October 2022 arXiv papers — page 96

Xinwei Ju, Frank Po Wen Lo, Jianing Qiu, Peilun Shi

Food recommendation system has proven as an effective technology to provide guidance on dietary choices, and this is especially important for patients suffering from chronic diseases. Unlike other multimedia recommendations, such as books and movies, food recommendation task is highly relied on the context at the moment, since users' food preference can be h

Yingjian Wang, Xiangyong Wen, Yanjun Cao, Chao Xu

Mutual localization provides a consensus of reference frame as an essential basis for cooperation in multirobot systems. Previous works have developed certifiable and robust solvers for relative transformation estimation between each pair of robots. However, recovering relative poses for robotic swarm with partially mutual observations is still an unexploite

Jeffrey M. La Fortune

Gaia HyperVelocity Star (HVS) kinematic observations favor a local escape velocity of ~700 km/s, nearly forty-percent greater than conventional estimates. Combining HVS and dwarf galaxy satellite data reveal the global escape velocity profile for the Galaxy smoothly traces an unbroken Keplerian decline from the central bar to the most remote satellite galaxy

Sheel Shah, Shubham Gupta

ConnectX is a two-player game that generalizes the popular game Connect 4. The objective is to get X coins across a row, column, or diagonal of an M x N board. The first player to do so wins the game. The parameters (M, N, X) are allowed to change in each game, making ConnectX a novel and challenging problem. In this paper, we present our work on the impleme

Haoran Hong, Eduardo Pavez, Antonio Ortega, Ryosuke Watanabe

In point cloud compression, exploiting temporal redundancy for inter predictive coding is challenging because of the irregular geometry. This paper proposes an efficient block-based inter-coding scheme for color attribute compression. The scheme includes integer-precision motion estimation and an adaptive graph based in-loop filtering scheme for improved att

Jianchun Chu, Man-Chun Lee, Jintian Zhu

Motivated by the recent progress on positive mass theorem for asymptotically flat manifolds with arbitrary ends and the Gromov's definition of scalar curvature lower bound for continuous metrics, we start a program on the positive mass theorem for asymptotically flat manifolds with $C^0$ arbitrary ends. In this work as the first step, we establish the positi

Rohit Kumar, Tuhina Mukherjee, Abhishek Sarkar

In this article, our main concern is to study the existence of bound and ground state solutions for the following fractional system of Schr\"{o}dinger equations with Hardy potentials: \begin{equation*} \left\{ \begin{aligned} (-\Delta)^{s_{1}} u - \lambda_{1} \frac{u~~}{|x|^{2s_{1}}} - u^{2_{s_{1}}^{*}-1} = \nu \alpha h(x) u^{\alpha-1}v^{\beta} & \quad \mbox

Manjun Ma, Wentao Meng, Chunhua Ou, Jiajun Yue

Seasonality frequently occurs in population models, and the corresponding seasonal patterns have been of great interest to scientists. This paper is concerned with traveling waves to a time-periodic bistable Lotka-Volterra competition system with nonlocal dispersal. We first establish the existence, uniqueness and stability of traveling wave solutions for th

Mingxuan Liu, Siqi Li, Han Yuan, Marcus Eng Hock Ong

Objective: The proper handling of missing values is critical to delivering reliable estimates and decisions, especially in high-stakes fields such as clinical research. The increasing diversity and complexity of data have led many researchers to develop deep learning (DL)-based imputation techniques. We conducted a systematic review to evaluate the use of th

Pilar Benito, Jorge Roldán-López

A Lie algebra is said to be quadratic if it admits a symmetric invariant and non-degenerated bilinear form. Semisimple algebras with the Killing form are examples of these algebras, while orthogonal subspaces provide abelian quadatric algebras. The class of quadratic algebras is outsize, but at first sight it is not clear weather an algebra is quadratic. Som

Aryan Garg, Renu M. Rameshan

Navigating dynamic physical environments without obstructing or damaging human assets is of quintessential importance for social robots. In this work, we solve autonomous drone navigation's sub-problem of predicting out-of-domain human and agent trajectories using a deep generative model. Our method: General-PECNet or G-PECNet observes an improvement of 9.5\

Huanjie Wang, Haibin Liu, Wenshuo Wang, Lijun Sun

Humans are experts in making decisions for challenging driving tasks with uncertainties. Many efforts have been made to model the decision-making process of human drivers at the behavior level. However, limited studies explain how human drivers actively make reliable sequential decisions to complete interactive driving tasks in an uncertain environment. This

