December 2020 arXiv papers — page 122

David Martínez-Rubio

We further research on the accelerated optimization phenomenon on Riemannian manifolds by introducing accelerated global first-order methods for the optimization of $L$-smooth and geodesically convex (g-convex) or $\mu$-strongly g-convex functions defined on the hyperbolic space or a subset of the sphere. For a manifold other than the Euclidean space, these

Gerassimos Barbatis, Panagiotis Branikas

We obtain heat kernel estimates for a class of fourth order non-uniformly elliptic operators in two dimensions. Contrary to existing results, the operators considered have symbols that are not strongly convex. This rises certain difficulties as it is known that, as opposed to the strongly convex case, there is no absolute exponential constant. Our estimates

Jianming Huang, Zhongxi Fang, Hiroyuki Kasai

For graph learning tasks, many existing methods utilize a message-passing mechanism where vertex features are updated iteratively by aggregation of neighbor information. This strategy provides an efficient means for graph features extraction, but obtained features after many iterations might contain too much information from other vertices, and tend to be si

Evan D. Hall, Kevin Kuns, Joshua R. Smith, Yuntao Bai

Cosmic Explorer (CE) is a next-generation ground-based gravitational-wave observatory concept, envisioned to begin operation in the 2030s, and expected to be capable of observing binary neutron star and black hole mergers back to the time of the first stars. Cosmic Explorer's sensitive band will extend below 10 Hz, where the design is predominantly limit

P. Schering, G. S. Uhrig

The optically induced polarization of localized electron spins in an ensemble of quantum dots (QDs) dephases due to the interaction with the surrounding nuclear spins. Despite this dephasing, the spins in the QDs can be controlled to respond coherently by applying periodic laser pulses, leading to a revival of the spin polarization before each pulse. This ef

Dávid Csercsik

In this paper we study the so-called minimum income condition order, which is used in some day-ahead electricity power exchanges to represent the production-related costs of generating units. This order belongs to the family of complex orders, which imply non-convexities in the market clearing problem. We demonstrate via simple numerical examples that if mor

T. Schäfer, A. Toschi

While calculations and measurements of single-particle spectral properties often offer the most direct route to study correlated electron systems, the underlying physics may remain quite elusive, if information at higher particle levels is not explicitly included. Here, we present a comprehensive overview of the different approaches which have been recently

Tommaso Lisini Baldi, Nicole D'Aurizio, Sergio Gurgone, Daniele Borzelli

Supernumerary robotic limbs (SRLs) gained increasing interest in the last years for their applicability as healthcare and assistive technologies. These devices can either support or augment human sensorimotor capabilities, allowing users to complete tasks that are more complex than those feasible for their natural limbs. However, for a successful coordinatio

Guangshuai Gao, Qingjie Liu, Zhenghui Hu, Lu Li

Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention. However, challenges such as large scale variation, complex background interference, and non-uniform density distribution greatly limit the counting accuracy, particularly striking in remote sensing imagery. To mitigate the abov

Mikhail Mrykhin, Alexander Okhotin

In 1973, Greibach ("The hardest context-free language", SIAM J. Comp., 1973) constructed a context-free language $L_0$ with the property that every context-free language can be reduced to $L_0$ by a homomorphism, thus representing it as an inverse homomorphic image $h^{-1}(L_0)$. In this paper, a similar characterization is established for a family of gramma

Eike J. Nustede, Jörn Anemüller

We investigate the viability of a variational U-Net architecture for denoising of single-channel audio data. Deep network speech enhancement systems commonly aim to estimate filter masks, or opt to work on the waveform signal, potentially neglecting relationships across higher dimensional spectro-temporal features. We study the adoption of a probabilistic bo

Eliana Duarte, Liam Solus

We investigate the algebra and geometry of general interventions in discrete DAG models. To this end, we introduce a theory for modeling soft interventions in the more general family of staged tree models and develop the formalism to study these models as parametrized subvarieties of a product of probability simplices. We then consider the problem of finding

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using a data sample of $(1310.6 \pm 7.0) \times 10^{6}$ $J/ψ$ events taken with the BESIII detector at the center-of-mass energy of 3.097 GeV, we search for the first time for the lepton number violating decay $Σ^{-} \to p e^{-} e^{-}$ and the rare inclusive decay $Σ^{-} \to Σ^{+} X$, where $X$ denotes any possible particle combination. The $Σ^-$ candidates

