March 2025 arXiv papers — page 10

Alpar Turkoglu, A. Nihat Berker

The phase diagram of the Ashkin-Tellerized XY model in spatial dimension $d=3$ is calculated by renormalization-group theory. In this system, each site has two spins, each spin being an XY spin, that is having orientation continuously varying in 2\pi radians. Nearest-neighbor sites are coupled by two-spin and four-spin interactions. The phase diagram has ord

Xinhao Li, Yizhong Huang, Xu Han, Xianjing Zhou

Electrons trapped on solid neon surfaces serve as low-noise charge qubits with long coherence times and high operational fidelities. Such charge qubits offer full electrical control and compact device footprints, convenient for scaling up with quantum circuits. Realizing two-qubit gates on this platform is a critical step towards practical quantum informatio

Rujiang Li, Wencai Wang, Xiangyu Kong, Bo Lv

The Haldane model is the simplest yet most powerful topological lattice model exhibiting various phases, including the Dirac semimetal phase and the anomalous quantum Hall phase (also known as the Chern insulator). Although considered unlikely to be physically directly realizable in condensed matter systems, it has been experimentally demonstrated in other p

Lingyu Liu, Yaxiong Wang, Li Zhu, Zhedong Zheng

We introduce a training-free framework specifically designed to bring real-world static paintings to life through image-to-video (I2V) synthesis, addressing the persistent challenge of aligning these motions with textual guidance while preserving fidelity to the original artworks. Existing I2V methods, primarily trained on natural video datasets, often strug

Seunghun Lee, Jihong Park, Jinho Choi, Hyuncheol Park

Text-based communication is expected to be prevalent in 6G applications such as wireless AI-generated content (AIGC). Motivated by this, this paper addresses the challenges of transmitting text prompts over erasure channels for a text-to-image AIGC task by developing the semantic segmentation and repeated transmission (SMART) algorithm. SMART groups words in

Yiyang Du, Xiaochen Wang, Chi Chen, Jiabo Ye

Recently, model merging methods have demonstrated powerful strengths in combining abilities on various tasks from multiple Large Language Models (LLMs). While previous model merging methods mainly focus on merging homogeneous models with identical architecture, they meet challenges when dealing with Multimodal Large Language Models (MLLMs) with inherent hete

Badr Elmansouri, Mohamed El Otmani

This paper addresses the existence and uniqueness of solutions to Reflected Generalized Backward Stochastic Differential Equations (GRBSDEs) within a general filtration that supports a Brownian motion and an independent integer-valued random measure. Our study focuses on cases where the given data satisfy appropriate $\mathbb{L}^2$-integrability conditions a

Yinq-Rong Chern, Yuhao Lee, Hsiao-Ching Lin, Guan-Ting Chen

Purpose: Research has revealed that strength training can reduce the incidence of chronic diseases and physical deterioration at any age. Therefore, having a movement diagnostic system is crucial for training alone. Hence, this study developed an artificial intelligence and computer vision-based barbell squat coaching system with a real-time mode that immedi

Yoonshik Kim, Jaeyoon Jung

The recent emergence of Large Vision-Language Models(VLMs) has resulted in a variety of different benchmarks for evaluating such models. Despite this, we observe that most existing evaluation methods suffer from the fact that they either require the model to choose from pre-determined responses, sacrificing open-endedness, or evaluate responses using a judge

Xiaodong Feng, Haojiong Shangguan, Tao Tang, Xiaoliang Wan

Evolution equations, including both ordinary differential equations (ODEs) and partial differential equations (PDEs), play a pivotal role in modeling dynamic systems. However, achieving accurate long-time integration for these equations remains a significant challenge. While physics-informed neural networks (PINNs) provide a mesh-free framework for solving P

Cheng Fan, Maodong Li, Sihao Yuan, Zhaoxin Xie

This study employed an artificial intelligence-enhanced molecular simulation framework to enable efficient Path Integral Molecular Dynamics (PIMD) simulations. Owing to its modular architecture and high-throughput capabilities, the framework effectively mitigates the computational complexity and resource-intensive limitations associated with conventional PIM

Kelsey A. Lund, Payel Mukhopadhyay, Jonah M. Miller, G. C. McLaughlin

The remnant black hole-accretion disk system resulting from binary neutron star mergers has proven to be a promising site for synthesizing the heaviest elements via rapid neutron capture (r-process). A critical factor in determining the full r-process pattern in these environments is the neutron richness of the ejecta, which is strongly influenced by neutrin

Lina Wang, Yunsheng Yuan, Chunxiao Wang, Feng Li

In the paradigm of decentralized learning, a group of agents collaborates to learn a global model using distributed datasets without a central server. However, due to the heterogeneity of the local data across the different agents, learning a robust global model is rather challenging. Moreover, the collaboration of the agents relies on their gradient informa

