March 2025 arXiv papers — page 62

Heng-Tong Ding, Jin-Biao Gu, Arpith Kumar, Sheng-Tai Li

We present a first-principles lattice QCD investigation of second-order fluctuations of and correlations among conserved charges -- baryon number (B), electric charge (Q), and strangeness (S) -- in the presence of external magnetic fields. Our study employs lattice simulations of (2+1)-flavor QCD with physical pion masses using highly improved staggered ferm

Mahwish Sarwar, Renata Ratajczak, Cyprian Mieszczynski, Sylwia Gierałtowska

Radiation-induced crystal lattice damage and its recovery in wide bandgap oxides, in particular beta-gallium oxide (beta-Ga2O3), is a complex process. This paper presents the first study on the process of the defects accumulation in beta-Ga2O3 implanted with Rare Earth (RE) ions and the impact of Rapid Thermal Annealing (RTA) on the defects formed. (-201) or

Steffen Seligmann, Martin Holthaus

We investigate numerically computed Floquet states of a Bose-Hubbard dimer which is subjected to strong, time-periodic forcing with respect to their coherence, invoking a measure for their degree of simplicity previously suggested by Leggett. This serves to ascertain the validity of the mean-field approximation under conditions such that the time-dependent n

Zhaoyu Zhang, Xiaoyu Chen, Yuqiong Du, Luyi Zheng

One-dimensional (1-D) Golay complementary sets(GCSs) possess numerous well-known properties and have achieved extensive use in communication engineering. The concept of 1-D GCSs can be extended to two-dimensional (2-D) Golay complementary array sets(GCASs). The letter proposes two constructions of 2-D GCASs based on two-dimensional extended generalized Boole

Sixian Ding, Xu Jiang, Zhongjing Du, Jiaqi Cui

Semi-supervised deep facial expression recognition (SS-DFER) has gained increasingly research interest due to the difficulty in accessing sufficient labeled data in practical settings. However, existing SS-DFER methods mainly utilize generated semantic-level pseudo-labels for supervised learning, the unreliability of which compromises their performance and u

Tadeusz Dziarmaga, Marcin Kądziołka, Artur Kasymov, Marcin Mazur

Deep generative models (DGMs) have caused a paradigm shift in the field of machine learning, yielding noteworthy advancements in domains such as image synthesis, natural language processing, and other related areas. However, a comprehensive evaluation of these models that accounts for the trichotomy between fidelity, diversity, and novelty in generated sampl

Lingting Zhu, Jingrui Ye, Runze Zhang, Zeyu Hu

Current methods for 3D generation still fall short in physically based rendering (PBR) texturing, primarily due to limited data and challenges in modeling multi-channel materials. In this work, we propose MuMA, a method for 3D PBR texturing through Multi-channel Multi-view generation and Agentic post-processing. Our approach features two key innovations: 1)

Jiahui Xiang, Tong Ye, Peiyu Liu, Yinan Zhang

Modelica is a widely adopted language for simulating complex physical systems, yet effective model creation and optimization require substantial domain expertise. Although large language models (LLMs) have demonstrated promising capabilities in code generation, their application to modeling remains largely unexplored. To address this gap, we have developed b

Haoyu Chen, Yunqiao Yang, Nan Zhong, Kede Ma

Hiding data using neural networks (i.e., neural steganography) has achieved remarkable success across both discriminative classifiers and generative adversarial networks. However, the potential of data hiding in diffusion models remains relatively unexplored. Current methods exhibit limitations in achieving high extraction accuracy, model fidelity, and hidin

Ngoc Luyen Le, Marie-Hélène Abel

Recent advancements in language models and pre-trained language models like BERT and RoBERTa have revolutionized natural language processing, enabling a deeper understanding of human-like language. In this paper, we explore enhancing recommender systems using textual embeddings from pre-trained language models to address the limitations of traditional recomm

Luchao Wang, Qian Ren, Kaimin Liao, Hua Wang

3D Gaussian Splatting (3DGS) reconstructions are plagued by stubborn ``floater" artifacts that degrade their geometric and visual fidelity. We are the first to reveal the root cause: a fundamental conflict in the 3DGS optimization process where the opacity gradients of floaters vanish when their blended color reaches a pseudo-equilibrium of canceling errors

Priya R. Baral, Nguyen Duy Khanh, Masaki Gen, Hajime Sagayama

The two-dimensional triangular lattice (TAL) is a model system of magnetic frustration and competing interactions, where skyrmion spin vortices can be induced by a vertical magnetic field $B$. We target the binary compound GdGa$_2$ with an undistorted TAL of Gd$^{3+}$ Heisenberg moments. At higher temperature ($T > 5$ K, $B = 0$, phase II), we reveal the cyc

