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1,801.10269
The Impact of Class Rebalancing Techniques on the Performance and Interpretation of Defect Prediction Models
Defect prediction models that are trained on class imbalanced datasets (i.e., the proportion of defective and clean modules is not equally represented) are highly susceptible to produce inaccurate prediction models. Prior research compares the impact of class rebalancing techniques on the performance of defect prediction models. Prior research efforts arrive at contradictory conclusions due to the use of different choice of datasets, classification techniques, and performance measures. Such contradictory conclusions make it hard to derive practical guidelines for whether class rebalancing techniques should be applied in the context of defect prediction models. In this paper, we investigate the impact of 4 popularly-used class rebalancing techniques on 10 commonly-used performance measures and the interpretation of defect prediction models. We also construct statistical models to better understand in which experimental design settings that class rebalancing techniques are beneficial for defect prediction models. Through a case study of 101 datasets that span across proprietary and open-source systems, we recommend that class rebalancing techniques are necessary when quality assurance teams wish to increase the completeness of identifying software defects (i.e., Recall). However, class rebalancing techniques should be avoided when interpreting defect prediction models. We also find that class rebalancing techniques do not impact the AUC measure. Hence, AUC should be used as a standard measure when comparing defect prediction models.
cs.SE
defect prediction models that are trained on class imbalanced datasets ie the proportion of defective and clean modules is not equally represented are highly susceptible to produce inaccurate prediction models prior research compares the impact of class rebalancing techniques on the performance of defect prediction models prior research efforts arrive at contradictory conclusions due to the use of different choice of datasets classification techniques and performance measures such contradictory conclusions make it hard to derive practical guidelines for whether class rebalancing techniques should be applied in the context of defect prediction models in this paper we investigate the impact of 4 popularlyused class rebalancing techniques on 10 commonlyused performance measures and the interpretation of defect prediction models we also construct statistical models to better understand in which experimental design settings that class rebalancing techniques are beneficial for defect prediction models through a case study of 101 datasets that span across proprietary and opensource systems we recommend that class rebalancing techniques are necessary when quality assurance teams wish to increase the completeness of identifying software defects ie recall however class rebalancing techniques should be avoided when interpreting defect prediction models we also find that class rebalancing techniques do not impact the auc measure hence auc should be used as a standard measure when comparing defect prediction models
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1,801.1027
The Impact of Automated Parameter Optimization on Defect Prediction Models
Defect prediction models---classifiers that identify defect-prone software modules---have configurable parameters that control their characteristics (e.g., the number of trees in a random forest). Recent studies show that these classifiers underperform when default settings are used. In this paper, we study the impact of automated parameter optimization on defect prediction models. Through a case study of 18 datasets, we find that automated parameter optimization: (1) improves AUC performance by up to 40 percentage points; (2) yields classifiers that are at least as stable as those trained using default settings; (3) substantially shifts the importance ranking of variables, with as few as 28% of the top-ranked variables in optimized classifiers also being top-ranked in non-optimized classifiers; (4) yields optimized settings for 17 of the 20 most sensitive parameters that transfer among datasets without a statistically significant drop in performance; and (5) adds less than 30 minutes of additional computation to 12 of the 26 studied classification techniques. While widely-used classification techniques like random forest and support vector machines are not optimization-sensitive, traditionally overlooked techniques like C5.0 and neural networks can actually outperform widely-used techniques after optimization is applied. This highlights the importance of exploring the parameter space when using parameter-sensitive classification techniques.
cs.SE
defect prediction modelsclassifiers that identify defectprone software moduleshave configurable parameters that control their characteristics eg the number of trees in a random forest recent studies show that these classifiers underperform when default settings are used in this paper we study the impact of automated parameter optimization on defect prediction models through a case study of 18 datasets we find that automated parameter optimization 1 improves auc performance by up to 40 percentage points 2 yields classifiers that are at least as stable as those trained using default settings 3 substantially shifts the importance ranking of variables with as few as 28 of the topranked variables in optimized classifiers also being topranked in nonoptimized classifiers 4 yields optimized settings for 17 of the 20 most sensitive parameters that transfer among datasets without a statistically significant drop in performance and 5 adds less than 30 minutes of additional computation to 12 of the 26 studied classification techniques while widelyused classification techniques like random forest and support vector machines are not optimizationsensitive traditionally overlooked techniques like c50 and neural networks can actually outperform widelyused techniques after optimization is applied this highlights the importance of exploring the parameter space when using parametersensitive classification techniques
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1,801.10271
The Impact of Correlated Metrics on Defect Models
Defect models are analytical models that are used to build empirical theories that are related to software quality. Prior studies often derive knowledge from such models using interpretation techniques, such as ANOVA Type-I. Recent work raises concerns that prior studies rarely remove correlated metrics when constructing such models. Such correlated metrics may impact the interpretation of models. Yet, the impact of correlated metrics in such models has not been investigated. In this paper, we set out to investigate the impact of correlated metrics, and the benefits and costs of removing correlated metrics on defect models. Through a case study of 15 publicly-available defect datasets, we find that (1) correlated metrics impact the ranking of the highest ranked metric for all of the 9 studied model interpretation techniques. On the other hand, removing correlated metrics (2) improves the consistency of the highest ranked metric regardless of how a model is specified for all of the studied interpretation techniques (except for ANOVA Type-I); and (3) negligibly impacts the performance and stability of defect models. Thus, researchers must (1) mitigate (e.g., remove) correlated metrics prior to constructing a defect model; and (2) avoid using ANOVA Type-I even if all correlated metrics are removed.
cs.SE
defect models are analytical models that are used to build empirical theories that are related to software quality prior studies often derive knowledge from such models using interpretation techniques such as anova typei recent work raises concerns that prior studies rarely remove correlated metrics when constructing such models such correlated metrics may impact the interpretation of models yet the impact of correlated metrics in such models has not been investigated in this paper we set out to investigate the impact of correlated metrics and the benefits and costs of removing correlated metrics on defect models through a case study of 15 publiclyavailable defect datasets we find that 1 correlated metrics impact the ranking of the highest ranked metric for all of the 9 studied model interpretation techniques on the other hand removing correlated metrics 2 improves the consistency of the highest ranked metric regardless of how a model is specified for all of the studied interpretation techniques except for anova typei and 3 negligibly impacts the performance and stability of defect models thus researchers must 1 mitigate eg remove correlated metrics prior to constructing a defect model and 2 avoid using anova typei even if all correlated metrics are removed
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1,801.10272
A new route to negative refractive index from topological metals
We theoretically discuss the possibility of realizing the negative refractive index in Weyl/Dirac semimetals. We consider the Maxwell equations with the plasma gap and the chiral magnetic effect. We study the dispersion relations of electromagnetic waves, and show that the refractive index becomes negative at frequencies (just) below the plasma frequency. We find that axial anomaly, or more specifically, negative magnetoresistance (electric current parallel to magnetic fields) opens a new route to realize the negative refractive index. Reflection and transmission coefficients are computed in a slab of Weyl/Dirac semimetals.
cond-mat.mes-hall hep-th physics.optics
we theoretically discuss the possibility of realizing the negative refractive index in weyldirac semimetals we consider the maxwell equations with the plasma gap and the chiral magnetic effect we study the dispersion relations of electromagnetic waves and show that the refractive index becomes negative at frequencies just below the plasma frequency we find that axial anomaly or more specifically negative magnetoresistance electric current parallel to magnetic fields opens a new route to realize the negative refractive index reflection and transmission coefficients are computed in a slab of weyldirac semimetals
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1,801.10273
Kernel Distillation for Fast Gaussian Processes Prediction
Gaussian processes (GPs) are flexible models that can capture complex structure in large-scale dataset due to their non-parametric nature. However, the usage of GPs in real-world application is limited due to their high computational cost at inference time. In this paper, we introduce a new framework, \textit{kernel distillation}, to approximate a fully trained teacher GP model with kernel matrix of size $n\times n$ for $n$ training points. We combine inducing points method with sparse low-rank approximation in the distillation procedure. The distilled student GP model only costs $O(m^2)$ storage for $m$ inducing points where $m \ll n$ and improves the inference time complexity. We demonstrate empirically that kernel distillation provides better trade-off between the prediction time and the test performance compared to the alternatives.
stat.ML cs.LG
gaussian processes gps are flexible models that can capture complex structure in largescale dataset due to their nonparametric nature however the usage of gps in realworld application is limited due to their high computational cost at inference time in this paper we introduce a new framework textitkernel distillation to approximate a fully trained teacher gp model with kernel matrix of size ntimes n for n training points we combine inducing points method with sparse lowrank approximation in the distillation procedure the distilled student gp model only costs om2 storage for m inducing points where m ll n and improves the inference time complexity we demonstrate empirically that kernel distillation provides better tradeoff between the prediction time and the test performance compared to the alternatives
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1,801.10274
Two-dimensional vortex quantum droplets
It was recently found that the Lee-Huang-Yang (LHY) correction to the mean-field Hamiltonian suppresses the collapse and creates stable localized modes (two-component "quantum droplets", QDs) in two and three dimensions. We construct two-dimensional\ self-trapped modes in the form of QDs with vorticity $S$ embedded into each component. The QDs feature a flat-top shape, which expands with the increase of $S$ and norm $N$. An essential finding, produced by a systematic numerical analysis and analytical estimates, is that the vortical QDs are \emph{stable} (which is a critical issue for vortex solitons in nonlinear models) up to $S=5$, for $N$ exceeding a certain threshold value. In the condensate of $^{39}$K atoms, in which QDs with $S=0$ and a quasi-2D shape were created recently, the vortical droplets may have radial size $\lesssim 30$ $\mathrm{\mu}$m, with the number of atoms in the range of $10^{4}-10^{5}$. It is worthy to note that \textit{hidden-vorticity} states in QDs with topological charges $% S_{+}=-S_{-}=1$ in its components, which are prone to strong instability in other settings, have their stability region too, although it may be located beyond applicability limits of the underlying model. Dynamics of elliptically deformed QDs, which form rotating elongated patterns or ones with strong oscillations of the eccentricity, as well as collisions of QDs, are also addressed.
cond-mat.quant-gas nlin.PS physics.flu-dyn quant-ph
it was recently found that the leehuangyang lhy correction to the meanfield hamiltonian suppresses the collapse and creates stable localized modes twocomponent quantum droplets qds in two and three dimensions we construct twodimensional selftrapped modes in the form of qds with vorticity s embedded into each component the qds feature a flattop shape which expands with the increase of s and norm n an essential finding produced by a systematic numerical analysis and analytical estimates is that the vortical qds are emphstable which is a critical issue for vortex solitons in nonlinear models up to s5 for n exceeding a certain threshold value in the condensate of 39k atoms in which qds with s0 and a quasi2d shape were created recently the vortical droplets may have radial size lesssim 30 mathrmmum with the number of atoms in the range of 104105 it is worthy to note that textithiddenvorticity states in qds with topological charges s_s_1 in its components which are prone to strong instability in other settings have their stability region too although it may be located beyond applicability limits of the underlying model dynamics of elliptically deformed qds which form rotating elongated patterns or ones with strong oscillations of the eccentricity as well as collisions of qds are also addressed
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1,801.10275
The Detection of Hot Cores and Complex Organic Molecules in the Large Magellanic Cloud
We report the first extragalactic detection of the complex organic molecules (COMs) dimethyl ether (CH$_3$OCH$_3$) and methyl formate (CH$_3$OCHO) with the Atacama Large Millimeter/submillimeter Array (ALMA). These COMs together with their parent species methanol (CH$_3$OH), were detected toward two 1.3 mm continuum sources in the N 113 star-forming region in the low-metallicity Large Magellanic Cloud (LMC). Rotational temperatures ($T_{\rm rot}\sim130$ K) and total column densities ($N_{\rm rot}\sim10^{16}$ cm$^{-2}$) have been calculated for each source based on multiple transitions of CH$_3$OH. We present the ALMA molecular emission maps for COMs and measured abundances for all detected species. The physical and chemical properties of two sources with COMs detection, and the association with H$_2$O and OH maser emission indicate that they are hot cores. The fractional abundances of COMs scaled by a factor of 2.5 to account for the lower metallicity in the LMC are comparable to those found at the lower end of the range in Galactic hot cores. Our results have important implications for studies of organic chemistry at higher redshift.
astro-ph.GA
we report the first extragalactic detection of the complex organic molecules coms dimethyl ether ch_3och_3 and methyl formate ch_3ocho with the atacama large millimetersubmillimeter array alma these coms together with their parent species methanol ch_3oh were detected toward two 13 mm continuum sources in the n 113 starforming region in the lowmetallicity large magellanic cloud lmc rotational temperatures t_rm rotsim130 k and total column densities n_rm rotsim1016 cm2 have been calculated for each source based on multiple transitions of ch_3oh we present the alma molecular emission maps for coms and measured abundances for all detected species the physical and chemical properties of two sources with coms detection and the association with h_2o and oh maser emission indicate that they are hot cores the fractional abundances of coms scaled by a factor of 25 to account for the lower metallicity in the lmc are comparable to those found at the lower end of the range in galactic hot cores our results have important implications for studies of organic chemistry at higher redshift
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1,801.10276
Sums with the Mobius function twisted by characters with powerful moduli
In their recent work, the authors (2016) have combined classical ideas of A. G. Postnikov (1956) and N. M. Korobov (1974) to derive improved bounds on short character sums for certain nonprincipal characters with powerful moduli. In the present paper, these results are used to bound sums of the Mobius function twisted by characters of the same type, complementing and improving some earlier work of B. Green (2012). To achieve this, we obtain a series of results about the size and zero-free region of $L$-functions with the same class of moduli.
math.NT
in their recent work the authors 2016 have combined classical ideas of a g postnikov 1956 and n m korobov 1974 to derive improved bounds on short character sums for certain nonprincipal characters with powerful moduli in the present paper these results are used to bound sums of the mobius function twisted by characters of the same type complementing and improving some earlier work of b green 2012 to achieve this we obtain a series of results about the size and zerofree region of lfunctions with the same class of moduli
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1,801.10277
Cataloging the Visible Universe through Bayesian Inference at Petascale
Astronomical catalogs derived from wide-field imaging surveys are an important tool for understanding the Universe. We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia. Using over 1.3 million threads on 650,000 Intel Xeon Phi cores of the Cori Phase II supercomputer, Celeste achieves a peak rate of 1.54 DP PFLOP/s. Celeste is able to jointly optimize parameters for 188M stars and galaxies, loading and processing 178 TB across 8192 nodes in 14.6 minutes. To achieve this, Celeste exploits parallelism at multiple levels (cluster, node, and thread) and accelerates I/O through Cori's Burst Buffer. Julia's native performance enables Celeste to employ high-level constructs without resorting to hand-written or generated low-level code (C/C++/Fortran), and yet achieve petascale performance.
cs.DC astro-ph.IM
astronomical catalogs derived from widefield imaging surveys are an important tool for understanding the universe we construct an astronomical catalog from 55 tb of imaging data using celeste a bayesian variational inference code written entirely in the highproductivity programming language julia using over 13 million threads on 650000 intel xeon phi cores of the cori phase ii supercomputer celeste achieves a peak rate of 154 dp pflops celeste is able to jointly optimize parameters for 188m stars and galaxies loading and processing 178 tb across 8192 nodes in 146 minutes to achieve this celeste exploits parallelism at multiple levels cluster node and thread and accelerates io through coris burst buffer julias native performance enables celeste to employ highlevel constructs without resorting to handwritten or generated lowlevel code ccfortran and yet achieve petascale performance
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1,801.10278
Ultra-High Resolution Neutron Spectroscopy of Low-Energy Spin Dynamics in UGe$_2$
Studying the prototypical ferromagnetic superconductor UGe$_2$ we demonstrate the potential of the Modulated IntEnsity by Zero Effort (MIEZE) technique---a novel neutron spectroscopy method with ultra-high energy resolution of at least 1~$\mu$eV---for the study of quantum matter. We reveal purely longitudinal spin fluctuations in UGe$_2$ with a dual nature arising from $5f$ electrons that are hybridized with the conduction electrons. Local spin fluctuations are perfectly described by the Ising universality class in three dimensions, whereas itinerant spin fluctuations occur over length scales comparable to the superconducting coherence length, showing that MIEZE is able to spectroscopically disentangle the complex low-energy behavior characteristic of quantum materials.
cond-mat.str-el
studying the prototypical ferromagnetic superconductor uge_2 we demonstrate the potential of the modulated intensity by zero effort mieze techniquea novel neutron spectroscopy method with ultrahigh energy resolution of at least 1muevfor the study of quantum matter we reveal purely longitudinal spin fluctuations in uge_2 with a dual nature arising from 5f electrons that are hybridized with the conduction electrons local spin fluctuations are perfectly described by the ising universality class in three dimensions whereas itinerant spin fluctuations occur over length scales comparable to the superconducting coherence length showing that mieze is able to spectroscopically disentangle the complex lowenergy behavior characteristic of quantum materials
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1,801.10279
Chain length effects of $p$-oligophenyls with comparison of benzene by Raman scattering
Raman scattering measurements are performed on benzene and a number of $p$-oligophenyls including biphenyl, $p$-terphenyl, $p$-quaterphenyl, $p$-quinquephenyl, and $p$-sexiphenyl at ambient conditions. The vibrational modes of the intra- and intermolecular terms in these materials are analyzed and compared. Chain length effects on the vibrational properties are examined for the C-H in-plane bending mode and the inter-ring C-C stretching mode at around 1200 cm$^{-1}$ and 1280 cm$^{-1}$, respectively, and the C-C stretching modes at around 1600 cm$^{-1}$. The complex and fluctuating properties of these modes result in an imprecise estimation of the chain length of these molecules. Meanwhile, the obtained ratio of the intensities of the 1200 cm$^{-1}$ mode and 1280 cm$^{-1}$ mode is sensitive to the applied lasers. A librational motion mode with the lowest energy is found to have a monotonous change with the increase in the chain length. This mode is simply relevant to the $c$ axis of the unit cell. Such an obvious trend makes it a better indicator for determining the chain length effects on the physical and chemical properties in these molecules.
cond-mat.mtrl-sci cond-mat.supr-con
raman scattering measurements are performed on benzene and a number of poligophenyls including biphenyl pterphenyl pquaterphenyl pquinquephenyl and psexiphenyl at ambient conditions the vibrational modes of the intra and intermolecular terms in these materials are analyzed and compared chain length effects on the vibrational properties are examined for the ch inplane bending mode and the interring cc stretching mode at around 1200 cm1 and 1280 cm1 respectively and the cc stretching modes at around 1600 cm1 the complex and fluctuating properties of these modes result in an imprecise estimation of the chain length of these molecules meanwhile the obtained ratio of the intensities of the 1200 cm1 mode and 1280 cm1 mode is sensitive to the applied lasers a librational motion mode with the lowest energy is found to have a monotonous change with the increase in the chain length this mode is simply relevant to the c axis of the unit cell such an obvious trend makes it a better indicator for determining the chain length effects on the physical and chemical properties in these molecules
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1,801.1028
Dugundji systems and a retract characterization of effective zero-dimensionality
In this paper (as in [Ken15]), we consider an effective version of the characterization of separable metric spaces as zero-dimensional iff every nonempty closed subset is a retract of the space (actually, it is a relative result for closed zero-dimensional subspaces of a fixed space that we have proved). This uses (in the converse direction) local compactness & bilocated sets as in [Ken15], but in the forward direction the newer version has a simpler proof and no compactness assumption. Furthermore, the proof of the forward implication relates to so-called Dugundji systems: we elaborate both a general construction of such systems for a proper nonempty closed subspace (using a computable form of countable paracompactness), and modifications -- to make the sets pairwise disjoint if the subspace is zero-dimensional, or to avoid the restriction to proper subspaces. In a different direction, a second theorem applies in $p$-adic analysis the ideas of the first theorem to compute a more general form of retraction, given a Dugundji system (possibly without disjointness). Finally, we complement the effective retract characterization of zero-dimensional subspaces mentioned above by improving to equivalence the implications (or Weihrauch reductions in some cases), for closed at-most-zero-dimensional subsets with some negative information, among separate conditions of computability of operations $N,M,B,S$ introduced in [Ken15,\S 4] and corresponding to vanishing large inductive dimension, vanishing small inductive dimension, existence of a countable basis of relatively clopen sets, and the reduction principle for sequences of open sets. Thus, similarly to the robust notion of effective zero-dimensionality of computable metric spaces in [Ken15], there is a robust notion of `uniform effective zero-dimensionality' for a represented pointclass consisting of at-most-zero-dimensional closed subsets.
