March 2025 arXiv papers — page 34

Rosalia Moreddu

In the past few decades, the life sciences have experienced an unprecedented accumulation of data, ranging from genomic sequences and proteomic profiles to heavy-content imaging, clinical assays, and commercial biological products for research. Traditional static databases have been invaluable in providing standardized and structured information. However, th

Laure Coutin, Benjamin Massat, Anthony Réveillac

In this article, we fill a gap in the literature regarding quantitative functional central limit theorems (qfCLT) for Hawkes processes by providing an upper bound for the convergence of a nearly unstable Hawkes process toward a Cox-Ingersoll-Ross (CIR) process. Note that in this case no speed of convergence has been established even for one-dimensional margi

Jiaqi Han, Jingwen Ye, Shunyu Liu, Haofei Zhang

The success of large language models has garnered widespread attention for model merging techniques, especially training-free methods which combine model capabilities within the parameter space. However, two challenges remain: (1) uniform treatment of all parameters leads to performance degradation; (2) search-based algorithms are often inefficient. In this

Christopher Stone, Yusuke Koroyasu, Yoichi Ochiai, Akiko Kaneko

Mid-air acoustic streaming, where ultrasound induces steady fluid motion, could significantly affect the perception of haptic sensations, stability of levitation systems, and enable controlled transfer of odours (smells) through air by directing volatile compounds to specific locations. Despite its importance, the streaming behavior in airborne phased-array

S Tiengo, R Eid, T Bourdel

In ultracold atomic gases, radio-frequency coupling between two spin states can lead to atoms being in a stable coherent superposition of the two states (dressed states). When the two-body interactions (scattering lengths) are different between the two states, it offers the possibility not only to control the interatomic interaction but also to modify the eq

Zhaoyi Yan, Kangjun Liu, Qixiang Ye

Knowledge distillation has become a cornerstone technique in deep learning, facilitating the transfer of knowledge from complex models to lightweight counterparts. Traditional distillation approaches focus on transferring knowledge at the instance level, but fail to capture nuanced semantic relationships within the data. In response, this paper introduces a

Ming Yan, Xincheng Lin, Yuhua Luo, Shuqi Fan

Human Motion Recovery (HMR) research mainly focuses on ground-based motions such as running. The study on capturing climbing motion, an off-ground motion, is sparse. This is partly due to the limited availability of climbing motion datasets, especially large-scale and challenging 3D labeled datasets. To address the insufficiency of climbing motion datasets,

Rene Aquarius, Elisabeth M. Bik, David Bimler, Morten P. Oksvold

Introduction: Taylor & Francis journal Bioengineered has been targeted by paper mills. The goal of this study is to identify problematic articles published in Bioengineered during the period 2010 to 2024. Methods: Dimensions was used to search for articles that contained the terms mouse OR mice OR rat OR rats in title or abstract, published in Bioengineered

S. de Regt, I. A. G. Snellen, N. F. Allard, D. González Picos

Brown dwarfs at the L-T transition likely experience an inhomogeneous clearing of the clouds in their atmospheres. The resulting surface of thin and thick cloudy patches has been put forward to explain the observed variability, J-band brightening, and re-emergence of FeH absorption. We study the closest brown dwarf binary, Luhman 16A and B, in an effort to c

Daisuke Nakamura, Taiki Shibata, Kenichi Shimizu

We give a complete list of indecomposable exact module categories over the finite tensor category $\mathrm{Rep}(u_q(\mathfrak{sl}_2))$ of representations of the small quantum group $u_q(\mathfrak{sl}_2)$, where $q$ is a root of unity of odd order. Each of them is given as the category of representations of a left comodule algebra over $u_q(\mathfrak{sl}_2)$

Hamidreza Eivazi, Jendrik-Alexander Tröger, Stefan Wittek, Stefan Hartmann

Multiscale problems are ubiquitous in physics. Numerical simulations of such problems by solving partial differential equations (PDEs) at high resolution are computationally too expensive for many-query scenarios, such as uncertainty quantification, remeshing applications, and topology optimization. This limitation has motivated the development of data-drive

Francesco A. Genco

We present a standard calculus for logical grounding based on well-established grounding principles [Schnieder, 2011, Fine, 2012, Correia, 2014, Correia, 2024] and provide a very direct characterisation of the provable grounding claims exclusively relying on the syntactic tree of the grounded formula. The technical features of the characterisation imply that

