December 2024 arXiv papers — page 52

Souhaib Attaiki, Paul Guerrero, Duygu Ceylan, Niloy J. Mitra

We train a feed-forward text-to-3D diffusion generator for human characters using only single-view 2D data for supervision. Existing 3D generative models cannot yet match the fidelity of image or video generative models. State-of-the-art 3D generators are either trained with explicit 3D supervision and are thus limited by the volume and diversity of existing

Rodrigo Moreira, Larissa F. Rodrigues Moreira, Tereza C. Carvalho, Flávio de Oliveira Silva

Modern network architectures have shaped market segments, governments, and communities with intelligent and pervasive applications. Ongoing digital transformation through technologies such as softwarization, network slicing, and AI drives this evolution, along with research into Beyond 5G (B5G) and 6G architectures. Network slices require seamless management

Zijiang Yang, Zhongwei Qiu, Tiancheng Lin, Hanqing Chao

It is clinically crucial and potentially very beneficial to be able to analyze and model directly the spatial distributions of cells in histopathology whole slide images (WSI). However, most existing WSI datasets lack cell-level annotations, owing to the extremely high cost over giga-pixel images. Thus, it remains an open question whether deep learning model

Daniel Marahrens, Angeliki Menegaki, Clément Mouhot

We propose a simple quantitative method for studying the hydrodynamic limit of interacting particle systems on lattices. It is applied to the diffusive scaling of the symmetric Zero-Range Process (in dimensions one and two). The rate of convergence is estimated in a Monge-Kantorovich distance asymptotic to the L^1 stability estimate of Kruzkhov, as well as i

Guy B. Oldaker, Maria Emelianenko

Clustering algorithms remain valuable tools for grouping and summarizing the most important aspects of data. Example areas where this is the case include image segmentation, dimension reduction, signals analysis, model order reduction, numerical analysis, and others. As a consequence, many clustering approaches have been developed to satisfy the unique needs

Fabiano Feleppa, Valerio Bozza, Oleg Yu. Tsupko

Near a gravitating compact object, massive particles traveling along timelike geodesics are gravitationally deflected similarly to light. In this paper, we study the deflection angles of these particles in the strong deflection limit. This analytical approximation applies when particles in unbound orbits approach the compact object very closely, circle aroun

Zhongwei Qiu, Hanqing Chao, Tiancheng Lin, Wanxing Chang

Histopathology plays a critical role in medical diagnostics, with whole slide images (WSIs) offering valuable insights that directly influence clinical decision-making. However, the large size and complexity of WSIs may pose significant challenges for deep learning models, in both computational efficiency and effective representation learning. In this work,

Gustav Mårdby, Julie Rowlett, Felix Rydell

In 1964, John Milnor, using a construction of two lattices by Witt, produced the first example of two flat tori that are not globally isometric and whose Laplacians for exterior forms have the same sequence of eigenvalues. The aforementioned flat tori are sixteen-dimensional. One is reducible while the second is irreducible. In the ensuing years, pairs of no

Jinyan Su, Jin Peng Zhou, Zhengxin Zhang, Preslav Nakov

Retrieval-Augmented Generation (RAG) systems have emerged as a promising solution to mitigate LLM hallucinations and enhance their performance in knowledge-intensive domains. However, these systems are vulnerable to adversarial poisoning attacks, where malicious passages injected into the retrieval corpus can mislead models into producing factually incorrect

Dharmaraj Ramachandran, Radhika Vathsan

Despite their elegance and widespread use, the current Geometric Measures (GMs) of entanglement exhibit a significant limitation: they fail to effectively distinguish Local Unitary (LU) inequivalent states due to the inherent nature of their definition. We illustrate the impact of this limitation using the fidelity of the teleportation protocol as an example

Vladimir Kolobov, Lucius Schoenbaum

We compare traditional finite volume and Physics Informed Neural Network (PINN) solvers for elliptic (Poisson), hyperbolic (advection), and parabolic (diffusion) equations in 2d settings. We describe the challenges of using traditional and PINN solvers for electron kinetic equations in collisional plasmas. The advantages and drawbacks of PINNs over state-of-

Nojod M. Alotaibi, Areej M. Alhothali, Manar S. Ali

Major depressive disorder (MDD) is one of the most common mental disorders, with significant impacts on many daily activities and quality of life. It stands as one of the most common mental disorders globally and ranks as the second leading cause of disability. The current diagnostic approach for MDD primarily relies on clinical observations and patient-repo

Yulián A. Alvarez-Ballesteros, Mario A. Quiroz-Juarez, José L. Del-Rio-Correa, Adrian M. Escobar-Ruiz

