March 2025 arXiv papers — page 198

Yongchao Long, Chao Yang, Gongzheng Tang, Jinwei Wang

Privacy-preserving medical decision support for kidney disease requires localized deployment of large language models (LLMs) while maintaining clinical reasoning capabilities. Current solutions face three challenges: 1) Cloud-based LLMs pose data security risks; 2) Local model deployment demands technical expertise; 3) General LLMs lack mechanisms to integra

Xue-Yi Guo

The irreversibility and thermalization of many-body systems can be attributed to the erasure of spread non-equilibrium state information by local operations. This thermalization mechanism can be demonstrated by the sequence of $\hat{O}_i(t_i)$, where $\hat{O_i}$ is a local operator, $\hat{O_i}(t_i) = e^{i\hat{H}t_i} \hat{O_i} e^{-i\hat{H}t_i}$, $\hat{H}$ is

Jie Xu, Na Zhao, Gang Niu, Masashi Sugiyama

Recently, multi-view learning (MVL) has garnered significant attention due to its ability to fuse discriminative information from multiple views. However, real-world multi-view datasets are often heterogeneous and imperfect, which usually causes MVL methods designed for specific combinations of views to lack application potential and limits their effectivene

Xue Han, Qian Hu, Yitong Wang, Wenchun Gao

Large language models (LLMs) suffer from temporal misalignment issues especially across long span of time. The issue arises from knowing that LLMs are trained on large amounts of data where temporal information is rather sparse over long times, such as thousands of years, resulting in insufficient learning or catastrophic forgetting by the LLMs. This paper p

Simin Chen, Pranav Pusarla, Baishakhi Ray

The rapid evolution of code largelanguage models underscores the need for effective and transparent benchmarking of their reasoning capabilities. However, the current benchmarking approach heavily depends on publicly available, human-created datasets. The widespread use of these fixed benchmark datasets makes the benchmarking process to be static and thus pa

Varatheepan Paramanayakam, Andreas Karatzas, Dimitrios Stamoulis, Iraklis Anagnostopoulos

Edge computing systems struggle to efficiently manage multiple concurrent deep neural network (DNN) workloads while meeting strict latency requirements, minimizing power consumption, and maintaining environmental sustainability. This paper introduces Ecomap, a sustainability-driven framework that dynamically adjusts the maximum power threshold of edge device

Long Zhang, Yizhe Zhao, Halvin Yang, Guangming Liang

Integrated data and energy transfer (IDET) is considered as a key enabler of 6G, as it can provide both wireless energy transfer (WET) and wireless data transfer (WDT) services towards low power devices. Thanks to the extra degree of freedom provided by fluid antenna (FA), incorporating FA into IDET systems presents a promising approach to enhance energy eff

Xin Hong, Dingchao Gao, Sanjiang Li, Shenggang Ying

With the rapid progress in quantum hardware and software, the need for verification of quantum systems becomes increasingly crucial. While model checking is a dominant and very successful technique for verifying classical systems, its application to quantum systems is still an underdeveloped research area. This paper advances the development of model checkin

Emil Albrychiewicz, Andrés Franco Valiente, Vi Hong

We explore the tropical analog of spinors by representing tropical geometries as foliated Riemann surfaces endowed with degenerate complex structures. We investigate tropical limits of the Laplace-Beltrami operator and explicitly construct its square root, which defines a tropical Dirac operator. We find that the tropical Clifford algebra is classified as a

Yating Liu, Zimo Liu, Xiangyuan Lan, Wenming Yang

Text-based person retrieval (TPR) has gained significant attention as a fine-grained and challenging task that closely aligns with practical applications. Tailoring CLIP to person domain is now a emerging research topic due to the abundant knowledge of vision-language pretraining, but challenges still remain during fine-tuning: (i) Previous full-model fine-t

Huian Yang, Rajeev Sahay

Deep learning has been shown to be highly effective for automatic modulation classification (AMC), which is a pivotal technology for next-generation cognitive communications. Yet, existing deep learning methods for AMC often lack robust mechanisms for uncertainty quantification (UQ). This limitation restricts their ability to produce accurate and reliable pr

Sangyeop Kim, Hangyeul Lee, Yohan Lee

The growth of conversational AI services has increased demand for effective information retrieval from dialogue data. However, existing methods often face challenges in capturing semantic intent or require extensive labeling and fine-tuning. This paper introduces HEISIR (Hierarchical Expansion of Inverted Semantic Indexing for Retrieval), a novel framework t

