634588115, -0.04889388309110568, 0.023720307442456928, -0.09915783290348168, 0.156598834327853, 0.08564959807623762, 0.04170811540836385, -0.10456908621422167, 0.35134327622936734, -0.013526880901668276, -0.29885650873740216, 0.12248531289732278, -0.15910469871292363, -0.1674494566534882, 0.10007789586462192, 0.16703743273531324, 0.1919993858562032, -0.16481511185029105, 0.20726309973770865, -0.16715405458834634, 0.07821883406462286, 0.07741754185130346, 0.05348103271741698, 0.08187196539965139, 0.16447894791585962, 0.22469748720637897, 0.21736188033428877, 0.07938887951756592, -0.0021932796225883067, -0.3620812491194081, -0.13701227474123684, -0.1899614776519419, 0.07881545777946933, -0.12774497683402394, -0.23969952144951961, 0.35765800891277283, 0.04362066534917746, 0.1684665748599305, 0.1587724410598653, 0.2208344022840706, 0.0425842674214518, 0.019653158651581452, 0.14805805936122118, 0.0893644951005925, 0.1216413940567134, -0.08336614525473829, -0.12335697092713593, -0.03206283270047783, 0.21091670868223283] |
1,802.03482 | A Continuation Method for Discrete Optimization and its Application to
Nearest Neighbor Classification | The continuation method is a popular approach in non-convex optimization and
computer vision. The main idea is to start from a simple function that can be
minimized efficiently, and gradually transform it to the more complicated
original objective function. The solution of the simpler problem is used as the
starting point to solve the original problem. We show a continuation method for
discrete optimization problems. Ideally, we would like the evolved function to
be hill-climbing friendly and to have the same global minima as the original
function. We show that the proposed continuation method is the best affine
approximation of a transformation that is guaranteed to transform the function
to a hill-climbing friendly function and to have the same global minima.
We show the effectiveness of the proposed technique in the problem of nearest
neighbor classification. Although nearest neighbor methods are often
competitive in terms of sample efficiency, the computational complexity in the
test phase has been a major obstacle in their applicability in big data
problems. Using the proposed continuation method, we show an improved
graph-based nearest neighbor algorithm. The method is readily understood and
easy to implement. We show how the computational complexity of the method in
the test phase scales gracefully with the size of the training set, a property
that is particularly important in big data applications.
| cs.LG | the continuation method is a popular approach in nonconvex optimization and computer vision the main idea is to start from a simple function that can be minimized efficiently and gradually transform it to the more complicated original objective function the solution of the simpler problem is used as the starting point to solve the original problem we show a continuation method for discrete optimization problems ideally we would like the evolved function to be hillclimbing friendly and to have the same global minima as the original function we show that the proposed continuation method is the best affine approximation of a transformation that is guaranteed to transform the function to a hillclimbing friendly function and to have the same global minima we show the effectiveness of the proposed technique in the problem of nearest neighbor classification although nearest neighbor methods are often competitive in terms of sample efficiency the computational complexity in the test phase has been a major obstacle in their applicability in big data problems using the proposed continuation method we show an improved graphbased nearest neighbor algorithm the method is readily understood and easy to implement we show how the computational complexity of the method in the test phase scales gracefully with the size of the training set a property that is particularly important in big data applications | [['the', 'continuation', 'method', 'is', 'a', 'popular', 'approach', 'in', 'nonconvex', 'optimization', 'and', 'computer', 'vision', 'the', 'main', 'idea', 'is', 'to', 'start', 'from', 'a', 'simple', 'function', 'that', 'can', 'be', 'minimized', 'efficiently', 'and', 'gradually', 'transform', 'it', 'to', 'the', 'more', 'complicated', 'original', 'objective', 'function', 'the', 'solution', 'of', 'the', 'simpler', 'problem', 'is', 'used', 'as', 'the', 'starting', 'point', 'to', 'solve', 'the', 'original', 'problem', 'we', 'show', 'a', 'continuation', 'method', 'for', 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1,802.03483 | Coherence properties of shallow donor qubits in ZnO | Defects in crystals are leading candidates for photon-based quantum
technologies, but progress in developing practical devices critically depends
on improving defect optical and spin properties. Motivated by this need, we
study a new defect qubit candidate, the shallow donor in ZnO. We demonstrate
all-optical control of the electron spin state of the donor qubits and measure
the spin coherence properties. We find a longitudinal relaxation time T$_1$
exceeding 100 ms, an inhomogeneous dephasing time T$_2^*$ of $17\pm2$ ns, and a
Hahn spin-echo time T$_2$ of $50\pm13$ $\mu$s. The magnitude of T$_2^*$ is
consistent with the inhomogeneity of the nuclear hyperfine field in natural
ZnO. Possible mechanisms limiting T$_2$ include instantaneous diffusion and
nuclear spin diffusion (spectral diffusion). These results are comparable to
the phosphorous donor system in natural silicon, suggesting that with isotope
and chemical purification long qubit coherence times can be obtained for donor
spins in a direct band gap semiconductor. This work motivates further research
on high-purity material growth, quantum device fabrication, and high-fidelity
control of the donor:ZnO system for quantum technologies.
| quant-ph cond-mat.mes-hall | defects in crystals are leading candidates for photonbased quantum technologies but progress in developing practical devices critically depends on improving defect optical and spin properties motivated by this need we study a new defect qubit candidate the shallow donor in zno we demonstrate alloptical control of the electron spin state of the donor qubits and measure the spin coherence properties we find a longitudinal relaxation time t_1 exceeding 100 ms an inhomogeneous dephasing time t_2 of 17pm2 ns and a hahn spinecho time t_2 of 50pm13 mus the magnitude of t_2 is consistent with the inhomogeneity of the nuclear hyperfine field in natural zno possible mechanisms limiting t_2 include instantaneous diffusion and nuclear spin diffusion spectral diffusion these results are comparable to the phosphorous donor system in natural silicon suggesting that with isotope and chemical purification long qubit coherence times can be obtained for donor spins in a direct band gap semiconductor this work motivates further research on highpurity material growth quantum device fabrication and highfidelity control of the donorzno system for quantum technologies | [['defects', 'in', 'crystals', 'are', 'leading', 'candidates', 'for', 'photonbased', 'quantum', 'technologies', 'but', 'progress', 'in', 'developing', 'practical', 'devices', 'critically', 'depends', 'on', 'improving', 'defect', 'optical', 'and', 'spin', 'properties', 'motivated', 'by', 'this', 'need', 'we', 'study', 'a', 'new', 'defect', 'qubit', 'candidate', 'the', 'shallow', 'donor', 'in', 'zno', 'we', 'demonstrate', 'alloptical', 'control', 'of', 'the', 'electron', 'spin', 'state', 'of', 'the', 'donor', 'qubits', 'and', 'measure', 'the', 'spin', 'coherence', 'properties', 'we', 'find', 'a', 'longitudinal', 'relaxation', 'time', 't_1', 'exceeding', '100', 'ms', 'an', 'inhomogeneous', 'dephasing', 'time', 't_2', 'of', '17pm2', 'ns', 'and', 'a', 'hahn', 'spinecho', 'time', 't_2', 'of', 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1,802.03484 | Relationships between solid spherical and toroidal harmonics | We derive new relationships expressing solid spherical harmonics as series of
toroidal harmonics and vice versa. The expansions include regular and irregular
spherical harmonics, ring and axial toroidal harmonics of even and odd parity
about the plane of the torus. The expansion coefficients are given in terms of
a recurrence relation. As an example application we apply one of the expansions
to express the potential of a charged conducting torus on a basis of spherical
harmonics.
| math-ph math.MP physics.class-ph | we derive new relationships expressing solid spherical harmonics as series of toroidal harmonics and vice versa the expansions include regular and irregular spherical harmonics ring and axial toroidal harmonics of even and odd parity about the plane of the torus the expansion coefficients are given in terms of a recurrence relation as an example application we apply one of the expansions to express the potential of a charged conducting torus on a basis of spherical harmonics | [['we', 'derive', 'new', 'relationships', 'expressing', 'solid', 'spherical', 'harmonics', 'as', 'series', 'of', 'toroidal', 'harmonics', 'and', 'vice', 'versa', 'the', 'expansions', 'include', 'regular', 'and', 'irregular', 'spherical', 'harmonics', 'ring', 'and', 'axial', 'toroidal', 'harmonics', 'of', 'even', 'and', 'odd', 'parity', 'about', 'the', 'plane', 'of', 'the', 'torus', 'the', 'expansion', 'coefficients', 'are', 'given', 'in', 'terms', 'of', 'a', 'recurrence', 'relation', 'as', 'an', 'example', 'application', 'we', 'apply', 'one', 'of', 'the', 'expansions', 'to', 'express', 'the', 'potential', 'of', 'a', 'charged', 'conducting', 'torus', 'on', 'a', 'basis', 'of', 'spherical', 'harmonics']] | [-0.18182821578873126, 0.1191979990615341, -0.09111964446186137, 0.0825270329062876, -0.09505353489351508, -0.056523852326024915, -0.04875511243804603, 0.3163670916110277, -0.20663571084151044, -0.211502724627376, 0.12300321194312633, -0.2436116935293141, -0.14454150167416388, 0.19894960084256078, 0.0179807464262826, -0.03449774181813394, -0.02440088467770501, 0.06740657358712174, -0.11760387704156242, -0.17463843055118464, 0.2961456740373059, 0.00784746308712975, 0.20791523309583826, 0.0007415038509000288, 0.07489764836224679, 0.02376209674280529, -0.038423725918523575, -0.011215275888772388, -0.11502159101042994, 0.16630016502581144, 0.18044681070176394, 0.06129277313103605, 0.14876165571867636, -0.4867024711872402, -0.1190688937020145, 0.08119375702287805, 0.17395429639145732, 0.07858606508785956, -0.03551402504978653, -0.18355394103319236, -0.0034428879152983427, -0.2205914826251836, -0.2432759957829196, -0.14929962667979693, 0.08692684328477633, 0.10485436010027402, -0.3065374341523765, 0.10784746584556017, 0.16279285363117724, 0.13011391213359802, -0.05927821092528144, -0.11740095766359254, -0.037746514855442864, 0.10950971508129059, 0.03139657420596402, -0.015482060549976794, 0.09636518406975818, -0.12501407095451691, -0.09474630209641825, 0.40170550502616126, -0.039918173204473294, -0.25858277195182283, 0.13935813349426576, -0.23704908029053753, -0.08192015270776067, 0.11656961613334715, 0.2104836917898961, 0.14200665110317795, -0.023526262669627995, 0.07005675579727579, -0.06353887937668908, 0.13378154317156019, 0.22024563102566294, -0.015639063534571937, 0.24301892696990093, 0.03457686607725918, 0.020105400343661466, 0.21752462876764567, -0.06447489452732139, -0.04528517106940087, -0.33694164831142287, -0.16205170944235042, -0.1971006284446431, 0.032210018007869, -0.15745356898849805, -0.19000803878972014, 0.4298943570864044, -0.01927829338972898, 0.18232224563038663, 0.020624658061454904, 0.29210576085422774, 0.12910467892039665, 0.03600248923574231, 0.011489622448846748, 0.19236355915812678, 0.20893453005211135, 0.06875443272062846, -0.19904351472168377, -0.06887971959689534, 0.1610360485440316] |
1,802.03485 | Theory of Probability. An Elementary Treatise against a Historical
Background | A popular scientific contribution should not contradict any established facts
and ought to be understandable. I complied with both these requirements and am
offering a sufficiently full introduction to probability theory. Furthermore, I
enlivened my text with much information about the history of probability and
commentaries on the occurring notions which do not belong to that theory.
Indeed, a student is not a container to be filled but a torch to be kindled. I
hope therefore that my piece will essentially benefit beginners, be also
methodically useful to advanced readers, and even attract some inquisitive
people to probability.
| math.HO | a popular scientific contribution should not contradict any established facts and ought to be understandable i complied with both these requirements and am offering a sufficiently full introduction to probability theory furthermore i enlivened my text with much information about the history of probability and commentaries on the occurring notions which do not belong to that theory indeed a student is not a container to be filled but a torch to be kindled i hope therefore that my piece will essentially benefit beginners be also methodically useful to advanced readers and even attract some inquisitive people to probability | [['a', 'popular', 'scientific', 'contribution', 'should', 'not', 'contradict', 'any', 'established', 'facts', 'and', 'ought', 'to', 'be', 'understandable', 'i', 'complied', 'with', 'both', 'these', 'requirements', 'and', 'am', 'offering', 'a', 'sufficiently', 'full', 'introduction', 'to', 'probability', 'theory', 'furthermore', 'i', 'enlivened', 'my', 'text', 'with', 'much', 'information', 'about', 'the', 'history', 'of', 'probability', 'and', 'commentaries', 'on', 'the', 'occurring', 'notions', 'which', 'do', 'not', 'belong', 'to', 'that', 'theory', 'indeed', 'a', 'student', 'is', 'not', 'a', 'container', 'to', 'be', 'filled', 'but', 'a', 'torch', 'to', 'be', 'kindled', 'i', 'hope', 'therefore', 'that', 'my', 'piece', 'will', 'essentially', 'benefit', 'beginners', 'be', 'also', 'methodically', 'useful', 'to', 'advanced', 'readers', 'and', 'even', 'attract', 'some', 'inquisitive', 'people', 'to', 'probability']] | [-0.02248081958101948, 0.1207334087943012, -0.1574851405920879, 0.12188512606341961, -0.21871157983146913, -0.2232180442138366, 0.06449072811115837, 0.37638495854881343, -0.24506153011152862, -0.28517547418002265, 0.10597493911247548, -0.3061442939298494, -0.12051219802720434, 0.1709592327230838, -0.15538548139679453, -0.06707938286304778, 0.085328089762289, 0.07111113079424415, -0.018647454733656227, -0.32467008801177144, 0.24579992177373522, 0.0849937123379537, 0.22555504556821318, 0.08875366248076363, 0.030271335374278834, -0.05709245900281382, -0.03671432233757663, 0.08008424289600581, -0.116836436737444, 0.14271377244660136, 0.3679022474585063, 0.20720354450049297, 0.31683153102212414, -0.4563001929687298, -0.16805808502724584, 0.06778764027189843, 0.1136429020137127, 0.10759680624932469, -0.02045702033947051, -0.2916284130163947, 0.10113836163940022, -0.2126511338509011, -0.12848474777645755, -0.07413163843947197, 0.026674914216546684, 0.011816927269861405, -0.21515473966695825, -0.037293440904955344, 0.11851096552338128, 0.04423608712623922, 0.00905359339067589, -0.13183009200634396, 0.03655157469174996, 0.14782157448614586, 0.05668338242566631, 0.07422095158835873, 0.1129894798623911, -0.12903358818659064, -0.06802904524849918, 0.3799067653088393, -0.002643823074842138, -0.20959852495211728, 0.2004004187444795, -0.14243248804491393, -0.15216980115938172, 0.09351097455970486, 0.11847998524008661, 0.060180073024286906, -0.15454236284962722, 0.010733012972418599, 0.00399910008053091, 0.2174538957496763, 0.04653658835711528, 0.04556117818348718, 0.2627226501722269, 0.07882238664588302, 0.005692549375342015, -0.010036819140493338, 0.03006594447533087, -0.09580231606200033, -0.30245266601025145, -0.1852538989457701, -0.15377887864407075, 0.10080907180978399, 0.03974814338155794, -0.15366577551871233, 0.3400880014333798, 0.21715838121896497, 0.11952739083968407, 0.045123621017187455, 0.24765873205258834, 0.04148106723584292, 0.07700068525653998, 0.08589606625693184, 0.17334858082592422, 0.06265217504388064, 0.16175996272454077, -0.05958451507422997, 0.10632352580373385, -0.0032656561792352976] |
1,802.03486 | An LSTM Recurrent Network for Step Counting | Smartphones with sensors such as accelerometer and gyroscope can be used as
pedometers and navigators. In this paper, we propose to use an LSTM recurrent
network for counting the number of steps taken by both blind and sighted users,
based on an annotated smartphone sensor dataset, WeAllWork. The models were
trained separately for sighted people, blind people with a long cane or a guide
dog for Leave-One-Out training modality. It achieved 5% overcount and
undercount rate.
| cs.LG cs.HC | smartphones with sensors such as accelerometer and gyroscope can be used as pedometers and navigators in this paper we propose to use an lstm recurrent network for counting the number of steps taken by both blind and sighted users based on an annotated smartphone sensor dataset weallwork the models were trained separately for sighted people blind people with a long cane or a guide dog for leaveoneout training modality it achieved 5 overcount and undercount rate | [['smartphones', 'with', 'sensors', 'such', 'as', 'accelerometer', 'and', 'gyroscope', 'can', 'be', 'used', 'as', 'pedometers', 'and', 'navigators', 'in', 'this', 'paper', 'we', 'propose', 'to', 'use', 'an', 'lstm', 'recurrent', 'network', 'for', 'counting', 'the', 'number', 'of', 'steps', 'taken', 'by', 'both', 'blind', 'and', 'sighted', 'users', 'based', 'on', 'an', 'annotated', 'smartphone', 'sensor', 'dataset', 'weallwork', 'the', 'models', 'were', 'trained', 'separately', 'for', 'sighted', 'people', 'blind', 'people', 'with', 'a', 'long', 'cane', 'or', 'a', 'guide', 'dog', 'for', 'leaveoneout', 'training', 'modality', 'it', 'achieved', '5', 'overcount', 'and', 'undercount', 'rate']] | [-0.03638367964056869, 0.02718319011930287, -0.029265579563399702, 0.06104442042024321, -0.12644596765940525, -0.2612897364984024, 0.0677899536628863, 0.4766763243058773, -0.2335139248418073, -0.3755642609441117, 0.10738311942558328, -0.31752368057631464, -0.14627771625097535, 0.21637285370315898, -0.1946026400139887, 0.07509206276552828, 0.11370004208324706, 0.11680950614789577, 0.06715516808238646, -0.3063876052703613, 0.22054571179315857, 0.05074995063398391, 0.28657060301518195, -0.025413477781973462, 0.1718241031702659, 0.02377556333930729, -0.03532491269049375, -0.004350382491805884, -0.027264604038775783, 0.11023297091652576, 0.3115323007843646, 0.19381036029567253, 0.3252982195364694, -0.47773971041179686, -0.1725493374165811, 0.08457636547415223, 0.15696092729005415, 0.07984453119490653, -0.0482412373586494, -0.42979697206963413, 0.09468757134721908, -0.22398366271326803, 0.011406090506033539, -0.08427705341465261, 0.0023452193565564614, 0.03298429779912511, -0.2997555503978917, 0.012771981014002573, -0.01420069656857889, 0.136225455019572, -0.0360132297500968, -0.08011813637838788, 0.05372581831243348, 0.2368470386901794, 0.03619996320507298, 0.03982579697255198, 0.1592157792287228, -0.16443737490064375, -0.12943095960718107, 0.31504231415791056, -0.042111632324857255, -0.19599524816523675, 0.1696544078893143, 0.0146012607234696, -0.09475537007419418, 0.030097145727542164, 0.23424889861720882, 0.1316396495684573, -0.2099042417615499, -0.08800359958565909, -0.03259176570182181, 0.22538961133320037, 0.09090779969558017, -0.02389435790326089, 0.18062900270380028, 0.2644184154381797, 0.024821879510442433, 0.09159092527131665, -0.22156726738297675, 0.04933824035504909, -0.20089576358919684, -0.12882679312375106, -0.20058157847403255, 0.01680846930774924, -0.05764142046794168, -0.12140114258732392, 0.3385873556213632, 0.20551099269076772, 0.1860939819263677, 0.12007633097827027, 0.34692192901155516, -0.0021847012318823842, 0.16108456939739518, 0.060281368444533384, 0.13544340704070174, -0.026384726874785474, 0.1839291189848254, -0.09866898473709414, 0.08292467460954843, 0.03932134409065116] |
1,802.03487 | Small nonlinearities in activation functions create bad local minima in
neural networks | We investigate the loss surface of neural networks. We prove that even for
one-hidden-layer networks with "slightest" nonlinearity, the empirical risks
have spurious local minima in most cases. Our results thus indicate that in
general "no spurious local minima" is a property limited to deep linear
networks, and insights obtained from linear networks may not be robust.
Specifically, for ReLU(-like) networks we constructively prove that for almost
all practical datasets there exist infinitely many local minima. We also
present a counterexample for more general activations (sigmoid, tanh, arctan,
ReLU, etc.), for which there exists a bad local minimum. Our results make the
least restrictive assumptions relative to existing results on spurious local
optima in neural networks. We complete our discussion by presenting a
comprehensive characterization of global optimality for deep linear networks,
which unifies other results on this topic.
| cs.LG math.OC stat.ML | we investigate the loss surface of neural networks we prove that even for onehiddenlayer networks with slightest nonlinearity the empirical risks have spurious local minima in most cases our results thus indicate that in general no spurious local minima is a property limited to deep linear networks and insights obtained from linear networks may not be robust specifically for relulike networks we constructively prove that for almost all practical datasets there exist infinitely many local minima we also present a counterexample for more general activations sigmoid tanh arctan relu etc for which there exists a bad local minimum our results make the least restrictive assumptions relative to existing results on spurious local optima in neural networks we complete our discussion by presenting a comprehensive characterization of global optimality for deep linear networks which unifies other results on this topic | [['we', 'investigate', 'the', 'loss', 'surface', 'of', 'neural', 'networks', 'we', 'prove', 'that', 'even', 'for', 'onehiddenlayer', 'networks', 'with', 'slightest', 'nonlinearity', 'the', 'empirical', 'risks', 'have', 'spurious', 'local', 'minima', 'in', 'most', 'cases', 'our', 'results', 'thus', 'indicate', 'that', 'in', 'general', 'no', 'spurious', 'local', 'minima', 'is', 'a', 'property', 'limited', 'to', 'deep', 'linear', 'networks', 'and', 'insights', 'obtained', 'from', 'linear', 'networks', 'may', 'not', 'be', 'robust', 'specifically', 'for', 'relulike', 'networks', 'we', 'constructively', 'prove', 'that', 'for', 'almost', 'all', 'practical', 'datasets', 'there', 'exist', 'infinitely', 'many', 'local', 'minima', 'we', 'also', 'present', 'a', 'counterexample', 'for', 'more', 'general', 'activations', 'sigmoid', 'tanh', 'arctan', 'relu', 'etc', 'for', 'which', 'there', 'exists', 'a', 'bad', 'local', 'minimum', 'our', 'results', 'make', 'the', 'least', 'restrictive', 'assumptions', 'relative', 'to', 'existing', 'results', 'on', 'spurious', 'local', 'optima', 'in', 'neural', 'networks', 'we', 'complete', 'our', 'discussion', 'by', 'presenting', 'a', 'comprehensive', 'characterization', 'of', 'global', 'optimality', 'for', 'deep', 'linear', 'networks', 'which', 'unifies', 'other', 'results', 'on', 'this', 'topic']] | [-0.11933479147457364, -0.024378782808244657, -0.06696736557985382, 0.13821796121799368, -0.06815222800515003, -0.18951571677818144, 0.06416475761132381, 0.4196586485385975, -0.25856167685136655, -0.2348599713128194, 0.09017709544072558, -0.272572012202774, -0.28179055001425873, 0.21619990545830844, -0.11083109404811327, 0.08011623335710774, 0.13228408464987715, -0.00298581587606304, -0.0702912307096471, -0.3143188574505474, 0.3048514820779372, 0.0030477699206425076, 0.27681157178171784, 0.0672770307044224, 0.08327761259961793, -0.027221395948184856, 0.008683330424652575, 0.03805532233326362, -0.11114065303619433, 0.1298640000285487, 0.2820806346244092, 0.15391826237344164, 0.3537978250131333, -0.48842698567138193, -0.2781380128798832, 0.19196942421815377, 0.10222621810314306, 0.12769862493467032, -0.04124566838499109, -0.2474781494426856, 0.14991015235042507, -0.09049566330475291, -0.08082757257603497, -0.1715088888915698, -0.005184910987108517, 0.047393560034336805, -0.3078164203300161, 0.08769063542495656, 0.13621102485225978, 0.07795432771442176, -0.03707080774876899, -0.14094919083378674, -0.01035966567541037, 0.0804132921520701, 0.03597540980399245, 0.026050092917096486, 0.08113229863694031, -0.16540214559900834, -0.11052247809025077, 0.26709250837778875, -0.05947022903509599, -0.1878932554998415, 0.2087688976061269, -0.0644011275437426, -0.2054046637046975, 0.10587796158150589, 0.1673451822829808, 0.09085733576831843, -0.15254964364990725, 0.020448913820767977, -0.10145352390534479, 0.1497874711221419, 0.0823303033872474, 0.050784012145302496, 0.1534738562592446, 0.15444319080829405, 0.15541580803533211, 0.10377365614793417, -0.04305250770219756, -0.08353218402237826, -0.2865285572441791, -0.06926821808082809, -0.1705177823967709, 0.04090789087937907, -0.12200232324173269, -0.17277684396052426, 0.40094584271499367, 0.16335362987509064, 0.21542982553071577, 0.15784318390865174, 0.2844645452757681, 0.06701935976056932, 0.11190746947720968, 0.14240329724019593, 0.26893004197828313, 0.05690452817780562, 0.08331321261608772, -0.11765894178332922, 0.0678839625701938, 0.020318668345726435] |
1,802.03488 | Generalization of an Upper Bound on the Number of Nodes Needed to
Achieve Linear Separability | An important issue in neural network research is how to choose the number of
nodes and layers such as to solve a classification problem. We provide new
intuitions based on earlier results by An et al. (2015) by deriving an upper
bound on the number of nodes in networks with two hidden layers such that
linear separability can be achieved. Concretely, we show that if the data can
be described in terms of N finite sets and the used activation function f is
non-constant, increasing and has a left asymptote, we can derive how many nodes
are needed to linearly separate these sets. This will be an upper bound that
depends on the structure of the data. This structure can be analyzed using an
algorithm. For the leaky rectified linear activation function, we prove
separately that under some conditions on the slope, the same number of layers
and nodes as for the aforementioned activation functions is sufficient. We
empirically validate our claims.
| stat.ML cs.LG | an important issue in neural network research is how to choose the number of nodes and layers such as to solve a classification problem we provide new intuitions based on earlier results by an et al 2015 by deriving an upper bound on the number of nodes in networks with two hidden layers such that linear separability can be achieved concretely we show that if the data can be described in terms of n finite sets and the used activation function f is nonconstant increasing and has a left asymptote we can derive how many nodes are needed to linearly separate these sets this will be an upper bound that depends on the structure of the data this structure can be analyzed using an algorithm for the leaky rectified linear activation function we prove separately that under some conditions on the slope the same number of layers and nodes as for the aforementioned activation functions is sufficient we empirically validate our claims | [['an', 'important', 'issue', 'in', 'neural', 'network', 'research', 'is', 'how', 'to', 'choose', 'the', 'number', 'of', 'nodes', 'and', 'layers', 'such', 'as', 'to', 'solve', 'a', 'classification', 'problem', 'we', 'provide', 'new', 'intuitions', 'based', 'on', 'earlier', 'results', 'by', 'an', 'et', 'al', '2015', 'by', 'deriving', 'an', 'upper', 'bound', 'on', 'the', 'number', 'of', 'nodes', 'in', 'networks', 'with', 'two', 'hidden', 'layers', 'such', 'that', 'linear', 'separability', 'can', 'be', 'achieved', 'concretely', 'we', 'show', 'that', 'if', 'the', 'data', 'can', 'be', 'described', 'in', 'terms', 'of', 'n', 'finite', 'sets', 'and', 'the', 'used', 'activation', 'function', 'f', 'is', 'nonconstant', 'increasing', 'and', 'has', 'a', 'left', 'asymptote', 'we', 'can', 'derive', 'how', 'many', 'nodes', 'are', 'needed', 'to', 'linearly', 'separate', 'these', 'sets', 'this', 'will', 'be', 'an', 'upper', 'bound', 'that', 'depends', 'on', 'the', 'structure', 'of', 'the', 'data', 'this', 'structure', 'can', 'be', 'analyzed', 'using', 'an', 'algorithm', 'for', 'the', 'leaky', 'rectified', 'linear', 'activation', 'function', 'we', 'prove', 'separately', 'that', 'under', 'some', 'conditions', 'on', 'the', 'slope', 'the', 'same', 'number', 'of', 'layers', 'and', 'nodes', 'as', 'for', 'the', 'aforementioned', 'activation', 'functions', 'is', 'sufficient', 'we', 'empirically', 'validate', 'our', 'claims']] | [-0.10587865075119483, 0.09946695160248999, -0.05013296174828285, 0.03600146196406036, -0.06889883034868327, -0.11817456274667042, 0.05035445864174377, 0.3862867609333293, -0.2949552505354707, -0.3253515534637206, 0.08972936691960642, -0.27041935502195064, -0.20722932281277293, 0.203789390058021, -0.07679200297175551, 0.057748540178125286, 0.03440433287314703, 0.06088048110900728, -0.032087142668365515, -0.3201219587052172, 0.3310404398053149, 0.06335813665394614, 0.27442029783111294, 0.07207011009306348, 0.0770717156910119, -0.028825747171117935, 0.02361582017670397, 0.029723625129266976, -0.12081957565290698, 0.11143738609655289, 0.2500187923921916, 0.16826906960520802, 0.2828586413381322, -0.4715503296573405, -0.20101689420249544, 0.12411867205720441, 0.16840463737313302, 0.08695988047416725, -0.007559458976668984, -0.2477982098892055, 0.12544003972944653, -0.12643670737398444, -0.07166875735281703, -0.09652226119888602, 0.008502499174533619, 0.04055898568742437, -0.29403672900617145, 0.01909336990987261, 0.05860681963774065, 0.018815816090841788, -0.062174081385572566, -0.1235817676391395, -0.024372719215332635, 0.13833856583935106, 0.006756820987599592, 0.023504870899171096, 0.07798197627194042, -0.10985258557362321, -0.11658077404178956, 0.2810863251107986, -0.0729377393576282, -0.2518076207143841, 0.17716618963189562, -0.05127828883260121, -0.14430396668153045, 0.047259489262626034, 0.20771467230386204, 0.11036301035876674, -0.14580390645035854, 0.06893706052528334, -0.11537915524267764, 0.1861081955333551, 0.06569065426959207, 0.006100261936044711, 0.1455812178204342, 0.15672747467724998, 0.09607637716122375, 0.16132495156633037, -0.076379935563266, -0.022895005427050853, -0.2995933338562832, -0.12123210496559486, -0.2105913719415297, 0.026897406975888175, -0.09245753045198163, -0.13659098728870353, 0.38554697131945026, 0.14197997668079665, 0.2691832178927682, 0.07283270122328152, 0.24992664039916831, 0.16718086071296617, 0.06288737709307755, 0.12844514792762052, 0.22406931459579624, 0.10063980763525139, 0.025185319301844748, -0.17307208193017654, 0.10141381259754668, 0.06356188121544956] |
1,802.03489 | Signal-background discrimination with convolutional neural networks in
the PandaX-III experiment using MC simulation | The PandaX-III experiment will search for neutrinoless double beta decay of
$^{136}$Xe with high pressure gaseous time projection chambers at the China
Jin-Ping underground Laboratory. The tracking feature of gaseous detectors
helps suppress the background level, resulting in the improvement of the
detection sensitivity. We study a method based on the convolutional neural
networks to discriminate double beta decay signals against the background from
high energy gammas generated by $^{214}$Bi and $^{208}$Tl decays based on
detailed Monte Carlo simulation. Using the 2-dimensional projections of
recorded tracks on two planes, the method successfully suppresses the
background level by a factor larger than 100 with a high signal efficiency. An
improvement of $62\%$ on the efficiency ratio of
$\epsilon_{s}/\sqrt{\epsilon_{b}}$ is achieved in comparison with the baseline
in the PandaX-III conceptual design report.
| physics.ins-det hep-ex nucl-ex | the pandaxiii experiment will search for neutrinoless double beta decay of 136xe with high pressure gaseous time projection chambers at the china jinping underground laboratory the tracking feature of gaseous detectors helps suppress the background level resulting in the improvement of the detection sensitivity we study a method based on the convolutional neural networks to discriminate double beta decay signals against the background from high energy gammas generated by 214bi and 208tl decays based on detailed monte carlo simulation using the 2dimensional projections of recorded tracks on two planes the method successfully suppresses the background level by a factor larger than 100 with a high signal efficiency an improvement of 62 on the efficiency ratio of epsilon_ssqrtepsilon_b is achieved in comparison with the baseline in the pandaxiii conceptual design report | [['the', 'pandaxiii', 'experiment', 'will', 'search', 'for', 'neutrinoless', 'double', 'beta', 'decay', 'of', '136xe', 'with', 'high', 'pressure', 'gaseous', 'time', 'projection', 'chambers', 'at', 'the', 'china', 'jinping', 'underground', 'laboratory', 'the', 'tracking', 'feature', 'of', 'gaseous', 'detectors', 'helps', 'suppress', 'the', 'background', 'level', 'resulting', 'in', 'the', 'improvement', 'of', 'the', 'detection', 'sensitivity', 'we', 'study', 'a', 'method', 'based', 'on', 'the', 'convolutional', 'neural', 'networks', 'to', 'discriminate', 'double', 'beta', 'decay', 'signals', 'against', 'the', 'background', 'from', 'high', 'energy', 'gammas', 'generated', 'by', '214bi', 'and', '208tl', 'decays', 'based', 'on', 'detailed', 'monte', 'carlo', 'simulation', 'using', 'the', '2dimensional', 'projections', 'of', 'recorded', 'tracks', 'on', 'two', 'planes', 'the', 'method', 'successfully', 'suppresses', 'the', 'background', 'level', 'by', 'a', 'factor', 'larger', 'than', '100', 'with', 'a', 'high', 'signal', 'efficiency', 'an', 'improvement', 'of', '62', 'on', 'the', 'efficiency', 'ratio', 'of', 'epsilon_ssqrtepsilon_b', 'is', 'achieved', 'in', 'comparison', 'with', 'the', 'baseline', 'in', 'the', 'pandaxiii', 'conceptual', 'design', 'report']] | [-0.03716780078893368, 0.13681992793615003, -0.04457847092863779, 0.05038245926636394, 0.015151034697688134, -0.10898267656073783, 0.03601146773561518, 0.34634084576495394, -0.18999807843794078, -0.35819289253577014, 0.0517115115969579, -0.33908845260450543, -0.03289499316119062, 0.23937793793481624, 0.029631410494389, 0.06025544268461738, 0.11052169728764268, 0.004498999305935793, -0.11787733683983485, -0.21345065184688383, 0.22993143712163203, 0.22607328780284225, 0.3444677931834157, 0.03137681187065535, 0.13456135199183253, -0.02877512870513466, -0.08597069877640222, -0.06959649401919035, -0.09818264201297894, 0.0511967483907938, 0.2398642998643566, 0.11977140805431345, 0.19644592992614868, -0.41991050075017666, -0.15522873118996275, 0.12279740330409403, 0.10635454466479809, 0.009054915748478831, -0.1237747824404302, -0.3413697918949201, 0.09382280878372519, -0.17279203484816721, -0.07815787743040642, -0.019991170089735194, -0.06256079519990572, 0.010429159499878106, -0.26770924088046993, 0.05939890816311402, -0.024413931241273418, 0.04596566675274178, -0.009474264869336472, -0.1713742814207262, 0.07383625203916847, 0.05863107909455664, 0.02322643197253491, 0.05642371725827066, 0.1982692887017489, -0.14591919836252495, -0.14407797554478974, 0.3035773799011065, -0.14140962175323263, -0.15332098252161644, 0.18316836544319756, -0.18025316820703735, -0.10461283314525503, 0.24164165336234394, 0.20452579427077328, 0.10124494484806246, -0.13209012162496242, 0.03963877914009558, 0.04325292216498376, 0.21674012776117685, 0.07755471734409473, -0.013090398667972217, 0.20753166863203856, 0.3130600256646817, 0.04783745942449616, 0.09471541743404269, -0.23995333388195078, -0.032173113265731766, -0.2843910875967559, -0.14413176504028744, -0.10569684118909631, 0.016539365576594606, -0.05888011783725571, -0.1084601513666007, 0.39946844061302356, 0.11239886401372767, 0.16007517461029655, 0.015726246168952462, 0.33003982183227476, 0.0320541048061478, 0.06990749584944096, -0.009218206831304601, 0.29441378416507974, 0.11845679255202413, 0.09650309952770093, -0.2554481462286713, 0.06559299521126546, 0.04274518980622985] |
1,802.0349 | A whitening approach to probabilistic canonical correlation analysis for
omics data integration | Background: Canonical correlation analysis (CCA) is a classic statistical
tool for investigating complex multivariate data. Correspondingly, it has found
many diverse applications, ranging from molecular biology and medicine to
social science and finance. Intriguingly, despite the importance and
pervasiveness of CCA, only recently a probabilistic understanding of CCA is
developing, moving from an algorithmic to a model-based perspective and
enabling its application to large-scale settings.
Results: Here, we revisit CCA from the perspective of statistical whitening
of random variables and propose a simple yet flexible probabilistic model for
CCA in the form of a two-layer latent variable generative model. The advantages
of this variant of probabilistic CCA include non-ambiguity of the latent
variables, provisions for negative canonical correlations, possibility of
non-normal generative variables, as well as ease of interpretation on all
levels of the model. In addition, we show that it lends itself to
computationally efficient estimation in high-dimensional settings using
regularized inference. We test our approach to CCA analysis in simulations and
apply it to two omics data sets illustrating the integration of gene expression
data, lipid concentrations and methylation levels.
Conclusions: Our whitening approach to CCA provides a unifying perspective on
CCA, linking together sphering procedures, multivariate regression and
corresponding probabilistic generative models. Furthermore, we offer an
efficient computer implementation in the "whitening" R package available at
https://CRAN.R-project.org/package=whitening .
| stat.ME | background canonical correlation analysis cca is a classic statistical tool for investigating complex multivariate data correspondingly it has found many diverse applications ranging from molecular biology and medicine to social science and finance intriguingly despite the importance and pervasiveness of cca only recently a probabilistic understanding of cca is developing moving from an algorithmic to a modelbased perspective and enabling its application to largescale settings results here we revisit cca from the perspective of statistical whitening of random variables and propose a simple yet flexible probabilistic model for cca in the form of a twolayer latent variable generative model the advantages of this variant of probabilistic cca include nonambiguity of the latent variables provisions for negative canonical correlations possibility of nonnormal generative variables as well as ease of interpretation on all levels of the model in addition we show that it lends itself to computationally efficient estimation in highdimensional settings using regularized inference we test our approach to cca analysis in simulations and apply it to two omics data sets illustrating the integration of gene expression data lipid concentrations and methylation levels conclusions our whitening approach to cca provides a unifying perspective on cca linking together sphering procedures multivariate regression and corresponding probabilistic generative models furthermore we offer an efficient computer implementation in the whitening r package available at httpscranrprojectorgpackagewhitening | [['background', 'canonical', 'correlation', 'analysis', 'cca', 'is', 'a', 'classic', 'statistical', 'tool', 'for', 'investigating', 'complex', 'multivariate', 'data', 'correspondingly', 'it', 'has', 'found', 'many', 'diverse', 'applications', 'ranging', 'from', 'molecular', 'biology', 'and', 'medicine', 'to', 'social', 'science', 'and', 'finance', 'intriguingly', 'despite', 'the', 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0.08064067310368701, 0.00462495651389536] |
1,802.03491 | Energy transfer in compressible magnetohydrodynamic turbulence for
isothermal self-gravitating fluids | Three-dimensional, compressible, magnetohydrodynamic turbulence of an
isothermal, self-gravitating fluid is analyzed using two-point statistics in
the asymptotic limit of large Reynolds numbers (both kinetic and magnetic).