cs.LO math.LO
in this paper as in ken15 we consider an effective version of the characterization of separable metric spaces as zerodimensional iff every nonempty closed subset is a retract of the space actually it is a relative result for closed zerodimensional subspaces of a fixed space that we have proved this uses in the converse direction local compactness bilocated sets as in ken15 but in the forward direction the newer version has a simpler proof and no compactness assumption furthermore the proof of the forward implication relates to socalled dugundji systems we elaborate both a general construction of such systems for a proper nonempty closed subspace using a computable form of countable paracompactness and modifications to make the sets pairwise disjoint if the subspace is zerodimensional or to avoid the restriction to proper subspaces in a different direction a second theorem applies in padic analysis the ideas of the first theorem to compute a more general form of retraction given a dugundji system possibly without disjointness finally we complement the effective retract characterization of zerodimensional subspaces mentioned above by improving to equivalence the implications or weihrauch reductions in some cases for closed atmostzerodimensional subsets with some negative information among separate conditions of computability of operations nmbs introduced in ken15s 4 and corresponding to vanishing large inductive dimension vanishing small inductive dimension existence of a countable basis of relatively clopen sets and the reduction principle for sequences of open sets thus similarly to the robust notion of effective zerodimensionality of computable metric spaces in ken15 there is a robust notion of uniform effective zerodimensionality for a represented pointclass consisting of atmostzerodimensional closed subsets
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1,801.10281
Learning Video-Story Composition via Recurrent Neural Network
In this paper, we propose a learning-based method to compose a video-story from a group of video clips that describe an activity or experience. We learn the coherence between video clips from real videos via the Recurrent Neural Network (RNN) that jointly incorporates the spatial-temporal semantics and motion dynamics to generate smooth and relevant compositions. We further rearrange the results generated by the RNN to make the overall video-story compatible with the storyline structure via a submodular ranking optimization process. Experimental results on the video-story dataset show that the proposed algorithm outperforms the state-of-the-art approach.
cs.CV
in this paper we propose a learningbased method to compose a videostory from a group of video clips that describe an activity or experience we learn the coherence between video clips from real videos via the recurrent neural network rnn that jointly incorporates the spatialtemporal semantics and motion dynamics to generate smooth and relevant compositions we further rearrange the results generated by the rnn to make the overall videostory compatible with the storyline structure via a submodular ranking optimization process experimental results on the videostory dataset show that the proposed algorithm outperforms the stateoftheart approach
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1,801.10282
Stochastic Optimization and Control Framework for 5G Network Slicing with Effective Isolation
Network slicing is an emerging technique for providing resources to diverse wireless services with heterogeneous quality-of-service needs. However, beyond satisfying end-to-end requirements of network users, network slicing needs to also provide isolation between slices so as to prevent one slice's faults and congestion from affecting other slices. In this paper, the problem of network slicing is studied in the context of a wireless system having a time-varying number of users that require two types of slices: reliable low latency (RLL) and self-managed (capacity limited) slices. To address this problem, a novel control framework for stochastic optimization is proposed based on the Lyapunov drift-plus-penalty method. This new framework enables the system to minimize power, maintain slice isolation, and provide reliable and low latency end-to-end communication for RLL slices. Simulation results show that the proposed approach can maintain the system's reliability while providing effective slice isolation in the event of sudden changes in the network environment.
cs.IT cs.NI math.IT
network slicing is an emerging technique for providing resources to diverse wireless services with heterogeneous qualityofservice needs however beyond satisfying endtoend requirements of network users network slicing needs to also provide isolation between slices so as to prevent one slices faults and congestion from affecting other slices in this paper the problem of network slicing is studied in the context of a wireless system having a timevarying number of users that require two types of slices reliable low latency rll and selfmanaged capacity limited slices to address this problem a novel control framework for stochastic optimization is proposed based on the lyapunov driftpluspenalty method this new framework enables the system to minimize power maintain slice isolation and provide reliable and low latency endtoend communication for rll slices simulation results show that the proposed approach can maintain the systems reliability while providing effective slice isolation in the event of sudden changes in the network environment
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1,801.10283
Giant photocurrent in asymmetric Weyl semimetals from the helical magnetic effect
We propose a new type of photoresponse induced in asymmetric Weyl semimetals in an external magnetic field. In usual symmetric Weyl semimetals in a magnetic field, the particles and holes produced by an incident light in different Weyl cones have opposite helicities and hence move in opposite directions, canceling each others's contributions to the photocurrent. However this cancelation does not occur if the Weyl semimetal possesses both a broken particle-hole symmetry and a broken spatial inversion symmetry. We call the resulting generation of photocurrent the helical magnetic effect because it is induced by the helicity imbalance in a magnetic field. We find that due to the large density of states in a magnetic field, the helical magnetic effect induces a remarkable large photocurrent for incident THz frequency light. This suggests a potential application of asymmetric Weyl semimetals for creating THz photosensors.
cond-mat.mes-hall
we propose a new type of photoresponse induced in asymmetric weyl semimetals in an external magnetic field in usual symmetric weyl semimetals in a magnetic field the particles and holes produced by an incident light in different weyl cones have opposite helicities and hence move in opposite directions canceling each otherss contributions to the photocurrent however this cancelation does not occur if the weyl semimetal possesses both a broken particlehole symmetry and a broken spatial inversion symmetry we call the resulting generation of photocurrent the helical magnetic effect because it is induced by the helicity imbalance in a magnetic field we find that due to the large density of states in a magnetic field the helical magnetic effect induces a remarkable large photocurrent for incident thz frequency light this suggests a potential application of asymmetric weyl semimetals for creating thz photosensors
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1,801.10284
Chemical abundances of primary stars in the Sirius-like binary systems
Study of primary stars lying in Sirius-like systems with various masses of WD companions and orbital separations is one of the key aspects to understand the origin and nature of Barium (Ba) stars. In this paper, based on high resolution and high S/N spectra, we present systematic analysis of photospheric abundances for 18 FGK primary stars of Sirius-like systems including six giants and 12 dwarfs. Atmospheric parameters, stellar masses, and abundances of 24 elements (C, Na, Mg, Al, Si, S, K, Ca, Sc, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Sr, Y, Zr, Ba, La, Ce and Nd) are determined homogeneously. The abundance patterns in these sample stars show that most of the elements in our sample follow the behavior of field stars with similar metallicity. As expected, s-process elements in four known Ba giants show overabundance. A weak correlation was found between anomalies of s-process elemental abundance and orbital separation, suggesting the orbital separation of the binaries could not be the main constraint to differentiate strong Ba stars from mild Ba stars. Our study shows that the large mass (>0.51 M ) of a WD companion in a binary system is not a sufficient condition to form a Ba star, even if the separation between the two components is small. Although not sufficient it seems to be a necessary condition since Ba stars with lower mass WDs in the observed sample were not found. Our results support that [s/Fe] and [hs/ls] ratios of Ba stars are anti-correlated with the metallicity. However, the different levels of s-process overabundance among Ba stars may not to be dominated mainly by the metallicity.
astro-ph.GA astro-ph.SR
study of primary stars lying in siriuslike systems with various masses of wd companions and orbital separations is one of the key aspects to understand the origin and nature of barium ba stars in this paper based on high resolution and high sn spectra we present systematic analysis of photospheric abundances for 18 fgk primary stars of siriuslike systems including six giants and 12 dwarfs atmospheric parameters stellar masses and abundances of 24 elements c na mg al si s k ca sc ti v cr mn fe co ni cu sr y zr ba la ce and nd are determined homogeneously the abundance patterns in these sample stars show that most of the elements in our sample follow the behavior of field stars with similar metallicity as expected sprocess elements in four known ba giants show overabundance a weak correlation was found between anomalies of sprocess elemental abundance and orbital separation suggesting the orbital separation of the binaries could not be the main constraint to differentiate strong ba stars from mild ba stars our study shows that the large mass 051 m of a wd companion in a binary system is not a sufficient condition to form a ba star even if the separation between the two components is small although not sufficient it seems to be a necessary condition since ba stars with lower mass wds in the observed sample were not found our results support that sfe and hsls ratios of ba stars are anticorrelated with the metallicity however the different levels of sprocess overabundance among ba stars may not to be dominated mainly by the metallicity
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1,801.10285
Optimal Configurations in Coverage Control with Polynomial Costs
We revisit the static coverage control problem for placement of vehicles with simple motion on the real line, under the assumption that the cost is a polynomial function of the locations of the vehicles. The main contribution of this paper is to demonstrate the use of tools from numerical algebraic geometry, in particular, a numerical polynomial homotopy continuation method that guarantees to find all solutions of polynomial equations, in order to characterize the \emph{global minima} for the coverage control problem. The results are then compared against a classic distributed approach involving the use of Lloyd descent, which is known to converge only to a local minimum under certain technical conditions.
cs.SY math.AG math.OC
we revisit the static coverage control problem for placement of vehicles with simple motion on the real line under the assumption that the cost is a polynomial function of the locations of the vehicles the main contribution of this paper is to demonstrate the use of tools from numerical algebraic geometry in particular a numerical polynomial homotopy continuation method that guarantees to find all solutions of polynomial equations in order to characterize the emphglobal minima for the coverage control problem the results are then compared against a classic distributed approach involving the use of lloyd descent which is known to converge only to a local minimum under certain technical conditions
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1,801.10286
Unconventional superconductivity and Surface pairing symmetry in Half-Heusler Compounds
Signatures of nodal line/point superconductivity have been observed in half-Heusler compounds, such as LnPtBi (Ln = Y, Lu). Topologically non-trivial band structures, as well as topological surface states, has also been confirmed by angular-resolved photoemission spectroscopy in these compounds. In this work, we present a systematical classification of possible gap functions of bulk states and surface states in half-Heusler compounds and the corresponding topological properties based on the representations of crystalline symmetry group. Different from all the previous studies based on four band Luttinger model, our study starts with the six-band Kane model, which involves both four p-orbital type of {\Gamma}8 bands and two s-orbital type of {\Gamma}6 bands. Although the {\Gamma}6 bands are away from the Fermi energy, our results reveal the importance of topological surface states, which originate from the band inversion between {\Gamma}6 and {\Gamma}8 bands, in determining surface properties of these compounds in the superconducting regime by combining topological bulk state picture and non-trivial surface state picture.
cond-mat.supr-con cond-mat.mes-hall
signatures of nodal linepoint superconductivity have been observed in halfheusler compounds such as lnptbi ln y lu topologically nontrivial band structures as well as topological surface states has also been confirmed by angularresolved photoemission spectroscopy in these compounds in this work we present a systematical classification of possible gap functions of bulk states and surface states in halfheusler compounds and the corresponding topological properties based on the representations of crystalline symmetry group different from all the previous studies based on four band luttinger model our study starts with the sixband kane model which involves both four porbital type of gamma8 bands and two sorbital type of gamma6 bands although the gamma6 bands are away from the fermi energy our results reveal the importance of topological surface states which originate from the band inversion between gamma6 and gamma8 bands in determining surface properties of these compounds in the superconducting regime by combining topological bulk state picture and nontrivial surface state picture
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1,801.10287
An Incremental Off-policy Search in a Model-free Markov Decision Process Using a Single Sample Path
In this paper, we consider a modified version of the control problem in a model free Markov decision process (MDP) setting with large state and action spaces. The control problem most commonly addressed in the contemporary literature is to find an optimal policy which maximizes the value function, i.e., the long run discounted reward of the MDP. The current settings also assume access to a generative model of the MDP with the hidden premise that observations of the system behaviour in the form of sample trajectories can be obtained with ease from the model. In this paper, we consider a modified version, where the cost function is the expectation of a non-convex function of the value function without access to the generative model. Rather, we assume that a sample trajectory generated using a priori chosen behaviour policy is made available. In this restricted setting, we solve the modified control problem in its true sense, i.e., to find the best possible policy given this limited information. We propose a stochastic approximation algorithm based on the well-known cross entropy method which is data (sample trajectory) efficient, stable, robust as well as computationally and storage efficient. We provide a proof of convergence of our algorithm to a policy which is globally optimal relative to the behaviour policy. We also present experimental results to corroborate our claims and we demonstrate the superiority of the solution produced by our algorithm compared to the state-of-the-art algorithms under appropriately chosen behaviour policy.
cs.AI
in this paper we consider a modified version of the control problem in a model free markov decision process mdp setting with large state and action spaces the control problem most commonly addressed in the contemporary literature is to find an optimal policy which maximizes the value function ie the long run discounted reward of the mdp the current settings also assume access to a generative model of the mdp with the hidden premise that observations of the system behaviour in the form of sample trajectories can be obtained with ease from the model in this paper we consider a modified version where the cost function is the expectation of a nonconvex function of the value function without access to the generative model rather we assume that a sample trajectory generated using a priori chosen behaviour policy is made available in this restricted setting we solve the modified control problem in its true sense ie to find the best possible policy given this limited information we propose a stochastic approximation algorithm based on the wellknown cross entropy method which is data sample trajectory efficient stable robust as well as computationally and storage efficient we provide a proof of convergence of our algorithm to a policy which is globally optimal relative to the behaviour policy we also present experimental results to corroborate our claims and we demonstrate the superiority of the solution produced by our algorithm compared to the stateoftheart algorithms under appropriately chosen behaviour policy
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1,801.10288
Visually Explainable Recommendation
Images account for a significant part of user decisions in many application scenarios, such as product images in e-commerce, or user image posts in social networks. It is intuitive that user preferences on the visual patterns of image (e.g., hue, texture, color, etc) can be highly personalized, and this provides us with highly discriminative features to make personalized recommendations. Previous work that takes advantage of images for recommendation usually transforms the images into latent representation vectors, which are adopted by a recommendation component to assist personalized user/item profiling and recommendation. However, such vectors are hardly useful in terms of providing visual explanations to users about why a particular item is recommended, and thus weakens the explainability of recommendation systems. As a step towards explainable recommendation models, we propose visually explainable recommendation based on attentive neural networks to model the user attention on images, under the supervision of both implicit feedback and textual reviews. By this, we can not only provide recommendation results to the users, but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner. Experimental results show that our models are not only able to improve the recommendation performance, but also can provide persuasive visual explanations for the users to take the recommendations.
cs.IR cs.MM
images account for a significant part of user decisions in many application scenarios such as product images in ecommerce or user image posts in social networks it is intuitive that user preferences on the visual patterns of image eg hue texture color etc can be highly personalized and this provides us with highly discriminative features to make personalized recommendations previous work that takes advantage of images for recommendation usually transforms the images into latent representation vectors which are adopted by a recommendation component to assist personalized useritem profiling and recommendation however such vectors are hardly useful in terms of providing visual explanations to users about why a particular item is recommended and thus weakens the explainability of recommendation systems as a step towards explainable recommendation models we propose visually explainable recommendation based on attentive neural networks to model the user attention on images under the supervision of both implicit feedback and textual reviews by this we can not only provide recommendation results to the users but also tell the users why an item is recommended by providing intuitive visual highlights in a personalized manner experimental results show that our models are not only able to improve the recommendation performance but also can provide persuasive visual explanations for the users to take the recommendations
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1,801.10289
Topological Flat Band and Parity-Time Symmetry in a Honeycomb Lattice of Coupled Resonant Optical Waveguides
Two-dimensional (2D) coupled resonant optical waveguide (CROW), exhibiting topological edge states, provides an efficient platform for designing integrated topological photonic devices. In this paper, we propose an experimentally feasible design of 2D honeycomb CROW photonic structure. The characteristic optical system possesses two-fold and three-fold Dirac points at different positions in the Brillouin zone. The effective gauge fields implemented by the intrinsic pseudo-spin-orbit interaction open up topologically nontrivial bandgaps through the Dirac points. Spatial lattice geometries allow destructive wave interference, leading to a dispersionless, nearly-flat energy band in the vicinity of the three-fold Dirac point in the telecommunication frequency regime. This nontrivial nearly-flat band yields topologically protected edge states. The pertinent physical effects brought about due to non-Hermitian gain/loss medium into the honeycomb CROW device are discussed. The generalized gain-loss lattice with parity-time symmetry decouples the gain and the loss at opposite zigzag edges, leading to purely gain or loss edge channels. Meanwhile, the gain and loss effects on the armchair boundary cancel each other, giving rise to dissipationless edge states in non-Hermitian optical systems. These characteristics underpin the fundamental importance as well as the potential applications in various optical devices such as polarizers, optical couplers, beam splitters and slow light delay lines.
physics.optics
twodimensional 2d coupled resonant optical waveguide crow exhibiting topological edge states provides an efficient platform for designing integrated topological photonic devices in this paper we propose an experimentally feasible design of 2d honeycomb crow photonic structure the characteristic optical system possesses twofold and threefold dirac points at different positions in the brillouin zone the effective gauge fields implemented by the intrinsic pseudospinorbit interaction open up topologically nontrivial bandgaps through the dirac points spatial lattice geometries allow destructive wave interference leading to a dispersionless nearlyflat energy band in the vicinity of the threefold dirac point in the telecommunication frequency regime this nontrivial nearlyflat band yields topologically protected edge states the pertinent physical effects brought about due to nonhermitian gainloss medium into the honeycomb crow device are discussed the generalized gainloss lattice with paritytime symmetry decouples the gain and the loss at opposite zigzag edges leading to purely gain or loss edge channels meanwhile the gain and loss effects on the armchair boundary cancel each other giving rise to dissipationless edge states in nonhermitian optical systems these characteristics underpin the fundamental importance as well as the potential applications in various optical devices such as polarizers optical couplers beam splitters and slow light delay lines
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1,801.1029
From Sticky-Hard-Sphere to Lennard-Jones-Type Clusters
A relation $\mathcal{M}_{\mathrm{SHS}\to\mathrm{LJ}}$ between the set of non-isomorphic sticky hard sphere clusters $\mathcal{M}_\mathrm{SHS}$ and the sets of local energy minima $\mathcal{M}_{LJ}$ of the $(m,n)$-Lennard-Jones potential $V^\mathrm{LJ}_{mn}(r) = \frac{\varepsilon}{n-m} [ m r^{-n} - n r^{-m} ]$ is established. The number of nonisomorphic stable clusters depends strongly and nontrivially on both $m$ and $n$, and increases exponentially with increasing cluster size $N$ for $N \gtrsim 10$. While the map from $\mathcal{M}_\mathrm{SHS}\to \mathcal{M}_{\mathrm{SHS}\to\mathrm{LJ}}$ is non-injective and non-surjective, the number of Lennard-Jones structures missing from the map is relatively small for cluster sizes up to $N=13$, and most of the missing structures correspond to energetically unfavourable minima even for fairly low $(m,n)$. Furthermore, even the softest Lennard-Jones potential predicts that the coordination of 13 spheres around a central sphere is problematic (the Gregory-Newton problem). A more realistic extended Lennard-Jones potential chosen from coupled-cluster calculations for a rare gas dimer leads to a substantial increase in the number of nonisomorphic clusters, even though the potential curve is very similar to a (6,12)-Lennard-Jones potential.
physics.atm-clus
a relation mathcalm_mathrmshstomathrmlj between the set of nonisomorphic sticky hard sphere clusters mathcalm_mathrmshs and the sets of local energy minima mathcalm_lj of the mnlennardjones potential vmathrmlj_mnr fracvarepsilonnm m rn n rm is established the number of nonisomorphic stable clusters depends strongly and nontrivially on both m and n and increases exponentially with increasing cluster size n for n gtrsim 10 while the map from mathcalm_mathrmshsto mathcalm_mathrmshstomathrmlj is noninjective and nonsurjective the number of lennardjones structures missing from the map is relatively small for cluster sizes up to n13 and most of the missing structures correspond to energetically unfavourable minima even for fairly low mn furthermore even the softest lennardjones potential predicts that the coordination of 13 spheres around a central sphere is problematic the gregorynewton problem a more realistic extended lennardjones potential chosen from coupledcluster calculations for a rare gas dimer leads to a substantial increase in the number of nonisomorphic clusters even though the potential curve is very similar to a 612lennardjones potential
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1,801.10291
A Cross Entropy based Optimization Algorithm with Global Convergence Guarantees
The cross entropy (CE) method is a model based search method to solve optimization problems where the objective function has minimal structure. The Monte-Carlo version of the CE method employs the naive sample averaging technique which is inefficient, both computationally and space wise. We provide a novel stochastic approximation version of the CE method, where the sample averaging is replaced with incremental geometric averaging. This approach can save considerable computational and storage costs. Our algorithm is incremental in nature and possesses additional attractive features such as accuracy, stability, robustness and convergence to the global optimum for a particular class of objective functions. We evaluate the algorithm on a variety of global optimization benchmark problems and the results obtained corroborate our theoretical findings.