Piyush Mani Tripathi, Saptarshi Basu

This paper discusses the numerical investigation of the wall roughness effect on the supercritical water flow susceptible to heat transfer deterioration (HTD). The simulation was carried in the vertical circular pipe using the SST k-omega turbulence model for different sets of heat flux (220kW/m2 & 1810kW/m2) and mass flow rate (0.0106kg/s &0.022kg/s) at a m

Ievgeniia Kuzminykh, Bogdan Ghita, Jose M. Such

Many companies consider IoT as a central element for increasing competitiveness. Despite the growing number of cyberattacks on IoT devices and the importance of IoT security, no study has yet primarily focused on the impact of IoT security measures on the security challenges. This paper presents a review of the current state of security of IoT in companies t

Alessandro Erba, Nils Ole Tippenhauer

In recent years, a number of process-based anomaly detection schemes for Industrial Control Systems were proposed. In this work, we provide the first systematic analysis of such schemes, and introduce a taxonomy of properties that are verified by those detection systems. We then present a novel general framework to generate adversarial spoofing signals that

K. Kritos, V. De Luca, G. Franciolini, A. Kehagias

Mainly motivated by the recent GW190521 mass gap event which we take as a benchmark point, we critically assess if binaries made of a primordial black hole and a black hole of astrophysical origin may form, merge in stellar clusters and reproduce the LIGO/Virgo detection rate. While two previously studied mechanisms -- the direct capture and the three body i

Francesco Picetti, Sara Mandelli, Paolo Bestagini, Vincenzo Lipari

Source device identification is an important topic in image forensics since it allows to trace back the origin of an image. Its forensics counter-part is source device anonymization, that is, to mask any trace on the image that can be useful for identifying the source device. A typical trace exploited for source device identification is the Photo Response No

Joanna Berteaud, Francesca Calore, Maica Clavel, Pasquale Dario Serpico

If the mysterious Fermi-LAT GeV gamma-ray excess is due to an unresolved population of millisecond pulsars (MSP) in the Galactic bulge, one expects this very same population to shine in X rays. For the first time, we address the question of what is the sensitivity of current X-ray telescopes to an MSP population in the Galactic bulge. To this end, we create

Wei-Shu Hou, Tanmoy Modak, Tilman Plehn

Electroweak baryogenesis in a two-Higgs doublet model is a well-motivated and testable scenario for physics beyond the Standard Model. An attractive way of providing $CP$ violation is through flavor-changing Higgs couplings, where the top-charm coupling is hardly constrained. This minimal scenario can be tested by searching for heavy charged and neutral Higg

P. J. LLoreda-Jurado, Jesus Hernandez-Saz, E. Chicardi, A. Paul

Chemical looping water splitting systems operate at relatively high temperatures (450-800 degree C) to produce, purify, or store hydrogen by the cyclic reduction and oxidation (redox) of a solid oxygen carrier. Therefore, to improve long-term operation, it is necessary to develop highly stable oxygen carriers with large specific surface areas. In this work,

Lev V. Ginzburg, Carolin Gold, Marc P. Röösli, Christian Reichl

We measure electronic transport through point contacts in the high-mobility electron gas in a Ga[Al]As heterostructure at different temperatures and bulk electron densities. The conductance through all point contacts increases with increasing temperature in a temperature window around $T \sim 10 K$ for all investigated electron densities and point contact wi

Darryl D. Holm, Jonathan I. Rawlinson, Cesare Tronci

Starting with the exact factorization of the molecular wavefunction, this paper presents the results from the numerical implementation in nonadiabatic molecular dynamics of the recently proposed bohmion method. Within the context of quantum hydrodynamics, we introduce a regularized nuclear Bohm potential admitting solutions comprising a train of $\delta$-fun

Hidenori Sonoda, Hiroshi Suzuki

The gradient flow bears a close resemblance to the coarse graining, the guiding principle of the renormalization group (RG). In the case of scalar field theory, a precise connection has been made between the gradient flow and the RG flow of the Wilson action in the exact renormalization group (ERG) formalism. By imitating the structure of this connection, we

Rocco Duvenhage

We show how one can obtain a class of quadratic Wasserstein metrics, that is to say, Wasserstein metrics of order 2, on the set of faithful normal states of a von Neumann algebra $A$, via transport plans, rather than through a dynamical approach. Two key points to make this work, are a suitable formulation of the cost of transport arising from Tomita-Takesak

Tomasz Witczak

In this paper we adhere to the definition of infra-topological space as it was introduced by Al-Odhari. Namely, we speak about families of subsets which contain empty set and the whole universe X, being at the same time closed under finite intersections (but not necessarily under arbitrary or even finite unions). This slight modification allows us to disting

