February 2021 arXiv papers — page 14

Jianzheng Wang, Guoqiang Hu

In this paper, we consider solving a composite optimization problem with coupling constraints in a multi-agent network based on proximal gradient method. In this problem, all the agents jointly minimize the sum of individual cost functions composed of smooth and possibly non-smooth parts. To this end, we derive the dual problem by the concept of Fenchel conj

Francesca Anceschi, Yuzhe Zhu

We investigate the Cauchy problem and the diffusion asymptotics for a spatially inhomogeneous kinetic model associated to a nonlinear Fokker-Planck operator. We derive the global well-posedness result with instantaneous smoothness effect, when the initial data lies below a Maxwellian. The proof relies on the hypoelliptic analog of classical parabolic theory,

Chen Wu, Ruqing Zhang, Jiafeng Guo, Yixing Fan

Ranked list truncation is of critical importance in a variety of professional information retrieval applications such as patent search or legal search. The goal is to dynamically determine the number of returned documents according to some user-defined objectives, in order to reach a balance between the overall utility of the results and user efforts. Existi

Changyong Oh, Efstratios Gavves, Max Welling

The sample efficiency of Bayesian optimization(BO) is often boosted by Gaussian Process(GP) surrogate models. However, on mixed variable spaces, surrogate models other than GPs are prevalent, mainly due to the lack of kernels which can model complex dependencies across different types of variables. In this paper, we propose the frequency modulated (FM) kerne

J. W. Shu, D. T. Adroja, A. D. Hillier, Y. J. Zhang

CeRh$_6$Ge$_4$ is an unusual example of a stoichiometric heavy fermion ferromagnet, which can be cleanly tuned by hydrostatic pressure to a quantum critical point. In order to understand the origin of this anomalous behavior, we have characterized the magnetic ordering and crystalline electric field (CEF) scheme of this system. While magnetic Bragg peaks are

Yuval Emek, Eyal Keren

Introduced by Emek and Wattenhofer (PODC 2013), the \emph{stone age (SA)} model provides an abstraction for network algorithms distributed over randomized finite state machines. This model, designed to resemble the dynamics of biological processes in cellular networks, assumes a weak communication scheme that is built upon the nodes' ability to sense the

Antonio Fernandez Anta, Chryssis Georgiou, Theophanis Hadjistasi, Nicolas Nicolaou

This work examines strategies to handle large shared data objects in distributed storage systems (DSS), while boosting the number of concurrent accesses, maintaining strong consistency guarantees, and ensuring good operation performance. To this respect, we define the notion of fragmented objects:con-current objects composed of a list of fragments (or blocks

Kwangmin Jung, Donggyu Kim, Seunghyeon Yu

This paper proposes a dynamic process of portfolio risk measurement to address potential information loss. The proposed model takes advantage of financial big data to incorporate out-of-target-portfolio information that may be missed when one considers the Value at Risk (VaR) measures only from certain assets of the portfolio. We investigate how the curse of

Harshay Shah, Prateek Jain, Praneeth Netrapalli

Post-hoc gradient-based interpretability methods [Simonyan et al., 2013, Smilkov et al., 2017] that provide instance-specific explanations of model predictions are often based on assumption (A): magnitude of input gradients -- gradients of logits with respect to input -- noisily highlight discriminative task-relevant features. In this work, we test the valid

Masaki Honda, Matsuo Sato, Taiki Tohshima

String geometry theory is a candidate of the non-perturbative formulation of string theory. In order to determine the string vacuum, we need to clarify how superstring backgrounds are described in string geometry theory. In this paper, we show that all the type IIA, IIB, SO(32) type I, and SO(32) and $E_8 \times E_8$ heterotic superstring backgrounds are emb

Elena Celledoni, Ergys Çokaj, Andrea Leone, Davide Murari

Since they were introduced in the 1990s, Lie group integrators have become a method of choice in many application areas. These include multibody dynamics, shape analysis, data science, image registration and biophysical simulations. Two important classes of intrinsic Lie group integrators are the Runge--Kutta--Munthe--Kaas methods and the commutator free Lie

Oliver Müller, Eva Schnider

Countless low-surface brightness objects - including spiral galaxies, dwarf galaxies, and noise patterns - have been detected in recent large surveys. Classically, astronomers visually inspect those detections to distinguish between real low-surface brightness galaxies and artefacts. Employing the Dark Energy Survey (DES) and machine learning techniques, Tan

