May 2023 arXiv papers — page 163

Dan Ofer, Michal Linial

Scientific research trends and interests evolve over time. The ability to identify and forecast these trends is vital for educational institutions, practitioners, investors, and funding organizations. In this study, we predict future trends in scientific publications using heterogeneous sources, including historical publication time series from PubMed, resea

Michael Bersudsky, Hao Xing

We study the distribution of orbits of a lattice $\Gamma\leq\text{SL}(3,\mathbb R)$ in the moduli space $X_{2,3}$ of covolume one rank-two discrete subgroups in $\mathbb R^3$. Each orbit is dense, and our main result is the limiting distribution of these orbits with respect to norm balls, where the norm is given by the sum of squares. Specifically, we consid

Jiaoyang Huang, Colin McSwiggen

This paper studies the asymptotic behavior of several central objects in Dunkl theory as the dimension of the underlying space grows large. Our starting point is the observation that a recent result from the random matrix theory literature implies a large deviations principle for the hydrodynamic limit of radial Dunkl processes. Using this fact, we prove a v

Marco Gambarini, Gabriele Ciaramella, Edie Miglio, Tommaso Vanzan

This work presents a new computational optimization framework for the robust control of parks of Wave Energy Converters (WEC) in irregular waves. The power of WEC parks is maximized with respect to the individual control damping and stiffness coefficients of each device. The results are robust with respect to the incident wave direction, which is treated as

Damien P. Huet, Antoine Morente, Guodong Gai, Anthony Wachs

We investigate the inertial and non-inertial dynamics of three-dimensional elastic capsules flowing through a square channel presenting a sharp corner. Our study analyzes the trajectory, surface area, velocity and membrane stress of the capsules in the case of a single capsule, a system of two interacting capsules and a train of ten capsules released upstrea

Zilong Wang, Thomas R. Shultz, Ardvan S. Nobandegani

Recent empirical work has shown that human children are adept at learning and reasoning with probabilities. Here, we model a recent experiment investigating the development of school-age children's non-symbolic probability reasoning ability using the Neural Probability Learner and Sampler (NPLS) system. We demonstrate that NPLS can accurately simulate childr

Wai Ming Tai, Bryon Aragam

We study the problem of learning mixtures of Gaussians with censored data. Statistical learning with censored data is a classical problem, with numerous practical applications, however, finite-sample guarantees for even simple latent variable models such as Gaussian mixtures are missing. Formally, we are given censored data from a mixture of univariate Gauss

Steven Flynn

We give an explicit Singular Value Decomposition of the sub-Riemannian X-ray transform on the Heisenberg group with compact center. By studying the singular values, we obtain a two-radius theorem for integrals over sub-Riemannian geodesics. We also state intertwining properties of distinguished differential operators. We conclude with a description of ongoin

Shihabul Haque, Jayanta K. Bhattacharjee

We apply perturbative techniques to a driven undamped sinusoidal oscillator at resonance. The angular displacement, $\theta$, obeys the dynamics $\Ddot{\theta} + \omega^{2} \sin{\theta} = H\cos{\omega t}$. The linearized approximation gives a divergent response (at long times) but the nonlinear terms make the response finite. We address the nonlinearity-indu

Santosh Sharma, Qifeng Li

The electric power distribution network (PDN) and the transportation network (TN) are generally operated/coordinated by different entities. However, they are coupled with each other due to electric vehicle charging stations (EVCSs). This paper proposes to coordinate the operation of the two systems via a fully decentralized framework where the PDN and TN ope

Daizong Liu, Xiaoye Qu, Jianfeng Dong, Pan Zhou

This paper addresses the temporal sentence grounding (TSG). Although existing methods have made decent achievements in this task, they not only severely rely on abundant video-query paired data for training, but also easily fail into the dataset distribution bias. To alleviate these limitations, we introduce a novel Equivariant Consistency Regulation Learnin

Rolf Sören Kraußhar, Dmitrii Legatiuk

In this paper, we continue the development of a fundament of discrete octonionic analysis that is associated to the discrete first order Cauchy-Riemann operator acting on octonions. In particular, we establish a discrete octonionic version of the Borel-Pompeiu formula and of Cauchy's integral formula. The latter then is exploited to introduce a discrete mono

Cong Fu, Keqiang Yan, Limei Wang, Wing Yee Au

Proteins are complex biomolecules that perform a variety of crucial functions within living organisms. Designing and generating novel proteins can pave the way for many future synthetic biology applications, including drug discovery. However, it remains a challenging computational task due to the large modeling space of protein structures. In this study, we