cs.AI math.OC
the cross entropy ce method is a model based search method to solve optimization problems where the objective function has minimal structure the montecarlo version of the ce method employs the naive sample averaging technique which is inefficient both computationally and space wise we provide a novel stochastic approximation version of the ce method where the sample averaging is replaced with incremental geometric averaging this approach can save considerable computational and storage costs our algorithm is incremental in nature and possesses additional attractive features such as accuracy stability robustness and convergence to the global optimum for a particular class of objective functions we evaluate the algorithm on a variety of global optimization benchmark problems and the results obtained corroborate our theoretical findings
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1,801.10292
On the Optimal Recovery Threshold of Coded Matrix Multiplication
We provide novel coded computation strategies for distributed matrix-matrix products that outperform the recent "Polynomial code" constructions in recovery threshold, i.e., the required number of successful workers. When $m$-th fraction of each matrix can be stored in each worker node, Polynomial codes require $m^2$ successful workers, while our MatDot codes only require $2m-1$ successful workers, albeit at a higher communication cost from each worker to the fusion node. We also provide a systematic construction of MatDot codes. Further, we propose "PolyDot" coding that interpolates between Polynomial codes and MatDot codes to trade off communication cost and recovery threshold. Finally, we demonstrate a coding technique for multiplying $n$ matrices ($n \geq 3$) by applying MatDot and PolyDot coding ideas.
cs.IT cs.DC math.IT
we provide novel coded computation strategies for distributed matrixmatrix products that outperform the recent polynomial code constructions in recovery threshold ie the required number of successful workers when mth fraction of each matrix can be stored in each worker node polynomial codes require m2 successful workers while our matdot codes only require 2m1 successful workers albeit at a higher communication cost from each worker to the fusion node we also provide a systematic construction of matdot codes further we propose polydot coding that interpolates between polynomial codes and matdot codes to trade off communication cost and recovery threshold finally we demonstrate a coding technique for multiplying n matrices n geq 3 by applying matdot and polydot coding ideas
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1,801.10293
Paraphrase-Supervised Models of Compositionality
Compositional vector space models of meaning promise new solutions to stubborn language understanding problems. This paper makes two contributions toward this end: (i) it uses automatically-extracted paraphrase examples as a source of supervision for training compositional models, replacing previous work which relied on manual annotations used for the same purpose, and (ii) develops a context-aware model for scoring phrasal compositionality. Experimental results indicate that these multiple sources of information can be used to learn partial semantic supervision that matches previous techniques in intrinsic evaluation tasks. Our approaches are also evaluated for their impact on a machine translation system where we show improvements in translation quality, demonstrating that compositionality in interpretation correlates with compositionality in translation.
cs.CL
compositional vector space models of meaning promise new solutions to stubborn language understanding problems this paper makes two contributions toward this end i it uses automaticallyextracted paraphrase examples as a source of supervision for training compositional models replacing previous work which relied on manual annotations used for the same purpose and ii develops a contextaware model for scoring phrasal compositionality experimental results indicate that these multiple sources of information can be used to learn partial semantic supervision that matches previous techniques in intrinsic evaluation tasks our approaches are also evaluated for their impact on a machine translation system where we show improvements in translation quality demonstrating that compositionality in interpretation correlates with compositionality in translation
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1,801.10294
Protecting Privacy in VANETs Using Mix Zones With Virtual Pseudonym Change
Vehicular ad hoc networks VANETs use pseudonyms to communicate among them and with road side units, these pseudonyms are used to authenticate these vehicles and to hide real identities behind these pseudonyms, to better enhance privacy, these pseudonyms are changed frequently so that it will not be that easy to link these pseudonyms together and hence reveal real identities. However, changing pseudonyms will not be that useful if previous and current pseudonyms are easily linked together. Therefore different techniques have been proposed to hide the pseudonym changes and make it difficult to link pseudonyms together. Most of these techniques do not fully quarantine privacy when changing a pseudonym under some situations such as low traffic. In this paper, we provide a technique for changing pseudonyms that has the same privacy level under all traffic conditions. The technique relies on fixed mix zones that are planted and distributed over the roads. By this technique, a vehicle guarantees a high level of security when changing its pseudonym at that mix zone which will make it very difficult for an adversary to link particular pseudonyms together and hence reveal real identity. Performance analysis showed that our model works efficiently with very few computational costs
cs.NI cs.CR
vehicular ad hoc networks vanets use pseudonyms to communicate among them and with road side units these pseudonyms are used to authenticate these vehicles and to hide real identities behind these pseudonyms to better enhance privacy these pseudonyms are changed frequently so that it will not be that easy to link these pseudonyms together and hence reveal real identities however changing pseudonyms will not be that useful if previous and current pseudonyms are easily linked together therefore different techniques have been proposed to hide the pseudonym changes and make it difficult to link pseudonyms together most of these techniques do not fully quarantine privacy when changing a pseudonym under some situations such as low traffic in this paper we provide a technique for changing pseudonyms that has the same privacy level under all traffic conditions the technique relies on fixed mix zones that are planted and distributed over the roads by this technique a vehicle guarantees a high level of security when changing its pseudonym at that mix zone which will make it very difficult for an adversary to link particular pseudonyms together and hence reveal real identity performance analysis showed that our model works efficiently with very few computational costs
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1,801.10295
A Delay-Tolerant Payment Scheme Based on the Ethereum Blockchain
Banking as an essential service can be hard to access in remote, rural regions where the network connectivity is intermittent. Although micro-banking has been made possible by SMS or USSD messages in some places, their security flaws and session-based nature prevent them from a wider adoption. Global level cryptocurrencies enable low-cost, secure and pervasive money transferring among distributed peers, but are still limited in their ability to reach more people in remote communities. We proposed to take advantage of the delay-tolerant nature of blockchains to deliver banking services to remote communities that only connect to the broader Internet intermittently. Using a base station that offers connectivity within the local area, regular transaction processing is solely handled by blockchain miners. The bank only joins to process currency exchange requests, reward miners and track user balances when the connection is available. By distributing the verification and storage tasks among peers, our system design saves on the overall deployment and operational costs without sacrificing the reliability and trustwor- thiness. Through theoretical and empirical analysis, we provided insights to system design, tested its robustness against network disturbances, and demonstrated the feasibility of implementation on off-the-shelf computers and mobile devices.
cs.CY cs.CR
banking as an essential service can be hard to access in remote rural regions where the network connectivity is intermittent although microbanking has been made possible by sms or ussd messages in some places their security flaws and sessionbased nature prevent them from a wider adoption global level cryptocurrencies enable lowcost secure and pervasive money transferring among distributed peers but are still limited in their ability to reach more people in remote communities we proposed to take advantage of the delaytolerant nature of blockchains to deliver banking services to remote communities that only connect to the broader internet intermittently using a base station that offers connectivity within the local area regular transaction processing is solely handled by blockchain miners the bank only joins to process currency exchange requests reward miners and track user balances when the connection is available by distributing the verification and storage tasks among peers our system design saves on the overall deployment and operational costs without sacrificing the reliability and trustwor thiness through theoretical and empirical analysis we provided insights to system design tested its robustness against network disturbances and demonstrated the feasibility of implementation on offtheshelf computers and mobile devices
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1,801.10296
Reinforced Self-Attention Network: a Hybrid of Hard and Soft Attention for Sequence Modeling
Many natural language processing tasks solely rely on sparse dependencies between a few tokens in a sentence. Soft attention mechanisms show promising performance in modeling local/global dependencies by soft probabilities between every two tokens, but they are not effective and efficient when applied to long sentences. By contrast, hard attention mechanisms directly select a subset of tokens but are difficult and inefficient to train due to their combinatorial nature. In this paper, we integrate both soft and hard attention into one context fusion model, "reinforced self-attention (ReSA)", for the mutual benefit of each other. In ReSA, a hard attention trims a sequence for a soft self-attention to process, while the soft attention feeds reward signals back to facilitate the training of the hard one. For this purpose, we develop a novel hard attention called "reinforced sequence sampling (RSS)", selecting tokens in parallel and trained via policy gradient. Using two RSS modules, ReSA efficiently extracts the sparse dependencies between each pair of selected tokens. We finally propose an RNN/CNN-free sentence-encoding model, "reinforced self-attention network (ReSAN)", solely based on ReSA. It achieves state-of-the-art performance on both Stanford Natural Language Inference (SNLI) and Sentences Involving Compositional Knowledge (SICK) datasets.
cs.CL
many natural language processing tasks solely rely on sparse dependencies between a few tokens in a sentence soft attention mechanisms show promising performance in modeling localglobal dependencies by soft probabilities between every two tokens but they are not effective and efficient when applied to long sentences by contrast hard attention mechanisms directly select a subset of tokens but are difficult and inefficient to train due to their combinatorial nature in this paper we integrate both soft and hard attention into one context fusion model reinforced selfattention resa for the mutual benefit of each other in resa a hard attention trims a sequence for a soft selfattention to process while the soft attention feeds reward signals back to facilitate the training of the hard one for this purpose we develop a novel hard attention called reinforced sequence sampling rss selecting tokens in parallel and trained via policy gradient using two rss modules resa efficiently extracts the sparse dependencies between each pair of selected tokens we finally propose an rnncnnfree sentenceencoding model reinforced selfattention network resan solely based on resa it achieves stateoftheart performance on both stanford natural language inference snli and sentences involving compositional knowledge sick datasets
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1,801.10297
Spectral dynamics of topological shift-current in ferroelectric semiconductor SbSI
Photoexcitation in solids brings about transitions of electrons/holes between different electronic bands. If the solid lacks an inversion symmetry, these electronic transitions support spontaneous photocurrent due to the topological character of the constituting electronic bands; the Berry connection. This photocurrent, termed shift current, is expected to emerge on the time-scale of primary photoexcitation process. We observed ultrafast time evolution of the shift current in a prototypical ferroelectric semiconductor by detecting emitted terahertz electromagnetic waves. By sweeping the excitation photon energy across the band gap, ultrafast electron dynamics as a source of terahertz emission abruptly changes its nature, reflecting a contribution of Berry connection upon interband optical transition. The shift excitation carries a net charge flow, and is followed by a swing-over of the electron cloud on the sub-picosecond time-scale of electron-phonon interaction. Understanding these substantive characters of the shift current will pave the way for its application to ultrafast sensors and solar cells.
cond-mat.mtrl-sci
photoexcitation in solids brings about transitions of electronsholes between different electronic bands if the solid lacks an inversion symmetry these electronic transitions support spontaneous photocurrent due to the topological character of the constituting electronic bands the berry connection this photocurrent termed shift current is expected to emerge on the timescale of primary photoexcitation process we observed ultrafast time evolution of the shift current in a prototypical ferroelectric semiconductor by detecting emitted terahertz electromagnetic waves by sweeping the excitation photon energy across the band gap ultrafast electron dynamics as a source of terahertz emission abruptly changes its nature reflecting a contribution of berry connection upon interband optical transition the shift excitation carries a net charge flow and is followed by a swingover of the electron cloud on the subpicosecond timescale of electronphonon interaction understanding these substantive characters of the shift current will pave the way for its application to ultrafast sensors and solar cells
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1,801.10298
$(\mathfrak{g},K)$-module of $\mathrm{O}(p,q)$ associated with the finite-dimensional representation of $\mathfrak{sl}_2$
The main aim of this paper is to construct irreducible $(\mathfrak{g},K)$-modules of $\mathrm{O}(p,q)$ corresponding to the finite-dimensional representation of $\mathfrak{sl}_2$ of dimension $m+1$ under the Howe duality, to find the $K$-type formula, the Gelfand-Kirillov dimension and the Bernstein degree of them, where $m$ is a non-negative integer. The $K$-type formula for $m=0$ shows that it is nothing but the $(\mathfrak{g},K)$-module of the minimal representation of $\mathrm{O}(p,q)$. One finds that the Gelfand-Kirillov dimension is equal to $p+q-3$ not only for $m=0$ but for any $m$ satisfying $m+3 \leq (p+q)/2$ when $p, q \geq 2$ and $p+q$ is even, and that the Bernstein degree for $m$ is equal to $(m+1)$ times that for $m=0$.
math.RT
the main aim of this paper is to construct irreducible mathfrakgkmodules of mathrmopq corresponding to the finitedimensional representation of mathfraksl_2 of dimension m1 under the howe duality to find the ktype formula the gelfandkirillov dimension and the bernstein degree of them where m is a nonnegative integer the ktype formula for m0 shows that it is nothing but the mathfrakgkmodule of the minimal representation of mathrmopq one finds that the gelfandkirillov dimension is equal to pq3 not only for m0 but for any m satisfying m3 leq pq2 when p q geq 2 and pq is even and that the bernstein degree for m is equal to m1 times that for m0
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1,801.10299
Underwater Quantum Key Distribution in Outdoor Conditions with Twisted Photons
Quantum communication has been successfully implemented in optical fibres and through free-space [1-3]. Fibre systems, though capable of fast key rates and low quantum bit error rates (QBERs), are impractical in communicating with destinations without an established fibre link [4]. Free-space quantum channels can overcome such limitations and reach long distances with the advent of satellite-to-ground links [5-8]. Shorter line-of-sight free-space links have also been realized for intra-city conditions [2, 9]. However, turbulence, resulting from local fluctuations in refractive index, becomes a major challenge by adding errors and losses [10]. Recently, an interest in investigating the possibility of underwater quantum channels has arisen, which could provide global secure communication channels among submersibles and boats [11-13]. Here, we investigate the effect of turbulence on an underwater quantum channel using twisted photons in outdoor conditions. We study the effect of turbulence on transmitted QBERs, and compare different QKD protocols in an underwater quantum channel showing the feasibility of high-dimensional encoding schemes. Our work may open the way for secure high-dimensional quantum communication between submersibles, and provides important input for potential submersibles-to-satellite quantum communication.
quant-ph physics.optics
quantum communication has been successfully implemented in optical fibres and through freespace 13 fibre systems though capable of fast key rates and low quantum bit error rates qbers are impractical in communicating with destinations without an established fibre link 4 freespace quantum channels can overcome such limitations and reach long distances with the advent of satellitetoground links 58 shorter lineofsight freespace links have also been realized for intracity conditions 2 9 however turbulence resulting from local fluctuations in refractive index becomes a major challenge by adding errors and losses 10 recently an interest in investigating the possibility of underwater quantum channels has arisen which could provide global secure communication channels among submersibles and boats 1113 here we investigate the effect of turbulence on an underwater quantum channel using twisted photons in outdoor conditions we study the effect of turbulence on transmitted qbers and compare different qkd protocols in an underwater quantum channel showing the feasibility of highdimensional encoding schemes our work may open the way for secure highdimensional quantum communication between submersibles and provides important input for potential submersiblestosatellite quantum communication
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1,801.103
Netizen-Style Commenting on Fashion Photos: Dataset and Diversity Measures
Recently, deep neural network models have achieved promising results in image captioning task. Yet, "vanilla" sentences, only describing shallow appearances (e.g., types, colors), generated by current works are not satisfied netizen style resulting in lacking engagements, contexts, and user intentions. To tackle this problem, we propose Netizen Style Commenting (NSC), to automatically generate characteristic comments to a user-contributed fashion photo. We are devoted to modulating the comments in a vivid "netizen" style which reflects the culture in a designated social community and hopes to facilitate more engagement with users. In this work, we design a novel framework that consists of three major components: (1) We construct a large-scale clothing dataset named NetiLook, which contains 300K posts (photos) with 5M comments to discover netizen-style comments. (2) We propose three unique measures to estimate the diversity of comments. (3) We bring diversity by marrying topic models with neural networks to make up the insufficiency of conventional image captioning works. Experimenting over Flickr30k and our NetiLook datasets, we demonstrate our proposed approaches benefit fashion photo commenting and improve image captioning tasks both in accuracy and diversity.
cs.CV
recently deep neural network models have achieved promising results in image captioning task yet vanilla sentences only describing shallow appearances eg types colors generated by current works are not satisfied netizen style resulting in lacking engagements contexts and user intentions to tackle this problem we propose netizen style commenting nsc to automatically generate characteristic comments to a usercontributed fashion photo we are devoted to modulating the comments in a vivid netizen style which reflects the culture in a designated social community and hopes to facilitate more engagement with users in this work we design a novel framework that consists of three major components 1 we construct a largescale clothing dataset named netilook which contains 300k posts photos with 5m comments to discover netizenstyle comments 2 we propose three unique measures to estimate the diversity of comments 3 we bring diversity by marrying topic models with neural networks to make up the insufficiency of conventional image captioning works experimenting over flickr30k and our netilook datasets we demonstrate our proposed approaches benefit fashion photo commenting and improve image captioning tasks both in accuracy and diversity
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1,801.10301
Molecular Gas Toward the Gemini OB1 Molecular Cloud Complex II: CO Outflow Candidates with Possible WISE Associations
We present a large scale survey of CO outflows in the Gem OB1 molecular cloud complex and its surroundings using the Purple Mountain Observatory Delingha 13.7 m telescope. A total of 198 outflow candidates were identified over a large area ($\sim$ 58.5 square degrees), of which 193 are newly detected. Approximately 68% (134/198) are associated with the Gem OB1 molecular cloud complex, including clouds GGMC 1, GGMC 2, BFS 52, GGMC 3 and GGMC 4. Other regions studied are: Local Arm (Local Lynds, West Front), Swallow, Horn, and Remote cloud. Outflow candidates in GGMC 1, BFS 52, and Swallow are mainly located at ring-like or filamentary structures. To avoid excessive uncertainty in distant regions ($\gtrsim$ 3.8 kpc), we only estimated the physical parameters for clouds in the Gem OB1 molecular cloud complex and in the Local arm. In those clouds, the total kinetic energy and the energy injection rate of the identified outflow candidates are $\lesssim$ 1% and $\lesssim$ 3% of the turbulent energy and the turbulent dissipation rate of each cloud, indicating that the identified outflow candidates cannot provide enough energy to balance turbulence of their host cloud at the scale of the entire cloud (several pc to dozens of pc). The gravitational binding energy of each cloud is $\gtrsim$ 135 times the total kinetic energy of the identified outflow candidates within the corresponding cloud, indicating that the identified outflow candidates cannot cause major disruptions to the integrity of their host cloud at the scale of the entire cloud.
astro-ph.GA
we present a large scale survey of co outflows in the gem ob1 molecular cloud complex and its surroundings using the purple mountain observatory delingha 137 m telescope a total of 198 outflow candidates were identified over a large area sim 585 square degrees of which 193 are newly detected approximately 68 134198 are associated with the gem ob1 molecular cloud complex including clouds ggmc 1 ggmc 2 bfs 52 ggmc 3 and ggmc 4 other regions studied are local arm local lynds west front swallow horn and remote cloud outflow candidates in ggmc 1 bfs 52 and swallow are mainly located at ringlike or filamentary structures to avoid excessive uncertainty in distant regions gtrsim 38 kpc we only estimated the physical parameters for clouds in the gem ob1 molecular cloud complex and in the local arm in those clouds the total kinetic energy and the energy injection rate of the identified outflow candidates are lesssim 1 and lesssim 3 of the turbulent energy and the turbulent dissipation rate of each cloud indicating that the identified outflow candidates cannot provide enough energy to balance turbulence of their host cloud at the scale of the entire cloud several pc to dozens of pc the gravitational binding energy of each cloud is gtrsim 135 times the total kinetic energy of the identified outflow candidates within the corresponding cloud indicating that the identified outflow candidates cannot cause major disruptions to the integrity of their host cloud at the scale of the entire cloud
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1,801.10302
Endoscopic character identities for metaplectic groups
In this paper, we prove the conjectural endoscopic character identities for tempered representations of metaplectic group $Mp_{2n}$ based on the formalism of endoscopy theory by J. Adams, D. Renard and W.W. Li.