Carlos E S Bernardes, Ricardo G Simões, M Soledade, C S Santos

In this work insights into the structural rearrangements occurring in aqueous solution, prior to the nucleation of different 4'-hydroxyacetophenone (HAP) forms from water were obtained, through a combination of thermomicroscopy, micro-differential scanning calorimetry, density and speed of sound measurements, and molecular dynamics simulations. The results c

Kechi Zhang, Huangzhao Zhang, Ge Li, Jinliang You

Recent advances in code generation models have demonstrated impressive capabilities in automating software development tasks, yet these models still struggle in real-world software engineering scenarios. Although current training methods, particularly post-training, excel at solving competitive programming problems, they fail to adequately prepare models for

Yunhong Lu, Qichao Wang, Hengyuan Cao, Xierui Wang

Without using explicit reward, direct preference optimization (DPO) employs paired human preference data to fine-tune generative models, a method that has garnered considerable attention in large language models (LLMs). However, exploration of aligning text-to-image (T2I) diffusion models with human preferences remains limited. In comparison to supervised fi

Benoît Collins, Manasa Nagatsu

We introduce and study the Weingarten calculus for centered random permutation matrices in the symmetric group S_N. After presenting a formulation of the Weingarten calculus on the symmetric group, we derive a formula in the centered case, as well as a sign-respecting formula. Our investigations uncover the fact that a building block of this Weingarten calcu

Erwann Delay

Let (M, g) be a compact Einstein Riemannian manifold with boundary. We show that under certain conditions, the map that associates to a metric on M its Ricci curvature, its induced conformal class on the boundary, and its mean curvature on the boundary is locally invertible near g. The contravariant Ricci operator, as well as other operators such as the Eins

M-A Torres-Arredondo, Julián Sierra-Pérez, Guénaël Cabanes

The global trends in the construction of modern structures require the integration of sensors together with data recording and analysis modules so that their integrity can be continuously monitored for safe-life, economic and ecological reasons. This process of measuring and analyzing the data from a distributed sensor network all over a structural system in

Christophe Profeta

Let X be a critical branching L{\'e}vy process whose offspring distribution is in the domain of attraction of a stable random variable. We study the tail probability of the maximum location ever reached by a particle in two different situations: first when the underlying L{\'e}vy process L admits moments of order at least two and is not centered, and then wh

Diego Chamorro, Maxence Mansais

In this article we study mild solutions for the forced, incompressible fractional Navier-Stokes equations. These solutions are classically obtained via a fixed-point argument which relies on suitable estimates for the initial data, the nonlinearity and the external forces. Many functional spaces can be considered, however we are mainly interested here in a c

Oscar Kivinen, Alexei Oblomkov, Dimitri Wyss

We study invariants of a plane cuve singularity $(f,0)$ coming from motivic integration on symmetric powers of a formal deformation of $f$. We show that a natural discriminant integral recovers the motivic classes of the principal Hilbert schemes of points on $f$, while the orbifold integral gives the plethystic exponential of the motivic Igusa zeta function

Frédéric Chapoton

Some Dirichlet-like functions, attached to a pair (periodic function, polynomial) are introduced and studied. These functions generalize the standard Dirichlet L-functions of Dirichlet characters. They have similar properties, being holomorphic on thefull complex plane and having simple values on negative integers.

M. Bouchenaki, L. I. Karaouzène, B. N. Brahmi, M. Kaid Slimane

The structural, elastic, thermodynamic and thermoelectric characteristics of the $\textrm{CeIr}_\textrm{4} \textrm{P}_\textrm{12}$ skutterudite have been predicted for the first time by applying density functional theory and the semi-classical Boltzmann simulations. Firstly, the structural-magnetic stability was verified through ground-state energy calculati

Jinho Jeong, Sangmin Han, Jinwoo Kim, Seon Joo Kim

In this paper, we propose LSRNA, a novel framework for higher-resolution (exceeding 1K) image generation using diffusion models by leveraging super-resolution directly in the latent space. Existing diffusion models struggle with scaling beyond their training resolutions, often leading to structural distortions or content repetition. Reference-based methods a

Chenfei Liao, Kaiyu Lei, Xu Zheng, Junha Moon

Multi-modal semantic segmentation (MMSS) addresses the limitations of single-modality data by integrating complementary information across modalities. Despite notable progress, a significant gap persists between research and real-world deployment due to variability and uncertainty in multi-modal data quality. Robustness has thus become essential for practica