Handi Chen, Rui Zhou, Yun-Hin Chan, Zhihan Jiang

Leveraging blockchain in Federated Learning (FL) emerges as a new paradigm for secure collaborative learning on Massive Edge Networks (MENs). As the scale of MENs increases, it becomes more difficult to implement and manage a blockchain among edge devices due to complex communication topologies, heterogeneous computation capabilities, and limited storage cap

Yilun Chen, Jiaqi Lu

We characterize a joint CLT of the number of pulls and the sample mean reward of the arms in a stochastic two-armed bandit environment under UCB algorithms. Several implications of this result are in place: (1) a nonstandard CLT of the number of pulls hence pseudo-regret that smoothly interpolates between a standard form in the large arm gap regime and a slo

Junchi Feng, Giles Hamilton-Fletcher, Nikhil Ballem, Michael Batavia

Purpose: Navigating urban environments poses significant challenges for individuals who are blind or have low vision, especially in areas affected by construction. Construction zones introduce hazards such as uneven surfaces, barriers, hazardous materials, excessive noise, and altered routes that obstruct familiar paths and compromise safety. Although naviga

Kaiwen Wu, Craig Sanders, Benjamin Letham, Phillip Guan

Gaussian processes (GPs) are powerful models for human-in-the-loop experiments due to their flexibility and well-calibrated uncertainty. However, GPs modeling human responses typically ignore auxiliary information, including a priori domain expertise and non-task performance information like user confidence ratings. We propose mixed likelihood variational GP

Bruce Nguyen, Yan Zhang

Diabetes is a prevalent chronic disease with significant health and economic burdens worldwide. Early prediction and diagnosis can aid in effective management and prevention of complications. This study explores the use of machine learning models to predict diabetes based on lifestyle factors using data from the Behavioral Risk Factor Surveillance System (BR

Jiyue Jiang, Zikang Wang, Yuheng Shan, Heyan Chai

Large Language models (LLMs) have emerged as powerful tools for addressing challenges across diverse domains. Notably, recent studies have demonstrated that large language models significantly enhance the efficiency of biomolecular analysis and synthesis, attracting widespread attention from academics and medicine. In this paper, we systematically investigat

Zhenyu Hou, Senming Tan, Zhihao Zhang, Long Xu

Terrain analysis is critical for the practical ap- plication of ground mobile robots in real-world tasks, espe- cially in outdoor unstructured environments. In this paper, we propose a novel spatial-temporal traversability assessment method, which aims to enable autonomous robots to effectively navigate through complex terrains. Our approach utilizes sparse

Kamal Choudhary

The Joint Automated Repository for Various Integrated Simulations (JARVIS) is a unified platform for multiscale, multimodal, forward, and inverse materials design. It integrates diverse theoretical and experimental approaches, including density functional theory, quantum Monte Carlo, tight-binding, classical force fields, machine learning, microscopy, diffra

Youngook Choi, Flaminio Flamini, Seonja Kim

This paper replaces the previous longer version and focuses on the specialty $2$ case. More precisely, in this paper we address the Brill-Noether theory for rank-two, degree $d$ stable bundles of speciality $2$ on a general $\nu$-gonal curve $C$ of genus $g$, $3 \leq \nu < \lfloor \frac{g+3}{2}\rfloor$, leveraging universal extension spaces, modular maps and

Jie Liu, Tiexin Qin, Hui Liu, Yilei Shi

In this work, we address the challenge of adaptive pediatric Left Ventricular Ejection Fraction (LVEF) assessment. While Test-time Training (TTT) approaches show promise for this task, they suffer from two significant limitations. Existing TTT works are primarily designed for classification tasks rather than continuous value regression, and they lack mechani

Jindong Jiang, Xiuyu Li, Zhijian Liu, Muyang Li

Recent advances in video-based multimodal large language models (Video-LLMs) have significantly improved video understanding by processing videos as sequences of image frames. However, many existing methods treat frames independently in the vision backbone, lacking explicit temporal modeling, which limits their ability to capture dynamic patterns and efficie

Ahan Basu, Bhabani Shankar Dey, Pushpak Jagtap

This work aims to synthesize a controller that ensures that an unknown discrete-time system is incrementally input-to-state stable ($\delta$-ISS). In this work, we introduce the notion of $\delta$-ISS control Lyapunov function ($\delta$-ISS-CLF), which, in conjunction with the controller, ensures that the closed-loop system is incrementally ISS. To address t

Georgios P. Georgiou

Pronunciation instruction in foreign language classrooms has often been an overlooked area of focus. With the widespread adoption of Artificial Intelligence (AI) and its potential benefits, investigating how AI is utilized in pronunciation teaching and understanding the beliefs of teachers about this tool is essential for improving learning outcomes. This st