Roy M. Howard

In this paper a spline based integral approximation is utilized to propose a sequence of approximations to the error function that converge at a significantly faster manner than the default Taylor series. The approximations can be improved by utilizing the approximation erf(x) approximately equal to one for x>>1. Two generalizations are possible, the first i

Saeid Alirezazadeh, Luís A. Alexandre

A scheduling method in a robotic network cloud system with minimal makespan is beneficial as the system can complete all the tasks assigned to it in the fastest way. Robotic network cloud systems can be translated into graphs where nodes represent hardware with independent computing power and edges represent data transmissions between nodes. Time window cons

Fabian Rupp

We consider a closed surface in $\mathbb{R}^3$ evolving by the volume-preserving Willmore flow and prove a lower bound for the existence time of smooth solutions. For spherical initial surfaces with Willmore energy below $8\pi$ we show long time existence and convergence to a round sphere by performing a suitable blow-up and by proving a constrained Lojasiew

Hao Li, Zixuan Li, Kenli Li, Jan S. Rellermeyer

Sparse Tucker Decomposition (STD) algorithms learn a core tensor and a group of factor matrices to obtain an optimal low-rank representation feature for the \underline{H}igh-\underline{O}rder, \underline{H}igh-\underline{D}imension, and \underline{S}parse \underline{T}ensor (HOHDST). However, existing STD algorithms face the problem of intermediate variables

Victor Chabu, Clotilde Fermanian Kammerer, Fabricio Macià

We study a Schr{\"o}dinger equation modeling the dynamics of an electron in a crystal in the asymptotic regime of small wavelength comparable to the characteristic scale of the crystal. Using Floquet Bloch decomposition, we obtain a description of the limit of time averaged energy densities. We make rather general assumption assuming that the initial data ar

Kevin Lu, Aditya Grover, Pieter Abbeel, Igor Mordatch

The objective of lifelong reinforcement learning (RL) is to optimize agents which can continuously adapt and interact in changing environments. However, current RL approaches fail drastically when environments are non-stationary and interactions are non-episodic. We propose Lifelong Skill Planning (LiSP), an algorithmic framework for non-episodic lifelong RL

Samim Ahmadi, Jan Christian Hauffen, Linh Kästner, Peter Jung

Block-sparse regularization is already well-known in active thermal imaging and is used for multiple measurement based inverse problems. The main bottleneck of this method is the choice of regularization parameters which differs for each experiment. To avoid time-consuming manually selected regularization parameter, we propose a learned block-sparse optimiza

Jianfeng Wang, Lin Song, Zeming Li, Hongbin Sun

Mainstream object detectors based on the fully convolutional network has achieved impressive performance. While most of them still need a hand-designed non-maximum suppression (NMS) post-processing, which impedes fully end-to-end training. In this paper, we give the analysis of discarding NMS, where the results reveal that a proper label assignment plays a c

Hidenori Fukaya, Mikio Furuta, Yoshiyuki Matsuki, Shinichiroh Matsuo

We reformulate the mod-two Atiyah-Patodi-Singer (APS) index in a physicist-friendly way using the domain-wall fermion. Our new formulation is given on a closed manifold, which is extended from the original manifold with boundary, where we instead give a fermion mass term changing its sign at the location of the original boundary. This new setup does not need

Jason Sang Hun Lee, Inkyu Park, Ian James Watson, Seungjin Yang

In high-energy particle physics events, it can be advantageous to find the jets associated with the decays of intermediate states, for example, the three jets produced by the hadronic decay of the top quark. Typically, a goodness-of-association measure, such as a $\chi^2$ related to the mass of the associated jets, is constructed, and the best jet combinatio

Yunyi Yang, Yunhao Li, Xiaojun Quan

This paper presents our task-oriented dialog system UBAR which models task-oriented dialogs on a dialog session level. Specifically, UBAR is acquired by fine-tuning the large pre-trained unidirectional language model GPT-2 on the sequence of the entire dialog session which is composed of user utterance, belief state, database result, system act, and system r

Hiroki Ohata, Hideo Suganuma

We study spontaneous chiral-symmetry breaking in SU(3) QCD in terms of the dual superconductor picture for quark confinement in the maximally Abelian (MA) gauge, using lattice QCD Monte Carlo simulations with four different lattices of $16^4$, $24^4$, $24^3\times 6$ at $β=6.0$ (i.e., the spacing $a \simeq$ 0.1 fm), and $32^4$ at $β=6.2$ (i.e., $a \simeq$ 0.0