Vishnudatta Thota, Swati Priya, Twinkle Tripathy

The paper focuses on the phenomenon of asymmetric polarization arising in the presence of a dominant group in the network. The existing works in the literature analyze polarization primarily in structurally and quasi-structurally balanced networks. In this work, we introduce generalized quasi-structurally balanced (GQSB) networks, which include both of these

Yijie Huang, Kaixin Yan, Qinyi Zhang

This paper studies a type of consumption preference where some adjustment costs are incured whenever the past spending maximum and the past spending minimum records are updated. This preference can capture the adverse effects of the historical consumption high and low values on the agent's consumption performance, thereby matching with some empirically obser

M. V. Tokarchuk, B. M. Markovych, O. S. Zakharyash, O. L. Ivankiv

The main mechanisms and conditions of interaction of lava-like fuel-containing materials (LFCM) with the atmosphere, water and aqueous solutions are presented. The mechanisms of destruction of the LFCM surface, including ion-exchange processes, hydrolysis, dissolution, oxidation, etc., were analyzed. Inhomogeneous diffusion coefficients for UO$_{2}^{2+}$, Cs

Florian Honz, Bernhard Schrenk

We demonstrate robust light coupling between two single-mode fibers for an out-door FSO link through a focal plane array beamformer with 61 fine-pitched fiber cores as antenna elements. We show that favourable coupling conditions are established for this 10Gb/s Fi-Wi-Fi bridge after rough initial pointing.

Thiago Carvalho Corso

In this paper, we show that the ground-state of many-body Schr\"odinger operators for electrons in one dimension is non-degenerate. More precisely, we consider Schr\"odinger operators of the form $H_N(v,w) = -\Delta + \sum_{i\neq j}^N w(x_i,x_j) + \sum_{j=1}^N v(x_i)$ acting on $\wedge^N \mathrm{L}^2([0,1])$, where the external and interaction potentials $v$

Zhaoyang Shan, Yangjie Jiao, Jiayu Guo, Yifan Wang

Low-dimensional spin fluctuations play a crucial role in unconventional superconductors, with quasi-one-dimensional spin excitations potentially linked with spin-triplet superconductivity. The heavy fermion superconductor CeSb$_2$ exhibits an unusual large inverted S-shaped upper critical field that suggests a possible triplet pairing state within its pressu

Guosheng Zhao, Xiaofeng Wang, Chaojun Ni, Zheng Zhu

Combining reconstruction models with generative models has emerged as a promising paradigm for closed-loop simulation in autonomous driving. For example, ReconDreamer has demonstrated remarkable success in rendering large-scale maneuvers. However, a significant gap remains between the generated data and real-world sensor observations, particularly in terms o

Hua Liu, Jiaqi Men, Shouxia Wang, Jinhong You

To address the challenge of utilizing patient data from other organ transplant centers (source cohorts) to improve survival time estimation and inference for a target center (target cohort) with limited samples and strict data-sharing privacy constraints, we propose the Similarity-Informed Transfer Learning (SITL) method. This approach estimates multivariate

Wen Bai, Yi Wong, Xiao Qiao, Chin Pang Ho

Federated learning (FL) aims to train machine learning (ML) models collaboratively using decentralized data, bypassing the need for centralized data aggregation. Standard FL models often assume that all data come from the same unknown distribution. However, in practical situations, decentralized data frequently exhibit heterogeneity. We propose a novel FL mo

Junteng Liu, Weihao Zeng, Xiwen Zhang, Yijun Wang

Chart understanding requires models to effectively analyze and reason about numerical data, textual elements, and complex visual components. Our observations reveal that the perception capabilities of existing large vision-language models (LVLMs) constitute a critical bottleneck in this process. In this study, we delve into this perception bottleneck by deco

Zhaoqing Zhu, Chuwei Luo, Zirui Shao, Feiyu Gao

Recent methods that integrate spatial layouts with text for document understanding in large language models (LLMs) have shown promising results. A commonly used method is to represent layout information as text tokens and interleave them with text content as inputs to the LLMs. However, such a method still demonstrates limitations, as it requires additional

Saman Hosseini, Lee W. Cohnstaedt, Matin Marjani, Caterina Scoglio

This paper presents a novel probabilistic approach for assessing the risk of West Nile Disease (WND) spillover to the human population. The assessment has been conducted under two different scenarios: (1) assessment of the onset of spillover, and (2) assessment of the severity of the epidemic after the onset of the disease. A compartmental model of different

Junsong Li, Jie Zhou, Yutao Yang, Bihao Zhan

Automatic math correction aims to check students' solutions to mathematical problems via artificial intelligence technologies. Most existing studies focus on judging the final answer at the problem level, while they ignore detailed feedback on each step in a math problem-solving process, which requires abilities of semantic understanding and reasoning. In th