Following an alternative formulation proposed by S. Banerjee and S. Galtier
(Phys. Rev. E,93, 033120, 2016) and S. Banerjee and S. Galtier (J. Phys. A,
Math. and Theor.,50, 015501, 2017), an exact relation has been derived for the
total energy transfer. This approach results in a simpler relation expressed
entirely in terms of mixed second-order structure functions. The kinetic,
thermodynamic, magnetic and gravitational contributions to the energy transfer
rate can be easily separated in the present form. By construction, the new
formalism includes such additional effects as global rotation, the Hall term in
the induction equation, etc. The analysis shows that solid-body rotation cannot
alter the energy flux rate of compressible turbulence. However, the
contribution of a uniform background magnetic field to the flux is shown to be
non-trivial unlike in the incompressible case. Finally, the compressible,
turbulent energy flux rate does not vanish completely due to simple alignments,
which leads to a zero turbulent energy flux rate in the incompressible case.
| physics.flu-dyn astro-ph.SR | threedimensional compressible magnetohydrodynamic turbulence of an isothermal selfgravitating fluid is analyzed using twopoint statistics in the asymptotic limit of large reynolds numbers both kinetic and magnetic following an alternative formulation proposed by s banerjee and s galtier phys rev e93 033120 2016 and s banerjee and s galtier j phys a math and theor50 015501 2017 an exact relation has been derived for the total energy transfer this approach results in a simpler relation expressed entirely in terms of mixed secondorder structure functions the kinetic thermodynamic magnetic and gravitational contributions to the energy transfer rate can be easily separated in the present form by construction the new formalism includes such additional effects as global rotation the hall term in the induction equation etc the analysis shows that solidbody rotation cannot alter the energy flux rate of compressible turbulence however the contribution of a uniform background magnetic field to the flux is shown to be nontrivial unlike in the incompressible case finally the compressible turbulent energy flux rate does not vanish completely due to simple alignments which leads to a zero turbulent energy flux rate in the incompressible case | [['threedimensional', 'compressible', 'magnetohydrodynamic', 'turbulence', 'of', 'an', 'isothermal', 'selfgravitating', 'fluid', 'is', 'analyzed', 'using', 'twopoint', 'statistics', 'in', 'the', 'asymptotic', 'limit', 'of', 'large', 'reynolds', 'numbers', 'both', 'kinetic', 'and', 'magnetic', 'following', 'an', 'alternative', 'formulation', 'proposed', 'by', 's', 'banerjee', 'and', 's', 'galtier', 'phys', 'rev', 'e93', '033120', '2016', 'and', 's', 'banerjee', 'and', 's', 'galtier', 'j', 'phys', 'a', 'math', 'and', 'theor50', '015501', '2017', 'an', 'exact', 'relation', 'has', 'been', 'derived', 'for', 'the', 'total', 'energy', 'transfer', 'this', 'approach', 'results', 'in', 'a', 'simpler', 'relation', 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1,802.03492 | An index theory with applications to homoclinic Orbits of Hamiltonian
systems and Dirac equations | In this paper, we will define the index pair $(i_A(B),\nu_A(B))$ by the dual
variational method, and show the relationship between the indices defined by
different methods. As applications, we apply the index $(i_A(B),\nu_A(B))$ to
study the existence and multiplicity of homoclinic orbits of nonlinear
Hamiltonian systems and solutions of nonlinear Dirac equations
| math.FA | in this paper we will define the index pair i_abnu_ab by the dual variational method and show the relationship between the indices defined by different methods as applications we apply the index i_abnu_ab to study the existence and multiplicity of homoclinic orbits of nonlinear hamiltonian systems and solutions of nonlinear dirac equations | [['in', 'this', 'paper', 'we', 'will', 'define', 'the', 'index', 'pair', 'i_abnu_ab', 'by', 'the', 'dual', 'variational', 'method', 'and', 'show', 'the', 'relationship', 'between', 'the', 'indices', 'defined', 'by', 'different', 'methods', 'as', 'applications', 'we', 'apply', 'the', 'index', 'i_abnu_ab', 'to', 'study', 'the', 'existence', 'and', 'multiplicity', 'of', 'homoclinic', 'orbits', 'of', 'nonlinear', 'hamiltonian', 'systems', 'and', 'solutions', 'of', 'nonlinear', 'dirac', 'equations']] | [-0.1564959673024714, 0.07746676300186664, -0.0958203287795186, 0.0881998667283915, -0.03969687771517783, -0.09259883277118205, -0.004460715556051582, 0.3260828935727477, -0.2829680670797825, -0.30508105096407234, 0.06774639955023304, -0.2762271030433476, -0.2509545676410198, 0.1914955712284427, -0.0654232433065772, 0.08441363528370857, 0.06617957480251789, 0.006649286600295454, -0.1195736419595778, -0.17299142671283335, 0.4132234843820333, -0.04020907633006573, 0.23745765505358576, 0.02814238354563713, 0.09184304503723979, 0.021997973658144473, -0.014290987784042954, 0.04157648127526045, -0.1893591332435608, 0.1704637401457876, 0.21866453791037202, 0.1054730398580432, 0.26325980325229464, -0.35409521669149396, -0.19118843405507505, 0.1165865246206522, 0.13007443740963937, 0.048748426917009056, -0.020129534229636193, -0.27600524134933946, 0.11987011957913637, -0.17706725474447013, -0.19251791389659048, -0.08560154587030411, 0.019811285585165025, 0.09327765799593181, -0.2262993463501334, 0.07433938706410118, 0.058922160062938926, 0.053578306287527085, -0.09242602046579122, -0.037020942680537704, -0.07206773560727014, 0.10685743188485503, 0.08257713421713561, -0.07720454465132207, 0.023482927083969117, -0.0893395925546065, -0.18103390886448323, 0.3687254487723112, -0.0882665427820757, -0.2352492930367589, 0.2063477822393179, -0.09835783077403903, -0.11814905557781458, 0.05717955211177468, 0.22315731639973818, 0.17794652842916547, -0.13308327422477306, 0.11385677924961783, -0.027254810091108084, 0.12158941060304641, 0.07087580723688007, 0.005500899255275726, 0.17188937582075595, 0.08746384631842374, 0.1235998179204762, 0.16005106184631587, -0.04361492035910487, -0.08613578208256513, -0.3032692669332027, -0.18420158367604017, -0.14140138678252698, 0.033879752978682516, -0.08226690010080347, -0.16831410320475698, 0.45246143408119677, 0.15630613951012493, 0.1794032957032323, 0.02682821374386549, 0.2368936231872067, 0.19755721550201996, -0.022578864861279725, 0.06256373697891832, 0.22979521221481264, 0.18687275194562972, 0.10509799323976039, -0.23540737143252044, -0.05204771880991757, 0.18989778373390437] |
1,802.03493 | More Robust Doubly Robust Off-policy Evaluation | We study the problem of off-policy evaluation (OPE) in reinforcement learning
(RL), where the goal is to estimate the performance of a policy from the data
generated by another policy(ies). In particular, we focus on the doubly robust
(DR) estimators that consist of an importance sampling (IS) component and a
performance model, and utilize the low (or zero) bias of IS and low variance of
the model at the same time. Although the accuracy of the model has a huge
impact on the overall performance of DR, most of the work on using the DR
estimators in OPE has been focused on improving the IS part, and not much on
how to learn the model. In this paper, we propose alternative DR estimators,
called more robust doubly robust (MRDR), that learn the model parameter by
minimizing the variance of the DR estimator. We first present a formulation for
learning the DR model in RL. We then derive formulas for the variance of the DR
estimator in both contextual bandits and RL, such that their gradients
w.r.t.~the model parameters can be estimated from the samples, and propose
methods to efficiently minimize the variance. We prove that the MRDR estimators
are strongly consistent and asymptotically optimal. Finally, we evaluate MRDR
in bandits and RL benchmark problems, and compare its performance with the
existing methods.
| cs.AI | we study the problem of offpolicy evaluation ope in reinforcement learning rl where the goal is to estimate the performance of a policy from the data generated by another policyies in particular we focus on the doubly robust dr estimators that consist of an importance sampling is component and a performance model and utilize the low or zero bias of is and low variance of the model at the same time although the accuracy of the model has a huge impact on the overall performance of dr most of the work on using the dr estimators in ope has been focused on improving the is part and not much on how to learn the model in this paper we propose alternative dr estimators called more robust doubly robust mrdr that learn the model parameter by minimizing the variance of the dr estimator we first present a formulation for learning the dr model in rl we then derive formulas for the variance of the dr estimator in both contextual bandits and rl such that their gradients wrtthe model parameters can be estimated from the samples and propose methods to efficiently minimize the variance we prove that the mrdr estimators are strongly consistent and asymptotically optimal finally we evaluate mrdr in bandits and rl benchmark problems and compare its performance with the existing methods | [['we', 'study', 'the', 'problem', 'of', 'offpolicy', 'evaluation', 'ope', 'in', 'reinforcement', 'learning', 'rl', 'where', 'the', 'goal', 'is', 'to', 'estimate', 'the', 'performance', 'of', 'a', 'policy', 'from', 'the', 'data', 'generated', 'by', 'another', 'policyies', 'in', 'particular', 'we', 'focus', 'on', 'the', 'doubly', 'robust', 'dr', 'estimators', 'that', 'consist', 'of', 'an', 'importance', 'sampling', 'is', 'component', 'and', 'a', 'performance', 'model', 'and', 'utilize', 'the', 'low', 'or', 'zero', 'bias', 'of', 'is', 'and', 'low', 'variance', 'of', 'the', 'model', 'at', 'the', 'same', 'time', 'although', 'the', 'accuracy', 'of', 'the', 'model', 'has', 'a', 'huge', 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1,802.03494 | AMC: AutoML for Model Compression and Acceleration on Mobile Devices | Model compression is a critical technique to efficiently deploy neural
network models on mobile devices which have limited computation resources and
tight power budgets. Conventional model compression techniques rely on
hand-crafted heuristics and rule-based policies that require domain experts to
explore the large design space trading off among model size, speed, and
accuracy, which is usually sub-optimal and time-consuming. In this paper, we
propose AutoML for Model Compression (AMC) which leverage reinforcement
learning to provide the model compression policy. This learning-based
compression policy outperforms conventional rule-based compression policy by
having higher compression ratio, better preserving the accuracy and freeing
human labor. Under 4x FLOPs reduction, we achieved 2.7% better accuracy than
the handcrafted model compression policy for VGG-16 on ImageNet. We applied
this automated, push-the-button compression pipeline to MobileNet and achieved
1.81x speedup of measured inference latency on an Android phone and 1.43x
speedup on the Titan XP GPU, with only 0.1% loss of ImageNet Top-1 accuracy.
| cs.CV | model compression is a critical technique to efficiently deploy neural network models on mobile devices which have limited computation resources and tight power budgets conventional model compression techniques rely on handcrafted heuristics and rulebased policies that require domain experts to explore the large design space trading off among model size speed and accuracy which is usually suboptimal and timeconsuming in this paper we propose automl for model compression amc which leverage reinforcement learning to provide the model compression policy this learningbased compression policy outperforms conventional rulebased compression policy by having higher compression ratio better preserving the accuracy and freeing human labor under 4x flops reduction we achieved 27 better accuracy than the handcrafted model compression policy for vgg16 on imagenet we applied this automated pushthebutton compression pipeline to mobilenet and achieved 181x speedup of measured inference latency on an android phone and 143x speedup on the titan xp gpu with only 01 loss of imagenet top1 accuracy | [['model', 'compression', 'is', 'a', 'critical', 'technique', 'to', 'efficiently', 'deploy', 'neural', 'network', 'models', 'on', 'mobile', 'devices', 'which', 'have', 'limited', 'computation', 'resources', 'and', 'tight', 'power', 'budgets', 'conventional', 'model', 'compression', 'techniques', 'rely', 'on', 'handcrafted', 'heuristics', 'and', 'rulebased', 'policies', 'that', 'require', 'domain', 'experts', 'to', 'explore', 'the', 'large', 'design', 'space', 'trading', 'off', 'among', 'model', 'size', 'speed', 'and', 'accuracy', 'which', 'is', 'usually', 'suboptimal', 'and', 'timeconsuming', 'in', 'this', 'paper', 'we', 'propose', 'automl', 'for', 'model', 'compression', 'amc', 'which', 'leverage', 'reinforcement', 'learning', 'to', 'provide', 'the', 'model', 'compression', 'policy', 'this', 'learningbased', 'compression', 'policy', 'outperforms', 'conventional', 'rulebased', 'compression', 'policy', 'by', 'having', 'higher', 'compression', 'ratio', 'better', 'preserving', 'the', 'accuracy', 'and', 'freeing', 'human', 'labor', 'under', '4x', 'flops', 'reduction', 'we', 'achieved', '27', 'better', 'accuracy', 'than', 'the', 'handcrafted', 'model', 'compression', 'policy', 'for', 'vgg16', 'on', 'imagenet', 'we', 'applied', 'this', 'automated', 'pushthebutton', 'compression', 'pipeline', 'to', 'mobilenet', 'and', 'achieved', '181x', 'speedup', 'of', 'measured', 'inference', 'latency', 'on', 'an', 'android', 'phone', 'and', '143x', 'speedup', 'on', 'the', 'titan', 'xp', 'gpu', 'with', 'only', '01', 'loss', 'of', 'imagenet', 'top1', 'accuracy']] | [-0.0713493668226405, -0.05378867024471307, -0.031211693996691235, 0.05078605282075343, -0.12101109183783973, -0.23810207028723052, 0.14931301239111852, 0.4995200663564667, -0.2531566025989671, -0.3722242174970527, 0.07472624278762528, -0.20713460923683258, -0.12638257558818064, 0.23165672358065362, -0.18666206136206917, 0.13724800429096626, 0.13888314637550783, 0.004017220755415638, -0.08329045112515168, -0.357724318268799, 0.18691015692367668, 0.11973557916034254, 0.41991680242842244, 0.018222818068123512, 0.12232041666885057, -0.0733283651061356, 0.026465159656870508, -0.06650683385443934, -0.05939356543169725, 0.18957409130169017, 0.27555924059267833, 0.226163988806788, 0.32358687651013174, -0.43848388639669267, -0.21839340320758283, 0.0764599139351518, 0.12908207745321335, 0.035932304302320604, -0.007460405785710581, -0.27749803741162105, 0.12791790179607848, -0.25479756712673174, 0.0821642060836424, -0.18909069230299322, -0.04729881676875295, -0.020560309308911524, -0.3224947499772233, 0.0487141988810993, 0.061894533020113746, 0.1176292389030418, -0.03196527913875592, -0.14985149704040057, 0.0822515586871774, 0.0928384781875197, 0.026525043427283245, 0.1096064566259062, 0.22170438051464098, -0.19645517016460579, -0.1700758817094949, 0.3631129292530879, -0.03297091531013364, -0.20831555473588168, 0.18483167817429338, 0.028303364626762847, -0.12845409604750813, 0.13443571654178443, 0.29321439053521553, 0.07566922645327717, -0.11528121113116221, 0.012393784146350359, 0.03166140432379419, 0.2677203248585424, 0.12524804229727915, -0.022141204078141, 0.07255305972611231, 0.30733133208892877, 0.0709305886734998, 0.11997093585274753, -0.10794747917492303, -0.0887750733042917, -0.13477296848751363, -0.07559193853108633, -0.16803883071913714, 0.0030583260701067985, -0.1875126293853804, -0.11496966991633657, 0.32424329309874483, 0.2476250519765721, 0.13636585995855352, 0.16429588373735426, 0.43254684847929786, 0.03418541164588063, 0.15124196643911061, 0.18443006073995943, 0.18780644790348297, -0.09317402232047771, 0.16070052281082156, -0.19110216451733703, 0.09182120617479086, 0.06087179850005815] |
1,802.03495 | Generative Adversarial Networks and Probabilistic Graph Models for
Hyperspectral Image Classification | High spectral dimensionality and the shortage of annotations make
hyperspectral image (HSI) classification a challenging problem. Recent studies
suggest that convolutional neural networks can learn discriminative spatial
features, which play a paramount role in HSI interpretation. However, most of
these methods ignore the distinctive spectral-spatial characteristic of
hyperspectral data. In addition, a large amount of unlabeled data remains an
unexploited gold mine for efficient data use. Therefore, we proposed an
integration of generative adversarial networks (GANs) and probabilistic
graphical models for HSI classification. Specifically, we used a
spectral-spatial generator and a discriminator to identify land cover
categories of hyperspectral cubes. Moreover, to take advantage of a large
amount of unlabeled data, we adopted a conditional random field to refine the
preliminary classification results generated by GANs. Experimental results
obtained using two commonly studied datasets demonstrate that the proposed
framework achieved encouraging classification accuracy using a small number of
data for training.
| cs.CV cs.AI | high spectral dimensionality and the shortage of annotations make hyperspectral image hsi classification a challenging problem recent studies suggest that convolutional neural networks can learn discriminative spatial features which play a paramount role in hsi interpretation however most of these methods ignore the distinctive spectralspatial characteristic of hyperspectral data in addition a large amount of unlabeled data remains an unexploited gold mine for efficient data use therefore we proposed an integration of generative adversarial networks gans and probabilistic graphical models for hsi classification specifically we used a spectralspatial generator and a discriminator to identify land cover categories of hyperspectral cubes moreover to take advantage of a large amount of unlabeled data we adopted a conditional random field to refine the preliminary classification results generated by gans experimental results obtained using two commonly studied datasets demonstrate that the proposed framework achieved encouraging classification accuracy using a small number of data for training | [['high', 'spectral', 'dimensionality', 'and', 'the', 'shortage', 'of', 'annotations', 'make', 'hyperspectral', 'image', 'hsi', 'classification', 'a', 'challenging', 'problem', 'recent', 'studies', 'suggest', 'that', 'convolutional', 'neural', 'networks', 'can', 'learn', 'discriminative', 'spatial', 'features', 'which', 'play', 'a', 'paramount', 'role', 'in', 'hsi', 'interpretation', 'however', 'most', 'of', 'these', 'methods', 'ignore', 'the', 'distinctive', 'spectralspatial', 'characteristic', 'of', 'hyperspectral', 'data', 'in', 'addition', 'a', 'large', 'amount', 'of', 'unlabeled', 'data', 'remains', 'an', 'unexploited', 'gold', 'mine', 'for', 'efficient', 'data', 'use', 'therefore', 'we', 'proposed', 'an', 'integration', 'of', 'generative', 'adversarial', 'networks', 'gans', 'and', 'probabilistic', 'graphical', 'models', 'for', 'hsi', 'classification', 'specifically', 'we', 'used', 'a', 'spectralspatial', 'generator', 'and', 'a', 'discriminator', 'to', 'identify', 'land', 'cover', 'categories', 'of', 'hyperspectral', 'cubes', 'moreover', 'to', 'take', 'advantage', 'of', 'a', 'large', 'amount', 'of', 'unlabeled', 'data', 'we', 'adopted', 'a', 'conditional', 'random', 'field', 'to', 'refine', 'the', 'preliminary', 'classification', 'results', 'generated', 'by', 'gans', 'experimental', 'results', 'obtained', 'using', 'two', 'commonly', 'studied', 'datasets', 'demonstrate', 'that', 'the', 'proposed', 'framework', 'achieved', 'encouraging', 'classification', 'accuracy', 'using', 'a', 'small', 'number', 'of', 'data', 'for', 'training']] | [-0.005343190759778122, -0.037124656873025824, -0.05933339866063255, 0.12636674108563473, -0.11407635253329841, -0.15380740398849477, 0.00836308862989312, 0.45688829314432394, -0.2631316903758091, -0.3431326917292464, 0.08495910341900755, -0.26874054503775147, -0.1770920796036128, 0.1853035879984424, -0.17515861856078865, 0.10899569757786944, 0.20385443531470188, 0.00045354734483292166, -0.021409759631022714, -0.30207493925923545, 0.3134748661483558, 0.03699347723448119, 0.391821827147378, 0.015097563432541905, 0.1513736757876919, -0.027980964507488226, -0.07804832209536917, 0.015805449416947285, -0.05154047154141852, 0.2191884422247662, 0.3642283271336729, 0.20153387047470678, 0.3241999535512993, -0.4178230883553624, -0.2800755673627807, 0.13121827136083747, 0.12597654158687405, 0.11591245559059987, -0.1001609115867524, -0.34037595789953573, 0.10152924709470745, -0.14342314431702854, 0.013306877936625125, -0.19899712317308646, -0.017985538732945082, -0.055135193953925854, -0.3287541721889669, 0.027343097879915245, 0.07780116395940018, 0.10228560869859545, -0.05786477909377153, -0.13623896052456455, -0.012279592725812204, 0.15729154004030768, 0.025362711045921884, 0.016043526490545825, 0.08216530597762556, -0.2064632627055271, -0.120745255384403, 0.3210143301487956, -0.06658717123359836, -0.1877554092704263, 0.18932151964662092, -0.018789594971408315, -0.1836306739179109, 0.12646068009665076, 0.24831521163219647, 0.11670005969727869, -0.14542742183937735, -0.00609805991384355, -0.049378687077948194, 0.19969729174680959, 0.050719939778244356, 0.010196224132565079, 0.16452026024571811, 0.2864579326590363, -0.016016283442593173, 0.15320642242849564, -0.21460524153593458, -0.008557015752407493, -0.19924196218044438, -0.08667301907765353, -0.26958735476846235, -0.025566514635216506, -0.1474146841658435, -0.15879597121917885, 0.3896280470248682, 0.2480142780468608, 0.25574303136842436, 0.07252466382338536, 0.3341875947789838, -0.020943953920871217, 0.13112373849599074, 0.04453015137334286, 0.14875306423933904, 0.06032717806620166, 0.12330249852396005, -0.11901701421828459, 0.055361288965877074, 0.022766833195142104] |
1,802.03496 | On the maximum of discretely sampled fractional Brownian motion with
small Hurst parameter | We show that the distribution of the maximum of the fractional Brownian
motion $B^H$ with Hurst parameter $H\to 0$ over an $n$-point set $\tau \subset
[0,1]$ can be approximated by the normal law with mean $\sqrt{\ln n}$ and
variance $1/2$ provided that $n\to \infty$ slowly enough and the points in
$\tau$ are not too close to each other.
| math.PR | we show that the distribution of the maximum of the fractional brownian motion bh with hurst parameter hto 0 over an npoint set tau subset 01 can be approximated by the normal law with mean sqrtln n and variance 12 provided that nto infty slowly enough and the points in tau are not too close to each other | [['we', 'show', 'that', 'the', 'distribution', 'of', 'the', 'maximum', 'of', 'the', 'fractional', 'brownian', 'motion', 'bh', 'with', 'hurst', 'parameter', 'hto', '0', 'over', 'an', 'npoint', 'set', 'tau', 'subset', '01', 'can', 'be', 'approximated', 'by', 'the', 'normal', 'law', 'with', 'mean', 'sqrtln', 'n', 'and', 'variance', '12', 'provided', 'that', 'nto', 'infty', 'slowly', 'enough', 'and', 'the', 'points', 'in', 'tau', 'are', 'not', 'too', 'close', 'to', 'each', 'other']] | [-0.08494612723107225, 0.202145081176272, -0.05526867078552986, 0.04847197738026494, -0.03276539452630898, -0.16340129207498555, 0.06542567307217817, 0.37651320283525974, -0.3141334610027743, -0.24711432574509545, 0.07874996883288861, -0.3402086986582084, -0.05677603167915267, 0.17937922535528397, -0.05030523863588941, 0.0692690394272835, 0.033842965009108446, 0.07512557723334637, 0.002288922707646571, -0.23315697005982028, 0.21573715232882712, -0.03777171701899376, 0.1487781313601239, -0.07301190232352257, 0.05191435510742253, -0.02629982584541471, -0.002028738148510456, 0.04834358966588203, -0.19231340471491548, -0.006358361449734918, 0.17892832964144903, 0.06722235658149846, 0.2666251004256051, -0.3144204583789768, -0.13589496403161822, 0.2037914778138029, 0.22072856352817075, -0.04460916546141279, 0.06038187888194004, -0.2946056379647604, 0.14266681530641329, -0.15636199179651408, -0.14833063991933032, -0.05158318553505273, 0.1277911699585745, 0.08308715620559864, -0.34372604677678437, 0.11066474993552627, 0.09084923234607639, -0.004667939224826364, -0.027368486728438914, -0.17431327153061485, -0.05040691092465844, 0.117085135076195, 0.10881017447160236, 0.11243243431190736, 0.1103913846504995, -0.11091058050956705, -0.07250422078128584, 0.33415510657179204, -0.13034492116100316, -0.28041525400275813, 0.12178506759990906, -0.3076682656698314, -0.09050328362379866, 0.1490218714200731, 0.09307502569171103, 0.13171507354189968, -0.10322113655676166, 0.1663083695818606, 0.008221766750874191, 0.1814800119669787, 0.04810220357191203, 0.0051685606033123775, 0.1600150303626112, 0.09245920986547296, 0.1179080029752844, 0.0798710210174563, -0.15059433969797503, -0.06603572753675539, -0.3816646821796894, -0.14588795682608052, -0.22261770209297538, 0.15751493067489655, -0.20933411483891742, -0.13670767855565547, 0.2648583270111603, 0.10192298493348062, 0.2734275369037842, 0.1581405525673823, 0.18663446322597307, 0.1963389214945672, -0.018517492290843148, 0.1264326175160963, 0.16021758300284372, 0.10292310624560021, 0.04037849699620735, -0.21547889789759084, 0.04463252641967144, 0.05731846791981109] |
1,802.03497 | Modeling Global Dynamics from Local Snapshots with Deep Generative
Neural Networks | Complex high dimensional stochastic dynamic systems arise in many
applications in the natural sciences and especially biology. However, while
these systems are difficult to describe analytically, "snapshot" measurements
that sample the output of the system are often available. In order to model the
dynamics of such systems given snapshot data, or local transitions, we present
a deep neural network framework we call Dynamics Modeling Network or DyMoN.
DyMoN is a neural network framework trained as a deep generative Markov model
whose next state is a probability distribution based on the current state.
DyMoN is trained using samples of current and next-state pairs, and thus does
not require longitudinal measurements. We show the advantage of DyMoN over
shallow models such as Kalman filters and hidden Markov models, and other deep
models such as recurrent neural networks in its ability to embody the dynamics
(which can be studied via perturbation of the neural network) and generate
longitudinal hypothetical trajectories. We perform three case studies in which
we apply DyMoN to different types of biological systems and extract features of
the dynamics in each case by examining the learned model.
| cs.LG stat.ML | complex high dimensional stochastic dynamic systems arise in many applications in the natural sciences and especially biology however while these systems are difficult to describe analytically snapshot measurements that sample the output of the system are often available in order to model the dynamics of such systems given snapshot data or local transitions we present a deep neural network framework we call dynamics modeling network or dymon dymon is a neural network framework trained as a deep generative markov model whose next state is a probability distribution based on the current state dymon is trained using samples of current and nextstate pairs and thus does not require longitudinal measurements we show the advantage of dymon over shallow models such as kalman filters and hidden markov models and other deep models such as recurrent neural networks in its ability to embody the dynamics which can be studied via perturbation of the neural network and generate longitudinal hypothetical trajectories we perform three case studies in which we apply dymon to different types of biological systems and extract features of the dynamics in each case by examining the learned model | [['complex', 'high', 'dimensional', 'stochastic', 'dynamic', 'systems', 'arise', 'in', 'many', 'applications', 'in', 'the', 'natural', 'sciences', 'and', 'especially', 'biology', 'however', 'while', 'these', 'systems', 'are', 'difficult', 'to', 'describe', 'analytically', 'snapshot', 'measurements', 'that', 'sample', 'the', 'output', 'of', 'the', 'system', 'are', 'often', 'available', 'in', 'order', 'to', 'model', 'the', 'dynamics', 'of', 'such', 'systems', 'given', 'snapshot', 'data', 'or', 'local', 'transitions', 'we', 'present', 'a', 'deep', 'neural', 'network', 'framework', 'we', 'call', 'dynamics', 'modeling', 'network', 'or', 'dymon', 'dymon', 'is', 'a', 'neural', 'network', 'framework', 'trained', 'as', 'a', 'deep', 'generative', 'markov', 'model', 'whose', 'next', 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1,802.03498 | The Strange Attractor Model of Bipedal Locomotion and its Consequences
on Motor Control | Despite decades of study, many unknowns exist about the mechanisms governing
human locomotion. Current models and motor control theories can only partially
capture the phenomenon. This may be a major cause of the reduced efficacy of
lower limb rehabilitation therapies. Recently, it has been proposed that human
locomotion can be planned in the task-space by taking advantage of the
gravitational pull acting on the Centre of Mass (CoM) by modelling the
attractor dynamics. The model proposed represents the CoM transversal
trajectory as a harmonic oscillator propagating on the attractor manifold.
However, the vertical trajectory of the CoM, controlled through ankle
strategies, has not been accurately captured yet. Research Questions: Is it
possible to improve the model accuracy by introducing a mathematical model of
the ankle strategies by coordinating the heel-strike and toe-off strategies
with the CoM movement? Our solution consists of closed-form equations that plan
human-like trajectories for the CoM, the foot swing, and the ankle strategies.
We have tested our model by extracting the biomechanics data and postural
during locomotion from the motion capture trajectories of 12 healthy subjects
at 3 self-selected speeds to generate a virtual subject using our model. Our
virtual subject has been based on the average of the collected data. The model
output shows our virtual subject has walking trajectories that have their
features consistent with our motion capture data. Additionally, it emerged from
the data analysis that our model regulates the stance phase of the foot as
humans do. The model proves that locomotion can be modelled as an attractor
dynamics, proving the existence of a nonlinear map that our nervous system
learns. It can support a deeper investigation of locomotion motor control,
potentially improving locomotion rehabilitation and assistive technologies.
| cs.RO | despite decades of study many unknowns exist about the mechanisms governing human locomotion current models and motor control theories can only partially capture the phenomenon this may be a major cause of the reduced efficacy of lower limb rehabilitation therapies recently it has been proposed that human locomotion can be planned in the taskspace by taking advantage of the gravitational pull acting on the centre of mass com by modelling the attractor dynamics the model proposed represents the com transversal trajectory as a harmonic oscillator propagating on the attractor manifold however the vertical trajectory of the com controlled through ankle strategies has not been accurately captured yet research questions is it possible to improve the model accuracy by introducing a mathematical model of the ankle strategies by coordinating the heelstrike and toeoff strategies with the com movement our solution consists of closedform equations that plan humanlike trajectories for the com the foot swing and the ankle strategies we have tested our model by extracting the biomechanics data and postural during locomotion from the motion capture trajectories of 12 healthy subjects at 3 selfselected speeds to generate a virtual subject using our model our virtual subject has been based on the average of the collected data the model output shows our virtual subject has walking trajectories that have their features consistent with our motion capture data additionally it emerged from the data analysis that our model regulates the stance phase of the foot as humans do the model proves that locomotion can be modelled as an attractor dynamics proving the existence of a nonlinear map that our nervous system learns it can support a deeper investigation of locomotion motor control potentially improving locomotion rehabilitation and assistive technologies | [['despite', 'decades', 'of', 'study', 'many', 'unknowns', 'exist', 'about', 'the', 'mechanisms', 'governing', 'human', 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1,802.03499 | Local Contrast Learning | Learning a deep model from small data is yet an opening and challenging
problem. We focus on one-shot classification by deep learning approach based on
a small quantity of training samples. We proposed a novel deep learning
approach named Local Contrast Learning (LCL) based on the key insight about a
human cognitive behavior that human recognizes the objects in a specific
context by contrasting the objects in the context or in her/his memory. LCL is
used to train a deep model that can contrast the recognizing sample with a
couple of contrastive samples randomly drawn and shuffled. On one-shot
classification task on Omniglot, the deep model based LCL with 122 layers and
1.94 millions of parameters, which was trained on a tiny dataset with only 60
classes and 20 samples per class, achieved the accuracy 97.99% that outperforms
human and state-of-the-art established by Bayesian Program Learning (BPL)
trained on 964 classes. LCL is a fundamental idea which can be applied to
alleviate parametric model's overfitting resulted by lack of training samples.
| cs.LG cs.AI cs.CV | learning a deep model from small data is yet an opening and challenging problem we focus on oneshot classification by deep learning approach based on a small quantity of training samples we proposed a novel deep learning approach named local contrast learning lcl based on the key insight about a human cognitive behavior that human recognizes the objects in a specific context by contrasting the objects in the context or in herhis memory lcl is used to train a deep model that can contrast the recognizing sample with a couple of contrastive samples randomly drawn and shuffled on oneshot classification task on omniglot the deep model based lcl with 122 layers and 194 millions of parameters which was trained on a tiny dataset with only 60 classes and 20 samples per class achieved the accuracy 9799 that outperforms human and stateoftheart established by bayesian program learning bpl trained on 964 classes lcl is a fundamental idea which can be applied to alleviate parametric models overfitting resulted by lack of training samples | [['learning', 'a', 'deep', 'model', 'from', 'small', 'data', 'is', 'yet', 'an', 'opening', 'and', 'challenging', 'problem', 'we', 'focus', 'on', 'oneshot', 'classification', 'by', 'deep', 'learning', 'approach', 'based', 'on', 'a', 'small', 'quantity', 'of', 'training', 'samples', 'we', 'proposed', 'a', 'novel', 'deep', 'learning', 'approach', 'named', 'local', 'contrast', 'learning', 'lcl', 'based', 'on', 'the', 'key', 'insight', 'about', 'a', 'human', 'cognitive', 'behavior', 'that', 'human', 'recognizes', 'the', 'objects', 'in', 'a', 'specific', 'context', 'by', 'contrasting', 'the', 'objects', 'in', 'the', 'context', 'or', 'in', 'herhis', 'memory', 'lcl', 'is', 'used', 'to', 'train', 'a', 'deep', 'model', 'that', 'can', 'contrast', 'the', 'recognizing', 'sample', 'with', 'a', 'couple', 'of', 'contrastive', 'samples', 'randomly', 'drawn', 'and', 'shuffled', 'on', 'oneshot', 'classification', 'task', 'on', 'omniglot', 'the', 'deep', 'model', 'based', 'lcl', 'with', '122', 'layers', 'and', '194', 'millions', 'of', 'parameters', 'which', 'was', 'trained', 'on', 'a', 'tiny', 'dataset', 'with', 'only', '60', 'classes', 'and', '20', 'samples', 'per', 'class', 'achieved', 'the', 'accuracy', '9799', 'that', 'outperforms', 'human', 'and', 'stateoftheart', 'established', 'by', 'bayesian', 'program', 'learning', 'bpl', 'trained', 'on', '964', 'classes', 'lcl', 'is', 'a', 'fundamental', 'idea', 'which', 'can', 'be', 'applied', 'to', 'alleviate', 'parametric', 'models', 'overfitting', 'resulted', 'by', 'lack', 'of', 'training', 'samples']] | [-0.0030763461635780263, 0.019657856984317917, -0.04096290218638398, 0.06552676643498176, -0.12979121972386023, -0.192604381893353, 0.08419213296671273, 0.42841575244626806, -0.2588618270159639, -0.39792243268732963, 0.07185757514674826, -0.2704823143311847, -0.1758701984943517, 0.21469074024475718, -0.15967735793844562, 0.0696456292807533, 0.13105355364907728, 0.06003824550772838, -0.051692032636475496, -0.29687213109825245, 0.30877653836022123, 0.014029958035370705, 0.36303698807976575, -0.03946933663369848, 0.15977895479684706, -0.031716320285170574, -0.004109878110704802, 0.008985098941545738, -0.03845260412447061, 0.18481753280519764, 0.2877892116870371, 0.17529697090725016, 0.3613594253841932, -0.3860350912325789, -0.2281503129317507, 0.10861345846478267, 0.11923943707387219, 0.10179123927414287, -0.05049906235953455, -0.35018052845343683, 0.08644981272191855, -0.1367088199990709, 0.05228669648957357, -0.10165546286567959, -0.037768269187437364, -0.03745218935662238, -0.2812003647440184, 0.04669057918244112, 0.09638264080324369, 0.09345474099462624, -0.054156800523694405, -0.12762045853618773, 0.03820824229065874, 0.1237765934811197, 0.00485594482610427, 0.08453504867002107, 0.13049288466572762, -0.18837367463856936, -0.13371651150947927, 0.3288052320716581, -0.06184776871851836, -0.17222074588640915, 0.21233972056485625, -0.003555540137036502, -0.1483532874477341, 0.11144044850980155, 0.26054886195329374, 0.15928878417314843, -0.18123815662625153, 0.020259003795898515, -0.08285448262849224, 0.23115915989187377, 0.017295590122584362, -0.04431587583194786, 0.1798485714400307, 0.2928126454625649, -0.03494054450767867, 0.1392010688524399, -0.15127478828735388, -0.06378750759040752, -0.19122384028639972, -0.05555969926498678, -0.23732802683857884, 0.00790933250961553, -0.09757290187123986, -0.12235422666342548, 0.3912780376951815, 0.18983401970984481, 0.23152338174220763, 0.12482445048064822, 0.2915395263647824, 0.0039873835086081805, 0.15602746952804383, 0.09802028779941202, 0.202272270612789, 0.005038514139664452, 0.08174019065081027, -0.15475402089865192, 0.12095022832903989, 0.04875422867249196] |
1,802.035 | A veracity preserving model for synthesizing scalable electricity load
profiles | Electricity users are the major players of the electric systems, and
electricity consumption is growing at an extraordinary rate. The research on
electricity consumption behaviors is becoming increasingly important to design
and deployment of the electric systems. Unfortunately, electricity load
profiles are difficult to acquire. Data synthesis is one of the best approaches
to solving the lack of data, and the key is the model that preserves the real
electricity consumption behaviors. In this paper, we propose a hierarchical
multi-matrices Markov Chain (HMMC) model to synthesize scalable electricity
load profiles that preserve the real consumption behavior on three time scales:
per day, per week, and per year. To promote the research on the electricity
consumption behavior, we use the HMMC approach to model two distinctive raw
electricity load profiles. One is collected from the resident sector, and the
other is collected from the non-resident sectors, including different
industries such as education, finance, and manufacturing. The experiments show
our model performs much better than the classical Markov Chain model. We
publish two trained models online, and researchers can directly use these
trained models to synthesize scalable electricity load profiles for further
researches.
| cs.OH | electricity users are the major players of the electric systems and electricity consumption is growing at an extraordinary rate the research on electricity consumption behaviors is becoming increasingly important to design and deployment of the electric systems unfortunately electricity load profiles are difficult to acquire data synthesis is one of the best approaches to solving the lack of data and the key is the model that preserves the real electricity consumption behaviors in this paper we propose a hierarchical multimatrices markov chain hmmc model to synthesize scalable electricity load profiles that preserve the real consumption behavior on three time scales per day per week and per year to promote the research on the electricity consumption behavior we use the hmmc approach to model two distinctive raw electricity load profiles one is collected from the resident sector and the other is collected from the nonresident sectors including different industries such as education finance and manufacturing the experiments show our model performs much better than the classical markov chain model we publish two trained models online and researchers can directly use these trained models to synthesize scalable electricity load profiles for further researches | [['electricity', 'users', 'are', 'the', 'major', 'players', 'of', 'the', 'electric', 'systems', 'and', 'electricity', 'consumption', 'is', 'growing', 'at', 'an', 'extraordinary', 'rate', 'the', 'research', 'on', 'electricity', 'consumption', 'behaviors', 'is', 'becoming', 'increasingly', 'important', 'to', 'design', 'and', 'deployment', 'of', 'the', 'electric', 'systems', 'unfortunately', 'electricity', 'load', 'profiles', 'are', 'difficult', 'to', 'acquire', 'data', 'synthesis', 'is', 'one', 'of', 'the', 'best', 'approaches', 'to', 'solving', 'the', 'lack', 'of', 'data', 'and', 'the', 'key', 'is', 'the', 'model', 'that', 'preserves', 'the', 'real', 'electricity', 'consumption', 'behaviors', 'in', 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1,802.03501 | Path Consistency Learning in Tsallis Entropy Regularized MDPs | We study the sparse entropy-regularized reinforcement learning (ERL) problem
in which the entropy term is a special form of the Tsallis entropy. The optimal
policy of this formulation is sparse, i.e.,~at each state, it has non-zero
probability for only a small number of actions. This addresses the main
drawback of the standard Shannon entropy-regularized RL (soft ERL) formulation,
in which the optimal policy is softmax, and thus, may assign a non-negligible
probability mass to non-optimal actions. This problem is aggravated as the
number of actions is increased. In this paper, we follow the work of Nachum et
al. (2017) in the soft ERL setting, and propose a class of novel path
consistency learning (PCL) algorithms, called {\em sparse PCL}, for the sparse
ERL problem that can work with both on-policy and off-policy data. We first
derive a {\em sparse consistency} equation that specifies a relationship
between the optimal value function and policy of the sparse ERL along any
system trajectory. Crucially, a weak form of the converse is also true, and we
quantify the sub-optimality of a policy which satisfies sparse consistency, and
show that as we increase the number of actions, this sub-optimality is better
than that of the soft ERL optimal policy. We then use this result to derive the
sparse PCL algorithms. We empirically compare sparse PCL with its soft
counterpart, and show its advantage, especially in problems with a large number
of actions.
| cs.AI cs.LG stat.ML | we study the sparse entropyregularized reinforcement learning erl problem in which the entropy term is a special form of the tsallis entropy the optimal policy of this formulation is sparse ieat each state it has nonzero probability for only a small number of actions this addresses the main drawback of the standard shannon entropyregularized rl soft erl formulation in which the optimal policy is softmax and thus may assign a nonnegligible probability mass to nonoptimal actions this problem is aggravated as the number of actions is increased in this paper we follow the work of nachum et al 2017 in the soft erl setting and propose a class of novel path consistency learning pcl algorithms called em sparse pcl for the sparse erl problem that can work with both onpolicy and offpolicy data we first derive a em sparse consistency equation that specifies a relationship between the optimal value function and policy of the sparse erl along any system trajectory crucially a weak form of the converse is also true and we quantify the suboptimality of a policy which satisfies sparse consistency and show that as we increase the number of actions this suboptimality is better than that of the soft erl optimal policy we then use this result to derive the sparse pcl algorithms we empirically compare sparse pcl with its soft counterpart and show its advantage especially in problems with a large number of actions | [['we', 'study', 'the', 'sparse', 'entropyregularized', 'reinforcement', 'learning', 'erl', 'problem', 'in', 'which', 'the', 'entropy', 'term', 'is', 'a', 'special', 'form', 'of', 'the', 'tsallis', 'entropy', 'the', 'optimal', 'policy', 'of', 'this', 'formulation', 'is', 'sparse', 'ieat', 'each', 'state', 'it', 'has', 'nonzero', 'probability', 'for', 'only', 'a', 'small', 'number', 'of', 'actions', 'this', 'addresses', 'the', 'main', 'drawback', 'of', 'the', 'standard', 'shannon', 'entropyregularized', 'rl', 'soft', 'erl', 'formulation', 'in', 'which', 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1,802.03502 | Large Scale Lorentz Violation Gravity and Dark Energy | The accelerating expansion of universe can be described by the non-zero
cosmological constant or the dark energy. However, the origin of the dark
energy remains a mystery of modern physics. The local Lorentz invariance is the
most exact symmetry of the Nature on the one hand, but all quantum gravity
theories predict Lorentz violation on the other hand. The local Lorentz
violation induced by the quantum gravity at the very early universe may be
transformed into large scale by inflation. Combining the low-$l$ anomalies of
the CMB spectrum, we propose that the local Lorentz invariance may be broken at
the large scale. We construct the effective gravity at the cosmic scale with a
local $SO(3)$ symmetry. The theory exhibits non-trivial contortion distribution
even with scalar matter source. The FRW like solution of the theory is analyzed
and the contortion distribution contributes a dark energy like effect which is
responsible for the accelerating expansion of the universe. It reveals that the
dark energy may be the remnants of quantum gravity in this sense.
| gr-qc astro-ph.CO hep-th | the accelerating expansion of universe can be described by the nonzero cosmological constant or the dark energy however the origin of the dark energy remains a mystery of modern physics the local lorentz invariance is the most exact symmetry of the nature on the one hand but all quantum gravity theories predict lorentz violation on the other hand the local lorentz violation induced by the quantum gravity at the very early universe may be transformed into large scale by inflation combining the lowl anomalies of the cmb spectrum we propose that the local lorentz invariance may be broken at the large scale we construct the effective gravity at the cosmic scale with a local so3 symmetry the theory exhibits nontrivial contortion distribution even with scalar matter source the frw like solution of the theory is analyzed and the contortion distribution contributes a dark energy like effect which is responsible for the accelerating expansion of the universe it reveals that the dark energy may be the remnants of quantum gravity in this sense | [['the', 'accelerating', 'expansion', 'of', 'universe', 'can', 'be', 'described', 'by', 'the', 'nonzero', 'cosmological', 'constant', 'or', 'the', 'dark', 'energy', 'however', 'the', 'origin', 'of', 'the', 'dark', 'energy', 'remains', 'a', 'mystery', 'of', 'modern', 'physics', 'the', 'local', 'lorentz', 'invariance', 'is', 'the', 'most', 'exact', 'symmetry', 'of', 'the', 'nature', 'on', 'the', 'one', 'hand', 'but', 'all', 'quantum', 'gravity', 'theories', 'predict', 'lorentz', 'violation', 'on', 'the', 'other', 'hand', 'the', 'local', 'lorentz', 'violation', 'induced', 'by', 'the', 'quantum', 'gravity', 'at', 'the', 'very', 'early', 'universe', 'may', 'be', 'transformed', 'into', 'large', 'scale', 'by', 'inflation', 'combining', 'the', 'lowl', 'anomalies', 'of', 'the', 'cmb', 'spectrum', 'we', 'propose', 'that', 'the', 'local', 'lorentz', 'invariance', 'may', 'be', 'broken', 'at', 'the', 'large', 'scale', 'we', 'construct', 'the', 'effective', 'gravity', 'at', 'the', 'cosmic', 'scale', 'with', 'a', 'local', 'so3', 'symmetry', 'the', 'theory', 'exhibits', 'nontrivial', 'contortion', 'distribution', 'even', 'with', 'scalar', 'matter', 'source', 'the', 'frw', 'like', 'solution', 'of', 'the', 'theory', 'is', 'analyzed', 'and', 'the', 'contortion', 'distribution', 'contributes', 'a', 'dark', 'energy', 'like', 'effect', 'which', 'is', 'responsible', 'for', 'the', 'accelerating', 'expansion', 'of', 'the', 'universe', 'it', 'reveals', 'that', 'the', 'dark', 'energy', 'may', 'be', 'the', 'remnants', 'of', 'quantum', 'gravity', 'in', 'this', 'sense']] | [-0.16799206245381948, 0.20429705167839, -0.14322804181490006, 0.13923007777356, -0.10791822748940957, -0.12490651393336309, -0.06829278484571638, 0.25921329301457074, -0.2581998221339322, -0.32918044707066446, 0.0750372794200937, -0.23758397410558754, -0.07374571224398435, 0.12892398229383237, 0.03634081189087484, -0.012953114248460245, -0.05051913752956966, 0.051992157793625496, -0.0619039288870882, -0.2310052448910726, 0.3287000945108661, 0.13279556490137068, 0.2823668684712936, 0.03797913168497427, 0.10969811305251422, -0.06840903740809408, -0.02072407915879604, 0.028667142381891608, -0.09577906204343213, 0.08289349321573757, 0.156031534067615, 0.09193079925188254, 0.2048313004062186, -0.43924235761555475, -0.2567355023659116, 0.1524317669790498, 0.1219198337578767, 0.15722774207960208, -0.07921851757605192, -0.30639933352900106, 0.03515361072928753, -0.1413252792310316, -0.16441282387660427, -0.06240326733339231, -0.03399112428603477, -0.08259577576550221, -0.18660138826411174, 0.16456204760047408, 0.0057758248308240326, -0.02352635195576261, -0.0595544942189008, -0.03690342173362043, -0.0004174229309915803, 0.04647277681790509, 0.12871004826034593, 0.03259334169723483, 0.17318204835701603, -0.19580800229613127, -0.06637360995803245, 0.4752282707188495, -0.12342804825599414, -0.13975092741054332, 0.1424430186946898, -0.18301052081691058, -0.16503813513825366, 0.09524993987640279, 0.0861530584461857, 0.06741422801657558, -0.1230675768930205, 0.1937616355226901, 0.0008736896223042074, 0.14716666190733396, 0.07564130356416193, 0.07306849599685954, 0.3313570723619832, 0.09866649817897354, 0.06898261606016434, 0.055918068552273326, -0.06413175898799023, -0.084358730475245, -0.3925349701813227, -0.14529171427048548, -0.20165178221230784, 0.09024206073656894, -0.14115424846225555, -0.13639107090691754, 0.38617569381414457, 0.09730037420563031, 0.16135376991249276, 0.008962032274846637, 0.2628329418123115, 0.1132681153032409, 0.09016763899193773, 0.1012943162796694, 0.31899801981743686, 0.09218106229097832, 0.10126709947795716, -0.29254130151415203, 0.026889924057434466, 0.05687682461643288] |
1,802.03503 | A New Approach of Exploiting Self-Adjoint Matrix Polynomials of Large
Random Matrices for Anomaly Detection and Fault Location | Synchronized measurements of a large power grid enable an unprecedented
opportunity to study the spatialtemporal correlations. Statistical analytics
for those massive datasets start with high-dimensional data matrices.
