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1,802.0436 | The consequences of a nearby supernova on the early Solar System | If the Sun was born in a relatively compact open cluster, it is quite likely
that a massive (10MSun) star was nearby when it exploded in a supernova. The
repercussions of a supernova can be rather profound, and the current Solar
System may still bear the memory of this traumatic event. The truncation of the
Kuiper belt and the tilt of the ecliptic plane with respect to the Sun's
rotation axis could be such signatures. We simulated the effect of a nearby
supernova on the young Solar System using the Astronomical Multipurpose
Software Environment. Our calculations are realized in two subsequent steps in
which we study the effect of the supernova irradiation on the circumstellar
disk and the effect of the impact of the nuclear blast-wave which arrives a few
decades later. We find that the blastwave of our adopted supernova exploding at
a distance of $0.15$--$0.40$\,pc and at an angle of $35^\circ$--$65^\circ$ with
respect to the angular-momentum axis of the circumsolar disk would induce a
misalignment between the Sun's equator and its disk to $5^\circ.6\pm1^\circ.2$,
consistent with the current value. The blast of a supernova truncates the disk
at a radius between $42$ and $55$\,au, which is consistent with the current
edge of the Kuiper belt. For the most favored parameters, the irradiation by
the supernova as well as the blast wave heat the majority of the disk to $\sim
1200$\,K, which is sufficiently hot to melt chondrules in the circumstellar
disk. The majority of planetary system may have been affected by a nearby
supernova, some of its repercussions, such as truncation and tilting of the
disk, may still be visible in their current planetary system's topology. The
amount of material from the supernova blast wave that is accreted by the
circumstellar disk is too small by several orders of magnitude to explain the
current abundance of the short live radionuclide $^{26}$Al.
| astro-ph.SR astro-ph.EP | if the sun was born in a relatively compact open cluster it is quite likely that a massive 10msun star was nearby when it exploded in a supernova the repercussions of a supernova can be rather profound and the current solar system may still bear the memory of this traumatic event the truncation of the kuiper belt and the tilt of the ecliptic plane with respect to the suns rotation axis could be such signatures we simulated the effect of a nearby supernova on the young solar system using the astronomical multipurpose software environment our calculations are realized in two subsequent steps in which we study the effect of the supernova irradiation on the circumstellar disk and the effect of the impact of the nuclear blastwave which arrives a few decades later we find that the blastwave of our adopted supernova exploding at a distance of 015040pc and at an angle of 35circ65circ with respect to the angularmomentum axis of the circumsolar disk would induce a misalignment between the suns equator and its disk to 5circ6pm1circ2 consistent with the current value the blast of a supernova truncates the disk at a radius between 42 and 55au which is consistent with the current edge of the kuiper belt for the most favored parameters the irradiation by the supernova as well as the blast wave heat the majority of the disk to sim 1200k which is sufficiently hot to melt chondrules in the circumstellar disk the majority of planetary system may have been affected by a nearby supernova some of its repercussions such as truncation and tilting of the disk may still be visible in their current planetary systems topology the amount of material from the supernova blast wave that is accreted by the circumstellar disk is too small by several orders of magnitude to explain the current abundance of the short live radionuclide 26al | [['if', 'the', 'sun', 'was', 'born', 'in', 'a', 'relatively', 'compact', 'open', 'cluster', 'it', 'is', 'quite', 'likely', 'that', 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1,802.04361 | Dust-to-gas ratio resurgence in circumstellar disks due to the formation
of giant planets: the case of HD 163296 | The amount of dust present in circumstellar disks is expected to steadily
decrease with age due to the growth from micron-sized particles to
planetesimals and planets. Mature circumstellar disks, however, can be observed
to contain significant amounts of dust and possess high dust-to-gas ratios.
Using HD 163296 as our case study, we explore how the formation of giant
planets in disks can create the conditions for collisionally rejuvenating the
dust population, halting or reversing the expected trend. We combine N-body
simulations with statistical methods and impact scaling laws to estimate the
dynamical and collisional excitation of the planetesimals due to the formation
of HD 163296's giant planets. We show that this process creates a violent
collisional environment across the disk that can inject collisionally produced
second-generation dust into it, significantly contributing to the observed
dust-to-gas ratio. The spatial distribution of the dust production can explain
the observed local enrichments in HD 163296's inner regions. The results
obtained for HD 163296 can be extended to any disk with embedded forming giant
planets and may indicate a common evolutionary stage in the life of such
circumstellar disks. Furthermore, the dynamical excitation of the planetesimals
could result in the release of transient, non-equilibrium gas species like H2O,
CO2, NH3 and CO in the disk due to ice sublimation during impacts and, due to
the excited planetesimals being supersonic with respect to the gas, could
produce bow shocks in the latter that could heat it and cause a broadening of
its emission lines.
| astro-ph.EP astro-ph.SR | the amount of dust present in circumstellar disks is expected to steadily decrease with age due to the growth from micronsized particles to planetesimals and planets mature circumstellar disks however can be observed to contain significant amounts of dust and possess high dusttogas ratios using hd 163296 as our case study we explore how the formation of giant planets in disks can create the conditions for collisionally rejuvenating the dust population halting or reversing the expected trend we combine nbody simulations with statistical methods and impact scaling laws to estimate the dynamical and collisional excitation of the planetesimals due to the formation of hd 163296s giant planets we show that this process creates a violent collisional environment across the disk that can inject collisionally produced secondgeneration dust into it significantly contributing to the observed dusttogas ratio the spatial distribution of the dust production can explain the observed local enrichments in hd 163296s inner regions the results obtained for hd 163296 can be extended to any disk with embedded forming giant planets and may indicate a common evolutionary stage in the life of such circumstellar disks furthermore the dynamical excitation of the planetesimals could result in the release of transient nonequilibrium gas species like h2o co2 nh3 and co in the disk due to ice sublimation during impacts and due to the excited planetesimals being supersonic with respect to the gas could produce bow shocks in the latter that could heat it and cause a broadening of its emission lines | [['the', 'amount', 'of', 'dust', 'present', 'in', 'circumstellar', 'disks', 'is', 'expected', 'to', 'steadily', 'decrease', 'with', 'age', 'due', 'to', 'the', 'growth', 'from', 'micronsized', 'particles', 'to', 'planetesimals', 'and', 'planets', 'mature', 'circumstellar', 'disks', 'however', 'can', 'be', 'observed', 'to', 'contain', 'significant', 'amounts', 'of', 'dust', 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1,802.04362 | The frame bundle picture of Gaussian wave packet dynamics in
semiclassical mechanics | Recently Ohsawa has studied the Marsden-Weinstein-Meyer quotient of the
manifold $T^*\mathbb{R}^n\times T^*\mathbb{R}^{2n^2}$ under a
$\operatorname{O}(2n)$-symmetry, and has used this quotient to describe the
relationship between two different parametrisations of Gaussian wave packet
dynamics commonly used in semiclassical mechanics. In this paper we suggest a
new interpretation of (a subset of) the unreduced space as being the frame
bundle $\mathcal{F}(T^*\mathbb{R}^n)$ of $T^*\mathbb{R}^n$. We outline some
advantages of this interpretation, and explain how it can be extended to more
general symplectic manifolds using the notion of the diagonal lift of a
symplectic form due to Cordero and de Le\'on.
| math-ph math.MP math.SG | recently ohsawa has studied the marsdenweinsteinmeyer quotient of the manifold tmathbbrntimes tmathbbr2n2 under a operatornameo2nsymmetry and has used this quotient to describe the relationship between two different parametrisations of gaussian wave packet dynamics commonly used in semiclassical mechanics in this paper we suggest a new interpretation of a subset of the unreduced space as being the frame bundle mathcalftmathbbrn of tmathbbrn we outline some advantages of this interpretation and explain how it can be extended to more general symplectic manifolds using the notion of the diagonal lift of a symplectic form due to cordero and de leon | [['recently', 'ohsawa', 'has', 'studied', 'the', 'marsdenweinsteinmeyer', 'quotient', 'of', 'the', 'manifold', 'tmathbbrntimes', 'tmathbbr2n2', 'under', 'a', 'operatornameo2nsymmetry', 'and', 'has', 'used', 'this', 'quotient', 'to', 'describe', 'the', 'relationship', 'between', 'two', 'different', 'parametrisations', 'of', 'gaussian', 'wave', 'packet', 'dynamics', 'commonly', 'used', 'in', 'semiclassical', 'mechanics', 'in', 'this', 'paper', 'we', 'suggest', 'a', 'new', 'interpretation', 'of', 'a', 'subset', 'of', 'the', 'unreduced', 'space', 'as', 'being', 'the', 'frame', 'bundle', 'mathcalftmathbbrn', 'of', 'tmathbbrn', 'we', 'outline', 'some', 'advantages', 'of', 'this', 'interpretation', 'and', 'explain', 'how', 'it', 'can', 'be', 'extended', 'to', 'more', 'general', 'symplectic', 'manifolds', 'using', 'the', 'notion', 'of', 'the', 'diagonal', 'lift', 'of', 'a', 'symplectic', 'form', 'due', 'to', 'cordero', 'and', 'de', 'leon']] | [-0.12305186568518875, 0.04424890104916643, -0.15160911092419524, 0.09963265314293963, -0.10540174243582802, -0.09684963925526692, -0.01444491866725282, 0.3409169816033362, -0.2877793748463903, -0.23822546809666104, 0.08094278662363169, -0.19716044635112798, -0.21675915988085404, 0.1683602948560492, -0.14247933480438296, 0.02111499346178639, 0.025080034629554375, 0.05211424541014891, -0.11727267388090655, -0.21263108722185, 0.39230916989905823, 0.054435880087152284, 0.2562898439815739, 0.0352626187889325, 0.12374736060603307, 0.00023046799085952425, -0.01617479349478854, 0.014220482961128844, -0.12630082734676262, 0.1383450238934749, 0.25666672399441537, 0.14201353781007148, 0.2440656281414104, -0.42812094383023597, -0.21182890685664102, 0.13942213028996855, 0.1485635847528721, 0.07398353905447728, -0.0017643410254814815, -0.289361377901674, 0.05486995348346082, -0.18712340045597528, -0.15831525129481003, -0.09688113161086381, 0.03099774272222031, -0.0021401679010263513, -0.16221489267587497, -0.00718342495831446, 0.0738432585456214, 0.042420655442913485, -0.048204858076072984, -0.07820076484023337, -0.00448735566697196, 0.06448146475192446, 0.0358016421112672, 0.07244842579705989, 0.0702315253809422, -0.04800424202611404, -0.10641695048332542, 0.40610793691415054, -0.07980493278048195, -0.27708620197334133, 0.16726644932814352, -0.12171363612575518, -0.16005289503424378, 0.09862855587482125, 0.13840602442704059, 0.10099512520041522, -0.11491011335401909, 0.1145924378658522, -0.07067386432698605, 0.05939886420786094, 0.11024227532414863, 0.031973407141337236, 0.1508153256458732, 0.15185892174102283, 0.029080716606516104, 0.13263545166777835, -0.06095257658347651, -0.11963060493120453, -0.30958075674025565, -0.2256276598903811, -0.13334565874250545, 0.10648134320121704, -0.05358537482459144, -0.14281626795879104, 0.40717497222627014, 0.12356615799816918, 0.22539309398936375, 0.04122715583582828, 0.24815354852394744, 0.08914841940641853, 0.037286232046496406, 0.03471035285146682, 0.21377235593352273, 0.19274353893002966, 0.053476589115254665, -0.1669009009310285, 0.0039026149558824496, 0.09644937589457565] |
1,802.04363 | On convective terms approximation approach that corresponds to pure
convection | Recent decades are put lots of efforts to develop a higher-order scheme for
convective terms approximation that is stable and reliable. The idea presented
here is that approximation approach has to correspond to the physical
phenomenon described by approximated terms. Pure convection (advection) that is
described by convective terms is transporting a property along the streamline,
and the information propagation is unidirectional, i.e., transported property
depends on previous values along the streamline but does not depend on the next
ones. The proposed approach represents streamlines on mesh as discrete
streamlines and is called Discrete Stream(line) Method (DStreaM). A discrete
streamline here is represented as a narrow triangle with one vertex of the
approximated node and two others neighbor upstream nodes. Discrete streamlines
are orientated using local flow direction as skew upwind schemes. DStreaM
corresponds to pure convection. Here are considered standard test problems:
advection of a step profile, advection of a double-step profile, advection of a
sinusoidal profile, and Smith and Hutton problem. DStreaM solutions were
compared with upwind-first order scheme and second-order Total Variation
Diminishing (TVD) schemes with limiters Min-Mod, QUICK, and SUPERBEE solutions.
DStreaM demonstrated second-order accuracy and rapid convergence. Upwind and
DStreaM need 2 or 4 iterations to reach a final solution while TVD schemes need
from 15 to 93.5 more iterations. DStreaM approach looks promising for
calculation of convective-dominated problems because it approximates naturally
first derivatives and is straightforwardly applicable as a meshfree method or
on unstructured meshes.
| math.NA physics.comp-ph | recent decades are put lots of efforts to develop a higherorder scheme for convective terms approximation that is stable and reliable the idea presented here is that approximation approach has to correspond to the physical phenomenon described by approximated terms pure convection advection that is described by convective terms is transporting a property along the streamline and the information propagation is unidirectional ie transported property depends on previous values along the streamline but does not depend on the next ones the proposed approach represents streamlines on mesh as discrete streamlines and is called discrete streamline method dstream a discrete streamline here is represented as a narrow triangle with one vertex of the approximated node and two others neighbor upstream nodes discrete streamlines are orientated using local flow direction as skew upwind schemes dstream corresponds to pure convection here are considered standard test problems advection of a step profile advection of a doublestep profile advection of a sinusoidal profile and smith and hutton problem dstream solutions were compared with upwindfirst order scheme and secondorder total variation diminishing tvd schemes with limiters minmod quick and superbee solutions dstream demonstrated secondorder accuracy and rapid convergence upwind and dstream need 2 or 4 iterations to reach a final solution while tvd schemes need from 15 to 935 more iterations dstream approach looks promising for calculation of convectivedominated problems because it approximates naturally first derivatives and is straightforwardly applicable as a meshfree method or on unstructured meshes | [['recent', 'decades', 'are', 'put', 'lots', 'of', 'efforts', 'to', 'develop', 'a', 'higherorder', 'scheme', 'for', 'convective', 'terms', 'approximation', 'that', 'is', 'stable', 'and', 'reliable', 'the', 'idea', 'presented', 'here', 'is', 'that', 'approximation', 'approach', 'has', 'to', 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1,802.04364 | Junction Tree Variational Autoencoder for Molecular Graph Generation | We seek to automate the design of molecules based on specific chemical
properties. In computational terms, this task involves continuous embedding and
generation of molecular graphs. Our primary contribution is the direct
realization of molecular graphs, a task previously approached by generating
linear SMILES strings instead of graphs. Our junction tree variational
autoencoder generates molecular graphs in two phases, by first generating a
tree-structured scaffold over chemical substructures, and then combining them
into a molecule with a graph message passing network. This approach allows us
to incrementally expand molecules while maintaining chemical validity at every
step. We evaluate our model on multiple tasks ranging from molecular generation
to optimization. Across these tasks, our model outperforms previous
state-of-the-art baselines by a significant margin.
| cs.LG cs.NE stat.ML | we seek to automate the design of molecules based on specific chemical properties in computational terms this task involves continuous embedding and generation of molecular graphs our primary contribution is the direct realization of molecular graphs a task previously approached by generating linear smiles strings instead of graphs our junction tree variational autoencoder generates molecular graphs in two phases by first generating a treestructured scaffold over chemical substructures and then combining them into a molecule with a graph message passing network this approach allows us to incrementally expand molecules while maintaining chemical validity at every step we evaluate our model on multiple tasks ranging from molecular generation to optimization across these tasks our model outperforms previous stateoftheart baselines by a significant margin | [['we', 'seek', 'to', 'automate', 'the', 'design', 'of', 'molecules', 'based', 'on', 'specific', 'chemical', 'properties', 'in', 'computational', 'terms', 'this', 'task', 'involves', 'continuous', 'embedding', 'and', 'generation', 'of', 'molecular', 'graphs', 'our', 'primary', 'contribution', 'is', 'the', 'direct', 'realization', 'of', 'molecular', 'graphs', 'a', 'task', 'previously', 'approached', 'by', 'generating', 'linear', 'smiles', 'strings', 'instead', 'of', 'graphs', 'our', 'junction', 'tree', 'variational', 'autoencoder', 'generates', 'molecular', 'graphs', 'in', 'two', 'phases', 'by', 'first', 'generating', 'a', 'treestructured', 'scaffold', 'over', 'chemical', 'substructures', 'and', 'then', 'combining', 'them', 'into', 'a', 'molecule', 'with', 'a', 'graph', 'message', 'passing', 'network', 'this', 'approach', 'allows', 'us', 'to', 'incrementally', 'expand', 'molecules', 'while', 'maintaining', 'chemical', 'validity', 'at', 'every', 'step', 'we', 'evaluate', 'our', 'model', 'on', 'multiple', 'tasks', 'ranging', 'from', 'molecular', 'generation', 'to', 'optimization', 'across', 'these', 'tasks', 'our', 'model', 'outperforms', 'previous', 'stateoftheart', 'baselines', 'by', 'a', 'significant', 'margin']] | [-0.10105704295269564, 0.023583077092761875, -0.052209355250062026, 0.008288666727135843, -0.10673042913501868, -0.11922510499974377, 0.1247360554416893, 0.44255722906119876, -0.2879784754553779, -0.3354652558499184, 0.002111124758394893, -0.2358292895313171, -0.15282996604028234, 0.17180632866659493, -0.03045291788532536, 0.06766903724475595, 0.12998054796432862, -0.012033889472835744, -0.03197510167192973, -0.24817016079652574, 0.2881161048912947, 0.020438079231754554, 0.26773753985151894, 0.04503917583401819, 0.1382757853563936, -0.030811958507344615, -0.01990541674055495, -0.025794724255349854, -0.07744698214237808, 0.19128194085291786, 0.28405576826790807, 0.1525903780043858, 0.26041853469872817, -0.4476797594409722, -0.2797026516503242, 0.09934019852147179, 0.14517049865461276, 0.14772790925936072, -0.01240187987375558, -0.28674531062362624, 0.07723907737794225, -0.14921933531639028, -0.01030655043031715, -0.10605418102693606, -0.019179448118952454, 0.0213407744862498, -0.27283281273376503, 0.02455880681098607, 0.048668887958571806, 0.04703054698680329, -0.0039500364595756975, -0.14341913536524964, -0.003133025998440495, 0.15512781307009643, -0.03336817768906228, 0.08207248589481975, 0.17748210195034025, -0.1368645735778135, -0.19006231265356305, 0.38149951922432446, -0.05880809252314483, -0.18908160050078981, 0.21596358958288234, -0.019110811311397587, -0.22285487580105479, 0.13371941102592305, 0.16635016932106408, 0.13943161223022665, -0.172500512348946, 0.04042714792131981, -0.03059033725074813, 0.18982802645959815, 0.10641204445722101, -0.032008309096678116, 0.222056455080031, 0.2615580448392229, 0.06086343386378445, 0.20340427263166572, -0.06466135782114284, -0.1248236375891405, -0.2011606244749168, -0.115593296017681, -0.20357312696421, -0.007480200208326588, -0.12956044992258328, -0.14490078023986006, 0.4449500236812918, 0.18673629737108446, 0.18320259054145607, 0.1316298763055381, 0.33632024563085594, -0.005591938352750492, 0.08981335096893313, 0.07042403633565046, 0.12163030984429796, 0.09149785878587148, 0.07789091055651699, -0.19356461713800482, 0.09749528713291511, 0.08479174637983812] |
1,802.04365 | Learning a Neural-network-based Representation for Open Set Recognition | Open set recognition problems exist in many domains. For example in security,
new malware classes emerge regularly; therefore malware classification systems
need to identify instances from unknown classes in addition to discriminating
between known classes. In this paper we present a neural network based
representation for addressing the open set recognition problem. In this
representation instances from the same class are close to each other while
instances from different classes are further apart, resulting in statistically
significant improvement when compared to other approaches on three datasets
from two different domains.
| cs.LG cs.CR stat.ML | open set recognition problems exist in many domains for example in security new malware classes emerge regularly therefore malware classification systems need to identify instances from unknown classes in addition to discriminating between known classes in this paper we present a neural network based representation for addressing the open set recognition problem in this representation instances from the same class are close to each other while instances from different classes are further apart resulting in statistically significant improvement when compared to other approaches on three datasets from two different domains | [['open', 'set', 'recognition', 'problems', 'exist', 'in', 'many', 'domains', 'for', 'example', 'in', 'security', 'new', 'malware', 'classes', 'emerge', 'regularly', 'therefore', 'malware', 'classification', 'systems', 'need', 'to', 'identify', 'instances', 'from', 'unknown', 'classes', 'in', 'addition', 'to', 'discriminating', 'between', 'known', 'classes', 'in', 'this', 'paper', 'we', 'present', 'a', 'neural', 'network', 'based', 'representation', 'for', 'addressing', 'the', 'open', 'set', 'recognition', 'problem', 'in', 'this', 'representation', 'instances', 'from', 'the', 'same', 'class', 'are', 'close', 'to', 'each', 'other', 'while', 'instances', 'from', 'different', 'classes', 'are', 'further', 'apart', 'resulting', 'in', 'statistically', 'significant', 'improvement', 'when', 'compared', 'to', 'other', 'approaches', 'on', 'three', 'datasets', 'from', 'two', 'different', 'domains']] | [-0.05773703425915705, 0.007722188164997432, -0.0024938178662624625, 0.07554581648307956, -0.06501232268185252, -0.20298239667382506, 0.026844727432924428, 0.4107643364618222, -0.31463320538815526, -0.3693747377643983, 0.10390655209517313, -0.3004611804253525, -0.15000379631916683, 0.2959892595652491, -0.1263173142551548, 0.06194892409774992, 0.11395866163721723, 0.05442733984026644, -0.07861482234568232, -0.2820161651199063, 0.38968448175324333, -0.12151916191602746, 0.3009152953099046, 0.0008522925898432731, 0.025616901419642898, -0.0894617900511043, -0.011193297971557412, 0.02734770597760669, -0.036580749019049105, 0.138972321306614, 0.3789995735718144, 0.16772523434936173, 0.2887803821100129, -0.36047251572211586, -0.20873075950269898, 0.17091756573386696, 0.14932778515956469, 0.1621126335620324, -0.040233192992551875, -0.3345741924208899, 0.0970405433107064, -0.14178581007031932, -0.014416686662783226, -0.08581021364467839, 0.03132541486476031, -0.00360295212142066, -0.2202709228421251, -0.0024310692782617276, 0.0487613862587346, 0.0732563326238758, -0.0881148512268232, -0.15682481752527463, 0.09311399990175333, 0.21538854480831005, 0.09483990440559056, 0.026420306243623296, 0.0789229154948973, -0.1689385306297077, -0.19230869921545188, 0.3607245274922914, 0.020373055550994144, -0.2286789053844081, 0.3011184025517044, -0.050972593648152216, -0.25492172742055524, 0.11118042011383093, 0.25541285909712313, 0.0969749307518618, -0.19948100063565005, 0.009480068175536063, -0.07702153222635388, 0.174016448821769, 0.1123468332613508, 0.026366653769380518, 0.1987862852608992, 0.1967925841247456, 0.06318033329249981, 0.18064211604004313, -0.06218160177167091, -0.07968003736136274, -0.2324780362451242, -0.06461282240019904, -0.1380964330294066, -0.014985098011998666, -0.07955779266647167, -0.1696690639687909, 0.43112661169014044, 0.2159931976115331, 0.17606038467751609, 0.0953149758770855, 0.2594558671944671, -0.02560722830433709, 0.0882804155142771, 0.1369519780842691, 0.1574557595084318, -0.01494553555837936, 0.06837854558964157, -0.1114801812341385, 0.06696015279481395, 0.02286754099962612] |
1,802.04366 | Bouncy Hybrid Sampler as a Unifying Device | This work introduces a class of rejection-free Markov chain Monte Carlo
(MCMC) samplers, named the Bouncy Hybrid Sampler, which unifies several
existing methods from the literature. Examples include the Bouncy Particle
Sampler of Peters and de With (2012), Bouchard-Cote et al. (2015) and the
Hamiltonian MCMC. Following the introduced general framework, we derive a new
sampler called the Quadratic Bouncy Hybrid Sampler. We apply this novel sampler
to the problem of sampling from a truncated Gaussian distribution.
| stat.CO | this work introduces a class of rejectionfree markov chain monte carlo mcmc samplers named the bouncy hybrid sampler which unifies several existing methods from the literature examples include the bouncy particle sampler of peters and de with 2012 bouchardcote et al 2015 and the hamiltonian mcmc following the introduced general framework we derive a new sampler called the quadratic bouncy hybrid sampler we apply this novel sampler to the problem of sampling from a truncated gaussian distribution | [['this', 'work', 'introduces', 'a', 'class', 'of', 'rejectionfree', 'markov', 'chain', 'monte', 'carlo', 'mcmc', 'samplers', 'named', 'the', 'bouncy', 'hybrid', 'sampler', 'which', 'unifies', 'several', 'existing', 'methods', 'from', 'the', 'literature', 'examples', 'include', 'the', 'bouncy', 'particle', 'sampler', 'of', 'peters', 'and', 'de', 'with', '2012', 'bouchardcote', 'et', 'al', '2015', 'and', 'the', 'hamiltonian', 'mcmc', 'following', 'the', 'introduced', 'general', 'framework', 'we', 'derive', 'a', 'new', 'sampler', 'called', 'the', 'quadratic', 'bouncy', 'hybrid', 'sampler', 'we', 'apply', 'this', 'novel', 'sampler', 'to', 'the', 'problem', 'of', 'sampling', 'from', 'a', 'truncated', 'gaussian', 'distribution']] | [0.012389468114921138, 0.1021729776586749, -0.1578670192485381, 0.10049400458458542, -0.09301944776732278, -0.17061901513462593, 0.03737257407537024, 0.3895129609951063, -0.2957440117943885, -0.36599802856309044, 0.029097884498217975, -0.18792266042069777, -0.12899845806685717, 0.1884206800497946, -0.11010252847336233, 0.09338740073144436, 0.12691512771293914, -0.1325314777511123, -0.05251686638343687, -0.2563629239233897, 0.22082055360078812, 0.16628794335319022, 0.29071591287165094, -0.12439676843611155, 0.21253366640303284, 0.08051372252674283, -0.04329891162189214, -0.04760348931641171, -0.27175630546652857, 0.12765400664801219, 0.15829019323236457, 0.16775712661910802, 0.32444770783063415, -0.29655819126453836, -0.22907054896380655, 0.15211588262555828, 0.1271146610072863, 0.18964800685123345, -0.08989789531762271, -0.3143566663769123, -0.04284661339663312, -0.25327754202053737, -0.052661604309258495, -0.09338521594059114, -0.1803343278282371, 0.062344323059445934, -0.30436299271930595, 0.06078596094525174, 0.1388657818131737, -0.007575219962745905, 0.06697327596760404, -0.18597471987136246, 0.040513473048217986, -0.067396924322422, -0.004124110124968483, 0.023176366383650976, 0.06957718576794784, 0.010674819791395413, -0.22578722497105205, 0.24914474287039698, -0.03708525708306728, -0.20005347900472484, 0.1975747624926857, -0.0043324549363828015, -0.28199902668252197, 0.12283218685096424, 0.15731198296232737, 0.22672797870626182, -0.20519559459736278, 0.21672582841864296, -0.03617247720984252, 0.04560251231918013, -0.04678003377232112, -0.10261091769069408, 0.09599338208729598, 0.14572828430314794, 0.07168111213678985, 0.22318747031845546, -0.14528910840530634, -0.28975448820902355, -0.2625875648013071, -0.20966626371649144, -0.26517643141070085, -0.0410105274012925, -0.07670831342655124, -0.25475671946218137, 0.40174812123816656, 0.2683552932495978, 0.1812307261371691, 0.12759344719846635, 0.27479328157220917, 0.061536342885933425, -0.030307144490315727, 0.15552344086187841, 0.08445128898969606, 0.14414887499113224, 0.08594909283381544, -0.1503888629315617, 0.03648214144509678, 0.1436528362652385] |
1,802.04367 | Computational Optimal Transport: Complexity by Accelerated Gradient
Descent Is Better Than by Sinkhorn's Algorithm | We analyze two algorithms for approximating the general optimal transport
(OT) distance between two discrete distributions of size $n$, up to accuracy
$\varepsilon$. For the first algorithm, which is based on the celebrated
Sinkhorn's algorithm, we prove the complexity bound
$\widetilde{O}\left({n^2/\varepsilon^2}\right)$ arithmetic operations. For the
second one, which is based on our novel Adaptive Primal-Dual Accelerated
Gradient Descent (APDAGD) algorithm, we prove the complexity bound
$\widetilde{O}\left(\min\left\{n^{9/4}/\varepsilon, n^{2}/\varepsilon^2
\right\}\right)$ arithmetic operations. Both bounds have better dependence on
$\varepsilon$ than the state-of-the-art result given by
$\widetilde{O}\left({n^2/\varepsilon^3}\right)$. Our second algorithm not only
has better dependence on $\varepsilon$ in the complexity bound, but also is not
specific to entropic regularization and can solve the OT problem with different
regularizers.
| cs.DS math.OC | we analyze two algorithms for approximating the general optimal transport ot distance between two discrete distributions of size n up to accuracy varepsilon for the first algorithm which is based on the celebrated sinkhorns algorithm we prove the complexity bound widetildeoleftn2varepsilon2right arithmetic operations for the second one which is based on our novel adaptive primaldual accelerated gradient descent apdagd algorithm we prove the complexity bound widetildeoleftminleftn94varepsilon n2varepsilon2 rightright arithmetic operations both bounds have better dependence on varepsilon than the stateoftheart result given by widetildeoleftn2varepsilon3right our second algorithm not only has better dependence on varepsilon in the complexity bound but also is not specific to entropic regularization and can solve the ot problem with different regularizers | [['we', 'analyze', 'two', 'algorithms', 'for', 'approximating', 'the', 'general', 'optimal', 'transport', 'ot', 'distance', 'between', 'two', 'discrete', 'distributions', 'of', 'size', 'n', 'up', 'to', 'accuracy', 'varepsilon', 'for', 'the', 'first', 'algorithm', 'which', 'is', 'based', 'on', 'the', 'celebrated', 'sinkhorns', 'algorithm', 'we', 'prove', 'the', 'complexity', 'bound', 'widetildeoleftn2varepsilon2right', 'arithmetic', 'operations', 'for', 'the', 'second', 'one', 'which', 'is', 'based', 'on', 'our', 'novel', 'adaptive', 'primaldual', 'accelerated', 'gradient', 'descent', 'apdagd', 'algorithm', 'we', 'prove', 'the', 'complexity', 'bound', 'widetildeoleftminleftn94varepsilon', 'n2varepsilon2', 'rightright', 'arithmetic', 'operations', 'both', 'bounds', 'have', 'better', 'dependence', 'on', 'varepsilon', 'than', 'the', 'stateoftheart', 'result', 'given', 'by', 'widetildeoleftn2varepsilon3right', 'our', 'second', 'algorithm', 'not', 'only', 'has', 'better', 'dependence', 'on', 'varepsilon', 'in', 'the', 'complexity', 'bound', 'but', 'also', 'is', 'not', 'specific', 'to', 'entropic', 'regularization', 'and', 'can', 'solve', 'the', 'ot', 'problem', 'with', 'different', 'regularizers']] | [-0.09345692506864328, 0.03716993730684573, -0.12712950330566275, 0.10196610029511662, -0.08298080826855518, -0.20873358889736912, 0.06353712042962963, 0.38181337780234487, -0.30210409750315276, -0.3598706733774055, 0.09234025965931571, -0.22883430951359598, -0.15313020581591197, 0.24509573156352749, -0.11531478131867268, 0.10998428011821075, 0.03180544393306429, 0.05712662555954673, -0.1126458335583183, -0.3422029241242192, 0.2674693366470323, 0.03594389835541899, 0.2633663335730406, 0.058729747352613645, 0.12272581685842439, -0.035555184447333556, 0.033702695936980574, -0.004171922553161328, -0.13844922055193984, 0.14443998120365326, 0.19145643764006143, 0.1437801905526695, 0.32234344420192595, -0.39482521956617184, -0.1406863012520427, 0.13912109545516696, 0.1638769724914296, 0.10009136614601381, -0.020152038149535655, -0.21794414424803107, 0.10241258964365856, -0.0798789651472775, -0.017288974484174765, -0.09613257318904454, -0.011632899550551717, 0.031450124971822584, -0.2981119582852857, 0.05567625549129761, 0.08168785845522177, -4.103860225189816e-05, -0.042343038390390574, -0.1724291337516413, 0.08101415827616372, 0.07153183448297734, 0.01703756732061844, 0.06894591466989368, 0.07011119913394478, -0.045800862867723814, -0.14853914048184047, 0.315209768881852, -0.07980571002977244, -0.2329456812278791, 0.13367436332204802, -0.08832982771352611, -0.17912571757862514, 0.09650887134908274, 0.21505358930338514, 0.1986051268367605, -0.07820183448705145, 0.11915674868664196, -0.0601414948024533, 0.1799938077615066, 0.06502946111458269, 0.05068125921904787, -0.005429259983992035, 0.16492895717224615, 0.19362140250265258, 0.14041455960409208, -0.049288780814756385, -0.1207158682232892, -0.26794038480359383, -0.13672129169307565, -0.23948191389949483, -0.011164949042458003, -0.17464370389709735, -0.15355597846950828, 0.334025210311467, 0.1462952694539457, 0.19747882923957977, 0.1657399343940514, 0.3604365522719242, 0.12266107333866372, 0.024242970015620813, 0.20183507372963835, 0.20987949188036675, 0.08770404578482902, 0.03272205882451751, -0.25791724691305057, 0.12141077823259613, 0.1859006154181605] |
1,802.04368 | Numerical Solution of the Schr\"odinger Equation for a Short-Range 1/r
Singular Potential with any L Angular Momentum | Recently, the Asymptotic Iteration Method (AIM) was used to calculate the
energy spectrum for a short rang three parameter central potential which was
introduced by H. Bahlouli and A. D. Alhaidari. The S-orbital wave solution of
the Schr\"odinger equation was obtained for different parameters of the
potential. In this work a non-zero angular momentum term were introduced to the
problem and the energy eigenvalues were obtained for different potential
parameters. Our results show very good agreements compared with other methods
such as potential parameter spectrum method (PPSM) and the complex scaling
method (CSM).
| quant-ph | recently the asymptotic iteration method aim was used to calculate the energy spectrum for a short rang three parameter central potential which was introduced by h bahlouli and a d alhaidari the sorbital wave solution of the schrodinger equation was obtained for different parameters of the potential in this work a nonzero angular momentum term were introduced to the problem and the energy eigenvalues were obtained for different potential parameters our results show very good agreements compared with other methods such as potential parameter spectrum method ppsm and the complex scaling method csm | [['recently', 'the', 'asymptotic', 'iteration', 'method', 'aim', 'was', 'used', 'to', 'calculate', 'the', 'energy', 'spectrum', 'for', 'a', 'short', 'rang', 'three', 'parameter', 'central', 'potential', 'which', 'was', 'introduced', 'by', 'h', 'bahlouli', 'and', 'a', 'd', 'alhaidari', 'the', 'sorbital', 'wave', 'solution', 'of', 'the', 'schrodinger', 'equation', 'was', 'obtained', 'for', 'different', 'parameters', 'of', 'the', 'potential', 'in', 'this', 'work', 'a', 'nonzero', 'angular', 'momentum', 'term', 'were', 'introduced', 'to', 'the', 'problem', 'and', 'the', 'energy', 'eigenvalues', 'were', 'obtained', 'for', 'different', 'potential', 'parameters', 'our', 'results', 'show', 'very', 'good', 'agreements', 'compared', 'with', 'other', 'methods', 'such', 'as', 'potential', 'parameter', 'spectrum', 'method', 'ppsm', 'and', 'the', 'complex', 'scaling', 'method', 'csm']] | [-0.09110480677950514, 0.053317066614649126, -0.09171017198976907, 0.07711595265517154, -0.04720995577236453, -0.1458933791299197, 0.008787347740688166, 0.34335314584793625, -0.2239267142376975, -0.33695657186947026, 0.05444502637460623, -0.2783218026979939, -0.12814509127657492, 0.20075739621297345, 0.011244519713467785, 0.13467403963863195, 0.07829410006077243, 0.03872142303325645, -0.04527028447405978, -0.21132986701943063, 0.31178805643979174, 0.09109282469028955, 0.25378209228402226, 0.06960292867190399, 0.09313979771776268, -0.01887315097469632, -0.02455625403160741, 0.027683496188658934, -0.16462302904647702, 0.09153143913039917, 0.19955115008505656, 0.044487115643038366, 0.2636834482079024, -0.3700658413735065, -0.25143380480521654, 0.10193199979571196, 0.12330204357935505, 0.09914325033749484, -0.05331469789802373, -0.2720215456089476, 0.0681992290454538, -0.21120939746621872, -0.1983933149693677, -0.08116737977284325, 0.040005151121007214, 0.05624352053708428, -0.29679216028979194, 0.0907248966267926, -0.001860557892631642, -0.01101580755989794, -0.11888028691512542, -0.17280230769928503, -0.03590147962261524, 0.09743321298590892, 0.06954294887811437, 0.05928834085608577, 0.03965995313886266, -0.08718193806247315, -0.05628556036646222, 0.35278396627732683, -0.08446791458997753, -0.19187371058205327, 0.16133885482674118, -0.10305402075339641, -0.0853771209655391, 0.11899928429297038, 0.11814283884602476, 0.1331838451960421, -0.15561625228396483, 0.11827911463486808, 0.015292255637897332, 0.12908951346608963, 0.08040643706508391, -0.005364249941411909, 0.11040423526991527, 0.10061539479295958, 0.0232507896271872, 0.10368785570480522, -0.10421196648155104, -0.07548912032251502, -0.24441328771657997, -0.13557757283714447, -0.2235253362271648, -0.005913454596148958, -0.10793950689325217, -0.10196150552767974, 0.439034944389957, 0.10097477727845966, 0.22011617788082952, 0.03022050555759611, 0.26117899442848924, 0.17139170012324229, 0.05328258896594519, 0.0436171033102405, 0.2538061981386208, 0.13421044931081788, 0.15032876852981664, -0.23602128491460622, 0.004811850233706467, 0.10860825095653207] |
1,802.04369 | Long-term regularity of the periodic Euler--Poisson system for electrons
in 2D | We study a basic plasma physics model--the one-fluid Euler--Poisson system on
the square torus, in which a compressible electron fluid flows under its own
electrostatic field. In this paper we prove long-term regularity of periodic
solutions of this system in 2 spatial dimensions.
Our main conclusion is that on a square torus of side length $R$, if the
initial data is sufficiently close to a constant solution, then the solution is
wellposed for a time at least $R/\epsilon^2(\log R)^{O(1)}$, where $\epsilon$
is the size of the initial data.
| math.AP | we study a basic plasma physics modelthe onefluid eulerpoisson system on the square torus in which a compressible electron fluid flows under its own electrostatic field in this paper we prove longterm regularity of periodic solutions of this system in 2 spatial dimensions our main conclusion is that on a square torus of side length r if the initial data is sufficiently close to a constant solution then the solution is wellposed for a time at least repsilon2log ro1 where epsilon is the size of the initial data | [['we', 'study', 'a', 'basic', 'plasma', 'physics', 'modelthe', 'onefluid', 'eulerpoisson', 'system', 'on', 'the', 'square', 'torus', 'in', 'which', 'a', 'compressible', 'electron', 'fluid', 'flows', 'under', 'its', 'own', 'electrostatic', 'field', 'in', 'this', 'paper', 'we', 'prove', 'longterm', 'regularity', 'of', 'periodic', 'solutions', 'of', 'this', 'system', 'in', '2', 'spatial', 'dimensions', 'our', 'main', 'conclusion', 'is', 'that', 'on', 'a', 'square', 'torus', 'of', 'side', 'length', 'r', 'if', 'the', 'initial', 'data', 'is', 'sufficiently', 'close', 'to', 'a', 'constant', 'solution', 'then', 'the', 'solution', 'is', 'wellposed', 'for', 'a', 'time', 'at', 'least', 'repsilon2log', 'ro1', 'where', 'epsilon', 'is', 'the', 'size', 'of', 'the', 'initial', 'data']] | [-0.19014398869188587, 0.09113488545180994, -0.09882670144775305, 0.031718377957934106, -0.017527582428964048, -0.15931916992640358, -0.022209965678777588, 0.3045889872886326, -0.2808530181688163, -0.2143957683302719, 0.18191643840306834, -0.25889886235925313, -0.08432924218647096, 0.15045926373066573, -0.05358561088769943, 0.0402711867488621, 0.07759736763613148, 0.05961337313591235, -0.03434769667674327, -0.24463337841818386, 0.33559007062469665, 0.04321774343649546, 0.21290980446709043, 0.04482447834693473, 0.13358384188018396, -0.03333610836844677, 0.04930947448030628, 0.037101361129818294, -0.20121619702226834, 0.04345643539088725, 0.18012065662542123, 0.05322211928767334, 0.3046701977393408, -0.41712954852344664, -0.20650669448505873, 0.05067514146988591, 0.13558066214552555, 0.11256731218550001, -0.029685431313915073, -0.20152202017616008, 0.10666350298828778, -0.12287486370267539, -0.21100740357912307, 0.012423039997403306, 0.09608731412023691, 0.045526428777060805, -0.31579076916236304, 0.06970462846387734, 0.0905350250159872, 0.04228217478027974, -0.12706572283728412, -0.05829530445612625, 0.004716044910059407, 0.06749879244993302, 0.08123400722277062, 0.07971460691749535, 0.08425444805052602, -0.14552039859131322, -0.013375633396208286, 0.38345705306735534, -0.0788496910358897, -0.25223884033574456, 0.20064297583403773, -0.19145750192304453, -0.09534491666581953, 0.13635248169517036, 0.18508619077011257, 0.108122487514997, -0.11664648649507556, 0.1417711183385945, -0.13498705585524653, 0.2501267683531704, 0.06599659783144791, -0.030638133363006102, 0.17571681816045237, 0.19823783333680925, 0.12301276734998, 0.11650094750669153, -0.07113092714096365, -0.08433787417651593, -0.3459204966309427, -0.1575147176332686, -0.22336899146608924, 0.10807345709067652, -0.1230257394959101, -0.17068806570023298, 0.3911340175405273, 0.14719376422935862, 0.13392742616177974, 0.050726631159182294, 0.2947005824956658, 0.09639708192660419, -0.017384844631406254, 0.1446440021732244, 0.1933629869927663, 0.10164656428936013, 0.12822004196758585, -0.21453943249673166, -0.00019193877433908396, 0.13158141285309505] |
1,802.0437 | Higher dimensional fractional time independent Schr\"{o}dinger equation
via Jumarie fractional derivative with generalized pseudoharmonic potential | In this paper we obtain approximate bound state solutions of $N$-dimensional
time independent fractional Schr\"{o}dinger equation for generalised
pseudoharmonic potential which has the form
$V(r^{\alpha})=a_1r^{2\alpha}+\frac{a_2}{r^{2\alpha}}+a_3$. Here
$\alpha(0<\alpha<1)$ acts like a fractional parameter for the space variable
$r$. The entire study is composed with the Jumarie type derivative and the
elegance of Laplace transform. As a result we successfully able to express the
approximate bound state solution in terms of Mittag-Leffler function and
fractionally defined confluent hypergeometric function. Our study may be
treated as a generalization of all previous works carried out on this topic
when $\alpha=1$ and $N$ arbitrary. We provide numerical result of energy
eigenvalues and eigenfunctions for a typical diatomic molecule for different
$\alpha$ close to unity. Finally, we try to correlate our work with Cornell
potential model which corresponds to $\alpha=\frac{1}{2}$ with $a_3=0$ and
predict the approximate mass spectra of quarkonia.
| quant-ph | in this paper we obtain approximate bound state solutions of ndimensional time independent fractional schrodinger equation for generalised pseudoharmonic potential which has the form vralphaa_1r2alphafraca_2r2alphaa_3 here alpha0alpha1 acts like a fractional parameter for the space variable r the entire study is composed with the jumarie type derivative and the elegance of laplace transform as a result we successfully able to express the approximate bound state solution in terms of mittagleffler function and fractionally defined confluent hypergeometric function our study may be treated as a generalization of all previous works carried out on this topic when alpha1 and n arbitrary we provide numerical result of energy eigenvalues and eigenfunctions for a typical diatomic molecule for different alpha close to unity finally we try to correlate our work with cornell potential model which corresponds to alphafrac12 with a_30 and predict the approximate mass spectra of quarkonia | [['in', 'this', 'paper', 'we', 'obtain', 'approximate', 'bound', 'state', 'solutions', 'of', 'ndimensional', 'time', 'independent', 'fractional', 'schrodinger', 'equation', 'for', 'generalised', 'pseudoharmonic', 'potential', 'which', 'has', 'the', 'form', 'vralphaa_1r2alphafraca_2r2alphaa_3', 'here', 'alpha0alpha1', 'acts', 'like', 'a', 'fractional', 'parameter', 'for', 'the', 'space', 'variable', 'r', 'the', 'entire', 'study', 'is', 'composed', 'with', 'the', 'jumarie', 'type', 'derivative', 'and', 'the', 'elegance', 'of', 'laplace', 'transform', 'as', 'a', 'result', 'we', 'successfully', 'able', 'to', 'express', 'the', 'approximate', 'bound', 'state', 'solution', 'in', 'terms', 'of', 'mittagleffler', 'function', 'and', 'fractionally', 'defined', 'confluent', 'hypergeometric', 'function', 'our', 'study', 'may', 'be', 'treated', 'as', 'a', 'generalization', 'of', 'all', 'previous', 'works', 'carried', 'out', 'on', 'this', 'topic', 'when', 'alpha1', 'and', 'n', 'arbitrary', 'we', 'provide', 'numerical', 'result', 'of', 'energy', 'eigenvalues', 'and', 'eigenfunctions', 'for', 'a', 'typical', 'diatomic', 'molecule', 'for', 'different', 'alpha', 'close', 'to', 'unity', 'finally', 'we', 'try', 'to', 'correlate', 'our', 'work', 'with', 'cornell', 'potential', 'model', 'which', 'corresponds', 'to', 'alphafrac12', 'with', 'a_30', 'and', 'predict', 'the', 'approximate', 'mass', 'spectra', 'of', 'quarkonia']] | [-0.0669345972587315, 0.04973718341977768, -0.08650583065275601, 0.06551127349810255, -0.07392802076007594, -0.1583190438219569, 0.03895521975978291, 0.32492834426672523, -0.24151534652403017, -0.2859570200810812, 0.07447442260872521, -0.2870013654481252, -0.14377120938676763, 0.17233723699456263, -0.01862106779688628, 0.08463324256516783, 0.023227336076186628, 0.06902981948654305, -0.07888366769946797, -0.2265183810328304, 0.321302513305006, 0.011145976385180379, 0.19880713428810082, 0.045770843057486583, 0.08481205991652965, -0.011738469970123974, -0.007918554364742947, -0.03132759502283002, -0.1765841194100573, 0.08412213300266237, 0.2332545111782219, 0.061371788253356346, 0.25968296402199587, -0.38986975209243124, -0.18853319153635645, 0.15044080466851856, 0.18020409352841302, 0.050743176980586856, -0.011094310281459656, -0.2735346321436301, 0.05220333710034102, -0.19521123519770697, -0.22241832376260992, -0.10112534115471365, 0.04869684497629043, 0.06654140917243252, -0.30021278920885125, 0.11063184940033186, 0.05297765154457113, -0.02881805048417218, -0.11263042877626632, -0.15243629413955306, 0.006958120682893295, 0.06936761485613574, 0.024192694827920795, 0.05919389176456271, 0.06891545328528056, -0.0955142017853984, -0.08503103222120816, 0.3562450221621654, -0.10045316188906084, -0.27431636057879355, 0.12404165687006702, -0.13205960494282484, -0.12405727181950925, 0.06805379191662493, 0.1673015958570521, 0.16005395533366154, -0.11863319886551964, 0.11254160000383526, -0.06587082000029548, 0.1504799799475504, 0.09292202123480153, 0.04488527126820155, 0.13430044539666303, 0.1057473863294663, 0.0534160240376237, 0.1746067528630948, -0.028719826498147572, -0.12906313124834232, -0.3211082519871563, -0.201466933762635, -0.20585772941168973, 0.08352602962863592, -0.06502921077450262, -0.18824358750790568, 0.40899649158966217, 0.103301162691295, 0.1983114984279997, 0.0891475539316725, 0.22721976062125512, 0.21665277695414467, -0.0001435349252678349, 0.043201463058030125, 0.16631603603657913, 0.13618965927859059, 0.09003529049010134, -0.19764476771873069, 0.0030745548346984976, 0.10790617411142923] |
1,802.04371 | A Direct Method for the Transient Stability Analysis of Transmission
Switching Events | In this paper, we propose an energy-based method for the transient stability
analysis of a power system transmission switching event. In this method the
exit point of pseudo-fault trajectory is used to determine a relevant
controlling unstable equilibrium point (CUEP) for a switching event, the
stability of the switching event is then assessed based on the energy margin
between the computed relevant CUEP and the post-switching initial point. The
effectiveness of the method is demonstrated on switching events in the
structure-preserving models of a heavily loaded version of the WSCC 9-bus
3-machine system, and the base case IEEE 145-bus 50-machine system. A scheme
for the detailed analysis of power system switching events is then proposed.
| eess.SP math.DS | in this paper we propose an energybased method for the transient stability analysis of a power system transmission switching event in this method the exit point of pseudofault trajectory is used to determine a relevant controlling unstable equilibrium point cuep for a switching event the stability of the switching event is then assessed based on the energy margin between the computed relevant cuep and the postswitching initial point the effectiveness of the method is demonstrated on switching events in the structurepreserving models of a heavily loaded version of the wscc 9bus 3machine system and the base case ieee 145bus 50machine system a scheme for the detailed analysis of power system switching events is then proposed | [['in', 'this', 'paper', 'we', 'propose', 'an', 'energybased', 'method', 'for', 'the', 'transient', 'stability', 'analysis', 'of', 'a', 'power', 'system', 'transmission', 'switching', 'event', 'in', 'this', 'method', 'the', 'exit', 'point', 'of', 'pseudofault', 'trajectory', 'is', 'used', 'to', 'determine', 'a', 'relevant', 'controlling', 'unstable', 'equilibrium', 'point', 'cuep', 'for', 'a', 'switching', 'event', 'the', 'stability', 'of', 'the', 'switching', 'event', 'is', 'then', 'assessed', 'based', 'on', 'the', 'energy', 'margin', 'between', 'the', 'computed', 'relevant', 'cuep', 'and', 'the', 'postswitching', 'initial', 'point', 'the', 'effectiveness', 'of', 'the', 'method', 'is', 'demonstrated', 'on', 'switching', 'events', 'in', 'the', 'structurepreserving', 'models', 'of', 'a', 'heavily', 'loaded', 'version', 'of', 'the', 'wscc', '9bus', '3machine', 'system', 'and', 'the', 'base', 'case', 'ieee', '145bus', '50machine', 'system', 'a', 'scheme', 'for', 'the', 'detailed', 'analysis', 'of', 'power', 'system', 'switching', 'events', 'is', 'then', 'proposed']] | [-0.17215391261708163, 0.0013675080889188726, -0.11427305612730188, 0.034973424451357316, -7.369393192300009e-06, -0.12837035264905303, 0.09562816514543881, 0.33197646861502883, -0.23860077867540744, -0.2600770942341331, 0.15727197530640954, -0.24157306266555545, -0.16587092149831834, 0.23811162968055097, -0.05529558883313466, 0.09504215397931841, 0.09301009378823187, 0.050538251689131106, -0.02059891244071886, -0.20167537039538863, 0.296774237989149, 0.09589870737164939, 0.34426616180107134, 0.01608013439445047, 0.14840663967807904, 0.016723195618496986, -0.00030682904970481855, 0.02324759693700957, -0.11332849092747367, 0.10265118958227712, 0.19308947677951152, 0.12359767672904862, 0.2684521958095218, -0.372589312698863, -0.20471443482802823, 0.07348805013189622, 0.09815519871275633, 0.07790787292269503, -0.05307421692216623, -0.27670286467444116, 0.12868826694513133, -0.19493841673802892, -0.10783087912468461, -0.04496502672928736, -0.002071674161274499, 0.061615908902924545, -0.32452124170958996, 0.0338756038226324, 0.06593187808682886, 0.06661301447129851, -0.08081141176607905, -0.0310405486972507, -0.023185075551203086, 0.1033508679445047, 0.021973832925875594, -0.025910226799069194, 0.15015270164954142, -0.08022939688483373, -0.12874986751671505, 0.3597785883813823, -0.028623237923949683, -0.18724348976217006, 0.13998801321637125, -0.07673176760793826, -0.09331707514549067, 0.1339594700262634, 0.2315444128733094, 0.1630153422491676, -0.17013897627181962, 0.035447116769260704, 0.023938257893367988, 0.1804720565498862, 0.028367373237916087, -0.03802315609469805, 0.1778022592135389, 0.27770173535126885, 0.07237669115527234, 0.1624948045050879, -0.14731966282685346, -0.13388486162101457, -0.3037246130449578, -0.1375769377950955, -0.18458034854100316, 0.024387032991924552, -0.07599055294108731, -0.14423377118116126, 0.4442554819003458, 0.17398629157265666, 0.16323235881335418, 0.009913790569461267, 0.33662397376851205, 0.1609217563618419, -0.017871358547222996, 0.07095895127239508, 0.24273011778746176, 0.06567341020894707, 0.14147031332425977, -0.26473609398230225, 0.0709155184695275, 0.0805957088580167] |
1,802.04372 | Cohomologie non ramifi\'ee dans le produit avec une courbe elliptique | A method of Gabber (2002) produces unramified cohomology classes in the
products of certain varieties with an elliptic curve. The connection between
third unramified cohomology and integral Hodge conjecture for codimension 2
cycles (2012, which builds upon results from algebraic K-Theory, then gives
many examples of such a product for which this conjecture fails. The special
case of the product with an Enriques surface was established by Benoist and
Ottem (2018).