Qianliang Wu, Haobo Jiang, Yaqing Ding, Lei Luo

Establishing reliable correspondences is crucial for all registration tasks, including 2D image registration, 3D point cloud registration, and 2D-3D image-to-point cloud registration. However, these tasks are often complicated by challenges such as scale inconsistencies, symmetry, and large deformations, which can lead to ambiguous matches. Previous feature-

Joshua Bird, Jan Blumenkamp, Amanda Prorok

Cooperative Simultaneous Localization and Mapping (C-SLAM) enables multiple agents to work together in mapping unknown environments while simultaneously estimating their own positions. This approach enhances robustness, scalability, and accuracy by sharing information between agents, reducing drift, and enabling collective exploration of larger areas. In thi

Changsen Feng, Zhongliang Huang, Jun Lin, Licheng Wang

The declining provision of inertia by synchronous generators in modern power systems necessitates aggregating distributed energy resources (DERs) into virtual power plants (VPPs) to unlock their potential in delivering inertia and primary frequency response (IPFR) through ancillary service markets. To facilitate DER participation in the IPFR market, this pap

Jiayi Chen, Shiquan Ruan

Motivated by the construction of $\imath$Hall algebras and $\Delta$-Hall algebras, we introduce $\imath$Hopf algebras associated with symmetrically self-dual Hopf algebras. We prove that the $\imath$Hopf algebra is an associative algebra with a unit, where the associativity relies on an analogue of Green's formula in the framework of Hopf algebras. As an app

Shinya Fujita, Boram Park

For a graph $G$, a subset $S$ of $V(G)$ is a {\it hop dominating set} of $G$ if every vertex not in $S$ has a $2$-step neighbor in $S$. The {\it hop domination number}, $\gamma_h(G)$, of $G$ is the minimum cardinality of a hop dominating set of $G$. In this paper, we show that for a connected triangle-free graph $G$ with $n\ge 15$ vertices, if $\delta(G)\ge

Tao Zhong, Christine Allen-Blanchette

We propose GAGrasp, a novel framework for dexterous grasp generation that leverages geometric algebra representations to enforce equivariance to SE(3) transformations. By encoding the SE(3) symmetry constraint directly into the architecture, our method improves data and parameter efficiency while enabling robust grasp generation across diverse object poses.

Wieb Bosma, Rene Bruin, Robbert Fokkink, Jonathan Grube

We use the automatic theorem prover Walnut to resolve various open problems from the OEIS and beyond. Specifically, we clarify the structure of sequence A260311, which concerns runs of sums of upper Wythoff numbers. We extend a result of Hajdu, Tijdeman, and Varga on polynomials with nonzero coefficients modulo a prime. Additionally, we settle open problems

Yansong Gao, Huaibing Peng, Hua Ma, Zhiyang Dai

For the first time, we unveil discernible temporal (or historical) trajectory imprints resulting from adversarial example (AE) attacks. Standing in contrast to existing studies all focusing on spatial (or static) imprints within the targeted underlying victim models, we present a fresh temporal paradigm for understanding these attacks. Of paramount discovery

Alan Luo, Kaiwen Yuan

Vision Transformers (ViTs) have demonstrated exceptional performance in various vision tasks. However, they tend to underperform on smaller datasets due to their inherent lack of inductive biases. Current approaches address this limitation implicitly-often by pairing ViTs with pretext tasks or by distilling knowledge from convolutional neural networks (CNNs)

Andrew Yuan, Rajeev Sahay

Deep learning is an effective approach for performing radio frequency (RF) fingerprinting, which aims to identify the transmitter corresponding to received RF signals. However, beyond the intended receiver, malicious eavesdroppers can also intercept signals and attempt to fingerprint transmitters communicating over a wireless channel. Recent studies suggest

Chunnan Shang, Zhizhong Wang, Hongwei Wang, Xiangming Meng

Attention-based arbitrary style transfer methods, including CNN-based, Transformer-based, and Diffusion-based, have flourished and produced high-quality stylized images. However, they perform poorly on the content and style images with the same semantics, i.e., the style of the corresponding semantic region of the generated stylized image is inconsistent wit

Congxi Xiao, Jingbo Zhou, Yixiong Xiao, Xinjiang Lu

We present TimeFound, an encoder-decoder transformer-based time series foundation model for out-of-the-box zero-shot forecasting. To handle time series data from various domains, TimeFound employs a multi-resolution patching strategy to capture complex temporal patterns at multiple scales. We pre-train our model with two sizes (200M and 710M parameters) on a