math.RT
in this paper we prove the conjectural endoscopic character identities for tempered representations of metaplectic group mp_2n based on the formalism of endoscopy theory by j adams d renard and ww li
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1,801.10303
Water's interfacial hydrogen bonding structure reveals the effective strength of surface-water interactions
The interactions of a hydrophilic surface with water can significantly influence the characteristics of the liquid water interface. In this manuscript, we explore this influence by studying the molecular structure of liquid water at a disordered surface with tunable surface-water interactions. We combine all-atom molecular dynamics simulations with a mean field model of interfacial hydrogen bonding to analyze the effect of surface-water interactions on the structural and energetic properties of the liquid water interface. We find that the molecular structure of water at a weakly interacting (i.e., hydrophobic) surface is resistant to change unless the strength of surface-water interactions are above a certain threshold. We find that below this threshold water's interfacial structure is homogeneous and insensitive to the details of the disordered surface, however, above this threshold water's interfacial structure is heterogeneous. Despite this heterogeneity, we demonstrate that the equilibrium distribution of molecular orientations can be used to quantify the energetic component of the surface-water interactions that contribute specifically to modifying the interfacial hydrogen bonding network. We identify this specific energetic component as a new measure of hydrophilicity, which we refer to as the intrinsic hydropathy.
cond-mat.soft
the interactions of a hydrophilic surface with water can significantly influence the characteristics of the liquid water interface in this manuscript we explore this influence by studying the molecular structure of liquid water at a disordered surface with tunable surfacewater interactions we combine allatom molecular dynamics simulations with a mean field model of interfacial hydrogen bonding to analyze the effect of surfacewater interactions on the structural and energetic properties of the liquid water interface we find that the molecular structure of water at a weakly interacting ie hydrophobic surface is resistant to change unless the strength of surfacewater interactions are above a certain threshold we find that below this threshold waters interfacial structure is homogeneous and insensitive to the details of the disordered surface however above this threshold waters interfacial structure is heterogeneous despite this heterogeneity we demonstrate that the equilibrium distribution of molecular orientations can be used to quantify the energetic component of the surfacewater interactions that contribute specifically to modifying the interfacial hydrogen bonding network we identify this specific energetic component as a new measure of hydrophilicity which we refer to as the intrinsic hydropathy
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1,801.10304
Action Recognition with Spatio-Temporal Visual Attention on Skeleton Image Sequences
Action recognition with 3D skeleton sequences is becoming popular due to its speed and robustness. The recently proposed Convolutional Neural Networks (CNN) based methods have shown good performance in learning spatio-temporal representations for skeleton sequences. Despite the good recognition accuracy achieved by previous CNN based methods, there exist two problems that potentially limit the performance. First, previous skeleton representations are generated by chaining joints with a fixed order. The corresponding semantic meaning is unclear and the structural information among the joints is lost. Second, previous models do not have an ability to focus on informative joints. The attention mechanism is important for skeleton based action recognition because there exist spatio-temporal key stages while the joint predictions can be inaccurate. To solve these two problems, we propose a novel CNN based method for skeleton based action recognition. We first redesign the skeleton representations with a depth-first tree traversal order, which enhances the semantic meaning of skeleton images and better preserves the associated structural information. We then propose the idea of a two-branch attention architecture that focuses on spatio-temporal key stages and filters out unreliable joint predictions. A base attention model with the simplest structure is first introduced. By improving the structures in both branches, we further propose a Global Long-sequence Attention Network (GLAN). Furthermore, in order to adjust the kernel's spatio-temporal aspect ratios and better capture long term dependencies, we propose a Sub-Sequence Attention Network (SSAN) that takes sub-image sequences as inputs. Our experiment results on NTU RGB+D and SBU Kinetic Interaction outperforms the state-of-the-art. The model is further validated on noisy estimated poses from UCF101 and Kinetics.
cs.CV
action recognition with 3d skeleton sequences is becoming popular due to its speed and robustness the recently proposed convolutional neural networks cnn based methods have shown good performance in learning spatiotemporal representations for skeleton sequences despite the good recognition accuracy achieved by previous cnn based methods there exist two problems that potentially limit the performance first previous skeleton representations are generated by chaining joints with a fixed order the corresponding semantic meaning is unclear and the structural information among the joints is lost second previous models do not have an ability to focus on informative joints the attention mechanism is important for skeleton based action recognition because there exist spatiotemporal key stages while the joint predictions can be inaccurate to solve these two problems we propose a novel cnn based method for skeleton based action recognition we first redesign the skeleton representations with a depthfirst tree traversal order which enhances the semantic meaning of skeleton images and better preserves the associated structural information we then propose the idea of a twobranch attention architecture that focuses on spatiotemporal key stages and filters out unreliable joint predictions a base attention model with the simplest structure is first introduced by improving the structures in both branches we further propose a global longsequence attention network glan furthermore in order to adjust the kernels spatiotemporal aspect ratios and better capture long term dependencies we propose a subsequence attention network ssan that takes subimage sequences as inputs our experiment results on ntu rgbd and sbu kinetic interaction outperforms the stateoftheart the model is further validated on noisy estimated poses from ucf101 and kinetics
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1,801.10305
A gap theorem for positive Einstein metrics on the four-sphere
We show that there exists a universal positive constant $\varepsilon_0 > 0$ with the following property: Let $g$ be a positive Einstein metric on $S^4$. If the Yamabe constant of the conformal class $[g]$ satisfies $$ Y(S^4, [g]) >\frac{1}{\sqrt{3}} Y(S^4, [g_{\mathbb S}]) - \varepsilon_0 $$ where $g_{\mathbb S}$ denotes the standard round metric on $S^4$, then, up to rescaling, $g$ is isometric to $g_{\mathbb S}$. This is an extension of Gursky's gap theorem for positive Einstein metrics on the four-sphere.
math.DG
we show that there exists a universal positive constant varepsilon_0 0 with the following property let g be a positive einstein metric on s4 if the yamabe constant of the conformal class g satisfies ys4 g frac1sqrt3 ys4 g_mathbb s varepsilon_0 where g_mathbb s denotes the standard round metric on s4 then up to rescaling g is isometric to g_mathbb s this is an extension of gurskys gap theorem for positive einstein metrics on the foursphere
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1,801.10306
Positiveness of the permanent of 4-dimensional polystochastic matrices of order 4
A nonnegative multidimensional matrix is called polystochastic if the sum of its entries over each line is equal to $1$. In this paper we overview known results on positiveness of the permanent of polystochastic matrices and prove that the permanent of every $4$-dimensional polystochastic matrix of order $4$ is greater than zero.
math.CO
a nonnegative multidimensional matrix is called polystochastic if the sum of its entries over each line is equal to 1 in this paper we overview known results on positiveness of the permanent of polystochastic matrices and prove that the permanent of every 4dimensional polystochastic matrix of order 4 is greater than zero
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1,801.10307
Non-Orthogonal Multi-Slater Determinant Expansions in Auxiliary Field Quantum Monte Carlo
The Auxiliary-Field Quantum Monte Carlo (AFQMC) algorithm is a powerful quantum many-body method that can be used successfully as an alternative to standard quantum chemistry approaches to compute the ground state of many body systems, such as molecules and solids, with high accuracy. In this article we use AFQMC with trial wave-functions built from non-orthogonal multi Slater determinant expansions to study the energetics of molecular systems, including the 55 molecules of the G1 test set and the isomerization path of the $[Cu_{2}O_{2}]^{2+}$ molecule. The main goal of this study is to show the ability of non-orthogonal multi Slater determinant expansions to produce high-quality, compact trial wave-functions for quantum Monte Carlo methods. We obtain systematically improvable results as the number of determinants is increased, with high accuracy typically obtained with tens of determinants. Great reduction in the average error and traditional statistical indicators are observed in the total and absorption energies of the molecules in the G1 test set with as few as 10-20 determinants. In the case of the relative energies along the isomerization path of the $[Cu_{2}O_{2}]^{2+}$, our results compare favorably with other advanced quantum many-body methods, including DMRG and complete-renormalized CCSD(T). Discrepancies in previous studies for this molecular problem are identified and attributed to the differences in the number of electrons and active spaces considered in such calculations.
physics.chem-ph
the auxiliaryfield quantum monte carlo afqmc algorithm is a powerful quantum manybody method that can be used successfully as an alternative to standard quantum chemistry approaches to compute the ground state of many body systems such as molecules and solids with high accuracy in this article we use afqmc with trial wavefunctions built from nonorthogonal multi slater determinant expansions to study the energetics of molecular systems including the 55 molecules of the g1 test set and the isomerization path of the cu_2o_22 molecule the main goal of this study is to show the ability of nonorthogonal multi slater determinant expansions to produce highquality compact trial wavefunctions for quantum monte carlo methods we obtain systematically improvable results as the number of determinants is increased with high accuracy typically obtained with tens of determinants great reduction in the average error and traditional statistical indicators are observed in the total and absorption energies of the molecules in the g1 test set with as few as 1020 determinants in the case of the relative energies along the isomerization path of the cu_2o_22 our results compare favorably with other advanced quantum manybody methods including dmrg and completerenormalized ccsdt discrepancies in previous studies for this molecular problem are identified and attributed to the differences in the number of electrons and active spaces considered in such calculations
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1,801.10308
Nested LSTMs
We propose Nested LSTMs (NLSTM), a novel RNN architecture with multiple levels of memory. Nested LSTMs add depth to LSTMs via nesting as opposed to stacking. The value of a memory cell in an NLSTM is computed by an LSTM cell, which has its own inner memory cell. Specifically, instead of computing the value of the (outer) memory cell as $c^{outer}_t = f_t \odot c_{t-1} + i_t \odot g_t$, NLSTM memory cells use the concatenation $(f_t \odot c_{t-1}, i_t \odot g_t)$ as input to an inner LSTM (or NLSTM) memory cell, and set $c^{outer}_t$ = $h^{inner}_t$. Nested LSTMs outperform both stacked and single-layer LSTMs with similar numbers of parameters in our experiments on various character-level language modeling tasks, and the inner memories of an LSTM learn longer term dependencies compared with the higher-level units of a stacked LSTM.
cs.CL cs.LG
we propose nested lstms nlstm a novel rnn architecture with multiple levels of memory nested lstms add depth to lstms via nesting as opposed to stacking the value of a memory cell in an nlstm is computed by an lstm cell which has its own inner memory cell specifically instead of computing the value of the outer memory cell as couter_t f_t odot c_t1 i_t odot g_t nlstm memory cells use the concatenation f_t odot c_t1 i_t odot g_t as input to an inner lstm or nlstm memory cell and set couter_t hinner_t nested lstms outperform both stacked and singlelayer lstms with similar numbers of parameters in our experiments on various characterlevel language modeling tasks and the inner memories of an lstm learn longer term dependencies compared with the higherlevel units of a stacked lstm
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1,801.10309
Demonstration of the Relationship between Sensitivity and Identifiability for Inverse Uncertainty Quantification
Inverse Uncertainty Quantification (UQ), or Bayesian calibration, is the process to quantify the uncertainties of random input parameters based on experimental data. The introduction of model discrepancy term is significant because "over-fitting" can theoretically be avoided. But it also poses challenges in the practical applications. One of the mostly concerned and unresolved problem is the "lack of identifiability" issue. With the presence of model discrepancy, inverse UQ becomes "non-identifiable" in the sense that it is difficult to precisely distinguish between the parameter uncertainties and model discrepancy when estimating the calibration parameters. Previous research to alleviate the non-identifiability issue focused on using informative priors for the calibration parameters and the model discrepancy, which is usually not a viable solution because one rarely has such accurate and informative prior knowledge. In this work, we show that identifiability is largely related to the sensitivity of the calibration parameters with regards to the chosen responses. We adopted an improved modular Bayesian approach for inverse UQ that does not require priors for the model discrepancy term. The relationship between sensitivity and identifiability was demonstrated with a practical example in nuclear engineering. It was shown that, in order for a certain calibration parameter to be statistically identifiable, it should be significant to at least one of the responses whose data are used for inverse UQ. Good identifiability cannot be achieved for a certain calibration parameter if it is not significant to any of the responses. It is also demonstrated that "fake identifiability" is possible if model responses are not appropriately chosen, or inaccurate but informative priors are specified.
stat.AP
inverse uncertainty quantification uq or bayesian calibration is the process to quantify the uncertainties of random input parameters based on experimental data the introduction of model discrepancy term is significant because overfitting can theoretically be avoided but it also poses challenges in the practical applications one of the mostly concerned and unresolved problem is the lack of identifiability issue with the presence of model discrepancy inverse uq becomes nonidentifiable in the sense that it is difficult to precisely distinguish between the parameter uncertainties and model discrepancy when estimating the calibration parameters previous research to alleviate the nonidentifiability issue focused on using informative priors for the calibration parameters and the model discrepancy which is usually not a viable solution because one rarely has such accurate and informative prior knowledge in this work we show that identifiability is largely related to the sensitivity of the calibration parameters with regards to the chosen responses we adopted an improved modular bayesian approach for inverse uq that does not require priors for the model discrepancy term the relationship between sensitivity and identifiability was demonstrated with a practical example in nuclear engineering it was shown that in order for a certain calibration parameter to be statistically identifiable it should be significant to at least one of the responses whose data are used for inverse uq good identifiability cannot be achieved for a certain calibration parameter if it is not significant to any of the responses it is also demonstrated that fake identifiability is possible if model responses are not appropriately chosen or inaccurate but informative priors are specified
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1,801.1031
Ten Years of PAMELA in Space
The PAMELA cosmic ray detector was launched on June 15th 2006 on board the Russian Resurs-DK1 satellite, and during ten years of nearly continuous data-taking it has observed new interesting features in cosmic rays (CRs). In a decade of operation it has provided plenty of scientific data, covering different issues related to cosmic ray physics. Its discoveries might change our basic vision of the mechanisms of production, acceleration and propagation of cosmic rays in the Galaxy. The antimatter measurements, focus of the experiment, have set strong constraints to the nature of Dark Matter. Search for signatures of more exotic processes (such as the ones involving Strange Quark Matter) was also pursued. Furthermore, the long-term operation of the instrument had allowed a constant monitoring of the solar activity during its maximum and a detailed and prolonged study of the solar modulation, improving the comprehension of the heliosphere mechanisms. PAMELA had also measured the radiation environment around the Earth, and it detected for the first time the presence of an antiproton radiation belt surrounding our planet. The operation of Resurs-DK1 was terminated in 2016. In this article we will review the main features of the PAMELA instrument and its constructing phases. Main part of the article will be dedicated to the summary of the most relevant PAMELA results over a decade of observation
astro-ph.HE
the pamela cosmic ray detector was launched on june 15th 2006 on board the russian resursdk1 satellite and during ten years of nearly continuous datataking it has observed new interesting features in cosmic rays crs in a decade of operation it has provided plenty of scientific data covering different issues related to cosmic ray physics its discoveries might change our basic vision of the mechanisms of production acceleration and propagation of cosmic rays in the galaxy the antimatter measurements focus of the experiment have set strong constraints to the nature of dark matter search for signatures of more exotic processes such as the ones involving strange quark matter was also pursued furthermore the longterm operation of the instrument had allowed a constant monitoring of the solar activity during its maximum and a detailed and prolonged study of the solar modulation improving the comprehension of the heliosphere mechanisms pamela had also measured the radiation environment around the earth and it detected for the first time the presence of an antiproton radiation belt surrounding our planet the operation of resursdk1 was terminated in 2016 in this article we will review the main features of the pamela instrument and its constructing phases main part of the article will be dedicated to the summary of the most relevant pamela results over a decade of observation
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1,801.10311
Could scientists use Altmetric.com scores to predict longer term citation counts?
Altmetrics from Altmetric.com are widely used by publishers and researchers to give earlier evidence of attention than citation counts. This article assesses whether Altmetric.com scores are reliable early indicators of likely future impact and whether they may also reflect non-scholarly impacts. A preliminary factor analysis suggests that the main altmetric indicator of scholarly impact is Mendeley reader counts, with weaker news, informational and social network discussion/promotion dimensions in some fields. Based on a regression analysis of Altmetric.com data from November 2015 and Scopus citation counts from October 2017 for articles in 30 narrow fields, only Mendeley reader counts are consistent predictors of future citation impact. Most other Altmetric.com scores can help predict future impact in some fields. Overall, the results confirm that early Altmetric.com scores can predict later citation counts, although less well than journal impact factors, and the optimal strategy is to consider both Altmetric.com scores and journal impact factors. Altmetric.com scores can also reflect dimensions of non-scholarly impact in some fields.
cs.DL
altmetrics from altmetriccom are widely used by publishers and researchers to give earlier evidence of attention than citation counts this article assesses whether altmetriccom scores are reliable early indicators of likely future impact and whether they may also reflect nonscholarly impacts a preliminary factor analysis suggests that the main altmetric indicator of scholarly impact is mendeley reader counts with weaker news informational and social network discussionpromotion dimensions in some fields based on a regression analysis of altmetriccom data from november 2015 and scopus citation counts from october 2017 for articles in 30 narrow fields only mendeley reader counts are consistent predictors of future citation impact most other altmetriccom scores can help predict future impact in some fields overall the results confirm that early altmetriccom scores can predict later citation counts although less well than journal impact factors and the optimal strategy is to consider both altmetriccom scores and journal impact factors altmetriccom scores can also reflect dimensions of nonscholarly impact in some fields
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1,801.10312
A Deep Ranking Model for Spatio-Temporal Highlight Detection from a 360 Video
We address the problem of highlight detection from a 360 degree video by summarizing it both spatially and temporally. Given a long 360 degree video, we spatially select pleasantly-looking normal field-of-view (NFOV) segments from unlimited field of views (FOV) of the 360 degree video, and temporally summarize it into a concise and informative highlight as a selected subset of subshots. We propose a novel deep ranking model named as Composition View Score (CVS) model, which produces a spherical score map of composition per video segment, and determines which view is suitable for highlight via a sliding window kernel at inference. To evaluate the proposed framework, we perform experiments on the Pano2Vid benchmark dataset and our newly collected 360 degree video highlight dataset from YouTube and Vimeo. Through evaluation using both quantitative summarization metrics and user studies via Amazon Mechanical Turk, we demonstrate that our approach outperforms several state-of-the-art highlight detection methods. We also show that our model is 16 times faster at inference than AutoCam, which is one of the first summarization algorithms of 360 degree videos
cs.CV
we address the problem of highlight detection from a 360 degree video by summarizing it both spatially and temporally given a long 360 degree video we spatially select pleasantlylooking normal fieldofview nfov segments from unlimited field of views fov of the 360 degree video and temporally summarize it into a concise and informative highlight as a selected subset of subshots we propose a novel deep ranking model named as composition view score cvs model which produces a spherical score map of composition per video segment and determines which view is suitable for highlight via a sliding window kernel at inference to evaluate the proposed framework we perform experiments on the pano2vid benchmark dataset and our newly collected 360 degree video highlight dataset from youtube and vimeo through evaluation using both quantitative summarization metrics and user studies via amazon mechanical turk we demonstrate that our approach outperforms several stateoftheart highlight detection methods we also show that our model is 16 times faster at inference than autocam which is one of the first summarization algorithms of 360 degree videos
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1,801.10313
From Spinning Primaries to Permutation Orbifolds
We carry out a systematic study of primary operators in the conformal field theory of a free Weyl fermion. Using SO(4,2) characters we develop counting formulas for primaries constructed using a fixed number of fermion fields. By specializing to particular classes of primaries, we derive very explicit formulas for the generating functions for the number of primaries in these classes. We present a duality map between primary operators in the fermion field theory and polynomial functions. This allows us to construct the primaries that were counted. Next we show that these classes of primary fields correspond to polynomial functions on certain permutation orbifolds. These orbifolds have palindromic Hilbert series.