Lorenzo Gavassino, Jorge Noronha

We develop the first causal and stable theory of a bulk-viscous relativistic pseudoplastic (or dilatant) fluid. This new formalism brings to light the rheological properties of several relativistic physical systems. Neutron star collisions can behave as a relativistic pseudoplastic material with viscous properties dictated by the non-conservation of lepton c

Zhiwei He, Tian Liang, Wenxiang Jiao, Zhuosheng Zhang

Large language models (LLMs) have demonstrated impressive capabilities in general scenarios, exhibiting a level of aptitude that approaches, in some aspects even surpasses, human-level intelligence. Among their numerous skills, the translation abilities of LLMs have received considerable attention. Compared to typical machine translation that focuses solely

Andreas Maletti, Andreea-Teodora Nász, Erik Paul

The HOM-problem asks whether the image of a regular tree language under a given tree homomorphism is again regular. It was recently shown to be decidable by Godoy, Gim\'enez, Ramos, and \`Alvarez. In this paper, the N-weighted version of this problem is considered and its decidability is proved. More precisely, it is decidable in polynomial time whether the

Joshua C. Lederman, Weipeng Zhang, Thomas Ferreira de Lima, Eric C. Blow

Multiple-input multiple-output (MIMO) mmWave devices broadcast multiple spatially-separated data streams simultaneously in order to increase data transfer rates. Data transfer can, however, be compromised by interference. Conventional techniques for mitigating interference require additional space and power not generally available in handheld mobile devices.

Sivaraman Balakrishnan, Edward H. Kennedy, Larry Wasserman

Many recent developments in causal inference, and functional estimation problems more generally, have been motivated by the fact that classical one-step (first-order) debiasing methods, or their more recent sample-split double machine-learning avatars, can outperform plugin estimators under surprisingly weak conditions. These first-order corrections improve

Ichiro Kawashima

Ternary logic is expected to increase the area efficiency of VLSI due to its expressiveness compared to the traditional binary logic. This paper proposes a new symmetric ternary logic and a systematic logic composition methodology that enables us to design any ternary logic circuits. The methodology is demonstrated by implementing the ternary inverters, tern

Andre C. R. Martins

The Continuous Opinions and Discrete Actions (CODA) model has been widely used to study the emergence of extremism in social networks. However, this standard model has been shown to generate unrealistic extreme opinions due to the reinforcement among agents. To address this issue, this paper introduces memory effects into the CODA model to explore how the dy

Noirrit Kiran Chandra, David B. Dunson, Jason Xu

Factor analysis provides a canonical framework for imposing lower-dimensional structure such as sparse covariance in high-dimensional data. High-dimensional data on the same set of variables are often collected under different conditions, for instance in reproducing studies across research groups. In such cases, it is natural to seek to learn the shared vers

Piotr Kokoszka, Neda Mohammadi, Haonan Wang, Shixuan Wang

We propose a stochastic volatility model for time series of curves. It is motivated by dynamics of intraday price curves that exhibit both between days dependence and intraday price evolution. The curves are suitably normalized to stationary in a function space and are functional analogs of point-to-point daily returns. The between curves dependence is model

Xiaohui Chen, Jiaxing He, Xu Han, Li-Ping Liu

Diffusion-based generative graph models have been proven effective in generating high-quality small graphs. However, they need to be more scalable for generating large graphs containing thousands of nodes desiring graph statistics. In this work, we propose EDGE, a new diffusion-based generative graph model that addresses generative tasks with large graphs. T

Barbara Kaltenbacher

In this paper we prove uniqueness for some parameter identification problems for the JMGT equation, a third order in time quasilinear PDE in nonlinear acoustics. The coefficients to be recovered are the space dependent nonlinearity parameter, sound speed, and attenuation parameter, and the observation available is a single time trace of the acoustic pressure

Luca Da Col

We describe the action of the mapping class group $M(g,n)$ on the fundamental group of $T_{g,n}$, a compact orientable topological surface of positive genus $g$ with $n$ marked points. This is achieved by computing the image of the generators of $M(g,n)$ as outer automorphisms of the fundamental group.