Soumya Sahu, Thomas Mathew, Dulal K. Bhaumik

A generalization of the classical concordance correlation coefficient (CCC) is considered under a three-level design where multiple raters rate every subject over time, and each rater is rating every subject multiple times at each measuring time point. The ratings can be discrete or continuous. A methodology is developed for the interval estimation of the CC

Rakesh Kumar, Shiv Parsad

Let $S_g$ be a closed orientable surface of genus $g\geq 2$. A collection $\Omega = \{ \gamma_1, \dots, \gamma_s\}$ of pairwise non-homotopic simple closed curves on $S_g$ such that $\gamma_i$ and $\gamma_j$ are in minimal position, is called a \emph{filling system} or a \emph{filling} of $S_g$ if the complement $S_g\setminus \Omega$ is a disjoint union of $

Chandra M. Varma

Non-reciprocal critical currents without applying an external magnetic field have been observed recently in several superconductors, in various forms of Graphene, a Kagome compound and in an under-doped cuprate. A necessary requirement for this is that the usual supercurrent be accompanied by an anti-symmetric chiral super-current, i.e. with the symmetry of

Ti-Chung Cheng, Yutong Zhang, Yi-Hung Chou, Vinay Koshy

Quadratic Surveys (QSs) elicit more accurate preferences than traditional methods like Likert-scale surveys. However, the cognitive load associated with QSs has hindered their adoption in digital surveys for collective decision-making. We introduce a two-phase "organize-then-vote" QS to reduce cognitive load. As interface design significantly impacts survey

Erik Jones, Arjun Patrawala, Jacob Steinhardt

Humans often rely on subjective natural language to direct language models (LLMs); for example, users might instruct the LLM to write an enthusiastic blogpost, while developers might train models to be helpful and harmless using LLM-based edits. The LLM's operational semantics of such subjective phrases -- how it adjusts its behavior when each phrase is incl

Leonardo Santens, Diego S. D'Antonio, Shuhang Hou, David Saldaña

This paper presents the Spinning Blimp, a novel lighter-than-air (LTA) aerial vehicle designed for low-energy stable flight. Utilizing an oblate spheroid helium balloon for buoyancy, the vehicle achieves minimal energy consumption while maintaining prolonged airborne states. The unique and low-cost design employs a passively arranged wing coupled with a prop

Lijia Yu, Yibo Miao, Yifan Zhu, Xiao-Shan Gao

The primary objective of learning methods is generalization. Classic uniform generalization bounds, which rely on VC-dimension or Rademacher complexity, fail to explain the significant attribute that over-parameterized models in deep learning exhibit nice generalizability. On the other hand, algorithm-dependent generalization bounds, like stability bounds, o

Juntong Chen, Jiang Wu, Jiajing Guo, Vikram Mohanty

The rise of Large Language Models (LLMs) and generative visual analytics systems has transformed data-driven insights, yet significant challenges persist in accurately interpreting users' analytical and interaction intents. While language inputs offer flexibility, they often lack precision, making the expression of complex intents inefficient, error-prone, a

Kenzi Odani

Poincar\'e gave a criterion which determines the shape of equilibrium for planar differential equations. In his statement, he excluded the case of repeated eigenvalues. In fact, in such a case, we can give a $C^1$ counter-example to his assertion. In this note, we show that if we strengthen the condition to $C^{1,\alpha}$ ($0<\alpha<1$), his assertion become

Rutwig Campoamor-Stursberg, Danilo Latini, Ian Marquette, Junze Zhang

The supermultiplet model, based on the reduction chain $\mathfrak{su}(4) \supset \mathfrak{su}(2) \times \mathfrak{su}(2)$, is revisited through the lens of commutants within universal enveloping algebras of Lie algebras. From this analysis, a collection of twenty polynomials up to degree nine emerges from the commutant associated with the $\mathfrak{su}(2)

Masoumeh Zareapoor, Pourya Shamsolmoali, Huiyu Zhou, Yue Lu

The Detection Transformer (DETR), by incorporating the Hungarian algorithm, has significantly simplified the matching process in object detection tasks. This algorithm facilitates optimal one-to-one matching of predicted bounding boxes to ground-truth annotations during training. While effective, this strict matching process does not inherently account for t

Haoran Wang, Lian Huai, Wenbin Li, Lei Qi

We have witnessed remarkable progress in foundation models in vision tasks. Currently, several recent works have utilized the segmenting anything model (SAM) to boost the segmentation performance in medical images, where most of them focus on training an adaptor for fine-tuning a large amount of pixel-wise annotated medical images following a fully supervise

