December 2020 arXiv papers — page 103

Corbinian Schlosser, Milan Korda

In this paper, we propose a general sparse decomposition of dynamical systems provided that the vector field and constraint set possess certain sparse structures, which we call subsystems. This notion is based on causal dependence in the dynamics between the different states. This results in sparse descriptions for fundamental problems from nonlinear dynamic

Linfeng Jiang, Cheng Wang, Shuang Liu, Chao Sun

We successfully perform the three-dimensional tracking in a turbulent fluid flow of small asymmetrical particles that are neutrally-buoyant and bottom-heavy, i.e., they have a non-homogeneous mass distribution along their symmetry axis. We experimentally show how a tiny mass inhomogeneity can affect the particle orientation along the preferred vertical direc

Lukas Ophaus, Edgar Knobloch, Svetlana V. Gurevich, Uwe Thiele

The active phase-field-crystal (active PFC) model provides a simple microscopic mean field description of crystallization in active systems. It combines the PFC model (or conserved Swift-Hohenberg equation) of colloidal crystallization and aspects of the Toner-Tu theory for self-propelled particles. We employ the active PFC model to study the occurrence of l

Wencan Zhang, Mariella Dimiccoli, Brian Y. Lim

Model explanations such as saliency maps can improve user trust in AI by highlighting important features for a prediction. However, these become distorted and misleading when explaining predictions of images that are subject to systematic error (bias) by perturbations and corruptions. Furthermore, the distortions persist despite model fine-tuning on images b

Samudra Sur, Diptiman Sen

We study the effects of a periodically driven electric field applied to a variety of tight-binding models in one dimension. We first consider a non-interacting system with or without a staggered on-site potential, and we find that that periodic driving can generate states localized completely or partially near the ends of a finite-sized system. Depending on

Franziska Barkhausen, Matthias Pukrop, Stefan Schumacher, Xuekai Ma

We investigate the current flow of microcavity polariton condensates loaded into concentric ring-shaped potentials. The tunneling of the condensates between different potential rings results in different phase-locked states, depending on the separation of the potential rings. As a consequence, the condensate currents in different rings can flow either in the

Tsuneya Yoshida, Tomonari Mizoguchi, Yasuhiro Hatsugai

We theoretically demonstrate the realization of a chiral edge mode in a system beyond natural science. Specifically, we elucidate that a kagome network of rock-paper-scissors (K-RPS) hosts a chiral edge mode of the population density which is protected by the non-trivial topology in the bulk. The emergence of the chiral edge mode is demonstrated by numerical

Sam A. Mutter, Aura-Cristiana Radu, Alina Vdovina

We unite elements of category theory, K-theory, and geometric group theory, by defining a class of groups called $k$-cube groups, which act freely and transitively on the product of $k$ trees, for arbitrary $k$. The quotient of this action on the product of trees defines a $k$-dimensional cube complex, which induces a higher-rank graph. We make deductions ab

Naomi Schneider, Volker Michel

We propose a novel dictionary learning add-on for existing approximation algorithms for spherical inverse problems such as the downward continuation of the gravitational potential. The Inverse Problem Matching Pursuit (IPMP) algorithms iteratively minimize the Tikhonov functional in order to construct a weighted linear combination of so-called dictionary ele

John Southworth

$ζ$ Phoenicis is a bright binary system containing B6V and B8V stars. It has deep total and annular eclipses, a slightly eccentric orbit with a period of 1.669 d, apsidal motion and a third body on a wider orbit. The Transiting Exoplanet Survey Satellite light curve and published radial velocities of this system are analysed to determine masses of 3.91 +/- 0

Vaishakh Kedambaimoole, Neelotpala Kumar, Vijay Shirhatti, Suresh Nuthalapati

The human body is punctuated with wide array of sensory systems that provide a high evolutionary advantage by facilitating formation of a detailed picture of the immediate surroundings. The sensors range across a wide spectrum, acquiring input from non-contact audio-visual means to contact based input via pressure and temperature. The ambit of sensing can be

Markus Wallerberger, Hiroshi Shinaoka, Anna Kauch

The Bethe-Salpeter equation plays a crucial role in understanding the physics of correlated fermions, relating to optical excitations in solids as well as resonances in high-energy physics. Yet, it is notoriously difficult to control numerically, typically requiring an effort that scales polynomially with energy scales and accuracy. This puts many interestin