Uncertainty is ubiquitous in a future's power grid. These data matrices are
recognized as random matrices. This new point of view is fundamental in our
theoretical analysis since true covariance matrices cannot be estimated
accurately in a high-dimensional regime. As an alternative, we consider
large-dimensional sample covariance matrices in the asymptotic regime to
replace the true covariance matrices. The self-adjoint polynomials of
large-dimensional random matrices are studied as statistics for big data
analytics. The calculation of the asymptotic spectrum distribution (ASD) for
such a matrix polynomial is understandably challenging. This task is made
possible by a recent breakthrough in free probability, an active research
branch in random matrix theory. This is the very reason why the work of this
paper is inspired initially. The new approach is interesting in many aspects.
The mathematical reason may be most critical. The real-world problems can be
solved using this approach, however.
| stat.AP eess.SP | synchronized measurements of a large power grid enable an unprecedented opportunity to study the spatialtemporal correlations statistical analytics for those massive datasets start with highdimensional data matrices uncertainty is ubiquitous in a futures power grid these data matrices are recognized as random matrices this new point of view is fundamental in our theoretical analysis since true covariance matrices cannot be estimated accurately in a highdimensional regime as an alternative we consider largedimensional sample covariance matrices in the asymptotic regime to replace the true covariance matrices the selfadjoint polynomials of largedimensional random matrices are studied as statistics for big data analytics the calculation of the asymptotic spectrum distribution asd for such a matrix polynomial is understandably challenging this task is made possible by a recent breakthrough in free probability an active research branch in random matrix theory this is the very reason why the work of this paper is inspired initially the new approach is interesting in many aspects the mathematical reason may be most critical the realworld problems can be solved using this approach however | [['synchronized', 'measurements', 'of', 'a', 'large', 'power', 'grid', 'enable', 'an', 'unprecedented', 'opportunity', 'to', 'study', 'the', 'spatialtemporal', 'correlations', 'statistical', 'analytics', 'for', 'those', 'massive', 'datasets', 'start', 'with', 'highdimensional', 'data', 'matrices', 'uncertainty', 'is', 'ubiquitous', 'in', 'a', 'futures', 'power', 'grid', 'these', 'data', 'matrices', 'are', 'recognized', 'as', 'random', 'matrices', 'this', 'new', 'point', 'of', 'view', 'is', 'fundamental', 'in', 'our', 'theoretical', 'analysis', 'since', 'true', 'covariance', 'matrices', 'can', 'not', 'be', 'estimated', 'accurately', 'in', 'a', 'highdimensional', 'regime', 'as', 'an', 'alternative', 'we', 'consider', 'largedimensional', 'sample', 'covariance', 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1,802.03504 | Complexity of a quadratic penalty accelerated inexact proximal point
method for solving linearly constrained nonconvex composite programs | This paper analyzes the iteration-complexity of a quadratic penalty
accelerated inexact proximal point method for solving linearly constrained
nonconvex composite programs. More specifically, the objective function is of
the form $f + h$ where $f$ is a differentiable function whose gradient is
Lipschitz continuous and $h$ is a closed convex function with bounded domain.
The method, basically, consists of applying an accelerated inexact proximal
point method for solving approximately a sequence of quadratic penalized
subproblems associated to the linearly constrained problem. Each subproblem of
the proximal point method is in turn approximately solved by an accelerated
composite gradient (ACG) method. It is shown that the proposed scheme generates
a $\rho$-approximate stationary point in at most ${\cal{O}}(\rho^{-3})$ ACG
iterations. Finally, numerical results showing the efficiency of the proposed
method are also given.
| math.OC | this paper analyzes the iterationcomplexity of a quadratic penalty accelerated inexact proximal point method for solving linearly constrained nonconvex composite programs more specifically the objective function is of the form f h where f is a differentiable function whose gradient is lipschitz continuous and h is a closed convex function with bounded domain the method basically consists of applying an accelerated inexact proximal point method for solving approximately a sequence of quadratic penalized subproblems associated to the linearly constrained problem each subproblem of the proximal point method is in turn approximately solved by an accelerated composite gradient acg method it is shown that the proposed scheme generates a rhoapproximate stationary point in at most calorho3 acg iterations finally numerical results showing the efficiency of the proposed method are also given | [['this', 'paper', 'analyzes', 'the', 'iterationcomplexity', 'of', 'a', 'quadratic', 'penalty', 'accelerated', 'inexact', 'proximal', 'point', 'method', 'for', 'solving', 'linearly', 'constrained', 'nonconvex', 'composite', 'programs', 'more', 'specifically', 'the', 'objective', 'function', 'is', 'of', 'the', 'form', 'f', 'h', 'where', 'f', 'is', 'a', 'differentiable', 'function', 'whose', 'gradient', 'is', 'lipschitz', 'continuous', 'and', 'h', 'is', 'a', 'closed', 'convex', 'function', 'with', 'bounded', 'domain', 'the', 'method', 'basically', 'consists', 'of', 'applying', 'an', 'accelerated', 'inexact', 'proximal', 'point', 'method', 'for', 'solving', 'approximately', 'a', 'sequence', 'of', 'quadratic', 'penalized', 'subproblems', 'associated', 'to', 'the', 'linearly', 'constrained', 'problem', 'each', 'subproblem', 'of', 'the', 'proximal', 'point', 'method', 'is', 'in', 'turn', 'approximately', 'solved', 'by', 'an', 'accelerated', 'composite', 'gradient', 'acg', 'method', 'it', 'is', 'shown', 'that', 'the', 'proposed', 'scheme', 'generates', 'a', 'rhoapproximate', 'stationary', 'point', 'in', 'at', 'most', 'calorho3', 'acg', 'iterations', 'finally', 'numerical', 'results', 'showing', 'the', 'efficiency', 'of', 'the', 'proposed', 'method', 'are', 'also', 'given']] | [-0.11846259716458007, 0.008375420568199843, -0.09369597735712887, 0.020216213678395434, -0.06650488446393865, -0.19085958010327886, 0.023521749010797066, 0.4207263886492001, -0.39608687754389393, -0.23715959653054597, 0.09322501181122789, -0.2446652329481367, -0.17893614256621504, 0.2090675991585158, -0.07300573357497342, 0.08925813223140722, 0.05851534414796333, 0.026580602818285115, -0.1263987368420203, -0.3003665179139716, 0.2721856495118118, 0.008834581727569457, 0.21069959076703526, 0.007456004317646148, 0.17667240896389558, -0.032823937995999586, 0.015485661861930566, 0.06524657920454047, -0.02839676462127727, 0.1408024514284989, 0.25497237661329564, 0.17254397474789585, 0.42368898808490485, -0.3402574225910939, -0.14514818805764662, 0.13725067149061942, 0.16236780321560218, 0.009012965732836165, -0.08725754615772985, -0.20660543161284295, 0.1036195553951984, -0.07056991772242327, -0.1310078367787355, -0.05077791601070203, -0.059656760131474584, 0.03635557120287558, -0.3685289570275927, 0.07093299061102698, 0.05939133484935155, -0.0008956137498898897, -0.08313547561465384, -0.14414890328225738, 0.0071025027355062775, -0.030501018030918203, 0.05793834395080921, 0.15115820706341765, 0.13959982646338176, -0.041537926210367004, -0.06913113308655738, 0.31768173105228925, -0.0670838352220926, -0.26444410675321706, 0.12198467272537528, -0.03720739306663745, -0.11738038644034532, 0.2014455573298619, 0.17842073645442724, 0.2825838262360776, -0.19231105987091723, 0.15522203148384506, -0.07108129731568624, 0.12769554263377358, 0.028941666587343207, -0.09853074831698905, 0.08749057271052152, 0.14991313623971791, 0.22490955169632798, 0.19028562369385327, -0.019025958049041947, -0.12640253845893312, -0.33117997576482594, -0.12215606257814215, -0.24938306346120953, -0.042777477099662065, -0.12277305607483413, -0.18152776588317465, 0.38626292621219704, 0.08763103183446219, 0.16121452318839147, 0.11091607975595252, 0.3153629296866711, 0.1985258223794517, 0.02506687254208373, 0.15527609734999714, 0.17247122645494528, 0.10032933100592345, 0.04529196851217421, -0.26252827515418176, 0.04676745976030361, 0.19476581004380478] |
1,802.03505 | Coulomb Autoencoders | Learning the true density in high-dimensional feature spaces is a well-known
problem in machine learning. In this work, we consider generative autoencoders
based on maximum-mean discrepancy (MMD) and provide theoretical insights. In
particular, (i) we prove that MMD coupled with Coulomb kernels has optimal
convergence properties, which are similar to convex functionals, thus improving
the training of autoencoders, and (ii) we provide a probabilistic bound on the
generalization performance, highlighting some fundamental conditions to achieve
better generalization. We validate the theory on synthetic examples and on the
popular dataset of celebrities' faces, showing that our model, called Coulomb
autoencoders, outperform the state-of-the-art.
| cs.LG cs.CV cs.NE | learning the true density in highdimensional feature spaces is a wellknown problem in machine learning in this work we consider generative autoencoders based on maximummean discrepancy mmd and provide theoretical insights in particular i we prove that mmd coupled with coulomb kernels has optimal convergence properties which are similar to convex functionals thus improving the training of autoencoders and ii we provide a probabilistic bound on the generalization performance highlighting some fundamental conditions to achieve better generalization we validate the theory on synthetic examples and on the popular dataset of celebrities faces showing that our model called coulomb autoencoders outperform the stateoftheart | [['learning', 'the', 'true', 'density', 'in', 'highdimensional', 'feature', 'spaces', 'is', 'a', 'wellknown', 'problem', 'in', 'machine', 'learning', 'in', 'this', 'work', 'we', 'consider', 'generative', 'autoencoders', 'based', 'on', 'maximummean', 'discrepancy', 'mmd', 'and', 'provide', 'theoretical', 'insights', 'in', 'particular', 'i', 'we', 'prove', 'that', 'mmd', 'coupled', 'with', 'coulomb', 'kernels', 'has', 'optimal', 'convergence', 'properties', 'which', 'are', 'similar', 'to', 'convex', 'functionals', 'thus', 'improving', 'the', 'training', 'of', 'autoencoders', 'and', 'ii', 'we', 'provide', 'a', 'probabilistic', 'bound', 'on', 'the', 'generalization', 'performance', 'highlighting', 'some', 'fundamental', 'conditions', 'to', 'achieve', 'better', 'generalization', 'we', 'validate', 'the', 'theory', 'on', 'synthetic', 'examples', 'and', 'on', 'the', 'popular', 'dataset', 'of', 'celebrities', 'faces', 'showing', 'that', 'our', 'model', 'called', 'coulomb', 'autoencoders', 'outperform', 'the', 'stateoftheart']] | [0.014269538601805078, -0.02939762243318676, -0.09593481258811927, 0.1431816106542281, -0.09973535605355877, -0.18531771394248941, 0.02957758061601642, 0.42949848741034763, -0.2503149257834521, -0.28159566910125977, 0.03486259239218613, -0.2838365090319352, -0.23860720776500974, 0.21725243830472452, -0.1733388941052674, 0.08577715505910392, 0.136838835060117, 0.038282564108716416, -0.09754585471963233, -0.3378172627774685, 0.3182137256677235, 0.02825687794328326, 0.357939335342386, 0.0597075651823958, 0.11945817985175418, -0.041981523768921, 0.02276299390849648, 0.005250025817220754, -0.14935820350855114, 0.21024805602115015, 0.25426850102768084, 0.186208885446615, 0.3477899009122117, -0.4048131218014082, -0.23501603464863383, 0.11697501730690203, 0.11285112466718449, 0.08086807398231571, -0.06850009795398183, -0.31375532143629425, 0.06404414293955606, -0.09156033800140319, -0.009448055634367288, -0.17563135717709613, -0.06935834936281242, 0.03068442118819803, -0.31976478556078847, 0.05525162529274084, 0.11795876154187365, 0.04323402834315047, -0.07021943463178554, -0.17145940173177585, 0.05674947696129358, 0.07622212052603464, 0.0371634594930133, 0.048871671264567, 0.05726480507522379, -0.14751034232587232, -0.16668231000588968, 0.325602809133211, -0.07820288726303837, -0.2406961776288503, 0.21656417989835955, -0.03421794335901885, -0.17900590301551256, 0.03458989224866918, 0.25417109050483694, 0.1092793517561611, -0.11710230737813923, 0.08441256788745984, -0.09744961118662158, 0.10053287472317715, 0.03115545701590542, 0.050096220295359886, 0.11135135567286006, 0.27464839020089277, 0.06316858662827181, 0.1518109724810808, -0.10402678076343813, -0.1376370725372926, -0.24048093668909024, -0.11777259862928716, -0.22017464162505204, 0.010270897308272301, -0.13461847912642924, -0.16209586297558382, 0.37199235833215405, 0.23618529672909638, 0.19925609560646484, 0.13506013528549915, 0.30128758304780073, 0.06356880459404264, 0.049150414266946295, 0.1189367154401054, 0.22480505252099078, 0.1168296279404128, 0.043121405296248964, -0.18550426742329382, 0.05705765425342845, 0.09997957095383934] |
1,802.03506 | Equivalence of edge bicolored graphs on surfaces | Consider the collection of edge bicolorings of a graph that is cellularly
embedded on an orientable surface. In this work, we count the number of
equivalence classes of such colorings under two relations: reversing colors
around a face and reversing colors around a vertex. In the case of the plane,
this is well studied, but for other surfaces, the computation is more subtle.
While this question can be stated purely graph theoretically, it has
interesting applications in knot theory.
| math.GT math.CO | consider the collection of edge bicolorings of a graph that is cellularly embedded on an orientable surface in this work we count the number of equivalence classes of such colorings under two relations reversing colors around a face and reversing colors around a vertex in the case of the plane this is well studied but for other surfaces the computation is more subtle while this question can be stated purely graph theoretically it has interesting applications in knot theory | [['consider', 'the', 'collection', 'of', 'edge', 'bicolorings', 'of', 'a', 'graph', 'that', 'is', 'cellularly', 'embedded', 'on', 'an', 'orientable', 'surface', 'in', 'this', 'work', 'we', 'count', 'the', 'number', 'of', 'equivalence', 'classes', 'of', 'such', 'colorings', 'under', 'two', 'relations', 'reversing', 'colors', 'around', 'a', 'face', 'and', 'reversing', 'colors', 'around', 'a', 'vertex', 'in', 'the', 'case', 'of', 'the', 'plane', 'this', 'is', 'well', 'studied', 'but', 'for', 'other', 'surfaces', 'the', 'computation', 'is', 'more', 'subtle', 'while', 'this', 'question', 'can', 'be', 'stated', 'purely', 'graph', 'theoretically', 'it', 'has', 'interesting', 'applications', 'in', 'knot', 'theory']] | [-0.18088362125025567, 0.11457859027612795, -0.07908131123249289, 0.06195300380711125, -0.09147398588778097, -0.12486829540892681, 0.028101588073304467, 0.4083087649667942, -0.27857032026270334, -0.3373491675180348, 0.09049544239540502, -0.29305295052030417, -0.17564885202748112, 0.177607679403611, -0.1742943126757635, -0.007978208999656424, 0.055094428938117966, 0.06620090521129343, -0.011172050176942838, -0.2688344379340275, 0.31676025675090996, -0.06874896917798662, 0.20569094936540233, 0.07875288121293217, 0.03247375872363396, 0.03893047233577818, 0.002248284707576791, 0.10427579579615517, -0.1376354762304558, 0.10990869062083267, 0.2566209266645999, 0.10118564231839927, 0.18846281429552297, -0.39575314825943, -0.20332267992434244, 0.16176464992775758, 0.12369788072626048, 0.07452714589094342, -0.04592239673936863, -0.22206646757778017, 0.10751096780789143, -0.11698688519529149, -0.11440683772811029, 0.0052452279502360885, 0.09692879170886701, -0.04472359200966061, -0.16350124156267581, -0.02561691055616623, 0.12157571750922906, 0.09564096330889041, 0.03811041935040509, -0.07300140211193622, -0.04439094678286604, 0.13627010074302648, -4.7689065030669865e-06, 0.06782334554303862, 0.06880727795223836, -0.17790459202826495, -0.15155491065469723, 0.40186901968208294, -0.004061523907452445, -0.207824502423217, 0.18359650624326512, -0.11349047884297898, -0.18799329797819822, 0.10011151549701072, 0.1224120332258223, 0.16609762158669247, -0.12374911814503654, 0.12818436768964003, -0.1417874177929627, 0.10808312640042056, 0.12970804533909394, 0.013061136579070287, 0.21671445677764242, 0.15056154388297774, 0.13170004527002008, 0.18922370609671138, -0.028244120637662237, -0.03376097630567943, -0.2824011539474507, -0.1593568683896638, -0.20223311482946377, 0.04859714531870205, -0.10058172616523343, -0.17783057945485733, 0.43924510731255706, 0.09030881716834405, 0.2035423648201778, 0.04958688253772598, 0.27449500089204765, 0.1020033018445431, 0.062390558309617296, 0.11284164250297826, 0.16051640385125257, 0.14763389466495452, -0.029063896396892924, -0.15636328158935509, 0.04473686963909223, 0.10820993239979577] |
1,802.03507 | A bijection between necklaces and multisets with divisible subset sum | Consider these two distinct combinatorial objects: (1) the necklaces of
length $n$ with at most $q$ colors, and (2) the multisets of integers modulo
$n$ with subset sum divisible by $n$ and with the multiplicity of each element
being strictly less than $q$. We show that these two objects have the same
cardinality when $q$ and $n$ are mutually coprime. Additionally, when $q$ is a
prime power, we construct a bijection between these two objects by viewing
necklaces as cyclic polynomials over the finite field of size $q$. Specializing
to $q=2$ answers a bijective problem posed by Richard Stanley.
| math.CO | consider these two distinct combinatorial objects 1 the necklaces of length n with at most q colors and 2 the multisets of integers modulo n with subset sum divisible by n and with the multiplicity of each element being strictly less than q we show that these two objects have the same cardinality when q and n are mutually coprime additionally when q is a prime power we construct a bijection between these two objects by viewing necklaces as cyclic polynomials over the finite field of size q specializing to q2 answers a bijective problem posed by richard stanley | [['consider', 'these', 'two', 'distinct', 'combinatorial', 'objects', '1', 'the', 'necklaces', 'of', 'length', 'n', 'with', 'at', 'most', 'q', 'colors', 'and', '2', 'the', 'multisets', 'of', 'integers', 'modulo', 'n', 'with', 'subset', 'sum', 'divisible', 'by', 'n', 'and', 'with', 'the', 'multiplicity', 'of', 'each', 'element', 'being', 'strictly', 'less', 'than', 'q', 'we', 'show', 'that', 'these', 'two', 'objects', 'have', 'the', 'same', 'cardinality', 'when', 'q', 'and', 'n', 'are', 'mutually', 'coprime', 'additionally', 'when', 'q', 'is', 'a', 'prime', 'power', 'we', 'construct', 'a', 'bijection', 'between', 'these', 'two', 'objects', 'by', 'viewing', 'necklaces', 'as', 'cyclic', 'polynomials', 'over', 'the', 'finite', 'field', 'of', 'size', 'q', 'specializing', 'to', 'q2', 'answers', 'a', 'bijective', 'problem', 'posed', 'by', 'richard', 'stanley']] | [-0.18882656583769453, 0.21082298975023958, -0.012575609299043814, 0.018410492242746657, -0.04765716821397419, -0.2063947264058045, -0.004452058988990206, 0.3614101638932797, -0.3021113858946289, -0.3038138927459115, 0.022637676311224098, -0.3024390662521726, -0.0762155230857921, 0.15600833175421663, -0.0906596306353958, -0.01249637795409959, 0.004707953269414679, 0.07928471263460439, -0.011683077921763397, -0.35908708174129705, 0.3783994146006539, -0.08188099796987242, 0.13822133577607496, -0.037658477757088464, 0.07556989229745184, 0.03325580788386463, -0.03273739805974236, 0.05126104137455725, -0.14833823789060738, 0.08765250295953299, 0.3175662545618987, 0.1311581756618589, 0.2469577258734992, -0.33497645298367124, -0.08190992718205006, 0.2079820583358106, 0.1706178132068328, -0.02790048551002536, 0.0054148903323544394, -0.16957902428317778, 0.1860915938923794, -0.14816154350968774, -0.09788991635514781, -0.010746613222484788, 0.16360327536169667, 0.06333678979144404, -0.3062293675041409, -0.042451468738492086, 0.1078506292295501, 0.13132252583204948, 0.01257583230846759, -0.2467285034792366, 0.014845554881512818, 0.08166975394712121, 0.029877112671554164, 0.04808340509859563, -0.002873933053871786, -0.09926018002207833, -0.15790187360511887, 0.35594522604963397, 0.052635094199554214, -0.21809991571412543, 0.12193619574636522, -0.20634864230941266, -0.10306925993445659, 0.14615956308188463, 0.07520167724314061, 0.17471601946086557, 0.003832173962357708, 0.12180707000979138, -0.16861534200025477, 0.16613097628696136, 0.2052752610361862, 0.019191848739718246, 0.19733906911704877, 0.02605018891262436, 0.05233771029643414, 0.1504278281760035, 0.003080102624465721, 0.017829409527658213, -0.31993778447874566, -0.11610308082567321, -0.20091941816299552, 0.12186874587540374, -0.18666967169591578, -0.1305006750003256, 0.3092183104499873, 0.0729563000960031, 0.24802262693963417, 0.1514549985612658, 0.2471246040153383, 0.04691013927698474, 0.032850474965843285, 0.09072050826172486, 0.04484585409447776, 0.14856325645314886, -0.07976504763931676, -0.15720615998343235, -0.027149135971234903, 0.17996144104711334] |
1,802.03508 | Interfacial properties of black phosphorus/transition metal carbide van
der Waals heterostructures | Owing to its outstanding electronic properties, black phosphorus (BP) is
considered as a promising material for next-generation optoelectronic devices.
In this work, devices based on BP/MXene (Zrn+1CnT2, T = O, F, OH, n = 1, 2) van
der Waals (vdW) heterostructures are designed via first-principles
calculations. Zrn+1CnT2 compositions with appropriate work functions lead to
the formation of Ohmic contact with BP in the vertical direction. Low Schottky
barriers are found along the lateral direction in BP/Zr2CF2, BP/Zr2CO2H2,
BP/Zr3C2F2, and BP/Zr3C2O2H2 bilayers, and BP/Zr3C2O2 even exhibits Ohmic
contact behavior. BP/Zr2CO2 is a semiconducting heterostructure with type-II
band alignment, which facilitates the separation of electron-hole pairs. The
band structure of BP/Zr2CO2 can be effectively tuned via a perpendicular
electric field, and BP is predicted to undergo a transition from donor to
acceptor at a 0.4 V/{\AA} electric field. The versatile electronic properties
of the BP/MXene heterostructures examined in this work highlight their
promising potential for applications in electronics.
| cond-mat.mtrl-sci | owing to its outstanding electronic properties black phosphorus bp is considered as a promising material for nextgeneration optoelectronic devices in this work devices based on bpmxene zrn1cnt2 t o f oh n 1 2 van der waals vdw heterostructures are designed via firstprinciples calculations zrn1cnt2 compositions with appropriate work functions lead to the formation of ohmic contact with bp in the vertical direction low schottky barriers are found along the lateral direction in bpzr2cf2 bpzr2co2h2 bpzr3c2f2 and bpzr3c2o2h2 bilayers and bpzr3c2o2 even exhibits ohmic contact behavior bpzr2co2 is a semiconducting heterostructure with typeii band alignment which facilitates the separation of electronhole pairs the band structure of bpzr2co2 can be effectively tuned via a perpendicular electric field and bp is predicted to undergo a transition from donor to acceptor at a 04 vaa electric field the versatile electronic properties of the bpmxene heterostructures examined in this work highlight their promising potential for applications in electronics | [['owing', 'to', 'its', 'outstanding', 'electronic', 'properties', 'black', 'phosphorus', 'bp', 'is', 'considered', 'as', 'a', 'promising', 'material', 'for', 'nextgeneration', 'optoelectronic', 'devices', 'in', 'this', 'work', 'devices', 'based', 'on', 'bpmxene', 'zrn1cnt2', 't', 'o', 'f', 'oh', 'n', '1', '2', 'van', 'der', 'waals', 'vdw', 'heterostructures', 'are', 'designed', 'via', 'firstprinciples', 'calculations', 'zrn1cnt2', 'compositions', 'with', 'appropriate', 'work', 'functions', 'lead', 'to', 'the', 'formation', 'of', 'ohmic', 'contact', 'with', 'bp', 'in', 'the', 'vertical', 'direction', 'low', 'schottky', 'barriers', 'are', 'found', 'along', 'the', 'lateral', 'direction', 'in', 'bpzr2cf2', 'bpzr2co2h2', 'bpzr3c2f2', 'and', 'bpzr3c2o2h2', 'bilayers', 'and', 'bpzr3c2o2', 'even', 'exhibits', 'ohmic', 'contact', 'behavior', 'bpzr2co2', 'is', 'a', 'semiconducting', 'heterostructure', 'with', 'typeii', 'band', 'alignment', 'which', 'facilitates', 'the', 'separation', 'of', 'electronhole', 'pairs', 'the', 'band', 'structure', 'of', 'bpzr2co2', 'can', 'be', 'effectively', 'tuned', 'via', 'a', 'perpendicular', 'electric', 'field', 'and', 'bp', 'is', 'predicted', 'to', 'undergo', 'a', 'transition', 'from', 'donor', 'to', 'acceptor', 'at', 'a', '04', 'vaa', 'electric', 'field', 'the', 'versatile', 'electronic', 'properties', 'of', 'the', 'bpmxene', 'heterostructures', 'examined', 'in', 'this', 'work', 'highlight', 'their', 'promising', 'potential', 'for', 'applications', 'in', 'electronics']] | [-0.17837631510459928, 0.1117265083911744, -0.008008654300576089, -0.013548688331319319, -0.032307624916213896, -0.22091878285644562, 0.07532313660151892, 0.4578371652030757, -0.2666201830607142, -0.322984012074596, -0.06234958425076301, -0.2925052709089449, -0.17684225841764023, 0.22133962474795957, 0.02892342151526909, 0.025313009657340214, 0.009206193255039153, -0.10929057395111744, -0.09736594383709028, -0.16672216801109588, 0.24710106191883138, 0.05906407906872487, 0.3058609675853741, 0.10900196552209855, 0.005189406744145847, -0.013029565438758451, 0.153373873900049, 0.003083248290565464, -0.14973112324682566, 0.11039813298087281, 0.27145899755398295, -0.1525032784060373, 0.2626704396761407, -0.4712323656930894, -0.20425423670403817, -0.03769319142879254, 0.1464170480173055, 0.13384612071265994, -0.12646352616708623, -0.2454205128312163, 0.13362818089854364, -0.14620480827752735, -0.0989597218395683, -0.032297996752016195, 0.015187699302145235, 0.06378199661417597, -0.22196398610843449, 0.06434608114251568, 0.03228761841888194, 0.03045641620274246, -0.0841258306911466, -0.14113809682682835, -0.09242613810290407, 0.06646439338663032, 0.008515324507077987, 0.06923041355770107, 0.2488954185371424, -0.12651396815590168, -0.08202120711558036, 0.3858138025523378, -0.04709926944623304, -0.10786980917429799, 0.19268254307424323, -0.13221365158687104, -0.04934533083668122, 0.13288745499652374, 0.12635939596285978, 0.11800035232209138, -0.1525920188104102, 0.12303686013402111, 0.08820460177042157, 0.12495025637123239, 0.07318108685321167, 0.0897544766613445, 0.3102287583857436, 0.1866297944533554, 0.037931670828794055, 0.10747816342245006, -0.11405705590406759, -0.0033032317447209067, -0.16058824594308446, -0.228372453965924, -0.20366110544573593, 0.1206800730319342, -0.07016123002596522, -0.22825620052489368, 0.39540389980599805, 0.12120297793754851, 0.16518177258150352, -0.06586908903282292, 0.25317373560982515, 0.08668756678820932, 0.11279307405620657, 0.011143590216166697, 0.2874925769474748, 0.19128885976253793, 0.1324408816640148, -0.23140124413933788, 0.08863985125900253, -0.020555846581977982] |
1,802.03509 | Levy-Steinitz for countable sets of series | The Levy-Steinitz theorem characterizes the values that a conditionally
convergent sequence in of real numbers can attain under permutations. We extend
this analysis to sequences of countable sequences of real numbers, under
pointwise convergence, reproving a theorem of Stanimir Troyanski.
| math.CA | the levysteinitz theorem characterizes the values that a conditionally convergent sequence in of real numbers can attain under permutations we extend this analysis to sequences of countable sequences of real numbers under pointwise convergence reproving a theorem of stanimir troyanski | [['the', 'levysteinitz', 'theorem', 'characterizes', 'the', 'values', 'that', 'a', 'conditionally', 'convergent', 'sequence', 'in', 'of', 'real', 'numbers', 'can', 'attain', 'under', 'permutations', 'we', 'extend', 'this', 'analysis', 'to', 'sequences', 'of', 'countable', 'sequences', 'of', 'real', 'numbers', 'under', 'pointwise', 'convergence', 'reproving', 'a', 'theorem', 'of', 'stanimir', 'troyanski']] | [-0.21806148745310613, 0.13285131683238807, -0.14981279858889487, 0.1059183278091204, -0.01145520147222739, -0.06066940137400077, 0.11563939016643697, 0.30704355134795874, -0.2940747970953966, -0.17587714432141718, 0.1015899014653256, -0.20065049884411004, -0.12464972907820573, 0.27956992076285997, -0.20088635112803715, 0.051091535972470105, 0.09938874050306204, 0.06978604321678479, -0.06156665673598838, -0.3051098934732951, 0.32260992356504387, -0.08733191649214579, 0.25184329408101547, 0.0019410732411182462, 0.0909003351504604, 0.02379121448701391, 0.012405280023813248, -0.03335381834170757, -0.12960451990101063, 0.10817604354367806, 0.258186304798493, 0.1757474946192442, 0.3284190222620964, -0.29896690223652583, -0.08803848473307414, 0.23695714413546598, 0.12756970472251758, 0.013446207946309676, 0.002939875404804181, -0.2781005691593656, 0.1925456646638803, -0.09332090574734582, -0.1578554500801823, -0.15967589736175844, -0.009826978764090782, 0.1414497019484257, -0.35820924270993626, 0.05441961038666658, 0.16688871980668643, 0.11365333951723117, -0.06394953058602718, -0.0780657617470775, 0.011762152043863749, 0.11912108267633578, 0.09075056919111656, -0.024956010282039642, 0.05077947357382912, 0.01409015060068132, -0.14972732975505865, 0.2903065344748589, -0.06533283420289174, -0.24600034445906296, 0.10154991350972499, -0.19572248882972276, -0.2363570689295347, 0.1539586653264287, 0.09177812067075418, 0.19086256976693106, -0.0247029533657508, 0.1269505983696152, -0.16466455400372162, 0.1482762597883359, 0.1908849023688489, 0.021853716244013645, 0.10773872605596597, 0.07845083679048678, 0.10396953789182962, 0.1834266665033423, 0.013668653483574208, -0.08233087452558371, -0.30747701313633186, -0.14971130785460657, -0.20887755576330116, 0.11975441885312112, -0.14593624108088896, -0.2236521483804935, 0.353880690697294, 0.1389887905202042, 0.15110386065685022, 0.24471660638944462, 0.22599223289543238, 0.09228630705426137, -0.010374071595903773, 0.007857098100850215, 0.05849080378762805, 0.2650959308330829, 0.025598927854727477, -0.08237713584915185, 0.04774067667163669, 0.18292925260865536] |
1,802.0351 | On-device Scalable Image-based Localization via Prioritized Cascade
Search and Fast One-Many RANSAC | We present the design of an entire on-device system for large-scale urban
localization using images. The proposed design integrates compact image
retrieval and 2D-3D correspondence search to estimate the location in extensive
city regions. Our design is GPS agnostic and does not require network
connection. In order to overcome the resource constraints of mobile devices, we
propose a system design that leverages the scalability advantage of image
retrieval and accuracy of 3D model-based localization. Furthermore, we propose
a new hashing-based cascade search for fast computation of 2D-3D
correspondences. In addition, we propose a new one-many RANSAC for accurate
pose estimation. The new one-many RANSAC addresses the challenge of repetitive
building structures (e.g. windows, balconies) in urban localization. Extensive
experiments demonstrate that our 2D-3D correspondence search achieves
state-of-the-art localization accuracy on multiple benchmark datasets.