| math.AG | a method of gabber 2002 produces unramified cohomology classes in the products of certain varieties with an elliptic curve the connection between third unramified cohomology and integral hodge conjecture for codimension 2 cycles 2012 which builds upon results from algebraic ktheory then gives many examples of such a product for which this conjecture fails the special case of the product with an enriques surface was established by benoist and ottem 2018 | [['a', 'method', 'of', 'gabber', '2002', 'produces', 'unramified', 'cohomology', 'classes', 'in', 'the', 'products', 'of', 'certain', 'varieties', 'with', 'an', 'elliptic', 'curve', 'the', 'connection', 'between', 'third', 'unramified', 'cohomology', 'and', 'integral', 'hodge', 'conjecture', 'for', 'codimension', '2', 'cycles', '2012', 'which', 'builds', 'upon', 'results', 'from', 'algebraic', 'ktheory', 'then', 'gives', 'many', 'examples', 'of', 'such', 'a', 'product', 'for', 'which', 'this', 'conjecture', 'fails', 'the', 'special', 'case', 'of', 'the', 'product', 'with', 'an', 'enriques', 'surface', 'was', 'established', 'by', 'benoist', 'and', 'ottem', '2018']] | [-0.1885664958218244, 0.019131121314606224, -0.1465854466774724, 0.03757258747789589, -0.07402890477277024, -0.13986695041014274, -0.032554218243040195, 0.2504548595195085, -0.2920935232107016, -0.2743191312159031, 0.10593512747272797, -0.21624861918406194, -0.1844744042191707, 0.2680872762706918, -0.22898851274053605, -0.021958580642322104, 0.05116068653132714, 0.07116399039711122, -0.0624290771887336, -0.38999595833470074, 0.4459885702481572, -0.0068681345413058575, 0.21647848844738074, 0.11461138276015999, 0.07564595899820117, 0.057499550292793085, -0.01747577729910402, -0.09894594226137433, -0.12843350475837648, 0.15720929608116269, 0.36207455000512195, 0.047274253718082755, 0.16374801515116238, -0.3818481556651458, -0.11742105487484852, 0.18713653786107898, 0.04616578196791906, 0.023100915610868956, 0.012626150859073855, -0.27825702735695096, 0.031419748860135883, -0.21354505742414737, -0.20035250779424965, -0.09283169697720925, 0.07771186116085925, 0.013462036677544385, -0.23262307678126323, -0.002317626173542419, 0.13041838982575377, 0.17952494101572625, -0.09050685480814165, -0.09572356653196448, -0.09142917607137015, 0.04190079257233252, 0.027544198603644758, 0.05791546466012656, 0.041576234807430855, -0.06382939601185876, -0.16250930784899795, 0.3048495969958079, -0.0429013854312435, -0.09861939670649213, 0.1275207106627419, -0.12413155937462415, -0.2000630338623805, 0.16260824765203338, 0.03646266440742872, 0.1941774971242076, 0.032617855200570235, 0.17525848703504815, -0.15718832960955695, 0.05136481699743636, 0.137253190469469, -0.11671635125514487, 0.1143055514294759, 0.03921088590745775, 0.06610476523293445, 0.12892792159101177, 0.000154218629894542, -0.03400041097381585, -0.35186629700408856, -0.22569974106539722, -0.08943917620664751, 0.15182146884855147, -0.11327762816480072, -0.16855214537501756, 0.43471711858267514, 0.011716663732495107, 0.17547608533559342, 0.13277039450304715, 0.21457993598576164, 0.04267942376802085, 0.019326529085216388, 0.09136645480150908, 0.1351932219072232, 0.2780943082704563, 0.0007850236889027374, -0.07153081958523204, -0.02495078145014778, 0.27558272469683853] |
1,802.04373 | Spherical confinement of Coulombic systems inside an impenetrable box: H
atom and the Hulth\'en potential | The generalized pseudospectral method is employed to study spherical
confinement in two simple Coulombic systems: (i) well celebrated and heavily
studied H atom (ii) relatively less explored Hulth\'en potential. In both
instances, arbitrary cavity size, as well as low and higher states are
considered. Apart from bound state eigenvalues, eigenfunctions, expectation
values, quite accurate estimates of the critical cage radius for H atom for all
the 55 states corresponding to $n \leq 10$, are also examined. Some of the
latter are better than previously reported values. Degeneracy and energy
ordering under the isotropic confinement situation are discussed as well. The
method produces consistently high-quality results for both potentials for small
as well as large cavity size. For the H atom, present results are comparable to
best theoretical values, while for the latter, this work gives considerably
better estimates than all existing work so far.
| quant-ph physics.atom-ph | the generalized pseudospectral method is employed to study spherical confinement in two simple coulombic systems i well celebrated and heavily studied h atom ii relatively less explored hulthen potential in both instances arbitrary cavity size as well as low and higher states are considered apart from bound state eigenvalues eigenfunctions expectation values quite accurate estimates of the critical cage radius for h atom for all the 55 states corresponding to n leq 10 are also examined some of the latter are better than previously reported values degeneracy and energy ordering under the isotropic confinement situation are discussed as well the method produces consistently highquality results for both potentials for small as well as large cavity size for the h atom present results are comparable to best theoretical values while for the latter this work gives considerably better estimates than all existing work so far | [['the', 'generalized', 'pseudospectral', 'method', 'is', 'employed', 'to', 'study', 'spherical', 'confinement', 'in', 'two', 'simple', 'coulombic', 'systems', 'i', 'well', 'celebrated', 'and', 'heavily', 'studied', 'h', 'atom', 'ii', 'relatively', 'less', 'explored', 'hulthen', 'potential', 'in', 'both', 'instances', 'arbitrary', 'cavity', 'size', 'as', 'well', 'as', 'low', 'and', 'higher', 'states', 'are', 'considered', 'apart', 'from', 'bound', 'state', 'eigenvalues', 'eigenfunctions', 'expectation', 'values', 'quite', 'accurate', 'estimates', 'of', 'the', 'critical', 'cage', 'radius', 'for', 'h', 'atom', 'for', 'all', 'the', '55', 'states', 'corresponding', 'to', 'n', 'leq', '10', 'are', 'also', 'examined', 'some', 'of', 'the', 'latter', 'are', 'better', 'than', 'previously', 'reported', 'values', 'degeneracy', 'and', 'energy', 'ordering', 'under', 'the', 'isotropic', 'confinement', 'situation', 'are', 'discussed', 'as', 'well', 'the', 'method', 'produces', 'consistently', 'highquality', 'results', 'for', 'both', 'potentials', 'for', 'small', 'as', 'well', 'as', 'large', 'cavity', 'size', 'for', 'the', 'h', 'atom', 'present', 'results', 'are', 'comparable', 'to', 'best', 'theoretical', 'values', 'while', 'for', 'the', 'latter', 'this', 'work', 'gives', 'considerably', 'better', 'estimates', 'than', 'all', 'existing', 'work', 'so', 'far']] | [-0.048845088216088094, 0.14533029073936632, 0.01811590587021783, 0.1124080572497203, -0.03454443494491796, -0.19894859892600733, 0.02351175442325055, 0.3887921469544785, -0.17375417142385333, -0.3546786636579782, 0.08340805189962136, -0.2885843621334061, -0.06359285747425424, 0.22577054627860585, -0.00675172096372181, 0.07983296402815337, 0.061167949066859566, 0.046313897690399446, -0.061328885879548684, -0.21183651267428003, 0.25395665430793696, 0.08542383335669809, 0.26647677452840274, 0.061112048380083356, -0.0028683381193938353, -0.028756409957875602, 0.04588389862328768, 0.03386681302476467, -0.14929645436738306, 0.08360194734591965, 0.2276207611624462, 0.029346509380654123, 0.23326414017679376, -0.393048377138459, -0.20032221521250904, 0.08240552190303181, 0.19398842725948068, 0.1418071395132251, -0.034754304920271456, -0.2647809852011657, 0.06752936802998495, -0.15421366051305085, -0.16572964818786, -0.10522148283780552, 0.056339368647766404, 0.022899507353738753, -0.2674412608094927, 0.10487616711806266, 0.06299900112046292, 0.040209634565851755, -0.1006417174584284, -0.25174133316613734, -0.009406679956656363, 0.07847399635162826, 0.027304145111379005, 0.019034437315793993, 0.1142974258238812, -0.10733131718977044, -0.05211181641789153, 0.40555882182282704, -0.06676079045448408, -0.19422111645851853, 0.19462199039440342, -0.16783746590630877, -0.07605676118206854, 0.12631734368753517, 0.09873290698225093, 0.15236702023604368, -0.10631160984080958, 0.06862263128338479, -0.02688872972228435, 0.12603738852259186, 0.08038074314633074, 0.09080640347868514, 0.10609363450849843, 0.10826054721837863, 0.0843112323523706, 0.1291773716641021, -0.05776565182572489, -0.1264679451770563, -0.282920011655531, -0.1059526486718217, -0.22088717005196182, 0.013922445100939108, -0.09980612567323988, -0.13902455332249197, 0.3448898082586109, 0.10931360302816352, 0.24402602492288375, 0.053307206953629956, 0.27820658662434045, 0.11092983899854719, 0.06205722412172084, 0.06907944808254898, 0.27478654196056596, 0.13053552504344326, 0.06350442699396, -0.18628978503531674, 0.04050282287385522, 0.04147651761660301] |
1,802.04374 | Tempered Adversarial Networks | Generative adversarial networks (GANs) have been shown to produce realistic
samples from high-dimensional distributions, but training them is considered
hard. A possible explanation for training instabilities is the inherent
imbalance between the networks: While the discriminator is trained directly on
both real and fake samples, the generator only has control over the fake
samples it produces since the real data distribution is fixed by the choice of
a given dataset. We propose a simple modification that gives the generator
control over the real samples which leads to a tempered learning process for
both generator and discriminator. The real data distribution passes through a
lens before being revealed to the discriminator, balancing the generator and
discriminator by gradually revealing more detailed features necessary to
produce high-quality results. The proposed module automatically adjusts the
learning process to the current strength of the networks, yet is generic and
easy to add to any GAN variant. In a number of experiments, we show that this
can improve quality, stability and/or convergence speed across a range of
different GAN architectures (DCGAN, LSGAN, WGAN-GP).
| stat.ML cs.CR cs.LG | generative adversarial networks gans have been shown to produce realistic samples from highdimensional distributions but training them is considered hard a possible explanation for training instabilities is the inherent imbalance between the networks while the discriminator is trained directly on both real and fake samples the generator only has control over the fake samples it produces since the real data distribution is fixed by the choice of a given dataset we propose a simple modification that gives the generator control over the real samples which leads to a tempered learning process for both generator and discriminator the real data distribution passes through a lens before being revealed to the discriminator balancing the generator and discriminator by gradually revealing more detailed features necessary to produce highquality results the proposed module automatically adjusts the learning process to the current strength of the networks yet is generic and easy to add to any gan variant in a number of experiments we show that this can improve quality stability andor convergence speed across a range of different gan architectures dcgan lsgan wgangp | [['generative', 'adversarial', 'networks', 'gans', 'have', 'been', 'shown', 'to', 'produce', 'realistic', 'samples', 'from', 'highdimensional', 'distributions', 'but', 'training', 'them', 'is', 'considered', 'hard', 'a', 'possible', 'explanation', 'for', 'training', 'instabilities', 'is', 'the', 'inherent', 'imbalance', 'between', 'the', 'networks', 'while', 'the', 'discriminator', 'is', 'trained', 'directly', 'on', 'both', 'real', 'and', 'fake', 'samples', 'the', 'generator', 'only', 'has', 'control', 'over', 'the', 'fake', 'samples', 'it', 'produces', 'since', 'the', 'real', 'data', 'distribution', 'is', 'fixed', 'by', 'the', 'choice', 'of', 'a', 'given', 'dataset', 'we', 'propose', 'a', 'simple', 'modification', 'that', 'gives', 'the', 'generator', 'control', 'over', 'the', 'real', 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1,802.04375 | The effective increase in atomic scale disorder by doping and
superconductivity in Ca$_3$Rh$_4$Sn$_{13}$ | The comprehensive research of the electronic structure, thermodynamic and
electrical transport properties reveals the existence of inhomogeneous
superconductivity due to structural disorder in Ca$_3$Rh$_4$Sn$_{13}$ doped
with La (Ca$_{3-x}$La$_x$Rh$_4$Sn$_{13}$) or Ce
(Ca$_{3-x}$Ce$_x$Rh$_4$Sn$_{13}$) with superconducting critical temperatures
$T_c^{\star}$ higher than those ($T_c$) observed in the parent compounds. The
$T-x$ diagrams and the entropy $S(x)_T$ isotherms well document the relation
between degree of an atomic disorder and separation of the {\it
high-temperature} $T_c^{\star}$ and $T_c$-bulk phases. In these dirty
superconductors with the mean free path much smaller than the coherence length,
the Werthamer-Helfand-Hohenber theoretical model does not well fits the
$H_{c2}(T)$ data. We suggest that this can result from two-band
superconductivity or from the presence of strong inhomogeneity in these
systems. The multiband model very well describes the $H-T$ dependencies, but
the present results as well as our previous studies give arguments for the
scenario based on the presence of nanoscopic inhomogeneity of the
superconducting state. We also revisited the nature of structural phase
transition at $T^{\star}\sim 130-170$ K and documented that there might be
another precursor transition at higher temperatures. The impact of the magnetic
Ce-Ce correlations on the increase of $T_c$ in respect to the critical
temperatures of Ca$_{3-x}$La$_x$Rh$_4$Sn$_{13}$ is also discussed.
| cond-mat.supr-con | the comprehensive research of the electronic structure thermodynamic and electrical transport properties reveals the existence of inhomogeneous superconductivity due to structural disorder in ca_3rh_4sn_13 doped with la ca_3xla_xrh_4sn_13 or ce ca_3xce_xrh_4sn_13 with superconducting critical temperatures t_cstar higher than those t_c observed in the parent compounds the tx diagrams and the entropy sx_t isotherms well document the relation between degree of an atomic disorder and separation of the it hightemperature t_cstar and t_cbulk phases in these dirty superconductors with the mean free path much smaller than the coherence length the werthamerhelfandhohenber theoretical model does not well fits the h_c2t data we suggest that this can result from twoband superconductivity or from the presence of strong inhomogeneity in these systems the multiband model very well describes the ht dependencies but the present results as well as our previous studies give arguments for the scenario based on the presence of nanoscopic inhomogeneity of the superconducting state we also revisited the nature of structural phase transition at tstarsim 130170 k and documented that there might be another precursor transition at higher temperatures the impact of the magnetic cece correlations on the increase of t_c in respect to the critical temperatures of ca_3xla_xrh_4sn_13 is also discussed | [['the', 'comprehensive', 'research', 'of', 'the', 'electronic', 'structure', 'thermodynamic', 'and', 'electrical', 'transport', 'properties', 'reveals', 'the', 'existence', 'of', 'inhomogeneous', 'superconductivity', 'due', 'to', 'structural', 'disorder', 'in', 'ca_3rh_4sn_13', 'doped', 'with', 'la', 'ca_3xla_xrh_4sn_13', 'or', 'ce', 'ca_3xce_xrh_4sn_13', 'with', 'superconducting', 'critical', 'temperatures', 't_cstar', 'higher', 'than', 'those', 't_c', 'observed', 'in', 'the', 'parent', 'compounds', 'the', 'tx', 'diagrams', 'and', 'the', 'entropy', 'sx_t', 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1,802.04376 | Few-Shot Learning with Metric-Agnostic Conditional Embeddings | Learning high quality class representations from few examples is a key
problem in metric-learning approaches to few-shot learning. To accomplish this,
we introduce a novel architecture where class representations are conditioned
for each few-shot trial based on a target image. We also deviate from
traditional metric-learning approaches by training a network to perform
comparisons between classes rather than relying on a static metric comparison.
This allows the network to decide what aspects of each class are important for
the comparison at hand. We find that this flexible architecture works well in
practice, achieving state-of-the-art performance on the Caltech-UCSD birds
fine-grained classification task.
| cs.LG stat.ML | learning high quality class representations from few examples is a key problem in metriclearning approaches to fewshot learning to accomplish this we introduce a novel architecture where class representations are conditioned for each fewshot trial based on a target image we also deviate from traditional metriclearning approaches by training a network to perform comparisons between classes rather than relying on a static metric comparison this allows the network to decide what aspects of each class are important for the comparison at hand we find that this flexible architecture works well in practice achieving stateoftheart performance on the caltechucsd birds finegrained classification task | [['learning', 'high', 'quality', 'class', 'representations', 'from', 'few', 'examples', 'is', 'a', 'key', 'problem', 'in', 'metriclearning', 'approaches', 'to', 'fewshot', 'learning', 'to', 'accomplish', 'this', 'we', 'introduce', 'a', 'novel', 'architecture', 'where', 'class', 'representations', 'are', 'conditioned', 'for', 'each', 'fewshot', 'trial', 'based', 'on', 'a', 'target', 'image', 'we', 'also', 'deviate', 'from', 'traditional', 'metriclearning', 'approaches', 'by', 'training', 'a', 'network', 'to', 'perform', 'comparisons', 'between', 'classes', 'rather', 'than', 'relying', 'on', 'a', 'static', 'metric', 'comparison', 'this', 'allows', 'the', 'network', 'to', 'decide', 'what', 'aspects', 'of', 'each', 'class', 'are', 'important', 'for', 'the', 'comparison', 'at', 'hand', 'we', 'find', 'that', 'this', 'flexible', 'architecture', 'works', 'well', 'in', 'practice', 'achieving', 'stateoftheart', 'performance', 'on', 'the', 'caltechucsd', 'birds', 'finegrained', 'classification', 'task']] | [-0.008558293338865042, -0.017562082459620546, -0.05764726333447969, 0.08024334035329886, -0.15094926795812652, -0.2114222036336786, 0.05034216378322419, 0.45989814970423193, -0.22290587720150748, -0.3249118790468749, 0.05375427285702351, -0.2555310033815605, -0.18983869723446084, 0.2341597479238522, -0.15981361730133786, 0.061376173323129904, 0.1319526851168085, 0.08459735351304214, -0.12563724699201903, -0.2603604718015584, 0.3509741135777029, 0.029685698088952432, 0.3780379117849995, -0.009856093122496032, 0.12536678104387486, -0.033501104191493464, 0.003803767327803607, 0.006790924518296093, -0.08780391570433173, 0.19547083129188703, 0.319462480242638, 0.16966972331626012, 0.3540597781402004, -0.36497573013127527, -0.21220892994332483, 0.11046607975674537, 0.11765630523601145, 0.13665694743166587, -0.023522979578724606, -0.31444477418656736, 0.08776153116738059, -0.17834515862312972, 0.03455660360472679, -0.13546814521610298, -0.04052848434338218, -0.020206035708314647, -0.2967339163810453, 0.005876618454303574, 0.047312239962219095, 0.06013342112704527, -0.04514129518350933, -0.11914189171283414, 0.09976380810771576, 0.1750626716415818, 0.018281188909895718, 0.07245688586422772, 0.14746102832392424, -0.18530353148896977, -0.150210239143823, 0.3754062413457124, -0.018696614028229697, -0.25226117897888317, 0.2514467226482891, -0.015785086311071235, -0.20880806553131884, 0.06664445370794662, 0.2367535144382832, 0.16725207101918904, -0.13315687602495446, -0.00854773850034119, -0.0637821729075821, 0.17326306398985358, 0.04858763181768796, -0.0046341791269643344, 0.17123624844266577, 0.2962253636488801, 0.05438144431522518, 0.1400619316024377, -0.08820246081735354, -0.08017123616574441, -0.22921997496821717, -0.10162060315191161, -0.1648174378833752, 0.0032511761448546952, -0.06390300633239687, -0.12807908623168865, 0.41486987992025476, 0.23293049383324152, 0.23166119594209114, 0.14963870407903895, 0.35522903439899284, 0.0017448395865001514, 0.0915152451717386, 0.1105366384321778, 0.16225196015404755, -0.011194191870790925, 0.10263181113473632, -0.1259849538624871, 0.09283812111700136, 0.09174109310340867] |
1,802.04377 | Design of high brightness Plasma Wakefield Acceleration experiment at
SPARC\_LAB test facility with particle-in-cell simulations | The present numerical investigation of a Plasma Wakefield Acceleration
scenario in the weakly non linear regime with external injection is motivated
by the upcoming campaigns at the SPARC\_LAB test facility where the final goal
is to demonstrate modest gradient acceleration ($\sim$1 GV/m) with no quality
loss. The accelerated bunch can be envisioned to seed a free electron laser.
The numerical study has been conducted with the particle-in-cell code ${\tt
ALaDyn}$, an exhaustive description of the plasma-acceleration version is
provided. The configuration consider a two bunches setup with parameters in the
facility range, the bunches are generated and pre-accelerated up to 100 MeV by
a high brightness photo-injector prior plasma injection. To verify the working
point robustness we have considered case scenario where the driver bunch
reaches the plasma or with a larger dimension or with large emittance. We also
present an analytical approach based on the envelope equation that allows to
reduce the matching condition in the presence of a ramp. Here, we limit our
interest to a simplified theoretical case with a linear plasma ramp. As a final
aspect we propose to combine classical integrated bunch diagnostics with the
test by Shapiro-Wilk, a mathematical test to diagnose bunch deviation from a
Gaussian distribution.
| physics.plasm-ph physics.acc-ph | the present numerical investigation of a plasma wakefield acceleration scenario in the weakly non linear regime with external injection is motivated by the upcoming campaigns at the sparc_lab test facility where the final goal is to demonstrate modest gradient acceleration sim1 gvm with no quality loss the accelerated bunch can be envisioned to seed a free electron laser the numerical study has been conducted with the particleincell code tt aladyn an exhaustive description of the plasmaacceleration version is provided the configuration consider a two bunches setup with parameters in the facility range the bunches are generated and preaccelerated up to 100 mev by a high brightness photoinjector prior plasma injection to verify the working point robustness we have considered case scenario where the driver bunch reaches the plasma or with a larger dimension or with large emittance we also present an analytical approach based on the envelope equation that allows to reduce the matching condition in the presence of a ramp here we limit our interest to a simplified theoretical case with a linear plasma ramp as a final aspect we propose to combine classical integrated bunch diagnostics with the test by shapirowilk a mathematical test to diagnose bunch deviation from a gaussian distribution | [['the', 'present', 'numerical', 'investigation', 'of', 'a', 'plasma', 'wakefield', 'acceleration', 'scenario', 'in', 'the', 'weakly', 'non', 'linear', 'regime', 'with', 'external', 'injection', 'is', 'motivated', 'by', 'the', 'upcoming', 'campaigns', 'at', 'the', 'sparc_lab', 'test', 'facility', 'where', 'the', 'final', 'goal', 'is', 'to', 'demonstrate', 'modest', 'gradient', 'acceleration', 'sim1', 'gvm', 'with', 'no', 'quality', 'loss', 'the', 'accelerated', 'bunch', 'can', 'be', 'envisioned', 'to', 'seed', 'a', 'free', 'electron', 'laser', 'the', 'numerical', 'study', 'has', 'been', 'conducted', 'with', 'the', 'particleincell', 'code', 'tt', 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1,802.04378 | Fundamental limitations for measurements in quantum many-body systems | Dynamical measurement schemes are an important tool for the investigation of
quantum many-body systems, especially in the age of quantum simulation. Here,
we address the question whether generic measurements can be implemented
efficiently if we have access to a certain set of experimentally realizable
measurements and can extend it through time evolution. For the latter, two
scenarios are considered (a) evolution according to unitary circuits and (b)
evolution due to Hamiltonians that we can control in a time-dependent fashion.
We find that the time needed to realize a certain measurement to a predefined
accuracy scales in general exponentially with the system size -- posing a
fundamental limitation. The argument is based, on the construction of
$\varepsilon$-packings for manifolds of observables with identical spectra and
a comparison of their cardinalities to those of $\varepsilon$-coverings for
quantum circuits and unitary time-evolution operators. The former is related to
the study of Grassmann manifolds.
| quant-ph cond-mat.stat-mech math-ph math.MP | dynamical measurement schemes are an important tool for the investigation of quantum manybody systems especially in the age of quantum simulation here we address the question whether generic measurements can be implemented efficiently if we have access to a certain set of experimentally realizable measurements and can extend it through time evolution for the latter two scenarios are considered a evolution according to unitary circuits and b evolution due to hamiltonians that we can control in a timedependent fashion we find that the time needed to realize a certain measurement to a predefined accuracy scales in general exponentially with the system size posing a fundamental limitation the argument is based on the construction of varepsilonpackings for manifolds of observables with identical spectra and a comparison of their cardinalities to those of varepsiloncoverings for quantum circuits and unitary timeevolution operators the former is related to the study of grassmann manifolds | [['dynamical', 'measurement', 'schemes', 'are', 'an', 'important', 'tool', 'for', 'the', 'investigation', 'of', 'quantum', 'manybody', 'systems', 'especially', 'in', 'the', 'age', 'of', 'quantum', 'simulation', 'here', 'we', 'address', 'the', 'question', 'whether', 'generic', 'measurements', 'can', 'be', 'implemented', 'efficiently', 'if', 'we', 'have', 'access', 'to', 'a', 'certain', 'set', 'of', 'experimentally', 'realizable', 'measurements', 'and', 'can', 'extend', 'it', 'through', 'time', 'evolution', 'for', 'the', 'latter', 'two', 'scenarios', 'are', 'considered', 'a', 'evolution', 'according', 'to', 'unitary', 'circuits', 'and', 'b', 'evolution', 'due', 'to', 'hamiltonians', 'that', 'we', 'can', 'control', 'in', 'a', 'timedependent', 'fashion', 'we', 'find', 'that', 'the', 'time', 'needed', 'to', 'realize', 'a', 'certain', 'measurement', 'to', 'a', 'predefined', 'accuracy', 'scales', 'in', 'general', 'exponentially', 'with', 'the', 'system', 'size', 'posing', 'a', 'fundamental', 'limitation', 'the', 'argument', 'is', 'based', 'on', 'the', 'construction', 'of', 'varepsilonpackings', 'for', 'manifolds', 'of', 'observables', 'with', 'identical', 'spectra', 'and', 'a', 'comparison', 'of', 'their', 'cardinalities', 'to', 'those', 'of', 'varepsiloncoverings', 'for', 'quantum', 'circuits', 'and', 'unitary', 'timeevolution', 'operators', 'the', 'former', 'is', 'related', 'to', 'the', 'study', 'of', 'grassmann', 'manifolds']] | [-0.1428301838752465, 0.1473507189618932, -0.09680446771727431, 0.06537976706571573, -0.028785587405646535, -0.15620984075366057, 0.033357091978205, 0.3692447481879673, -0.25911790249096295, -0.2997150990199678, 0.11537257062198379, -0.23725231588805462, -0.13084925231872266, 0.23604699923992664, -0.0519935290937369, 0.1113024395795501, 0.07272141516449399, 0.028962933393448693, -0.09445306660269298, -0.2464658019994544, 0.34948343646294455, 0.052247673818733874, 0.2393445435081463, 0.03879726098432225, 0.06665166571964415, -0.014597420726006305, -0.007928470301688934, 0.019895155025124598, -0.10981210897612104, 0.10895417766569525, 0.26357237408001, 0.11820967520466771, 0.24718851252097865, -0.4255744568610779, -0.19384776402188014, 0.1281564256436109, 0.15067343244671214, 0.14994425076965978, -0.0214696526376936, -0.26880827875091967, 0.08376530653616528, -0.14604444553454718, -0.12826018847804674, -0.10910221954592231, 0.024979564617863114, -0.006595644528096935, -0.26415571666696447, 0.0317421970959455, 0.046017669403227046, 0.0347848123186553, -0.04741255015286864, -0.024775092284112763, 0.03904536303899371, 0.15452417513976494, -0.03760740372627897, -0.010818898732469854, 0.12207250791557488, -0.08870774169838956, -0.15229203773751146, 0.4041807458729667, -0.04027855185337276, -0.22510474688392512, 0.20327669476653087, -0.12417094000824253, -0.13397673386636944, 0.05000451671555131, 0.18722297608231506, 0.12036627058104492, -0.15489317504071384, 0.08005338788609065, -0.029681439042648895, 0.15933788568256613, 0.011867414893848556, 0.07839514792391548, 0.18361202856449751, 0.14695825999868767, 0.069587523607081, 0.12025926920005633, -0.02546977929036025, -0.12774950386259426, -0.2911914469306769, -0.1564156410530238, -0.17742197458841363, 0.09715859364911432, -0.05080524228670045, -0.1474905244461229, 0.41249216211085415, 0.1759861311938639, 0.21485515282216933, 0.051645424171053125, 0.2734025549396974, 0.13261903574475212, 0.07235311967404685, 0.04854478057930056, 0.20276938824832033, 0.16213880373532472, 0.04342004320028691, -0.23519623638637566, 0.05406105596109667, 0.037054388156654884] |
1,802.04379 | Search for Neutrinos in Super-Kamiokande associated with the GW170817
neutron-star merger | We report the results of a neutrino search in Super-Kamiokande for coincident
signals with the first detected gravitational wave produced by a binary neutron
star merger, GW170817, which was followed by a short gamma-ray burst,
GRB170817A, and a kilonova/macronova. We searched for coincident neutrino
events in the range from 3.5 MeV to $\sim$100 PeV, in a time window $\pm$500
seconds around the gravitational wave detection time, as well as during a
14-day period after the detection. No significant neutrino signal was observed
for either time window. We calculated 90% confidence level upper limits on the
neutrino fluence for GW170817. From the upward-going-muon events in the energy
region above 1.6 GeV, the neutrino fluence limit is $16.0^{+0.7}_{-0.6}$
($21.3^{+1.1}_{-0.8}$) cm$^{-2}$ for muon neutrinos (muon antineutrinos), with
an error range of $\pm5^{\circ}$ around the zenith angle of NGC4993, and the
energy spectrum is under the assumption of an index of $-2$. The fluence limit
for neutrino energies less than 100 MeV, for which the emission mechanism would
be different than for higher-energy neutrinos, is also calculated. It is $6.6
\times 10^7$ cm$^{-2}$ for anti-electron neutrinos under the assumption of a
Fermi-Dirac spectrum with average energy of 20 MeV.
| astro-ph.HE | we report the results of a neutrino search in superkamiokande for coincident signals with the first detected gravitational wave produced by a binary neutron star merger gw170817 which was followed by a short gammaray burst grb170817a and a kilonovamacronova we searched for coincident neutrino events in the range from 35 mev to sim100 pev in a time window pm500 seconds around the gravitational wave detection time as well as during a 14day period after the detection no significant neutrino signal was observed for either time window we calculated 90 confidence level upper limits on the neutrino fluence for gw170817 from the upwardgoingmuon events in the energy region above 16 gev the neutrino fluence limit is 16007_06 21311_08 cm2 for muon neutrinos muon antineutrinos with an error range of pm5circ around the zenith angle of ngc4993 and the energy spectrum is under the assumption of an index of 2 the fluence limit for neutrino energies less than 100 mev for which the emission mechanism would be different than for higherenergy neutrinos is also calculated it is 66 times 107 cm2 for antielectron neutrinos under the assumption of a fermidirac spectrum with average energy of 20 mev | [['we', 'report', 'the', 'results', 'of', 'a', 'neutrino', 'search', 'in', 'superkamiokande', 'for', 'coincident', 'signals', 'with', 'the', 'first', 'detected', 'gravitational', 'wave', 'produced', 'by', 'a', 'binary', 'neutron', 'star', 'merger', 'gw170817', 'which', 'was', 'followed', 'by', 'a', 'short', 'gammaray', 'burst', 'grb170817a', 'and', 'a', 'kilonovamacronova', 'we', 'searched', 'for', 'coincident', 'neutrino', 'events', 'in', 'the', 'range', 'from', '35', 'mev', 'to', 'sim100', 'pev', 'in', 'a', 'time', 'window', 'pm500', 'seconds', 'around', 'the', 'gravitational', 'wave', 'detection', 'time', 'as', 'well', 'as', 'during', 'a', '14day', 'period', 'after', 'the', 'detection', 'no', 'significant', 'neutrino', 'signal', 'was', 'observed', 'for', 'either', 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'mev']] | [-0.07985536805305553, 0.2596220285475586, 0.006188054057323218, 0.17809196119439244, -0.029618551773789175, -0.057930958251745324, 0.09486449900092946, 0.39084803478423663, -0.14154486086166193, -0.3827743404932941, 0.05419192445780633, -0.3382027734575483, 0.05798180674901232, 0.26265890578967327, 0.08123401967031896, 0.00892916070491386, 0.0945977748488076, 0.04983596300371573, -0.10230860830051824, -0.1685630724271808, 0.21034189107255466, 0.19171054559531817, 0.20240549898881, 0.08875580293715757, 0.12150878609221157, -0.03106727698589869, -0.006721744089039324, -0.1420227648341097, -0.08999375413016726, -0.042223845264137104, 0.22341678918989297, 0.1216276322932875, 0.12705663521046517, -0.37135638476077776, -0.21883534715622469, 0.1497385482871323, 0.10916477029725986, 0.007955943048727931, -0.062183438802094315, -0.3451206754671148, 0.0989712707025016, -0.22346176060455036, -0.13928461729665287, 0.11434011975992082, 0.03981142593693221, -0.013825671058536196, 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1,802.0438 | Randomized Empirical Processes and Confidence Bands via Virtual
Resampling | Let $X,X_1,X_2,\cdots$ be independent real valued random variables with a
common distribution function $F$, and consider $\{X_1,\cdots,X_N \}$, possibly
a big concrete data set, or an imaginary random sample of size $N\geq 1$ on
$X$. In the latter case, or when a concrete data set in hand is too big to be
entirely processed, then the sample distribution function $F_N$ and the the
population distribution function $F$ are both to be estimated. This, in this
paper, is achieved via viewing $\{X_1,\cdots,X_N \}$ as above, as a finite
population of real valued random variables with $N$ labeled units, and sampling
its indices $\{1,\cdots,N \}$ with replacement $m_N:= \sum_{i=1}^N w_{i}^{(N)}$
times so that for each $1\leq i \leq N$, $w_{i}^{(N)}$ is the count of number
of times the index $i$ of $X_i$ is chosen in this virtual resampling process.
This exposition extends the Doob-Donsker classical theory of weak convergence
of empirical processes to that of the thus created randomly weighted empirical
processes when $N, m_N \rightarrow \infty$ so that $m_N=o(N^2)$.
| stat.ME | let xx_1x_2cdots be independent real valued random variables with a common distribution function f and consider x_1cdotsx_n possibly a big concrete data set or an imaginary random sample of size ngeq 1 on x in the latter case or when a concrete data set in hand is too big to be entirely processed then the sample distribution function f_n and the the population distribution function f are both to be estimated this in this paper is achieved via viewing x_1cdotsx_n as above as a finite population of real valued random variables with n labeled units and sampling its indices 1cdotsn with replacement m_n sum_i1n w_in times so that for each 1leq i leq n w_in is the count of number of times the index i of x_i is chosen in this virtual resampling process this exposition extends the doobdonsker classical theory of weak convergence of empirical processes to that of the thus created randomly weighted empirical processes when n m_n rightarrow infty so that m_non2 | [['let', 'xx_1x_2cdots', 'be', 'independent', 'real', 'valued', 'random', 'variables', 'with', 'a', 'common', 'distribution', 'function', 'f', 'and', 'consider', 'x_1cdotsx_n', 'possibly', 'a', 'big', 'concrete', 'data', 'set', 'or', 'an', 'imaginary', 'random', 'sample', 'of', 'size', 'ngeq', '1', 'on', 'x', 'in', 'the', 'latter', 'case', 'or', 'when', 'a', 'concrete', 'data', 'set', 'in', 'hand', 'is', 'too', 'big', 'to', 'be', 'entirely', 'processed', 'then', 'the', 'sample', 'distribution', 'function', 'f_n', 'and', 'the', 'the', 'population', 'distribution', 'function', 'f', 'are', 'both', 'to', 'be', 'estimated', 'this', 'in', 'this', 'paper', 'is', 'achieved', 'via', 'viewing', 'x_1cdotsx_n', 'as', 'above', 'as', 'a', 'finite', 'population', 'of', 'real', 'valued', 'random', 'variables', 'with', 'n', 'labeled', 'units', 'and', 'sampling', 'its', 'indices', '1cdotsn', 'with', 'replacement', 'm_n', 'sum_i1n', 'w_in', 'times', 'so', 'that', 'for', 'each', '1leq', 'i', 'leq', 'n', 'w_in', 'is', 'the', 'count', 'of', 'number', 'of', 'times', 'the', 'index', 'i', 'of', 'x_i', 'is', 'chosen', 'in', 'this', 'virtual', 'resampling', 'process', 'this', 'exposition', 'extends', 'the', 'doobdonsker', 'classical', 'theory', 'of', 'weak', 'convergence', 'of', 'empirical', 'processes', 'to', 'that', 'of', 'the', 'thus', 'created', 'randomly', 'weighted', 'empirical', 'processes', 'when', 'n', 'm_n', 'rightarrow', 'infty', 'so', 'that', 'm_non2']] | [-0.09043166347422034, 0.17454441862318842, -0.052941319916350475, 0.03268865012582664, -0.02465635090012033, -0.13411459875287218, 0.06620770979836677, 0.37997764161547776, -0.30572897746995, -0.2655892907553967, 0.09680808043332899, -0.3024223315523668, -0.08105948987217772, 0.14726321150246002, -0.07851501444424942, 0.004066457344093155, 0.036170401589933246, 0.07574856280109042, -0.024790956452781766, -0.289926627046083, 0.30643285939370357, -0.019996691310672828, 0.21547081928155473, -0.05363418929478001, 0.07763713509072813, 0.03933459007526812, -0.0037846876309786297, -0.0004160144152529772, -0.08932237356600796, 0.0764114444126498, 0.24090617207470555, 0.1743553296106691, 0.3046032572458, -0.3662085880465189, -0.16372784031496482, 0.22593594039643897, 0.17742077559935596, -0.022684163929390832, -0.009327883747956896, -0.2576513204155431, 0.13189025380964972, -0.1235554018416647, -0.13474570868431396, -0.014160576330661867, 0.08597116564172057, 0.06816333175619256, -0.37310050074128237, 0.03318145010311379, 0.06169527859585184, 0.07196377194340124, 0.011655070750868687, -0.1811279209283819, -0.02683534128181402, 0.09675912492002109, 0.05478998304270764, 0.08134023202348899, 0.10080631567258974, -0.06355800509815977, -0.06620506437659357, 0.34057277888528203, -0.04389262175972357, -0.2236162856830366, 0.11227221272405495, -0.17585028781784032, -0.1515031021246511, 0.09434994476197182, 0.16742205375120184, 0.16874931543573457, -0.11177318398317222, 0.1517147936484097, -0.07368189239370748, 0.16084449383307178, 0.04157554588731636, -0.010519592756253181, 0.12210623881012295, 0.1199990864872417, 0.06759719261841017, 0.1177015680696745, -0.03457425680832367, -0.054021773480593505, -0.35680535006916747, -0.14419145725737764, -0.2688510974476688, 0.16079316379168546, -0.15959663614134653, -0.18550245817047809, 0.2966400154766703, 0.11630250581023253, 0.26669682147209794, 0.1169182042255939, 0.2714004052228898, 0.139328671789343, 0.010269176961748668, 0.046165305825313495, 0.054564234554627035, 0.12168690861999379, 0.03189317453008309, -0.11253491880184731, 0.0646720626508618, 0.04107835573150114] |
1,802.04381 | Classification from Pairwise Similarity and Unlabeled Data | Supervised learning needs a huge amount of labeled data, which can be a big
bottleneck under the situation where there is a privacy concern or labeling
cost is high. To overcome this problem, we propose a new weakly-supervised
learning setting where only similar (S) data pairs (two examples belong to the
same class) and unlabeled (U) data points are needed instead of fully labeled
data, which is called SU classification. We show that an unbiased estimator of
the classification risk can be obtained only from SU data, and the estimation
error of its empirical risk minimizer achieves the optimal parametric
convergence rate. Finally, we demonstrate the effectiveness of the proposed
method through experiments.
| cs.LG | supervised learning needs a huge amount of labeled data which can be a big bottleneck under the situation where there is a privacy concern or labeling cost is high to overcome this problem we propose a new weaklysupervised learning setting where only similar s data pairs two examples belong to the same class and unlabeled u data points are needed instead of fully labeled data which is called su classification we show that an unbiased estimator of the classification risk can be obtained only from su data and the estimation error of its empirical risk minimizer achieves the optimal parametric convergence rate finally we demonstrate the effectiveness of the proposed method through experiments | [['supervised', 'learning', 'needs', 'a', 'huge', 'amount', 'of', 'labeled', 'data', 'which', 'can', 'be', 'a', 'big', 'bottleneck', 'under', 'the', 'situation', 'where', 'there', 'is', 'a', 'privacy', 'concern', 'or', 'labeling', 'cost', 'is', 'high', 'to', 'overcome', 'this', 'problem', 'we', 'propose', 'a', 'new', 'weaklysupervised', 'learning', 'setting', 'where', 'only', 'similar', 's', 'data', 'pairs', 'two', 'examples', 'belong', 'to', 'the', 'same', 'class', 'and', 'unlabeled', 'u', 'data', 'points', 'are', 'needed', 'instead', 'of', 'fully', 'labeled', 'data', 'which', 'is', 'called', 'su', 'classification', 'we', 'show', 'that', 'an', 'unbiased', 'estimator', 'of', 'the', 'classification', 'risk', 'can', 'be', 'obtained', 'only', 'from', 'su', 'data', 'and', 'the', 'estimation', 'error', 'of', 'its', 'empirical', 'risk', 'minimizer', 'achieves', 'the', 'optimal', 'parametric', 'convergence', 'rate', 'finally', 'we', 'demonstrate', 'the', 'effectiveness', 'of', 'the', 'proposed', 'method', 'through', 'experiments']] | [-0.07996257823125451, -0.0030893032691200403, -0.044459387223211536, 0.08250598365387393, -0.11154660239271753, -0.1763813735307318, 0.11621497514599984, 0.4014820716698217, -0.3090276807810353, -0.31147089354957624, 0.12274845280948268, -0.3162825927797672, -0.14090953161733935, 0.18970945798297437, -0.2031569219569354, 0.07733452555762636, 0.16240174280875155, 0.08643783783886284, -0.03926791631115963, -0.33553105035582476, 0.31858244151458043, 0.014153533796491348, 0.34627430515355573, -0.0020269697343526755, 0.12809219468174815, -0.03858172192765746, 0.008835930212409096, 0.007663369871082559, -0.078894724377415, 0.16971943837660572, 0.32229805717953536, 0.21514611172827736, 0.37477379925219356, -0.3416964372916691, -0.1896026731361594, 0.19197417292496669, 0.13133770364658098, 0.09771840737316659, -0.08113846471698015, -0.29645012608168864, 0.11112536386116943, -0.15419122205832891, -0.008828779657791674, -0.13610437068371184, -0.0721626619363319, -0.0051686727829211585, -0.37489312061364144, 0.06191251040601163, 0.04504728330550633, 0.05064633401292088, -0.042265102692747865, -0.08367253916000172, -0.014264818905251084, 0.11978366133855838, 0.07593271976328594, 0.04066174197529929, 0.07580588593980116, -0.1324507136132766, -0.1172332528945619, 0.3298032336525281, -0.0320959025514889, -0.20655694263533944, 0.1845715955410779, -0.07617179921465571, -0.1326457805420577, 0.13629141508599546, 0.21609102894111115, 0.12602154791882608, -0.18957891050557218, 0.07356757288942921, -0.06366810029933015, 0.1704409478368254, 0.02274203936124336, -0.009282835833398641, 0.09954286830714054, 0.2251648940599268, 0.09619444891587889, 0.13186682991279636, -0.14578988241542756, -0.035462776536896695, -0.28896233507028196, -0.11998464901461801, -0.25606718800335593, 0.012678442259146048, -0.10531858138228041, -0.118070087651104, 0.3416169524693499, 0.17772636883191567, 0.2402375076559529, 0.08328902237817842, 0.32065901658044454, 0.09108030165553885, 0.07015598530312424, 0.13628594232805535, 0.1764227727724783, -0.0031996916204822803, 0.04632922801430197, -0.1867569221731737, 0.09663129891310118, 0.03376215461237702] |
1,802.04382 | Non-classical point of view of the Brownian motion generation via
Fractional deterministic model | In this paper we present a dynamical system to generate Brownian motion based
on the Langevin equation without stochastic term and using fractional
derivatives, i.e., a deterministic Brownian motion model is proposed. The
stochastic process is replaced by considering an additional degree of freedom
in the second order Langevin equation. Thus it is transformed into a system of
three first order linear differential equations, additionally
$\alpha$-fractional derivative are considered which allow us obtain better
statistical properties. Switching Surfaces are established as a part of
fluctuating acceleration. The final system of three $\alpha$-order linear
differential equations does not contain a stochastic term, so the system
generates motion in a deterministic way. Nevertheless, from the time series
analysis, we found that the behavior of the system exhibits statistics
properties of Brownian motion, such as, a linear growth in time of the mean
square displacement, a Gaussian distribution. Furthermore, we use the detrended
fluctuation analysis to prove the Brownian character of this motion.
| nlin.CD | in this paper we present a dynamical system to generate brownian motion based on the langevin equation without stochastic term and using fractional derivatives ie a deterministic brownian motion model is proposed the stochastic process is replaced by considering an additional degree of freedom in the second order langevin equation thus it is transformed into a system of three first order linear differential equations additionally alphafractional derivative are considered which allow us obtain better statistical properties switching surfaces are established as a part of fluctuating acceleration the final system of three alphaorder linear differential equations does not contain a stochastic term so the system generates motion in a deterministic way nevertheless from the time series analysis we found that the behavior of the system exhibits statistics properties of brownian motion such as a linear growth in time of the mean square displacement a gaussian distribution furthermore we use the detrended fluctuation analysis to prove the brownian character of this motion | [['in', 'this', 'paper', 'we', 'present', 'a', 'dynamical', 'system', 'to', 'generate', 'brownian', 'motion', 'based', 'on', 'the', 'langevin', 'equation', 'without', 'stochastic', 'term', 'and', 'using', 'fractional', 'derivatives', 'ie', 'a', 'deterministic', 'brownian', 'motion', 'model', 'is', 'proposed', 'the', 'stochastic', 'process', 'is', 'replaced', 'by', 'considering', 'an', 'additional', 'degree', 'of', 'freedom', 'in', 'the', 'second', 'order', 'langevin', 'equation', 'thus', 'it', 'is', 'transformed', 'into', 'a', 'system', 'of', 'three', 'first', 'order', 'linear', 'differential', 'equations', 'additionally', 'alphafractional', 'derivative', 'are', 'considered', 'which', 'allow', 'us', 'obtain', 'better', 'statistical', 'properties', 'switching', 'surfaces', 'are', 'established', 'as', 'a', 'part', 'of', 'fluctuating', 'acceleration', 'the', 'final', 'system', 'of', 'three', 'alphaorder', 'linear', 'differential', 'equations', 'does', 'not', 'contain', 'a', 'stochastic', 'term', 'so', 'the', 'system', 'generates', 'motion', 'in', 'a', 'deterministic', 'way', 'nevertheless', 'from', 'the', 'time', 'series', 'analysis', 'we', 'found', 'that', 'the', 'behavior', 'of', 'the', 'system', 'exhibits', 'statistics', 'properties', 'of', 'brownian', 'motion', 'such', 'as', 'a', 'linear', 'growth', 'in', 'time', 'of', 'the', 'mean', 'square', 'displacement', 'a', 'gaussian', 'distribution', 'furthermore', 'we', 'use', 'the', 'detrended', 'fluctuation', 'analysis', 'to', 'prove', 'the', 'brownian', 'character', 'of', 'this', 'motion']] | [-0.1364763303368818, 0.07886862673703945, -0.15360175179303043, 0.0329444285420891, -0.0873021564737428, -0.09546461830395855, -0.01747240650947788, 0.3165690398454899, -0.33346936302259567, -0.27170239933766427, 0.09594561649137176, -0.27873007051530296, -0.179775418585632, 0.18210372021858348, -0.04659434267305187, 0.08700505863816943, 0.03407184805255383, 0.05200697880063672, -0.024244596286735032, -0.22237569563585566, 0.26317976370919494, -0.008551904349587858, 0.21555552945646922, -0.06796862306291587, 0.19989248495621723, 0.007384436116262804, -0.026437349381740204, 0.04101064253627555, -0.11827694351545688, 0.09191927137726452, 0.13388632419955684, 0.024151278431236278, 0.2951102867082227, -0.4214671349443961, -0.22744469293975272, 0.08975016752374358, 0.13204834997250145, 0.11448882611221052, -0.016288968590379226, -0.28324356796219946, 0.02823471406591125, -0.14823223701760072, -0.1516873815082363, -0.10342373274033889, 0.017027024418348448, 0.06205115361008211, -0.2822957131284056, 0.14926263181259855, 0.12988884020796831, 0.04474765310878866, -0.06626927131728735, -0.08293256240940536, -0.009718977595912292, 0.09940325260104146, 0.02099745056239044, -0.016489911325334106, 0.11773896461527329, -0.11233498219771718, -0.12762077469815267, 0.3853669530246407, -0.1359929628808459, -0.2960044135339558, 0.14178297088801628, -0.16926211827958468, -0.1161443699427764, 0.1457015648693755, 0.196059609844815, 0.12827120125875807, -0.24440029805991798, 0.07660084468479908, 0.01837955921946559, 0.2014109051437117, 0.037237697881937495, 0.003317639029410202, 0.1422231721051503, 0.176896633929573, 0.09794016075902619, 0.1570599913276965, -0.07519180892122676, -0.18965988104610004, -0.34331813915632664, -0.17896586986062174, -0.16886246313806624, 0.07850597745427876, -0.08585559358461978, -0.18358334323565942, 0.37738639626186343, 0.15071317587789962, 0.16806541701953392, 0.08278554539792822, 0.262801535113249, 0.21173339036031394, 0.003686316331732087, 0.05943679392657941, 0.18247305976110512, 0.12876717781909974, 0.136416202338296, -0.2304410403698057, 0.06747799062632112, 0.11156711061630631] |
1,802.04383 | Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph
Total Variation | We present an extension of the cut-pursuit algorithm, introduced by Landrieu
and Obozinski (2017), to the graph total-variation regularization of functions
with a separable nondifferentiable part. We propose a modified algorithmic
scheme as well as adapted proofs of convergence. We also present a heuristic
approach for handling the cases in which the values associated to each vertex
of the graph are multidimensional. The performance of our algorithm, which we
demonstrate on difficult, ill-conditioned large-scale inverse and learning
problems, is such that it may in practice extend the scope of application of
the total-variation regularization.
| math.OC | we present an extension of the cutpursuit algorithm introduced by landrieu and obozinski 2017 to the graph totalvariation regularization of functions with a separable nondifferentiable part we propose a modified algorithmic scheme as well as adapted proofs of convergence we also present a heuristic approach for handling the cases in which the values associated to each vertex of the graph are multidimensional the performance of our algorithm which we demonstrate on difficult illconditioned largescale inverse and learning problems is such that it may in practice extend the scope of application of the totalvariation regularization | [['we', 'present', 'an', 'extension', 'of', 'the', 'cutpursuit', 'algorithm', 'introduced', 'by', 'landrieu', 'and', 'obozinski', '2017', 'to', 'the', 'graph', 'totalvariation', 'regularization', 'of', 'functions', 'with', 'a', 'separable', 'nondifferentiable', 'part', 'we', 'propose', 'a', 'modified', 'algorithmic', 'scheme', 'as', 'well', 'as', 'adapted', 'proofs', 'of', 'convergence', 'we', 'also', 'present', 'a', 'heuristic', 'approach', 'for', 'handling', 'the', 'cases', 'in', 'which', 'the', 'values', 'associated', 'to', 'each', 'vertex', 'of', 'the', 'graph', 'are', 'multidimensional', 'the', 'performance', 'of', 'our', 'algorithm', 'which', 'we', 'demonstrate', 'on', 'difficult', 'illconditioned', 'largescale', 'inverse', 'and', 'learning', 'problems', 'is', 'such', 'that', 'it', 'may', 'in', 'practice', 'extend', 'the', 'scope', 'of', 'application', 'of', 'the', 'totalvariation', 'regularization']] | [-0.03810654050135821, -0.016028072176965537, -0.08870648513394562, 0.09516915115195193, -0.11090697251915277, -0.11992104728655009, 0.024291930633218408, 0.4110136222258046, -0.31169535021635364, -0.29444721330898804, 0.15096954889544337, -0.20808548528565463, -0.2387068980465551, 0.18607712612272456, -0.13227594595985834, 0.11116239958034015, 0.0922955110730542, 0.02537555789196311, -0.0709235274135698, -0.2754163588685045, 0.31836687465924973, 0.03663701039053254, 0.2148593244818295, 0.0699793548875867, 0.12607959778160677, 0.03645499906717585, -0.028450812658315505, 0.056235379006106444, -0.09918955420593252, 0.14137063694063698, 0.2764425424470024, 0.17356315805259478, 0.35220238083339, -0.3644772244944841, -0.18973285918748314, 0.1144224301410409, 0.14219031578625788, 0.10075030616832549, -0.030602739946046783, -0.2750412848583617, 0.08742926287168003, -0.16780076929173626, -0.07174813172749274, -0.13020071064910063, -0.0400126880740972, 0.022248368607936324, -0.31687221148020617, 0.0500320631285981, 0.08635646548598064, 0.0020748877508954687, -0.04532323841128375, -0.13754407924364556, 0.08634725543107469, 0.05284797994338058, 0.04963033624597221, 0.04721388399887543, 0.08047926879825663, -0.09874011292685192, -0.15791332902857563, 0.37819943958791075, -0.03538958649509228, -0.23257954757415003, 0.19228817687298244, -0.03151709789533045, -0.16417262312741235, 0.06763616353682764, 0.20667607638125235, 0.1772105077634678, -0.10738373521183218, 0.11023428811881036, -0.05276587404883825, 0.10788409236900426, 0.019594777421514085, 0.011577853996941185, 0.06892142059527091, 0.17425900950495685, 0.14753133749747957, 0.20261511075627672, -0.06536045876538872, -0.07261953926594047, -0.32925570168747353, -0.12073839042376686, -0.22047330004132384, -0.021955742949752936, -0.12235772183419533, -0.21390862957379975, 0.41201883449185106, 0.1894712450537974, 0.20717837041305318, 0.08195966793541479, 0.32258601074495896, 0.12595821400607968, 0.07587698012958843, 0.11541395098646427, 0.17064185573586396, 0.09483566376953743, 0.1095761313348763, -0.20485244197566266, 0.0453298292276296, 0.12859894163333452] |
1,802.04384 | Inhomogeneous Diophantine approximation in the coprime setting | Given $n\in N$ and $x,\gamma\in R$, let
\begin{equation*}
||\gamma-nx||^\prime=\min\{|\gamma-nx+m|:m\in Z, \gcd (n,m)=1\},
\end{equation*} %where $(n,m)$ is the largest common divisor of $n$ and $m$.
Two conjectures in the coprime inhomogeneous Diophantine approximation state
that for any irrational number $\alpha$ and almost every $\gamma\in R$,
\begin{equation*}
\liminf_{n\to \infty}n||\gamma -n\alpha||^{\prime}=0 \end{equation*} and that
there exists $C>0$, such that for all $\alpha\in R\backslash Q$ and $\gamma\in
[0,1)$ ,
\begin{equation*}
\liminf_{n\to \infty}n||\gamma -n\alpha||^{\prime} < C.