hep-th
we carry out a systematic study of primary operators in the conformal field theory of a free weyl fermion using so42 characters we develop counting formulas for primaries constructed using a fixed number of fermion fields by specializing to particular classes of primaries we derive very explicit formulas for the generating functions for the number of primaries in these classes we present a duality map between primary operators in the fermion field theory and polynomial functions this allows us to construct the primaries that were counted next we show that these classes of primary fields correspond to polynomial functions on certain permutation orbifolds these orbifolds have palindromic hilbert series
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1,801.10314
Complex Sequential Question Answering: Towards Learning to Converse Over Linked Question Answer Pairs with a Knowledge Graph
While conversing with chatbots, humans typically tend to ask many questions, a significant portion of which can be answered by referring to large-scale knowledge graphs (KG). While Question Answering (QA) and dialog systems have been studied independently, there is a need to study them closely to evaluate such real-world scenarios faced by bots involving both these tasks. Towards this end, we introduce the task of Complex Sequential QA which combines the two tasks of (i) answering factual questions through complex inferencing over a realistic-sized KG of millions of entities, and (ii) learning to converse through a series of coherently linked QA pairs. Through a labor intensive semi-automatic process, involving in-house and crowdsourced workers, we created a dataset containing around 200K dialogs with a total of 1.6M turns. Further, unlike existing large scale QA datasets which contain simple questions that can be answered from a single tuple, the questions in our dialogs require a larger subgraph of the KG. Specifically, our dataset has questions which require logical, quantitative, and comparative reasoning as well as their combinations. This calls for models which can: (i) parse complex natural language questions, (ii) use conversation context to resolve coreferences and ellipsis in utterances, (iii) ask for clarifications for ambiguous queries, and finally (iv) retrieve relevant subgraphs of the KG to answer such questions. However, our experiments with a combination of state of the art dialog and QA models show that they clearly do not achieve the above objectives and are inadequate for dealing with such complex real world settings. We believe that this new dataset coupled with the limitations of existing models as reported in this paper should encourage further research in Complex Sequential QA.
cs.CL
while conversing with chatbots humans typically tend to ask many questions a significant portion of which can be answered by referring to largescale knowledge graphs kg while question answering qa and dialog systems have been studied independently there is a need to study them closely to evaluate such realworld scenarios faced by bots involving both these tasks towards this end we introduce the task of complex sequential qa which combines the two tasks of i answering factual questions through complex inferencing over a realisticsized kg of millions of entities and ii learning to converse through a series of coherently linked qa pairs through a labor intensive semiautomatic process involving inhouse and crowdsourced workers we created a dataset containing around 200k dialogs with a total of 16m turns further unlike existing large scale qa datasets which contain simple questions that can be answered from a single tuple the questions in our dialogs require a larger subgraph of the kg specifically our dataset has questions which require logical quantitative and comparative reasoning as well as their combinations this calls for models which can i parse complex natural language questions ii use conversation context to resolve coreferences and ellipsis in utterances iii ask for clarifications for ambiguous queries and finally iv retrieve relevant subgraphs of the kg to answer such questions however our experiments with a combination of state of the art dialog and qa models show that they clearly do not achieve the above objectives and are inadequate for dealing with such complex real world settings we believe that this new dataset coupled with the limitations of existing models as reported in this paper should encourage further research in complex sequential qa
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1,801.10315
Cluster nonequilibrium relaxation in Ising models observed with the Binder ratio
The Binder ratios exhibit discrepancy from the Gaussian behavior of the magnetic cumulants, and their size independence at the critical point has been widely utilized in numerical studies of critical phenomena. In the present article we reformulate the nonequilibrium relaxation (NER) analysis in cluster algorithms using the $(2,1)$-Binder ratio, and apply this scheme to the two- and three-dimensional Ising models. Although the stretched-exponential relaxation behavior at the critical point is not explicitly observed in this quantity, we find that there exists a logarithmic finite-size scaling formula which can be related with a similar formula recently derived in cluster NER of the correlation length, and that the formula enables precise evaluation of the critical point and the stretched-exponential relaxation exponent $\sigma$. Physical background of this novel behavior is explained by the simulation-time dependence of the distribution function of magnetization in two dimensions and temperature dependence of $\sigma$ obtained from magnetization in three dimensions.
cond-mat.stat-mech
the binder ratios exhibit discrepancy from the gaussian behavior of the magnetic cumulants and their size independence at the critical point has been widely utilized in numerical studies of critical phenomena in the present article we reformulate the nonequilibrium relaxation ner analysis in cluster algorithms using the 21binder ratio and apply this scheme to the two and threedimensional ising models although the stretchedexponential relaxation behavior at the critical point is not explicitly observed in this quantity we find that there exists a logarithmic finitesize scaling formula which can be related with a similar formula recently derived in cluster ner of the correlation length and that the formula enables precise evaluation of the critical point and the stretchedexponential relaxation exponent sigma physical background of this novel behavior is explained by the simulationtime dependence of the distribution function of magnetization in two dimensions and temperature dependence of sigma obtained from magnetization in three dimensions
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1,801.10316
Collapse of critical nematic fluctuations in FeSe under pressure
We report the evolution of the electronic nematic susceptibility in FeSe via Raman scattering as a function of hydrostatic pressure up to 5.8 GPa where the superconducting transition temperature $T_{c}$ reaches its maximum. The critical nematic fluctuations observed at low pressure vanish above 1.6 GPa, indicating they play a marginal role in the four-fold enhancement of $T_{c}$ at higher pressures. The collapse of nematic fluctuations appears to be linked to a suppression of low energy electronic excitations which manifests itself by optical phonon anomalies at around 2 GPa, in agreement with lattice dynamical and electronic structure calculations using local density approximation combined with dynamical mean field theory. Our results reveal two different regimes of nematicity in the phase diagram of FeSe under pressure: a d-wave Pomeranchuk instability of the Fermi surface at low pressure and a magnetic driven orthorhombic distortion at higher pressure.
cond-mat.supr-con cond-mat.str-el
we report the evolution of the electronic nematic susceptibility in fese via raman scattering as a function of hydrostatic pressure up to 58 gpa where the superconducting transition temperature t_c reaches its maximum the critical nematic fluctuations observed at low pressure vanish above 16 gpa indicating they play a marginal role in the fourfold enhancement of t_c at higher pressures the collapse of nematic fluctuations appears to be linked to a suppression of low energy electronic excitations which manifests itself by optical phonon anomalies at around 2 gpa in agreement with lattice dynamical and electronic structure calculations using local density approximation combined with dynamical mean field theory our results reveal two different regimes of nematicity in the phase diagram of fese under pressure a dwave pomeranchuk instability of the fermi surface at low pressure and a magnetic driven orthorhombic distortion at higher pressure
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1,801.10317
Highly efficient visible colloidal lead-halide perovskite nanocrystal light-emitting diodes
Lead-halide perovskites have been attracting attention for potential use in solid-state lighting. Following the footsteps of solar cells, the field of perovskite light-emitting diodes (PeLEDs) has been growing rapidly. Their application prospects in lighting, however, remain still uncertain due to a variety of shortcomings in device performance including their limited levels of luminous efficiency achievable thus far. Here we show high-efficiency PeLEDs based on colloidal perovskite nanocrystals (PeNCs) synthesized at room temperature possessing dominant first-order excitonic radiation (enabling a photoluminescence quantum yield of 71% in solid film), unlike in the case of bulk perovskites with slow electron-hole bimolecular radiative recombination (a second-order process). In these PeLEDs, by reaching charge balance in the recombination zone, we find that the Auger nonradiative recombination, with its significant role in emission quenching, is effectively suppressed in low driving current density range. In consequence, these devices reach a record high maximum external quantum efficiency of 12.9% reported to date and an unprecedentedly high power efficiency of 30.3 lm W-1 at luminance levels above 1000 cd m-2 as required for various applications. These findings suggest that, with feasible levels of device performance, the PeNCs hold great promise for their use in LED lighting and displays.
physics.app-ph
leadhalide perovskites have been attracting attention for potential use in solidstate lighting following the footsteps of solar cells the field of perovskite lightemitting diodes peleds has been growing rapidly their application prospects in lighting however remain still uncertain due to a variety of shortcomings in device performance including their limited levels of luminous efficiency achievable thus far here we show highefficiency peleds based on colloidal perovskite nanocrystals pencs synthesized at room temperature possessing dominant firstorder excitonic radiation enabling a photoluminescence quantum yield of 71 in solid film unlike in the case of bulk perovskites with slow electronhole bimolecular radiative recombination a secondorder process in these peleds by reaching charge balance in the recombination zone we find that the auger nonradiative recombination with its significant role in emission quenching is effectively suppressed in low driving current density range in consequence these devices reach a record high maximum external quantum efficiency of 129 reported to date and an unprecedentedly high power efficiency of 303 lm w1 at luminance levels above 1000 cd m2 as required for various applications these findings suggest that with feasible levels of device performance the pencs hold great promise for their use in led lighting and displays
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1,801.10318
Probability of detection of an extraneous mobile object by autonomous unmanned underwater vehicles as a solution of the Buffon problem
Underwater robotics addresses the problem of object detection apparatus. Offers a probabilistic formulation of the problem, which uses the reduction of the detection task to a classical task of Buffon. This formulation arises naturally in the formulation of the problem in the coordinate system associated with the apparatus. It is shown that the problem allows analysis in the presence of an asymptotic parameter, determined by the ratio of the local scan size of the apparatus to the global size of the problem under consideration.
cs.RO physics.data-an
underwater robotics addresses the problem of object detection apparatus offers a probabilistic formulation of the problem which uses the reduction of the detection task to a classical task of buffon this formulation arises naturally in the formulation of the problem in the coordinate system associated with the apparatus it is shown that the problem allows analysis in the presence of an asymptotic parameter determined by the ratio of the local scan size of the apparatus to the global size of the problem under consideration
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1,801.10319
SESR: Single Image Super Resolution with Recursive Squeeze and Excitation Networks
Single image super resolution is a very important computer vision task, with a wide range of applications. In recent years, the depth of the super-resolution model has been constantly increasing, but with a small increase in performance, it has brought a huge amount of computation and memory consumption. In this work, in order to make the super resolution models more effective, we proposed a novel single image super resolution method via recursive squeeze and excitation networks (SESR). By introducing the squeeze and excitation module, our SESR can model the interdependencies and relationships between channels and that makes our model more efficiency. In addition, the recursive structure and progressive reconstruction method in our model minimized the layers and parameters and enabled SESR to simultaneously train multi-scale super resolution in a single model. After evaluating on four benchmark test sets, our model is proved to be above the state-of-the-art methods in terms of speed and accuracy.
cs.CV
single image super resolution is a very important computer vision task with a wide range of applications in recent years the depth of the superresolution model has been constantly increasing but with a small increase in performance it has brought a huge amount of computation and memory consumption in this work in order to make the super resolution models more effective we proposed a novel single image super resolution method via recursive squeeze and excitation networks sesr by introducing the squeeze and excitation module our sesr can model the interdependencies and relationships between channels and that makes our model more efficiency in addition the recursive structure and progressive reconstruction method in our model minimized the layers and parameters and enabled sesr to simultaneously train multiscale super resolution in a single model after evaluating on four benchmark test sets our model is proved to be above the stateoftheart methods in terms of speed and accuracy
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1,801.1032
Birational maps conjugate to the rank 2 cluster mutations of affine types and their geometry
Mutations of the cluster variables generating the cluster algebra of type $A^{(2)}_2$ reduce to a two-dimensional discrete integrable system given by a quartic birational map. The invariant curve of the map is a singular quartic curve, and its resolution of the singularity induces a discrete integrable system on a conic governed by a cubic birational map conjugate to the cluster mutations of type $A^{(2)}_2$. Moreover, it is shown that the conic is also the invariant curve of the quadratic birational map arising from the cluster mutations of type $A^{(1)}_1$ and the two birational maps on the conic are commutative. Finally, the commutative birational maps are reduced as singular limits of additions of points on an elliptic curve arising as the spectral curve of the discrete Toda lattice of type $A^{(1)}_1$.
nlin.SI math.AG
mutations of the cluster variables generating the cluster algebra of type a2_2 reduce to a twodimensional discrete integrable system given by a quartic birational map the invariant curve of the map is a singular quartic curve and its resolution of the singularity induces a discrete integrable system on a conic governed by a cubic birational map conjugate to the cluster mutations of type a2_2 moreover it is shown that the conic is also the invariant curve of the quadratic birational map arising from the cluster mutations of type a1_1 and the two birational maps on the conic are commutative finally the commutative birational maps are reduced as singular limits of additions of points on an elliptic curve arising as the spectral curve of the discrete toda lattice of type a1_1
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1,801.10321
Constraint Estimation and Derivative-Free Recovery for Robot Learning from Demonstrations
Learning from human demonstrations can facilitate automation but is risky because the execution of the learned policy might lead to collisions and other failures. Adding explicit constraints to avoid unsafe states is generally not possible when the state representations are complex. Furthermore, enforcing these constraints during execution of the learned policy can be challenging in environments where dynamics are difficult to model such as push mechanics in grasping. In this paper, we propose Derivative-Free Recovery (DFR), a two-phase method for generating robust policies from demonstrations in robotic manipulation tasks where the system comes to rest at each time step. In the first phase, we use support estimation of supervisor demonstrations and treat the support as implicit constraints on states. We also propose a time-varying modification for sequential tasks. In the second phase, we use this support estimate to derive a switching policy that employs the learned policy in the interior of the support and switches to a recovery policy to steer the robot away from the boundary of the support if it drifts too close. We present additional conditions, which linearly bound the difference in state at each time step by the magnitude of control, allowing us to prove that the robot will not violate the constraints using the recovery policy. A simulated pushing task in MuJoCo suggests that DFR can reduce collisions by 83\%. On a physical line tracking task using a da Vinci Surgical Robot and a moving Stewart platform, DFR reduced collisions by 84\%.
cs.RO
learning from human demonstrations can facilitate automation but is risky because the execution of the learned policy might lead to collisions and other failures adding explicit constraints to avoid unsafe states is generally not possible when the state representations are complex furthermore enforcing these constraints during execution of the learned policy can be challenging in environments where dynamics are difficult to model such as push mechanics in grasping in this paper we propose derivativefree recovery dfr a twophase method for generating robust policies from demonstrations in robotic manipulation tasks where the system comes to rest at each time step in the first phase we use support estimation of supervisor demonstrations and treat the support as implicit constraints on states we also propose a timevarying modification for sequential tasks in the second phase we use this support estimate to derive a switching policy that employs the learned policy in the interior of the support and switches to a recovery policy to steer the robot away from the boundary of the support if it drifts too close we present additional conditions which linearly bound the difference in state at each time step by the magnitude of control allowing us to prove that the robot will not violate the constraints using the recovery policy a simulated pushing task in mujoco suggests that dfr can reduce collisions by 83 on a physical line tracking task using a da vinci surgical robot and a moving stewart platform dfr reduced collisions by 84
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1,801.10322
Kinetic Energy Matrix Elements for a two-electron Atom with Extended Hylleraas-CI Wave Function
Some typical kinetic energy integrals which arise in the application of extended Hylleraas-configuration interaction (E-Hy-CI) function in the framework of Rayleigh-Ritz method of variation, have been evaluated analytically for two-electron atomic systems. Closed-form expressions for the corresponding integrals which occur in the application of Hylleraas-CI functions, have also been derived as special cases.
physics.atom-ph
some typical kinetic energy integrals which arise in the application of extended hylleraasconfiguration interaction ehyci function in the framework of rayleighritz method of variation have been evaluated analytically for twoelectron atomic systems closedform expressions for the corresponding integrals which occur in the application of hylleraasci functions have also been derived as special cases
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1,801.10323
Privacy-Preserving Secret Shared Computations using MapReduce
Data outsourcing allows data owners to keep their data at \emph{untrusted} clouds that do not ensure the privacy of data and/or computations. One useful framework for fault-tolerant data processing in a distributed fashion is MapReduce, which was developed for \emph{trusted} private clouds. This paper presents algorithms for data outsourcing based on Shamir's secret-sharing scheme and for executing privacy-preserving SQL queries such as count, selection including range selection, projection, and join while using MapReduce as an underlying programming model. Our proposed algorithms prevent an adversary from knowing the database or the query while also preventing output-size and access-pattern attacks. Interestingly, our algorithms do not involve the database owner, which only creates and distributes secret-shares once, in answering any query, and hence, the database owner also cannot learn the query. Logically and experimentally, we evaluate the efficiency of the algorithms on the following parameters: (\textit{i}) the number of communication rounds (between a user and a server), (\textit{ii}) the total amount of bit flow (between a user and a server), and (\textit{iii}) the computational load at the user and the server.\B
cs.DB cs.CR cs.DC cs.IT math.IT
data outsourcing allows data owners to keep their data at emphuntrusted clouds that do not ensure the privacy of data andor computations one useful framework for faulttolerant data processing in a distributed fashion is mapreduce which was developed for emphtrusted private clouds this paper presents algorithms for data outsourcing based on shamirs secretsharing scheme and for executing privacypreserving sql queries such as count selection including range selection projection and join while using mapreduce as an underlying programming model our proposed algorithms prevent an adversary from knowing the database or the query while also preventing outputsize and accesspattern attacks interestingly our algorithms do not involve the database owner which only creates and distributes secretshares once in answering any query and hence the database owner also cannot learn the query logically and experimentally we evaluate the efficiency of the algorithms on the following parameters textiti the number of communication rounds between a user and a server textitii the total amount of bit flow between a user and a server and textitiii the computational load at the user and the serverb
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1,801.10324
From BoW to CNN: Two Decades of Texture Representation for Texture Classification
Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention. Since 2000, texture representations based on Bag of Words (BoW) and on Convolutional Neural Networks (CNNs) have been extensively studied with impressive performance. Given this period of remarkable evolution, this paper aims to present a comprehensive survey of advances in texture representation over the last two decades. More than 200 major publications are cited in this survey covering different aspects of the research, which includes (i) problem description; (ii) recent advances in the broad categories of BoW-based, CNN-based and attribute-based methods; and (iii) evaluation issues, specifically benchmark datasets and state of the art results. In retrospect of what has been achieved so far, the survey discusses open challenges and directions for future research.
cs.CV cs.LG
texture is a fundamental characteristic of many types of images and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention since 2000 texture representations based on bag of words bow and on convolutional neural networks cnns have been extensively studied with impressive performance given this period of remarkable evolution this paper aims to present a comprehensive survey of advances in texture representation over the last two decades more than 200 major publications are cited in this survey covering different aspects of the research which includes i problem description ii recent advances in the broad categories of bowbased cnnbased and attributebased methods and iii evaluation issues specifically benchmark datasets and state of the art results in retrospect of what has been achieved so far the survey discusses open challenges and directions for future research
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1,801.10325
Meromorphic function fields closed by partial derivatives
We characterize meromorphic function fields closed by partial derivatives in n variables.
math.CV
we characterize meromorphic function fields closed by partial derivatives in n variables
[['we', 'characterize', 'meromorphic', 'function', 'fields', 'closed', 'by', 'partial', 'derivatives', 'in', 'n', 'variables']]
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1,801.10326
Incidence structures near configurations of type $(n_3)$
An $(n_3)$ configuration is an incidence structure equivalent to a linear hypergraph on $n$ vertices which is both 3-regular and 3-uniform. We investigate a variant in which one constraint, say 3-regularity, is present, and we allow exactly one line to have size four, exactly one line to have size two, and all other lines to have size three. In particular, we study planar (Euclidean or projective) representations, settling the existence question and adapting Steinitz' theorem for this setting.
math.CO
an n_3 configuration is an incidence structure equivalent to a linear hypergraph on n vertices which is both 3regular and 3uniform we investigate a variant in which one constraint say 3regularity is present and we allow exactly one line to have size four exactly one line to have size two and all other lines to have size three in particular we study planar euclidean or projective representations settling the existence question and adapting steinitz theorem for this setting
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1,801.10327
Merger Rate Distribution of Primordial-Black-Hole Binaries
Up to now several gravitational-wave events from the coalescences of black hole binaries have been reported by LIGO/VIRGO, and imply that black holes should have an extended mass function. We work out the merger rate distribution of primordial-black-hole binaries with a general mass function by taking into account the torques by all primordial black holes and linear density perturbations. In the future, many more coalescences of black hole binaries are expected to be detected, and the one-dimensional and two-dimensional merger rate distributions will be crucial for reconstructing the mass function of primordial black holes.