Guglielmo Lami, Alessandro Santini, Mario Collura

The dynamics of a quantum system, undergoing unitary evolution and continuous monitoring, can be described in term of quantum trajectories. Although the averaged state fully characterises expectation values, the entire ensamble of stochastic trajectories goes beyond simple linear observables, keeping a more attentive description of the entire dynamics. Here

Aditya Joglekar, Hongrui Chen, Levent Burak Kara

We propose a direct mesh-free method for performing topology optimization by integrating a density field approximation neural network with a displacement field approximation neural network. We show that this direct integration approach can give comparable results to conventional topology optimization techniques, with an added advantage of enabling seamless i

Martin Weyssow, Xin Zhou, Kisub Kim, David Lo

Pre-trained language models (PLMs) have become a prevalent technique in deep learning for code, utilizing a two-stage pre-training and fine-tuning procedure to acquire general knowledge about code and specialize in a variety of downstream tasks. However, the dynamic nature of software codebases poses a challenge to the effectiveness and robustness of PLMs. I

Faria Binte Kader, Nafisa Hossain Nujat, Tasmia Binte Sogir, Mohsinul Kabir

Sarcasm is a form of figurative language that serves as a humorous tool for mockery and ridicule. We present a novel architecture for sarcasm generation with emoji from a non-sarcastic input sentence in English. We divide the generation task into two sub tasks: one for generating textual sarcasm and another for collecting emojis associated with those sarcast

Miaomiao Wang, Abdelhamid Tayebi

This paper explores the design of hybrid feedback for a class of affine nonlinear systems with topological constraints that prevent global asymptotic stability. A new hybrid control strategy is introduced, which differs conceptually from the commonly used synergistic hybrid approaches. The key idea involves the construction of a generalized synergistic Lyapu

Marianne A Jonker, Steven Teerenstra

In a clinical trial with a survival outcome, an interim analysis is often performed to allow for early stopping for efficacy. If the interim analysis is early in the trial, one might conclude that a new treatment is more effective (compared to e.g.\ a placebo) and stop the trial, whereas the survival curves in the trial arms are not mature for the research q

Mahender Kumar, Gregory Epiphaniou, Carsten Maple

Additive manufacturing (AM) offers numerous benefits, such as manufacturing complex and customised designs quickly and cost-effectively, reducing material waste, and enabling on-demand production. However, several security challenges are associated with AM, making it increasingly attractive to attackers ranging from individual hackers to organised criminal g

Yuanchun Shen

Information retrieval based knowledge base question answering (KBQA) first retrieves a subgraph to reduce search space, then reasons on the subgraph to select answer entities. Existing approaches have three issues that impede the retrieval of such subgraphs. Firstly, there is no off-the-shelf toolkit for semantic-relevant subgraph retrieval. Secondly, existi

Anshika Gupta, Shaz Furniturewala, Vijay Kumari, Yashvardhan Sharma

A legal document is usually long and dense requiring human effort to parse it. It also contains significant amounts of jargon which make deriving insights from it using existing models a poor approach. This paper presents the approaches undertaken to perform the task of rhetorical role labelling on Indian Court Judgements as part of SemEval Task 6: understan

Ho Fung Tsoi, Adrian Alan Pol, Vladimir Loncar, Ekaterina Govorkova

The high-energy physics community is investigating the potential of deploying machine-learning-based solutions on Field-Programmable Gate Arrays (FPGAs) to enhance physics sensitivity while still meeting data processing time constraints. In this contribution, we introduce a novel end-to-end procedure that utilizes a machine learning technique called symbolic

Kirsty M. Butler, Paul P. van der Werf, Alain Omont, Pierre Cox

OH+ absorption is a powerful tracer of inflowing and outflowing gas in the predominantly atomic diffuse and turbulent halo surrounding galaxies. In this letter, we present observations of OH+(1_1-1_0), CO(9-8) and the underlying dust continuum in 5 strongly lensed z~2-4 QSOs, using ALMA to detect outflowing neutral gas. Blue-shifted OH+ absorption is detecte

Jiasheng Li, Zeyu Yan, Arush Shah, Jonathan Lazar

Despite their growing popularity, many public kiosks with touchscreens are inaccessible to blind people. Toucha11y is a working prototype that allows blind users to use existing inaccessible touchscreen kiosks independently and with little effort. Toucha11y consists of a mechanical bot that can be instrumented to an arbitrary touchscreen kiosk by a blind use

Guy Avni, Pranav Ghorpade, Shibashis Guha

We introduce and study pawn games, a class of two-player zero-sum turn-based graph games. A turn-based graph game proceeds by placing a token on an initial vertex, and whoever controls the vertex on which the token is located, chooses its next location. This leads to a path in the graph, which determines the winner. Traditionally, the control of vertices is

