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null | {
"abstract": " We study the interplay between Steinberg algebras and partial skew rings: For\na partial action of a group in a Hausdorff, locally compact, totally\ndisconnected topological space, we realize the associated partial skew group\nring as a Steinberg algebra (over the transformation groupoid attached to the\npartial action). We then apply this realization to characterize diagonal\npreserving isomorphisms of partial skew group rings, over commutative algebras,\nin terms of continuous orbit equivalence of the associated partial actions.\nFinally, we show that any Steinberg algebra, associated to a Hausdorff ample\ngroupoid, can be seen as a partial skew inverse semigroup ring.\n",
"title": "The interplay between Steinberg algebras and partial skew rings"
} | null | null | null | null | true | null | 20301 | null | Default | null | null |
null | {
"abstract": " The Dirac equation for relativistic electron waves is the parent model for\nWeyl and Majorana fermions as well as topological insulators. Simulation of\nDirac physics in three-dimensional photonic crystals, though fundamentally\nimportant for topological phenomena at optical frequencies, encounters the\nchallenge of synthesis of both Kramers double degeneracy and parity inversion.\nHere we show how type-II Dirac points---exotic Dirac relativistic waves yet to\nbe discovered---are robustly realized through the nonsymmorphic screw symmetry.\nThe emergent type-II Dirac points carry nontrivial topology and are the mother\nstates of type-II Weyl points. The proposed all-dielectric architecture enables\nrobust cavity states at photonic-crystal---air interfaces and anomalous\nrefraction, with very low energy dissipation.\n",
"title": "Type-II Dirac Photons"
} | null | null | null | null | true | null | 20302 | null | Default | null | null |
null | {
"abstract": " Though Convolutional Neural Networks (CNNs) have surpassed human-level\nperformance on tasks such as object classification and face verification, they\ncan easily be fooled by adversarial attacks. These attacks add a small\nperturbation to the input image that causes the network to misclassify the\nsample. In this paper, we focus on neutralizing adversarial attacks by compact\nfeature learning. In particular, we show that learning features in a closed and\nbounded space improves the robustness of the network. We explore the effect of\nL2-Softmax Loss, that enforces compactness in the learned features, thus\nresulting in enhanced robustness to adversarial perturbations. Additionally, we\npropose compact convolution, a novel method of convolution that when\nincorporated in conventional CNNs improves their robustness. Compact\nconvolution ensures feature compactness at every layer such that they are\nbounded and close to each other. Extensive experiments show that Compact\nConvolutional Networks (CCNs) neutralize multiple types of attacks, and perform\nbetter than existing methods in defending adversarial attacks, without\nincurring any additional training overhead compared to CNNs.\n",
"title": "Improving Network Robustness against Adversarial Attacks with Compact Convolution"
} | null | null | [
"Computer Science",
"Statistics"
]
| null | true | null | 20303 | null | Validated | null | null |
null | {
"abstract": " We study the crossover between the sudden quench limit and the adiabatic\ndynamics of superconducting states in the attractive Hubbard model. We focus on\nthe dynamics induced by the change of the attractive interaction during a\nfinite ramp time which is varied in order to track the evolution of the\ndynamical phase diagram from the sudden quench to the equilibrium limit. Two\ndifferent dynamical regimes are realized for quenches towards weak and strong\ncoupling interactions. At weak coupling the dynamics depends only on the energy\ninjected into the system, whereas a dynamics retaining memory of the initial\nstate takes place at strong coupling. We show that this is related to a sharp\ntransition between a weak and a strong coupling quench dynamical regime, which\ndefines the boundaries beyond which a dynamics independent from the initial\nstate is recovered. Comparing the dynamics in the superconducting and\nnon-superconducting phases we argue that this is due to the lack of an\nadiabatic connection to the equilibrium ground state for non-equilibrium\nsuperconducting states in the strong coupling quench regime.\n",
"title": "From sudden quench to adiabatic dynamics in the attractive Hubbard model"
} | null | null | [
"Physics"
]
| null | true | null | 20304 | null | Validated | null | null |
null | {
"abstract": " We report strong interfacial exchange coupling in Bi2Se3/yttrium iron garnet\n(YIG) bilayers manifested as large in-plane interfacial magnetic anisotropy\n(IMA) and enhancement of damping probed by ferromagnetic resonance (FMR). The\nIMA and spin mixing conductance reached a maximum when Bi2Se3 was around 6\nquintuple-layer (QL) thick. The unconventional Bi2Se3 thickness dependence of\nthe IMA and spin mixing conductance are correlated with the evolution of\nsurface band structure of Bi2Se3, indicating that topological surface states\nplay an important role in the magnetization dynamics of YIG.\nTemperature-dependent FMR of Bi2Se3/YIG revealed signatures of magnetic\nproximity effect of $T_c$ as high as 180 K, and an effective field parallel to\nthe YIG magnetization direction at low temperature. Our study sheds light on\nthe effects of topological insulators on magnetization dynamics, essential for\ndevelopment of TI-based spintronic devices.\n",
"title": "Strongly exchange-coupled and surface-state-modulated magnetization dynamics in Bi2Se3/YIG heterostructures"
} | null | null | [
"Physics"
]
| null | true | null | 20305 | null | Validated | null | null |
null | {
"abstract": " Software-driven cloud networking is a new paradigm in orchestrating physical\nresources (CPU, network bandwidth, energy, storage) allocated to network\nfunctions, services, and applications, which is commonly modeled as a\ncross-layer network. This model carries a physical network representing the\nphysical infrastructure, a logical network showing demands, and\nlogical-to-physical node/link mappings. In such networks, a single failure in\nthe physical network may trigger cascading failures in the logical network and\ndisable network services and connectivity. In this paper, we propose an\nevaluation metric, survivable probability, to evaluate the reliability of such\nnetworks under random physical link failure(s). We propose the concept of base\nprotecting spanning tree and prove the necessary and sufficient conditions for\nits existence and relation to survivability. We then develop mathematical\nprogramming formulations for reliable cross-layer network routing design with\nthe maximal reliable probability. Computation results demonstrate the viability\nof our approach.\n",
"title": "Survivable Probability of SDN-enabled Cloud Networking with Random Physical Link Failure"
} | null | null | null | null | true | null | 20306 | null | Default | null | null |
null | {
"abstract": " PID control architectures are widely used in industrial applications. Despite\ntheir low number of open parameters, tuning multiple, coupled PID controllers\ncan become tedious in practice. In this paper, we extend PILCO, a model-based\npolicy search framework, to automatically tune multivariate PID controllers\npurely based on data observed on an otherwise unknown system. The system's\nstate is extended appropriately to frame the PID policy as a static state\nfeedback policy. This renders PID tuning possible as the solution of a finite\nhorizon optimal control problem without further a priori knowledge. The\nframework is applied to the task of balancing an inverted pendulum on a seven\ndegree-of-freedom robotic arm, thereby demonstrating its capabilities of fast\nand data-efficient policy learning, even on complex real world problems.\n",
"title": "Model-Based Policy Search for Automatic Tuning of Multivariate PID Controllers"
} | null | null | null | null | true | null | 20307 | null | Default | null | null |
null | {
"abstract": " The Min-Hashing approach to sketching has become an important tool in data\nanalysis, information retrial, and classification. To apply it to real-valued\ndatasets, the ICWS algorithm has become a seminal approach that is widely used,\nand provides state-of-the-art performance for this problem space. However, ICWS\nsuffers a computational burden as the sketch size K increases. We develop a new\nSimplified approach to the ICWS algorithm, that enables us to obtain over 20x\nspeedups compared to the standard algorithm. The veracity of our approach is\ndemonstrated empirically on multiple datasets and scenarios, showing that our\nnew Simplified CWS obtains the same quality of results while being an order of\nmagnitude faster.\n",
"title": "Engineering a Simplified 0-Bit Consistent Weighted Sampling"
} | null | null | null | null | true | null | 20308 | null | Default | null | null |
null | {
"abstract": " Among other macroeconomic indicators, the monthly release of U.S.\nunemployment rate figures in the Employment Situation report by the U.S. Bureau\nof Labour Statistics gets a lot of media attention and strongly affects the\nstock markets. I investigate whether a profitable investment strategy can be\nconstructed by predicting the likely changes in U.S. unemployment before the\nofficial news release using Google query volumes for related search terms. I\nfind that massive new data sources of human interaction with the Internet not\nonly improves U.S. unemployment rate predictability, but can also enhance\nmarket timing of trading strategies when considered jointly with macroeconomic\ndata. My results illustrate the potential of combining extensive behavioural\ndata sets with economic data to anticipate investor expectations and stock\nmarket moves.\n",
"title": "Quantifying macroeconomic expectations in stock markets using Google Trends"
} | null | null | null | null | true | null | 20309 | null | Default | null | null |
null | {
"abstract": " Understanding the origin of unintentional doping in Ga2O3 is key to\nincreasing breakdown voltages of Ga2O3 based power devices. Therefore,\ntransport and capacitance spectroscopy studies have been performed to better\nunderstand the origin of unintentional doping in Ga2O3. Previously unobserved\nunintentional donors in commercially available (-201) Ga2O3 substrates have\nbeen electrically characterized via temperature dependent Hall effect\nmeasurements up to 1000 K and found to have a donor energy of 110 meV. The\nexistence of the unintentional donor is confirmed by temperature dependent\nadmittance spectroscopy, with an activation energy of 131 meV determined via\nthat technique, in agreement with Hall effect measurements. With the\nconcentration of this donor determined to be in the mid to high 10^16 cm^-3\nrange, elimination of this donor from the drift layer of Ga2O3 power\nelectronics devices will be key to pushing the limits of device performance.\nIndeed, analytical assessment of the specific on-resistance (Ronsp) and\nbreakdown voltage of Schottky diodes containing the 110 meV donor indicates\nthat incomplete ionization increases Ronsp and decreases breakdown voltage as\ncompared to Ga2O3 Schottky diodes containing only the shallow donor. The\nreduced performance due to incomplete ionization occurs in addition to the\nusual tradeoff between Ronsp and breakdown voltage. To achieve 10 kV operation\nin Ga2O3 Schottky diode devices, analysis indicates that the concentration of\n110 meV donors must be reduced below 5x10^14 cm^-3 to limit the increase in\nRonsp to one percent.\n",
"title": "Effects of Incomplete Ionization on Beta - Ga2O3 Power Devices: Unintentional Donor with Energy 110 meV"
} | null | null | null | null | true | null | 20310 | null | Default | null | null |
null | {
"abstract": " Optic flow is two dimensional, but no special qualities are attached to one\nor other of these dimensions. For binocular disparity, on the other hand, the\nterms 'horizontal' and 'vertical' disparities are commonly used. This is odd,\nsince binocular disparity and optic flow describe essentially the same thing.\nThe difference is that, generally, people tend to fixate relatively close to\nthe direction of heading as they move, meaning that fixation is close to the\noptic flow epipole, whereas, for binocular vision, fixation is close to the\nhead-centric midline, i.e. approximately 90 degrees from the binocular epipole.\nFor fixating animals, some separations of flow may lead to simple algorithms\nfor the judgement of surface structure and the control of action. We consider\nthe following canonical flow patterns that sum to produce overall flow: (i)\n'towards' flow, the component of translational flow produced by approaching (or\nretreating from) the fixated object, which produces pure radial flow on the\nretina; (ii) 'sideways' flow, the remaining component of translational flow,\nwhich is produced by translation of the optic centre orthogonal to the\ncyclopean line of sight and (iii) 'vergence' flow, rotational flow produced by\na counter-rotation of the eye in order to maintain fixation. A general flow\npattern could also include (iv) 'cyclovergence' flow, produced by rotation of\none eye relative to the other about the line of sight. We consider some\npractical advantages of dividing up flow in this way when an observer fixates\nas they move. As in some previous treatments, we suggest that there are certain\ntasks for which it is sensible to consider 'towards' flow as one component and\n'sideways' + 'vergence' flow as another.\n",
"title": "A single coordinate framework for optic flow and binocular disparity"
} | null | null | null | null | true | null | 20311 | null | Default | null | null |
null | {
"abstract": " Neurons process information by transforming barrages of synaptic inputs into\nspiking activity. Synaptic inhibition suppresses the output firing activity of\na neuron, and is commonly classified as having a subtractive or divisive effect\non a neuron's output firing activity. Subtractive inhibition can narrow the\nrange of inputs that evoke spiking activity by eliminating responses to\nnon-preferred inputs. Divisive inhibition is a form of gain control: it\nmodifies firing rates while preserving the range of inputs that evoke firing\nactivity. Since these two \"modes\" of inhibition have distinct impacts on neural\ncoding, it is important to understand the biophysical mechanisms that\ndistinguish these response profiles.\nWe use simulations and mathematical analysis of a neuron model to find the\nspecific conditions for which inhibitory inputs have subtractive or divisive\neffects. We identify a novel role for the A-type Potassium current (IA). In our\nmodel, this fast-activating, slowly- inactivating outward current acts as a\nswitch between subtractive and divisive inhibition. If IA is strong (large\nmaximal conductance) and fast (activates on a time-scale similar to spike\ninitiation), then inhibition has a subtractive effect on neural firing. In\ncontrast, if IA is weak or insufficiently fast-activating, then inhibition has\na divisive effect on neural firing. We explain these findings using dynamical\nsystems methods to define how a spike threshold condition depends on synaptic\ninputs and IA.\nOur findings suggest that neurons can \"self-regulate\" the gain control\neffects of inhibition via combinations of synaptic plasticity and/or modulation\nof the conductance and kinetics of A-type Potassium channels. This novel role\nfor IA would add flexibility to neurons and networks, and may relate to recent\nobservations of divisive inhibitory effects on neurons in the nucleus of the\nsolitary tract.\n",
"title": "Gain control with A-type potassium current: IA as a switch between divisive and subtractive inhibition"