Wenxuan Lu, Jiangyang He, Zhanqiu Zhang, Yiwen Guo

Developing agents capable of fluid gameplay in first/third-person games without API access remains a critical challenge in Artificial General Intelligence (AGI). Recent efforts leverage Vision Language Models (VLMs) as direct controllers, frequently pausing the game to analyze screens and plan action through language reasoning. However, this inefficient para

Yunusa Haruna, Adamu Lawan, Shamsuddeen Hassan Muhammad, Jiaquan Zhang

Capturing long-range dependencies (LRD) efficiently is a core challenge in visual recognition, and state-space models (SSMs) have recently emerged as a promising alternative to self-attention for addressing it. However, adapting SSMs into CNN-based bottlenecks remains challenging, as existing approaches require complex pre-processing and multiple SSM replica

Asmaa Abada, Gabriela Barenboim, Toni Bertólez-Martínez, Sandipan Bhattacherjee

This document summarises discussions on future directions in theoretical neutrino physics, which are the outcome of a neutrino theory workshop held at CERN in February 2025. The starting point is the realisation that neutrino physics offers unique opportunities to address some of the most fundamental questions in physics. This motivates a vigorous experiment

Seonggon Kim, Juncheol Shin, Seung-taek Woo, Eunhyeok Park

It has become increasingly important to optimize backpropagation to reduce memory usage and computational overhead. Achieving this goal is highly challenging, as multiple objectives must be considered jointly while maintaining training quality. In this paper, we focus on matrix multiplication, which accounts for the largest portion of training costs, and ana

Zihua Niu, Alice-Agnes Gabriel, Yehuda Ben-Zion

Using a novel high-performance computing implementation of a nonlinear continuum damage breakage model, we explore interactions between 3D co-seismic off-fault damage, seismic radiation, and rupture dynamics. Our simulations demonstrate that off-fault damage enhances high-frequency wave radiation above 1~Hz, reduces rupture speed and alters the total kinetic

Wencheng Han, Dongqian Guo, Xiao Chen, Pang Lyu

Metal artifacts in CT slices have long posed challenges in medical diagnostics. These artifacts degrade image quality, resulting in suboptimal visualization and complicating the accurate interpretation of tissues adjacent to metal implants. To address these issues, we introduce the Latent Gemstone Spectral Imaging (GSI) Alignment Framework, which effectively

Jizhou Han, Chenhao Ding, Yuhang He, Songlin Dong

Few-shot class-incremental Learning (FSCIL) enables models to learn new classes from limited data while retaining performance on previously learned classes. Traditional FSCIL methods often require fine-tuning parameters with limited new class data and suffer from a separation between learning new classes and utilizing old knowledge. Inspired by the analogica

Yongxu Wang, Weiyun Yi, Xinhao Kong, Wanting Li

With the rapid development of embodied intelligence, leveraging large-scale human data for high-level imitation learning on humanoid robots has become a focal point of interest in both academia and industry. However, applying humanoid robots to precision operation domains remains challenging due to the complexities they face in perception and control process

Patrick Ling

Although the valuation of life contingent assets has been thoroughly investigated under the framework of mathematical statistics, little financial economics research pays attention to the pricing of these assets in a non-arbitrage, complete market. In this paper, we first revisit the Fundamental Theorem of Asset Pricing (FTAP) and the short proof of it. Then

Cong Li, Bing Dong

In this paper, we derive the equations of motion for a system composed of a spinless quantum dot coupled to two normal leads and two Majorana bound states (MBSs), utilizing the auxiliary-mode expansion method and the nonequilibrium Green function technique. Subsequently, we use these equations to analyze the linear conductance, adiabatic linear capacitance,

Zhaokai Wang, Chenxi Bao, Le Zhuo, Jingrui Han

Vision-to-music Generation, including video-to-music and image-to-music tasks, is a significant branch of multimodal artificial intelligence demonstrating vast application prospects in fields such as film scoring, short video creation, and dance music synthesis. However, compared to the rapid development of modalities like text and images, research in vision

Changqing Li, Ran Xia, Yutai Zhao, Yifang Li

Mamyshev oscillators (MOs), a novel class of passively mode-locked fiber lasers, serve as an excellent platform to explore complex nonlinear dynamics, ranging from localized structures to chaos. Despite their versatility, achieving self-starting mode-locking remains a significant challenge. In this study, we unveil the critical role of the dissipative Farada

Benedikt Klein, Mario Ohlberger

This article builds on the recently proposed RB-ML-ROM approach for parameterized parabolic PDEs and proposes a novel hierarchical Trust Region algorithm for solving parabolic PDE constrained optimization problems. Instead of using a traditional offline/online splitting approach for model order reduction, we adopt an active learning or enrichment strategy to