Hongwei Ren, Xiaopeng Lin, Hongxiang Huang, Yue Zhou

Eye-tracking is a vital technology for human-computer interaction, especially in wearable devices such as AR, VR, and XR. The realization of high-speed and high-precision eye-tracking using frame-based image sensors is constrained by their limited temporal resolution, which impairs the accurate capture of rapid ocular dynamics, such as saccades and blinks. E

Sajid Husain, Maya Ramesh, Xinyan Li, Sergei Prokhorenko

Since Felix Bloch's introduction of the concept of spin waves in 1930, magnons (the quanta of spin waves) have been extensively studied in a range of materials for spintronics, particularly for non-volatile logic-in-memory devices. Controlling magnons in conventional antiferromagnets and harnessing them in practical applications, however, remains a challenge

Georgios Korpas, Vyacheslav Kungurtsev, Jakub Mareček

Variational Quantum Algorithms (VQAs), such as the Variational Quantum Eigensolver (VQE) and the Quantum Approximate Optimization Algorithm (QAOA), are widely studied as candidates for near-term quantum advantage. Recent work has shown that training VQAs is NP-hard in general. In this paper, we present a conditional result suggesting that the training of VQA

Pingping Zhang, Xiang Hu, Yuhao Wang, Huchuan Lu

As an important task in intelligent transportation systems, Aerial-Ground person Re-IDentification (AG-ReID) aims to retrieve specific persons across heterogeneous cameras in different viewpoints. Previous methods typically adopt deep learning-based models, focusing on extracting view-invariant features. However, they usually overlook the semantic informatio

Jiagen Li, Rui Yu, Huihao Huang, Huaicheng Yan

Multimodal Emotion Recognition in Conversations (MERC) identifies emotional states across text, audio and video, which is essential for intelligent dialogue systems and opinion analysis. Existing methods emphasize heterogeneous modal fusion directly for cross-modal integration, but often suffer from disorientation in multimodal learning due to modal heteroge

Jiaqi Chen, Li Lin Yang, Yiyang Zhang

We present generic expressions for the integrands of canonical bases under maximal cut in elliptic Feynman integral families with multiple kinematic scales. Such integrals frequently arise in phenomenologically relevant scattering processes. The derivation of our results starts from the Legendre normal form of elliptic curves, where the geometric properties

Chenhao Li, Simon Gross, Leonardo de S. Menezes, Stefan A. Maier

Orbital angular momentum (OAM) modes have emerged as a promising solution for enhancing the capacity of optical multiplexing systems, leveraging their theoretically unbounded set of orthogonal spatial modes. However, the generation and detection of OAM multiplexing signals are predominantly reliant on bulky optical components within complex optical setups. W

Shiraj Pokharel, Georg P. Roßrucker, Mario M. Kubek

Carrying out research tasks is only inadequately supported, if not hindered, by current web search engines. This paper therefore proposes functional extensions of WebMap, a semantically induced overlay linking structure on the web to inherently facilitate research activities. These add-ons support the dynamic determination and regrouping of document clusters

Xingshuang Lin, Qinge Xie, Binbin Zhao, Yuan Tian

Smart contracts are fundamental pillars of the blockchain, playing a crucial role in facilitating various business transactions. However, these smart contracts are vulnerable to exploitable bugs that can lead to substantial monetary losses. A recent study reveals that over 80% of these exploitable bugs, which are primarily functional bugs, can evade the dete

David Sweeney, Alberto Krone-Martins, Daniel Stern, Peter Tuthill

Lensed quasars are key to many areas of study in astronomy, offering a unique probe into the intermediate and far universe. However, finding lensed quasars has proved difficult despite significant efforts from large collaborations. These challenges have limited catalogues of confirmed lensed quasars to the hundreds, despite theoretical predictions that they

Yi Liu, Wengen Li, Jihong Guan, Shuigeng Zhou

Cloud removal (CR) remains a challenging task in remote sensing image processing. Although diffusion models (DM) exhibit strong generative capabilities, their direct applications to CR are suboptimal, as they generate cloudless images from random noise, ignoring inherent information in cloudy inputs. To overcome this drawback, we develop a new CR model EMRDM

Jing Li, Cui Ning, Xiaofei Zhao

In this paper, we study the dispersion-managed nonlinear Schr\"odinger (DM-NLS) equation $$ i\partial_t u(t,x)+\gamma(t)\Delta u(t,x)=|u(t,x)|^{\frac4d}u(t,x),\quad x\in\R^d, $$ and the nonlinearity-managed NLS (NM-NLS) equation: $$ i\partial_t u(t,x)+\Delta u(t,x)=\gamma(t)|u(t,x)|^{\frac4d}u(t,x), \quad x\in\R^d, $$ where $\gamma(t)$ is a periodic function