In this work, we applied the Chaos Game Representation (CGR) to the complete human genomic sequence T2T-CHM13v2.0, analyzing the entire chromosome assembly and each chromosome separately, including mitochondrial DNA. Multifractal spectra were determined using two types of box-counting coverage, revealing slight variations across most chromosomes. While the g

P. E. Trevisanutto, S. Taioli, M. Dapor, C. S. Allen

High-resolution imaging has revolutionized materials science by offering detailed insights into the atomic structures of materials. Electron microscopy and spectroscopy rely on analysing backscattered and transmitted electrons as well as stimulated radiation emission to form structural and chemical maps. These signals contain information about the elastic an

Barun Kumar Pal

We have re-examined Mukhanov parametrization for inflationary equation of state, $1+\omega=\frac{\beta}{({N}+1)^\alpha}$, in the light of Planck 2018 results and latest bound of tensor-to-scalar ratio employing Hamilton-Jacobi formalism. We have found that the current observational values of scalar spectral index and tensor-to-scalar ratio can be used effici

B. Chomet, D. Gacemi, O. Lopez, L. Del Balzo

A continuous-wave Fabry-Perot quantum cascade laser (QCL) emitting at 8.1 $\mu$m operating in the single mode regime has been coherently phase locked to a turn-key low-noise commercial mid-Infrared (mid-IR) optical frequency comb. The stability of the comb used as a reference is transferred to the QCL resulting in an integrated residual phase error of 0.4 ra

Aritra Kumar Lahiri, Qinmin Vivian Hu

Recent advancements in generative AI have fostered the development of highly adept Large Language Models (LLMs) that integrate diverse data types to empower decision-making. Among these, multimodal retrieval-augmented generation (RAG) applications are promising because they combine the strengths of information retrieval and generative models, enhancing their

Haocheng Huang, Jiaxin Chen, Jinyang Guo, Ruiyi Zhan

Diffusion models have achieved remarkable success in the image and video generation tasks. Nevertheless, they often require a large amount of memory and time overhead during inference, due to the complex network architecture and considerable number of timesteps for iterative diffusion. Recently, the post-training quantization (PTQ) technique has proved a pro

Jinlin Li, Xintong Li, Xiao Zhou

As global populations age rapidly, incorporating age-specific considerations into urban planning has become essential to addressing the urgent demand for age-friendly built environments and ensuring sustainable urban development. However, current practices often overlook these considerations, resulting in inadequate and unevenly distributed elderly services

Tongfei Bian, Yiming Ma, Mathieu Chollet, Victor Sanchez

For efficient human-agent interaction, an agent should proactively recognize their target user and prepare for upcoming interactions. We formulate this challenging problem as the novel task of jointly forecasting a person's intent to interact with the agent, their attitude towards the agent and the action they will perform, from the agent's (egocentric) pers

Gregorio Falqui, Tamara Grava, Christian Puntini

We study a deterministic gas of breathers for the Focusing Nonlinear Schr\"odinger equation. The gas of breathers is obtained from a $N$-breather solution in the limit $N\to \infty$.\\ The limit is performed at the level of scattering data by letting the $N$-breather spectrum to fill uniformly a suitable compact domain of the complex plane in the limit $N\to

Sandip Saha

In dynamical systems theory, there is a lack of a straightforward rule to distinguish exact center solutions from decaying center-like solutions, as both require the damping force function to be zero [1, 2]. By adopting a multi-scale perturbative method, we have demonstrated a general rule for the decaying center-like power law behavior, characterized by an

Aritra Kumar Lahiri, Qinmin Vivian Hu

This paper proposes a novel approach to develop an open-domain and long-form Over-The-Top (OTT) Question-Answering (QA) dataset, DragonVerseQA, specifically oriented to the fantasy universe of "House of the Dragon" and "Game Of Thrones" TV series. Most existing QA datasets focus on short, fact-based answers sourced almost solely from Wikipedia articles, devo

Sankalp Mittal

The increasing volume of traffic (especially from IoT devices) is posing a challenge to the current anomaly detection systems. Existing systems are forced to take the support of the control plane for a more thorough and accurate detection of malicious traffic (anomalies). This introduces latency in making decisions regarding fast incoming traffic and therefo

Siyuan Li, Stephan Narison, Davidson Rabetiarivony, Tom Steele

We improve the determination of the mass and coupling of the $2^{++}$ tensor di-gluonium by using relativistic QCD Laplace sum rules (LSR). In so doing, we evaluate the next-to-leading order (NLO) corrections to the perturbative (PT) and $\langle \alpha_s G^2 \rangle$ condensate and the lowest order (LO) $\langle G^3 \rangle$ contributions to the $2^{++}$ di

