October 2023 arXiv papers — page 156

Yu Huang, Yuan Cheng, Yingbin Liang

Transformers have recently revolutionized many domains in modern machine learning and one salient discovery is their remarkable in-context learning capability, where models can solve an unseen task by utilizing task-specific prompts without further parameters fine-tuning. This also inspired recent theoretical studies aiming to understand the in-context learn

Mikhail Lavrov, Jennifer Vandenbussche

Let $G$ be a bipartite graph with bipartition $(X,Y)$. Inspired by a hypergraph problem, we seek an upper bound on the number of disjoint paths needed to cover all the vertices of $X$. We conjecture that a Hall-type sufficient condition holds based on the maximum value of $|S|-|\mathsf\Lambda(S)|$, where $S\subseteq X$ and $\mathsf\Lambda(S)$ is the set of a

Liran Shaul

Given a two-sided noetherian ring $A$ with a dualizing complex, we show that the big finitistic dimension of $A$ is finite if and only if every bounded below Gorenstein-projective-acyclic cochain complex of Gorenstein-projective $A$-modules is contractible. If $A$ is further assumed to be an Artin algebra, we also prove a Gorenstein variant of a theorem of R

Jiayu Zhang

Remote state preparation with verifiability (RSPV) is an important quantum cryptographic primitive [GV19,Zha22]. In this primitive, a client would like to prepare a quantum state (sampled or chosen from a state family) on the server side, such that ideally the client knows its full description, while the server holds and only holds the state itself. In this

Ye Li, Hanjiang Hu, Zuxin Liu, Xiaohao Xu

Cameras and LiDARs are both important sensors for autonomous driving, playing critical roles in 3D object detection. Camera-LiDAR Fusion has been a prevalent solution for robust and accurate driving perception. In contrast to the vast majority of existing arts that focus on how to improve the performance of 3D target detection through cross-modal schemes, de

Robin Buehler, Roman Kolyada, Vincent Desjacques

In a gaseous medium, dynamical friction (DF) reaches a maximum when the orbital speed of a (point-like) perturber moving on a circular orbit is close to the sound speed. Therefore, in a quasi-steady state, eccentric orbits of perturbers approaching the sound barrier (from below) should rapidly circularize as they experience the strongest drag at pericenter p

D. Efimov, M. Sydorov, K. Sysak

Let $K$ be an algebraically closed field of characteristic zero, $P_n=K[x_1, ..., x_n]$ the polynomial ring, and $W_n(K)$ the Lie algebra of all $K$-derivations on $P_n$. One of the most important subalgebras of $W_n(K)$ is the triangular subalgebra $u_n(K) = P_0\partial_1+\cdots+P_{n-1}\partial_n$, where $\partial_i:=\partial/\partial x_i$ are partial deriv

Tianyang Zhong, Wei Zhao, Yutong Zhang, Yi Pan

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and norma

Sunjae Yoon, Gwanhyeong Koo, Dahyun Kim, Chang D. Yoo

Video moment retrieval aims to localize moments in video corresponding to a given language query. To avoid the expensive cost of annotating the temporal moments, weakly-supervised VMR (wsVMR) systems have been studied. For such systems, generating a number of proposals as moment candidates and then selecting the most appropriate proposal has been a popular a

Shaddin Dughmi, Yusuf Hakan Kalayci, Neel Patel

Motivated by the growing interest in correlation-robust stochastic optimization, we investigate stochastic selection problems beyond independence. Specifically, we consider the instructive case of pairwise-independent priors and matroid constraints. We obtain essentially-optimal bounds for contention resolution and prophet inequalities. The impetus for our w

Reihaneh Mirjalili, Michael Krawez, Yannik Blei, Simone Silenzi

In this paper, we propose Lan-grasp, a novel approach towards more appropriate semantic grasping and placing. We leverage foundation models to equip the robot with a semantic understanding of object geometry, enabling it to identify the right place to grasp, which parts to avoid, and the natural pose for placement. This is an important contribution to graspi

Roberto Moretti, Hervè Atsè Corti, Danilo Labranca, Felix Ahrens

Quantum Sensing is a rapidly expanding research field that finds one of its applications in Fundamental Physics, as the search for Dark Matter. Devices based on superconducting qubits have already been successfully applied in detecting few-GHz single photons via Quantum Non-Demolition measurement (QND). This technique allows us to perform repeatable measurem

Md Selim, Jie Zhang, Faraneh Fathi, Michael A. Brooks

Computed tomography (CT) serves as an effective tool for lung cancer screening, diagnosis, treatment, and prognosis, providing a rich source of features to quantify temporal and spatial tumor changes. Nonetheless, the diversity of CT scanners and customized acquisition protocols can introduce significant inconsistencies in texture features, even when assessi