Daniela Kirilova, Mariana Panayotova

We calculate the baryon asymmetry value generated in the Scalar Field Condensate (SCF) baryogenesis model obtained in several inflationary scenarios and different reheating models. We provide analysis of the baryon asymmetry value obtained for more than 70 sets of parameters of the SCF model and the following inflationary scenarios, namely: new inflation, ch

Sergey Norin, Alex Scott, David R. Wood

The "clustered chromatic number" of a class of graphs is the minimum integer $k$ such that for some integer $c$ every graph in the class is $k$-colourable with monochromatic components of size at most $c$. We determine the clustered chromatic number of any minor-closed class with bounded treedepth, and prove a best possible upper bound on the clustered chrom

Lizxandra Flores-Rivera, Susan Terebey, Karen Willacy, Andrea Isella

Sub-millimeter spectral line and continuum emission from the protoplanetary disks and envelopes of protostars are powerful probes of their structure, chemistry, and dynamics. Here we present a benchmark study of our modeling code, RadChemT, that for the first time uses a chemical model to reproduce ALMA C$^{18}$O (2-1) and CARMA $^{12}$CO (1-0) and N$_{2}$H$

Fukun Liu, Chunyang Cao, Marek A. Abramowicz, Maciek Wielgus

Elliptical accretion disk models for tidal disruption events (TDEs) have been recently proposed and independently developed by two groups. Although these two models are characterized by a similar geometry, their physical properties differ considerably. In this paper, we further investigate the properties of the elliptical accretion disk of the nearly uniform

Jiahui Huang, Shi-Sheng Huang, Haoxuan Song, Shi-Min Hu

Previous online 3D dense reconstruction methods struggle to achieve the balance between memory storage and surface quality, largely due to the usage of stagnant underlying geometry representation, such as TSDF (truncated signed distance functions) or surfels, without any knowledge of the scene priors. In this paper, we present DI-Fusion (Deep Implicit Fusion

Laurent Bonnasse-Gahot, Jean-Pierre Nadal

A well-known perceptual consequence of categorization in humans and other animals, called categorical perception, is notably characterized by a within-category compression and a between-category separation: two items, close in input space, are perceived closer if they belong to the same category than if they belong to different categories. Elaborating on exp

Gyula Fodor, Etevaldo dos Santos Costa Filho, Betti Hartmann

The multipole moments of stationary axially symmetric vacuum or electrovacuum spacetimes can be expressed in terms of the power series expansion coefficients of the Ernst potential on the axis. In this paper we present a simpler, more efficient calculation of the multipole moments, applying methods introduced by B\"ackdahl and Herberthson. For the non-vacuum

Sven Bodenstedt, Morgan Mitchell, Michael Tayler

Optically pumped magnetometers (OPMs) based on alkali-atom vapors are ultra-sensitive devices for dc and low-frequency ac magnetic measurements. Here, in combination with fast-field-cycling hardware and high-resolution spectroscopic detection, we demonstrate applicability of OPMs in quantifying nuclear magnetic relaxation phenomena. Relaxation rate dispersio

Zeliang Song, Xiaofei Zhou, Zhendong Mao, Jianlong Tan

Image captioning is a challenging computer vision task, which aims to generate a natural language description of an image. Most recent researches follow the encoder-decoder framework which depends heavily on the previous generated words for the current prediction. Such methods can not effectively take advantage of the future predicted information to learn co

Xueshan Zhao, Lijun Gou, Yanting Dong, Youli Tuo

MAXI J1820+070 is a newly-discovered black hole X-ray binary, whose dynamical parameters, namely the black hole mass, the inclination angle and the source distance, have been estimated recently. \emph{Insight}-HXMT have observed its entire outburst from March 14th, 2018. In this work, we attempted to estimate the spin parameter~$a_*$, using the continuum-fit

Lennart Dabelow, Stefano Bo, Ralf Eichhorn

The defining feature of active particles is that they constantly propel themselves by locally converting chemical energy into directed motion. This active self-propulsion prevents them from equilibrating with their thermal environment (e.g., an aqueous solution), thus keeping them permanently out of equilibrium. Nevertheless, the spatial dynamics of active p

Qiyao Peng, Fred Vermolen, Daphne Weihs

The phenomenological model for cell shape deformation and cell migration (Chen et.al. 2018; Vermolen and Gefen 2012) is extended with the incorporation of cell traction forces and the evolution of cell equilibrium shapes as a result of cell differentiation. Plastic deformations of the extracellular matrix are modelled using morphoelasticity theory. The resul