Shan Shan

This research presents a three-step causal inference framework that integrates correlation analysis, machine learning-based causality discovery, and LLM-driven interpretations to identify socioeconomic factors influencing carbon emissions and contributing to climate change. The approach begins with identifying correlations, progresses to causal analysis, and

Dirk Oliver Theis

We propose a quantum-state-certification protocol for stabilizer states, motivated by application in in-situ testing of NISQ-era quantum computer systems: The number of qubits is bounded, and in terms of cost of running the protocol, identical repetition of quantum circuits contribute negligibly compared to switching the measurement bases. The method builds

Majd Zayyad, Yossi Adi

The integration of retrieval-augmented techniques with LLMs has shown promise in improving performance across various domains. However, their utility in tasks requiring advanced reasoning, such as generating and evaluating mathematical statements and proofs, remains underexplored. This study explores the use of Lean, a programming language for writing mathem

Jérôme Deprince, Gururaj Wagle, Sirine Ben Nasr, Helena Carvajal Gallego

In the context of kilonova (KN) modeling, the present work focusses on large-scale atomic data and opacity computations for all heavy elements from Ca to Lr, with a special effort on lanthanides and actinides, for a grid of typical KN ejecta conditions between one day and one week after the merger (corresponding to the LTE photosphere phase of the KN ejecta)

Amirhossein Mesbah, Reshad Hosseini, Seyed Pooya Shariatpanahi, Majid Nili Ahmadabadi

Reinforcement learning (RL) plays a major role in solving complex sequential decision-making tasks. Hierarchical and goal-conditioned RL are promising methods for dealing with two major problems in RL, namely sample inefficiency and difficulties in reward shaping. These methods tackle the mentioned problems by decomposing a task into simpler subtasks and tem

Minda Hu, Qiyuan Zhang, Yufei Wang, Bowei He

As a crucial step to enhance LLMs alignment with human intentions, Instruction Fine-Tuning (IFT) has a high demand on dataset quality. However, existing IFT datasets often contain knowledge that is inconsistent with LLMs' internal knowledge learned from the pre-training phase, which can greatly affect the efficacy of IFT. To address this issue, we introduce

M. V. Garzelli, G. Limatola, S. -O. Moch, M. Steinhauser

We provide an update on QCD predictions for top-quark pair production close to threshold including bound state effects at the Large Hadron Collider. We compute the top-quark pair invariant mass distribution $d\sigma/dM_{t\bar{t}}$, including Coulomb resummation for bound-state effects, as well as threshold resummation for emissions of soft and collinear gluo

Zexi Cai, Wenbo Fei, Doudou Zhou

The two-sample test is a fundamental problem in statistics with a wide range of applications. In the realm of high-dimensional data, nonparametric methods have gained prominence due to their flexibility and minimal distributional assumptions. However, many existing methods tend to be more effective when the two distributions differ primarily in their first a

Songtao Lu, Yingdong Lu, Tomasz Nowicki

We derive the system of differential equations for the gradient flow characterizing the training process of linear in-context learning in full generality. Next, we explore the geometric structure of the gradient flows in two instances, including identifying its invariants, optimum, and saddle points. This understanding allows us to quantify the behavior of t

Feiran Jia, Tong Wu, Xin Qin, Anna Squicciarini

Large Language Model (LLM) agents are increasingly being deployed as conversational assistants capable of performing complex real-world tasks through tool integration. This enhanced ability to interact with external systems and process various data sources, while powerful, introduces significant security vulnerabilities. In particular, indirect prompt inject

Clay Cordova, Davi B. Costa, Po-Shen Hsin

In recent work, we developed a method to construct invertible and non-invertible symmetries of finite-group gauge theories as topological domain walls on the lattice. In the present work, we consider abelian and non-abelian finite-group gauge theories in general spacetime dimension, and demonstrate how to realize these symmetries as condensation defects, i.e

R. Kord Valeshabadi, S. Rezaie

Collinear parton distribution functions (cPDFs) and transverse momentum dependent distributions (TMDs) are essential for calculating cross sections in high-energy physics, particularly within collinear and kt-factorization frameworks. Currently, there exists two libraries, such as LHAPDF and TMDLib, to obtain these physical objects. However, there are limita

Clémence Fontanive, Luigi R. Bedin, Loïc Albert, Daniella C. Bardalez Gagliuffi

We present astrometric results from a Hubble Space Telescope (HST) campaign aimed at determining precise distances for cold Y-type brown dwarfs. Combining observations from a dedicated HST/WFC3 program with archival data, we derive astrometric solutions for 15 nearby Y dwarfs, by linking the high-precision relative astrometry from Hubble to the high-accuracy