astro-ph.CO gr-qc hep-ph hep-th
up to now several gravitationalwave events from the coalescences of black hole binaries have been reported by ligovirgo and imply that black holes should have an extended mass function we work out the merger rate distribution of primordialblackhole binaries with a general mass function by taking into account the torques by all primordial black holes and linear density perturbations in the future many more coalescences of black hole binaries are expected to be detected and the onedimensional and twodimensional merger rate distributions will be crucial for reconstructing the mass function of primordial black holes
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1,801.10328
Dual Frobenius manifolds of minimal gravity on disk
Liouville field theory approach to 2-dimensional gravity possesses the duality ($b \leftrightarrow b^{-1}$). The matrix counterpart of minimal gravity $\mathcal{M}(q,p)$ ($q<p$ co-prime) is effectively described on $A_{q-1}$ Frobenius manifold, which may exhibit a similar duality $p\leftrightarrow q$, and allow a description on $A_{p-1}$ Frobenius manifold. We have positive results from the bulk one-point and the bulk-boundary two-point correlations on disk that the dual description of the Frobenius manifold works for the unitary series $\mathcal{M}(q, q+1)$. However, for the Lee-Yang series $\mathcal{M}(2, 2q+1)$ on disk the duality is checked only partially. The main difficulty lies in the absence of a canonical description of trace in the continuum limit.
hep-th
liouville field theory approach to 2dimensional gravity possesses the duality b leftrightarrow b1 the matrix counterpart of minimal gravity mathcalmqp qp coprime is effectively described on a_q1 frobenius manifold which may exhibit a similar duality pleftrightarrow q and allow a description on a_p1 frobenius manifold we have positive results from the bulk onepoint and the bulkboundary twopoint correlations on disk that the dual description of the frobenius manifold works for the unitary series mathcalmq q1 however for the leeyang series mathcalm2 2q1 on disk the duality is checked only partially the main difficulty lies in the absence of a canonical description of trace in the continuum limit
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1,801.10329
Effective Hamiltonian approach to optical activity in Weyl spin-orbit system
Chirality or handedness in condensed matter induces anomalous optical responses such as natural optical activity, rotation of the plane of light polarization, as a result of breaking of spatial-inversion symmetry. In this study, optical properties of a Weyl spin-orbit system with quadratic dispersion, a typical chiral system invariant under time-reversal, are investigated theoretically by deriving an effective Hamiltonian based on an imaginary-time path-integral formalism. We show that the effective Hamiltonian can be indeed written in terms of an optical chirality order parameter suggested by Lipkin. The natural optical activity is discussed based on the Hamiltonian.
cond-mat.mes-hall
chirality or handedness in condensed matter induces anomalous optical responses such as natural optical activity rotation of the plane of light polarization as a result of breaking of spatialinversion symmetry in this study optical properties of a weyl spinorbit system with quadratic dispersion a typical chiral system invariant under timereversal are investigated theoretically by deriving an effective hamiltonian based on an imaginarytime pathintegral formalism we show that the effective hamiltonian can be indeed written in terms of an optical chirality order parameter suggested by lipkin the natural optical activity is discussed based on the hamiltonian
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1,801.1033
On correctors for linear elliptic homogenization in the presence of local defects: the case of advection-diffusion
We follow-up on our works devoted to homogenization theory for linear second-order elliptic equations with coefficients that are perturbations of periodic coefficients. We have first considered equations in divergence form in [6, 7, 8]. We have next shown, in our recent work [9], using a slightly different strategy of proof than in our earlier works, that we may also address the equation --aij$\partial$iju = f. The present work is devoted to advection-diffusion equations: --aij$\partial$iju + bj$\partial$ju = f. We prove, under suitable assumptions on the coefficients aij, bj, 1 $\le$ i, j $\le$ d (typically that they are the sum of a periodic function and some perturbation in L p , for suitable p < +$\infty$), that the equation admits a (unique) invariant measure and that this measure may be used to transform the problem into a problem in divergence form, amenable to the techniques we have previously developed for the latter case.
math.AP
we followup on our works devoted to homogenization theory for linear secondorder elliptic equations with coefficients that are perturbations of periodic coefficients we have first considered equations in divergence form in 6 7 8 we have next shown in our recent work 9 using a slightly different strategy of proof than in our earlier works that we may also address the equation aijpartialiju f the present work is devoted to advectiondiffusion equations aijpartialiju bjpartialju f we prove under suitable assumptions on the coefficients aij bj 1 le i j le d typically that they are the sum of a periodic function and some perturbation in l p for suitable p infty that the equation admits a unique invariant measure and that this measure may be used to transform the problem into a problem in divergence form amenable to the techniques we have previously developed for the latter case
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1,801.10331
Disks and outflows in the S255IR area of high mass star formation from ALMA observations
We describe the general structure of the well known S255IR high mass star forming region, as revealed by our recent ALMA observations. The data indicate a physical relation of the major clumps SMA1 and SMA2. The driving source of the extended high velocity well collimated bipolar outflow is not the most pronounced disk-like SMA1 clump harboring a 20 M$_\odot$ young star (S255 NIRS3), as it was assumed earlier. Apparently it is the less evolved SMA2 clump, which drives the outflow and contains a compact rotating structure (probably a disk). At the same time the SMA1 clump drives another outflow, with a larger opening angle. The molecular line data do not show an outflow from the SMA3 clump (NIRS1), which was suggested by IR studies of this region.
astro-ph.GA
we describe the general structure of the well known s255ir high mass star forming region as revealed by our recent alma observations the data indicate a physical relation of the major clumps sma1 and sma2 the driving source of the extended high velocity well collimated bipolar outflow is not the most pronounced disklike sma1 clump harboring a 20 m_odot young star s255 nirs3 as it was assumed earlier apparently it is the less evolved sma2 clump which drives the outflow and contains a compact rotating structure probably a disk at the same time the sma1 clump drives another outflow with a larger opening angle the molecular line data do not show an outflow from the sma3 clump nirs1 which was suggested by ir studies of this region
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1,801.10332
How to manipulate magnetic states of antiferromagnets
Antiferromagnetic materials, which have drawn considerable attention recently, have fascinating features: they are robust against perturbation, produce no stray fields, and exhibit ultrafast dynamics. Discerning how to efficiently manipulate the magnetic state of an antiferromagnet is key to the development of antiferromagnetic spintronics. In this review, we introduce four main methods (magnetic, strain, electrical, and optical) to mediate the magnetic states and elaborate on intrinsic origins of different antiferromagnetic materials. Magnetic control includes a strong magnetic field, exchange bias, and field cooling, which are traditional and basic. Strain control involves the magnetic anisotropy effect or metamagnetic transition. Electrical control can be divided into two parts, electric field and electric current, both of which are convenient for practical applications. Optical control includes thermal and electronic excitation, an inertia-driven mechanism, and terahertz laser control, with the potential for ultrafast antiferromagnetic manipulation. This review sheds light on effective usage of antiferromagnets and provides a new perspective on antiferromagnetic spintronics.
cond-mat.mtrl-sci
antiferromagnetic materials which have drawn considerable attention recently have fascinating features they are robust against perturbation produce no stray fields and exhibit ultrafast dynamics discerning how to efficiently manipulate the magnetic state of an antiferromagnet is key to the development of antiferromagnetic spintronics in this review we introduce four main methods magnetic strain electrical and optical to mediate the magnetic states and elaborate on intrinsic origins of different antiferromagnetic materials magnetic control includes a strong magnetic field exchange bias and field cooling which are traditional and basic strain control involves the magnetic anisotropy effect or metamagnetic transition electrical control can be divided into two parts electric field and electric current both of which are convenient for practical applications optical control includes thermal and electronic excitation an inertiadriven mechanism and terahertz laser control with the potential for ultrafast antiferromagnetic manipulation this review sheds light on effective usage of antiferromagnets and provides a new perspective on antiferromagnetic spintronics
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1,801.10333
Root Cause Analysis and Correction of Single Metal Contact Open-Induced Scan Chain Failure in 90nm node VLSI
In this paper, the localization of open metal contact for 90nm node SOC is reported based on Electron Beam Absorbed Current (EBAC) technique and scan diagnosis for the first time. According to the detected excess carbon, silicon and oxygen signals obtained from X-ray energy dispersive spectroscopy (EDX), the failure was deemed to be caused by the incomplete removal of silicate photoresist polymer formed during the O2 plasma dry clean before copper plating. Based on this, we proposed to replace the dry clean with diluted HF clean prior to the copper plating, which can significantly remove the silicate polymers and increase the yield.
physics.app-ph
in this paper the localization of open metal contact for 90nm node soc is reported based on electron beam absorbed current ebac technique and scan diagnosis for the first time according to the detected excess carbon silicon and oxygen signals obtained from xray energy dispersive spectroscopy edx the failure was deemed to be caused by the incomplete removal of silicate photoresist polymer formed during the o2 plasma dry clean before copper plating based on this we proposed to replace the dry clean with diluted hf clean prior to the copper plating which can significantly remove the silicate polymers and increase the yield
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1,801.10334
Quantitative recurrence properties and homogeneous self-similar sets
Let $K$ be a homogeneous self-similar set satisfying the strong separation condition. This paper is concerned with the quantitative recurrence properties of the natural map $T: K\rightarrow K$ induced by the shift. Let $\mu$ be the natural self-similar measure supported on $K$. For a positive function $\varphi$ defined on $\mathbb{N}$, we show that the $\mu$-measure of the following set \begin{equation*} R(\varphi):=\{x\in K: |T^n x-x|<\varphi(n) \; \text{for infinitely many} \; n\in\mathbb{N}\} \end{equation*} is null or full according to convergence or divergence of a certain series. Moreover, a similar dichotomy law holds for the general Hausdorff measure, which completes the metric theory of this set.
math.DS math.NT
let k be a homogeneous selfsimilar set satisfying the strong separation condition this paper is concerned with the quantitative recurrence properties of the natural map t krightarrow k induced by the shift let mu be the natural selfsimilar measure supported on k for a positive function varphi defined on mathbbn we show that the mumeasure of the following set beginequation rvarphixin k tn xxvarphin textfor infinitely many ninmathbbn endequation is null or full according to convergence or divergence of a certain series moreover a similar dichotomy law holds for the general hausdorff measure which completes the metric theory of this set
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1,801.10335
On correctors for linear elliptic homogenization in the presence of local defects
We consider the corrector equation associated, in homogenization theory , to a linear second-order elliptic equation in divergence form --$\partial$i(aij$\partial$ju) = f , when the diffusion coefficient is a locally perturbed periodic coefficient. The question under study is the existence (and uniqueness) of the corrector, strictly sublinear at infinity, with gradient in L r if the local perturbation is itself L r , r < +$\infty$. The present work follows up on our works [7, 8, 9], providing an alternative, more general and versatile approach , based on an a priori estimate, for this well-posedness result. Equations in non-divergence form such as --aij$\partial$iju = f are also considered, along with various extensions. The case of general advection-diffusion equations --aij$\partial$iju + bj$\partial$ju = f is postponed until our future work [10]. An appendix contains a corrigendum to our earlier publication [9].
math.AP
we consider the corrector equation associated in homogenization theory to a linear secondorder elliptic equation in divergence form partialiaijpartialju f when the diffusion coefficient is a locally perturbed periodic coefficient the question under study is the existence and uniqueness of the corrector strictly sublinear at infinity with gradient in l r if the local perturbation is itself l r r infty the present work follows up on our works 7 8 9 providing an alternative more general and versatile approach based on an a priori estimate for this wellposedness result equations in nondivergence form such as aijpartialiju f are also considered along with various extensions the case of general advectiondiffusion equations aijpartialiju bjpartialju f is postponed until our future work 10 an appendix contains a corrigendum to our earlier publication 9
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1,801.10336
Nonautonomous gradient-like ODEs on the circle: classification, structural stability and autonomization
We study a class of scalar differential equations on the circle $S^1$. This class is characterized mainly by the property that any solution of such an equation possesses exponential dichotomy both on the semi-axes $\R_+$ and $\R_+$. Also we impose some other assumptions on the structure of the foliation into integral curves for such the equation. Differential equations of this class are called gradient-like ones. As a result, we describe the global behavior of the foliation, introduce a complete invariant of uniform equivalency, give standard models for the equations of the distinguished class. The case of almost periodic gradient-like equations is also studied, their classification is presented.
math.DS
we study a class of scalar differential equations on the circle s1 this class is characterized mainly by the property that any solution of such an equation possesses exponential dichotomy both on the semiaxes r_ and r_ also we impose some other assumptions on the structure of the foliation into integral curves for such the equation differential equations of this class are called gradientlike ones as a result we describe the global behavior of the foliation introduce a complete invariant of uniform equivalency give standard models for the equations of the distinguished class the case of almost periodic gradientlike equations is also studied their classification is presented
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1,801.10337
Proposal for detecting nodal-line semimetal surface-states with resonant spin-flipped reflection
Topological nodal-line semimetals are predicted to exhibit unique drumhead-like surface states (DSS). Yet, a direct detection of such states remains a challenge. Here, we propose spin-resolved transport in a junction between a normal metal and a spin-orbit coupled nodal-line semimetal as the mechanism for their detection. Specifically, we find that in such a device, the DSS induce resonant spin-flipped reflection. This effect can be probed by both vertical spin transport and lateral charge transport between anti-parallel magnetic terminals. In the tunneling limit of the junction, both spin and charge conductances exhibit a resonant peak around zero energy, providing a unique evidence of the DSS. This signature is robust to both dispersive DSS and interface disorder. Based on numerical calculations, we show that the scheme can be implemented in the topological semimetal HgCr$_2$Se$_4$.
cond-mat.mes-hall cond-mat.str-el
topological nodalline semimetals are predicted to exhibit unique drumheadlike surface states dss yet a direct detection of such states remains a challenge here we propose spinresolved transport in a junction between a normal metal and a spinorbit coupled nodalline semimetal as the mechanism for their detection specifically we find that in such a device the dss induce resonant spinflipped reflection this effect can be probed by both vertical spin transport and lateral charge transport between antiparallel magnetic terminals in the tunneling limit of the junction both spin and charge conductances exhibit a resonant peak around zero energy providing a unique evidence of the dss this signature is robust to both dispersive dss and interface disorder based on numerical calculations we show that the scheme can be implemented in the topological semimetal hgcr_2se_4
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1,801.10338
Laser acceleration of highly energetic carbon ions using a double-layer target composed of slightly underdense plasma and ultrathin foil
We report the experimental generation of highly energetic carbon ions up to 48 MeV per nucleon by shooting double-layer targets composed of well-controlled slightly underdense plasma (SUP) and ultrathin foils with ultra-intense femtosecond laser pulses. Particle-in-cell simulations reveal that carbon ions residing in the ultrathin foils undergo radiation pressure acceleration and long-time sheath field acceleration in sequence due to the existence of the SUP in front of the foils. Such an acceleration scheme is especially suited for heavy ion acceleration with femtosecond laser pulses. The breakthrough of heavy ion energy up to multi-tens of MeV/u at high-repetition-rate would be able to trigger significant advances in nuclear physics, high energy density physics, and medical physics.
physics.plasm-ph
we report the experimental generation of highly energetic carbon ions up to 48 mev per nucleon by shooting doublelayer targets composed of wellcontrolled slightly underdense plasma sup and ultrathin foils with ultraintense femtosecond laser pulses particleincell simulations reveal that carbon ions residing in the ultrathin foils undergo radiation pressure acceleration and longtime sheath field acceleration in sequence due to the existence of the sup in front of the foils such an acceleration scheme is especially suited for heavy ion acceleration with femtosecond laser pulses the breakthrough of heavy ion energy up to multitens of mevu at highrepetitionrate would be able to trigger significant advances in nuclear physics high energy density physics and medical physics
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1,801.10339
A Continuous - Time Quantum Walk for Attributed Graphs Matching
Diverse facets Of the Theory of Quantum Walks on Graph are reviewed Till now .In specific, Quantum network routing, Quantum Walk Search Algorithm, Element distinctness associated to the eigenvalues of Graphs and the use of these relation /connection in the study of Quantum walks is furthermore described. Different Researchers had contribution and put their benchmark idea Pertaining with this research concept. I furthermore try to investigate recent Application of Quantum walks, In specific the problem pertained with Graph matching i.e Matching nodes(vertices) of the Graphs. In this research paper,I consider how Continuous-time quantum walk (CTQW) can be directed to Graph-matching problems. The matching problem is abstracted using weighted(attributed) Graphs that connects vertices's of one Graph to other and Try to compute the distance b/n those Graphs Node's Beside that finding the matched nodes and the Cost related to Matching. eventually measuring the distance between two Graphs which might have different size then by using k-nearest neighbor (k-NN) method try to classifying those graph based on closest training examples in the feature space.
cs.DS
diverse facets of the theory of quantum walks on graph are reviewed till now in specific quantum network routing quantum walk search algorithm element distinctness associated to the eigenvalues of graphs and the use of these relation connection in the study of quantum walks is furthermore described different researchers had contribution and put their benchmark idea pertaining with this research concept i furthermore try to investigate recent application of quantum walks in specific the problem pertained with graph matching ie matching nodesvertices of the graphs in this research paperi consider how continuoustime quantum walk ctqw can be directed to graphmatching problems the matching problem is abstracted using weightedattributed graphs that connects verticess of one graph to other and try to compute the distance bn those graphs nodes beside that finding the matched nodes and the cost related to matching eventually measuring the distance between two graphs which might have different size then by using knearest neighbor knn method try to classifying those graph based on closest training examples in the feature space
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1,801.1034
Cyber-Physical Microservices: An IoT-based Framework for Manufacturing Systems
Recent advances in ICT enable the evolution of the manufacturing industry to meet the new requirements of the society. Cyber-physical systems, Internet-of-Things (IoT), and Cloud computing, play a key role in the fourth industrial revolution known as Industry 4.0. The microservice architecture has evolved as an alternative to SOA and promises to address many of the challenges in software development. In this paper, we adopt the concept of microservice and describe a framework for manufacturing systems that has the cyber-physical microservice as the key construct. The manufacturing plant processes are defined as compositions of primitive cyber-physical microservices adopting either the orchestration or the choreography pattern. IoT technologies are used for system integration and model-driven engineering is utilized to semi-automate the development process for the industrial engineer, who is not familiar with microservices and IoT. Two case studies demonstrate the feasibility of the proposed approach.
cs.SE
recent advances in ict enable the evolution of the manufacturing industry to meet the new requirements of the society cyberphysical systems internetofthings iot and cloud computing play a key role in the fourth industrial revolution known as industry 40 the microservice architecture has evolved as an alternative to soa and promises to address many of the challenges in software development in this paper we adopt the concept of microservice and describe a framework for manufacturing systems that has the cyberphysical microservice as the key construct the manufacturing plant processes are defined as compositions of primitive cyberphysical microservices adopting either the orchestration or the choreography pattern iot technologies are used for system integration and modeldriven engineering is utilized to semiautomate the development process for the industrial engineer who is not familiar with microservices and iot two case studies demonstrate the feasibility of the proposed approach
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1,801.10341
An Infinitesimal Probabilistic Model for Principal Component Analysis of Manifold Valued Data
We provide a probabilistic and infinitesimal view of how the principal component analysis procedure (PCA) can be generalized to analysis of nonlinear manifold valued data. Starting with the probabilistic PCA interpretation of the Euclidean PCA procedure, we show how PCA can be generalized to manifolds in an intrinsic way that does not resort to linearization of the data space. The underlying probability model is constructed by mapping a Euclidean stochastic process to the manifold using stochastic development of Euclidean semimartingales. The construction uses a connection and bundles of covariant tensors to allow global transport of principal eigenvectors, and the model is thereby an example of how principal fiber bundles can be used to handle the lack of global coordinate system and orientations that characterizes manifold valued statistics. We show how curvature implies non-integrability of the equivalent of Euclidean principal subspaces, and how the stochastic flows provide an alternative to explicit construction of such subspaces. We describe estimation procedures for inference of parameters and prediction of principal components, and we give examples of properties of the model on embedded surfaces.