Dominik Wilde, Andreas Krämer, Dirk Reith, Holger Foysi

Turbulent compressible flows are traditionally simulated using explicit time integrators applied to discretized versions of the Navier-Stokes equations. However, the associated Courant-Friedrichs-Lewy condition severely restricts the maximum time step size. Exploiting the Lagrangian nature of the Boltzmann equation's material derivative, we now introduce a f

Yuanqi Chen, Ge Li, Cece Jin, Shan Liu

This paper observes that there is an issue of high frequencies missing in the discriminator of standard GAN, and we reveal it stems from downsampling layers employed in the network architecture. This issue makes the generator lack the incentive from the discriminator to learn high-frequency content of data, resulting in a significant spectrum discrepancy bet

Guillaume Le Moing, Phongtharin Vinayavekhin, Don Joven Agravante, Tadanobu Inoue

Deep neural networks have recently led to promising results for the task of multiple sound source localization. Yet, they require a lot of training data to cover a variety of acoustic conditions and microphone array layouts. One can leverage acoustic simulators to inexpensively generate labeled training data. However, models trained on synthetic data tend to

Mitja Nedic

In this paper, we give several characterizations of Herglotz-Nevanlinna functions in terms of a specific type of positive semi-definite functions called Poisson-type functions. This allows us to propose a multidimensional analogue of the classical Nevanlinna kernel and a definition of generalized Nevanlinna functions in several variables. Furthermore, a char

Guillaume Le Moing, Don Joven Agravante, Tadanobu Inoue, Jayakorn Vongkulbhisal

This paper introduces an ensemble of discriminators that improves the accuracy of a domain adaptation technique for the localization of multiple sound sources. Recently, deep neural networks have led to promising results for this task, yet they require a large amount of labeled data for training. Recording and labeling such datasets is very costly, especiall

Tomohiro Okuma, Maria Evelina Rossi, Kei-ichi Watanabe, Ken-ichi Yoshida

Let $(A,\mathfrak m)$ be an excellent two-dimensional normal local domain. In this paper we study the elliptic and the strongly elliptic ideals of $A$ with the aim to characterize elliptic and strongly elliptic singularities, according to the definitions given by Wagreich and by Yau. In analogy with the rational singularities, in the main result we character

Ziang Long, Penghang Yin, Jack Xin

Deep neural networks (DNNs) are quantized for efficient inference on resource-constrained platforms. However, training deep learning models with low-precision weights and activations involves a demanding optimization task, which calls for minimizing a stage-wise loss function subject to a discrete set-constraint. While numerous training methods have been pro

Thibault Scoquart, Pierre-Élie Larré, Dominique Delande, Nicolas Cherroret

We explore the quench dynamics of a two-dimensional, weakly interacting disordered Bose gas for various relative strengths of interactions and disorder. This allows us to identify two well distinct out-of-equilibrium regimes. When interactions are smaller than the disorder, the gas experiences multiple scattering and exhibits a short-range spatial coherence.

Xiaoyuan Huang, Qiang Yuan, Yi-Zhong Fan

Cosmic rays are important probe of a number of fundamental physical problems such as the acceleration of high and very high energy particles in extreme astrophysical environments. The Galactic center is widely anticipated to be an important cosmic-ray source and the observations of some Imaging Atmospheric Cherenkov Telescopes did successfully reveal a compo

Jiashun Wang, Huazhe Xu, Jingwei Xu, Sifei Liu

Synthesizing 3D human motion plays an important role in many graphics applications as well as understanding human activity. While many efforts have been made on generating realistic and natural human motion, most approaches neglect the importance of modeling human-scene interactions and affordance. On the other hand, affordance reasoning (e.g., standing on t

Jingya Li, Demia Della Penda, Henrik Sahlin, Paul Schliwa-Bertling

5G system is characterized by its capability to support a wide range of use cases and services. Supporting accessibility differentiation becomes therefore essential to preserve a stable network condition during high traffic load, while ensuring connection and service quality to prioritized devices and services. In this article, we describe some use cases and

Liyang Han, Jalal Kazempour, Pierre Pinson

When energy customers schedule loads ahead of time, this information, if acquired by their energy retailer, can improve the retailer's load forecasts. Better forecasts lead to wholesale purchase decisions that are likely to result in lower energy imbalance costs, and thus higher profits for the retailer. Therefore, this paper monetizes the value of the custo

Alexander Menovschikov, Anastasia Molchanova, Luca Scarpa

We propose an extension of the classical variational theory of evolution equations that accounts for dynamics also in possibly non-reflexive and non-separable spaces. The pivoting point is to establish a novel variational structure, based on abstract modular spaces associated to a given convex function. Firstly, we show that the new variational triple is sui