Kun Liu, Qi Liu, Xinchen Liu, Jie Li

Text-to-video (T2V) generation has made tremendous progress in generating complicated scenes based on texts. However, human-object interaction (HOI) often cannot be precisely generated by current T2V models due to the lack of large-scale videos with accurate captions for HOI. To address this issue, we introduce HOIGen-1M, the first largescale dataset for HOI

Youmi Ma, Sakae Mizuki, Kazuki Fujii, Taishi Nakamura

Instruction tuning is crucial for enabling Large Language Models (LLMs) to solve real-world tasks. Prior work has shown the effectiveness of instruction-tuning data synthesized solely from LLMs, raising a fundamental question: Do we still need human-originated signals for instruction tuning? This work answers the question affirmatively: we build state-of-the

Priyanka Gautam, Balasubramaniam Natarajan, Sai Munikoti, S M Ferdous

In an age where information spreads rapidly across social media, effectively identifying influential nodes in dynamic networks is critical. Traditional influence maximization strategies often fail to keep up with rapidly evolving relationships and structures, leading to missed opportunities and inefficiencies. To address this, we propose a novel learning-bas

Jie Cheng, Hao Zheng, Meiguang Zheng, Lei Wang

Source-Free Domain Adaptation (SFDA) aims to train a target model without source data, and the key is to generate pseudo-labels using a pre-trained source model. However, we observe that the source model often produces highly uncertain pseudo-labels for hard samples, particularly those heavily affected by domain shifts, leading to these noisy pseudo-labels b

Manit Paul, Arun Kumar Kuchibhotla

Estimating the mode of a unimodal distribution is a classical problem in statistics. Although there are several approaches for point-estimation of mode in the literature, very little has been explored about the interval-estimation of mode. Our work proposes a collection of novel methods of obtaining finite sample valid confidence set of the mode of a unimoda

Zhiyuan Wen, Jiannong Cao, Zian Wang, Beichen Guo

The exponential growth of academic literature creates urgent demands for comprehensive survey papers, yet manual writing remains time-consuming and labor-intensive. Recent advances in large language models (LLMs) and retrieval-augmented generation (RAG) facilitate studies in synthesizing survey papers from multiple references, but most existing works restric

Jie Pan, Seungwon Lee, Cheligeer Cheligeer, Elliot A. Martin

Objective: Electronic health records (EHR) are widely available to complement administrative data-based disease surveillance and healthcare performance evaluation. Defining conditions from EHR is labour-intensive and requires extensive manual labelling of disease outcomes. This study developed an efficient strategy based on advanced large language models to

Fangzhou Jiang, Zixiang Jia, Haonan Zheng, Luis C. Ho

We present a statistical study of black hole (BH) formation and growth seeded by gravothermal core collapse of self-interacting dark matter (SIDM) halos at high redshift, using a cosmological semi-analytical framework based on Monte Carlo merger trees. We demonstrate that gravothermal collapse naturally leads to BH formation in high-concentration halos at a

Xulong Shi, Caiyi Sun, Zhi Qi, Liu Hao

While binary neural networks (BNNs) offer significant benefits in terms of speed, memory and energy, they encounter substantial accuracy degradation in challenging tasks compared to their real-valued counterparts. Due to the binarization of weights and activations, the possible values of each entry in the feature maps generated by BNNs are strongly constrain

Jingzheng Li, Xianglong Liu, Shikui Wei, Zhijun Chen

Autonomous driving has made significant progress in both academia and industry, including performance improvements in perception task and the development of end-to-end autonomous driving systems. However, the safety and robustness assessment of autonomous driving has not received sufficient attention. Current evaluations of autonomous driving are typically c

Yuto Asano, Naruya Kondo, Tatsuki Fushimi, Yoichi Ochiai

3D layout tasks have traditionally concentrated on geometric constraints, but many practical applications demand richer contextual understanding that spans social interactions, cultural traditions, and usage conventions. Existing methods often rely on rule-based heuristics or narrowly trained learning models, making them difficult to generalize and frequentl

Jie Pan, Seungwon Lee, Cheligeer Cheligeer, Bing Li

Objectives: Administrative data is commonly used to inform chronic disease prevalence and support health informatics research. This study assessed the validity of coding comorbidity in the International Classification of Diseases, 10th Revision (ICD-10) administrative data. Methods: We analyzed three chart review cohorts (4,008 patients in 2003, 3,045 in 201