math.ST cs.CV stat.TH
we provide a probabilistic and infinitesimal view of how the principal component analysis procedure pca can be generalized to analysis of nonlinear manifold valued data starting with the probabilistic pca interpretation of the euclidean pca procedure we show how pca can be generalized to manifolds in an intrinsic way that does not resort to linearization of the data space the underlying probability model is constructed by mapping a euclidean stochastic process to the manifold using stochastic development of euclidean semimartingales the construction uses a connection and bundles of covariant tensors to allow global transport of principal eigenvectors and the model is thereby an example of how principal fiber bundles can be used to handle the lack of global coordinate system and orientations that characterizes manifold valued statistics we show how curvature implies nonintegrability of the equivalent of euclidean principal subspaces and how the stochastic flows provide an alternative to explicit construction of such subspaces we describe estimation procedures for inference of parameters and prediction of principal components and we give examples of properties of the model on embedded surfaces
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1,801.10342
ConvCSNet: A Convolutional Compressive Sensing Framework Based on Deep Learning
Compressive sensing (CS), aiming to reconstruct an image/signal from a small set of random measurements has attracted considerable attentions in recent years. Due to the high dimensionality of images, previous CS methods mainly work on image blocks to avoid the huge requirements of memory and computation, i.e., image blocks are measured with Gaussian random matrices, and the whole images are recovered from the reconstructed image blocks. Though efficient, such methods suffer from serious blocking artifacts. In this paper, we propose a convolutional CS framework that senses the whole image using a set of convolutional filters. Instead of reconstructing individual blocks, the whole image is reconstructed from the linear convolutional measurements. Specifically, the convolutional CS is implemented based on a convolutional neural network (CNN), which performs both the convolutional CS and nonlinear reconstruction. Through end-to-end training, the sensing filters and the reconstruction network can be jointly optimized. To facilitate the design of the CS reconstruction network, a novel two-branch CNN inspired from a sparsity-based CS reconstruction model is developed. Experimental results show that the proposed method substantially outperforms previous state-of-the-art CS methods in term of both PSNR and visual quality.
cs.CV
compressive sensing cs aiming to reconstruct an imagesignal from a small set of random measurements has attracted considerable attentions in recent years due to the high dimensionality of images previous cs methods mainly work on image blocks to avoid the huge requirements of memory and computation ie image blocks are measured with gaussian random matrices and the whole images are recovered from the reconstructed image blocks though efficient such methods suffer from serious blocking artifacts in this paper we propose a convolutional cs framework that senses the whole image using a set of convolutional filters instead of reconstructing individual blocks the whole image is reconstructed from the linear convolutional measurements specifically the convolutional cs is implemented based on a convolutional neural network cnn which performs both the convolutional cs and nonlinear reconstruction through endtoend training the sensing filters and the reconstruction network can be jointly optimized to facilitate the design of the cs reconstruction network a novel twobranch cnn inspired from a sparsitybased cs reconstruction model is developed experimental results show that the proposed method substantially outperforms previous stateoftheart cs methods in term of both psnr and visual quality
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1,801.10343
Josephson effect in junctions of conventional and topological superconductors
We present a theoretical analysis of the equilibrium Josephson current-phase relation in hybrid devices made of conventional s-wave spin-singlet superconductors (S) and topological superconductor (TS) wires featuring Majorana end states. Using Green's function techniques, the topological superconductor is alternatively described by the low-energy continuum limit of a Kitaev chain or by a more microscopic spinful nanowire model. We show that for the simplest S-TS tunnel junction, only the s-wave pairing correlations in a spinful TS nanowire model can generate a Josephson effect. The critical current is much smaller in the topological regime and exhibits a kink-like dependence on the Zeeman field along the wire. When a correlated quantum dot (QD) in the magnetic regime is present in the junction region, however, the Josephson current becomes finite also in the deep topological phase as shown for the cotunneling regime and by a mean-field analysis. Remarkably, we find that the S-QD-TS setup can support $\varphi_0$-junction behavior, where a finite supercurrent flows at vanishing phase difference. Finally, we also address a multi-terminal S-TS-S geometry, where the TS wire acts as tunable parity switch on the Andreev bound states in a superconducting atomic contact.
cond-mat.mes-hall
we present a theoretical analysis of the equilibrium josephson currentphase relation in hybrid devices made of conventional swave spinsinglet superconductors s and topological superconductor ts wires featuring majorana end states using greens function techniques the topological superconductor is alternatively described by the lowenergy continuum limit of a kitaev chain or by a more microscopic spinful nanowire model we show that for the simplest sts tunnel junction only the swave pairing correlations in a spinful ts nanowire model can generate a josephson effect the critical current is much smaller in the topological regime and exhibits a kinklike dependence on the zeeman field along the wire when a correlated quantum dot qd in the magnetic regime is present in the junction region however the josephson current becomes finite also in the deep topological phase as shown for the cotunneling regime and by a meanfield analysis remarkably we find that the sqdts setup can support varphi_0junction behavior where a finite supercurrent flows at vanishing phase difference finally we also address a multiterminal stss geometry where the ts wire acts as tunable parity switch on the andreev bound states in a superconducting atomic contact
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1,801.10344
Enhanced axion-photon coupling in GUT with hidden photon
We show that the axion coupling to photons can be enhanced in simple models with a single Peccei-Quinn field, if the gauge coupling unification is realized by a large kinetic mixing $\chi = {\cal O}(0.1)$ between hypercharge and unbroken hidden U(1)$_H$. The key observation is that the U(1)$_H$ gauge coupling should be rather strong to induce such large kinetic mixing, leading to enhanced contributions of hidden matter fields to the electromagnetic anomaly. We find that the axion-photon coupling is enhanced by about a factor of 10-100 with respect to the GUT-axion models with $E/N = 8/3$.
hep-ph astro-ph.CO hep-ex
we show that the axion coupling to photons can be enhanced in simple models with a single pecceiquinn field if the gauge coupling unification is realized by a large kinetic mixing chi cal o01 between hypercharge and unbroken hidden u1_h the key observation is that the u1_h gauge coupling should be rather strong to induce such large kinetic mixing leading to enhanced contributions of hidden matter fields to the electromagnetic anomaly we find that the axionphoton coupling is enhanced by about a factor of 10100 with respect to the gutaxion models with en 83
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1,801.10345
Band tail interface states and quantum capacitance in a monolayer molybdenum disulfide field-effect-transistor
Although MoS2 field-effect transistors (FETs) with high-k dielectrics are promising for electron device applications, the underlying physical origin of interface degradation remains largely unexplored. Here, we present a systematic analysis of the energy distribution of the interface state density (Dit) and the quantum capacitance (CQ) in a dual-gate monolayer exfoliated MoS2 FET. The CQ analysis enabled us to construct a Dit extraction method as a function of EF. A band tail distribution of Dit with the lowest value of 8*1011 cm-2eV-1 suggests that Dit is not directly related to the sharp peak energy distribution of the S vacancy. Therefore, the Mo-S bond bending related to the strain at the interface or the surface roughness of the SiO2/Si substrate might be the origin. It is also shown that ultra-thin 2D materials are more sensitive to interface disorder due to the reduced density of states. Since all the constituents for the measured capacitance are well understood, I-V characteristics can be reproduced by utilizing the drift current model. As a result, one of the physical origins of the metal/insulator transition is suggested to be the external outcome of interface traps and quantum capacitance.
cond-mat.mtrl-sci
although mos2 fieldeffect transistors fets with highk dielectrics are promising for electron device applications the underlying physical origin of interface degradation remains largely unexplored here we present a systematic analysis of the energy distribution of the interface state density dit and the quantum capacitance cq in a dualgate monolayer exfoliated mos2 fet the cq analysis enabled us to construct a dit extraction method as a function of ef a band tail distribution of dit with the lowest value of 81011 cm2ev1 suggests that dit is not directly related to the sharp peak energy distribution of the s vacancy therefore the mos bond bending related to the strain at the interface or the surface roughness of the sio2si substrate might be the origin it is also shown that ultrathin 2d materials are more sensitive to interface disorder due to the reduced density of states since all the constituents for the measured capacitance are well understood iv characteristics can be reproduced by utilizing the drift current model as a result one of the physical origins of the metalinsulator transition is suggested to be the external outcome of interface traps and quantum capacitance
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1,801.10346
The k-PDTM : a coreset for robust geometric inference
Analyzing the sub-level sets of the distance to a compact sub-manifold of R d is a common method in TDA to understand its topology. The distance to measure (DTM) was introduced by Chazal, Cohen-Steiner and M{\'e}rigot in [7] to face the non-robustness of the distance to a compact set to noise and outliers. This function makes possible the inference of the topology of a compact subset of R d from a noisy cloud of n points lying nearby in the Wasserstein sense. In practice, these sub-level sets may be computed using approximations of the DTM such as the q-witnessed distance [10] or other power distance [6]. These approaches lead eventually to compute the homology of unions of n growing balls, that might become intractable whenever n is large. To simultaneously face the two problems of large number of points and noise, we introduce the k-power distance to measure (k-PDTM). This new approximation of the distance to measure may be thought of as a k-coreset based approximation of the DTM. Its sublevel sets consist in union of k-balls, k << n, and this distance is also proved robust to noise. We assess the quality of this approximation for k possibly dramatically smaller than n, for instance k = n 1 3 is proved to be optimal for 2-dimensional shapes. We also provide an algorithm to compute this k-PDTM.
math.ST cs.CG stat.TH
analyzing the sublevel sets of the distance to a compact submanifold of r d is a common method in tda to understand its topology the distance to measure dtm was introduced by chazal cohensteiner and merigot in 7 to face the nonrobustness of the distance to a compact set to noise and outliers this function makes possible the inference of the topology of a compact subset of r d from a noisy cloud of n points lying nearby in the wasserstein sense in practice these sublevel sets may be computed using approximations of the dtm such as the qwitnessed distance 10 or other power distance 6 these approaches lead eventually to compute the homology of unions of n growing balls that might become intractable whenever n is large to simultaneously face the two problems of large number of points and noise we introduce the kpower distance to measure kpdtm this new approximation of the distance to measure may be thought of as a kcoreset based approximation of the dtm its sublevel sets consist in union of kballs k n and this distance is also proved robust to noise we assess the quality of this approximation for k possibly dramatically smaller than n for instance k n 1 3 is proved to be optimal for 2dimensional shapes we also provide an algorithm to compute this kpdtm
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1,801.10347
Star Mean Curvature Flow on 3 manifolds and its B\"acklund Transformations
The Hodge star mean curvature flow on a 3-dimensional Riemannian or pseudo-Riemannian manifold is a natural nonlinear dispersive curve flow in geometric analysis. A curve flow is integrable if the local differential invariants of a solution to the curve flow evolve according to a soliton equation. In this paper, we show that this flow on $\mathbb{S}^3$ and $\mathbb{H}^3$ are integrable, and describe algebraically explicit solutions to such curve flows. The Cauchy problem of the curve flows on $\mathbb{S}^3$ and $\mathbb{H}^3$ and its B\"acklund transformations follow from this construction.
math.DG math.AP nlin.SI
the hodge star mean curvature flow on a 3dimensional riemannian or pseudoriemannian manifold is a natural nonlinear dispersive curve flow in geometric analysis a curve flow is integrable if the local differential invariants of a solution to the curve flow evolve according to a soliton equation in this paper we show that this flow on mathbbs3 and mathbbh3 are integrable and describe algebraically explicit solutions to such curve flows the cauchy problem of the curve flows on mathbbs3 and mathbbh3 and its backlund transformations follow from this construction
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1,801.10348
Numerical analytic continuation of Euclidean data
In this work we present a direct comparison of three different numerical analytic continuation methods: the Maximum Entropy Method, the Backus-Gilbert method and the Schlessinger point or Resonances Via Pad\'{e} method. First, we perform a benchmark test based on a model spectral function and study the regime of applicability of these methods depending on the number of input points and their statistical error. We then apply these methods to more realistic examples, namely to numerical data on Euclidean propagators obtained from a Functional Renormalization Group calculation, to data from a lattice Quantum Chromodynamics simulation and to data obtained from a tight-binding model for graphene in order to extract the electrical conductivity.
hep-ph hep-lat hep-th
in this work we present a direct comparison of three different numerical analytic continuation methods the maximum entropy method the backusgilbert method and the schlessinger point or resonances via pade method first we perform a benchmark test based on a model spectral function and study the regime of applicability of these methods depending on the number of input points and their statistical error we then apply these methods to more realistic examples namely to numerical data on euclidean propagators obtained from a functional renormalization group calculation to data from a lattice quantum chromodynamics simulation and to data obtained from a tightbinding model for graphene in order to extract the electrical conductivity
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1,801.10349
QRMW: Quantum representation of multi wavelength images
In this study, we propose quantum representation of multi wavelength images (QRMW) which gives preparation and retrieving procedures of quantum images. Proposed QRMW model represents multi-channel and 2^n x 2^m images. Also, we present image comparison and some image operations based on QRMW model. Comparing our model with the models in literature, QRMW model has less time complexity. Also QRMW model uses fewer qubits than existing models in the literature.
quant-ph cs.IT math.IT
in this study we propose quantum representation of multi wavelength images qrmw which gives preparation and retrieving procedures of quantum images proposed qrmw model represents multichannel and 2n x 2m images also we present image comparison and some image operations based on qrmw model comparing our model with the models in literature qrmw model has less time complexity also qrmw model uses fewer qubits than existing models in the literature
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1,801.1035
Tribaryon configurations and the inevitable three nucleon repulsions at short distance
We decompose the tribaryon configuration in terms of SU(3) flavor and spin state and analyse their color-spin-flavor wave function following Pauli principle. By comparing the color-color and color-spin interactions of compact tribaryon configuration against the lowest three nucleon threshold within a constituent quark model, we show that the three nucleon forces have to be repulsive at short distance for all possible quantum numbers and all values of the SU(3) symmetry breaking parameter. Our work identifies the origin of the repulsive nuclear three body forces including the hyperons at short distance that are called for from phenomenological considerations starting from nuclear matter to the maximum mass of a neutron star.
hep-ph nucl-th
we decompose the tribaryon configuration in terms of su3 flavor and spin state and analyse their colorspinflavor wave function following pauli principle by comparing the colorcolor and colorspin interactions of compact tribaryon configuration against the lowest three nucleon threshold within a constituent quark model we show that the three nucleon forces have to be repulsive at short distance for all possible quantum numbers and all values of the su3 symmetry breaking parameter our work identifies the origin of the repulsive nuclear three body forces including the hyperons at short distance that are called for from phenomenological considerations starting from nuclear matter to the maximum mass of a neutron star
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1,801.10351
Fast and Accurate Reconstruction of Compressed Color Light Field
Light field photography has been studied thoroughly in recent years. One of its drawbacks is the need for multi-lens in the imaging. To compensate that, compressed light field photography has been proposed to tackle the trade-offs between the spatial and angular resolutions. It obtains by only one lens, a compressed version of the regular multi-lens system. The acquisition system consists of a dedicated hardware followed by a decompression algorithm, which usually suffers from high computational time. In this work, we propose a computationally efficient neural network that recovers a high-quality color light field from a single coded image. Unlike previous works, we compress the color channels as well, removing the need for a CFA in the imaging system. Our approach outperforms existing solutions in terms of recovery quality and computational complexity. We propose also a neural network for depth map extraction based on the decompressed light field, which is trained in an unsupervised manner without the ground truth depth map.
cs.CV
light field photography has been studied thoroughly in recent years one of its drawbacks is the need for multilens in the imaging to compensate that compressed light field photography has been proposed to tackle the tradeoffs between the spatial and angular resolutions it obtains by only one lens a compressed version of the regular multilens system the acquisition system consists of a dedicated hardware followed by a decompression algorithm which usually suffers from high computational time in this work we propose a computationally efficient neural network that recovers a highquality color light field from a single coded image unlike previous works we compress the color channels as well removing the need for a cfa in the imaging system our approach outperforms existing solutions in terms of recovery quality and computational complexity we propose also a neural network for depth map extraction based on the decompressed light field which is trained in an unsupervised manner without the ground truth depth map
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1,801.10352
Entanglement entropy: holography and renormalization group
Entanglement entropy plays a variety of roles in quantum field theory, including the connections between quantum states and gravitation through the holographic principle. This article provides a review of entanglement entropy from a mixed viewpoint of field theory and holography. A set of basic methods for the computation is developed and illustrated with simple examples such as free theories and conformal field theories. The structures of the ultraviolet divergences and the universal parts are determined and compared with the holographic descriptions of entanglement entropy. The utility of quantum inequalities of entanglement are discussed and shown to derive the C-theorem that constrains renormalization group flows of quantum field theories in diverse dimensions.
hep-th
entanglement entropy plays a variety of roles in quantum field theory including the connections between quantum states and gravitation through the holographic principle this article provides a review of entanglement entropy from a mixed viewpoint of field theory and holography a set of basic methods for the computation is developed and illustrated with simple examples such as free theories and conformal field theories the structures of the ultraviolet divergences and the universal parts are determined and compared with the holographic descriptions of entanglement entropy the utility of quantum inequalities of entanglement are discussed and shown to derive the ctheorem that constrains renormalization group flows of quantum field theories in diverse dimensions
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1,801.10353
Uniqueness of axisymmetric viscous flows originating from positive linear combinations of circular vortex filaments
Following the recent papers [9] and [10] by T. Gallay and V. \u{S}ver\'ak, in the line of work initiated by H. Feng and V. \u{S}ver\'ak in their paper [3], we prove the uniqueness of a solution of the axisymmetric Navier-Stokes equations without swirl when the initial data is a positive linear combination of Dirac masses.
math.AP
following the recent papers 9 and 10 by t gallay and v usverak in the line of work initiated by h feng and v usverak in their paper 3 we prove the uniqueness of a solution of the axisymmetric navierstokes equations without swirl when the initial data is a positive linear combination of dirac masses
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1,801.10354
The kinetic Fokker-Planck equation with weak confinement force
We consider the kinetic Fokker-Planck equation with weak confinement force. We proved some (polynomial and sub-exponential) rate of convergence to the equilibrium (depending on the space to which the initial datum belongs). Our results generalized the result in [4, 5, 21, 11, 10, 9, 1, 14] to weak confinement case.
math.AP
we consider the kinetic fokkerplanck equation with weak confinement force we proved some polynomial and subexponential rate of convergence to the equilibrium depending on the space to which the initial datum belongs our results generalized the result in 4 5 21 11 10 9 1 14 to weak confinement case
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1,801.10355
A CNN-based Spatial Feature Fusion Algorithm for Hyperspectral Imagery Classification
The shortage of training samples remains one of the main obstacles in applying the artificial neural networks (ANN) to the hyperspectral images classification. To fuse the spatial and spectral information, pixel patches are often utilized to train a model, which may further aggregate this problem. In the existing works, an ANN model supervised by center-loss (ANNC) was introduced. Training merely with spectral information, the ANNC yields discriminative spectral features suitable for the subsequent classification tasks. In this paper, a CNN-based spatial feature fusion (CSFF) algorithm is proposed, which allows a smart fusion of the spatial information to the spectral features extracted by ANNC. As a critical part of CSFF, a CNN-based discriminant model is introduced to estimate whether two paring pixels belong to the same class. At the testing stage, by applying the discriminant model to the pixel-pairs generated by the test pixel and its neighbors, the local structure is estimated and represented as a customized convolutional kernel. The spectral-spatial feature is obtained by a convolutional operation between the estimated kernel and the corresponding spectral features within a neighborhood. At last, the label of the test pixel is predicted by classifying the resulting spectral-spatial feature. Without increasing the number of training samples or involving pixel patches at the training stage, the CSFF framework achieves the state-of-the-art by declining $20\%-50\%$ classification failures in experiments on three well-known hyperspectral images.