Furthermore, our experiments on a large Google Street View (GSV) image dataset
show the potential of large-scale localization entirely on a typical mobile
device.
| cs.CV | we present the design of an entire ondevice system for largescale urban localization using images the proposed design integrates compact image retrieval and 2d3d correspondence search to estimate the location in extensive city regions our design is gps agnostic and does not require network connection in order to overcome the resource constraints of mobile devices we propose a system design that leverages the scalability advantage of image retrieval and accuracy of 3d modelbased localization furthermore we propose a new hashingbased cascade search for fast computation of 2d3d correspondences in addition we propose a new onemany ransac for accurate pose estimation the new onemany ransac addresses the challenge of repetitive building structures eg windows balconies in urban localization extensive experiments demonstrate that our 2d3d correspondence search achieves stateoftheart localization accuracy on multiple benchmark datasets furthermore our experiments on a large google street view gsv image dataset show the potential of largescale localization entirely on a typical mobile device | [['we', 'present', 'the', 'design', 'of', 'an', 'entire', 'ondevice', 'system', 'for', 'largescale', 'urban', 'localization', 'using', 'images', 'the', 'proposed', 'design', 'integrates', 'compact', 'image', 'retrieval', 'and', '2d3d', 'correspondence', 'search', 'to', 'estimate', 'the', 'location', 'in', 'extensive', 'city', 'regions', 'our', 'design', 'is', 'gps', 'agnostic', 'and', 'does', 'not', 'require', 'network', 'connection', 'in', 'order', 'to', 'overcome', 'the', 'resource', 'constraints', 'of', 'mobile', 'devices', 'we', 'propose', 'a', 'system', 'design', 'that', 'leverages', 'the', 'scalability', 'advantage', 'of', 'image', 'retrieval', 'and', 'accuracy', 'of', '3d', 'modelbased', 'localization', 'furthermore', 'we', 'propose', 'a', 'new', 'hashingbased', 'cascade', 'search', 'for', 'fast', 'computation', 'of', '2d3d', 'correspondences', 'in', 'addition', 'we', 'propose', 'a', 'new', 'onemany', 'ransac', 'for', 'accurate', 'pose', 'estimation', 'the', 'new', 'onemany', 'ransac', 'addresses', 'the', 'challenge', 'of', 'repetitive', 'building', 'structures', 'eg', 'windows', 'balconies', 'in', 'urban', 'localization', 'extensive', 'experiments', 'demonstrate', 'that', 'our', '2d3d', 'correspondence', 'search', 'achieves', 'stateoftheart', 'localization', 'accuracy', 'on', 'multiple', 'benchmark', 'datasets', 'furthermore', 'our', 'experiments', 'on', 'a', 'large', 'google', 'street', 'view', 'gsv', 'image', 'dataset', 'show', 'the', 'potential', 'of', 'largescale', 'localization', 'entirely', 'on', 'a', 'typical', 'mobile', 'device']] | [-0.13111883344330416, -0.06611463830242405, -0.04734030965668191, 0.03571977140678532, -0.07926970420873895, -0.1466535914208811, 0.05149439641476199, 0.44753012004171966, -0.22192419796683796, -0.3565190129076979, 0.10772277467296856, -0.24118962134961275, -0.21972941329508464, 0.22517245553380508, -0.1558841977127202, 0.11605972415696426, 0.16005801201047626, -0.0034785768963325366, -0.09531894000098821, -0.21378515327978095, 0.26196990786031316, 0.04084899948292952, 0.3944703026668756, 0.05883080567749348, 0.12077507239641846, 0.003039742493705385, -0.05131068130492405, -0.021804309652519123, -0.04839266002429189, 0.21113062048313108, 0.29610493352523987, 0.21536051101128387, 0.2772016418522378, -0.40083776815396965, -0.23349429482511083, 0.05453568491135623, 0.17787886218338014, 0.08782731451082658, -0.12769783494006368, -0.3895680544316579, 0.109851881665931, -0.15966008395359965, -0.0021425533748141365, -0.1445961557328701, -0.027346881414217886, -0.024512649561237, -0.3029958396218717, 0.025453735527784155, 0.01584586471609866, 0.06123804484915202, -0.048471276667343514, -0.046826075491121714, 0.08580023106513841, 0.19342568364872295, -0.05871966079835465, 0.02509103214463136, 0.16652886302856978, -0.19467047082592442, -0.18179192015408852, 0.3868176904477321, -0.018083822475242053, -0.16698622083955794, 0.2243098279203223, -0.02750352263201479, -0.1526934981559682, 0.11374988626380256, 0.2669661241883092, 0.12482493807810839, -0.1494077437610668, 0.030287910761848495, -0.07302109448084976, 0.21607418633866937, 0.01183007051575051, -0.0034189973364637536, 0.18220563146291643, 0.30389063769453173, 0.11550384546647881, 0.14150253947005625, -0.2213770381610983, -0.05915088058452294, -0.22294666070326463, -0.14060649280590198, -0.20353313504457238, -0.04376778813740046, -0.13186632013691746, -0.17780982655588728, 0.4144465842041051, 0.31187275460191594, 0.19083390456096383, 0.09264272313185368, 0.3657818520738251, -0.01579354949233235, 0.08148709373774042, 0.08349068883871719, 0.16425399544536118, -0.06970147475612107, 0.1412271701224344, -0.18814955093559754, 0.029548340420695436, 0.07743248030703491] |
1,802.03511 | A General Framework For Frequentist Model Averaging | Model selection strategies have been routinely employed to determine a model
for data analysis in statistics, and further study and inference then often
proceed as though the selected model were the true model that were known a
priori. This practice does not account for the uncertainty introduced by the
selection process and the fact that the selected model can possibly be a wrong
one. Model averaging approaches try to remedy this issue by combining
estimators for a set of candidate models. Specifically, instead of deciding
which model is the 'right' one, a model averaging approach suggests to fit a
set of candidate models and average over the estimators using certain data
adaptive weights. In this paper we establish a general frequentist model
averaging framework that does not set any restrictions on the set of candidate
models. It greatly broadens the scope of the existing methodologies under the
frequentist model averaging development. Assuming the data is from an unknown
model, we derive the model averaging estimator and study its limiting
distributions and related predictions while taking possible modeling biases
into account. We propose a set of optimal weights to combine the individual
estimators so that the expected mean squared error of the average estimator is
minimized. Simulation studies are conducted to compare the performance of the
estimator with that of the existing methods. The results show the benefits of
the proposed approach over traditional model selection approaches as well as
existing model averaging methods.
| stat.ME | model selection strategies have been routinely employed to determine a model for data analysis in statistics and further study and inference then often proceed as though the selected model were the true model that were known a priori this practice does not account for the uncertainty introduced by the selection process and the fact that the selected model can possibly be a wrong one model averaging approaches try to remedy this issue by combining estimators for a set of candidate models specifically instead of deciding which model is the right one a model averaging approach suggests to fit a set of candidate models and average over the estimators using certain data adaptive weights in this paper we establish a general frequentist model averaging framework that does not set any restrictions on the set of candidate models it greatly broadens the scope of the existing methodologies under the frequentist model averaging development assuming the data is from an unknown model we derive the model averaging estimator and study its limiting distributions and related predictions while taking possible modeling biases into account we propose a set of optimal weights to combine the individual estimators so that the expected mean squared error of the average estimator is minimized simulation studies are conducted to compare the performance of the estimator with that of the existing methods the results show the benefits of the proposed approach over traditional model selection approaches as well as existing model averaging methods | [['model', 'selection', 'strategies', 'have', 'been', 'routinely', 'employed', 'to', 'determine', 'a', 'model', 'for', 'data', 'analysis', 'in', 'statistics', 'and', 'further', 'study', 'and', 'inference', 'then', 'often', 'proceed', 'as', 'though', 'the', 'selected', 'model', 'were', 'the', 'true', 'model', 'that', 'were', 'known', 'a', 'priori', 'this', 'practice', 'does', 'not', 'account', 'for', 'the', 'uncertainty', 'introduced', 'by', 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1,802.03512 | Quantum measurement of a rapidly rotating spin qubit in diamond | A controlled qubit in a rotating frame opens new opportunities to probe
fundamental quantum physics, such as geometric phases in physically rotating
frames, and can potentially enhance detection of magnetic fields. Realising a
single qubit that can be measured and controlled during physical rotation is
experimentally challenging. In this work, we demonstrate quantum control of a
single nitrogen-vacancy (NV) centre within a diamond rotated at 200,000rpm, a
rotational period comparable to the NV spin coherence time $T_2$. We
stroboscopically image individual NV centres that execute rapid circular motion
in addition to rotation, and demonstrate preparation, control and readout of
the qubit quantum state with lasers and microwaves. Using spin-echo
interferometry of the rotating qubit, we are able to detect modulation of the
NV Zeeman shift arising from the rotating NV axis and an external DC magnetic
field. Our work establishes single NV qubits in diamond as quantum sensors in
the physically rotating frame, and paves the way for the realisation of
single-qubit diamond-based rotation sensors.
| quant-ph | a controlled qubit in a rotating frame opens new opportunities to probe fundamental quantum physics such as geometric phases in physically rotating frames and can potentially enhance detection of magnetic fields realising a single qubit that can be measured and controlled during physical rotation is experimentally challenging in this work we demonstrate quantum control of a single nitrogenvacancy nv centre within a diamond rotated at 200000rpm a rotational period comparable to the nv spin coherence time t_2 we stroboscopically image individual nv centres that execute rapid circular motion in addition to rotation and demonstrate preparation control and readout of the qubit quantum state with lasers and microwaves using spinecho interferometry of the rotating qubit we are able to detect modulation of the nv zeeman shift arising from the rotating nv axis and an external dc magnetic field our work establishes single nv qubits in diamond as quantum sensors in the physically rotating frame and paves the way for the realisation of singlequbit diamondbased rotation sensors | [['a', 'controlled', 'qubit', 'in', 'a', 'rotating', 'frame', 'opens', 'new', 'opportunities', 'to', 'probe', 'fundamental', 'quantum', 'physics', 'such', 'as', 'geometric', 'phases', 'in', 'physically', 'rotating', 'frames', 'and', 'can', 'potentially', 'enhance', 'detection', 'of', 'magnetic', 'fields', 'realising', 'a', 'single', 'qubit', 'that', 'can', 'be', 'measured', 'and', 'controlled', 'during', 'physical', 'rotation', 'is', 'experimentally', 'challenging', 'in', 'this', 'work', 'we', 'demonstrate', 'quantum', 'control', 'of', 'a', 'single', 'nitrogenvacancy', 'nv', 'centre', 'within', 'a', 'diamond', 'rotated', 'at', '200000rpm', 'a', 'rotational', 'period', 'comparable', 'to', 'the', 'nv', 'spin', 'coherence', 'time', 't_2', 'we', 'stroboscopically', 'image', 'individual', 'nv', 'centres', 'that', 'execute', 'rapid', 'circular', 'motion', 'in', 'addition', 'to', 'rotation', 'and', 'demonstrate', 'preparation', 'control', 'and', 'readout', 'of', 'the', 'qubit', 'quantum', 'state', 'with', 'lasers', 'and', 'microwaves', 'using', 'spinecho', 'interferometry', 'of', 'the', 'rotating', 'qubit', 'we', 'are', 'able', 'to', 'detect', 'modulation', 'of', 'the', 'nv', 'zeeman', 'shift', 'arising', 'from', 'the', 'rotating', 'nv', 'axis', 'and', 'an', 'external', 'dc', 'magnetic', 'field', 'our', 'work', 'establishes', 'single', 'nv', 'qubits', 'in', 'diamond', 'as', 'quantum', 'sensors', 'in', 'the', 'physically', 'rotating', 'frame', 'and', 'paves', 'the', 'way', 'for', 'the', 'realisation', 'of', 'singlequbit', 'diamondbased', 'rotation', 'sensors']] | [-0.14628763530114466, 0.22597868423903625, -0.00018342519278365482, -0.05518510885516795, -0.006100427867594834, -0.2004344787337908, 0.043526279157249076, 0.4793099499954956, -0.26109213163088096, -0.30951308498795077, 0.02715822066345093, -0.22851079665465143, -0.016178849347009572, 0.2661144283696691, -0.08124157168658269, 0.07274728870905209, 0.047118142118717246, 0.019581096167335423, -0.06734393495944238, -0.14916099509133435, 0.2020652533349831, 0.016378192552620907, 0.32060127345867817, -0.024637829914842393, 0.13724616173052842, 0.01941133259579812, 0.09369319628426527, -0.02445163511920993, -0.0695951045324874, 0.11729989734637301, 0.2812283098422241, 0.05388852798169842, 0.20190721598515754, -0.45241235120494555, -0.18555034417091165, 0.07166089866560225, 0.17267603113986096, 0.2710013099287341, -0.12043044831825239, -0.35070193448762704, 0.016154955051805277, -0.13100244945572495, -0.136933165267337, -0.12003184571896293, 0.019362118090190585, -0.023909869126793815, -0.2215231989351351, 0.04605270083141854, 0.05665662411171602, 0.07783970272696646, -0.06480572549569444, 0.043370810893886697, 0.01905027738198774, 0.10502393138538138, -0.0739628193948259, 0.10485966805732105, 0.289746721226881, -0.08959814236022351, -0.20471728005923512, 0.355792090501182, -0.08007365505767577, -0.126342088375922, 0.14425852967045702, -0.18607190198120563, -0.08116531556063308, 0.03442234659238105, 0.14394560824261932, 0.15660142963297846, -0.16179937758573854, 0.016283808932313126, 0.026743932020220133, 0.19804932692985436, 0.10068587462627897, 0.1312272959763593, 0.2958900997324324, 0.1605225987441372, 0.10938678403305481, 0.17304719633155888, -0.18495842531499476, -0.0802678776686307, -0.2453923349285771, -0.17485473006708185, -0.1910986381741922, 0.12433863862803797, -0.05743824502834042, -0.08497427776455879, 0.4139939267841372, 0.1491968632908538, 0.16108882420542991, -0.09852769582038291, 0.32097743097238424, 0.06421984343934327, 0.10890014718612159, 0.027603508149296407, 0.277086773961101, 0.23152271182866904, 0.08820697959376181, -0.3464023765565532, -0.0405184627587271, -0.053759912411517626] |
1,802.03513 | The blow-up of the conformal mean curvature flow | In this paper, we introduce and study the conformal mean curvature flow of
submanifolds of higher codimension in the Euclidean space $\bbr^n$. This kind
of flow is a special case of a general modified mean curvature flow which is of
various origination. As the main result, we prove a blow-up theorem concluding
that, under the conformal mean curvature flow in $\bbr^n$, the maximum of the
square norm of the second fundamental form of any compact submanifold tends to
infinity in finite time. Furthermore, by using the idea of Andrews and Baker
for studying the mean curvature flow of submanifolds in the Euclidean space, we
also derive some more evolution formulas and inequalities which we believe to
be useful in our further study of conformal mean curvature flow. Presently,
these computations together with our main theorem are applied to provide a
direct proof of a convergence theorem concluding that the external conformal
forced mean curvature flow of a compact submanifold in $\bbr^n$ with the same
pinched condition as Andrews-Baker's will be convergent to a round point in
finite time.
| math.DG | in this paper we introduce and study the conformal mean curvature flow of submanifolds of higher codimension in the euclidean space bbrn this kind of flow is a special case of a general modified mean curvature flow which is of various origination as the main result we prove a blowup theorem concluding that under the conformal mean curvature flow in bbrn the maximum of the square norm of the second fundamental form of any compact submanifold tends to infinity in finite time furthermore by using the idea of andrews and baker for studying the mean curvature flow of submanifolds in the euclidean space we also derive some more evolution formulas and inequalities which we believe to be useful in our further study of conformal mean curvature flow presently these computations together with our main theorem are applied to provide a direct proof of a convergence theorem concluding that the external conformal forced mean curvature flow of a compact submanifold in bbrn with the same pinched condition as andrewsbakers will be convergent to a round point in finite time | [['in', 'this', 'paper', 'we', 'introduce', 'and', 'study', 'the', 'conformal', 'mean', 'curvature', 'flow', 'of', 'submanifolds', 'of', 'higher', 'codimension', 'in', 'the', 'euclidean', 'space', 'bbrn', 'this', 'kind', 'of', 'flow', 'is', 'a', 'special', 'case', 'of', 'a', 'general', 'modified', 'mean', 'curvature', 'flow', 'which', 'is', 'of', 'various', 'origination', 'as', 'the', 'main', 'result', 'we', 'prove', 'a', 'blowup', 'theorem', 'concluding', 'that', 'under', 'the', 'conformal', 'mean', 'curvature', 'flow', 'in', 'bbrn', 'the', 'maximum', 'of', 'the', 'square', 'norm', 'of', 'the', 'second', 'fundamental', 'form', 'of', 'any', 'compact', 'submanifold', 'tends', 'to', 'infinity', 'in', 'finite', 'time', 'furthermore', 'by', 'using', 'the', 'idea', 'of', 'andrews', 'and', 'baker', 'for', 'studying', 'the', 'mean', 'curvature', 'flow', 'of', 'submanifolds', 'in', 'the', 'euclidean', 'space', 'we', 'also', 'derive', 'some', 'more', 'evolution', 'formulas', 'and', 'inequalities', 'which', 'we', 'believe', 'to', 'be', 'useful', 'in', 'our', 'further', 'study', 'of', 'conformal', 'mean', 'curvature', 'flow', 'presently', 'these', 'computations', 'together', 'with', 'our', 'main', 'theorem', 'are', 'applied', 'to', 'provide', 'a', 'direct', 'proof', 'of', 'a', 'convergence', 'theorem', 'concluding', 'that', 'the', 'external', 'conformal', 'forced', 'mean', 'curvature', 'flow', 'of', 'a', 'compact', 'submanifold', 'in', 'bbrn', 'with', 'the', 'same', 'pinched', 'condition', 'as', 'andrewsbakers', 'will', 'be', 'convergent', 'to', 'a', 'round', 'point', 'in', 'finite', 'time']] | [-0.14654602418919382, 0.10425058293937689, -0.1303700956630739, 0.06723524088376744, -0.08457114040262281, -0.1028706042416983, -0.03133365836952771, 0.32682142338825987, -0.2620069534730924, -0.20388639834051753, 0.13928394754796083, -0.23737156350234007, -0.16111770187146793, 0.1850878268988056, -0.12652436637746808, 0.04906961742241336, 0.042525418409235814, 0.09304498886774389, -0.09216937608852467, -0.2702659398730381, 0.3762789853285514, -0.002825098880435114, 0.22278162355845174, 0.11343032841164208, 0.0944403670481564, -0.02083754131707263, -0.012656773316889665, 0.060023180227845085, -0.20481624278559885, 0.1421366778128691, 0.21295904437748261, 0.07940749033080614, 0.2807169826096187, -0.39453834956143535, -0.22869293735294188, 0.14507926605368232, 0.14283344269622286, 0.06157620965981972, -0.028287058853097707, -0.27132726032786747, 0.1088293150118631, -0.07680624136227672, -0.2099917902924268, -0.0640236683042541, -0.010489962674745773, 0.02623314778833508, -0.21367603094622198, 0.08909279877161423, 0.13781512689896422, 0.08274577695437008, -0.07979249205482376, -0.06870748106644116, -0.0276940123310923, 0.10632942544854125, 0.08555727191975618, 0.07686781883798803, 0.08883609692171468, -0.06703025365437744, -0.07086719237373205, 0.35222309522625417, -0.1236954364310003, -0.2728425896684455, 0.0965476511940781, -0.17456134728361236, -0.14353051065798786, 0.09716993137223298, 0.19993334887338812, 0.17205992141622775, -0.1066910504984359, 0.09091341316517138, -0.055353452598854984, 0.07146417800061494, 0.10078302453308877, -0.025601723832196666, 0.13647014974737487, 0.10832196791356398, 0.1599673261329279, 0.17214954496982196, -0.04818159719602873, -0.110436964893219, -0.41325718451733306, -0.2487491086706793, -0.18118006315629528, 0.13937423970960713, -0.17068285733817531, -0.1728381961373623, 0.3702725759525862, 0.07485728657582122, 0.19068856105881896, 0.12764863015578712, 0.27651757932147064, 0.09802978761799339, 0.05182909289122385, 0.1308361961622065, 0.22561672380628817, 0.19936646282341394, 0.07777990506814614, -0.15713480139025593, -0.04013985399542229, 0.1492929292874316] |
1,802.03514 | Angle-dependent magnetoresistance as a probe of Fermi surface warping in
HgBa$_2$CuO$_{4+\delta}$ | We develop a model for the angle-dependent magnetoresistance of
HgBa$_2$CuO$_{4+\delta}$ in the underdoped regime where the Fermi surface is
thought to be reconstructed by an ordered state such as a charge density wave.
We show that such measurements can be employed to unambiguously distinguish the
form of the Fermi surface's interlayer warping, placing severe contraints on
the symmetry and nature of the reconstructing order. We describe experimentally
accessible conditions in which our calculations can be put to the test.
| cond-mat.supr-con | we develop a model for the angledependent magnetoresistance of hgba_2cuo_4delta in the underdoped regime where the fermi surface is thought to be reconstructed by an ordered state such as a charge density wave we show that such measurements can be employed to unambiguously distinguish the form of the fermi surfaces interlayer warping placing severe contraints on the symmetry and nature of the reconstructing order we describe experimentally accessible conditions in which our calculations can be put to the test | [['we', 'develop', 'a', 'model', 'for', 'the', 'angledependent', 'magnetoresistance', 'of', 'hgba_2cuo_4delta', 'in', 'the', 'underdoped', 'regime', 'where', 'the', 'fermi', 'surface', 'is', 'thought', 'to', 'be', 'reconstructed', 'by', 'an', 'ordered', 'state', 'such', 'as', 'a', 'charge', 'density', 'wave', 'we', 'show', 'that', 'such', 'measurements', 'can', 'be', 'employed', 'to', 'unambiguously', 'distinguish', 'the', 'form', 'of', 'the', 'fermi', 'surfaces', 'interlayer', 'warping', 'placing', 'severe', 'contraints', 'on', 'the', 'symmetry', 'and', 'nature', 'of', 'the', 'reconstructing', 'order', 'we', 'describe', 'experimentally', 'accessible', 'conditions', 'in', 'which', 'our', 'calculations', 'can', 'be', 'put', 'to', 'the', 'test']] | [-0.14105185255691222, 0.16855941355818604, -0.1178497878858183, 0.0989301361724192, -0.0754580974712956, -0.11391112893301097, 0.05467562202977229, 0.34100620950796184, -0.2739485984290891, -0.31024209242549877, 0.060333869965228284, -0.26930150005353404, -0.14679482840944694, 0.1907272580775279, 0.011696697519266908, 0.020097961849709856, -0.020038849299867885, -0.02320676748758724, -0.11053843169968246, -0.2157872258390807, 0.31808370930626045, 0.040565634824194105, 0.2795841174459533, 0.09725084473803357, 0.043525856053102996, -0.010041476828576644, 0.09462950065661269, 0.0729026348813425, -0.15880404121312092, 0.0701248820729648, 0.2921775059974929, -0.017199271629694143, 0.13811162225548415, -0.45687230160059056, -0.23302032929385388, 0.047155037828827205, 0.12510337921808462, 0.1497608089180003, -0.0393581959386533, -0.32902196795927197, 0.056177597151055365, -0.13329617631963536, -0.12781769169871754, -0.13941204646908784, -0.06064180506369735, -0.04601248933733264, -0.2608121700903189, 0.0818415976931117, 0.010307162873376208, -0.005715901383638671, -0.08356081588853952, -0.056050252581981916, -0.05313514827019712, 0.09028804771413532, 0.04976857102275649, 0.06391191965888572, 0.112192677512258, -0.11248476815091658, -0.0758412414808062, 0.3852485771514947, -0.060415727871800246, -0.1914321028544933, 0.1705521932520161, -0.20481118051146593, -0.0951362006142264, 0.1077446324538581, 0.18468210992367962, 0.10840698596549846, -0.12932834503111207, 0.054998787755848065, -0.04979238864988578, 0.16684817013484013, 0.05688190200554702, 0.031176654427441993, 0.24160273177431354, 0.16871537867921724, 0.04218963425182089, 0.12088455892421447, -0.18623694030957036, 0.003657389992164283, -0.2888259365683115, -0.1630815789103508, -0.2348230144073833, 0.06593514324952053, -0.0051651764069787, -0.16609859268499327, 0.39858439292086734, 0.16927726447723712, 0.24768491783709842, -0.046973333498050426, 0.23278244669678844, 0.14674740642326872, 0.08588525360520882, 0.0419285528014025, 0.28491167713817256, 0.10088490278908063, 0.02019498312217331, -0.25077865117146997, 0.10761218858128295, 0.011010287193982284] |
1,802.03515 | Vehicle Pose and Shape Estimation through Multiple Monocular Vision | In this paper, we present an accurate approach to estimate vehicles' pose and
shape from off-board multiview images. The images are taken by monocular
cameras and have small overlaps. We utilize state-of-the-art convolutional
neural networks (CNNs) to extract vehicles' semantic keypoints and introduce a
Cross Projection Optimization (CPO) method to estimate the 3D pose. During the
iterative CPO process, an adaptive shape adjustment method named Hierarchical
Wireframe Constraint (HWC) is implemented to estimate the shape. Our approach
is evaluated under both simulated and real-world scenes for performance
verification. It's shown that our algorithm outperforms other existing
monocular and stereo methods for vehicles' pose and shape estimation. This
approach provides a new and robust solution for off-board visual vehicle
localization and tracking, which can be applied to massive surveillance camera
networks for intelligent transportation.
| cs.CV cs.RO | in this paper we present an accurate approach to estimate vehicles pose and shape from offboard multiview images the images are taken by monocular cameras and have small overlaps we utilize stateoftheart convolutional neural networks cnns to extract vehicles semantic keypoints and introduce a cross projection optimization cpo method to estimate the 3d pose during the iterative cpo process an adaptive shape adjustment method named hierarchical wireframe constraint hwc is implemented to estimate the shape our approach is evaluated under both simulated and realworld scenes for performance verification its shown that our algorithm outperforms other existing monocular and stereo methods for vehicles pose and shape estimation this approach provides a new and robust solution for offboard visual vehicle localization and tracking which can be applied to massive surveillance camera networks for intelligent transportation | [['in', 'this', 'paper', 'we', 'present', 'an', 'accurate', 'approach', 'to', 'estimate', 'vehicles', 'pose', 'and', 'shape', 'from', 'offboard', 'multiview', 'images', 'the', 'images', 'are', 'taken', 'by', 'monocular', 'cameras', 'and', 'have', 'small', 'overlaps', 'we', 'utilize', 'stateoftheart', 'convolutional', 'neural', 'networks', 'cnns', 'to', 'extract', 'vehicles', 'semantic', 'keypoints', 'and', 'introduce', 'a', 'cross', 'projection', 'optimization', 'cpo', 'method', 'to', 'estimate', 'the', '3d', 'pose', 'during', 'the', 'iterative', 'cpo', 'process', 'an', 'adaptive', 'shape', 'adjustment', 'method', 'named', 'hierarchical', 'wireframe', 'constraint', 'hwc', 'is', 'implemented', 'to', 'estimate', 'the', 'shape', 'our', 'approach', 'is', 'evaluated', 'under', 'both', 'simulated', 'and', 'realworld', 'scenes', 'for', 'performance', 'verification', 'its', 'shown', 'that', 'our', 'algorithm', 'outperforms', 'other', 'existing', 'monocular', 'and', 'stereo', 'methods', 'for', 'vehicles', 'pose', 'and', 'shape', 'estimation', 'this', 'approach', 'provides', 'a', 'new', 'and', 'robust', 'solution', 'for', 'offboard', 'visual', 'vehicle', 'localization', 'and', 'tracking', 'which', 'can', 'be', 'applied', 'to', 'massive', 'surveillance', 'camera', 'networks', 'for', 'intelligent', 'transportation']] | [-0.047926414928651115, -0.06483367121322396, -0.10417641514450088, 0.04550992063895978, -0.09689837314286515, -0.21535112913627935, -0.047570783481232934, 0.49629666831316355, -0.2450059459971866, -0.3703182628728393, 0.07935925507375312, -0.23718472054802386, -0.2136591861258205, 0.20444571231036707, -0.24521151113610967, 0.14660234768267483, 0.2066590793822941, 0.005399262434557865, -0.05467864102618314, -0.22429517528245269, 0.22574408813659802, 0.002192473480183827, 0.33893040549605247, 0.008815705398958932, 0.18840635557750002, 0.032205736479911026, -0.034502377981299015, 0.008271927155792377, -0.06484856362012784, 0.21492425360737888, 0.3060054971829505, 0.21717898732513413, 0.23799394060948625, -0.42720791432810457, -0.2261426700629238, 0.06018898411062231, 0.15904059433343268, 0.09067883595379587, -0.04192483707863305, -0.4418210764100453, 0.09296866496709785, -0.18139626365154982, -0.028514527983585357, -0.14714970663750082, -0.021904616914269395, -0.027578283360410006, -0.33742034330936077, 0.01516804438875638, -0.004073078525287653, 0.055432907459711224, -0.10284040451900599, -0.07443803838363156, 0.055942011013262925, 0.2200789217858162, 0.00565944244377, 0.05763616739191059, 0.18158005856159598, -0.18279128547589807, -0.11706896503350901, 0.36499079084373953, -0.005579217102490645, -0.2266555716094554, 0.1640519415189449, 0.01955332448776055, -0.1376518783040513, 0.14969819372421816, 0.26483144715456364, 0.148835731189719, -0.19044157500112696, -0.020579251785468506, -0.03526164212831317, 0.17776224842729202, 0.03062940252020507, -0.05072532419843557, 0.1662880750968212, 0.24697373292752003, 0.14462218832931853, 0.0959749662179913, -0.26404443658062055, -0.045318695898622946, -0.15468883403415853, -0.07571656123594525, -0.19913142344641283, -0.08650764507325576, -0.1077958502188759, -0.14924929994119057, 0.37438649009156943, 0.2954771314061394, 0.18642887814824743, 0.12596873028809555, 0.45442920802791315, 0.04184521758697815, 0.09684654071408891, 0.08713043598752272, 0.1962569656605041, -0.011369428691923394, 0.14654436478517333, -0.1925462500587862, 0.08216224503318283, 0.12136063121672355] |
1,802.03516 | A sequence of neighborhood contingency logics | This note proposes various axiomatizations of contingency logic under
neighborhood semantics. In particular, by defining a suitable canonical
neighborhood function, we give sound and complete axiomatizations of monotone
contingency logic and regular contingency logic, thereby answering two open
questions raised by Bakhtiari, van Ditmarsch, and Hansen. The canonical
function is inspired by a function proposed by Kuhn in 1995. We show that
Kuhn's function is actually equal to a related function originally given by
Humberstone.
| math.LO cs.LO | this note proposes various axiomatizations of contingency logic under neighborhood semantics in particular by defining a suitable canonical neighborhood function we give sound and complete axiomatizations of monotone contingency logic and regular contingency logic thereby answering two open questions raised by bakhtiari van ditmarsch and hansen the canonical function is inspired by a function proposed by kuhn in 1995 we show that kuhns function is actually equal to a related function originally given by humberstone | [['this', 'note', 'proposes', 'various', 'axiomatizations', 'of', 'contingency', 'logic', 'under', 'neighborhood', 'semantics', 'in', 'particular', 'by', 'defining', 'a', 'suitable', 'canonical', 'neighborhood', 'function', 'we', 'give', 'sound', 'and', 'complete', 'axiomatizations', 'of', 'monotone', 'contingency', 'logic', 'and', 'regular', 'contingency', 'logic', 'thereby', 'answering', 'two', 'open', 'questions', 'raised', 'by', 'bakhtiari', 'van', 'ditmarsch', 'and', 'hansen', 'the', 'canonical', 'function', 'is', 'inspired', 'by', 'a', 'function', 'proposed', 'by', 'kuhn', 'in', '1995', 'we', 'show', 'that', 'kuhns', 'function', 'is', 'actually', 'equal', 'to', 'a', 'related', 'function', 'originally', 'given', 'by', 'humberstone']] | [-0.09759961405437287, 0.05799470367930727, -0.03525186864633675, 0.12865485825850181, -0.1010933155512871, -0.13437728769439336, 0.10773467178749915, 0.3209773734313984, -0.2636994409106978, -0.2949932058124918, 0.04772511171495976, -0.2204270305698865, -0.20271938888687793, 0.14259335886023633, -0.1941319878327928, 0.09301673340266697, 0.015089791000195562, -0.020426791493002684, -0.06918077242611716, -0.2951423125213956, 0.3603385975488704, 0.034591256219842666, 0.21008823823489964, 0.07972867553017089, 0.12435818740090177, 0.12869221397535238, -0.0367032259013759, 0.07384839734981118, -0.1758663663549918, 0.10778847617441661, 0.31913560067545876, 0.26994158002254487, 0.34584043431414724, -0.3671969716271309, -0.13829808755879838, 0.10276638281576285, 0.05634882384974969, 0.03908536715866768, 0.012438148583448173, -0.2931044180048247, 0.09224265966243876, -0.183261104591497, -0.09760418492815878, -0.10213305554607857, 0.12334815792943517, 0.0598444537935804, -0.24240915389246728, -0.0007184549834426136, 0.20040302943081073, 0.13272708838754524, -0.011164309750374865, -0.09634285571997704, 0.047518298277082816, 0.003676461317410616, -0.04262229187209925, 0.07212770520709455, 0.07162190597402314, -0.043431277115739984, -0.1629742239538121, 0.2970398849335639, -0.033961956976109174, -0.21761280604421276, 0.10205364470969733, -0.07098865162657753, -0.1420240860228261, 0.03315753309847149, 0.026315662225274598, 0.10802349496683845, -0.1605871552984192, 0.15248440892821535, -0.10844446412504535, 0.14052278829630055, 0.1949788931731696, -0.050079949882697976, 0.22665286070526872, 0.1402966820360608, 0.052819936657452014, 0.19614861335299194, 0.08774308768445499, -0.11658124210415026, -0.2725403475166898, -0.13556935832746428, -0.1398633752936778, 0.02722171542221961, -0.07029994069347445, -0.1984123190839405, 0.40001266296595744, 0.14143244452672463, 0.19717737384242554, 0.13378506544211957, 0.24953894270902932, 0.13377615700679998, 0.011253081823456777, 0.057812583865597844, 0.10343720353119178, 0.1527819669652373, 0.06741536184762643, -0.13303377384906762, 0.11362117651390703, 0.1698144989706933] |
1,802.03517 | Disturbance Grassmann Kernels for Subspace-Based Learning | In this paper, we focus on subspace-based learning problems, where data
elements are linear subspaces instead of vectors. To handle this kind of data,
Grassmann kernels were proposed to measure the space structure and used with
classifiers, e.g., Support Vector Machines (SVMs). However, the existing
discriminative algorithms mostly ignore the instability of subspaces, which
would cause the classifiers misled by disturbed instances. Thus we propose
considering all potential disturbance of subspaces in learning processes to
obtain more robust classifiers. Firstly, we derive the dual optimization of
linear classifiers with disturbance subject to a known distribution, resulting
in a new kernel, Disturbance Grassmann (DG) kernel. Secondly, we research into
two kinds of disturbance, relevant to the subspace matrix and singular values
of bases, with which we extend the Projection kernel on Grassmann manifolds to
two new kernels. Experiments on action data indicate that the proposed kernels
perform better compared to state-of-the-art subspace-based methods, even in a
worse environment.
| cs.LG | in this paper we focus on subspacebased learning problems where data elements are linear subspaces instead of vectors to handle this kind of data grassmann kernels were proposed to measure the space structure and used with classifiers eg support vector machines svms however the existing discriminative algorithms mostly ignore the instability of subspaces which would cause the classifiers misled by disturbed instances thus we propose considering all potential disturbance of subspaces in learning processes to obtain more robust classifiers firstly we derive the dual optimization of linear classifiers with disturbance subject to a known distribution resulting in a new kernel disturbance grassmann dg kernel secondly we research into two kinds of disturbance relevant to the subspace matrix and singular values of bases with which we extend the projection kernel on grassmann manifolds to two new kernels experiments on action data indicate that the proposed kernels perform better compared to stateoftheart subspacebased methods even in a worse environment | [['in', 'this', 'paper', 'we', 'focus', 'on', 'subspacebased', 'learning', 'problems', 'where', 'data', 'elements', 'are', 'linear', 'subspaces', 'instead', 'of', 'vectors', 'to', 'handle', 'this', 'kind', 'of', 'data', 'grassmann', 'kernels', 'were', 'proposed', 'to', 'measure', 'the', 'space', 'structure', 'and', 'used', 'with', 'classifiers', 'eg', 'support', 'vector', 'machines', 'svms', 'however', 'the', 'existing', 'discriminative', 'algorithms', 'mostly', 'ignore', 'the', 'instability', 'of', 'subspaces', 'which', 'would', 'cause', 'the', 'classifiers', 'misled', 'by', 'disturbed', 'instances', 'thus', 'we', 'propose', 'considering', 'all', 'potential', 'disturbance', 'of', 'subspaces', 'in', 'learning', 'processes', 'to', 'obtain', 'more', 'robust', 'classifiers', 'firstly', 'we', 'derive', 'the', 'dual', 'optimization', 'of', 'linear', 'classifiers', 'with', 'disturbance', 'subject', 'to', 'a', 'known', 'distribution', 'resulting', 'in', 'a', 'new', 'kernel', 'disturbance', 'grassmann', 'dg', 'kernel', 'secondly', 'we', 'research', 'into', 'two', 'kinds', 'of', 'disturbance', 'relevant', 'to', 'the', 'subspace', 'matrix', 'and', 'singular', 'values', 'of', 'bases', 'with', 'which', 'we', 'extend', 'the', 'projection', 'kernel', 'on', 'grassmann', 'manifolds', 'to', 'two', 'new', 'kernels', 'experiments', 'on', 'action', 'data', 'indicate', 'that', 'the', 'proposed', 'kernels', 'perform', 'better', 'compared', 'to', 'stateoftheart', 'subspacebased', 'methods', 'even', 'in', 'a', 'worse', 'environment']] | [-0.07143502172518663, 0.026415924162004785, -0.05865220558254203, 0.09112532184663923, -0.11845640449007605, -0.17165074811893913, 0.02328673702335448, 0.41441625136004134, -0.2922538069720457, -0.23502470371402373, 0.11565020670936366, -0.2639323989712414, -0.1695703480713022, 0.18602363160028343, -0.1267965225425733, 0.11321708986731795, 0.1270755069755955, 0.04737779541700414, -0.1289457768842483, -0.30719791715045214, 0.3889315395547777, 0.05695770363542874, 0.2866979019633334, -0.04794504717704217, 0.12810787166080254, -0.020231361185040347, -0.05094936856655938, 0.013080541019077312, -0.05202765503749407, 0.16367117407624937, 0.31907951214890573, 0.18689371053734496, 0.30281019299943596, -0.4226555674259735, -0.2169839483863751, 0.16274361948076352, 0.13358351254750822, 0.0745512936664686, 0.025762862038252145, -0.3243218623049806, 0.06153225412838469, -0.12908819822481815, -0.02260682679220417, -0.16889279910695448, -0.04239007730072566, -0.009742015044376919, -0.31992071943400596, 0.038589764435354404, 0.10235225176735288, 0.053124095301387037, -0.09145175620830458, -0.16588684379651097, 0.058436150793104796, 0.056940554769466495, 0.03514389766107319, 0.0290353489538453, 0.12557933405694213, -0.11483512175785508, -0.15461786676666872, 0.3327783568640637, -0.055840567165873, -0.29557000248912413, 0.22003356740768454, -0.07470071380720682, -0.12957327648890532, 0.09523550417774007, 0.2723573565091582, 0.1017469384330829, -0.12956001292980887, 0.031065132978348547, -0.03652354718963052, 0.12135631718174525, 0.040791342540638174, 0.03624183175465089, 0.10965292091528843, 0.1439430751099257, 0.09665541578629974, 0.15463246215004356, -0.09226342110307353, -0.07246654606305632, -0.23075722747001867, -0.09054326053981664, -0.17739935066909857, -0.019861656525854473, -0.11123577549524907, -0.17942771212802902, 0.38635591565850813, 0.19918127781652198, 0.24748994219917686, 0.07043257732668025, 0.3171975155165241, 0.06512062679560986, 0.1172139955496202, 0.1438932226152177, 0.1973552603325006, 0.13302677873355948, 0.0477393359319562, -0.1613244051617944, 0.04935719464626757, 0.09992376971651747] |
1,802.03518 | Hydra: an Ensemble of Convolutional Neural Networks for Geospatial Land
Classification | We describe in this paper Hydra, an ensemble of convolutional neural networks
(CNN) for geospatial land classification. The idea behind Hydra is to create an
initial CNN that is coarsely optimized but provides a good starting pointing
for further optimization, which will serve as the Hydra's body. Then, the
obtained weights are fine-tuned multiple times with different augmentation
techniques, crop styles, and classes weights to form an ensemble of CNNs that
represent the Hydra's heads. By doing so, we prompt convergence to different
endpoints, which is a desirable aspect for ensembles. With this framework, we
were able to reduce the training time while maintaining the classification
performance of the ensemble. We created ensembles for our experiments using two
state-of-the-art CNN architectures, ResNet and DenseNet. We have demonstrated
the application of our Hydra framework in two datasets, FMOW and NWPU-RESISC45,
achieving results comparable to the state-of-the-art for the former and the
best reported performance so far for the latter. Code and CNN models are
available at https://github.com/maups/hydra-fmow
| cs.CV | we describe in this paper hydra an ensemble of convolutional neural networks cnn for geospatial land classification the idea behind hydra is to create an initial cnn that is coarsely optimized but provides a good starting pointing for further optimization which will serve as the hydras body then the obtained weights are finetuned multiple times with different augmentation techniques crop styles and classes weights to form an ensemble of cnns that represent the hydras heads by doing so we prompt convergence to different endpoints which is a desirable aspect for ensembles with this framework we were able to reduce the training time while maintaining the classification performance of the ensemble we created ensembles for our experiments using two stateoftheart cnn architectures resnet and densenet we have demonstrated the application of our hydra framework in two datasets fmow and nwpuresisc45 achieving results comparable to the stateoftheart for the former and the best reported performance so far for the latter code and cnn models are available at httpsgithubcommaupshydrafmow | [['we', 'describe', 'in', 'this', 'paper', 'hydra', 'an', 'ensemble', 'of', 'convolutional', 'neural', 'networks', 'cnn', 'for', 'geospatial', 'land', 'classification', 'the', 'idea', 'behind', 'hydra', 'is', 'to', 'create', 'an', 'initial', 'cnn', 'that', 'is', 'coarsely', 'optimized', 'but', 'provides', 'a', 'good', 'starting', 'pointing', 'for', 'further', 'optimization', 'which', 'will', 'serve', 'as', 'the', 'hydras', 'body', 'then', 'the', 'obtained', 'weights', 'are', 'finetuned', 'multiple', 'times', 'with', 'different', 'augmentation', 'techniques', 'crop', 'styles', 'and', 'classes', 'weights', 'to', 'form', 'an', 'ensemble', 'of', 'cnns', 'that', 'represent', 'the', 'hydras', 'heads', 'by', 'doing', 'so', 'we', 'prompt', 'convergence', 'to', 'different', 'endpoints', 'which', 'is', 'a', 'desirable', 'aspect', 'for', 'ensembles', 'with', 'this', 'framework', 'we', 'were', 'able', 'to', 'reduce', 'the', 'training', 'time', 'while', 'maintaining', 'the', 'classification', 'performance', 'of', 'the', 'ensemble', 'we', 'created', 'ensembles', 'for', 'our', 'experiments', 'using', 'two', 'stateoftheart', 'cnn', 'architectures', 'resnet', 'and', 'densenet', 'we', 'have', 'demonstrated', 'the', 'application', 'of', 'our', 'hydra', 'framework', 'in', 'two', 'datasets', 'fmow', 'and', 'nwpuresisc45', 'achieving', 'results', 'comparable', 'to', 'the', 'stateoftheart', 'for', 'the', 'former', 'and', 'the', 'best', 'reported', 'performance', 'so', 'far', 'for', 'the', 'latter', 'code', 'and', 'cnn', 'models', 'are', 'available', 'at', 'httpsgithubcommaupshydrafmow']] | [-0.031100534361207903, 0.030450037938507882, -0.06962390435666482, 0.05786283424751629, -0.05293219438583658, -0.18231956207011712, 0.00604552145913902, 0.4807990099780443, -0.24179163659218608, -0.3159188629957143, 0.0628868205765285, -0.2473611486053503, -0.16076485259549284, 0.2149813750236318, -0.10201745650095002, 0.09735637521314458, 0.17557964818168817, 0.023139011526548462, -0.05445160377322037, -0.3249388502274708, 0.29210003988999056, 0.09217439524022998, 0.3302045371213065, 0.006795811762170094, 0.12659879019533612, -0.07536375490386357, 0.008048711507865115, 0.006444612622300799, -0.06869232585654017, 0.17650170980884536, 0.2716940749466475, 0.17771167949228206, 0.2754852616216806, -0.4181265553588008, -0.2054922256005428, 0.0857005821478485, 0.16540529916954522, 0.10951786952616048, -0.005560363361770969, -0.29650827503206617, 0.13452394684299646, -0.1772047252473762, -0.04035969818771885, -0.12317781234387218, -0.03842049045204299, 0.011394291620335268, -0.2910141776111431, -0.007523322030731545, 0.06657891747507654, 0.03994871942871591, -0.06622405299808966, -0.1679899058866928, 0.030672991983365358, 0.1996594662366367, 0.000666631466606859, 0.05339291744308955, 0.12896747281881357, -0.15617980140619117, -0.1188591131855302, 0.3364748277462928, -0.04201852114553132, -0.18702817559043491, 0.22129689752998785, -0.01483059890579614, -0.16357943131701974, 0.06179399083073183, 0.1956444364239848, 0.09201824785885969, -0.1467530077306281, -0.023600272767930065, -0.05598703471421287, 0.160374186671816, 0.06821073317139341, 0.004365571076050401, 0.18258802876126293, 0.23794298035251657, 0.023365252947991307, 0.1491652304582048, -0.15700329242314714, -0.07427497552821367, -0.22122933979965348, -0.11513114957956642, -0.17040802993976353, -0.01404254826455854, -0.09799639187515756, -0.13733581068539402, 0.4223764027715888, 0.21046671421244378, 0.21589704167712537, 0.14512462724602168, 0.3210011445860401, 0.011390455265205763, 0.1522269146800859, 0.12431163464604718, 0.22685383349194238, 0.01383717203649079, 0.10660424574119289, -0.15695852798157053, 0.03618073535400937, 0.0653217076948594] |
1,802.03519 | On Weak Supercyclicity II | This paper considers weak supercyclicity for bounded linear operators on a
normed space. On the one hand, weak supercyclicity is investigated for classes
of Hilbert-space operators: (i) self-adjoint operators are not weakly
supercyclic, (ii) diagonalizable operators are not weakly l-sequentially
supercyclic, and (iii) weak l-sequential supercyclicity is preserved between a
unitary operator and its adjoint. On the other hand, weak supercyclicity is
investigated for classes of normed-space operators: (iv) the point spectrum of
the normed-space adjoint of a power bounded supercyclic operator is either
empty or is a singleton in the open unit disk, (v) weak l-sequential
supercyclicity coincides with supercyclicity for compact operators, and (vi)
every compact weakly l-sequentially supercyclic operator is quasinilpotent.