Zhongcheng Yu, Jinyuan Tian, Peng Peng, Dekai Mao

The ultracold atoms in a moving optical lattice with its high controllability is a feasible platform to research the transport phenomenon. Here, we study the transport process of ultracold atoms at the D band in a one-dimensional optical lattice, and perform the manipulation of atomic transport by modulating the population proportion of S band and D band. In

Zewei Xiong, Meng-Ru Wu, Gabriel Martínez-Pinedo, Tobias Fischer

We implement a multi-group and discrete-ordinate neutrino transport model in spherical symmetry which allows to simulate collective neutrino oscillations by including realistic collisional rates in a self-consistent way. We utilize this innovative model, based on strategic parameter rescaling, to study a recently proposed collisional flavor instability cause

Muhammad Ahsan, Sacheendra Talluri, Alexandru Iosup

Cloud services are important for societal function such as healthcare, commerce, entertainment and education. Cloud can provide a variety of features such as increased collaboration and inexpensive computing. Failures are unavoidable in cloud services due to the large size and complexity, resulting in decreased reliability and efficiency. For example, due to

David Alonso López, Jose A. R. Cembranos, David Díaz-Guerra, Andrés Mínguez Sánchez

In this manuscript we study the Wehrl entropy of entangled oscillators. This semiclassical entropy associated with the phase-space description of quantum mechanics can be used for formulating uncertainty relations and for a quantification of entanglement. We focus on a system of two coupled oscillators described within its Segal-Bargmann space. This Hilbert

Siamak Layeghy, Mahsa Baktashmotlagh, Marius Portmann

The performance of machine learning based network intrusion detection systems (NIDSs) severely degrades when deployed on a network with significantly different feature distributions from the ones of the training dataset. In various applications, such as computer vision, domain adaptation techniques have been successful in mitigating the gap between the distr

Jiaqi Sun, Lin Zhang, Shenglin Zhao, Yujiu Yang

Graph neural networks (GNNs) hold the promise of learning efficient representations of graph-structured data, and one of its most important applications is semi-supervised node classification. However, in this application, GNN frameworks tend to fail due to the following issues: over-smoothing and heterophily. The most popular GNNs are known to be focused on

H. Y. Wu, Yu-Chin Tzeng, Z. Y. Xie, K. Ji

In this work, the cross derivative of the Gibbs free energy, initially proposed for phase transitions in classical spin models [Phys. Rev. B 101, 165123 (2020)], is extended for quantum systems. We take the spin-1 XXZ chain with anisotropies as an example to demonstrate its effectiveness and convenience for the Gaussian-type quantum phase transitions therein

Yongwei Zhou, Junwei Bao, Chaoqun Duan, Youzheng Wu

Question answering requiring discrete reasoning, e.g., arithmetic computing, comparison, and counting, over knowledge is a challenging task. In this paper, we propose UniRPG, a semantic-parsing-based approach advanced in interpretability and scalability, to perform unified discrete reasoning over heterogeneous knowledge resources, i.e., table and text, as pr

Hanlin Zhang, Xuechen Li, Prithviraj Sen, Salim Roukos

We systematically study the calibration of classifiers trained with differentially private stochastic gradient descent (DP-SGD) and observe miscalibration across a wide range of vision and language tasks. Our analysis identifies per-example gradient clipping in DP-SGD as a major cause of miscalibration, and we show that existing approaches for improving cali

Ioannis P. A. Papadopoulos, Sheehan Olver

We develop a sparse spectral method for a class of fractional differential equations, posed on $\mathbb{R}$, in one dimension. These equations can include sqrt-Laplacian, Hilbert, derivative and identity terms. The numerical method utilizes a basis consisting of weighted Chebyshev polynomials of the second kind in conjunction with their Hilbert transforms. T

Steven Duplij, Wend Werner

We investigate finite field extensions of the unital 3-field, consisting of the unit element alone, and find considerable differences to classical field theory. Furthermore, the structure of their automorphism groups is clarified and the respective subfields are determined. In an attempt to better understand the structure of 3-fields that show up here we loo

Chao Wang, Joerg Deigmoeller

As knowledge graph has the potential to bridge the gap between commonsense knowledge and reasoning over actionable capabilities of mobile robotic platforms, incorporating knowledge graph into robotic system attracted increasing attention in recent years. Previously, graph visualization has been used wildly by developers to make sense of knowledge representat

Yakov Krasnov, Vladimir Tkachev

The purpose of this paper is to give a systematic study of two new classes of commutative nonassociative algebras, the so-called isospectral and medial algebras. An isospectral algebra $\mathbb{A}$ is a generic commutative nonassociative algebra whose idempotents have the same Peirce spectrum. A medial algebra is algebra with identity $(xy)(zw)=(xz)(yw)$. We