} | null | null | null | null | true | null | 20312 | null | Default | null | null |
null | {
"abstract": " Cyclic data structures, such as cyclic lists, in functional programming are\ntricky to handle because of their cyclicity. This paper presents an\ninvestigation of categorical, algebraic, and computational foundations of\ncyclic datatypes. Our framework of cyclic datatypes is based on second-order\nalgebraic theories of Fiore et al., which give a uniform setting for syntax,\ntypes, and computation rules for describing and reasoning about cyclic\ndatatypes. We extract the \"fold\" computation rules from the categorical\nsemantics based on iteration categories of Bloom and Esik. Thereby, the rules\nare correct by construction. We prove strong normalisation using the General\nSchema criterion for second-order computation rules. Rather than the fixed\npoint law, we particularly choose Bekic law for computation, which is a key to\nobtaining strong normalisation. We also prove the property of \"Church-Rosser\nmodulo bisimulation\" for the computation rules. Combining these results, we\nhave a remarkable decidability result of the equational theory of cyclic data\nand fold.\n",
"title": "Cyclic Datatypes modulo Bisimulation based on Second-Order Algebraic Theories"
} | null | null | null | null | true | null | 20313 | null | Default | null | null |
null | {
"abstract": " High-order harmonic generation (HHG) from aligned acetylene molecules\ninteracting with mid infra-red (IR), linearly polarized laser pulses is studied\ntheoretically using a mixed quantum-classical approach in which the electrons\nare described using time-dependent density functional theory while the ions are\ntreated classically. We find that for molecules aligned perpendicular to the\nlaser polarization axis, HHG arises from the highest-occupied molecular orbital\n(HOMO) while for molecules aligned along the laser polarization axis, HHG is\ndominated by the HOMO-1. In the parallel orientation we observe a double\nplateau with an inner plateau that is produced by ionization from and\nrecombination back to an autoionizing state. Two pieces of evidence support\nthis idea. Firstly, by choosing a suitably tuned vacuum ultraviolet pump pulse\nthat directly excites the autoionizing state we observe a dramatic enhancement\nof all harmonics in the inner plateau. Secondly, in certain circumstances, the\nposition of the inner plateau cut-off does not agree with the classical\nthree-step model. We show that this discrepancy can be understood in terms of a\nminimum in the dipole recombination matrix element from the continuum to the\nautoionizing state. As far as we are aware, this represents the first\nobservation of harmonic enhancement over a wide range of frequencies arising\nfrom autoionizing states in molecules.\n",
"title": "High-order harmonic generation from highly-excited states in acetylene"
} | null | null | [
"Physics"
]
| null | true | null | 20314 | null | Validated | null | null |
null | {
"abstract": " Here we report on a set of programs developed at the ZMBH Bio-Imaging\nFacility for tracking real-life images of cellular processes. These programs\nperform 1) automated tracking; 2) quantitative and comparative track analyses\nof different images in different groups; 3) different interactive visualization\nschemes; and 4) interactive realistic simulation of different cellular\nprocesses for validation and optimal problem-specific adjustment of image\nacquisition parameters (tradeoff between speed, resolution, and quality with\nfeedback from the very final results). The collection of programs is primarily\ndeveloped for the common bio-image analysis software ImageJ (as a single Java\nPlugin). Some programs are also available in other languages (C++ and\nJavascript) and may be run simply with a web-browser; even on a low-end Tablet\nor Smartphone. The programs are available at\nthis https URL\n",
"title": "Cellulyzer - Automated analysis and interactive visualization/simulation of select cellular processes"
} | null | null | null | null | true | null | 20315 | null | Default | null | null |
null | {
"abstract": " Many computationally-efficient methods for Bayesian deep learning rely on\ncontinuous optimization algorithms, but the implementation of these methods\nrequires significant changes to existing code-bases. In this paper, we propose\nVprop, a method for Gaussian variational inference that can be implemented with\ntwo minor changes to the off-the-shelf RMSprop optimizer. Vprop also reduces\nthe memory requirements of Black-Box Variational Inference by half. We derive\nVprop using the conjugate-computation variational inference method, and\nestablish its connections to Newton's method, natural-gradient methods, and\nextended Kalman filters. Overall, this paper presents Vprop as a principled,\ncomputationally-efficient, and easy-to-implement method for Bayesian deep\nlearning.\n",
"title": "Vprop: Variational Inference using RMSprop"
} | null | null | null | null | true | null | 20316 | null | Default | null | null |
null | {
"abstract": " The optical properties of a multilayer system of dielectric media with\narbitrary $N$ layers is investigated. Each layer is one of two dielectric\nmedia, with thickness one-quarter the wavelength of light in that medium,\ncorresponding to a central frequency. Using the transfer matrix method, the\ntransmittance $T$ is calculated for all possible $2^N$ sequences for small $N$.\nUnexpectedly, it is found that instead of $2^N$ different values of $T$ at the\ncentral frequency ($T_0$), there are either $(N/2+1)$ or $(N+1)$ discrete\nvalues of $T_0$ for even or odd $N$, respectively. We explain the high\ndegeneracy in the $T_0$ values by defining new symmetry operations that do not\nchange $T_0$. Analytical formulae were derived for the $T_0$ values and their\ndegeneracy as functions of $N$ and an integer parameter for each sequence we\ncall \"charge\". Additionally, the bandwidth of the transmission spectra at $f_0$\nis investigated, revealing some asymptotic behavior at large $N$.\n",
"title": "Hidden symmetries in $N$-layer dielectric stacks"
} | null | null | null | null | true | null | 20317 | null | Default | null | null |
null | {
"abstract": " Quadratic systems of equations appear in several applications. The results in\nthis paper are motivated by quadratic systems of equations that describe\nequilibrium behavior of physical infrastructure networks like the power and gas\ngrids. The quadratic systems in infrastructure networks are parameterized- the\nparameters can represent uncertainty (estimation error in resistance/inductance\nof a power transmission line, for example)or controllable decision variables\n(power outputs of generators,for example). It is then of interest to understand\nconditions on the parameters under which the quadratic system is guaranteed to\nhave a solution within a specified set (for example, bounds on voltages and\nflows in a power grid). Given nominal values of the parameters at which the\nquadratic system has a solution and the Jacobian of the quadratic system at the\nsolution i snon-singular, we develop a general framework to construct convex\nregions around the nominal value such that the system is guaranteed to have a\nsolution within a given distance of the nominal solution. We show that several\nresults from recen tliterature can be recovered as special cases of our\nframework,and demonstrate our approach on several benchmark power systems.\n",
"title": "Solvability regions of affinely parameterized quadratic equations"
} | null | null | [
"Computer Science",
"Mathematics"
]
| null | true | null | 20318 | null | Validated | null | null |
null | {
"abstract": " In most illiquid markets, there is no obvious proxy for the market price of\nan asset. The European corporate bond market is an archetypal example of such\nan illiquid market where mid-prices can only be estimated with a statistical\nmodel. In this OTC market, dealers / market makers only have access, indeed, to\npartial information about the market. In real-time, they know the price\nassociated with their trades on the dealer-to-dealer (D2D) and dealer-to-client\n(D2C) markets, they know the result of the requests for quotes (RFQ) they\nanswered, and they have access to composite prices (e.g., Bloomberg CBBT). This\npaper presents a Bayesian method for estimating the mid-price of corporate\nbonds by using the real-time information available to a dealer. This method\nrelies on recent ideas coming from the particle filtering (PF) / sequential\nMonte-Carlo (SMC) literature.\n",
"title": "Mid-price estimation for European corporate bonds: a particle filtering approach"
} | null | null | null | null | true | null | 20319 | null | Default | null | null |
null | {
"abstract": " We propose a novel method for 3D object pose estimation in RGB images, which\ndoes not require pose annotations of objects in images in the training stage.\nWe tackle the pose estimation problem by learning how to establish\ncorrespondences between RGB images and rendered depth images of CAD models.\nDuring training, our approach only requires textureless CAD models and aligned\nRGB-D frames of a subset of object instances, without explicitly requiring pose\nannotations for the RGB images. We employ a deep quadruplet convolutional\nneural network for joint learning of suitable keypoints and their associated\ndescriptors in pairs of rendered depth images which can be matched across\nmodalities with aligned RGB-D views. During testing, keypoints are extracted\nfrom a query RGB image and matched to keypoints extracted from rendered depth\nimages, followed by establishing 2D-3D correspondences. The object's pose is\nthen estimated using the RANSAC and PnP algorithms. We conduct experiments on\nthe recently introduced Pix3D dataset and demonstrate the efficacy of our\nproposed approach in object pose estimation as well as generalization to object\ninstances not seen during training.\n",
"title": "Matching RGB Images to CAD Models for Object Pose Estimation"
} | null | null | null | null | true | null | 20320 | null | Default | null | null |
null | {
"abstract": " In human microbiome studies, sequencing reads data are often summarized as\ncounts of bacterial taxa at various taxonomic levels specified by a taxonomic\ntree. This paper considers the problem of analyzing two repeated measurements\nof microbiome data from the same subjects. Such data are often collected to\nassess the change of microbial composition after certain treatment, or the\ndifference in microbial compositions across body sites. Existing models for\nsuch count data are limited in modeling the covariance structure of the counts\nand in handling paired multinomial count data. A new probability distribution\nis proposed for paired-multinomial count data, which allows flexible covariance\nstructure and can be used to model repeatedly measured multivariate count data.\nBased on this distribution, a test statistic is developed for testing the\ndifference in compositions based on paired multinomial count data. The proposed\ntest can be applied to the count data observed on a taxonomic tree in order to\ntest difference in microbiome compositions and to identify the subtrees with\ndifferent subcompositions. Simulation results indicate that proposed test has\ncorrect type 1 errors and increased power compared to some commonly used\nmethods. An analysis of an upper respiratory tract microbiome data set is used\nto illustrate the proposed methods.\n",
"title": "A Model for Paired-Multinomial Data and Its Application to Analysis of Data on a Taxonomic Tree"
} | null | null | null | null | true | null | 20321 | null | Default | null | null |
null | {
"abstract": " Transition points mark qualitative changes in the macroscopic properties of\nlarge complex systems. Explosive transitions, exhibiting properties of both\ncontinuous and discontinuous phase transitions, have recently been uncovered in\nnetwork growth processes. Real networks not only grow but often also\nrestructure, yet common network restructuring processes, such as small world\nrewiring, do not exhibit phase transitions. Here, we uncover a class of\nintrinsically discontinuous transitions emerging in network restructuring\nprocesses controlled by \\emph{adhesion} -- the preference of a chosen link to\nremain connected to its end node. Deriving a master equation for the temporal\nnetwork evolution and working out an analytic solution, we identify genuinely\ndiscontinuous transitions in non-growing networks, separating qualitatively\ndistinct phases with monotonic and with peaked degree distributions.\nIntriguingly, our analysis of heuristic data indicates a separation between the\nsame two forms of degree distributions distinguishing abstract from\nface-to-face social networks.\n",
"title": "Adhesion-induced Discontinuous Transitions and Classifying Social Networks"
} | null | null | null | null | true | null | 20322 | null | Default | null | null |
null | {
"abstract": " We provide a hybrid method that captures the polynomial speed of convergence\nand polynomial speed of mixing for Markov processes. The hybrid method that we\nintroduce is based on the coupling technique and renewal theory. We propose to\nreplace some estimates in classical results about the ergodicity of Markov\nprocesses by numerical simulations when the corresponding analytical proof is\ndifficult. After that, all remaining conclusions can be derived from rigorous\nanalysis. Then we apply our results to two 1D microscopic heat conduction\nmodels. The mixing rate of these two models are expected to be polynomial but\nvery difficult to prove. In both examples, our numerical results match the\nexpected polynomial mixing rate well.\n",
"title": "Numerical simulation of polynomial-speed convergence phenomenon"
} | null | null | null | null | true | null | 20323 | null | Default | null | null |
null | {
"abstract": " Primarily motivated by the drug development process, several publications\nhave now presented methodology for the design of multi-arm multi-stage\nexperiments with normally distributed outcome variables of known variance.\nHere, we extend these past considerations to allow the design of what we refer\nto as an abcd multi-arm multi-stage experiment. We provide a proof of how\nstrong control of the a-generalised type-I familywise error-rate can be\nensured. We then describe how to attain the power to reject at least b out of c\nfalse hypotheses, which is related to controlling the b-generalised type-II\nfamilywise error-rate. Following this, we detail how a design can be optimised\nfor a scenario in which rejection of any d null hypotheses brings about\ntermination of the experiment. We achieve this by proposing a highly\ncomputationally efficient approach for evaluating the performance of a\ncandidate design. Finally, using a real clinical trial as a motivating example,\nwe explore the effect of the design's control parameters on the statistical\noperating characteristics.\n",
"title": "Efficient determination of optimised multi-arm multi-stage experimental designs with control of generalised error-rates"
} | null | null | null | null | true | null | 20324 | null | Default | null | null |
null | {
"abstract": " In this paper we present the co-simulation of a PID class power converter\ncontroller and an electrical circuit by means of the waveform relaxation\ntechnique. The simulation of the controller model is characterized by a\nfixed-time stepping scheme reflecting its digital implementation, whereas a\ncircuit simulation usually employs an adaptive time stepping scheme in order to\naccount for a wide range of time constants within the circuit model. In order\nto maintain the characteristic of both models as well as to facilitate model\nreplacement, we treat them separately by means of input/output relations and\npropose an application of a waveform relaxation algorithm. Furthermore, the\nmaximum and minimum number of iterations of the proposed algorithm are\nmathematically analyzed. The concept of controller/circuit coupling is\nillustrated by an example of the co-simulation of a PI power converter\ncontroller and a model of the main dipole circuit of the Large Hadron Collider.\n",