| math.FA | this paper considers weak supercyclicity for bounded linear operators on a normed space on the one hand weak supercyclicity is investigated for classes of hilbertspace operators i selfadjoint operators are not weakly supercyclic ii diagonalizable operators are not weakly lsequentially supercyclic and iii weak lsequential supercyclicity is preserved between a unitary operator and its adjoint on the other hand weak supercyclicity is investigated for classes of normedspace operators iv the point spectrum of the normedspace adjoint of a power bounded supercyclic operator is either empty or is a singleton in the open unit disk v weak lsequential supercyclicity coincides with supercyclicity for compact operators and vi every compact weakly lsequentially supercyclic operator is quasinilpotent | [['this', 'paper', 'considers', 'weak', 'supercyclicity', 'for', 'bounded', 'linear', 'operators', 'on', 'a', 'normed', 'space', 'on', 'the', 'one', 'hand', 'weak', 'supercyclicity', 'is', 'investigated', 'for', 'classes', 'of', 'hilbertspace', 'operators', 'i', 'selfadjoint', 'operators', 'are', 'not', 'weakly', 'supercyclic', 'ii', 'diagonalizable', 'operators', 'are', 'not', 'weakly', 'lsequentially', 'supercyclic', 'and', 'iii', 'weak', 'lsequential', 'supercyclicity', 'is', 'preserved', 'between', 'a', 'unitary', 'operator', 'and', 'its', 'adjoint', 'on', 'the', 'other', 'hand', 'weak', 'supercyclicity', 'is', 'investigated', 'for', 'classes', 'of', 'normedspace', 'operators', 'iv', 'the', 'point', 'spectrum', 'of', 'the', 'normedspace', 'adjoint', 'of', 'a', 'power', 'bounded', 'supercyclic', 'operator', 'is', 'either', 'empty', 'or', 'is', 'a', 'singleton', 'in', 'the', 'open', 'unit', 'disk', 'v', 'weak', 'lsequential', 'supercyclicity', 'coincides', 'with', 'supercyclicity', 'for', 'compact', 'operators', 'and', 'vi', 'every', 'compact', 'weakly', 'lsequentially', 'supercyclic', 'operator', 'is', 'quasinilpotent']] | [-0.18545586737205408, 0.17103568965103477, 0.03963709268386343, 0.10118533054628642, -0.11289167040376924, -0.23806049850619665, -0.026355270548914893, 0.39279084699228406, -0.30305609200149775, -0.029095644607358345, 0.17256846199390047, -0.35294692262375194, -0.07056191038697891, 0.15006573413224292, -0.07876969970363591, 0.006130706098442066, 0.0719195284972167, 0.12107196826920179, -0.1279363908189615, -0.1926747772730388, 0.484452476259321, -0.08781640814517491, 0.1599410865601385, 0.05731294811162765, 0.1078565278689244, 0.004388759185011233, -0.028076752294769643, 0.005013421784626969, -0.027842536626849324, 0.10385987515785798, 0.22065828522733813, 0.11536794715045419, 0.27236304112843107, -0.3470775944076325, -0.10941302240618304, 0.210817817482166, 0.08888000140931192, -0.07607786761114507, -0.009534506532613054, -0.34146414334619685, 0.12013092932258067, -0.11265387376105147, -0.11656637100012242, -0.062483412330038846, 0.09637498484724867, 0.02912102783739101, -0.35694585835985243, 0.0775424405702714, 0.1816349378870135, 0.06727334908334472, -0.09112000114587968, -0.040500699288843735, -0.08950454647700619, 0.024742154672691998, -0.10578042666228223, 0.017895489677487473, 0.08475426917876965, -0.04328183716487339, -0.05338807985286361, 0.324122707692108, -0.12211631899533261, -0.2551648732125094, 0.2079709328411679, -0.2064655199937988, -0.14245178030146885, 0.03244166196548447, 0.05669909764713209, 0.21200269471903863, -0.14212517551745155, 0.29750250315503635, -0.10831875567756859, 0.13102201550775913, 0.03334816377329324, 0.10021078404887314, 0.09301955709608072, 0.06994586175174586, 0.2161695387996068, 0.06400071518614173, 0.06317795744689647, -0.04547668483740251, -0.35359959496418014, -0.12386868472926185, -0.13752392575510644, 0.0510382670870188, -0.036476822831511005, -0.22707954158041893, 0.3524535711081366, 0.024645556500347863, 0.1829479408001394, 0.06837238090728144, 0.20643329532634067, 0.20048072349163704, 0.06507584251812659, 0.11436854667928335, 0.18790964160247572, 0.20437177228658193, 0.05872891208023897, -0.1891662231729632, 0.002114076150714287, 0.232639729597005] |
1,802.0352 | HESS J1640-465 -- a Gamma-ray emitting pulsar wind nebula ? | HESS J1640-465 is an extended TeV $\gamma$-ray source and its $\gamma$-ray
emission whether from the shell of a supernova remnant (SNR) or a pulsar wind
nebula (PWN) is still under debate. We reanalyze the GeV $\gamma$-ray data in
the field of HESS J1640-465 using eight years of Pass 8 data recorded by the
Fermi Large Area Telescope. An extended GeV $\gamma$-ray source positionally
coincident with HESS J1640-465 is found. Its photon spectrum can be described
by a power-law with an index of $1.42\pm0.19$ in the energy range of 10-500
GeV, and smoothly connects with the TeV spectrum of HESS J1640-465. The
broadband spectrum of HESS J1640-465 can be well fit by a leptonic model with a
broken power-law spectrum of electrons with an exponential cut-off at $\sim$
300 TeV. The spectral properties of HESS J1640-465 are broadly consistent with
the characteristics of other sources identified as PWNe, such as the
correlations between high-energy luminosity ratios and the physical parameters
of pulsar, including spin-down luminosity $\dot{E}$ and characteristic age
$\tau_c$. All these pieces of evidence support that the $\gamma$-ray emission
of HESS J1640-465 may originate from the PWN powered by PSR J1640-4631 rather
than the shell of the SNR G338.3-0.0.
| astro-ph.HE | hess j1640465 is an extended tev gammaray source and its gammaray emission whether from the shell of a supernova remnant snr or a pulsar wind nebula pwn is still under debate we reanalyze the gev gammaray data in the field of hess j1640465 using eight years of pass 8 data recorded by the fermi large area telescope an extended gev gammaray source positionally coincident with hess j1640465 is found its photon spectrum can be described by a powerlaw with an index of 142pm019 in the energy range of 10500 gev and smoothly connects with the tev spectrum of hess j1640465 the broadband spectrum of hess j1640465 can be well fit by a leptonic model with a broken powerlaw spectrum of electrons with an exponential cutoff at sim 300 tev the spectral properties of hess j1640465 are broadly consistent with the characteristics of other sources identified as pwne such as the correlations between highenergy luminosity ratios and the physical parameters of pulsar including spindown luminosity dote and characteristic age tau_c all these pieces of evidence support that the gammaray emission of hess j1640465 may originate from the pwn powered by psr j16404631 rather than the shell of the snr g338300 | [['hess', 'j1640465', 'is', 'an', 'extended', 'tev', 'gammaray', 'source', 'and', 'its', 'gammaray', 'emission', 'whether', 'from', 'the', 'shell', 'of', 'a', 'supernova', 'remnant', 'snr', 'or', 'a', 'pulsar', 'wind', 'nebula', 'pwn', 'is', 'still', 'under', 'debate', 'we', 'reanalyze', 'the', 'gev', 'gammaray', 'data', 'in', 'the', 'field', 'of', 'hess', 'j1640465', 'using', 'eight', 'years', 'of', 'pass', '8', 'data', 'recorded', 'by', 'the', 'fermi', 'large', 'area', 'telescope', 'an', 'extended', 'gev', 'gammaray', 'source', 'positionally', 'coincident', 'with', 'hess', 'j1640465', 'is', 'found', 'its', 'photon', 'spectrum', 'can', 'be', 'described', 'by', 'a', 'powerlaw', 'with', 'an', 'index', 'of', '142pm019', 'in', 'the', 'energy', 'range', 'of', '10500', 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1,802.03521 | Self-similar solutions of curvature flows in warped products | In this paper we study self-similar solutions in warped products satisfying
$F-\mathcal{F}=\bar{g}(\lambda(r)\partial_{r},\nu)$, where $\mathcal{F}$ is a
nonnegative constant and $F$ is in a class of general curvature functions
including powers of mean curvature and Gauss curvature. We show that slices are
the only closed strictly convex self-similar solutions in the hemisphere for
such $F$. We also obtain a similar uniqueness result in hyperbolic space
$\mathbb{H}^{3}$ for Gauss curvature $F$ and $\mathcal{F}\geq 1$.
| math.DG math.AP | in this paper we study selfsimilar solutions in warped products satisfying fmathcalfbarglambdarpartial_rnu where mathcalf is a nonnegative constant and f is in a class of general curvature functions including powers of mean curvature and gauss curvature we show that slices are the only closed strictly convex selfsimilar solutions in the hemisphere for such f we also obtain a similar uniqueness result in hyperbolic space mathbbh3 for gauss curvature f and mathcalfgeq 1 | [['in', 'this', 'paper', 'we', 'study', 'selfsimilar', 'solutions', 'in', 'warped', 'products', 'satisfying', 'fmathcalfbarglambdarpartial_rnu', 'where', 'mathcalf', 'is', 'a', 'nonnegative', 'constant', 'and', 'f', 'is', 'in', 'a', 'class', 'of', 'general', 'curvature', 'functions', 'including', 'powers', 'of', 'mean', 'curvature', 'and', 'gauss', 'curvature', 'we', 'show', 'that', 'slices', 'are', 'the', 'only', 'closed', 'strictly', 'convex', 'selfsimilar', 'solutions', 'in', 'the', 'hemisphere', 'for', 'such', 'f', 'we', 'also', 'obtain', 'a', 'similar', 'uniqueness', 'result', 'in', 'hyperbolic', 'space', 'mathbbh3', 'for', 'gauss', 'curvature', 'f', 'and', 'mathcalfgeq', '1']] | [-0.19792516570804375, 0.08770838547497987, -0.026227557765586034, 0.08627380407581638, -0.08473575862257608, -0.10374440025272114, -0.06665736211829686, 0.3714833064270871, -0.26508569485907046, -0.16701943509812867, 0.12286485017310562, -0.28186164999912894, -0.1693821730424783, 0.20100000074931554, -0.0990033212450466, 0.040982013561629824, 0.020297045513455358, 0.09661253452567117, -0.09144929423595645, -0.23142573570200642, 0.4192088092544249, -0.09960580843367747, 0.165732331613877, 0.0908341697799707, 0.11318138573052627, -0.09722699534946254, 0.004997215127306325, 0.0912910480879613, -0.25349831327382294, 0.09504445320926606, 0.20661434112781926, 0.1100649022896375, 0.2741096336494333, -0.3359439956822566, -0.2244812624529004, 0.20093956801907292, 0.12518778302640254, -0.029776907020381518, -0.07753961723563926, -0.22910196470495844, 0.11940991009718606, -0.07453393297536033, -0.17587514646085245, -0.0791913591192237, 0.05619230161953185, 0.050843740477492766, -0.29884907640516756, 0.11332382749658011, 0.13768523181788622, 0.041304204599665745, -0.13267108203976283, -0.08979886984951528, -0.03167335245359157, 0.04653454031130033, 0.06962152321024664, 0.0859889935535778, 0.032317251678822295, -0.08665330060425082, -0.033677896611126405, 0.34744830512707786, -0.15569338582988296, -0.3241827396675944, 0.0773287218529731, -0.2213553696870804, -0.17734723672536867, 0.13433437069345797, 0.19134456564539246, 0.22741291773771602, -0.07738549251641547, 0.20757194276250499, -0.09085518196890396, 0.08353249471900719, 0.16898252562885838, -0.011622467406726043, 0.1349097902786785, 0.03821862204266446, 0.17357822111142532, 0.16730674550469432, 0.0020774428133985825, -0.06361895534209908, -0.38029520117810794, -0.2406123979116923, -0.15207778009186898, 0.17509539127349855, -0.1534252862905435, -0.21726739129849842, 0.40488720482348334, -0.024229452067187855, 0.17645366002813848, 0.18615369562591824, 0.23535241447389127, 0.07265350816388881, 0.008872690880005913, 0.1510472135485283, 0.187046251605664, 0.13691190326852457, 0.032945598369198185, -0.07632797561985041, -0.04382284664149795, 0.12214530103706887] |
1,802.03522 | Enhanced version of AdaBoostM1 with J48 Tree learning method | Machine Learning focuses on the construction and study of systems that can
learn from data. This is connected with the classification problem, which
usually is what Machine Learning algorithms are designed to solve. When a
machine learning method is used by people with no special expertise in machine
learning, it is important that the method be robust in classification, in the
sense that reasonable performance is obtained with minimal tuning of the
problem at hand. Algorithms are evaluated based on how robust they can classify
the given data. In this paper, we propose a quantifiable measure of robustness,
and describe a particular learning method that is robust according to this
measure in the context of classification problem. We proposed Adaptive Boosting
(AdaBoostM1) with J48(C4.5 tree) as a base learner with tuning weight threshold
(P) and number of iterations (I) for boosting algorithm. To benchmark the
performance, we used the baseline classifier, AdaBoostM1 with Decision Stump as
base learner without tuning parameters. By tuning parameters and using J48 as
base learner, we are able to reduce the overall average error rate ratio
(errorC/errorNB) from 2.4 to 0.9 for development sets of data and 2.1 to 1.2
for evaluation sets of data.
| stat.ML cs.LG | machine learning focuses on the construction and study of systems that can learn from data this is connected with the classification problem which usually is what machine learning algorithms are designed to solve when a machine learning method is used by people with no special expertise in machine learning it is important that the method be robust in classification in the sense that reasonable performance is obtained with minimal tuning of the problem at hand algorithms are evaluated based on how robust they can classify the given data in this paper we propose a quantifiable measure of robustness and describe a particular learning method that is robust according to this measure in the context of classification problem we proposed adaptive boosting adaboostm1 with j48c45 tree as a base learner with tuning weight threshold p and number of iterations i for boosting algorithm to benchmark the performance we used the baseline classifier adaboostm1 with decision stump as base learner without tuning parameters by tuning parameters and using j48 as base learner we are able to reduce the overall average error rate ratio errorcerrornb from 24 to 09 for development sets of data and 21 to 12 for evaluation sets of data | [['machine', 'learning', 'focuses', 'on', 'the', 'construction', 'and', 'study', 'of', 'systems', 'that', 'can', 'learn', 'from', 'data', 'this', 'is', 'connected', 'with', 'the', 'classification', 'problem', 'which', 'usually', 'is', 'what', 'machine', 'learning', 'algorithms', 'are', 'designed', 'to', 'solve', 'when', 'a', 'machine', 'learning', 'method', 'is', 'used', 'by', 'people', 'with', 'no', 'special', 'expertise', 'in', 'machine', 'learning', 'it', 'is', 'important', 'that', 'the', 'method', 'be', 'robust', 'in', 'classification', 'in', 'the', 'sense', 'that', 'reasonable', 'performance', 'is', 'obtained', 'with', 'minimal', 'tuning', 'of', 'the', 'problem', 'at', 'hand', 'algorithms', 'are', 'evaluated', 'based', 'on', 'how', 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1,802.03523 | Real-time evolution method and its application to 3$\alpha$ cluster
system | A new theoretical method is proposed to describe the ground and excited
cluster states of atomic nuclei. The method utilizes the equation-of-motion of
the Gaussian wave packets to generate the basis wave functions having various
cluster configurations. The generated basis wave functions are superposed to
diagonalize the Hamiltonian. In other words, this method uses the real time as
the generator coordinate. The application to the $3\alpha$ system as a
benchmark shows that the new method works efficiently and yields the result
consistent with or better than the other cluster models. Brief discussion on
the structure of the excited $0^+$ and $1^-$ states is also made.
| nucl-th nucl-ex | a new theoretical method is proposed to describe the ground and excited cluster states of atomic nuclei the method utilizes the equationofmotion of the gaussian wave packets to generate the basis wave functions having various cluster configurations the generated basis wave functions are superposed to diagonalize the hamiltonian in other words this method uses the real time as the generator coordinate the application to the 3alpha system as a benchmark shows that the new method works efficiently and yields the result consistent with or better than the other cluster models brief discussion on the structure of the excited 0 and 1 states is also made | [['a', 'new', 'theoretical', 'method', 'is', 'proposed', 'to', 'describe', 'the', 'ground', 'and', 'excited', 'cluster', 'states', 'of', 'atomic', 'nuclei', 'the', 'method', 'utilizes', 'the', 'equationofmotion', 'of', 'the', 'gaussian', 'wave', 'packets', 'to', 'generate', 'the', 'basis', 'wave', 'functions', 'having', 'various', 'cluster', 'configurations', 'the', 'generated', 'basis', 'wave', 'functions', 'are', 'superposed', 'to', 'diagonalize', 'the', 'hamiltonian', 'in', 'other', 'words', 'this', 'method', 'uses', 'the', 'real', 'time', 'as', 'the', 'generator', 'coordinate', 'the', 'application', 'to', 'the', '3alpha', 'system', 'as', 'a', 'benchmark', 'shows', 'that', 'the', 'new', 'method', 'works', 'efficiently', 'and', 'yields', 'the', 'result', 'consistent', 'with', 'or', 'better', 'than', 'the', 'other', 'cluster', 'models', 'brief', 'discussion', 'on', 'the', 'structure', 'of', 'the', 'excited', '0', 'and', '1', 'states', 'is', 'also', 'made']] | [-0.08930365550110028, 0.10192355101129838, -0.1008026282347384, 0.0739280929844383, -0.04787891960392396, -0.09621566274159012, 0.024291470841992468, 0.37088260778359006, -0.24638352086324067, -0.2650403504792069, 0.04193847835219155, -0.2834951655939221, -0.11981289228202686, 0.20545498499746567, 0.06354060177469538, 0.07439541115115086, 0.0942591161600181, 0.06521927826771778, -0.0828633859066204, -0.21508829226229517, 0.34106122046886456, 0.059102840823609205, 0.27728651393914505, -0.009540013982249157, 0.08893916126163233, 0.004801918541835178, -0.0062161622214175405, -0.050475250681241354, -0.0671019931194245, 0.13018560105202986, 0.19321850204020544, 0.1570354021863923, 0.24339469386752519, -0.42648704931849524, -0.19414136164067758, 0.04326046007419271, 0.14034504413028204, 0.1854708408803812, -0.02044294787947798, -0.3359993927579905, 0.05574242851386468, -0.19353165254440335, -0.19259389418931233, -0.09381737494397731, -0.009457781729066656, 0.06769042125337624, -0.27650952762757824, 0.056128010049273834, 0.025043254381134396, -0.012214377687667453, -0.08663098151751217, -0.1639541657186956, -0.024036553702760664, 0.10010435004009023, 0.01304847093720344, 0.09719835635762485, 0.11763046563469938, -0.05875070040014439, -0.11835853082260915, 0.3867133815728483, -0.052603644020217916, -0.2085221341589377, 0.1990756743243851, -0.10371456367983704, -0.1044046195002184, 0.11278521992665316, 0.15933828962345917, 0.13014439861068414, -0.10669184624317235, 0.038994122505010595, -0.05149390833144675, 0.18518642664310478, 0.032461268439268075, 0.04336473053498637, 0.12817689083028763, 0.13574515724377262, 0.05743483931180977, 0.14732943267361925, -0.09519960782934157, -0.11748819517503892, -0.2806287295584168, -0.12800319479955805, -0.21116006558849698, -0.014315663126070008, -0.02864054392626886, -0.1776749107809294, 0.4588450595647806, 0.13498181150782676, 0.17705318688281946, 0.025236246184933753, 0.2945533910411454, 0.1235735994164965, 0.0660109831463723, 0.0636902539270176, 0.19679444175036181, 0.14532842309258523, 0.046558422715004, -0.20925811138891037, 0.020792290333303667, 0.07188726495286184] |
1,802.03524 | Evolutionary value of collective sensing | We propose a mathematical model for collective sensing in a population
growing in a stochastically varying environment. In the population, individuals
use an information channel for sensing the environment, and two channels for
signal production and comprehension to communicate among themselves. We show
that existence of such system has a positive effect on population growth, hence
can have a positive evolutionary effect. We show that the gain in growth due to
the collective sensing is related to information theoretic entities, which can
be considered as the information content of this system from the environment.
We further show that heterogeneity in communication resulted from network or
spatial structure increases growth. We compute the growth rate of a population
residing on a lattice and show that growth rate near the maximum noise level in
observation or communication, increases exponentially as noise decreases. This
exponential effect makes the emergence of collective observation an easy
outcome in an evolutionary process. Furthermore, we are able to quantify
interesting effects such as accelerated growth, and simplification of decision
making due to information amplification by communication. Finally, we show that
an amount of noise in representation formation has more disadvantageous effect
compared to the same noise in signal production.
| q-bio.PE cond-mat.stat-mech | we propose a mathematical model for collective sensing in a population growing in a stochastically varying environment in the population individuals use an information channel for sensing the environment and two channels for signal production and comprehension to communicate among themselves we show that existence of such system has a positive effect on population growth hence can have a positive evolutionary effect we show that the gain in growth due to the collective sensing is related to information theoretic entities which can be considered as the information content of this system from the environment we further show that heterogeneity in communication resulted from network or spatial structure increases growth we compute the growth rate of a population residing on a lattice and show that growth rate near the maximum noise level in observation or communication increases exponentially as noise decreases this exponential effect makes the emergence of collective observation an easy outcome in an evolutionary process furthermore we are able to quantify interesting effects such as accelerated growth and simplification of decision making due to information amplification by communication finally we show that an amount of noise in representation formation has more disadvantageous effect compared to the same noise in signal production | [['we', 'propose', 'a', 'mathematical', 'model', 'for', 'collective', 'sensing', 'in', 'a', 'population', 'growing', 'in', 'a', 'stochastically', 'varying', 'environment', 'in', 'the', 'population', 'individuals', 'use', 'an', 'information', 'channel', 'for', 'sensing', 'the', 'environment', 'and', 'two', 'channels', 'for', 'signal', 'production', 'and', 'comprehension', 'to', 'communicate', 'among', 'themselves', 'we', 'show', 'that', 'existence', 'of', 'such', 'system', 'has', 'a', 'positive', 'effect', 'on', 'population', 'growth', 'hence', 'can', 'have', 'a', 'positive', 'evolutionary', 'effect', 'we', 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1,802.03525 | The Design for a Nanoscale Single-Photon Spin Splitter | We propose using the effective spin-orbit interaction of light in
Bragg-modulated cylindrical waveguides for the effcient separation of spin-up
and spin-down photons emitted by a single photon emitter. Due to the spin and
directional dependence of photonic stopbands in the waveguides, spin-up (down)
photon propagation in the negative (positive) direction along the waveguide
axis is blocked while the same photon freely propagates in the opposite
direction.
| physics.optics | we propose using the effective spinorbit interaction of light in braggmodulated cylindrical waveguides for the effcient separation of spinup and spindown photons emitted by a single photon emitter due to the spin and directional dependence of photonic stopbands in the waveguides spinup down photon propagation in the negative positive direction along the waveguide axis is blocked while the same photon freely propagates in the opposite direction | [['we', 'propose', 'using', 'the', 'effective', 'spinorbit', 'interaction', 'of', 'light', 'in', 'braggmodulated', 'cylindrical', 'waveguides', 'for', 'the', 'effcient', 'separation', 'of', 'spinup', 'and', 'spindown', 'photons', 'emitted', 'by', 'a', 'single', 'photon', 'emitter', 'due', 'to', 'the', 'spin', 'and', 'directional', 'dependence', 'of', 'photonic', 'stopbands', 'in', 'the', 'waveguides', 'spinup', 'down', 'photon', 'propagation', 'in', 'the', 'negative', 'positive', 'direction', 'along', 'the', 'waveguide', 'axis', 'is', 'blocked', 'while', 'the', 'same', 'photon', 'freely', 'propagates', 'in', 'the', 'opposite', 'direction']] | [-0.18442722003763684, 0.2737677795239366, -0.011583473504735873, 0.009133307706976596, -0.0777008675182095, -0.18658094878595036, 0.021862612535747197, 0.5088483638488329, -0.24108555153585398, -0.28453201688826085, -0.04466316624759482, -0.31506602447838167, 0.023246110030091725, 0.2040255159139633, 0.06342725738023336, 0.014318272908433126, 0.02079740557103203, -0.04829644288581151, -0.04713296721187921, -0.0851529719379659, 0.2502115057136577, 0.017619318332379827, 0.35213210814130996, 0.10000326897089298, 0.14252457071113614, 0.10890901115221473, 0.05238802623576843, -0.09556483569053503, -0.02438170418653709, 0.07368779298491203, 0.1571699326687779, -0.08085469059073008, 0.2090722160342221, -0.4519704586897905, -0.15897027004796724, 0.029576842236117673, 0.2436538231988939, 0.1608149278586587, -0.08936104650537555, -0.29036905473241437, -0.04556507453895532, -0.1462117733218922, -0.15878366728205806, 0.08358168917206618, 0.018555683391885115, 0.013735090410157752, -0.17020185253081413, 0.0822448306694591, 0.07376139390353974, -0.022359048345914255, -0.016671989951282738, -0.013624129372720535, -0.07632970200278438, 0.03701341270397489, 0.08521722483162124, 0.0549776002460231, 0.16301784196581978, -0.10619205746513147, -0.12399925184077941, 0.3429824761186655, -0.1222577234568934, -0.2186021216070423, 0.09606844627370055, -0.21241353365655344, 0.018686212943150447, 0.20652862511790143, 0.21095842342250623, 0.1049481954282293, -0.07446338727067296, 0.011189671690002657, 0.020160996104375675, 0.1709677969607023, 0.14669833563698026, 0.06718481120008689, 0.3299545454004636, 0.11636314426477139, 0.045789463289726814, 0.20101291679132444, -0.16751064021283618, -0.015622061508922622, -0.2593210151848885, -0.17315345085942402, -0.2254163829394831, 0.040699226323228616, -0.1087377298977047, -0.13450277611756553, 0.43867196338967634, 0.10233917459845543, 0.14850615293074113, -0.03320588436550819, 0.3436077092702572, 0.15736103522089812, 0.07422721731798866, 0.08666593180969358, 0.3555163475756462, 0.2067594638488327, 0.11674298660184901, -0.29967185564769, 0.03705024808788529, -0.06901791400204484] |
1,802.03526 | Propagating bound states in the continuum in dielectric gratings | We consider propagating bound states in the continuum in dielectric gratings.
The gratings consist of a slab with ridges periodically arranged ether on top
or on the both sides of the slab. Based on the Fourier modal approach we
recover the leaky zones above the line of light to identify the geometries of
the gratings supporting Bloch bound states propagating in the direction
perpendicular to the ridges. Most importantly, it is demonstrated that if a
two-side grating possesses either mirror or glide symmetry the Bloch bound
states are stable to variation of parameters as far as the above symmetries are
preserved.
| physics.optics | we consider propagating bound states in the continuum in dielectric gratings the gratings consist of a slab with ridges periodically arranged ether on top or on the both sides of the slab based on the fourier modal approach we recover the leaky zones above the line of light to identify the geometries of the gratings supporting bloch bound states propagating in the direction perpendicular to the ridges most importantly it is demonstrated that if a twoside grating possesses either mirror or glide symmetry the bloch bound states are stable to variation of parameters as far as the above symmetries are preserved | [['we', 'consider', 'propagating', 'bound', 'states', 'in', 'the', 'continuum', 'in', 'dielectric', 'gratings', 'the', 'gratings', 'consist', 'of', 'a', 'slab', 'with', 'ridges', 'periodically', 'arranged', 'ether', 'on', 'top', 'or', 'on', 'the', 'both', 'sides', 'of', 'the', 'slab', 'based', 'on', 'the', 'fourier', 'modal', 'approach', 'we', 'recover', 'the', 'leaky', 'zones', 'above', 'the', 'line', 'of', 'light', 'to', 'identify', 'the', 'geometries', 'of', 'the', 'gratings', 'supporting', 'bloch', 'bound', 'states', 'propagating', 'in', 'the', 'direction', 'perpendicular', 'to', 'the', 'ridges', 'most', 'importantly', 'it', 'is', 'demonstrated', 'that', 'if', 'a', 'twoside', 'grating', 'possesses', 'either', 'mirror', 'or', 'glide', 'symmetry', 'the', 'bloch', 'bound', 'states', 'are', 'stable', 'to', 'variation', 'of', 'parameters', 'as', 'far', 'as', 'the', 'above', 'symmetries', 'are', 'preserved']] | [-0.1707129802711603, 0.17476945475555292, -0.08383685749227016, 0.00014522902340176376, -0.05753554620475757, -0.1625583415924765, 0.0426472331902086, 0.4516574646724333, -0.21883646987198788, -0.26201372234021675, 0.1102964490257886, -0.29906663118797067, -0.09671399948713923, 0.15848579474881586, 0.007657643352257143, 0.03576483352151284, 0.0008663262867617725, -0.0030845594223551823, -0.034623572360816424, -0.17162392076593594, 0.2981561022755312, -0.003044071095525333, 0.3233249800667019, 0.04201597413785829, 0.04700077286607927, 0.02548730085686882, 0.07216475238230559, -0.022710120522112836, -0.08707208313904576, 0.09614105359017204, 0.18706225721386843, -0.009326631014262982, 0.17260492917629752, -0.5009227245123965, -0.21394634994881062, -0.002934466189898477, 0.15516234547969435, 0.09564778678082299, -0.020102119597717013, -0.30368846546083983, 0.031035856512089323, -0.026714995357906446, -0.19289481713169135, -0.010130467612554532, -0.03794701191271646, 0.006925473819569786, -0.1807471063278505, 0.04964578261432966, 0.08532140811554867, 0.015669250307549343, -0.060007372693536745, -0.0852503847805691, -0.11497766220836098, 0.04397835842066324, 0.03286317797806483, -0.049778271157589586, 0.13722836670793112, -0.10714976570688851, -0.10269284600594847, 0.3767776225548293, -0.08896641600485153, -0.20926832702785436, 0.1605047271955795, -0.16269364990155663, -0.01748669057586553, 0.15284304177425434, 0.1697556954753207, 0.13535882082611028, -0.02522638536030703, 0.036327643903193645, -0.10551306392564115, 0.20050333708635357, 0.1969452999661317, 0.04956878826372547, 0.24312031328087985, 0.12462281604472658, 0.08884170630688568, 0.17519179436534937, -0.13614013071614547, -0.07153230687455818, -0.30394044154240646, -0.15192990264530284, -0.17877157982254382, -0.025319639667130932, -0.04817733970369295, -0.25026079381604005, 0.39762851643827885, 0.04882572752211501, 0.19162290923223638, -0.02270888800889549, 0.2837438261145634, 0.11607442225218925, 0.09854442204837457, 0.08610893380920412, 0.30626107214046366, 0.17807586002394116, 0.03662938210827363, -0.20726594942538898, 0.0022357765649731205, 0.027457060135605903] |
1,802.03527 | A generalized matrix Krylov subspace method for TV regularization | This paper presents an efficient algorithm to solve total variation (TV)
regularizations of images contaminated by a both blur and noise. The
unconstrained structure of the problem suggests that one can solve a
constrained optimization problem by transforming the original unconstrained
minimization problem to an equivalent constrained minimization one. An
augmented Lagrangian method is developed to handle the constraints when the
model is given with matrix variables, and an alternating direction method (ADM)
is used to iteratively find solutions. The solutions of some sub-problems are
belonging to subspaces generated by application of successive orthogonal
projections onto a class of generalized matrix Krylov subspaces of increasing
dimension.
| cs.NA | this paper presents an efficient algorithm to solve total variation tv regularizations of images contaminated by a both blur and noise the unconstrained structure of the problem suggests that one can solve a constrained optimization problem by transforming the original unconstrained minimization problem to an equivalent constrained minimization one an augmented lagrangian method is developed to handle the constraints when the model is given with matrix variables and an alternating direction method adm is used to iteratively find solutions the solutions of some subproblems are belonging to subspaces generated by application of successive orthogonal projections onto a class of generalized matrix krylov subspaces of increasing dimension | [['this', 'paper', 'presents', 'an', 'efficient', 'algorithm', 'to', 'solve', 'total', 'variation', 'tv', 'regularizations', 'of', 'images', 'contaminated', 'by', 'a', 'both', 'blur', 'and', 'noise', 'the', 'unconstrained', 'structure', 'of', 'the', 'problem', 'suggests', 'that', 'one', 'can', 'solve', 'a', 'constrained', 'optimization', 'problem', 'by', 'transforming', 'the', 'original', 'unconstrained', 'minimization', 'problem', 'to', 'an', 'equivalent', 'constrained', 'minimization', 'one', 'an', 'augmented', 'lagrangian', 'method', 'is', 'developed', 'to', 'handle', 'the', 'constraints', 'when', 'the', 'model', 'is', 'given', 'with', 'matrix', 'variables', 'and', 'an', 'alternating', 'direction', 'method', 'adm', 'is', 'used', 'to', 'iteratively', 'find', 'solutions', 'the', 'solutions', 'of', 'some', 'subproblems', 'are', 'belonging', 'to', 'subspaces', 'generated', 'by', 'application', 'of', 'successive', 'orthogonal', 'projections', 'onto', 'a', 'class', 'of', 'generalized', 'matrix', 'krylov', 'subspaces', 'of', 'increasing', 'dimension']] | [-0.10270292772296465, 0.0225795110723856, -0.04345357958403117, 0.0684233994315431, -0.09472110030627898, -0.15959021627006806, -0.014581013193028926, 0.37127144525776495, -0.39325980177887204, -0.3087460281511874, 0.14721534484426296, -0.24956973389950846, -0.17066179749222016, 0.1357550575147506, -0.12748193303298838, 0.08016854959083194, 0.09021805049763676, -0.015226409045698226, -0.15771334870709633, -0.29435278794877584, 0.3069781307570355, 0.00745318242555203, 0.22392939632969364, -0.047364775669093265, 0.18256340008053296, 0.029517339191065646, -0.03216855613655358, 0.048115344893819884, -0.030879163253903587, 0.1746028014557559, 0.2512565082892389, 0.18669192113623656, 0.3132177661458593, -0.40981333078992255, -0.18397917322383947, 0.15386624131263849, 0.17415326328087105, 0.08290667304734015, -0.030484334964366665, -0.26740762710852445, 0.07529628694162897, -0.11607542024257611, -0.10741232186643723, -0.06982298093004469, -0.06025495904800921, -0.040425535212358775, -0.350859053563085, 0.05794944657553163, 0.02112846968493442, -0.021600639107109944, -0.12514955756135882, -0.1422211308700015, 0.04460297867585466, 0.024310757775345096, 0.09499968915303536, 0.06734501838438073, 0.08601377839398272, -0.08138109381389716, -0.09953670368424423, 0.3876193661444803, -0.026299874156599266, -0.31833915599019985, 0.12658094839667375, 0.024282768065601867, -0.10693883985052553, 0.18445967534943572, 0.1964570419370847, 0.17534239422793518, -0.19497677850669343, 0.1083378739037797, -0.08307514826433277, 0.15328598382689482, 0.03059853802276951, -0.047720196472512744, 0.1158540354040012, 0.1184317104618355, 0.20972267621337384, 0.19310525559626943, -0.04831217053205759, -0.08341150040263837, -0.2089959412158625, -0.0985509115095549, -0.21430123868653164, -0.03374360386668792, -0.08542705470018466, -0.1588483647298384, 0.4098722839130546, 0.13410814965301948, 0.21927419770509005, 0.06355994927942893, 0.3393985607819456, 0.16247162885770625, 0.047813077271094476, 0.083385947472328, 0.15808352266294434, 0.1630979259300731, 0.03577156098700076, -0.22408728303282327, 0.015142611478744206, 0.1643660464324057] |
1,802.03528 | Coverless information hiding based on Generative Model | A new coverless image information hiding method based on generative model is
proposed, we feed the secret image to the generative model database, and
generate a meaning-normal and independent image different from the secret
image, then, the generated image is transmitted to the receiver and is fed to
the generative model database to generate another image visually the same as
the secret image. So we only need to transmit the meaning-normal image which is
not related to the secret image, and we can achieve the same effect as the
transmission of the secret image. This is the first time to propose the
coverless image information hiding method based on generative model, compared
with the traditional image steganography, the transmitted image does not embed
any information of the secret image in this method, therefore, can effectively
resist steganalysis tools. Experimental results show that our method has high
capacity, safety and reliability.
| cs.CV | a new coverless image information hiding method based on generative model is proposed we feed the secret image to the generative model database and generate a meaningnormal and independent image different from the secret image then the generated image is transmitted to the receiver and is fed to the generative model database to generate another image visually the same as the secret image so we only need to transmit the meaningnormal image which is not related to the secret image and we can achieve the same effect as the transmission of the secret image this is the first time to propose the coverless image information hiding method based on generative model compared with the traditional image steganography the transmitted image does not embed any information of the secret image in this method therefore can effectively resist steganalysis tools experimental results show that our method has high capacity safety and reliability | [['a', 'new', 'coverless', 'image', 'information', 'hiding', 'method', 'based', 'on', 'generative', 'model', 'is', 'proposed', 'we', 'feed', 'the', 'secret', 'image', 'to', 'the', 'generative', 'model', 'database', 'and', 'generate', 'a', 'meaningnormal', 'and', 'independent', 'image', 'different', 'from', 'the', 'secret', 'image', 'then', 'the', 'generated', 'image', 'is', 'transmitted', 'to', 'the', 'receiver', 'and', 'is', 'fed', 'to', 'the', 'generative', 'model', 'database', 'to', 'generate', 'another', 'image', 'visually', 'the', 'same', 'as', 'the', 'secret', 'image', 'so', 'we', 'only', 'need', 'to', 'transmit', 'the', 'meaningnormal', 'image', 'which', 'is', 'not', 'related', 'to', 'the', 'secret', 'image', 'and', 'we', 'can', 'achieve', 'the', 'same', 'effect', 'as', 'the', 'transmission', 'of', 'the', 'secret', 'image', 'this', 'is', 'the', 'first', 'time', 'to', 'propose', 'the', 'coverless', 'image', 'information', 'hiding', 'method', 'based', 'on', 'generative', 'model', 'compared', 'with', 'the', 'traditional', 'image', 'steganography', 'the', 'transmitted', 'image', 'does', 'not', 'embed', 'any', 'information', 'of', 'the', 'secret', 'image', 'in', 'this', 'method', 'therefore', 'can', 'effectively', 'resist', 'steganalysis', 'tools', 'experimental', 'results', 'show', 'that', 'our', 'method', 'has', 'high', 'capacity', 'safety', 'and', 'reliability']] | [-0.03691982379493614, -0.04925523418777933, -0.1363697559107095, 0.06498287527433907, -0.10420891617735227, -0.22981641239797077, 0.029080880352606375, 0.4223698636641105, -0.32584570042788985, -0.3169931175497671, 0.11304738820142424, -0.29628738669057686, -0.13384736830523858, 0.16001775902385512, -0.21242864227543273, 0.0999612852341185, 0.04952087470795959, 0.09675006586126983, -0.018619433571584524, -0.32056055195008715, 0.2976028802928825, 0.04468188324943185, 0.39095110502714914, 0.018628188786096872, 0.13944481309018253, -0.019912332923461992, -0.04791245620387296, -0.08114716356775413, -0.06056287631571953, 0.16196304383066792, 0.29112563005958997, 0.24229151739928056, 0.2306411582805837, -0.40018458229800064, -0.23869220765307544, 0.10032113256553808, 0.15794967914621036, 0.18357184604974464, -0.11106512280957152, -0.3327071543969214, 0.14125324467197062, -0.14450833064193527, 0.07811785897550484, -0.07641913193588455, -0.10375000799074768, -0.02662054520798847, -0.31245660881822307, 0.0023514371582617362, 0.06267216147854925, 0.01063616677497824, -0.03238822444846543, -0.01983096995080511, -0.028567084004171194, 0.2258382185548544, -0.005188724782007436, 0.10181116138352081, 0.15225692553135256, -0.1719426501255172, -0.08520858069327611, 0.38538936941574015, -0.0648649023178344, -0.2141011503835519, 0.10265635393947983, -0.07309767602632443, -0.08078388562736412, 0.13806672189074257, 0.1955961714188258, 0.08456422771016757, -0.15150706846266984, -0.02809506991451296, -0.08599153405055404, 0.2744564886018634, 0.04301817590448385, 0.04243984745039294, 0.17241535225262244, 0.16419106919318438, 0.020122274812310933, 0.17570879137609155, -0.20077669595678646, -0.03895234902078907, -0.21597895946353673, -0.14427998465796313, -0.2588062576732288, -0.03004238732450176, -0.08354365253151627, -0.10837252167363962, 0.41509542599941296, 0.2736162715529402, 0.1919481478041659, 0.048172891106611736, 0.453744007324179, 0.030277210960630328, 0.16164624781658252, 0.07590380396383505, 0.16274929113298034, 0.03529924745671451, 0.1018877553505202, -0.14300520031247288, 0.11007374177065989, 0.071756387486433] |
1,802.03529 | Revealing hidden scenes by photon-efficient occlusion-based
opportunistic active imaging | The ability to see around corners, i.e., recover details of a hidden scene
from its reflections in the surrounding environment, is of considerable
interest in a wide range of applications. However, the diffuse nature of light
reflected from typical surfaces leads to mixing of spatial information in the
collected light, precluding useful scene reconstruction. Here, we employ a
computational imaging technique that opportunistically exploits the presence of
occluding objects, which obstruct probe-light propagation in the hidden scene,
to undo the mixing and greatly improve scene recovery. Importantly, our
technique obviates the need for the ultrafast time-of-flight measurements
employed by most previous approaches to hidden-scene imaging. Moreover, it does
so in a photon-efficient manner based on an accurate forward model and a
computational algorithm that, together, respect the physics of three-bounce
light propagation and single-photon detection. Using our methodology, we
demonstrate reconstruction of hidden-surface reflectivity patterns in a
meter-scale environment from non-time-resolved measurements. Ultimately, our
technique represents an instance of a rich and promising new imaging modality
with important potential implications for imaging science.