Qingdi Yu, Zhiwei Cao, Ruihang Wang, Zhen Yang

Time series forecasting is crucial for applications like resource scheduling and risk management, where multi-step predictions provide a comprehensive view of future trends. Uncertainty Quantification (UQ) is a mainstream approach for addressing forecasting uncertainties, with Conformal Prediction (CP) gaining attention due to its model-agnostic nature and s

Mohamed Lamine Mekhalfi, Paul Chippendale, Francisco Fraile, Marcos Rico

Orange grading is a crucial step in the fruit industry, as it helps to sort oranges according to different criteria such as size, quality, ripeness, and health condition, ensuring safety for human consumption and better price allocation and client satisfaction. Automated grading enables faster processing, precision, and reduced human labor. In this paper, we

Yufei Bo, Meixia Tao

This paper investigates distributed joint source-channel coding (JSCC) for correlated image semantic transmission over wireless channels. In this setup, correlated images at different transmitters are separately encoded and transmitted through dedicated channels for joint recovery at the receiver. We propose a novel distributed nonlinear transform source-cha

Yujie Liu, Zonglin Yang, Tong Xie, Jinjie Ni

Large language models (LLMs) have shown potential in assisting scientific research, yet their ability to discover high-quality research hypotheses remains unexamined due to the lack of a dedicated benchmark. To address this gap, we introduce the first large-scale benchmark for evaluating LLMs on a sufficient set of scientific discovery sub-tasks-inspiration

Yi C. Huang, Ryunosuke Kusaba, Tohru Ozawa

We consider the commutation relations between the Gauss--Weierstrass semigroup on $\mathbb{R}^{n}$, generated by convolution with the complex Gaussian kernel, and monomial weights. We provide an explicit representation and a sharper estimate of the commutation relations with more concise proofs than those in previous works.

Haoyu Zhao, Zhongang Qi, Cong Wang, Qingping Zheng

With diffusion transformer (DiT) excelling in video generation, its use in specific tasks has drawn increasing attention. However, adapting DiT for pose-guided human image animation faces two core challenges: (a) existing U-Net-based pose control methods may be suboptimal for the DiT backbone; and (b) removing text guidance, as in previous approaches, often

Hardy Chan, Xavier Fernández-Real, Alessio Figalli, Joaquim Serra

A long-standing conjecture of De Giorgi asserts that every monotone solution of the Allen--Cahn equation in \(\mathbb{R}^{n+1}\) is one-dimensional if \(n \leq 7\). A stronger version of the conjecture, also widely studied and often called ``the stable De Giorgi conjecture'', proposes that every stable solution in \(\mathbb{R}^n\) must be one-dimensional for

Mario García-Márquez, Nuria Rodríguez-Barroso, M. Victoria Luzón, Francisco Herrera

The rapid development of artificial intelligence systems has amplified societal concerns regarding their usage, necessitating regulatory frameworks that encompass data privacy. Federated Learning (FL) is posed as potential solution to data privacy challenges in distributed machine learning by enabling collaborative model training {without data sharing}. Howe

Vinod Kumar Bhardwaj, Anil Kumar Yadav, Manish Kalra, Pankaj

In this study, we have explored the cosmological dynamics of an isotropic, homogeneous universe in Rastall gravity. For this purpose, we use the parameterization of the EoS parameter in the form $\omega(z) = \frac{\omega_{0}}{(z+1)} $ to derive the explicit solution of the field equations in Rastall gravity. We constrained the cosmological parameters for the

Bashar Tahir, Philipp Svoboda, Markus Rupp

Integrated sensing and communication (ISAC) is envisioned be to one of the paradigms upon which next-generation mobile networks will be built, extending localization and tracking capabilities, as well as giving birth to environment-aware wireless access. A key aspect of sensing integration is parameter estimation, which involves extracting information about

HyeYoung Lee, Pavel Tsoi

Accurate patient mortality prediction enables effective risk stratification, leading to personalized treatment plans and improved patient outcomes. However, predicting mortality in healthcare remains a significant challenge, with existing studies often focusing on specific diseases or limited predictor sets. This study evaluates machine learning models for a

Ningyu He, Shangtong Cao, Haoyu Wang, Yao Guo

As JavaScript has been criticized for performance and security issues in web applications, WebAssembly (Wasm) was proposed in 2017 and is regarded as the complementation for JavaScript. Due to its advantages like compact-size, native-like speed, and portability, Wasm binaries are gradually used as the compilation target for industrial projects in other high-