"title": "Application of the Waveform Relaxation Technique to the Co-Simulation of Power Converter Controller and Electrical Circuit Models"
} | null | null | null | null | true | null | 20325 | null | Default | null | null |
null | {
"abstract": " Here, in this paper it has been considered a sub family of exponential\nfamily. Maximum likelihood estimations (MLE) for the parameter of this family,\nprobability density function, and cumulative density function based on a sample\nand based on lower record values have been obtained. It has been considered\nMean Square Error (MSE) as a criterion for determining which is better in\ndifferent situations. Additionally, it has been proved some theories about the\nrelations between MLE based on lower record values and based on a random\nsample. Also, some interesting asymptotically properties for these estimations\nhave been shown during some theories.\n",
"title": "Some Investigations about the Properties of Maximum Likelihood Estimations Based on Lower Record Values for a Sub-Family of the Exponential Family"
} | null | null | [
"Mathematics",
"Statistics"
]
| null | true | null | 20326 | null | Validated | null | null |
null | {
"abstract": " This paper revisits the problem of optimal control law design for linear\nsystems using the global optimal control framework introduced by Vadim Krotov.\nKrotov's approach is based on the idea of total decomposition of the original\noptimal control problem (OCP) with respect to time, by an $ad$ $hoc$ choice of\nthe so-called Krotov's function or solving function, thereby providing\nsufficient conditions for the existence of global solution based on another\noptimization problem, which is completely equivalent to the original OCP. It is\nwell known that the solution of this equivalent optimization problem is\nobtained using an iterative method. In this paper, we propose suitable Krotov's\nfunctions for linear quadratic OCP and subsequently, show that by imposing\nconvexity condition on this equivalent optimization problem, there is no need\nto compute an iterative solution. We also give some key insights into the\nsolution procedure of the linear quadratic OCP using the proposed methodology\nin contrast to the celebrated Calculus of Variations (CoV) and\nHamilton-Jacobi-Bellman (HJB) equation based approach.\n",
"title": "Some Insights on Synthesizing Optimal Linear Quadratic Controller Using Krotov's Sufficiency Conditions"
} | null | null | [
"Computer Science"
]
| null | true | null | 20327 | null | Validated | null | null |
null | {
"abstract": " Schumann resonance transients which propagate around the globe can\npotentially generate a correlated background in widely separated gravitational\nwave detectors. We show that due to the distribution of lightning hotspots\naround the globe these transients have characteristic time lags, and this\nfeature can be useful to further suppress such a background, especially in\nsearches of the stochastic gravitational-wave background. A brief review of the\ncorresponding literature on Schumann resonances and lightnings is also given.\n",
"title": "Schumann resonance transients and the search for gravitational waves"
} | null | null | null | null | true | null | 20328 | null | Default | null | null |
null | {
"abstract": " How do you learn to navigate an Unmanned Aerial Vehicle (UAV) and avoid\nobstacles? One approach is to use a small dataset collected by human experts:\nhowever, high capacity learning algorithms tend to overfit when trained with\nlittle data. An alternative is to use simulation. But the gap between\nsimulation and real world remains large especially for perception problems. The\nreason most research avoids using large-scale real data is the fear of crashes!\nIn this paper, we propose to bite the bullet and collect a dataset of crashes\nitself! We build a drone whose sole purpose is to crash into objects: it\nsamples naive trajectories and crashes into random objects. We crash our drone\n11,500 times to create one of the biggest UAV crash dataset. This dataset\ncaptures the different ways in which a UAV can crash. We use all this negative\nflying data in conjunction with positive data sampled from the same\ntrajectories to learn a simple yet powerful policy for UAV navigation. We show\nthat this simple self-supervised model is quite effective in navigating the UAV\neven in extremely cluttered environments with dynamic obstacles including\nhumans. For supplementary video see: this https URL\n",
"title": "Learning to Fly by Crashing"
} | null | null | null | null | true | null | 20329 | null | Default | null | null |
null | {
"abstract": " Exploratory analysis over network data is often limited by our ability to\nefficiently calculate graph statistics, which can provide a model-free\nunderstanding of macroscopic properties of a network. This work introduces a\nframework for estimating the graphlet count - the number of occurrences of a\nsmall subgraph motif (e.g. a wedge or a triangle) in the network. For massive\ngraphs, where accessing the whole graph is not possible, the only viable\nalgorithms are those which act locally by making a limited number of vertex\nneighborhood queries.\nWe introduce a Monte Carlo sampling technique for graphlet counts, called\nlifting, which can simultaneously sample all graphlets of size up to $k$\nvertices. We outline three variants of lifted graphlet counts: the ordered,\nunordered, and shotgun estimators. We prove that our graphlet count updates are\nunbiased for the true graphlet count, have low correlation between samples, and\nhave a controlled variance. We compare the experimental performance of lifted\ngraphlet counts to the state-of-the art graphlet sampling procedures: Waddling\nand the pairwise subgraph random walk.\n",
"title": "Estimating Graphlet Statistics via Lifting"
} | null | null | null | null | true | null | 20330 | null | Default | null | null |
null | {
"abstract": " As algorithms increasingly inform and influence decisions made about\nindividuals, it becomes increasingly important to address concerns that these\nalgorithms might be discriminatory. The output of an algorithm can be\ndiscriminatory for many reasons, most notably: (1) the data used to train the\nalgorithm might be biased (in various ways) to favor certain populations over\nothers; (2) the analysis of this training data might inadvertently or\nmaliciously introduce biases that are not borne out in the data. This work\nfocuses on the latter concern.\nWe develop and study multicalbration -- a new measure of algorithmic fairness\nthat aims to mitigate concerns about discrimination that is introduced in the\nprocess of learning a predictor from data. Multicalibration guarantees accurate\n(calibrated) predictions for every subpopulation that can be identified within\na specified class of computations. We think of the class as being quite rich;\nin particular, it can contain many overlapping subgroups of a protected group.\nWe show that in many settings this strong notion of protection from\ndiscrimination is both attainable and aligned with the goal of obtaining\naccurate predictions. Along the way, we present new algorithms for learning a\nmulticalibrated predictor, study the computational complexity of this task, and\ndraw new connections to computational learning models such as agnostic\nlearning.\n",
"title": "Calibration for the (Computationally-Identifiable) Masses"
} | null | null | [
"Computer Science",
"Statistics"
]
| null | true | null | 20331 | null | Validated | null | null |
null | {
"abstract": " (abridged) In this paper we revisit the problem of inferring the innermost\nstructure of the Milky Way's nuclear star cluster via star counts, to clarify\nwhether it displays a core or a cusp around the central black hole. Through\nimage stacking and improved PSF fitting we push the completeness limit about\none magnitude deeper than in previous, comparable work. Contrary to previous\nwork, we analyse the stellar density in well-defined magnitude ranges in order\nto be able to constrain stellar masses and ages. The RC and brighter giant\nstars display a core-like surface density profile within a projected radius\nR<0.3 pc of the central black hole, in agreement with previous studies, but\nshow a cusp-like surface density distribution at larger R. The surface density\nof the fainter stars can be described well by a single power-law at R<2 pc. The\ncusp-like profile of the faint stars persists even if we take into account the\npossible contamination of stars in this brightness range by young pre-main\nsequence stars. The data are inconsistent with a core-profile for the faint\nstars.Finally, we show that a 3D Nuker law provides a very good description of\nthe cluster structure. We conclude that the observed stellar density at the\nGalactic Centre, as it can be inferred with current instruments, is consistent\nwith the existence of a stellar cusp around the Milky Way's central black hole,\nSgr A*. This cusp is well developed inside the influence radius of about 3 pc\nof Sgr A* and can be described by a single three-dimensional power-law with an\nexponent gamma=1.23+-0.05. The apparent lack of RC stars and brighter giants at\nprojected distances of R < 0.3 pc (R<8\") of the massive black hole may indicate\nthat some mechanism has altered their distribution or intrinsic luminosity.\n",
"title": "The distribution of old stars around the Milky Way's central black hole I: Star counts"
} | null | null | null | null | true | null | 20332 | null | Default | null | null |
null | {
"abstract": " In this paper we discuss some general properties of viscoelastic models\ndefined in terms of constitutive equations involving infinitely many\nderivatives (of integer and fractional order). In particular, we consider as a\nworking example the recently developed Bessel models of linear viscoelasticiy\nthat, for short times, behave like fractional Maxwell bodies of order $1/2$.\n",
"title": "On infinite order differential operators in fractional viscoelasticity"
} | null | null | [
"Physics",
"Mathematics"
]
| null | true | null | 20333 | null | Validated | null | null |
null | {
"abstract": " Quasiparticle excitations in FeSe were studied by means of specific heat\n($C$) measurements on a high-quality single crystal under rotating magnetic\nfields. The field dependence of $C$ shows three-stage behavior with different\nslopes, indicating the existence of three gaps ($\\Delta_1$, $\\Delta_2$, and\n$\\Delta_3$). In the low-temperature and low-field region, the azimuthal-angle\n($\\phi$) dependence of $C$ shows a four-fold symmetric oscillation with sign\nchange. On the other hand, the polar-angle ($\\theta$) dependence manifests as\nan anisotropy-inverted two-fold symmetry with unusual shoulder behavior.\nCombining the angle-resolved results and the theoretical calculation, the\nsmaller gap $\\Delta_1$ is proved to have two vertical-line nodes or gap minima\nalong the $k_z$ direction, and is determined to reside on the electron-type\n$\\varepsilon$ band. $\\Delta_2$ is found to be related to the electron-type\n$\\delta$ band, and is isotropic in the $ab$-plane but largely anisotropic out\nof the plane. $\\Delta_3$ residing on the hole-type $\\alpha$ band shows a small\nout-of-plane anisotropy with a strong Pauli-paramagnetic effect.\n",
"title": "Gap structure of FeSe determined by field-angle-resolved specific heat measurements"
} | null | null | null | null | true | null | 20334 | null | Default | null | null |
null | {
"abstract": " This paper presents a model based on Deep Learning algorithms of LSTM and GRU\nfor facilitating an anomaly detection in Large Hadron Collider superconducting\nmagnets. We used high resolution data available in Post Mortem database to\ntrain a set of models and chose the best possible set of their\nhyper-parameters. Using Deep Learning approach allowed to examine a vast body\nof data and extract the fragments which require further experts examination and\nare regarded as anomalies. The presented method does not require tedious manual\nthreshold setting and operator attention at the stage of the system setup.\nInstead, the automatic approach is proposed, which achieves according to our\nexperiments accuracy of 99%. This is reached for the largest dataset of 302 MB\nand the following architecture of the network: single layer LSTM, 128 cells, 20\nepochs of training, look_back=16, look_ahead=128, grid=100 and optimizer Adam.\nAll the experiments were run on GPU Nvidia Tesla K80\n",
"title": "Recurrent Neural Networks for anomaly detection in the Post-Mortem time series of LHC superconducting magnets"
} | null | null | null | null | true | null | 20335 | null | Default | null | null |
null | {
"abstract": " Janus type Water-Splitting Catalysts have attracted highest attention as a\ntool of choice for solar to fuel conversion. AISI Ni 42 steel was upon harsh\nanodization converted in a bifunctional electrocatalyst. Oxygen evolution\nreaction- (OER) and hydrogen evolution reaction (HER) are highly efficiently\nand steadfast catalyzed at pH 7, 13, 14, 14.6 (OER) respectively at pH 0, 1,\n13, 14, 14.6 (HER). The current density taken from long-term OER measurements\nin pH 7 buffer solution upon the electro activated steel at 491 mV\noverpotential was around 4 times higher (4 mA/cm2) in comparison with recently\ndeveloped OER electrocatalysts. The very strong voltage-current behavior of the\ncatalyst shown in OER polarization experiments at both pH 7 and at pH 13 were\neven superior to those known for IrO2-RuO2. No degradation of the catalyst was\ndetected even when conditions close to standard industrial operations were\napplied to the catalyst. A stable Ni-, Fe- oxide based passivating layer\nsufficiently protected the bare metal for further oxidation. Quantitative\ncharge to oxygen- (OER) and charge to hydrogen (HER) conversion was confirmed.\nHigh resolution XPS spectra showed that most likely gamma-NiO(OH) and FeO(OH)\nare the catalytic active OER and NiO is the catalytic active HER species.\n",
"title": "Electro-Oxidation of Ni42 Steel: A highly Active Bifunctional Electrocatalyst"
} | null | null | null | null | true | null | 20336 | null | Default | null | null |
null | {
"abstract": " A prevailing challenge in the biomedical and social sciences is to estimate a\npopulation mean from a sample obtained with unknown selection probabilities.\nUsing a well-known ratio estimator, Aronow and Lee (2013) proposed a method for\npartial identification of the mean by allowing the unknown selection\nprobabilities to vary arbitrarily between two fixed extreme values. In this\npaper, we show how to leverage auxiliary shape constraints on the population\noutcome distribution, such as symmetry or log-concavity, to obtain tighter\nbounds on the population mean. We use this method to estimate the performance\nof Aymara students---an ethnic minority in the north of Chile---in a national\neducational standardized test. We implement this method in the new statistical\nsoftware package scbounds for R.\n",
"title": "Shape-constrained partial identification of a population mean under unknown probabilities of sample selection"
} | null | null | null | null | true | null | 20337 | null | Default | null | null |
null | {