Sida Xing, Feihu Han, Suiyang Khoo

The emergence of Long Short-Term Memory (LSTM) solves the problems of vanishing gradient and exploding gradient in traditional Recurrent Neural Networks (RNN). LSTM, as a new type of RNN, has been widely used in various fields, such as text prediction, Wind Speed Forecast, depression prediction by EEG signals, etc. The results show that improving the efficie

Jiye Kim, Seungbeom Lee, Dongwoo Kim, Sungsoo Ahn

Designing a neural network architecture for molecular representation is crucial for AI-driven drug discovery and molecule design. In this work, we propose a new framework for molecular representation learning. Our contribution is threefold: (a) demonstrating the usefulness of incorporating substructures to node-wise features from molecules, (b) designing two

Yuzhe Zhang, Min Cen, Tongzhou Wu, Hong Zhang

Few-shot relation extraction (FSRE) aims at recognizing unseen relations by learning with merely a handful of annotated instances. To generalize to new relations more effectively, this paper proposes a novel pipeline for the FSRE task based on queRy-information guided Attention and adaptive Prototype fuSion, namely RAPS. Specifically, RAPS first derives the

Ling Tang, Yanjun Zhang, Hanyu Li

We first propose the regular sketch-and-project method for solving tensor equations with respect to the popular t-product. Then, three adaptive sampling strategies and three corresponding adaptive sketch-and-project methods are derived. We prove that all the proposed methods have linear convergence in expectation. Furthermore, we investigate the Fourier doma

A. S. Sanz

Airy beams are solutions to the paraxial Helmholtz equation known for exhibiting shape invariance along their self-accelerated propagation in free space. These two properties are associated with the fact that they are not square integrable, that is, they carry infinite energy. To circumvent this drawback, families of so-called finite-energy Airy-type beams h

Amitav Sahu, Vivek N. Bhat, Sanjoy Patra, Vivek Tiwari

Background-free fluorescence detection in multidimensional electronic spectroscopy promises high sensitivity compared to conventional approaches. Here we explore the sensitivity limits of multidimensional electronic spectroscopy. We present a fluorescence-detected multidimensional electronic spectrometer based on a visible white-light continuum. As a demonst

Zihan Zhang, Yuhang Jiang, Yuan Zhou, Xiangyang Ji

In this paper, we study the episodic reinforcement learning (RL) problem modeled by finite-horizon Markov Decision Processes (MDPs) with constraint on the number of batches. The multi-batch reinforcement learning framework, where the agent is required to provide a time schedule to update policy before everything, which is particularly suitable for the scenar

Leonid Positselski, Jan Stovicek

The concept of an abelian DG-category, introduced by the first-named author in arXiv:2110.08237, unites the notions of abelian categories and (curved) DG-modules in a common framework. In this paper we consider coderived and contraderived categories in the sense of Becker. Generalizing some constructions and results from the preceding papers by Becker arXiv:

Anju Bhasin, Meenakshi Sharma

We present the new studies which are performed with the aim of better understanding the production mechanisms for strange particles, and hence the strangeness enhancement phenomenon, in small-collision systems. In one of the recent studies, the very forward energy transported by beam remnants (spectators) and detected by the Zero Degree Calorimeters (ZDC) is

Wenpo Yao, Wenli Yao, Rongshuang Xu, Jun Wang

Although time irreversibility (TIR) and amplitude irreversibility (AIR) are relevant concepts for nonequilibrium analysis, their association has received little attention. This paper conducts a systematic comparative analysis of the relationship between TIR and AIR based on statistical descriptions and numerical simulations. To simplify the quantification of

Tsung-Hsuan Tsai

Gromov showed that (1993) with high probability, every bounded and reduced van Kampen diagram $D$ of a random group at density $d$ satisfies the isoperimetric inequality $|\partial D|\geq (1-2d-s)|D|\ell$. In this article, we adapt Gruber-Mackay's prove for random triangular groups, showing a non-reduced 2-complex version of this inequality. Moreover, for an

Yinger Zhang, Zhouyi Wu, Peiying Lin, Yang Pan

A lensless camera is an imaging system that uses a mask in place of a lens, making it thinner, lighter, and less expensive than a lensed camera. However, additional complex computation and time are required for image reconstruction. This work proposes a deep learning model named Raw3dNet that recognizes hand gestures directly on raw videos captured by a lens

Tesla Zhang

This tutorial explains (one way) how to implement De Morgan cubical type theory to people who know how to implement a dependent type theory. It contains an introduction to basic concepts of cubes, type checking algorithms under a cofibration, the idea of "transportation rules" and cubical operations. This tutorial is a by-product of an experimental implement