Jhih-Wei Chen, Tsu-Jui Fu, Chen-Kang Lee, Wei-Yun Ma

Although distant supervision automatically generates training data for relation extraction, it also introduces false-positive (FP) and false-negative (FN) training instances to the generated datasets. Whereas both types of errors degrade the final model performance, previous work on distant supervision denoising focuses more on suppressing FP noise and less

Takanori Taniguchi, Kota Kudo, Shun Asano, Motofumi Takahama

Despite its unique structural features, the magnetism of single-layered cuprate with five oxygen coordination ($T$*-type structure) has not been investigated thus far. Here, we report the results of muon spin relaxation and magnetic susceptibility measurements to elucidate the magnetism of $T$*-type La$_{1-x/2}$Eu$_{1-x/2}$Sr$_x$CuO$_4$ (LESCO) via magnetic

Yajun Zhou

Through algebraic manipulations on Wro\'nskian matrices whose entries are reducible to Bessel moments, we present a new analytic proof of the quadratic relations conjectured by Broadhurst and Roberts, along with some generalizations. In the Wro\'nskian framework, we reinterpret the de Rham intersection pairing through polynomial coefficients in Vanhove's dif

Weicheng Ma, Ruibo Liu, Lili Wang, Soroush Vosoughi

Metaphors are ubiquitous in human language. The metaphor detection task (MD) aims at detecting and interpreting metaphors from written language, which is crucial in natural language understanding (NLU) research. In this paper, we introduce a pre-trained Transformer-based model into MD. Our model outperforms the previous state-of-the-art models by large margi

Seo-Hyun Lee, Minji Lee, Seong-Whan Lee

Recent advances in brain-computer interface technology have shown the potential of imagined speech and visual imagery as a robust paradigm for intuitive brain-computer interface communication. However, the internal dynamics of the two paradigms along with their intrinsic features haven't been revealed. In this paper, we investigated the functional connec

Ruoyu Zeng, Qi Zhao, Yuanjie Yang

We theoretically and experimentally studied a novel class of vortex beams named open vortex beams (OVBs). Such beams are generated using Gaussian beams diffracted by partially blocked fork-shaped gratings (PB-FSGs).The analytical model of OVBs in the near field and far field is given by superpositions of Hypergeometric (HyG) modes. Unlike conventional intege

Guy Bukchin, Eli Schwartz, Kate Saenko, Ori Shahar

Few-shot learning methods offer pre-training techniques optimized for easier later adaptation of the model to new classes (unseen during training) using one or a few examples. This adaptivity to unseen classes is especially important for many practical applications where the pre-trained label space cannot remain fixed for effective use and the model needs to

Takanori Anegawa, Norihiro Iizuka, Kotaro Tamaoka, Tomonori Ugajin

We study a class of decoherence process which admits a 3 dimensional holographic bulk. Starting from a thermo-field double dual to a wormhole, we prepare another thermo-field double which plays the role of environment. By allowing the energy flow between the original and environment thermo-field double, the entanglement of the original thermo-field double ev

Zhaoqiang Chen, Qun Chen, Youcef Nafa, Tianyi Duan

The state-of-the-art performance on entity resolution (ER) has been achieved by deep learning. However, deep models are usually trained on large quantities of accurately labeled training data, and can not be easily tuned towards a target workload. Unfortunately, in real scenarios, there may not be sufficient labeled training data, and even worse, their distr

Junsup Shim, Changbom Park, Juhan Kim, Ho Seong Hwang

We develop a method to identify cosmic voids from the matter density field by adopting a physically-motivated concept that voids are the counterpart of massive clusters. To prove the concept we use a pair of $Λ$CDM simulations, a reference and its initial density-inverted mirror simulation, and study the relation between the effective size of voids and the m

Dae-Hyeok Lee, Hyung-Ju Ahn, Ji-Hoon Jeong, Seong-Whan Lee

Non-invasive brain-computer interface (BCI) has been developed for understanding users' intentions by using electroencephalogram (EEG) signals. With the recent development of artificial intelligence, there have been many developments in the drone control system. BCI characteristic that can reflect the users' intentions led to the BCI-based drone cont

Anoushka Vyas, Sambaran Bandyopadhyay

Soil moisture is an important component of precision agriculture as it directly impacts the growth and quality of vegetation. Forecasting soil moisture is essential to schedule the irrigation and optimize the use of water. Physics based soil moisture models need rich features and heavy computation which is not scalable. In recent literature, conventional mac

