March 2025 arXiv papers — page 217

Alan Dow, Raul Figueroa-Sierra, Osvaldo Guzmán, Michael Hrušák

We provide a counterexample to the Category Dichotomy in the framework of $\textsf{ZFC}$. That is, we prove the existence of an ideal on $\omega$ that is not Kat\v{e}tov below $\mathsf{nwd}$ and does not have restrictions above $\mathcal{ED}$. We also prove that in the Laver model every tall $\mathsf{P}$-ideal is Kat\v{e}tov-Blass above $\mathcal{ED}_{\maths

Kun Zhao, Jiayi Wang, Yifei Lou

This paper focuses on recovering an underlying matrix from its noisy partial entries, a problem commonly known as matrix completion. We delve into the investigation of a non-convex regularization, referred to as transformed $L_1$ (TL1), which interpolates between the rank and the nuclear norm of matrices through a hyper-parameter $a \in (0, \infty)$. While s

Ava Polzin

A point spread function (PSF) describes the distribution of light for a pure point source in an astronomical image due to the optics of the instrument. An accurate PSF is key for deconvolution, point source photometry and source removal. Space-based telescopes can then pose a challenge because their PSFs are influenced by their complex construction, and the

David J. Moss

Optical polarizers, which selectively transmit light with specific polarization states, are essential components in modern optical systems. Here, we experimentally demonstrate integrated waveguide and microring resonator (MRR) polarizers incorporating reduced graphene oxide (rGO). 2D graphene oxide (GO) films are integrated onto silicon photonic devices with

Yoshiyuki Y. Yamaguchi, Julien Barré

We investigate the universality in collisionless nonlinear dynamics of a codimension-two bifurcation where two eigenvalues collide at the origin, and two lines of continuous bifurcation and discontinuous jump meet. Through linear analysis and direct numerical simulations, we show that this bifurcation does occur, both for two-dimensional shear flows and for

Shivang Garde, Jaya Prakash Champati, Arpan Chattopadhyay

Monitoring a process/phenomenon of specific interest is prevalent in Cyber-Physical Systems (CPS), remote healthcare, smart buildings, intelligent transport, industry 4.0, etc. A key building block of the monitoring system is a sensor sampling the process and communicating the status updates to a monitor for detecting events of interest. Measuring the freshn

Seokun Kang, Taehwan Kim

Video action recognition is a challenging but important task for understanding and discovering what the video does. However, acquiring annotations for a video is costly, and semi-supervised learning (SSL) has been studied to improve performance even with a small number of labeled data in the task. Prior studies for semi-supervised video action recognition ha

XinWei Feng, Yu Jiang, Zhi Liu, Zhe Meng

In this paper, we first investigate the estimation of the empirical joint Laplace transform of volatilities of two semi-martingales within a fixed time interval [0, T] by using overlapped increments of high-frequency data. The proposed estimator is robust to the presence of finite variation jumps in price processes. The related functional central limit theor

Tabia Tanzin Prama, Jannatul Ferdaws Amrin, Md. Mushfique Anwar, Iqbal H. Sarker

The rise of social media has significantly increased the prevalence of cyberbullying (CB), posing serious risks to both mental and physical well-being. Effective detection systems are essential for mitigating its impact. While several machine learning (ML) models have been developed, few incorporate victims' psychological, demographic, and behavioral factors

Taekyun Kim, Dae San Kim

Spivey found a recurrence relation for the Bell numbers by using combinatorial method. The aim of this paper is to derive Spivey's type recurrence relations for the degenerate Bell polynomials and the degenerate Dowling polynomials by using the boson annihilation and creation operators satisfying the commutation relation aa+-a+a=1. In addition, we derive a S

Ahmad Mohammad Saber, Max Mauro Dias Santos, Mohammad Al Janaideh, Amr Youssef

The increasing adoption of Electric Vehicles (EVs) and the expansion of charging infrastructure and their reliance on communication expose Electric Vehicle Supply Equipment (EVSE) to cyberattacks. This paper presents a novel Kolmogorov-Arnold Network (KAN)-based framework for detecting cyberattacks on EV chargers using only power consumption measurements. Le