ShengLi Tzeng, Bo-Yu Chen, Hsin-Cheng Huang

In this research, we propose a novel technique for visualizing nonstationarity in geostatistics, particularly when confronted with a single realization of data at irregularly spaced locations. Our method hinges on formulating a statistic that tracks a stable microergodic parameter of the exponential covariance function, allowing us to address the intricate c

Alberto Facchini, Leila Heidari Zadeh

The category of all $k$-algebras with a bilinear form, whose objects are all pairs $(R,b)$ where $R$ is a $k$-algebra and $b\colon R\times R\to k$ is a bilinear mapping, is equivalent to the category of unital $k$-algebras $A$ for which the canonical homomorphism $(k,1)\to(A,1_A)$ of unital $k$-algebras is a splitting monomorphism in the category of $k$-modu

Hengsheng Zhang, Xueyi Zou, Jiaming Guo, Youliang Yan

Online processing of compressed videos to increase their resolutions attracts increasing and broad attention. Video Super-Resolution (VSR) using recurrent neural network architecture is a promising solution due to its efficient modeling of long-range temporal dependencies. However, state-of-the-art recurrent VSR models still require significant computation t

Lukang Huang, Wei Huang, Oliver Linton, Zheng Zhang

To investigate causal mechanisms, causal mediation analysis decomposes the total treatment effect into the natural direct and indirect effects. This paper examines the estimation of the direct and indirect effects in a general treatment effect model, where the treatment can be binary, multi-valued, continuous, or a mixture. We propose generalized weighting e

Bo Wang, Lu Meng, Shi-Lin Zhu

We studied the $D\bar{D}^\ast/\bar{D}D^\ast$, $D^\ast\bar{D}^\ast$, $\bar{D}_s D^\ast/\bar{D}_s^\ast D$, $B\bar{B}^\ast/\bar{B}B^\ast$ and $B^\ast\bar{B}^\ast$ di-hadron interactions in chiral effective field theory ($\chi$EFT) up to the next-to-leading (NLO) order. The above-threshold tetraquark states $Z_c(3900)$, $Z_c(4020)$, $Z_{cs}(3985)$, $Z_b(10610)$,

Adriano Cardace, Riccardo Spezialetti, Pierluigi Zama Ramirez, Samuele Salti

Point cloud classification is a popular task in 3D vision. However, previous works, usually assume that point clouds at test time are obtained with the same procedure or sensor as those at training time. Unsupervised Domain Adaptation (UDA) instead, breaks this assumption and tries to solve the task on an unlabeled target domain, leveraging only on a supervi

Yung-Han Ho, Chih-Hsuan Lin, Peng-Yu Chen, Mu-Jung Chen

This paper proposes a learning-based video compression framework for variable-rate coding on YUV 4:2:0 content. Most existing learning-based video compression models adopt the traditional hybrid-based coding architecture, which involves temporal prediction followed by residual coding. However, recent studies have shown that residual coding is sub-optimal fro

Frank Schweitzer, Christian Zingg, Giona Casiraghi

Collectives form non-equilibrium social structures characterised by a volatile dynamics. Individuals join or leave. Social relations change quickly. Therefore, differently from engineered or ecological systems, a resilient reference state cannot be defined. We propose a novel resilience measure combining two dimensions: robustness and adaptivity. We demonstr

Franco Barbanera, Ivan Lanese, Emilio Tuosto

We introduce a meta-model based on formal languages, dubbed formal choreographic languages, to study message-passing systems. Our framework allows us to generalise standard constructions from the literature and to compare them. In particular, we consider notions such as global view, local view, and projections from the former to the latter. The correctness o

Vaclav Zatloukal, Simon Vedl

We introduce the concept of shape operator and rotating blade (also known in the theory of embedded Riemannian manifolds as the second fundamental form and the Gauss map) in the realm of Yang-Mills theories. Hence we arrive at new gauge-invariant variables, which can serve as an alternative to the usual gauge potentials.