"abstract": " This paper argues that a class of Riemannian metrics, called warped metrics,\nplays a fundamental role in statistical problems involving location-scale\nmodels. The paper reports three new results : i) the Rao-Fisher metric of any\nlocation-scale model is a warped metric, provided that this model satisfies a\nnatural invariance condition, ii) the analytic expression of the sectional\ncurvature of this metric, iii) the exact analytic solution of the geodesic\nequation of this metric. The paper applies these new results to several\nexamples of interest, where it shows that warped metrics turn location-scale\nmodels into complete Riemannian manifolds of negative sectional curvature. This\nis a very suitable situation for developing algorithms which solve problems of\nclassification and on-line estimation. Thus, by revealing the connection\nbetween warped metrics and location-scale models, the present paper paves the\nway to the introduction of new efficient statistical algorithms.\n",
"title": "Warped metrics for location-scale models"
} | null | null | null | null | true | null | 20338 | null | Default | null | null |
null | {
"abstract": " This work develops techniques for the sequential detection and location\nestimation of transient changes in the volatility (standard deviation) of time\nseries data. In particular, we introduce a class of change detection algorithms\nbased on the windowed volatility filter. The first method detects changes by\nemploying a convex combination of two such filters with differing window sizes,\nsuch that the adaptively updated convex weight parameter is then used as an\nindicator for the detection of instantaneous power changes. Moreover, the\nproposed adaptive filtering based method is readily extended to the\nmultivariate case by using recent advances in distributed adaptive filters,\nthereby using cooperation between the data channels for more effective\ndetection of change points. Furthermore, this work also develops a novel change\npoint location estimator based on the differenced output of the volatility\nfilter. Finally, the performance of the proposed methods were evaluated on both\nsynthetic and real world data.\n",
"title": "Detecting Changes in Time Series Data using Volatility Filters"
} | null | null | [
"Computer Science"
]
| null | true | null | 20339 | null | Validated | null | null |
null | {
"abstract": " We propose the kl-UCB ++ algorithm for regret minimization in stochastic\nbandit models with exponential families of distributions. We prove that it is\nsimultaneously asymptotically optimal (in the sense of Lai and Robbins' lower\nbound) and minimax optimal. This is the first algorithm proved to enjoy these\ntwo properties at the same time. This work thus merges two different lines of\nresearch with simple and clear proofs.\n",
"title": "A minimax and asymptotically optimal algorithm for stochastic bandits"
} | null | null | null | null | true | null | 20340 | null | Default | null | null |
null | {
"abstract": " Ordinary differential operators with periodic coefficients analytic in a\nstrip act on a Hardy-Hilbert space of analytic functions with inner product\ndefined by integration over a period on the boundary of the strip. Simple\nexamples show that eigenfunctions may form a complete set for a narrow strip,\nbut completeness may be lost for a wide strip. Completeness of the\neigenfunctions in the Hardy-Hilbert space is established for regular second\norder operators with matrix-valued coefficients when the leading coefficient\nsatisfies a positive real part condition throughout the strip.\n",
"title": "Eigenfunctions of Periodic Differential Operators Analytic in a Strip"
} | null | null | [
"Mathematics"
]
| null | true | null | 20341 | null | Validated | null | null |
null | {
"abstract": " The set of all possible configurations of the Ehrenfest wind-tree model\nendowed with the Hausdorff topology is a compact metric space. For a typical\nconfiguration we show that the wind-tree dynamics has infinite ergodic index in\nalmost every direction. In particular some ergodic theorems can be applied to\nshow that if we start with a large number of initially parallel particles their\ndirections decorrelate as the dynamics evolve answering the question posed by\nthe Ehrenfests.\n",
"title": "Infinite ergodic index of the ehrenfest wind-tree model"
} | null | null | null | null | true | null | 20342 | null | Default | null | null |
null | {
"abstract": " We study the cooperative optical coupling between regularly spaced atoms in a\none-dimensional waveguide using decompositions to subradiant and superradiant\ncollective excitation eigenmodes, direct numerical solutions, and analytical\ntransfer-matrix methods. We illustrate how the spectrum of transmitted light\nthrough the waveguide including the emergence of narrow Fano resonances can be\nunderstood by the resonance features of the eigenmodes. We describe a method\nbased on superradiant and subradiant modes to engineer the optical response of\nthe waveguide and to store light. The stopping of light is obtained by\ntransferring an atomic excitation to a subradiant collective mode with the zero\nradiative resonance linewidth by controlling the level shift of an atom in the\nwaveguide. Moreover, we obtain an exact analytic solution for the transmitted\nlight through the waveguide for the case of a regular lattice of atoms and\nprovide a simple description how the light transmission may present large\nresonance shifts when the lattice spacing is close, but not exactly equal, to\nhalf of the wavelength of the light. Experimental imperfections such as\nfluctuations of the positions of the atoms and loss of light from the waveguide\nare easily quantified in the numerical simulations, which produce the natural\nresult that the optical response of the atomic array tends toward the response\nof a gas with random atomic positions.\n",
"title": "Arrays of strongly-coupled atoms in a one-dimensional waveguide"
} | null | null | null | null | true | null | 20343 | null | Default | null | null |
null | {
"abstract": " Measures of neuroelectric activity from each of 18 automatically identified\nwhite matter tracts were extracted from resting MEG recordings from a\nnormative, n=588, and a chronic TBI, traumatic brain injury, n=63, cohort, 60\nof whose TBIs were mild. Activity in the TBI cohort was significantly reduced\ncompared with the norms for ten of the tracts, p < 10-6 for each. Significantly\nreduced activity (p < 10-3) was seen in more than one tract in seven mTBI\nindividuals and one member of the normative cohort.\n",
"title": "MEG-Derived Functional Tractography, Results for Normal and Concussed Cohorts"
} | null | null | null | null | true | null | 20344 | null | Default | null | null |
null | {
"abstract": " We present accurate electrical resistivity measurements along the two\nprinciple crystallographic axes of the pressure-induced heavy-fermion\nsuperconductor CeRhIn5 up to 5.63 GPa. For both directions, a valence crossover\nline is identified in the p-T plane and the extrapolation of this line to zero\ntemperature coincides with the collapse of the magnetic ordering temperature.\nFurthermore, it is found that the p-T phase diagram of CeRhIn5 in the valence\ncrossover region is very similar to that of CeCu2Si2. These results point to\nthe essential role of Ce-4f electron delocalization in both destroying magnetic\norder and realizing superconductivity in CeRhIn5 under pressure.\n",
"title": "Coincidence of magnetic and valence quantum critical points in CeRhIn5 under pressure"
} | null | null | null | null | true | null | 20345 | null | Default | null | null |
null | {
"abstract": " One of the goals in scaling sequential machine learning methods pertains to\ndealing with high-dimensional data spaces. A key related challenge is that many\nmethods heavily depend on obtaining the inverse covariance matrix of the data.\nIt is well known that covariance matrix estimation is problematic when the\nnumber of observations is relatively small compared to the number of variables.\nA common way to tackle this problem is through the use of a shrinkage estimator\nthat offers a compromise between the sample covariance matrix and a\nwell-conditioned matrix, with the aim of minimizing the mean-squared error. We\nderived sequential update rules to approximate the inverse shrinkage estimator\nof the covariance matrix. The approach paves the way for improved large-scale\nmachine learning methods that involve sequential updates.\n",
"title": "Sequential Inverse Approximation of a Regularized Sample Covariance Matrix"
} | null | null | [
"Statistics"
]
| null | true | null | 20346 | null | Validated | null | null |
null | {
"abstract": " A triple array is a rectangular array containing letters, each letter\noccurring equally often with no repeats in rows or columns, such that the\nnumber of letters common to two rows, two columns, or a row and a column are\n(possibly different) non-zero constants. Deleting the condition on the letters\ncommon to a row and a column gives a double array. We propose the term\n\\emph{sesqui-array} for such an array when only the condition on pairs of\ncolumns is deleted. Thus all triple arrays are sesqui-arrays.\nIn this paper we give three constructions for sesqui-arrays. The first gives\n$(n+1)\\times n^2$ arrays on $n(n+1)$ letters for $n\\geq 2$. (Such an array for\n$n=2$ was found by Bagchi.) This construction uses Latin squares. The second\nuses the \\emph{Sylvester graph}, a subgraph of the Hoffman--Singleton graph, to\nbuild a good block design for $36$ treatments in $42$ blocks of size~$6$, and\nthen uses this in a $7\\times 36$ sesqui-array for $42$ letters.\nWe also give a construction for $K\\times(K-1)(K-2)/2$ sesqui-arrays on\n$K(K-1)/2$ letters. This construction uses biplanes. It starts with a block of\na biplane and produces an array which satisfies the requirements for a\nsesqui-array except possibly that of having no repeated letters in a row or\ncolumn. We show that this condition holds if and only if the \\emph{Hussain\nchains} for the selected block contain no $4$-cycles. A sufficient condition\nfor the construction to give a triple array is that each Hussain chain is a\nunion of $3$-cycles; but this condition is not necessary, and we give a few\nfurther examples.\nWe also discuss the question of which of these arrays provide good designs\nfor experiments.\n",
"title": "Sesqui-arrays, a generalisation of triple arrays"
} | null | null | null | null | true | null | 20347 | null | Default | null | null |
null | {
"abstract": " Learning algorithms for natural language processing (NLP) tasks traditionally\nrely on manually defined relevant contextual features. On the other hand,\nneural network models using an only distributional representation of words have\nbeen successfully applied for several NLP tasks. Such models learn features\nautomatically and avoid explicit feature engineering. Across several domains,\nneural models become a natural choice specifically when limited characteristics\nof data are known. However, this flexibility comes at the cost of\ninterpretability. In this paper, we define three different methods to\ninvestigate ability of bi-directional recurrent neural networks (RNNs) in\ncapturing contextual features. In particular, we analyze RNNs for sequence\ntagging tasks. We perform a comprehensive analysis on general as well as\nbiomedical domain datasets. Our experiments focus on important contextual words\nas features, which can easily be extended to analyze various other feature\ntypes. We also investigate positional effects of context words and show how the\ndeveloped methods can be used for error analysis.\n",
"title": "Investigating how well contextual features are captured by bi-directional recurrent neural network models"
} | null | null | null | null | true | null | 20348 | null | Default | null | null |
null | {
"abstract": " In axisymmetric fusion reactors, the equilibrium magnetic configuration can\nbe expressed in terms of the solution to a semi-linear elliptic equation known\nas the Grad-Shafranov equation, the solution of which determines the poloidal\ncomponent of the magnetic field. When the geometry of the confinement region is\nknown, the problem becomes an interior Dirichlet boundary value problem. We\npropose a high order solver based on the Hybridizable Discontinuous Galerkin\nmethod. The resulting algorithm (1) provides high order of convergence for the\nflux function and its gradient, (2) incorporates a novel method for handling\npiecewise smooth geometries by extension from polygonal meshes, (3) can handle\ngeometries with non-smooth boundaries and x-points, and (4) deals with the\nsemi-linearity through an accelerated two-grid fixed-point iteration. The\neffectiveness of the algorithm is verified with computations for cases where\nanalytic solutions are known on configurations similar to those of actual\ndevices (ITER with single null and double null divertor, NSTX, ASDEX upgrade,\nand Field Reversed Configurations).\n",
"title": "A Hybridizable Discontinuous Galerkin solver for the Grad-Shafranov equation"
} | null | null | null | null | true | null | 20349 | null | Default | null | null |
null | {
"abstract": " We introduce a new sub-linear space sketch---the Weight-Median Sketch---for\nlearning compressed linear classifiers over data streams while supporting the\nefficient recovery of large-magnitude weights in the model. This enables\nmemory-limited execution of several statistical analyses over streams,\nincluding online feature selection, streaming data explanation, relative\ndeltoid detection, and streaming estimation of pointwise mutual information.\nUnlike related sketches that capture the most frequently-occurring features (or\nitems) in a data stream, the Weight-Median Sketch captures the features that\nare most discriminative of one stream (or class) compared to another. The\nWeight-Median Sketch adopts the core data structure used in the Count-Sketch,\nbut, instead of sketching counts, it captures sketched gradient updates to the\nmodel parameters. We provide a theoretical analysis that establishes recovery\nguarantees for batch and online learning, and demonstrate empirical\nimprovements in memory-accuracy trade-offs over alternative memory-budgeted\nmethods, including count-based sketches and feature hashing.\n",
"title": "Sketching Linear Classifiers over Data Streams"
} | null | null | null | null | true | null | 20350 | null | Default | null | null |
null | {
"abstract": " Weiyi Zhang noticed recently a gap in the proof of the main theorem of the\nauthors article \"Tamed to compatible: Symplectic forms via moduli space\nintegration\" [T] for the case when the symplectic 4-manifold in question has\nfirst Betti number 2 (and necessarily self-dual second Betti number 1). This\nnote explains how to fill this gap.\n",
"title": "Tamed to compatible when b^(2+) = 1 and b^1 = 2"
} | null | null | null | null | true | null | 20351 | null | Default | null | null |
null | {
"abstract": " In this article we discuss the Mass Transference Principle due to Beresnevich\nand Velani and survey several generalisations and variants, both deterministic\nand random. Using a Hausdorff measure analogue of the inhomogeneous\nKhintchine-Groshev Theorem, proved recently via an extension of the Mass\nTransference Principle to systems of linear forms, we give an alternative proof\nof a general inhomogeneous Jarn\\'{\\i}k-Besicovitch Theorem which was originally\nproved by Levesley. We additionally show that without monotonicity Levesley's\ntheorem no longer holds in general. Thereafter, we discuss recent advances by\nWang, Wu and Xu towards mass transference principles where one transitions from\n$\\limsup$ sets defined by balls to $\\limsup$ sets defined by rectangles (rather\nthan from \"balls to balls\" as is the case in the original Mass Transference\nPrinciple). Furthermore, we consider mass transference principles for\ntransitioning from rectangles to rectangles and extend known results using a\nslicing technique. We end this article with a brief survey of random analogues\nof the Mass Transference Principle.\n",
"title": "The Mass Transference Principle: Ten Years On"
} | null | null | null | null | true | null | 20352 | null | Default | null | null |
null | {