Thierry Coquand, Henri Lombardi, Stefan Neuwirth

We provide a constructive treatment of basic results in the theory of central simple algebras. One main issue is the fact that one starting result, Wedderburn's Theorem stating that a simple algebra is a matrix algebra over a skew field, is not constructively valid. We solve this problem by proving instead a dynamical version of this theorem. One can use

Qing Yu, Xing-Gang Wu, Hua Zhou, Xu-Dong Huang

In the present paper, we first give a detailed study on the pQCD corrections to the leading-twist part of BSR. Previous pQCD corrections to the leading-twist part derived under conventional scale-setting approach up to ${\cal O}(α_s^4)$-level still show strong renormalization scale dependence. The principle of maximum conformality (PMC) provides a systematic

Martin Grambow, Tobias Pfandzelter, Luk Burchard, Carsten Schubert

Following the increasing interest and adoption of FaaS systems, benchmarking frameworks for determining non-functional properties have also emerged. While existing (microbenchmark) frameworks only evaluate single aspects of FaaS platforms, a more holistic, application-driven approach is still missing. In this paper, we design and present BeFaaS, an extensibl

Anze Bozic, Stefano Franzini, Simon Copar

Topology and geometry of a sphere create constraints for particles that lie on its surface which they otherwise do not experience in Euclidean space. Notably, the number of particles and the size of the system can be varied separately, requiring a careful treatment of systems with one or several characteristic length scales. All this can make it difficult to

John A. Gracey

We use the large $N$ critical point formalism to compute $d$-dimensional critical exponents at several orders in $1/N$ in an Ising Gross-Neveu universality class where the core interaction includes a Lie group generator. Specifying a particular symmetry group or taking the abelian limit of the final exponents recovers known results but also provides expressi

Jeannette Jansen, Jan Knoll, Elke Beyreuther, Jörg Pawelke

Purpose: To investigate experimentally, if FLASH irradiation depletes oxygen within water for different radiation types such as photons, protons and carbon ions. Methods: This study presents measurements of the oxygen consumption in sealed, 3D printed water phantoms during irradiation with X-rays, protons and carbon ions at varying dose rates up to 340 Gy/s.

Konrad Aguilar, Jens Kaad, David Kyed

We prove that the Podles spheres $S_q^2$ converge in quantum Gromov-Hausdorff distance to the classical 2-sphere as the deformation parameter $q$ tends to 1. Moreover, we construct a $q$-deformed analogue of the fuzzy spheres, and prove that they converge to $S_q^2$ as their linear dimension tends to infinity, thus providing a quantum counterpart to a classi

Timur V. Tscherbul, Jacek Kłos

We use numerically exact quantum dynamics calculations to demonstrate universal stereoselectivity of cold collisions of $^2Π$ molecules with $^1$S-state atoms in an external electric field. We show that cold collisions of OH molecules in their low-field-seeking $f$ states, whose dipole moments are oriented against the field direction, are much more likely to

Oliver J. D. Barrowclough, Georg Muntingh, Varatharajan Nainamalai, Ivar Stangeby

We propose a novel approach to image segmentation based on combining implicit spline representations with deep convolutional neural networks. This is done by predicting the control points of a bivariate spline function whose zero-set represents the segmentation boundary. We adapt several existing neural network architectures and design novel loss functions t

Joan Sola, Adria Gomez-Valent, Javier de Cruz Perez, Cristian Moreno-Pulido

The cosmological term, $Λ$, was introduced $104$ years ago by Einstein in his gravitational field equations. Whether $Λ$ is a rigid quantity or a dynamical variable in cosmology has been a matter of debate for many years, especially after the introduction of the general notion of dark energy (DE). $Λ$ is associated to the vacuum energy density, $ρ_{\rm vac}$

Edgar Beck, Carsten Bockelmann, Armin Dekorsy

Following the great success of Machine Learning (ML), especially Deep Neural Networks (DNNs), in many research domains in 2010s, several ML-based approaches were proposed for detection in large inverse linear problems, e.g., massive MIMO systems. The main motivation behind is that the complexity of Maximum A-Posteriori (MAP) detection grows exponentially wit

Mahnaz Sadat Qafari, Wil van der Aalst

Process mining is widely used to diagnose processes and uncover performance and compliance problems. It is also possible to see relations between different behavioral aspects, e.g., cases that deviate more at the beginning of the process tend to get delayed in the later part of the process. However, correlations do not necessarily reveal causalities. Moreove