Jiaqi Wei, Boyu Zhang, Michel Hehn, Wei Zhang

Ultra-fast magnetization switching induced by a single femtosecond laser pulse, under no applied magnetic field has attracted a lot of attention during the last 10 years because of its high potential for low energy and ultra-fast memory applications. Single-pulse helicity-independent switching has mostly been demonstrated for Gd based materials. It is now im

Hanbaek Lyu, Yuchen Li

Block majorization-minimization (BMM) is a simple iterative algorithm for constrained nonconvex optimization that sequentially minimizes majorizing surrogates of the objective function in each block while the others are held fixed. BMM entails a large class of optimization algorithms such as block coordinate descent and its proximal-point variant, expectatio

Xiachong Feng, Xiaocheng Feng, Bing Qin, Xinwei Geng

Meeting summarization is a challenging task due to its dynamic interaction nature among multiple speakers and lack of sufficient training data. Existing methods view the meeting as a linear sequence of utterances while ignoring the diverse relations between each utterance. Besides, the limited labeled data further hinders the ability of data-hungry neural mo

Ameek Malhotra, Ema Dimastrogiovanni, Matteo Fasiello, Maresuke Shiraishi

We explore and corroborate, by working out explicit examples, the effectiveness of cross-correlating stochastic gravitational wave background anisotropies with CMB temperature fluctuations as a way to establish the primordial nature of a given gravitational wave signal. We consider the case of gravitational wave anisotropies induced by scalar-tensor-tensor p

Frederik vom Ende

In quantum systems theory one of the fundamental problems boils down to: Given an initial state, which final states can be reached by the dynamic system in question? Formulated in the framework of bilinear control systems, the evolution shall be governed by an inevitable Hamiltonian drift term, finitely many control Hamiltonians allowing for (at least) piece

Tetsuo Yamaguchi, Akira Akamine, Yoshinori Sawae

In this study, the adhesion-detachment behaviour of a gecko-inspired adhesive pad was investigated to understand the on/off switching mechanisms of adhesion in gecko feet. A macroscopic spatula model was fabricated using silicone rubber, and adhesion tests combining lateral sliding and vertical debonding were conducted. It was observed that the contact state

Anton Smerdov, Evgeny Burnaev, Andrey Somov, Anton Stepanov

The emerging progress of eSports lacks the tools for ensuring high-quality analytics and training in Pro and amateur eSports teams. We report on an Artificial Intelligence (AI) enabled solution for predicting the eSports player in-game performance using exclusively the data from sensors. For this reason, we collected the physiological, environmental, and the

Lipeng Wan, Xuwei Song, Xuguang Lan, Nanning Zheng

Cooperative multi-agent tasks require agents to deduce their own contributions with shared global rewards, known as the challenge of credit assignment. General methods for policy based multi-agent reinforcement learning to solve the challenge introduce differentiate value functions or advantage functions for individual agents. In multi-agent system, polices

Kevin Li

Regression trees and random forests are popular and effective non-parametric estimators in practical applications. A recent paper by Athey and Wager shows that the random forest estimate at any point is asymptotically Gaussian; in this paper, we extend this result to the multivariate case and show that the vector of estimates at multiple points is jointly no

Souvik Das, Anirudh Shankar, Vaneet Aggarwal

A novel multi-agent evolutionary robotics (ER) based framework, inspired by competitive evolutionary environments in nature, is demonstrated for training Spiking Neural Networks (SNN). The weights of a population of SNNs along with morphological parameters of bots they control in the ER environment are treated as phenotypes. Rules of the framework select cer

Mingzhou Xu, Liangyou Li, Derek. F. Wong, Qun Liu

Previous works have shown that contextual information can improve the performance of neural machine translation (NMT). However, most existing document-level NMT methods only consider a few number of previous sentences. How to make use of the whole document as global contexts is still a challenge. To address this issue, we hypothesize that a document can be r

Rui Yang, Wenrui Dai, Chenglin Li, Junni Zou

Message passing has evolved as an effective tool for designing Graph Neural Networks (GNNs). However, most existing methods for message passing simply sum or average all the neighboring features to update node representations. They are restricted by two problems, i.e., (i) lack of interpretability to identify node features significant to the prediction of GN

Xiao Guo, Youjun Lu

Wave optics may need to be considered when studying the lensed waveforms of gravitational waves (GWs). However, the computation of the diffraction integral (amplification factor) in wave optics is challenging and time-consuming. It is vital to develop an accurate and efficient method to calculate the amplification factor for detecting lensed GW systems. In t

Hidetoshi Omiya, Takuya Takahashi, Takahiro Tanaka

There are strong interests in considering ultra-light scalar fields (especially axion) around a rapidly rotating black hole because of the possibility of observing gravitational waves from axion condensate (axion cloud) around black holes. Motivated by this consideration, we propose a new method to study the dynamics of an ultra-light scalar field with self-