\end{equation*}
We prove the first conjecture and disprove the second one.
| math.NT | given nin n and xgammain r let beginequation gammanxprimemingammanxmmin z gcd nm1 endequation where nm is the largest common divisor of n and m two conjectures in the coprime inhomogeneous diophantine approximation state that for any irrational number alpha and almost every gammain r beginequation liminf_nto inftyngamma nalphaprime0 endequation and that there exists c0 such that for all alphain rbackslash q and gammain 01 beginequation liminf_nto inftyngamma nalphaprime c endequation we prove the first conjecture and disprove the second one | [['given', 'nin', 'n', 'and', 'xgammain', 'r', 'let', 'beginequation', 'gammanxprimemingammanxmmin', 'z', 'gcd', 'nm1', 'endequation', 'where', 'nm', 'is', 'the', 'largest', 'common', 'divisor', 'of', 'n', 'and', 'm', 'two', 'conjectures', 'in', 'the', 'coprime', 'inhomogeneous', 'diophantine', 'approximation', 'state', 'that', 'for', 'any', 'irrational', 'number', 'alpha', 'and', 'almost', 'every', 'gammain', 'r', 'beginequation', 'liminf_nto', 'inftyngamma', 'nalphaprime0', 'endequation', 'and', 'that', 'there', 'exists', 'c0', 'such', 'that', 'for', 'all', 'alphain', 'rbackslash', 'q', 'and', 'gammain', '01', 'beginequation', 'liminf_nto', 'inftyngamma', 'nalphaprime', 'c', 'endequation', 'we', 'prove', 'the', 'first', 'conjecture', 'and', 'disprove', 'the', 'second', 'one']] | [-0.2928341340424644, 0.09221285703667514, 0.04453615660502298, -0.014828669641959807, 0.021139379993483826, -0.3226845096796751, -0.035838014181904694, 0.309814583497575, -0.3008680912238118, -0.14673915877623633, 0.05566682398312952, -0.41615990241926565, -0.06588875137999453, 0.1651422750122996, -0.032935491567981, 0.055511735228080665, -0.012086454174808553, 0.08612174696817591, 0.0038701333026905115, -0.28831376892835453, 0.3178698908210405, -0.2224174183164094, 0.06163413979968912, 0.05059050156961422, 0.10659415069086528, -0.027231508016435278, 0.13066375663982252, -0.08816209992054044, -0.3446689138826686, -0.015341830930697757, 0.2781299223267549, 0.09851343446801342, 0.35219482508664196, -0.29575374938753696, -0.087737603556692, 0.2806796699800101, 0.21895783395538693, -0.16419538884499185, 0.030785605849421305, -0.2237810640646195, 0.2273407142131111, -0.05248070004824046, -0.18873025421556588, -0.011116687812515208, 0.22625937851498257, 0.04935476042305094, -0.4292260729770705, 0.07790609767874451, 0.142110856271953, 0.05258698302301078, 0.0113921917320506, -0.25587378667566824, -0.028731238276294962, 0.054572321568942955, -0.012681481054027539, 0.17878385459393817, -0.0714263329667832, -0.03714182714277224, -0.014687073675009448, 0.3612915442098637, -0.08558229808821469, -0.19627222657908458, -0.022589066109896916, -0.25642907221776406, -0.21573689378596642, 0.06492246139945614, 0.03132636831857805, 0.20882120789532121, 0.03192592073685011, 0.2704559632756345, -0.13982157159999414, 0.21561354222531254, 0.19876975783876874, -0.05522706303619654, 0.017740123255518138, 0.0015216230782931922, 0.07511962944566197, 0.005897551993650661, 0.005274051161659126, 0.06650273561653858, -0.3931621053331607, -0.190901129896677, -0.1960429310455692, 0.255123607814312, -0.1666393757131536, -0.12039275877051861, 0.1984445010437756, 0.1220425880746916, 0.16369837738347012, 0.142921770218055, 0.1524353976820822, 0.09355533377242249, -0.09505604082133269, 0.13678321986761247, 0.009294405025807587, 0.1715384186245501, -0.002914763181596189, -0.16952928667612738, 0.016528450600752555, 0.15670468415309852] |
1,802.04385 | Certified Roundoff Error Bounds using Bernstein Expansions and Sparse
Krivine-Stengle Representations | Floating point error is a drawback of embedded systems implementation that is
difficult to avoid. Computing rigorous upper bounds of roundoff errors is
absolutely necessary for the validation of critical software. This problem of
computing rigorous upper bounds is even more challenging when addressing
non-linear programs. In this paper, we propose and compare two new algorithms
based on Bernstein expansions and sparse Krivine-Stengle representations,
adapted from the field of the global optimization, to compute upper bounds of
roundoff errors for programs implementing polynomial and rational functions. We
also provide the convergence rate of these two algorithms. We release two
related software package FPBern and FPKriSten, and compare them with the
state-of-the-art tools. We show that these two methods achieve competitive
performance, while providing accurate upper bounds by comparison with the other
tools.
| cs.NA cs.MS | floating point error is a drawback of embedded systems implementation that is difficult to avoid computing rigorous upper bounds of roundoff errors is absolutely necessary for the validation of critical software this problem of computing rigorous upper bounds is even more challenging when addressing nonlinear programs in this paper we propose and compare two new algorithms based on bernstein expansions and sparse krivinestengle representations adapted from the field of the global optimization to compute upper bounds of roundoff errors for programs implementing polynomial and rational functions we also provide the convergence rate of these two algorithms we release two related software package fpbern and fpkristen and compare them with the stateoftheart tools we show that these two methods achieve competitive performance while providing accurate upper bounds by comparison with the other tools | [['floating', 'point', 'error', 'is', 'a', 'drawback', 'of', 'embedded', 'systems', 'implementation', 'that', 'is', 'difficult', 'to', 'avoid', 'computing', 'rigorous', 'upper', 'bounds', 'of', 'roundoff', 'errors', 'is', 'absolutely', 'necessary', 'for', 'the', 'validation', 'of', 'critical', 'software', 'this', 'problem', 'of', 'computing', 'rigorous', 'upper', 'bounds', 'is', 'even', 'more', 'challenging', 'when', 'addressing', 'nonlinear', 'programs', 'in', 'this', 'paper', 'we', 'propose', 'and', 'compare', 'two', 'new', 'algorithms', 'based', 'on', 'bernstein', 'expansions', 'and', 'sparse', 'krivinestengle', 'representations', 'adapted', 'from', 'the', 'field', 'of', 'the', 'global', 'optimization', 'to', 'compute', 'upper', 'bounds', 'of', 'roundoff', 'errors', 'for', 'programs', 'implementing', 'polynomial', 'and', 'rational', 'functions', 'we', 'also', 'provide', 'the', 'convergence', 'rate', 'of', 'these', 'two', 'algorithms', 'we', 'release', 'two', 'related', 'software', 'package', 'fpbern', 'and', 'fpkristen', 'and', 'compare', 'them', 'with', 'the', 'stateoftheart', 'tools', 'we', 'show', 'that', 'these', 'two', 'methods', 'achieve', 'competitive', 'performance', 'while', 'providing', 'accurate', 'upper', 'bounds', 'by', 'comparison', 'with', 'the', 'other', 'tools']] | [-0.12087186501862911, -0.006105825208270779, -0.08101724116489864, 0.11988412571282914, -0.07981466577889827, -0.17145739542678573, 0.06578295055257444, 0.3653811686887191, -0.24261382164147038, -0.332001892502348, 0.18281109214384253, -0.2359218152024998, -0.147686218470335, 0.3044407201178658, -0.09445513032663327, 0.1408275691505808, 0.10837579758909459, -0.016463452939150067, -0.11909775888869682, -0.3041748616557855, 0.2728322691624411, 0.04604433754172463, 0.2552191536801939, 0.06590267897655185, 0.053926296084402844, -0.049945588189159305, -0.06409802821536477, -0.027126806899188803, -0.1692398823104808, 0.22098994578207987, 0.2823871706110927, 0.18083932659087273, 0.30377869631092136, -0.4377892934717238, -0.09678151227283077, 0.12394200179797525, 0.15712310907144386, 0.10663755725030429, -0.03872046920508952, -0.2436782849809298, 0.09905249750904309, -0.14121487736701965, -0.08286345839285507, -0.1444514486222313, -0.02829813918439098, 0.0327439101094989, -0.29450860718408456, 0.0408951915372969, 0.06886381867628258, 0.07490748663414987, -0.016150722591779552, -0.17259905826873503, 0.06745729049882637, 0.13441619243620573, 0.019967290569240084, 0.013361342014888158, 0.10463902258242552, -0.10527896458963648, -0.16106096054069124, 0.34286511543994913, -0.06881227963604034, -0.23439484910609631, 0.19972547229128676, -0.05489560670326822, -0.17644410183151754, 0.13183879115117283, 0.22345304862739376, 0.10827456810511649, -0.15535618373284635, 0.10944086306576187, 0.022337865134557853, 0.1514507858890843, 0.03942607341764065, 0.03479697275906801, 0.11937199194485751, 0.15343830620404333, 0.13995167286350177, 0.16444715475708319, -0.03613880301104823, -0.08528839367656754, -0.28189525145750777, -0.1360451743444383, -0.15740491734781803, -0.05102467754981122, -0.103956069884477, -0.1925574300201753, 0.35109098012236734, 0.22519048316619145, 0.12428828274401335, 0.16027513347172107, 0.40029026095110637, 0.12346619884685005, 0.004321750060010415, 0.1521205912129237, 0.21828049028363938, 0.10988104889849917, 0.02819037302934493, -0.16135271381980812, 0.06961298716397812, 0.0962574629435459] |
1,802.04386 | The Hopf monoid of Megagreedoids | We introduce megagreedoids, which generalize polymatroids, megamatroids, and
greedoids. We define a quasisymmetric function invariant for a megagreedoid,
and show that it has a positive expansion in the basis of fundamental
quasisymmetric functions. Our proof involves lexicographic shellability. We
also show that megagreedoids form a Hopf monoid. A running example is a
megagreedoid associated to a rooted connected graph, and the resulting
generalization of the chromatic symmetric function.
| math.CO | we introduce megagreedoids which generalize polymatroids megamatroids and greedoids we define a quasisymmetric function invariant for a megagreedoid and show that it has a positive expansion in the basis of fundamental quasisymmetric functions our proof involves lexicographic shellability we also show that megagreedoids form a hopf monoid a running example is a megagreedoid associated to a rooted connected graph and the resulting generalization of the chromatic symmetric function | [['we', 'introduce', 'megagreedoids', 'which', 'generalize', 'polymatroids', 'megamatroids', 'and', 'greedoids', 'we', 'define', 'a', 'quasisymmetric', 'function', 'invariant', 'for', 'a', 'megagreedoid', 'and', 'show', 'that', 'it', 'has', 'a', 'positive', 'expansion', 'in', 'the', 'basis', 'of', 'fundamental', 'quasisymmetric', 'functions', 'our', 'proof', 'involves', 'lexicographic', 'shellability', 'we', 'also', 'show', 'that', 'megagreedoids', 'form', 'a', 'hopf', 'monoid', 'a', 'running', 'example', 'is', 'a', 'megagreedoid', 'associated', 'to', 'a', 'rooted', 'connected', 'graph', 'and', 'the', 'resulting', 'generalization', 'of', 'the', 'chromatic', 'symmetric', 'function']] | [-0.163803703048163, 0.08089324498608236, -0.11566216498613358, 0.10651643637249926, -0.15939889141609745, -0.08458344730740738, 0.018158985069021583, 0.3598708699028643, -0.3373717563374648, -0.18183423966051093, 0.07817897567015496, -0.2287372736822045, -0.28339789118913433, 0.1662093230656215, -0.07339012627268121, -0.02041661502083852, 0.0639232076918854, 0.0709733363122694, -0.08322402479363576, -0.1882744083418474, 0.37926660467767054, -0.026063102208787485, 0.20657838393180145, 0.11567245392380135, 0.15432989023466934, 0.013612598816435489, -0.04546954808017564, 0.07084695107972486, -0.1828512008136068, 0.1129661218544084, 0.23059885520192366, 0.1679218172553986, 0.2519133650062103, -0.30651997890146004, -0.11174808810686782, 0.16674063441949705, 0.10441677136317132, 0.02515471652926256, -0.06648261076770723, -0.20953448937969313, 0.08835604860787354, -0.22470903349301172, -0.1263218334218162, -0.07434646477774969, 0.06359325052933797, 0.04304031226488333, -0.3412559977510855, -0.02992652349648923, 0.13440242111091577, 0.04830180170750874, 0.010650524357703351, -0.08038967745082956, -0.037116229478713306, 0.07106607128673839, -0.06530761466332016, 0.11821276817049477, 0.056026742837968324, -0.08836002258901736, -0.1856229840408242, 0.3737804762028631, -0.04929653018535603, -0.2644214249910816, 0.11925190599406109, -0.16029181471833634, -0.20054593173757432, 0.05836298547330357, 0.1195223124047357, 0.15711025661596703, -0.02947121975381696, 0.11869040218510828, -0.19759033173913992, 0.1031616711453165, 0.14165891029886782, 0.012139815615401381, 0.1403201094252013, 0.07384533370431098, 0.1257890263601901, 0.2899638589708105, 0.08229030584380592, -0.031127915606789645, -0.309503669303561, -0.20259593993366237, -0.17590478432941295, 0.09017532482920658, -0.1254975255423998, -0.25656121655825587, 0.4732561120430806, 0.09453103979236431, 0.1586277081172854, 0.17325147862420492, 0.21963107156833367, 0.11454451572896528, 0.10811164221977668, 0.08223199416395455, 0.11309448510615362, 0.22240324138796755, -0.005236287113456499, -0.12283662299159914, 0.03484389697024155, 0.22123889637637942] |
1,802.04387 | Spatially dispersive circular photogalvanic effect in a Weyl semimetal | Weyl semimetals are gapless topological states of matter with broken
inversion and/or time reversal symmetry, which can support unconventional
responses to externally applied electrical, optical and magnetic fields. Here
we report a new photogalvanic effect in type-II WSMs, MoTe2 and Mo0.9W0.1Te2,
which are observed to support a circulating photocurrent when illuminated by
circularly polarized light at normal incidence. This effect occurs exclusively
in the inversion broken phase, where crucially we find that it is associated
with a spatially varying beam profile via a new dispersive contribution to the
circular photogalvanic effect (s-CPGE). The response functions derived for
s-CPGE reveal the microscopic mechanism of this photocurrent, which are
controlled by terms that are allowed in the absence of inversion symmetry,
along with asymmetric carrier excitation and relaxation. By evaluating this
response for a minimal model of a Weyl semimetal, we obtain the frequency
dependent scaling behavior of this form of photocurrent. These results
demonstrate opportunities for controlling photoresponse by patterning optical
fields to store, manipulate and transmit information over a wide spectral
range.
| cond-mat.mtrl-sci cond-mat.mes-hall | weyl semimetals are gapless topological states of matter with broken inversion andor time reversal symmetry which can support unconventional responses to externally applied electrical optical and magnetic fields here we report a new photogalvanic effect in typeii wsms mote2 and mo09w01te2 which are observed to support a circulating photocurrent when illuminated by circularly polarized light at normal incidence this effect occurs exclusively in the inversion broken phase where crucially we find that it is associated with a spatially varying beam profile via a new dispersive contribution to the circular photogalvanic effect scpge the response functions derived for scpge reveal the microscopic mechanism of this photocurrent which are controlled by terms that are allowed in the absence of inversion symmetry along with asymmetric carrier excitation and relaxation by evaluating this response for a minimal model of a weyl semimetal we obtain the frequency dependent scaling behavior of this form of photocurrent these results demonstrate opportunities for controlling photoresponse by patterning optical fields to store manipulate and transmit information over a wide spectral range | [['weyl', 'semimetals', 'are', 'gapless', 'topological', 'states', 'of', 'matter', 'with', 'broken', 'inversion', 'andor', 'time', 'reversal', 'symmetry', 'which', 'can', 'support', 'unconventional', 'responses', 'to', 'externally', 'applied', 'electrical', 'optical', 'and', 'magnetic', 'fields', 'here', 'we', 'report', 'a', 'new', 'photogalvanic', 'effect', 'in', 'typeii', 'wsms', 'mote2', 'and', 'mo09w01te2', 'which', 'are', 'observed', 'to', 'support', 'a', 'circulating', 'photocurrent', 'when', 'illuminated', 'by', 'circularly', 'polarized', 'light', 'at', 'normal', 'incidence', 'this', 'effect', 'occurs', 'exclusively', 'in', 'the', 'inversion', 'broken', 'phase', 'where', 'crucially', 'we', 'find', 'that', 'it', 'is', 'associated', 'with', 'a', 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1,802.04388 | Statistical Sensor Fusion of a 9-DoF MEMS IMU for Indoor Navigation | Sensor fusion of a MEMS IMU with a magnetometer is a popular system design,
because such 9-DoF (degrees of freedom) systems are capable of achieving
drift-free 3D orientation tracking. However, these systems are often vulnerable
to ambient magnetic distortions and lack useful position information; in the
absence of external position aiding (e.g. satellite/ultra-wideband positioning
systems) the dead-reckoned position accuracy from a 9-DoF MEMS IMU deteriorates
rapidly due to unmodelled errors. Positioning information is valuable in many
satellite-denied geomatics applications (e.g. indoor navigation, location-based
services, etc.). This paper proposes an improved 9-DoF IMU indoor pose tracking
method using batch optimization. By adopting a robust in-situ user
self-calibration approach to model the systematic errors of the accelerometer,
gyroscope, and magnetometer simultaneously in a tightly-coupled post-processed
least-squares framework, the accuracy of the estimated trajectory from a 9-DoF
MEMS IMU can be improved. Through a combination of relative magnetic
measurement updates and a robust weight function, the method is able to
tolerate a high level of magnetic distortions. The proposed auto-calibration
method was tested in-use under various heterogeneous magnetic field conditions
to mimic a person walking with the sensor in their pocket, a person checking
their phone, and a person walking with a smartwatch. In these experiments, the
presented algorithm improved the in-situ dead-reckoning orientation accuracy by
79.8 - 89.5% and the dead-reckoned positioning accuracy by 72.9 - 92.8%, thus
reducing the relative positioning error from metre-level to decimetre-level
after ten seconds of integration, without making assumptions about the user's
dynamics.
| eess.SP | sensor fusion of a mems imu with a magnetometer is a popular system design because such 9dof degrees of freedom systems are capable of achieving driftfree 3d orientation tracking however these systems are often vulnerable to ambient magnetic distortions and lack useful position information in the absence of external position aiding eg satelliteultrawideband positioning systems the deadreckoned position accuracy from a 9dof mems imu deteriorates rapidly due to unmodelled errors positioning information is valuable in many satellitedenied geomatics applications eg indoor navigation locationbased services etc this paper proposes an improved 9dof imu indoor pose tracking method using batch optimization by adopting a robust insitu user selfcalibration approach to model the systematic errors of the accelerometer gyroscope and magnetometer simultaneously in a tightlycoupled postprocessed leastsquares framework the accuracy of the estimated trajectory from a 9dof mems imu can be improved through a combination of relative magnetic measurement updates and a robust weight function the method is able to tolerate a high level of magnetic distortions the proposed autocalibration method was tested inuse under various heterogeneous magnetic field conditions to mimic a person walking with the sensor in their pocket a person checking their phone and a person walking with a smartwatch in these experiments the presented algorithm improved the insitu deadreckoning orientation accuracy by 798 895 and the deadreckoned positioning accuracy by 729 928 thus reducing the relative positioning error from metrelevel to decimetrelevel after ten seconds of integration without making assumptions about the users dynamics | [['sensor', 'fusion', 'of', 'a', 'mems', 'imu', 'with', 'a', 'magnetometer', 'is', 'a', 'popular', 'system', 'design', 'because', 'such', '9dof', 'degrees', 'of', 'freedom', 'systems', 'are', 'capable', 'of', 'achieving', 'driftfree', '3d', 'orientation', 'tracking', 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1,802.04389 | Non-Conforming Multiscale Finite Element Method for Stokes Flows in
Heterogeneous Media. Part II: error estimates for periodic microstructure | This paper is dedicated to the rigorous numerical analysis of a Multiscale
Finite Element Method (MsFEM) for the Stokes system, when dealing with highly
heterogeneous media, as proposed in [B.P.~Muljadi et al., arXiv:1404.2837]. The
method is in the vein of the classical Crouzeix-Raviart approach. It is
generalized here to arbitrary sets of weighting functions used to enforced
continuity across the mesh edges. We provide error bounds for a particular set
of weighting functions in a periodic setting, using an accurate estimate of the
homogenization error. Numerical experiments demonstrate an improved accuracy of
the present variant with respect to that of Part I, both in the periodic case
and in a broader setting.
| math.NA | this paper is dedicated to the rigorous numerical analysis of a multiscale finite element method msfem for the stokes system when dealing with highly heterogeneous media as proposed in bpmuljadi et al arxiv14042837 the method is in the vein of the classical crouzeixraviart approach it is generalized here to arbitrary sets of weighting functions used to enforced continuity across the mesh edges we provide error bounds for a particular set of weighting functions in a periodic setting using an accurate estimate of the homogenization error numerical experiments demonstrate an improved accuracy of the present variant with respect to that of part i both in the periodic case and in a broader setting | [['this', 'paper', 'is', 'dedicated', 'to', 'the', 'rigorous', 'numerical', 'analysis', 'of', 'a', 'multiscale', 'finite', 'element', 'method', 'msfem', 'for', 'the', 'stokes', 'system', 'when', 'dealing', 'with', 'highly', 'heterogeneous', 'media', 'as', 'proposed', 'in', 'bpmuljadi', 'et', 'al', 'arxiv14042837', 'the', 'method', 'is', 'in', 'the', 'vein', 'of', 'the', 'classical', 'crouzeixraviart', 'approach', 'it', 'is', 'generalized', 'here', 'to', 'arbitrary', 'sets', 'of', 'weighting', 'functions', 'used', 'to', 'enforced', 'continuity', 'across', 'the', 'mesh', 'edges', 'we', 'provide', 'error', 'bounds', 'for', 'a', 'particular', 'set', 'of', 'weighting', 'functions', 'in', 'a', 'periodic', 'setting', 'using', 'an', 'accurate', 'estimate', 'of', 'the', 'homogenization', 'error', 'numerical', 'experiments', 'demonstrate', 'an', 'improved', 'accuracy', 'of', 'the', 'present', 'variant', 'with', 'respect', 'to', 'that', 'of', 'part', 'i', 'both', 'in', 'the', 'periodic', 'case', 'and', 'in', 'a', 'broader', 'setting']] | [-0.07019068525934762, -0.002676788617761552, -0.07791370942934671, 0.020509312731552532, -0.04542279890103435, -0.08249993071976033, 0.036906965243079785, 0.3655517862263051, -0.2756510363383727, -0.2757623670479833, 0.10769210995005613, -0.24884090168316933, -0.15857406220748088, 0.2265824817375026, -0.11179121139628644, 0.10703907443041151, 0.09046346655284816, -0.014783631921057928, -0.07186233500569043, -0.2615003282034939, 0.31857939139008523, 0.055684083492749115, 0.28611891458488325, 0.03737416980280118, 0.09379705829461189, 0.00286839707034894, -0.07063161280768161, 0.06662896443158388, -0.11517562085414697, 0.1609604705548422, 0.23564544694963843, 0.0797804629790004, 0.3276284173384986, -0.39558623103551904, -0.22898624176371166, 0.08162199111062694, 0.1449991359968077, 0.11995516173033552, -0.05471033743498, -0.2794134441234002, 0.12029104740570554, -0.16656824927777053, -0.1415457360785116, -0.08791614770042626, -0.00038624626838348127, 0.03803414748981595, -0.35759927413680337, 0.0809654424432665, 0.049380889378318735, 0.06588495635342868, -0.06021990592337467, -0.09398138863352043, 0.0492457131114365, 0.0999932486725315, -0.012287823870693418, 0.019558655258945443, 0.030922554149715738, -0.07181496591815217, -0.0949089870788157, 0.3843320341103456, -0.07676534052218564, -0.29469602910632436, 0.1756457507102327, -0.09255458532239903, -0.11396192923526872, 0.10812747107649391, 0.21182533526285127, 0.1595452345145697, -0.12538771768039705, 0.0652502462558914, -0.07533359515099701, 0.15663342151621526, 0.03553827339571647, -0.01536210716956041, 0.06308053348382765, 0.1945310579909181, 0.11001566926610064, 0.14588072016115555, -0.07536154316357252, -0.07500457312245007, -0.33526641348884867, -0.15065079121096378, -0.20723312656522136, -0.04271955009317026, -0.11126454575397392, -0.23240691318033813, 0.385430553148035, 0.1932245872930666, 0.15720502348108725, 0.06954109654523728, 0.30065657827071846, 0.1309808997057685, -0.004344997097822753, 0.07104827534322712, 0.1954068025870418, 0.12578511601862952, 0.0988806074993177, -0.20794679563577204, 0.05054334351284937, 0.13143832435492764] |
1,802.0439 | Veneziano Amplitude of Vasiliev Theory | We compute the four-point function of scalar operators in CFTs with weakly
broken higher spin symmetry at arbitrary 't Hooft coupling. We use the known
three-point functions in these theories, the Lorentzian OPE inversion formula
and crossing to fix the result up to the addition of three functions of the
cross ratios. These are given by contact Witten diagrams in AdS and manifest
non-analyticity of the OPE data in spin. We use Schwinger-Dyson equations to
show that such terms are absent in the large $N$ Chern-Simons matter theories.
The result is that the OPE data is analytic in spin up to $J=0$.
| hep-th | we compute the fourpoint function of scalar operators in cfts with weakly broken higher spin symmetry at arbitrary t hooft coupling we use the known threepoint functions in these theories the lorentzian ope inversion formula and crossing to fix the result up to the addition of three functions of the cross ratios these are given by contact witten diagrams in ads and manifest nonanalyticity of the ope data in spin we use schwingerdyson equations to show that such terms are absent in the large n chernsimons matter theories the result is that the ope data is analytic in spin up to j0 | [['we', 'compute', 'the', 'fourpoint', 'function', 'of', 'scalar', 'operators', 'in', 'cfts', 'with', 'weakly', 'broken', 'higher', 'spin', 'symmetry', 'at', 'arbitrary', 't', 'hooft', 'coupling', 'we', 'use', 'the', 'known', 'threepoint', 'functions', 'in', 'these', 'theories', 'the', 'lorentzian', 'ope', 'inversion', 'formula', 'and', 'crossing', 'to', 'fix', 'the', 'result', 'up', 'to', 'the', 'addition', 'of', 'three', 'functions', 'of', 'the', 'cross', 'ratios', 'these', 'are', 'given', 'by', 'contact', 'witten', 'diagrams', 'in', 'ads', 'and', 'manifest', 'nonanalyticity', 'of', 'the', 'ope', 'data', 'in', 'spin', 'we', 'use', 'schwingerdyson', 'equations', 'to', 'show', 'that', 'such', 'terms', 'are', 'absent', 'in', 'the', 'large', 'n', 'chernsimons', 'matter', 'theories', 'the', 'result', 'is', 'that', 'the', 'ope', 'data', 'is', 'analytic', 'in', 'spin', 'up', 'to', 'j0']] | [-0.16899048537928976, 0.17535161200509058, -0.03955686859348241, 0.10417075205238208, -0.05564552286466328, -0.12848681704524686, -0.013326118992385911, 0.3352233382380184, -0.17071858720452177, -0.2573712428200844, 0.04735895047904741, -0.3324436087015213, -0.1597630038798111, 0.09725940812780869, 0.040574668870106634, 0.06149243400660435, -0.038469237163571604, 0.033721819068031275, -0.1585433102788075, -0.25939084211473956, 0.3652327773435151, -0.057426038362524086, 0.23322442269829266, 0.12959770886136182, 0.06396885163963352, 0.021148809619868796, -0.00578819728894707, -0.053154264083679986, -0.12428974949680018, 0.08372480388475108, 0.2689581011358064, 0.03356571825148136, 0.0894085647830484, -0.4185021484599394, -0.12974773266343584, 0.06873943668666899, 0.1735280095143061, 0.14039871252287983, 0.05697860911045698, -0.24929384569473126, 0.05731513405072631, -0.19479172426622873, -0.16908483594021412, -0.14723937530253156, -0.007710763102616458, -0.08637707561765816, -0.2767693879740203, 0.10819908381238871, 0.018161112506526942, 0.039863846118689754, -0.03791718307754719, -0.10146585052979051, -0.08350579019215908, 0.1164810750542172, 0.14131388489348704, 0.06363993712846994, 0.10100795891633549, -0.20149508089411491, -0.13508295949991317, 0.3082113458464543, -0.11967083310489268, -0.2485763630555833, 0.14289581762425893, -0.2350820107202904, -0.18515438183128616, 0.08734031195076657, 0.10579106423492525, 0.17636958983562448, -0.13441941151212827, 0.18867420001462212, -0.015592545855279062, 0.08214177605787329, 0.11233017474924233, 0.024028656543141194, 0.20686401511250319, 0.023069989465761418, 0.03667878918549475, 0.1656938567392382, -0.002919782907245498, -0.10863195275705234, -0.3966141280845976, -0.1341745961713148, -0.18341779487901458, 0.10178020590112782, -0.1786385260424782, -0.16967397129309236, 0.3326034612386424, 0.14691506806925378, 0.2128206014036265, 0.09096092317143784, 0.22938813179658324, 0.19132578720032767, 0.15933327327993715, 0.06127617555195648, 0.22242290641160628, 0.17287272396702869, 0.08490786695505913, -0.26431489352812515, -0.06707959639264599, 0.13848749707525998] |
1,802.04391 | Shake and sink: liquefaction without pressurization | Soil liquefaction is a significant natural hazard associated with
earthquakes. Some of its devastating effects include tilting and sinking of
buildings and bridges, and destruction of pipelines. Conventional geotechnical
engineering practice assumes liquefaction occurs via shear-driven compaction
and consequent elevation of pore pressure. This assumption guides construction
for seismically hazardous locations, yet evidence suggests that liquefaction
strikes also under currently unpredicted conditions. Here we show, using
theory, simulations and experiments, another mechanism for liquefaction in
saturated soils, without necessitating high pore fluid pressure or special
soils, whereby seismically triggered liquefaction is controlled by buoyancy
forces. This new mechanism supplements the conventional pore pressure
mechanism, enlarges the window of conditions under which liquefaction is
predicted to occur, and may explain previously not understood cases such as
liquefaction in well-compacted soils, under drained conditions, repeated
liquefaction cases, and the basics of sinking in quicksand. These results may
greatly impact hazard assessment and mitigation in seismically active areas.
| physics.geo-ph cond-mat.soft | soil liquefaction is a significant natural hazard associated with earthquakes some of its devastating effects include tilting and sinking of buildings and bridges and destruction of pipelines conventional geotechnical engineering practice assumes liquefaction occurs via sheardriven compaction and consequent elevation of pore pressure this assumption guides construction for seismically hazardous locations yet evidence suggests that liquefaction strikes also under currently unpredicted conditions here we show using theory simulations and experiments another mechanism for liquefaction in saturated soils without necessitating high pore fluid pressure or special soils whereby seismically triggered liquefaction is controlled by buoyancy forces this new mechanism supplements the conventional pore pressure mechanism enlarges the window of conditions under which liquefaction is predicted to occur and may explain previously not understood cases such as liquefaction in wellcompacted soils under drained conditions repeated liquefaction cases and the basics of sinking in quicksand these results may greatly impact hazard assessment and mitigation in seismically active areas | [['soil', 'liquefaction', 'is', 'a', 'significant', 'natural', 'hazard', 'associated', 'with', 'earthquakes', 'some', 'of', 'its', 'devastating', 'effects', 'include', 'tilting', 'and', 'sinking', 'of', 'buildings', 'and', 'bridges', 'and', 'destruction', 'of', 'pipelines', 'conventional', 'geotechnical', 'engineering', 'practice', 'assumes', 'liquefaction', 'occurs', 'via', 'sheardriven', 'compaction', 'and', 'consequent', 'elevation', 'of', 'pore', 'pressure', 'this', 'assumption', 'guides', 'construction', 'for', 'seismically', 'hazardous', 'locations', 'yet', 'evidence', 'suggests', 'that', 'liquefaction', 'strikes', 'also', 'under', 'currently', 'unpredicted', 'conditions', 'here', 'we', 'show', 'using', 'theory', 'simulations', 'and', 'experiments', 'another', 'mechanism', 'for', 'liquefaction', 'in', 'saturated', 'soils', 'without', 'necessitating', 'high', 'pore', 'fluid', 'pressure', 'or', 'special', 'soils', 'whereby', 'seismically', 'triggered', 'liquefaction', 'is', 'controlled', 'by', 'buoyancy', 'forces', 'this', 'new', 'mechanism', 'supplements', 'the', 'conventional', 'pore', 'pressure', 'mechanism', 'enlarges', 'the', 'window', 'of', 'conditions', 'under', 'which', 'liquefaction', 'is', 'predicted', 'to', 'occur', 'and', 'may', 'explain', 'previously', 'not', 'understood', 'cases', 'such', 'as', 'liquefaction', 'in', 'wellcompacted', 'soils', 'under', 'drained', 'conditions', 'repeated', 'liquefaction', 'cases', 'and', 'the', 'basics', 'of', 'sinking', 'in', 'quicksand', 'these', 'results', 'may', 'greatly', 'impact', 'hazard', 'assessment', 'and', 'mitigation', 'in', 'seismically', 'active', 'areas']] | [-0.09678617518322614, 0.25304727721203407, -0.06341473417743654, 0.06720399453876201, -0.0794906912026806, -0.1195390759785841, 0.0845372755973987, 0.33491438193331385, -0.231331672592016, -0.295608188902548, 0.15715253751954766, -0.19402932819710778, -0.20785270003728906, 0.19225266627935608, -0.16989702738627985, 0.02969591234252764, 0.03319659366804574, -0.06055458717943205, 0.018531064530581315, -0.17715971124836233, 0.2264940628068304, 0.12660007975850399, 0.3349817759983314, 0.13078523443085022, 0.06500526589217487, -0.0586721537163305, -0.011365282247236071, 0.025401236373330572, -0.17501177635013054, -0.04128548303579718, 0.2656837825227964, 0.07741137866729073, 0.2742185307479185, -0.5343666001307693, -0.3199526662195582, 0.07946352333029998, 0.06193010512037904, 0.07949570386501198, -0.0747425994138814, -0.2432013196060313, 0.0035619285749876268, -0.19217856954964627, -0.14353019730853184, -0.04443205595698719, 0.012311122207841897, 0.027557165600830866, -0.2997579482866318, 0.12805416373904135, 0.1034028480803086, 0.1391942931230289, -0.12510617006518696, -0.09261241976123352, -0.05270138859760935, 0.0839265277208579, 0.12341920960718741, -0.07827471001617997, 0.2910997084970888, -0.1544335817467639, 0.009788524338298223, 0.43197362833995034, 0.004535001829605211, -0.12964675449705743, 0.2610548598776487, -0.10933254515470986, -0.08526101410872751, 0.18137003195036638, 0.18161617621307732, 0.04048430917832952, -0.14769177028739994, -0.043834136130723664, 0.017969081752824707, 0.10150822342143624, 0.15864138108490689, -0.08951268506133567, 0.22254382595442562, 0.22966422061375402, 0.06206643892527337, 0.12832456861785915, -0.11834983952538497, -0.08698375998427729, -0.28999686904566047, -0.13790130325122102, -0.08416126688107751, -0.027907873718457314, -0.07116394943494181, -0.18094091907191973, 0.27420127111208903, 0.14992062192464659, 0.09959264356316394, -0.017493309555938103, 0.2682836114658744, -0.008388782997295115, 0.0964994180989875, 0.0461682466723557, 0.2580068325421655, 0.06962475788858175, 0.11442894427492828, -0.20152812929334143, 0.21258728223997955, 0.02596680316983347] |
1,802.04392 | Image Retargetability | Real-world applications could benefit from the ability to automatically
retarget an image to different aspect ratios and resolutions, while preserving
its visually and semantically important content. However, not all images can be
equally well processed that way. In this work, we introduce the notion of image
retargetability to describe how well a particular image can be handled by
content-aware image retargeting. We propose to learn a deep convolutional
neural network to rank photo retargetability in which the relative ranking of
photo retargetability is directly modeled in the loss function. Our model
incorporates joint learning of meaningful photographic attributes and image
content information which can help regularize the complicated retargetability
rating problem. To train and analyze this model, we have collected a database
which contains retargetability scores and meaningful image attributes assigned
by six expert raters. Experiments demonstrate that our unified model can
generate retargetability rankings that are highly consistent with human labels.
To further validate our model, we show applications of image retargetability in
retargeting method selection, retargeting method assessment and photo collage
generation.
| cs.CV | realworld applications could benefit from the ability to automatically retarget an image to different aspect ratios and resolutions while preserving its visually and semantically important content however not all images can be equally well processed that way in this work we introduce the notion of image retargetability to describe how well a particular image can be handled by contentaware image retargeting we propose to learn a deep convolutional neural network to rank photo retargetability in which the relative ranking of photo retargetability is directly modeled in the loss function our model incorporates joint learning of meaningful photographic attributes and image content information which can help regularize the complicated retargetability rating problem to train and analyze this model we have collected a database which contains retargetability scores and meaningful image attributes assigned by six expert raters experiments demonstrate that our unified model can generate retargetability rankings that are highly consistent with human labels to further validate our model we show applications of image retargetability in retargeting method selection retargeting method assessment and photo collage generation | [['realworld', 'applications', 'could', 'benefit', 'from', 'the', 'ability', 'to', 'automatically', 'retarget', 'an', 'image', 'to', 'different', 'aspect', 'ratios', 'and', 'resolutions', 'while', 'preserving', 'its', 'visually', 'and', 'semantically', 'important', 'content', 'however', 'not', 'all', 'images', 'can', 'be', 'equally', 'well', 'processed', 'that', 'way', 'in', 'this', 'work', 'we', 'introduce', 'the', 'notion', 'of', 'image', 'retargetability', 'to', 'describe', 'how', 'well', 'a', 'particular', 'image', 'can', 'be', 'handled', 'by', 'contentaware', 'image', 'retargeting', 'we', 'propose', 'to', 'learn', 'a', 'deep', 'convolutional', 'neural', 'network', 'to', 'rank', 'photo', 'retargetability', 'in', 'which', 'the', 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1,802.04393 | On the convergence of data assimilation for the one-dimensional shallow
water equations with sparse observations | The shallow water equations (SWE) are a widely used model for the propagation
of surface waves on the oceans. We consider the problem of optimally
determining the initial conditions for the one-dimensional SWE in an unbounded
domain from a small set of observations of the sea surface height. In the
linear case we prove a theorem that gives sufficient conditions for convergence
to the true initial conditions. At least two observation points must be used
and at least one pair of observation points must be spaced more closely than
half the effective minimum wavelength of the energy spectrum of the initial
conditions. This result also applies to the linear wave equation. Our analysis
is confirmed by numerical experiments for both the linear and nonlinear SWE
data assimilation problems. These results show that convergence rates improve
with increasing numbers of observation points and that at least three
observation points are required for the practically useful results. Better
results are obtained for the nonlinear equations provided more than two
observation points are used. This paper is a first step in understanding the
conditions for observability of the SWE for small numbers of observation points
in more physically realistic settings.
| physics.flu-dyn math.OC physics.comp-ph | the shallow water equations swe are a widely used model for the propagation of surface waves on the oceans we consider the problem of optimally determining the initial conditions for the onedimensional swe in an unbounded domain from a small set of observations of the sea surface height in the linear case we prove a theorem that gives sufficient conditions for convergence to the true initial conditions at least two observation points must be used and at least one pair of observation points must be spaced more closely than half the effective minimum wavelength of the energy spectrum of the initial conditions this result also applies to the linear wave equation our analysis is confirmed by numerical experiments for both the linear and nonlinear swe data assimilation problems these results show that convergence rates improve with increasing numbers of observation points and that at least three observation points are required for the practically useful results better results are obtained for the nonlinear equations provided more than two observation points are used this paper is a first step in understanding the conditions for observability of the swe for small numbers of observation points in more physically realistic settings | [['the', 'shallow', 'water', 'equations', 'swe', 'are', 'a', 'widely', 'used', 'model', 'for', 'the', 'propagation', 'of', 'surface', 'waves', 'on', 'the', 'oceans', 'we', 'consider', 'the', 'problem', 'of', 'optimally', 'determining', 'the', 'initial', 'conditions', 'for', 'the', 'onedimensional', 'swe', 'in', 'an', 'unbounded', 'domain', 'from', 'a', 'small', 'set', 'of', 'observations', 'of', 'the', 'sea', 'surface', 'height', 'in', 'the', 'linear', 'case', 'we', 'prove', 'a', 'theorem', 'that', 'gives', 'sufficient', 'conditions', 'for', 'convergence', 'to', 'the', 'true', 'initial', 'conditions', 'at', 'least', 'two', 'observation', 'points', 'must', 'be', 'used', 'and', 'at', 'least', 'one', 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1,802.04394 | M-Walk: Learning to Walk over Graphs using Monte Carlo Tree Search | Learning to walk over a graph towards a target node for a given query and a
source node is an important problem in applications such as knowledge base
completion (KBC). It can be formulated as a reinforcement learning (RL) problem
with a known state transition model. To overcome the challenge of sparse
rewards, we develop a graph-walking agent called M-Walk, which consists of a
deep recurrent neural network (RNN) and Monte Carlo Tree Search (MCTS). The RNN
encodes the state (i.e., history of the walked path) and maps it separately to
a policy and Q-values. In order to effectively train the agent from sparse
rewards, we combine MCTS with the neural policy to generate trajectories
yielding more positive rewards. From these trajectories, the network is
improved in an off-policy manner using Q-learning, which modifies the RNN
policy via parameter sharing. Our proposed RL algorithm repeatedly applies this
policy-improvement step to learn the model. At test time, MCTS is combined with
the neural policy to predict the target node. Experimental results on several
graph-walking benchmarks show that M-Walk is able to learn better policies than
other RL-based methods, which are mainly based on policy gradients. M-Walk also
outperforms traditional KBC baselines.
| cs.AI cs.CL cs.LG | learning to walk over a graph towards a target node for a given query and a source node is an important problem in applications such as knowledge base completion kbc it can be formulated as a reinforcement learning rl problem with a known state transition model to overcome the challenge of sparse rewards we develop a graphwalking agent called mwalk which consists of a deep recurrent neural network rnn and monte carlo tree search mcts the rnn encodes the state ie history of the walked path and maps it separately to a policy and qvalues in order to effectively train the agent from sparse rewards we combine mcts with the neural policy to generate trajectories yielding more positive rewards from these trajectories the network is improved in an offpolicy manner using qlearning which modifies the rnn policy via parameter sharing our proposed rl algorithm repeatedly applies this policyimprovement step to learn the model at test time mcts is combined with the neural policy to predict the target node experimental results on several graphwalking benchmarks show that mwalk is able to learn better policies than other rlbased methods which are mainly based on policy gradients mwalk also outperforms traditional kbc baselines | [['learning', 'to', 'walk', 'over', 'a', 'graph', 'towards', 'a', 'target', 'node', 'for', 'a', 'given', 'query', 'and', 'a', 'source', 'node', 'is', 'an', 'important', 'problem', 'in', 'applications', 'such', 'as', 'knowledge', 'base', 'completion', 'kbc', 'it', 'can', 'be', 'formulated', 'as', 'a', 'reinforcement', 'learning', 'rl', 'problem', 'with', 'a', 'known', 'state', 'transition', 'model', 'to', 'overcome', 'the', 'challenge', 'of', 'sparse', 'rewards', 'we', 'develop', 'a', 'graphwalking', 'agent', 'called', 'mwalk', 'which', 'consists', 'of', 'a', 'deep', 'recurrent', 'neural', 'network', 'rnn', 'and', 'monte', 'carlo', 'tree', 'search', 'mcts', 'the', 'rnn', 'encodes', 'the', 'state', 'ie', 'history', 'of', 'the', 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'on', 'policy', 'gradients', 'mwalk', 'also', 'outperforms', 'traditional', 'kbc', 'baselines']] | [-0.013099161945259775, 0.017117104910433452, -0.10452532027682601, 0.07958855132423436, -0.15358898694140052, -0.21127296730563952, 0.10110841764297884, 0.5068255283108671, -0.32304895925453775, -0.2967980790807662, 0.05979783389407688, -0.263590188791531, -0.1869603785057434, 0.15771434848140073, -0.10997481231112809, 0.0750273946285021, 0.12096402773779562, 0.09590032815148458, -0.017840083994604836, -0.2663953570564228, 0.28257361741802717, 0.06914431165751934, 0.28273215454289224, -0.05640080140497025, 0.1945290487415635, -0.01959367337764293, 0.014075436643389796, -0.014088964622779773, -0.03883230753928017, 0.12979030675670786, 0.341361470514587, 0.21337386357319083, 0.36003327029308085, -0.3994308096985451, -0.22654790523447815, 0.11258216257039269, 0.12138692865167097, 0.12123400113382259, -0.006654045024906228, -0.34433919972584104, 0.08848065463353308, -0.19297449994680996, 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1,802.04395 | Spatially-resolved Fluorescence-detected Two-dimensional Electronic
Spectroscopy Probes Varying Electronic Couplings in Photosynthetic Bacteria | We present a variation of two-dimensional electronic spectroscopy that is
capable of mapping spatially-varying differences in electronic couplings using
a correlated map of excitation and detection frequencies, with sensitivity
orders of magnitude better than conventional spatially-averaged electronic
spectroscopies. The approach performs fluorescence-detection-based fully
collinear two-dimensional electronic spectroscopy in a microscope, combining
femtosecond time-resolution, sub-micron spatial resolution, and the sensitivity
of fluorescence detection. We demonstrate the approach on a mixture of
photosynthetic bacteria that are known to exhibit variations in electronic
structure with growth conditions. Spatial variations in the constitution of
mixed bacterial colonies manifests as spatially-varying peak intensities in the
measured two-dimensional contour maps, which exhibit well-resolved electronic
couplings between excited electronic states of the bacterial proteins.
| physics.chem-ph physics.bio-ph | we present a variation of twodimensional electronic spectroscopy that is capable of mapping spatiallyvarying differences in electronic couplings using a correlated map of excitation and detection frequencies with sensitivity orders of magnitude better than conventional spatiallyaveraged electronic spectroscopies the approach performs fluorescencedetectionbased fully collinear twodimensional electronic spectroscopy in a microscope combining femtosecond timeresolution submicron spatial resolution and the sensitivity of fluorescence detection we demonstrate the approach on a mixture of photosynthetic bacteria that are known to exhibit variations in electronic structure with growth conditions spatial variations in the constitution of mixed bacterial colonies manifests as spatiallyvarying peak intensities in the measured twodimensional contour maps which exhibit wellresolved electronic couplings between excited electronic states of the bacterial proteins | [['we', 'present', 'a', 'variation', 'of', 'twodimensional', 'electronic', 'spectroscopy', 'that', 'is', 'capable', 'of', 'mapping', 'spatiallyvarying', 'differences', 'in', 'electronic', 'couplings', 'using', 'a', 'correlated', 'map', 'of', 'excitation', 'and', 'detection', 'frequencies', 'with', 'sensitivity', 'orders', 'of', 'magnitude', 'better', 'than', 'conventional', 'spatiallyaveraged', 'electronic', 'spectroscopies', 'the', 'approach', 'performs', 'fluorescencedetectionbased', 'fully', 'collinear', 'twodimensional', 'electronic', 'spectroscopy', 'in', 'a', 'microscope', 'combining', 'femtosecond', 'timeresolution', 'submicron', 'spatial', 'resolution', 'and', 'the', 'sensitivity', 'of', 'fluorescence', 'detection', 'we', 'demonstrate', 'the', 'approach', 'on', 'a', 'mixture', 'of', 'photosynthetic', 'bacteria', 'that', 'are', 'known', 'to', 'exhibit', 'variations', 'in', 'electronic', 'structure', 'with', 'growth', 'conditions', 'spatial', 'variations', 'in', 'the', 'constitution', 'of', 'mixed', 'bacterial', 'colonies', 'manifests', 'as', 'spatiallyvarying', 'peak', 'intensities', 'in', 'the', 'measured', 'twodimensional', 'contour', 'maps', 'which', 'exhibit', 'wellresolved', 'electronic', 'couplings', 'between', 'excited', 'electronic', 'states', 'of', 'the', 'bacterial', 'proteins']] | [-0.10850267854101699, 0.1513199869152921, -0.062648748336681, 0.040074419816313635, 0.005577413410405982, -0.10964301035032961, 0.011479871712849829, 0.45196685236316686, -0.25421987300546006, -0.30681555013268674, -0.009547836621942255, -0.3217071682407424, -0.17937132436782122, 0.1861727879832274, 0.02765018161354137, 0.0331333217277304, 0.03514872289238626, -0.09384178108905025, -0.0569184831753052, -0.12729456353479002, 0.24164279452364507, 0.03944467626319363, 0.33117005090100754, 0.046812665721016194, 0.13209975841515376, -0.010695310381787091, 0.03125261963778657, -0.006827657929910668, -0.11880520862541744, 0.1511321607309771, 0.2680298945894625, -0.012716835663766312, 0.1825417187887822, -0.44400050984053263, -0.24946440631977346, 0.03639800700555748, 0.15232486108012883, 0.11375357622806594, -0.042026216313764776, -0.28425400156591984, -0.0036625021944175764, -0.08426293466543248, -0.1217599037155541, -0.15210704921327275, -0.04503866992007684, 0.04810192633835711, -0.22704561131631007, 0.16480456380944314, -0.030813754102664775, 0.15887085796365963, -0.11975546435509198, -0.07674728325118535, -0.06460551052065246, 0.09467928639837894, -0.03490766859814075, -0.01916330303306338, 0.21462094821919012, -0.1354532814905818, -0.12558701831108793, 0.3727780261020786, -0.11949228162835614, -0.12501623006417126, 0.2032796896207307, -0.2041143271510072, -0.11787728504940129, 0.19794201226262698, 0.1299820184137608, 0.12332848867337251, -0.1396855001728219, 0.02095144530577617, 0.01144781432532031, 0.2503171447174752, 0.10470140440156683, 0.15141358367038954, 0.18600548782931833, 0.2331398020825622, 0.049094503725365064, 0.13526817181464768, -0.21069089313447154, -0.03905553968848083, -0.14615018410479713, -0.10973517408048541, -0.1746362381743203, 0.06854407483278292, -0.06302684350797289, -0.18964945167656347, 0.459887974172959, 0.12267370918637206, 0.18309795066858803, -0.019871725298359927, 0.29098206270357657, 0.05860666532354462, 0.08874087173547916, -0.044051465874433454, 0.2054353953531847, 0.13911580936059934, 0.08100761945664497, -0.3130579468159904, 0.07670925800078388, -0.03744799407712858] |
1,802.04396 | Proximity-induced topological phases in bilayer graphene | We study the band structure of phases induced by depositing bilayer graphene
on a transition metal dichalcogenide monolayer. Tight-binding and low-energy
effective Hamiltonian calculations show that it is possible to induce
topologically nontrivial phases that should exhibit spin Hall effect in these
systems. We classify bulk insulating phases through calculation of the Z$_2$
invariant, which unequivocally identifies the topology of the structure. The
study of these and similar hybrid systems under applied gate voltage opens the
possibility for tunable topological structures in real experimental systems.
| cond-mat.mes-hall | we study the band structure of phases induced by depositing bilayer graphene on a transition metal dichalcogenide monolayer tightbinding and lowenergy effective hamiltonian calculations show that it is possible to induce topologically nontrivial phases that should exhibit spin hall effect in these systems we classify bulk insulating phases through calculation of the z_2 invariant which unequivocally identifies the topology of the structure the study of these and similar hybrid systems under applied gate voltage opens the possibility for tunable topological structures in real experimental systems | [['we', 'study', 'the', 'band', 'structure', 'of', 'phases', 'induced', 'by', 'depositing', 'bilayer', 'graphene', 'on', 'a', 'transition', 'metal', 'dichalcogenide', 'monolayer', 'tightbinding', 'and', 'lowenergy', 'effective', 'hamiltonian', 'calculations', 'show', 'that', 'it', 'is', 'possible', 'to', 'induce', 'topologically', 'nontrivial', 'phases', 'that', 'should', 'exhibit', 'spin', 'hall', 'effect', 'in', 'these', 'systems', 'we', 'classify', 'bulk', 'insulating', 'phases', 'through', 'calculation', 'of', 'the', 'z_2', 'invariant', 'which', 'unequivocally', 'identifies', 'the', 'topology', 'of', 'the', 'structure', 'the', 'study', 'of', 'these', 'and', 'similar', 'hybrid', 'systems', 'under', 'applied', 'gate', 'voltage', 'opens', 'the', 'possibility', 'for', 'tunable', 'topological', 'structures', 'in', 'real', 'experimental', 'systems']] | [-0.2492387936121839, 0.2173182395999046, -0.05709151384585044, 0.01434674400541767, -0.011658459726501914, -0.17961773313034107, 0.10443400016383213, 0.4300253446706954, -0.267481347107712, -0.294290945941911, -0.02573594373979551, -0.2688080256476122, -0.2593391711371677, 0.17706707098576077, 0.05922012777232072, 0.035873844868996564, -0.024890071553561616, -0.11952139948878218, -0.15157837266828317, -0.17252198147094425, 0.31826473151826684, -0.041971702917533764, 0.3279932215809822, 0.07347729549236486, 0.007845148203127525, -0.021465390674112475, 0.12787361885420978, 0.042698957962805734, -0.17597689698261948, 0.03726060284192071, 0.2434645569718936, -0.1434558122939266, 0.1003533228957916, -0.4596339290413786, -0.21646295336677748, -0.0010575863998383284, 0.090514479259796, 0.14924370720845592, -0.10195455815889598, -0.33313050375265235, 0.11713694062303094, -0.18626488454859047, -0.10522701999391704, -0.17205356273471434, -0.0140735656625646, -0.08466306516681524, -0.1794929576695294, 0.049295042078216174, 0.041596900123883694, 0.053177806175474816, -0.09239742067532943, -0.07920632938690045, -0.13574416412807563, 0.08669112655094496, 0.013183878501877189, -0.0012426313697634374, 0.18285848410142697, -0.09608179820044076, -0.1606951234423939, 0.39822828969096435, -0.03760553389404188, -0.11247362189871424, 0.1979907819234273, -0.16476632206313832, -0.11198860771734925, 0.15627156526726835, 0.12492570800104123, 0.0892474656815038, -0.11933673388078152, 0.11547575355644393, -0.036545239881995845, 0.15372968705864076, -0.008697278087493033, 0.10123811424435938, 0.29109332100214325, 0.1943920163109022, 0.06913741431472932, 0.16444920377586694, -0.06663914581295102, -0.055455811185460974, -0.21939298718729439, -0.19462551461423144, -0.21610581405899104, 0.10373693778272033, -0.028443359134047677, -0.23664661413058638, 0.49503786815878226, 0.16718836332643952, 0.1523579160176108, -0.08203189138131325, 0.22355242032448158, 0.09190170268582946, 0.0874575171400519, -0.01486363688702969, 0.23828576214830666, 0.15636426456108252, 0.03270128517795135, -0.2710401126210961, 0.04894512043991948, 0.029730991120724116] |
1,802.04397 | Identifiability of Nonparametric Mixture Models and Bayes Optimal
Clustering | Motivated by problems in data clustering, we establish general conditions
under which families of nonparametric mixture models are identifiable, by
introducing a novel framework involving clustering overfitted \emph{parametric}
(i.e. misspecified) mixture models. These identifiability conditions generalize
existing conditions in the literature, and are flexible enough to include for
example mixtures of Gaussian mixtures. In contrast to the recent literature on
estimating nonparametric mixtures, we allow for general nonparametric mixture
components, and instead impose regularity assumptions on the underlying mixing
measure. As our primary application, we apply these results to partition-based
clustering, generalizing the notion of a Bayes optimal partition from classical
parametric model-based clustering to nonparametric settings. Furthermore, this
framework is constructive so that it yields a practical algorithm for learning
identified mixtures, which is illustrated through several examples on real
data. The key conceptual device in the analysis is the convex, metric geometry
of probability measures on metric spaces and its connection to the Wasserstein
convergence of mixing measures. The result is a flexible framework for
nonparametric clustering with formal consistency guarantees.