Xiaochao Luo, Chuntao Li, Jintian Lin, Renhong Gao

We report the simultaneous generation of second-harmonic generation (SHG) and Brillouin microlaser in a high-quality thin-film lithium niobate (TFLN) microdisk resonator. The microdisk is fabricated with ultrahigh-Q factor of 4X10(6) by photolithography-assisted chemo-mechanical etching, enabling significant cavity-enhancement effect for boosting nonlinear f

George Rapakoulias, Ali Reza Pedram, Panagiotis Tsiotras

The Mean-Field Schrodinger Bridge (MFSB) problem is an optimization problem aiming to find the minimum effort control policy to drive a McKean-Vlassov stochastic differential equation from one probability measure to another. In the context of multi-agent control, the objective is to control the configuration of a swarm of identical, interacting cooperative a

Nelly Natsch, Tara N. Tošić, Jian-Rui Soh, Nicola A. Spaldin

We use spin dynamics simulations to determine the origin of the unusual correlated diffuse scattering, characterised by half-moon shapes bridging the magnetic Bragg peaks, observed in the polarised elastic neutron scattering from manganese tungstate, MnWO\textsubscript{4}. We first fit a Heisenberg Hamiltonian with twelve nearest-neighbour exchange interacti

Dima Grigoriev, Cristhian Garay López

We introduce and study minimal (with respect to inclusion) solutions of systems of tropical linear differential equations. We describe the set of all minimal solutions for a single equation. It is shown that any tropical linear differential equation in a single unknown has either a solution or a solution at infinity. For a generic system of $n$ tropical line

Shufan Xi, Zexian Liu, Junlin Chang, Hongyu Wu

3D intraoral scan mesh is widely used in digital dentistry diagnosis, segmenting 3D intraoral scan mesh is a critical preliminary task. Numerous approaches have been devised for precise tooth segmentation. Currently, the deep learning-based methods are capable of the high accuracy segmentation of crown. However, the segmentation accuracy at the junction betw

Yuto Muramatsu, Daigorou Hirai, Mitsuaki Kawamura, Susumu Minami

We elucidate the transport properties and electronic structures of distorted rutile-type WO2. Electrical resistivity and Hall effect measurements of high-quality single crystals revealed the transport property characteristics of topological materials; these characteristics included an extremely large magnetoresistance of 13,200% (2 K and 9 T) and a very high

Zhiqiang Wang, Pengbin Feng, Yanbin Lin, Shuzhang Cai

We propose Fuzzy Group Relative Policy Reward (FGRPR), a novel framework that integrates Group Relative Policy Optimization (GRPO) with a fuzzy reward function to enhance learning efficiency. Unlike the conventional binary 0/1 accuracy reward, our fuzzy reward model provides nuanced incentives, encouraging more precise outputs. Experimental results demonstra

Fengwei Xu, Xing Lu, Ke Wang, Hauyu Baobab Liu

The Milky Way's Central Molecular Zone (CMZ) is measured to form stars 10 times less efficiently than in the Galactic disk, based on emission from high-mass stars. However, the CMZ's low-mass protostellar population, which accounts for most of the initial stellar mass budget and star formation rate (SFR), is poorly constrained observationally due to limited

Hansaka Aluvihare, Levi Lingsch, Xianqi Li, Sirani M. Perera

Data-driven learning is rapidly evolving and places a new perspective on realizing state-space dynamical systems. However, dynamical systems derived from nonlinear ordinary differential equations (ODEs) suffer from limitations in computational efficiency. Thus, this paper stems from data-driven learning to advance states of dynamical systems utilizing a stru

Daigorou Hirai

Heteroanionic compounds, which contain two or more types of anions, have emerged as a promising class of materials with diverse properties and functionalities. In this paper, I review the experimental findings on Ca3ReO5Cl2 and related com-pounds that exhibit remarkable pleochroism and novel quantum magnetism. I discuss how the heteroanionic coordination aff

Yuxuan Chen, Dewen Guo, Sen Mei, Xinze Li

Retrieval-Augmented Generation (RAG) significantly enhances the performance of large language models (LLMs) in downstream tasks by integrating external knowledge. To facilitate researchers in deploying RAG systems, various RAG toolkits have been introduced. However, many existing RAG toolkits lack support for knowledge adaptation tailored to specific applica

A. Abdelhamid, D. Acosta, P. Affleck, G. Agarwal

This document is being submitted to the 2024-2026 European Strategy for Particle Physics Update (ESPPU) process on behalf of the US Muon Collider community, with its preparation coordinated by the interim US Muon Collider Coordination Group. The US Muon Collider Community comprises a few hundred American scientists. The purpose of the document is to inform E