Hanchi Ren, Jingjing Deng, Xianghua Xie

Federated Learning (FL) is a widely adopted privacy-preserving machine learning approach where private data remains local, enabling secure computations and the exchange of local model gradients between local clients and third-party parameter servers. However, recent findings reveal that privacy may be compromised and sensitive information potentially recover

Connor H. Ligeikis, Jeffrey T. Scruggs

We implement a causal model predictive control (MPC) strategy to maximize power generation from a wave energy converter (WEC) system, for which the power take-off (PTO) systems have both hard stroke (i.e., displacement) limits and force ratings. The approach models the WEC dynamics in discrete-time, in a manner that exactly preserves energy-flow quantities,

Tunazzina Islam, Ruqi Zhang, Dan Goldwasser

Climate change is the defining issue of our time, and we are at a defining moment. Various interest groups, social movement organizations, and individuals engage in collective action on this issue on social media. In addition, issue advocacy campaigns on social media often arise in response to ongoing societal concerns, especially those faced by energy indus

Nishant A. Mehta

In the setting of stochastic online learning with undirected feedback graphs, Lykouris et al. (2020) previously analyzed the pseudo-regret of the upper confidence bound-based algorithm UCB-N and the Thompson Sampling-based algorithm TS-N. In this note, we show how to improve their pseudo-regret analysis. Our improvement involves refining a key lemma of the p

Yating Wang, David S. Dean, Sophie Marbach, Ruben Zakine

Understanding particle motion in narrow channels is essential to guide progress in numerous applications, from filtration to vascular transport. Thermal or active fluctuations of channel walls for fluid-filled channels can slow down or increase the dispersion of tracer particles. Entropic trapping in the wall bulges slows dispersion, and hydrodynamic flows i

Lei Wang, Wanyu Xu, Yihuai Lan, Zhiqiang Hu

Large language models (LLMs) have recently been shown to deliver impressive performance in various NLP tasks. To tackle multi-step reasoning tasks, few-shot chain-of-thought (CoT) prompting includes a few manually crafted step-by-step reasoning demonstrations which enable LLMs to explicitly generate reasoning steps and improve their reasoning task accuracy.

Alfred Michel Grundland

In this paper, we establish a relation between two seemingly unrelated concepts for solving first-order hyperbolic quasilinear systems of partial differential equations in many dimensions. These concepts are based on a variant of the conditional symmetry method and on the generalized method of characteristics. We present the outline of recent results on mult

Gopal Chandra Dutta

We study the homology self-closeness numbers of simply connected CW complex and those of the homotopy cofiber. Self-maps of spaces in cofibrations which appear in Homology decomposition are studied. We also consider Postnikov tower and studied the relation of homology self-closeness numbers between homology decomposition and homotopy decomposition.

Hanyan Fang, Harshitra Mahalingam, Xinzhe Li, Xu Han

Patterning antidots ("voids") into well-defined antidot lattices creates an intriguing class of artificial structures for the periodic modulation of 2D electron systems, leading to anomalous transport properties and exotic quantum phenomena as well as enabling the precise bandgap engineering of 2D materials to address technological bottleneck issues. However

Kechi Zhang, Zhuo Li, Jia Li, Ge Li

Large language models (LLMs) have demonstrated an impressive ability to generate codes on competitive programming tasks. However, with limited sample numbers, LLMs still suffer from poor accuracy. Inspired by the process of human programming, we propose a generate-and-edit approach named Self-Edit that utilizes execution results of the generated code from LL

Gongbo Zhang, Sihua Chen, Kuihua Huang, Yijie Peng

We consider a simulation optimization problem for a context-dependent decision-making, which aims to determine the top-m designs for all contexts. Under a Bayesian framework, we formulate the optimal dynamic sampling decision as a stochastic dynamic programming problem, and develop a sequential sampling policy to efficiently learn the performance of each des

Louise Sadoine, Zacharie De Grève, Thomas Brihaye

We propose two market designs for the optimal day-ahead scheduling of energy exchanges within renewable energy communities. The first one implements a cooperative demand side management scheme inside a community where members objectives are coupled through grid tariffs, whereas the second allows in addition the valuation of excess generation in the community

Vincent Hardel, Paul-Antoine Hervieux, Giovanni Manfredi

Nelson's stochastic quantum mechanics provides an ideal arena to test how the Born rule is established from an initial probability distribution that is not identical to the square modulus of the wave function. Here, we investigate numerically this problem for three relevant cases: a double-slit interference setup, a harmonic oscillator, and a quantum particl