Bichai Wang, Xiuhong Wei, Xueru Li, Yongxing Zhou

Integrated sensing and communications (ISAC) has been identified as one of the six usage scenarios for IMT-2030. Compared with communication performance, sensing performance is much more vulnerable to interference, and the received backscattered sensing signal with target information is usually too weak to be detected. It is interesting to understand the opt

Zhizi Guan, Zhiwei Peng, David J. Srolovitz, Jacob Khurgin

Symmetry governs the selection rules of light-matter interactions in crystalline materials, making symmetry manipulation a powerful tool for tuning their optical properties. Here, we demonstrate that the hot-electron injection from a plasmonic resonator breaks the centrosymmtry of an adjacent transition metal dichalcogenide bilayer, probed via second-harmoni

Karanbir Singh, William Ngu

Advancements in retrieving accessible information have evolved faster in the last few years compared to the decades since the internet's creation. Search engines, like Google, have been the number one way to find relevant data. They have always relied on the user's abilities to find the best information in its billions of links and sources at everybody's fin

Shuai Li, Jie Zhang, Yuang Qi, Kejiang Chen

Large-scale image retrieval using deep hashing has become increasingly popular due to the exponential growth of image data and the remarkable feature extraction capabilities of deep neural networks (DNNs). However, deep hashing methods are vulnerable to malicious attacks, including adversarial and backdoor attacks. It is worth noting that these attacks typic

Aryan Esmailpour, Sainyam Galhotra, Rahul Raychaudhury, Stavros Sintos

Effective data discovery is a cornerstone of modern data-driven decision-making. Yet, identifying datasets with specific distributional characteristics, such as percentiles or preferences, remains challenging. While recent proposals have enabled users to search based on percentile predicates, much of the research in data discovery relies on heuristics. This

W. C. Wu, H. Y. Dong, K. Wang

This paper focuses on continuous data assimilation (CDA) for the Navier-Stokes equations with nonlinear slip boundary conditions. CDA methods are typically employed to recover the original system when initial data or viscosity coefficients are unknown, by incorporating a feedback control term generated by observational data over a time period. In this study,

Jie-Min Ding, Liu Min, Zihuan Xuan, Mengli Liu

Aims. /e aim of this study was to evaluate the protective effects of Er Miao San (EMS) and the regulative function of bone marrow-derived dendritic cells (BMDCs) on adjuvant arthritis (AA) in rats. Methods. /e ethyl acetate part of EMS (3 g/kg, 1.5 g/kg, and 0.75 g/kg) was orally administered from day 15 after immunization to day 29. /e polyarthritis index a

Ayush Bheemaiah, Seungyong Yang

The inability of autonomous vehicles (AVs) to infer the material properties of obstacles limits their decision-making capacity. While AVs rely on sensor systems such as cameras, LiDAR, and radar to detect obstacles, this study suggests combining sensors with a knowledge graph (KG)-based world model to improve AVs' comprehension of physical material qualities

Qinghai Zhong

For a finite abelian group $G$ and a positive integer $k$, let $\mathsf{D}_k(G)$ denote the smallest integer $\ell$ such that each sequence over $G$ of length at least $\ell$ has $k$ disjoint nontrivial zero-sum subsequences. It is known that $\mathsf D_k(G)=n_1+kn_2-1$ if $G\cong C_{n_1}\oplus C_{n_2}$ is a rank $2$ group, where $1<n_1\t n_2$. We investigat

Hyunjin Shim, Junhyun Baek, Dohyeong Kim, Minjin Kim

We present CO(1-0) observations of 50 star-forming galaxies at 0.01<z<0.35, for which 3.3$\,\mu$m PAH emission flux or its upper limit is available. A scaling relation between 3.3$\,\mu$m PAH luminosity and CO(1-0) luminosity is established covering ~2 orders of magnitude in total IR luminosity and CO luminosity, with a scatter of ~0.23 dex: $\mathrm{log}\,L

Johan Muhamad, Kanya Kusano

Solar flares are frequently accompanied by coronal mass ejections (CMEs) that release significant amount of energetic plasma into interplanetary space, potentially causing geomagnetic disturbances on Earth. However, many solar flares have no association with CMEs. The relationship between solar flare and CME occurrences remains unclear. Therefore, it is valu

Martijn H. H. Schoot Uiterkamp, Willian J. van Dijk, Hans Heesterbeek, Remco van der Hofstad