Alícia G. Borges, Ilya L. Shapiro

The derivative expansion of the effective action is considered in the model with two interacting real scalar fields in curved spacetime. Using the functional approach and local momentum representation, the coefficient of the derivative term is calculated up to the first order in curvature in the one-scalar theory. The two-scalar problem is solved by extracti

Y. Tachibana, C. Sirimanna, A. Majumder, A. Angerami

We investigate medium-induced modifications to jet substructure observables that characterize hard components in central Pb-Pb collisions at $\sqrt{s_{NN}}=5.02$~TeV. Using a multistage Monte Carlo simulation of in-medium jet shower evolution, we explore flavor-dependent medium effects through simulations of inclusive and $\gamma$-tagged jets. The results sh

Daigorou Hirai, Naoto Uematsu, Koh Saitoh, Naoyuki Katayama

The high-entropy concept was applied to the synthesis of transition-metal antimonides, M1-xPtxSb (M = equimolar Ru, Rh, Pd, and Ir). High-entropy antimonide samples crystallized in a pseudo-hexagonal NiAs-type crystal structure with a P63/mmc space group were successfully synthesized through a conventional solid-state reaction and subsequent quenching. A det

Xiaomei Li, Alex Whan, Meredith McNeil, David Starns

Genome annotation is essential for understanding the functional elements within genomes. While automated methods are indispensable for processing large-scale genomic data, they often face challenges in accurately predicting gene structures and functions. Consequently, manual curation by domain experts remains crucial for validating and refining these predict

Ruiren Shi, Michael Drewsen, Jesús Pérez-Ríos

In the study of ion-atom interactions, the ion often remain trapped during the experiments. However, the effects of the trapping potential of the ion on ion-neutral interactions remain largely unexplored. Although trap-assisted ion-neutral complex formation has been experimentally studied and described by applying semiclassical theories where the ion is trea

Mingxiang Li, Biao Ma

Given a smooth function $f(x)$ on $\mathbb{R}^n$ which is positive somewhere and satisfies $f(x)=O(|x|^{-l})$ for any $l>\frac{n}{2}$, we show that there exists a complete and conformal metric $g=e^{2u}|dx|^2$ with finite total Q-curvature such that its Q-curvature equals to $f(x)$.

Sharad Duwal

Reliability of LLMs is questionable even as they get better at more tasks. A wider adoption of LLMs is contingent on whether they are usably factual. And if they are not, on whether they can properly calibrate their confidence in their responses. This work focuses on utilizing the multilingual knowledge of an LLM to inform its decision to abstain or answer w

Oliver T. Schmidt

Space-Time Projection (STP) is introduced as a data-driven forecasting approach for high-dimensional and time-resolved data. The method computes extended space-time proper orthogonal modes from training data spanning a prediction horizon comprising both hindcast and forecast intervals. Forecasts are then generated by projecting the hindcast portion of these

Zijian Zhao, Varun Darshana Parekh, Po-Kai Hsu, Yixin Qin

Neuromorphic vision sensors require efficient real-time pattern recognition, yet conventional architectures struggle with energy and latency constraints. Here, we present a novel in-situ spatiotemporal sequence detector that leverages vertical NAND storage to achieve massively parallel pattern detection. By encoding each cell with two single-transistor-based

Haitao Tian, Junyang Li, Chenxing Wang, Helong Jiang

Multi-view stereo methods have achieved great success for depth estimation based on the coarse-to-fine depth learning frameworks, however, the existing methods perform poorly in recovering the depth of object boundaries and detail regions. To address these issues, we propose a detail-aware multi-view stereo network (DA-MVSNet) with a coarse-to-fine framework

Sam Greydanus, Zachary Wimpee

Transformers trained on tokenized text, audio, and images can generate high-quality autoregressive samples. But handwriting data, represented as sequences of pen coordinates, remains underexplored. We introduce a novel tokenization scheme that converts pen stroke offsets to polar coordinates, discretizes them into bins, and then turns them into sequences of

Nantao Zhang

We study the relation between perverse stability conditions and geometric stability conditions under blow up. We confirm a conjecture of Toda in some special cases and show that geometric stability conditions can be induced from perverse stability conditions from semiorthogonal decompositions associated to blowups.