Fan He, Mingzhen He, Lei Shi, Xiaolin Huang

The lack of sufficient flexibility is the key bottleneck of kernel-based learning that relies on manually designed, pre-given, and non-trainable kernels. To enhance kernel flexibility, this paper introduces the concept of Locally-Adaptive-Bandwidths (LAB) as trainable parameters to enhance the Radial Basis Function (RBF) kernel, giving rise to the LAB RBF ke

Robin Algayres, Pablo Diego-Simon, Benoit Sagot, Emmanuel Dupoux

Due to the absence of explicit word boundaries in the speech stream, the task of segmenting spoken sentences into word units without text supervision is particularly challenging. In this work, we leverage the most recent self-supervised speech models that have proved to quickly adapt to new tasks through fine-tuning, even in low resource conditions. Taking i

Yun Tian, Didi Ma

In this paper, we study the bifurcation of limit cycles near a homoclinic cuspidal loop in a planar cubic near-Hamiltonian system by high-order Melnikov functions. We present a method combining the algebraic structure of Abelian integrals and Picard-Fuchs equation for computing the corresponding asymptotic expansion of Melnikov functions near the cuspidal lo

Yan Gao, Yu Du, Yun-Yun Bai, Weikang Wu

Exploring various topological states (TS) and topological phase transitions (TPT) has attracted great attention in condensed matter physics. However, so far, there is rarely a typical material system that can be used as a platform to study the TS and TPT as the system transforms from one-dimensional (1D) nanoribbons to two-dimensional (2D) sheet then to thre

Song Gao, Julio M. Ottino, Richard M. Lueptow, Paul B. Umbanhowar

We study the percolation velocity, $v_p,$ of a fine spherical particle in a sheared large-particle bed under gravity using discrete element method simulations for large-to-fine particle diameter ratios, $R=d/d_f,$ below and above the free-sifting threshold, $R_t\approx6.5.$ For $R<R_t,$ $v_p$ initially increases with increasing shear rate, $\dot\gamma,$ as s

Taewan Kim, Seolyeong Bae, Hyun Ah Kim, Su-woo Lee

In the mental health domain, Large Language Models (LLMs) offer promising new opportunities, though their inherent complexity and low controllability have raised questions about their suitability in clinical settings. We present MindfulDiary, a mobile journaling app incorporating an LLM to help psychiatric patients document daily experiences through conversa

Shicong Cen, Yuejie Chi

Policy gradient methods, where one searches for the policy of interest by maximizing the value functions using first-order information, become increasingly popular for sequential decision making in reinforcement learning, games, and control. Guaranteeing the global optimality of policy gradient methods, however, is highly nontrivial due to nonconcavity of th

Seyed Amir Alavi, Samin Ishtiaq, Nick Johnson, Rojalin Mishra

This paper describes the verification of the classical software and hardware stack that is used to control cold atom- and superconducting-based quantum computing hardware. The paper serves both as an introduction to quantum computing and to how classical device verification techniques can be employed there. Two main challenges in building a quantum control s

Bai-Cian Ke, Jonna Koponen, Hai-Bo Li, Yangheng Zheng

We present a comprehensive review of purely leptonic and semileptonic decays of $D^{0(+)}$, $D_s^{+}$, and charmed baryons (including $\Lambda_c^{+}$, $\Xi_c$ and $\Omega_c$). The precise studies of these decays help deepen our understanding and knowledge of quantum chromodynamics via measuring decay constants and form factors, and test the Standard Model th

Qingsong Xu, Yilei Shi, Jonathan Bamber, Ye Tuo

Accurate hydrological understanding and water cycle prediction are crucial for addressing scientific and societal challenges associated with the management of water resources, particularly under the dynamic influence of anthropogenic climate change. Existing reviews predominantly concentrate on the development of machine learning (ML) in this field, yet ther

Rahnuma Islam, Akif Ibragimov

We study the movement of the living organism in a band form towards the presence of chemical substrates based on a system of partial differential evolution equations. We incorporate Einstein's method of Brownian motion to deduce the chemotactic model exhibiting a traveling band. It is the first time that Einstein's method has been used to motivate equations

Sean Gearoid Fitzgerald, Marvin Anas Hahn, Síofra Kelly

Hurwitz numbers enumerate branched morphisms between Riemannn surfaces with fixed numerical data. They represent important objects in enumerative geometry that are accessible by combinatorial techniques. In the past decade, many variants of Hurwitz numbers have appeared in the literature. In this paper, we focus on an exciting such variant that arises natura