In this paper, we present a simple method to integrate risk-contact data, obtained via digital contact monitoring (DCM) apps, in conventional compartmental transmission models. During the recent COVID-19 pandemic, many such data have been collected for the first time via newly developed DCM apps. However, it is unclear what the added value of these data is,

Hengyuan Zhao, Ziqin Wang, Qixin Sun, Kaiyou Song

Mixture of Experts (MoE) architectures have recently advanced the scalability and adaptability of large language models (LLMs) for continual multimodal learning. However, efficiently extending these models to accommodate sequential tasks remains challenging. As new tasks arrive, naive model expansion leads to rapid parameter growth, while modifying shared ro

Yishai Lavi, Leo Segre, Shai Avidan

3D Gaussian Splatting (3D-GS) enables efficient novel view synthesis, but treats all frequencies uniformly, making it difficult to separate coarse structure from fine detail. Recent works have started to exploit frequency signals, but lack explicit frequency decomposition of the 3D representation itself. We propose a frequency-aware decomposition that organi

Alif Al Hasan, Md. Musfique Anwar

One of the most important challenges for improving personalized services in industries like tourism is predicting users' near-future movements based on prior behavior and current circumstances. Next POI (Point of Interest) recommendation is essential for helping users and service providers by providing personalized recommendations. The intricacy of this work

Matthieu Meunier, Christoph Reisinger, Yufei Zhang

Designing efficient learning algorithms with complexity guarantees for Markov decision processes (MDPs) with large or continuous state and action spaces remains a fundamental challenge. We address this challenge for entropy-regularized MDPs with Polish state and action spaces, assuming access to a generative model of the environment. We propose a novel famil

Haolong Yan, Kaijun Tan, Yeqing Shen, Xin Huang

We investigate a critical yet under-explored question in Large Vision-Language Models (LVLMs): Do LVLMs genuinely comprehend interleaved image-text in the document? Existing document understanding benchmarks often assess LVLMs using question-answer formats, which are information-sparse and difficult to guarantee the coverage of long-range dependencies. To ad

Zhihan Zhang, Xunkai Li, Lei Zhu, Guang Zeng

Recently, the emergence of large language models (LLMs) has motivated integrating language descriptions into graphs, forming text-attributed graphs (TAGs) that enhance model encoding capabilities from a data-centric perspective. A review of prior advancements highlights that graph structure learning (GSL) is a pivotal technique for improving data utility, ma

András Czégel, Boglárka G. -Tóth

We propose a new approach to utilize quantum computers for binary linear programming (BLP), which can be extended to general integer linear programs (ILP). Quantum optimization algorithms, hybrid or quantum-only, are currently general purpose, standalone solvers for ILP. However, to consider them practically useful, we expect them to overperform the current

Amey Nitin Agharkar, Anmol Singh, Kush Kumar Dewangan, Dipshikha Chakravortty

Hypothesis Droplets ejected from the host can directly settle on a substrate as fomite. In industrial environments, especially the food processing industries, the components maintained at specific temperatures can act as a substrate, leading to the fomite mode of infection. We hypothesize that substrate temperature influences the desiccation dynamics, bacter

Hidetsugu Sakaguchi

Explicit forms of nonequilibrium Gaussian distributions and heat flows are obtained for the Fokker-Planck equation corresponding to the coupled linear Langevin equations of two and three variables.

Sibo Wu, Congrong Xu, Binbin Huang, Andreas Geiger

Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency. However, a notable conditioning gap can be observed between these two fields, e.g., scalable 3D scene reconstruction often requires densely captured views, whereas 3D generation typically relies on a single or no input

Yan Peng

Based on the orbital period of Kerr black holes, Hod proposed a conjecture that a general lower bound on the orbital period may exist. In this work, we examined this bound by exploring the orbital period of Kerr-Newman black holes using analytical and numerical methods. By choosing different charge and spin of Kerr-Newman black holes, we found a lower bound

Misa Yamaguchi, Masao Mori, Takanobu Kirihara, Yohei Miki

We investigate a minor merger event in M31 that simultaneously forms the Andromeda Giant Southern Stream (AGSS), Eastern Extent (EE), North-Eastern Shelf (NES), and Western Shel (WS), offering a unified model for these substructures. By varying the scale radius and mass of the progenitor's dark matter halo (DMH), around the range predicted by the $\Lambda$CD

Alan Dao, Norapat Buppodom

Comprehending 3D environments is vital for intelligent systems in domains like robotics and autonomous navigation. Voxel grids offer a structured representation of 3D space, but extracting high-level semantic meaning remains challenging. This paper proposes a novel approach utilizing a Vision-Language Model (VLM) to extract "voxel semantics"-object identity,