Jun Ohkubo

The path-integral control, which stems from the stochastic Hamilton-Jacobi-Bellman equation, is one of the methods to control stochastic nonlinear systems. This paper gives a new insight into nonlinear stochastic optimal control problems from the perspective of Koopman operators. When a finite-dimensional dynamical system is nonlinear, the corresponding Koop

Maxime Lavaud, Thomas Salez, Yann Louyer, Yacine Amarouchene

Brownian motion in confinement and at interfaces is a canonical situation, encountered from fundamental biophysics to nanoscale engineering. Using the Lorenz-Mie framework, we optically record the thermally-induced tridimensional trajectories of individual microparticles, within salty aqueous solutions, in the vicinity of a rigid wall, and in the presence of

Guoqing Bao, Huai Chen, Tongliang Liu, Guanzhong Gong

There is an urgent need for automated methods to assist accurate and effective assessment of COVID-19. Radiology and nucleic acid test (NAT) are complementary COVID-19 diagnosis methods. In this paper, we present an end-to-end multitask learning (MTL) framework (COVID-MTL) that is capable of automated and simultaneous detection (against both radiology and NA

Giulia Fracastoro, Enrico Magli, Giovanni Poggi, Giuseppe Scarpa

Synthetic aperture radar (SAR) images are affected by a spatially-correlated and signal-dependent noise called speckle, which is very severe and may hinder image exploitation. Despeckling is an important task that aims at removing such noise, so as to improve the accuracy of all downstream image processing tasks. The first despeckling methods date back to th

Thomas Barthel, Yikang Zhang

In the thermodynamic limit, the steady states of open quantum many-body systems can undergo nonequilibrium phase transitions due to a competition between coherent and driven-dissipative dynamics. Here, we consider Markovian systems and elucidate structures of the Liouville super-operator that generates the time evolution. In many cases of interest, an operat

Apostolos Chalkis, Vissarion Fisikopoulos, Elias Tsigaridas, Haris Zafeiropoulos

Systems Biology is a fundamental field and paradigm that introduces a new era in Biology. The crux of its functionality and usefulness relies on metabolic networks that model the reactions occurring inside an organism and provide the means to understand the underlying mechanisms that govern biological systems. Even more, metabolic networks have a broader imp

Rohit Gupta, Satyajit Jena

A good understanding of the transverse momentum $(p_T)$ spectra is pivotal in the study of QCD matter created during the heavy-ion collision. Considering the difference in the underlying particle production mechanism, $p_T$ spectra can be divided into two distinct regions. Low-$p_T$ region corresponds to particle produced in soft-processes whereas particles

Lulu Fang, Hiroki Takahasi, Yiwei Zhang

We establish two precise asymptotic results on the Birkhoff sums for dynamical systems. These results are parallel to that on the arithmetic sums of independent and identically distributed random variables previously obtained by Hsu and Robbins, Erd\H{o}s, Heyde. We apply our results to the Gauss map and obtain new precise asymptotics in the theorem of L\'ev

Raffaele Marino

Many natural optimization problems are NP-hard, which implies that they are probably hard to solve exactly in the worst-case. However, it suffices to get reasonably good solutions for all (or even most) instances in practice. This paper presents a new algorithm for computing approximate solutions in ${\Theta(N})$ for the Maximum Exact 3-Satisfiability (MAX-E

Leonid Pekker

In this article, we construct a novel 1D-model of microfluidic laminar flows in tapered circular and rectangular channels assuming the flow in channels fully developed. In the model, we take into account the inertance and dynamic pressure terms. The model can be used for a wide range of flows: from the pure capillary flow regime, where the capillary forces a

Song-Jin O, Yong-Hwan Kim, Ok-Gyong Pak, Kum-Hyok Jong

Motivated by recent discovery of correlated insulating and superconducting behavior in twisted bilayer graphene, we revisit graphene's honeycomb lattice doped close to the van Hove singularity, using the truncated unity functional renormalization group approach. We consider an extended Hubbard model on the honeycomb lattice including on-site and nearest-neig

Kaifeng Cui, Sijia Chao, Chenglong Sun, Shaomao Wang

Quantum-logic-based ${}^{27}\textrm{Al}^+$ optical clock has been demonstrated in several schemes as there are different choices of the auxiliary ion species. In this paper, we present the first detailed evaluation of the systematic shift and the total uncertainty of an ${}^{27}\textrm{Al}^+$ optical clock sympathetically cooled by a ${}^{40}\textrm{Ca}^+$ i