Robin Algayres, Yossi Adi, Tu Anh Nguyen, Jade Copet

In NLP, text language models based on words or subwords are known to outperform their character-based counterparts. Yet, in the speech community, the standard input of spoken LMs are 20ms or 40ms-long discrete units (shorter than a phoneme). Taking inspiration from word-based LM, we introduce a Generative Spoken Language Model (GSLM) based on word-size conti

Martin Gruber, Muhammad Firhard Roslan, Owain Parry, Fabian Scharnböck

Non-deterministic test behavior, or flakiness, is common and dreaded among developers. Researchers have studied the issue and proposed approaches to mitigate it. However, the vast majority of previous work has only considered developer-written tests. The prevalence and nature of flaky tests produced by test generation tools remain largely unknown. We ask whe

Amin Mahnamfar, Kemal Bicakci, Yusuf Uzunay

The challenge of achieving passwordless user authentication is real given the prevalence of web applications that keep asking passwords. Complicating this issue further, in an enterprise environment, a single sign-on (SSO) service is often maintained but not all applications can be integrated with it. We envision a passwordless future which provides a fricti

Gabriel German, Juan Carlos Hidalgo, Luis E. Padilla

The study of reheating in inflationary models is crucial for understanding the early universe and gaining insights into inflationary dynamics and parameters. The reheating temperature $T_{re}$ and the duration of the reheating phase, quantified by the number of $e$-folds $N_{re}$, have significant implications for particle production, thermalization, and the

Yun Tian, Tingting Jing, Zhe Zhang

In this paper, we consider the bifurcation of small-amplitude limit cycles near the origin in perturbed pendulum systems of the form $\dot x= y$, $\dot y=-\sin(x)+\varepsilon Q(x,y)$, where $Q(x,y)$ is a smooth or piecewise smooth polynomial in the triple $(\sin(x),\cos(x), y)$ with free coefficients. We obtain the sharp upper bound on the number of positive

Nils Niggemann, Nikita Astrakhantsev, Arnaud Ralko, Francesco Ferrari

$\require{mhchem}$The square-kagome lattice Heisenberg antiferromagnet is a highly frustrated Hamiltonian whose material realizations have been scarce. We theoretically investigate the recently synthesized $\ce{Na6Cu7BiO4(PO4)4Cl3}$ where a Cu$^{2+}$ spin-$1/2$ square-kagome lattice (with six site unit cell) is decorated by a seventh magnetic site alternatin

Roman Snytsar

Sliding Window Sum algorithms have been successfully used for training and inference of Deep Neural Networks. We have shown before how both pooling and convolution 1-D primitives could be expressed as sliding sums and evaluated by the compute kernels with a shared structure. In this paper, we present an extensive study of the Sliding Window convolution techn

Rong Zhang, Rongqing Yuan, Boxue Tian

Predicting quantum chemical properties is a fundamental challenge for computational chemistry. While the development of graph neural networks has advanced molecular representation learning and property prediction, their performance could be further enhanced by incorporating 3D structural geometry into 2D molecular graph representation. In this study, we intr

Nicola Scafetta

Schmidt, Jones, and Kennedy's (SJK) (2023, https://doi.org/10.1029/2022GL102530) critique of Scafetta (2022, https://doi.org/10.1029/2022GL097716) is flawed. Their assessment of the error of the ERA-T2m 2011-2021 mean (about 0.10 {\deg}C) is 5-10 times overestimated and contradicts published literature. SJK confused natural variability with random noise and

Xintong Wang, Xiaoyu Li, Xingshan Li, Chris Biemann

Large Language Models (LLMs) have emerged as dominant foundational models in modern NLP. However, the understanding of their prediction processes and internal mechanisms, such as feed-forward networks (FFN) and multi-head self-attention (MHSA), remains largely unexplored. In this work, we probe LLMs from a human behavioral perspective, correlating values fro

Jose Luis Ponton

Real-time animation of virtual characters has traditionally been accomplished by playing short sequences of animations structured in the form of a graph. These methods are time-consuming to set up and scale poorly with the number of motions required in modern virtual environments. The ever-increasing need for highly-realistic virtual characters in fields suc

Alemayehu Nana Koya, Longnan Li, Wei Li

Controlled trapping of light absorbing nanoparticles with low-power optical tweezers is crucial for remote manipulation of small objects. This study takes advantage of the synergetic effects of tightly confined local fields of plasmonic nanoaperture, self-induced back-action of nanoparticles, and resonant optical trapping method to demonstrate enhanced manip