| math.ST cs.AI cs.LG stat.ML stat.TH | motivated by problems in data clustering we establish general conditions under which families of nonparametric mixture models are identifiable by introducing a novel framework involving clustering overfitted emphparametric ie misspecified mixture models these identifiability conditions generalize existing conditions in the literature and are flexible enough to include for example mixtures of gaussian mixtures in contrast to the recent literature on estimating nonparametric mixtures we allow for general nonparametric mixture components and instead impose regularity assumptions on the underlying mixing measure as our primary application we apply these results to partitionbased clustering generalizing the notion of a bayes optimal partition from classical parametric modelbased clustering to nonparametric settings furthermore this framework is constructive so that it yields a practical algorithm for learning identified mixtures which is illustrated through several examples on real data the key conceptual device in the analysis is the convex metric geometry of probability measures on metric spaces and its connection to the wasserstein convergence of mixing measures the result is a flexible framework for nonparametric clustering with formal consistency guarantees | [['motivated', 'by', 'problems', 'in', 'data', 'clustering', 'we', 'establish', 'general', 'conditions', 'under', 'which', 'families', 'of', 'nonparametric', 'mixture', 'models', 'are', 'identifiable', 'by', 'introducing', 'a', 'novel', 'framework', 'involving', 'clustering', 'overfitted', 'emphparametric', 'ie', 'misspecified', 'mixture', 'models', 'these', 'identifiability', 'conditions', 'generalize', 'existing', 'conditions', 'in', 'the', 'literature', 'and', 'are', 'flexible', 'enough', 'to', 'include', 'for', 'example', 'mixtures', 'of', 'gaussian', 'mixtures', 'in', 'contrast', 'to', 'the', 'recent', 'literature', 'on', 'estimating', 'nonparametric', 'mixtures', 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1,802.04398 | $\beta$-delayed fission in $r$-process nucleosynthesis | We present $\beta$-delayed neutron emission and $\beta$-delayed fission
calculations for heavy, neutron-rich nuclei using the coupled Quasi-Particle
Random Phase Approximation plus Hauser-Feshbach (QRPA+HF) approach. From the
initial population of a compound nucleus after $\beta$-decay, we follow the
statistical decay taking into account competition between neutrons,
$\gamma$-rays, and fission. We find a region of the chart of nuclides where the
probability of $\beta$-delayed fission is $\sim100$%, that likely prevents the
production of superheavy elements in nature. For a subset of nuclei near the
neutron dripline, neutron multiplicity and the probability of fission are both
large, leading to the intriguing possibility of multi-chance $\beta$-delayed
fission, a new decay mode for extremely neutron-rich heavy nuclei. In this new
decay mode, $\beta$-decay can be followed by multiple neutron emission leading
to subsequent daughter generations which each have a probability to fission. We
explore the impact of $\beta$-delayed fission in rapid neutron capture process
($r$-process) nucleosynthesis in the tidal ejecta of a neutron star--neutron
star merger and show that it is a key fission channel that shapes the final
abundances near the second $r$-process peak.
| nucl-th astro-ph.SR | we present betadelayed neutron emission and betadelayed fission calculations for heavy neutronrich nuclei using the coupled quasiparticle random phase approximation plus hauserfeshbach qrpahf approach from the initial population of a compound nucleus after betadecay we follow the statistical decay taking into account competition between neutrons gammarays and fission we find a region of the chart of nuclides where the probability of betadelayed fission is sim100 that likely prevents the production of superheavy elements in nature for a subset of nuclei near the neutron dripline neutron multiplicity and the probability of fission are both large leading to the intriguing possibility of multichance betadelayed fission a new decay mode for extremely neutronrich heavy nuclei in this new decay mode betadecay can be followed by multiple neutron emission leading to subsequent daughter generations which each have a probability to fission we explore the impact of betadelayed fission in rapid neutron capture process rprocess nucleosynthesis in the tidal ejecta of a neutron starneutron star merger and show that it is a key fission channel that shapes the final abundances near the second rprocess peak | [['we', 'present', 'betadelayed', 'neutron', 'emission', 'and', 'betadelayed', 'fission', 'calculations', 'for', 'heavy', 'neutronrich', 'nuclei', 'using', 'the', 'coupled', 'quasiparticle', 'random', 'phase', 'approximation', 'plus', 'hauserfeshbach', 'qrpahf', 'approach', 'from', 'the', 'initial', 'population', 'of', 'a', 'compound', 'nucleus', 'after', 'betadecay', 'we', 'follow', 'the', 'statistical', 'decay', 'taking', 'into', 'account', 'competition', 'between', 'neutrons', 'gammarays', 'and', 'fission', 'we', 'find', 'a', 'region', 'of', 'the', 'chart', 'of', 'nuclides', 'where', 'the', 'probability', 'of', 'betadelayed', 'fission', 'is', 'sim100', 'that', 'likely', 'prevents', 'the', 'production', 'of', 'superheavy', 'elements', 'in', 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1,802.04399 | Robust multifrequency imaging with MUSIC | In this paper, we study the MUltiple SIgnal Classification (MUSIC) algorithm
often used to image small targets when multiple measurement vectors are
available. We show that this algorithm may be used when the imaging problem can
be cast as a linear system that admits a special factorization. We discuss
several active array imaging configurations where this factorization is exact,
as well as other configurations where the factorization only holds
approximately and, hence, the results provided by MUSIC deteriorate. We give
special attention to the most general setting where an active array with an
arbitrary number of transmitters and receivers uses signals of multiple
frequencies to image the targets. This setting provides all the possible
diversity of information that can be obtained from the illuminations. We give a
theorem that shows that MUSIC is robust with respect to additive noise provided
that the targets are well separated. The theorem also shows the relevance of
using appropriate sets of controlled parameters, such as excitations, to form
the images with MUSIC robustly. We present numerical experiments that support
our theoretical results.
| math.NA math.AP math.OC | in this paper we study the multiple signal classification music algorithm often used to image small targets when multiple measurement vectors are available we show that this algorithm may be used when the imaging problem can be cast as a linear system that admits a special factorization we discuss several active array imaging configurations where this factorization is exact as well as other configurations where the factorization only holds approximately and hence the results provided by music deteriorate we give special attention to the most general setting where an active array with an arbitrary number of transmitters and receivers uses signals of multiple frequencies to image the targets this setting provides all the possible diversity of information that can be obtained from the illuminations we give a theorem that shows that music is robust with respect to additive noise provided that the targets are well separated the theorem also shows the relevance of using appropriate sets of controlled parameters such as excitations to form the images with music robustly we present numerical experiments that support our theoretical results | [['in', 'this', 'paper', 'we', 'study', 'the', 'multiple', 'signal', 'classification', 'music', 'algorithm', 'often', 'used', 'to', 'image', 'small', 'targets', 'when', 'multiple', 'measurement', 'vectors', 'are', 'available', 'we', 'show', 'that', 'this', 'algorithm', 'may', 'be', 'used', 'when', 'the', 'imaging', 'problem', 'can', 'be', 'cast', 'as', 'a', 'linear', 'system', 'that', 'admits', 'a', 'special', 'factorization', 'we', 'discuss', 'several', 'active', 'array', 'imaging', 'configurations', 'where', 'this', 'factorization', 'is', 'exact', 'as', 'well', 'as', 'other', 'configurations', 'where', 'the', 'factorization', 'only', 'holds', 'approximately', 'and', 'hence', 'the', 'results', 'provided', 'by', 'music', 'deteriorate', 'we', 'give', 'special', 'attention', 'to', 'the', 'most', 'general', 'setting', 'where', 'an', 'active', 'array', 'with', 'an', 'arbitrary', 'number', 'of', 'transmitters', 'and', 'receivers', 'uses', 'signals', 'of', 'multiple', 'frequencies', 'to', 'image', 'the', 'targets', 'this', 'setting', 'provides', 'all', 'the', 'possible', 'diversity', 'of', 'information', 'that', 'can', 'be', 'obtained', 'from', 'the', 'illuminations', 'we', 'give', 'a', 'theorem', 'that', 'shows', 'that', 'music', 'is', 'robust', 'with', 'respect', 'to', 'additive', 'noise', 'provided', 'that', 'the', 'targets', 'are', 'well', 'separated', 'the', 'theorem', 'also', 'shows', 'the', 'relevance', 'of', 'using', 'appropriate', 'sets', 'of', 'controlled', 'parameters', 'such', 'as', 'excitations', 'to', 'form', 'the', 'images', 'with', 'music', 'robustly', 'we', 'present', 'numerical', 'experiments', 'that', 'support', 'our', 'theoretical', 'results']] | [-0.10444192928844132, 0.06886134510797716, -0.08037270401248688, 0.06174910699247561, -0.08932052790239621, -0.1500027524938498, 0.007966751171919528, 0.41192622265119233, -0.27425799860286765, -0.2964850229677467, 0.14519545474296353, -0.2562638891813753, -0.19092506364647174, 0.22064931738828675, -0.07777803057668668, 0.039088276343739306, 0.10032348078806372, 0.041636271395699614, -0.027582828628142583, -0.24947503487464417, 0.3023908190596639, 0.05524391508033353, 0.262628958408794, 0.016455674899762934, 0.11021646661317666, 0.034553154774554326, -0.02371763156496742, 0.04623895754587701, -0.07031271368567799, 0.09658953199114682, 0.3438275746852055, 0.17575254215953057, 0.24388088968587684, -0.399193161993884, -0.19451684327900745, 0.10308101336377558, 0.14870478632320974, 0.13386671756017493, -0.07563377727409092, -0.28691682690398745, 0.12220156914031322, -0.13634654595464302, -0.0736063821398224, -0.09930111027814531, -0.04215349553589803, 0.04805683549572033, -0.32596587718809755, 0.03172102730621171, 0.07281888492753352, 0.01639180105137691, -0.07305918615102099, -0.13865139888039674, 0.04739078681973552, 0.16936592788996666, 0.05735091156891307, 0.028850752190592584, 0.09512991924100461, -0.10465525663353167, -0.11771131928727617, 0.3811246269340679, -0.0669908419989175, -0.23133146419618908, 0.19157916450049936, -0.10979035934119412, -0.13188586386804876, 0.10919198437902575, 0.172789117600311, 0.12355848463001044, -0.12341627341860596, 0.023895330467454963, -0.1047576924555757, 0.2087980691981868, 0.06118856255734193, 0.06611935344813757, 0.15595664271530224, 0.1570388235575572, 0.06890154494694696, 0.17711929883644392, -0.10984079500534728, -0.03622026227740309, -0.2758008089966894, -0.11061558850159282, -0.19090047569846044, 0.018493989479905937, -0.0916503967788905, -0.14133085836551748, 0.3692256342521555, 0.17767825837605475, 0.21519141342082804, 0.06109398315932727, 0.3315510317667523, 0.09517018945319997, 0.056454159431892, 0.05724840818412518, 0.20705811333845192, 0.08679527415386454, 0.060214929026289936, -0.1461301004223714, 0.026245869892345886, 0.002414493282649959] |
1,802.044 | Demolishing prejudices to get to the foundations: a criterion of
demarcation for fundamentality | In this paper, we reject commonly accepted views on fundamentality in
science, either based on bottom-up construction or top-down reduction to
isolate the alleged fundamental entities. We do not introduce any new
scientific methodology, but rather describe the current scientific methodology
and show how it entails an inherent search for foundations of science. This is
achieved by phrasing (minimal sets of) metaphysical assumptions into
falsifiable statements and define as fundamental those that survive empirical
tests. The ones that are falsified are rejected, and the corresponding
philosophical concept is demolished as a prejudice. Furthermore, we show the
application of this criterion in concrete examples of the search for
fundamentality in quantum physics and biophysics.
| physics.hist-ph | in this paper we reject commonly accepted views on fundamentality in science either based on bottomup construction or topdown reduction to isolate the alleged fundamental entities we do not introduce any new scientific methodology but rather describe the current scientific methodology and show how it entails an inherent search for foundations of science this is achieved by phrasing minimal sets of metaphysical assumptions into falsifiable statements and define as fundamental those that survive empirical tests the ones that are falsified are rejected and the corresponding philosophical concept is demolished as a prejudice furthermore we show the application of this criterion in concrete examples of the search for fundamentality in quantum physics and biophysics | [['in', 'this', 'paper', 'we', 'reject', 'commonly', 'accepted', 'views', 'on', 'fundamentality', 'in', 'science', 'either', 'based', 'on', 'bottomup', 'construction', 'or', 'topdown', 'reduction', 'to', 'isolate', 'the', 'alleged', 'fundamental', 'entities', 'we', 'do', 'not', 'introduce', 'any', 'new', 'scientific', 'methodology', 'but', 'rather', 'describe', 'the', 'current', 'scientific', 'methodology', 'and', 'show', 'how', 'it', 'entails', 'an', 'inherent', 'search', 'for', 'foundations', 'of', 'science', 'this', 'is', 'achieved', 'by', 'phrasing', 'minimal', 'sets', 'of', 'metaphysical', 'assumptions', 'into', 'falsifiable', 'statements', 'and', 'define', 'as', 'fundamental', 'those', 'that', 'survive', 'empirical', 'tests', 'the', 'ones', 'that', 'are', 'falsified', 'are', 'rejected', 'and', 'the', 'corresponding', 'philosophical', 'concept', 'is', 'demolished', 'as', 'a', 'prejudice', 'furthermore', 'we', 'show', 'the', 'application', 'of', 'this', 'criterion', 'in', 'concrete', 'examples', 'of', 'the', 'search', 'for', 'fundamentality', 'in', 'quantum', 'physics', 'and', 'biophysics']] | [-0.03578473864347741, 0.0893817399130837, -0.10586031570398648, 0.12794577294906567, -0.1422955221163907, -0.1379589690934933, 0.07243788190680789, 0.334851274495512, -0.24977909519137548, -0.3287614459092895, 0.09473426564363763, -0.23608233388845942, -0.18271885038328556, 0.20720890919723647, -0.14063287987041154, 0.013009046553634107, 0.04750228947211456, 0.017520366953769035, -0.029844758739013093, -0.24480758787623408, 0.31814384694111403, 0.039257531469047535, 0.3056697423536597, 0.06340833089364294, 0.0567957924960995, -0.0012624816944090916, -0.08997577450437737, 0.011749474302632734, -0.14229805577594692, 0.14801470354931162, 0.2898084332700819, 0.26387339485310285, 0.31565879057494123, -0.4425410941309695, -0.19202970257460297, 0.10168970905111305, 0.08793905723307814, 0.10483251912644066, -0.046322268284192045, -0.28520318475784734, 0.0813811192555087, -0.14043402120504261, -0.11646898964993722, -0.12164079808384745, -0.010127243951761298, -0.036664791022693474, -0.1754851281709437, 0.03423791261489636, 0.10858807300350495, 0.09674696871661581, -0.013299811259327856, -0.11050406401877158, 0.042664347910821174, 0.08743672181611016, 0.036797125794691965, -0.004974898761637243, 0.12334773657078456, -0.12589233585874485, -0.20670234112601196, 0.41810822744654225, 0.010654269469731454, -0.21113599875908612, 0.2255479030531465, -0.07862158134854066, -0.18820155929798993, 0.017655253518439298, 0.14510456408191072, 0.07343139322931945, -0.15674764449275763, 0.08571064484671556, -0.03621719099464826, 0.15363236296356522, 0.07570220393452994, 0.02517324025601348, 0.2526715618046832, 0.16084739394968242, 0.026812082865425118, 0.09410068439943384, 0.0019914720427810346, -0.10614681702905468, -0.3685391985345632, -0.17331392115348926, -0.16584094874685565, 0.05486600091236531, -0.008702300837675076, -0.18035848730089907, 0.33816316384348155, 0.2517327770141752, 0.12509693156295856, 0.03129454946715019, 0.29790576527219464, 0.05757983367116789, 0.056499355451835855, 0.022751573464899724, 0.2560369971075228, 0.047549548020469956, 0.0671311066585726, -0.10361819225363433, 0.10533330454823694, 0.03530330203024538] |
1,802.04401 | On gravity's role in the genesis of rest masses of classical fields | It is shown that in the Einstein-conformally coupled Higgs--Maxwell system
with Friedman-Robertson-Walker symmetries the energy density of the Higgs field
has stable local minimum only if the mean curvature of the $t={\rm const}$
hypersurfaces is less than a finite critical value $\chi_c$, while for greater
mean curvature the energy density is not bounded from below. Therefore, there
are extreme gravitational situations in which even quasi-locally defined
instantaneous vacuum states of the Higgs sector cannot exist, and hence one
cannot at all define the rest mass of all the classical fields. On
hypersurfaces with mean curvature less than $\chi_c$ the energy density has the
`wine bottle' (rather than the familiar `Mexican hat') shape, and the gauge
field can get rest mass via the Brout--Englert--Higgs mechanism. The spacelike
hypersurface with the critical mean curvature represents the moment of
`genesis' of rest masses.
| gr-qc hep-th | it is shown that in the einsteinconformally coupled higgsmaxwell system with friedmanrobertsonwalker symmetries the energy density of the higgs field has stable local minimum only if the mean curvature of the trm const hypersurfaces is less than a finite critical value chi_c while for greater mean curvature the energy density is not bounded from below therefore there are extreme gravitational situations in which even quasilocally defined instantaneous vacuum states of the higgs sector cannot exist and hence one cannot at all define the rest mass of all the classical fields on hypersurfaces with mean curvature less than chi_c the energy density has the wine bottle rather than the familiar mexican hat shape and the gauge field can get rest mass via the broutenglerthiggs mechanism the spacelike hypersurface with the critical mean curvature represents the moment of genesis of rest masses | [['it', 'is', 'shown', 'that', 'in', 'the', 'einsteinconformally', 'coupled', 'higgsmaxwell', 'system', 'with', 'friedmanrobertsonwalker', 'symmetries', 'the', 'energy', 'density', 'of', 'the', 'higgs', 'field', 'has', 'stable', 'local', 'minimum', 'only', 'if', 'the', 'mean', 'curvature', 'of', 'the', 'trm', 'const', 'hypersurfaces', 'is', 'less', 'than', 'a', 'finite', 'critical', 'value', 'chi_c', 'while', 'for', 'greater', 'mean', 'curvature', 'the', 'energy', 'density', 'is', 'not', 'bounded', 'from', 'below', 'therefore', 'there', 'are', 'extreme', 'gravitational', 'situations', 'in', 'which', 'even', 'quasilocally', 'defined', 'instantaneous', 'vacuum', 'states', 'of', 'the', 'higgs', 'sector', 'can', 'not', 'exist', 'and', 'hence', 'one', 'can', 'not', 'at', 'all', 'define', 'the', 'rest', 'mass', 'of', 'all', 'the', 'classical', 'fields', 'on', 'hypersurfaces', 'with', 'mean', 'curvature', 'less', 'than', 'chi_c', 'the', 'energy', 'density', 'has', 'the', 'wine', 'bottle', 'rather', 'than', 'the', 'familiar', 'mexican', 'hat', 'shape', 'and', 'the', 'gauge', 'field', 'can', 'get', 'rest', 'mass', 'via', 'the', 'broutenglerthiggs', 'mechanism', 'the', 'spacelike', 'hypersurface', 'with', 'the', 'critical', 'mean', 'curvature', 'represents', 'the', 'moment', 'of', 'genesis', 'of', 'rest', 'masses']] | [-0.14298000940904418, 0.23502652125816184, -0.08889451361400016, 0.10550488619355101, -0.05630754186699154, -0.15065955281825671, -0.055500858662739155, 0.3150357902297711, -0.1975532491354549, -0.27259336011682417, 0.07996980799279499, -0.2875477832458015, -0.04531231559548818, 0.14961407487842102, -0.035542525123170715, 0.012748246929931937, -0.007993787224899898, 0.1629017450308414, -0.08324843409334158, -0.2287213498237708, 0.35002232092624225, 0.03981501139462628, 0.24852259712729682, 0.0839977587923302, 0.08101674021347195, -0.00921899743100747, 0.06588482329991797, 0.02374883963898194, -0.15012540438581723, 0.05007237147544808, 0.15578544226058938, 0.08629960070549121, 0.24846951486972155, -0.36253886619675246, -0.2220706709838928, 0.20949905269766056, 0.1356568840953211, 0.06995447523023351, -0.0034730974312356497, -0.25589231386159855, 0.09000759056260717, -0.10459077257682826, -0.1637346599464047, -0.03485628402732471, 0.005390066939161707, -0.036942543838823755, -0.2183440478340267, 0.1574338878148255, 0.00721677669888719, 0.06460769384676683, -0.0732922411444498, -0.1437111654482346, -0.14351527843333728, 0.03926953671876579, 0.10888988574709188, 0.08790017638342283, 0.17372306112979427, -0.16519693667993796, -0.03998795388173972, 0.3939876226140252, -0.08898108715597286, -0.21594271068923598, 0.11966663674312703, -0.20564795916087608, -0.036810612005465634, 0.1599030550833501, 0.11620834866763217, 0.11978469275479726, -0.10523111823655612, 0.13772349625558375, 0.009388552140609645, 0.14951979374869706, 0.09056121280998097, 0.013484823010970858, 0.24096137262444864, 0.07678862919713905, 0.11609659769988441, 0.05024974175946827, -0.055795501899436536, -0.1303860814086036, -0.369967702373281, -0.16752976914603554, -0.176995260782989, 0.09412324106754613, -0.11638065153078163, -0.16358935201589303, 0.3879865238208535, 0.06527111309222176, 0.18705422787246764, 0.06815382850220995, 0.2807998544850687, 0.11956260091598733, 0.109153984421666, 0.14291568327998966, 0.31515344128290074, 0.1410664432923184, 0.09702003703630985, -0.18459815820736863, 0.01416348821833624, 0.05293399472223473] |
1,802.04402 | Recurrent Slice Networks for 3D Segmentation of Point Clouds | Point clouds are an efficient data format for 3D data. However, existing 3D
segmentation methods for point clouds either do not model local dependencies
\cite{pointnet} or require added computations \cite{kd-net,pointnet2}. This
work presents a novel 3D segmentation framework, RSNet\footnote{Codes are
released here https://github.com/qianguih/RSNet}, to efficiently model local
structures in point clouds. The key component of the RSNet is a lightweight
local dependency module. It is a combination of a novel slice pooling layer,
Recurrent Neural Network (RNN) layers, and a slice unpooling layer. The slice
pooling layer is designed to project features of unordered points onto an
ordered sequence of feature vectors so that traditional end-to-end learning
algorithms (RNNs) can be applied. The performance of RSNet is validated by
comprehensive experiments on the S3DIS\cite{stanford}, ScanNet\cite{scannet},
and ShapeNet \cite{shapenet} datasets. In its simplest form, RSNets surpass all
previous state-of-the-art methods on these benchmarks. And comparisons against
previous state-of-the-art methods \cite{pointnet, pointnet2} demonstrate the
efficiency of RSNets.
| cs.CV | point clouds are an efficient data format for 3d data however existing 3d segmentation methods for point clouds either do not model local dependencies citepointnet or require added computations citekdnetpointnet2 this work presents a novel 3d segmentation framework rsnetfootnotecodes are released here httpsgithubcomqianguihrsnet to efficiently model local structures in point clouds the key component of the rsnet is a lightweight local dependency module it is a combination of a novel slice pooling layer recurrent neural network rnn layers and a slice unpooling layer the slice pooling layer is designed to project features of unordered points onto an ordered sequence of feature vectors so that traditional endtoend learning algorithms rnns can be applied the performance of rsnet is validated by comprehensive experiments on the s3discitestanford scannetcitescannet and shapenet citeshapenet datasets in its simplest form rsnets surpass all previous stateoftheart methods on these benchmarks and comparisons against previous stateoftheart methods citepointnet pointnet2 demonstrate the efficiency of rsnets | [['point', 'clouds', 'are', 'an', 'efficient', 'data', 'format', 'for', '3d', 'data', 'however', 'existing', '3d', 'segmentation', 'methods', 'for', 'point', 'clouds', 'either', 'do', 'not', 'model', 'local', 'dependencies', 'citepointnet', 'or', 'require', 'added', 'computations', 'citekdnetpointnet2', 'this', 'work', 'presents', 'a', 'novel', '3d', 'segmentation', 'framework', 'rsnetfootnotecodes', 'are', 'released', 'here', 'httpsgithubcomqianguihrsnet', 'to', 'efficiently', 'model', 'local', 'structures', 'in', 'point', 'clouds', 'the', 'key', 'component', 'of', 'the', 'rsnet', 'is', 'a', 'lightweight', 'local', 'dependency', 'module', 'it', 'is', 'a', 'combination', 'of', 'a', 'novel', 'slice', 'pooling', 'layer', 'recurrent', 'neural', 'network', 'rnn', 'layers', 'and', 'a', 'slice', 'unpooling', 'layer', 'the', 'slice', 'pooling', 'layer', 'is', 'designed', 'to', 'project', 'features', 'of', 'unordered', 'points', 'onto', 'an', 'ordered', 'sequence', 'of', 'feature', 'vectors', 'so', 'that', 'traditional', 'endtoend', 'learning', 'algorithms', 'rnns', 'can', 'be', 'applied', 'the', 'performance', 'of', 'rsnet', 'is', 'validated', 'by', 'comprehensive', 'experiments', 'on', 'the', 's3discitestanford', 'scannetcitescannet', 'and', 'shapenet', 'citeshapenet', 'datasets', 'in', 'its', 'simplest', 'form', 'rsnets', 'surpass', 'all', 'previous', 'stateoftheart', 'methods', 'on', 'these', 'benchmarks', 'and', 'comparisons', 'against', 'previous', 'stateoftheart', 'methods', 'citepointnet', 'pointnet2', 'demonstrate', 'the', 'efficiency', 'of', 'rsnets']] | [-0.07476571997058656, -0.03443334655796053, -0.05838757355715489, 0.04939110299431339, -0.08322233874941061, -0.15771931185900287, 0.025613444165366008, 0.475105705106972, -0.2992231883862856, -0.2887902051918733, 0.07779511969379137, -0.2806640512434582, -0.15114543530770289, 0.1826377221814383, -0.08390636554866715, 0.10256289091972884, 0.15792467031778742, -0.00011834870158156879, -0.07859412585954922, -0.27670748271441586, 0.3132075497513296, 0.06778358490678521, 0.3686131618270965, -0.02280822498428727, 0.11661984118416538, -0.07982126568627715, -0.03162232631469854, -0.00555046766579912, -0.05705818294861697, 0.16734372672065764, 0.2617077335295543, 0.1567218992576285, 0.27178001965881443, -0.48167783662047187, -0.2672230761931081, 0.039767029357444444, 0.16617866361539968, 0.08734737077662738, -0.03220419993978256, -0.2954617492120031, 0.11532116892196657, -0.1662868245752384, 0.018075016717439597, -0.1586279618261959, -0.05024790158618281, -0.004384754941319968, -0.30381174129106, 0.02497502908759921, 0.096699952346299, 0.07162553737182695, -0.05270828317742611, -0.11414787754691927, -0.006034388079006277, 0.1592133065183896, -0.045857922997193254, 0.09642439858961693, 0.16443916859800442, -0.18541625889696994, -0.14457610551215394, 0.3421674496035131, -0.05802655415150376, -0.22953737721348863, 0.20603158558279433, 0.00730419925964949, -0.1427381197580884, 0.12077983451523887, 0.19260393323140665, 0.16656043421162267, -0.14329920813415042, 0.03995622776024057, -0.07089453866675785, 0.20494150321805213, 0.007169977799517063, -0.03246561251908646, 0.21560534690736308, 0.25737773675248554, 0.04634259652279147, 0.131226690176999, -0.1668718633550206, -0.08507893241705819, -0.20909586434029567, -0.12865384440528047, -0.1930674650455484, -0.06931841177166961, -0.09295781510588992, -0.17811386720736472, 0.3988195461414518, 0.2128399884767673, 0.22110394557649402, 0.07759092360223369, 0.40610998121260755, 0.0061006395139692115, 0.1701722093365929, 0.1124731348040448, 0.17199101144428247, 0.0021542094904951127, 0.10040062394412891, -0.09231197008941795, 0.08948330092899712, 0.11051864437693336] |
1,802.04403 | TVAE: Triplet-Based Variational Autoencoder using Metric Learning | Deep metric learning has been demonstrated to be highly effective in learning
semantic representation and encoding information that can be used to measure
data similarity, by relying on the embedding learned from metric learning. At
the same time, variational autoencoder (VAE) has widely been used to
approximate inference and proved to have a good performance for directed
probabilistic models. However, for traditional VAE, the data label or feature
information are intractable. Similarly, traditional representation learning
approaches fail to represent many salient aspects of the data. In this project,
we propose a novel integrated framework to learn latent embedding in VAE by
incorporating deep metric learning. The features are learned by optimizing a
triplet loss on the mean vectors of VAE in conjunction with standard evidence
lower bound (ELBO) of VAE. This approach, which we call Triplet based
Variational Autoencoder (TVAE), allows us to capture more fine-grained
information in the latent embedding. Our model is tested on MNIST data set and
achieves a high triplet accuracy of 95.60% while the traditional VAE (Kingma &
Welling, 2013) achieves triplet accuracy of 75.08%.
| stat.ML cs.AI cs.CV cs.LG | deep metric learning has been demonstrated to be highly effective in learning semantic representation and encoding information that can be used to measure data similarity by relying on the embedding learned from metric learning at the same time variational autoencoder vae has widely been used to approximate inference and proved to have a good performance for directed probabilistic models however for traditional vae the data label or feature information are intractable similarly traditional representation learning approaches fail to represent many salient aspects of the data in this project we propose a novel integrated framework to learn latent embedding in vae by incorporating deep metric learning the features are learned by optimizing a triplet loss on the mean vectors of vae in conjunction with standard evidence lower bound elbo of vae this approach which we call triplet based variational autoencoder tvae allows us to capture more finegrained information in the latent embedding our model is tested on mnist data set and achieves a high triplet accuracy of 9560 while the traditional vae kingma welling 2013 achieves triplet accuracy of 7508 | [['deep', 'metric', 'learning', 'has', 'been', 'demonstrated', 'to', 'be', 'highly', 'effective', 'in', 'learning', 'semantic', 'representation', 'and', 'encoding', 'information', 'that', 'can', 'be', 'used', 'to', 'measure', 'data', 'similarity', 'by', 'relying', 'on', 'the', 'embedding', 'learned', 'from', 'metric', 'learning', 'at', 'the', 'same', 'time', 'variational', 'autoencoder', 'vae', 'has', 'widely', 'been', 'used', 'to', 'approximate', 'inference', 'and', 'proved', 'to', 'have', 'a', 'good', 'performance', 'for', 'directed', 'probabilistic', 'models', 'however', 'for', 'traditional', 'vae', 'the', 'data', 'label', 'or', 'feature', 'information', 'are', 'intractable', 'similarly', 'traditional', 'representation', 'learning', 'approaches', 'fail', 'to', 'represent', 'many', 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1,802.04404 | Topological mechanics of edge waves in Kagome lattices | Topological insulators are new phases of matter whose properties are derived
from a number of qualitative yet robust topological invariants rather than
specific geometric features or constitutive parameters. Here, Kagome lattices
are classified based on a topological invariant directly related to the
handedness of a couple of elliptically polarized stationary eigenmodes in the
context of what is known as the "quantum valley Hall effect" in physics
literature. An interface separating two topologically distinct lattices, i.e.,
two lattices with different topological invariants, is then proven to host two
topological Stoneley waves whose frequencies, shapes and decay and propagation
velocities are quantified. Conversely, an interface separating two
topologically equivalent lattices will host no Stoneley waves. Analysis is
based on an asymptotic model derived through a modified high-frequency
homogenization procedure. This case study constitutes the first implementation
of the quantum valley Hall effect in in-plane elasticity. A preliminary
discussion of 1D lattices is included to provide relevant background on
topological effects in a simple analytical framework.
| cond-mat.mes-hall | topological insulators are new phases of matter whose properties are derived from a number of qualitative yet robust topological invariants rather than specific geometric features or constitutive parameters here kagome lattices are classified based on a topological invariant directly related to the handedness of a couple of elliptically polarized stationary eigenmodes in the context of what is known as the quantum valley hall effect in physics literature an interface separating two topologically distinct lattices ie two lattices with different topological invariants is then proven to host two topological stoneley waves whose frequencies shapes and decay and propagation velocities are quantified conversely an interface separating two topologically equivalent lattices will host no stoneley waves analysis is based on an asymptotic model derived through a modified highfrequency homogenization procedure this case study constitutes the first implementation of the quantum valley hall effect in inplane elasticity a preliminary discussion of 1d lattices is included to provide relevant background on topological effects in a simple analytical framework | [['topological', 'insulators', 'are', 'new', 'phases', 'of', 'matter', 'whose', 'properties', 'are', 'derived', 'from', 'a', 'number', 'of', 'qualitative', 'yet', 'robust', 'topological', 'invariants', 'rather', 'than', 'specific', 'geometric', 'features', 'or', 'constitutive', 'parameters', 'here', 'kagome', 'lattices', 'are', 'classified', 'based', 'on', 'a', 'topological', 'invariant', 'directly', 'related', 'to', 'the', 'handedness', 'of', 'a', 'couple', 'of', 'elliptically', 'polarized', 'stationary', 'eigenmodes', 'in', 'the', 'context', 'of', 'what', 'is', 'known', 'as', 'the', 'quantum', 'valley', 'hall', 'effect', 'in', 'physics', 'literature', 'an', 'interface', 'separating', 'two', 'topologically', 'distinct', 'lattices', 'ie', 'two', 'lattices', 'with', 'different', 'topological', 'invariants', 'is', 'then', 'proven', 'to', 'host', 'two', 'topological', 'stoneley', 'waves', 'whose', 'frequencies', 'shapes', 'and', 'decay', 'and', 'propagation', 'velocities', 'are', 'quantified', 'conversely', 'an', 'interface', 'separating', 'two', 'topologically', 'equivalent', 'lattices', 'will', 'host', 'no', 'stoneley', 'waves', 'analysis', 'is', 'based', 'on', 'an', 'asymptotic', 'model', 'derived', 'through', 'a', 'modified', 'highfrequency', 'homogenization', 'procedure', 'this', 'case', 'study', 'constitutes', 'the', 'first', 'implementation', 'of', 'the', 'quantum', 'valley', 'hall', 'effect', 'in', 'inplane', 'elasticity', 'a', 'preliminary', 'discussion', 'of', '1d', 'lattices', 'is', 'included', 'to', 'provide', 'relevant', 'background', 'on', 'topological', 'effects', 'in', 'a', 'simple', 'analytical', 'framework']] | [-0.18818977656574246, 0.21184689616682678, -0.10594922085464047, 0.06478259073833913, -0.12275916748205942, -0.17671419440717237, -0.004887671456986501, 0.3800443573254932, -0.2694791685189892, -0.24712769387495573, 0.0739792847157416, -0.2728815652155803, -0.18895318905788652, 0.2223481817775909, 0.0072054786599052645, 0.04461583046334013, -0.037875236940507324, -0.03724228284600979, -0.0903114128795001, -0.20976625421181597, 0.2993840265967683, -0.03934668039365963, 0.3232336360704085, 0.028615704954007608, 0.03523592009677615, -0.03252798864943621, -0.005798240062963514, 0.05588267034811019, -0.16800333068426127, 0.09040833383718608, 0.21965944543595145, -0.03322672344847051, 0.16511844186557947, -0.4421346092745205, -0.21154012176136064, 0.025359788326049463, 0.12787920383999318, 0.16266689274711477, -0.05886237647890062, -0.31443250675377166, 0.06140278910780954, -0.1365149822628891, -0.12243215023237138, -0.07350622515616927, 0.0353351407884363, -0.031448039034112966, -0.18387900624264236, 0.059772276799508764, 0.07396820274701604, 0.12209152468148779, -0.05358941053994503, -0.10608579932020669, -0.08689738072432075, 0.08544385655885962, 0.014109816161509992, -0.022347106085518844, 0.10723241667143224, -0.13653086302830536, -0.16653938207238272, 0.40867399538587207, -0.028494857210666656, -0.23346702076574685, 0.20798639784012835, -0.11271895859253013, -0.10074771737588421, 0.13658377137092267, 0.16078562446030006, 0.10127561030897991, -0.13234448017112546, 0.046026507226116044, -0.0539168312637124, 0.1591117073927486, 0.031539231858736166, 0.10214564012737797, 0.29357271536647633, 0.13353415741085253, 0.05297208396843606, 0.18454179715542293, -0.047776201625634555, -0.08422391138152374, -0.30301733303563727, -0.1306017679876528, -0.210458758385692, 0.06159293644224505, -0.04858742678874355, -0.23051148169228317, 0.4322303084919423, 0.10813243135867392, 0.15113051818466625, -0.006112345657168539, 0.2700796180327589, 0.10551953305950262, 0.009634902513660263, 0.035768819494355765, 0.245917275567979, 0.1874170168731476, 0.018709275147311983, -0.20109742197230962, 0.021658816154863387, 0.10153253481614809] |
1,802.04405 | On the Properties of Blue Large-Amplitude Pulsators. No BLAPs in the
Magellanic Clouds | We present the properties of the recently discovered class of variable stars,
Blue Large-Amplitude Pulsators (BLAPs). These extremely rare, short-period
pulsating objects were detected thanks to regular, high-cadence observations of
hundreds of millions of Milky Way stars by the OGLE variability survey. The new
variables closely resemble classical pulsators, Cepheids, and RR Lyrae-type
stars, but at effective temperatures at which pulsations are due to the
presence of iron-group elements. Theory shows that BLAPs are evolved low-mass
stars with a giant-like structure, but their origin remains a mystery. In this
contribution, we report the negative result of a search for BLAPs in the whole
Magellanic System.
| astro-ph.SR | we present the properties of the recently discovered class of variable stars blue largeamplitude pulsators blaps these extremely rare shortperiod pulsating objects were detected thanks to regular highcadence observations of hundreds of millions of milky way stars by the ogle variability survey the new variables closely resemble classical pulsators cepheids and rr lyraetype stars but at effective temperatures at which pulsations are due to the presence of irongroup elements theory shows that blaps are evolved lowmass stars with a giantlike structure but their origin remains a mystery in this contribution we report the negative result of a search for blaps in the whole magellanic system | [['we', 'present', 'the', 'properties', 'of', 'the', 'recently', 'discovered', 'class', 'of', 'variable', 'stars', 'blue', 'largeamplitude', 'pulsators', 'blaps', 'these', 'extremely', 'rare', 'shortperiod', 'pulsating', 'objects', 'were', 'detected', 'thanks', 'to', 'regular', 'highcadence', 'observations', 'of', 'hundreds', 'of', 'millions', 'of', 'milky', 'way', 'stars', 'by', 'the', 'ogle', 'variability', 'survey', 'the', 'new', 'variables', 'closely', 'resemble', 'classical', 'pulsators', 'cepheids', 'and', 'rr', 'lyraetype', 'stars', 'but', 'at', 'effective', 'temperatures', 'at', 'which', 'pulsations', 'are', 'due', 'to', 'the', 'presence', 'of', 'irongroup', 'elements', 'theory', 'shows', 'that', 'blaps', 'are', 'evolved', 'lowmass', 'stars', 'with', 'a', 'giantlike', 'structure', 'but', 'their', 'origin', 'remains', 'a', 'mystery', 'in', 'this', 'contribution', 'we', 'report', 'the', 'negative', 'result', 'of', 'a', 'search', 'for', 'blaps', 'in', 'the', 'whole', 'magellanic', 'system']] | [-0.11190834112820171, 0.1781530998203726, -0.0876397366090844, 0.0939677094275664, -0.18900231522108826, -0.06786824460806591, 0.13782785375314277, 0.3534705931764273, -0.18527936933429112, -0.35305986747677837, 0.040953870018988495, -0.3232698542416897, -0.10953026685331549, 0.22551426942282843, -0.09144810907365311, -0.031055463956935064, 0.16077398749157076, -0.06287348834143597, 0.022545396369172348, -0.2853735028921316, 0.2596723679231391, -0.019262860777477422, 0.09210204714909195, -0.16017125589506967, 0.011314647427449623, -0.1574316435560052, -0.08922691299092202, -0.05622533042693422, -0.08906570240097131, 0.06456756254746801, 0.24097070262013448, 0.10212071681661265, 0.21707797435644482, -0.3132834380692137, -0.21097820403852632, 0.05405940772699458, 0.20851505862637645, 0.04435711219529843, -0.07884801227954172, -0.2738174962944218, 0.10772089552400367, -0.11499549321874622, -0.2517582257854797, -0.023928732992637727, 0.11504408762391125, 0.07952283289993094, -0.16201245068278652, 0.10106335879120576, 0.04708886802019108, 0.1496930872932786, -0.11370390532981782, -0.13196194976723444, 0.00674598440084429, 0.0641522333070281, 0.04221944093172039, 0.04391342263580078, 0.06320894817777332, -0.12580711735146385, -0.032862648678322635, 0.40423490451205346, -0.10553505768600319, 0.04914298497938684, 0.22945005566413915, -0.21822845647111536, -0.25114520432072734, 0.11691041191973324, 0.14753363332932903, 0.22946757874229834, -0.22278008021114926, 0.02131786518009557, 0.01227660331697691, 0.18384735194434013, 0.07069588047452272, 0.15081738559529184, 0.35907213017344475, 0.14622416849514203, -0.035347604913459646, 0.10653992220315905, -0.24463495104351923, -0.0686699835095732, -0.22676069365725632, -0.07603644276747391, -0.07270174448910569, 0.06878890128185351, -0.06184136743776478, -0.21753075976545613, 0.3542105665519124, 0.07141849255089515, 0.16912228090112053, 0.001657137222632411, 0.24498250353194417, 0.05692870647901492, 0.14108087932469235, 0.12991956117607298, 0.33665274616685653, 0.2209245116804682, 0.13534283043727988, -0.23457545033168226, 0.11526698745188436, 0.005643458856225369] |
1,802.04406 | A wide-spectrum language for verification of programs on weak memory
models | Modern processors deploy a variety of weak memory models, which for
efficiency reasons may (appear to) execute instructions in an order different
to that specified by the program text. The consequences of instruction
reordering can be complex and subtle, and can impact on ensuring correctness.
Previous work on the semantics of weak memory models has focussed on the
behaviour of assembler-level programs. In this paper we utilise that work to
extract some general principles underlying instruction reordering, and apply
those principles to a wide-spectrum language encompassing abstract data types
as well as low-level assembler code. The goal is to support reasoning about
implementations of data structures for modern processors with respect to an
abstract specification.
Specifically, we define an operational semantics, from which we derive some
properties of program refinement, and encode the semantics in the rewriting
engine Maude as a model-checking tool. The tool is used to validate the
semantics against the behaviour of a set of litmus tests (small assembler
programs) run on hardware, and also to model check implementations of data
structures from the literature against their abstract specifications.
| cs.PL cs.LO | modern processors deploy a variety of weak memory models which for efficiency reasons may appear to execute instructions in an order different to that specified by the program text the consequences of instruction reordering can be complex and subtle and can impact on ensuring correctness previous work on the semantics of weak memory models has focussed on the behaviour of assemblerlevel programs in this paper we utilise that work to extract some general principles underlying instruction reordering and apply those principles to a widespectrum language encompassing abstract data types as well as lowlevel assembler code the goal is to support reasoning about implementations of data structures for modern processors with respect to an abstract specification specifically we define an operational semantics from which we derive some properties of program refinement and encode the semantics in the rewriting engine maude as a modelchecking tool the tool is used to validate the semantics against the behaviour of a set of litmus tests small assembler programs run on hardware and also to model check implementations of data structures from the literature against their abstract specifications | [['modern', 'processors', 'deploy', 'a', 'variety', 'of', 'weak', 'memory', 'models', 'which', 'for', 'efficiency', 'reasons', 'may', 'appear', 'to', 'execute', 'instructions', 'in', 'an', 'order', 'different', 'to', 'that', 'specified', 'by', 'the', 'program', 'text', 'the', 'consequences', 'of', 'instruction', 'reordering', 'can', 'be', 'complex', 'and', 'subtle', 'and', 'can', 'impact', 'on', 'ensuring', 'correctness', 'previous', 'work', 'on', 'the', 'semantics', 'of', 'weak', 'memory', 'models', 'has', 'focussed', 'on', 'the', 'behaviour', 'of', 'assemblerlevel', 'programs', 'in', 'this', 'paper', 'we', 'utilise', 'that', 'work', 'to', 'extract', 'some', 'general', 'principles', 'underlying', 'instruction', 'reordering', 'and', 'apply', 'those', 'principles', 'to', 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1,802.04407 | Adversarially Regularized Graph Autoencoder for Graph Embedding | Graph embedding is an effective method to represent graph data in a low
dimensional space for graph analytics. Most existing embedding algorithms
typically focus on preserving the topological structure or minimizing the
reconstruction errors of graph data, but they have mostly ignored the data
distribution of the latent codes from the graphs, which often results in
inferior embedding in real-world graph data. In this paper, we propose a novel
adversarial graph embedding framework for graph data. The framework encodes the
topological structure and node content in a graph to a compact representation,
on which a decoder is trained to reconstruct the graph structure. Furthermore,
the latent representation is enforced to match a prior distribution via an
adversarial training scheme. To learn a robust embedding, two variants of
adversarial approaches, adversarially regularized graph autoencoder (ARGA) and
adversarially regularized variational graph autoencoder (ARVGA), are developed.
Experimental studies on real-world graphs validate our design and demonstrate
that our algorithms outperform baselines by a wide margin in link prediction,
graph clustering, and graph visualization tasks.
| cs.LG stat.ML | graph embedding is an effective method to represent graph data in a low dimensional space for graph analytics most existing embedding algorithms typically focus on preserving the topological structure or minimizing the reconstruction errors of graph data but they have mostly ignored the data distribution of the latent codes from the graphs which often results in inferior embedding in realworld graph data in this paper we propose a novel adversarial graph embedding framework for graph data the framework encodes the topological structure and node content in a graph to a compact representation on which a decoder is trained to reconstruct the graph structure furthermore the latent representation is enforced to match a prior distribution via an adversarial training scheme to learn a robust embedding two variants of adversarial approaches adversarially regularized graph autoencoder arga and adversarially regularized variational graph autoencoder arvga are developed experimental studies on realworld graphs validate our design and demonstrate that our algorithms outperform baselines by a wide margin in link prediction graph clustering and graph visualization tasks | [['graph', 'embedding', 'is', 'an', 'effective', 'method', 'to', 'represent', 'graph', 'data', 'in', 'a', 'low', 'dimensional', 'space', 'for', 'graph', 'analytics', 'most', 'existing', 'embedding', 'algorithms', 'typically', 'focus', 'on', 'preserving', 'the', 'topological', 'structure', 'or', 'minimizing', 'the', 'reconstruction', 'errors', 'of', 'graph', 'data', 'but', 'they', 'have', 'mostly', 'ignored', 'the', 'data', 'distribution', 'of', 'the', 'latent', 'codes', 'from', 'the', 'graphs', 'which', 'often', 'results', 'in', 'inferior', 'embedding', 'in', 'realworld', 'graph', 'data', 'in', 'this', 'paper', 'we', 'propose', 'a', 'novel', 'adversarial', 'graph', 'embedding', 'framework', 'for', 'graph', 'data', 'the', 'framework', 'encodes', 'the', 'topological', 'structure', 'and', 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1,802.04408 | REAS: Combining Numerical Optimization with SAT Solving | In this paper, we present ReaS, a technique that combines numerical
optimization with SAT solving to synthesize unknowns in a program that involves
discrete and floating point computation. ReaS makes the program end-to-end
differentiable by smoothing any Boolean expression that introduces
discontinuity such as conditionals and relaxing the Boolean unknowns so that
numerical optimization can be performed. On top of this, ReaS uses a SAT solver
to help the numerical search overcome local solutions by incrementally fixing
values to the Boolean expressions. We evaluated the approach on 5 case studies
involving hybrid systems and show that ReaS can synthesize programs that could
not be solved by previous SMT approaches.
| cs.PL cs.AI | in this paper we present reas a technique that combines numerical optimization with sat solving to synthesize unknowns in a program that involves discrete and floating point computation reas makes the program endtoend differentiable by smoothing any boolean expression that introduces discontinuity such as conditionals and relaxing the boolean unknowns so that numerical optimization can be performed on top of this reas uses a sat solver to help the numerical search overcome local solutions by incrementally fixing values to the boolean expressions we evaluated the approach on 5 case studies involving hybrid systems and show that reas can synthesize programs that could not be solved by previous smt approaches | [['in', 'this', 'paper', 'we', 'present', 'reas', 'a', 'technique', 'that', 'combines', 'numerical', 'optimization', 'with', 'sat', 'solving', 'to', 'synthesize', 'unknowns', 'in', 'a', 'program', 'that', 'involves', 'discrete', 'and', 'floating', 'point', 'computation', 'reas', 'makes', 'the', 'program', 'endtoend', 'differentiable', 'by', 'smoothing', 'any', 'boolean', 'expression', 'that', 'introduces', 'discontinuity', 'such', 'as', 'conditionals', 'and', 'relaxing', 'the', 'boolean', 'unknowns', 'so', 'that', 'numerical', 'optimization', 'can', 'be', 'performed', 'on', 'top', 'of', 'this', 'reas', 'uses', 'a', 'sat', 'solver', 'to', 'help', 'the', 'numerical', 'search', 'overcome', 'local', 'solutions', 'by', 'incrementally', 'fixing', 'values', 'to', 'the', 'boolean', 'expressions', 'we', 'evaluated', 'the', 'approach', 'on', '5', 'case', 'studies', 'involving', 'hybrid', 'systems', 'and', 'show', 'that', 'reas', 'can', 'synthesize', 'programs', 'that', 'could', 'not', 'be', 'solved', 'by', 'previous', 'smt', 'approaches']] | [-0.0918297700417322, -0.004159682379061475, -0.09299431535748577, 0.04393088655816519, -0.11080244532999915, -0.16814929451537106, 0.07651182593042971, 0.3810319828313835, -0.29667901891975773, -0.3492680571699908, 0.12568823161637496, -0.22792984641033198, -0.17339085922106554, 0.22990214659559002, -0.04698475784330561, 0.13956048371125582, 0.10091813920181109, -0.07750930041587407, -0.13002743181097534, -0.2568152775996527, 0.267565895147502, 0.016353719660567992, 0.1827979310407141, 0.0061528605297891375, 0.10037378790369685, 0.0157923096098826, -0.017536978873877077, 0.0763367772905515, -0.06593652932310302, 0.11267048504135203, 0.29905274098551055, 0.2150880726024406, 0.30149863987152864, -0.4763701282304908, -0.17831005131261884, 0.06064691927728303, 0.1471066416920496, 0.11823123954920046, -0.04523752649835901, -0.2541441904923477, 0.11578458210948957, -0.1446457563326993, -0.05791124099557963, -0.1367535427246892, -0.04802998469783625, 0.03146998476986816, -0.30352972036466824, -0.009615780584890089, 0.037724632436597565, 0.028116303162837246, -0.025700979656753463, -0.14608138656124062, 0.028931300459139116, 0.031687707926462826, 0.012023212768376694, 0.06752949037136288, 0.12174863792387683, -0.06700101828441844, -0.18573338251962507, 0.348062543924727, -0.047365414166669235, -0.27145784327316036, 0.1678873699888963, -0.022823886360562998, -0.1761241500573489, 0.14054443808528808, 0.17998133971753164, 0.16650712611837262, -0.15877395858902724, 0.11720204559943015, -0.06272513769276508, 0.25276675981115937, 0.06927577815437262, -0.09902535196086136, 0.15253790991312458, 0.15720110092688044, 0.07726243572360879, 0.1737672620123987, -0.027653281481876766, -0.12130920817955919, -0.27928229428212575, -0.13570459524528422, -0.16990378826275604, 0.002992746947945198, -0.053094216893291434, -0.1511885649140138, 0.3438004111580619, 0.23080247352668726, 0.11705332439939636, 0.14408498344763498, 0.33117680010650685, 0.15793617998800932, 0.12344128936059226, 0.08437689682554214, 0.1673212286700076, 0.044844509869249996, 0.1036814328052377, -0.21054888868794153, 0.09549696829144357, 0.11730534345800177] |
1,802.04409 | Dynamic Bounds on Stochastic Chemical Kinetic Systems Using Semidefinite
Programming | Applying the method of moments to the chemical master equation (CME)
appearing in stochastic chemical kinetics often leads to the so-called closure
problem. Recently, several authors showed that this problem can be partially
overcome using moment-based semidefinite programs (SDPs). In particular, they
showed that moment-based SDPs can be used to calculate rigorous bounds on
various descriptions of the stochastic chemical kinetic system's stationary
distribution(s) -- for example, mean molecular counts, variances in these
counts, and so on. In this paper, we show that these ideas can be extended to
the corresponding dynamic problem, calculating time-varying bounds on the same
descriptions.
| math.PR math.OC q-bio.MN q-bio.QM | applying the method of moments to the chemical master equation cme appearing in stochastic chemical kinetics often leads to the socalled closure problem recently several authors showed that this problem can be partially overcome using momentbased semidefinite programs sdps in particular they showed that momentbased sdps can be used to calculate rigorous bounds on various descriptions of the stochastic chemical kinetic systems stationary distributions for example mean molecular counts variances in these counts and so on in this paper we show that these ideas can be extended to the corresponding dynamic problem calculating timevarying bounds on the same descriptions | [['applying', 'the', 'method', 'of', 'moments', 'to', 'the', 'chemical', 'master', 'equation', 'cme', 'appearing', 'in', 'stochastic', 'chemical', 'kinetics', 'often', 'leads', 'to', 'the', 'socalled', 'closure', 'problem', 'recently', 'several', 'authors', 'showed', 'that', 'this', 'problem', 'can', 'be', 'partially', 'overcome', 'using', 'momentbased', 'semidefinite', 'programs', 'sdps', 'in', 'particular', 'they', 'showed', 'that', 'momentbased', 'sdps', 'can', 'be', 'used', 'to', 'calculate', 'rigorous', 'bounds', 'on', 'various', 'descriptions', 'of', 'the', 'stochastic', 'chemical', 'kinetic', 'systems', 'stationary', 'distributions', 'for', 'example', 'mean', 'molecular', 'counts', 'variances', 'in', 'these', 'counts', 'and', 'so', 'on', 'in', 'this', 'paper', 'we', 'show', 'that', 'these', 'ideas', 'can', 'be', 'extended', 'to', 'the', 'corresponding', 'dynamic', 'problem', 'calculating', 'timevarying', 'bounds', 'on', 'the', 'same', 'descriptions']] | [-0.05939903382136665, 0.06417043953357886, -0.10437144937388824, 0.1354405196040229, -0.07381150255807573, -0.08472925344613766, 0.030969928352707864, 0.34670692107480283, -0.31910523192750084, -0.33611140167811, 0.0794920940798792, -0.23449264018975122, -0.1766280224829009, 0.20899974328590407, -0.07780349527419818, 0.09206638810450607, 0.05895215043365353, -0.026040073684087457, -0.09139377954815787, -0.2620434696795513, 0.2766515920220902, 0.016311110081997784, 0.23270338794423473, 0.06162103015172173, 0.06751644785393669, -0.05123025866112474, -0.035462921763996055, 0.08249416804844231, -0.13001248297495285, 0.11871378248289341, 0.28025695439098836, 0.14270022777238428, 0.25926310025894017, -0.47528179170507373, -0.22585649286029916, 0.13609072695853133, 0.16074484111823498, 0.16809531452952686, -0.018985055931467525, -0.2685068400783671, 0.07689994771616777, -0.1361897599676417, -0.09083081733886943, -0.1157137569054171, -0.040127596502766164, 0.07447836936611418, -0.26474942434390986, 0.08054173043505712, 0.04377901259659216, 0.0028026554300779045, -0.05977317247765534, -0.17339994557992075, 0.01807882321758591, 0.0782650643724459, 0.029901096122245296, -0.029193682335034915, 0.1104216004834708, -0.05939982044381193, -0.15127428550498956, 0.3327688082977377, -0.06741014235850537, -0.25995242168581245, 0.18791278055398677, -0.11361377399339519, -0.20250913338039558, 0.12298097670539235, 0.20128295702314136, 0.14225476496674197, -0.22940719554746392, 0.09054183445793267, -0.04797648884017359, 0.1266020398793949, 0.08070888341112871, -0.028628021168212097, 0.1581491369029714, 0.08086825665697982, 0.07053164017358513, 0.1368441680326324, -0.02016325263221833, -0.14119376169254233, -0.24615732933197057, -0.11310891030389214, -0.18678146136943438, 0.04019661041710413, -0.07949493491461834, -0.1538406191652434, 0.36648882421279194, 0.19478454805600146, 0.15944901294065544, 0.08531802415029344, 0.2801677820253929, 0.20220915829254824, 0.019225377989274383, 0.0805512880177397, 0.1964191747523581, 0.17735439695116847, 0.09836272478357634, -0.23701913112943823, 0.11963320531967012, 0.10790015968748114] |
1,802.0441 | Smart Contract-Based Access Control for the Internet of Things | This paper investigates a critical access control issue in the Internet of
Things (IoT). In particular, we propose a smart contract-based framework, which
consists of multiple access control contracts (ACCs), one judge contract (JC)
and one register contract (RC), to achieve distributed and trustworthy access
control for IoT systems. Each ACC provides one access control method for a
subject-object pair, and implements both static access right validation based
on predefined policies and dynamic access right validation by checking the
behavior of the subject. The JC implements a misbehavior-judging method to
facilitate the dynamic validation of the ACCs by receiving misbehavior reports
from the ACCs, judging the misbehavior and returning the corresponding penalty.