Isaías Rodríguez, Renela M. Valladares, Alexander Valladares, David Hinojosa-Romero

For almost a century, since Bernal\'s attempts at a molecular theory of liquid structure(Bernal [1]), correlation functions have been the bridge to compare theoretical calculations with experimental measurements in the study of disordered materials. Pair Distribution Functions (g(r)), Radial Distribution Functions (J(r)), Plane Angle Distributions (g({\theta

Hao Li, Huan Wang, Guanghua Liu

Nowadays, social network platforms have been the prime source for people to experience news and events due to their capacities to spread information rapidly, which inevitably provides a fertile ground for the dissemination of fake news. Thus, it is significant to detect fake news otherwise it could cause public misleading and panic. Existing deep learning mo

Nipun Wijerathne, Billy Pik Lik Lau, Benny Kai Kiat Ng, Chau Yuen

Urban data mining can be identified as a highly potential area that can enhance the smart city services towards better sustainable development especially in the urban residential activity tracking. While existing human activity tracking systems have demonstrated the capability to unveil the hidden aspects of citizens' behavior, they often come with a hig

Ulises Nucamendi, Alfredo Herrera-Aguilar, Raul Lizardo-Castro, Omar Lopez Cruz

We construct from first principles a general relativistic approach to study Schwarzschild black hole (BH) rotation curves and estimate the mass-to-distance ratio of the active galactic nucleus of NGC 4258 in terms of astrophysical observable quantities. The presented method allows one to clearly distinguish and quantify the general and special relativistic c

Ian Gleason

We introduce the specialization map in Scholzes theory of diamonds. We consider v-sheaves that behave like formal schemes and call them kimberlites. We attach to them: a reduced special fiber, an analytic locus, a specialization map, a Zariski site, and an etale site. When the kimberlite comes from a formal scheme, our sites recover the classical ones. We pr

Binbin Zhang, Di Wu, Zhuoyuan Yao, Xiong Wang

In this paper, we present a novel two-pass approach to unify streaming and non-streaming end-to-end (E2E) speech recognition in a single model. Our model adopts the hybrid CTC/attention architecture, in which the conformer layers in the encoder are modified. We propose a dynamic chunk-based attention strategy to allow arbitrary right context length. At infer

Jinliang Wang, Binhua Li, Xiliang Zhang

Lucky imaging is a high-resolution astronomical image recovery technique with two classic implementation algorithms, i.e. image selecting, shifting and adding in image space and data selecting and image synthesizing in Fourier space. This paper proposes a novel lucky imaging algorithm where with space-domain and frequency-domain selection rates as a link, th

Musarrat Hussain, Fahad Ahmed Satti, Jamil Hussain, Taqdir Ali

Objective: Causality mining is an active research area, which requires the application of state-of-the-art natural language processing techniques. In the healthcare domain, medical experts create clinical text to overcome the limitation of well-defined and schema driven information systems. The objective of this research work is to create a framework, which

Kingshuk Mukhuti, Sudip Sinha, Subhasis Sinha, Bhavtosh Bansal

Lead and tin-based chalcogenide semiconductors like PbTe or SnSe have long been known to exhibit an unusually low thermal conductivity that makes them very attractive thermoelectric materials. An apparently unrelated fact is that the excitonic bandgap in these materials increases with temperature, whereas for most semiconductors one observes the opposite tre

Viktor T. Toth, Slava G. Turyshev

We report on the initial results obtained with an image convolution/deconvolution computer code that we developed and used to study the image formation capabilities of the solar gravitational lens (SGL). Although the SGL of a spherical Sun creates a greatly blurred image, knowledge of the SGL's point-spread function (PSF) makes it possible to reconstruct the

Matthew T. Scoggins, Armin Rahmani

In this work, we address the challenge of uncovering patterns in variational optimal protocols for taking the system to ground states of many-body Hamiltonians, using variational quantum algorithms. We develop highly optimized classical Monte Carlo (MC) algorithms to find the optimal protocols for transformations between the ground states of the square-latti

Enwei Zhang, Xinyang Jiang, Hao Cheng, Ancong Wu

Current training objectives of existing person Re-IDentification (ReID) models only ensure that the loss of the model decreases on selected training batch, with no regards to the performance on samples outside the batch. It will inevitably cause the model to over-fit the data in the dominant position (e.g., head data in imbalanced class, easy samples or nois