Jonas A. Krieger, Samuel Stolz, Inigo Robredo, Kaustuv Manna

Spin-momentum locking in solids describes a directional relationship between the electron's spin angular momentum and its linear momentum over the entire Fermi surface. While orthogonal spin-momentum locking, such as Rashba spin-orbit coupling, has been studied for decades and inspired a vast number of applications, its natural counterpart, the purely parall

Mame Gor Ngom, Ibrahima Faye, Diaraf Seck

In this paper, we perform a rigourous version of shape and topological derivatives for optimizations problems under constraint Helmoltz problems. A shape and topological optimization problem is formulated by introducing cost functional. We derive first by considering the lagradian method the shape derivative of the functional. It is also proven a topological

Raffaele Paolino, Aleksandar Bojchevski, Stephan Günnemann, Gitta Kutyniok

A powerful framework for studying graphs is to consider them as geometric graphs: nodes are randomly sampled from an underlying metric space, and any pair of nodes is connected if their distance is less than a specified neighborhood radius. Currently, the literature mostly focuses on uniform sampling and constant neighborhood radius. However, real-world grap

Huangping Jin, Kunpeng Liu, Gilwon Lee, Emad J. Farag

Since the introduction of fifth-generation new radio (5G-NR) in Third Generation Partnership Project (3GPP) Release 15, swift progress has been made to evolve 5G with 3GPP Release 18 emerging. A critical aspect is the design of massive multiple-input multiple-output (MIMO) technology. In this line, this paper makes several important contributions: We provide

Kuang-Huei Lee, Ted Xiao, Adrian Li, Paul Wohlhart

The predictive information, the mutual information between the past and future, has been shown to be a useful representation learning auxiliary loss for training reinforcement learning agents, as the ability to model what will happen next is critical to success on many control tasks. While existing studies are largely restricted to training specialist agents

Yonghui Wang, Wengang Zhou, Zhenbo Lu, Houqiang Li

Document images captured by mobile devices are usually degraded by uncontrollable illumination, which hampers the clarity of document content. Recently, a series of research efforts have been devoted to correcting the uneven document illumination. However, existing methods rarely consider the use of ambient light information, and usually rely on paired sampl

Qiongling Li, Takuro Mochizuki

We prove that a generically regular semisimple Higgs bundle equipped with a non-degenerate symmetric pairing on any Riemann surface always has a harmonic metric compatible with the pairing. We also study the classification of such compatible harmonic metrics in the case where the Riemann surface is the complement of a finite set $D$ in a compact Riemann surf

Luis Daniel Abreu, Peter Balazs, Smiljana Jakšić

We introduce the affine ensemble, a class of determinantal point processes (DPP) in the half-plane C^+ associated with the ax+b (affine) group, depending on an admissible Hardy function {\psi}. We obtain the asymptotic behavior of the variance, the exact value of the asymptotic constant, and non-asymptotic upper and lower bounds for the variance on a compact

Bo Gao, Yongjia Wang, Zepeng Gao, Qingfeng Li

Within the ultrarelativistic quantum molecular dynamics (UrQMD) model, by reverse tracing nucleons that are finally emitted at mid-rapidity (|$y_0$| < 0.1) in the entire reaction process, the time evolution of elliptic flow ($v_2$) of these traced nucleons produced in Au+Au collisions at beam energy of 0.4 GeV$/$nucleon with different impact parameters ($b$)

Shuli Jiang, Robson Leonardo Ferreira Cordeiro, Leman Akoglu

How can we detect outliers, both scattered and clustered, and also explicitly assign them to respective micro-clusters, without knowing apriori how many micro-clusters exist? How can we perform both tasks in-house, i.e., without any post-hoc processing, so that both detection and assignment can benefit simultaneously from each other? Presenting outliers in s

Pengtao Li, Hanchao Wang

Catoni proposed a robust M-estimator and gave the deviation inequality for one fixed test function. The present paper is devoted to the uniform concentration inequality for a family of test functions. As an application, we consider empirical risk minimization for heavy-tailed losses.

Mohammad Mahdi Karimi, Azin Heidarshenas, William W. Edmonson

This paper proposes a self-explainable Deep Learning (SE-DL) system for an image classification problem that performs self-error detection. The self-error detection is key to improving the DL system's safe operation, especially in safety-critical applications such as automotive systems. A SE-DL system outputs both the class prediction and an explanation for

Tanmay Chavan, Aditya Kane

The spread of propaganda through the internet has increased drastically over the past years. Lately, propaganda detection has started gaining importance because of the negative impact it has on society. In this work, we describe our approach for the WANLP 2022 shared task which handles the task of propaganda detection in a multi-label setting. The task deman

Yuankui Ma, Taekyun Kim, Hongze Li

Many mathematicians have been studying various degenerate versions of special polynomials and numbers in some arithmetic and combinatorial aspects. Our main focus here is a new type of degenerate poly-Euler polynomials and numbers. This focus stems from their nascent importance for applications in combinatorics, number theory and in other aspects of applied