Aimin Li, Shaohua Wu, Sumei Sun

The intricate interplay of source dynamics, unreliable channels, and staleness of information has long been recognized as a significant impediment for the receiver to achieve accurate, timely, and most importantly, goal-oriented decision making. Thus, a plethora of promising metrics, such as Age of Information, Value of Information, and Mean Square Error, ha

Dongwon Kelvin Ryu, Meng Fang, Shirui Pan, Gholamreza Haffari

Text-based games (TGs) are language-based interactive environments for reinforcement learning. While language models (LMs) and knowledge graphs (KGs) are commonly used for handling large action space in TGs, it is unclear whether these techniques are necessary or overused. In this paper, we revisit the challenge of exploring the action space in TGs and propo

Jiaxi Yang, Sheng Cao, Cuifang Zhao, Weina Niu

In recent years, there has been a significant increase in attention towards designing incentive mechanisms for federated learning (FL). Tremendous existing studies attempt to design the solutions using various approaches (e.g., game theory, reinforcement learning) under different settings. Yet the design of incentive mechanism could be significantly biased i

HanQin Cai, Zehan Chao, Longxiu Huang, Deanna Needell

We study the tensor robust principal component analysis (TRPCA) problem, a tensorial extension of matrix robust principal component analysis (RPCA), that aims to split the given tensor into an underlying low-rank component and a sparse outlier component. This work proposes a fast algorithm, called Robust Tensor CUR Decompositions (RTCUR), for large-scale non

Jakob Svennevik Notland, Mariusz Nowostawski, Jingyue Li

Blockchain systems run consensus rules as code to agree on the state of the distributed ledger and secure the network. Changing these rules can be risky and challenging. In addition, it can often be controversial and take much effort to make all the necessary participants agree to adopt a change. Arguably, Bitcoin has seen centralisation tendencies in pools

Paolo Acampora, Emanuele Cristoforoni, Carlo Nitsch, Cristina Trombetti

We are interested in the thermal insulation of a bounded open set $\Omega$ surrounded by a set whose thickness is locally described by $\varepsilon h$, where $h$ is a non-negative function defined on the boundary $\partial\Omega$. We study the problem in the limit for $\varepsilon$ going to zero using a first-order asymptotic development by $\Gamma$-converge

Ankit Bhojak, Surjeet Singh Choudhary, Saurabh Shrivastava

In this paper we introduce bilinear Bochner-Riesz means associated with convex domains in the plane $\mathbb R^2$ and study their $L^p-$boundedness properties for a wide range of exponents. One of the important aspects of our proof involves the use of bilinear Kakeya maximal function in the context of bilinear Bochner-Riesz problem. This amounts to establish

Shuzheng Si, Zefan Cai, Shuang Zeng, Guoqiang Feng

Distantly-Supervised Named Entity Recognition effectively alleviates the burden of time-consuming and expensive annotation in the supervised setting. But the context-free matching process and the limited coverage of knowledge bases introduce inaccurate and incomplete annotation noise respectively. Previous studies either considered only incomplete annotation

Zhiqiang Shen, Xiaoxiao Sheng, Longguang Wang, Yulan Guo

Self-supervised learning can extract representations of good quality from solely unlabeled data, which is appealing for point cloud videos due to their high labelling cost. In this paper, we propose a contrastive mask prediction (PointCMP) framework for self-supervised learning on point cloud videos. Specifically, our PointCMP employs a two-branch structure

Benoît Claudon, Patrick Graf, Henri Guenancia

Let $(X, \Delta)$ be a compact K\"ahler klt pair, where $K_X + \Delta$ is ample or numerically trivial, and $\Delta$ has standard coefficients. We show that if equality holds in the orbifold Miyaoka-Yau inequality for $(X, \Delta)$, then its orbifold universal cover is either the unit ball (ample case) or the affine space (numerically trivial case).

Shripad Vilasrao Deshmukh, Arpan Dasgupta, Balaji Krishnamurthy, Nan Jiang

Explanation is a key component for the adoption of reinforcement learning (RL) in many real-world decision-making problems. In the literature, the explanation is often provided by saliency attribution to the features of the RL agent's state. In this work, we propose a complementary approach to these explanations, particularly for offline RL, where we attribu

Minyi Zhao, Jinpeng Wang, Dongliang Liao, Yiru Wang

In addition to relevance, diversity is an important yet less studied performance metric of cross-modal image retrieval systems, which is critical to user experience. Existing solutions for diversity-aware image retrieval either explicitly post-process the raw retrieval results from standard retrieval systems or try to learn multi-vector representations of im