Alexander Yu. Vlasov

Currently, generalizations of quantum communication protocols from qubits to systems with higher-dimensional state spaces (qudits) typically use mutually unbiased bases (MUB). The construction with maximal number of MUB is known in any dimension equal to a prime power and at least two such bases exist in any dimension. However, in small dimensions, there als

Zhichao Sun, Huazhang Hu, Yidong Ma, Gang Liu

With the exponential growth of data, traditional object detection methods are increasingly struggling to handle vast vocabulary object detection tasks effectively. We analyze two key limitations of classification-based detectors: positive gradient dilution, where rare positive categories receive insufficient learning signals, and hard negative gradient dilut

Dingcheng Zhen, Shunshun Yin, Shiyang Qin, Hou Yi

In this work, we introduce the first autoregressive framework for real-time, audio-driven portrait animation, a.k.a, talking head. Beyond the challenge of lengthy animation times, a critical challenge in realistic talking head generation lies in preserving the natural movement of diverse body parts. To this end, we propose Teller, the first streaming audio-d

Changho Keem

We denote by $\mathcal{H}_{d,g,r}$ the Hilbert scheme of smooth curves, which is the union of components whose general point corresponds to a smooth irreducible and non-degenerate curve of degree $d$ and genus $g$ in $\mathbb{P}^r$. In this article, we study $\mathcal{H}_{16,g,5}$ for almost every possible genus $g$ and chasing after its irreducibility. We a

Yingchen Song, Yaobin Wang, Yi Luo, Huan Wu

Coordinating the design of sampling and sparse-dense matrix multiplication (SpMM) is crucial for accelerating graph neural networks (GNNs). However, due to irrational sampling strategies, existing methods face a trade-off between accuracy and speed. Moreover, as computational optimizations progress, data loading has gradually become the primary bottleneck in

Yu-An Liu, Haya Nachimovsky, Ruqing Zhang, Oren Kurland

With the advancement of information retrieval (IR) technologies, robustness is increasingly attracting attention. When deploying technology into practice, we consider not only its average performance under normal conditions but, more importantly, its ability to maintain functionality across a variety of exceptional situations. In recent years, the research o

Itai Boneh, Shay Golan, Shay Mozes, Daniel Prigan

We show how to preprocess a weighted undirected $n$-vertex planar graph in $\tilde O(n^{4/3})$ time, such that the distance between any pair of vertices can then be reported in $\tilde O(1)$ time. This improves the previous $\tilde O(n^{3/2})$ preprocessing time [JACM'23]. Our main technical contribution is a near optimal construction of \emph{additively wei

Abdulrezzak Zekiye, Ouns Bouachir, Öznur Özkasap, Moayad Aloqaily

Energy is a fundamental component of modern life, driving nearly all aspects of daily activities. As such, the inability to access energy when needed is a significant issue that requires innovative solutions. In this paper, we propose ED-DAO, a novel fully transparent and community-driven decentralized autonomous organization (DAO) designed to facilitate ene

P. V. Golovko, D. O. Ignatyeva, S. Xia, P. E. Zimnyakova

We present novel type of tunable magneto-optical metasurfaces performing Faraday rotation, the sign and value of which are not fixed after the structure fabrication but can be tuned in a wide range via heating of the metasurface. We demonstrate both experimentally and theoretically that the Faraday rotation angle is enhanced in the vicinity of the magnetodip

Handong Li, Yiyuan Zhang, Longteng Guo, Xiangyu Yue

Most Video-Large Language Models (Video-LLMs) adopt an encoder-decoder framework, where a vision encoder extracts frame-wise features for processing by a language model. However, this approach incurs high computational costs, introduces resolution biases, and struggles to capture fine-grained multimodal interactions. To overcome these limitations, we propose

Qiang Hu, Zihan Zheng, Houqiang Zhong, Sihua Fu

3D Gaussian Splatting (3DGS) has substantial potential for enabling photorealistic Free-Viewpoint Video (FVV) experiences. However, the vast number of Gaussians and their associated attributes poses significant challenges for storage and transmission. Existing methods typically handle dynamic 3DGS representation and compression separately, neglecting motion

Dian Zheng, Cheng Zhang, Xiao-Ming Wu, Cao Li

Generating 360-degree panoramas from narrow field of view (NFoV) image is a promising computer vision task for Virtual Reality (VR) applications. Existing methods mostly assess the generated panoramas with InceptionNet or CLIP based metrics, which tend to perceive the image quality and is \textbf{not suitable for evaluating the distortion}. In this work, we