Gerold Schmitt-Ulms, Xinzhu Wang, Joel Watts, Stephanie Booth

Decades after their initial observation in prion-infected brain tissues, the identities of virus-like dense particles, varicose tubules, and oval bodies containing parallel bands and fibrils have remained elusive. Our recent work revealed that a phenotype of dilation of the endoplasmic reticulum (ER), most notable for the perinuclear space (PNS), contributes

Chi Zhang, Yuanzhi Liang, Xi Qiu, Fangqiu Yi

Generating high-quality videos from textual descriptions poses challenges in maintaining temporal coherence and control over subject motion. We propose VAST (Video As Storyboard from Text), a two-stage framework to address these challenges and enable high-quality video generation. In the first stage, StoryForge transforms textual descriptions into detailed s

Yihui Tong, Wenjie Liu, Zhichang Guo, Wenjuan Yao

This paper investigates a class of degenerate forward-backward diffusion equations with a nonlinear source term, proposed as a model for removing multiplicative noise in images. Based on Rothe's method, the relaxation theorem, and Schauder's fixed-point theorem, we establish the existence of Young measure solutions for the corresponding initial boundary prob

Meselem Karras

Let f be an arithmetic function satisfying certain conditions. In this paper, we give an asymptotic formula for the sum \[\sum_{n_1 n_2 \cdots n_r \leq x} f\left(\left\lfloor \frac{x}{n_1 n_2 \cdots n_r} \right\rfloor\right), \quad r \geq 2.\], where $\lfloor . \rfloor$ denotes the integer part function.

Jieyi Wang, Yue Huang, Zeming Liu, Dexuan Xu

Online psychological counseling dialogue systems are trending, offering a convenient and accessible alternative to traditional in-person therapy. However, existing psychological counseling dialogue systems mainly focus on basic empathetic dialogue or QA with minimal professional knowledge and without goal guidance. In many real-world counseling scenarios, cl

Daniel Pereira Monteiro, Lucas Nardelli de Freitas Botelho Saar, Larissa Ferreira Rodrigues Moreira, Rodrigo Moreira

Novel applications demand high throughput, low latency, and high reliability connectivity and still pose significant challenges to slicing orchestration architectures. The literature explores network slicing techniques that employ canonical methods, artificial intelligence, and combinatorial optimization to address errors and ensure throughput for network sl

Jumin Qiu, Ganqing Lu, Tingting Liu, Dejian Zhang

Artificial intelligence generative content technology has experienced remarkable breakthroughs in recent years and is quietly leading a profound transformation. Diffractive optical networks provide a promising solution for implementing generative model with high-speed and low-power consumption. In this work, we present the implementation of a generative mode

Agustin Moreno, Arthur Limoge

In this note, we show there exist infinitely many trajectories which are bi-normal (i.e. normal at initial and final times) to the xz-plane, in the Spatial Circular Restricted Three-Body Problem, for energies below or slightly above the first critical value and near the primaries, under the assumption of the twist condition as defined by Moreno-van-Koert in

Boyuan Li, Xihua Wang, Ruihua Song, Wenbing Huang

Multi-person interactive motion generation, a critical yet under-explored domain in computer character animation, poses significant challenges such as intricate modeling of inter-human interactions beyond individual motions and generating two motions with huge differences from one text condition. Current research often employs separate module branches for in

Philip Zmushko, Marat Mansurov, Ruslan Svirschevski, Denis Kuznedelev

As deep learning models become larger and more expensive, many practitioners turn to fine-tuning APIs. These web services allow fine-tuning a model between two parties: the client that provides the data, and the server that hosts the model. While convenient, these APIs raise a new concern: the data of the client is at risk of privacy breach during the traini

Christoffer Hansen, Xiang I. A. Yang, Mahdi Abkar

This work investigates the current wall-modeled large-eddy simulation (WMLES) capabilities of the open-source computational fluid dynamics solver OpenFOAM, which is used widely in academia and industry. This is achieved by a simulation campaign that covers both attached and smooth body separation cases. The campaign includes simulations using four different

Nicolás E. Díaz Ferreyra, Sirine Khelifi, Nalin Arachchilage, Riccardo Scandariato

Privacy and security are central to the design of information systems endowed with sound data protection and cyber resilience capabilities. Still, developers often struggle to incorporate these properties into software projects as they either lack proper cybersecurity training or do not consider them a priority. Prior work has tried to support privacy and se