Rahul Chhimpa, Avinash Chand Yadav

We simulate the $N$-spin critical Ising model on a square lattice using Glauber dynamics and consider the typical one-unit time equal to $N$ single-spin-flip attempts. The divergence of correlation time with the linear extent of the system results in critical slowing down, a challenge to equilibration because the spin configurations generated in such a way a

Zichong Li, Xinyu Feng, Yuheng Cai, Zixuan Zhang

Large Language Models (LLMs) have shown remarkable capabilities across tasks, yet they often require additional prompting techniques when facing complex problems. While approaches like self-correction and response selection have emerged as popular solutions, recent studies have shown these methods perform poorly when relying on the LLM itself to provide feed

Yining Cao, Yiyi Huang, Anh Truong, Hijung Valentina Shin

It has been increasingly recognized that effective human-AI co-creation requires more than prompts and results, but an environment with empowering structures that facilitate exploration, planning, iteration, as well as control and inspection of AI generation. Yet, a concrete design approach to such an environment has not been established. Our literature anal

József Balogh, Haoran Luo

Let $\alpha(\mathbb{F}_q^{d},p)$ be the maximum possible size of a point set in general position in the $p$-random subset of $\mathbb{F}_q^d$. In this note, we determine the order of magnitude of $\alpha(\mathbb{F}_q^{3},p)$ up to a polylogarithmic factor by proving a balanced supersaturation result for the sets of $4$ points in the same plane.

Gleb Smirnov, Roman Vershynin

We show how to improve the discrepancy of an iid sample by moving only a few points. Specifically, modifying \( O(m) \) sample points on average reduces the Kolmogorov-Smirnov distance to the population distribution to \(1/m\).

Runtao Zhou, Guangya Wan, Saadia Gabriel, Sheng Li

Large Language Models (LLMs) have demonstrated remarkable capabilities in reasoning tasks, leading to their widespread deployment. However, recent studies have highlighted concerning biases in these models, particularly in their handling of dialectal variations like African American English (AAE). In this work, we systematically investigate dialectal dispari

Jacqueline E. McCleary, Eric M. Huff, James W. Bartlett, Brandon S. Hensley

One of the more surprising astrophysical discoveries of the last decade has been the presence of enormous quantities of dust at megaparsec distances from galaxies, which has important implications for galaxy evolution, the circumgalactic and intergalactic medium, and observational cosmology. In this work, we present a novel method for studying these vast hal

Yassine El Gantouh, Yang Liu, Jianquan Lu, Jinde Cao

In this paper, we investigate well-posedness and stability properties of distributed parameter systems, with particular emphasis on linear positive control systems. We establish a characterization of the well-posedness in the Banach lattice setting. Furthermore, we derive a resolvent condition for admissibility of control operators for positive semigroups. I

Beverley Gorry, Tobias Fischer, Michael Milford, Alejandro Fontan

Effective monitoring of underwater ecosystems is crucial for tracking environmental changes, guiding conservation efforts, and ensuring long-term ecosystem health. However, automating underwater ecosystem management with robotic platforms remains challenging due to the complexities of underwater imagery, which pose significant difficulties for traditional vi

Xiangnan Chen, Yuancheng Fang, Qian Xiao, Juncheng Li

Multimodal Large Language Models (MLLMs) have garnered significant attention for their strong visual-semantic understanding. Most existing chart benchmarks evaluate MLLMs' ability to parse information from charts to answer questions. However, they overlook the inherent output biases of MLLMs, where models rely on their parametric memory to answer questions r

Seth Karten, Andy Luu Nguyen, Chi Jin

We introduce Pok\'eChamp, a minimax agent powered by Large Language Models (LLMs) for Pok\'emon battles. Built on a general framework for two-player competitive games, Pok\'eChamp leverages the generalist capabilities of LLMs to enhance minimax tree search. Specifically, LLMs replace three key modules: (1) player action sampling, (2) opponent modeling, and (

Mahsa Ashouri, Nicholas C. Henderson

Developing tools for estimating heterogeneous treatment effects (HTE) and individualized treatment effects has been an area of active research in recent years. While these tools have proven to be useful in many contexts, a concern when deploying such methods is the degree to which incorporating HTE into a prediction model provides an advantage over predictiv

Miriam Löcke, Pim van Ooij, Cristóbal Bertoglio

4D Flow MRI is the state of the art technique for measuring blood flow, and it provides valuable information for inverse problems in the cardiovascular system. However, 4D Flow MRI has a very long acquisition time, straining healthcare resources and inconveniencing patients. Due to this, usually only a part of the frequency space is acquired, where then furt