Bhada Yun, Dana Feng, Ace S. Chen, Afshin Nikzad

Our study of 20 knowledge workers revealed a common challenge: the difficulty of synthesizing unstructured information scattered across multiple platforms to make informed decisions. Drawing on their vision of an ideal knowledge synthesis tool, we developed Yodeai, an AI-enabled system, to explore both the opportunities and limitations of AI in knowledge wor

Baran Düzgün, Ago-Erik Riet, Vladislav Taranchuk

In 1979, Erd\H{o}s conjectured that if $m = O(n^{2/3})$, then $ex(n, m, \{C_4, C_6 \}) = O(n)$. This conjecture was disproven by several papers and the current best-known bounds for this problem are $$ c_1n^{1 + \frac{1}{15}} \leq ex(n, n^{2/3}, \{C_4, C_6\}) \leq c_2n^{1 + 1/9} $$ for some constants $c_1, c_2$. A consequence of our work here proves that $$

Junseok Lee, Kai Murai, Fuminobu Takahashi, Wen Yin

Recently, results from the Atacama Cosmology Telescope (ACT) DR6 have shown a preference for isotropic cosmic birefringence, consistent with previous analyses based on Planck and WMAP data. Separately, the Dark Energy Spectroscopic Instrument (DESI) DR2 results suggest that dark energy evolves over cosmic history, pointing to new physics in the late-time uni

N. A. Pozdnyakov

Additional elementary species and primordial black holes are common candidates for dark matter. Their co-existence in the early Universe leads to accretion if particles are heavy. We solve equation of motion affected by expansion which enhances black hole growth rates. They depend upon particles freeze-out time rather than their mass. Taking into account fri

Viktória Klász, René Marczinzik, Anton Mellit, Martin Rubey

We study the global dimension of Nakayama algebras. In the case of linear Nakayama algebras, which are in canonical bijection to Dyck paths, we show that the global dimension has the same distribution as the height of Dyck paths. For cyclic Nakayama algebras an explicit classification of finite global dimension is not known. However, we show that in certain

Yuchuan Tian, Hanting Chen, Mengyu Zheng, Yuchen Liang

Representation Alignment (REPA) that aligns Diffusion Transformer (DiT) hidden-states with ViT visual encoders has proven highly effective in DiT training, demonstrating superior convergence properties, but it has not been validated on the canonical diffusion U-Net architecture that shows faster convergence compared to DiTs. However, adapting REPA to U-Net a

Yuzhou Han, Hao Zhang, Lixin He

Rydberg atom arrays have emerged as a powerful platform for experimental research and a challenging subject for theoretical investigation in quantum science. In this study, we investigate the finite-temperature properties of two-dimensional square-lattice Rydberg atom arrays using the projected entangled pair states (PEPS) method. By analyzing the thermal be

Liang-Biao Chen, Peng-Fei Yao

We study exponents of thickness in Frieseck-James-M\"uller's inequalities for shells. We derive the following results: (a) the exponent of thickness $\mu(S)\leq15/8$ if the middle surface $S$ is parabolic; (b) the exponent of thickness $\mu(S)\leq11/6$ if the middle surface $S$ is a minimal surface with negative curvature; (c) the exponent of thickness $\mu(

Qing Guo, Angela Pistoia, Shixin Wen

In this paper we study the existence of solutions to nonlinear Schr\"odinger systems with mixed couplings of attractive and repulsive forces, which arise from the models in Bose-Einstein condensates and nonlinear optics. In particular, we build solutions whose first component has one bump and the other components have several peaks forming a regular polygon

Masaki Kashima

A 2-factor of a graph is a 2-regular spanning subgraph. For a graph $G$ and an independent set $I$ of $G$, let $\delta_G(I)$ denote the minimum degree of vertices contained in $I$. We show that (1) if every independent set $I$ of $G$ satisfies $|I|\leq \delta_G(I)-1$, then $G$ has a 2-factor and that (2) if every independent set $I$ of $G$ satisfies $|I|\leq

Qi Tao, Yin Jinhua, Cai Dongqi, Xie Yueqi

In light of scaling laws, many AI institutions are intensifying efforts to construct advanced AIs on extensive collections of high-quality human data. However, in a rush to stay competitive, some institutions may inadvertently or even deliberately include unauthorized data (like privacy- or intellectual property-sensitive content) for AI training, which infr