Jitendra Kumar, Puja Bharti

This paper investigates a spherically symmetric compact relativistic body with isotropic pressure profiles within the framework of general relativity. In order to solve the Einstein&#39;s field equations, we have considered the Vaidya-Tikekar type metric potential, which depends upon parameter K. We have presented a perfect fluid model, considering K<0 or K>

Minseok Shin, Donggyu Kim, Jianqing Fan

Several novel statistical methods have been developed to estimate large integrated volatility matrices based on high-frequency financial data. To investigate their asymptotic behaviors, they require a sub-Gaussian or finite high-order moment assumption for observed log-returns, which cannot account for the heavy-tail phenomenon of stock-returns. Recently, a

Jitendra Kumar, Puja Bharti

In this paper, we have constructed a model for well behaved isotropic compact star in the presence of charged perfect fluid, by considering a static and spherically symmetric metric in Schwarzschild&#39;s canonical coordinate system. To put the resulting differential equations into a closed system, we have employed the Vaidya & Tikekar (J. Astrophys. Astron.

Geoffrey Compère, Lorenzo Küchler

The transition motion of a point particle around the last stable orbit of Kerr is described at leading order in the transition-timescale expansion. Taking systematically into account all self-force effects, we prove that the transition motion is still described by the Painlevé transcendent equation of the first kind. Using an asymptotically matched expansion

Hoang-Son Do, Quang Dieu Nguyen

In this paper, we study some properties of viscosity sub/super-solutions of a class of fully nonlinear elliptic equations relative to the eigenvalues of the complex Hessian. We show that every viscosity subsolution is approximated by a decreasing sequence of smooth subsolutions. When the equations satisfy some conditions on the limit at infinity, we verify t

Aravind Sankaran, Paolo Bientinesi

In a typical Internet-of-Things setting that involves scientific applications, a target computation can be evaluated in many different ways depending on the split of computations among various devices. On the one hand, different implementations (or algorithms)--equivalent from a mathematical perspective--might exhibit significant difference in terms of perfo

Luis Fredes, Jean-Francois Marckert

Consider a connected graph $G=(E,V)$ with $N=|V|$ vertices. The main purpose of this paper is to explore the question of uniform sampling of a subtree of $G$ with $n$ nodes, for some $n\leq N$ (the spanning tree case correspond to $n=N$, and is already deeply studied in the literature). We provide new asymptotically exact simulation methods using Markov chai

Erik Daniel, Florian Tschorsch

Decentralized, distributed storage offers a way to reduce the impact of data silos as often fostered by centralized cloud storage. While the intentions of this trend are not new, the topic gained traction due to technological advancements, most notably blockchain networks. As a consequence, we observe that a new generation of peer-to-peer data networks emerg

Jose Blanchet, Fernando Hernandez, Viet Anh Nguyen, Markus Pelger

Missing time-series data is a prevalent practical problem. Imputation methods in time-series data often are applied to the full panel data with the purpose of training a model for a downstream out-of-sample task. For example, in finance, imputation of missing returns may be applied prior to training a portfolio optimization model. Unfortunately, this practic

Jeongmin Chae, Songnam Hong

We consider the problem of learning a nonlinear function over a network of learners in a fully decentralized fashion. Online learning is additionally assumed, where every learner receives continuous streaming data locally. This learning model is called a fully distributed online learning (or a fully decentralized online federated learning). For this model, w

Mohammad Akil, Ibtissam Issa, Ali Wehbe

In this paper, we investigate the energy decay of hyperbolic systems of wave-wave, wave-Euler- Bernoulli beam and beam-beam types. The two equations are coupled through boundary connection with only one localized non-smooth fractional Kelvin-Voigt damping. First, we reformulate each system into an augmented model and using a general criteria of Arendt-Batty,

Gaspard Beugnot, Aude Genevay, Kristjan Greenewald, Justin Solomon

Optimal transport (OT) is a popular tool in machine learning to compare probability measures geometrically, but it comes with substantial computational burden. Linear programming algorithms for computing OT distances scale cubically in the size of the input, making OT impractical in the large-sample regime. We introduce a practical algorithm, which relies on

Shravan Narayan, Craig Disselkoen, Daniel Moghimi, Sunjay Cauligi

We describe Swivel, a new compiler framework for hardening WebAssembly (Wasm) against Spectre attacks. Outside the browser, Wasm has become a popular lightweight, in-process sandbox and is, for example, used in production to isolate different clients on edge clouds and function-as-a-service platforms. Unfortunately, Spectre attacks can bypass Wasm&#39;s isol