Jun Liu, Yunming Liao, Hongli Xu, Yang Xu

Fine-tuning large language models (LLMs) via federated learning, i.e., FedLLM, has been proposed to adapt LLMs for various downstream applications in a privacy-preserving way. To reduce the fine-tuning costs on resource-constrained devices, FedLoRA is proposed to fine-tune only a small subset of model parameters by integrating low-rank adaptation (LoRA) into

Jiancheng Zhao, Xingda Yu, Zhen Yang

Parameter-Efficient Fine-Tuning (PEFT) has become an essential approach for adapting large-scale pre-trained models while reducing computational costs. Among PEFT methods, LoRA significantly reduces trainable parameters by decomposing weight updates into low-rank matrices. However, traditional LoRA applies a fixed rank across all layers, failing to account f

P. S. Bhupal Dev

The neutrino sector is the least known in the Standard Model. We briefly review the open questions in neutrino physics and the future experimental prospects of answering them. We argue that a synergistic approach at multiple frontiers is needed.

Rixu Hao, Yuxin Zhao, Shaoqing Zhang, Guihua Wang

El Ni\~no-Southern Oscillation (ENSO) exerts global climate and societal impacts, but real-time prediction with lead times beyond one year remains challenging. Dynamical models suffer from large biases and uncertainties, while deep learning struggles with interpretability and multi-scale dynamics. Here, we introduce PTSTnet, an interpretable model that unifi

Yueying Gao, Dongliang Chang, Bingyao Yu, Haotian Qin

Accurate and interpretable detection of AI-generated images is essential for mitigating risks associated with AI misuse. However, the substantial domain gap among generative models makes it challenging to develop a generalizable forgery detection model. Moreover, since every pixel in an AI-generated image is synthesized, traditional saliency-based forgery ex

Puspita Parui, Bheema Lingam Chittari

We study the topological properties of Rashba spin-orbit coupling and exchange coupling induced pseudospin-$1$ system Dice lattice under the influence of a staggered electric potential and magnetization. The band structure and topological phases of the system are investigated and compared with the pseudospin-$\frac{1}{2}$ system honeycomb lattice. Under indi

Wenxuan Qiu, Chengxin Xie, Jingui Huang

This paper presents an enhanced waste classification framework based on EfficientNetV2 to address challenges in data acquisition cost, generalization, and real-time performance. We propose a Channel-Efficient Attention (CE-Attention) module that mitigates feature loss during global pooling without introducing dimensional scaling, effectively enhancing critic

Yuntao Gui, Peiqi Yin, Xiao Yan, Chaorui Zhang

Approximate Nearest Neighbor Search (ANNS) has become fundamental to modern deep learning applications, having gained particular prominence through its integration into recent generative models that work with increasingly complex datasets and higher vector dimensions. Existing CPU-only solutions, even the most efficient graph-based ones, struggle to meet the

Arvind Saini, Rajiblochan Sahoo, Rajarshi Chakrabarti, Sayantan Dutta

Heteropolymers are ubiquitous in both synthetic systems, such as block copolymers, and biological macromolecules, including proteins and nucleic acids. Beyond their chemical composition, these polymers often exhibit spatial variations in physical properties. For instance, in biopolymers such as chromatin, stiffness heterogeneity arises from inherent molecula

Nicholas Robert Fisk, Matthew Ng Kok Ming, Zahratu Shabrina

This paper aims at improving predictive crime models by extending the mathematical framework of Artificial Neural Networks (ANNs) tailored to general spatiotemporal problems and appropriately applying them. Recent advancements in the geospatial-temporal modelling field have focused on the inclusion of geographical weighting in their deep learning models to a

Boris Bermúdez-Cárdenas, Oscar Lasso Andino

In a recent article PRD 111, 064001 (2025) a new geometric a approach for studying massive particle surfaces was proposed. Using the Gaussian and geodesic curvatures of a two dimensional Riemannian metric a criteria for the existence of massive particle surfaces was provided. In this work we generalize these results by including stationary spacetime metrics.