Ziqiao Wang, Cheng Long, Yongyi Mao

Federated learning (FL) is a widely adopted privacy-preserving distributed learning framework, yet its generalization performance remains less explored compared to centralized learning. In FL, the generalization error consists of two components: the out-of-sample gap, which measures the gap between the empirical and true risk for participating clients, and t

Yuzuho Komori, Nobuhito Maru

We propose a model of six dimensional $SU(7)$ grand gauge-Higgs unification compactified on $S^1/Z_2 /times S^1/Z_2$, which is a six dimensional extension of five dimensional $SU(4)$ gauge-Higgs unification predicting the weak mixing angle $/sin^2 /theta_W=1/4$ at the compactification scale. We investigate whether the correct pattern of electroweak symmetry

Hao Ding, Yiming Zeng, Zhaoliang Wan, Hui Cheng

Goal-oriented grasping in dense clutter, a fundamental challenge in robotics, demands an adaptive policy to handle occluded target objects and diverse configurations. Previous methods typically learn policies based on partially observable segments of the occluded target to generate motions. However, these policies often struggle to generate optimal motions d

Yuqing Wang, Xiao Yang

With the rapid expansion of cloud computing infrastructure, energy consumption has become a critical challenge, driving the need for accurate and efficient prediction models. This study proposes a novel Vector Weighted Average Kernel Extreme Learning Machine (VWAA-KELM) model to enhance energy consumption prediction in cloud computing environments. By integr

Sheikh Moonwara Anjum Monisha, Ratun Rahman

One of the primary challenges in medical diagnostics is the accurate and efficient use of magnetic resonance imaging (MRI) for the detection of brain tumors. But the current machine learning (ML) approaches have two major limitations, data privacy and high latency. To solve the problem, in this work we propose a federated learning architecture for a better a

Tung T. Nguyen, Nguyen Duy Tân

Gcd-graphs represent an interesting and historically important class of integral graphs. Since the pioneering work of Klotz and Sander, numerous incarnations of these graphs have been explored in the literature. In this article, we define and establish some foundational properties of gcd-graphs defined over a general finite commutative ring. In particular, w

Arash Mozhdehi, Yunli Wang, Sun Sun, Xin Wang

Deep reinforcement learning (DRL)-based frameworks, featuring Transformer-style policy networks, have demonstrated their efficacy across various vehicle routing problem (VRP) variants. However, the application of these methods to the multi-trip time-dependent vehicle routing problem (MTTDVRP) with maximum working hours constraints -- a pivotal element of urb

Hongyuan Yang, Siqi Peng, Akihiro Yamamoto

We propose a novel and efficient method for link prediction in bipartite networks, using \textit{formal concept analysis} (FCA) and the Transformer encoder. Link prediction in bipartite networks finds practical applications in various domains such as product recommendation in online sales, and prediction of chemical-disease interaction in medical science. Si

Yining Cao, Peiling Jiang, Haijun Xia

Unlike static and rigid user interfaces, generative and malleable user interfaces offer the potential to respond to diverse users' goals and tasks. However, current approaches primarily rely on generating code, making it difficult for end-users to iteratively tailor the generated interface to their evolving needs. We propose employing task-driven data models

Yingzhuo Han, Yingbo Wang, Yucheng Xue, Jiefei Shi

The nature of the pseudogap state is widely believed as a key to understanding the pairing mechanism underlying unconventional superconductivity. Over the past two decades, significant efforts have been devoted to searching for spontaneous symmetry breaking or potential order parameters associated with these pseudogap states, aiming to better characterize th

Shuojue Yang, Zijian Wu, Mingxuan Hong, Qian Li

Real2Sim is becoming increasingly important with the rapid development of surgical artificial intelligence (AI) and autonomy. In this work, we propose a novel Real2Sim methodology, Instrument-Splatting, that leverages 3D Gaussian Splatting to provide fully controllable 3D reconstruction of surgical instruments from monocular surgical videos. To maintain both

V. A. Dzuba, V. V. Flambaum, E. Peik

We propose that a nuclear clock based on the Th V ion can surpass the accuracy of clocks built with other thorium ions. The Th$^{4+}$ ion has a rigid closed-shell core with zero total electron angular momentum, suppressing frequency shifts from black-body radiation and stray external fields that act mainly on electrons. We calculate the energy shift of the n

Younes Nikdelan

We establish sufficient conditions, involving Rankin--Cohen (RC) brackets, under which certain combinations of meromorphic quasi-modular forms and their derivatives yield meromorphic modular forms. To achieve this, we adopt an algebraic perspective by working within the framework of RC algebras. First, we prove that any canonical RC algebra, whose underlying