Akhila Sri Manasa Venigalla, Sridhar Chimalakonda

Developers use and contribute to repositories on GitHub. Documentation present in the repositories serves as an important source by helping developers to understand, maintain and contribute to the project. Currently, documentation in a repository is diversified, among various files, with most of it present in ReadMe files. However, other software artifacts i

Weifeng Ding, Zhaoying Wang

In this paper, ABCD matrix is introduced to study the paraxial transmission of light on a constant gaussian curvature surface (CGCS), which is the first time to our knowledge. It is also proved that the curved surface can be used as the implementation of fractional Fourier transform, we further generalize that we can obtain the transfer matrix of an arbitrar

Jing Dong, Ke Li, Shuai Li, Baoxiang Wang

Strategic behavior against sequential learning methods, such as &#34;click framing&#34; in real recommendation systems, have been widely observed. Motivated by such behavior we study the problem of combinatorial multi-armed bandits (CMAB) under strategic manipulations of rewards, where each arm can modify the emitted reward signals for its own interest. This

Tom Mutabazi

We report distance measurements for the Norma cluster based on the near-infrared $J$- and $K_s$-band Fundamental Plane (FP) relations. Our simultaneous $J$ and $K_s$-band photometry analyses were performed using 31 early-type galaxies in the nearby Norma cluster obtained using the 1.4 m InfraRed Survey Facility (IRSF) at the South African Astronomical Observ

Raphael van Kempen, Bastian Lampe, Timo Woopen, Lutz Eckstein

Evidential occupancy grid maps (OGMs) are a popular representation of the environment of automated vehicles. Inverse sensor models (ISMs) are used to compute OGMs from sensor data such as lidar point clouds. Geometric ISMs show a limited performance when estimating states in unobserved but inferable areas and have difficulties dealing with ambiguous input. D

Hoda Taheri, Faeze Ramezani, Neda Mohammadi, Parisa Khoshdel

In a cloud environment, a cloud broker is an important entity that works as an independent middleware between cloud customers and providers to address issues and conduct negotiations related to satisfying both customer preferences and service provider profits. In recent years, researchers have published many articles which directly or indirectly address this

Tolson H. Bell, Jerrell M. Cockerham, Clayton M. Mizgerd, Melita F. Wiles

We present a method for computing transition points of the random cluster model using a generalization of the Newman-Ziff algorithm, a celebrated technique in numerical percolation, to the random cluster model. The new method is straightforward to implement and works for real cluster weight $q>0$. Furthermore, results for an arbitrary number of values of $q$

Thomas Berger, Achim Ilchmann, Stephan Trenn

We investigate feedback forms for linear time-invariant systems described by differential-algebraic equations. Feedback forms are representatives of certain equivalence classes. For example state space transformations, invertible transformations from the left, and proportional state feedback constitute an equivalence relation. The representative of such an e

Wadia Mansouri, Ahmad Oufkou

We give a complete classification of left invariant para-Kähler structures on four-dimensional simply connected Lie groups up to an automorphism. As an application we discuss some curvatures properties of the canonical connection associated to these structures as flat, Ricci flat and existence of Ricci solitons.

Valeriy Bardakov, Mahender Singh

Cycle sets are known to give non-degenerate unitary solutions of the Yang--Baxter equation and linear cycle sets are enriched versions of these algebraic systems. The paper explores the recently developed cohomology and extension theory for linear cycle sets. We derive a four term exact sequence relating 1-cocycles, second cohomology and certain groups of au

Michael Maiworm, Christian Wagner, Taner Esat, Philipp Leinen

Scanning quantum dot microscopy is a recently developed high-resolution microscopy technique that is based on atomic force microscopy and is capable of imaging the electrostatic potential of nanostructures like molecules or single atoms. Recently, it could be shown that it not only yields qualitatively but also quantitatively cutting edge images even on an a

Jean-Claude Cuenin

We establish quantitative upper and lower bounds for Schrödinger operators with complex potentials that satisfy some weak form of sparsity. Our first result is a quantitative version of an example, due to S.\ Boegli (Comm. Math. Phys., 2017, 352, 629-639), of a Schrödinger operator with eigenvalues accumulating to every point of the essential spectrum. The s

Werner Kirsch, Gabor Toth

We analyse optimal voting weights in two-tier voting systems. In our model, the overall population (or union) is split in groups (or member states) of different sizes. The individuals comprising the overall population constitute the first tier, and the council is the second tier. Each group has a representative in the council that casts votes on their behalf