Jiacheng Wu, Ruiqi Zhang, Jie Chen, Hui Zhang

Efficiently modeling relightable human avatars from sparse-view videos is crucial for AR/VR applications. Current methods use neural implicit representations to capture dynamic geometry and reflectance, which incur high costs due to the need for dense sampling in volume rendering. To overcome these challenges, we introduce Physically-based Neural Explicit Su

Wencheng Zhu, Yuexin Wang, Hongxuan Li, Pengfei Zhu

Vision-language models bridge visual and linguistic understanding and have proven to be powerful for video recognition tasks. Existing approaches primarily rely on parameter-efficient fine-tuning of image-text pre-trained models, yet they often suffer from limited interpretability and poor generalization due to inadequate temporal modeling. To address these,

Sherry X. Chen, Misha Sra, Pradeep Sen

Although natural language instructions offer an intuitive way to guide automated image editing, deep-learning models often struggle to achieve high-quality results, largely due to the difficulty of creating large, high-quality training datasets. To do this, previous approaches have typically relied on text-to-image (T2I) generative models to produce pairs of

Xu Fan, Yuetan Lin, Bing Gong, Hao Li

Air pollution has become a major threat to human health, making accurate forecasting crucial for pollution control. Traditional physics-based models forecast global air pollution by coupling meteorology and pollution processes, using either online or offline methods depending on whether fully integrated with meteorological models and run simultaneously. Howe

Ruslan A. Abramchuk

In this paper the non-perturbative suppression of the Chiral Magnetic Effect (CME) is investigated in the deconfined region of the QCD phase diagram (as a model for Quark-Gluon Plasma (QGP) emerging in Heavy Ion Collisions (HIC)), using the Kubo formula to calculate a linear response to the chiral imbalance, and the Field Correlator Method to address the str

Xusheng Cao, Haori Lu, Linlan Huang, Fei Yang

Continual learning in computer vision faces the critical challenge of catastrophic forgetting, where models struggle to retain prior knowledge while adapting to new tasks. Although recent studies have attempted to leverage the generalization capabilities of pre-trained models to mitigate overfitting on current tasks, models still tend to forget details of pr

Youyu Chen, Junjun Jiang, Kui Jiang, Xiao Tang

3D Gaussian Splatting (3DGS) renders pixels by rasterizing Gaussian primitives, where the rendering resolution and the primitive number, concluded as the optimization complexity, dominate the time cost in primitive optimization. In this paper, we propose DashGaussian, a scheduling scheme over the optimization complexity of 3DGS that strips redundant complexi

Vinesh Vijayan, Karpagavalli K, Sandhiya Jenifer J, Prakash R

This letter seeks to illuminate the profound connection between complexity, self-organization, emergent behaviour, pattern formation, and entropy concepts that are foundational to understanding our universe. By examining these ideas through the lenses of physics, information theory, and nonlinear dynamics, we uncover a fascinating narrative. Starting with a

Teppei Ogihara, Futo Ueno

This paper introduces a quasi-likelihood ratio testing procedure for diffusion processes observed under nonsynchronous sampling schemes. High-frequency data, particularly in financial econometrics, are often recorded at irregular time points, challenging conventional synchronous methods for parameter estimation and hypothesis testing. To address these challe

D. I. Palade, L. M. Pomarjanschi

We introduce the Turbulent Transport in Tokamaks via Stochastic Trajectories (T3ST) code, designed to address the problem of turbulent transport using a statistical approach complementary to gyrokinetics. The code employs test-particle methods to track the dynamics of charged particles in axisymmetric magnetic equilibria, accounting for both turbulence and C

Jinhua Zhao, Xinguo Yu, Rui Ding, Cuiling Gu

Global cooperation often falters despite shared objectives, as misaligned interests and unequal incentives undermine collective efforts, such as those in international climate change collaborations. To tackle this issue, this paper introduces a multi-level game-theoretic model to analyze the dynamics of complex interactions within hierarchical systems. The m

Sebastiano Vigna

We describe a simple and yet very scalable implementation of static functions (VFunc) and of static filters (VFilter) based on hypergraphs. We introduce the idea of {\epsilon}-cost sharding, which allows us to build structures that can manage trillions of keys, at the same time increasing memory locality in hypergraph-based constructions. Contrarily to the c

Xiao Wang, Xiaodong He, Jianda Wu

We study many-body quantum geometric effects in time-dependent system with emergent quantum integrable field theory instantaneously. We establish a theorem stating that the Berry connection matrix thus all associated geometric quantities of the system can be precisely characterized by excitations up to two particles from the initial quantum integrable system

Rong Chen, Shuzhi Cao, Ailong He, Shuguang Han

The two primary tasks in the search recommendation system are search relevance matching and click-through rate (CTR) prediction -- the former focuses on seeking relevant items for user queries whereas the latter forecasts which item may better match user interest. Prior research typically develops two models to predict the CTR and search relevance separately