The RC registers the information of the access control and misbehavior-judging
methods as well as their smart contracts, and also provides functions (e.g.,
register, update and delete) to manage these methods. To demonstrate the
application of the framework, we provide a case study in an IoT system with one
desktop computer, one laptop and two Raspberry Pi single-board computers, where
the ACCs, JC and RC are implemented based on the Ethereum smart contract
platform to achieve the access control.
| cs.CR | this paper investigates a critical access control issue in the internet of things iot in particular we propose a smart contractbased framework which consists of multiple access control contracts accs one judge contract jc and one register contract rc to achieve distributed and trustworthy access control for iot systems each acc provides one access control method for a subjectobject pair and implements both static access right validation based on predefined policies and dynamic access right validation by checking the behavior of the subject the jc implements a misbehaviorjudging method to facilitate the dynamic validation of the accs by receiving misbehavior reports from the accs judging the misbehavior and returning the corresponding penalty the rc registers the information of the access control and misbehaviorjudging methods as well as their smart contracts and also provides functions eg register update and delete to manage these methods to demonstrate the application of the framework we provide a case study in an iot system with one desktop computer one laptop and two raspberry pi singleboard computers where the accs jc and rc are implemented based on the ethereum smart contract platform to achieve the access control | [['this', 'paper', 'investigates', 'a', 'critical', 'access', 'control', 'issue', 'in', 'the', 'internet', 'of', 'things', 'iot', 'in', 'particular', 'we', 'propose', 'a', 'smart', 'contractbased', 'framework', 'which', 'consists', 'of', 'multiple', 'access', 'control', 'contracts', 'accs', 'one', 'judge', 'contract', 'jc', 'and', 'one', 'register', 'contract', 'rc', 'to', 'achieve', 'distributed', 'and', 'trustworthy', 'access', 'control', 'for', 'iot', 'systems', 'each', 'acc', 'provides', 'one', 'access', 'control', 'method', 'for', 'a', 'subjectobject', 'pair', 'and', 'implements', 'both', 'static', 'access', 'right', 'validation', 'based', 'on', 'predefined', 'policies', 'and', 'dynamic', 'access', 'right', 'validation', 'by', 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1,802.04411 | Poincar\'e type and spectral gap inequalities with fractional Laplacians
on Hamming cube | We prove here some dimension free Poincar\'e-type inequalities on Hamming
cube for functions with different spectral properties and for fractional
Laplacians. In this note the main attention is paid to estimates in $L^1$ norm
on Hamming cube. We build the examples showing that our assumptions on spectral
properties of functions cannot be dropped in general.
| math.AP math.CA math.PR | we prove here some dimension free poincaretype inequalities on hamming cube for functions with different spectral properties and for fractional laplacians in this note the main attention is paid to estimates in l1 norm on hamming cube we build the examples showing that our assumptions on spectral properties of functions cannot be dropped in general | [['we', 'prove', 'here', 'some', 'dimension', 'free', 'poincaretype', 'inequalities', 'on', 'hamming', 'cube', 'for', 'functions', 'with', 'different', 'spectral', 'properties', 'and', 'for', 'fractional', 'laplacians', 'in', 'this', 'note', 'the', 'main', 'attention', 'is', 'paid', 'to', 'estimates', 'in', 'l1', 'norm', 'on', 'hamming', 'cube', 'we', 'build', 'the', 'examples', 'showing', 'that', 'our', 'assumptions', 'on', 'spectral', 'properties', 'of', 'functions', 'can', 'not', 'be', 'dropped', 'in', 'general']] | [-0.041189450255062966, 0.031290622877090106, -0.0846480702046912, 0.15402707510760852, -0.0825457148436856, -0.09914686911672886, -0.0027385563257017304, 0.46331235200964976, -0.2859361706235047, -0.21954041709458189, 0.15686110912897025, -0.2945556897071323, -0.1538719006015786, 0.21464805857976899, -0.1757444209069945, 0.06910769505027149, 0.06936323431520057, 0.054953972804859016, -0.14357034542315109, -0.28698776190036107, 0.4049466658782746, -0.005527673771471849, 0.19747011340223253, 0.12358165810915775, 0.028548176259001985, -0.023861129870056175, -0.021391215035691857, -0.016977776906320026, -0.2326332448996514, 0.17359930257745354, 0.193951196236802, 0.1115606114950164, 0.28285459701770116, -0.3850739758927375, -0.22366664324155344, 0.16570488654542714, 0.08783238086782928, 0.019012211406204318, -0.027594922428501638, -0.2512855159917048, 0.10916429478675127, -0.08354062400758266, -0.11902477317822299, -0.09398150968731248, 0.021850947440335795, 0.08513027409623776, -0.275930988444348, 0.03742827180706497, 0.10747810170453574, 0.04002346569281404, -0.09697757407723527, -0.16774431289897102, 0.021460388757986948, 0.07398585267323401, 0.02727164898117605, -0.006107438571884164, 0.037847892264835536, -0.048669796532651945, -0.10877919952119035, 0.33864544474220437, -0.03632624094773616, -0.3311201379900532, 0.1513409500475973, -0.1887721078944326, -0.22217828427840555, 0.0179027371175055, 0.18408375171046437, 0.13619720134218888, -0.08392353803252003, 0.10935203948819046, -0.07977871595150125, 0.18262129003414884, 0.11047792709099927, 0.12863601303979522, 0.059824936191684434, 0.07027013426912683, 0.14531488891225308, 0.2150481590547965, -0.03482065217186963, -0.08115817825975162, -0.3411564310081303, -0.1470699571696709, -0.2658504687715322, 0.1046031811152456, -0.16537309304086584, -0.157480565564973, 0.375842008939279, 0.11181148401893941, 0.184015956710417, 0.10924532209589545, 0.22927730001642235, 0.12026226193951775, 0.09080987438626055, 0.0994955184121084, 0.17461804286670354, 0.15521381249085867, 0.03241366521355563, -0.13494703151185863, 0.018005842384549657, 0.18356640671845526] |
1,802.04412 | Efficient Exploration through Bayesian Deep Q-Networks | We study reinforcement learning (RL) in high dimensional episodic Markov
decision processes (MDP). We consider value-based RL when the optimal Q-value
is a linear function of d-dimensional state-action feature representation. For
instance, in deep-Q networks (DQN), the Q-value is a linear function of the
feature representation layer (output layer). We propose two algorithms, one
based on optimism, LINUCB, and another based on posterior sampling, LINPSRL. We
guarantee frequentist and Bayesian regret upper bounds of O(d sqrt{T}) for
these two algorithms, where T is the number of episodes. We extend these
methods to deep RL and propose Bayesian deep Q-networks (BDQN), which uses an
efficient Thompson sampling algorithm for high dimensional RL. We deploy the
double DQN (DDQN) approach, and instead of learning the last layer of Q-network
using linear regression, we use Bayesian linear regression, resulting in an
approximated posterior over Q-function. This allows us to directly incorporate
the uncertainty over the Q-function and deploy Thompson sampling on the learned
posterior distribution resulting in efficient exploration/exploitation
trade-off. We empirically study the behavior of BDQN on a wide range of Atari
games. Since BDQN carries out more efficient exploration and exploitation, it
is able to reach higher return substantially faster compared to DDQN.
| cs.AI cs.LG stat.ML | we study reinforcement learning rl in high dimensional episodic markov decision processes mdp we consider valuebased rl when the optimal qvalue is a linear function of ddimensional stateaction feature representation for instance in deepq networks dqn the qvalue is a linear function of the feature representation layer output layer we propose two algorithms one based on optimism linucb and another based on posterior sampling linpsrl we guarantee frequentist and bayesian regret upper bounds of od sqrtt for these two algorithms where t is the number of episodes we extend these methods to deep rl and propose bayesian deep qnetworks bdqn which uses an efficient thompson sampling algorithm for high dimensional rl we deploy the double dqn ddqn approach and instead of learning the last layer of qnetwork using linear regression we use bayesian linear regression resulting in an approximated posterior over qfunction this allows us to directly incorporate the uncertainty over the qfunction and deploy thompson sampling on the learned posterior distribution resulting in efficient explorationexploitation tradeoff we empirically study the behavior of bdqn on a wide range of atari games since bdqn carries out more efficient exploration and exploitation it is able to reach higher return substantially faster compared to ddqn | [['we', 'study', 'reinforcement', 'learning', 'rl', 'in', 'high', 'dimensional', 'episodic', 'markov', 'decision', 'processes', 'mdp', 'we', 'consider', 'valuebased', 'rl', 'when', 'the', 'optimal', 'qvalue', 'is', 'a', 'linear', 'function', 'of', 'ddimensional', 'stateaction', 'feature', 'representation', 'for', 'instance', 'in', 'deepq', 'networks', 'dqn', 'the', 'qvalue', 'is', 'a', 'linear', 'function', 'of', 'the', 'feature', 'representation', 'layer', 'output', 'layer', 'we', 'propose', 'two', 'algorithms', 'one', 'based', 'on', 'optimism', 'linucb', 'and', 'another', 'based', 'on', 'posterior', 'sampling', 'linpsrl', 'we', 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1,802.04413 | What is the Sharpe Ratio, and how can everyone get it wrong? | The Sharpe ratio is the most widely used risk metric in the quantitative
finance community - amazingly, essentially everyone gets it wrong. In this
note, we will make a quixotic effort to rectify the situation.
| q-fin.PM q-fin.RM | the sharpe ratio is the most widely used risk metric in the quantitative finance community amazingly essentially everyone gets it wrong in this note we will make a quixotic effort to rectify the situation | [['the', 'sharpe', 'ratio', 'is', 'the', 'most', 'widely', 'used', 'risk', 'metric', 'in', 'the', 'quantitative', 'finance', 'community', 'amazingly', 'essentially', 'everyone', 'gets', 'it', 'wrong', 'in', 'this', 'note', 'we', 'will', 'make', 'a', 'quixotic', 'effort', 'to', 'rectify', 'the', 'situation']] | [-0.04753596098585562, 0.01979304043660787, -0.14592301594375662, 0.19279961423440414, -0.13767827948498906, -0.14821785805521137, 0.10890439367204001, 0.4159764814557451, -0.27363453719722614, -0.2790514272496556, 0.10864610654640604, -0.32231374796141277, -0.19479910241947931, 0.15514836584528288, -0.23192153072379756, 0.019711269563148646, 0.07072703935431712, 0.07257146749532584, 0.05336566921323538, -0.32255964308525575, 0.21572940209598251, 0.15150249269649838, 0.32568194288195984, 0.07896471507565091, -0.011098574705873474, -0.05037954555485736, -0.07245122943299287, 0.028038242718938625, -0.153033269968353, 0.13103460540261233, 0.378142555780483, 0.1511525639304609, 0.3747080679644238, -0.37117190359894076, -0.1054144053663494, 0.19629055035836768, 0.09569192599244133, 0.13842390632877746, 0.02506057855983575, -0.20483018977172446, -0.009983932456662209, -0.2148430733518167, -0.1416437950154597, -0.03552132222632116, 0.039154613207122595, -0.11963786467006712, -0.23800522235758376, 0.028302142876341488, 0.020927512129260736, 0.030936541600209293, 0.03021787573357649, -0.16860241606605775, 0.057088428318048966, 0.16241055790504272, 0.19261933879390586, 0.05830740341634461, 0.13676492018963804, -0.14035504640108257, -0.047793305732987144, 0.39972443954849785, -0.07534734388305382, -0.23002385370659106, 0.16334863639239108, -0.1399827101126765, -0.15049665330937415, 0.05233356746082956, 0.11958651940310092, 0.060347563502463425, -0.21564218507740984, 0.06115290562086033, -0.04223051991765246, 0.2010708540452249, 0.06616359943467559, -0.04939595835678505, 0.14877854265046841, 0.14129132876229106, 0.1315509442409331, 0.1006418036184076, 0.007727643044552568, -0.09718051109688752, -0.2449526090852239, -0.15552710290208008, -0.12135852556887337, 0.070858105128123, -0.03535799095124909, -0.18180693211880597, 0.33902500949493103, 0.25624123723669484, 0.11411125435860771, 0.01862350792736944, 0.30343508387379575, 0.09116587923331694, 0.0003876190801913088, 0.05789022614727869, 0.3015818044994817, 0.07550737069863261, 0.2072167233775624, -0.09697026399556886, 0.18674434808957757, 0.025812776674601166] |
1,802.04414 | Motion of a Rigid Body in a Special Lorentz Gas: Loss of Memory Effect | Linear motion of a rigid body in a special kind of Lorentz gas is
mathematically analyzed. The rigid body moves against gas drag according to
Newton's equation. The gas model is a special Lorentz gas consisting of gas
molecules and background obstacles, which was introduced in~(Tsuji and Aoki: J.
Stat. Phys. \textbf{146}, 620--645, 2012). The specular boundary condition is
imposed on the resulting kinetic equation. This study complements the numerical
study by Tsuji and Aoki cited above --- although the setting in this paper is
slightly different from theirs, qualitatively the same asymptotic behavior is
proved: The velocity $V(t)$ of the rigid body decays exponentially if the
obstacles undergo thermal motion; if the obstacles are motionless, then the
velocity $V(t)$ decays algebraically with a rate $t^{-5}$ independent of the
spatial dimension. This demonstrates the idea that interaction of the molecules
with the background obstacles destroy the memory effect due to recollision.
| math-ph math.AP math.MP | linear motion of a rigid body in a special kind of lorentz gas is mathematically analyzed the rigid body moves against gas drag according to newtons equation the gas model is a special lorentz gas consisting of gas molecules and background obstacles which was introduced intsuji and aoki j stat phys textbf146 620645 2012 the specular boundary condition is imposed on the resulting kinetic equation this study complements the numerical study by tsuji and aoki cited above although the setting in this paper is slightly different from theirs qualitatively the same asymptotic behavior is proved the velocity vt of the rigid body decays exponentially if the obstacles undergo thermal motion if the obstacles are motionless then the velocity vt decays algebraically with a rate t5 independent of the spatial dimension this demonstrates the idea that interaction of the molecules with the background obstacles destroy the memory effect due to recollision | [['linear', 'motion', 'of', 'a', 'rigid', 'body', 'in', 'a', 'special', 'kind', 'of', 'lorentz', 'gas', 'is', 'mathematically', 'analyzed', 'the', 'rigid', 'body', 'moves', 'against', 'gas', 'drag', 'according', 'to', 'newtons', 'equation', 'the', 'gas', 'model', 'is', 'a', 'special', 'lorentz', 'gas', 'consisting', 'of', 'gas', 'molecules', 'and', 'background', 'obstacles', 'which', 'was', 'introduced', 'intsuji', 'and', 'aoki', 'j', 'stat', 'phys', 'textbf146', '620645', '2012', 'the', 'specular', 'boundary', 'condition', 'is', 'imposed', 'on', 'the', 'resulting', 'kinetic', 'equation', 'this', 'study', 'complements', 'the', 'numerical', 'study', 'by', 'tsuji', 'and', 'aoki', 'cited', 'above', 'although', 'the', 'setting', 'in', 'this', 'paper', 'is', 'slightly', 'different', 'from', 'theirs', 'qualitatively', 'the', 'same', 'asymptotic', 'behavior', 'is', 'proved', 'the', 'velocity', 'vt', 'of', 'the', 'rigid', 'body', 'decays', 'exponentially', 'if', 'the', 'obstacles', 'undergo', 'thermal', 'motion', 'if', 'the', 'obstacles', 'are', 'motionless', 'then', 'the', 'velocity', 'vt', 'decays', 'algebraically', 'with', 'a', 'rate', 't5', 'independent', 'of', 'the', 'spatial', 'dimension', 'this', 'demonstrates', 'the', 'idea', 'that', 'interaction', 'of', 'the', 'molecules', 'with', 'the', 'background', 'obstacles', 'destroy', 'the', 'memory', 'effect', 'due', 'to', 'recollision']] | [-0.130128102619787, 0.17655724834422676, -0.08318231403067404, 0.0010724389374408188, -0.06609826638374389, -0.13947969240446886, 0.008684006993988289, 0.2881271621389758, -0.2436162325059425, -0.25391305668190833, 0.042875723550603, -0.2827156563004365, -0.10830351230244906, 0.12272273895305386, -0.10592887568965453, 0.03607876047525289, 0.03818869912604086, 0.017679018652712812, -0.01601896503921218, -0.23567893171087415, 0.29557756785316025, 0.03159518852563841, 0.2550469716041916, 0.03490992741291823, 0.0956781600510106, -0.0101312148182013, -0.020706427489489723, 0.03838286260383474, -0.16307317607642705, 0.0414954335460452, 0.16110657536318576, -0.002678813302557247, 0.23776475338544062, -0.40300881890516704, -0.21945112786686594, 0.07839049562038918, 0.1300464194017735, 0.11852638822818669, -0.01656634116554823, -0.3280470711464176, 0.025640583707203415, -0.15878655964217217, -0.21881066064103222, 0.004460003508926452, 0.08655455883997841, 0.01919700082892007, -0.25283497635300467, 0.14786027186885983, 0.09902288678552018, 0.044917154125869274, -0.07536179239411844, -0.06879408311947775, -0.019315263354631306, 0.044629888931371674, 0.11153934339495362, 0.07637543283833735, 0.192722923945965, -0.09738709942158945, -0.03174186785242894, 0.4223626045871298, -0.05033993972627744, -0.23269077035642804, 0.23670489923889135, -0.16474090654681733, -0.07985775329673118, 0.18349308386242308, 0.114293112844026, 0.09584932440264644, -0.17032338521082183, 0.1171054441880175, -0.07350959284088518, 0.13774887974006536, 0.10885768782562849, -0.03715813456168145, 0.17764010349388368, 0.11557866995647346, 0.04043099407081296, 0.14165046842147908, -0.07902366504212524, -0.10219049803456481, -0.30558251646556417, -0.1437715678222376, -0.1774316906087975, 0.06253245098953497, -0.04833324395603624, -0.13738003701326393, 0.3266030466437441, 0.10691536077120829, 0.18601620378492234, 0.023619213655470636, 0.31708701764119374, 0.0881603328054961, 0.006103357412617932, 0.11685438639782116, 0.25981996130502344, 0.17373855504346694, 0.10855574842833844, -0.26193141920941876, 0.054589371200093405, 0.10546254296945472] |
1,802.04415 | Excitation Mechanisms for Jovian Seismic Modes | Recent (2011) results from the Nice Observatory indicate the existence of
global seismic modes on Jupiter in the frequency range between 0.7 and 1.5mHz
with amplitudes of tens of cm/s. Currently, the driving force behind these
modes is a mystery; the measured amplitudes are many orders of magnitude larger
than anticipated based on theory analogous to heliosiesmology (that is,
turbulent convection as a source of stochastic excitation). One of the most
promising hypotheses is that these modes are driven by Jovian storms. This work
constructs a framework to analytically model the expected equilibrium normal
mode amplitudes arising from convective columns in storms. We also place rough
constraints on Jupiter's seismic modal quality factor. Using this model,
neither meteor strikes, turbulent convection, nor water storms can feasibly
excite the order of magnitude of observed amplitudes. Next we speculate about
the potential role of rock storms deeper in Jupiter's atmosphere, because the
rock storms' expected energy scales make them promising candidates to be the
chief source of excitation for Jovian seismic modes, based on simple scaling
arguments. We also suggest some general trends in the expected partition of
energy between different frequency modes. Finally we supply some commentary on
potential applications to gravity, Juno, Cassini and Saturn, and future
missions to Uranus and Neptune.
| astro-ph.EP | recent 2011 results from the nice observatory indicate the existence of global seismic modes on jupiter in the frequency range between 07 and 15mhz with amplitudes of tens of cms currently the driving force behind these modes is a mystery the measured amplitudes are many orders of magnitude larger than anticipated based on theory analogous to heliosiesmology that is turbulent convection as a source of stochastic excitation one of the most promising hypotheses is that these modes are driven by jovian storms this work constructs a framework to analytically model the expected equilibrium normal mode amplitudes arising from convective columns in storms we also place rough constraints on jupiters seismic modal quality factor using this model neither meteor strikes turbulent convection nor water storms can feasibly excite the order of magnitude of observed amplitudes next we speculate about the potential role of rock storms deeper in jupiters atmosphere because the rock storms expected energy scales make them promising candidates to be the chief source of excitation for jovian seismic modes based on simple scaling arguments we also suggest some general trends in the expected partition of energy between different frequency modes finally we supply some commentary on potential applications to gravity juno cassini and saturn and future missions to uranus and neptune | [['recent', '2011', 'results', 'from', 'the', 'nice', 'observatory', 'indicate', 'the', 'existence', 'of', 'global', 'seismic', 'modes', 'on', 'jupiter', 'in', 'the', 'frequency', 'range', 'between', '07', 'and', '15mhz', 'with', 'amplitudes', 'of', 'tens', 'of', 'cms', 'currently', 'the', 'driving', 'force', 'behind', 'these', 'modes', 'is', 'a', 'mystery', 'the', 'measured', 'amplitudes', 'are', 'many', 'orders', 'of', 'magnitude', 'larger', 'than', 'anticipated', 'based', 'on', 'theory', 'analogous', 'to', 'heliosiesmology', 'that', 'is', 'turbulent', 'convection', 'as', 'a', 'source', 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1,802.04416 | Neural Tensor Factorization | Neural collaborative filtering (NCF) and recurrent recommender systems (RRN)
have been successful in modeling user-item relational data. However, they are
also limited in their assumption of static or sequential modeling of relational
data as they do not account for evolving users' preference over time as well as
changes in the underlying factors that drive the change in user-item
relationship over time. We address these limitations by proposing a Neural
Tensor Factorization (NTF) model for predictive tasks on dynamic relational
data. The NTF model generalizes conventional tensor factorization from two
perspectives: First, it leverages the long short-term memory architecture to
characterize the multi-dimensional temporal interactions on relational data.
Second, it incorporates the multi-layer perceptron structure for learning the
non-linearities between different latent factors. Our extensive experiments
demonstrate the significant improvement in rating prediction and link
prediction on dynamic relational data by our NTF model over both neural network
based factorization models and other traditional methods.
| cs.LG | neural collaborative filtering ncf and recurrent recommender systems rrn have been successful in modeling useritem relational data however they are also limited in their assumption of static or sequential modeling of relational data as they do not account for evolving users preference over time as well as changes in the underlying factors that drive the change in useritem relationship over time we address these limitations by proposing a neural tensor factorization ntf model for predictive tasks on dynamic relational data the ntf model generalizes conventional tensor factorization from two perspectives first it leverages the long shortterm memory architecture to characterize the multidimensional temporal interactions on relational data second it incorporates the multilayer perceptron structure for learning the nonlinearities between different latent factors our extensive experiments demonstrate the significant improvement in rating prediction and link prediction on dynamic relational data by our ntf model over both neural network based factorization models and other traditional methods | [['neural', 'collaborative', 'filtering', 'ncf', 'and', 'recurrent', 'recommender', 'systems', 'rrn', 'have', 'been', 'successful', 'in', 'modeling', 'useritem', 'relational', 'data', 'however', 'they', 'are', 'also', 'limited', 'in', 'their', 'assumption', 'of', 'static', 'or', 'sequential', 'modeling', 'of', 'relational', 'data', 'as', 'they', 'do', 'not', 'account', 'for', 'evolving', 'users', 'preference', 'over', 'time', 'as', 'well', 'as', 'changes', 'in', 'the', 'underlying', 'factors', 'that', 'drive', 'the', 'change', 'in', 'useritem', 'relationship', 'over', 'time', 'we', 'address', 'these', 'limitations', 'by', 'proposing', 'a', 'neural', 'tensor', 'factorization', 'ntf', 'model', 'for', 'predictive', 'tasks', 'on', 'dynamic', 'relational', 'data', 'the', 'ntf', 'model', 'generalizes', 'conventional', 'tensor', 'factorization', 'from', 'two', 'perspectives', 'first', 'it', 'leverages', 'the', 'long', 'shortterm', 'memory', 'architecture', 'to', 'characterize', 'the', 'multidimensional', 'temporal', 'interactions', 'on', 'relational', 'data', 'second', 'it', 'incorporates', 'the', 'multilayer', 'perceptron', 'structure', 'for', 'learning', 'the', 'nonlinearities', 'between', 'different', 'latent', 'factors', 'our', 'extensive', 'experiments', 'demonstrate', 'the', 'significant', 'improvement', 'in', 'rating', 'prediction', 'and', 'link', 'prediction', 'on', 'dynamic', 'relational', 'data', 'by', 'our', 'ntf', 'model', 'over', 'both', 'neural', 'network', 'based', 'factorization', 'models', 'and', 'other', 'traditional', 'methods']] | [-0.06123469049086819, 0.004525949415756849, -0.05948130504489731, 0.10686081020250711, -0.13174456923099404, -0.18129441788262846, 0.028343383910727094, 0.484557238099008, -0.3305681684665237, -0.31329959575328736, 0.06826489950598644, -0.26584982649459465, -0.2132301916982132, 0.13453757740306221, -0.053994956891227804, 0.10582342884894884, 0.1066890654770972, 0.049834637124811045, -0.07386046609623576, -0.25181274754962846, 0.33246314118452996, 0.04942658646395067, 0.3632218411325344, 0.022259812569245696, 0.11936460430023273, 0.043372827751392666, -0.09504719919150138, 0.014909574476995978, -0.020982059264072147, 0.1272191870787998, 0.31566512580559297, 0.2040103118301236, 0.3151770008504149, -0.4653850999697075, -0.2847753314924269, 0.08546263603614522, 0.10630743861404042, 0.058112288834285236, 0.022862206643308187, -0.31546849107926156, 0.06883738642216769, -0.2236798943334876, 0.06155150544726858, -0.1861781848574892, -0.011292491591983027, 0.011909641454740149, -0.2749073939906681, 0.05487832388528411, 0.11882408917957142, 0.05576690320868964, -0.07785800995468464, -0.12649795428722002, 0.0473484852662697, 0.1614195386132274, 0.03831837297033204, -0.0011218802961097522, 0.11423130694915867, -0.15503841027611406, -0.2020410070824739, 0.3542304675082688, -0.06425925812913806, -0.20840923080081725, 0.16736121012605484, -0.028889787074952544, -0.19458915015029443, 0.053477314726431406, 0.2699366135476611, 0.05154514733653564, -0.16695081184156293, 0.057542416092096045, -0.053470037324208915, 0.19916160128309163, 0.017001154041824888, 0.013951750943896833, 0.18070932951235733, 0.2782094231262416, -0.020556217724723474, 0.058397940165354184, -0.0670599294508892, -0.15549482911970322, -0.16830255845319722, -0.06750220843808508, -0.18312812521569916, -0.0050642663996036215, -0.17303036097650698, -0.1678082289524957, 0.3942736094611903, 0.21053513254389747, 0.20700043426636097, 0.10532688348729453, 0.3552530351663371, 0.03772000308593058, 0.16411497001082626, 0.09124462337341982, 0.15826330683798856, 0.05291857433735163, 0.1765142067361248, -0.1569426694687217, 0.14743579675552326, 0.011022366604891715] |
1,802.04417 | Jets, Arcs and Shocks: NGC 5195 at radio wavelengths | We studied the nearby, interacting galaxy NGC 5195 (M51b) in the radio,
optical and X-ray bands. We mapped the extended, low-surface-brightness
features of its radio-continuum emission; determined the energy content of its
complex structure of shock-ionized gas; constrained the current activity level
of its supermassive nuclear black hole. In particular, we combined data from
the European Very Long Baseline Interferometry Network (~1-pc scale), from our
new e-MERLIN observations (~10-pc scale), and from the Very Large Array
(~100-1000-pc scale), to obtain a global picture of energy injection in this
galaxy. We put an upper limit to the luminosity of the (undetected)
flat-spectrum radio core. We find steep-spectrum, extended emission within 10
pc of the nuclear position, consistent with optically-thin synchrotron emission
from nuclear star formation or from an outflow powered by an active galactic
nucleus (AGN). A linear spur of radio emission juts out of the nuclear source
towards the kpc-scale arcs (detected in radio, Halpha and X-ray bands). From
the size, shock velocity, and Balmer line luminosity of the kpc-scale bubble,
we estimate that it was inflated by a long-term-average mechanical power ~3-6 x
10^{41} erg/s over the last 3-6 Myr. This is an order of magnitude more power
than can be provided by the current level of star formation, and by the current
accretion power of the supermassive black hole. We argue that a jet-inflated
bubble scenario associated with previous episodes of AGN activity is the most
likely explanation for the kpc-scale structures.
| astro-ph.GA astro-ph.HE | we studied the nearby interacting galaxy ngc 5195 m51b in the radio optical and xray bands we mapped the extended lowsurfacebrightness features of its radiocontinuum emission determined the energy content of its complex structure of shockionized gas constrained the current activity level of its supermassive nuclear black hole in particular we combined data from the european very long baseline interferometry network 1pc scale from our new emerlin observations 10pc scale and from the very large array 1001000pc scale to obtain a global picture of energy injection in this galaxy we put an upper limit to the luminosity of the undetected flatspectrum radio core we find steepspectrum extended emission within 10 pc of the nuclear position consistent with opticallythin synchrotron emission from nuclear star formation or from an outflow powered by an active galactic nucleus agn a linear spur of radio emission juts out of the nuclear source towards the kpcscale arcs detected in radio halpha and xray bands from the size shock velocity and balmer line luminosity of the kpcscale bubble we estimate that it was inflated by a longtermaverage mechanical power 36 x 1041 ergs over the last 36 myr this is an order of magnitude more power than can be provided by the current level of star formation and by the current accretion power of the supermassive black hole we argue that a jetinflated bubble scenario associated with previous episodes of agn activity is the most likely explanation for the kpcscale structures | [['we', 'studied', 'the', 'nearby', 'interacting', 'galaxy', 'ngc', '5195', 'm51b', 'in', 'the', 'radio', 'optical', 'and', 'xray', 'bands', 'we', 'mapped', 'the', 'extended', 'lowsurfacebrightness', 'features', 'of', 'its', 'radiocontinuum', 'emission', 'determined', 'the', 'energy', 'content', 'of', 'its', 'complex', 'structure', 'of', 'shockionized', 'gas', 'constrained', 'the', 'current', 'activity', 'level', 'of', 'its', 'supermassive', 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1,802.04418 | Measuring Electromagnetic and Gravitational Responses of Photonic Landau
Levels | The topology of an object describes global properties that are insensitive to
local perturbations. Classic examples include string knots and the genus
(number of handles) of a surface: no manipulation of a closed string short of
cutting it changes its "knottedness"; and no deformation of a closed surface,
short of puncturing it, changes how many handles it has. Topology has recently
become an intense focus of condensed matter physics, where it arises in the
context of the quantum Hall effect [1] and topological insulators [2]. In each
case, topology is defined through invariants of the material's bulk [3-5], but
experimentally measured through chiral/helical properties of the material's
edges. In this work we measure topological invariants of a quantum Hall
material through local response of the bulk: treating the material as a
many-port circulator enables direct measurement of the Chern number as the
spatial winding of the circulator phase; excess density accumulation near
spatial curvature quantifies the curvature-analog of charge known as mean
orbital spin, while the moment of inertia of this excess density reflects the
chiral central charge. We observe that the topological invariants converge to
their global values when probed over a few magnetic lengths lB, consistent with
intuition that the bulk/edge distinction exists only for samples larger than a
few lB. By performing these experiments in photonic Landau levels of a twisted
resonator [6], we apply quantum-optics tools to topological matter. Combined
with developments in Rydberg-mediated interactions between resonator photons
[7], this work augurs an era of precision characterization of topological
matter in strongly correlated fluids of light.
| cond-mat.quant-gas cond-mat.str-el physics.atom-ph | the topology of an object describes global properties that are insensitive to local perturbations classic examples include string knots and the genus number of handles of a surface no manipulation of a closed string short of cutting it changes its knottedness and no deformation of a closed surface short of puncturing it changes how many handles it has topology has recently become an intense focus of condensed matter physics where it arises in the context of the quantum hall effect 1 and topological insulators 2 in each case topology is defined through invariants of the materials bulk 35 but experimentally measured through chiralhelical properties of the materials edges in this work we measure topological invariants of a quantum hall material through local response of the bulk treating the material as a manyport circulator enables direct measurement of the chern number as the spatial winding of the circulator phase excess density accumulation near spatial curvature quantifies the curvatureanalog of charge known as mean orbital spin while the moment of inertia of this excess density reflects the chiral central charge we observe that the topological invariants converge to their global values when probed over a few magnetic lengths lb consistent with intuition that the bulkedge distinction exists only for samples larger than a few lb by performing these experiments in photonic landau levels of a twisted resonator 6 we apply quantumoptics tools to topological matter combined with developments in rydbergmediated interactions between resonator photons 7 this work augurs an era of precision characterization of topological matter in strongly correlated fluids of light | [['the', 'topology', 'of', 'an', 'object', 'describes', 'global', 'properties', 'that', 'are', 'insensitive', 'to', 'local', 'perturbations', 'classic', 'examples', 'include', 'string', 'knots', 'and', 'the', 'genus', 'number', 'of', 'handles', 'of', 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1,802.04419 | Iwasawa theory for Rankin--Selberg products of $p$-non-ordinary
eigenforms | Let $f$ and $g$ be two modular forms which are non-ordinary at $p$. The
theory of Beilinson-Flach elements gives rise to four rank-one non-integral
Euler systems for the Rankin-Selberg convolution $f \otimes g$, one for each
choice of $p$-stabilisations of $f$ and $g$. We prove (modulo a hypothesis on
non-vanishing of $p$-adic $L$-fuctions) that the $p$-parts of these four
objects arise as the images under appropriate projection maps of a single class
in the wedge square of Iwasawa cohomology, confirming a conjecture of
Lei-Loeffler-Zerbes.
Furthermore, we define an explicit logarithmic matrix using the theory of
Wach modules, and show that this describes the growth of the Euler systems and
$p$-adic $L$-functions associated to $f \otimes g$ in the cyclotomic tower.
This allows us to formulate "signed" Iwasawa main conjectures for $f\otimes g$
in the spirit of Kobayashi's $\pm$-Iwasawa theory for supersingular elliptic
curves; and we prove one inclusion in these conjectures under our running
hypotheses.
| math.NT | let f and g be two modular forms which are nonordinary at p the theory of beilinsonflach elements gives rise to four rankone nonintegral euler systems for the rankinselberg convolution f otimes g one for each choice of pstabilisations of f and g we prove modulo a hypothesis on nonvanishing of padic lfuctions that the pparts of these four objects arise as the images under appropriate projection maps of a single class in the wedge square of iwasawa cohomology confirming a conjecture of leiloefflerzerbes furthermore we define an explicit logarithmic matrix using the theory of wach modules and show that this describes the growth of the euler systems and padic lfunctions associated to f otimes g in the cyclotomic tower this allows us to formulate signed iwasawa main conjectures for fotimes g in the spirit of kobayashis pmiwasawa theory for supersingular elliptic curves and we prove one inclusion in these conjectures under our running hypotheses | [['let', 'f', 'and', 'g', 'be', 'two', 'modular', 'forms', 'which', 'are', 'nonordinary', 'at', 'p', 'the', 'theory', 'of', 'beilinsonflach', 'elements', 'gives', 'rise', 'to', 'four', 'rankone', 'nonintegral', 'euler', 'systems', 'for', 'the', 'rankinselberg', 'convolution', 'f', 'otimes', 'g', 'one', 'for', 'each', 'choice', 'of', 'pstabilisations', 'of', 'f', 'and', 'g', 'we', 'prove', 'modulo', 'a', 'hypothesis', 'on', 'nonvanishing', 'of', 'padic', 'lfuctions', 'that', 'the', 'pparts', 'of', 'these', 'four', 'objects', 'arise', 'as', 'the', 'images', 'under', 'appropriate', 'projection', 'maps', 'of', 'a', 'single', 'class', 'in', 'the', 'wedge', 'square', 'of', 'iwasawa', 'cohomology', 'confirming', 'a', 'conjecture', 'of', 'leiloefflerzerbes', 'furthermore', 'we', 'define', 'an', 'explicit', 'logarithmic', 'matrix', 'using', 'the', 'theory', 'of', 'wach', 'modules', 'and', 'show', 'that', 'this', 'describes', 'the', 'growth', 'of', 'the', 'euler', 'systems', 'and', 'padic', 'lfunctions', 'associated', 'to', 'f', 'otimes', 'g', 'in', 'the', 'cyclotomic', 'tower', 'this', 'allows', 'us', 'to', 'formulate', 'signed', 'iwasawa', 'main', 'conjectures', 'for', 'fotimes', 'g', 'in', 'the', 'spirit', 'of', 'kobayashis', 'pmiwasawa', 'theory', 'for', 'supersingular', 'elliptic', 'curves', 'and', 'we', 'prove', 'one', 'inclusion', 'in', 'these', 'conjectures', 'under', 'our', 'running', 'hypotheses']] | [-0.22974236941409132, 0.049367955387111244, -0.14894763760219346, 0.05269363312056868, -0.07170723128249708, -0.14365771802339627, -0.04661469799693865, 0.2708046689031288, -0.31748047995300877, -0.2359371633077694, 0.05466490120639322, -0.21421871624822614, -0.1467469683162165, 0.22133176280062225, -0.126501949849409, 0.01392529196790512, 0.020660661036049983, 0.10103533555142927, -0.08668557664114267, -0.31837901266812907, 0.4055400516757655, -0.08052258683323847, 0.19745029534971872, 0.04126501781017896, 0.09671064399318485, 0.03317446480597684, 0.0009128046499577579, -0.0851766139250372, -0.14364361615178087, 0.1683071804670802, 0.3060724096425149, 0.05999979668255242, 0.24579105063323056, -0.3969005879680842, -0.11099821932471993, 0.18528657601776205, 0.07757058084671793, -0.029156632928812987, 0.02506264722027156, -0.2412186252533008, 0.12498549785193623, -0.17892799901449127, -0.18489199631619235, -0.07229550389591481, 0.058216643881280936, 0.01374753012779841, -0.315876777660077, 0.016378773412099014, 0.08478514618542514, 0.12815272104957245, -0.0795834759294228, -0.17604070035617378, -0.015926793361058, 0.06712895806952818, 0.029865627482360758, 0.04541690775581839, 0.07432978552309734, -0.11639345525409286, -0.10980135416735383, 0.35725689215573253, -0.09843003577768605, -0.17703749172833583, 0.11115415299632, -0.1708352046645561, -0.2333283779544398, 0.09738709698030293, 0.08480325129055818, 0.1428031904040065, 0.013949635350731331, 0.17463449414454044, -0.1539698421167956, 0.06362270322683829, 0.1195507699111785, -0.041050169342475025, 0.1418255658462545, 0.0146940461874921, 0.07637992935156487, 0.147853826425911, -0.01145989411943808, 0.005043696627538923, -0.3862387803537364, -0.21152022008056326, -0.11807534740374281, 0.14363280826229727, -0.11993078584888786, -0.17288559684930802, 0.4136196154521297, 0.09714992095821148, 0.16596656441472668, 0.16086070543380196, 0.16620662481376025, 0.09846822027924983, 0.07106931355245263, 0.031496642673980224, 0.08615752337007038, 0.25747796034576087, -0.05448858075102927, -0.16221401710772504, -0.03681415234636885, 0.21480579950248466] |
1,802.0442 | Towards Understanding the Generalization Bias of Two Layer Convolutional
Linear Classifiers with Gradient Descent | A major challenge in understanding the generalization of deep learning is to
explain why (stochastic) gradient descent can exploit the network architecture
to find solutions that have good generalization performance when using high
capacity models. We find simple but realistic examples showing that this
phenomenon exists even when learning linear classifiers --- between two linear
networks with the same capacity, the one with a convolutional layer can
generalize better than the other when the data distribution has some underlying
spatial structure. We argue that this difference results from a combination of
the convolution architecture, data distribution and gradient descent, all of
which are necessary to be included in a meaningful analysis. We provide a
general analysis of the generalization performance as a function of data
distribution and convolutional filter size, given gradient descent as the
optimization algorithm, then interpret the results using concrete examples.
Experimental results show that our analysis is able to explain what happens in
our introduced examples.
| cs.LG stat.ML | a major challenge in understanding the generalization of deep learning is to explain why stochastic gradient descent can exploit the network architecture to find solutions that have good generalization performance when using high capacity models we find simple but realistic examples showing that this phenomenon exists even when learning linear classifiers between two linear networks with the same capacity the one with a convolutional layer can generalize better than the other when the data distribution has some underlying spatial structure we argue that this difference results from a combination of the convolution architecture data distribution and gradient descent all of which are necessary to be included in a meaningful analysis we provide a general analysis of the generalization performance as a function of data distribution and convolutional filter size given gradient descent as the optimization algorithm then interpret the results using concrete examples experimental results show that our analysis is able to explain what happens in our introduced examples | [['a', 'major', 'challenge', 'in', 'understanding', 'the', 'generalization', 'of', 'deep', 'learning', 'is', 'to', 'explain', 'why', 'stochastic', 'gradient', 'descent', 'can', 'exploit', 'the', 'network', 'architecture', 'to', 'find', 'solutions', 'that', 'have', 'good', 'generalization', 'performance', 'when', 'using', 'high', 'capacity', 'models', 'we', 'find', 'simple', 'but', 'realistic', 'examples', 'showing', 'that', 'this', 'phenomenon', 'exists', 'even', 'when', 'learning', 'linear', 'classifiers', 'between', 'two', 'linear', 'networks', 'with', 'the', 'same', 'capacity', 'the', 'one', 'with', 'a', 'convolutional', 'layer', 'can', 'generalize', 'better', 'than', 'the', 'other', 'when', 'the', 'data', 'distribution', 'has', 'some', 'underlying', 'spatial', 'structure', 'we', 'argue', 'that', 'this', 'difference', 'results', 'from', 'a', 'combination', 'of', 'the', 'convolution', 'architecture', 'data', 'distribution', 'and', 'gradient', 'descent', 'all', 'of', 'which', 'are', 'necessary', 'to', 'be', 'included', 'in', 'a', 'meaningful', 'analysis', 'we', 'provide', 'a', 'general', 'analysis', 'of', 'the', 'generalization', 'performance', 'as', 'a', 'function', 'of', 'data', 'distribution', 'and', 'convolutional', 'filter', 'size', 'given', 'gradient', 'descent', 'as', 'the', 'optimization', 'algorithm', 'then', 'interpret', 'the', 'results', 'using', 'concrete', 'examples', 'experimental', 'results', 'show', 'that', 'our', 'analysis', 'is', 'able', 'to', 'explain', 'what', 'happens', 'in', 'our', 'introduced', 'examples']] | [-0.03611319666067958, -0.0043038737831297535, -0.1111101766099345, 0.11248884649373263, -0.09463134971183707, -0.17024027883608198, 0.003997245942588028, 0.42223241170809706, -0.30591489871342975, -0.3102774711958361, 0.09624101120594936, -0.25139335079891784, -0.24882666239772672, 0.2036186985542658, -0.11195251609216321, 0.034742609953944154, 0.12008624562463553, 0.037532172600904176, -0.09271389581468073, -0.28271344268794096, 0.2956041613456049, 0.0646433678430757, 0.3224056014416259, 0.025885168523029804, 0.11030853775098522, -0.06117591682423211, 0.01713062496562986, 0.03495420912594048, -0.08346667137299371, 0.13583399134222418, 0.26841136858075637, 0.1681481233421631, 0.3097173357924224, -0.416709502221747, -0.2452673818271771, 0.11647094270132435, 0.14623225618826677, 0.12859300048011443, -0.07218511734134371, -0.23834356428534026, 0.11290976150421156, -0.158127189086715, -0.07259271480435939, -0.14822109369268208, -0.05085899588892107, 0.04449064292104434, -0.28695788580533294, 0.029985308041649655, 0.09204146915566434, 0.03421196466999688, -0.04198415435077447, -0.12092345865639571, 0.011145439230303906, 0.1165473776290694, 0.03236124083786359, 0.04321972898693767, 0.09336635766919914, -0.1396226903113226, -0.12163550166834919, 0.3328801895661643, -0.09322823870551125, -0.22810183379111984, 0.17909467262479495, -0.09596607208825976, -0.1544024936690833, 0.07215008037838405, 0.201072926023893, 0.09051816288159235, -0.13796416721656354, 0.00574940073072512, -0.0872560744788849, 0.1709738082176297, 0.015335313006415495, -0.0003418317544215458, 0.14166863696538382, 0.22030923994178972, 0.07503607495887053, 0.16567518830163194, -0.09010793106138425, -0.11919790286931016, -0.29089419652104564, -0.11283501048437641, -0.18048833684377819, 0.013513881762207445, -0.11900587167877136, -0.12667023491278384, 0.38266334614757475, 0.19998233525943504, 0.2707310169818079, 0.11737036421007635, 0.32832673116830186, 0.09031941636857933, 0.11802446375946579, 0.11694730703467093, 0.21004154805900124, 0.11340095109889084, 0.1011035084876718, -0.1737974444440853, 0.08998504684617122, 0.03625982817930152] |
1,802.04421 | X-Ray sum frequency generation; direct imaging of ultrafast electron
dynamics | X-ray diffraction from molecules in the ground state produces an image of
their charge density, and time-resolved X-ray diffraction can thus monitor the
motion of the nuclei. However, the density change of excited valence electrons
upon optical excitation can barely be monitored with regular diffraction
techniques due to the overwhelming background contribution of the core
electrons. We present a nonlinear X-ray technique made possible by novel free
electron laser sources, which provides a spatial electron density image of
valence electron excitations. The technique, sum frequency generation carried
out with a visible pump and a broadband X-ray diffraction pulse, yields
snapshots of the transition charge densities, which represent the electron
density variations upon optical excitation. The technique is illustrated by ab
initio simulations of transition charge density imaging for the optically
induced electronic dynamics in a donor/acceptor substituted stilbene.
| physics.chem-ph | xray diffraction from molecules in the ground state produces an image of their charge density and timeresolved xray diffraction can thus monitor the motion of the nuclei however the density change of excited valence electrons upon optical excitation can barely be monitored with regular diffraction techniques due to the overwhelming background contribution of the core electrons we present a nonlinear xray technique made possible by novel free electron laser sources which provides a spatial electron density image of valence electron excitations the technique sum frequency generation carried out with a visible pump and a broadband xray diffraction pulse yields snapshots of the transition charge densities which represent the electron density variations upon optical excitation the technique is illustrated by ab initio simulations of transition charge density imaging for the optically induced electronic dynamics in a donoracceptor substituted stilbene | [['xray', 'diffraction', 'from', 'molecules', 'in', 'the', 'ground', 'state', 'produces', 'an', 'image', 'of', 'their', 'charge', 'density', 'and', 'timeresolved', 'xray', 'diffraction', 'can', 'thus', 'monitor', 'the', 'motion', 'of', 'the', 'nuclei', 'however', 'the', 'density', 'change', 'of', 'excited', 'valence', 'electrons', 'upon', 'optical', 'excitation', 'can', 'barely', 'be', 'monitored', 'with', 'regular', 'diffraction', 'techniques', 'due', 'to', 'the', 'overwhelming', 'background', 'contribution', 'of', 'the', 'core', 'electrons', 'we', 'present', 'a', 'nonlinear', 'xray', 'technique', 'made', 'possible', 'by', 'novel', 'free', 'electron', 'laser', 'sources', 'which', 'provides', 'a', 'spatial', 'electron', 'density', 'image', 'of', 'valence', 'electron', 'excitations', 'the', 'technique', 'sum', 'frequency', 'generation', 'carried', 'out', 'with', 'a', 'visible', 'pump', 'and', 'a', 'broadband', 'xray', 'diffraction', 'pulse', 'yields', 'snapshots', 'of', 'the', 'transition', 'charge', 'densities', 'which', 'represent', 'the', 'electron', 'density', 'variations', 'upon', 'optical', 'excitation', 'the', 'technique', 'is', 'illustrated', 'by', 'ab', 'initio', 'simulations', 'of', 'transition', 'charge', 'density', 'imaging', 'for', 'the', 'optically', 'induced', 'electronic', 'dynamics', 'in', 'a', 'donoracceptor', 'substituted', 'stilbene']] | [-0.07735875690612348, 0.22576463043251482, -0.08452840676477207, 0.05031425165319545, 0.011983146330854599, -0.08455630052713273, 0.10773983572616472, 0.4602556913767172, -0.24613953323062995, -0.3110614148360016, -0.007241097088241815, -0.3425672941763575, -0.04112444667186102, 0.1619776883095071, 0.05809196914154766, 0.05316494335462609, 0.022592175019689013, -0.05449656534778035, -0.07427360747301398, -0.11089401369057325, 0.25738199234035786, 0.0945394697221185, 0.31467792377773457, 0.07685426165066335, 0.09198390544000742, 0.06842767983418552, -0.0083847065705914, -0.011056869837414959, -0.08762410512882406, 0.10884322797663618, 0.28116088778968307, 0.007924805117764281, 0.1822705805517625, -0.4781330617725093, -0.27966528163170035, -0.03187366728535921, 0.14316366480130271, 0.0974152623726379, -0.11195250092060778, -0.27589248151828843, -0.029446942063615374, -0.14695681741400418, -0.16312036921094725, -0.0960658028474216, -0.029081673563822456, 0.07099953087542774, -0.25698409092180646, 0.11265819701298639, -0.0034032003005858564, 0.03388776486837373, -0.1334771237967104, -0.050309130746493305, -0.06892849496174333, 0.04192977787781696, 0.0012878148489213293, 0.06202027645380492, 0.2214234763974884, -0.1082219289281689, -0.10065929255232323, 0.3498963647441048, -0.03674409944105191, -0.052857871066974614, 0.13080119540108423, -0.21107184897055445, -0.05605742887195632, 0.28213728402835736, 0.07354988989317317, 0.15565355236480094, -0.14194942687121828, 0.03243173223808813, 0.0024554673337674553, 0.2801180879544953, 0.11271261541253846, 0.09828111756107082, 0.2819633651811603, 0.17498772802924656, 0.03474631598489224, 0.13632925447208158, -0.23864809465527104, 0.00944004340588277, -0.17636832432564942, -0.08375434651026044, -0.232829793923251, 0.10419548763488622, -0.025960816724851822, -0.16917860705322801, 0.4127010210430708, 0.09806400941401396, 0.09968058296573767, -0.09955939908167752, 0.34406754870872025, 0.14322754933348641, 0.032330792230229985, -0.0035843302814654357, 0.23657177635417928, 0.18164368462748826, 0.10914677468573918, -0.31254734815709106, 0.019768786123967257, 0.018647884978723807] |
1,802.04422 | A comparative study of fairness-enhancing interventions in machine
learning | Computers are increasingly used to make decisions that have significant
impact in people's lives. Often, these predictions can affect different
population subgroups disproportionately. As a result, the issue of fairness has
received much recent interest, and a number of fairness-enhanced classifiers
and predictors have appeared in the literature. This paper seeks to study the
following questions: how do these different techniques fundamentally compare to
one another, and what accounts for the differences? Specifically, we seek to
bring attention to many under-appreciated aspects of such fairness-enhancing
interventions. Concretely, we present the results of an open benchmark we have
developed that lets us compare a number of different algorithms under a variety
of fairness measures, and a large number of existing datasets. We find that
although different algorithms tend to prefer specific formulations of fairness
preservations, many of these measures strongly correlate with one another. In
addition, we find that fairness-preserving algorithms tend to be sensitive to
fluctuations in dataset composition (simulated in our benchmark by varying
training-test splits), indicating that fairness interventions might be more
brittle than previously thought.
| stat.ML cs.CY cs.LG | computers are increasingly used to make decisions that have significant impact in peoples lives often these predictions can affect different population subgroups disproportionately as a result the issue of fairness has received much recent interest and a number of fairnessenhanced classifiers and predictors have appeared in the literature this paper seeks to study the following questions how do these different techniques fundamentally compare to one another and what accounts for the differences specifically we seek to bring attention to many underappreciated aspects of such fairnessenhancing interventions concretely we present the results of an open benchmark we have developed that lets us compare a number of different algorithms under a variety of fairness measures and a large number of existing datasets we find that although different algorithms tend to prefer specific formulations of fairness preservations many of these measures strongly correlate with one another in addition we find that fairnesspreserving algorithms tend to be sensitive to fluctuations in dataset composition simulated in our benchmark by varying trainingtest splits indicating that fairness interventions might be more brittle than previously thought | [['computers', 'are', 'increasingly', 'used', 'to', 'make', 'decisions', 'that', 'have', 'significant', 'impact', 'in', 'peoples', 'lives', 'often', 'these', 'predictions', 'can', 'affect', 'different', 'population', 'subgroups', 'disproportionately', 'as', 'a', 'result', 'the', 'issue', 'of', 'fairness', 'has', 'received', 'much', 'recent', 'interest', 'and', 'a', 'number', 'of', 'fairnessenhanced', 'classifiers', 'and', 'predictors', 'have', 'appeared', 'in', 'the', 'literature', 'this', 'paper', 'seeks', 'to', 'study', 'the', 'following', 'questions', 'how', 'do', 'these', 'different', 'techniques', 'fundamentally', 'compare', 'to', 'one', 'another', 'and', 'what', 'accounts', 'for', 'the', 'differences', 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1,802.04423 | Geodesic planes in geometrically finite acylindrical 3-manifolds | Let $M$ be a geometrically finite acylindrical hyperbolic 3-manifold and let
$M^*$ denote the interior of the convex core of M. We show that any geodesic
plane in $M^*$ is either closed or dense, and that there are only countably
many closed geodesic planes in $M^*$. These results were obtained earlier by
McMullen, Mohammadi, and the second named author when M is convex cocompact.