Cornelia Vogel

We consider an isolated macroscopic quantum system in a pure state $\psi_t$ evolving unitarily in a separable Hilbert space $\mathcal{H}$ and take for granted that different macro states $\nu$ correspond to mutually orthogonal subspaces $\mathcal{H}_\nu\subset\mathcal{H}$. Let $P_\nu$ be the projection to $\mathcal{H}_\nu$. It was recently shown that for all

CMS Collaboration

The value of the strong coupling $\alpha_\mathrm{S}$ is determined in a comprehensive analysis at next-to-next-to-leading order accuracy in quantum chromodynamics. The analysis uses double-differential cross section measurements from the CMS Collaboration at the CERN LHC of inclusive jet production in proton-proton collisions at centre-of-mass energies of 2.

Yan Zhu, Shihao Wang, Yong Han, Yao Lu

Air pollution, particularly airborne particulate matter (PM), poses a significant threat to public health globally. It is crucial to comprehend the association between PM-associated toxic components and their cellular targets in humans to understand the mechanisms by which air pollution impacts health and to establish causal relationships between air polluti

Martin Houde, Fereshteh Rajabi, Lamies Sati, Vahid Anari

The origin of dark matter in galactic halos, one of the deepest unsolved problems in astrophysics, may find an unexpected contribution from the quantum mechanics of ordinary atomic hydrogen. We show that quantum entanglement and coherence among hydrogen atoms in a gas at thermal equilibrium can naturally lead to subradiance, a cooperative suppression of radi

Moorad Alexanian

An ansatz applied to the two-dimensional Ising model in an external magnetic field h gives rise to an exactly soluble model. The singularity in the magnetization found by Onsager does not survive the presence of the external magnetic field as found earlier by Lee and Yang in 1952. However, the singularity in the heat capacity remains even in the presence of

Yahe Yang

Adversarial attacks on image classification systems have always been an important problem in the field of machine learning, and generative adversarial networks (GANs), as popular models in the field of image generation, have been widely used in various novel scenarios due to their powerful generative capabilities. However, with the popularity of generative a

Fouad Et-Tahri, Jon Asier Bárcena-Petisco, Idriss Boutaayamou, Lahcen Maniar

This paper aims to address an interesting open problem, posed in the paper "Singular Optimal Control for a Transport-Diffusion Equation" of Sergio Guerrero and Gilles Lebeau in 2007. The problem involves studying the null controllability cost of a transport-diffusion equation with Neumann conditions, where the diffusivity coefficient is denoted by $\varepsil

Alessandro De Gregorio, Dario Frisardi, Francesco Iafrate, Stefano Iacus

Penalized estimation methods for diffusion processes and dependent data have recently gained significant attention due to their effectiveness in handling high-dimensional stochastic systems. In this work, we introduce an adaptive Elastic-Net estimator for ergodic diffusion processes observed under high-frequency sampling schemes. Our method combines the leas

Carolina van Baalen, Laura Alvarez, Robert Style, Lucio Isa

Active systems comprising micron-sized self-propelling units, also termed microswimmers, are promising candidates for the bottom-up assembly of small structures and reconfigurable materials. Here we leverage field-driven colloidal assembly to induce structural transformations in dense layers of microswimmers driven by an alternating current (AC) electric fie

Yesim Beril Soguksu, Ayse Bilicioglu Gunes, Hatice Gurdil

The purpose of this study is to provide a step-by-step demonstration of item recovery for the Multidimensional Graded Response Model (MGRM) in R. Within this scope, a sample simulation design was constructed where the test lengths were set to 20 and 40, the interdimensional correlations were varied as 0.3 and 0.7, and the sample size was fixed at 2000. Param

Qiaojun Yu, Ce Hao, Xibin Yuan, Li Zhang

Manipulating articulated objects with robotic arms is challenging due to the complex kinematic structure, which requires precise part segmentation for efficient manipulation. In this work, we introduce a novel superpoint-based perception method designed to improve part segmentation in 3D point clouds of articulated objects. We propose a learnable, part-aware

Simon J. A. Malham, Anke Wiese, Yifan Xu

In this paper we derive a new direct inversion method to simulate squared Bessel processes. Since the transition probability of these processes can be represented by a non-central chi-square distribution, we construct an efficient and accurate algorithm to simulate non-central chi-square variables. In this method, the dimension of the squared Bessel process,

Yaming Zhang, Chenqiang Gao, Fangcen Liu, Junjie Guo

Existing infrared and visible (IR-VIS) methods inherit the general representations of Pre-trained Visual Models (PVMs) to facilitate complementary learning. However, our analysis indicates that under the full fine-tuning paradigm, the feature space becomes highly constrained and low-ranked, which has been proven to seriously impair generalization. One remedy