| stat.AP physics.optics quant-ph | the ability to see around corners ie recover details of a hidden scene from its reflections in the surrounding environment is of considerable interest in a wide range of applications however the diffuse nature of light reflected from typical surfaces leads to mixing of spatial information in the collected light precluding useful scene reconstruction here we employ a computational imaging technique that opportunistically exploits the presence of occluding objects which obstruct probelight propagation in the hidden scene to undo the mixing and greatly improve scene recovery importantly our technique obviates the need for the ultrafast timeofflight measurements employed by most previous approaches to hiddenscene imaging moreover it does so in a photonefficient manner based on an accurate forward model and a computational algorithm that together respect the physics of threebounce light propagation and singlephoton detection using our methodology we demonstrate reconstruction of hiddensurface reflectivity patterns in a meterscale environment from nontimeresolved measurements ultimately our technique represents an instance of a rich and promising new imaging modality with important potential implications for imaging science | [['the', 'ability', 'to', 'see', 'around', 'corners', 'ie', 'recover', 'details', 'of', 'a', 'hidden', 'scene', 'from', 'its', 'reflections', 'in', 'the', 'surrounding', 'environment', 'is', 'of', 'considerable', 'interest', 'in', 'a', 'wide', 'range', 'of', 'applications', 'however', 'the', 'diffuse', 'nature', 'of', 'light', 'reflected', 'from', 'typical', 'surfaces', 'leads', 'to', 'mixing', 'of', 'spatial', 'information', 'in', 'the', 'collected', 'light', 'precluding', 'useful', 'scene', 'reconstruction', 'here', 'we', 'employ', 'a', 'computational', 'imaging', 'technique', 'that', 'opportunistically', 'exploits', 'the', 'presence', 'of', 'occluding', 'objects', 'which', 'obstruct', 'probelight', 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1,802.0353 | Aurora: Providing Trusted System Services for Enclaves On an Untrusted
System | Intel SGX provisions shielded executions for security-sensitive computation,
but lacks support for trusted system services (TSS), such as clock, network and
filesystem. This makes \textit{enclaves} vulnerable to Iago
attacks~\cite{DBLP:conf/asplos/CheckowayS13} in the face of a powerful
malicious system. To mitigate this problem, we present Aurora, a novel
architecture that provides TSSes via a secure channel between enclaves and
devices on top of an untrusted system, and implement two types of TSSes, i.e.
clock and end-to-end network. We evaluate our solution by porting SQLite and
OpenSSL into Aurora, experimental results show that SQLite benefits from a
\textit{microsecond} accuracy trusted clock and OpenSSL gains end-to-end secure
network with about 1ms overhead.
| cs.CR | intel sgx provisions shielded executions for securitysensitive computation but lacks support for trusted system services tss such as clock network and filesystem this makes textitenclaves vulnerable to iago attackscitedblpconfasploscheckoways13 in the face of a powerful malicious system to mitigate this problem we present aurora a novel architecture that provides tsses via a secure channel between enclaves and devices on top of an untrusted system and implement two types of tsses ie clock and endtoend network we evaluate our solution by porting sqlite and openssl into aurora experimental results show that sqlite benefits from a textitmicrosecond accuracy trusted clock and openssl gains endtoend secure network with about 1ms overhead | [['intel', 'sgx', 'provisions', 'shielded', 'executions', 'for', 'securitysensitive', 'computation', 'but', 'lacks', 'support', 'for', 'trusted', 'system', 'services', 'tss', 'such', 'as', 'clock', 'network', 'and', 'filesystem', 'this', 'makes', 'textitenclaves', 'vulnerable', 'to', 'iago', 'attackscitedblpconfasploscheckoways13', 'in', 'the', 'face', 'of', 'a', 'powerful', 'malicious', 'system', 'to', 'mitigate', 'this', 'problem', 'we', 'present', 'aurora', 'a', 'novel', 'architecture', 'that', 'provides', 'tsses', 'via', 'a', 'secure', 'channel', 'between', 'enclaves', 'and', 'devices', 'on', 'top', 'of', 'an', 'untrusted', 'system', 'and', 'implement', 'two', 'types', 'of', 'tsses', 'ie', 'clock', 'and', 'endtoend', 'network', 'we', 'evaluate', 'our', 'solution', 'by', 'porting', 'sqlite', 'and', 'openssl', 'into', 'aurora', 'experimental', 'results', 'show', 'that', 'sqlite', 'benefits', 'from', 'a', 'textitmicrosecond', 'accuracy', 'trusted', 'clock', 'and', 'openssl', 'gains', 'endtoend', 'secure', 'network', 'with', 'about', '1ms', 'overhead']] | [-0.21124165302856815, -0.06233842650445933, -0.020057869561975007, 0.015766888248815555, -0.07693900218266243, -0.2864431989225514, 0.15313734806784726, 0.42572045224962884, -0.23388225530970444, -0.33223640273135263, 0.11745551550756345, -0.26763707280014326, -0.1321560387291665, 0.22992066795784002, -0.1510334130977303, 0.09628530168562259, 0.12211548846929325, -0.010401391749749484, 0.008135180321332175, -0.24924311580733188, 0.25372304457682865, 0.08119025496422376, 0.30415354320978366, 0.08444603349000794, 0.11588883274280042, -0.032533975613068394, 0.02335396266623753, -0.07546985348250301, 0.00775017057931462, 0.13982965894726115, 0.2757264391962186, 0.24544506725737625, 0.28687132634584184, -0.4751606589562974, -0.1429886996601392, 0.03645910111084146, 0.11976643219517852, 0.08984930747306173, -0.05367714502663325, -0.365392017295783, 0.15874304543746617, -0.33675873548117946, -0.029673612174590445, -0.12289128682657498, -0.029183217446383053, -0.01428745801897394, -0.2817272985335982, -0.027857873715387966, 0.015625230945393587, 0.07618536362967011, 0.007529120965045877, -0.05729211230414187, 0.022524775966585985, 0.17502008659710871, -0.04782571432687053, 0.031971633149473704, 0.19523193850860143, -0.09966685606817459, -0.1778495677575035, 0.3659258381083825, -0.024899078915076492, -0.13228834325191113, 0.19079479282819078, 0.0512743179812955, -0.1474102287987265, 0.06572004762089369, 0.2581116507842558, 0.01776485150065092, -0.1762725047821414, -0.0068749108468182385, 0.006236364798240581, 0.2594981761558831, 0.033490604644153016, 0.09725646130758726, 0.18410407764457384, 0.22822193829433143, 0.09098448668543285, 0.18511008146869978, -0.07389297519665826, -0.0803868161916769, -0.2214077875578722, -0.1792613288423824, -0.15156531389757485, -0.003985863402757106, -0.06167009331424974, -0.13673779523922402, 0.337957126205986, 0.2539131717611719, 0.0979295348414012, 0.1211104813637664, 0.47508044171969865, -0.05370456793625812, 0.1435328605901269, 0.2063565753870508, 0.16037678914777573, -0.03216250696969987, 0.18309756116127796, -0.14875859267403851, 0.1364563418398566, -0.022359151663586443] |
1,802.03531 | Collaborative Learning for Weakly Supervised Object Detection | Weakly supervised object detection has recently received much attention,
since it only requires image-level labels instead of the bounding-box labels
consumed in strongly supervised learning. Nevertheless, the save in labeling
expense is usually at the cost of model accuracy. In this paper, we propose a
simple but effective weakly supervised collaborative learning framework to
resolve this problem, which trains a weakly supervised learner and a strongly
supervised learner jointly by enforcing partial feature sharing and prediction
consistency. For object detection, taking WSDDN-like architecture as weakly
supervised detector sub-network and Faster-RCNN-like architecture as strongly
supervised detector sub-network, we propose an end-to-end Weakly Supervised
Collaborative Detection Network. As there is no strong supervision available to
train the Faster-RCNN-like sub-network, a new prediction consistency loss is
defined to enforce consistency of predictions between the two sub-networks as
well as within the Faster-RCNN-like sub-networks. At the same time, the two
detectors are designed to partially share features to further guarantee the
model consistency at perceptual level. Extensive experiments on PASCAL VOC 2007
and 2012 data sets have demonstrated the effectiveness of the proposed
framework.
| cs.CV | weakly supervised object detection has recently received much attention since it only requires imagelevel labels instead of the boundingbox labels consumed in strongly supervised learning nevertheless the save in labeling expense is usually at the cost of model accuracy in this paper we propose a simple but effective weakly supervised collaborative learning framework to resolve this problem which trains a weakly supervised learner and a strongly supervised learner jointly by enforcing partial feature sharing and prediction consistency for object detection taking wsddnlike architecture as weakly supervised detector subnetwork and fasterrcnnlike architecture as strongly supervised detector subnetwork we propose an endtoend weakly supervised collaborative detection network as there is no strong supervision available to train the fasterrcnnlike subnetwork a new prediction consistency loss is defined to enforce consistency of predictions between the two subnetworks as well as within the fasterrcnnlike subnetworks at the same time the two detectors are designed to partially share features to further guarantee the model consistency at perceptual level extensive experiments on pascal voc 2007 and 2012 data sets have demonstrated the effectiveness of the proposed framework | [['weakly', 'supervised', 'object', 'detection', 'has', 'recently', 'received', 'much', 'attention', 'since', 'it', 'only', 'requires', 'imagelevel', 'labels', 'instead', 'of', 'the', 'boundingbox', 'labels', 'consumed', 'in', 'strongly', 'supervised', 'learning', 'nevertheless', 'the', 'save', 'in', 'labeling', 'expense', 'is', 'usually', 'at', 'the', 'cost', 'of', 'model', 'accuracy', 'in', 'this', 'paper', 'we', 'propose', 'a', 'simple', 'but', 'effective', 'weakly', 'supervised', 'collaborative', 'learning', 'framework', 'to', 'resolve', 'this', 'problem', 'which', 'trains', 'a', 'weakly', 'supervised', 'learner', 'and', 'a', 'strongly', 'supervised', 'learner', 'jointly', 'by', 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1,802.03532 | Bayesian Optimization Using Monotonicity Information and Its Application
in Machine Learning Hyperparameter | We propose an algorithm for a family of optimization problems where the
objective can be decomposed as a sum of functions with monotonicity properties.
The motivating problem is optimization of hyperparameters of machine learning
algorithms, where we argue that the objective, validation error, can be
decomposed as monotonic functions of the hyperparameters. Our proposed
algorithm adapts Bayesian optimization methods to incorporate the monotonicity
constraints. We illustrate the advantages of exploiting monotonicity using
illustrative examples and demonstrate the improvements in optimization
efficiency for some machine learning hyperparameter tuning applications.
| cs.LG stat.ML | we propose an algorithm for a family of optimization problems where the objective can be decomposed as a sum of functions with monotonicity properties the motivating problem is optimization of hyperparameters of machine learning algorithms where we argue that the objective validation error can be decomposed as monotonic functions of the hyperparameters our proposed algorithm adapts bayesian optimization methods to incorporate the monotonicity constraints we illustrate the advantages of exploiting monotonicity using illustrative examples and demonstrate the improvements in optimization efficiency for some machine learning hyperparameter tuning applications | [['we', 'propose', 'an', 'algorithm', 'for', 'a', 'family', 'of', 'optimization', 'problems', 'where', 'the', 'objective', 'can', 'be', 'decomposed', 'as', 'a', 'sum', 'of', 'functions', 'with', 'monotonicity', 'properties', 'the', 'motivating', 'problem', 'is', 'optimization', 'of', 'hyperparameters', 'of', 'machine', 'learning', 'algorithms', 'where', 'we', 'argue', 'that', 'the', 'objective', 'validation', 'error', 'can', 'be', 'decomposed', 'as', 'monotonic', 'functions', 'of', 'the', 'hyperparameters', 'our', 'proposed', 'algorithm', 'adapts', 'bayesian', 'optimization', 'methods', 'to', 'incorporate', 'the', 'monotonicity', 'constraints', 'we', 'illustrate', 'the', 'advantages', 'of', 'exploiting', 'monotonicity', 'using', 'illustrative', 'examples', 'and', 'demonstrate', 'the', 'improvements', 'in', 'optimization', 'efficiency', 'for', 'some', 'machine', 'learning', 'hyperparameter', 'tuning', 'applications']] | [-0.04409023487824015, -0.062124143792309704, -0.0978618042215451, 0.07337064917903097, -0.13050970834129574, -0.16530059664298527, 0.04476112722444337, 0.44099305680190976, -0.3457001279404556, -0.3391298862301152, 0.11820664184713016, -0.20293531573208218, -0.20934054721146822, 0.2245902564291927, -0.13046194120860574, 0.14593577687628567, 0.110106724483723, -0.05318010261874984, -0.149456511846934, -0.31664847231216053, 0.27730198615145957, 0.007270514922724528, 0.27268283240581775, 0.05328594427026639, 0.1508524760799694, 0.025136499808550896, 0.036005038249475714, 0.034942459579641844, -0.11637719683072976, 0.14979915211932332, 0.31922154741054826, 0.28923489674079145, 0.41951027868146246, -0.3419040428813208, -0.22219189801465042, 0.15474886503282256, 0.18415147351863032, 0.029330494665456088, -0.04646757820790464, -0.2535952804398469, 0.03970871741396629, -0.13135250546673144, -0.02025076232067394, -0.19767864752264525, -0.1048570477234369, 0.09351335719905117, -0.3923508962192996, 0.014945623503246514, 0.05380605753320693, 0.03441484126431698, -0.06587942413699982, -0.19357992881189354, 0.04664639620882967, 0.0675962682315995, 0.09528712516343645, 0.0022741054840893908, 0.1848384578611744, -0.1259472564085048, -0.22013005455532533, 0.3423109125281123, -0.04417959121267565, -0.28507721015590837, 0.09986230550566688, 0.05702076230557974, -0.18490616244856606, 0.07452594748677009, 0.25304521595403046, 0.1504187460734763, -0.13647817853118546, 0.08834334219401088, -0.011298591927201911, 0.14550542619756676, 0.0021307498258961873, 0.0027588205253282054, 0.10563450536309657, 0.22610489948949014, 0.1319121911179867, 0.23865052425588312, -0.04144874128343707, -0.09458193332433108, -0.3122053196315061, -0.13907060579433825, -0.22600714341652664, -0.029042146634310484, -0.14254142796149114, -0.14811210632747548, 0.41193242102268746, 0.19398699216121298, 0.18409066428747875, 0.12291325561024925, 0.33784093449569563, 0.13158442883683497, 0.06453223060816526, 0.11023240370824086, 0.20446739331137823, 0.08800416297956624, 0.056950511230917815, -0.21239704665177586, 0.09689263937021182, 0.039390701823074116] |
1,802.03533 | Hartree-Fock study of an Anderson metal-insulator transition in the
presence of Coulomb interaction: Two types of mobility edges and their
multifractal scaling exponents | In summary, we investigated the role of Coulomb interactions in the nature of
eigenfunction multifractality of an Anderson metal-insulator transition, based
on the Hartree-Fock approximation and the Ewald summation technique. As a
result, we showed that two types of mobility edges appear near the Fermi energy
and at a high energy, respectively, where the low-energy mobility edge results
from Coulomb interactions while the high-energy one is nothing but the mobility
edge of the Anderson localization transition without electron correlations.
Indeed, not only multifractal scaling exponents but also the multifractal
singularity spectrum confirms the existence of two kinds of mobility edges:
Their values differ from those of the Anderson metal-insulator transition and
the singularity spectrum collapses into two types of curves, implying two kinds
of scale-invariance, which depends on the energy scale. We speculate that this
novel nature of the eigenfunction multifractality would serve as valuable
information for possible instabilities near a metal-insulator transition in the
presence of Coulomb interactions.
| cond-mat.dis-nn | in summary we investigated the role of coulomb interactions in the nature of eigenfunction multifractality of an anderson metalinsulator transition based on the hartreefock approximation and the ewald summation technique as a result we showed that two types of mobility edges appear near the fermi energy and at a high energy respectively where the lowenergy mobility edge results from coulomb interactions while the highenergy one is nothing but the mobility edge of the anderson localization transition without electron correlations indeed not only multifractal scaling exponents but also the multifractal singularity spectrum confirms the existence of two kinds of mobility edges their values differ from those of the anderson metalinsulator transition and the singularity spectrum collapses into two types of curves implying two kinds of scaleinvariance which depends on the energy scale we speculate that this novel nature of the eigenfunction multifractality would serve as valuable information for possible instabilities near a metalinsulator transition in the presence of coulomb interactions | [['in', 'summary', 'we', 'investigated', 'the', 'role', 'of', 'coulomb', 'interactions', 'in', 'the', 'nature', 'of', 'eigenfunction', 'multifractality', 'of', 'an', 'anderson', 'metalinsulator', 'transition', 'based', 'on', 'the', 'hartreefock', 'approximation', 'and', 'the', 'ewald', 'summation', 'technique', 'as', 'a', 'result', 'we', 'showed', 'that', 'two', 'types', 'of', 'mobility', 'edges', 'appear', 'near', 'the', 'fermi', 'energy', 'and', 'at', 'a', 'high', 'energy', 'respectively', 'where', 'the', 'lowenergy', 'mobility', 'edge', 'results', 'from', 'coulomb', 'interactions', 'while', 'the', 'highenergy', 'one', 'is', 'nothing', 'but', 'the', 'mobility', 'edge', 'of', 'the', 'anderson', 'localization', 'transition', 'without', 'electron', 'correlations', 'indeed', 'not', 'only', 'multifractal', 'scaling', 'exponents', 'but', 'also', 'the', 'multifractal', 'singularity', 'spectrum', 'confirms', 'the', 'existence', 'of', 'two', 'kinds', 'of', 'mobility', 'edges', 'their', 'values', 'differ', 'from', 'those', 'of', 'the', 'anderson', 'metalinsulator', 'transition', 'and', 'the', 'singularity', 'spectrum', 'collapses', 'into', 'two', 'types', 'of', 'curves', 'implying', 'two', 'kinds', 'of', 'scaleinvariance', 'which', 'depends', 'on', 'the', 'energy', 'scale', 'we', 'speculate', 'that', 'this', 'novel', 'nature', 'of', 'the', 'eigenfunction', 'multifractality', 'would', 'serve', 'as', 'valuable', 'information', 'for', 'possible', 'instabilities', 'near', 'a', 'metalinsulator', 'transition', 'in', 'the', 'presence', 'of', 'coulomb', 'interactions']] | [-0.16826166451246766, 0.1480396515053716, -0.10466530252469165, 0.11911251825872082, -0.00729697252735027, -0.1479519392120646, 0.06937334004426433, 0.3258452321257759, -0.23054343433874958, -0.27880264675016253, 0.029647703350627056, -0.35795592250150143, -0.16881895292866625, 0.13263094233079525, 0.0512828518015643, 0.013486645011024099, 0.01489499202818147, 0.014711849034160562, -0.06393906984620276, -0.1671144679648532, 0.35027937603376386, 0.03771356990946744, 0.30421822766462964, 0.1545014718588465, -0.01736774544799759, 0.03723946443477751, 0.061394211754155985, 0.021154248310873907, -0.12266939337141651, 0.0454873217140916, 0.2265673701134481, -0.03610385086609976, 0.24499204894234924, -0.41783638429716696, -0.23236553406096855, 0.05292603764214418, 0.1647145752780397, 0.09105038139808033, -0.0410700065315168, -0.3067559192115265, 0.031190186012653436, -0.14639866092599602, -0.1464842987252669, -0.043532230312871574, 0.0024584866972323857, 0.014413708890861474, -0.235648466520152, 0.14415894457232062, 0.0895059165374944, 0.048138925467424515, -0.07913763174763734, -0.07426916938802262, -0.045939248763961986, 0.1387383109837208, 0.06831155700642767, -0.04551332701457395, 0.1294266101992074, -0.14148338259572643, -0.12865474518482228, 0.36907747775058514, -0.030778911045266304, -0.10705251155315705, 0.21918003673594938, -0.20181662895652297, -0.12544150336545967, 0.1911252047603001, 0.10369994869139397, 0.044757896258185305, -0.106154804076976, 0.0818025222119184, 0.013101257064596474, 0.13077459578788928, 0.0703406200843787, 0.09267895843294806, 0.22566726076293667, 0.13472431988613504, 0.05527757405180414, 0.09119098902015167, -0.11719217081114931, -0.10086633919616693, -0.2811536263182478, -0.11251219828560385, -0.2669958715040073, 0.07749301939772563, -0.11039678337326662, -0.22706374170188354, 0.4606913808664035, 0.1760323890807025, 0.2303825516016486, -0.011717548405710584, 0.20779210551928892, 0.1325109038200323, 0.03928603155907454, 0.07514913612390933, 0.2443877018449254, 0.06732540944028857, 0.10277167940951602, -0.25213350570883675, 0.04284960932044148, 0.11195557597444726] |
1,802.03534 | A family of ovoids in PG(3, 2^m) from cyclic codes | Ovoids in $\PG(3, q)$ have been an interesting topic in coding theory,
combinatorics, and finite geometry for a long time. So far only two families
are known. The first is the elliptic quadratics and the second is the Tits
ovoids. In this article, we present a family of ovoids in $\PG(3, 2^m)$ for all
$m$ which are from a family of irreducible cyclic codes.
| math.CO cs.IT math.IT | ovoids in pg3 q have been an interesting topic in coding theory combinatorics and finite geometry for a long time so far only two families are known the first is the elliptic quadratics and the second is the tits ovoids in this article we present a family of ovoids in pg3 2m for all m which are from a family of irreducible cyclic codes | [['ovoids', 'in', 'pg3', 'q', 'have', 'been', 'an', 'interesting', 'topic', 'in', 'coding', 'theory', 'combinatorics', 'and', 'finite', 'geometry', 'for', 'a', 'long', 'time', 'so', 'far', 'only', 'two', 'families', 'are', 'known', 'the', 'first', 'is', 'the', 'elliptic', 'quadratics', 'and', 'the', 'second', 'is', 'the', 'tits', 'ovoids', 'in', 'this', 'article', 'we', 'present', 'a', 'family', 'of', 'ovoids', 'in', 'pg3', '2m', 'for', 'all', 'm', 'which', 'are', 'from', 'a', 'family', 'of', 'irreducible', 'cyclic', 'codes']] | [-0.19960494555198238, 0.11304598892661488, -0.08146910912546446, 0.0378214832253434, -0.07630430148128653, -0.14023326058668317, -0.05891838806383021, 0.3673430402559461, -0.3060579155571759, -0.2270713835314382, 0.15999141462089028, -0.3058768844421138, -0.14428473613224924, 0.19936076596422936, -0.11881244792311918, 0.03280895279021934, -0.023374129959847778, 0.09986949362792075, -0.08357744735258166, -0.2858557050349191, 0.3493042351838085, -0.04683613574889023, 0.1843455445341533, 0.009563229948980734, 0.08813980004924815, -0.02211305917444406, -0.01323739904910326, -0.011912232381291687, -0.15386934460548218, 0.11033768288325518, 0.329008787884959, 0.07674537101047463, 0.2397718532665749, -0.3058862060861429, -0.1923327449185308, 0.14058819809179113, 0.1614995759591693, 0.09978721643528843, -0.053168447673670016, -0.18250120141601656, 0.08028081522206776, -0.17718382897146512, -0.0998166489880532, -0.009398335922014667, 0.08250291975127766, 0.028898868586111348, -0.2097953083430184, -0.021612158003335935, 0.12301588774425909, 0.14458265689609107, -0.005285305487632286, -0.17584506557614077, 0.016632950686471304, 0.11513353150076, 0.0036715915048262104, 0.03203350487456191, -0.07499696999730077, -0.03587841508669953, -0.12422675707784947, 0.3554759917078627, 0.01182590461394284, -0.18935353858978488, 0.10837556089609279, -0.15207384564564563, -0.190098322207632, 0.14967277152754832, 0.16989162215031683, 0.21687647618091432, -0.10393107892014086, 0.17015550647010969, -0.15597307589632692, 0.09836755489232019, 0.12652495315705892, 0.010794452304253355, 0.1664330131825409, 0.10160281004209537, 0.05614954952397966, 0.13210368068394018, -0.010729991990956478, -0.06238658309302991, -0.3345976414857432, -0.15943684740341268, -0.15101164006227918, 0.07121533145254944, -0.07756312991068626, -0.18222000567038776, 0.396844038303243, 0.03129816090222448, 0.16127792035695165, 0.06233160868760024, 0.21854200583766215, 0.0037811004440300167, 0.0635217979142908, 0.12086155462020542, 0.20194048632401973, 0.1685571380512556, -0.009152256272500381, -0.10245143286783787, -0.0005390076594267157, 0.16045731122721918] |
1,802.03535 | Geometric Regularity Criteria for Incompressible Navier--Stokes
Equations with Navier Boundary Conditions | We study the regularity criteria for weak solutions to the $3D$
incompressible Navier--Stokes equations in terms of the geometry of vortex
structures, taking into account the boundary effects. A boundary regularity
theorem is proved on regular domains with a class of oblique derivative
boundary conditions, providing that the vorticity of the fluid is coherently
aligned. In particular, we establish the boundary regularity on round balls,
half-spaces and right circular cylindrical ducts, subject to the classical
Navier and kinematic boundary conditions.
| math.AP math-ph math.MP physics.flu-dyn | we study the regularity criteria for weak solutions to the 3d incompressible navierstokes equations in terms of the geometry of vortex structures taking into account the boundary effects a boundary regularity theorem is proved on regular domains with a class of oblique derivative boundary conditions providing that the vorticity of the fluid is coherently aligned in particular we establish the boundary regularity on round balls halfspaces and right circular cylindrical ducts subject to the classical navier and kinematic boundary conditions | [['we', 'study', 'the', 'regularity', 'criteria', 'for', 'weak', 'solutions', 'to', 'the', '3d', 'incompressible', 'navierstokes', 'equations', 'in', 'terms', 'of', 'the', 'geometry', 'of', 'vortex', 'structures', 'taking', 'into', 'account', 'the', 'boundary', 'effects', 'a', 'boundary', 'regularity', 'theorem', 'is', 'proved', 'on', 'regular', 'domains', 'with', 'a', 'class', 'of', 'oblique', 'derivative', 'boundary', 'conditions', 'providing', 'that', 'the', 'vorticity', 'of', 'the', 'fluid', 'is', 'coherently', 'aligned', 'in', 'particular', 'we', 'establish', 'the', 'boundary', 'regularity', 'on', 'round', 'balls', 'halfspaces', 'and', 'right', 'circular', 'cylindrical', 'ducts', 'subject', 'to', 'the', 'classical', 'navier', 'and', 'kinematic', 'boundary', 'conditions']] | [-0.19785326974233614, 0.0615759554086253, -0.0770116099549341, 0.03400028345931787, -0.11757281033787877, -0.11982841371791438, -0.08000106520485133, 0.30067665769020097, -0.27830445660511033, -0.1903926865576068, 0.174865025166946, -0.2346644024597481, -0.040777246994548474, 0.1494716552726459, -0.10124358332250268, 0.12463145783403888, 0.09124930901452899, -0.00681712847435847, -0.06621556543104816, -0.20913759584072977, 0.389576931274496, -0.09232547350111417, 0.2857874757377431, 0.05576754428911954, 0.11796392547912546, -0.03628287554020062, 0.018516233027912676, 0.09046234819106758, -0.2774014384752263, 0.07314159562229179, 0.17408981737680734, -0.02592118089669384, 0.2461016171902884, -0.5278754542116075, -0.222758200915996, 0.04792379637947306, 0.0895465598499868, 0.0611206773435697, -0.012356028510839678, -0.3307094997027889, 0.10176879430073313, -0.02102818160201423, -0.21245200010889675, 0.006343765615019948, -0.00130262253805995, 0.032766943340539, -0.3298273248481564, 0.10046054854756221, 0.14312792423588688, 0.057502650225069375, -0.20420970430132002, -0.03883586803567596, -0.08140235210303218, 0.07268994641490281, 0.07093195559282321, 5.47434581676498e-05, 0.06628748608927708, -0.18596686059609055, -0.025859760510502382, 0.37035668701864777, -0.015116075181867927, -0.35076394653879106, 0.20894892201758922, -0.19728745663669542, -0.04250101741636172, 0.10553251825156167, 0.18844135647523216, 0.13936812481260857, -0.12333550541079603, 0.13347084785200422, -0.09995161937404191, 0.08137304635019973, 0.17783456426113844, -0.019577632035361604, 0.1620984551263973, 0.12236504941538442, 0.15123252369230614, 0.2030657670693472, -0.07828872977115679, -0.12630744293273893, -0.38065485914703456, -0.16111803745152428, -0.10769581308704801, 0.05750149553950905, -0.14064826033391, -0.2450741848908365, 0.3478888667305, 0.09144549623688362, 0.1446251040291827, 0.040925851941574366, 0.26478060050867497, 0.12164370762184262, 0.003350597135022326, 0.10263092582463287, 0.2146691050962545, 0.19627873558201828, 0.13741835374967196, -0.18718409148277715, 0.017279307544231414, 0.1979421748081222] |
1,802.03536 | Supereigenvalue Models and Topological Recursion | We show that the Eynard-Orantin topological recursion, in conjunction with
simple auxiliary equations, can be used to calculate all correlation functions
of supereigenvalue models.
| hep-th math-ph math.MP | we show that the eynardorantin topological recursion in conjunction with simple auxiliary equations can be used to calculate all correlation functions of supereigenvalue models | [['we', 'show', 'that', 'the', 'eynardorantin', 'topological', 'recursion', 'in', 'conjunction', 'with', 'simple', 'auxiliary', 'equations', 'can', 'be', 'used', 'to', 'calculate', 'all', 'correlation', 'functions', 'of', 'supereigenvalue', 'models']] | [-0.10069852462038398, 0.09438787373558928, -0.1422099800159534, 0.1884393113044401, -0.10214489308418706, -0.144745705028375, -0.04126363294199109, 0.361638518050313, -0.3165323454886675, -0.2666212832555175, 0.04183396720812501, -0.29205205850303173, -0.22886434585476914, 0.1923278181347996, -0.03646547455961505, 0.08086707287778457, 0.02633215448198219, -0.0028675549353162446, -0.19173320102466582, -0.255234703887254, 0.3170819457930823, -0.08535046386532485, 0.24971262319013476, 0.020266713031257193, 0.09257410202796261, 0.03289473477828627, -0.007271173972791682, 0.06750308442860842, -0.12283141822899779, 0.11481108306907117, 0.26669629791967964, 0.0963926794550692, 0.09995276608970016, -0.4720756517102321, -0.21853326148508737, 0.09821637778077275, 0.15463818854186684, 0.12435556009101371, 0.03677134308964014, -0.22312913392670453, 0.10111065765765186, -0.20411776223530373, -0.129476258958069, -0.19991138317467025, -0.046802899858448654, 0.08505605157309522, -0.280632321179534, 0.01353299718660613, -0.054148152897444866, 0.0034070586940894523, -0.05511661638350537, -0.07889025328525652, -0.11618666670013529, 0.09672611486166716, -0.03001555700514776, 0.051174476490511246, 0.004458789345032225, -0.10886365625386436, -0.17805467984241355, 0.30315565682637197, -0.15637214272283018, -0.2738826606267442, 0.16800687035235265, -0.11697729110407333, -0.19062494987156242, 0.04518297454342246, 0.14945242487980673, 0.11069206598525246, -0.17470041262762, 0.09939585075092812, -0.042805850272998214, 0.1686280119853715, 0.04482349019963294, 0.02464342041639611, 0.17981252058719596, 0.007066457377125819, -0.028506822922887903, 0.20194637212262023, -0.011876492761075497, -0.08136808602527405, -0.32411531390001375, -0.1764515364775434, -0.14258899757502755, 0.04823390312958509, -0.10834114779330169, -0.19250551409398517, 0.39337970157309127, 0.20977071767750508, 0.17621949985429333, 0.11234843672718853, 0.22891146022205552, 0.28177330633237335, 0.13550172792747617, 0.06273261302461226, 0.16134320183967552, 0.15113533660769463, 4.007170597712199e-06, -0.19538552042407295, 0.037358683398148664, 0.19995397558280578] |
1,802.03537 | Automatic physical inference with information maximising neural networks | Compressing large data sets to a manageable number of summaries that are
informative about the underlying parameters vastly simplifies both frequentist
and Bayesian inference. When only simulations are available, these summaries
are typically chosen heuristically, so they may inadvertently miss important
information. We introduce a simulation-based machine learning technique that
trains artificial neural networks to find non-linear functionals of data that
maximise Fisher information: information maximising neural networks (IMNNs). In
test cases where the posterior can be derived exactly, likelihood-free
inference based on automatically derived IMNN summaries produces nearly exact
posteriors, showing that these summaries are good approximations to sufficient
statistics. In a series of numerical examples of increasing complexity and
astrophysical relevance we show that IMNNs are robustly capable of
automatically finding optimal, non-linear summaries of the data even in cases
where linear compression fails: inferring the variance of Gaussian signal in
the presence of noise; inferring cosmological parameters from mock simulations
of the Lyman-{\alpha} forest in quasar spectra; and inferring frequency-domain
parameters from LISA-like detections of gravitational waveforms. In this final
case, the IMNN summary outperforms linear data compression by avoiding the
introduction of spurious likelihood maxima. We anticipate that the automatic
physical inference method described in this paper will be essential to obtain
both accurate and precise cosmological parameter estimates from complex and
large astronomical data sets, including those from LSST and Euclid.
| astro-ph.IM | compressing large data sets to a manageable number of summaries that are informative about the underlying parameters vastly simplifies both frequentist and bayesian inference when only simulations are available these summaries are typically chosen heuristically so they may inadvertently miss important information we introduce a simulationbased machine learning technique that trains artificial neural networks to find nonlinear functionals of data that maximise fisher information information maximising neural networks imnns in test cases where the posterior can be derived exactly likelihoodfree inference based on automatically derived imnn summaries produces nearly exact posteriors showing that these summaries are good approximations to sufficient statistics in a series of numerical examples of increasing complexity and astrophysical relevance we show that imnns are robustly capable of automatically finding optimal nonlinear summaries of the data even in cases where linear compression fails inferring the variance of gaussian signal in the presence of noise inferring cosmological parameters from mock simulations of the lymanalpha forest in quasar spectra and inferring frequencydomain parameters from lisalike detections of gravitational waveforms in this final case the imnn summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima we anticipate that the automatic physical inference method described in this paper will be essential to obtain both accurate and precise cosmological parameter estimates from complex and large astronomical data sets including those from lsst and euclid | [['compressing', 'large', 'data', 'sets', 'to', 'a', 'manageable', 'number', 'of', 'summaries', 'that', 'are', 'informative', 'about', 'the', 'underlying', 'parameters', 'vastly', 'simplifies', 'both', 'frequentist', 'and', 'bayesian', 'inference', 'when', 'only', 'simulations', 'are', 'available', 'these', 'summaries', 'are', 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1,802.03538 | Constraining the halo size from possible density profiles of hydrogen
gas of Milky Way Galaxy | Galactic magnetic field (GMF) and secondary cosmic rays (CRs) (e.g.
$^{10}$beryllium, boron, antiproton) are important components to understand the
propagation of CRs in the Milky Way Galaxy. Realistic modeling of GMF is based
on the Faraday rotation measurements of various Galactic and extragalactic
radio sources and synchrotron emission from CR leptons in the radio frequency
range, thereby providing information of halo height. On the other hand,
diffusion coefficient and halo height are also estimated from the
$^{10}$Be/$^{9}$Be and B/C ratios. Moreover, density distribution of gaseous
components of interstellar medium (ISM) also plays an important role as
secondary CRs are produced due to interaction of primary CRs with the gaseous
components of ISM. We consider mainly molecular, atomic, and ionized components
of hydrogen gas for our study. Recent observations and hydrodynamical
simulations provide new forms of density profiles of hydrogen gas in Milky Way
Galaxy. In the \texttt{DRAGON} code, we have implemented our chosen density
profiles, based on realistic observations in radio, X-ray and $\gamma$-ray
wavebands, and hydrodynamical simulations of interstellar hydrogen gas to study
the variation in the height of the halo required to fit the observed CR
spectra. Our results show the halo height ($z_{t}$) varies in the range of 2 to
6 kpc for the density profiles considered in our work.
| astro-ph.HE | galactic magnetic field gmf and secondary cosmic rays crs eg 10beryllium boron antiproton are important components to understand the propagation of crs in the milky way galaxy realistic modeling of gmf is based on the faraday rotation measurements of various galactic and extragalactic radio sources and synchrotron emission from cr leptons in the radio frequency range thereby providing information of halo height on the other hand diffusion coefficient and halo height are also estimated from the 10be9be and bc ratios moreover density distribution of gaseous components of interstellar medium ism also plays an important role as secondary crs are produced due to interaction of primary crs with the gaseous components of ism we consider mainly molecular atomic and ionized components of hydrogen gas for our study recent observations and hydrodynamical simulations provide new forms of density profiles of hydrogen gas in milky way galaxy in the textttdragon code we have implemented our chosen density profiles based on realistic observations in radio xray and gammaray wavebands and hydrodynamical simulations of interstellar hydrogen gas to study the variation in the height of the halo required to fit the observed cr spectra our results show the halo height z_t varies in the range of 2 to 6 kpc for the density profiles considered in our work | [['galactic', 'magnetic', 'field', 'gmf', 'and', 'secondary', 'cosmic', 'rays', 'crs', 'eg', '10beryllium', 'boron', 'antiproton', 'are', 'important', 'components', 'to', 'understand', 'the', 'propagation', 'of', 'crs', 'in', 'the', 'milky', 'way', 'galaxy', 'realistic', 'modeling', 'of', 'gmf', 'is', 'based', 'on', 'the', 'faraday', 'rotation', 'measurements', 'of', 'various', 'galactic', 'and', 'extragalactic', 'radio', 'sources', 'and', 'synchrotron', 'emission', 'from', 'cr', 'leptons', 'in', 'the', 'radio', 'frequency', 'range', 'thereby', 'providing', 'information', 'of', 'halo', 'height', 'on', 'the', 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1,802.03539 | Stability and convergence of a conservative finite difference scheme for
the modified Hunter--Saxton equation | The modified Hunter--Saxton equation models the propagation of short
capillary-gravity waves. As it involves a mixed derivative, its initial value
problem on the periodic domain is much more complicated than the standard
evolutionary equations. Although its local well-posedness has recently been
proved, the behavior of its solution is yet to be investigated. In this paper,
to develop a reliable numerical method for this problem, we derive a
conservative finite difference scheme. Then, we rigorously prove not only its
stability in the sense of the uniform norm but also its uniform convergence to
sufficiently smooth exact solutions. Discrete conservation laws are used to
overcome the difficulty due to the mixed derivative.
| math.NA | the modified huntersaxton equation models the propagation of short capillarygravity waves as it involves a mixed derivative its initial value problem on the periodic domain is much more complicated than the standard evolutionary equations although its local wellposedness has recently been proved the behavior of its solution is yet to be investigated in this paper to develop a reliable numerical method for this problem we derive a conservative finite difference scheme then we rigorously prove not only its stability in the sense of the uniform norm but also its uniform convergence to sufficiently smooth exact solutions discrete conservation laws are used to overcome the difficulty due to the mixed derivative | [['the', 'modified', 'huntersaxton', 'equation', 'models', 'the', 'propagation', 'of', 'short', 'capillarygravity', 'waves', 'as', 'it', 'involves', 'a', 'mixed', 'derivative', 'its', 'initial', 'value', 'problem', 'on', 'the', 'periodic', 'domain', 'is', 'much', 'more', 'complicated', 'than', 'the', 'standard', 'evolutionary', 'equations', 'although', 'its', 'local', 'wellposedness', 'has', 'recently', 'been', 'proved', 'the', 'behavior', 'of', 'its', 'solution', 'is', 'yet', 'to', 'be', 'investigated', 'in', 'this', 'paper', 'to', 'develop', 'a', 'reliable', 'numerical', 'method', 'for', 'this', 'problem', 'we', 'derive', 'a', 'conservative', 'finite', 'difference', 'scheme', 'then', 'we', 'rigorously', 'prove', 'not', 'only', 'its', 'stability', 'in', 'the', 'sense', 'of', 'the', 'uniform', 'norm', 'but', 'also', 'its', 'uniform', 'convergence', 'to', 'sufficiently', 'smooth', 'exact', 'solutions', 'discrete', 'conservation', 'laws', 'are', 'used', 'to', 'overcome', 'the', 'difficulty', 'due', 'to', 'the', 'mixed', 'derivative']] | [-0.12347412473966622, 0.04222922706129876, -0.1406101521361217, 0.1307996428148313, -0.12465228586542336, -0.1238063515621682, 0.010699758897747167, 0.3361941822719845, -0.3215033452428708, -0.24000794621269134, 0.1701620273059234, -0.21094107762910425, -0.13354074555999515, 0.1676348293898627, -0.0858923175710846, 0.10567170014679009, 0.05681190603751351, 0.03612384618344632, -0.08156381487338381, -0.2547356107991866, 0.28643893449651925, -2.0197279412638056e-05, 0.2782907272625545, 0.05351703478954732, 0.1437948444180868, -0.06383230580355634, 0.008652078258720311, 0.026088557371192356, -0.15776867569762876, 0.11142589037137275, 0.21006499199684187, 0.06699271934445608, 0.32591759816912763, -0.43686364956030793, -0.2550868091342801, 0.14758952031420036, 0.14862402457511054, 0.14152542731978676, -0.046614489573800134, -0.25878233650953253, 0.14330545566044747, -0.15705627192192795, -0.20867556816136296, -0.07969305417923765, 0.05001401635394855, 0.03035697168882259, -0.2679967323275791, 0.11427925089136062, 0.07799841748889197, 0.011472911827943542, -0.1027395354859023, -0.0580412788993933, -0.011262661614455282, 0.06822364200753245, 0.08285461576359177, 0.01476132657632909, 0.008290073783560233, -0.10277205916036937, -0.03103713403900408, 0.39321161741390825, -0.0595224333743152, -0.3043011310277507, 0.19946168158203365, -0.0925764634451215, -0.09961616492999548, 0.12371128409930944, 0.13812907724641263, 0.19274131805127995, -0.16457644088024442, 0.12102505007589405, -0.0302433124743402, 0.161491598544473, 0.06547814902680164, 0.008537889166142452, 0.11480344997041604, 0.1434067594416609, 0.15472512257454749, 0.1333209235803224, -0.014794705650472844, -0.14555951821702448, -0.2850090337578546, -0.12393122292388316, -0.19484448337250135, 0.04296380956882124, -0.0581675085874105, -0.20152987929653715, 0.3838849775060226, 0.13714796275069768, 0.11335345327705991, 0.08898214460773901, 0.2976037732198496, 0.22537144783947785, -0.004929975102740255, 0.06818618949672038, 0.257059280407107, 0.17497129652395166, 0.14899752213280987, -0.22052473888880658, 0.09602953830987893, 0.12506082684478975] |
1,802.0354 | Hierarchy of the nonlocal advantage of quantum coherence and Bell
nonlocality | Quantum coherence and nonlocality capture nature of quantumness from
different aspects. For the two-qubit states with diagonal correlation matrix,
we prove strictly a hierarchy between the nonlocal advantage of quantum
coherence (NAQC) and Bell nonlocality by showing geometrically that the NAQC
created on one qubit by local measurement on another qubit captures quantum
correlation which is stronger than Bell nonlocality. For general states, our
numerical results present strong evidence that this hierarchy may still hold.