Takuya Taira, Yusuke Kato, Masanori Ichioka, Hiroto Adachi

We theoretically investigate the vortex spin Hall effect, i.e., a novel spin Hall effect driven by the motion of superconducting vortices, by focusing on the role of superconducting fluctuations. Within the BCS-Gor'kov microscopic approach combined with the Kubo formula, we find a strong similarity between the vortex spin Hall effect and the vortex Nerns

David Coupier, Lucas Flammant, Viet Chi Tran

We define and analyze an extension to the $d$-dimensional hyperbolic space of the Radial Spanning Tree (RST) introduced by Baccelli and Bordenave in the two-dimensional Euclidean space (2007). In particular, we will focus on the description of the infinite branches of the tree. The properties of the two-dimensional Euclidean RST are extended to the hyperboli

Pratap Roy, K. Banerjee, T. K. Rana, S. Kundu

The nuclear level density of $^{115}$Sn has been measured in an excitation energy range of $\sim $2 - 9 MeV using the experimental neutron evaporation spectra from the $^{115}$In($p,n$)$^{115}$Sn reaction. The experimental level densities were compared with the microscopic Hartree-Fock BCS (HFBCS), Hartree-Fock-Bogoliubov plus combinatorial (HFB+C), and an e

Sarah E. Dietz, Jinmi Yoon, Timothy C. Beers, Vinicius Placco

We present a chemo-dynamical analysis of low-resolution ($R \sim 1300$) spectroscopy of stars from the AAOmega Evolution of Galactic Structure (AEGIS) survey, focusing on two key populations of carbon-enhanced metal-poor (CEMP) stars within the disk system of the Milky Way: a mildly prograde population ($L_z < 1000\,$kpc$\,$km$\,$s$^{-1}$) and a strongly pro

Lei Wang, Xin Liu, Yin Zhang

In this paper, we develop an algorithm for federated principal component analysis (PCA) with emphases on both communication efficiency and data privacy. Generally speaking, federated PCA algorithms based on direct adaptations of classic iterative methods, such as simultaneous subspace iterations (SSI), are unable to preserve data privacy, while algorithms ba

Ria Vinod, Pin-Yu Chen, Payel Das

Recent advancements in transfer learning have made it a promising approach for domain adaptation via transfer of learned representations. This is especially when relevant when alternate tasks have limited samples of well-defined and labeled data, which is common in the molecule data domain. This makes transfer learning an ideal approach to solve molecular le

Mingda Qiao, Gregory Valiant

We consider an online binary prediction setting where a forecaster observes a sequence of $T$ bits one by one. Before each bit is revealed, the forecaster predicts the probability that the bit is $1$. The forecaster is called well-calibrated if for each $p \in [0, 1]$, among the $n_p$ bits for which the forecaster predicts probability $p$, the actual number

Ryan Lopez, Paul J. Atzberger

We develop data-driven methods for incorporating physical information for priors to learn parsimonious representations of nonlinear systems arising from parameterized PDEs and mechanics. Our approach is based on Variational Autoencoders (VAEs) for learning from observations nonlinear state space models. We develop ways to incorporate geometric and topologica

E. Barrientos, S. Mendoza, P. Padilla

We present a toy model for extending the Friedmann equations of relativistic cosmology using fractional derivatives. We do this by replacing the integer derivatives, in a few well-known cosmological results with fractional derivatives leaving their order as a free parameter. All this with the intention to explain the current observed acceleration of the Univ

Xu-Ping Yao, Yonghao Gao, Gang Chen

SU($N$) Mott insulators have been proposed and/or realized in solid-state materials and with ultracold atoms on optical lattices. We study the two-dimensional SU($N$) antiferromagnets on the triangular lattice. Starting from an SU($N$) Heisenberg model with the fundamental representation on each site in the large-$N$ limit, we perform a self-consistent calcu

Raditya Weda Bomantara, Sen Mu, Jiangbin Gong

Studies of periodically driven one-dimensional many-body systems have advanced our understanding of complex systems and stimulated promising developments in quantum simulation. It is hence of interest to go one step further, by investigating the topological and dynamical aspects of periodically driven spin ladders as clean quasi-one-dimensional systems with

Nikolai I. Lebovka, Mykhailo O. Tatochenko, Nikolai V. Vygornitskii, Andrei V. Eserkepov