Jiayu Zhang

How could quantum cryptography help us achieve what are not achievable in classical cryptography? In this work we study the classical cryptographic problem that two parties would like to perform secure computations with long outputs. As a basic primitive and example, we first consider the following problem which we call secure function sampling with long out

David Kupeev, Eyal Nitzany

Every day, humans perceive objects and communicate these perceptions through various channels. In this paper, we present a computational model designed to track and simulate the perception of objects, as well as their representations as conveyed in communication. We delineate two fundamental components of our internal representation, termed "observed" and "s

Davide Marcato, Achille Giacometti, Amos Maritan, Angelo Rosa

We study a solution of interacting semiflexible polymers with curvature energy in poor-solvent conditions on the d-dimensional cubic lattice using mean-field theory and Monte Carlo computer simulations. Building upon past studies on a single chain, we construct a field-theory representation of the system and solve it within a mean-field approximation support

Qing Zong, Zhaowei Wang, Baixuan Xu, Tianshi Zheng

A main goal of Argument Mining (AM) is to analyze an author's stance. Unlike previous AM datasets focusing only on text, the shared task at the 10th Workshop on Argument Mining introduces a dataset including both text and images. Importantly, these images contain both visual elements and optical characters. Our new framework, TILFA (A Unified Framework for T

Xiaoran Liu, Hang Yan, Shuo Zhang, Chenxin An

The extrapolation capability of Large Language Models (LLMs) based on Rotary Position Embedding is currently a topic of considerable interest. The mainstream approach to addressing extrapolation with LLMs involves modifying RoPE by replacing 10000, the rotary base of $\theta_n={10000}^{-2n/d}$ in the original RoPE, with a larger value and providing longer fi

Xihuai Wang, Shao Zhang, Wenhao Zhang, Wentao Dong

Zero-shot coordination (ZSC) is a new cooperative multi-agent reinforcement learning (MARL) challenge that aims to train an ego agent to work with diverse, unseen partners during deployment. The significant difference between the deployment-time partners' distribution and the training partners' distribution determined by the training algorithm makes ZSC a un

Ziqiao Shang, Li Yu

Recently how to introduce large amounts of unlabeled facial images in the wild into supervised Facial Action Unit (AU) detection frameworks has become a challenging problem. In this paper, we propose a new AU detection framework where multi-task learning is introduced to jointly learn AU domain separation and reconstruction and facial landmark detection by s

Zhengyi Chen, Helmut Dannerbauer, Matthew Lehnert, Bjorn Emonts

This paper presents the results of 475 hours of interferometric observations with the Australia Telescope Compact Array towards the Spiderweb protocluster at \(z=2.16\). We search for large, extended molecular gas reservoirs among 46 previously detected CO(1-0) emitters, employing a customised method we developed. Based on the CO emission images and position

Hanjing Wang, Man-Kit Sit, Congjie He, Ying Wen

This paper introduces a distributed, GPU-centric experience replay system, GEAR, designed to perform scalable reinforcement learning (RL) with large sequence models (such as transformers). With such models, existing systems such as Reverb face considerable bottlenecks in memory, computation, and communication. GEAR, however, optimizes memory efficiency by en

Pei-Fu Guo, Ying-Hsuan Chen, Yun-Da Tsai, Shou-De Lin

In this work, we conduct an assessment of the optimization capabilities of LLMs across various tasks and data sizes. Each of these tasks corresponds to unique optimization domains, and LLMs are required to execute these tasks with interactive prompting. That is, in each optimization step, the LLM generates new solutions from the past generated solutions with

Ryuichi Yamamoto, Reo Yoneyama, Lester Phillip Violeta, Wen-Chin Huang

This paper presents our systems (denoted as T13) for the singing voice conversion challenge (SVCC) 2023. For both in-domain and cross-domain English singing voice conversion (SVC) tasks (Task 1 and Task 2), we adopt a recognition-synthesis approach with self-supervised learning-based representation. To achieve data-efficient SVC with a limited amount of targ

Cong Duan, Zixuan Liu, Jiahao Xia, Minghai Zhang

Deep neural networks enable real-time monitoring of in-vehicle drivers, facilitating the timely prediction of distractions, fatigue, and potential hazards. This technology is now integral to intelligent transportation systems. Recent research has exposed unreliable cross-dataset driver behavior recognition due to a limited number of data samples and backgrou

Yuta Murakami, Denis Golež, Martin Eckstein, Philipp Werner

The study of nonequilibrium phenomena in interacting lattice systems can provide new perspectives on correlation effects, and information on metastable states of matter. Mott insulators are a promising class of systems for nonequilibrium studies, since they exhibit exotic phenomena and complex phase diagrams upon doping, and because a large Mott gap provides