U. A. N. Ungku Chulan, M. T. Islam, N. Misran

Currently, the potential of microsimulation in space time trellis code has not been thoroughly ascertained. Therefore, this letter explores the possibility of using microsimulation in performing a pairwise comparison between competing generator matrices in code design. The validation of code construction is often done with simulation, which can be inherently

Nikola Surjanovic, Thomas M. Loughin

The Hosmer-Lemeshow (HL) test is a commonly used global goodness-of-fit (GOF) test that assesses the quality of the overall fit of a logistic regression model. In this paper, we give results from simulations showing that the type 1 error rate (and hence power) of the HL test decreases as model complexity grows, provided that the sample size remains fixed and

Brendan Keith, Akshay Khadse, Scott E. Field

We introduce a gravitational waveform inversion strategy that discovers mechanical models of binary black hole (BBH) systems. We show that only a single time series of (possibly noisy) waveform data is necessary to construct the equations of motion for a BBH system. Starting with a class of universal differential equations parameterized by feed-forward neura

Shangkun Weng, Yan Zhou

In this paper, we investigate the decay properties of axially symmetric solutions to the steady incompressible magnetohydrodynamic equations in $\mathbb{R}^3$ with finite Dirichlet integral. We first derive the decay rates of general D-solutions to the axisymmetric MHD equations. In the special case where the magnetic field only has the swirl component ${\te

Chunguang Pan, Bingyan Song, Shengguang Wang, Zhipeng Luo

Acronym disambiguation (AD) task aims to find the correct expansions of an ambiguous ancronym in a given sentence. Although it is convenient to use acronyms, sometimes they could be difficult to understand. Identifying the appropriate expansions of an acronym is a practical task in natural language processing. Since few works have been done for AD in scienti

Takao Komatsu, Rusen Li

In this paper, several weighted summation formulas of $q$-hyperharmonic numbers are derived. As special cases, several formulas of hyperharmonic numbers of type $\sum_{\ell=1}^{n} {\ell}^{p} H_{\ell}^{(r)}$ and $\sum_{\ell=0}^{n} {\ell}^{p} H_{n-\ell}^{(r)}$ are obtained.

Zhuangyan Fang, Yue Liu, Zhi Geng, Shengyu Zhu

Causality is important for designing interpretable and robust methods in artificial intelligence research. We propose a local approach to identify whether a variable is a cause of a given target under the framework of causal graphical models of directed acyclic graphs (DAGs). In general, the causal relation between two variables may not be identifiable from

Claire Liang, Wil Thomason, E. Andy Ricci, Soham Sankaran

In environments where multiple robots must coordinate in a shared space, decentralized approaches allow for decoupled planning at the cost of global guarantees, while centralized approaches make the opposite trade-off. These solutions make a range of assumptions - commonly, that all the robots share the same planning strategies. In this work, we present a fr

Jakub Maksymilian Fober

A wide choice of cinematic lenses enables motion-picture creators to adapt image visual-appearance to their creative vision. Such choice does not exist in the realm of real-time computer graphics, where only one type of perspective projection is widely used, a linear perspective. This paper presents an extended perspective imaging model, which can represent

Youzhou Zhou

From the Poisson-Dirichlet diffusions to the $Z$-measure diffusions, they all have explicit transition densities. In this paper, we will show that the transition densities of the $Z$-measure diffusions can also be expressed as a mixture of a sequence of probability measures on the Thoma simplex. The coefficients are still the transition probabilities of the

Sooyong Jang, Radoslav Ivanov, Insup Lee, James Weimer

As machine learning techniques become widely adopted in new domains, especially in safety-critical systems such as autonomous vehicles, it is crucial to provide accurate output uncertainty estimation. As a result, many approaches have been proposed to calibrate neural networks to accurately estimate the likelihood of misclassification. However, while these m

Da Yu, Huishuai Zhang, Wei Chen, Tie-Yan Liu

The privacy leakage of the model about the training data can be bounded in the differential privacy mechanism. However, for meaningful privacy parameters, a differentially private model degrades the utility drastically when the model comprises a large number of trainable parameters. In this paper, we propose an algorithm \emph{Gradient Embedding Perturbation

Hoshin V Gupta, Mohammed Reza Ehsani, Tirthankar Roy, Maria A Sans-Fuentes

We develop a simple Quantile Spacing (QS) method for accurate probabilistic estimation of one-dimensional entropy from equiprobable random samples, and compare it with the popular Bin-Counting (BC) method. In contrast to BC, which uses equal-width bins with varying probability mass, the QS method uses estimates of the quantiles that divide the support of the