Connectedness percolation phenomena in two-dimensional packings of elongated particles (discorectangles) were studied numerically. The packings were produced using random sequential adsorption (RSA) off-lattice model with preferential orientations of particles along a given direction. The partial ordering was characterized by order parameter $S$, with $S=0$

Tianyu Dai, Jean-François Paquet, Derek Teaney, Steffen A. Bass

An energetic parton travelling through a quark-gluon plasma loses energy via occasional hard scatterings and frequent softer interactions. Whether or not these interactions admit a perturbative description, the effect of the soft interactions can be factorized and encoded in a small number of transport coefficients. In this work, we present a hard-soft facto

Jicong Fan, Lijun Ding, Chengrun Yang, Zhao Zhang

The nuclear norm and Schatten-$p$ quasi-norm are popular rank proxies in low-rank matrix recovery. However, computing the nuclear norm or Schatten-$p$ quasi-norm of a tensor is hard in both theory and practice, hindering their application to low-rank tensor completion (LRTC) and tensor robust principal component analysis (TRPCA). In this paper, we propose a

Florian Beyer, Todd A. Oliynyk

We establish the existence of a stable family of solutions to the Euler equations on Kasner backgrounds near the singularity with the full expected asymptotic data degrees of freedom and no symmetry or isotropy restrictions. Existence is achieved through transforming the Euler equations into the form of a symmetric hyperbolic Fuchsian system followed by an a

Woo-Jeoung Nam, Jaesik Choi, Seong-Whan Lee

The clear transparency of Deep Neural Networks (DNNs) is hampered by complex internal structures and nonlinear transformations along deep hierarchies. In this paper, we propose a new attribution method, Relative Sectional Propagation (RSP), for fully decomposing the output predictions with the characteristics of class-discriminative attributions and clear ob

Mustapha Azreg-Aïnou

Given an anisotropic fluid source, we determine in closed forms, upon solving the field equations of general relativity (GR) and teleparallel gravity (TEGR) coupled to a cosmological constant, cylindrically symmetric four-dimensional cosmological rotating wormholes, satisfying all local energy conditions, and cosmological rotating solutions with two axes of

Yuanqiang Zhou, Dewei Li, Yugeng Xi, Yunwen Xu

This paper develops a data-driven learning framework for approximating the feasible region and invariant set of a nonlinear system under the nonlinear Model Predictive Control (MPC) scheme. The developed approach is based on the feasibility information of a point-wise data set using low-discrepancy sequence. Using kernel-based Support Vector Machine (SVM) le

Kai-Chun Liu, Kuo-Hsuan Hung, Chia-Yeh Hsieh, Hsiang-Yun Huang

In the last two decades, fall detection (FD) systems have been developed as a popular assistive technology. Such systems automatically detect critical fall events and immediately alert medical professionals or caregivers. To support long-term FD services, various power-saving strategies have been implemented. Among them, a reduced sampling rate is a common a

Seok-Jun Chang, Hee-Won Lee

Emission features formed through Raman scattering with atomic hydrogen provide unique and crucial information to probe the distribution and kinematics of a thick neutral region illuminated by a strong far UV emission source. We introduce a new 3 dimensional Monte-Carlo code in order to describe the radiative transfer of line photons that are subject to Raman

Thanh V. Pham, Thang V. Nguyen, Anh T. Pham

In this paper, we newly present a closed-form bit-error rate (BER) expression for an $M$-ary pulse-amplitude modulation ($M$-PAM) over additive white Gaussian noise (AWGN) channels by analytically characterizing the bit decision regions and positions. The obtained expression is then used to derive the conditional BER of a rectangular quadrature amplitude mod

Asier Ozaeta, Wim van Dam, Peter L. McMahon

We report on the energy-expectation-value landscapes produced by the single-layer ($p=1$) Quantum Approximate Optimization Algorithm (QAOA) when being used to solve Ising problems. The landscapes are obtained using an analytical formula that we derive. The formula allows us to predict the landscape for any given Ising problem instance and consequently predic

Minkai Xu, Zhiming Zhou, Guansong Lu, Jian Tang

Wasserstein GANs (WGANs), built upon the Kantorovich-Rubinstein (KR) duality of Wasserstein distance, is one of the most theoretically sound GAN models. However, in practice it does not always outperform other variants of GANs. This is mostly due to the imperfect implementation of the Lipschitz condition required by the KR duality. Extensive work has been do

Daniel Flores-Alfonso, Román Linares, Marco Maceda

Recent work has shown the existence of a unique nonlinear extension of electromagnetism which preserves conformal symmetry and allows for the freedom of duality rotations. Moreover, black holes and gravitational waves have been found to exist in this nonlinearly extended electrovacuum. We generalise these dyonic black holes in two major ways: with the relaxa