Tamara Evstafyeva, Roxana Rosca-Mead, Ulrich Sperhake, Bernd Brugmann

We study phenomenological features and stability of boson stars in massless and massive scalar-tensor theory of gravity with Damour-Esposito-Farese coupling. This coupling between the tensor and scalar sectors of the theory leads to a phenomenon called spontaneous scalarization, the onset of which we investigate by numerically computing families of boson-sta

Wei Shen, Rui Zheng, Wenyu Zhan, Jun Zhao

Reinforcement learning from human feedback serves as a crucial bridge, aligning large language models with human and societal values. This alignment requires a vast corpus of human feedback to learn a reward model, which is subsequently used to finetune language models. However, we have identified that the reward model often finds shortcuts to bypass its int

S. V. Bolokhov

We show that perturbations of a scalar field in the background of the black hole obtained with the Starobinsky-Bel-Robinson Gravity is unstable unless the dimensionless coupling $\beta$ describing the compactification of M-theory is small enough. In the sector of stability quasinormal spectrum show peculiar behavior both in the frequency and time domains: th

Xibo Li, Shruti Patel, David Stronzek-Pfeifer, Christof Büskens

Global navigation satellite systems readily provide accurate position information when localizing a robot outdoors. However, an analogous standard solution does not exist yet for mobile robots operating indoors. This paper presents an integrated framework for indoor localization and experimental validation of an autonomous driving system based on an advanced

Zihan Tan, Vania Calandrini, Jan K. G. Dhont, Gerhard Nägele

Competing short-range attractive (SA) and long range repulsive (LR) interactions have been invoked to describe colloid or protein solutions, as well as membrane proteins interactions mediated by lipid molecules. Using Langevin dynamics simulations, we determine the generalized phase diagram, the cluster shapes and size distributions of a generic Q2D dispersi

Bert van Geemen, Matthias Schütt

We exhibit large families of K3 surfaces with real multiplication, both abstractly using lattice theory, the Torelli theorem and the surjectivity of the period map, as well as explicitly using dihedral covers and isogenies.

Yuting Wang, Jinpeng Wang, Bin Chen, Ziyun Zeng

Given a text query, partially relevant video retrieval (PRVR) seeks to find untrimmed videos containing pertinent moments in a database. For PRVR, clip modeling is essential to capture the partial relationship between texts and videos. Current PRVR methods adopt scanning-based clip construction to achieve explicit clip modeling, which is information-redundan

Glennys R. Farrar, Nico Wintergerst

We derive and explicitly display the internal wave function of a color-, flavor- and spin- singlet dibaryon composed of uuddss quarks in a spatially symmetric state, in the approximation of exact SU(3) flavor symmetry. This wavefunction shows that the often-used superposition of Lambda Lambda, N Xi and Sigma Sigma baryons, relevant for di-baryon molecules, a

Chenzhuang Du, Yue Zhao, Chonghua Liao, Jiacheng You

This paper investigates how to better leverage large-scale pre-trained uni-modal models to further enhance discriminative multi-modal learning. Even when fine-tuned with only uni-modal data, these models can outperform previous multi-modal models in certain tasks. It's clear that their incorporation into multi-modal learning would significantly improve perfo

Eungyeom Ha, Heemook Kim, Sung Chul Hong, Dongbin Na

Recent multi-media data such as images and videos have been rapidly spread out on various online services such as social network services (SNS). With the explosive growth of online media services, the number of image content that may harm users is also growing exponentially. Thus, most recent online platforms such as Facebook and Instagram have adopted conte

Jieun Han, Haneul Yoo, Junho Myung, Minsun Kim

In the context of English as a Foreign Language (EFL) writing education, LLM-as-a-tutor can assist students by providing real-time feedback on their essays. However, challenges arise in assessing LLM-as-a-tutor due to differing standards between educational and general use cases. To bridge this gap, we integrate pedagogical principles to assess student-LLM i

Yun-He Lei, Zhen-Hao Yang, Xiao-Mei Kuang

We consider the quasinormal modes, quasibound states and superradiant instability of a rotating hairy black hole, which possesses a Horndeski hair as deviation from Kerr black hole, under the perturbation of massive scalar field. With the use of the matrix method, we mainly calculate the eigenfrequencies related to those modes of the perturbation. Under the

Isabelle Augenstein, Timothy Baldwin, Meeyoung Cha, Tanmoy Chakraborty

The emergence of tools based on Large Language Models (LLMs), such as OpenAI's ChatGPT, Microsoft's Bing Chat, and Google's Bard, has garnered immense public attention. These incredibly useful, natural-sounding tools mark significant advances in natural language generation, yet they exhibit a propensity to generate false, erroneous, or misleading content --