Nishanth Upadhyaya, Raghavendra Sridharamurthy

In this article, we introduce 'Internalized Self-Correction' (InSeC) for large language models (LLMs). While many approaches exist for self-reflection at inference time, we propose a novel method that combines ideas from negative sampling, self-reflection during training, and inference time. InSeC allows LLMs to correct themselves by introducing mistakes and

S. Pérez-Esteva, A. Uribe, C. Villegas-Blas

We study the spectrum of the Dirichlet to Neumann operator of the two-sphere associated to a Schr\"odinger operator in the unit ball. The spectrum forms clusters of size $O(1/k)$ around the sequence of natural numbers $k=1,2,\ldots$, and we compute the first three terms in the asymptotic distribution of the eigenvalues within the clusters, as $k\to\infty$ (b

Bruce Hoeneisen

Recent observations by Mistele et al. show that the circular velocity curves of isolated galaxies remain flat out to the largest radii probed so far, i.e. $\approx 1$ Mpc. The velocity decline beyond the expected virial radius is not observed. These results imply that the galaxy halo is in thermal equilibrium even at large radii where particles did not have

Yufei Song, Ziqi Zhou, Minghui Li, Xianlong Wang

With the rapid advancement of deep learning, the model robustness has become a significant research hotspot, \ie, adversarial attacks on deep neural networks. Existing works primarily focus on image classification tasks, aiming to alter the model's predicted labels. Due to the output complexity and deeper network architectures, research on adversarial exampl

Naeem Akhtar, Jia-Xin Peng, Xiaosen Yang, Yuanping Chen

The challenge of developing high-precision temperature sensors is an important issue that has recently received a lot of attention. In this work, we introduce an estimation technique to precisely measure the temperature of a quantum reservoir using a Kerr-nonlinear resonator with drive. Thermalization in our suggested protocol is assessed using Uhlmann-Jozsa

Kazuki Yoshida, Junki Tanaka

The quasi-free nucleon knockout reaction has been revealed the single-particle nature of nuclei. Thanks to the advances in experimental techniques and reaction theory, various new aspects of nuclei are being revealed by knockout reactions. In this article, we review the basic concept of the quasi-free knockout reaction, and recent achievements in the SEASTAR

Jiazhen Liu, Kunal Tamang, Dashun Wang, Chaoming Song

The study of causal structure in complex systems has gained increasing attention, with many recent studies exploring causal networks that capture cause-effect relationships across diverse fields. Despite increasing empirical evidence linking causal structures to network topological correlations, the mechanisms underlying the emergence of these correlations i

Frédérique Charles, Zhe Chen, François Golse

This article presents a common setting for the collision integrals $\mathrm{St}$ appearing in the kinetic theory of dense gases. It includes the collision integrals of the Enskog equation, of (a variant of) the Povzner equation, and of a model for soft sphere collisions proposed by Cercignani [Comm. Pure Appl. Math. 36 (1983), 479-494]. All these collision i

Yuchen Wang, Hongyuan Wang, Lizhi Wang, Xin Wang

Existing single-image denoising algorithms often struggle to restore details when dealing with complex noisy images. The introduction of near-infrared (NIR) images offers new possibilities for RGB image denoising. However, due to the inconsistency between NIR and RGB images, the existing works still struggle to balance the contributions of two fields in the

Jianghang Gu, Ling Wen, Yuntian Chen, Shiyi Chen

Traditional numerical methods, such as the finite element method and finite volume method, adress partial differential equations (PDEs) by discretizing them into algebraic equations and solving these iteratively. However, this process is often computationally expensive and time-consuming. An alternative approach involves transforming PDEs into integral equat

Silin Yang, Dong Wang, Haoqi Zheng, Ruochun Jin

Although the rise of large language models (LLMs) has introduced new opportunities for time series forecasting, existing LLM-based solutions require excessive training and exhibit limited transferability. In view of these challenges, we propose TimeRAG, a framework that incorporates Retrieval-Augmented Generation (RAG) into time series forecasting LLMs, whic

Junxuan Zhang, Zhengxue Cheng, Yan Zhao, Shihao Wang

Learning-based probabilistic models can be combined with an entropy coder for data compression. However, due to the high complexity of learning-based models, their practical application as text compressors has been largely overlooked. To address this issue, our work focuses on a low-complexity design while maintaining compression performance. We introduce a