Idris O. Sunmola, Zhenjun Zhao, Samuel Schmidgall, Yumeng Wang

Accurate geometric reconstruction of deformable tissues in monocular endoscopic video remains a fundamental challenge in robot-assisted minimally invasive surgery. Although recent volumetric and point primitive methods based on neural radiance fields (NeRF) and 3D Gaussian primitives have efficiently rendered surgical scenes, they still struggle with handlin

Qing Chang, Wei Dai, Zhihao Shuai, Limin Yu

Naturalistic driving action recognition is essential for vehicle cabin monitoring systems. However, the complexity of real-world backgrounds presents significant challenges for this task, and previous approaches have struggled with practical implementation due to their limited ability to observe subtle behavioral differences and effectively learn inter-frame

Ali Naseri Sadr

We prove that a pair of continuous disjoint periodic curves in $\mathbb{C}$ inscribes an isosceles trapezoid with any similarity type. The case of smooth curves can be identified with a Lagrangian intersection problem for a pair of Lagrangian cylinders in $\mathbb{R}\times S^1\times\mathbb{C}$, and the continuous case follows from the smooth one by a standar

Yiwen Dong, Zhenyang Xu, Yongqiang Tian, Chengnian Sun

Type inference is crucial for reusing online code snippets. Although snippets are prevalently shared on platforms like StackOverflow, they often lack essential type information, such as fully qualified names (FQNs). Recent studies have leveraged Large Language Models (LLMs) to perform type inference for such code snippets, showing promising results. However,

Graciela Camacho-Fidalgo, Blain Judkins, Kylee Friederichs, Lara Soberanis

Augmented Reality Head-Mounted Displays (AR-HMDs) have proven effective to assist workers. However, they may degrade their Safety and Situational Awareness (SSA), particularly in complex and hazardous industrial settings. This paper analyzes, objectively and subjectively, the effects of AR-HMDs' on workers' SSA in a simulated hazardous industrial environment

Hongyao Tang

Learning the optimal policy from a random network initialization is the theme of deep Reinforcement Learning (RL). As the scale of DRL training increases, treating DRL policy network weights as a new data modality and exploring the potential becomes appealing and possible. In this work, we focus on the policy learning path in deep RL, represented by the traj

A. Makai Baker, Paul D. Lasky, Eric Thrane, Jacob Golomb

The next generation of gravitational-wave observatories will achieve unprecedented strain sensitivities with an expanded observing band. They will detect ${\cal O}(10^5)$ binary neutron star (BNS) mergers every year, the loudest of which will be in the band for $\approx 90$ minutes with signal-to-noise ratios $\approx 1500$. Current techniques will not be ab

Zhou Fang, Arie Israel

In this paper, we introduce a robust market making framework based on Wasserstein distance, utilizing a stochastic policy approach enhanced by entropy regularization. We demonstrate that, under mild assumptions, the robust market making problem can be reformulated as a convex optimization question. Additionally, we outline a methodology for selecting the opt

Miao Li, Michael Klamkin, Russell Bent, Pascal Van Hentenryck

Decision makers routinely use constrained optimization technology to plan and operate complex systems like global supply chains or power grids. In this context, practitioners must assess how close a computed solution is to optimality in order to make operational decisions, such as whether the current solution is sufficient or whether additional computation i

Aaron D. Kaplan, Runze Liu, Ji Qi, Tsz Wai Ko

Accurate potential energy surface (PES) descriptions are essential for atomistic simulations of materials. Universal machine learning interatomic potentials (UMLIPs)$^{1-3}$ offer a computationally efficient alternative to density functional theory (DFT)$^4$ for PES modeling across the periodic table. However, their accuracy today is fundamentally constraine

Lihao Xiao, Tingyu Zhang, Yun Liu, Chayanis Sutcharitchan

Chronic superficial gastritis (CSG) severely affects quality of life and can progress to worse gastric pathologies. Traditional Chinese Medicine (TCM) effectively treats CSG, as exemplified by Jinhong Tablets (JHT) with known anti-inflammatory properties, though their mechanism remains unclear. This study integrated network pharmacology, untargeted metabolom

Karthik Elamvazhuthi

In this note, we revisit the problem of flow approximation properties of neural ordinary differential equations (NODEs). The approximation properties have been considered as a flow controllability problem in recent literature. The neural ODE is considered {\it narrow} when the parameters have dimension equal to the input of the neural network, and hence have