Mihir Godbole, Parth Dandavate, Aditya Kane

Meaning of words constantly changes given the events in modern civilization. Large Language Models use word embeddings, which are often static and thus cannot cope with this semantic change. Thus,it is important to resolve ambiguity in word meanings. This paper is an effort in this direction, where we explore methods for word sense disambiguation for the Evo

Namita Uppal, Shashikiran Ganesh, Mathias Schultheis

Aims: Our aim is to provide an observational view of the old Disc structure of the Milky Way galaxy using the distribution of red clump stars. The spiral arms, warp structure, and other asymmetries present in the Disc are re-visited using a systematic study of red clump star counts over the disc of the Galaxy. Methods: We developed a method to systematically

Ryoma Sato

Labeling data is one of the most costly processes in machine learning pipelines. Active learning is a standard approach to alleviating this problem. Pool-based active learning first builds a pool of unlabelled data and iteratively selects data to be labeled so that the total number of required labels is minimized, keeping the model performance high. Many eff

Unni VS, Ruturaj Gavaskar, Kunal Narayan Chaudhury

Compressive Sensing (CS) has recently attracted attention for ECG data compression. In CS, an ECG signal is projected onto a small set of random vectors. Recovering the original signal from such compressed measurements remains a challenging problem. Traditional recovery methods are based on solving a regularized minimization problem, where a sparsity-promoti

Ang Li, Song Jiang, Yizhou Sun, Judea Pearl

The unit selection problem is to identify a group of individuals who are most likely to exhibit a desired mode of behavior, for example, selecting individuals who would respond one way if incentivized and a different way if not. The unit selection problem consists of evaluation and search subproblems. Li and Pearl defined the "benefit function" to evaluate t

Changwoon Choi, Juhyeon Kim, Young Min Kim

We propose IBL-NeRF, which decomposes the neural radiance fields (NeRF) of large-scale indoor scenes into intrinsic components. Recent approaches further decompose the baked radiance of the implicit volume into intrinsic components such that one can partially approximate the rendering equation. However, they are limited to representing isolated objects with

Mriganka Basu RoyChowdhury, Arabin K Dey

In this paper we propose a kernel based COBRA which is a direct approximation of the original COBRA. We propose a novel tuning procedure for original COBRA parameters based on this kernel approximation. We show that our proposed algorithm provides much better accuracy than other COBRAs and faster than usual Gridsearch COBRA. We use two datasets to illustrate

Masaru Hamano, Hiroaki Kikuchi, Minami Watanabe

This paper addresses the focusing cubic-quintic nonlinear Schrodinger equation in three space dimensions. Especially, we study the global dynamics of solutions whose energy and mass equal to those of the ground state in the sprits of Duyckaerts and Merle (2009). When we try to obtain the corresponding result, we meet several difficulties due to the cubic-qui

D. E. Kedzierski, P. Krasoń

Let $A={\mathbb F}_q[t]$ be the polynomial ring over a finite field ${\mathbb F}_q$ and let $\phi $ and $\psi$ be $A-$Drinfeld modules. In this paper we consider the group ${\mathrm{Ext}}^1(\phi ,\psi )$ with the Baer addition. We show that if $\mathrm{rank}\phi >\mathrm{rank}\psi$ then $\mathrm{Ext^1}(\phi,\psi)$ has the structure of a \tm module. We give c

Binbing Wu, Jie Liu

We investigate the intense X-ray laser-induced proton emission from halo nuclei within the framework of a nonperturbative quantum $S$-matrix approach. We have analytically deduced the angular differential as well as the total multi-photon rates of the proton emissions. For a linearly polarized X-ray laser field, we find that the angular distributions of prot

Ruidi Chen, Boran Hao, Ioannis Ch. Paschalidis

We develop a Distributionally Robust Optimization (DRO) formulation for Multiclass Logistic Regression (MLR), which could tolerate data contaminated by outliers. The DRO framework uses a probabilistic ambiguity set defined as a ball of distributions that are close to the empirical distribution of the training set in the sense of the Wasserstein metric. We re

Kiana Asgari, Aida Afshar Mohammadian, Mojtaba Tefagh

In recent years, with the development of easy to use learning environments, implementing and reproducible benchmarking of reinforcement learning algorithms has been largely accelerated by utilizing these frameworks. In this article, we introduce the Dynamic Fee learning Environment (DyFEn), an open-source real-world financial network model. It can provide a

Ayush K Tarun, Vikram S Chundawat, Murari Mandal, Mohan Kankanhalli

With the introduction of data protection and privacy regulations, it has become crucial to remove the lineage of data on demand from a machine learning (ML) model. In the last few years, there have been notable developments in machine unlearning to remove the information of certain training data efficiently and effectively from ML models. In this work, we ex