T. A. Khudaiberganov, S. M. Arakelian

A quantum behavior of the light emitted by exciton polaritons excited in a pillar semiconductor microcavity with embedded quantum well is investigated. Considering the bare excitons and photon modes as coupled quantum oscillators allows for an accurate accounting of the nonlinear and dissipative effects. In particular, using the method of quantum states pres

Yuting Qiang, Yongxin Yang, Xueting Zhang, Yanwen Guo

We present a novel Tensor Composition Net (TCN) to predict visual relationships in images. Visual Relationship Prediction (VRP) provides a more challenging test of image understanding than conventional image tagging and is difficult to learn due to a large label-space and incomplete annotation. The key idea of our TCN is to exploit the low-rank property of t

Chang Chen, Yin Li, Francisco Villaescusa-Navarro, Shirley Ho

Understanding the physics of large cosmological surveys down to small (nonlinear) scales will significantly improve our knowledge of the Universe. Large N-body simulations have been built to obtain predictions in the non-linear regime. However, N-body simulations are computationally expensive and generate large amount of data, putting burdens on storage. The

Marvin Häuser, Vitaly Cheptsov

The Unified Extensible Firmware Interface (UEFI) is a standardised interface between the firmware and the operating system used in all x86-based platforms over the past ten years, which continues to spread to other architectures such as ARM and RISC-V. The UEFI incorporates a modular design based on images containing a driver or an application in a Common Ob

Ryota Fukai, Sota Arakawa

Describing the comprehensive evolutionary scenario for asteroids is key to explaining the various physical processes of the solar system. Bulk-scale carbonaceous chondrites (CCs) possibly record the primordial information associated with the formation processes of their parent bodies. In this study, we tried to estimate the relative formation region of volat

Hongxiang Qiu, Alex Luedtke

Bayes estimators are well known to provide a means to incorporate prior knowledge that can be expressed in terms of a single prior distribution. However, when this knowledge is too vague to express with a single prior, an alternative approach is needed. Gamma-minimax estimators provide such an approach. These estimators minimize the worst-case Bayes risk ove

Tomoki Ohsawa

This paper concerns an approximation of the expectation values of the position and momentum of the solution to the semiclassical Schr\"odinger equation with a Gaussian as the initial condition. Of particular interest is the approximation obtained by our symplectic/Hamiltonian formulation of the Gaussian wave packet dynamics that introduces a correction term

Nolan J. Coble, Matthew Coudron

We present a classical algorithm that, for any 3D geometrically-local, polylogarithmic-depth quantum circuit $C$ acting on $n$ qubits, and any bit string $x\in\{0,1\}^n$, can compute the quantity $|< x |C|0^{\otimes n}>|^2$ to within any inverse-polynomial additive error in quasi-polynomial time. It is known that it is $\#P$-hard to compute this same quantit

Anup Pradhan Sakhya, P. L. Paulose, A. Thamizhavel, Kalobaran Maiti

Realization of semimetals with non-trivial topologies such as Dirac and Weyl semimetals, have provided a boost in the study of these quantum materials. Presence of electron correlation makes the system even more exotic due to enhanced scattering of charge carriers, Kondo screening etc. Here, we studied the electronic properties of single crystalline, SmBi em

Guanya Shi, Wolfgang Hönig, Xichen Shi, Yisong Yue

We present Neural-Swarm2, a learning-based method for motion planning and control that allows heterogeneous multirotors in a swarm to safely fly in close proximity. Such operation for drones is challenging due to complex aerodynamic interaction forces, such as downwash generated by nearby drones and ground effect. Conventional planning and control methods ne

Shu-Ichiro Suzuki, Takashi Hirai, Matthias Eschrig, Yukio Tanaka

We investigate the effects of Andreev bound states due to the unconventional pairing on the inverse proximity effect of ferromagnet/superconductor junctions. Utilizing quasiclassical Eilenberger theory, we obtain the magnetization penetrating into the superconductor. We show that in a wide parameter range the direction of the induced magnetization is determi

Adrian Joseph Alva, Harjinder Singh

Synaptic integration is a prominent aspect of neuronal information processing. The detailed mechanisms that modulate synaptic inputs determine the computational properties of any given neuron. We study a simple model for the summation of excitatory inputs from synapses and illustrate its use by characterizing some functional properties of postsynaptic neuron

Vivek Khetan, Roshni Ramnani, Mayuresh Anand, Shubhashis Sengupta

Causality understanding between events is a critical natural language processing task that is helpful in many areas, including health care, business risk management and finance. On close examination, one can find a huge amount of textual content both in the form of formal documents or in content arising from social media like Twitter, dedicated to communicat