Azarakhsh Keipour, Guilherme A. S. Pereira, Sebastian Scherer

We propose a new algorithm for real-time detection and tracking of elliptic patterns suitable for real-world robotics applications. The method fits ellipses to each contour in the image frame and rejects ellipses that do not yield a good fit. The resulting detection and tracking method is lightweight enough to be used on robots&#39; resource-limited onboard

Hiroyasu Tsukamoto, Soon-Jo Chung

This paper presents Learning-based Autonomous Guidance with RObustness and Stability guarantees (LAG-ROS), which provides machine learning-based nonlinear motion planners with formal robustness and stability guarantees, by designing a differential Lyapunov function using contraction theory. LAG-ROS utilizes a neural network to model a robust tracking control

Xuesu Xiao, Joydeep Biswas, Peter Stone

This paper presents a learning-based approach to consider the effect of unobservable world states in kinodynamic motion planning in order to enable accurate high-speed off-road navigation on unstructured terrain. Existing kinodynamic motion planners either operate in structured and homogeneous environments and thus do not need to explicitly account for terra

Jiangtao Duan, Jushan Bai, Xu Han

This paper estimates the break point for large-dimensional factor models with a single structural break in factor loadings at a common unknown date. First, we propose a quasi-maximum likelihood (QML) estimator of the change point based on the second moments of factors, which are estimated by principal component analysis. We show that the QML estimator perfor

Chris Whittle, Evan D. Hall, Sheila Dwyer, Nergis Mavalvala

The motion of a mechanical object -- even a human-sized object -- should be governed by the rules of quantum mechanics. Coaxing them into a quantum state is, however, difficult: the thermal environment masks any quantum signature of the object&#39;s motion. Indeed, the thermal environment also masks effects of proposed modifications of quantum mechanics at l

Sandro Mattarei

A thin Lie algebras is a Lie algebra $L$, graded over the positive integers, with its first homogeneous component $L_1$ of dimension two and generating $L$, and such that each nonzero ideal of $L$ lies between consecutive terms of its lower central series. All its homogeneous components have dimension one or two, and the two-dimensional components are called

Mehdi Jafarnia-Jahromi, Rahul Jain, Ashutosh Nayyar

Solving Partially Observable Markov Decision Processes (POMDPs) is hard. Learning optimal controllers for POMDPs when the model is unknown is harder. Online learning of optimal controllers for unknown POMDPs, which requires efficient learning using regret-minimizing algorithms that effectively tradeoff exploration and exploitation, is even harder, and no sol

Travis Garrett, Jennifer Elle, Michael White, Remington Reid

Recent experiments have shown that femtosecond filamentation plasmas generate ultra-broadband radio frequency radiation (RF). We show that a combination of plasma dynamics is responsible for the RF: a plasma wake field develops behind the laser pulse, and this wake excites (and copropagates with) a surface wave on the plasma column. The surface wave proceeds

Nam-Hoon Lee

We show that there exists a non-Kähler Calabi-Yau fourfold, constructing an example by smoothing a normal crossing variety.

Yudan Liu, Hamid Ossareh

This paper presents a constraint management strategy based on Scalar Reference Governors (SRG) to enforce output, state, and control constraints while taking into account the preview information of the reference and/or disturbances signals. The strategy, referred to as the Preview Reference Governor (PRG), can outperform SRG while maintaining the highly-attr

Dian P. Triani, Manodeep Sinha, Darren J. Croton, Eli Dwek

We explore the relation between dust and several fundamental properties of simulated galaxies using the Dusty SAGE semi-analytic model. In addition to tracing the standard galaxy properties, Dusty SAGE also tracks cold dust mass in the interstellar medium (ISM), hot dust mass in the halo and dust mass ejected by feedback activity. Based on their ISM dust con

M. L. Pereira Júnior, R. T. de Sousa Junior, G. D. Amvame Nze, W. F. Giozza

Virtual screening is a computational technique widely used for identifying small molecules which are most likely to bind to a protein target. Here, we performed a molecular docking study to propose potential candidates to prevent the RBD/ACE2 attachment. These candidates are sixteen different flavonoids present in the peppermint leaf. Results showed that Lut