Antos Cheeramban Varghese, Anamitra Pal

Uncalibrated instrument transformers (ITs) can degrade the performance of downstream applications that rely on the voltage and current measurements that ITs provide. It is also well-known that phasor measurement unit (PMU)-based system-wide IT calibration and line parameter estimation (LPE) are interdependent problems. In this paper, we present a statistical

Sicong Liu, Yang Shu, Chenjuan Guo, Bin Yang

Learning cooperative multi-agent policy from offline multi-task data that can generalize to unseen tasks with varying numbers of agents and targets is an attractive problem in many scenarios. Although aggregating general behavior patterns among multiple tasks as skills to improve policy transfer is a promising approach, two primary challenges hinder the furt

Séverin Philip

Silverberg and Zarhin introduced the notion of a $(p,t,a)$-inertial group in the hope of having a group theoretic characterization of the finite groups that appear as finite monodromy groups -- the groups that represent the local obstruction to semi-stable reduction -- of abelian varieties in fixed dimension $t+a$. In this text, we provide a positive answer

Jack Hirschman, Erfan Abedi, Minyang Wang, Hao Zhang

Modeling second-order ($\chi^{(2)}$) nonlinear optical processes remains computationally expensive due to the need to resolve fast field oscillations and simulate wave propagation using methods like the split-step Fourier method (SSFM). This can become a bottleneck in real-time applications, such as high-repetition-rate laser systems requiring rapid feedback

Bingyan Xie, Yongpeng Wu, Yuxuan Shi, Biqian Feng

Existing wireless video transmission schemes directly conduct video coding in pixel level, while neglecting the inner semantics contained in videos. In this paper, we propose a wireless video semantic communication framework, abbreviated as WVSC, which integrates the idea of semantic communication into wireless video transmission scenarios. WVSC first encode

Meenu Upadhyay, Silvan Käser, Jayakrushna Sahoo, Yohann Scribano

The reaction dynamics for the H + HeH$^+$ $\rightarrow$ He + H$_2^+$ reaction in its electronic ground state is investigated using two different representations of the potential energy surface (PES). The first uses a combined kernel and neural network representation of UCCSD(T) reference data whereas the second is a corrected PES (cR-PES) that eliminates an

Sora Kang

This study presents a dual-mode interface design concept for social media platforms aimed at reducing social comparison in health-related content among Korean MZ (Millennials and Gen-Z) users. The proposed "Inspiration" and "Reality" modes allow users to toggle between curated, idealized posts and more realistic, candid content. This approach aims to allevia

Boning Meng, Yicheng Pan

In this article, we give a sufficient and necessary condition for determining whether a matchgate signature retains its property under a certain variable permutation, which can be checked in polynomial time. We also define the concept of permutable matchgate signatures, and use it to erase the gap between Pl-\#CSP and \#CSP on planar graphs in the previous s

Hongxuan Tang, Hao Liu, Xinyan Xiao

We introduce UGen, a unified autoregressive multimodal model that demonstrates strong performance across text processing, image understanding, and image generation tasks simultaneously. UGen converts both texts and images into discrete token sequences and utilizes a single transformer to generate them uniformly in an autoregressive manner. To address the cha

Xinwei Cai, Xuejin Li, Xin Bian

A comprehensive understanding of surface wetting phenomena in microchannels is essential for optimizing particle transport and filtration processes. This study numerically investigates the dynamics of a freely suspended elliptical cylinder in confined Poiseuille flow, with a focus on Navier slip boundary conditions. The smoothed particle hydrodynamics method

Sora Kang, Kaiwen Yu, Xinyi Zhou, Joonhwan Lee

Generative design, an AI-assisted technology for optimizing design through algorithmic processes, is propelling advancements across numerous fields. As the use of immersive environments such as Augmented Reality (AR) continues to rise, integrating generative design into such platforms presents a potent opportunity for innovation. However, a vital challenge t

Erika Mori, Yue Qiu, Hirokatsu Kataoka, Yoshimitsu Aoki

Social intelligence, the ability to interpret emotions, intentions, and behaviors, is essential for effective communication and adaptive responses. As robots and AI systems become more prevalent in caregiving, healthcare, and education, the demand for AI that can interact naturally with humans grows. However, creating AI that seamlessly integrates multiple m

Sora Kang, Mingu Lee

The global rise of K-pop and the digital revolution have paved the way for new dimensions in artist recommendations. With platforms like Twitter serving as a hub for fans to interact, share and discuss K-pop, a vast amount of data is generated that can be analyzed to understand listener preferences. However, current recommendation systems often overlook K- p

Aixin Sun

Many studies in recommender systems (RecSys) adopt a general problem definition, i.e., to recommend preferred items to users based on past interactions. Such abstraction often lacks the domain-specific nuances necessary for practical deployment. However, models are frequently evaluated using datasets collected from online recommender platforms, which inheren