Jiawei Yan, Nanshun Huang, Haixiao Deng, Bo Liu

As a new-generation light source, free-electron lasers (FELs) provide high-brightness X-ray pulses at the angstrom-femtosecond space and time scales. The fundamental physics behind the FEL is the interaction between an electromagnetic wave and a relativistic electron beam in an undulator, which consists of hundreds or thousands of dipole magnets with an alte

Moise Blanchard, M. Amine Bennouna

In this paper, we analyze the number of neurons and training parameters that a neural networks needs to approximate multivariate functions of bounded second mixed derivatives -- Korobov functions. We prove upper bounds on these quantities for shallow and deep neural networks, breaking the curse of dimensionality. Our bounds hold for general activation functi

Asma Ben Saber, Abderrazek Karoui

In the first part of this work, we develop a novel scheme for solving nonparametric regression problems. That is the approximation of possibly low regular and noised functions from the knowledge of their approximate values given at some random points. Our proposed scheme is based on the use of the pseudo-inverse of a random projection matrix, combined with s

Kuan Zhang, Yuan-Yuan Li, Yi-Kai Huo, Andreas Schäfer

We analyze the lattice spacing dependence for the pion unpolarized matrix element of a quark bilinear operator with Wilson link (quasi-PDF operator) in the rest frame, using 13 lattice spacings ranging from 0.032 fm to 0.121 fm. We compare results for three different fermion actions with or without good chiral symmetry on dynamical gauge ensembles from three

Michael J. Horry, Subrata Chakraborty, Biswajeet Pradhan, Manoranjan Paul

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective at detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced by the medical community due to several practical, ethical, and regulatory constraints stemming from the black-box nature

Ting Wang, Zongkai Wu, Donglin Wang

Vision-and-language navigation (VLN) is a challenging task that requires an agent to navigate in real-world environments by understanding natural language instructions and visual information received in real-time. Prior works have implemented VLN tasks on continuous environments or physical robots, all of which use a fixed camera configuration due to the lim

Seung-Hwan Do, Hao Zhang, Travis J. Williams, Tao Hong

An ongoing challenge in the study of quantum materials, is to reveal and explain collective quantum effects in spin systems where interactions between different modes types are important. Here we approach this problem through a combined experimental and theoretical study of interacting transverse and longitudinal modes in an easy-plane quantum magnet near a

Umesh Kumar, Shi-Zeng Lin

The recent successful experimental observation of quantum anomalous Hall effect in graphene under laser irradiation demonstrates the feasibility of controlling single particle band structure by lasers. Here we study superconductivity in a Hubbard honeycomb model in the presence of an electromagnetic drive. We start with Hubbard honeycomb model in the presenc

Sukuse Abe

We prove the volume conjecture for any twist knots by using an equivalence relation, complex analysis, analytic continuation, and function of several complex variables on the basis of colored Jones polynomials.

Maxat Alibayev, David Paulius, Yu Sun

In this work, we propose a motion embedding strategy known as motion codes, which is a vectorized representation of motions based on a manipulation's salient mechanical attributes. These motion codes provide a robust motion representation, and they are obtained using a hierarchy of features called the motion taxonomy. We developed and trained a deep neural n

Vaibhav Mohanty, Ard A. Louis

Across many scientific and engineering disciplines, it is important to consider how much the output of a given system changes due to perturbations of the input. Here, we investigate the glassy phase of $\pm J$ spin glasses at zero temperature by calculating the robustness of the ground states to flips in the sign of single interactions. For random graphs and

Y. T. Albert Law

We study the manifestly covariant and local 1-loop path integrals on $S^{d+1}$ for general massive, shift-symmetric and (partially) massless totally symmetric tensor fields of arbitrary spin $s\geq 0$ in any dimensions $d\geq 2$. After reviewing the cases of massless fields with spin $s=1,2$, we provide a detailed derivation for path integrals of massless fi

Risheng Liu, Zhu Liu, Pan Mu, Xin Fan

In recent years, there has been a growing interest in combining learnable modules with numerical optimization to solve low-level vision tasks. However, most existing approaches focus on designing specialized schemes to generate image/feature propagation. There is a lack of unified consideration to construct propagative modules, provide theoretical analysis t