So the NAQC states form a subset of the states that can exhibit Bell
nonlocality. We further propose a measure of NAQC that can be used for a
quantitative study of it in bipartite states.
| quant-ph | quantum coherence and nonlocality capture nature of quantumness from different aspects for the twoqubit states with diagonal correlation matrix we prove strictly a hierarchy between the nonlocal advantage of quantum coherence naqc and bell nonlocality by showing geometrically that the naqc created on one qubit by local measurement on another qubit captures quantum correlation which is stronger than bell nonlocality for general states our numerical results present strong evidence that this hierarchy may still hold so the naqc states form a subset of the states that can exhibit bell nonlocality we further propose a measure of naqc that can be used for a quantitative study of it in bipartite states | [['quantum', 'coherence', 'and', 'nonlocality', 'capture', 'nature', 'of', 'quantumness', 'from', 'different', 'aspects', 'for', 'the', 'twoqubit', 'states', 'with', 'diagonal', 'correlation', 'matrix', 'we', 'prove', 'strictly', 'a', 'hierarchy', 'between', 'the', 'nonlocal', 'advantage', 'of', 'quantum', 'coherence', 'naqc', 'and', 'bell', 'nonlocality', 'by', 'showing', 'geometrically', 'that', 'the', 'naqc', 'created', 'on', 'one', 'qubit', 'by', 'local', 'measurement', 'on', 'another', 'qubit', 'captures', 'quantum', 'correlation', 'which', 'is', 'stronger', 'than', 'bell', 'nonlocality', 'for', 'general', 'states', 'our', 'numerical', 'results', 'present', 'strong', 'evidence', 'that', 'this', 'hierarchy', 'may', 'still', 'hold', 'so', 'the', 'naqc', 'states', 'form', 'a', 'subset', 'of', 'the', 'states', 'that', 'can', 'exhibit', 'bell', 'nonlocality', 'we', 'further', 'propose', 'a', 'measure', 'of', 'naqc', 'that', 'can', 'be', 'used', 'for', 'a', 'quantitative', 'study', 'of', 'it', 'in', 'bipartite', 'states']] | [-0.140076848635958, 0.17439104908315295, -0.10850416855801913, 0.13126202323897318, 0.017938267236406152, -0.22891611932692202, 0.040500916201959954, 0.31935594505207104, -0.2349337455446816, -0.251918015121059, 0.016037924667100675, -0.27339069063521243, -0.1323264551327818, 0.18406352821568198, -0.024610031827945602, 0.08797324239382182, 0.10220410312229598, 0.010947620546953245, -0.08290793791159311, -0.23220226800873536, 0.36886639962480827, -0.030022835662715474, 0.2883523511361669, 0.09925924766258422, 0.0741654931101948, -0.0008444480597972869, 0.08617822354320775, 0.046557876315306536, -0.08155011599169071, 0.12280867708084936, 0.25131002247164197, 0.1809488087024709, 0.2715557940050282, -0.43504748522219333, -0.21174949342384936, 0.11095276644919068, 0.0897052285330243, 0.15615849097322843, -0.020853626302612775, -0.3961466384458948, 0.011420956431803379, -0.16595805047333917, -0.0644255436279557, -0.16770683875180442, 0.032814920270307496, -0.09078217902517116, -0.2572668406943029, 0.15340536442500624, 0.12748491289940747, 0.03511974777264351, 0.0554428936786611, -0.01752863573651253, 0.03866445841479369, 0.09391097171359103, -0.0798217083613219, -0.03474949277217754, 0.07420209084044803, -0.09278846220325002, -0.19222428967054425, 0.314551341178065, -0.03474909376884832, -0.23555308983618903, 0.17158886959554034, -0.15775506453884935, -0.1202338888860223, 0.010907253639941866, 0.09179992311295461, 0.066494498770176, -0.1074240663533353, 0.006720478183970871, -0.12810424323701722, 0.2311245640062473, 0.0561660035894337, 0.19522596265623263, 0.17391685405746102, 0.06352533615956252, 0.12242463220926848, 0.19068839060333134, -0.0370169621358152, -0.15850899191573262, -0.3507228438149799, -0.21312930902296848, -0.26243464878590944, 0.1425877807407894, -0.07324818992876152, -0.09290021099996838, 0.42657473930792716, 0.11603280494349416, 0.14455047074297908, 0.03690553344329766, 0.19906970838254148, 0.0937871611518362, 0.08561628943584351, 0.0614060072101314, 0.2700459623455324, 0.17680562834086067, 0.021834130805324425, -0.27007617010861973, 0.1403720358545384, 0.02727148852480406] |
1,802.03541 | Harmonics of Solar Radio Spikes at Metric Wavelengths | This paper presents the latest observations from the newly-built solar radio
spectrograph at the \emph{Chashan Solar Observatory}. On July 18 2016, the
spectrograph records a solar spike burst event, which has several episodes
showing harmonic structures, with the second, third, and fourth harmonics. The
lower harmonic radio spike emissions are observed later than the higher
harmonic bands, and the temporal delay of the second (third) harmonic relative
to the fourth harmonic is about 30\ --\ 40 (10) ms. Based on the electron
cyclotron maser emission mechanism, we analyze possible causes of the temporal
delay and further infer relevant coronal parameters, such as the magnetic field
strength and the electron density at the radio source.
| astro-ph.SR | this paper presents the latest observations from the newlybuilt solar radio spectrograph at the emphchashan solar observatory on july 18 2016 the spectrograph records a solar spike burst event which has several episodes showing harmonic structures with the second third and fourth harmonics the lower harmonic radio spike emissions are observed later than the higher harmonic bands and the temporal delay of the second third harmonic relative to the fourth harmonic is about 30 40 10 ms based on the electron cyclotron maser emission mechanism we analyze possible causes of the temporal delay and further infer relevant coronal parameters such as the magnetic field strength and the electron density at the radio source | [['this', 'paper', 'presents', 'the', 'latest', 'observations', 'from', 'the', 'newlybuilt', 'solar', 'radio', 'spectrograph', 'at', 'the', 'emphchashan', 'solar', 'observatory', 'on', 'july', '18', '2016', 'the', 'spectrograph', 'records', 'a', 'solar', 'spike', 'burst', 'event', 'which', 'has', 'several', 'episodes', 'showing', 'harmonic', 'structures', 'with', 'the', 'second', 'third', 'and', 'fourth', 'harmonics', 'the', 'lower', 'harmonic', 'radio', 'spike', 'emissions', 'are', 'observed', 'later', 'than', 'the', 'higher', 'harmonic', 'bands', 'and', 'the', 'temporal', 'delay', 'of', 'the', 'second', 'third', 'harmonic', 'relative', 'to', 'the', 'fourth', 'harmonic', 'is', 'about', '30', '40', '10', 'ms', 'based', 'on', 'the', 'electron', 'cyclotron', 'maser', 'emission', 'mechanism', 'we', 'analyze', 'possible', 'causes', 'of', 'the', 'temporal', 'delay', 'and', 'further', 'infer', 'relevant', 'coronal', 'parameters', 'such', 'as', 'the', 'magnetic', 'field', 'strength', 'and', 'the', 'electron', 'density', 'at', 'the', 'radio', 'source']] | [-0.10646447306083116, 0.17168987284302212, 0.01619020037053685, 0.08917354674901747, -0.09513716539160148, -0.07522613326623936, 0.028702410105324816, 0.39096557137755944, -0.18669546073735027, -0.37296984534311506, 0.08709215397331198, -0.30130969994102735, -0.0916015133053796, 0.24378853233065456, 0.0026868151372972143, -0.0039587895776743865, 0.06092266724278618, 0.034967548155691475, -0.025925420740739873, -0.20583056567660865, 0.26756958854717333, 0.16872055281419307, 0.2396728074776807, 0.036501655471511185, 0.1267709431953595, -0.10611433438524338, -0.07663021760527045, -0.10095734433603606, -0.0614044650262388, 0.10600417228332455, 0.18293760688642838, 0.08944922148449612, 0.23705021699424833, -0.4234335704001881, -0.21118374331750342, 0.004192079153394194, 0.09045752061813671, 0.01596046422569738, 0.00032540598372829014, -0.27838010809084934, 0.03472912763079096, -0.1690099367905142, -0.13348244780874147, 0.10165207051302007, 0.03088551450075881, 0.08203081366290073, -0.22855773383551942, 0.09770148738919358, 0.024148215832155984, 0.11326856246783532, -0.11421670019210849, -0.04438411618217027, -0.04272636000366349, 0.10590666643942573, 0.05122639086039271, 0.05345933737719731, 0.12541456830928968, -0.043338527929985764, -0.10548272189251813, 0.3825119727324428, -0.09972606258257397, 0.021752650447264647, 0.15628495919892366, -0.27513410467820776, -0.18156923541599618, 0.17019181401701644, 0.18070990247153013, 0.08557919525940504, -0.14736978177097626, -0.02045530058551646, 0.029896207177703867, 0.2457001195954425, 0.13391377761893505, 0.03903692438949032, 0.24470658828587538, 0.11678442092878479, 0.05888123195784699, 0.11495399665311977, -0.2914588255433565, -0.0295651224748456, -0.25336154690947815, -0.0875706868163044, -0.12390836379303696, 0.0357925118379561, -0.09700509800573595, -0.10441865310089529, 0.48453510778823067, 0.10885133067910958, 0.15242865534466027, 0.013897712540970783, 0.3239762604394595, 0.15824102265988976, 0.058479476192068044, 0.07665857318040382, 0.31140084416672054, 0.17176241718698293, 0.21875083990744315, -0.2245472072791017, 0.01291011597952872, 0.05917060279378867] |
1,802.03542 | Tubule segmentation of fluorescence microscopy images based on
convolutional neural networks with inhomogeneity correction | Fluorescence microscopy has become a widely used tool for studying various
biological structures of in vivo tissue or cells. However, quantitative
analysis of these biological structures remains a challenge due to their
complexity which is exacerbated by distortions caused by lens aberrations and
light scattering. Moreover, manual quantification of such image volumes is an
intractable and error-prone process, making the need for automated image
analysis methods crucial. This paper describes a segmentation method for
tubular structures in fluorescence microscopy images using convolutional neural
networks with data augmentation and inhomogeneity correction. The segmentation
results of the proposed method are visually and numerically compared with other
microscopy segmentation methods. Experimental results indicate that the
proposed method has better performance with correctly segmenting and
identifying multiple tubular structures compared to other methods.
| cs.CV cs.LG eess.IV | fluorescence microscopy has become a widely used tool for studying various biological structures of in vivo tissue or cells however quantitative analysis of these biological structures remains a challenge due to their complexity which is exacerbated by distortions caused by lens aberrations and light scattering moreover manual quantification of such image volumes is an intractable and errorprone process making the need for automated image analysis methods crucial this paper describes a segmentation method for tubular structures in fluorescence microscopy images using convolutional neural networks with data augmentation and inhomogeneity correction the segmentation results of the proposed method are visually and numerically compared with other microscopy segmentation methods experimental results indicate that the proposed method has better performance with correctly segmenting and identifying multiple tubular structures compared to other methods | [['fluorescence', 'microscopy', 'has', 'become', 'a', 'widely', 'used', 'tool', 'for', 'studying', 'various', 'biological', 'structures', 'of', 'in', 'vivo', 'tissue', 'or', 'cells', 'however', 'quantitative', 'analysis', 'of', 'these', 'biological', 'structures', 'remains', 'a', 'challenge', 'due', 'to', 'their', 'complexity', 'which', 'is', 'exacerbated', 'by', 'distortions', 'caused', 'by', 'lens', 'aberrations', 'and', 'light', 'scattering', 'moreover', 'manual', 'quantification', 'of', 'such', 'image', 'volumes', 'is', 'an', 'intractable', 'and', 'errorprone', 'process', 'making', 'the', 'need', 'for', 'automated', 'image', 'analysis', 'methods', 'crucial', 'this', 'paper', 'describes', 'a', 'segmentation', 'method', 'for', 'tubular', 'structures', 'in', 'fluorescence', 'microscopy', 'images', 'using', 'convolutional', 'neural', 'networks', 'with', 'data', 'augmentation', 'and', 'inhomogeneity', 'correction', 'the', 'segmentation', 'results', 'of', 'the', 'proposed', 'method', 'are', 'visually', 'and', 'numerically', 'compared', 'with', 'other', 'microscopy', 'segmentation', 'methods', 'experimental', 'results', 'indicate', 'that', 'the', 'proposed', 'method', 'has', 'better', 'performance', 'with', 'correctly', 'segmenting', 'and', 'identifying', 'multiple', 'tubular', 'structures', 'compared', 'to', 'other', 'methods']] | [-0.013310034531926694, 0.00888144550946552, -0.06940890070729593, 0.07633510373436944, -0.05129326726923617, -0.1513415094241211, -0.0283686634367007, 0.4785232623360416, -0.25881322789504085, -0.32836478487985543, 0.08790395887525276, -0.28309450641115624, -0.25877076061262005, 0.2679612377077312, -0.13351161082369067, 0.1453750574629205, 0.14433206438606735, -0.04799464513915916, 0.01214736140722972, -0.23446886132781705, 0.2621990229010351, 0.06475281107145571, 0.3736608575148753, 0.049203038284222976, 0.08969039764966412, 0.002350568852582297, -0.07870011605221287, 0.05030539962499179, -0.08559841492472536, 0.19793799956141458, 0.32674331121803657, 0.13186984369892252, 0.28512287558987737, -0.45809811460532884, -0.28205926813069704, 0.08093382041655885, 0.20885965967318043, 0.12813873605914558, -0.08669335911656882, -0.32217673111216044, 0.10380606874534788, -0.07869349853604916, -0.05336893619175336, -0.17116740064753233, -0.028183845069454334, -0.017259167649877047, -0.24795119147323605, 0.10018802658012209, 0.02485311352972721, 0.11764520154760559, -0.09804455389795724, -0.05960413010235038, 0.030737221675073048, 0.1763783799062028, 0.030790005850040738, 0.043908631483222854, 0.1716579036000221, -0.20165180698824078, -0.13016723833848223, 0.38571670116827816, 0.027306836011797883, -0.21666458820880846, 0.20007881185296172, -0.05886068203768065, -0.12092777215623, 0.19488173547424664, 0.13099831659635602, 0.12228325740325936, -0.16964798400452893, -0.0017178596142686671, 0.044828361824385876, 0.21085713231979414, 0.11184399251198006, -0.012122789296761964, 0.15369454637899013, 0.2773671693262435, -0.010062045307176606, 0.11758547734429468, -0.1927615674862335, 0.004822480773856473, -0.1471402674228451, -0.13212392062612976, -0.1622174826284596, -0.03135068547666477, -0.09314983893244964, -0.21099067867423096, 0.3367484043417282, 0.19184392896415883, 0.1793226138589355, 0.01804655372046021, 0.41282128343221464, -0.0070092939490149185, 0.12250667330881133, -0.020898075835678237, 0.17374151132192261, 0.10226763429683308, 0.10529729556436687, -0.19657909073830807, 0.11886389671947604, 0.04080898678940974] |
1,802.03543 | Creative geometry | This work is a continuation of [1]. As in the previous article, here we will
describe some interesting ideas and a lot of new theorems in plane geometry
related to them.
| math.HO | this work is a continuation of 1 as in the previous article here we will describe some interesting ideas and a lot of new theorems in plane geometry related to them | [['this', 'work', 'is', 'a', 'continuation', 'of', '1', 'as', 'in', 'the', 'previous', 'article', 'here', 'we', 'will', 'describe', 'some', 'interesting', 'ideas', 'and', 'a', 'lot', 'of', 'new', 'theorems', 'in', 'plane', 'geometry', 'related', 'to', 'them']] | [-0.102868574160722, 0.05011079132917427, -0.12991417966212235, 0.06365856480213904, -0.15177479224099266, -0.077662086504842, 0.05365757157485331, 0.338363139739921, -0.27393787388779944, -0.26970681752408704, 0.1423516789105751, -0.27174538157639966, -0.2491141181559332, 0.24561039544641972, -0.16640308284531197, 0.03134391691175199, 0.009612373046336635, 0.0017220994097090537, -0.09020133166303558, -0.22200730467034924, 0.3589143838372923, 0.031881868088197325, 0.18998722108681837, 0.0942729479603229, 0.028161032741228418, -0.014735871475310094, -0.08603670372958144, 0.022725503891706467, -0.24355696361031262, 0.1927205921871768, 0.2803396270159752, 0.13243510739337053, 0.3019412683112727, -0.40374795173204714, -0.183209732385172, 0.0792406034024973, 0.1692651292128909, 0.16304037047009315, -0.07358672103333858, -0.21145269714836631, 0.08454147503981667, -0.15597979742432794, -0.19531555272518628, -0.07162550416204237, 0.03257614079742662, 0.03980862984674111, -0.13358398379697914, -0.02763912434719743, 0.14441349753929722, 0.03187527636727018, -0.009425517712389269, -0.09607354230097225, 0.10323566704567883, 0.09948104188867635, 0.0974416371299735, 0.09901429669210507, 0.05466744042332134, -0.09368949893471454, -0.14715244648076833, 0.3295199981620235, -0.00902472947153353, -0.19469656230461213, 0.22426938397749777, -0.14425220487699394, -0.24253450201884394, 0.034969614037582954, 0.19315278518103784, 0.15146023679464574, -0.17751395122538652, 0.10472369591908288, -0.10993341646427589, 0.08887385668593549, 0.06860725989475125, 0.02388685340842893, 0.1605764054843495, 0.1284585967059097, 0.05152601643555587, 0.16813376296551957, -0.017898894749739536, -0.09216948084893727, -0.3543403130625525, -0.21163927466278115, -0.14355275784093405, 0.0928910496614633, -0.0037170341083471994, -0.11642123302144389, 0.4563486284666484, 0.19756073384515702, 0.2308616401447404, -0.008663463338668789, 0.28436363713755725, 0.057509258385717625, -0.004524989233862969, -0.012424040961289597, 0.22716786970493325, 0.1477105731084462, 0.1638032206664643, -0.06337131945897014, -0.03584849470925908, 0.06617856417752561] |
1,802.03544 | To the problem of "The Instrumental complex for ontological engineering
purpose" software system design | The given work describes methodological principles of design instrumental
complex of ontological purpose. Instrumental complex intends for the
implementation of the integrated information technologies automated build of
domain ontologies. Results focus on enhancing the effectiveness of the
automatic analysis and understanding of natural-language texts, building of
knowledge description of subject areas (primarily in the area of science and
technology) and for interdisciplinary research in conjunction with the solution
of complex problems.
| cs.AI cs.DL | the given work describes methodological principles of design instrumental complex of ontological purpose instrumental complex intends for the implementation of the integrated information technologies automated build of domain ontologies results focus on enhancing the effectiveness of the automatic analysis and understanding of naturallanguage texts building of knowledge description of subject areas primarily in the area of science and technology and for interdisciplinary research in conjunction with the solution of complex problems | [['the', 'given', 'work', 'describes', 'methodological', 'principles', 'of', 'design', 'instrumental', 'complex', 'of', 'ontological', 'purpose', 'instrumental', 'complex', 'intends', 'for', 'the', 'implementation', 'of', 'the', 'integrated', 'information', 'technologies', 'automated', 'build', 'of', 'domain', 'ontologies', 'results', 'focus', 'on', 'enhancing', 'the', 'effectiveness', 'of', 'the', 'automatic', 'analysis', 'and', 'understanding', 'of', 'naturallanguage', 'texts', 'building', 'of', 'knowledge', 'description', 'of', 'subject', 'areas', 'primarily', 'in', 'the', 'area', 'of', 'science', 'and', 'technology', 'and', 'for', 'interdisciplinary', 'research', 'in', 'conjunction', 'with', 'the', 'solution', 'of', 'complex', 'problems']] | [-0.08008012219174752, -0.020541463739199326, -0.03140027621272348, 0.037467919132659136, -0.14650897233104201, -0.07910981370319783, 0.030507464987442265, 0.34391314399914, -0.2040064414359734, -0.35700151065862934, 0.12286041925897376, -0.2488362600052939, -0.18100543178513018, 0.24157498084323506, -0.11214067824316067, 0.07288569246064609, 0.08433341432992421, -0.00869675071805086, -0.024230496033134177, -0.2048318030470064, 0.3319411642757625, 0.05802739677216175, 0.39762944947193624, 0.10041049966247569, 0.09683651855530244, 0.02725148269854171, -0.2073643850556142, -0.035973760673225344, -0.10361572419142377, 0.2691166372051541, 0.41332758747985665, 0.2662159316110569, 0.3599494149396852, -0.45822709251824817, -0.2070730928574282, 0.01606557926189312, 0.11002760408052677, 0.06312025552222007, -0.04380316315809081, -0.31923990122134416, 0.02778444357845985, -0.11457865943633755, -0.1500039548731186, -0.06889787608836319, 0.023515682142805045, 0.014741824841467847, -0.18317409001395735, -0.04275487073447923, 0.1280813837869906, 0.15714999830717563, -0.06585127342177528, -0.14104004978189166, 0.06595462777579701, 0.19015053938418416, 0.008998484075279303, 0.04457732058469613, 0.16425225212120673, -0.18907951708542, -0.166619494216333, 0.3763005343646231, 0.007372503749317896, -0.17671009087541573, 0.16157272271811962, -0.053263140340584894, -0.1732161734777857, 0.06713083630401484, 0.2598236765266514, 0.0654136974718684, -0.20265115033441655, 0.10249450932417563, 0.07291291277645759, 0.19296131905456873, 0.013736530807210316, 0.04990068316774469, 0.24317533842904468, 0.3024757649474056, 0.0032640047909193474, 0.10848942245881428, -0.01589300754663705, -0.1343414598393818, -0.2789483334554333, -0.18440222643105916, -0.15754043085264488, -0.005709332333628456, -0.042713485388650926, -0.16926901989524634, 0.4187696061792298, 0.2242546934555744, 0.057270340601795575, -0.01987648774987795, 0.33130348316380676, -0.006722582963255929, 0.0570701390085086, -0.02373665291160135, 0.16788101276304102, 0.09114702164956277, 0.19303480376043475, -0.16172066881832942, 0.08408029547984332, 0.01866456401914778] |
1,802.03545 | Correspondence between the physics of extremal black holes and that of
stable heavy atomic nuclei | Extremal black holes are immune of Hawking evaporation. On the other hand,
some heavy atomic nuclei feature extraordinary stability to spontaneous
transmutations changing their mass numbers. The fact that extremal black holes
and stable nuclei share a common trait, that of defying spontaneous ejection of
their constituents, suggests that a good part of nuclear physics is modelled on
physics of extremal black holes through a simple version of gauge/gravity
duality. A general criterion for discriminating between stable and unstable
microscopic systems can be formulated to gain a new insight into some
imperfectly understood phenomena, such as instability of truly neutral spinless
particles (Higgs bosons, $\pi_0$, quarkonia, glueballs).
| hep-th gr-qc hep-ph nucl-th | extremal black holes are immune of hawking evaporation on the other hand some heavy atomic nuclei feature extraordinary stability to spontaneous transmutations changing their mass numbers the fact that extremal black holes and stable nuclei share a common trait that of defying spontaneous ejection of their constituents suggests that a good part of nuclear physics is modelled on physics of extremal black holes through a simple version of gaugegravity duality a general criterion for discriminating between stable and unstable microscopic systems can be formulated to gain a new insight into some imperfectly understood phenomena such as instability of truly neutral spinless particles higgs bosons pi_0 quarkonia glueballs | [['extremal', 'black', 'holes', 'are', 'immune', 'of', 'hawking', 'evaporation', 'on', 'the', 'other', 'hand', 'some', 'heavy', 'atomic', 'nuclei', 'feature', 'extraordinary', 'stability', 'to', 'spontaneous', 'transmutations', 'changing', 'their', 'mass', 'numbers', 'the', 'fact', 'that', 'extremal', 'black', 'holes', 'and', 'stable', 'nuclei', 'share', 'a', 'common', 'trait', 'that', 'of', 'defying', 'spontaneous', 'ejection', 'of', 'their', 'constituents', 'suggests', 'that', 'a', 'good', 'part', 'of', 'nuclear', 'physics', 'is', 'modelled', 'on', 'physics', 'of', 'extremal', 'black', 'holes', 'through', 'a', 'simple', 'version', 'of', 'gaugegravity', 'duality', 'a', 'general', 'criterion', 'for', 'discriminating', 'between', 'stable', 'and', 'unstable', 'microscopic', 'systems', 'can', 'be', 'formulated', 'to', 'gain', 'a', 'new', 'insight', 'into', 'some', 'imperfectly', 'understood', 'phenomena', 'such', 'as', 'instability', 'of', 'truly', 'neutral', 'spinless', 'particles', 'higgs', 'bosons', 'pi_0', 'quarkonia', 'glueballs']] | [-0.10867057568975724, 0.24698893181222412, -0.11118901187606225, 0.22703712417583077, -0.07196076755699153, -0.22310964554704935, 0.0466611324570133, 0.30297517658115547, -0.21950800242511748, -0.2357769297940709, 0.03516346266902287, -0.30207228188829444, -0.09095041952792242, 0.15048138388578353, -0.027708403478924917, 0.045166972160739734, 0.017529691834977577, 0.010855746864576205, -0.04255591375779395, -0.2100942726007654, 0.33239380142729835, 0.06463600048864974, 0.2384291441510562, 0.06257102373012594, 0.07202428940102343, -0.00046382400981872997, 0.046105240345453824, 0.010299766918200408, -0.10927031006304727, 0.10844821501610295, 0.2478465159797919, 0.11350643410234251, 0.1980603503750933, -0.399890420575426, -0.22023026828836895, 0.11074825368438647, 0.189165309557874, 0.15135479874247423, -0.169737846170885, -0.27366238231466056, 0.04477255926336919, -0.21254277290236728, -0.1831625727370869, -0.07954715113982419, 0.03913990601355419, -0.024090632058627832, -0.21978726560928832, 0.10426739549751733, 0.08190275914858261, -0.0005655672943010647, -0.09854747254888867, -0.08280791428318753, -0.08281721334434419, 0.06809625992220696, 0.132684836483987, -0.03513506787097969, 0.23738258474867652, -0.1473423736735691, -0.14974803303881923, 0.38482942456536207, -0.005468125272298527, -0.15095360352326986, 0.24463623498183476, -0.1860542301715256, -0.12952312354485843, 0.15372874706147988, 0.18795261037683933, 0.16121482649365457, -0.13666898556158086, 0.06051747885067016, -0.062095473520457745, 0.15667407690747598, 0.10564746292064263, 0.14503377654522256, 0.4019167274932995, 0.15314467639932147, -0.03620352896330791, 0.12020969869096755, 0.04406219411415057, -0.12773618314232432, -0.29618744717510503, -0.14814893892208966, -0.13656535804506706, 0.1272137931191197, -0.09055580075576719, -0.18639527463406394, 0.3346688881707944, 0.04457455953130516, 0.20253169066142115, -0.04790869058614649, 0.22452362946766535, 0.023520432888077588, 0.0024108924798528168, 0.020767547681962498, 0.3163208945063552, 0.19520230131728628, 0.08066707024368171, -0.25680336514139707, 0.007134872823779549, 0.1284158572284766] |
1,802.03546 | Construction of Grothendieck categories with enough compressible objects
using colored quivers | We introduce a new method to construct a Grothendieck category from a given
colored quiver. This is a variant of the construction used to prove that every
partially ordered set arises as the atom spectrum of a Grothendieck category.
Using the new method, we prove that for every finite partially ordered set,
there exists a locally noetherian Grothendieck category such that every nonzero
object contains a compressible subobject and its atom spectrum is isomorphic to
the given partially ordered set.
| math.RA math.CT math.RT | we introduce a new method to construct a grothendieck category from a given colored quiver this is a variant of the construction used to prove that every partially ordered set arises as the atom spectrum of a grothendieck category using the new method we prove that for every finite partially ordered set there exists a locally noetherian grothendieck category such that every nonzero object contains a compressible subobject and its atom spectrum is isomorphic to the given partially ordered set | [['we', 'introduce', 'a', 'new', 'method', 'to', 'construct', 'a', 'grothendieck', 'category', 'from', 'a', 'given', 'colored', 'quiver', 'this', 'is', 'a', 'variant', 'of', 'the', 'construction', 'used', 'to', 'prove', 'that', 'every', 'partially', 'ordered', 'set', 'arises', 'as', 'the', 'atom', 'spectrum', 'of', 'a', 'grothendieck', 'category', 'using', 'the', 'new', 'method', 'we', 'prove', 'that', 'for', 'every', 'finite', 'partially', 'ordered', 'set', 'there', 'exists', 'a', 'locally', 'noetherian', 'grothendieck', 'category', 'such', 'that', 'every', 'nonzero', 'object', 'contains', 'a', 'compressible', 'subobject', 'and', 'its', 'atom', 'spectrum', 'is', 'isomorphic', 'to', 'the', 'given', 'partially', 'ordered', 'set']] | [-0.14866812494583428, 0.10553825990282348, -0.08468864765018225, 0.05883700783087988, -0.10302159497514367, -0.163013099366799, 0.012239486072212457, 0.37169254892505704, -0.416677533229813, -0.14331485732691363, 0.06827002514037303, -0.22911140089854598, -0.12879185774363577, 0.1311151563655585, -0.16197632824187166, -0.08541255231029936, 0.09944514776580035, 0.11825778079219162, -0.021006804311764427, -0.25828349295479713, 0.40254861224675553, -0.08036093050613999, 0.2162153859535465, 0.01568942073499784, 0.1883692172676092, 0.0338477180688642, 0.04348577444325201, 0.0959796681534499, -0.10529298529136213, 0.140204153911327, 0.32424244017747694, 0.13598555516800842, 0.2549565390858334, -0.312295135471868, -0.10736235890035459, 0.220958916132804, 0.08948404544498771, 0.11555754976288882, -0.09550643271504669, -0.2968353717355058, 0.16522126914933324, -0.2590570444939658, -0.10328393014788162, -0.07360675124218688, 0.06496657041134313, -0.040996818814892325, -0.2925352131365798, -0.06674694267567247, 0.055216910364106295, 0.06468596168269869, -0.0849156986703747, -0.03573709726333618, -0.07693044937041123, 0.08296727897250093, -0.11216379695688375, 0.09178274677396984, 0.07703774377368973, -0.044857528124339296, -0.1297825254732743, 0.41068855540361254, -0.08835448622703553, -0.21097176804905757, 0.15294110300019384, -0.13197418953059242, -0.16587317510857247, 0.1715017210226506, 0.01084664799273014, 0.16202228344627656, -0.02585060363635421, 0.1763919788405474, -0.2392600852996111, 0.1543472503311932, 0.08051091617671773, 0.016743491630768402, 0.18673414466902613, 0.15148713816888632, 0.12152127634035423, 0.207894456962822, -0.02173424949287437, 0.01787790555972606, -0.37163464905461296, -0.14645114824852498, -0.1775486599712167, 0.11660970359807835, -0.04016180591406737, -0.2761587501969188, 0.38022670259233565, 0.14615433801955077, 0.18131887984927744, 0.1001806135289371, 0.2350922124038334, 0.048801928683496955, 0.030294491787208243, 0.0873574945377186, 0.060657878045458344, 0.1995174912386574, -0.01954328496940434, -0.07772973130631726, 0.0220400374266319, 0.24802036333130673] |
1,802.03547 | Extreme waves in crossing sea states | The evolution of crossing sea states and the emergence of rogue waves in such
systems are studied via numerical simulations performed using a higher order
spectral method to solve the free surface Euler equations with a flat bottom.
Two classes of crossing sea states are analysed: one using directional spectra
from the Draupner wave crossing at different angles, another considering a
Draupner-like spectra crossed with a narrowband JONSWAP state to model spectral
growth between wind sea and swell. These two classes of crossing sea states are
constructed using the spectral output of a WAVEWATCH III hindcast on the
Draupner rogue wave event. We measure ensemble statistical moments as functions
of time, finding that although the crossing angle influences the statistical
evolution to some degree, there are no significant third order effects present.
Additionally, we pay particular attention to the mean sea level measured
beneath extreme crest heights, the elevation of which (set up or set down) is
shown to be related to the spectral content in the low wavenumber region of the
corresponding spectrum.
| physics.flu-dyn | the evolution of crossing sea states and the emergence of rogue waves in such systems are studied via numerical simulations performed using a higher order spectral method to solve the free surface euler equations with a flat bottom two classes of crossing sea states are analysed one using directional spectra from the draupner wave crossing at different angles another considering a draupnerlike spectra crossed with a narrowband jonswap state to model spectral growth between wind sea and swell these two classes of crossing sea states are constructed using the spectral output of a wavewatch iii hindcast on the draupner rogue wave event we measure ensemble statistical moments as functions of time finding that although the crossing angle influences the statistical evolution to some degree there are no significant third order effects present additionally we pay particular attention to the mean sea level measured beneath extreme crest heights the elevation of which set up or set down is shown to be related to the spectral content in the low wavenumber region of the corresponding spectrum | [['the', 'evolution', 'of', 'crossing', 'sea', 'states', 'and', 'the', 'emergence', 'of', 'rogue', 'waves', 'in', 'such', 'systems', 'are', 'studied', 'via', 'numerical', 'simulations', 'performed', 'using', 'a', 'higher', 'order', 'spectral', 'method', 'to', 'solve', 'the', 'free', 'surface', 'euler', 'equations', 'with', 'a', 'flat', 'bottom', 'two', 'classes', 'of', 'crossing', 'sea', 'states', 'are', 'analysed', 'one', 'using', 'directional', 'spectra', 'from', 'the', 'draupner', 'wave', 'crossing', 'at', 'different', 'angles', 'another', 'considering', 'a', 'draupnerlike', 'spectra', 'crossed', 'with', 'a', 'narrowband', 'jonswap', 'state', 'to', 'model', 'spectral', 'growth', 'between', 'wind', 'sea', 'and', 'swell', 'these', 'two', 'classes', 'of', 'crossing', 'sea', 'states', 'are', 'constructed', 'using', 'the', 'spectral', 'output', 'of', 'a', 'wavewatch', 'iii', 'hindcast', 'on', 'the', 'draupner', 'rogue', 'wave', 'event', 'we', 'measure', 'ensemble', 'statistical', 'moments', 'as', 'functions', 'of', 'time', 'finding', 'that', 'although', 'the', 'crossing', 'angle', 'influences', 'the', 'statistical', 'evolution', 'to', 'some', 'degree', 'there', 'are', 'no', 'significant', 'third', 'order', 'effects', 'present', 'additionally', 'we', 'pay', 'particular', 'attention', 'to', 'the', 'mean', 'sea', 'level', 'measured', 'beneath', 'extreme', 'crest', 'heights', 'the', 'elevation', 'of', 'which', 'set', 'up', 'or', 'set', 'down', 'is', 'shown', 'to', 'be', 'related', 'to', 'the', 'spectral', 'content', 'in', 'the', 'low', 'wavenumber', 'region', 'of', 'the', 'corresponding', 'spectrum']] | [-0.13534166762871785, 0.1575418536846036, -0.07215619948558036, 0.11724024558657878, -0.022573487148108768, -0.07208519095199646, 0.028964532645569974, 0.3518496815282098, -0.24342544067844835, -0.31731954816866104, 0.10138860087696398, -0.30668823544684304, -0.11238601831638528, 0.18079714889159007, 0.031261906578074976, 0.05304449299791817, 0.05325576363971209, 0.013411576089831446, -0.0805615800482657, -0.18033075553898006, 0.3389132559094901, 0.04869599957584344, 0.2776605587323449, 0.040137455989122175, 0.09649133323718702, -0.0440660222215481, -0.007890596207920354, 0.011313504463390077, -0.11898235332193371, 0.08042591969876932, 0.20724039324128601, 0.07942534086558257, 0.2199575528421714, -0.4092560445383794, -0.22531469855215291, 0.07502362258245124, 0.10776553109202082, 0.08641718220718694, -0.012382643512649344, -0.28015719448899945, 0.04127400953624118, -0.14778869613983892, -0.1731512055603109, -0.009696875968032833, -0.017962099163324675, 0.034570550399689766, -0.21909611505816953, 0.08471073000288079, -0.01119692728725515, 0.07537183197177516, -0.036369462459848796, -0.1268638458797396, -0.12263506878775711, 0.12350041790383551, 0.06065864156261409, -0.007909659302666934, 0.07278389317848898, -0.11895892889315607, -0.07027157495939576, 0.36490552727606773, -0.07986493536198479, -0.17768556513080663, 0.19026495783247938, -0.20048263638328784, -0.07638543702640457, 0.2015975105220915, 0.20415984205040283, 0.08429431230249691, -0.09339829509797899, 0.0012059780357558969, -0.017794601218014513, 0.1551090798373038, 0.15367956684816175, 0.012140795988881304, 0.23878496437672678, 0.1246939996907466, 0.04215996984037102, 0.12136721597295362, -0.16438147218913415, -0.10461992275096325, -0.2848736457849686, -0.0955802331102549, -0.15418859966896, 0.019993872336492836, -0.040821359858739144, -0.17317077084737184, 0.4621009574557057, 0.1074449662980489, 0.18659868733143176, 0.04083752514985579, 0.2591451639578694, 0.1615601216728538, 0.04090267062460401, 0.09275844284720891, 0.21863729606945784, 0.11938269159385148, 0.08800385517305076, -0.21762348090418254, 0.02302275998033368, 0.05636766615506757] |
1,802.03548 | Constructing first-principles phase diagrams of amorphous LixSi using
machine-learning-assisted sampling with an evolutionary algorithm | The atomistic modeling of amorphous materials requires structure sizes and
sampling statistics that are challenging to achieve with first-principles
methods. Here, we propose a methodology to speed up the sampling of amorphous
and disordered materials using a combination of a genetic algorithm and a
specialized machine-learning potential based on artificial neural networks
(ANN). We show for the example of the amorphous LiSi alloy that around 1,000
first-principles calculations are sufficient for the ANN potential assisted
sampling of low-energy atomic configurations in the entire amorphous LixSi
phase space. The obtained phase diagram is validated by comparison with the
results from an extensive sampling of LixSi configurations using molecular
dynamics simulations and a general ANN potential trained to ~45,000
first-principles calculations. This demonstrates the utility of the approach
for the first-principles modeling of amorphous materials.