Bo-Yong Chen

Using the logarithmic capacity, we give quantitative estimates of the Green function, as well as lower bounds of the Bergman kernel for bounded pseudoconvex domains in $\mathbb C^n$ and the Bergman distance for bounded planar domains. In particular, it is shown that the Bergman kernel satisfies $K_Ω(z)\gtrsim δ_Ω(z)^{-2}$ for any bounded pseudoconvex domain

Yuanqing Wang, Theofanis Karaletsos

Graph neural networks (GNNs) manifest pathologies including over-smoothing and limited discriminating power as a result of suboptimally expressive aggregating mechanisms. We herein present a unifying framework for stochastic aggregation (STAG) in GNNs, where noise is (adaptively) injected into the aggregation process from the neighborhood to form node embedd

Heng Hao, Hankyu Moon, Sima Didari, Jae Oh Woo

We propose a highly data-efficient active learning framework for image classification. Our novel framework combines: (1) unsupervised representation learning of a Convolutional Neural Network and (2) the Gaussian Process (GP) method, in sequence to achieve highly data and label efficient classifications. Moreover, both elements are less sensitive to the prev

Vladimir Pletser

For all positive non-square integer multiplier k, there is an infinity of multiples of triangular numbers which are also triangular numbers. With a simple change of variables, these triangular numbers can be found using solutions of Pell equations. With some conditions on parities of fundamental solutions of the simple and generalized Pell equations, only od

Yuanhan Zhang, Zhenfei Yin, Jing Shao, Ziwei Liu

As facial interaction systems are prevalently deployed, security and reliability of these systems become a critical issue, with substantial research efforts devoted. Among them, face anti-spoofing emerges as an important area, whose objective is to identify whether a presented face is live or spoof. Recently, a large-scale face anti-spoofing dataset, CelebA-

Cai-Yun Zhao, Hui-Li Han, Ting-Yung Shi

We have studied the three-body recombination rates on both sides of the interspecies d-wave Feshbach resonance in the $^{85}$Rb\,-$^{87}$Rb-$^{87}$Rb system using the $R$-matrix propagation method in the hyperspherical coordinate frame. Two different mechanisms of recombination rate enhancement for positive and negative $^{85}$Rb\,-$^{87}$Rb d-wave scatterin

A. M. Marques, L. Madail, R. G. Dias

Square-root topology is a recently emerged subfield describing a class of insulators and superconductors whose topological nature is only revealed upon squaring their Hamiltonians, i.e., the finite energy edge states of the starting square-root model inherit their topological features from the zero-energy edge states of a known topological insulator/supercon

Yuan Li, Huazi Zhang, Rong Li, Jun Wang

Polar codes are the first class of channel codes achieving the symmetric capacity of the binary-input discrete memoryless channels with efficient encoding and decoding algorithms. But the weight spectrum of Polar codes is relatively poor compared to RM codes, which degrades their ML performance. Pre-transformation with an upper-triangular matrix (including c

Aritra Kundu, Atanu Rajak, Tanay Nag

Having established the fact that interacting classical kicked rotor systems exhibit long-lived prethermal phase with quasi-conserved average Hamiltonian before entering into chaotic heating regime, we use spatio-temporal fluctuation correlation of kinetic energy to probe the above dynamic phases. We remarkably find the diffusive transport of fluctuation in t

Wenjing Ma, Youqing Wu, Zhaoxia Yin

With the popularization of digital information technology, the reversible data hiding in encrypted images (RDHEI) has gradually become the research hotspot of privacy protection in cloud storage. As a technology which can embed additional information in encrypted domain, extract the embedded information correctly and recover the original image without loss,

Yoshifumi Nakata, Da Zhao, Takayuki Okuda, Eiichi Bannai

A unitary $t$-design is a powerful tool in quantum information science and fundamental physics. Despite its usefulness, only approximate implementations were known for general $t$. In this paper, we provide for the first time quantum circuits that generate exact unitary $t$-designs for any $t$ on an arbitrary number of qubits. Our construction is inductive a

Itamar López, Leonardo Castañeda

Two methods for mass profiles reconstruction in disc-like galaxies are presented in this work, the first is done with the fit of the rotation curve based on the data of circular velocity which are obtained observationally in a stars system, while the other method is focused in the Gravitational Lensed Effect (GLE). For these mass reconstructions, two routine

Yingqiang Qiu, Qichao Ying, Yuyan Yang, Huanqiang Zeng

While the existing vacating room before encryption (VRBE) based schemes can achieve decent embedding rate, the payloads of the existing vacating room after encryption (VRAE) based schemes are relatively low. To address this issue, this paper proposes a generalized framework for high-capacity RDHEI for both VRBE and VRAE cases. First, an efficient embedding r