J. Buete, B. M. A. Swinton-Bland, D. J. Hinde, K. J. Cook

We present the results of a broad, systematic study of heavy-ion induced fission mass distributions for every even-Z compound nucleus ($Z_\mathrm{CN}$) from $^{144}$Gd to $^{212}$Th. We find systematic evidence of shell-driven structure in every fission mass distribution. The change in shape of the mass distributions with $Z_\mathrm{CN}$ is consistent with t

Ethan Holbrook, Matthew P. Kroonblawd, Brenden W. Hamilton, H. Keo Springer

Many high explosive (HE) formulations are composite materials whose microstructure is understood to impact functional characteristics. Interfaces are known to mediate the formation of hot spots that control their safety and initiation. To study such processes at molecular scales, we developed all-atom force fields (FFs) for Octol, a prototypical HE formulati

Mingkai Tian, Guorong Li, Yuankai Qi, Amin Beheshti

Zero-shot video captioning requires that a model generate high-quality captions without human-annotated video-text pairs for training. State-of-the-art approaches to the problem leverage CLIP to extract visual-relevant textual prompts to guide language models in generating captions. These methods tend to focus on one key aspect of the scene and build a capti

Xiao-Ping Zheng, Wei-Hua Wang, Chun Huang, Jian-Ping Yuan

It was previously believed that, the long-term persistent increase in the spin-down rate of the Crab pulsar following a glitch is direct evidence of a starquake-induced glitch or at least related to a starquake. Using radio data covering 1710 days following the 2017 glitch, we obtain an extreme persistent increase of the spin-down rate, which allows to test

Jun S. Han, Nino Kordzakhia

We study the bias and the mean-squared error of the maximum likelihood estimators (MLE) of parameters associated with a two-parameter mean-reverting process for a finite time $T$. Using the likelihood ratio process, we derive the expressions for MLEs, then compute the bias and the MSE via the change of measure and Ito's formula. We apply the derived expressi

Chenguang Wan, Youngwoo Cho, Zhisong Qu, Yann Camenen

One of the main challenges in building high-fidelity surrogate models of tokamak turbulence is the substantial demand for high-quality data. Typically, producing high-quality data involves simulating complex physical processes, which requires extensive computing resources. In this work, we propose a fine tuning-based approach to develop the surrogate model t

Zhiyong Rao, Qi Liu, Qiong Wu, Zhouping Yin

In the past, Takahashi has introduced the James type constants $\mathcal{J}_{\mathcal{X} ,t}(\tau)$. Building upon this foundation, we introduce an innovative skew James type constant, denoted as $\mathcal{J}_t[\tau,\mathcal{X}]$, which is perceived as a skewed counterpart to the traditional James type constants. We delineate a novel constant, and proceed to

Xuanyu Li

Given two Riemannian manifolds $M$ and $N\subset\mathbb{R}^L$, we consider the energy concentration phenomena of the penalized energy functional $$E_{\epsilon}(u)=\int_M\frac{\vert\nabla u\vert^2}{2}+\frac{F(u)}{\epsilon^2},u\in W^{1,2}(M,\mathbb{R}^L),$$ where $F(x)$=dist$(x,N)$ in a small tubular neighborhood of $N$ and is constant away from $N$. It was sh

Cameron R. Jones, Benjamin K. Bergen

We evaluated 4 systems (ELIZA, GPT-4o, LLaMa-3.1-405B, and GPT-4.5) in two randomised, controlled, and pre-registered Turing tests on independent populations. Participants had 5 minute conversations simultaneously with another human participant and one of these systems before judging which conversational partner they thought was human. When prompted to adopt

Zhengyi Zhao, Shubo Zhang, Bin Liang, Binyang Li

In Biomedical Natural Language Processing (BioNLP) tasks, such as Relation Extraction, Named Entity Recognition, and Text Classification, the scarcity of high-quality data remains a significant challenge. This limitation poisons large language models to correctly understand relationships between biological entities, such as molecules and diseases, or drug in

Zi-Yi Zhang, Chen-Wu Wu

Cooled infrared detectors with high sensitivity and high performance are widely applied in many fields. However, environmental disturbances such as intense light may cause a decline in their performance and even lead to permanent damage. In this study, the multi-physical behaviors and the performance degradation processes of typical cooled infrared detectors

Tongke Ni, Yang Fan, Junru Zhou, Xiangping Wu

Text semantic segmentation involves partitioning a document into multiple paragraphs with continuous semantics based on the subject matter, contextual information, and document structure. Traditional approaches have typically relied on preprocessing documents into segments to address input length constraints, resulting in the loss of critical semantic inform

Xuli Cai, Poonam Lohan, Burak Kantarci

In critical situations such as natural disasters, network outages, battlefield communication, or large-scale public events, Unmanned Aerial Vehicles (UAVs) offer a promising approach to maximize wireless coverage for affected users in the shortest possible time. In this paper, we propose a novel framework where multiple UAVs are deployed with the objective t

Jiaxin Wu, Ting Zhang, Rubing Chen, Wengyu Zhang

Current molecular understanding approaches predominantly focus on the descriptive aspect of human perception, providing broad, topic-level insights. However, the referential aspect -- linking molecular concepts to specific structural components -- remains largely unexplored. To address this gap, we propose a molecular grounding benchmark designed to evaluate