Ziqi Ni, Ao Fu, Yi Zhou

Achieving high-fidelity lip-speech synchronization in audio-driven talking portrait synthesis remains challenging. While multi-stage pipelines or diffusion models yield high-quality results, they suffer from high computational costs. Some approaches perform well on specific individuals with low resources, yet still exhibit mismatched lip movements. The afore

T. Li, M. Lei, James Snead, C. S. Chen

In this paper, we propose several mathematical models for 3D surface reconstruction and volume estimation from a set of scattered cloud data. Three meshless methods including the interpolation-based method by RBF, PDE-based approach by Kansa's method and the Method of Fundamental Solutions are employed and compared. For the optimal recovery of the surfaces,

Alison A. Silva, D. Bazeia, Fabiano M. Andrade

Entanglement is a fundamental resource for many applications in quantum information processing. Here, we investigate how quantum transport in simple quantum graphs, modeled as controlled two-level quantum systems, can be utilized to generate entangled states through coherent control operations between two simple quantum graphs. A controlled operation is defi

Feng Ni, Kui Huang, Yao Lu, Wenyu Lv

With the rapid advancement of digitalization, various document images are being applied more extensively in production and daily life, and there is an increasingly urgent need for fast and accurate parsing of the content in document images. Therefore, this report presents PP-DocBee, a novel multimodal large language model designed for end-to-end document ima

Chenglong Wang, Haoyu Tang, Xiyuan Yang, Yueqi Xie

As large language models (LLMs) gradually become integral tools for problem solving in daily life worldwide, understanding linguistic inequality is becoming increasingly important. Existing research has primarily focused on static analyses that assess the disparities in the existing knowledge and capabilities of LLMs across languages. However, LLMs are conti

Taixian Hou, Yueqi Zhang, Xiaoyi Wei, Zhiyan Dong

We address the challenge of effectively controlling the locomotion of legged robots by incorporating precise frequency and phase characteristics, which is often ignored in locomotion policies that do not account for the periodic nature of walking. We propose a hierarchical architecture that integrates a low-level phase tracker, oscillators, and a high-level

Disheng Liu, Yiran Qiao, Wuche Liu, Yiren Lu

True intelligence hinges on the ability to uncover and leverage hidden causal relations. Despite significant progress in AI and computer vision (CV), there remains a lack of benchmarks for assessing models' abilities to infer latent causality from complex visual data. In this paper, we introduce \textsc{\textbf{Causal3D}}, a novel and comprehensive benchmark

Ruiya Chen, Xiangdong Xu, Jianqiang Li

Travel time is one of the key indicators monitored by intelligent transportation systems, helping the systems to gain real-time insights into traffic situations, predict congestion, and identify network bottlenecks. Travel time exhibits variability, and thus suitable probability distributions are necessary to accurately capture full information of travel tim

Maurice S. Fabien

We present a high-order hybridizable discontinuous Galerkin method for the numerical solution of time-dependent three-phase flow in heterogeneous porous media. The underlying algorithm is a semi-implicit operator splitting approach that relaxes the nonlinearity present in the governing equations. By treating the subsequent equations implicitly, we obtain sol

Qunqiang Feng, Hongpeng Ren, Yaru Tian

In this paper, we study the limiting behavior of the generalized Zagreb indices of the classical Erd\H{o}s-R\'{e}nyi (ER) random graph $G(n,p)$, as $n\to\infty$. For any integer $k\ge1$, we first give an expression for the $k$-th order generalized Zagreb index in terms of the number of star graphs of various sizes in any simple graph. The explicit formulas f

Yunxiao Shi, Hong Cai, Amin Ansari, Fatih Porikli

3D occupancy prediction has recently emerged as a new paradigm for holistic 3D scene understanding and provides valuable information for downstream planning in autonomous driving. Most existing methods, however, are computationally expensive, requiring costly attention-based 2D-3D transformation and 3D feature processing. In this paper, we present a novel 3D

Liang Zhang, Hua Pang, Chenghao Zhang, Song Li

In protein engineering, while computational models are increasingly used to predict mutation effects, their evaluations primarily rely on high-throughput deep mutational scanning (DMS) experiments that use surrogate readouts, which may not adequately capture the complex biochemical properties of interest. Many proteins and their functions cannot be assessed

Haiyang Yu, Mengyang Zhao, Jinghui Lu, Ke Niu

Video subtitles play a crucial role in short videos and movies, as they not only help models better understand video content but also support applications such as video translation and content retrieval. Existing video subtitle extraction methods typically rely on multi-stage frameworks, where errors accumulate across stages and temporal dependencies are und