Carolina Silva-Plata, Carlos Rosel, Barnabas Gavin Cangan, Hosam Alagi

In this paper, we present a touch technology to achieve tactile interactivity for human-robot interaction (HRI) in soft robotics. By combining a capacitive touch sensor with an online solid mechanics simulation provided by the SOFA framework, contact detection is achieved for arbitrary shapes. Furthermore, the implementation of the capacitive touch technolog

Qiang Li, Yinhan Lin, Qin Luo, Lina Yu

Reinforcement learning (RL) has evolved into a widely investigated technology for the development of smart TSC strategies. However, current RL algorithms necessitate excessive interaction with the environment to learn effective policies, making them impractical for large-scale tasks. The DreamerV3 algorithm presents compelling properties for policy learning.

Sourabh Saha, Hosho Katsura, Manoranjan Kumar

Flat band systems have recently attracted significant attention due to their instability under small perturbations, which can lead to the stabilization of many exotic quantum phases. We study a trimer ladder which shows a middle flat band in the absence of onsite Coulomb interaction. We investigate the quantum phases of the Hubbard model on this geometry usi

Muhan Hou, Koen Hindriks, A. E. Eiben, Kim Baraka

Learning from Demonstrations (LfD) allows robots to learn skills from human users, but its effectiveness can suffer due to sub-optimal teaching, especially from untrained demonstrators. Active LfD aims to improve this by letting robots actively request demonstrations to enhance learning. However, this may lead to frequent context switches between various tas

Immanuel Ben Porat, José A. Carrillo, Pierre-Emmanuel Jabin

We study the mean field limit for singular dynamics with time evolving weights. Our results are an extension of the work of Serfaty \cite{duerinckx2020mean} and Bresch-Jabin-Wang \cite{bresch2019modulated}, which consider singular Coulomb flows with weights which are constant time. The inclusion of time dependent weights necessitates the commutator estimates

Weijie Kuang, Hann Woei Ho, Ye Zhou, Shahrel Azmin Suandi

Autonomous Micro Air Vehicles (MAVs) are becoming essential in precision agriculture to enhance efficiency and reduce labor costs through targeted, real-time operations. However, existing unmanned systems often rely on GPS-based navigation, which is prone to inaccuracies in rural areas and limits flight paths to predefined routes, resulting in operational in

Junseon Park, Junhyun Lee, Hyeongon Park

In South Korea, power grid is currently operated based on the static line rating (SLR) method, where the transmission line capacity is determined based on extreme weather conditions. However, with global warming, there is a concern that the temperatures during summer may exceed the SLR criteria, posing safety risks. On the other hand, the conservative estima

Harsh Sharma, Juan Diego Draxl Giannoni, Boris Kramer

Existing model reduction techniques for high-dimensional models of conservative partial differential equations (PDEs) encounter computational bottlenecks when dealing with systems featuring non-polynomial nonlinearities. This work presents a nonlinear model reduction method that employs lifting variable transformations to derive structure-preserving quadrati

Enhyeok Jang, Youngmin Kim, Hyungseok Kim, Seungwoo Choi

A zoned neutral atom architecture achieves exceptional fidelity by segregating the execution spaces of 1- and 2-qubit gates, being a promising candidate for high-accuracy quantum systems. Unfortunately, naively applying programs designed for static qubit topologies to zoned architectures may result in most execution time being consumed by inter-zone travels

Tianyi Peng, Naimeng Ye, Andrew Zheng

Experiments in online platforms frequently suffer from network interference, in which a treatment applied to a given unit affects outcomes for other units connected via the platform. This SUTVA violation biases naive approaches to experiment design and estimation. A common solution is to reduce interference by clustering connected units, and randomizing trea