| cond-mat.dis-nn physics.comp-ph | the atomistic modeling of amorphous materials requires structure sizes and sampling statistics that are challenging to achieve with firstprinciples methods here we propose a methodology to speed up the sampling of amorphous and disordered materials using a combination of a genetic algorithm and a specialized machinelearning potential based on artificial neural networks ann we show for the example of the amorphous lisi alloy that around 1000 firstprinciples calculations are sufficient for the ann potential assisted sampling of lowenergy atomic configurations in the entire amorphous lixsi phase space the obtained phase diagram is validated by comparison with the results from an extensive sampling of lixsi configurations using molecular dynamics simulations and a general ann potential trained to 45000 firstprinciples calculations this demonstrates the utility of the approach for the firstprinciples modeling of amorphous materials | [['the', 'atomistic', 'modeling', 'of', 'amorphous', 'materials', 'requires', 'structure', 'sizes', 'and', 'sampling', 'statistics', 'that', 'are', 'challenging', 'to', 'achieve', 'with', 'firstprinciples', 'methods', 'here', 'we', 'propose', 'a', 'methodology', 'to', 'speed', 'up', 'the', 'sampling', 'of', 'amorphous', 'and', 'disordered', 'materials', 'using', 'a', 'combination', 'of', 'a', 'genetic', 'algorithm', 'and', 'a', 'specialized', 'machinelearning', 'potential', 'based', 'on', 'artificial', 'neural', 'networks', 'ann', 'we', 'show', 'for', 'the', 'example', 'of', 'the', 'amorphous', 'lisi', 'alloy', 'that', 'around', '1000', 'firstprinciples', 'calculations', 'are', 'sufficient', 'for', 'the', 'ann', 'potential', 'assisted', 'sampling', 'of', 'lowenergy', 'atomic', 'configurations', 'in', 'the', 'entire', 'amorphous', 'lixsi', 'phase', 'space', 'the', 'obtained', 'phase', 'diagram', 'is', 'validated', 'by', 'comparison', 'with', 'the', 'results', 'from', 'an', 'extensive', 'sampling', 'of', 'lixsi', 'configurations', 'using', 'molecular', 'dynamics', 'simulations', 'and', 'a', 'general', 'ann', 'potential', 'trained', 'to', '45000', 'firstprinciples', 'calculations', 'this', 'demonstrates', 'the', 'utility', 'of', 'the', 'approach', 'for', 'the', 'firstprinciples', 'modeling', 'of', 'amorphous', 'materials']] | [-0.05567888889323458, 0.08912271050540678, -0.08978940012533904, 0.00467956552450034, -0.002492798807859225, -0.09497914301875726, 0.108610260559428, 0.4487370368009223, -0.2477928792210506, -0.31501386105958235, 0.03937561212797278, -0.26296316621180876, -0.23320375470780677, 0.1862417451190041, 0.016087707695796302, 0.10254969828783121, 0.08067500890981882, -0.09092076855126936, -0.09782423169695233, -0.23599794513678976, 0.24983850181018724, 0.09006218936756477, 0.3108099856539944, 0.007450510103507598, 0.08885032781644871, -0.014867035533333043, 0.027944517967858372, 0.02463951838247415, -0.17447205678145383, 0.16682854908539502, 0.25501085122122913, 0.028578733223581447, 0.2632557939338897, -0.47641049522934553, -0.257625323063799, 0.026802891914389636, 0.0764683060053932, 0.15181778890914038, -0.08788189960072952, -0.26039339040399045, 0.10266127842180851, -0.1338502904032602, -0.07591763910788454, -0.16211160318400794, -0.023599749641086822, 0.04381846571980128, -0.29409377809650705, 0.05272060327093586, -0.010947867727285265, 0.1320655694242551, -0.11104453402690422, -0.15901833569151314, -0.04176486925663132, 0.05104774252171943, -0.04859956858602626, 0.03295556805446688, 0.19584853383467385, -0.11111930006925941, -0.14224226149306737, 0.3909489174786591, -0.03449547589630505, -0.133949360130985, 0.2195906255698826, -0.044070434910630375, -0.12815439599195033, 0.1680429759190271, 0.16523376762642897, 0.12012615610138141, -0.1729715237384347, 0.07567242417253442, 0.00945465405177521, 0.1770319337851571, 0.006111461515153261, -0.03461865090749303, 0.16887301774923094, 0.2888625869247362, -0.03849155715617694, 0.14392324266920245, -0.16458536284499542, -0.10518831645551704, -0.18862350757716967, -0.15523048598823466, -0.281705832812599, 0.040265504880003015, -0.12349462040299129, -0.1970713373074042, 0.38246840639460017, 0.1926432624231688, 0.12206890758939255, 0.043897107726705256, 0.27099500974654256, 0.02904693928461424, 0.05115007341198651, 0.03758967004338265, 0.22838985542521664, 0.1326499593398653, 0.09285221356352356, -0.1991117367664478, 0.055053986091677914, 0.01592890203657343] |
1,802.03549 | From acquaintance to best friend forever: robust and fine-grained
inference of social tie strengths | Social networks often provide only a binary perspective on social ties: two
individuals are either connected or not. While sometimes external information
can be used to infer the strength of social ties, access to such information
may be restricted or impractical. Sintos and Tsaparas (KDD 2014) first
suggested to infer the strength of social ties from the topology of the network
alone, by leveraging the Strong Triadic Closure (STC) property. The STC
property states that if person A has strong social ties with persons B and C, B
and C must be connected to each other as well (whether with a weak or strong
tie). Sintos and Tsaparas exploited this to formulate the inference of the
strength of social ties as NP-hard optimization problem, and proposed two
approximation algorithms. We refine and improve upon this landmark paper, by
developing a sequence of linear relaxations of this problem that can be solved
exactly in polynomial time. Usefully, these relaxations infer more fine-grained
levels of tie strength (beyond strong and weak), which also allows to avoid
making arbitrary strong/weak strength assignments when the network topology
provides inconclusive evidence. One of the relaxations simultaneously infers
the presence of a limited number of STC violations. An extensive theoretical
analysis leads to two efficient algorithmic approaches. Finally, our
experimental results elucidate the strengths of the proposed approach, and
sheds new light on the validity of the STC property in practice.
| cs.SI | social networks often provide only a binary perspective on social ties two individuals are either connected or not while sometimes external information can be used to infer the strength of social ties access to such information may be restricted or impractical sintos and tsaparas kdd 2014 first suggested to infer the strength of social ties from the topology of the network alone by leveraging the strong triadic closure stc property the stc property states that if person a has strong social ties with persons b and c b and c must be connected to each other as well whether with a weak or strong tie sintos and tsaparas exploited this to formulate the inference of the strength of social ties as nphard optimization problem and proposed two approximation algorithms we refine and improve upon this landmark paper by developing a sequence of linear relaxations of this problem that can be solved exactly in polynomial time usefully these relaxations infer more finegrained levels of tie strength beyond strong and weak which also allows to avoid making arbitrary strongweak strength assignments when the network topology provides inconclusive evidence one of the relaxations simultaneously infers the presence of a limited number of stc violations an extensive theoretical analysis leads to two efficient algorithmic approaches finally our experimental results elucidate the strengths of the proposed approach and sheds new light on the validity of the stc property in practice | [['social', 'networks', 'often', 'provide', 'only', 'a', 'binary', 'perspective', 'on', 'social', 'ties', 'two', 'individuals', 'are', 'either', 'connected', 'or', 'not', 'while', 'sometimes', 'external', 'information', 'can', 'be', 'used', 'to', 'infer', 'the', 'strength', 'of', 'social', 'ties', 'access', 'to', 'such', 'information', 'may', 'be', 'restricted', 'or', 'impractical', 'sintos', 'and', 'tsaparas', 'kdd', '2014', 'first', 'suggested', 'to', 'infer', 'the', 'strength', 'of', 'social', 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1,802.0355 | A note on a class of gyrogroups | In \cite{1}, a class of gyrogroups $(G,\odot)$ has been associated to certain
groups $(G,\cdot)$. We give a necessary and sufficient condition for
$(G,\odot)$ to be gyrocommutative. We also prove that under a suitable
assumption two finite groups central by a $2$-Engel group are isomorphic if and
only if their associated gyrogroups are isomorphic.
| math.GR | in cite1 a class of gyrogroups godot has been associated to certain groups gcdot we give a necessary and sufficient condition for godot to be gyrocommutative we also prove that under a suitable assumption two finite groups central by a 2engel group are isomorphic if and only if their associated gyrogroups are isomorphic | [['in', 'cite1', 'a', 'class', 'of', 'gyrogroups', 'godot', 'has', 'been', 'associated', 'to', 'certain', 'groups', 'gcdot', 'we', 'give', 'a', 'necessary', 'and', 'sufficient', 'condition', 'for', 'godot', 'to', 'be', 'gyrocommutative', 'we', 'also', 'prove', 'that', 'under', 'a', 'suitable', 'assumption', 'two', 'finite', 'groups', 'central', 'by', 'a', '2engel', 'group', 'are', 'isomorphic', 'if', 'and', 'only', 'if', 'their', 'associated', 'gyrogroups', 'are', 'isomorphic']] | [-0.16692581600597445, 0.1461232602859865, -0.10690427269294576, 0.10264162237114571, -0.1133573054564449, -0.16838202783662193, -0.013221864891677813, 0.4370229597783314, -0.26367284533269003, -0.18753113211044725, 0.20237145492288652, -0.2009700013430051, -0.14310876814261922, 0.20575542649570502, -0.15947571623508097, -0.041880794879892526, 0.06113363568723764, 0.14633071865394431, -0.054594014643884775, -0.2843498901446473, 0.36649046669591145, -0.050687136550275784, 0.20122542699693508, 0.09336833442442717, 0.09965121359476503, -0.04752989310907411, 0.010341901870286267, 0.06687522981807871, -0.1696340447026228, 0.041552231488925104, 0.25196401880316016, 0.09135345663910485, 0.30059443421679427, -0.367646147743008, -0.12836902494595018, 0.24483603251359934, 0.10273114580415049, -0.014096216237137341, -0.0834125698113568, -0.2476628535550158, 0.21369332728802032, -0.1983737784947427, -0.11250922582024392, -0.07621070783901608, 0.07924103645502396, 0.020960306959613314, -0.2648332298575145, 0.021296975189130225, 0.09101524689006356, 0.07132716677239481, -0.05914311036291831, -0.05538070002430171, -0.07599211132751321, 0.09858689553824798, -0.010899024066638272, 0.013147804916853894, 0.03452375793021242, -0.04648418678849373, -0.0646881756970202, 0.39326899147258615, -0.018264404287175188, -0.2219331617695543, 0.16822247706213087, -0.14882477816580883, -0.24621717672232749, 0.11820716003641346, 0.05187917555207914, 0.14195995378958168, -0.09967804125308077, 0.17142196505479865, -0.15758979831874934, 0.05453510274495578, 0.09405155799720648, 0.004638641835453938, 0.1094671315777133, 0.020045499317348003, 0.12842783693856788, 0.13459618354242575, 0.06400442242683878, 0.034501062758829235, -0.35038877088787423, -0.17449266507448452, -0.10290981472452294, 0.13358201699788277, -0.022914132410286978, -0.11844579753342946, 0.32901125193907405, 0.08526654935108041, 0.1517555415278898, 0.06812950083107319, 0.15139518607899827, 0.07455321943787155, 0.10104719805531204, 0.09219987451467875, 0.14206475645022573, 0.25594326288369046, -0.08961508786832949, -0.09584968864812322, 0.05232811987154045, 0.16176419811463863] |
1,802.03551 | On integrability of geodesics in near-horizon extremal geometries: Case
of Myers-Perry black holes in arbitrary dimensions | We investigate dynamics of probe particles moving in the near-horizon limit
of extremal Myers-Perry black holes in arbitrary dimensions. Employing
ellipsoidal coordinates we show that this problem is integrable and separable,
extending the results of the odd dimensional case discussed in
arXiv:1703.00713. We find the general solution of the Hamilton-Jacobi equations
for these systems and present explicit expressions for the Liouville integrals,
discuss Killing tensors and the associated constants of motion. We analyze
special cases of the background near-horizon geometry were the system possesses
more constants of motion and is hence superintegrable. Finally, we consider
near-horizon extremal vanishing horizon case which happens for Myers-Perry
black holes in odd dimensions and show that geodesic equations on this geometry
are also separable and work out its integrals of motion.
| hep-th gr-qc math-ph math.MP | we investigate dynamics of probe particles moving in the nearhorizon limit of extremal myersperry black holes in arbitrary dimensions employing ellipsoidal coordinates we show that this problem is integrable and separable extending the results of the odd dimensional case discussed in arxiv170300713 we find the general solution of the hamiltonjacobi equations for these systems and present explicit expressions for the liouville integrals discuss killing tensors and the associated constants of motion we analyze special cases of the background nearhorizon geometry were the system possesses more constants of motion and is hence superintegrable finally we consider nearhorizon extremal vanishing horizon case which happens for myersperry black holes in odd dimensions and show that geodesic equations on this geometry are also separable and work out its integrals of motion | [['we', 'investigate', 'dynamics', 'of', 'probe', 'particles', 'moving', 'in', 'the', 'nearhorizon', 'limit', 'of', 'extremal', 'myersperry', 'black', 'holes', 'in', 'arbitrary', 'dimensions', 'employing', 'ellipsoidal', 'coordinates', 'we', 'show', 'that', 'this', 'problem', 'is', 'integrable', 'and', 'separable', 'extending', 'the', 'results', 'of', 'the', 'odd', 'dimensional', 'case', 'discussed', 'in', 'arxiv170300713', 'we', 'find', 'the', 'general', 'solution', 'of', 'the', 'hamiltonjacobi', 'equations', 'for', 'these', 'systems', 'and', 'present', 'explicit', 'expressions', 'for', 'the', 'liouville', 'integrals', 'discuss', 'killing', 'tensors', 'and', 'the', 'associated', 'constants', 'of', 'motion', 'we', 'analyze', 'special', 'cases', 'of', 'the', 'background', 'nearhorizon', 'geometry', 'were', 'the', 'system', 'possesses', 'more', 'constants', 'of', 'motion', 'and', 'is', 'hence', 'superintegrable', 'finally', 'we', 'consider', 'nearhorizon', 'extremal', 'vanishing', 'horizon', 'case', 'which', 'happens', 'for', 'myersperry', 'black', 'holes', 'in', 'odd', 'dimensions', 'and', 'show', 'that', 'geodesic', 'equations', 'on', 'this', 'geometry', 'are', 'also', 'separable', 'and', 'work', 'out', 'its', 'integrals', 'of', 'motion']] | [-0.1920066944661639, 0.09657175814925087, -0.029033245356191718, 0.10806518668929736, -0.065197914824008, -0.14478815147017557, -0.05555469739469811, 0.30872388414683793, -0.19301932492470814, -0.2047118421437012, 0.11795844479130456, -0.31473194831974866, -0.1774975943475193, 0.1709586823920882, -0.07877603605470901, 0.056528156519302035, 0.05407635214376367, 0.06540256519691043, -0.12463973128118568, -0.24774503763506395, 0.3969390891337146, -0.001917365990165207, 0.21318235360862067, 0.011569557652350456, 0.16907463996626793, 0.04639116935025427, 0.016857699224991456, 0.06443967033716208, -0.20151094384715498, 0.09117805027118367, 0.2307590754584424, 0.08035466306480682, 0.13013184451985926, -0.42208726837166716, -0.18711382901713636, 0.07993066251381403, 0.1942603014786506, 0.1610997887060327, -0.021180165509923382, -0.2749193297913446, 0.036540765191487495, -0.15251600743079233, -0.24878746751577607, -0.1283198737794356, 0.09143812802161962, -0.018417961333715725, -0.20331233197320547, 0.1358221419918303, 0.1235260333060213, -0.0037488028155787598, -0.16314250022073143, -0.05923307318164642, -0.013131348102072638, 0.07760676312341636, 0.12157223080118586, -0.05691035690953925, 0.12027912589144849, -0.09536588686918664, -0.12695753910682267, 0.35821089676270884, -0.01201259416005101, -0.3133277239957972, 0.1438909116587306, -0.23148422839770477, -0.13050291706098333, 0.09058019180970621, 0.1965468927759618, 0.2594458994913905, -0.14291120039926017, 0.20895442596566494, -0.02177483491998698, 0.08300656547015976, 0.1611624574030025, 0.047883997810357765, 0.23024902200060232, 0.06309842589562611, 0.0543675966743779, 0.2225475905939848, -0.04239266949921979, -0.1504168532648316, -0.3544422007675859, -0.17551752358734135, -0.1251233746223743, 0.12914598836667007, -0.19103820668094576, -0.15698801978520813, 0.33000047171547536, 0.1176039043195834, 0.13819261193497195, 0.03814588954195469, 0.20325160850263765, 0.11612477272541986, -0.023917404021919955, 0.1256275219475067, 0.29964313919227276, 0.1384354677958606, 0.08039467479471886, -0.23235426991364905, -0.11136226517902244, 0.15290203827825036] |
1,802.03552 | A note on subgroup commutativity degrees of finite groups | In this note we give some new results concerning the subgroup commutativity
degree of a finite group $G$. These are obtained by considering the minimum of
subgroup commutativity degrees of all sections of $G$.
| math.GR | in this note we give some new results concerning the subgroup commutativity degree of a finite group g these are obtained by considering the minimum of subgroup commutativity degrees of all sections of g | [['in', 'this', 'note', 'we', 'give', 'some', 'new', 'results', 'concerning', 'the', 'subgroup', 'commutativity', 'degree', 'of', 'a', 'finite', 'group', 'g', 'these', 'are', 'obtained', 'by', 'considering', 'the', 'minimum', 'of', 'subgroup', 'commutativity', 'degrees', 'of', 'all', 'sections', 'of', 'g']] | [-0.28050837769200476, 0.12974866949405303, -0.07123153317062293, -0.019010173886165244, -0.09490797886460581, -0.06173001258524463, 0.060315085081931424, 0.34206498060238494, -0.29409170260324197, -0.2969574038596714, 0.094686404141077, -0.27063594112063155, -0.07424709348775008, 0.16773200821241036, -0.1461485874674776, -0.03589869950733641, 0.08130461944486289, 0.130641040227869, -0.07919518627664622, -0.3033095062436426, 0.3888179302653846, -0.046052600421449714, 0.15097593299715834, 0.0649624218178146, 0.09136333374087424, 0.02781895131749265, -0.05720794992521405, 0.018546874361003145, -0.16639135098632643, 0.11628202800912892, 0.24329375015462146, 0.09212424556779511, 0.22422137763351202, -0.3392716865055263, -0.16072261718320935, 0.20718805612448385, 0.06755082309246063, 0.026700261322891012, -0.06014510922079139, -0.2656753529958865, 0.1533974921604728, -0.20481187388384783, -0.17825400945730507, -0.05403192560462391, 0.051461378632880306, 0.03729900493122199, -0.1709803736439961, 0.04339980032733258, 0.11708970228210092, 0.20586282231242342, 0.039134205497034336, -0.20561920725466573, -0.03768214097191744, 0.0986077774740646, 0.03607938115430229, -0.033629046616089696, 0.022835214581230983, -0.10139696454108857, -0.09698690983521588, 0.4113967802704257, -0.04332169498700429, -0.17521648284266977, 0.10512555264593924, -0.2153282052536002, -0.21583337123122284, 0.08548854280482321, 0.10047280825400616, 0.16406257264316082, -0.09555496910915655, 0.14014770690461292, -0.16572481655406163, 0.03989656376378501, 0.07564863183206934, 0.04907110065002652, 0.06955482125939692, 0.06571432059247266, 0.09008036573033999, 0.15106639321482576, 0.06704347473843132, 0.1055069243781926, -0.47366616327096434, -0.13130588802125523, -0.09538111874409129, 0.11098977802869152, -0.1562220334787579, -0.09290426914744518, 0.45508814099080425, 0.10966351042118143, 0.13871023924473455, 0.11114743434111862, 0.16043726365794153, 0.07012739626909881, 0.03895480589776793, 0.10679609690080671, 0.15345489720365388, 0.28630041709744974, -0.14840892357203891, -0.2137288923977929, -0.00585991793366916, 0.18097731316297808] |
1,802.03553 | A nilpotency criterion for finite groups | Let $G$ be a finite group. In this short note, we give a criterion of
nilpotency of $G$ based on the existence of elements of certain order in each
section of $G$.
| math.GR | let g be a finite group in this short note we give a criterion of nilpotency of g based on the existence of elements of certain order in each section of g | [['let', 'g', 'be', 'a', 'finite', 'group', 'in', 'this', 'short', 'note', 'we', 'give', 'a', 'criterion', 'of', 'nilpotency', 'of', 'g', 'based', 'on', 'the', 'existence', 'of', 'elements', 'of', 'certain', 'order', 'in', 'each', 'section', 'of', 'g']] | [-0.23658886819612235, 0.12228681084525306, -0.15251958323642612, -0.04777853681662236, -0.09758416027761996, -0.08585262854467146, 0.032536321436055005, 0.3409429987077601, -0.237846402451396, -0.26513042362057604, 0.0788492889914778, -0.19960233429446816, -0.08444152947049588, 0.15357774766744114, -0.10812776896636933, -0.09710992220789194, 0.05376746796537191, 0.19716313516255468, -0.0828338615829125, -0.30579818616388366, 0.3504991338122636, -0.06970805360560917, 0.15639471876784228, 0.1104147908044979, 0.10737961367681237, 0.024891894179745577, 0.007429652439896017, 0.028808554947318044, -0.18774580903118476, 0.15849922702182084, 0.27805539791006595, 0.06336864478362259, 0.308108260971494, -0.36685181013308465, -0.13045322104881052, 0.23610188675229438, 0.08370607644610573, -0.009236355166649446, -0.03404140247221221, -0.24037830834276974, 0.2018302974611288, -0.20468212984269485, -0.1252991792280227, 0.03602983138989657, 0.13309926976216957, 0.06102394696790725, -0.26487152732443064, 0.014133282646071166, 0.09843801043462008, 0.14158187274006195, 0.01625183479336556, -0.10553494609484915, -0.016645092924591154, 0.07787013090273831, -0.020062839073943906, -0.0015998410090105608, 0.007218489263323136, -0.044609997181396466, -0.0955663537606597, 0.44501064787618816, -0.1439919188451313, -0.1639265512349084, 0.08579241944971727, -0.1952757173567079, -0.16446186922257766, 0.10180621058680117, 0.18397822744736914, 0.23891352125065168, -0.06214679416734725, 0.15792186858016066, -0.13499112869612873, 0.10815701098181307, 0.031175324897048995, 0.01381878659594804, 0.1327212325995788, 0.16028027819265844, 0.14075344719458371, 0.14622411332311458, 0.03769356068369234, 0.16821246477775276, -0.48731413995847106, -0.1561250175582245, -0.13444790129142348, 0.15837020706385374, -0.10825461277272552, -0.18880586916930042, 0.4446290085907094, 0.1321628452569712, 0.20380521361948922, 0.07804451650008559, 0.16402464843122289, 0.12180007886490785, 0.03619992302265018, 0.05303540581371635, 0.07492400228511542, 0.2620114656747319, -0.1129258726577973, -0.19108976557618007, 0.031733496420201845, 0.23533724312437698] |
1,802.03554 | Breaking points in centralizer lattices | In this note, we prove that the centralizer lattice ${\mathfrak C}(G)$ of a
group $G$ cannot be written as a union of two proper intervals. In particular,
it follows that ${\mathfrak C}(G)$ has no breaking point. As an application, we
show that the generalized quaternion $2$-groups are not capable.
| math.GR | in this note we prove that the centralizer lattice mathfrak cg of a group g cannot be written as a union of two proper intervals in particular it follows that mathfrak cg has no breaking point as an application we show that the generalized quaternion 2groups are not capable | [['in', 'this', 'note', 'we', 'prove', 'that', 'the', 'centralizer', 'lattice', 'mathfrak', 'cg', 'of', 'a', 'group', 'g', 'can', 'not', 'be', 'written', 'as', 'a', 'union', 'of', 'two', 'proper', 'intervals', 'in', 'particular', 'it', 'follows', 'that', 'mathfrak', 'cg', 'has', 'no', 'breaking', 'point', 'as', 'an', 'application', 'we', 'show', 'that', 'the', 'generalized', 'quaternion', '2groups', 'are', 'not', 'capable']] | [-0.17801578497514128, 0.10326485744939419, -0.10474539641290903, 0.0425049680727534, -0.12036341182887554, -0.10352232212200761, -0.023213364481925965, 0.4319310848414898, -0.33059809774160387, -0.16901638671755792, 0.11007701526861638, -0.21594821782782675, -0.15660173922777176, 0.19883166685700415, -0.14301652597263456, -0.09203550012782216, 0.08485176971182228, 0.12859622712247074, -0.0923852281877771, -0.2558827006071806, 0.2953030815254897, -0.07580808896338567, 0.21669122396036983, 0.032151785986498, 0.11854710043407976, -0.007044468531385064, 0.05630820404738188, 0.050150766149163244, -0.10676050936555839, 0.008517148047685623, 0.30190233513712883, 0.10969671335536986, 0.25407249383628366, -0.35255740948021413, -0.17882170083001256, 0.2340442170202732, 0.20584184964187444, 0.028401235807687043, -0.055181109993718565, -0.19635358784347773, 0.15395879071205854, -0.24544347101822495, -0.1286547744832933, -0.10116859890520573, 0.07198214460164308, -0.016317397952079774, -0.23944753035902977, 0.003851860202848911, 0.1262493688799441, 0.10563612185418605, 0.012338783950544894, -0.1322865610010922, -0.07181043270975351, 0.08107427733018994, -0.02245868350379169, 0.07750068975612522, 0.09210644511505961, -0.028521833359263837, -0.1362567551061511, 0.46096706226468087, -0.07986674444749951, -0.24525964444503187, 0.12266405465081334, -0.15222536624409258, -0.2464109360333532, 0.05906500535085797, 0.04963415754726157, 0.11160248959437012, -0.1017138028331101, 0.16984273908659817, -0.16443217895925044, 0.1104049079149263, 0.04502913034521043, -0.016337076479103416, 0.12660958020482213, 0.09192233593203128, 0.08761771395802498, 0.11175075470237061, 0.03874858535826206, 0.04156509857624769, -0.41133402615785597, -0.19758847955614328, -0.20350096136331558, 0.11051522315538023, -0.09283339189365507, -0.15756353691220285, 0.37700524136424063, 0.13204469706863164, 0.15138456942513584, 0.07280486576259136, 0.16698499870486558, 0.07494714988861233, 0.09762489563785494, 0.12374105228111149, 0.14145488779991866, 0.1397961651161313, -0.07729783705435693, -0.16730054885149, -0.038491419907659294, 0.16286160688847304] |
1,802.03555 | Breaking points in the poset of conjugacy classes of subgroups of a
finite group | In this note, we determine the finite groups whose poset of conjugacy classes
of subgroups has breaking points. This leads to a new characterization of the
generalized quaternion $2$-groups. A generalization of this property is also
studied.
| math.GR | in this note we determine the finite groups whose poset of conjugacy classes of subgroups has breaking points this leads to a new characterization of the generalized quaternion 2groups a generalization of this property is also studied | [['in', 'this', 'note', 'we', 'determine', 'the', 'finite', 'groups', 'whose', 'poset', 'of', 'conjugacy', 'classes', 'of', 'subgroups', 'has', 'breaking', 'points', 'this', 'leads', 'to', 'a', 'new', 'characterization', 'of', 'the', 'generalized', 'quaternion', '2groups', 'a', 'generalization', 'of', 'this', 'property', 'is', 'also', 'studied']] | [-0.1896603502635215, 0.11312466071968949, -0.11523669948046272, 0.0574532941776655, -0.12078812990236927, -0.05722745938377606, 0.05611631242130455, 0.3170360657008919, -0.35189803770265066, -0.22053925833992055, 0.07138361882204441, -0.21109945339628974, -0.15708633831928712, 0.151789415199813, -0.18806346581392996, -0.04539445666848002, 0.00849333722647783, 0.10182371599650061, -0.1634564719892837, -0.29245354869478457, 0.3815701629815472, -0.03907168728639246, 0.2593220753997967, 0.05917130657345862, 0.10545741245653983, -0.0317673418620551, -0.03454939245774939, -0.0068291848324037886, -0.18182254819249785, 0.13670538217332717, 0.2795123071787325, 0.045298389912696205, 0.24363800339602135, -0.2692985683176163, -0.1455534991329989, 0.2891113784264874, 0.1490826612288082, 0.06811949019823726, -0.07523832726921584, -0.2529515233394262, 0.13829322945880326, -0.21677313286911798, -0.18959059561225208, -0.05879956370571981, 0.06214483866367389, -0.019219846137472102, -0.22196472934573083, -0.014120431460370939, 0.14491665071329554, 0.15596052380223646, -0.021749668427415797, -0.06364861820396539, 0.03377899497344687, 0.09356332745252026, 0.034303267077366646, -0.04634303974642141, 0.008962256494104056, -0.0419271187416303, -0.17044809117372073, 0.4496505978341038, 0.04504749480936978, -0.22585403826087713, 0.13952635270165833, -0.1543603775569716, -0.25743990401561195, 0.13295005682251743, 0.14967852779286536, 0.15399312494775733, -0.11622180107578232, 0.16055587613703431, -0.20601312700356986, 0.06925598080144138, 0.10413441953022738, 0.01881975428880872, 0.09813991017841003, 0.14640265428838697, 0.09282182684369586, 0.2449309177495338, 0.05907262912070429, 0.02923128305858857, -0.340854083640954, -0.20891243601972992, -0.1257309929996326, 0.08720745942932931, -0.08338116695852699, -0.2401000217572358, 0.5118482841833217, 0.0985260898926975, 0.13405514677130692, 0.10925619649021207, 0.1503526834594841, 0.07023296618481746, 0.11187428176856122, 0.004269627262712331, 0.13033551226898626, 0.25857769697904587, -0.08151380309986102, -0.16502985754994223, -0.008697954384056298, 0.1981034254831438] |
1,802.03556 | Minimal non-Iwasawa finite groups | In this note, we describe first the structure of minimal non-Iwasawa finite
groups. Then we determine the minimal non-Iwasawa finite groups which are
modular. Also, we find connections between minimal non-Iwasawa finite groups
and the subgroup commutativity degree, and we give an example of a family of
non-nilpotent modular finite groups $G_n$, $n\in\mathbb{N}$, whose subgroup
commutativity degree tends to $1$ as $n$ tends to infinity.
| math.GR | in this note we describe first the structure of minimal noniwasawa finite groups then we determine the minimal noniwasawa finite groups which are modular also we find connections between minimal noniwasawa finite groups and the subgroup commutativity degree and we give an example of a family of nonnilpotent modular finite groups g_n ninmathbbn whose subgroup commutativity degree tends to 1 as n tends to infinity | [['in', 'this', 'note', 'we', 'describe', 'first', 'the', 'structure', 'of', 'minimal', 'noniwasawa', 'finite', 'groups', 'then', 'we', 'determine', 'the', 'minimal', 'noniwasawa', 'finite', 'groups', 'which', 'are', 'modular', 'also', 'we', 'find', 'connections', 'between', 'minimal', 'noniwasawa', 'finite', 'groups', 'and', 'the', 'subgroup', 'commutativity', 'degree', 'and', 'we', 'give', 'an', 'example', 'of', 'a', 'family', 'of', 'nonnilpotent', 'modular', 'finite', 'groups', 'g_n', 'ninmathbbn', 'whose', 'subgroup', 'commutativity', 'degree', 'tends', 'to', '1', 'as', 'n', 'tends', 'to', 'infinity']] | [-0.20037773204250978, 0.16595000331849755, -0.09011319606350018, 0.05853287090475743, -0.11117222268994038, -0.13469417669738715, 0.03597838220437272, 0.3507155976544779, -0.35112952320621565, -0.2230659924304256, 0.08507209518888535, -0.28813695050776006, -0.13126495190394613, 0.10626191519773924, -0.10999974691523956, -0.048036053611968575, -0.027455247895648845, 0.16914332089945674, -0.08603600392368838, -0.2986388625971113, 0.38301895283735715, -0.03628772286554942, 0.2127481786820751, 0.030206514486501246, 0.11502307705455818, -0.020185760172227254, -0.004692723602056503, -0.027137562202719543, -0.18300639094355015, 0.08251152540055605, 0.2871233799423163, 0.041376140570411314, 0.22923439904832496, -0.36421752274036406, -0.09749106882999722, 0.2816560667509643, 0.18147206558631016, 0.048060805373825136, -0.02342874318218002, -0.19774636932147238, 0.16861923321221883, -0.2389417971954502, -0.21382578981664174, -0.06384232342243194, 0.07626000195383452, 0.002729068553218475, -0.19081036905274512, -0.03490498703949225, 0.0944805318919512, 0.1657170357899024, -0.008889054693281651, -0.09678600439085411, -0.030732464759897153, 0.15347652172383208, -0.003366558568981978, -0.053980921330646824, 0.026922088879375503, -0.0287737266268008, -0.08031108940306764, 0.37041394313922726, -0.06046952363103628, -0.2211399094703106, 0.16866365593786423, -0.19099501726719048, -0.22683181584311218, 0.049073375546588346, 0.13163130960355585, 0.15454287538304926, -0.02536602683741456, 0.1902306588563638, -0.12780886149177184, 0.10863529066913404, 0.0704546979162842, -0.007667783217934461, 0.05604901365362681, 0.06542710393953782, 0.12267236011102796, 0.18584386262183006, 0.05134397502272175, 0.04663475164427207, -0.42788766679855494, -0.1910719874673165, -0.09641423719361998, 0.1329905402989915, -0.16079481476514887, -0.18973856691557628, 0.40552860524218814, 0.06178175564855337, 0.14585532600489948, 0.18603988795791968, 0.14018370765619553, 0.04887032137574771, 0.06278882009478716, 0.08937993476597163, 0.03706709503984222, 0.2292833075213891, -0.14878050895551076, -0.19549619233808838, -0.0361922590706784, 0.1605464922120938] |
1,802.03557 | Reachable Set Estimation and Verification for Neural Network Models of
Nonlinear Dynamic Systems | Neural networks have been widely used to solve complex real-world problems.
Due to the complicate, nonlinear, non-convex nature of neural networks, formal
safety guarantees for the behaviors of neural network systems will be crucial
for their applications in safety-critical systems. In this paper, the reachable
set estimation and verification problems for Nonlinear Autoregressive-Moving
Average (NARMA) models in the forms of neural networks are addressed. The
neural network involved in the model is a class of feed-forward neural networks
called Multi-Layer Perceptron (MLP). By partitioning the input set of an MLP
into a finite number of cells, a layer-by-layer computation algorithm is
developed for reachable set estimation for each individual cell. The union of
estimated reachable sets of all cells forms an over-approximation of reachable
set of the MLP. Furthermore, an iterative reachable set estimation algorithm
based on reachable set estimation for MLPs is developed for NARMA models. The
safety verification can be performed by checking the existence of intersections
of unsafe regions and estimated reachable set. Several numerical examples are
provided to illustrate our approach.
| cs.SY | neural networks have been widely used to solve complex realworld problems due to the complicate nonlinear nonconvex nature of neural networks formal safety guarantees for the behaviors of neural network systems will be crucial for their applications in safetycritical systems in this paper the reachable set estimation and verification problems for nonlinear autoregressivemoving average narma models in the forms of neural networks are addressed the neural network involved in the model is a class of feedforward neural networks called multilayer perceptron mlp by partitioning the input set of an mlp into a finite number of cells a layerbylayer computation algorithm is developed for reachable set estimation for each individual cell the union of estimated reachable sets of all cells forms an overapproximation of reachable set of the mlp furthermore an iterative reachable set estimation algorithm based on reachable set estimation for mlps is developed for narma models the safety verification can be performed by checking the existence of intersections of unsafe regions and estimated reachable set several numerical examples are provided to illustrate our approach | [['neural', 'networks', 'have', 'been', 'widely', 'used', 'to', 'solve', 'complex', 'realworld', 'problems', 'due', 'to', 'the', 'complicate', 'nonlinear', 'nonconvex', 'nature', 'of', 'neural', 'networks', 'formal', 'safety', 'guarantees', 'for', 'the', 'behaviors', 'of', 'neural', 'network', 'systems', 'will', 'be', 'crucial', 'for', 'their', 'applications', 'in', 'safetycritical', 'systems', 'in', 'this', 'paper', 'the', 'reachable', 'set', 'estimation', 'and', 'verification', 'problems', 'for', 'nonlinear', 'autoregressivemoving', 'average', 'narma', 'models', 'in', 'the', 'forms', 'of', 'neural', 'networks', 'are', 'addressed', 'the', 'neural', 'network', 'involved', 'in', 'the', 'model', 'is', 'a', 'class', 'of', 'feedforward', 'neural', 'networks', 'called', 'multilayer', 'perceptron', 'mlp', 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1,802.03558 | Mining Container Image Repositories for Software Configuration and
Beyond | This paper introduces the idea of mining container image repositories for
configuration and other deployment information of software systems. Unlike
traditional software repositories (e.g., source code repositories and app
stores), image repositories encapsulate the entire execution ecosystem for
running target software, including its configurations, dependent libraries and
components, and OS-level utilities, which contributes to a wealth of data and
information. We showcase the opportunities based on concrete software
engineering tasks that can benefit from mining image repositories. To
facilitate future mining efforts, we summarize the challenges of analyzing
image repositories and the approaches that can address these challenges. We
hope that this paper will stimulate exciting research agenda of mining this
emerging type of software repositories.
| cs.SE | this paper introduces the idea of mining container image repositories for configuration and other deployment information of software systems unlike traditional software repositories eg source code repositories and app stores image repositories encapsulate the entire execution ecosystem for running target software including its configurations dependent libraries and components and oslevel utilities which contributes to a wealth of data and information we showcase the opportunities based on concrete software engineering tasks that can benefit from mining image repositories to facilitate future mining efforts we summarize the challenges of analyzing image repositories and the approaches that can address these challenges we hope that this paper will stimulate exciting research agenda of mining this emerging type of software repositories | [['this', 'paper', 'introduces', 'the', 'idea', 'of', 'mining', 'container', 'image', 'repositories', 'for', 'configuration', 'and', 'other', 'deployment', 'information', 'of', 'software', 'systems', 'unlike', 'traditional', 'software', 'repositories', 'eg', 'source', 'code', 'repositories', 'and', 'app', 'stores', 'image', 'repositories', 'encapsulate', 'the', 'entire', 'execution', 'ecosystem', 'for', 'running', 'target', 'software', 'including', 'its', 'configurations', 'dependent', 'libraries', 'and', 'components', 'and', 'oslevel', 'utilities', 'which', 'contributes', 'to', 'a', 'wealth', 'of', 'data', 'and', 'information', 'we', 'showcase', 'the', 'opportunities', 'based', 'on', 'concrete', 'software', 'engineering', 'tasks', 'that', 'can', 'benefit', 'from', 'mining', 'image', 'repositories', 'to', 'facilitate', 'future', 'mining', 'efforts', 'we', 'summarize', 'the', 'challenges', 'of', 'analyzing', 'image', 'repositories', 'and', 'the', 'approaches', 'that', 'can', 'address', 'these', 'challenges', 'we', 'hope', 'that', 'this', 'paper', 'will', 'stimulate', 'exciting', 'research', 'agenda', 'of', 'mining', 'this', 'emerging', 'type', 'of', 'software', 'repositories']] | [-0.08672450152265936, -0.018698567725268418, -0.07052176911785299, 0.040917345769261815, -0.17516895773788466, -0.15225598444070282, 0.03945341369875803, 0.41843186643231534, -0.3169788059112134, -0.34556211918744995, 0.152640244361527, -0.33173905088614414, -0.11810953235093119, 0.22077467593219902, -0.12077694293914427, 0.05735809237902968, 0.16241560537560745, -0.04283924657722999, -0.028829543628521134, -0.2920876394973361, 0.32856452834374944, 0.06313636748858824, 0.38900604035006836, 0.11852132581206488, 0.009413981955731288, -0.01487177299287427, -0.13208696780051907, -0.062254877881436, -0.09697501727846272, 0.26974456823744075, 0.42031861587973507, 0.36041003519845805, 0.3415339900389442, -0.43534542857829867, -0.17246486446900486, 0.030427552165527796, 0.19538975906282852, 0.10479776918835906, -0.09750568130402826, -0.2931501531796851, 0.05252444473093231, -0.23947387465259767, -0.09779463364975527, -0.12693538557705952, 0.002433151275658145, 0.04501426254864782, -0.23066085096898264, -0.08971979481103862, 0.03232208316531514, 0.13088071983556487, -0.022588129810675785, -0.14382476701625976, 0.01078455011231889, 0.21637972791083077, 0.06810472965047791, 0.03201230714731882, 0.2202383827032714, -0.19156238721816093, -0.18434816858819525, 0.3839873806412878, 0.024597725851043802, -0.11295832858966856, 0.1734451049926755, 0.0356695672837367, -0.23231809196696648, 0.05312380404613013, 0.26525830247856935, 0.037432100232449715, -0.23059682257007807, 0.0790211426963275, 0.052162700685961495, 0.2290700080780441, 0.03700286484773046, 0.03206126694968934, 0.27070863737528017, 0.24122638310353947, 0.028305997483365555, 0.16653450222029592, -0.04154874162611969, -0.06770451761007405, -0.21918130488132914, -0.13709275809855298, -0.11210041384790735, -0.02877929070482737, -0.03888330447155348, -0.1805799279185333, 0.39036084917084923, 0.263346720607309, 0.030073149940641277, -0.03491114722946177, 0.3646716361801172, -0.11436232253266819, 0.14678006011446745, 0.14124494601554913, 0.05186699430361904, -0.08155364648174045, 0.26454954312166906, -0.12184676263433207, 0.09256836365916413, -0.04229453141833174] |
1,802.03559 | Dynamic Pricing in Shared Mobility on Demand Service | We consider a profit maximization problem in an urban mobility on-demand
service, of which the operator owns a fleet, provides both exclusive and shared
trip services, and dynamically determines prices of offers. With knowledge of
the traveler preference and the distribution of future trip requests, the
operator wants to find the pricing strategy that optimizes the total operating
profit of multiple trips during a specific period, namely, a day in this paper.
This problem is first formulated and analyzed within the dynamic programming
framework, where a general approach combining parametric rollout policy and
stochastic optimization is proposed. A discrete-choice-based price optimization
model is then used for the request level optimal decision problem and leads to
a practical and computationally tractable algorithm for the problem.
Our algorithm is evaluated with a simulated experiment in the urban traffic
network in Langfang, China, and it is shown to generate considerably higher
profit than naive strategies. Further analysis shows that this method also
leads to higher congestion level and lower service capacity in the urban
traffic system, which highlights a need for policy interventions that balance
the private profit making and the system level optimality.
| math.OC | we consider a profit maximization problem in an urban mobility ondemand service of which the operator owns a fleet provides both exclusive and shared trip services and dynamically determines prices of offers with knowledge of the traveler preference and the distribution of future trip requests the operator wants to find the pricing strategy that optimizes the total operating profit of multiple trips during a specific period namely a day in this paper this problem is first formulated and analyzed within the dynamic programming framework where a general approach combining parametric rollout policy and stochastic optimization is proposed a discretechoicebased price optimization model is then used for the request level optimal decision problem and leads to a practical and computationally tractable algorithm for the problem our algorithm is evaluated with a simulated experiment in the urban traffic network in langfang china and it is shown to generate considerably higher profit than naive strategies further analysis shows that this method also leads to higher congestion level and lower service capacity in the urban traffic system which highlights a need for policy interventions that balance the private profit making and the system level optimality | [['we', 'consider', 'a', 'profit', 'maximization', 'problem', 'in', 'an', 'urban', 'mobility', 'ondemand', 'service', 'of', 'which', 'the', 'operator', 'owns', 'a', 'fleet', 'provides', 'both', 'exclusive', 'and', 'shared', 'trip', 'services', 'and', 'dynamically', 'determines', 'prices', 'of', 'offers', 'with', 'knowledge', 'of', 'the', 'traveler', 'preference', 'and', 'the', 'distribution', 'of', 'future', 'trip', 'requests', 'the', 'operator', 'wants', 'to', 'find', 'the', 'pricing', 'strategy', 'that', 'optimizes', 'the', 'total', 'operating', 'profit', 'of', 'multiple', 'trips', 'during', 'a', 'specific', 'period', 'namely', 'a', 'day', 'in', 'this', 'paper', 'this', 'problem', 'is', 'first', 'formulated', 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