Kun Li, Xinwei Chen, Tianyou Song, Chengrui Zhou

In recent years, large language models (LLMs) have shown an impressive ability to perform arithmetic and symbolic reasoning tasks. However, we found that LLMs (e.g., ChatGPT) cannot perform well on reasoning that requires multiple rounds of dialogue, especially when solving situation puzzles. Specifically, LLMs intend to ask very detailed questions focusing

Xinhua Xu, Hong Liu, Jianbing Wu, Jinfu Liu

The integration of RGB and depth modalities significantly enhances the accuracy of segmenting complex indoor scenes, with depth data from RGB-D cameras playing a crucial role in this improvement. However, collecting an RGB-D dataset is more expensive than an RGB dataset due to the need for specialized depth sensors. Aligning depth and RGB images also poses c

Songchen Liu, Liyou Zhang

We first establish the weak stability results for solutions of complex Monge-Amp\`ere equations in relative full mass classes, extending the results known to hold in the full mass class. Building on weak stability, we then prove the $\mathcal{C}^{k,\alpha}$ stability of solutions to complex Monge-Amp\`ere equations on quasi-projective varieties. As an applic

Siddharth Chandak, Shaan Ul Haque, Nicholas Bambos

Two-time-scale Stochastic Approximation (SA) is an iterative algorithm with applications in reinforcement learning and optimization. Prior finite time analysis of such algorithms has focused on fixed point iterations with mappings contractive under Euclidean norm. Motivated by applications in reinforcement learning, we give the first mean square bound on non

A. I. Milstein, I. S. Terekhov

The interaction model of two electrons in the edge states of a two-dimensional topological insulator is investigated. Both solutions of the Schr\"odinger equation and solutions of the Bethe-Salpeter equation at different values of the Fermi energy are considered. It is shown that for the Bethe-Salpeter equation, which takes into account the existence of the

Alba Aguilera, Nardine Osman, Georgina Curto

The global rise in homelessness calls for urgent and alternative policy solutions. Non-profits and governmental organizations alert about the many challenges faced by people experiencing homelessness (PEH), which include not only the lack of shelter but also the lack of opportunities for personal development. In this context, the capability approach (CA), wh

Dongrun Jian, Jie Su, Jun Wang, Jin Wang

Chiral active matter widely exists in nature and emerges rich dynamical behaviors. Among these, chiral active particles (CAPs) with alignment effects show novel collective motions such as orderly rotating droplets and distinct phase transitions under different chirality degrees. However, the underlying dynamical and thermodynamical mechanisms of the phase tr

Joshua Krook

Large Language Model chatbots are increasingly taking the form and visage of human beings, adapting human faces, names, voices, personalities, and quirks, including those of celebrities and well-known political figures. Personifying AI chatbots could foreseeably increase their trust with users. However, it could also make them more capable of manipulation, b

Sicong Feng, Jielong Yang, Li Peng

Recent advances in diffusion models bring new vitality to visual content creation. However, current text-to-video generation models still face significant challenges such as high training costs, substantial data requirements, and difficulties in maintaining consistency between given text and motion of the foreground object. To address these challenges, we pr

Xudong Mou, Rui Wang, Bo Li, Tianyu Wo

The accumulation of time-series signals and the absence of labels make time-series Anomaly Detection (AD) a self-supervised task of deep learning. Methods based on normality assumptions face the following three limitations: (1) A single assumption could hardly characterize the whole normality or lead to some deviation. (2) Some assumptions may go against the

Yuan Gao, Shaobo Xia, Pu Wang, Xiaohuan Xi

Light detection and ranging (LiDAR) remote sensing encompasses two major directions: data interpretation and parameter inversion. However, both directions rely heavily on costly and labor-intensive labeled data and field measurements, which constrains their scalability and spatiotemporal adaptability. Weakly Supervised Learning (WSL) provides a unified frame

Mengzhou Sun

We investigate the logical strength of the cohesiveness principle when restricted to finite sequences of sets, denoted by fin-COH, over different base theories. Our main result shows that fin-COH entails $I\Sigma_1^0$ over the weaker base theory $RCA_0^*$, thereby answering a question posed by Fiori-Carones, Ko{\l}odziejczyk and Kowalik. In addition, we show

Hongen Liu, Cheng Cui, Yuning Du, Yi Liu

Formula recognition is an important task in document intelligence. It involves converting mathematical expressions from document images into structured symbolic formats that computers can easily work with. LaTeX is the most common format used for this purpose. In this work, we present PP-FormulaNet, a state-of-the-art formula recognition model that excels in