Nuo Chen, Zhiyuan Hu, Qingyun Zou, Jiaying Wu

Large Language Models (LLMs) are increasingly adopted as evaluators, offering a scalable alternative to human annotation. However, existing supervised fine-tuning (SFT) approaches often fall short in domains that demand complex reasoning. Judgment is inherently reasoning-intensive: beyond surface-level scoring, it requires verifying evidence, identifying err

Qichuan Ni, Qi Liu, Yuxin Wang, Jinyu Xia

In this article, we introduce a novel geometric constant $L_X(t)$, which provides an equivalent definition of the von Neumann-Jordan constant from an orthogonal perspective. First, we present some fundamental properties of the constant $L_X(t)$ in Banach spaces, including its upper and lower bounds, as well as its convexity, non-increasing continuity. Next,

Kotaro Inoue

Due to their high versatility in tasks such as image captioning, document analysis, and automated content generation, multimodal Large Language Models (LLMs) have attracted significant attention across various industrial fields. In particular, they have been shown to surpass specialized models in Optical Character Recognition (OCR). Nevertheless, their perfo

Jaewoo Kim, Taehyun Sung, Woonam Hwang, Jaemyung Ahn

This paper proposes a novel replenishment strategy that can jointly support multiple satellite constellations. In this approach, multiple constellations share launch opportunities and parking orbits to address the operational satellite failures and ensure the desired service level of the constellations. We develop an inventory management model based on param

Yuanjun Feng, Vivek Chodhary, Yash Raj Shrestha

This study examines the understudied role of algorithmic evaluation of human judgment in hybrid decision-making systems, a critical gap in management research. While extant literature focuses on human reluctance to follow algorithmic advice, we reverse the perspective by investigating how AI agents based on large language models (LLMs) assess and integrate h

Masahiko Tsuji, Hitoshi Niigaki, Ryuichi Tanida

Visual localization is to estimate the 6-DOF camera pose of a query image in a 3D reference map. We extract keypoints from the reference image and generate a 3D reference map with 3D reconstruction of the keypoints in advance. We emphasize that the more keypoints in the 3D reference map and the smaller the error of the 3D positions of the keypoints, the high

Yichuan Niu, Jianhui Chen

In online revenue systems, e.g. an advertising system, budget pacing plays a critical role in ensuring that the spend aligns with desired financial objectives. Pacing systems dynamically control the velocity of spending to balance auction intensity, traffic fluctuations, and other stochastic variables. Current industry practices rely heavily on trial-and-err

Ruoyu Wang, Huimin Miao, Xue Luo

The feedback particle filter (FPF) is an innovative, control-oriented and resampling-free adaptation of the traditional particle filter (PF). In the FPF, individual particles are regulated via a feedback gain, and the corresponding gain function serves as the solution to the Poisson's equation equipped with a probability-weighted Laplacian. Owing to the fact

Guang-Xin Pang, Zhi Li, Shan-Zhong Li, Yan-Yang Zhang

In this study, we investigate the problem of Anderson localization in a one-dimensional flat band lattice with a non-Hermitian quasiperiodic on-site potential. First of all, we discuss the influences of non-Hermitian potentials on the existence of critical states. Our findings show that, unlike in Hermitian cases, the non-Hermiticity of the potential leads t

Thomas C. Hull, Adham Ibrahim, Jacob Paltrowitz, Natalya Ter-Saakov

A strip of square stamps can be folded in many ways such that all of the stamps are stacked in a single pile in the folded state. The stamp folding problem asks for the number of such foldings and has previously been studied extensively. We consider this problem with the additional restriction of fixing the mountain-valley assignment of each crease in the st

Junjie Zheng, Zihao Chen, Chaofan Ding, Xinhan Di

Current movie dubbing technology can generate the desired voice from a given speech prompt, ensuring good synchronization between speech and visuals while accurately conveying the intended emotions. However, in movie dubbing, key aspects such as adapting to different dubbing styles, handling dialogue, narration, and monologue effectively, and understanding s

Xiaoxuan Sun, Yifei Duan, Yingnan Deng, Fan Guo

In this paper, an operating system scheduling algorithm based on Double DQN (Double Deep Q network) is proposed, and its performance under different task types and system loads is verified by experiments. Compared with the traditional scheduling algorithm, the algorithm based on Double DQN can dynamically adjust the task priority and resource allocation stra

Zhuoyi Zhao, Vishrant Tripathi, Igor Kadota

Modern sensing and monitoring applications typically consist of sources transmitting updates of different sizes, ranging from a few bytes (position, temperature, etc.) to multiple megabytes (images, video frames, LIDAR point scans, etc.). Existing approaches to wireless scheduling for information freshness typically ignore this mix of large and small updates