Laurent Demanet, Olivier Lafitte

This paper considers the question of characterizing the behavior of waves reflected by a fractional singularity of the wave speed profile, i.e., of the form \[ c(x_1, x_2, x_3) = c_0 \left(1 + \left( \frac{x_1}{\ell}\right)_{+}^\alpha \right)^{-1/2}, \] for $\alpha > 0$ not necessarily integer. We first focus on the case of one spatial dimension and a harmon

Kabir Chakravarti

The propagation of Gravitational Waves has been reliably recognised as a test-bed for beyond standard models of gravity and cosmology. We utilise this property to examine the effects of a class of parametrised beyond-$\Lambda$CDM cosmology on inferece of GW parameters. We find that the combined beyond-$\Lambda$CDM likelihood function exhibits correlations be

Adam Wills, Sergii Strelchuk

The quantum Schur transform is a fundamental building block that maps the computational basis to a coupled basis consisting of irreducible representations of the unitary and symmetric groups. Equivalently, it may be regarded as a change of basis from the computational basis to a simultaneous spin eigenbasis of Permutational Quantum Computing (PQC) [Quantum I

Yi Han

We construct unique martingale solutions to the damped stochastic wave equation $$ \mu \frac{\partial^2u}{\partial t^2}(t,x)=\Delta u(t,x)-\frac{\partial u}{\partial t}(t,x)+b(t,x,u(t,x))+\sigma(t,x,u(t,x))\frac{dW_t}{dt},$$ where $\Delta$ is the Laplacian on $[0,1]$ with Dirichlet boundary condition, $W$ is space-time white noise, $\sigma$ is $\frac{3}{4}+\

Heng Yang, Ke Li

Recent studies have revealed the vulnerability of pre-trained language models to adversarial attacks. Existing adversarial defense techniques attempt to reconstruct adversarial examples within feature or text spaces. However, these methods struggle to effectively repair the semantics in adversarial examples, resulting in unsatisfactory performance and limiti

Zhoubin Kou, Yun Ji, Xiaoxiong Zhong, Sheng Zhang

Over-the-air Computation (AirComp) has been demonstrated as an effective transmission scheme to boost the efficiency of federated edge learning (FEEL). However, existing FEEL systems with AirComp scheme often employ traditional synchronous aggregation mechanisms for local model aggregation in each global round, which suffer from the stragglers issues. In thi

Tegar Ari Widianto, Ahmad Khoirul Falah, Agus Suroso, Husin Alatas

Using dynamical system analysis, we investigate some cosmological consequences of Rastall gravity coupled to a scalar field (called the Klein-Gordon-Rastall theory) with exponential scalar potential turned on in higher dimensions. From the critical points of the autonomous equations, we can determine the dominant components of the energy density in different

Konstantinos Bourazas, Guido Consonni, Laura Deldossi

An ongoing "reproducibility crisis" calls into question scientific discoveries across a variety of disciplines ranging from life to social sciences. Replication studies aim to investigate the validity of findings in published research, and try to assess whether the latter are statistically consistent with those in the replications. While the majority of repl

Boyang Wang, DingFan Zhang, Tingyu Zhang, Chayanis Sutcharitchan

Objective: The purpose of this study is to reveal the mechanism of action of Yi Qi Tong Qiao Pill (YQTQP) in the treatment of allergic rhinitis (AR), as well as establish a paradigm for the researches on traditional Chinese medicine (TCM) from systematic perspective. Methods: Based on the data collected from TCM-related and disease-related databases, target

John M. Conroy, Neil P Molino, Brian Baughman, Rod Gomez

In this paper, we explore the role of matrix scaling on a matrix of counts when building a topic model using non-negative matrix factorization. We present a scaling inspired by the normalized Laplacian (NL) for graphs that can greatly improve the quality of a non-negative matrix factorization. The results parallel those in the spectral graph clustering work

Mingzhao Yang, Shangchao Su, Bin Li, Xiangyang Xue

Recently, semi-supervised federated learning (semi-FL) has been proposed to handle the commonly seen real-world scenarios with labeled data on the server and unlabeled data on the clients. However, existing methods face several challenges such as communication costs, data heterogeneity, and training pressure on client devices. To address these challenges, we

Sanghyeon Park, Junmo Lee, Soo-Mook Moon

As artificial intelligence (AI) continues to permeate various domains, concerns surrounding trust and transparency in AI-driven inference and training processes have emerged, particularly with respect to potential biases and traceability challenges. Decentralized solutions such as blockchain have been proposed to tackle these issues, but they often struggle