Chuanhuan Li, Yi Li, Kairui Xu

In this paper, we consider the Laplacian G_2 flow on a closed seven-dimensional manifold M with a closed G_2-structure. We first obtain the gradient estimates of positive solutions of the heat equation under the Laplacian G_2 flow and then we get the Harnack inequality on spacetime. As an application, we prove the monotonicity for positive solutions of the h

Maad Ebrahim, Abdelhakim Senhaji Hafid, Mohamed Riduan Abid

Fog computing emerged as a promising paradigm to address the challenges of processing and managing data generated by the Internet of Things (IoT). Load balancing (LB) plays a crucial role in Fog computing environments to optimize the overall system performance. It requires efficient resource allocation to improve resource utilization, minimize latency, and e

Yan Zhang, Hao Hao, Xiao He, Shuanhu Gao

Molecular retrosynthesis is a significant and complex problem in the field of chemistry, however, traditional manual synthesis methods not only need well-trained experts but also are time-consuming. With the development of big data and machine learning, artificial intelligence (AI) based retrosynthesis is attracting more attention and has become a valuable t

Haoran Luo, Haihong E, Yuhao Yang, Tianyu Yao

Beyond traditional binary relational facts, n-ary relational knowledge graphs (NKGs) are comprised of n-ary relational facts containing more than two entities, which are closer to real-world facts with broader applications. However, the construction of NKGs remains at a coarse-grained level, which is always in a single schema, ignoring the order and variable

Feng Lu, Lijun Zhang, Shuting Dong, Baifan Chen

Visual place recognition (VPR) is one of the research hotspots in robotics, which uses visual information to locate robots. Recently, the hierarchical two-stage VPR methods have become popular in this field due to the trade-off between accuracy and efficiency. These methods retrieve the top-k candidate images using the global features in the first stage, the

Zixuan Liu, Xin Zhang, Junjun He, Dan Fu

Learning with noisy labels has been studied to address incorrect label annotations in real-world applications. In this paper, we present ChiMera, a two-stage learning-from-noisy-labels framework based on semi-supervised learning, developed based on a novel contrastive learning technique MixCLR. The key idea of MixCLR is to learn and refine the representation

Wenpei Long, Yufeng Wei, Kun Xu

Nonequilibrium flows are common in aerospace engineering, and numerical simulations are vital in understanding non-equilibrium flow dynamics in spacecraft flight. The unified gas-kinetic wave-particle (UGKWP) method has been developed for multi-scale flow simulation, which models the coupled particle transport and collision within a numerical time in the flu

Shivam Mehta, Ruibo Tu, Simon Alexanderson, Jonas Beskow

As text-to-speech technologies achieve remarkable naturalness in read-aloud tasks, there is growing interest in multimodal synthesis of verbal and non-verbal communicative behaviour, such as spontaneous speech and associated body gestures. This paper presents a novel, unified architecture for jointly synthesising speech acoustics and skeleton-based 3D gestur

Yuntao Wang, Qinnan Hu, Zhendong Li, Zhou Su

Natural or man-made disasters pose significant challenges for delivering critical relief to affected populations due to disruptions in critical infrastructures and logistics networks. Unmanned aerial vehicles (UAVs)-aided disaster relief networks (UDRNs) leverage UAVs to assist existing ground relief networks by swiftly assessing affected areas and timely de

Yupeng Wu, Wenyun Li, Wenjie Huang, Chin Pang Ho

One of the main challenges in reinforcement learning (RL) is that the agent has to make decisions that would influence the future performance without having complete knowledge of the environment. Dynamically adjusting the level of epistemic risk during the learning process can help to achieve reliable policies in safety-critical settings with better efficien

Nii Osae Osae Dade, Margaret Lartey-Quaye, Emmanuel Teye-Kofi Odonkor, Paul Ammah

Programming has always been at the heart of technological innovation in the 21st century. With the advent of blockchain technologies and the proliferation of web3 paradigms of decentralised applications, smart contracts have been very instrumental in enabling developers to build applications that reside on decentralised blockchains. Despite the huge interest

Xuming Hu, Junzhe Chen, Xiaochuan Li, Yufei Guo

Large language models (LLMs) have recently driven striking performance improvements across a range of natural language processing tasks. The factual knowledge acquired during pretraining and instruction tuning can be useful in various downstream tasks, such as question answering, and language generation. Unlike conventional Knowledge Bases (KBs) that explici