Chris Lam

Systems thinking provides us with a way to model the algorithmic fairness problem by allowing us to encode prior knowledge and assumptions about where we believe bias might exist in the data generating process. We can then encode these beliefs as a series of causal graphs, enabling us to link AI/ML systems to politics and the law. This allows us to combine t

Sangwon Lee, Michael Pilipchuk, Can Yildirim, Duncan Greeley

At two-thirds the weight of aluminum, magnesium alloys have the potential to significantly reduce the fuel consumption of transportation vehicles. These advancements depend on our ability to optimize the desirable versus undesirable effects of deformation twins: three dimensional (3D) microstructural domains that form under mechanical stresses. Previously on

Yan Luo, Kaicheng Sheng

We consider a nonlinear pendulum whose suspension point undergoes stochastic vibrations in its plane of motion. Stochastic vibrations are constructed by stochastic differential equations with random periodic solutions. Averaging over these stochastic vibrations can be simplified with ergodicity. We give a complete description of the bifurcations of phase por

Ivo Dravins, Marcel Koch, Victoria Griehl, Katharina Kormann

This work focuses on the numerical study of a recently published class of Runge-Kutta methods designed for mixed-precision arithmetic. We employ the methods in solving partial differential equations on modern hardware. In particular we investigate what speedups are achievable by the use of mixed precision and the dependence of the methods algorithmic compati

Pavel Pudlák, Vojtěch Rödl, William J. Wesley

We will prove that $R_k(k+1,k+1)\geq 4 tw_{\lfloor k/4\rfloor -3}(2)$, where $tw$ is the tower function defined by ${tw}_1(x)=x$ and ${tw}_{i+1}(x)=2^{{tw}_i(x)}$. We also give proofs of $R_k(k+1,k+2)\geq 4 tw_{k-7}(2)$, $R_k(k+1,2k+1)\geq 4 tw_{k-3}(2)$, and $R_k(k+2,k+2)\geq 4 tw_{k-4}(2)$.

Zoey Zhiyuan Dong, Shu Yan Lau, Kent Yagi

Heavy neutron stars may contain solid quark cores as motivated by, e.g. the crystalline color superconducting phase, forming elastic hybrid stars (HSs). Many previous studies assumed an elastic core to be unsheared for the background, static and spherically symmetric configuration, and introduced shear deformation only at a perturbative level. This study rel

Raphael Schneider, Daniel Honerkamp, Tim Welschehold, Abhinav Valada

Recent interest in mobile manipulation has resulted in a wide range of new robot designs. A large family of these designs focuses on modular platforms that combine existing mobile bases with static manipulator arms. They combine these modules by mounting the arm in a tabletop configuration. However, the operating workspaces and heights for common mobile mani

Xiaolu Zhu, Tan Peng, Fang Lyu, Wei Cao

In recent years, the interplay between non-Hermiticity and band topology is expected to uncover numerous novel physical phenomena. However, the majority of research has focused on periodic crystalline structures, with comparatively fewer studies exploring quasicrystalline systems. In this paper, we delve into the influence of asymmetric hopping on the topolo

Xuancun Lu, Zhengxian Huang, Xinfeng Li, Chi Zhang

The integration of LLMs into robots has witnessed significant growth, where LLMs can convert instructions into executable robot policies. However, the inherent vulnerability of LLMs to jailbreak attacks brings critical security risks from the digital domain to the physical world. An attacked LLM-based robot could execute harmful policies and cause physical h

Sofiane Bouaziz, Adel Hafiane, Raphael Canals, Rachid Nedjai

Land Surface Temperature (LST) plays a key role in climate monitoring, urban heat assessment, and land-atmosphere interactions. However, current thermal infrared satellite sensors cannot simultaneously achieve high spatial and temporal resolution. Spatio-temporal fusion (STF) techniques address this limitation by combining complementary satellite data, one w

Nikolaos Athanasiou, Grigorios Fournodavlos

We construct local, in spacetime, singular solutions to the Einstein vacuum equations that exhibit Kasner-like behavior in their past boundary. Our result can be viewed as a localization (in space) of the construction in \cite{FL}. We also prove a refined uniqueness statement and give a simple argument that generates general asymptotic data for Kasner-like s

Antonio Lei, Robert Pollack, Naman Pratap

We investigate the $λ$-invariants of Mazur--Tate elements of elliptic curves defined over the field of rational numbers at primes of additive reduction. We explain their growth and how these invariants relate to other better understood invariants depending on the potential reduction type. We give examples and a conjecture for the additive potentially supersi

A. Nonato, G. G. S. Teles, C. C. Silva, R. X. Silva

We present a study of low-concentration Zn (II) impurities in L-Alanine Single Crystal (ZNLA) employing resonant Raman scattering and optical absorption spectroscopy. By analyzing the relative integrated intensity of the selective vibrational modes, we observe a resonant enhancement of Raman-active modes near the UV absorption band, associated with vibration

Rong Yang, Songxiao Li, Lian Hu

In this paper, we investigate the boundedness and compactness of generalized integration operators $T_g^{n,k}$ and $S_g^{n,0}$ from analytic tent spaces $AT_p^\infty(\alpha)$ to $AT_q^\infty(\beta)$ when $0<p,q<\infty,\alpha,\beta>-2$.