Thomas Şerban von Davier, Konrad Kollnig, Reuben Binns, Max Van Kleek

Since GDPR went into effect in 2018, many other data protection and privacy regulations have been released. With the new regulation, there has been an associated increase in industry professionals focused on data protection and privacy. Building on related work showing the potential benefits of knowledge management in organisational compliance and privacy en

Mark Philip Roach

Ptychography involves a sample being illuminated by a coherent, localised probe of illumination. When the probe interacts with the sample, the light is diffracted and a diffraction pattern is detected. Then the probe or sample is shifted laterally in space to illuminate a new area of the sample while ensuring there is sufficient overlap. Far-field Ptychograp

Johan Östman, Pablo Gomez, Vinutha Magal Shreenath, Gabriele Meoni

Onboard machine learning on the latest satellite hardware offers the potential for significant savings in communication and operational costs. We showcase the training of a machine learning model on a satellite constellation for scene classification using semi-supervised learning while accounting for operational constraints such as temperature and limited po

Myungho Choi, Hojin Chu, Suh-Ryung Kim

The Friendship Theorem states that if in a party any pair of persons has precisely one common friend, then there is always a person who is everybody's friend and the theorem has been proved by Paul Erd\H{o}s, Alfr\'{e}d R\'{e}nyi, and Vera T. S\'{o}s in 1966. ``What would happen if instead any pair of persons likes precisely one person?" While a friendship r

Cai fangzhou

In this paper we introduce the notion of Feldman-Katok pseudo-orbits and use it to study topological pressure. We prove that the topological pressure of a dynamical system can be computed by measuring the Feldman-Katok pseudo-orbits complexity of the shift map on the potential function, which extends Barge and Swanson's result on entropy and pseudo-orbits \c

F. Fatehi, Y. N. Kyrychko, K. B. Blyuss

In this paper we present a new method for deriving It\^{o} stochastic delay differential equations (SDDEs) from delayed chemical master equations (DCMEs). Considering alternative formulations of SDDEs that can be derived from the same DCME, we prove that they are equivalent both in distribution, and in sample paths they produce. This allows us to formulate a

Mahender Kumar, Ruby Rani, Mirko Botarelli, Gregory Epiophaniou

Ontologies play a critical role in Semantic Web technologies by providing a structured and standardized way to represent knowledge and enabling machines to understand the meaning of data. Several taxonomies and ontologies have been generated, but individuals target one domain, and only some of those have been found expensive in time and manual effort. Also,

Zeyu Cai, Jian Yu, Ziyu Zhang, Chengqian Jin

Spectral image reconstruction is an important task in snapshot compressed imaging. This paper aims to propose a new end-to-end framework with iterative capabilities similar to a deep unfolding network to improve reconstruction accuracy, independent of optimization conditions, and to reduce the number of parameters. A novel framework called the reversible-pri

K. B. Blyuss, Y. N. Kyrychko, O. B. Blyuss

In this paper, we analyse a recently proposed predator-prey model with ratio dependence and Holling type III functional response, with particular emphasis on the dynamics close to extinction. By using Briot-Bouquet transformation we transform the model into a system, where the extinction steady state is represented by up to three distinct steady states, whos

Weiren Zhao, Ruizhao Zi

In this paper, we prove the asymptotic stability of Couette flow in a strong uniform magnetic field for the Euler-MHD system, when the perturbations are in Gevrey-$\frac{1}{s}$, $(\frac12<s\leq 1)$ and of size smaller than the resistivity coefficient $\mu$. More precisely, we prove (1) the $\mu^{-\frac13}$-amplification of the perturbed vorticity, namely, th

Adrien Lefauve, Miles M. P. Couchman

Modeling fluid turbulence using a 'skeleton' of coherent structures has traditionally progressed by focusing on a few canonical experiments, such as pipe flow and Taylor-Couette flow. We here consider an alternative canonical experiment, the stratified inclined duct, a sustained shear flow whose density stratification allows for the exploration of a wealth o

Bar Karov, Moni Naor

Risk-limiting audits (RLAs) are a significant tool in increasing confidence in the accuracy of elections. They consist of randomized algorithms which check that an election's vote tally, as reported by a vote tabulation system, corresponds to the correct candidates winning. If an initial vote count leads to the wrong election winner, an RLA guarantees to ide

Yuxia Wu, Tianhao Dai, Zhedong Zheng, Lizi Liao

Existing task-oriented conversational search systems heavily rely on domain ontologies with pre-defined slots and candidate value sets. In practical applications, these prerequisites are hard to meet, due to the emerging new user requirements and ever-changing scenarios. To mitigate these issues for better interaction performance, there are efforts working t