Gulnara Suliyeva, Kuantay Boshkayev, Talgar Konysbayev, Yergali Kurmanov

In this work, we explore general relativistic effects and geometric properties of the Fan-Wang spacetime, one of the simplest regular solutions that can be obtained in nonlinear electrodynamics. In particular, we investigate the motion of test particles, the capture cross-section of neutral massive and massless particles, such as neutrinos and photons, and t

Seung-Lee Kim, Chung-Uk Lee, Kyeongsoo Hong, Jae Woo Lee

Blue large-amplitude pulsators (BLAPs), a recently classified type of variable stars, are evolved objects likely formed through interactions between stars in a binary system. However, only two BLAPs with stellar companions have been discovered to date. This paper presents photometric data from the Korea Microlensing Telescope Network (KMTNet) for three BLAPs

Tianpei Zhang, Yiming Zhu, Jufeng Zhao, Guangmang Cui

Deep learning techniques have revolutionized the infrared and visible image fusion (IVIF), showing remarkable efficacy on complex scenarios. However, current methods do not fully combine frequency domain features with global semantic information, which will result in suboptimal extraction of global features across modalities and insufficient preservation of

Chang Gao, Kang Zhao, Runqi Wang, Jianfei Chen

Large language models (LLMs) have demonstrated impressive capabilities, but their enormous size poses significant challenges for deployment in real-world applications. To address this issue, researchers have sought to apply network pruning techniques to LLMs. A critical challenge in pruning is allocation the sparsity for each layer. Recent sparsity allocatio

Mehrnoosh Ayazi, Ipsita Sahin, Caio Mucchiani, Elena Kokkoni

To enhance pediatric exosuit design, it is crucial to assess the actuator-generated forces. This work evaluates the contact forces exerted by soft fabric-based pneumatic actuators in an upper extremity pediatric exosuit. Two actuators were examined: a single-cell bidirectional actuator for shoulder abduction/adduction and a bellow-type actuator for elbow ext

Yunhao Quan, Chuang Gao, Nan Cheng, Zhijie Zhang

In Automatic Modulation Classification (AMC), deep learning methods have shown remarkable performance, offering significant advantages over traditional approaches and demonstrating their vast potential. Nevertheless, notable drawbacks, particularly in their high demands for storage, computational resources, and large-scale labeled data, which limit their pra

Hui-Ke Jin, Rong-Yang Sun, Hong-Hao Tu, Yi Zhou

The study of strongly correlated electron systems remains a fundamental challenge in condensed matter physics, particularly in two-dimensional (2D) systems hosting various exotic phases of matter including quantum spin liquids, unconventional superconductivity, and topological orders. Although Density Matrix Renormalization Group (DMRG) has established itsel

Oliver Tronn Scott-Simons, Chris Colman, FrostByte

Decentralised exchanges (DEXs) have transformed trading by enabling trustless, permissionless transactions, yet they face significant challenges such as impermanent loss and slippage, which undermine profitability for liquidity providers and traders. In this paper, we introduce QubitSwap, an innovative DEX model designed to tackle these issues through a hybr

Vikramjit Mitra, Amrit Romana, Dung T. Tran, Erdrin Azemi

Spontaneous speech emotion data usually contain perceptual grades where graders assign emotion score after listening to the speech files. Such perceptual grades introduce uncertainty in labels due to grader opinion variation. Grader variation is addressed by using consensus grades as groundtruth, where the emotion with the highest vote is selected. Consensus

Prof Dr Ray Wai Man Kong

There is applied research for the development of the Automated Stretch Elastic Waistband Sewing Machine represents a significant advancement in garment manufacturing, addressing the industry's need for increased efficiency, precision, and adaptability. This machine integrates innovative features such as a sensor-based automatic waistband expansion system, sy

Chang-geun Oh, Sun-Woo Kim, Kun Woo Kim, Bartomeu Monserrat

Mass is a defining property of particles, shaping their fundamental nature and interactions. In condensed matter systems, the effective mass of electrons has long been regarded as a key factor influencing material properties, including their transport and optical responses. In this work, we challenge this conventional wisdom by unveiling a mass-invariant uni

Hankyul Kang, Gregor Seifer, Donghyun Lee, Jongbin Ryu

According to the forgetting curve theory, we can enhance memory retention by learning extensive data and taking adequate rest. This means that in order to effectively retain new knowledge, it is essential to learn it thoroughly and ensure sufficient rest so that our brain can memorize without forgetting. The main takeaway from this theory is that learning ex