Wenjie Shang, Jiahang Zhou, J. P. Panda, Zhihao Xu

This paper introduces JAX-BTE, a GPU-accelerated, differentiable solver for the phonon Boltzmann Transport Equation (BTE) based on differentiable programming. JAX-BTE enables accurate, efficient and differentiable multiscale thermal modeling by leveraging high-performance GPU computing and automatic differentiation. The solver efficiently addresses the high-

Yi-Cai Zhang

In this work, we propose a double commutator method for a general two-band bosonic superfluid. First and foremost, we prove that the sum of the superfluid and normal densities is equal to the weight of the f-sum rule. This weight can be easily determined by analyzing the ground state wave function. Once we have determined the excitation gap of the upper band

Ciro Micheletti Diniz, Gabriella G. Damas, Norton G. de Almeida, Celso J. Villas-Bôas

Quantum correlations and non-classical states are indispensable resources for advancing quantum technologies, and their resilience at finite temperatures is crucial for practical experimental implementations. The two-qubit quantum Rabi model (2QQRM), a natural extension of the quantum Rabi model, describes two qubits coupled to a single bosonic mode and has

Kisung You, Yelim Lee, Hae-Jeong Park

The correlation matrix is a central representation of functional brain networks in neuroimaging. Traditional analyses often treat pairwise interactions independently in a Euclidean setting, overlooking the intrinsic geometry of correlation matrices. While earlier attempts have embraced the quotient geometry of the correlation manifold, they remain limited by

Sushrut Kumar, Matthew J. McHenry, Jung-Hee Seo, Rajat Mittal

Fish swim with flexible fins that stand in stark contrast to the rigid propulsors of engineered vehicles. Using numerical simulations of the dynamics of flow-structure interaction, we have found that dorso-ventral deformation in flexible caudal fins results in a 70% increase in efficiency of caudal fin swimmers compared to a rigid fin generating the same amo

Gregory Lupton, Nicholas A. Scoville, P. Christopher Staecker

The edge group of a simplicial complex is a well-known, combinatorial version of the fundamental group. It is a group associated to a simplicial complex that consists of equivalence classes of edge loops and that is isomorphic to the ordinary (topological) fundamental group of the spatial realization. We define a counterpart to the edge group that likewise g

Egor A. Maximenko, Carlos G. Pacheco

We study radial Carleson--Bergman measures on the unit disk and the corresponding Toeplitz operators acting in the Bergman space. First, we show that such Toeplitz operators are diagonal in the canonical basis, and we compute their eigenvalue sequences and Berezin transforms in terms of the radial component of the measure. Next, considering the average value

Noga Alon, Matija Bucić, Lior Gishboliner

The number of spanning trees of a graph $G$, denoted $\tau(G)$, is a well studied graph parameter with numerous connections to other areas of mathematics. In a recent remarkable paper, answering a question of Sedl\'a\v{c}ek from 1969, Chan, Kontorovich and Pak showed that $\tau(G)$ takes at least $1.1103^n$ different values across simple (and planar) $n$-ver

Said Ohamouddou, Mohamed Ohamouddou, Hanaa El Afia, Abdellatif El Afia

Accurate classification of tree species based on Terrestrial Laser Scanning (TLS) and Airborne Laser Scanning (ALS) is essential for biodiversity conservation. While advanced deep learning models for 3D point cloud classification have demonstrated strong performance in this domain, their high complexity often hinders the development of efficient, low-computa

Kateryna Andrych, Devika Kamath, Hans Van Wincke, Akke Corporaa

Second-generation circumbinary discs around evolved binary stars, such as post-Asymptotic Giant Branch (post-AGB) binaries, provide insights into poorly understood mechanisms of dust processing and disc evolution across diverse stellar environments. We present a multi-wavelength polarimetric survey of five evolved binary systems - AR Pup, HR 4049, HR 4226, U

Jianping Wang, Mingxin Wang

In this paper, we introduce the nonlinear diffusion term $\nabla\cdot(D(u)\nabla u)$ into the chemotaxis-May-Nowak model to investigate the effects of $D(u)$ and chemotaxis on the global existence, boundedness, and finite time blow-up of solutions. Here, $D(u)$ generalizes the prototype $(1+u)^{m-1}$ with $m\in\R$. For the parabolic-elliptic-parabolic case,

Chaojian Li, Sixu Li, Linrui Jiang, Jingqun Zhang

Recent advancements in neural rendering technologies and their supporting devices have paved the way for immersive 3D experiences, significantly transforming human interaction with intelligent devices across diverse applications. However, achieving the desired real-time rendering speeds for immersive interactions is still hindered by (1) the lack of a univer