Adrien Lefauve, Miles M. P. Couchman

We propose a dimensionality reduction and unsupervised clustering method for the automatic classification and reduced-order modeling of density-stratified turbulence in laboratory experiments. We apply this method to 113 long shadowgraph movies collected in a `Stratified Inclined Duct' (SID) experiment, where turbulence is generated by instabilities arising

Haijin Zeng, Jiezhang Cao, Kai Feng, Shaoguang Huang

Hyperspectral imaging (HI) has emerged as a powerful tool in diverse fields such as medical diagnosis, industrial inspection, and agriculture, owing to its ability to detect subtle differences in physical properties through high spectral resolution. However, hyperspectral images (HSIs) are often quite noisy because of narrow band spectral filtering. To reduc

Caiyue Zhang, Zipin Liu, Bowen Xu

Permanent magnet synchronous motors (PMSM) are widely used due to their numerous benefits. It is critical to get rotor position and speed information in order to operate the motor accurately. Sensorless control techniques have emerged as a popular study area both at home and overseas. The sliding mode observer (SMO) may indirectly detect rotor position and h

Fayadh Kadhem

In the construction of a cluster algebra on the homogeneous coordinate ring of a partial flag variety by Gei{\ss}, Leclerc and Schr{\"{o}}er, they defined a special map denoted by ``tilde". This map lifts each element $f$ of the coordinate ring of a Schubert cell uniquely to an element $\widetilde{f}$ of the (multi-homogeneous) coordinate ring of the corresp

Kun Zhou, Yifan Li, Wayne Xin Zhao, Ji-Rong Wen

Recently, continuous diffusion models (CDM) have been introduced into non-autoregressive (NAR) text-to-text generation. However, the discrete nature of text increases the difficulty of CDM to generate coherent and fluent texts, and also causes the incompatibility problem between CDM and advanced NLP techniques, especially the popular pre-trained language mod

Rui Hu, Yahan Tu, Jitao Sang

Neural networks often learn spurious correlations when exposed to biased training data, leading to poor performance on out-of-distribution data. A biased dataset can be divided, according to biased features, into bias-aligned samples (i.e., with biased features) and bias-conflicting samples (i.e., without biased features). Recent debiasing works typically as

Rui Prezado

We study effective descent $ \mathcal V $-functors for cartesian monoidal categories $ \mathcal V $ with finite limits. This study is carried out via the properties enjoyed by the $2$-functor $ \mathcal V \mapsto \mathsf{Fam}(\mathcal V) $, results about effective descent of bilimits of categories, and the fact that the enrichment $2$-functor preserves certa

Ahmed Zahari

The basic objective of the current research paper is to investigate the structure and the algebraic varieties of Hom-associative dialgebras. We elaborate a classification of $n$-dimensional Hom-associative dialgebras for $n\leq4$. Additionally, using the classification result of Hom-associative dialgebras, we characterize the $\alpha$-derivations and centroi

Wei Xiong, Peng-Cheng Li

This paper studies the quasinormal mode spectrum of the scalar perturbation on the background of the rotating accelerating black holes. The quasinormal frequency $\omega$ and the separation constant $\lambda$ are calculated using two methods: the continued fractions method and the directly integration method. The spectrum is found to include three families o

Tianqiang Yan, Tiansheng Xu

In this paper, we propose a simple yet efficient approach based on prompt engineering that leverages the large language model itself to optimize its answers without relying on auxiliary models. We introduce an iterative self-evaluating optimization mechanism, with the potential for improved output quality as iterations progress, removing the need for manual

Brandon Hanson, Misha Rudnev, Ilya Shkredov, Dmitrii Zhelezov

It was asked by E. Szemer\'edi if, for a finite set $A\subset\mathbb{Z}$, one can improve estimates for $\max\{|A+A|,|A\cdot A|\}$, under the constraint that all integers involved have a bounded number of prime factors -- that is, each $a\in A$ satisfies $\omega(a)\leq k$. In this paper, answer Szemer\'edi's question in the affirmative by showing that this m

H. Blas, Ronal A. DeLaCruz-Araujo, N. I. Reynaldo, N. Santos

A Boussinesq system for a non-linear shallow water is considered. The nonlinear and topological effects are examined through an associated matrix spectral problem. It is shown an equivalence relationship between the bound states and topological soliton charge densities which resembles a formula of the Atiyah-Patodi-Singer-type index theorem. The zero mode co