"abstract": " By two-color photoassociation of $^{40}$Ca four weakly bound vibrational\nlevels in the Ca$_2$ \\Xpot ground state potential were measured, using highly\nspin-forbidden transitions to intermediate states of the coupled system\n$^3\\Pi_{u}$ and $^3\\Sigma^+ _{u}$ near the ${^3P_1}$+${^1S_0}$ asymptote. From\nthe observed binding energies, including the least bound state, the long range\ndispersion coefficients $\\mathrm{C}_6, \\mathrm{C}_8,\\mathrm{C}_{10}$ and a\nprecise value for the s-wave scattering length of 308.5(50)~$a_0$ were derived.\nFrom mass scaling we also calculated the corresponding scattering length for\nother natural isotopes. From the Autler-Townes splitting of the spectra, the\nmolecular Rabi frequency has been determined as function of the laser intensity\nfor one bound-bound transition. The observed value for the Rabi-frequency is in\ngood agreement with calculated transition moments based on the derived\npotentials, assuming a dipole moment being independent of internuclear\nseparation for the atomic pair model.\n",
"title": "Ground-state properties of Ca$_2$ from narrow line two-color photoassociation"
} | null | null | null | null | true | null | 20353 | null | Default | null | null |
null | {
"abstract": " We provide criteria for the cyclotomic quiver Hecke algebras of type C to be\nsemisimple. In the semisimple case, we construct the irreducible modules.\n",
"title": "On the semisimplicity of the cyclotomic quiver Hecke algebra of type C"
} | null | null | null | null | true | null | 20354 | null | Default | null | null |
null | {
"abstract": " We compare electronic structures of single FeSe layer films on SrTiO$_3$\nsubstrate (FeSe/STO) and K$_x$Fe$_{2-y}$Se$_{2}$ superconductors obtained from\nextensive LDA and LDA+DMFT calculations with the results of ARPES experiments.\nIt is demonstrated that correlation effects on Fe-3d states are sufficient in\nprinciple to explain the formation of the shallow electron -- like bands at the\nM(X)-point. However, in FeSe/STO these effects alone are apparently\ninsufficient for the simultaneous elimination of the hole -- like Fermi surface\naround the $\\Gamma$-point which is not observed in ARPES experiments. Detailed\ncomparison of ARPES detected and calculated quasiparticle bands shows\nreasonable agreement between theory and experiment. Analysis of the bands with\nrespect to their origin and orbital composition shows, that for FeSe/STO system\nthe experimentally observed \"replica\" quasiparticle band at the M-point\n(usually attributed to forward scattering interactions with optical phonons in\nSrTiO$_3$ substrate) can be reasonably understood just as the LDA calculated\nFe-3d$_{xy}$ band, renormalized by electronic correlations. The only\nmanifestation of the substrate reduces to lifting the degeneracy between\nFe-3d$_{xz}$ and Fe-3d$_{yz}$ bands in the vicinity of M-point. For the case of\nK$_x$Fe$_{2-y}$Se$_{2}$ most bands observed in ARPES can also be understood as\ncorrelation renormalized Fe-3d LDA calculated bands, with overall semi --\nquantitative agreement with LDA+DMFT calculations.\n",
"title": "On the origin of the shallow and \"replica\" bands in FeSe monolayer superconductors"
} | null | null | [
"Physics"
]
| null | true | null | 20355 | null | Validated | null | null |
null | {
"abstract": " Regularization techniques are widely employed in optimization-based\napproaches for solving ill-posed inverse problems in data analysis and\nscientific computing. These methods are based on augmenting the objective with\na penalty function, which is specified based on prior domain-specific expertise\nto induce a desired structure in the solution. We consider the problem of\nlearning suitable regularization functions from data in settings in which\nprecise domain knowledge is not directly available. Previous work under the\ntitle of `dictionary learning' or `sparse coding' may be viewed as learning a\nregularization function that can be computed via linear programming. We\ndescribe generalizations of these methods to learn regularizers that can be\ncomputed and optimized via semidefinite programming. Our framework for learning\nsuch semidefinite regularizers is based on obtaining structured factorizations\nof data matrices, and our algorithmic approach for computing these\nfactorizations combines recent techniques for rank minimization problems along\nwith an operator analog of Sinkhorn scaling. Under suitable conditions on the\ninput data, our algorithm provides a locally linearly convergent method for\nidentifying the correct regularizer that promotes the type of structure\ncontained in the data. Our analysis is based on the stability properties of\nOperator Sinkhorn scaling and their relation to geometric aspects of\ndeterminantal varieties (in particular tangent spaces with respect to these\nvarieties). The regularizers obtained using our framework can be employed\neffectively in semidefinite programming relaxations for solving inverse\nproblems.\n",
"title": "A Matrix Factorization Approach for Learning Semidefinite-Representable Regularizers"
} | null | null | [
"Computer Science",
"Mathematics",
"Statistics"
]
| null | true | null | 20356 | null | Validated | null | null |
null | {
"abstract": " Network models are applied in numerous domains where data can be represented\nas a system of interactions among pairs of actors. While both statistical and\nmechanistic network models are increasingly capable of capturing various\ndependencies amongst these actors, these dependencies imply the lack of\nindependence. This poses statistical challenges for analyzing such data,\nespecially when there is only a single observed network, and often leads to\nintractable likelihoods regardless of the modeling paradigm, which limit the\napplication of existing statistical methods for networks. We explore a\nsubsampling bootstrap procedure to serve as the basis for goodness of fit and\nmodel selection with a single observed network that circumvents the\nintractability of such likelihoods. Our approach is based on flexible\nresampling distributions formed from the single observed network, allowing for\nfiner and higher dimensional comparisons than simply point estimates of\nquantities of interest. We include worked examples for model selection, with\nsimulation, and assessment of goodness of fit, with duplication-divergence\nmodel fits for yeast (S.cerevisiae) protein-protein interaction data from the\nliterature. The proposed procedure produces a flexible resampling distribution\nthat can be based on any statistics of one's choosing and can be employed\nregardless of choice of model.\n",
"title": "A Bootstrap Method for Goodness of Fit and Model Selection with a Single Observed Network"
} | null | null | null | null | true | null | 20357 | null | Default | null | null |
null | {
"abstract": " Extrapolation methods use the last few iterates of an optimization algorithm\nto produce a better estimate of the optimum. They were shown to achieve optimal\nconvergence rates in a deterministic setting using simple gradient iterates.\nHere, we study extrapolation methods in a stochastic setting, where the\niterates are produced by either a simple or an accelerated stochastic gradient\nalgorithm. We first derive convergence bounds for arbitrary, potentially biased\nperturbations, then produce asymptotic bounds using the ratio between the\nvariance of the noise and the accuracy of the current point. Finally, we apply\nthis acceleration technique to stochastic algorithms such as SGD, SAGA, SVRG\nand Katyusha in different settings, and show significant performance gains.\n",
"title": "Nonlinear Acceleration of Stochastic Algorithms"
} | null | null | null | null | true | null | 20358 | null | Default | null | null |
null | {
"abstract": " The motion of a massive particle in Rindler space has been studied and\nobtained the geodesics of motion. The orbits in Rindler space are found to be\nquite different from that of Schwarzschild case. The paths are not like the\nPerihelion Precession type. Further we have set up the non-relativistic\nSchrodinger equation for the particle in the quantum mechanical scenario in\npresence of background constant gravitational field and investigated the\nproblem of fall of the particle at the center. This problem is also treated\nclassically. Unlike the conventional scenario, here the fall occurs at the\nsurface of a sphere of unit radius.\n",
"title": "Motion of Massive Particles in Rindler Space and the Problem of Fall at the Centre"
} | null | null | null | null | true | null | 20359 | null | Default | null | null |
null | {
"abstract": " Approximate Markov chain Monte Carlo (MCMC) offers the promise of more rapid\nsampling at the cost of more biased inference. Since standard MCMC diagnostics\nfail to detect these biases, researchers have developed computable Stein\ndiscrepancy measures that provably determine the convergence of a sample to its\ntarget distribution. This approach was recently combined with the theory of\nreproducing kernels to define a closed-form kernel Stein discrepancy (KSD)\ncomputable by summing kernel evaluations across pairs of sample points. We\ndevelop a theory of weak convergence for KSDs based on Stein's method,\ndemonstrate that commonly used KSDs fail to detect non-convergence even for\nGaussian targets, and show that kernels with slowly decaying tails provably\ndetermine convergence for a large class of target distributions. The resulting\nconvergence-determining KSDs are suitable for comparing biased, exact, and\ndeterministic sample sequences and simpler to compute and parallelize than\nalternative Stein discrepancies. We use our tools to compare biased samplers,\nselect sampler hyperparameters, and improve upon existing KSD approaches to\none-sample hypothesis testing and sample quality improvement.\n",
"title": "Measuring Sample Quality with Kernels"
} | null | null | null | null | true | null | 20360 | null | Default | null | null |
null | {
"abstract": " In search engines, online marketplaces and other human-computer interfaces\nlarge collectives of individuals sequentially interact with numerous\nalternatives of varying quality. In these contexts, trial and error\n(exploration) is crucial for uncovering novel high-quality items or solutions,\nbut entails a high cost for individual users. Self-interested decision makers,\nare often better off imitating the choices of individuals who have already\nincurred the costs of exploration. Although imitation makes sense at the\nindividual level, it deprives the group of additional information that could\nhave been gleaned by individual explorers. In this paper we show that in such\nproblems, preference diversity can function as a welfare enhancing mechanism.\nIt leads to a consistent increase in the quality of the consumed alternatives\nthat outweighs the increased cost of search for the users.\n",
"title": "Diversity of preferences can increase collective welfare in sequential exploration problems"
} | null | null | null | null | true | null | 20361 | null | Default | null | null |
null | {
"abstract": " In the real world, many complex systems interact with other systems. In\naddition, the intra- or inter-systems for the spread of information about\ninfectious diseases and the transmission of infectious diseases are often not\nrandom, but with direction. Hence, in this paper, we build epidemic model based\non an interconnected directed network, which can be considered as the\ngeneralization of undirected networks and bipartite networks. By using the\nmean-field approach, we establish the Susceptible-Infectious-Susceptible model\non this network. We theoretically analyze the model, and obtain the basic\nreproduction number, which is also the generalization of the critical number\ncorresponding to undirected or bipartite networks. And we prove the global\nstability of disease-free and endemic equilibria via the basic reproduction\nnumber as a forward bifurcation parameter. We also give a condition for\nepidemic prevalence only on a single subnetwork. Furthermore, we carry out\nnumerical simulations, and find that the independence between each node's in-\nand out-degrees greatly reduce the impact of the network's topological\nstructure on disease spread.\n",
"title": "Epidemic spread in interconnected directed networks"
} | null | null | null | null | true | null | 20362 | null | Default | null | null |
null | {
"abstract": " Previously, the controllability problem of a linear time-invariant dynamical\nsystem was mapped to the maximum matching (MM) problem on the bipartite\nrepresentation of the underlying directed graph, and the sizes of MMs on random\nbipartite graphs were calculated analytically with the cavity method at zero\ntemperature limit. Here we present an alternative theory to estimate MM sizes\nbased on the core percolation theory and the perfect matching of cores. Our\ntheory is much more simplified and easily interpreted, and can estimate MM\nsizes on random graphs with or without symmetry between out- and in-degree\ndistributions. Our result helps to illuminate the fundamental connection\nbetween the controllability problem and the underlying structure of complex\nsystems.\n",
"title": "Controllability and maximum matchings of complex networks"
} | null | null | [
"Computer Science"
]
| null | true | null | 20363 | null | Validated | null | null |
null | {
"abstract": " We relate the counting of honeycomb dimer configurations on the cylinder to\nthe counting of certain vertices in Kirillov-Reshetikhin crystal graphs. We\nshow that these dimer configurations yield the quantum Kostka numbers of the\nsmall quantum cohomology ring of the Grassmannian, i.e. the expansion\ncoefficients when multiplying a Schubert class repeatedly with different Chern\nclasses. This allows one to derive sum rules for Gromov-Witten invariants.\n",
"title": "Dimers, crystals and quantum Kostka numbers"
} | null | null | null | null | true | null | 20364 | null | Default | null | null |
null | {
"abstract": " Programming by demonstration has recently gained much attention due to its\nuser-friendly and natural way to transfer human skills to robots. In order to\nfacilitate the learning of multiple demonstrations and meanwhile generalize to\nnew situations, a task-parameterized Gaussian mixture model (TP-GMM) has been\nrecently developed. This model has achieved reliable performance in areas such\nas human-robot collaboration and dual-arm manipulation. However, the crucial\ntask frames and associated parameters in this learning framework are often set\nby the human teacher, which renders three problems that have not been addressed\nyet: (i) task frames are treated equally, without considering their individual\nimportance, (ii) task parameters are defined without taking into account\nadditional task constraints, such as robot joint limits and motion smoothness,\nand (iii) a fixed number of task frames are pre-defined regardless of whether\nsome of them may be redundant or even irrelevant for the task at hand. In this\npaper, we generalize the task-parameterized learning by addressing the\naforementioned problems. Moreover, we provide a novel learning perspective\nwhich allows the robot to refine and adapt previously learned skills in a low\ndimensional space. Several examples are studied in both simulated and real\nrobotic systems, showing the applicability of our approach.\n",
"title": "Generalized Task-Parameterized Skill Learning"
} | null | null | null | null | true | null | 20365 | null | Default | null | null |
null | {