Junjie Xu, Enyan Dai, Xiang Zhang, Suhang Wang

Graph neural networks (GNNs) have achieved great success in various graph problems. However, most GNNs are Message Passing Neural Networks (MPNNs) based on the homophily assumption, where nodes with the same label are connected in graphs. Real-world problems bring us heterophily problems, where nodes with different labels are connected in graphs. MPNNs fail

August George, Doo Nam Kim, Trevor Moser, Ian T. Gildea

In this study, we present a method of pattern mining based on network theory that enables the identification of protein structures or complexes from synthetic volume densities, without the knowledge of predefined templates or human biases for refinement. We hypothesized that the topological connectivity of protein structures is invariant, and they are distin

A. Baez, N. Breton, I. Cabrera-Munguia

Relying on the Penrose process mechanism, we study the possibility of energy extraction from a binary system composed of two extreme electrostatic black holes (BHs) oppositely charged, separated by a strut described by Bonnor's metric (BM). We determined and plotted the generalized ergosphere that surrounds only one of the BH. We demonstrate the existence of

Uday Shankar Chakraborty

In this paper, we characterize the so called property $\textbf{L}_{o,o}$ as defined by Dantas and Rueda Zoca, for compact, weak-weak continuous bilinear maps. Motivated by this we weaken this property by defining the weak $\textbf{L}_{o,o}$ for bilinear maps. We provide equivalence of the weak $\textbf{L}_{o,o}$ property of $(X\hat{\otimes}_{\pi}Y,\mathbb{R}

R. W. Perry, S. C. Chapman, Ian Smail, F. Bertoldi

We present the resolved properties of the $z=2.82$ Hyper Luminous Infrared Galaxy (HyLIRG) HS170850.1, the brightest 850$\mu$m source found in the SCUBA-2 followup to the Keck Baryonic Structure Survey fields (S$_{\rm 850 \mu m}=$19.5 mJy), and amongst the most luminous starbursts known at any redshift. Using the IRAM-NOEMA interferometer in the highest reso

Jinglei Cheng, Hanrui Wang, Zhiding Liang, Yiyu Shi

Variational Quantum Algorithms (VQA) are promising to demonstrate quantum advantages on near-term devices. Designing ansatz, a variational circuit with parameterized gates, is of paramount importance for VQA as it lays the foundation for parameter optimizations. Due to the large noise on Noisy-Intermediate Scale Quantum (NISQ) machines, considering circuit s

Jiahao Liu, Dongsheng Li, Hansu Gu, Tun Lu

Dynamic interaction graphs have been widely adopted to model the evolution of user-item interactions over time. There are two crucial factors when modelling user preferences for link prediction in dynamic interaction graphs: 1) collaborative relationship among users and 2) user personalized interaction patterns. Existing methods often implicitly consider the

Themos Stafylakis, Ladislav Mosner, Sofoklis Kakouros, Oldrich Plchot

Self-supervised learning of speech representations from large amounts of unlabeled data has enabled state-of-the-art results in several speech processing tasks. Aggregating these speech representations across time is typically approached by using descriptive statistics, and in particular, using the first- and second-order statistics of representation coeffic

Haiyun He, Gholamali Aminian, Yuheng Bu, Miguel Rodrigues

We provide an exact characterization of the expected generalization error (gen-error) for semi-supervised learning (SSL) with pseudo-labeling via the Gibbs algorithm. The gen-error is expressed in terms of the symmetrized KL information between the output hypothesis, the pseudo-labeled dataset, and the labeled dataset. Distribution-free upper and lower bound

Clinton Enwerem, Ihechiluru Okoro

We present a controller tuning strategy for first-order plus time delay (FOPTD) processes, where the time delay in the model is approximated using the Pad\'e function. Using Routh-Hurwitz stability analysis, we derive the gain that gives rise to desirable PID controller settings. The resulting PID controller, now correctly tuned, produces satisfactory closed

Fidelia A. Orji, Julita Vassileva

This research aims to develop machine learning models for students academic performance and study strategies prediction which could be generalized to all courses in higher education. Key learning attributes (intrinsic, extrinsic, autonomy, relatedness, competence, and self-esteem) essential for students learning process were used in building the models. Dete

Wenqian Li, Yinchuan Li, Shengyu Zhu, Yunfeng Shao

Causal discovery aims to uncover causal structure among a set of variables. Score-based approaches mainly focus on searching for the best Directed Acyclic Graph (DAG) based on a predefined score function. However, most of them are not applicable on a large scale due to the limited searchability. Inspired by the active learning in generative flow networks, we