cs.CV
the shortage of training samples remains one of the main obstacles in applying the artificial neural networks ann to the hyperspectral images classification to fuse the spatial and spectral information pixel patches are often utilized to train a model which may further aggregate this problem in the existing works an ann model supervised by centerloss annc was introduced training merely with spectral information the annc yields discriminative spectral features suitable for the subsequent classification tasks in this paper a cnnbased spatial feature fusion csff algorithm is proposed which allows a smart fusion of the spatial information to the spectral features extracted by annc as a critical part of csff a cnnbased discriminant model is introduced to estimate whether two paring pixels belong to the same class at the testing stage by applying the discriminant model to the pixelpairs generated by the test pixel and its neighbors the local structure is estimated and represented as a customized convolutional kernel the spectralspatial feature is obtained by a convolutional operation between the estimated kernel and the corresponding spectral features within a neighborhood at last the label of the test pixel is predicted by classifying the resulting spectralspatial feature without increasing the number of training samples or involving pixel patches at the training stage the csff framework achieves the stateoftheart by declining 2050 classification failures in experiments on three wellknown hyperspectral images
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1,801.10356
Landau-Ginzburg theory of cortex dynamics: Scale-free avalanches emerge at the edge of synchronization
Understanding the origin, nature, and functional significance of complex patterns of neural activity, as recorded by diverse electrophysiological and neuroimaging techniques, is a central challenge in neuroscience. Such patterns include collective oscillations emerging out of neural synchronization as well as highly heterogeneous outbursts of activity interspersed by periods of quiescence, called "neuronal avalanches." Much debate has been generated about the possible scale invariance or criticality of such avalanches and its relevance for brain function. Aimed at shedding light onto this, here we analyze the large-scale collective properties of the cortex by using a mesoscopic approach following the principle of parsimony of Landau-Ginzburg. Our model is similar to that of Wilson-Cowan for neural dynamics but crucially, includes stochasticity and space; synaptic plasticity and inhibition are considered as possible regulatory mechanisms. Detailed analyses uncover a phase diagram including down-state, synchronous, asynchronous, and up-state phases and reveal that empirical findings for neuronal avalanches are consistently reproduced by tuning our model to the edge of synchronization. This reveals that the putative criticality of cortical dynamics does not correspond to a quiescent-to-active phase transition as usually assumed in theoretical approaches but to a synchronization phase transition, at which incipient oscillations and scale-free avalanches coexist. Furthermore, our model also accounts for up and down states as they occur (e.g., during deep sleep). This approach constitutes a framework to rationalize the possible collective phases and phase transitions of cortical networks in simple terms, thus helping to shed light on basic aspects of brain functioning from a very broad perspective.
q-bio.NC cond-mat.stat-mech nlin.AO physics.comp-ph
understanding the origin nature and functional significance of complex patterns of neural activity as recorded by diverse electrophysiological and neuroimaging techniques is a central challenge in neuroscience such patterns include collective oscillations emerging out of neural synchronization as well as highly heterogeneous outbursts of activity interspersed by periods of quiescence called neuronal avalanches much debate has been generated about the possible scale invariance or criticality of such avalanches and its relevance for brain function aimed at shedding light onto this here we analyze the largescale collective properties of the cortex by using a mesoscopic approach following the principle of parsimony of landauginzburg our model is similar to that of wilsoncowan for neural dynamics but crucially includes stochasticity and space synaptic plasticity and inhibition are considered as possible regulatory mechanisms detailed analyses uncover a phase diagram including downstate synchronous asynchronous and upstate phases and reveal that empirical findings for neuronal avalanches are consistently reproduced by tuning our model to the edge of synchronization this reveals that the putative criticality of cortical dynamics does not correspond to a quiescenttoactive phase transition as usually assumed in theoretical approaches but to a synchronization phase transition at which incipient oscillations and scalefree avalanches coexist furthermore our model also accounts for up and down states as they occur eg during deep sleep this approach constitutes a framework to rationalize the possible collective phases and phase transitions of cortical networks in simple terms thus helping to shed light on basic aspects of brain functioning from a very broad perspective
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1,801.10357
Slepton pair production at the LHC in NLO+NLL with resummation-improved parton densities
Novel PDFs taking into account resummation-improved matrix elements, albeit only in the fit of a reduced data set, allow for consistent NLO+NLL calculations of slepton pair production at the LHC. We apply a factorisation method to this process that minimises the effect of the data set reduction, avoids the problem of outlier replicas in the NNPDF method for PDF uncertainties and preserves the reduction of the scale uncertainty. For Run II of the LHC, left-handed selectron/smuon, right-handed and maximally mixed stau production, we confirm that the consistent use of threshold-improved PDFs partially compensates the resummation contributions in the matrix elements. Together with the reduction of the scale uncertainty at NLO+NLL, the described method further increases the reliability of slepton pair production cross sections at the LHC.
hep-ph
novel pdfs taking into account resummationimproved matrix elements albeit only in the fit of a reduced data set allow for consistent nlonll calculations of slepton pair production at the lhc we apply a factorisation method to this process that minimises the effect of the data set reduction avoids the problem of outlier replicas in the nnpdf method for pdf uncertainties and preserves the reduction of the scale uncertainty for run ii of the lhc lefthanded selectronsmuon righthanded and maximally mixed stau production we confirm that the consistent use of thresholdimproved pdfs partially compensates the resummation contributions in the matrix elements together with the reduction of the scale uncertainty at nlonll the described method further increases the reliability of slepton pair production cross sections at the lhc
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1,801.10358
Wave front sets with respect to Banach spaces of ultradistributions. Characterisation via the short-time Fourier transform
We define ultradistributional wave front sets with respect to translation-modulation invariant Banach spaces of ultradistributions having solid Fourier image. The main result is their characterisation by the short-time Fourier transform.
math.AP
we define ultradistributional wave front sets with respect to translationmodulation invariant banach spaces of ultradistributions having solid fourier image the main result is their characterisation by the shorttime fourier transform
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1,801.10359
Multi-factor approximation of rough volatility models
Rough volatility models are very appealing because of their remarkable fit of both historical and implied volatilities. However, due to the non-Markovian and non-semimartingale nature of the volatility process, there is no simple way to simulate efficiently such models, which makes risk management of derivatives an intricate task. In this paper, we design tractable multi-factor stochastic volatility models approximating rough volatility models and enjoying a Markovian structure. Furthermore, we apply our procedure to the specific case of the rough Heston model. This in turn enables us to derive a numerical method for solving fractional Riccati equations appearing in the characteristic function of the log-price in this setting.
math.PR q-fin.CP q-fin.PR
rough volatility models are very appealing because of their remarkable fit of both historical and implied volatilities however due to the nonmarkovian and nonsemimartingale nature of the volatility process there is no simple way to simulate efficiently such models which makes risk management of derivatives an intricate task in this paper we design tractable multifactor stochastic volatility models approximating rough volatility models and enjoying a markovian structure furthermore we apply our procedure to the specific case of the rough heston model this in turn enables us to derive a numerical method for solving fractional riccati equations appearing in the characteristic function of the logprice in this setting
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1,801.1036
Rotational broadening and conservation of angular momentum in post-extreme horizontal branch stars
We show that the recent realization that isolated post-extreme horizontal branch (post-EHB) stars are generally characterized by rotational broadening with values of $V_{\rm rot} \sin i$ between 25 and 30 km~s$^{-1}$ can be explained as a natural consequence of the conservation of angular momentum from the previous He-core burning phase on the EHB. The progenitors of these evolved objects, the EHB stars, are known to be slow rotators with an average value of $V_{\rm rot} \sin i$ of $\sim$7.7 km~s$^{-1}$. This implies significant spin-up between the EHB and post-EHB phases. Using representative evolutionary models of hot subdwarf stars, we demonstrate that angular momentum conservation in uniformly rotating strutures (rigid-body rotation) boosts that value of the projected equatorial rotation speed by a factor $\sim$3.6 by the time the model has reached the region of the surface gravity-effective temperature plane where the newly-studied post-EHB objects are found. This is exactly what is needed to account for their observed atmospheric broadening. We note that the decrease of the moment of inertia causing the spin-up is mostly due to the redistribution of matter that produces more centrally-condensed structures in the post-EHB phase of evolution, not to the decrease of the radius per se.
astro-ph.SR
we show that the recent realization that isolated postextreme horizontal branch postehb stars are generally characterized by rotational broadening with values of v_rm rot sin i between 25 and 30 kms1 can be explained as a natural consequence of the conservation of angular momentum from the previous hecore burning phase on the ehb the progenitors of these evolved objects the ehb stars are known to be slow rotators with an average value of v_rm rot sin i of sim77 kms1 this implies significant spinup between the ehb and postehb phases using representative evolutionary models of hot subdwarf stars we demonstrate that angular momentum conservation in uniformly rotating strutures rigidbody rotation boosts that value of the projected equatorial rotation speed by a factor sim36 by the time the model has reached the region of the surface gravityeffective temperature plane where the newlystudied postehb objects are found this is exactly what is needed to account for their observed atmospheric broadening we note that the decrease of the moment of inertia causing the spinup is mostly due to the redistribution of matter that produces more centrallycondensed structures in the postehb phase of evolution not to the decrease of the radius per se
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1,801.10361
Weil-Petersson Teichm\"{u}ller space II: smoothness of flow curves of $H^{\frac 32}$-vector fields
Given a continuous vector field $\lambda(t, \cdot)$ of Sobolev class $H^{\frac 32}$ on the unit circle $S^1$, the flow maps $\eta=g(t, \cdot)$ of the differential equation $$ \cases \frac{d\eta}{dt}=\lambda(t, \eta)\\ \eta(0,\zeta)=\zeta \endcases $$ are known to be quasisymmetric homeomorphisms. Very recently, Gay-Balmaz-Ratiu [GR] conjectured that the flow curve $g(t, \cdot)$ is in the Weil-Petersson class WP$(S^1)$ and is continuously differentiable with respect to the Hilbert manifold structure of WP$(S^1)$ introduced by Takhtajan-Teo [TT]. The first assertion had already been demonstrated in our previous paper [Sh2]. In this sequel to [Sh2], we will continue to deal with the Weil-Petersson class WP$(S^1)$ and completely solve this conjecture in the affirmative.
math.CV
given a continuous vector field lambdat cdot of sobolev class hfrac 32 on the unit circle s1 the flow maps etagt cdot of the differential equation cases fracdetadtlambdat eta eta0zetazeta endcases are known to be quasisymmetric homeomorphisms very recently gaybalmazratiu gr conjectured that the flow curve gt cdot is in the weilpetersson class wps1 and is continuously differentiable with respect to the hilbert manifold structure of wps1 introduced by takhtajanteo tt the first assertion had already been demonstrated in our previous paper sh2 in this sequel to sh2 we will continue to deal with the weilpetersson class wps1 and completely solve this conjecture in the affirmative
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1,801.10362
Measurement of $\Gamma_{ee}\times\mathcal{B}_{\mu\mu}$ for $\psi(2S)$ meson
The product of the electronic width of the $\psi(2S)$ meson and the branching fraction of its decay to the muon pair was measured in the $e^{+}e^{-} \to \psi(2S) \to \mu^{+}\mu^{-}$ process using nine data sets corresponding to an integrated luminosity of about 6.5 pb$^{-1}$ collected with the KEDR detector at the VEPP-4M electron-positron collider: \[ \Gamma_{ee}\times\mathcal{B}_{\mu\mu} = 19.3 \pm 0.3 \pm 0.5 ~\text{eV}. \] Adding the previous KEDR results on hadronic and leptonic channels, the values of the $\psi(2S)$ electronic width were obtained under two assumptions: either with the assumption of lepton universality \[ \Gamma_{ee} = 2.279 \pm 0.015 \pm 0.042 ~\text{keV} \] or without it, summing up hadronic and three independent leptonic channels: \[ \Gamma_{ee} = 2.282 \pm 0.015 \pm 0.042 ~\text{keV}. \]
hep-ex
the product of the electronic width of the psi2s meson and the branching fraction of its decay to the muon pair was measured in the ee to psi2s to mumu process using nine data sets corresponding to an integrated luminosity of about 65 pb1 collected with the kedr detector at the vepp4m electronpositron collider gamma_eetimesmathcalb_mumu 193 pm 03 pm 05 textev adding the previous kedr results on hadronic and leptonic channels the values of the psi2s electronic width were obtained under two assumptions either with the assumption of lepton universality gamma_ee 2279 pm 0015 pm 0042 textkev or without it summing up hadronic and three independent leptonic channels gamma_ee 2282 pm 0015 pm 0042 textkev
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1,801.10363
Detection of bosonic mode as a signature of magnetic excitation in one unit cell FeSe on SrTiO3
We report an in situ scanning tunneling spectroscopy study of one-unit-cell (1-UC) FeSe film on SrTiO3(001) (STO) substrate. In quasiparticle density of states, bosonic excitation mode characterized by the "dip-hump" structure is detected outside the larger superconducting gap with energy comparable with phonon and spin resonance modes in heavily electron-doped iron selenides. Statistically, the excitation mode, which is intimately correlated with superconductivity, shows an anticorrelation with pairing strength and yields an energy scale upper-bounded by twice the superconducting gap coinciding with the characteristics of magnetic resonance in cuprates and iron-based superconductors. The local response of tunneling spectra to magnetically different Se defects all exhibits the induced in-gap quasiparticle bound states, indicating an unconventional sign-reversing pairing. These results support the magnetic nature of the excitation mode and possibly reveal a signature of electron-magnetic-excitation coupling in high-temperature superconductivity of 1-UC FeSe/STO.
cond-mat.supr-con
we report an in situ scanning tunneling spectroscopy study of oneunitcell 1uc fese film on srtio3001 sto substrate in quasiparticle density of states bosonic excitation mode characterized by the diphump structure is detected outside the larger superconducting gap with energy comparable with phonon and spin resonance modes in heavily electrondoped iron selenides statistically the excitation mode which is intimately correlated with superconductivity shows an anticorrelation with pairing strength and yields an energy scale upperbounded by twice the superconducting gap coinciding with the characteristics of magnetic resonance in cuprates and ironbased superconductors the local response of tunneling spectra to magnetically different se defects all exhibits the induced ingap quasiparticle bound states indicating an unconventional signreversing pairing these results support the magnetic nature of the excitation mode and possibly reveal a signature of electronmagneticexcitation coupling in hightemperature superconductivity of 1uc fesesto
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1,801.10364
Security of NEQR Quantum Image by Using Quantum Fourier Transform with Blind Trent
In this study, the security of Novel Enhanced Quantum Representation (NEQR) of quantum images are suggested by using the Quantum Fourier Transform (QFT) with blind trent. In the protocol, QFT and keys are used to share signature with recipients. So all members know only their signature information which are encrypted output of the QFT. This improves the security of the protocol. In addition, the security of the protocol is provided by using reorder QFT output qubits with permutation of the blind trent. The security analysis expresses security of the transfer of the image with effective secret key usage.
quant-ph cs.IT math.IT
in this study the security of novel enhanced quantum representation neqr of quantum images are suggested by using the quantum fourier transform qft with blind trent in the protocol qft and keys are used to share signature with recipients so all members know only their signature information which are encrypted output of the qft this improves the security of the protocol in addition the security of the protocol is provided by using reorder qft output qubits with permutation of the blind trent the security analysis expresses security of the transfer of the image with effective secret key usage
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1,801.10365
Synchronized Detection and Recovery of Steganographic Messages with Adversarial Learning
In this work, we mainly study the mechanism of learning the steganographic algorithm as well as combining the learning process with adversarial learning to learn a good steganographic algorithm. To handle the problem of embedding secret messages into the specific medium, we design a novel adversarial modules to learn the steganographic algorithm, and simultaneously train three modules called generator, discriminator and steganalyzer. Different from existing methods, the three modules are formalized as a game to communicate with each other. In the game, the generator and discriminator attempt to communicate with each other using secret messages hidden in an image. While the steganalyzer attempts to analyze whether there is a transmission of confidential information. We show that through unsupervised adversarial training, the adversarial model can produce robust steganographic solutions, which act like an encryption. Furthermore, we propose to utilize supervised adversarial training method to train a robust steganalyzer, which is utilized to discriminate whether an image contains secret information. Numerous experiments are conducted on publicly available dataset to demonstrate the effectiveness of the proposed method.
cs.CV cs.CR
in this work we mainly study the mechanism of learning the steganographic algorithm as well as combining the learning process with adversarial learning to learn a good steganographic algorithm to handle the problem of embedding secret messages into the specific medium we design a novel adversarial modules to learn the steganographic algorithm and simultaneously train three modules called generator discriminator and steganalyzer different from existing methods the three modules are formalized as a game to communicate with each other in the game the generator and discriminator attempt to communicate with each other using secret messages hidden in an image while the steganalyzer attempts to analyze whether there is a transmission of confidential information we show that through unsupervised adversarial training the adversarial model can produce robust steganographic solutions which act like an encryption furthermore we propose to utilize supervised adversarial training method to train a robust steganalyzer which is utilized to discriminate whether an image contains secret information numerous experiments are conducted on publicly available dataset to demonstrate the effectiveness of the proposed method
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1,801.10366
Analysis of scalar and fermion quantum field theory on anti-de Sitter space-time
We study vacuum and thermal expectation values of quantum scalar and Dirac fermion fields on anti-de Sitter space-time. Anti-de Sitter space-time is maximally symmetric and this enables expressions for the scalar and fermion vacuum Feynman Green's functions to be derived in closed form. We employ Hadamard renormalization to find the vacuum expectation values. The thermal Feynman Green's functions are constructed from the vacuum Feynman Green's functions using the imaginary time periodicity/anti-periodicity property for scalars/fermions. Focussing on massless fields with either conformal or minimal coupling to the space-time curvature (these two cases being the same for fermions) we compute the differences between the thermal and vacuum expectation values. We compare the resulting energy densities, pressures and pressure deviators with the corresponding classical quantities calculated using relativistic kinetic theory.
hep-th
we study vacuum and thermal expectation values of quantum scalar and dirac fermion fields on antide sitter spacetime antide sitter spacetime is maximally symmetric and this enables expressions for the scalar and fermion vacuum feynman greens functions to be derived in closed form we employ hadamard renormalization to find the vacuum expectation values the thermal feynman greens functions are constructed from the vacuum feynman greens functions using the imaginary time periodicityantiperiodicity property for scalarsfermions focussing on massless fields with either conformal or minimal coupling to the spacetime curvature these two cases being the same for fermions we compute the differences between the thermal and vacuum expectation values we compare the resulting energy densities pressures and pressure deviators with the corresponding classical quantities calculated using relativistic kinetic theory
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1,801.10367
On a new type of divergence for spiky Wilson loops and related entanglement entropies
We study the divergences of Wilson loops for a contour with a cusp of zero opening angle, combined with a nonzero discontinuity of its curvature. The analysis is performed in lowest order, both for weak and strong coupling. Such a spike contributes a leading divergent term proportional to the inverse of the square root of the cutoff times the jump of the curvature. As nextleading term appears a logarithmic one in the supersymmetric case, but it is absent in QCD. The strong coupling result, obtained from minimal surfaces in AdS via holography, can be used also for applications to entanglement entropy in (2+1)-dimensional CFT's.
hep-th hep-ph
we study the divergences of wilson loops for a contour with a cusp of zero opening angle combined with a nonzero discontinuity of its curvature the analysis is performed in lowest order both for weak and strong coupling such a spike contributes a leading divergent term proportional to the inverse of the square root of the cutoff times the jump of the curvature as nextleading term appears a logarithmic one in the supersymmetric case but it is absent in qcd the strong coupling result obtained from minimal surfaces in ads via holography can be used also for applications to entanglement entropy in 21dimensional cfts
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1,801.10368
Generalized characters for glider representations of groups
Glider representations can be defined for a finite algebra filtration FKG determined by a chain of subgroups 1 < G_1 < ... < G_d = G. In this paper we develop the generalized character theory for such glider representations. We give the generalization of Artin's theorem and define a generalized inproduct. For finite abelian groups G with chain 1 < G, we explicitly calculate the generalized character ring and compute its semisimple quotient. The papers ends with a discussion of the quaternion group as a first non-abelian example.
math.GR
glider representations can be defined for a finite algebra filtration fkg determined by a chain of subgroups 1 g_1 g_d g in this paper we develop the generalized character theory for such glider representations we give the generalization of artins theorem and define a generalized inproduct for finite abelian groups g with chain 1 g we explicitly calculate the generalized character ring and compute its semisimple quotient the papers ends with a discussion of the quaternion group as a first nonabelian example
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