Yimin Xu, Fan Yang, Bin Xu

Despite the significant advancements in general image segmentation achieved by large-scale pre-trained foundation models (such as Meta's Segment Any-thing Model (SAM) series and DINOv2), their performance in specialized fields remains limited by two critical issues: the excessive training costs due to large model parameters, and the insufficient ability to r

P. Horoschenkoff, J. Henrich, R. Böhn, I. Khan

Quantum Key Distribution Networks (QKDN) enable secure communication even in the age of powerful quantum computers. In the hands of a network operator, which can offer its service to many users, the economic viability of a QKDN increases significantly. The highly challenging operator-user relationship in a large-scale network setting demands additional requi

L. Bottura, B. Auchmann, F. Boattini, B. Bordini

The Muon Collider, proposed under the International Muon Collider Collaboration (IMCC), represents a groundbreaking advancement in circular collider technology. By using muons instead of protons or electrons, this collider has the potential of unprecedented discovery reach, luminosity, and compact design, significantly increasing energy efficiency, reducing

Jagdev Singh, R. Ramesh, B. Raghavendra Prasad, V. Muthu Priyal

Aditya-L1, India's first dedicated mission to study the Sun and its atmosphere from the Sun-Earth Lagrangian L1 location was successfully launched on 2023 September 2. It carries seven payloads. The Visible Emission Line Coronagraph (VELC) is a major payload on Aditya-L1. VELC is designed to carry out imaging and spectroscopic observations (the latter in thr

Yuntao Zang

We prove for $C^\infty$ non-singular flows on three-dimensional compact manifolds with positive entropy, there are at most finitely many ergodic measures of maximal entropy. This result extends the notable work of Buzzi-Crovisier-Sarig (\emph{Ann. of Math.}, 2022) on surface diffeomorphisms. Our approach differs by addressing the continuity of Lyapunov expon

Gang Bao, Yixuan Zhang

The inverse reflector problem aims to design a freeform reflecting surface that can direct the light from a specified source to produce the desired illumination in the target area, which is significant in the field of geometrical non-imaging optics. Mathematically, it can be formulated as an optimization problem, which is exactly the optimal transportation p

Yuanchun Ren, Bochao Chen, Yixian Gao, Peijun Li

Inspired by [25], this paper investigates subwavelength bandgaps in phononic crystals consisting of periodically arranged hard elastic materials embedded in a soft elastic background medium. Our contributions are threefold. First, we introduce the quasi-periodic Dirichlet-to-Neumann map and an auxiliary sesquilinear form to characterize the subwavelength res

Nikolay Moshchevitin, Vasiliy Neckrasov

In this paper we develop a metric theory of inhomogeneous Diophantine approximation for the case of a fixed matrix. We use transference principle to connect uniform Diophantine properties of a pair $(\Theta, \pmb{\eta})$ of a matrix and a vector with the asymptotic Diophantine properties of the transposed matrix $\Theta^{\top}$, and vice versa, the asymptoti

L. Bottura, B. Auchmann, F. Boattini, B. Bordini

A proton-driven Muon Collider, in the configuration that has resulted from the efforts of the International Muon Collider Collaboration (IMCC), poses multiple and exceptional magnet system challenges. Addressing these challenges will require a focused effort to advance accelerator magnet technology well beyond the present state of the art, including activiti

Sadikshya Gyawali, Ashwini Mandal, Manish Dahal, Manish Awale

Chemical reaction network is an important method for modeling and exploring complex biological processes, bio-chemical interactions and the behavior of different dynamics in system biology. But, formulating such reaction kinetics takes considerable time. In this paper, we leverage the efficiency of modern large language models to automate the stochastic mont

Hao-Fan Wang, Shao-Ming Fei

We provide a class of lower bounds for concurrence based on symmetric measurements. We show that our lower bounds estimate the quantum entanglement better than some existing lower bounds by detailed examples. Moreover, our lower bounds can be experimentally identified without state tomography.

Mehmet Cagri Kaymak, Nicholas Lubbers, Christian F. A. Negre, Michael Wall

With recent advancements in machine learning for interatomic potentials, Python has become the go-to programming language for exploring new ideas. While machine-learning potentials are often developed in Python-based frameworks, existing molecular dynamics software is predominantly written in lower-level languages. This disparity complicates the integration

Xiaoyan Xu, Weishi Lim, Xing Zhang, Jeff Cai

In many service markets, expert providers possess an information advantage over consumers regarding the necessary services, creating opportunities for fraudulent practices. These may involve overtreatment through unnecessary services or undertreatment with ineffective solutions that fail to address consumers' problems. When issues are resolved, consumers exi