Alvaro Garcia-Recuero

Anonymous social networks present a number of new and challenging problems for existing Social Network Analysis techniques. Traditionally, existing methods for analysing graph structure, such as community detection, required global knowledge of the graph structure. That implies that a centralised entity must be given access to the edge list of each node in t

Bradford Snios, Daniel A. Schwartz, Aneta Siemiginowska, Małgosia Sobolewska

We present Chandra X-ray observations of 14 radio-loud quasars at redshifts $3 < z < 4$, selected from a well-defined sample. All quasars are detected in the 0.5-7.0 keV energy band, and resolved X-ray features are detected in five of the objects at distances of 1-12&#34; from the quasar core. The X-ray features are spatially coincident with known radio feat

Jianbo Cui, Liying Sun

In this paper, we prove the global existence and uniqueness of the solution of the stochastic logarithmic Schrödinger (SlogS) equation driven by additive noise or multiplicative noise. The key ingredient lies on the regularized stochastic logarithmic Schrödinger (RSlogS) equation with regularized energy and the strong convergence analysis of the solutions of

Huilong Ren, Xiaoying Zhuang, Erkan Oterkus, HeHua Zhu

The derivation of nonlocal strong forms for many physical problems remains cumbersome in traditional methods. In this paper, we apply the variational principle/weighted residual method based on nonlocal operator method for the derivation of nonlocal forms for elasticity, thin plate, gradient elasticity, electro-magneto-elasticity and phase field fracture met

Hideaki Takemura, Fumitaka Nakamura, Shuo Kong, Héctor G. Arce

Applying dendrogram analysis to the CARMA-NRO C$^{18}$O ($J$=1--0) data having an angular resolution of $\sim$ 8&#34;, we identified 692 dense cores in the Orion Nebula Cluster (ONC) region. Using this core sample, we compare the core and initial stellar mass functions in the same area to quantify the step from cores to stars. About 22 \% of the identified c

Hideaki Takemura, Fumitaka Nakamura, Shun Ishii, Yoshito Shimajiri

We have performed an unbiased dense core survey toward the Orion A Giant Molecular Cloud in the C$^{18}$O ($J$=1--0) emission line taken with the Nobeyama Radio Observatory (NRO) 45-m telescope. The effective angular resolution of the map is 26&#34;, which corresponds to $\sim$ 0.05 pc at a distance of 414 pc. By using the Herschel-Planck H$_2$ column densit

Roman Töngi

Sign language is the primary language for people with a hearing loss. Sign language recognition (SLR) is the automatic recognition of sign language, which represents a challenging problem for computers, though some progress has been made recently using deep learning. Huge amounts of data are generally required to train deep learning models. However, correspo

Babette Bühler, Heiko Paulheim

Tables on the web constitute a valuable data source for many applications, like factual search and knowledge base augmentation. However, as genuine tables containing relational knowledge only account for a small proportion of tables on the web, reliable genuine web table classification is a crucial first step of table extraction. Previous works usually rely

Vladimir Pletser

It is known that, for any positive non-square integer multiplier $k$, there is an infinity of multiples of triangular numbers which are triangular numbers. We analyze the congruence properties of the indices $ξ$ of triangular numbers that are multiples of other triangular numbers. We show that the remainders in the congruence relations of $ξ$ modulo k come a

Muhammad Shahroz, Farooq Ahmad, Muhammad Shahzad Younis, Nadeem Ahmad

The coronavirus disease 2019 (COVID-19) is a severe global pandemic that has claimed millions of lives and continues to overwhelm public health systems in many countries. The spread of COVID-19 pandemic has negatively impacted the human mobility patterns such as daily transportation-related behavior of the public. There is a requirement to understand the dis

Martina Ferrando, Francesco Causone, Tianzhen Hong, Yixing Chen

Regulations corroborate the importance of retrofitting existing building stocks or constructing new energy efficient district. There is, thus, a need for modeling tools to evaluate energy scenarios to better manage and design cities, and numerous methodologies and tools have been developed. Among them, Urban Building Energy Modeling (UBEM) tools allow the en

Juliette Millet, Jean-Remi King

Our ability to comprehend speech remains, to date, unrivaled by deep learning models. This feat could result from the brain&#39;s ability to fine-tune generic sound representations for speech-specific processes. To test this hypothesis, we compare i) five types of deep neural networks to ii) human brain responses elicited by spoken sentences and recorded in