Sergio R. Fenley, Rafael Potrie

Let $\mathcal{F}_1$ and $\mathcal{F}_2$ be transverse two dimensional foliations with Gromov hyperbolic leaves in a closed 3-manifold $M$ whose fundamental group is not solvable, and let $\mathcal{G}$ be the one dimensional foliation obtained by intersection. We show that $\mathcal{G}$ is \emph{leafwise quasigeodesic} in $\mathcal{F}_1$ and $\mathcal{F}_2$ i

Lu Yin, You Wu, Zhenyu Zhang, Cheng-Yu Hsieh

Large Language Models (LLMs), renowned for their remarkable performance across diverse domains, present a challenge when it comes to practical deployment due to their colossal model size. In response to this challenge, efforts have been directed toward the application of traditional network pruning techniques to LLMs, uncovering a massive number of parameter

Xi-guang Wang, Lu-lu Zeng, Guang-hua Guo, Jamal Berakdar

Magnons serve as a testing ground for fundamental aspects of Hermitian and non-Hermitian wave mechanics and are of high relevance for information technology. This study presents setups for realizing spatio-temporally driven parity-time (PT) symmetric magnonics based on coupled magnetic waveguides and magnonic crystals. A charge current in a metal layer with

Zhixun Li, Liang Wang, Xin Sun, Yifan Luo

Graph Structure Learning (GSL) has recently garnered considerable attention due to its ability to optimize both the parameters of Graph Neural Networks (GNNs) and the computation graph structure simultaneously. Despite the proliferation of GSL methods developed in recent years, there is no standard experimental setting or fair comparison for performance eval

M. Farnik

We classify quadratic polynomial mappings from $\mathbb{C}^3$ to $\mathbb{C}^2$ up to affine equivalence and topological equivalence. This is a part of a larger project, we have already classified mappings from $\mathbb{C}^2$ to $\mathbb{C}^n$ and inted to classify mappings from $\mathbb{C}^3$ to $\mathbb{C}^3$.

Anirban Chakraborty, Nimish Mishra, Sayandeep Saha, Sarani Bhattacharya

Randomizing the address-to-set mapping and partitioning of the cache has been shown to be an effective mechanism in designing secured caches. Several designs have been proposed on a variety of rationales: (1) randomized design, (2) randomized-and-partitioned design, and (3) psuedo-fully associative design. This work fills in a crucial gap in current literatu

Hongyu Zhao, Gongming Wei, Yang Xiao, Xianglei Xing

Multi-ship tracking (MST) as a core technology has been proven to be applied to situational awareness at sea and the development of a navigational system for autonomous ships. Despite impressive tracking outcomes achieved by multi-object tracking (MOT) algorithms for pedestrian and vehicle datasets, these models and techniques exhibit poor performance when a

Chengjie Lu, Tao Yue, Man Zhang, Shaukat Ali

Autonomous driving systems (ADSs) are capable of sensing the environment and making driving decisions autonomously. These systems are safety-critical, and testing them is one of the important approaches to ensure their safety. However, due to the inherent complexity of ADSs and the high dimensionality of their operating environment, the number of possible te

Dibyakanti Kumar, Anirbit Mukherjee

Physics Informed Neural Networks (PINNs) have been achieving ever newer feats of solving complicated PDEs numerically while offering an attractive trade-off between accuracy and speed of inference. A particularly challenging aspect of PDEs is that there exist simple PDEs which can evolve into singular solutions in finite time starting from smooth initial con

Hidekazu Yoshioka, Yumi Yoshioka

Evaluating environmental variables that vary stochastically is the principal topic for designing better environmental management and restoration schemes. Both the upper and lower estimates of these variables, such as water quality indices and flood and drought water levels, are important and should be consistently evaluated within a unified mathematical fram

Paul Mattes, Rainer Schlosser, Ralf Herbrich

One of the biggest challenges to modern deep reinforcement learning (DRL) algorithms is sample efficiency. Many approaches learn a world model in order to train an agent entirely in imagination, eliminating the need for direct environment interaction during training. However, these methods often suffer from either a lack of imagination accuracy, exploration

Han Zhou, Xingchen Ma, Matthew B Blaschko

Sequential maximization of expected improvement (EI) is one of the most widely used policies in Bayesian optimization because of its simplicity and ability to handle noisy observations. In particular, the improvement function often uses the best posterior mean as the best incumbent in noisy settings. However, the uncertainty associated with the incumbent sol

Xiao Pu, Jingyu Zhang, Xiaochuang Han, Yulia Tsvetkov

The rampant proliferation of large language models, fluent enough to generate text indistinguishable from human-written language, gives unprecedented importance to the detection of machine-generated text. This work is motivated by an important research question: How will the detectors of machine-generated text perform on outputs of a new generator, that the