"abstract": " Aiming at financial applications, we study the problem of learning the\nvolatility under market microstructure noise. Specifically, we consider noisy\ndiscrete time observations from a stochastic differential equation and develop\na novel computational method to learn the diffusion coefficient of the\nequation. We take a nonparametric Bayesian approach, where we model the\nvolatility function a priori as piecewise constant. Its prior is specified via\nthe inverse Gamma Markov chain. Sampling from the posterior is accomplished by\nincorporating the Forward Filtering Backward Simulation algorithm in the Gibbs\nsampler. Good performance of the method is demonstrated on two representative\nsynthetic data examples. Finally, we apply the method on the EUR/USD exchange\nrate dataset.\n",
"title": "Nonparametric Bayesian volatility learning under microstructure noise"
} | null | null | null | null | true | null | 20366 | null | Default | null | null |
null | {
"abstract": " Supervised learning based methods for source localization, being data driven,\ncan be adapted to different acoustic conditions via training and have been\nshown to be robust to adverse acoustic environments. In this paper, a\nconvolutional neural network (CNN) based supervised learning method for\nestimating the direction-of-arrival (DOA) of multiple speakers is proposed.\nMulti-speaker DOA estimation is formulated as a multi-class multi-label\nclassification problem, where the assignment of each DOA label to the input\nfeature is treated as a separate binary classification problem. The phase\ncomponent of the short-time Fourier transform (STFT) coefficients of the\nreceived microphone signals are directly fed into the CNN, and the features for\nDOA estimation are learnt during training. Utilizing the assumption of disjoint\nspeaker activity in the STFT domain, a novel method is proposed to train the\nCNN with synthesized noise signals. Through experimental evaluation with both\nsimulated and measured acoustic impulse responses, the ability of the proposed\nDOA estimation approach to adapt to unseen acoustic conditions and its\nrobustness to unseen noise type is demonstrated. Through additional empirical\ninvestigation, it is also shown that with an array of M microphones our\nproposed framework yields the best localization performance with M-1\nconvolution layers. The ability of the proposed method to accurately localize\nspeakers in a dynamic acoustic scenario with varying number of sources is also\nshown.\n",
"title": "Multi-Speaker DOA Estimation Using Deep Convolutional Networks Trained with Noise Signals"
} | null | null | null | null | true | null | 20367 | null | Default | null | null |
null | {
"abstract": " A revolution in galaxy cluster science is only a few years away. The survey\nmachines eROSITA and Euclid will provide cluster samples of never-before-seen\nstatistical quality. XMM-Newton will be the key instrument to exploit these\nrich datasets in terms of detailed follow-up of the cluster hot gas content,\nsystematically characterizing sub-samples as well as exotic new objects.\n",
"title": "Follow-up of eROSITA and Euclid Galaxy Clusters with XMM-Newton"
} | null | null | null | null | true | null | 20368 | null | Default | null | null |
null | {
"abstract": " Previous studies have shown that intermediate surface tension has a\ncounterintuitive destabilizing effect on 2-phase planar jets. Here, the\ntransition process in confined 2D jets of two fluids with varying viscosity\nratio is investigated using DNS. Neutral curves for persistent oscillations are\nfound by recording the norm of the velocity residuals in DNS for over 1000\nnondimensional time units, or until the signal has reached a constant level in\na logarithmic scale - either a converged steady state, or a \"statistically\nsteady\" oscillatory state. Oscillatory final states are found for all viscosity\nratios (0.1-10). For uniform viscosity (m=1), the first bifurcation is through\na surface tension-driven global instability. For low viscosity of the outer\nfluid, there is a mode competition between a steady asymmetric Coanda-type\nattachment mode and the surface tension-induced mode. At moderate surface\ntension, the Coanda-type attachment dominates and eventually triggers\ntime-dependent convective bursts. At high surface tension, the surface\ntension-dominated mode dominates. For high viscosity of the outer fluid,\npersistent oscillations appear due to a strong convective instability. Finally,\nthe m=1 jet remains unstable far from the inlet when the shear profile is\nnearly constant. Comparing this to a parallel Couette flow (without inflection\npoints), we show that in both flows, a hidden interfacial mode brought out by\nsurface tension becomes temporally and absolutely unstable in an intermediate\nWeber and Reynolds regime. An energy analysis of the Couette setup shows that\nsurface tension, although dissipative, induces a velocity field near the\ninterface which extracts energy from the flow through a viscous mechanism. This\nstudy highlights the rich dynamics of immiscible planar uniform-density jets,\nwhere several self-sustained and convective mechanisms compete depending on the\nexact parameters.\n",
"title": "Effect of viscosity ratio on the self-sustained instabilities in planar immiscible jets"
} | null | null | null | null | true | null | 20369 | null | Default | null | null |
null | {
"abstract": " Generative adversarial networks (GANs) are highly effective unsupervised\nlearning frameworks that can generate very sharp data, even for data such as\nimages with complex, highly multimodal distributions. However GANs are known to\nbe very hard to train, suffering from problems such as mode collapse and\ndisturbing visual artifacts. Batch normalization (BN) techniques have been\nintroduced to address the training. Though BN accelerates the training in the\nbeginning, our experiments show that the use of BN can be unstable and\nnegatively impact the quality of the trained model. The evaluation of BN and\nnumerous other recent schemes for improving GAN training is hindered by the\nlack of an effective objective quality measure for GAN models. To address these\nissues, we first introduce a weight normalization (WN) approach for GAN\ntraining that significantly improves the stability, efficiency and the quality\nof the generated samples. To allow a methodical evaluation, we introduce\nsquared Euclidean reconstruction error on a test set as a new objective\nmeasure, to assess training performance in terms of speed, stability, and\nquality of generated samples. Our experiments with a standard DCGAN\narchitecture on commonly used datasets (CelebA, LSUN bedroom, and CIFAR-10)\nindicate that training using WN is generally superior to BN for GANs, achieving\n10% lower mean squared loss for reconstruction and significantly better\nqualitative results than BN. We further demonstrate the stability of WN on a\n21-layer ResNet trained with the CelebA data set. The code for this paper is\navailable at this https URL\n",
"title": "On the Effects of Batch and Weight Normalization in Generative Adversarial Networks"
} | null | null | null | null | true | null | 20370 | null | Default | null | null |
null | {
"abstract": " We present and implement a non-destructive detection scheme for the\ntransition probability readout of an optical lattice clock. The scheme relies\non a differential heterodyne measurement of the dispersive properties of\nlattice-trapped atoms enhanced by a high finesse cavity. By design, this scheme\noffers a 1st order rejection of the technical noise sources, an enhanced\nsignal-to-noise ratio, and an homogeneous atom-cavity coupling. We\ntheoretically show that this scheme is optimal with respect to the photon shot\nnoise limit. We experimentally realize this detection scheme in an operational\nstrontium optical lattice clock. The resolution is on the order of a few atoms\nwith a photon scattering rate low enough to keep the atoms trapped after\ndetection. This scheme opens the door to various different interrogations\nprotocols, which reduce the frequency instability, including atom recycling,\nzero-dead time clocks with a fast repetition rate, and sub quantum projection\nnoise frequency stability.\n",
"title": "A noise-immune cavity-assisted non-destructive detection for an optical lattice clock in the quantum regime"
} | null | null | null | null | true | null | 20371 | null | Default | null | null |
null | {
"abstract": " The only open case of Vizing's conjecture that every planar graph with\n$\\Delta\\geq 6$ is a class 1 graph is $\\Delta = 6$. We give a short proof of the\nfollowing statement: there is no 6-critical plane graph $G$, such that every\nvertex of $G$ is incident to at most three 3-faces. A stronger statement\nwithout restriction to critical graphs is stated in \\cite{Wang_Xu_2013}.\nHowever, the proof given there works only for critical graphs. Furthermore, we\nshow that every 5-critical plane graph has a 3-face which is adjacent to a\n$k$-face $(k\\in \\{3,4\\})$.\nFor $\\Delta = 5$ our result gives insights into the structure of planar\n$5$-critical graphs, and the result for $\\Delta=6$ gives support for the truth\nof Vizing's planar graph conjecture.\n",
"title": "Remarks on planar edge-chromatic critical graphs"
} | null | null | [
"Mathematics"
]
| null | true | null | 20372 | null | Validated | null | null |
null | {
"abstract": " We present a study of the low-frequency radio properties of star forming (SF)\ngalaxies and active galactic nuclei (AGN) up to redshift $z=2.5$. The new\nspectral window probed by the Low Frequency Array (LOFAR) allows us to\nreconstruct the radio continuum emission from 150 MHz to 1.4 GHz to an\nunprecedented depth for a radio-selected sample of $1542$ galaxies in $\\sim 7~\n\\rm{deg}^2$ of the LOFAR Boötes field. Using the extensive multi-wavelength\ndataset available in Boötes and detailed modelling of the FIR to UV spectral\nenergy distribution (SED), we are able to separate the star-formation (N=758)\nand the AGN (N=784) dominated populations. We study the shape of the radio SEDs\nand their evolution across cosmic time and find significant differences in the\nspectral curvature between the SF galaxy and AGN populations. While the radio\nspectra of SF galaxies exhibit a weak but statistically significant flattening,\nAGN SEDs show a clear trend to become steeper towards lower frequencies. No\nevolution of the spectral curvature as a function of redshift is found for SF\ngalaxies or AGN. We investigate the redshift evolution of the infrared-radio\ncorrelation (IRC) for SF galaxies and find that the ratio of total infrared to\n1.4 GHz radio luminosities decreases with increasing redshift: $ q_{\\rm 1.4GHz}\n= (2.45 \\pm 0.04) \\times (1+z)^{-0.15 \\pm 0.03} $. Similarly, $q_{\\rm 150MHz}$\nshows a redshift evolution following $ q_{\\rm 150GHz} = (1.72 \\pm 0.04) \\times\n(1+z)^{-0.22 \\pm 0.05}$. Calibration of the 150 MHz radio luminosity as a star\nformation rate tracer suggests that a single power-law extrapolation from\n$q_{\\rm 1.4GHz}$ is not an accurate approximation at all redshifts.\n",
"title": "The LOFAR window on star-forming galaxies and AGN - curved radio SEDs and IR-radio correlation at $0 < z < 2.5$"
} | null | null | null | null | true | null | 20373 | null | Default | null | null |
null | {
"abstract": " It is known that the initial-boundary value problem for certain integrable\npartial differential equations (PDEs) on the half-line with integrable boundary\nconditions can be mapped to a special case of the Inverse Scattering Method\n(ISM) on the full-line. This can also be established within the so-called\nUnified Transform (UT) for initial-boundary value problems with linearizable\nboundary conditions. In this paper, we show a converse to this statement within\nthe Ablowitz-Kaup-Newell-Segur (AKNS) scheme: the ISM on the full-line can be\nmapped to an initial-boundary value problem with linearizable boundary\nconditions. To achieve this, we need a matrix version of the UT that was\nintroduced by the author to study integrable PDEs on star-graphs. As an\napplication of the result, we show that the new, nonlocal reduction of the AKNS\nscheme introduced by Ablowitz and Musslimani to obtain the nonlocal Nonlinear\nSchrödinger (NLS) equation can be recast as an old, local reduction, thus\nputting the nonlocal NLS and the NLS equations on equal footing from the point\nof view of the reduction group theory of Mikhailov.\n",
"title": "Interplay between the Inverse Scattering Method and Fokas's Unified Transform with an Application"
} | null | null | null | null | true | null | 20374 | null | Default | null | null |
null | {
"abstract": " Wheeled ground robots are limited from exploring extreme environments such as\ncaves, lava tubes and skylights. Small robots that can utilize unconventional\nmobility through hopping, flying or rolling can overcome these limitations.\nMul-tiple robots operating as a team offer significant benefits over a single\nlarge ro-bot, as they are not prone to single-point failure, enable distributed\ncommand and control and enable execution of tasks in parallel. These robots can\ncomplement large rovers and landers, helping to explore inaccessible sites,\nobtaining samples and for planning future exploration missions. Our robots, the\nSphereX, are 3-kg in mass, spherical and contain computers equivalent to\ncurrent smartphones. They contain an array of guidance, navigation and control\nsensors and electronics. SphereX contains room for a 1-kg science payload,\nincluding for sample return. Our work in this field has recognized the need for\nminiaturized chemical mobility systems that provide power and propulsion. Our\nresearch explored the use of miniature rockets, including solid rockets,\nbi-propellants including RP1/hydrogen-peroxide and\npolyurethane/ammonium-perchlorate. These propulsion options provide maximum\nflight times of 10 minutes on Mars. Flying, especially hovering consumes\nsignificant fuel; hence, we have been developing our robots to perform\nballistic hops that enable the robots to travel efficiently over long\ndistances. Techniques are being developed to enable mid-course correction\nduring a ballistic hop. Using multiple cameras, it is possible to reconstitute\nan image scene from motion blur. Hence our approach is to enable photo mapping\nas the robots travel on a ballistic hop. The same images would also be used for\nnavigation and path planning. Using our proposed design approach, we are\ndeveloping low-cost methods for surface exploration of planetary bodies using a\nnetwork of small robots.\n",
"title": "GNC of the SphereX Robot for Extreme Environment Exploration on Mars"
} | null | null | [
"Computer Science",
"Physics"
]
| null | true | null | 20375 | null | Validated | null | null |
null | {
"abstract": " When studying flocking/swarming behaviors in animals one is interested in\nquantifying and comparing the dynamics of the clustering induced by the\ncoalescence and disbanding of animals in different groups. In a similar vein,\nstudying the dynamics of social networks leads to the problem of characterizing\ngroups/communities as they form and disperse throughout time.\nMotivated by this, we study the problem of obtaining persistent homology\nbased summaries of time-dependent data. Given a finite dynamic graph (DG), we\nfirst construct a zigzag persistence module arising from linearizing the\ndynamic transitive graph naturally induced from the input DG. Based on standard\nresults, we then obtain a persistence diagram or barcode from this zigzag\npersistence module. We prove that these barcodes are stable under perturbations\nin the input DG under a suitable distance between DGs that we identify.\nMore precisely, our stability theorem can be interpreted as providing a lower\nbound for the distance between DGs. Since it relies on barcodes, and their\nbottleneck distance, this lower bound can be computed in polynomial time from\nthe DG inputs.\nSince DGs can be given rise by applying the Rips functor (with a fixed\nthreshold) to dynamic metric spaces, we are also able to derive related stable\ninvariants for these richer class of dynamic objects.\nAlong the way, we propose a summarization of dynamic graphs that captures\ntheir time-dependent clustering features which we call formigrams. These\nset-valued functions generalize the notion of dendrogram, a prevalent tool for\nhierarchical clustering. In order to elucidate the relationship between our\ndistance between two DGs and the bottleneck distance between their associated\nbarcodes, we exploit recent advances in the stability of zigzag persistence due\nto Botnan and Lesnick, and to Bjerkevik.\n",