Qimeng Guo, Zhuoran Zheng, Xiuyi Jia, Liancheng Xu

Label distribution learning can characterize the polysemy of an instance through label distributions. However, some noise and uncertainty may be introduced into the label space when processing label distribution data due to artificial or environmental factors. To alleviate this problem, we propose a \textbf{L}abel \textbf{C}orrelation \textbf{G}rid (LCG) to

Guillermo Currás-Lorenzo, Shlok Nahar, Norbert Lütkenhaus, Kiyoshi Tamaki

The performance of quantum key distribution (QKD) is severely limited by multiphoton emissions, due to the photon-number-splitting attack. The most efficient solution, the decoy-state method, requires that the phases of all transmitted pulses are independent and uniformly random. In practice, however, these phases are often correlated, especially in high-spe

Chendong Zhao, Jianzong Wang, Xiaoyang Qu, Haoqian Wang

Unsupervised representation learning for speech audios attained impressive performances for speech recognition tasks, particularly when annotated speech is limited. However, the unsupervised paradigm needs to be carefully designed and little is known about what properties these representations acquire. There is no guarantee that the model learns meaningful r

Keyu Yan, Man Zhou, Jie Huang, Feng Zhao

Panchromatic (PAN) and multi-spectral (MS) image fusion, named Pan-sharpening, refers to super-resolve the low-resolution (LR) multi-spectral (MS) images in the spatial domain to generate the expected high-resolution (HR) MS images, conditioning on the corresponding high-resolution PAN images. In this paper, we present a simple yet effective \textit{alternat

Eren Arkangil

Consumer behavior under social influence is a well-known phenomenon and computer scientists and economists are prevalently trying to analyze the dynamics behind decision making during the consumption process through agent-based modeling (ABM). Some articles tried to explain market inequality because of the social influencing, but the impact of advertising is

Jerome P. de Leon, John H. Livingston, James S. Jenkins, Jose I. Vines

Transiting exoplanets orbiting young nearby stars are ideal laboratories for testing theories of planet formation and evolution. However, to date only a handful of stars with age <1 Gyr have been found to host transiting exoplanets. Here we present the discovery and validation of a sub-Neptune around HD 18599, a young (300 Myr), nearby (d=40 pc) K star. We v

Sanjay Saha, Terence Sim

Face recognition is a popular form of biometric authentication and due to its widespread use, attacks have become more common as well. Recent studies show that Face Recognition Systems are vulnerable to attacks and can lead to erroneous identification of faces. Interestingly, most of these attacks are white-box, or they are manipulating facial images in ways

Omer Haq, Sergei Shabanov

Elastic waves scattering off a periodic single and double array of thin cylindrical defects is considered for isotropic materials. An analytical expression for the scattering matrix is obtained by means of the Lippmann-Schwinger formalism and analyzed in the long wavelength limit using Schloemilch series in order to obtain explicit expressions for the poles

Byeongkeun Ahn, Chiyoon Kim, Youngjoon Hong, Hyunwoo J. Kim

Normalizing flows model probability distributions by learning invertible transformations that transfer a simple distribution into complex distributions. Since the architecture of ResNet-based normalizing flows is more flexible than that of coupling-based models, ResNet-based normalizing flows have been widely studied in recent years. Despite their architectu

Zhenpeng Zhao, Niklas Elmqvist

Data videos -- motion graphics that incorporate visualizations -- have been recognized as an effective way to communicate ideas, but creating such video requires both time and expertise, precluding them from being created and streamed live. We introduce DataTV, a system for combining multiple media sources in real time. We validate our work through an expert

Dewi Yustikasari, Mirza Satriawan

We clarify that an ideal gas obeying infinite statistics cannot undergo condensation. Then we derive the dynamic equation for an identical particle system obeying infinite statistics under external potential and inter-particle interaction. The derivation utilizes the Hamiltonian written in terms of the number operators and the transition number operators. At

Jinming Zhao, Gholamreza Haffar, Ehsan Shareghi

Training end-to-end speech translation (ST) systems requires sufficiently large-scale data, which is unavailable for most language pairs and domains. One practical solution to the data scarcity issue is to convert machine translation data (MT) to ST data via text-to-speech (TTS) systems. Yet, using TTS systems can be tedious and slow, as the conversion needs

Lirong Xia, Weiqiang Zheng

The computational complexity of winner determination is a classical and important problem in computational social choice. Previous work based on worst-case analysis has established NP-hardness of winner determination for some classic voting rules, such as Kemeny, Dodgson, and Young. In this paper, we revisit the classical problem of winner determination thro