As a corollary we obtain that when $M$ covers an arithmetic hyperbolic
3-manifold $M_0$, the topological behavior of a geodesic plane in $M^*$ is
governed by that of the corresponding plane in $M_0$. We construct a
counterexample of this phenomenon when $M_0$ is non-arithmetic.
| math.DS math.GT | let m be a geometrically finite acylindrical hyperbolic 3manifold and let m denote the interior of the convex core of m we show that any geodesic plane in m is either closed or dense and that there are only countably many closed geodesic planes in m these results were obtained earlier by mcmullen mohammadi and the second named author when m is convex cocompact as a corollary we obtain that when m covers an arithmetic hyperbolic 3manifold m_0 the topological behavior of a geodesic plane in m is governed by that of the corresponding plane in m_0 we construct a counterexample of this phenomenon when m_0 is nonarithmetic | [['let', 'm', 'be', 'a', 'geometrically', 'finite', 'acylindrical', 'hyperbolic', '3manifold', 'and', 'let', 'm', 'denote', 'the', 'interior', 'of', 'the', 'convex', 'core', 'of', 'm', 'we', 'show', 'that', 'any', 'geodesic', 'plane', 'in', 'm', 'is', 'either', 'closed', 'or', 'dense', 'and', 'that', 'there', 'are', 'only', 'countably', 'many', 'closed', 'geodesic', 'planes', 'in', 'm', 'these', 'results', 'were', 'obtained', 'earlier', 'by', 'mcmullen', 'mohammadi', 'and', 'the', 'second', 'named', 'author', 'when', 'm', 'is', 'convex', 'cocompact', 'as', 'a', 'corollary', 'we', 'obtain', 'that', 'when', 'm', 'covers', 'an', 'arithmetic', 'hyperbolic', '3manifold', 'm_0', 'the', 'topological', 'behavior', 'of', 'a', 'geodesic', 'plane', 'in', 'm', 'is', 'governed', 'by', 'that', 'of', 'the', 'corresponding', 'plane', 'in', 'm_0', 'we', 'construct', 'a', 'counterexample', 'of', 'this', 'phenomenon', 'when', 'm_0', 'is', 'nonarithmetic']] | [-0.24310506721613584, 0.17404063646608847, -0.07799191798807846, 0.027774168289464864, -0.10960970439999881, -0.1278807053135501, -0.004197944041893438, 0.34935667325259634, -0.26474167184076375, -0.25645875477197544, 0.1111787579074088, -0.30777657242422857, -0.1389967972365277, 0.23526375151575646, -0.1568035649014982, -0.03365681323231439, 0.06355758486919243, 0.08062348666135222, -0.08334045364905183, -0.26217225125852833, 0.381687349881287, -0.11699165545777995, 0.14228788810174187, 0.04849883876859504, 0.1236773456050152, -0.007357970315583602, 0.027250304236076772, 0.07715935055037132, -0.21388730575051373, 0.07253390466104503, 0.2916560642188415, 0.07212455067093726, 0.19568957248271476, -0.3264695151247671, -0.18490969502643026, 0.19146136570230332, 0.16346329313927088, -0.0868748797931605, -0.045094761057515385, -0.25961094907553933, 0.16576407247447167, -0.0767714639659971, -0.17705527781853797, -0.011511041685783615, 0.10373591111662488, -0.011554884476397463, -0.20088544824264115, -0.002801713274998797, 0.18216013235764372, 0.09465465121212657, -0.026283080150649226, -0.10078846044079573, -0.09448553516132827, 0.079587971959511, -0.005578308297252214, 0.12785787008814858, 0.0586888664477522, 0.03127528937680095, -0.07178024744994387, 0.3933224242258196, -0.09360429087084615, -0.2712167251290036, 0.1109682632420488, -0.2040872476300811, -0.11984254794057321, 0.15579862937469174, 0.12158486451436248, 0.19086287384076664, -0.07048994259188206, 0.24302132792298733, -0.1893512009755122, 0.0892495044282879, 0.1171064136972375, -0.09127379945345672, 0.17670797025439916, 0.10582873512154307, 0.1460250902967312, 0.13270018920755117, 0.010304509808688803, 0.006356289912084186, -0.3546937896990804, -0.1867357282123218, -0.2229021424071915, 0.17274164064373407, -0.1312152216836424, -0.16954371703495444, 0.25050443789331, -0.0296489205211401, 0.20737890223110164, 0.11074033221605835, 0.23512394972042078, 0.03481836188843267, 0.011657926081507295, 0.15796306090325946, 0.11906919096320591, 0.16089860494550387, -0.06562785916375341, -0.1454905597689773, -0.037340816959773225, 0.1795845547070106] |
1,802.04424 | L^p operator algebras with approximate identities I | We initiate an investigation into how much the existing theory of
(nonselfadjoint) operator algebras on a Hilbert space generalizes to algebras
acting on L^p spaces. In particular we investigate the applicability of the
theory of real positivity, which has recently been useful in the study of
L^2-operator algebras and Banach algebras, to algebras of bounded operators on
Lp spaces.
| math.FA math-ph math.MP math.OA | we initiate an investigation into how much the existing theory of nonselfadjoint operator algebras on a hilbert space generalizes to algebras acting on lp spaces in particular we investigate the applicability of the theory of real positivity which has recently been useful in the study of l2operator algebras and banach algebras to algebras of bounded operators on lp spaces | [['we', 'initiate', 'an', 'investigation', 'into', 'how', 'much', 'the', 'existing', 'theory', 'of', 'nonselfadjoint', 'operator', 'algebras', 'on', 'a', 'hilbert', 'space', 'generalizes', 'to', 'algebras', 'acting', 'on', 'lp', 'spaces', 'in', 'particular', 'we', 'investigate', 'the', 'applicability', 'of', 'the', 'theory', 'of', 'real', 'positivity', 'which', 'has', 'recently', 'been', 'useful', 'in', 'the', 'study', 'of', 'l2operator', 'algebras', 'and', 'banach', 'algebras', 'to', 'algebras', 'of', 'bounded', 'operators', 'on', 'lp', 'spaces']] | [-0.08997954344534773, 0.058434393803709406, -0.08109898632392287, 0.12482846771382679, -0.13339305306405966, -0.06339440864937032, -0.056265750161956174, 0.3692296409379628, -0.3346271729807101, -0.13668986974220912, 0.18559829715118445, -0.24656757177248345, -0.16463898276610267, 0.2165088931540564, -0.17107821888905966, 0.021131540617068945, 0.04875758129282523, 0.0985513446631573, -0.18368660674383075, -0.2715865220022151, 0.4995642022971632, 0.020142720345313014, 0.20648324727948944, 0.08374812744431577, 0.07078080933730481, 0.07739405455146679, -0.023022026896981868, -0.012390954096868665, -0.18679929485197289, 0.1673363941579552, 0.29103728413179325, 0.08660938408841395, 0.33045590274288494, -0.4162018577236745, -0.18999057460450014, 0.2094765277737278, 0.1673908331983928, -0.02308792254090372, -0.03071238033244607, -0.3268620028334149, 0.024334836340809272, -0.16133452953486624, -0.07951828024460603, -0.13286659771860657, 0.062378665748036514, -0.04072386894564507, -0.2627122749779689, -0.007187475695006423, 0.10276700654836624, 0.10948263130829496, -0.20184152848008324, -0.0966584401805017, -0.050005349630521514, 0.05422172993737257, -0.07622767415813218, 0.011861580672626526, 0.09403042761230115, -0.004517055361263327, -0.19564821258566137, 0.3235668947888633, 0.014368777969354858, -0.23453495885103437, 0.14935752610533926, -0.23280869826863884, -0.19186114254644362, 0.013110318773631321, 0.18749550018080716, 0.19002806006978123, -0.04447748144236156, 0.273662524312632, -0.13275652885500153, 0.019074757788646018, 0.053507315263278406, 0.11295864077569065, 0.059462792504484115, 0.14525024346233803, 0.10603276124003075, 0.17455174059654444, 0.11393081261855313, -0.10827943804018768, -0.3038633023783312, -0.14804735359877852, -0.10646253533787646, 0.08097729964529053, -0.07851568239290165, -0.19472977645316367, 0.4229314170572591, 0.17013402728332302, 0.16992544194967565, 0.08868312018221024, 0.1887696575120849, 0.11459823104284578, 0.16127383636342266, -0.014551744078112355, 0.16737945609868077, 0.31064644354884907, 0.07996791445302888, -0.13855210755620215, -0.06017541793032975, 0.2501228628292881] |
1,802.04425 | "How Was Your Weekend?" A Generative Model of Phatic Conversation | Unspoken social rules, such as those that govern choosing a proper discussion
topic and when to change discussion topics, guide conversational behaviors. We
propose a computational model of conversation that can follow or break such
rules, with participant agents that respond accordingly. Additionally, we
demonstrate an application of the model: the Experimental Social Tutor (EST), a
first step toward a social skills training tool that generates human-readable
conversation and a conversational guideline at each point in the dialogue.
Finally, we discuss the design and results of a pilot study evaluating the EST.
Results show that our model is capable of producing conversations that follow
social norms.
| cs.CL cs.AI | unspoken social rules such as those that govern choosing a proper discussion topic and when to change discussion topics guide conversational behaviors we propose a computational model of conversation that can follow or break such rules with participant agents that respond accordingly additionally we demonstrate an application of the model the experimental social tutor est a first step toward a social skills training tool that generates humanreadable conversation and a conversational guideline at each point in the dialogue finally we discuss the design and results of a pilot study evaluating the est results show that our model is capable of producing conversations that follow social norms | [['unspoken', 'social', 'rules', 'such', 'as', 'those', 'that', 'govern', 'choosing', 'a', 'proper', 'discussion', 'topic', 'and', 'when', 'to', 'change', 'discussion', 'topics', 'guide', 'conversational', 'behaviors', 'we', 'propose', 'a', 'computational', 'model', 'of', 'conversation', 'that', 'can', 'follow', 'or', 'break', 'such', 'rules', 'with', 'participant', 'agents', 'that', 'respond', 'accordingly', 'additionally', 'we', 'demonstrate', 'an', 'application', 'of', 'the', 'model', 'the', 'experimental', 'social', 'tutor', 'est', 'a', 'first', 'step', 'toward', 'a', 'social', 'skills', 'training', 'tool', 'that', 'generates', 'humanreadable', 'conversation', 'and', 'a', 'conversational', 'guideline', 'at', 'each', 'point', 'in', 'the', 'dialogue', 'finally', 'we', 'discuss', 'the', 'design', 'and', 'results', 'of', 'a', 'pilot', 'study', 'evaluating', 'the', 'est', 'results', 'show', 'that', 'our', 'model', 'is', 'capable', 'of', 'producing', 'conversations', 'that', 'follow', 'social', 'norms']] | [-0.08535681962791197, 0.08224035869476073, -0.11082740875294889, 0.07525738247534049, -0.18254473023028728, -0.15821466147723906, 0.09703068749733129, 0.4325956644595794, -0.23152215298669376, -0.30527186272490137, 0.013435802873847831, -0.29489370628330364, -0.27099149598617034, 0.16401105274485847, -0.07443602902393015, -0.026679050456293207, 0.10877637484304185, 0.0532289636057784, 0.016169349192905257, -0.25160340676611326, 0.3232666799394449, 0.04756377149759879, 0.3256970897343291, 0.04810261683108039, 0.10320685877473498, 0.01703039547077046, -0.04311609780196241, -0.014326051193869338, -0.109381569186879, 0.12317452934693615, 0.37502108853890226, 0.23414318466928066, 0.40386315020469, -0.41353161030290825, -0.17454299224180364, 0.034141115281941754, 0.11671775532886386, 0.13092642454957626, -0.06390343426797047, -0.32671220749968066, 0.10075839261858249, -0.2184995148684566, -0.09308606401560302, -0.12222622240067653, -0.034705246396731795, 0.047596541281684106, -0.2923813840058052, -0.03058136062134268, 0.11487936569467837, 0.11569167346306988, -0.04885673082767511, -0.08202986786238638, 0.0064891717673437495, 0.2171066834578699, 0.08213808604423835, 0.031162223895080388, 0.1719096706398183, -0.17177462487323386, -0.1724606989531444, 0.40549290285639045, -0.02910063395393081, -0.16211098698978224, 0.16244420837083795, -0.016267886490754363, -0.17575990379225673, -0.0007277025384301285, 0.24321561224235855, 0.07981737336266856, -0.14029476800255306, -0.040707967794864915, -0.07204739995441346, 0.20690602565844288, 0.03483185167701691, -0.02996904578692508, 0.20397235690132837, 0.21608170621615466, 0.05065519532698365, 0.08361109077297854, 0.017574873692487843, -0.1073007098012516, -0.2953789547252697, -0.16368881736020996, -0.12732481916944655, 0.027026151298141142, -0.09286910549691936, -0.11776044354159033, 0.37086157901388295, 0.2216987987444297, 0.18145772005609273, 0.10563647709259728, 0.2449370086509383, 0.022449742091099185, 0.03706063450542542, 0.05624639011374762, 0.13933298583054599, -0.060111305570967916, 0.17006407826172434, -0.19830222542503112, 0.10254461980723548, 0.019026093615465006] |
1,802.04426 | High Mobility 2DEG in modulation-doped \b{eta}-(AlxGa1-x)2O3/Ga2O3
heterostructures | Beta-phase Ga2O3 has emerged as a promising candidate for a wide range of
device applications, including power electronic devices, radio-frequency
devices and solar-blind photodetectors. The wide bandgap energy and the
predicted high breakdown field, together with the availability of low-cost
native substrates, make \b{eta}-Ga2O3 a promising material compared to other
conventional wide bandgap materials, such as GaN and SiC. Alloying of Al with
\b{eta}-Ga2O3 could enable even larger band gap materials, and provide more
flexibility for electronic and optoelectronic device design. In this work, we
demonstrate a high mobility two-dimensional electron gas (2DEG) formed at the
\b{eta}-(AlxGa1-x)2O3/Ga2O3 interface through modulation doping. Shubnikov-de
Haas oscillation was observed for the first time in the modulation-doped
\b{eta}-(AlxGa1-x)2O3/Ga2O3 structure, indicating a high-quality channel formed
at the heterojunction interface. The formation of the 2DEG channel was further
confirmed by a weak temperature-dependence of the carrier density, and the peak
low temperature mobility was found to be 2790 cm2/Vs, which is significantly
higher than can be achieved in bulk-doped \b{eta}-Ga2O3. The demonstrated
modulation-doped \b{eta}-(AlxGa1-x)2O3/Ga2O3 structure lays the foundation for
future exploration of quantum physical phenomena as well as new semiconductor
device technologies based on the \b{eta}-Ga2O3 material system.
| cond-mat.mtrl-sci | betaphase ga2o3 has emerged as a promising candidate for a wide range of device applications including power electronic devices radiofrequency devices and solarblind photodetectors the wide bandgap energy and the predicted high breakdown field together with the availability of lowcost native substrates make betaga2o3 a promising material compared to other conventional wide bandgap materials such as gan and sic alloying of al with betaga2o3 could enable even larger band gap materials and provide more flexibility for electronic and optoelectronic device design in this work we demonstrate a high mobility twodimensional electron gas 2deg formed at the betaalxga1x2o3ga2o3 interface through modulation doping shubnikovde haas oscillation was observed for the first time in the modulationdoped betaalxga1x2o3ga2o3 structure indicating a highquality channel formed at the heterojunction interface the formation of the 2deg channel was further confirmed by a weak temperaturedependence of the carrier density and the peak low temperature mobility was found to be 2790 cm2vs which is significantly higher than can be achieved in bulkdoped betaga2o3 the demonstrated modulationdoped betaalxga1x2o3ga2o3 structure lays the foundation for future exploration of quantum physical phenomena as well as new semiconductor device technologies based on the betaga2o3 material system | [['betaphase', 'ga2o3', 'has', 'emerged', 'as', 'a', 'promising', 'candidate', 'for', 'a', 'wide', 'range', 'of', 'device', 'applications', 'including', 'power', 'electronic', 'devices', 'radiofrequency', 'devices', 'and', 'solarblind', 'photodetectors', 'the', 'wide', 'bandgap', 'energy', 'and', 'the', 'predicted', 'high', 'breakdown', 'field', 'together', 'with', 'the', 'availability', 'of', 'lowcost', 'native', 'substrates', 'make', 'betaga2o3', 'a', 'promising', 'material', 'compared', 'to', 'other', 'conventional', 'wide', 'bandgap', 'materials', 'such', 'as', 'gan', 'and', 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1,802.04427 | Deep Learning Models Delineates Multiple Nuclear Phenotypes in H&E
Stained Histology Sections | Nuclear segmentation is an important step for profiling aberrant regions of
histology sections. However, segmentation is a complex problem as a result of
variations in nuclear geometry (e.g., size, shape), nuclear type (e.g.,
epithelial, fibroblast), and nuclear phenotypes (e.g., vesicular, aneuploidy).
The problem is further complicated as a result of variations in sample
preparation. It is shown and validated that fusion of very deep convolutional
networks overcomes (i) complexities associated with multiple nuclear
phenotypes, and (ii) separation of overlapping nuclei. The fusion relies on
integrating of networks that learn region- and boundary-based representations.
The system has been validated on a diverse set of nuclear phenotypes that
correspond to the breast and brain histology sections.
| cs.CV q-bio.QM | nuclear segmentation is an important step for profiling aberrant regions of histology sections however segmentation is a complex problem as a result of variations in nuclear geometry eg size shape nuclear type eg epithelial fibroblast and nuclear phenotypes eg vesicular aneuploidy the problem is further complicated as a result of variations in sample preparation it is shown and validated that fusion of very deep convolutional networks overcomes i complexities associated with multiple nuclear phenotypes and ii separation of overlapping nuclei the fusion relies on integrating of networks that learn region and boundarybased representations the system has been validated on a diverse set of nuclear phenotypes that correspond to the breast and brain histology sections | [['nuclear', 'segmentation', 'is', 'an', 'important', 'step', 'for', 'profiling', 'aberrant', 'regions', 'of', 'histology', 'sections', 'however', 'segmentation', 'is', 'a', 'complex', 'problem', 'as', 'a', 'result', 'of', 'variations', 'in', 'nuclear', 'geometry', 'eg', 'size', 'shape', 'nuclear', 'type', 'eg', 'epithelial', 'fibroblast', 'and', 'nuclear', 'phenotypes', 'eg', 'vesicular', 'aneuploidy', 'the', 'problem', 'is', 'further', 'complicated', 'as', 'a', 'result', 'of', 'variations', 'in', 'sample', 'preparation', 'it', 'is', 'shown', 'and', 'validated', 'that', 'fusion', 'of', 'very', 'deep', 'convolutional', 'networks', 'overcomes', 'i', 'complexities', 'associated', 'with', 'multiple', 'nuclear', 'phenotypes', 'and', 'ii', 'separation', 'of', 'overlapping', 'nuclei', 'the', 'fusion', 'relies', 'on', 'integrating', 'of', 'networks', 'that', 'learn', 'region', 'and', 'boundarybased', 'representations', 'the', 'system', 'has', 'been', 'validated', 'on', 'a', 'diverse', 'set', 'of', 'nuclear', 'phenotypes', 'that', 'correspond', 'to', 'the', 'breast', 'and', 'brain', 'histology', 'sections']] | [-0.01937530274000509, 0.0509875848484195, -0.05620054940983098, 0.09320303958252464, -0.027603429186631712, -0.12819238662262233, -0.022010962201053638, 0.40772288031222526, -0.23566748250864053, -0.27496175237588194, 0.089194680321063, -0.2454882388803781, -0.1833732602852315, 0.19913759160165986, -0.10305776496304468, 0.04279051103482121, 0.12039015125156495, 0.025662097702080614, 0.007061404120912285, -0.20333912130036766, 0.33596493810897265, 0.04321837489898603, 0.3149037484994583, 0.0534830253342526, 0.13367959420480602, -0.012438151415584511, -0.05166172608733177, 0.02797550694984302, -0.037186397985989744, 0.18438769348799006, 0.34813944560786086, 0.17828601632438795, 0.28999983313240174, -0.44544621271849966, -0.2424107993515862, 0.11178473346425515, 0.1677057802619202, 0.11126181475099242, -0.045188144251720554, -0.2933948888104376, 0.0691152821926057, -0.13943128545948288, -0.02595282834852441, -0.10196784677049309, 0.06987047478507616, 0.025818184119232587, -0.27686004307153717, 0.09867275040596724, 0.03184740367884699, 0.09229911183284824, -0.11919794487358447, -0.13952975057518893, -0.0058824294859445405, 0.1734559421198802, 0.009424041245916956, 0.06051718378276156, 0.2505893770874919, -0.2175538656506034, -0.10824156160213119, 0.34182487904469955, 0.08955010945148963, -0.16934555920038533, 0.2267141733645347, -0.07753289853434053, -0.15265268067371818, 0.17328546326934247, 0.18926806237319843, 0.1233282487149138, -0.16095872046099158, 0.030653775999988347, -0.023751017116745442, 0.1924847860589803, 0.09265327138270725, -0.02250105674019116, 0.13210164486397907, 0.2923036138036926, 0.015967182826307977, 0.07759885580417815, -0.19266739872073868, -0.06666230572910424, -0.22127066819628358, -0.10680523888910548, -0.13666364668843975, 0.001963696204097288, -0.07712960991895607, -0.167412793968795, 0.38111841262792023, 0.0309500629327407, 0.25535456358308073, 0.0069510087967245724, 0.2629745157411028, -0.014614360879676767, 0.14579663878274068, -0.012843535055014255, 0.17611459378263375, 0.14796004239510613, 0.062129896730511336, -0.260138888228603, 0.139626253179781, 0.04403237989907594] |
1,802.04428 | Reconciling Enumerative and Symbolic Search in Syntax-Guided Synthesis | Syntax-guided synthesis aims to find a program satisfying semantic
specification as well as user-provided structural hypothesis. For syntax-guided
synthesis there are two main search strategies: concrete search, which
systematically or stochastically enumerates all possible solutions, and
symbolic search, which interacts with a constraint solver to solve the
synthesis problem. In this paper, we propose a concolic synthesis framework
which combines the best of the two worlds. Based on a decision tree
representation, our framework works by enumerating tree heights from the
smallest possible one to larger ones. For each fixed height, the framework
symbolically searches a solution through the counterexample-guided inductive
synthesis approach. To compensate the exponential blow-up problem with the
concolic synthesis framework, we identify two fragments of synthesis problems
and develop purely symbolic and more efficient procedures. The two fragments
are decidable as these procedures are terminating and complete. We implemented
our synthesis procedures and compared with state-of-the-art synthesizers on a
range of benchmarks. Experiments show that our algorithms are promising.
| cs.PL cs.LO | syntaxguided synthesis aims to find a program satisfying semantic specification as well as userprovided structural hypothesis for syntaxguided synthesis there are two main search strategies concrete search which systematically or stochastically enumerates all possible solutions and symbolic search which interacts with a constraint solver to solve the synthesis problem in this paper we propose a concolic synthesis framework which combines the best of the two worlds based on a decision tree representation our framework works by enumerating tree heights from the smallest possible one to larger ones for each fixed height the framework symbolically searches a solution through the counterexampleguided inductive synthesis approach to compensate the exponential blowup problem with the concolic synthesis framework we identify two fragments of synthesis problems and develop purely symbolic and more efficient procedures the two fragments are decidable as these procedures are terminating and complete we implemented our synthesis procedures and compared with stateoftheart synthesizers on a range of benchmarks experiments show that our algorithms are promising | [['syntaxguided', 'synthesis', 'aims', 'to', 'find', 'a', 'program', 'satisfying', 'semantic', 'specification', 'as', 'well', 'as', 'userprovided', 'structural', 'hypothesis', 'for', 'syntaxguided', 'synthesis', 'there', 'are', 'two', 'main', 'search', 'strategies', 'concrete', 'search', 'which', 'systematically', 'or', 'stochastically', 'enumerates', 'all', 'possible', 'solutions', 'and', 'symbolic', 'search', 'which', 'interacts', 'with', 'a', 'constraint', 'solver', 'to', 'solve', 'the', 'synthesis', 'problem', 'in', 'this', 'paper', 'we', 'propose', 'a', 'concolic', 'synthesis', 'framework', 'which', 'combines', 'the', 'best', 'of', 'the', 'two', 'worlds', 'based', 'on', 'a', 'decision', 'tree', 'representation', 'our', 'framework', 'works', 'by', 'enumerating', 'tree', 'heights', 'from', 'the', 'smallest', 'possible', 'one', 'to', 'larger', 'ones', 'for', 'each', 'fixed', 'height', 'the', 'framework', 'symbolically', 'searches', 'a', 'solution', 'through', 'the', 'counterexampleguided', 'inductive', 'synthesis', 'approach', 'to', 'compensate', 'the', 'exponential', 'blowup', 'problem', 'with', 'the', 'concolic', 'synthesis', 'framework', 'we', 'identify', 'two', 'fragments', 'of', 'synthesis', 'problems', 'and', 'develop', 'purely', 'symbolic', 'and', 'more', 'efficient', 'procedures', 'the', 'two', 'fragments', 'are', 'decidable', 'as', 'these', 'procedures', 'are', 'terminating', 'and', 'complete', 'we', 'implemented', 'our', 'synthesis', 'procedures', 'and', 'compared', 'with', 'stateoftheart', 'synthesizers', 'on', 'a', 'range', 'of', 'benchmarks', 'experiments', 'show', 'that', 'our', 'algorithms', 'are', 'promising']] | [-0.04810954909414578, 0.03614688304144364, -0.06701120639368435, 0.09694045385741486, -0.12701102630578073, -0.17629358442385962, 0.07748732833046054, 0.40021330239308395, -0.2844366299822652, -0.3616026007096095, 0.13261213117247686, -0.2218461981317474, -0.14245372290860153, 0.21477111455830145, -0.025848088854569597, 0.11876368688625152, 0.09123067326980866, -0.036616554086673664, -0.05609790640443912, -0.21786529567389767, 0.29749776647094794, 0.012550916816993543, 0.2721874962440846, -0.011952604529265018, 0.09387197316641364, -0.0012974247159264936, -0.03941298238475995, 0.009511817926598108, -0.10669875362384643, 0.15577396897719087, 0.31453551719267997, 0.2560858270896176, 0.2785872396287377, -0.4073137025365983, -0.16763598366177704, 0.0861198430240977, 0.12218795093404917, 0.15210287378418139, -0.08886876896045534, -0.25083048290864457, 0.11524074580991767, -0.1559287161206358, -0.03633097431513887, -0.08821952846929705, -0.025126040050405665, 0.00846399209730647, -0.2782569616415932, -0.05613113522238207, 0.09473161340585447, 0.032525050016748575, -0.08968112161782987, -0.17704575246291385, 0.025193586079917986, 0.09905240311504461, -0.010219525945789974, -0.004391481071436332, 0.10310022448878049, -0.06677767385705803, -0.216333648108483, 0.3762823409783694, -0.043900041001066106, -0.18724586168572627, 0.22747428458543964, 0.0066344475050805355, -0.2052337744450368, 0.10520229695444824, 0.16512288773600925, 0.21182415874937743, -0.16788454020133048, 0.0780791747794441, -0.03274805345601099, 0.20483509446722828, 0.059266457298450585, -0.04433181225550732, 0.20437452005668102, 0.24343093753180986, 0.08408514925912937, 0.1798705861795168, -0.01056776450307, -0.09778549230837498, -0.2831111900348422, -0.11475615153671956, -0.07550685503734333, -0.06248573181819331, -0.056322085624525, -0.19527396908175612, 0.3948442718368366, 0.19920821055952756, 0.1449089360143418, 0.1714160814931319, 0.35826192479909175, 0.08122584929095547, 0.08259890105273929, 0.06804071598416844, 0.14015964163821534, 0.0689931170578526, 0.055704084379267105, -0.20673149221925283, 0.10629686399397263, 0.11082568084692548] |
1,802.04429 | Accuracy of the Muskingum-Cunge method for constant-parameter
diffusion-wave channel routing with lateral inflow | Channel routing is important in flood forecasting and watershed modeling. The
general constant-parameter Muskingum-Cunge (CPMC) method is second-order
accurate and easy to implement. With specific discretizations such that the
temporal and spatial intervals maintain a unique relationship, the CPMC method
can be third-order accurate. In this paper, we derive the average lateral
inflow term in the second- and third-order accuracy CPMC method, and
demonstrate that For spatially and temporally variable lateral inflow, the
effect of lateral inflow on simulated discharge varies with spatial and
temporal discretizations, the value and spatial and temporal variations of
lateral inflow, wave celerity, and diffusion coefficient. Comparison of the
CPMC solution with the analytical solution shows that both the second- and
third-order accuracy schemes are more accurate than the simplified method by
which spatial derivatives of lateral inflow are ignored. For small time steps,
the third-order accuracy CPMC method results in higher accuracy than the
second-order scheme even when the third-order accuracy criterion is not fully
met. For large time steps, the temporal and spatial discretization of the
third- and second-order scheme becomes the same, but the third-order scheme
yields higher accuracy than the second-order scheme because of the third-order
accurate estimation of the lateral inflow term.
| physics.flu-dyn physics.comp-ph | channel routing is important in flood forecasting and watershed modeling the general constantparameter muskingumcunge cpmc method is secondorder accurate and easy to implement with specific discretizations such that the temporal and spatial intervals maintain a unique relationship the cpmc method can be thirdorder accurate in this paper we derive the average lateral inflow term in the second and thirdorder accuracy cpmc method and demonstrate that for spatially and temporally variable lateral inflow the effect of lateral inflow on simulated discharge varies with spatial and temporal discretizations the value and spatial and temporal variations of lateral inflow wave celerity and diffusion coefficient comparison of the cpmc solution with the analytical solution shows that both the second and thirdorder accuracy schemes are more accurate than the simplified method by which spatial derivatives of lateral inflow are ignored for small time steps the thirdorder accuracy cpmc method results in higher accuracy than the secondorder scheme even when the thirdorder accuracy criterion is not fully met for large time steps the temporal and spatial discretization of the third and secondorder scheme becomes the same but the thirdorder scheme yields higher accuracy than the secondorder scheme because of the thirdorder accurate estimation of the lateral inflow term | [['channel', 'routing', 'is', 'important', 'in', 'flood', 'forecasting', 'and', 'watershed', 'modeling', 'the', 'general', 'constantparameter', 'muskingumcunge', 'cpmc', 'method', 'is', 'secondorder', 'accurate', 'and', 'easy', 'to', 'implement', 'with', 'specific', 'discretizations', 'such', 'that', 'the', 'temporal', 'and', 'spatial', 'intervals', 'maintain', 'a', 'unique', 'relationship', 'the', 'cpmc', 'method', 'can', 'be', 'thirdorder', 'accurate', 'in', 'this', 'paper', 'we', 'derive', 'the', 'average', 'lateral', 'inflow', 'term', 'in', 'the', 'second', 'and', 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1,802.0443 | Relations between Transfinite Diameters on Affine Algebraic Varieties | Given a compact set $K$ one may define a transfinite diameter for $K$ via a
limiting process involving maximising a Vandermonde determinant over $K$ with
respect to a monomial basis. Different transfinite diameters may be obtained by
using different polynomial bases in the Vandermonde determinant calculation. We
show that if these bases are sufficiently similar that the transfinite diameter
of $K$ is unchanged. Utilising this result we show that the transfinite
diameters defined by Cox-Ma`u and Berman-Boucksom for algebraic varieties are
equal.
| math.CV math.AG | given a compact set k one may define a transfinite diameter for k via a limiting process involving maximising a vandermonde determinant over k with respect to a monomial basis different transfinite diameters may be obtained by using different polynomial bases in the vandermonde determinant calculation we show that if these bases are sufficiently similar that the transfinite diameter of k is unchanged utilising this result we show that the transfinite diameters defined by coxmau and bermanboucksom for algebraic varieties are equal | [['given', 'a', 'compact', 'set', 'k', 'one', 'may', 'define', 'a', 'transfinite', 'diameter', 'for', 'k', 'via', 'a', 'limiting', 'process', 'involving', 'maximising', 'a', 'vandermonde', 'determinant', 'over', 'k', 'with', 'respect', 'to', 'a', 'monomial', 'basis', 'different', 'transfinite', 'diameters', 'may', 'be', 'obtained', 'by', 'using', 'different', 'polynomial', 'bases', 'in', 'the', 'vandermonde', 'determinant', 'calculation', 'we', 'show', 'that', 'if', 'these', 'bases', 'are', 'sufficiently', 'similar', 'that', 'the', 'transfinite', 'diameter', 'of', 'k', 'is', 'unchanged', 'utilising', 'this', 'result', 'we', 'show', 'that', 'the', 'transfinite', 'diameters', 'defined', 'by', 'coxmau', 'and', 'bermanboucksom', 'for', 'algebraic', 'varieties', 'are', 'equal']] | [-0.13777965905121814, 0.16007775106225852, -0.05814400798192731, 0.027343204369403625, -0.06901113721139637, -0.12609720157091447, 0.03514526356701498, 0.37290568953311004, -0.3193152796385097, -0.228938108946076, 0.08762045859295967, -0.23492627264356908, -0.12126779628135724, 0.20523851163962706, -0.05990437319341265, 0.005353897292580869, 0.0673913279387318, 0.07372935659562548, -0.11825341448751221, -0.30364571415163855, 0.3545597858818961, -0.007843758198030201, 0.15708611459459787, 0.002574790844026907, 0.11507881413225407, -0.03754934677739202, 0.0013813004817491697, 0.07472316918457732, -0.14595193085165473, 0.14356157298625252, 0.28409838852081676, 0.13777381883083303, 0.24919264077174444, -0.3591578230491759, -0.10857883208419805, 0.17425770790281672, 0.14831887126934748, 0.013918172706056523, 0.04802056930867242, -0.20820190651158307, 0.15492552108372803, -0.12565926740658873, -0.18100399088781374, -0.0846754790134268, 0.05969213780567602, 0.05618675040472069, -0.3051111073244685, -0.03871463054078229, 0.07897773365501637, 0.147944805568751, 0.009583834915157453, -0.24637074319410057, 0.0003158854677077429, 0.05679940921541901, -0.04721041455222466, 0.010085243179842277, 0.07630960317903462, -0.010479853077458195, -0.11757304454452278, 0.31883510211367666, -0.08182895411219862, -0.22387439481638097, 0.13106498138887096, -0.1383215379613785, -0.09667895695217597, 0.16415828036397329, 0.059846245100215814, 0.15945006086042635, -0.08805345089669213, 0.11004030542307315, -0.12714387690303502, 0.13350382361584057, 0.20124319462496557, 0.004082521509931043, 0.16110057268797615, 0.0855745140083686, 0.07258740140493085, 0.17262196234272173, 0.02883614929691877, -0.03225363416735221, -0.3130522472438989, -0.1640427808024357, -0.22669746926813214, 0.15387325950921227, -0.201566142127612, -0.1427302564965722, 0.3072455162757341, 0.08125619472142447, 0.21698576866154684, 0.17634569679927312, 0.21904929523517228, 0.09076800878769804, 0.09017239062598457, 0.0527531888830349, 0.1397106443290357, 0.1931815962307155, -0.00796323679358825, -0.14021997156633456, 0.049965263419083236, 0.16774126121567356] |
1,802.04431 | Detecting Spacecraft Anomalies Using LSTMs and Nonparametric Dynamic
Thresholding | As spacecraft send back increasing amounts of telemetry data, improved
anomaly detection systems are needed to lessen the monitoring burden placed on
operations engineers and reduce operational risk. Current spacecraft monitoring
systems only target a subset of anomaly types and often require costly expert
knowledge to develop and maintain due to challenges involving scale and
complexity. We demonstrate the effectiveness of Long Short-Term Memory (LSTMs)
networks, a type of Recurrent Neural Network (RNN), in overcoming these issues
using expert-labeled telemetry anomaly data from the Soil Moisture Active
Passive (SMAP) satellite and the Mars Science Laboratory (MSL) rover,
Curiosity. We also propose a complementary unsupervised and nonparametric
anomaly thresholding approach developed during a pilot implementation of an
anomaly detection system for SMAP, and offer false positive mitigation
strategies along with other key improvements and lessons learned during
development.
| cs.LG stat.ML | as spacecraft send back increasing amounts of telemetry data improved anomaly detection systems are needed to lessen the monitoring burden placed on operations engineers and reduce operational risk current spacecraft monitoring systems only target a subset of anomaly types and often require costly expert knowledge to develop and maintain due to challenges involving scale and complexity we demonstrate the effectiveness of long shortterm memory lstms networks a type of recurrent neural network rnn in overcoming these issues using expertlabeled telemetry anomaly data from the soil moisture active passive smap satellite and the mars science laboratory msl rover curiosity we also propose a complementary unsupervised and nonparametric anomaly thresholding approach developed during a pilot implementation of an anomaly detection system for smap and offer false positive mitigation strategies along with other key improvements and lessons learned during development | [['as', 'spacecraft', 'send', 'back', 'increasing', 'amounts', 'of', 'telemetry', 'data', 'improved', 'anomaly', 'detection', 'systems', 'are', 'needed', 'to', 'lessen', 'the', 'monitoring', 'burden', 'placed', 'on', 'operations', 'engineers', 'and', 'reduce', 'operational', 'risk', 'current', 'spacecraft', 'monitoring', 'systems', 'only', 'target', 'a', 'subset', 'of', 'anomaly', 'types', 'and', 'often', 'require', 'costly', 'expert', 'knowledge', 'to', 'develop', 'and', 'maintain', 'due', 'to', 'challenges', 'involving', 'scale', 'and', 'complexity', 'we', 'demonstrate', 'the', 'effectiveness', 'of', 'long', 'shortterm', 'memory', 'lstms', 'networks', 'a', 'type', 'of', 'recurrent', 'neural', 'network', 'rnn', 'in', 'overcoming', 'these', 'issues', 'using', 'expertlabeled', 'telemetry', 'anomaly', 'data', 'from', 'the', 'soil', 'moisture', 'active', 'passive', 'smap', 'satellite', 'and', 'the', 'mars', 'science', 'laboratory', 'msl', 'rover', 'curiosity', 'we', 'also', 'propose', 'a', 'complementary', 'unsupervised', 'and', 'nonparametric', 'anomaly', 'thresholding', 'approach', 'developed', 'during', 'a', 'pilot', 'implementation', 'of', 'an', 'anomaly', 'detection', 'system', 'for', 'smap', 'and', 'offer', 'false', 'positive', 'mitigation', 'strategies', 'along', 'with', 'other', 'key', 'improvements', 'and', 'lessons', 'learned', 'during', 'development']] | [-0.10336665549811765, 0.05526441937394484, -0.01635953602950381, 0.044667167476901824, -0.14813297027789982, -0.20268810269368445, 0.10719714898371348, 0.38556235192180444, -0.2619456963504862, -0.3733317731764086, 0.1884963856559641, -0.2736097167647124, -0.197660406252682, 0.20183281769744887, -0.17381550188101555, 0.11050121642030558, 0.13334514960191166, 0.004268102930777155, -0.03496368558411181, -0.26301345003116194, 0.2192405067821013, 0.1318583270283348, 0.31885884207313075, -0.0010309859253738048, 0.14639625690340397, -0.018549464719478777, -0.08657589937512675, -0.059593649323601415, -0.024964591078664156, 0.13634697911261587, 0.34586406472465364, 0.22275454078862159, 0.3253327612323265, -0.504293405885951, -0.2561084272027233, 0.09976340467314215, 0.09288814284135145, 0.036441598906705196, -0.041474580558282015, -0.34956814209339176, 0.01836045841298943, -0.21804959311347155, -0.07012495575268773, -0.13135767636657522, -0.015574076450871725, -0.028877264398564823, -0.26555059627242333, 0.014634533133315275, 0.008941827245043034, 0.13744559259677347, -0.07703127555657913, -0.12672083102163254, 0.043558305098817945, 0.18124387854451898, 0.07662523756089219, 0.013978372836981758, 0.1996533121220278, -0.13716864600492112, -0.12407430320515903, 0.30451650742482206, -0.03484083517678898, -0.1370141687921935, 0.21029916720041653, -0.02429308247392195, -0.1679997073272311, 0.12967710216006223, 0.25431059541292894, 0.03759193690289764, -0.18440837999804216, -0.005996461893112337, 0.07215658369997557, 0.19023304789524226, 0.0530489335307362, 0.01408775940190756, 0.22648644164743337, 0.2436309220136762, 0.08307730981121587, 0.10006848726307728, -0.14987238920565668, -0.029184496488225013, -0.22443128893335423, -0.1361278881145519, -0.13527136942181384, -0.0063449818114954025, -0.055522272797525744, -0.0975624383903061, 0.34298607360326894, 0.23404122749683412, 0.12408622486489641, 0.06236636211388629, 0.3790708895684322, -0.022534355907836916, 0.13764378029757934, 0.08110568481225548, 0.19104849249056546, -0.0018474703355535974, 0.19251724048755572, -0.22138995820160168, 0.11450268909331737, 0.020518258397542212] |
1,802.04432 | Diagnosing the magnetic field structure of a coronal cavity observed
during the 2017 total solar eclipse | We present an investigation of a coronal cavity observed above the western
limb in the coronal red line Fe X 6374 {\AA} using a telescope of Peking
University and in the green line Fe XIV 5303 {\AA} using a telescope of Yunnan
Observatories, Chinese Academy of Sciences during the total solar eclipse on
2017 August 21. A series of magnetic field models are constructed based on the
magnetograms taken by the Helioseismic and Magnetic Imager onboard the Solar
Dynamics Observatory (SDO) one week before the eclipse. The model field lines
are then compared with coronal structures seen in images taken by the
Atmospheric Imaging Assembly on board SDO and in our coronal red line images.
The best-fit model consists of a flux rope with a twist angle of 3.1$\pi$,
which is consistent with the most probable value of the total twist angle of
interplanetary flux ropes observed at 1 AU. Linear polarization of the Fe XIII
10747 {\AA} line calculated from this model shows a "lagomorphic" signature
that is also observed by the Coronal Multichannel Polarimeter of the High
Altitude Observatory. We also find a ring-shaped structure in the line-of-sight
velocity of Fe XIII 10747 {\AA}, which implies hot plasma flows along a helical
magnetic field structure, in the cavity. These results suggest that the
magnetic structure of the cavity is a highly twisted flux rope, which may erupt
eventually. The temperature structure of the cavity has also been investigated
using the intensity ratio of Fe XIII 10747 {\AA} and Fe X 6374 {\AA}.
| astro-ph.SR astro-ph.EP | we present an investigation of a coronal cavity observed above the western limb in the coronal red line fe x 6374 aa using a telescope of peking university and in the green line fe xiv 5303 aa using a telescope of yunnan observatories chinese academy of sciences during the total solar eclipse on 2017 august 21 a series of magnetic field models are constructed based on the magnetograms taken by the helioseismic and magnetic imager onboard the solar dynamics observatory sdo one week before the eclipse the model field lines are then compared with coronal structures seen in images taken by the atmospheric imaging assembly on board sdo and in our coronal red line images the bestfit model consists of a flux rope with a twist angle of 31pi which is consistent with the most probable value of the total twist angle of interplanetary flux ropes observed at 1 au linear polarization of the fe xiii 10747 aa line calculated from this model shows a lagomorphic signature that is also observed by the coronal multichannel polarimeter of the high altitude observatory we also find a ringshaped structure in the lineofsight velocity of fe xiii 10747 aa which implies hot plasma flows along a helical magnetic field structure in the cavity these results suggest that the magnetic structure of the cavity is a highly twisted flux rope which may erupt eventually the temperature structure of the cavity has also been investigated using the intensity ratio of fe xiii 10747 aa and fe x 6374 aa | [['we', 'present', 'an', 'investigation', 'of', 'a', 'coronal', 'cavity', 'observed', 'above', 'the', 'western', 'limb', 'in', 'the', 'coronal', 'red', 'line', 'fe', 'x', '6374', 'aa', 'using', 'a', 'telescope', 'of', 'peking', 'university', 'and', 'in', 'the', 'green', 'line', 'fe', 'xiv', '5303', 'aa', 'using', 'a', 'telescope', 'of', 'yunnan', 'observatories', 'chinese', 'academy', 'of', 'sciences', 'during', 'the', 'total', 'solar', 'eclipse', 'on', '2017', 'august', '21', 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1,802.04433 | Strangeness $S=-1$ hyperon-nucleon interactions: chiral effective field
theory vs. lattice QCD | Hyperon-nucleon interactions serve as basic inputs to studies of hypernuclear
physics and dense (neutron) stars. Unfortunately, a precise understanding of
these important quantities have lagged far behind that of the nucleon-nucleon
interaction due to lack of high precision experimental data. Historically,
hyperon-nucleon interactions are either formulated in quark models or meson
exchange models. In recent years, lattice QCD simulations and chiral effective
field theory approaches start to offer new insights from first principles. In
the present work, we contrast the state of art lattice QCD simulations with the
latest chiral hyperon-nucleon forces and show that the leading order
relativistic chiral results can already describe the lattice QCD data
reasonably well. Given the fact that the lattice QCD simulations are performed
with pion masses ranging from the (almost) physical point to 700 MeV, such
studies provide a highly non-trivial check on both the chiral effective field
theory approaches as well as lattice QCD simulations. Nevertheless more precise
lattice QCD simulations are eagerly needed to refine our understanding of
hyperon-nucleon interactions.
| nucl-th hep-lat hep-ph | hyperonnucleon interactions serve as basic inputs to studies of hypernuclear physics and dense neutron stars unfortunately a precise understanding of these important quantities have lagged far behind that of the nucleonnucleon interaction due to lack of high precision experimental data historically hyperonnucleon interactions are either formulated in quark models or meson exchange models in recent years lattice qcd simulations and chiral effective field theory approaches start to offer new insights from first principles in the present work we contrast the state of art lattice qcd simulations with the latest chiral hyperonnucleon forces and show that the leading order relativistic chiral results can already describe the lattice qcd data reasonably well given the fact that the lattice qcd simulations are performed with pion masses ranging from the almost physical point to 700 mev such studies provide a highly nontrivial check on both the chiral effective field theory approaches as well as lattice qcd simulations nevertheless more precise lattice qcd simulations are eagerly needed to refine our understanding of hyperonnucleon interactions | [['hyperonnucleon', 'interactions', 'serve', 'as', 'basic', 'inputs', 'to', 'studies', 'of', 'hypernuclear', 'physics', 'and', 'dense', 'neutron', 'stars', 'unfortunately', 'a', 'precise', 'understanding', 'of', 'these', 'important', 'quantities', 'have', 'lagged', 'far', 'behind', 'that', 'of', 'the', 'nucleonnucleon', 'interaction', 'due', 'to', 'lack', 'of', 'high', 'precision', 'experimental', 'data', 'historically', 'hyperonnucleon', 'interactions', 'are', 'either', 'formulated', 'in', 'quark', 'models', 'or', 'meson', 'exchange', 'models', 'in', 'recent', 'years', 'lattice', 'qcd', 'simulations', 'and', 'chiral', 'effective', 'field', 'theory', 'approaches', 'start', 'to', 'offer', 'new', 'insights', 'from', 'first', 'principles', 'in', 'the', 'present', 'work', 'we', 'contrast', 'the', 'state', 'of', 'art', 'lattice', 'qcd', 'simulations', 'with', 'the', 'latest', 'chiral', 'hyperonnucleon', 'forces', 'and', 'show', 'that', 'the', 'leading', 'order', 'relativistic', 'chiral', 'results', 'can', 'already', 'describe', 'the', 'lattice', 'qcd', 'data', 'reasonably', 'well', 'given', 'the', 'fact', 'that', 'the', 'lattice', 'qcd', 'simulations', 'are', 'performed', 'with', 'pion', 'masses', 'ranging', 'from', 'the', 'almost', 'physical', 'point', 'to', '700', 'mev', 'such', 'studies', 'provide', 'a', 'highly', 'nontrivial', 'check', 'on', 'both', 'the', 'chiral', 'effective', 'field', 'theory', 'approaches', 'as', 'well', 'as', 'lattice', 'qcd', 'simulations', 'nevertheless', 'more', 'precise', 'lattice', 'qcd', 'simulations', 'are', 'eagerly', 'needed', 'to', 'refine', 'our', 'understanding', 'of', 'hyperonnucleon', 'interactions']] | [-0.0681255734325855, 0.19874081986311537, -0.11055609586575625, 0.09493922858813106, -0.09805283000222242, -0.08078550307091172, 0.05904666282589266, 0.3800894336681262, -0.18795311368020945, -0.288487733865278, 0.05086634719548692, -0.30941749940014623, -0.08782727304658682, 0.13112795399088611, 0.077599627751452, 0.11980465540838753, 0.09971210785408521, 0.028473773366563607, -0.07517678594663078, -0.22800418729384114, 0.28769178926454275, 0.06080738899501911, 0.22572018507266686, 0.16199763630621533, 0.023662634812482805, 0.002792090265708562, -0.04625876613110083, -0.026032483643995456, -0.1577263044354147, 0.08840609099936379, 0.2662047294422536, 0.030387616933097158, 0.1497272250611754, -0.4845122546370874, -0.2597858141108895, 0.037340320597165785, 0.1412278190310273, 0.1901072204702316, -0.06533091180083361, -0.28907887953383155, 0.06997903167909375, -0.21587329044092182, -0.15767811082810515, -0.20082910022541287, -0.032400949691427795, 0.019227828915897735, -0.28010874010723075, 0.04942933986130165, -0.03374364692442251, 0.08745113544761253, -0.046707707652877246, -0.2117317538410866, 0.009412479665427959, 0.12505395536534158, 0.09227454284558723, 0.11881061482447139, 0.12745330407016928, -0.20510032015836097, -0.17453632066053884, 0.4934587480952225, -0.005623149209567649, -0.10275319738302534, 0.21598599773429378, -0.12719545274430102, -0.14485989701410634, 0.09497545017811233, 0.17567012243016938, 0.07276902172344905, -0.1753130192825703, 0.07590975236187596, -0.033034157975075334, 0.18387239972028205, 0.011346484458669138, 0.06942630563614637, 0.2589285311250556, 0.19601494081858437, -0.055731556908717964, 0.015116217266492678, -0.01096820196731997, -0.17431144981463192, -0.3149524407093441, -0.005341212267948855, -0.1626394568934221, 0.07371290539771583, -0.10010170835297977, -0.12370513236844505, 0.32421626840558276, 0.20221749985226867, 0.1932105029514335, -0.0049743303455985505, 0.3194389904920871, 0.049661977062299405, 0.07026270022147374, 0.019096912028124698, 0.30018282004979235, 0.20229165103374677, 0.11740077706880471, -0.23209251897938593, -0.022499045569273318, 0.07175741318875742] |
1,802.04434 | signSGD: Compressed Optimisation for Non-Convex Problems | Training large neural networks requires distributing learning across multiple
workers, where the cost of communicating gradients can be a significant
bottleneck. signSGD alleviates this problem by transmitting just the sign of
each minibatch stochastic gradient. We prove that it can get the best of both
worlds: compressed gradients and SGD-level convergence rate. The relative
$\ell_1/\ell_2$ geometry of gradients, noise and curvature informs whether
signSGD or SGD is theoretically better suited to a particular problem. On the
practical side we find that the momentum counterpart of signSGD is able to
match the accuracy and convergence speed of Adam on deep Imagenet models. We
extend our theory to the distributed setting, where the parameter server uses
majority vote to aggregate gradient signs from each worker enabling 1-bit
compression of worker-server communication in both directions. Using a theorem
by Gauss we prove that majority vote can achieve the same reduction in variance
as full precision distributed SGD. Thus, there is great promise for sign-based
optimisation schemes to achieve fast communication and fast convergence. Code
to reproduce experiments is to be found at https://github.com/jxbz/signSGD .
| cs.LG cs.DC math.OC | training large neural networks requires distributing learning across multiple workers where the cost of communicating gradients can be a significant bottleneck signsgd alleviates this problem by transmitting just the sign of each minibatch stochastic gradient we prove that it can get the best of both worlds compressed gradients and sgdlevel convergence rate the relative ell_1ell_2 geometry of gradients noise and curvature informs whether signsgd or sgd is theoretically better suited to a particular problem on the practical side we find that the momentum counterpart of signsgd is able to match the accuracy and convergence speed of adam on deep imagenet models we extend our theory to the distributed setting where the parameter server uses majority vote to aggregate gradient signs from each worker enabling 1bit compression of workerserver communication in both directions using a theorem by gauss we prove that majority vote can achieve the same reduction in variance as full precision distributed sgd thus there is great promise for signbased optimisation schemes to achieve fast communication and fast convergence code to reproduce experiments is to be found at httpsgithubcomjxbzsignsgd | [['training', 'large', 'neural', 'networks', 'requires', 'distributing', 'learning', 'across', 'multiple', 'workers', 'where', 'the', 'cost', 'of', 'communicating', 'gradients', 'can', 'be', 'a', 'significant', 'bottleneck', 'signsgd', 'alleviates', 'this', 'problem', 'by', 'transmitting', 'just', 'the', 'sign', 'of', 'each', 'minibatch', 'stochastic', 'gradient', 'we', 'prove', 'that', 'it', 'can', 'get', 'the', 'best', 'of', 'both', 'worlds', 'compressed', 'gradients', 'and', 'sgdlevel', 'convergence', 'rate', 'the', 'relative', 'ell_1ell_2', 'geometry', 'of', 'gradients', 'noise', 'and', 'curvature', 'informs', 'whether', 'signsgd', 'or', 'sgd', 'is', 'theoretically', 'better', 'suited', 'to', 'a', 'particular', 'problem', 'on', 'the', 'practical', 'side', 'we', 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1,802.04435 | Finite-Control-Set Model Predictive Control (FCS-MPC) for Islanded
Hybrid Microgrids | Microgrids consisting of multiple distributed energy resources (DERs) provide
a promising solution to integrate renewable energies, e.g., solar photovoltaic
(PV) systems. Hybrid AC/DC microgrids leverage the merits of both AC and DC
power systems. In this paper, a control strategy for islanded multi-bus hybrid
microgrids is proposed based on the Finite-Control-Set Model Predictive Control
(FCS-MPC) technologies. The control loops are expedited by predicting the
future states and determining the optimal control action before switching
signals are sent. The proposed algorithm eliminates the needs of PI, PWM, and
droop components, and offers 1) accurate PV maximum power point tracking (MPPT)
and battery charging/discharging control, 2) DC and multiple AC bus
voltage/frequency regulation, 3) a precise power sharing scheme among DERs
without voltage or frequency deviation, and 4) a unified MPC design flow for
hybrid microgrids. Multiple case studies are carried out, which verify the
satisfactory performance of the proposed method.