Junyu Wang, Zizhen Lin, Tianrui Wang, Meng Ge

In recent speech enhancement (SE) research, transformer and its variants have emerged as the predominant methodologies. However, the quadratic complexity of the self-attention mechanism imposes certain limitations on practical deployment. Mamba, as a novel state-space model (SSM), has gained widespread application in natural language processing and computer

Jafar Khodagholizadeh, Ghadir Jafari, Alireza Allahyari, Ali Vahedi

We investigate a compelling model of a rotating black hole that is deformed by the effects of loop quantum gravity (LQG). We present a simplified metric and explore two distinct geometries: one in which the masses of the black hole and white hole are equal, and another in which they differ. Our analysis yields the radius of the innermost stable circular orbi

Pavan C Shekar, Vivek Kanhangad, Shishir Maheshwari, T Sunil Kumar

Gastrointestinal (GI) bleeding, a critical indicator of digestive system disorders, re quires efficient and accurate detection methods. This paper presents our solution to the Auto-WCEBleedGen Version V1 Challenge, where we achieved the consolation position. We developed a unified YOLOv8-X model for both detection and classification of bleeding regions in Wi

Paulo L. Dattori da Silva, Alexandre Kirilov, Ricardo Paleari da Silva

We investigate the global hypoellipticity and global solvability of systems of left-invariant differential operators on compact Lie groups. Focusing on diagonal systems, we establish necessary and sufficient conditions for these global properties. Specifically, we show that global solvability is characterized by a Diophantine condition on the symbol of the s

Guangdi Zhou, Wei Lv, Heng Wang, Zihao Nie

The discovery of bilayer nickelate superconductors under high pressure has opened a new chapter in high-transition temperature (high-TC) superconductivity. Here, we report ambient-pressure superconductivity onset above the McMillan limit (40 K) in bilayer nickelate epitaxial ultrathin films. Three-unit-cell (3UC) thick La2.85Pr0.15Ni2O7 single-phase-crystall

Ying Wai Lee

The study of Fourier transforms of probability measures on fractal sets plays an important role in recent research. Faster decay rates are known to yield enhanced results in areas such as metric number theory. This paper focuses on self-similar probability measures defined on self-similar sets. Explicit upper bounds are derived for their decay rates, improvi

Ye Shang, Quanjun Zhang, Chunrong Fang, Siqi Gu

Unit testing plays a pivotal role in software development, improving software quality and reliability. However, generating effective test cases manually is time-consuming, prompting interest in unit testing research. Recently, Large Language Models (LLMs) have shown potential in various unit testing tasks, including test generation, assertion generation, and

Tianqi Shen, Shaohua Liu, Jiaqi Feng, Ziye Ma

Gaussian Splatting (GS) has emerged as a crucial technique for representing discrete volumetric radiance fields. It leverages unique parametrization to mitigate computational demands in scene optimization. This work introduces Topology-Aware 3D Gaussian Splatting (Topology-GS), which addresses two key limitations in current approaches: compromised pixel-leve

Ryoya Ando

The class of semi-hereditary rings is an important class of rings in theories that do not assume the Noetherian condition, such as perfectoid ring theory. We prove several results concerning the structure theory of this class, focusing on the relationship between semi-hereditary rings and the flatness of torsion-free modules. We also consider Shimomoto's pro

Zhi-Fu Gao, Biaopeng Li, L. C. Garcia de Andrade

In this paper, we investigate Holst gravity by examining two distinct examples. The first example involves minimal coupling to torsion, while the second explores non-minimal coupling. The motivation for the first example stems from the recent work by Dombriz, which utilized a technique of imposing constraint constant coefficients to massive torsion in the mo

Divya Jyoti Bajpai, Manjesh Kumar Hanawal

The large size of DNNs poses a significant challenge for deployment on devices with limited resources, such as mobile, edge, and IoT platforms. To address this issue, a distributed inference framework can be utilized. In this framework, a small-scale DNN (initial layers) is deployed on mobile devices, a larger version on edge devices, and the full DNN on the