Mohamed K. Abdel-Aziz, Cristina Perfecto, Sumudu Samarakoon, Mehdi Bennis

Cooperative perception plays a vital role in extending a vehicle's sensing range beyond its line-of-sight. However, exchanging raw sensory data under limited communication resources is infeasible. Towards enabling an efficient cooperative perception, vehicles need to address the following fundamental question: What sensory data needs to be shared?, at which

Alexander Rodríguez, Bijaya Adhikari, Andrés D. González, Charles Nicholson

Can we infer all the failed components of an infrastructure network, given a sample of reachable nodes from supply nodes? One of the most critical post-disruption processes after a natural disaster is to quickly determine the damage or failure states of critical infrastructure components. However, this is non-trivial, considering that often only a fraction o

Jin Wang, Xinrong Ma

In this paper, by means of the classical Lagrange inversion formula, we establish a general nonlinear inverse relations which is a partial solution to the problem proposed in the paper [J. Wang, Nonlinear inverse relations for the Bell polynomials via the Lagrange inversion formula, J. Integer Seq., Vol. 22 (2019), Article 19.3.8. (https://cs.uwaterloo.ca/jo

Vineel Pratap, Qiantong Xu, Anuroop Sriram, Gabriel Synnaeve

This paper introduces Multilingual LibriSpeech (MLS) dataset, a large multilingual corpus suitable for speech research. The dataset is derived from read audiobooks from LibriVox and consists of 8 languages, including about 44.5K hours of English and a total of about 6K hours for other languages. Additionally, we provide Language Models (LM) and baseline Auto

Wenqing Chen, Lulu Liu, Wentao Yang, Dong Chen

The discovery of signatures of topological superconductivity in superconducting bulk materials with topological surface states has attracted intensive research interests recently. Utilizing angle-resolved photoemission spectroscopy and first-principles calculations, here, we demonstrate the existence of topological nodal-line states and drumheadlike surface

Xin Wen, Peng Xiang, Zhizhong Han, Yan-Pei Cao

The task of point cloud completion aims to predict the missing part for an incomplete 3D shape. A widely used strategy is to generate a complete point cloud from the incomplete one. However, the unordered nature of point clouds will degrade the generation of high-quality 3D shapes, as the detailed topology and structure of discrete points are hard to be capt

Alexander Ororbia, Daniel Kifer

Neural generative models can be used to learn complex probability distributions from data, to sample from them, and to produce probability density estimates. We propose a computational framework for developing neural generative models inspired by the theory of predictive processing in the brain. According to predictive processing theory, the neurons in the b

Changsheng You, Beixiong Zheng, Weidong Mei, Rui Zhang

The performance of wireless communication systems is fundamentally constrained by the random and uncontrollable wireless channel. By leveraging the recent advance in digitally-controlled metasurface, intelligent reflecting surface (IRS) has emerged as a promising solution to enhance the wireless network performance by smartly reconfiguring the radio propagat

Adrian Wheeldon, Alex Yakovlev, Rishad Shafik, Jordan Morris

Modern internet of things (IoT) devices leverage machine learning inference using sensed data on-device rather than offloading them to the cloud. Commonly known as inference at-the-edge, this gives many benefits to the users, including personalization and security. However, such applications demand high energy efficiency and robustness. In this paper we prop

Adrián Pérez-Suay, Gustau Camps-Valls

Establishing causal relations between random variables from observational data is perhaps the most important challenge in today&#39;s Science. In remote sensing and geosciences this is of special relevance to better understand the Earth&#39;s system and the complex and elusive interactions between processes. In this paper we explore a framework to derive cau

Aleksandra Wolanin, Gustau Camps-Valls, Luis Gómez-Chova, Gonzalo Mateo-García

Satellite remote sensing has been widely used in the last decades for agricultural applications, {both for assessing vegetation condition and for subsequent yield prediction.} Existing remote sensing-based methods to estimate gross primary productivity (GPP), which is an important variable to indicate crop photosynthetic function and stress, typically rely o

Daniel Heestermans Svendsen, Pablo Morales-Álvarez, Rafael Molina, Gustau Camps-Valls

This paper introduces deep Gaussian processes (DGPs) for geophysical parameter retrieval. Unlike the standard full GP model, the DGP accounts for complicated (modular, hierarchical) processes, provides an efficient solution that scales well to large datasets, and improves prediction accuracy over standard full and sparse GP models. We give empirical evidence