Xi-guang Wang, Guang-hua Guo, Jamal Berakdar

Devices for performing computation and logic operations with low-energy consumption are of key importance for environmentally friendly data-processing and information technology. Here, we present a design for magnetic elements that use excess heat to perform logic operations. The basic information channel is coupled non-conductive magnetic stripes with a nor

CMS Collaboration

The observation of WW$\gamma$ production in proton-proton collisions at a center-of-mass energy of 13 TeV with an integrated luminosity of 138 fb$^{-1}$ is presented. The observed (expected) significance is 5.6 (5.1) standard deviations. Events are selected by requiring exactly two leptons (one electron and one muon) of opposite charge, moderate missing tran

Chengwen Qi, Bowen Li, Binyuan Hui, Bailin Wang

Large Language Models (LLMs) have achieved remarkable success in many formal language oriented tasks, such as structural data-to-text and semantic parsing. However current benchmarks mostly follow the data distribution of the pre-training data of LLMs. Therefore, a natural question rises that do LLMs really understand the structured semantics of formal langu

Zhijie Chen, Houwang Li

We study the concentration phenomenon of the Lane-Emden equation with vanishing potentials \[\begin{cases} -\Delta u_n=W_n(x)u_n^{p_n},\quad u_n>0,\quad\text{in}~\Omega, u_n=0,\quad\text{on}~\partial\Omega, \int_\Omega p_n W_n(x)u_n^{p_n}dx\le C, \end{cases}\] where $\Omega$ is a smooth bounded domain in $\mathbb{R}^2$, $W_n(x)\geq 0$ are bounded functions w

Hafida Massit, Mohamed Rossafi, Choonkil Park

In this paper, we present controlled finite continuous frames in a finite dimensional Hilbert space and we study some properties of them. Parseval controlled integral frames are presented and we characterize operators that construct controlled integral finite frames.

Anej Svete, Ryan Cotterell

Studying language models (LMs) in terms of well-understood formalisms allows us to precisely characterize their abilities and limitations. Previous work has investigated the representational capacity of recurrent neural network (RNN) LMs in terms of their capacity to recognize unweighted formal languages. However, LMs do not describe unweighted formal langua

Paramjeet Banger

We investigate the vortex lattice configuration in a rotating spin orbit-coupled spin-2 Bose-Einstein condensate confined in a quasi-two-dimensional harmonic trap. By considering the interplay between rotation frequency, spin-orbit couplings, and inter atomic interactions, we explore a variety of vortex lattice structures emerging as a ground state solution.

Vanita Garg, Kusum Deep, Khalid Abdulaziz Alnowibet, Ali Wagdy Mohamed

In this paper, an ensemble metaheuristic algorithm (denoted as LX-BBSCA) is introduced. It combines the strengths of Laplacian Biogeography-Based Optimization (LX-BBO) and the Sine Cosine Algorithm (SCA) to address structural engineering design optimization problems. Our primary objective is to mitigate the risk of getting stuck in local minima and accelerat

Yu Wang, Yihong Wang, Tong Liu, Xiubao Sui

Noise, artifacts, and over-exposure are significant challenges in the field of low-light image enhancement. Existing methods often struggle to address these issues simultaneously. In this paper, we propose a novel Retinex-based method, called ITRE, which suppresses noise and artifacts from the origin of the model, prevents over-exposure throughout the enhanc

Yifan Wei, Yisong Su, Huanhuan Ma, Xiaoyan Yu

Large language models (LLMs) have shown nearly saturated performance on many natural language processing (NLP) tasks. As a result, it is natural for people to believe that LLMs have also mastered abilities such as time understanding and reasoning. However, research on the temporal sensitivity of LLMs has been insufficiently emphasized. To fill this gap, this

Xuanrui Feng, Zhenfu Wang

We derive the quantitative estimates of propagation of chaos for the large interacting particle systems in terms of the relative entropy between the joint law of the particles and the tensorized law of the mean field PDE. We resolve this problem for the first time for the viscous vortex model that approximates 2D Navier-Stokes equation in the vorticity formu

Cheng Qian, Chenyan Xiong, Zhenghao Liu, Zhiyuan Liu

Large Language Models (LLMs) have demonstrated remarkable progress in utilizing tools, but their closed-source nature and high inference costs pose limitations on their adaptability, necessitating a valid method that leverages smaller, open-sourced models. In this paper, we introduce Toolink, a comprehensive framework that performs task-solving by first crea