"title": "Stable Signatures for Dynamic Graphs and Dynamic Metric Spaces via Zigzag Persistence"
} | null | null | null | null | true | null | 20376 | null | Default | null | null |
null | {
"abstract": " We are developing a system for human-robot communication that enables people\nto communicate with robots in a natural way and is focused on solving problems\nin a shared space. Our strategy for developing this system is fundamentally\ndata-driven: we use data from multiple input sources and train key components\nwith various machine learning techniques. We developed a web application that\nis collecting data on how two humans communicate to accomplish a task, as well\nas a mobile laboratory that is instrumented to collect data on how two humans\ncommunicate to accomplish a task in a physically shared space. The data from\nthese systems will be used to train and fine-tune the second stage of our\nsystem, in which the robot will be simulated through software. A physical robot\nwill be used in the final stage of our project. We describe these instruments,\na test-suite and performance metrics designed to evaluate and automate the data\ngathering process as well as evaluate an initial data set.\n",
"title": "A Data-driven Approach Towards Human-robot Collaborative Problem Solving in a Shared Space"
} | null | null | [
"Computer Science"
]
| null | true | null | 20377 | null | Validated | null | null |
null | {
"abstract": " The recent turn towards quantitative text-as-data approaches in IR brought\nnew ways to study the discursive landscape of world politics. Here seen as\ncomplementary to qualitative approaches, quantitative assessments have the\nadvantage of being able to order and make comprehensible vast amounts of text.\nHowever, the validity of unsupervised methods applied to the types of text\navailable in large quantities needs to be established before they can speak to\nother studies relying on text and discourse as data. In this paper, we\nintroduce a new text corpus of United Nations Security Council (UNSC) speeches\non Afghanistan between 2001 and 2017; we study this corpus through unsupervised\ntopic modeling (LDA) with the central aim to validate the topic categories that\nthe LDA identifies; and we discuss the added value, and complementarity, of\nquantitative text-as-data approaches. We set-up two tests using mixed- method\napproaches. Firstly, we evaluate the identified topics by assessing whether\nthey conform with previous qualitative work on the development of the situation\nin Afghanistan. Secondly, we use network analysis to study the underlying\nsocial structures of what we will call 'speaker-topic relations' to see whether\nthey correspondent to know divisions and coalitions in the UNSC. In both cases\nwe find that the unsupervised LDA indeed provides valid and valuable outputs.\nIn addition, the mixed-method approaches themselves reveal interesting patterns\ndeserving future qualitative research. Amongst these are the coalition and\ndynamics around the 'women and human rights' topic as part of the UNSC debates\non Afghanistan.\n",
"title": "Discursive Landscapes and Unsupervised Topic Modeling in IR: A Validation of Text-As-Data Approaches through a New Corpus of UN Security Council Speeches on Afghanistan"
} | null | null | null | null | true | null | 20378 | null | Default | null | null |
null | {
"abstract": " We introduce a lattice gas implementation that is based on coarse-graining a\nMolecular Dynamics (MD) simulation. Such a lattice gas is similar to standard\nlattice gases, but its collision operator is informed by an underlying MD\nsimulation. This can be considered an optimal lattice gas implementation\nbecause it allows for the representation of any system that can be simulated\nwith MD. We show here that equilibrium behavior of the popular lattice\nBoltzmann algorithm is consistent with this optimal lattice gas. This\ncomparison allows us to make a more accurate identification of the expressions\nfor temperature and pressure in lattice Boltzmann simulations which turn out to\nbe related not only to the physical temperature and pressure but also to the\nlattice discretization. We show that for any spatial discretization we need to\nchoose a particular temporal discretization to recover the lattice Boltzmann\nequilibrium.\n",
"title": "Lattice Gas with Molecular Dynamics Collision Operator"
} | null | null | [
"Physics"
]
| null | true | null | 20379 | null | Validated | null | null |
null | {
"abstract": " In the present paper we investigate the influence of the retarded access by a\ntime-varying delay on the dynamics of delay systems. We show that there are two\nuniversality classes of delays, which lead to fundamental differences in\ndynamical quantities such as the Lyapunov spectrum. Therefore we introduce an\noperator theoretic framework, where the solution operator of the delay system\nis decomposed into the Koopman operator describing the delay access and an\noperator similar to the solution operator known from systems with constant\ndelay. The Koopman operator corresponds to an iterated map, called access map,\nwhich is defined by the iteration of the delayed argument of the delay\nequation. The dynamics of this one-dimensional iterated map determines the\nuniversality classes of the infinite-dimensional state dynamics governed by the\ndelay differential equation. In this way, we connect the theory of time-delay\nsystems with the theory of circle maps and the framework of the Koopman\noperator. In the present paper we extend our previous work [Otto, Müller, and\nRadons, Phys. Rev. Lett. 118, 044104 (2017)], by elaborating the mathematical\ndetails and presenting further results also on the Lyapunov vectors.\n",
"title": "From dynamical systems with time-varying delay to circle maps and Koopmanism"
} | null | null | null | null | true | null | 20380 | null | Default | null | null |
null | {
"abstract": " Unconventional superconductivity or superfluidity are among the most exciting\nand fascinating quantum states in condensed matter physics. Usually these\nstates are characterized by non-trivial spatial symmetry of the pairing order\nparameter, such as in $^{3}He$ and high-$T_{c}$ cuprates. Besides spatial\ndependence the order parameter could have unconventional frequency dependence,\nwhich is also allowed by Fermi-Dirac statistics. For instance, odd-frequency\npairing is an exciting paradigm when discussing exotic superfluidity or\nsuperconductivity and is yet to be realized in the experiments. In this paper\nwe propose a symmetry-based method of controlling frequency dependence of the\npairing order parameter via manipulating the inversion symmetry of the system.\nFirst, a toy model is introduced to illustrate that frequency dependence of the\norder parameter can be adjusted by controlling the inversion symmetry of the\nsystem. Second, taking advantage of the recent rapid developments of shaken\noptical lattices in ultracold gases, we propose a Bose-Fermi mixture to realize\nsuch frequency dependent superfluids. The key idea is introducing the\nfrequency-dependent attraction between Fermions mediated by Bogoliubov phonons\nwith asymmetric dispersion. Our proposal should pave an alternative way for\nexploring frequency-dependent superconductors or superfluids with cold atoms.\n",
"title": "Engineering Frequency-dependent Superfluidity in Bose-Fermi Mixtures"
} | null | null | null | null | true | null | 20381 | null | Default | null | null |
null | {
"abstract": " The problem of controlling the mean and the variance of a species of interest\nin a simple gene expression is addressed. It is shown that the protein mean\nlevel can be globally and robustly tracked to any desired value using a simple\nPI controller that satisfies certain sufficient conditions. Controlling both\nthe mean and variance however requires an additional control input, e.g. the\nmRNA degradation rate, and local robust tracking of mean and variance is proved\nto be achievable using multivariable PI control, provided that the reference\npoint satisfies necessary conditions imposed by the system. Even more\nimportantly, it is shown that there exist PI controllers that locally, robustly\nand simultaneously stabilize all the equilibrium points inside the admissible\nregion. The results are then extended to the mean control of a gene expression\nwith protein dimerization. It is shown that the moment closure problem can be\ncircumvented without invoking any moment closure technique. Local stabilization\nand convergence of the average dimer population to any desired reference value\nis ensured using a pure integral control law. Explicit bounds on the controller\ngain are provided and shown to be valid for any reference value. As a\nbyproduct, an explicit upper-bound of the variance of the monomer species,\nacting on the system as unknown input due to the moment openness, is obtained.\nThe results are illustrated by simulation.\n",
"title": "In-Silico Proportional-Integral Moment Control of Stochastic Reaction Networks with Applications to Gene Expression (with Dimerization)"
} | null | null | null | null | true | null | 20382 | null | Default | null | null |
null | {
"abstract": " Big, fine-grained enterprise registration data that includes time and\nlocation information enables us to quantitatively analyze, visualize, and\nunderstand the patterns of industries at multiple scales across time and space.\nHowever, data quality issues like incompleteness and ambiguity, hinder such\nanalysis and application. These issues become more challenging when the volume\nof data is immense and constantly growing. High Performance Computing (HPC)\nframeworks can tackle big data computational issues, but few studies have\nsystematically investigated imputation methods for enterprise registration data\nin this type of computing environment. In this paper, we propose a big data\nimputation workflow based on Apache Spark as well as a bare-metal computing\ncluster, to impute enterprise registration data. We integrated external data\nsources, employed Natural Language Processing (NLP), and compared several\nmachine-learning methods to address incompleteness and ambiguity problems found\nin enterprise registration data. Experimental results illustrate the\nfeasibility, efficiency, and scalability of the proposed HPC-based imputation\nframework, which also provides a reference for other big georeferenced text\ndata processing. Using these imputation results, we visualize and briefly\ndiscuss the spatiotemporal distribution of industries in China, demonstrating\nthe potential applications of such data when quality issues are resolved.\n",
"title": "Big enterprise registration data imputation: Supporting spatiotemporal analysis of industries in China"
} | null | null | null | null | true | null | 20383 | null | Default | null | null |
null | {
"abstract": " This paper presents a novel technique that allows for both computationally\nfast and sufficiently plausible simulation of vehicles with non-deformable\ntracks. The method is based on an effect we have called Contact Surface Motion.\nA comparison with several other methods for simulation of tracked vehicle\ndynamics is presented with the aim to evaluate methods that are available\noff-the-shelf or with minimum effort in general-purpose robotics simulators.\nThe proposed method is implemented as a plugin for the open-source\nphysics-based simulator Gazebo using the Open Dynamics Engine.\n",
"title": "Fast Simulation of Vehicles with Non-deformable Tracks"
} | null | null | null | null | true | null | 20384 | null | Default | null | null |
null | {
"abstract": " Recurrent neural networks (RNNs) have been drawing much attention with great\nsuccess in many applications like speech recognition and neural machine\ntranslation. Long short-term memory (LSTM) is one of the most popular RNN units\nin deep learning applications. LSTM transforms the input and the previous\nhidden states to the next states with the affine transformation, multiplication\noperations and a nonlinear activation function, which makes a good data\nrepresentation for a given task. The affine transformation includes rotation\nand reflection, which change the semantic or syntactic information of\ndimensions in the hidden states. However, considering that a model interprets\nthe output sequence of LSTM over the whole input sequence, the dimensions of\nthe states need to keep the same type of semantic or syntactic information\nregardless of the location in the sequence. In this paper, we propose a simple\nvariant of the LSTM unit, persistent recurrent unit (PRU), where each dimension\nof hidden states keeps persistent information across time, so that the space\nkeeps the same meaning over the whole sequence. In addition, to improve the\nnonlinear transformation power, we add a feedforward layer in the PRU\nstructure. In the experiment, we evaluate our proposed methods with three\ndifferent tasks, and the results confirm that our methods have better\nperformance than the conventional LSTM.\n",
"title": "Persistent Hidden States and Nonlinear Transformation for Long Short-Term Memory"
} | null | null | null | null | true | null | 20385 | null | Default | null | null |
null | {
"abstract": " We propose a vision-based method that localizes a ground vehicle using\npublicly available satellite imagery as the only prior knowledge of the\nenvironment. Our approach takes as input a sequence of ground-level images\nacquired by the vehicle as it navigates, and outputs an estimate of the\nvehicle's pose relative to a georeferenced satellite image. We overcome the\nsignificant viewpoint and appearance variations between the images through a\nneural multi-view model that learns location-discriminative embeddings in which\nground-level images are matched with their corresponding satellite view of the\nscene. We use this learned function as an observation model in a filtering\nframework to maintain a distribution over the vehicle's pose. We evaluate our\nmethod on different benchmark datasets and demonstrate its ability localize\nground-level images in environments novel relative to training, despite the\nchallenges of significant viewpoint and appearance variations.\n",
"title": "Satellite Image-based Localization via Learned Embeddings"
} | null | null | null | null | true | null | 20386 | null | Default | null | null |
null | {
"abstract": " We consider geometrical optimization problems related to optimizing the error\nprobability in the presence of a Gaussian noise. One famous questions in the\nfield is the \"weak simplex conjecture\". We discuss possible approaches to it,\nand state related conjectures about the Gaussian measure, in particular, the\nconjecture about minimizing of the Gaussian measure of a simplex. We also\nconsider antipodal codes, apply the Šidák inequality and establish some\ntheoretical and some numerical results about their optimality.\n",
"title": "Optimality of codes with respect to error probability in Gaussian noise"
} | null | null | null | null | true | null | 20387 | null | Default | null | null |
null | {