| math.OC eess.SP | microgrids consisting of multiple distributed energy resources ders provide a promising solution to integrate renewable energies eg solar photovoltaic pv systems hybrid acdc microgrids leverage the merits of both ac and dc power systems in this paper a control strategy for islanded multibus hybrid microgrids is proposed based on the finitecontrolset model predictive control fcsmpc technologies the control loops are expedited by predicting the future states and determining the optimal control action before switching signals are sent the proposed algorithm eliminates the needs of pi pwm and droop components and offers 1 accurate pv maximum power point tracking mppt and battery chargingdischarging control 2 dc and multiple ac bus voltagefrequency regulation 3 a precise power sharing scheme among ders without voltage or frequency deviation and 4 a unified mpc design flow for hybrid microgrids multiple case studies are carried out which verify the satisfactory performance of the proposed method | [['microgrids', 'consisting', 'of', 'multiple', 'distributed', 'energy', 'resources', 'ders', 'provide', 'a', 'promising', 'solution', 'to', 'integrate', 'renewable', 'energies', 'eg', 'solar', 'photovoltaic', 'pv', 'systems', 'hybrid', 'acdc', 'microgrids', 'leverage', 'the', 'merits', 'of', 'both', 'ac', 'and', 'dc', 'power', 'systems', 'in', 'this', 'paper', 'a', 'control', 'strategy', 'for', 'islanded', 'multibus', 'hybrid', 'microgrids', 'is', 'proposed', 'based', 'on', 'the', 'finitecontrolset', 'model', 'predictive', 'control', 'fcsmpc', 'technologies', 'the', 'control', 'loops', 'are', 'expedited', 'by', 'predicting', 'the', 'future', 'states', 'and', 'determining', 'the', 'optimal', 'control', 'action', 'before', 'switching', 'signals', 'are', 'sent', 'the', 'proposed', 'algorithm', 'eliminates', 'the', 'needs', 'of', 'pi', 'pwm', 'and', 'droop', 'components', 'and', 'offers', '1', 'accurate', 'pv', 'maximum', 'power', 'point', 'tracking', 'mppt', 'and', 'battery', 'chargingdischarging', 'control', '2', 'dc', 'and', 'multiple', 'ac', 'bus', 'voltagefrequency', 'regulation', '3', 'a', 'precise', 'power', 'sharing', 'scheme', 'among', 'ders', 'without', 'voltage', 'or', 'frequency', 'deviation', 'and', '4', 'a', 'unified', 'mpc', 'design', 'flow', 'for', 'hybrid', 'microgrids', 'multiple', 'case', 'studies', 'are', 'carried', 'out', 'which', 'verify', 'the', 'satisfactory', 'performance', 'of', 'the', 'proposed', 'method']] | [-0.2293651705848522, 0.02192623650363716, 0.007266510563481245, -0.03485881414982731, -0.06122547590813669, -0.23867338845430283, 0.11359976368978442, 0.3670734581569222, -0.26717646923042887, -0.33275306849822806, 0.089389224602943, -0.2605113937475142, -0.08782501577013203, 0.26193379430055974, -0.10809952128349112, 0.12032352993937945, 0.033596442010724675, -0.0456793306307902, 0.04025379080185983, -0.16022411318730284, 0.18609887067320421, 0.08296107258140838, 0.41577507155000226, 0.0007614896879816541, 0.13142878466783103, -0.024193808932148782, -0.004164937769575995, 0.024216619672627525, -0.013795017107781086, 0.14873045928685982, 0.3148651288544718, 0.1040442002411349, 0.33760866198409983, -0.4467791849303813, -0.2329382757675283, 0.10052167319179493, 0.10588513761329177, -0.0004467284378158499, -0.06819709467140919, -0.18481776801090002, 0.14516391712011212, -0.2488254688547126, -0.06707372558524921, -0.09491407186375792, -0.045768965938489656, 0.10451071938740558, -0.3604247804325657, 0.017844373276132496, 0.007563092704659619, 0.05847084741139797, -0.10855156170254966, -0.13301612725988232, -0.024818790330532556, 0.14497209350965745, -0.03925503542580243, -0.05633617306546289, 0.22026823532963996, -0.07178788611722388, -0.18233436194476577, 0.34229987658414557, 0.06135138753978979, -0.1640632627104238, 0.07692727517514002, -0.033009189895360544, -0.08871722155168248, 0.08702433220432362, 0.22727678893894263, 0.09729990245755382, -0.23264880339871635, 0.016001059366849434, 0.073363782407963, 0.18609595896306347, 0.03333599798755125, 0.030869917706491386, 0.22163409724369088, 0.231846072878942, 0.16699576079579137, 0.09509737563769327, -0.07952349907781954, -0.12223207582814657, -0.2384902138855359, -0.10686976397415975, -0.10575365021406692, 0.015375793268460603, -0.07033058082010536, -0.0964013812303239, 0.4596595907779393, 0.16266111328130348, 0.07166816116164938, 0.0539508745063185, 0.4370289440361821, 0.13963426684099092, 0.05300823531626743, 0.13256709633369734, 0.21198121046482707, 0.05074357323753996, 0.20775572323956254, -0.31706179627439096, 0.03097211562895349, -0.023645728718818856] |
1,802.04436 | Ruelle-Bowen continuous-time random walk | We define the probability structure of a continuous-time time-homogeneous
Markov jump process, on a finite graph, that represents the continuous-time
counterpart of the so-called Ruelle-Bowen discrete-time random walk. It
constitutes the unique jump process having maximal entropy rate. Moreover, it
has the property that, given the number of jumps between any two specified
end-points on the graph, the probability of traversing any one of the
alternative paths that are consistent with the specified number of jumps and
end-points, is the same for all, and thereby depends only on the number of
jumps and the end-points and not the particular path being traversed.
| math.OC math.PR | we define the probability structure of a continuoustime timehomogeneous markov jump process on a finite graph that represents the continuoustime counterpart of the socalled ruellebowen discretetime random walk it constitutes the unique jump process having maximal entropy rate moreover it has the property that given the number of jumps between any two specified endpoints on the graph the probability of traversing any one of the alternative paths that are consistent with the specified number of jumps and endpoints is the same for all and thereby depends only on the number of jumps and the endpoints and not the particular path being traversed | [['we', 'define', 'the', 'probability', 'structure', 'of', 'a', 'continuoustime', 'timehomogeneous', 'markov', 'jump', 'process', 'on', 'a', 'finite', 'graph', 'that', 'represents', 'the', 'continuoustime', 'counterpart', 'of', 'the', 'socalled', 'ruellebowen', 'discretetime', 'random', 'walk', 'it', 'constitutes', 'the', 'unique', 'jump', 'process', 'having', 'maximal', 'entropy', 'rate', 'moreover', 'it', 'has', 'the', 'property', 'that', 'given', 'the', 'number', 'of', 'jumps', 'between', 'any', 'two', 'specified', 'endpoints', 'on', 'the', 'graph', 'the', 'probability', 'of', 'traversing', 'any', 'one', 'of', 'the', 'alternative', 'paths', 'that', 'are', 'consistent', 'with', 'the', 'specified', 'number', 'of', 'jumps', 'and', 'endpoints', 'is', 'the', 'same', 'for', 'all', 'and', 'thereby', 'depends', 'only', 'on', 'the', 'number', 'of', 'jumps', 'and', 'the', 'endpoints', 'and', 'not', 'the', 'particular', 'path', 'being', 'traversed']] | [-0.16428385426144243, 0.17489073241092995, -0.07425054807362019, 0.029750361822188293, -0.08918747856217783, -0.14524343563243747, 0.10958605979149248, 0.37778146232606147, -0.2895844722367531, -0.21642310064578174, 0.1294973347029037, -0.28767699887062986, -0.09044250411291917, 0.16596599585175806, -0.06629233604625744, 0.07499929738011868, 0.046752703554161334, 0.13098570042947197, -0.0003742459298604552, -0.21920136312152916, 0.30633834887910283, -0.00828274101128473, 0.2489277250316067, 0.019347234848229325, 0.1846308658155156, 0.06769735859159161, -0.02211928812560954, 0.04023440977028918, -0.11396221380888236, 0.06792447629256868, 0.1543610508704339, 0.13203815142989306, 0.2767952002936463, -0.38198477586767837, -0.1991773818153888, 0.21543200270218008, 0.09234583256857506, 0.06872133111727297, 0.009476791876687795, -0.2504919349562888, 0.0954634013389438, -0.11776150753904208, -0.10972184738071233, 0.061605691352822615, 0.03782921607204366, 0.06185525237906761, -0.2508824876647707, 0.019820176781721266, 0.08243910413162381, 0.010249573728252276, 0.02107494991437039, -0.07177117123273109, -0.07658749605602055, 0.15472681734108312, 0.020410063819772582, 0.0271727993545652, 0.1098055571052885, -0.09123022658019966, -0.17066858609905466, 0.3117597784993111, -0.03897393244893893, -0.22236759687646054, 0.19855902789105825, -0.17709644968785784, -0.15059970208334134, 0.1642904606460612, 0.09487841401577872, 0.10498131059256255, -0.15235583195474237, 0.11870459932149113, -0.04653235470109126, 0.10410196973837219, 0.06341188458963205, 0.035573668481197716, 0.1724436388316926, 0.14499051144629643, 0.1422293869111979, 0.13954968775586024, -0.05051353794988245, -0.18735538005335806, -0.3634711672624975, -0.19761653910573646, -0.25866518424499746, 0.04535275260366011, -0.16196742278521462, -0.25939739322034167, 0.3890104089754031, 0.14739212131712073, 0.2498756449158285, 0.1385356606732981, 0.23306248975032026, 0.18754716075993344, -0.011559796126961125, 0.12100901127354625, 0.1036028950931687, 0.12216892827064822, 0.02785111940694133, -0.19862863274437248, 0.20253683001204742, 0.08799463003992523] |
1,802.04437 | Diffusion in translucent media | Diffusion is the result of repeated random scattering. It governs a wide
range of phenomena from Brownian motion, to heat flow through window panes,
neutron flux in fuel rods, dispersion of light in human tissue, and electronic
conduction. It is universally acknowledged that the diffusion approach to
describing wave transport fails in translucent samples thinner than the
distance between scattering events such as are encountered in meteorology,
astronomy, biomedicine and communications. Here we show in optical measurements
and numerical simulations that the scaling of transmission and the intensity
profiles of transmission eigenchannels have the same form in translucent as in
opaque media. Paradoxically, the similarities in transport across translucent
and opaque samples explain the puzzling observations of suppressed optical and
ultrasonic delay times relative to predictions of diffusion theory well into
the diffusive regime.
| cond-mat.mes-hall | diffusion is the result of repeated random scattering it governs a wide range of phenomena from brownian motion to heat flow through window panes neutron flux in fuel rods dispersion of light in human tissue and electronic conduction it is universally acknowledged that the diffusion approach to describing wave transport fails in translucent samples thinner than the distance between scattering events such as are encountered in meteorology astronomy biomedicine and communications here we show in optical measurements and numerical simulations that the scaling of transmission and the intensity profiles of transmission eigenchannels have the same form in translucent as in opaque media paradoxically the similarities in transport across translucent and opaque samples explain the puzzling observations of suppressed optical and ultrasonic delay times relative to predictions of diffusion theory well into the diffusive regime | [['diffusion', 'is', 'the', 'result', 'of', 'repeated', 'random', 'scattering', 'it', 'governs', 'a', 'wide', 'range', 'of', 'phenomena', 'from', 'brownian', 'motion', 'to', 'heat', 'flow', 'through', 'window', 'panes', 'neutron', 'flux', 'in', 'fuel', 'rods', 'dispersion', 'of', 'light', 'in', 'human', 'tissue', 'and', 'electronic', 'conduction', 'it', 'is', 'universally', 'acknowledged', 'that', 'the', 'diffusion', 'approach', 'to', 'describing', 'wave', 'transport', 'fails', 'in', 'translucent', 'samples', 'thinner', 'than', 'the', 'distance', 'between', 'scattering', 'events', 'such', 'as', 'are', 'encountered', 'in', 'meteorology', 'astronomy', 'biomedicine', 'and', 'communications', 'here', 'we', 'show', 'in', 'optical', 'measurements', 'and', 'numerical', 'simulations', 'that', 'the', 'scaling', 'of', 'transmission', 'and', 'the', 'intensity', 'profiles', 'of', 'transmission', 'eigenchannels', 'have', 'the', 'same', 'form', 'in', 'translucent', 'as', 'in', 'opaque', 'media', 'paradoxically', 'the', 'similarities', 'in', 'transport', 'across', 'translucent', 'and', 'opaque', 'samples', 'explain', 'the', 'puzzling', 'observations', 'of', 'suppressed', 'optical', 'and', 'ultrasonic', 'delay', 'times', 'relative', 'to', 'predictions', 'of', 'diffusion', 'theory', 'well', 'into', 'the', 'diffusive', 'regime']] | [-0.08530121855568071, 0.17015575522334495, -0.058811235533499005, 0.04059057915930761, -0.03463199850060602, -0.10810301487278472, 0.0387898371858981, 0.4386321613426084, -0.3126453866820727, -0.29367297468706965, 0.037397325236989715, -0.3396946323319658, -0.16511599756707784, 0.21744946451551878, -0.019907927529802964, 0.06716789272837533, 0.012982216561143635, -0.0619831531788153, 0.015561778532730332, -0.14722941608651222, 0.23515592264220006, 0.043285468780894336, 0.3101637671430553, 0.06185558978329176, 0.0692392232734015, 0.014234235942878051, -0.035590275147332075, 0.016409393321764446, -0.11653740719588883, 0.059106932077278844, 0.28633267312473965, 0.03197672731701785, 0.20269911110734762, -0.4798782985212643, -0.32385060109142494, 0.03342448582580841, 0.181088341552931, 0.08288621804120937, -0.027538317707436744, -0.2466932981650331, -0.01858510313305392, -0.11169876661082145, -0.1311756837765561, 0.0018298808943980666, 0.05273820700492265, 0.056061476802648, -0.22212550864179634, 0.151769067730699, 0.009284752118151458, 0.04441023198779109, -0.09157980906207171, -0.054050043426843275, -0.010558163491772738, 0.12568238041964847, 0.06479060985087139, -0.02684192624335775, 0.18283811078490272, -0.18775801255312094, -0.044978554986083684, 0.41186403927268156, -0.0750941110349394, -0.11930152294838474, 0.2158739939193005, -0.21854357523478762, -0.046740573886503926, 0.1915786561413682, 0.19406722627650536, 0.09169054570618267, -0.16070331626140805, -0.008602325327576264, -0.039297769772155565, 0.13241677781490527, 0.10168068852851606, 0.06596146861035655, 0.1992156628950207, 0.19973996855360246, 0.014975761009533125, 0.07414825546291115, -0.1432529096213628, -0.106303469449935, -0.23696047612187912, -0.15952839990799775, -0.167498682305884, 0.0881422819640729, -0.09642778280727156, -0.15619871267743074, 0.29937804711169436, 0.18506323930515506, 0.17115875272820494, 0.003696271748092623, 0.30553666064376706, 0.09576979212535879, 0.06212899933305027, 0.07654381268344987, 0.2535891338119478, 0.16692753385669037, 0.1692846140775607, -0.23699997138030438, 0.09879603919072716, -0.03520970551797481] |
1,802.04438 | Bekenstein Bounds, Penrose Inequalities, and Black Hole Formation | A universal geometric inequality for bodies relating energy, size, angular
momentum, and charge is naturally implied by Bekenstein's entropy bounds. We
establish versions of this inequality for axisymmetric bodies satisfying
appropriate energy conditions, thus lending credence to the most general form
of Bekenstein's bound. Similar techniques are then used to prove a Penrose-like
inequality in which the ADM energy is bounded from below in terms of horizon
area, angular momentum, and charge. Lastly, new criteria for the formation of
black holes is presented involving concentration of angular momentum, charge,
and nonelectromagnetic matter energy.
| gr-qc math-ph math.DG math.MP | a universal geometric inequality for bodies relating energy size angular momentum and charge is naturally implied by bekensteins entropy bounds we establish versions of this inequality for axisymmetric bodies satisfying appropriate energy conditions thus lending credence to the most general form of bekensteins bound similar techniques are then used to prove a penroselike inequality in which the adm energy is bounded from below in terms of horizon area angular momentum and charge lastly new criteria for the formation of black holes is presented involving concentration of angular momentum charge and nonelectromagnetic matter energy | [['a', 'universal', 'geometric', 'inequality', 'for', 'bodies', 'relating', 'energy', 'size', 'angular', 'momentum', 'and', 'charge', 'is', 'naturally', 'implied', 'by', 'bekensteins', 'entropy', 'bounds', 'we', 'establish', 'versions', 'of', 'this', 'inequality', 'for', 'axisymmetric', 'bodies', 'satisfying', 'appropriate', 'energy', 'conditions', 'thus', 'lending', 'credence', 'to', 'the', 'most', 'general', 'form', 'of', 'bekensteins', 'bound', 'similar', 'techniques', 'are', 'then', 'used', 'to', 'prove', 'a', 'penroselike', 'inequality', 'in', 'which', 'the', 'adm', 'energy', 'is', 'bounded', 'from', 'below', 'in', 'terms', 'of', 'horizon', 'area', 'angular', 'momentum', 'and', 'charge', 'lastly', 'new', 'criteria', 'for', 'the', 'formation', 'of', 'black', 'holes', 'is', 'presented', 'involving', 'concentration', 'of', 'angular', 'momentum', 'charge', 'and', 'nonelectromagnetic', 'matter', 'energy']] | [-0.12777082197448258, 0.13429564071358532, -0.10808672287291096, 0.17319345183580392, -0.08402696244608891, -0.1234364584789321, 0.029186582387430014, 0.23023696012684816, -0.2072443889156084, -0.33562559359056016, 0.053313743310569914, -0.26394034319266835, -0.036356425313379175, 0.2241885961804499, -0.11100105947304156, 0.062308256966262174, -0.007144053679420262, 0.02770453571937778, -0.09548800236104878, -0.18521706227173088, 0.3793417815474533, 0.07525883478823528, 0.23781047931682037, 0.13592734220512812, 0.10531826220673099, 0.011474344073244, -0.006190099903652745, 0.05309754336673406, -0.2504802584267592, 0.1296572213471737, 0.22340041026473045, 0.10464858579930038, 0.21209241478373447, -0.40121565134294573, -0.16996963075312074, 0.11704498492381586, 0.10957438784328499, 0.09540377088486948, -0.09058089252631431, -0.22747419939015903, 0.042790311558913156, -0.1873609327676115, -0.2176350284598127, -0.07474363867133375, 0.05505031627422619, 0.04540528572741545, -0.298514091960525, 0.21189503984085173, 0.10943925965537307, -0.03879513935015727, -0.15874437029693797, -0.11147491204156791, -0.06193397357378916, -0.013440553003781906, 0.12659901593591497, 0.019485662998293615, 0.16981775322628598, -0.07366149861275428, -0.08351900972806478, 0.29190651151121305, 0.01948719942361437, -0.24589513259487958, 0.1239256361920026, -0.17001535770012646, -0.10055544527788315, 0.0893880095112548, 0.14635862512964634, 0.1205151980011774, -0.1609600580407567, 0.1325146565520485, -0.04256289372200607, 0.13502601085491078, 0.15907674887147483, 0.1430146364546481, 0.2800793033493783, 0.07005996441328398, 0.13032275442314403, 0.13435730891382341, -0.057324990124431666, -0.13456671961623254, -0.3321515418669229, -0.21293433211676235, -0.22169376625328935, 0.09497304596207155, -0.1501379266216342, -0.07934791674575849, 0.30809451540261107, 0.04116730338403134, 0.1748869447913822, 0.08498067101238356, 0.27103384771661726, 0.12058049126938787, 0.06181673662857026, 0.13450209148508566, 0.26838409649683864, 0.16241767058419365, 0.1443823529407382, -0.1898175108519393, -0.01109943441265533, 0.12061682431154348] |
1,802.04439 | On Whitehead's theorem beyond pointed connected spaces | We prove that the 2-category of spaces admits a strong generator made up of
the tori. In other words, Whitehead's theorem holds for the 2-category of (not
necessarily connected, not pointed) spaces.
| math.AT | we prove that the 2category of spaces admits a strong generator made up of the tori in other words whiteheads theorem holds for the 2category of not necessarily connected not pointed spaces | [['we', 'prove', 'that', 'the', '2category', 'of', 'spaces', 'admits', 'a', 'strong', 'generator', 'made', 'up', 'of', 'the', 'tori', 'in', 'other', 'words', 'whiteheads', 'theorem', 'holds', 'for', 'the', '2category', 'of', 'not', 'necessarily', 'connected', 'not', 'pointed', 'spaces']] | [-0.1984619270078838, 0.07187956440611742, -0.07303921025595628, 0.147909157036338, -0.10281816177302971, -0.13665561574453022, 0.02353931945981458, 0.40040066512301564, -0.31714621774153784, -0.12492034297611099, 0.0675429849907232, -0.22772705310489982, -0.0883182148099877, 0.2368442061724636, -0.22308683808660135, -0.09176857181591913, 0.11573975963983685, 0.10158944758586586, -0.08410387057665503, -0.28079056637216127, 0.4552625154319685, -0.09500475495588034, 0.22327971171762329, 0.07244477386120707, 0.18394286080729216, 0.027453237315057777, -0.031909708777675405, 0.04449010008829646, -0.13164363816872537, 0.08271858609259652, 0.3063360981468577, 0.07166638250782853, 0.23469461518106982, -0.3677814407274127, -0.13068023772211745, 0.2607253345195204, 0.07285452645737678, 0.009783608969883062, 0.0029416783327178564, -0.332086100068409, 0.11765812881640159, -0.20292615762446076, -0.18271397109492682, -0.0990178263746202, 0.05352062580641359, 0.01029015226959018, -0.20317587773752166, -0.06919331062817946, 0.26125017541926354, 0.04419903494999744, -0.0647897880844539, -0.012067292555002496, -0.08272113466227893, 0.11522609046369325, -0.02802314260043204, 0.07254908584582154, 0.08892770200327504, -0.05711995082674548, -0.1472384826047346, 0.40419416017539334, -0.055385694722644985, -0.2299777926527895, 0.12123038675053976, -0.16241122828796506, -0.2280520735657774, 0.13414779992308468, -0.00857143010944128, 0.15338083781534806, -0.011723647825419903, 0.18443326012311445, -0.18893837710493244, 0.12887128126749303, 0.13193312563817017, 0.07744443918636534, 0.12277006344811525, 0.08460001309867948, 0.09423829962906893, 0.1090763305855944, 0.06708299482124858, -0.04854300339866313, -0.3689160434878431, -0.1749828330357559, -0.11192390011274256, 0.13645011793505546, -0.06536923452222254, -0.25610029060044326, 0.263045724175754, 0.10483840825327206, 0.11715418787207454, 0.19985394962714054, 0.15572460391558707, -0.0139206456951797, 0.10426161272334866, 0.11501247048727237, 0.21493591007310897, 0.25560516701079905, 0.026020785779110156, -0.0065958954510279, -0.012939718246343546, 0.24139763506536838] |
1,802.0444 | Bivariate representation and conjugacy class zeta functions associated
to unipotent group schemes, I: Arithmetic properties | This is the first of two papers in which we introduce and study two bivariate
zeta functions associated to unipotent group schemes over rings of integers of
number fields. One of these zeta functions encodes the numbers of isomorphism
classes of irreducible complex representations of finite dimensions of
congruence quotients of the associated group and the other one encodes the
numbers of conjugacy classes of each size of such quotients. In this paper, we
show that these zeta functions satisfy Euler factorizations and almost all of
their Euler factors are rational and satisfy functional equations. Moreover, we
show that such bivariate zeta functions specialize to (univariate) class number
zeta functions. In case of nilpotency class 2, bivariate representation zeta
functions also specialize to (univariate) twist representation zeta functions.
| math.GR | this is the first of two papers in which we introduce and study two bivariate zeta functions associated to unipotent group schemes over rings of integers of number fields one of these zeta functions encodes the numbers of isomorphism classes of irreducible complex representations of finite dimensions of congruence quotients of the associated group and the other one encodes the numbers of conjugacy classes of each size of such quotients in this paper we show that these zeta functions satisfy euler factorizations and almost all of their euler factors are rational and satisfy functional equations moreover we show that such bivariate zeta functions specialize to univariate class number zeta functions in case of nilpotency class 2 bivariate representation zeta functions also specialize to univariate twist representation zeta functions | [['this', 'is', 'the', 'first', 'of', 'two', 'papers', 'in', 'which', 'we', 'introduce', 'and', 'study', 'two', 'bivariate', 'zeta', 'functions', 'associated', 'to', 'unipotent', 'group', 'schemes', 'over', 'rings', 'of', 'integers', 'of', 'number', 'fields', 'one', 'of', 'these', 'zeta', 'functions', 'encodes', 'the', 'numbers', 'of', 'isomorphism', 'classes', 'of', 'irreducible', 'complex', 'representations', 'of', 'finite', 'dimensions', 'of', 'congruence', 'quotients', 'of', 'the', 'associated', 'group', 'and', 'the', 'other', 'one', 'encodes', 'the', 'numbers', 'of', 'conjugacy', 'classes', 'of', 'each', 'size', 'of', 'such', 'quotients', 'in', 'this', 'paper', 'we', 'show', 'that', 'these', 'zeta', 'functions', 'satisfy', 'euler', 'factorizations', 'and', 'almost', 'all', 'of', 'their', 'euler', 'factors', 'are', 'rational', 'and', 'satisfy', 'functional', 'equations', 'moreover', 'we', 'show', 'that', 'such', 'bivariate', 'zeta', 'functions', 'specialize', 'to', 'univariate', 'class', 'number', 'zeta', 'functions', 'in', 'case', 'of', 'nilpotency', 'class', '2', 'bivariate', 'representation', 'zeta', 'functions', 'also', 'specialize', 'to', 'univariate', 'twist', 'representation', 'zeta', 'functions']] | [-0.2265251300195814, 0.06547323635459179, -0.10281156119890511, 0.09354560584597493, -0.0832986625864578, -0.11543805827386677, -0.04050654924412811, 0.32706587499706075, -0.3506719584111124, -0.20425429268652806, 0.04064267259764165, -0.23616406638393528, -0.19295272658200702, 0.2161854870719253, -0.03963692381637429, 0.04249374013761553, -0.007586559806895821, 0.06668366334633902, -0.13149325678750756, -0.33356788920900726, 0.4642392452860804, -0.11489901317713702, 0.17822333407821134, -0.0013156379754946101, 0.11033569621213246, 0.009576613028912107, -0.07329929135812563, -0.04347740567027358, -0.1248672541811402, 0.11344104949239409, 0.3066907601460116, 0.09642821311672378, 0.23965489319925837, -0.35373872553827823, -0.1411199669346388, 0.21845051534182858, 0.13615111169383454, -0.06079977679019066, 0.05144819893757813, -0.18698504755593603, 0.10137584748008521, -0.2033265747668338, -0.15249988400501024, -0.09311196196904348, 0.04826786500416347, 0.14789309397565376, -0.2927695747130201, 0.05725285518360579, 0.028974815108995244, 0.1253694471452036, -0.07454624537786003, -0.18409261832130142, 0.0008886238538252655, 0.1130990237579681, 0.0847964430358843, -0.01591429010113643, 0.06686202502532979, -0.16899547369030188, -0.1180000582280627, 0.3374401136825327, 0.0049778430184233, -0.27200139974593185, 0.15945098472002428, -0.24099431964714313, -0.24389348190379678, 0.11210330813628389, 0.13910611484607216, 0.19288253552804235, -0.008820617600576952, 0.1265196462827589, -0.14299348232452758, 0.07601075321599637, 0.156657936382544, 0.020402616836292964, 0.1221926633224939, -0.014794663795328233, 0.011418297799536958, 0.1892166835173157, 0.024977906969979813, -0.037014300763985375, -0.3518751863448415, -0.23191614564711927, -0.13754098527260794, 0.08750695833623467, -0.1429649346714541, -0.23057245628297096, 0.4692483298422303, 0.10277006209980755, 0.15975667476595845, 0.1961679884525438, 0.18377573084399046, 0.13505808716763568, 0.09260649568568624, 0.030789103850111132, 0.02085655534028774, 0.205690910705016, -0.0633847169128785, -0.11500072740818723, 0.01816558277278091, 0.24495686898444546] |
1,802.04441 | Texture Classification in Extreme Scale Variations using GANet | Research in texture recognition often concentrates on recognizing textures
with intraclass variations such as illumination, rotation, viewpoint and small
scale changes. In contrast, in real-world applications a change in scale can
have a dramatic impact on texture appearance, to the point of changing
completely from one texture category to another. As a result, texture
variations due to changes in scale are amongst the hardest to handle. In this
work we conduct the first study of classifying textures with extreme variations
in scale. To address this issue, we first propose and then reduce scale
proposals on the basis of dominant texture patterns. Motivated by the
challenges posed by this problem, we propose a new GANet network where we use a
Genetic Algorithm to change the units in the hidden layers during network
training, in order to promote the learning of more informative semantic texture
patterns. Finally, we adopt a FVCNN (Fisher Vector pooling of a Convolutional
Neural Network filter bank) feature encoder for global texture representation.
Because extreme scale variations are not necessarily present in most standard
texture databases, to support the proposed extreme-scale aspects of texture
understanding we are developing a new dataset, the Extreme Scale Variation
Textures (ESVaT), to test the performance of our framework. It is demonstrated
that the proposed framework significantly outperforms gold-standard texture
features by more than 10% on ESVaT. We also test the performance of our
proposed approach on the KTHTIPS2b and OS datasets and a further dataset
synthetically derived from Forrest, showing superior performance compared to
the state of the art.
| cs.CV | research in texture recognition often concentrates on recognizing textures with intraclass variations such as illumination rotation viewpoint and small scale changes in contrast in realworld applications a change in scale can have a dramatic impact on texture appearance to the point of changing completely from one texture category to another as a result texture variations due to changes in scale are amongst the hardest to handle in this work we conduct the first study of classifying textures with extreme variations in scale to address this issue we first propose and then reduce scale proposals on the basis of dominant texture patterns motivated by the challenges posed by this problem we propose a new ganet network where we use a genetic algorithm to change the units in the hidden layers during network training in order to promote the learning of more informative semantic texture patterns finally we adopt a fvcnn fisher vector pooling of a convolutional neural network filter bank feature encoder for global texture representation because extreme scale variations are not necessarily present in most standard texture databases to support the proposed extremescale aspects of texture understanding we are developing a new dataset the extreme scale variation textures esvat to test the performance of our framework it is demonstrated that the proposed framework significantly outperforms goldstandard texture features by more than 10 on esvat we also test the performance of our proposed approach on the kthtips2b and os datasets and a further dataset synthetically derived from forrest showing superior performance compared to the state of the art | [['research', 'in', 'texture', 'recognition', 'often', 'concentrates', 'on', 'recognizing', 'textures', 'with', 'intraclass', 'variations', 'such', 'as', 'illumination', 'rotation', 'viewpoint', 'and', 'small', 'scale', 'changes', 'in', 'contrast', 'in', 'realworld', 'applications', 'a', 'change', 'in', 'scale', 'can', 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1,802.04442 | Central splitting of manifolds with no conjugate points | Each compact Riemannian manifold with no conjugate points admits a family of
functions whose integrals vanish exactly when central Busemann functions split
linearly. These functions vanish when all central Busemann functions are sub-
or superharmonic. When central Busemann functions are convex or concave, they
must be totally geodesic. These yield generalizations of the splitting theorems
of O'Sullivan and Eberlein for manifolds with no focal points and,
respectively, nonpositive curvature.
| math.DG | each compact riemannian manifold with no conjugate points admits a family of functions whose integrals vanish exactly when central busemann functions split linearly these functions vanish when all central busemann functions are sub or superharmonic when central busemann functions are convex or concave they must be totally geodesic these yield generalizations of the splitting theorems of osullivan and eberlein for manifolds with no focal points and respectively nonpositive curvature | [['each', 'compact', 'riemannian', 'manifold', 'with', 'no', 'conjugate', 'points', 'admits', 'a', 'family', 'of', 'functions', 'whose', 'integrals', 'vanish', 'exactly', 'when', 'central', 'busemann', 'functions', 'split', 'linearly', 'these', 'functions', 'vanish', 'when', 'all', 'central', 'busemann', 'functions', 'are', 'sub', 'or', 'superharmonic', 'when', 'central', 'busemann', 'functions', 'are', 'convex', 'or', 'concave', 'they', 'must', 'be', 'totally', 'geodesic', 'these', 'yield', 'generalizations', 'of', 'the', 'splitting', 'theorems', 'of', 'osullivan', 'and', 'eberlein', 'for', 'manifolds', 'with', 'no', 'focal', 'points', 'and', 'respectively', 'nonpositive', 'curvature']] | [-0.170702895068604, 0.15240212092581001, -0.09627557578294174, 0.13915238267593627, -0.10001126427095437, -0.2170295910437362, -0.05408147624457606, 0.4095117092618476, -0.2566554680520642, -0.11257592330187344, 0.1560437425269165, -0.35215247962353885, -0.11367180599979516, 0.20433474008155905, -0.10372542135039534, 0.028755979423505672, 0.06636031449981628, 0.04763738227927167, -0.14423241545412474, -0.2639409085293638, 0.4494804670575304, -0.09846348218970756, 0.17555345087042212, 0.09362234694856232, 0.11965810147829462, 0.04420722246615459, 0.008697368639885732, 0.05158097274682444, -0.1168829299563515, 0.07849571594388048, 0.32666808793294255, 0.07181269608006097, 0.26388312846530176, -0.3586088005399358, -0.12184347242346384, 0.2604724189462275, 0.10229754488935451, -0.08948240814683284, 0.021426300504717274, -0.25285860273879074, 0.08861881018737736, 0.000752734728967366, -0.20253754737854435, -0.1068645910481396, 0.008094189033938059, 0.11733363427059806, -0.1881328526330923, 0.02865367142510587, 0.1340075401670259, 0.038805708977515285, -0.07402291748524252, -0.19756328099427503, -0.15008229775813178, 0.06484741874442408, 0.022086718686572884, 0.11309537193670437, 0.1173174939914674, 0.011226574471895245, -0.09550154960268865, 0.28498277542150946, -0.021639806117847278, -0.3878340801575046, 0.13156954168031612, -0.2121697733502673, -0.15442348640088155, 0.15924840409011728, 0.12751863040434924, 0.19865872557747408, -0.09588996286860808, 0.1520121193261466, -0.016974594821964485, 0.04028873053629059, 0.20038956317348758, 0.006720678513005808, 0.21205736061666106, -0.055786852869947536, 0.20869307251701105, 0.10610365383563213, 0.021663632864753406, -0.12993325441535833, -0.3920539611734558, -0.12625401314563942, -0.1684822809993816, 0.13289683447151945, -0.20600431063170274, -0.2745414153821226, 0.32292452578743297, -0.11323998466242051, 0.1875049200826797, 0.17345758738077205, 0.21925908262315003, 0.14422773803665262, 0.09677094308848398, 0.17341455868512823, 0.15645571972202996, 0.1664720184399166, -0.04339740150000738, -0.06239759268752043, -0.06161790245981968, 0.14403101670272325] |
1,802.04443 | On Characterizing the Capacity of Neural Networks using Algebraic
Topology | The learnability of different neural architectures can be characterized
directly by computable measures of data complexity. In this paper, we reframe
the problem of architecture selection as understanding how data determines the
most expressive and generalizable architectures suited to that data, beyond
inductive bias. After suggesting algebraic topology as a measure for data
complexity, we show that the power of a network to express the topological
complexity of a dataset in its decision region is a strictly limiting factor in
its ability to generalize. We then provide the first empirical characterization
of the topological capacity of neural networks. Our empirical analysis shows
that at every level of dataset complexity, neural networks exhibit topological
phase transitions. This observation allowed us to connect existing theory to
empirically driven conjectures on the choice of architectures for
fully-connected neural networks.
| cs.LG cs.CG cs.NE math.AT stat.ML | the learnability of different neural architectures can be characterized directly by computable measures of data complexity in this paper we reframe the problem of architecture selection as understanding how data determines the most expressive and generalizable architectures suited to that data beyond inductive bias after suggesting algebraic topology as a measure for data complexity we show that the power of a network to express the topological complexity of a dataset in its decision region is a strictly limiting factor in its ability to generalize we then provide the first empirical characterization of the topological capacity of neural networks our empirical analysis shows that at every level of dataset complexity neural networks exhibit topological phase transitions this observation allowed us to connect existing theory to empirically driven conjectures on the choice of architectures for fullyconnected neural networks | [['the', 'learnability', 'of', 'different', 'neural', 'architectures', 'can', 'be', 'characterized', 'directly', 'by', 'computable', 'measures', 'of', 'data', 'complexity', 'in', 'this', 'paper', 'we', 'reframe', 'the', 'problem', 'of', 'architecture', 'selection', 'as', 'understanding', 'how', 'data', 'determines', 'the', 'most', 'expressive', 'and', 'generalizable', 'architectures', 'suited', 'to', 'that', 'data', 'beyond', 'inductive', 'bias', 'after', 'suggesting', 'algebraic', 'topology', 'as', 'a', 'measure', 'for', 'data', 'complexity', 'we', 'show', 'that', 'the', 'power', 'of', 'a', 'network', 'to', 'express', 'the', 'topological', 'complexity', 'of', 'a', 'dataset', 'in', 'its', 'decision', 'region', 'is', 'a', 'strictly', 'limiting', 'factor', 'in', 'its', 'ability', 'to', 'generalize', 'we', 'then', 'provide', 'the', 'first', 'empirical', 'characterization', 'of', 'the', 'topological', 'capacity', 'of', 'neural', 'networks', 'our', 'empirical', 'analysis', 'shows', 'that', 'at', 'every', 'level', 'of', 'dataset', 'complexity', 'neural', 'networks', 'exhibit', 'topological', 'phase', 'transitions', 'this', 'observation', 'allowed', 'us', 'to', 'connect', 'existing', 'theory', 'to', 'empirically', 'driven', 'conjectures', 'on', 'the', 'choice', 'of', 'architectures', 'for', 'fullyconnected', 'neural', 'networks']] | [-0.10974659435861908, 0.018232337856555685, -0.08662633373580106, 0.0920208725720466, -0.09862832025871338, -0.16092696803217918, 0.10441724079667919, 0.3968579482478911, -0.2936642333456, -0.2983350823290975, 0.06173006748962079, -0.23845636160866193, -0.22711072776035188, 0.20589850016403943, -0.10696635193466243, 0.07319297803812386, 0.06533197544152192, 0.03593470564181403, -0.06459846666171763, -0.274653129882234, 0.3422032626926118, 0.05235916358309195, 0.33697430325521377, 0.04927723594900111, 0.07349080118172638, -0.0646949085742509, 0.008336344641386806, 0.0376790270974572, -0.1175437389559139, 0.1715323038489955, 0.31542980660143416, 0.22049657454429988, 0.29306641671642225, -0.41758967532244723, -0.222308128614232, 0.10957254016059725, 0.12748037423668446, 0.06729524866129984, 0.023980089445317657, -0.28923457072061654, 0.08669605632466675, -0.1463914303768594, -0.06342980935005471, -0.15542679385330035, -0.0009509857661802979, -0.009014329886721337, -0.2600145239377846, 0.010750935368431622, 0.10162111799753107, 0.057037423522321176, -0.032052354828920215, -0.06778174385972842, -0.007656496292089715, 0.14895489179145763, 0.004065851497323499, 0.029677987397650107, 0.11351278756346266, -0.15896184921867268, -0.18163419937413625, 0.3166992689606131, -0.04476290891933091, -0.18424142024698942, 0.17533669514437755, -0.09220766418901108, -0.20406678106690593, 0.09984424947213162, 0.21984207030960962, 0.0608379524158355, -0.13600580018315025, 0.05781595021450331, -0.03452156984670129, 0.18576675769872963, 0.012953082068741103, 0.05810020700001212, 0.1588962626958485, 0.28158276853055747, 0.05498198211631354, 0.18992904838609426, -0.06777369793716763, -0.07733700344311621, -0.22408594954621924, -0.1109295091305017, -0.20949481183368548, 0.03391786092491683, -0.11295876337670266, -0.16883919289616375, 0.42780122091747164, 0.22263712157591192, 0.20680214621482745, 0.17639136516406914, 0.30864189354949356, 0.05836592885401949, 0.10839867970635704, 0.08375690234927735, 0.18684808366507402, 0.11862964942702092, 0.12058987715692424, -0.18135599962689036, 0.10370295378721922, 0.034536891308722686] |
1,802.04444 | A General Method for Demand Inversion | This paper describes a numerical method to solve for mean product qualities
which equates the real market share to the market share predicted by a discrete
choice model. The method covers a general class of discrete choice model,
including the pure characteristics model in Berry and Pakes(2007) and the
random coefficient logit model in Berry et al.(1995) (hereafter BLP). The
method transforms the original market share inversion problem to an
unconstrained convex minimization problem, so that any convex programming
algorithm can be used to solve the inversion. Moreover, such results also imply
that the computational complexity of inverting a demand model should be no more
than that of a convex programming problem. In simulation examples, I show the
method outperforms the contraction mapping algorithm in BLP. I also find the
method remains robust in pure characteristics models with near-zero market
shares.
| econ.EM | this paper describes a numerical method to solve for mean product qualities which equates the real market share to the market share predicted by a discrete choice model the method covers a general class of discrete choice model including the pure characteristics model in berry and pakes2007 and the random coefficient logit model in berry et al1995 hereafter blp the method transforms the original market share inversion problem to an unconstrained convex minimization problem so that any convex programming algorithm can be used to solve the inversion moreover such results also imply that the computational complexity of inverting a demand model should be no more than that of a convex programming problem in simulation examples i show the method outperforms the contraction mapping algorithm in blp i also find the method remains robust in pure characteristics models with nearzero market shares | [['this', 'paper', 'describes', 'a', 'numerical', 'method', 'to', 'solve', 'for', 'mean', 'product', 'qualities', 'which', 'equates', 'the', 'real', 'market', 'share', 'to', 'the', 'market', 'share', 'predicted', 'by', 'a', 'discrete', 'choice', 'model', 'the', 'method', 'covers', 'a', 'general', 'class', 'of', 'discrete', 'choice', 'model', 'including', 'the', 'pure', 'characteristics', 'model', 'in', 'berry', 'and', 'pakes2007', 'and', 'the', 'random', 'coefficient', 'logit', 'model', 'in', 'berry', 'et', 'al1995', 'hereafter', 'blp', 'the', 'method', 'transforms', 'the', 'original', 'market', 'share', 'inversion', 'problem', 'to', 'an', 'unconstrained', 'convex', 'minimization', 'problem', 'so', 'that', 'any', 'convex', 'programming', 'algorithm', 'can', 'be', 'used', 'to', 'solve', 'the', 'inversion', 'moreover', 'such', 'results', 'also', 'imply', 'that', 'the', 'computational', 'complexity', 'of', 'inverting', 'a', 'demand', 'model', 'should', 'be', 'no', 'more', 'than', 'that', 'of', 'a', 'convex', 'programming', 'problem', 'in', 'simulation', 'examples', 'i', 'show', 'the', 'method', 'outperforms', 'the', 'contraction', 'mapping', 'algorithm', 'in', 'blp', 'i', 'also', 'find', 'the', 'method', 'remains', 'robust', 'in', 'pure', 'characteristics', 'models', 'with', 'nearzero', 'market', 'shares']] | [-0.04471349042557579, 0.012114106272617522, -0.1232282129981156, 0.07771972179263165, -0.0975167463537738, -0.1957954977472712, 0.0416908196780631, 0.4039220318730388, -0.33237491490851556, -0.2966565241371947, 0.09969702161865175, -0.25309674981981517, -0.23383329500045094, 0.19499268174404277, -0.12150522046389857, 0.04502870138801102, 0.054780233123372976, -0.021619807579554617, -0.08822380849991791, -0.26011465764022434, 0.24572810080052088, -0.0011410230887122451, 0.2931443439996136, 0.013073860470571422, 0.10990777647654926, -0.005472148476733959, -0.021904009187294703, 0.05671044884781752, -0.08602903898816813, 0.11429421704877833, 0.23225940079877805, 0.15478596948752446, 0.31522714684251696, -0.3825821788715465, -0.19597106752168786, 0.13429083126331015, 0.08389894996237542, 0.09320837524303767, -0.012833855506219802, -0.22859281075320073, 0.059809973909120476, -0.177908035733604, -0.09736116397793272, -0.0805046341357021, -0.029536514167141703, -0.03281361442059279, -0.32508483217097817, 0.06609380970600925, 0.05485425384914769, 0.012016159621998668, -0.10259328697741564, -0.12031541839824059, -0.024019453062542846, 0.0547673677493419, 0.051987561516164404, 0.004988669005355665, 0.08251671441830695, -0.08961847598631201, -0.16705443608474785, 0.40508477935301407, -0.05322186290172145, -0.24645994996119822, 0.12616370465839283, -0.07243745817270662, -0.14722539143736607, 0.13302847628614733, 0.18330025120106128, 0.12376745455964867, -0.1268244690561135, 0.10712424551519299, -0.1187047476805414, 0.15995749355559902, 0.008156044593280447, -0.022990309375953595, 0.14773954588869986, 0.12850910739632257, 0.11594196853859882, 0.1492451114879389, -0.015418442388597344, -0.14924823561949388, -0.2547064218004899, -0.1532949768605509, -0.23168834133871963, -0.0071007050956333325, -0.10081960370194533, -0.181647644151235, 0.4028208561773811, 0.17949529467483183, 0.13143442375751743, 0.10109160508977116, 0.29512745307292787, 0.12965510747328932, 0.03192632971871977, 0.13713685858529062, 0.16399252213138554, 0.07464755585450412, 0.09469476799214525, -0.2449783930355417, 0.10729765402252919, 0.07733037583529949] |
1,802.04445 | Topological Defect Lines and Renormalization Group Flows in Two
Dimensions | We consider topological defect lines (TDLs) in two-dimensional conformal
field theories. Generalizing and encompassing both global symmetries and
Verlinde lines, TDLs together with their attached defect operators provide
models of fusion categories without braiding. We study the crossing relations
of TDLs, discuss their relation to the 't Hooft anomaly, and use them to
constrain renormalization group flows to either conformal critical points or
topological quantum field theories (TQFTs). We show that if certain
non-invertible TDLs are preserved along a RG flow, then the vacuum cannot be a
non-degenerate gapped state. For various massive flows, we determine the
infrared TQFTs completely from the consideration of TDLs together with modular
invariance.
| hep-th cond-mat.str-el | we consider topological defect lines tdls in twodimensional conformal field theories generalizing and encompassing both global symmetries and verlinde lines tdls together with their attached defect operators provide models of fusion categories without braiding we study the crossing relations of tdls discuss their relation to the t hooft anomaly and use them to constrain renormalization group flows to either conformal critical points or topological quantum field theories tqfts we show that if certain noninvertible tdls are preserved along a rg flow then the vacuum cannot be a nondegenerate gapped state for various massive flows we determine the infrared tqfts completely from the consideration of tdls together with modular invariance | [['we', 'consider', 'topological', 'defect', 'lines', 'tdls', 'in', 'twodimensional', 'conformal', 'field', 'theories', 'generalizing', 'and', 'encompassing', 'both', 'global', 'symmetries', 'and', 'verlinde', 'lines', 'tdls', 'together', 'with', 'their', 'attached', 'defect', 'operators', 'provide', 'models', 'of', 'fusion', 'categories', 'without', 'braiding', 'we', 'study', 'the', 'crossing', 'relations', 'of', 'tdls', 'discuss', 'their', 'relation', 'to', 'the', 't', 'hooft', 'anomaly', 'and', 'use', 'them', 'to', 'constrain', 'renormalization', 'group', 'flows', 'to', 'either', 'conformal', 'critical', 'points', 'or', 'topological', 'quantum', 'field', 'theories', 'tqfts', 'we', 'show', 'that', 'if', 'certain', 'noninvertible', 'tdls', 'are', 'preserved', 'along', 'a', 'rg', 'flow', 'then', 'the', 'vacuum', 'can', 'not', 'be', 'a', 'nondegenerate', 'gapped', 'state', 'for', 'various', 'massive', 'flows', 'we', 'determine', 'the', 'infrared', 'tqfts', 'completely', 'from', 'the', 'consideration', 'of', 'tdls', 'together', 'with', 'modular', 'invariance']] | [-0.18978316869417375, 0.17315912003290246, -0.07667710858301936, 0.06955827215728773, -0.0985692301955582, -0.20436239494010805, 0.014362137491645461, 0.362775393710895, -0.2630901892923496, -0.20897862506522374, 0.09338738428407603, -0.26433493504062594, -0.12637132678646595, 0.1134074666410346, -0.04865066556429321, 0.0273201063063673, -0.006835956130684777, 0.05602771066875324, -0.14707082173660058, -0.2342050406870178, 0.40402204204689374, -0.03005014758408916, 0.288431441411376, 0.051121198882835665, 0.045823033183643766, -0.007459724004465071, -0.0247781045565551, 0.04258049969900061, -0.13680207382455012, 0.10259394701146944, 0.25472154049351625, 0.030712345979091796, 0.10030785407765176, -0.44627620977434246, -0.23028844599823722, 0.08595229181122374, 0.1370031242322346, 0.10996955264656043, -0.0012159742457284169, -0.3173680244013667, 0.10330395926800769, -0.17371476442468436, -0.14712051972746848, -0.13537519966167483, -0.04391057464421134, -0.04836554367476228, -0.20311553001361476, 0.013348201442171227, 0.03869203220714222, 0.13726745678789237, -0.07438276824964718, -0.001228104019537568, -0.1071732235363346, 0.1328977045529014, 0.052490642678458245, -0.005090394749475474, 0.14613924800333652, -0.20548953162506223, -0.1387162291741168, 0.370043313824995, -0.06324384063482284, -0.1800834810953926, 0.20884113618469036, -0.13632678014480254, -0.18899667981842702, 0.07914776546372608, 0.07418854282745584, 0.10900168171237139, -0.09907642887820574, 0.1597461050445087, -0.04505618320016021, 0.057444567030126396, 0.07322434543377974, 0.04210342776019719, 0.282037125620991, -0.0075254736425863075, 0.0606301467992704, 0.14061677774668419, -0.020230974971094506, -0.08921313735614107, -0.3787788907235319, -0.20421474474609236, -0.07645944754847071, 0.11741509411116237, -0.09752395524265012, -0.15938817404041236, 0.37887773727998136, 0.14476216828572266, 0.20860394833778792, 0.06942495605908335, 0.17212536976757375, 0.11963599799200893, 0.13345155766318467, 0.0983118849425492, 0.18433267852841792, 0.20204587430291046, 0.028721101850863885, -0.2285592060874809, -0.125109047440558, 0.1757994981736622] |
1,802.04446 | No bursts detected from FRB121102 in two 5-hour observing campaigns with
the Robert C. Byrd Green Bank Telescope | Here, we report non-detection of radio bursts from Fast Radio Burst FRB
121102 during two 5-hour observation sessions on the Robert C. Byrd 100-m Green
Bank Telescope in West Virginia, USA, on December 11, 2017, and January 12,
2018. In addition, we report non-detection during an abutting 10-hour
observation with the Kunming 40-m telescope in China, which commenced UTC 10:00
January 12, 2018. These are among the longest published contiguous observations
of FRB 121102, and support the notion that FRB 121102 bursts are episodic.
These observations were part of a simultaneous optical and radio monitoring
campaign with the the Caltech HIgh- speed Multi-color CamERA (CHIMERA)
instrument on the Hale 5.1-m telescope.
| astro-ph.HE | here we report nondetection of radio bursts from fast radio burst frb 121102 during two 5hour observation sessions on the robert c byrd 100m green bank telescope in west virginia usa on december 11 2017 and january 12 2018 in addition we report nondetection during an abutting 10hour observation with the kunming 40m telescope in china which commenced utc 1000 january 12 2018 these are among the longest published contiguous observations of frb 121102 and support the notion that frb 121102 bursts are episodic these observations were part of a simultaneous optical and radio monitoring campaign with the the caltech high speed multicolor camera chimera instrument on the hale 51m telescope | [['here', 'we', 'report', 'nondetection', 'of', 'radio', 'bursts', 'from', 'fast', 'radio', 'burst', 'frb', '121102', 'during', 'two', '5hour', 'observation', 'sessions', 'on', 'the', 'robert', 'c', 'byrd', '100m', 'green', 'bank', 'telescope', 'in', 'west', 'virginia', 'usa', 'on', 'december', '11', '2017', 'and', 'january', '12', '2018', 'in', 'addition', 'we', 'report', 'nondetection', 'during', 'an', 'abutting', '10hour', 'observation', 'with', 'the', 'kunming', '40m', 'telescope', 'in', 'china', 'which', 'commenced', 'utc', '1000', 'january', '12', '2018', 'these', 'are', 'among', 'the', 'longest', 'published', 'contiguous', 'observations', 'of', 'frb', '121102', 'and', 'support', 'the', 'notion', 'that', 'frb', '121102', 'bursts', 'are', 'episodic', 'these', 'observations', 'were', 'part', 'of', 'a', 'simultaneous', 'optical', 'and', 'radio', 'monitoring', 'campaign', 'with', 'the', 'the', 'caltech', 'high', 'speed', 'multicolor', 'camera', 'chimera', 'instrument', 'on', 'the', 'hale', '51m', 'telescope']] | [-0.13541315782915903, 0.1420648478713308, -0.015354804258357297, 0.034608094483144114, -0.1954285720560431, -0.09858788369298987, 0.1055768461780505, 0.4859375295875309, -0.06218799031633246, -0.38373557911285927, 0.22930225727530112, -0.33189219422638416, -0.1248503286886524, 0.2578340996568603, -0.03948691120027221, -0.04616579322798832, 0.17863305515467046, -0.17716604737598482, 0.045039532603360376, -0.2742347201282108, 0.13016620689587718, 0.19255381532281907, 0.2718280796129424, -0.06209576190323443, 0.13973091559501383, -0.07773293341616974, -0.17033101145968865, -0.15979761263640882, -0.06321458273331544, 0.001794345039236653, 0.2667458784674202, 0.19065503119532992, 0.21340269920088956, -0.39932486748004015, -0.1315965601368933, 0.002936688306330292, 0.02328953715324452, -0.08326238514879102, 0.02744591897584263, -0.44923880551983647, -0.0011599007742227735, -0.24066632741072155, -0.15566969579840834, 0.23550071047098786, 0.12211636003098442, 0.1061848245963857, -0.13433414919150843, 0.0524184008355479, -0.09714856126732696, 0.1309410580224148, -0.1381078688490733, -0.04954955991575712, 0.06669589659295606, 0.08097029019877114, -0.02927174583194895, 0.12722348373987385, 0.07137882177139053, -0.02224393749425003, -0.12872027663665042, 0.26802622683153293, -0.0817463095341851, 0.19092916799625298, 0.19758889354715073, -0.2638742720271956, -0.31495947569508004, 0.1587213159639489, 0.19972806921446914, 0.07104765567592941, -0.14877575572982832, 0.015924222392149624, -0.06354250634290479, 0.24715991209991076, 0.1241285284679081, 0.036912587366427656, 0.280582453149396, 0.10030096874631068, 0.017196719131961063, 0.06817075574397202, -0.40404653731053947, 0.03842585809058971, -0.2488355176556889, -0.09488998174243649, -0.1563953923665591, 0.1366256436014229, -0.03097101476692137, -0.037717682535724446, 0.4065828754625342, 0.07180980789298946, 0.09840430063405284, 0.07140746455836773, 0.23206800460051913, -0.010437516134802823, 0.07873391272470791, 0.1367304143873421, 0.32950225127609195, 0.05736275206252865, 0.2437112509054845, -0.16027092531130457, 0.02598582363377015, 0.024480315411950018] |
1,802.04447 | Graph Coarsening with Preserved Spectral Properties | Large-scale graphs are widely used to represent object relationships in many
real world applications. The occurrence of large-scale graphs presents
significant computational challenges to process, analyze, and extract
information. Graph coarsening techniques are commonly used to reduce the
computational load while attempting to maintain the basic structural properties
of the original graph. As there is no consensus on the specific graph
properties preserved by coarse graphs, how to measure the differences between
original and coarse graphs remains a key challenge. In this work, we introduce
a new perspective regarding the graph coarsening based on concepts from
spectral graph theory. We propose and justify new distance functions that
characterize the differences between original and coarse graphs. We show that
the proposed spectral distance naturally captures the structural differences in
the graph coarsening process. In addition, we provide efficient graph
coarsening algorithms to generate graphs which provably preserve the spectral
properties from original graphs. Experiments show that our proposed algorithms
consistently achieve better results compared to previous graph coarsening
methods on graph classification and block recovery tasks.
| cs.SI cs.NA stat.AP | largescale graphs are widely used to represent object relationships in many real world applications the occurrence of largescale graphs presents significant computational challenges to process analyze and extract information graph coarsening techniques are commonly used to reduce the computational load while attempting to maintain the basic structural properties of the original graph as there is no consensus on the specific graph properties preserved by coarse graphs how to measure the differences between original and coarse graphs remains a key challenge in this work we introduce a new perspective regarding the graph coarsening based on concepts from spectral graph theory we propose and justify new distance functions that characterize the differences between original and coarse graphs we show that the proposed spectral distance naturally captures the structural differences in the graph coarsening process in addition we provide efficient graph coarsening algorithms to generate graphs which provably preserve the spectral properties from original graphs experiments show that our proposed algorithms consistently achieve better results compared to previous graph coarsening methods on graph classification and block recovery tasks | [['largescale', 'graphs', 'are', 'widely', 'used', 'to', 'represent', 'object', 'relationships', 'in', 'many', 'real', 'world', 'applications', 'the', 'occurrence', 'of', 'largescale', 'graphs', 'presents', 'significant', 'computational', 'challenges', 'to', 'process', 'analyze', 'and', 'extract', 'information', 'graph', 'coarsening', 'techniques', 'are', 'commonly', 'used', 'to', 'reduce', 'the', 'computational', 'load', 'while', 'attempting', 'to', 'maintain', 'the', 'basic', 'structural', 'properties', 'of', 'the', 'original', 'graph', 'as', 'there', 'is', 'no', 'consensus', 'on', 'the', 'specific', 'graph', 'properties', 'preserved', 'by', 'coarse', 'graphs', 'how', 'to', 'measure', 'the', 'differences', 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'classification', 'and', 'block', 'recovery', 'tasks']] | [-0.05148293080473585, 0.010767606047115156, -0.10659877948462963, 0.11313872823102948, -0.112538926581453, -0.10229818370193243, 0.02695016084704548, 0.4490723656929497, -0.3265245530687805, -0.35658342245966196, 0.1014896317318614, -0.24248816139131252, -0.22376830827710883, 0.13915141659921834, -0.160333208995206, 0.07659827334806323, 0.13631709176780923, 0.01992912243403095, -0.055487797418609264, -0.25276373946058034, 0.3101562085907374, 0.03407988931584571, 0.3149280565657786, 0.06220656107578959, 0.08619157679445509, -0.02791725354535239, -0.0507550419807168, 0.06342501999012061, -0.17515964136371623, 0.15489693658692497, 0.2836745848719563, 0.18410694273720896, 0.25259683384427, -0.4426867264615638, -0.2302396934785481, 0.14650659567011254, 0.11776607145156179, 0.10169106494618713, -0.012992696474040194, -0.24835412632141796, 0.13219820069415228, -0.08183237539803875, -0.009225767940016729, -0.13014099382662347, 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1,802.04448 | Inducing ferromagnetism and Kondo effect in platinum by paramagnetic
ionic gating | Electrically controllable magnetism, which requires the field-effect
manipulation of both charge and spin degrees of freedom, has attracted growing
interests since the emergence of spintronics. In this work, we report the
reversible electrical switching of ferromagnetic (FM) states in platinum (Pt)
thin films by introducing paramagnetic ionic liquid (PIL) as the gating media.