Beongjun Choi, Jy-yong Sohn, Dong-Jun Han, Jaekyun Moon

Federated learning has been spotlighted as a way to train neural networks using distributed data with no need for individual nodes to share data. Unfortunately, it has also been shown that adversaries may be able to extract local data contents off model parameters transmitted during federated learning. A recent solution based on the secure aggregation primit

Xin Li, Dongya Wu

In this paper, we investigate the matrix estimation problem in the multi-response regression model with measurement errors. A nonconvex error-corrected estimator based on a combination of the amended loss function and the nuclear norm regularizer is proposed to estimate the matrix parameter. Then under the (near) low-rank assumption, we analyse statistical a

Huanhuan Tian, Mohammad A. Alkhadra, Martin Z. Bazant

Shock electrodialysis (shock ED), an emerging electrokinetic process for water purification, leverages the new physics of deionization shock waves in porous media. In previous work, a simple leaky membrane model with surface conduction can explain the propagation of deionization shocks in a shock ED system, but it cannot quantitatively predict the deionizati

Saigopal Thota, Mridul Jain, Nishad Kamat, Saikiran Malikireddy

Large scale graph processing using distributed computing frameworks is becoming pervasive and efficient in the industry. In this work, we present a highly scalable and configurable distributed algorithm for building connected components, called Union Find Shuffle (UFS) with Path Compression. The scale and complexity of the algorithm are a function of the num

Ying Zhou, Xuefeng Liang, Yu Gu, Yifei Yin

In recent years, speech emotion recognition technology is of great significance in industrial applications such as call centers, social robots and health care. The combination of speech recognition and speech emotion recognition can improve the feedback efficiency and the quality of service. Thus, the speech emotion recognition has been attracted much attent

Deog Ki Hong, Chang Sub Shin, Seokhoon Yun

The standard cooling scenario in the presence of nucleon superfluidity fits rather well to the observation of the neutron stars. It implies that the stellar cooling arguments could place a stringent constraint on the properties of novel particles. We study in particular the cooling rate induced by dark gauge bosons for very young neutron stars: remnants of C

Yangming Li, Lemao Liu, Shuming Shi

In many scenarios, named entity recognition (NER) models severely suffer from unlabeled entity problem, where the entities of a sentence may not be fully annotated. Through empirical studies performed on synthetic datasets, we find two causes of performance degradation. One is the reduction of annotated entities and the other is treating unlabeled entities a

Elena Caceres, Arnab Kundu, Ayan K. Patra, Sanjit Shashi

We use the notion of double holography to study Hawking radiation emitted by the eternal BTZ black hole in equilibrium with a thermal bath, but in the form of warped CFT$_2$ degrees of freedom. In agreement with the literature, we find entanglement islands and a phase transition in the entanglement surface, but our results differ significantly from work in A

Ran Li, Jin Wang

We study the phase transition and the kinetics of the four dimensional charged AdS black hole in GB gravity based on the free energy landscape. Below the critical temperature, the free energy landscape topography has the shape of double basins with each representing one stable/unstable black hole phase. The thermodynamic small/large black hole phase transiti

Michelle Kunimoto, Steve Bryson

We present a framework for estimating exoplanet occurrence rates by synthesizing constraints from radial velocity and transit surveys simultaneously. We employ approximate Bayesian computation and various mass-radius (M-R) relations to explore the population models describing these surveys, both separately and in a joint fit. Using this approach, we fit a pl

Ziyang Wang, Wei Wei, Xian-Ling Mao, Xiao-Li Li

Session-based recommendation (SBR) is a challenging task, which aims to predict users' future interests based on anonymous behavior sequences. Existing methods leverage powerful representation learning approaches to encode sessions into a low-dimensional space. However, despite such achievements, all the existing studies focus on the instance-level session l

Weinan E, Stephan Wojtowytsch

A recent numerical study observed that neural network classifiers enjoy a large degree of symmetry in the penultimate layer. Namely, if $h(x) = Af(x) +b$ where $A$ is a linear map and $f$ is the output of the penultimate layer of the network (after activation), then all data points $x_{i, 1}, \dots, x_{i, N_i}$ in a class $C_i$ are mapped to a single point $

Harideep Nair, Prabhu Vellaisamy, Santha Bhasuthkar, John Paul Shen

A set of highly-optimized custom macro extensions is developed for a 7nm CMOS cell library for implementing Temporal Neural Networks (TNNs) that can mimic brain-like sensory processing with extreme energy efficiency. A TNN prototype (13,750 neurons and 315,000 synapses) for MNIST requires only 1.56mm2 die area and consumes only 1.69mW.