"abstract": " Time delay neural networks (TDNNs) are an effective acoustic model for large\nvocabulary speech recognition. The strength of the model can be attributed to\nits ability to effectively model long temporal contexts. However, current TDNN\nmodels are relatively shallow, which limits the modelling capability. This\npaper proposes a method of increasing the network depth by deepening the kernel\nused in the TDNN temporal convolutions. The best performing kernel consists of\nthree fully connected layers with a residual (ResNet) connection from the\noutput of the first to the output of the third. The addition of\nspectro-temporal processing as the input to the TDNN in the form of a\nconvolutional neural network (CNN) and a newly designed Grid-RNN was\ninvestigated. The Grid-RNN strongly outperforms a CNN if different sets of\nparameters for different frequency bands are used and can be further enhanced\nby using a bi-directional Grid-RNN. Experiments using the multi-genre broadcast\n(MGB3) English data (275h) show that deep kernel TDNNs reduces the word error\nrate (WER) by 6% relative and when combined with the frequency dependent\nGrid-RNN gives a relative WER reduction of 9%.\n",
"title": "Improved TDNNs using Deep Kernels and Frequency Dependent Grid-RNNs"
} | null | null | null | null | true | null | 20388 | null | Default | null | null |
null | {
"abstract": " This paper proposes a new model for extracting an interpretable sentence\nembedding by introducing self-attention. Instead of using a vector, we use a\n2-D matrix to represent the embedding, with each row of the matrix attending on\na different part of the sentence. We also propose a self-attention mechanism\nand a special regularization term for the model. As a side effect, the\nembedding comes with an easy way of visualizing what specific parts of the\nsentence are encoded into the embedding. We evaluate our model on 3 different\ntasks: author profiling, sentiment classification, and textual entailment.\nResults show that our model yields a significant performance gain compared to\nother sentence embedding methods in all of the 3 tasks.\n",
"title": "A Structured Self-attentive Sentence Embedding"
} | null | null | [
"Computer Science"
]
| null | true | null | 20389 | null | Validated | null | null |
null | {
"abstract": " We view a neural network as a distributed system of which neurons can fail\nindependently, and we evaluate its robustness in the absence of any (recovery)\nlearning phase. We give tight bounds on the number of neurons that can fail\nwithout harming the result of a computation. To determine our bounds, we\nleverage the fact that neural activation functions are Lipschitz-continuous.\nOur bound is on a quantity, we call the \\textit{Forward Error Propagation},\ncapturing how much error is propagated by a neural network when a given number\nof components is failing, computing this quantity only requires looking at the\ntopology of the network, while experimentally assessing the robustness of a\nnetwork requires the costly experiment of looking at all the possible inputs\nand testing all the possible configurations of the network corresponding to\ndifferent failure situations, facing a discouraging combinatorial explosion.\nWe distinguish the case of neurons that can fail and stop their activity\n(crashed neurons) from the case of neurons that can fail by transmitting\narbitrary values (Byzantine neurons). Interestingly, as we show in the paper,\nour bound can easily be extended to the case where synapses can fail.\nWe show how our bound can be leveraged to quantify the effect of memory cost\nreduction on the accuracy of a neural network, to estimate the amount of\ninformation any neuron needs from its preceding layer, enabling thereby a\nboosting scheme that prevents neurons from waiting for unnecessary signals. We\nfinally discuss the trade-off between neural networks robustness and learning\ncost.\n",
"title": "When Neurons Fail"
} | null | null | [
"Statistics"
]
| null | true | null | 20390 | null | Validated | null | null |
null | {
"abstract": " We consider a two-node tandem queueing network in which the upstream queue is\nM/G/1 and each job reuses its upstream service requirement when moving to the\ndownstream queue. Both servers employ the first-in-first-out policy. We\ninvestigate the amount of work in the second queue at certain embedded arrival\ntime points, namely when the upstream queue has just emptied. We focus on the\ncase of infinite-variance service times and obtain a heavy traffic process\nlimit for the embedded Markov chain.\n",
"title": "Heavy Traffic Limit for a Tandem Queue with Identical Service Times"
} | null | null | null | null | true | null | 20391 | null | Default | null | null |
null | {
"abstract": " In this letter, we present a new robotic harvester (Harvey) that can\nautonomously harvest sweet pepper in protected cropping environments. Our\napproach combines effective vision algorithms with a novel end-effector design\nto enable successful harvesting of sweet peppers. Initial field trials in\nprotected cropping environments, with two cultivar, demonstrate the efficacy of\nthis approach achieving a 46% success rate for unmodified crop, and 58% for\nmodified crop. Furthermore, for the more favourable cultivar we were also able\nto detach 90% of sweet peppers, indicating that improvements in the grasping\nsuccess rate would result in greatly improved harvesting performance.\n",
"title": "Autonomous Sweet Pepper Harvesting for Protected Cropping Systems"
} | null | null | null | null | true | null | 20392 | null | Default | null | null |
null | {
"abstract": " AMI observations towards CIZA J2242+5301, in comparison with observations of\nweak gravitational lensing and X-ray emission from the literature, are used to\ninvestigate the behaviour of non-baryonic dark matter (NBDM) and gas during the\nmerger. Analysis of the Sunyaev-Zel'dovich (SZ) signal indicates the presence\nof high pressure gas elongated perpendicularly to the X-ray and weak-lensing\nmorphologies which, given the merger-axis constraints in the literature,\nimplies that high pressure gas is pushed out into a linear structure during\ncore passing. Simulations in the literature closely matching the inferred\nmerger scenario show the formation of gas density and temperature structures\nperpendicular to the merger axis. These SZ observations are challenging for\nmodified gravity theories in which NBDM is not the dominant contributor to\ngalaxy-cluster gravity.\n",
"title": "AMI SZ observation of galaxy-cluster merger CIZA J2242+5301: perpendicular flows of gas and dark matter"
} | null | null | null | null | true | null | 20393 | null | Default | null | null |
null | {
"abstract": " In the orthognathic surgery, dental splints are important and necessary to\nhelp the surgeon reposition the maxilla or mandible. However, the traditional\nmethods of manual design of dental splints are difficult and time-consuming.\nThe research on computer-aided design software for dental splints is rarely\nreported. Our purpose is to develop a novel special software named EasySplint\nto design the dental splints conveniently and efficiently. The design can be\ndivided into two steps, which are the generation of initial splint base and the\nBoolean operation between it and the maxilla-mandibular model. The initial\nsplint base is formed by ruled surfaces reconstructed using the manually picked\npoints. Then, a method to accomplish Boolean operation based on the distance\nfiled of two meshes is proposed. The interference elimination can be conducted\non the basis of marching cubes algorithm and Boolean operation. The accuracy of\nthe dental splint can be guaranteed since the original mesh is utilized to form\nthe result surface. Using EasySplint, the dental splints can be designed in\nabout 10 minutes and saved as a stereo lithography (STL) file for 3D printing\nin clinical applications. Three phantom experiments were conducted and the\nefficiency of our method was demonstrated.\n",
"title": "Development of a computer-aided design software for dental splint in orthognathic surgery"
} | null | null | null | null | true | null | 20394 | null | Default | null | null |
null | {
"abstract": " Energy-transport equations for the transport of fermions in optical lattices\nare formally derived from a Boltzmann transport equation with a periodic\nlattice potential in the diffusive limit. The limit model possesses a formal\ngradient-flow structure like in the case of the energy-transport equations for\nsemiconductors. At the zeroth-order high temperature limit, the\nenergy-transport equations reduce to the whole-space logarithmic diffusion\nequation which has some unphysical properties. Therefore, the first-order\nexpansion is derived and analyzed. The existence of weak solutions to the\ntime-discretized system for the particle and energy densities with periodic\nboundary conditions is proved. The difficulties are the nonstandard degeneracy\nand the quadratic gradient term. The main tool of the proof is a result on the\nstrong convergence of the gradients of the approximate solutions. Numerical\nsimulations in one space dimension show that the particle density converges to\na constant steady state if the initial energy density is sufficiently large,\notherwise the particle density converges to a nonconstant steady state.\n",
"title": "Energy-transport systems for optical lattices: derivation, analysis, simulation"
} | null | null | null | null | true | null | 20395 | null | Default | null | null |
null | {
"abstract": " We report ab initio density functional calculations of the structural and\nmagnetic properties, and the electronic structure of CrAs. To simulate the\nobserved pressure-driven experimental results, we perform our analysis for\ndifferent volumes of the unit cell, showing that the structural, magnetic and\nelectronic properties strongly depend on the size of the cell. We find that the\ncalculated quantities are in good agreement with the experimental data, and we\nreview our results in terms of the observed superconductivity.\n",
"title": "First principles study of structural, magnetic and electronic properties of CrAs"
} | null | null | null | null | true | null | 20396 | null | Default | null | null |
null | {
"abstract": " Making sense of Wasserstein distances between discrete measures in\nhigh-dimensional settings remains a challenge. Recent work has advocated a\ntwo-step approach to improve robustness and facilitate the computation of\noptimal transport, using for instance projections on random real lines, or a\npreliminary quantization to reduce the number of points. We propose in this\nwork a new robust variant of the Wasserstein distance. This quantity captures\nthe maximal possible distance that can be realized between these two measures,\nafter they have been projected orthogonally on a lower k dimensional subspace.\nWe show that this distance inherits several favorably properties of OT, and\nthat computing it can be cast as a convex problem involving the top k\neigenvalues of the second order moment matrix of the displacements induced by a\ntransport plan. We provide algorithms to approximate the computation of this\nsaddle point using entropic regularization, and illustrate the interest of this\napproach empirically.\n",
"title": "Subspace Robust Wasserstein distances"
} | null | null | null | null | true | null | 20397 | null | Default | null | null |
null | {
"abstract": " X-ray emission associated to accretion onto compact objects displays\nimportant levels of photometric and spectroscopic time-variability. When the\naccretor orbits a Supergiant star, it captures a fraction of the supersonic\nradiatively-driven wind which forms shocks in its vicinity. The amplitude and\nstability of this gravitational beaming of the flow conditions the mass\naccretion rate responsible, in fine, for the X-ray luminosity of those\nSupergiant X-ray Binaries. The capacity of this low angular momentum inflow to\nform a disc-like structure susceptible to be the stage of well-known\ninstabilities remains at stake. Using state-of-the-art numerical setups, we\ncharacterized the structure of a Bondi-Hoyle-Lyttleton flow onto a compact\nobject, from the shock down to the vicinity of the accretor, typically five\norders of magnitude smaller. The evolution of the mass accretion rate and of\nthe bow shock which forms around the accretor (transverse structure, opening\nangle, stability, temperature profile) with the Mach number of the incoming\nflow is described in detail. The robustness of those simulations based on the\nHigh Performance Computing MPI-AMRVAC code is supported by the topology of the\ninner sonic surface, in agreement with theoretical expectations. We developed a\nsynthetic model of mass transfer in Supergiant X-ray Binaries which couples the\nlaunching of the wind accordingly to the stellar parameters, the orbital\nevolution of the streamlines in a modified Roche potential and the accretion\nprocess. We show that the shape of the permanent flow is entirely determined by\nthe mass ratio, the filling factor, the Eddington factor and the alpha-force\nmultiplier. Provided scales such as the orbital period are known, we can trace\nback the observables to evaluate the mass accretion rates, the accretion\nmechanism (stream or wind-dominated) and the shearing of the inflow.\n",
"title": "Wind accretion onto compact objects"
} | null | null | null | null | true | null | 20398 | null | Default | null | null |
null | {
"abstract": " We develop Riemannian Stein Variational Gradient Descent (RSVGD), a Bayesian\ninference method that generalizes Stein Variational Gradient Descent (SVGD) to\nRiemann manifold. The benefits are two-folds: (i) for inference tasks in\nEuclidean spaces, RSVGD has the advantage over SVGD of utilizing information\ngeometry, and (ii) for inference tasks on Riemann manifolds, RSVGD brings the\nunique advantages of SVGD to the Riemannian world. To appropriately transfer to\nRiemann manifolds, we conceive novel and non-trivial techniques for RSVGD,\nwhich are required by the intrinsically different characteristics of general\nRiemann manifolds from Euclidean spaces. We also discover Riemannian Stein's\nIdentity and Riemannian Kernelized Stein Discrepancy. Experimental results show\nthe advantages over SVGD of exploring distribution geometry and the advantages\nof particle-efficiency, iteration-effectiveness and approximation flexibility\nover other inference methods on Riemann manifolds.\n",
"title": "Riemannian Stein Variational Gradient Descent for Bayesian Inference"
} | null | null | null | null | true | null | 20399 | null | Default | null | null |
null | {
"abstract": " Spring-antispring systems have been investigated as possible low-frequency\nseismic isolation in high-precision optical experiments. These systems provide\nthe possibility to tune the fundamental resonance frequency to, in principle,\narbitrarily low values, and at the same time maintain a compact design of the\nisolation system. It was argued though that thermal noise in spring-antispring\nsystems would not be as small as one may naively expect from lowering the\nfundamental resonance frequency. In this paper, we present a detailed\ncalculation of the suspension thermal noise for a specific spring-antispring\nsystem, namely the Roberts linkage. We find a concise expression of the\nsuspension thermal noise spectrum, which assumes a form very similar to the\nwell-known expression for a simple pendulum. It is found that while the Roberts\nlinkage can provide strong seismic isolation due to a very low fundamental\nresonance frequency, its thermal noise is rather determined by the dimension of\nthe system. We argue that this is true for all horizontal mechanical isolation\nsystems with spring-antispring dynamics. This imposes strict requirements on\nmechanical spring-antispring systems for the seismic isolation in potential\nfuture low-frequency gravitational-wave detectors as we discuss for the four\nmain concepts: atom-interferometric, superconducting, torsion-bars, and\nconventional laser interferometer.\n",
"title": "Suspension-thermal noise in spring-antispring systems for future gravitational-wave detectors"
} | null | null | null | null | true | null | 20400 | null | Default | null | null |
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