The paramagnetic ionic gating controls the movement of ions with magnetic
moments, which induces itinerant ferromagnetism on the surface of Pt films with
large coercivity and perpendicular anisotropy mimicking the ideal
two-dimensional Ising-type FM state. The electrical transport of the induced FM
state shows Kondo effect at low temperature suggesting spatially separated
coexistence of Kondo scattering beneath the FM interface. The tunable FM state
indicates that paramagnetic ionic gating could serve as a versatile method to
induce rich transport phenomena combining field effect and magnetism at
PIL-gated interfaces.
| physics.app-ph cond-mat.mtrl-sci | electrically controllable magnetism which requires the fieldeffect manipulation of both charge and spin degrees of freedom has attracted growing interests since the emergence of spintronics in this work we report the reversible electrical switching of ferromagnetic fm states in platinum pt thin films by introducing paramagnetic ionic liquid pil as the gating media the paramagnetic ionic gating controls the movement of ions with magnetic moments which induces itinerant ferromagnetism on the surface of pt films with large coercivity and perpendicular anisotropy mimicking the ideal twodimensional isingtype fm state the electrical transport of the induced fm state shows kondo effect at low temperature suggesting spatially separated coexistence of kondo scattering beneath the fm interface the tunable fm state indicates that paramagnetic ionic gating could serve as a versatile method to induce rich transport phenomena combining field effect and magnetism at pilgated interfaces | [['electrically', 'controllable', 'magnetism', 'which', 'requires', 'the', 'fieldeffect', 'manipulation', 'of', 'both', 'charge', 'and', 'spin', 'degrees', 'of', 'freedom', 'has', 'attracted', 'growing', 'interests', 'since', 'the', 'emergence', 'of', 'spintronics', 'in', 'this', 'work', 'we', 'report', 'the', 'reversible', 'electrical', 'switching', 'of', 'ferromagnetic', 'fm', 'states', 'in', 'platinum', 'pt', 'thin', 'films', 'by', 'introducing', 'paramagnetic', 'ionic', 'liquid', 'pil', 'as', 'the', 'gating', 'media', 'the', 'paramagnetic', 'ionic', 'gating', 'controls', 'the', 'movement', 'of', 'ions', 'with', 'magnetic', 'moments', 'which', 'induces', 'itinerant', 'ferromagnetism', 'on', 'the', 'surface', 'of', 'pt', 'films', 'with', 'large', 'coercivity', 'and', 'perpendicular', 'anisotropy', 'mimicking', 'the', 'ideal', 'twodimensional', 'isingtype', 'fm', 'state', 'the', 'electrical', 'transport', 'of', 'the', 'induced', 'fm', 'state', 'shows', 'kondo', 'effect', 'at', 'low', 'temperature', 'suggesting', 'spatially', 'separated', 'coexistence', 'of', 'kondo', 'scattering', 'beneath', 'the', 'fm', 'interface', 'the', 'tunable', 'fm', 'state', 'indicates', 'that', 'paramagnetic', 'ionic', 'gating', 'could', 'serve', 'as', 'a', 'versatile', 'method', 'to', 'induce', 'rich', 'transport', 'phenomena', 'combining', 'field', 'effect', 'and', 'magnetism', 'at', 'pilgated', 'interfaces']] | [-0.20186253858264536, 0.27761313620638767, 0.004226727492641658, -0.0434095565354385, -0.0431700764656333, -0.21714894604603094, 0.0824078682128207, 0.40827277510106796, -0.29236542540935, -0.28398681346360327, -0.03633166471596009, -0.3192049724449004, -0.07924195809422858, 0.1338335512879504, 0.07522531966845106, -0.03125527772353962, -0.09163686478236091, -0.11682224761295532, -0.11198219461532842, -0.13480037935078143, 0.22588333909599376, -0.010959545798998858, 0.3696898713707924, 0.1576830162533692, 0.08697755307158721, 0.01606530732076083, 0.19713064694244947, 0.04851292693471935, -0.11422313571867042, 0.05071680880542512, 0.2733749401755631, -0.21732966747201446, 0.17628356601178113, -0.4901793492625334, -0.22832415106490123, -0.049680255692718285, 0.11107587083575449, 0.17830832843568975, -0.13769754019449465, -0.3155334485628243, 0.024344864585769496, -0.13291507566214672, -0.1259447972755879, -0.1220969726747301, -0.01718712362178069, -0.012744147477496881, -0.2734168064368922, 0.1146327133365308, 0.11362006778113677, 0.17351977785583586, -0.07711826723368306, -0.15233225312591198, -0.1240407005418092, 0.07289034681660789, 0.05599948460169669, 0.0759783431172504, 0.26084332130849364, -0.11260505464368699, -0.12759336092442805, 0.29048817422008144, -0.032116644932622354, -0.06546036209245877, 0.18494259745348246, -0.2028429905817445, -0.006259897135065071, 0.1901598143058696, 0.12565161049133167, 0.08220104369933584, -0.15045957486145198, 0.07247341466531257, 0.022876183036714792, 0.21653675594300564, 0.017539675739992943, 0.10378409343705114, 0.34138519779912063, 0.2643795089064432, 0.07111768491491342, 0.17373670674195246, -0.14100633416507793, -0.06708227012838636, -0.15657189072641944, -0.17694642416068485, -0.2411424783374449, 0.11095228601136893, -0.07539766713712846, -0.22762375395478948, 0.3890229861118964, 0.17091853609474908, 0.10543473446076469, -0.15442150160503973, 0.22815408589584488, 0.023101063201569818, 0.06980459920097408, 0.03232287773363558, 0.23356998588923097, 0.23430468171469068, 0.21035224828946023, -0.33978257824533753, 0.15082520823593118, -0.002912387585716455] |
1,802.04449 | Pseudorapidity distribution and decorrelation of anisotropic flow within
CLVisc hydrodynamics | Studies of fluctuations and correlations of soft hadrons and hard and
electromagnetic probes of the dense and strongly interacting medium require
event-by-event hydrodynamic simulations of high-energy heavy-ion collisions
that are computing intensive. We develop a (3+1)D viscous hydrodynamic model --
CLVisc that is parallelized on Graphics Processing Unit (GPU) using Open
Computing Language (OpenCL) with 60 times performance increase for space-time
evolution and more than 120 times for the Cooper-Frye particlization relative
to that without GPU parallelization. The pseudo-rapidity dependence of
anisotropic flow $v_n(\eta)$ are then computed in CLVisc with initial
conditions given by the A Multi-Phase Transport (AMPT) model, with energy
density fluctuations both in the transverse plane and along the longitudinal
direction. Although the magnitude of $v_n(\eta)$ and the ratios between
$v_2(\eta)$ and $v_3(\eta)$ are sensitive to the effective shear viscosity over
entropy density ratio $\eta_v/s$, the shape of the $v_{n}(\eta)$ distributions
in $\eta$ do not depend on the value of $\eta_v/s$. The decorrelation of $v_n$
along the pseudo-rapidity direction due to the twist and fluctuation of the
event-planes in the initial parton density distributions is also studied. The
decorrelation observable $r_n(\eta^a, \eta^b)$ between $v_n\{-\eta^a\}$ and
$v_n\{\eta^a\}$ with the auxiliary reference window $\eta^b$ is found not
sensitive to $\eta_v/s$ when there is no initial fluid velocity. For small
$\eta_v/s$, the initial fluid velocity from mini-jet partons introduces sizable
splitting of $r_n(\eta^a, \eta^b)$ between the two reference rapidity windows
$\eta^b \in [3, 4]$ and $\eta^b \in [4.4, 5.0]$, as has been observed in
experiment. The implementation of CLVisc and guidelines on how to efficiently
parallelize scientific programs on GPUs are also provided.
| nucl-th hep-ph physics.comp-ph physics.flu-dyn | studies of fluctuations and correlations of soft hadrons and hard and electromagnetic probes of the dense and strongly interacting medium require eventbyevent hydrodynamic simulations of highenergy heavyion collisions that are computing intensive we develop a 31d viscous hydrodynamic model clvisc that is parallelized on graphics processing unit gpu using open computing language opencl with 60 times performance increase for spacetime evolution and more than 120 times for the cooperfrye particlization relative to that without gpu parallelization the pseudorapidity dependence of anisotropic flow v_neta are then computed in clvisc with initial conditions given by the a multiphase transport ampt model with energy density fluctuations both in the transverse plane and along the longitudinal direction although the magnitude of v_neta and the ratios between v_2eta and v_3eta are sensitive to the effective shear viscosity over entropy density ratio eta_vs the shape of the v_neta distributions in eta do not depend on the value of eta_vs the decorrelation of v_n along the pseudorapidity direction due to the twist and fluctuation of the eventplanes in the initial parton density distributions is also studied the decorrelation observable r_netaa etab between v_netaa and v_netaa with the auxiliary reference window etab is found not sensitive to eta_vs when there is no initial fluid velocity for small eta_vs the initial fluid velocity from minijet partons introduces sizable splitting of r_netaa etab between the two reference rapidity windows etab in 3 4 and etab in 44 50 as has been observed in experiment the implementation of clvisc and guidelines on how to efficiently parallelize scientific programs on gpus are also provided | [['studies', 'of', 'fluctuations', 'and', 'correlations', 'of', 'soft', 'hadrons', 'and', 'hard', 'and', 'electromagnetic', 'probes', 'of', 'the', 'dense', 'and', 'strongly', 'interacting', 'medium', 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1,802.0445 | A High Performance Implementation of Spectral Clustering on CPU-GPU
Platforms | Spectral clustering is one of the most popular graph clustering algorithms,
which achieves the best performance for many scientific and engineering
applications. However, existing implementations in commonly used software
platforms such as Matlab and Python do not scale well for many of the emerging
Big Data applications. In this paper, we present a fast implementation of the
spectral clustering algorithm on a CPU-GPU heterogeneous platform. Our
implementation takes advantage of the computational power of the multi-core CPU
and the massive multithreading and SIMD capabilities of GPUs. Given the input
as data points in high dimensional space, we propose a parallel scheme to build
a sparse similarity graph represented in a standard sparse representation
format. Then we compute the smallest $k$ eigenvectors of the Laplacian matrix
by utilizing the reverse communication interfaces of ARPACK software and
cuSPARSE library, where $k$ is typically very large. Moreover, we implement a
very fast parallelized $k$-means algorithm on GPUs. Our implementation is shown
to be significantly faster compared to the best known Matlab and Python
implementations for each step. In addition, our algorithm scales to problems
with a very large number of clusters.
| cs.DC cs.MS | spectral clustering is one of the most popular graph clustering algorithms which achieves the best performance for many scientific and engineering applications however existing implementations in commonly used software platforms such as matlab and python do not scale well for many of the emerging big data applications in this paper we present a fast implementation of the spectral clustering algorithm on a cpugpu heterogeneous platform our implementation takes advantage of the computational power of the multicore cpu and the massive multithreading and simd capabilities of gpus given the input as data points in high dimensional space we propose a parallel scheme to build a sparse similarity graph represented in a standard sparse representation format then we compute the smallest k eigenvectors of the laplacian matrix by utilizing the reverse communication interfaces of arpack software and cusparse library where k is typically very large moreover we implement a very fast parallelized kmeans algorithm on gpus our implementation is shown to be significantly faster compared to the best known matlab and python implementations for each step in addition our algorithm scales to problems with a very large number of clusters | [['spectral', 'clustering', 'is', 'one', 'of', 'the', 'most', 'popular', 'graph', 'clustering', 'algorithms', 'which', 'achieves', 'the', 'best', 'performance', 'for', 'many', 'scientific', 'and', 'engineering', 'applications', 'however', 'existing', 'implementations', 'in', 'commonly', 'used', 'software', 'platforms', 'such', 'as', 'matlab', 'and', 'python', 'do', 'not', 'scale', 'well', 'for', 'many', 'of', 'the', 'emerging', 'big', 'data', 'applications', 'in', 'this', 'paper', 'we', 'present', 'a', 'fast', 'implementation', 'of', 'the', 'spectral', 'clustering', 'algorithm', 'on', 'a', 'cpugpu', 'heterogeneous', 'platform', 'our', 'implementation', 'takes', 'advantage', 'of', 'the', 'computational', 'power', 'of', 'the', 'multicore', 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1,802.04451 | Blockchain and Artificial Intelligence | It is undeniable that artificial intelligence (AI) and blockchain concepts
are spreading at a phenomenal rate. Both technologies have distinct degree of
technological complexity and multi-dimensional business implications. However,
a common misunderstanding about blockchain concept, in particular, is that
blockchain is decentralized and is not controlled by anyone. But the underlying
development of a blockchain system is still attributed to a cluster of core
developers. Take smart contract as an example, it is essentially a collection
of codes (or functions) and data (or states) that are programmed and deployed
on a blockchain (say, Ethereum) by different human programmers. It is thus,
unfortunately, less likely to be free of loopholes and flaws. In this article,
through a brief overview about how artificial intelligence could be used to
deliver bug-free smart contract so as to achieve the goal of blockchain 2.0, we
to emphasize that the blockchain implementation can be assisted or enhanced via
various AI techniques. The alliance of AI and blockchain is expected to create
numerous possibilities.
| cs.AI | it is undeniable that artificial intelligence ai and blockchain concepts are spreading at a phenomenal rate both technologies have distinct degree of technological complexity and multidimensional business implications however a common misunderstanding about blockchain concept in particular is that blockchain is decentralized and is not controlled by anyone but the underlying development of a blockchain system is still attributed to a cluster of core developers take smart contract as an example it is essentially a collection of codes or functions and data or states that are programmed and deployed on a blockchain say ethereum by different human programmers it is thus unfortunately less likely to be free of loopholes and flaws in this article through a brief overview about how artificial intelligence could be used to deliver bugfree smart contract so as to achieve the goal of blockchain 20 we to emphasize that the blockchain implementation can be assisted or enhanced via various ai techniques the alliance of ai and blockchain is expected to create numerous possibilities | [['it', 'is', 'undeniable', 'that', 'artificial', 'intelligence', 'ai', 'and', 'blockchain', 'concepts', 'are', 'spreading', 'at', 'a', 'phenomenal', 'rate', 'both', 'technologies', 'have', 'distinct', 'degree', 'of', 'technological', 'complexity', 'and', 'multidimensional', 'business', 'implications', 'however', 'a', 'common', 'misunderstanding', 'about', 'blockchain', 'concept', 'in', 'particular', 'is', 'that', 'blockchain', 'is', 'decentralized', 'and', 'is', 'not', 'controlled', 'by', 'anyone', 'but', 'the', 'underlying', 'development', 'of', 'a', 'blockchain', 'system', 'is', 'still', 'attributed', 'to', 'a', 'cluster', 'of', 'core', 'developers', 'take', 'smart', 'contract', 'as', 'an', 'example', 'it', 'is', 'essentially', 'a', 'collection', 'of', 'codes', 'or', 'functions', 'and', 'data', 'or', 'states', 'that', 'are', 'programmed', 'and', 'deployed', 'on', 'a', 'blockchain', 'say', 'ethereum', 'by', 'different', 'human', 'programmers', 'it', 'is', 'thus', 'unfortunately', 'less', 'likely', 'to', 'be', 'free', 'of', 'loopholes', 'and', 'flaws', 'in', 'this', 'article', 'through', 'a', 'brief', 'overview', 'about', 'how', 'artificial', 'intelligence', 'could', 'be', 'used', 'to', 'deliver', 'bugfree', 'smart', 'contract', 'so', 'as', 'to', 'achieve', 'the', 'goal', 'of', 'blockchain', '20', 'we', 'to', 'emphasize', 'that', 'the', 'blockchain', 'implementation', 'can', 'be', 'assisted', 'or', 'enhanced', 'via', 'various', 'ai', 'techniques', 'the', 'alliance', 'of', 'ai', 'and', 'blockchain', 'is', 'expected', 'to', 'create', 'numerous', 'possibilities']] | [-0.12304372212871864, 0.09129648614184398, -0.08719294058705518, 0.06501503135872347, -0.1491735353679006, -0.2047447603848389, 0.06163190282089357, 0.37732393822136395, -0.31184908617734014, -0.31861181934778915, 0.1632509327613509, -0.2806225725251646, -0.16953049547284352, 0.20126937260756458, -0.14173968515531746, 0.019482624616674677, 0.05302460857982117, 0.02080924869208279, 0.0363238175360564, -0.30420784925495437, 0.2807931165283401, 0.05865071532678997, 0.30934548038364706, 0.1066087290176196, 0.04832129061855041, -0.05303042992729203, 0.00401886261464236, -0.022282666217223433, -0.060827814260934074, 0.1623161705182921, 0.34715295285767245, 0.26752757737106325, 0.3757149625833085, -0.4592504844147556, -0.13247061055711287, 0.09288320610366628, 0.15073470690519086, 0.1043399111395646, -0.06431177351554064, -0.2882916144301702, 0.13699972175597075, -0.2597769875516648, -0.13098687374083226, -0.08547210405109461, 0.023340544512796546, 0.010754068893754107, -0.21302291402637868, -0.07737639966326024, 0.040742959652561694, 0.10346088513821185, 0.040538085782119704, -0.07553034420487677, -0.02065462476370332, 0.13371364442034886, 0.03231375303654792, 0.03793777505627382, 0.19743556903764428, -0.11860805234180284, -0.18327482470414394, 0.392683827474401, 0.050878734719621656, -0.14889105054142768, 0.18277565175932303, -0.020396398770773483, -0.13302161754248384, 0.055077806597542085, 0.1869314450067942, 0.035744228662398764, -0.20648606971720468, 0.044314566718394906, 0.020365453447901204, 0.1991991138620397, 0.044055931930274086, 0.051366974705618296, 0.2503198726816538, 0.21058842864774258, 0.09252445624784349, 0.05964840639976528, 0.035711011443632895, -0.14256235306474666, -0.21552585108794026, -0.1701931652981052, -0.18405502997784914, 0.07122284503143718, -0.03308038674178185, -0.14424614311881853, 0.3456186568908237, 0.2207218596828867, 0.09093834469541015, -0.017499959957925325, 0.33707382018025406, 0.0339277058569324, 0.1438248280552913, 0.08587585339980747, 0.20226520158888628, 0.02084029191814943, 0.16839456528531174, -0.1218833991299902, 0.17015020980382572, -0.036158136102060014] |
1,802.04452 | Bayesian comparison of latent variable models: Conditional vs marginal
likelihoods | Typical Bayesian methods for models with latent variables (or random effects)
involve directly sampling the latent variables along with the model parameters.
In high-level software code for model definitions (using, e.g., BUGS, JAGS,
Stan), the likelihood is therefore specified as conditional on the latent
variables. This can lead researchers to perform model comparisons via
conditional likelihoods, where the latent variables are considered model
parameters. In other settings, however, typical model comparisons involve
marginal likelihoods where the latent variables are integrated out. This
distinction is often overlooked despite the fact that it can have a large
impact on the comparisons of interest. In this paper, we clarify and illustrate
these issues, focusing on the comparison of conditional and marginal Deviance
Information Criteria (DICs) and Watanabe-Akaike Information Criteria (WAICs) in
psychometric modeling. The conditional/marginal distinction corresponds to
whether the model should be predictive for the clusters that are in the data or
for new clusters (where "clusters" typically correspond to higher-level units
like people or schools). Correspondingly, we show that marginal WAIC
corresponds to leave-one-cluster out (LOcO) cross-validation, whereas
conditional WAIC corresponds to leave-one-unit out (LOuO). These results lead
to recommendations on the general application of the criteria to models with
latent variables.
| stat.CO | typical bayesian methods for models with latent variables or random effects involve directly sampling the latent variables along with the model parameters in highlevel software code for model definitions using eg bugs jags stan the likelihood is therefore specified as conditional on the latent variables this can lead researchers to perform model comparisons via conditional likelihoods where the latent variables are considered model parameters in other settings however typical model comparisons involve marginal likelihoods where the latent variables are integrated out this distinction is often overlooked despite the fact that it can have a large impact on the comparisons of interest in this paper we clarify and illustrate these issues focusing on the comparison of conditional and marginal deviance information criteria dics and watanabeakaike information criteria waics in psychometric modeling the conditionalmarginal distinction corresponds to whether the model should be predictive for the clusters that are in the data or for new clusters where clusters typically correspond to higherlevel units like people or schools correspondingly we show that marginal waic corresponds to leaveonecluster out loco crossvalidation whereas conditional waic corresponds to leaveoneunit out louo these results lead to recommendations on the general application of the criteria to models with latent variables | [['typical', 'bayesian', 'methods', 'for', 'models', 'with', 'latent', 'variables', 'or', 'random', 'effects', 'involve', 'directly', 'sampling', 'the', 'latent', 'variables', 'along', 'with', 'the', 'model', 'parameters', 'in', 'highlevel', 'software', 'code', 'for', 'model', 'definitions', 'using', 'eg', 'bugs', 'jags', 'stan', 'the', 'likelihood', 'is', 'therefore', 'specified', 'as', 'conditional', 'on', 'the', 'latent', 'variables', 'this', 'can', 'lead', 'researchers', 'to', 'perform', 'model', 'comparisons', 'via', 'conditional', 'likelihoods', 'where', 'the', 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1,802.04453 | Anomalous bulk behaviour in the free parafermion $Z(N)$ spin chain | We demonstrate using direct numerical diagonalization and extrapolation
methods that boundary conditions have a profound effect on the bulk properties
of a simple $Z(N)$ model for $N \ge 3$ for which the model hamiltonian is
non-hermitian. For $N=2$ the model reduces to the well known quantum Ising
model in a transverse field. For open boundary conditions the $Z(N)$ model is
known to be solved exactly in terms of free parafermions. Once the ends of the
open chain are connected by considering the model on a ring, the bulk
properties, including the ground-state energy per site, are seen to differ
dramatically with increasing $N$. Other properties, such as the leading
finite-size corrections to the ground-state energy, the mass gap exponent and
the specific heat exponent, are also seen to be dependent on the boundary
conditions. We speculate that this anomalous bulk behaviour is a topological
effect.
| cond-mat.stat-mech hep-th math-ph math.MP quant-ph | we demonstrate using direct numerical diagonalization and extrapolation methods that boundary conditions have a profound effect on the bulk properties of a simple zn model for n ge 3 for which the model hamiltonian is nonhermitian for n2 the model reduces to the well known quantum ising model in a transverse field for open boundary conditions the zn model is known to be solved exactly in terms of free parafermions once the ends of the open chain are connected by considering the model on a ring the bulk properties including the groundstate energy per site are seen to differ dramatically with increasing n other properties such as the leading finitesize corrections to the groundstate energy the mass gap exponent and the specific heat exponent are also seen to be dependent on the boundary conditions we speculate that this anomalous bulk behaviour is a topological effect | [['we', 'demonstrate', 'using', 'direct', 'numerical', 'diagonalization', 'and', 'extrapolation', 'methods', 'that', 'boundary', 'conditions', 'have', 'a', 'profound', 'effect', 'on', 'the', 'bulk', 'properties', 'of', 'a', 'simple', 'zn', 'model', 'for', 'n', 'ge', '3', 'for', 'which', 'the', 'model', 'hamiltonian', 'is', 'nonhermitian', 'for', 'n2', 'the', 'model', 'reduces', 'to', 'the', 'well', 'known', 'quantum', 'ising', 'model', 'in', 'a', 'transverse', 'field', 'for', 'open', 'boundary', 'conditions', 'the', 'zn', 'model', 'is', 'known', 'to', 'be', 'solved', 'exactly', 'in', 'terms', 'of', 'free', 'parafermions', 'once', 'the', 'ends', 'of', 'the', 'open', 'chain', 'are', 'connected', 'by', 'considering', 'the', 'model', 'on', 'a', 'ring', 'the', 'bulk', 'properties', 'including', 'the', 'groundstate', 'energy', 'per', 'site', 'are', 'seen', 'to', 'differ', 'dramatically', 'with', 'increasing', 'n', 'other', 'properties', 'such', 'as', 'the', 'leading', 'finitesize', 'corrections', 'to', 'the', 'groundstate', 'energy', 'the', 'mass', 'gap', 'exponent', 'and', 'the', 'specific', 'heat', 'exponent', 'are', 'also', 'seen', 'to', 'be', 'dependent', 'on', 'the', 'boundary', 'conditions', 'we', 'speculate', 'that', 'this', 'anomalous', 'bulk', 'behaviour', 'is', 'a', 'topological', 'effect']] | [-0.1304593121825621, 0.17128295665365997, -0.04072311909483938, 0.0696680951622668, -0.036132673597252314, -0.16399407396735302, 0.029886848444183326, 0.362124055636854, -0.26089216642210195, -0.3007967216701343, 0.10248281390663108, -0.29176360351306485, -0.11363909628467056, 0.1965576628190947, 0.020608370837851844, 0.050884389819513105, 0.026814385591845576, 0.07397340325445964, -0.10205445730223738, -0.22877014811905422, 0.2978280219474229, 0.03364367816709624, 0.25179050490260124, 0.10878025435296626, 0.014638971283646492, -0.03259265670103246, 0.06090794516534641, 0.04812298586014016, -0.1675950051932636, 0.047637783247849036, 0.17107424824177447, -0.006950551846289429, 0.17344832559726361, -0.4198774090486354, -0.258061984394966, 0.07574106209885313, 0.14143514845777175, 0.12019061111796904, -0.03193656851553583, -0.2515941697975685, 0.0745652642582768, -0.15672033170944658, -0.1796616078739793, -0.03749040002324458, 0.001956627656834136, -0.008985957811618673, -0.26396958942621435, 0.1077338819823565, 0.05375667116524459, 0.04864554227137103, -0.07374278407800815, -0.12582615385281629, -0.06941615801208235, 0.13096929721429879, 0.036003828116146656, 0.01838189140443915, 0.11992925943019961, -0.1435387081354214, -0.10521177506748715, 0.3718749473931204, -0.042281238556486266, -0.21288746483623983, 0.1848642863590142, -0.1419366491235921, -0.1251691726999807, 0.11306985332470001, 0.11407421974722168, 0.1078396804177138, -0.1288062177855393, 0.15185420028463906, -0.03468644128036525, 0.14517281188244194, -0.0029303730208555174, 0.038533753511528004, 0.17772864317200307, 0.13872579093380222, 0.049643568076586736, 0.17264091152063538, -0.04734264762413784, -0.11665590578240567, -0.2915370845043197, -0.16859977980754498, -0.21417885928189961, 0.08010362264346974, -0.09940132839356325, -0.18744490138929465, 0.39003462321927834, 0.14616921016664053, 0.21694946699486725, 0.009996135707077539, 0.23272893467339975, 0.16817376337993634, 0.09097502316059224, 0.07113716780240166, 0.21481319275395624, 0.13217572188807716, 0.04761949611249669, -0.27823799791713727, 0.024980424005730913, 0.1014309904088491] |
1,802.04454 | Rigidity of Einstein metrics as critical points of quadratic curvature
functionals on closed manifolds | In this paper, we prove some rigidity results for the Einstein metrics as the
critical points of a family of known quadratic curvature functionals on closed
manifolds, characterized by some point-wise inequalities. Moreover, we also
provide a few rigidity results that involve the Weyl curvature, the trace-less
Ricci curvature and the Yamabe invariant, accordingly.
| math.DG | in this paper we prove some rigidity results for the einstein metrics as the critical points of a family of known quadratic curvature functionals on closed manifolds characterized by some pointwise inequalities moreover we also provide a few rigidity results that involve the weyl curvature the traceless ricci curvature and the yamabe invariant accordingly | [['in', 'this', 'paper', 'we', 'prove', 'some', 'rigidity', 'results', 'for', 'the', 'einstein', 'metrics', 'as', 'the', 'critical', 'points', 'of', 'a', 'family', 'of', 'known', 'quadratic', 'curvature', 'functionals', 'on', 'closed', 'manifolds', 'characterized', 'by', 'some', 'pointwise', 'inequalities', 'moreover', 'we', 'also', 'provide', 'a', 'few', 'rigidity', 'results', 'that', 'involve', 'the', 'weyl', 'curvature', 'the', 'traceless', 'ricci', 'curvature', 'and', 'the', 'yamabe', 'invariant', 'accordingly']] | [-0.2468024651106033, 0.10778526349545077, -0.07439363658152244, 0.1095152781064691, -0.10253184947564646, -0.17746514045736855, -0.05331293923168064, 0.3219986996716923, -0.24296753563814694, -0.2060504603303141, 0.1410426549436697, -0.28798545410649645, -0.254284040959276, 0.2014815030205581, -0.11955466690576738, 0.09415638971107977, 0.03212617223013054, 0.1361972678121809, -0.14161359041463584, -0.28218498043026086, 0.48063851341053293, -0.02972634317767289, 0.19165559169077487, 0.17772236752703233, 0.0869227782305744, -0.08417029850426372, 0.03382912598964241, 0.10456523814893982, -0.2766294928179478, 0.13200851383239584, 0.21185695607629088, 0.05515094835492265, 0.2404650111372272, -0.35737989373781065, -0.2385062145034748, 0.14984043863282917, 0.09668247967406556, 0.027713934494251455, -0.09304994222044048, -0.3368086873001798, 0.07870444114949485, -0.06342173947659493, -0.1741399423950093, -0.1553393672220409, -0.05242340566797389, 0.022277303729896194, -0.16497819898305116, 0.1148975590988778, 0.15167641474141014, 0.06973242642642516, -0.16229828560931814, -0.11658119712093915, -0.017618176665295054, 0.04646508238726744, 0.14474999163023852, -3.192312498059538e-05, 0.0927511148189229, -0.04142845465038489, -0.08823433811603873, 0.3137678092688374, -0.12072066586740592, -0.2911252687271271, 0.06164781330153346, -0.10575152498118028, -0.1954779506109103, 0.08019379457389866, 0.1704428257006738, 0.19401999873419604, -0.11412576558979237, 0.1418431653043997, -0.042418587349217246, 0.032856638025906354, 0.12829153999444787, 0.045716592171056954, 0.10841495598907824, -0.0015734726632082904, 0.20811283743629852, 0.12334162089973688, 0.006942904092957852, -0.07149451029383475, -0.4207738668278412, -0.24263056445245942, -0.19757922234324118, 0.18175119428283157, -0.19923742432379019, -0.21066243346366617, 0.4334020283487108, 0.008055385202169418, 0.1944304530043155, 0.20831865108468467, 0.20672255081848967, 0.026324513124698704, 0.0463483363079528, 0.14171805951951277, 0.2697245519731142, 0.20484301775987088, 0.08819193138602983, -0.11655876864851625, -0.0566335481212095, 0.16756931202555145] |
1,802.04455 | Asteroseismology of 16000 Kepler Red Giants: Global Oscillation
Parameters, Masses, and Radii | The Kepler mission has provided exquisite data to perform an ensemble
asteroseismic analysis on evolved stars. In this work we systematically
characterize solar-like oscillations and granulation for 16,094 oscillating red
giants, using end-of-mission long-cadence data. We produced a homogeneous
catalog of the frequency of maximum power (typical uncertainty
$\sigma_{\nu_{\rm max}}$=1.6\%), the mean large frequency separation
($\sigma_{\Delta\nu}$=0.6\%), oscillation amplitude ($\sigma_{\rm A}$=4.7\%),
granulation power ($\sigma_{\rm gran}$=8.6\%), power excess width ($\sigma_{\rm
width}$=8.8\%), seismically-derived stellar mass ($\sigma_{\rm M}$=7.8\%),
radius ($\sigma_{\rm R}$=2.9\%), and thus surface gravity ($\sigma_{\log
g}$=0.01 dex). Thanks to the large red giant sample, we confirm that
red-giant-branch (RGB) and helium-core-burning (HeB) stars collectively differ
in the distribution of oscillation amplitude, granulation power, and width of
power excess, which is mainly due to the mass difference. The distribution of
oscillation amplitudes shows an extremely sharp upper edge at fixed $\nu_{\rm
max}$, which might hold clues to understand the excitation and damping
mechanisms of the oscillation modes. We find both oscillation amplitude and
granulation power depend on metallicity, causing a spread of 15\% in
oscillation amplitudes and a spread of 25\% in granulation power from
[Fe/H]=-0.7 to 0.5 dex. Our asteroseismic stellar properties can be used as
reliable distance indicators and age proxies for mapping and dating galactic
stellar populations observed by Kepler. They will also provide an excellent
opportunity to test asteroseismology using Gaia parallaxes, and lift
degeneracies in deriving atmospheric parameters in large spectroscopic surveys
such as APOGEE and LAMOST.
| astro-ph.SR | the kepler mission has provided exquisite data to perform an ensemble asteroseismic analysis on evolved stars in this work we systematically characterize solarlike oscillations and granulation for 16094 oscillating red giants using endofmission longcadence data we produced a homogeneous catalog of the frequency of maximum power typical uncertainty sigma_nu_rm max16 the mean large frequency separation sigma_deltanu06 oscillation amplitude sigma_rm a47 granulation power sigma_rm gran86 power excess width sigma_rm width88 seismicallyderived stellar mass sigma_rm m78 radius sigma_rm r29 and thus surface gravity sigma_log g001 dex thanks to the large red giant sample we confirm that redgiantbranch rgb and heliumcoreburning heb stars collectively differ in the distribution of oscillation amplitude granulation power and width of power excess which is mainly due to the mass difference the distribution of oscillation amplitudes shows an extremely sharp upper edge at fixed nu_rm max which might hold clues to understand the excitation and damping mechanisms of the oscillation modes we find both oscillation amplitude and granulation power depend on metallicity causing a spread of 15 in oscillation amplitudes and a spread of 25 in granulation power from feh07 to 05 dex our asteroseismic stellar properties can be used as reliable distance indicators and age proxies for mapping and dating galactic stellar populations observed by kepler they will also provide an excellent opportunity to test asteroseismology using gaia parallaxes and lift degeneracies in deriving atmospheric parameters in large spectroscopic surveys such as apogee and lamost | [['the', 'kepler', 'mission', 'has', 'provided', 'exquisite', 'data', 'to', 'perform', 'an', 'ensemble', 'asteroseismic', 'analysis', 'on', 'evolved', 'stars', 'in', 'this', 'work', 'we', 'systematically', 'characterize', 'solarlike', 'oscillations', 'and', 'granulation', 'for', '16094', 'oscillating', 'red', 'giants', 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1,802.04456 | Global Optimal Power Flow over Large-Scale Power Transmission Network | Optimal power flow (OPF) over power transmission networks poses challenging
large-scale nonlinear optimization problems, which involve a large number of
quadratic equality and indefinite quadratic inequality constraints. These
computationally intractable constraints are often expressed by linear
constraints plus matrix additional rank-one constraints on the outer products
of the voltage vectors. The existing convex relaxation technique, which drops
the difficult rank-one constraints for tractable computation, cannot yield even
a feasible point. We address these computationally difficult problems by an
iterative procedure, which generates a sequence of improved points that
converge to a rank-one solution. Each iteration calls a semi-definite program.
Intensive simulations for the OPF problems over networks with a few thousands
of buses are provided to demonstrate the efficiency of our approach. The
suboptimal values of the OPF problems found by our computational procedure turn
out to be the global optimal value with computational tolerance less than
0.01%.
| cs.SY | optimal power flow opf over power transmission networks poses challenging largescale nonlinear optimization problems which involve a large number of quadratic equality and indefinite quadratic inequality constraints these computationally intractable constraints are often expressed by linear constraints plus matrix additional rankone constraints on the outer products of the voltage vectors the existing convex relaxation technique which drops the difficult rankone constraints for tractable computation cannot yield even a feasible point we address these computationally difficult problems by an iterative procedure which generates a sequence of improved points that converge to a rankone solution each iteration calls a semidefinite program intensive simulations for the opf problems over networks with a few thousands of buses are provided to demonstrate the efficiency of our approach the suboptimal values of the opf problems found by our computational procedure turn out to be the global optimal value with computational tolerance less than 001 | [['optimal', 'power', 'flow', 'opf', 'over', 'power', 'transmission', 'networks', 'poses', 'challenging', 'largescale', 'nonlinear', 'optimization', 'problems', 'which', 'involve', 'a', 'large', 'number', 'of', 'quadratic', 'equality', 'and', 'indefinite', 'quadratic', 'inequality', 'constraints', 'these', 'computationally', 'intractable', 'constraints', 'are', 'often', 'expressed', 'by', 'linear', 'constraints', 'plus', 'matrix', 'additional', 'rankone', 'constraints', 'on', 'the', 'outer', 'products', 'of', 'the', 'voltage', 'vectors', 'the', 'existing', 'convex', 'relaxation', 'technique', 'which', 'drops', 'the', 'difficult', 'rankone', 'constraints', 'for', 'tractable', 'computation', 'can', 'not', 'yield', 'even', 'a', 'feasible', 'point', 'we', 'address', 'these', 'computationally', 'difficult', 'problems', 'by', 'an', 'iterative', 'procedure', 'which', 'generates', 'a', 'sequence', 'of', 'improved', 'points', 'that', 'converge', 'to', 'a', 'rankone', 'solution', 'each', 'iteration', 'calls', 'a', 'semidefinite', 'program', 'intensive', 'simulations', 'for', 'the', 'opf', 'problems', 'over', 'networks', 'with', 'a', 'few', 'thousands', 'of', 'buses', 'are', 'provided', 'to', 'demonstrate', 'the', 'efficiency', 'of', 'our', 'approach', 'the', 'suboptimal', 'values', 'of', 'the', 'opf', 'problems', 'found', 'by', 'our', 'computational', 'procedure', 'turn', 'out', 'to', 'be', 'the', 'global', 'optimal', 'value', 'with', 'computational', 'tolerance', 'less', 'than', '001']] | [-0.15419771933565604, -0.007966439920104883, -0.03408703822080021, 0.058416231966658724, -0.12238555476569489, -0.21344530118993427, 0.07467035461145284, 0.33587043564891655, -0.35634225839436456, -0.3429903053059774, 0.16889908391788552, -0.23354792142647862, -0.1481909650899845, 0.24955587444714453, -0.07774799332884452, 0.1693989253735159, 0.12807123988066743, -0.03575388733735121, -0.11859962438395709, -0.30897566875217847, 0.23272292455658317, 0.05241240534756408, 0.25558433271069453, 0.015731948621621066, 0.11064197493079525, -0.026854994219271528, 0.011983380034165894, 0.08083856623969822, -0.07012417006343268, 0.15154955494717615, 0.33826693655898005, 0.18276842691366804, 0.35896577573562627, -0.4781252007506438, -0.18219636168937206, 0.15472210854629442, 0.12227253800870588, 0.06519414160334999, -0.010451348621129439, -0.18931686716004206, 0.08713618974082622, -0.12740423272585732, -0.04220114746318517, -0.13023965700604523, -0.04665287244196156, -0.005871507493879071, -0.3510040490225507, 0.08826586695375459, 0.019788212790591456, -0.004500230355500775, -0.03708194297169579, -0.16522379951698443, 0.026585728351976846, 0.03797363454648669, 0.07967144508702342, -0.003201662106501976, 0.15953250429061736, -0.09823836025238138, -0.10611979313969812, 0.37272409034655396, 0.03530299740688385, -0.25543039051453337, 0.1332076830280032, -0.03341078459153939, -0.15219160617366684, 0.2158391653816372, 0.21787425575972436, 0.19379771562080655, -0.15615664751287675, 0.0804527547431566, -0.06332349999426495, 0.1700143659022096, 0.03638835031314184, -0.008771782472570296, 0.1863551209040061, 0.11828554813568824, 0.21145955905718292, 0.13772394933390913, 0.004137098443485022, -0.11371801043516838, -0.24986381060443819, -0.038193574875293165, -0.19298610400295999, 0.04452918654304988, -0.15914569967947884, -0.1534848227571921, 0.36655715676399586, 0.10749567750116322, 0.17058755018617083, 0.1523149467664052, 0.3527884423957775, 0.1674346115874539, 0.06931460734406773, 0.11557014913441951, 0.22151177667156363, 0.08860787855236223, 0.087116127788408, -0.24777049210622606, 0.05979706966255685, 0.046915417593347546] |
1,802.04457 | Predicting Adversarial Examples with High Confidence | It has been suggested that adversarial examples cause deep learning models to
make incorrect predictions with high confidence. In this work, we take the
opposite stance: an overly confident model is more likely to be vulnerable to
adversarial examples. This work is one of the most proactive approaches taken
to date, as we link robustness with non-calibrated model confidence on noisy
images, providing a data-augmentation-free path forward. The adversarial
examples phenomenon is most easily explained by the trend of increasing
non-regularized model capacity, while the diversity and number of samples in
common datasets has remained flat. Test accuracy has incorrectly been
associated with true generalization performance, ignoring that training and
test splits are often extremely similar in terms of the overall representation
space. The transferability property of adversarial examples was previously used
as evidence against overfitting arguments, a perceived random effect, but
overfitting is not always random.
| cs.LG stat.ML | it has been suggested that adversarial examples cause deep learning models to make incorrect predictions with high confidence in this work we take the opposite stance an overly confident model is more likely to be vulnerable to adversarial examples this work is one of the most proactive approaches taken to date as we link robustness with noncalibrated model confidence on noisy images providing a dataaugmentationfree path forward the adversarial examples phenomenon is most easily explained by the trend of increasing nonregularized model capacity while the diversity and number of samples in common datasets has remained flat test accuracy has incorrectly been associated with true generalization performance ignoring that training and test splits are often extremely similar in terms of the overall representation space the transferability property of adversarial examples was previously used as evidence against overfitting arguments a perceived random effect but overfitting is not always random | [['it', 'has', 'been', 'suggested', 'that', 'adversarial', 'examples', 'cause', 'deep', 'learning', 'models', 'to', 'make', 'incorrect', 'predictions', 'with', 'high', 'confidence', 'in', 'this', 'work', 'we', 'take', 'the', 'opposite', 'stance', 'an', 'overly', 'confident', 'model', 'is', 'more', 'likely', 'to', 'be', 'vulnerable', 'to', 'adversarial', 'examples', 'this', 'work', 'is', 'one', 'of', 'the', 'most', 'proactive', 'approaches', 'taken', 'to', 'date', 'as', 'we', 'link', 'robustness', 'with', 'noncalibrated', 'model', 'confidence', 'on', 'noisy', 'images', 'providing', 'a', 'dataaugmentationfree', 'path', 'forward', 'the', 'adversarial', 'examples', 'phenomenon', 'is', 'most', 'easily', 'explained', 'by', 'the', 'trend', 'of', 'increasing', 'nonregularized', 'model', 'capacity', 'while', 'the', 'diversity', 'and', 'number', 'of', 'samples', 'in', 'common', 'datasets', 'has', 'remained', 'flat', 'test', 'accuracy', 'has', 'incorrectly', 'been', 'associated', 'with', 'true', 'generalization', 'performance', 'ignoring', 'that', 'training', 'and', 'test', 'splits', 'are', 'often', 'extremely', 'similar', 'in', 'terms', 'of', 'the', 'overall', 'representation', 'space', 'the', 'transferability', 'property', 'of', 'adversarial', 'examples', 'was', 'previously', 'used', 'as', 'evidence', 'against', 'overfitting', 'arguments', 'a', 'perceived', 'random', 'effect', 'but', 'overfitting', 'is', 'not', 'always', 'random']] | [-0.054965099588527716, 0.03725881664146596, -0.06554006612285564, 0.13706445235484727, -0.1243966835302186, -0.2158806370953991, 0.07267669340187352, 0.43777497411284544, -0.19435403868077603, -0.3338800000612088, 0.08464155339855982, -0.2994434424366623, -0.19070763556344103, 0.1745679603478144, -0.1881706004145823, 0.07789746939989241, 0.08411272027057735, 0.033382808351857356, 0.006707298913843011, -0.33286676195781834, 0.28168011857643216, 0.11194223260478282, 0.3220955081768844, 0.01977662216195811, 0.06884988300719183, -0.08904347399708955, -0.0043958340060246525, 0.04278608590140397, -0.03104164346158329, 0.07729527187331943, 0.2897432672049683, 0.14759383957246192, 0.36022775501574145, -0.3829368801200635, -0.2629405099656178, 0.13333811302037832, 0.14767564786279141, 0.14416085335369022, -0.04966326030690864, -0.31563997066066257, 0.11075151305046085, -0.19727820304719962, -0.07438044081129491, -0.11493965586621875, -0.004071984117155359, -0.059912483632478426, -0.2503581455006098, 0.05485765165616463, 0.11127805428847365, 0.06660753796692405, -0.03389721070153141, -0.10986451023538941, -0.028935460073949947, 0.12293048705969466, 0.12382300519974494, 0.06822602178786613, 0.08659964845490271, -0.1413893505248356, -0.12171154261059575, 0.3589724258851413, -0.028460291263723923, -0.2275619367587066, 0.23427307900130648, -0.05689819231438004, -0.13422477726540719, 0.13822951801647496, 0.18406219188122347, 0.08281491080630723, -0.14104409784410302, 0.021548492217608663, -0.03409553504842397, 0.15287521466204565, 0.06294024416025447, 0.00956123838622174, 0.1698869166013961, 0.18874356585031707, 0.01203743137151309, 0.14502971120934602, -0.10170381022210565, -0.0813833453105635, -0.23041684313817587, -0.07705167186293393, -0.18795501811697773, 0.021146739121906823, -0.08815748281501815, -0.14845853113913782, 0.3553164288918296, 0.23602410550835248, 0.23553682908045817, 0.0889230266911909, 0.353193103967311, 0.077921073161359, 0.101642475948248, 0.064561270776983, 0.2506048176809388, 0.05507188514417216, 0.036224756940038336, -0.10470565458303573, 0.21188667209576562, -0.006616815822581722] |
1,802.04458 | Disentangling superconducting and magnetic orders in NaFe_1-xNi_xAs
using muon spin rotation | Muon spin rotation and relaxation studies have been performed on a "111"
family of iron-based superconductors NaFe_1-xNi_xAs. Static magnetic order was
characterized by obtaining the temperature and doping dependences of the local
ordered magnetic moment size and the volume fraction of the magnetically
ordered regions. For x = 0 and 0.4 %, a transition to a nearly-homogeneous long
range magnetically ordered state is observed, while for higher x than 0.4 %
magnetic order becomes more disordered and is completely suppressed for x = 1.5
%. The magnetic volume fraction continuously decreases with increasing x. The
combination of magnetic and superconducting volumes implies that a
spatially-overlapping coexistence of magnetism and superconductivity spans a
large region of the T-x phase diagram for NaFe_1-xNi_xAs . A strong reduction
of both the ordered moment size and the volume fraction is observed below the
superconducting T_C for x = 0.6, 1.0, and 1.3 %, in contrast to other iron
pnictides in which one of these two parameters exhibits a reduction below TC,
but not both. The suppression of magnetic order is further enhanced with
increased Ni doping, leading to a reentrant non-magnetic state below T_C for x
= 1.3 %. The reentrant behavior indicates an interplay between
antiferromagnetism and superconductivity involving competition for the same
electrons. These observations are consistent with the sign-changing s-wave
superconducting state, which is expected to appear on the verge of microscopic
coexistence and phase separation with magnetism. We also present a universal
linear relationship between the local ordered moment size and the
antiferromagnetic ordering temperature TN across a variety of iron-based
superconductors. We argue that this linear relationship is consistent with an
itinerant-electron approach, in which Fermi surface nesting drives
antiferromagnetic ordering.
| cond-mat.supr-con cond-mat.str-el | muon spin rotation and relaxation studies have been performed on a 111 family of ironbased superconductors nafe_1xni_xas static magnetic order was characterized by obtaining the temperature and doping dependences of the local ordered magnetic moment size and the volume fraction of the magnetically ordered regions for x 0 and 04 a transition to a nearlyhomogeneous long range magnetically ordered state is observed while for higher x than 04 magnetic order becomes more disordered and is completely suppressed for x 15 the magnetic volume fraction continuously decreases with increasing x the combination of magnetic and superconducting volumes implies that a spatiallyoverlapping coexistence of magnetism and superconductivity spans a large region of the tx phase diagram for nafe_1xni_xas a strong reduction of both the ordered moment size and the volume fraction is observed below the superconducting t_c for x 06 10 and 13 in contrast to other iron pnictides in which one of these two parameters exhibits a reduction below tc but not both the suppression of magnetic order is further enhanced with increased ni doping leading to a reentrant nonmagnetic state below t_c for x 13 the reentrant behavior indicates an interplay between antiferromagnetism and superconductivity involving competition for the same electrons these observations are consistent with the signchanging swave superconducting state which is expected to appear on the verge of microscopic coexistence and phase separation with magnetism we also present a universal linear relationship between the local ordered moment size and the antiferromagnetic ordering temperature tn across a variety of ironbased superconductors we argue that this linear relationship is consistent with an itinerantelectron approach in which fermi surface nesting drives antiferromagnetic ordering | [['muon', 'spin', 'rotation', 'and', 'relaxation', 'studies', 'have', 'been', 'performed', 'on', 'a', 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1,802.04459 | Bang-Bang Charging of Electrical Vehicles by Smart Grid Technology | The success of the transportation electricification in this century
particularly requires the penentration of the internet of plug-in electric
vehicles (PEVs) into the smart power grid. Beside the function of serving the
traditional residential power demand, next-generation power grids also aim to
support the internet of PEVs at the same time. The distinct difference between
the traditional power demand and PEVs' power demand is that while the
statistics of the former is rich enough for treating it as inelastic/known
before hand, the latter is unknown until random PEVs' arrivals. Massive
penentration of PEVs certainly causes the grid unpredictable fluctuation. The
present paper considers the joint PEVs charging coordination and grid power
generation to minimizing both of the negative impact of PEVs' integration and
the cost of power generation while meeting the grid operating constraints and
all parties' demand. The bang-bang PEVs charging strategy is adopted to exploit
its simple implementation. By using a recently developed model predictive
control (MPC) model for this problem, the online compuation is based on a
predictive mixed integer nonlinear programming (MINP). A new solution
computation for this optimization problem is developed. Its capacity of
achieving the globally optimal solution is shown by numerical comparison
between its performance and that by an off-line optimal solution.
| cs.SY | the success of the transportation electricification in this century particularly requires the penentration of the internet of plugin electric vehicles pevs into the smart power grid beside the function of serving the traditional residential power demand nextgeneration power grids also aim to support the internet of pevs at the same time the distinct difference between the traditional power demand and pevs power demand is that while the statistics of the former is rich enough for treating it as inelasticknown before hand the latter is unknown until random pevs arrivals massive penentration of pevs certainly causes the grid unpredictable fluctuation the present paper considers the joint pevs charging coordination and grid power generation to minimizing both of the negative impact of pevs integration and the cost of power generation while meeting the grid operating constraints and all parties demand the bangbang pevs charging strategy is adopted to exploit its simple implementation by using a recently developed model predictive control mpc model for this problem the online compuation is based on a predictive mixed integer nonlinear programming minp a new solution computation for this optimization problem is developed its capacity of achieving the globally optimal solution is shown by numerical comparison between its performance and that by an offline optimal solution | [['the', 'success', 'of', 'the', 'transportation', 'electricification', 'in', 'this', 'century', 'particularly', 'requires', 'the', 'penentration', 'of', 'the', 'internet', 'of', 'plugin', 'electric', 'vehicles', 'pevs', 'into', 'the', 'smart', 'power', 'grid', 'beside', 'the', 'function', 'of', 'serving', 'the', 'traditional', 'residential', 'power', 'demand', 'nextgeneration', 'power', 'grids', 'also', 'aim', 'to', 'support', 'the', 'internet', 'of', 'pevs', 'at', 'the', 'same', 'time', 'the', 'distinct', 'difference', 'between', 'the', 'traditional', 'power', 'demand', 'and', 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