Gargi Panda, Soumitra Kundu, Saumik Bhattacharya, Aurobinda Routray

Salient object detection (SOD) in RGB-D images is an essential task in computer vision, enabling applications in scene understanding, robotics, and augmented reality. However, existing methods struggle to capture global dependency across modalities, lack comprehensive saliency priors from both RGB and depth data, and are ineffective in handling low-quality d

Yasuhiro Fujita

Experience replay is a key component in reinforcement learning for stabilizing learning and improving sample efficiency. Its typical implementation samples transitions with replacement from a replay buffer. In contrast, in supervised learning with a fixed dataset, it is a common practice to shuffle the dataset every epoch and consume data sequentially, which

Wenjia Jiang, Yangyang Zhuang, Chenxi Song, Xu Yang

Recent advancements in Large Language Models (LLMs) have led to the development of intelligent LLM-based agents capable of interacting with graphical user interfaces (GUIs). These agents demonstrate strong reasoning and adaptability, enabling them to perform complex tasks that traditionally required predefined rules. However, the reliance on step-by-step rea

Vladimir Sluchak

Flow-improving devices (FIDs) are used the most effectively for the reduction of noise and vibration induced by a propulsor. These devices may also increase the efficiency of a propulsor and in some cases should be considered and designed as a part of an Integrated Propulsor, for example, non-axis symmetrical ducts. The FIDs may be naturally subdivided into

Sourav Mishra, Shreya Hallikeri, Suresh Sundaram

Physics-Informed Neural Networks (PINNs) offer a promising approach to simulating physical systems. Still, their application is limited by optimization challenges, mainly due to the lack of activation functions that generalize well across several physical systems. Existing activation functions often lack such flexibility and generalization power. To address

Jeremiah Allis, Xin Jin, Riddhi Ghosh

The standard regression tree method applied to observations within clusters poses both methodological and implementation challenges. Effectively leveraging these data requires methods that account for both individual-level and sample-level effects. We propose Generalized Tree-Informed Mixed Model (GTIMM), which replaces the linear fixed effect in a generaliz

Ethan Kilmer, Joseph Chen, Jiawei Ge, Preksha Sarda

Autonomous robotic systems hold potential for improving renal tumor resection accuracy and patient outcomes. We present a fluorescence-guided robotic system capable of planning and executing incision paths around exophytic renal tumors with a clinically relevant resection margin. Leveraging point cloud observations, the system handles irregular tumor shapes

Go Soma, Tomohiro Akazawa, Eisaku Kato, Kento Komatsu

High-speed optical receivers are crucial in modern optical communication systems. While complex photonic integrated circuits (PICs) are widely employed to harness the full degrees of freedom (DOFs) of light for efficient data transmission, their waveguide nature inherently constrains two-dimensional spatial scaling to accommodate a large number of optical si

Van Tien Nguyen, Zhi-An Wang, Kaiqiang Zhang

Based on the method of matched asymptotic expansions and Banach fixed point theorem, we rigorously construct infinitely many self-similar blow-up profiles for the parabolic-elliptic Keller-Segel system \begin{equation*} \left\{\begin{array}{l} \partial_{t} u=\Delta u-\nabla \cdot\left(u \nabla \Phi_{u}\right), \\ 0=\Delta \Phi_{u}+u,\\ u(\cdot,0)=u_0 \geq 0

Roberto De Leo, James A. Yorke

In a recent article, we introduced the concept of streams and graphs of a semiflow. An important related concept is the one of semiflow with {\em compact dynamics}, which we defined as a semiflow $F$ with a {\em compact global trapping region}. In this follow-up, we restrict to the important case where the phase space $X$ is locally compact and we move the f

Zelin Li, Chenwei Wang, Zhaoke Huang, Yiming MA

3D fluorescence microscopy is essential for understanding fundamental life processes through long-term live-cell imaging. However, due to inherent issues in imaging principles, it faces significant challenges including spatially varying noise and anisotropic resolution, where the axial resolution lags behind the lateral resolution up to 4.5 times. Meanwhile,

Ross Street

The first word in the title is intended in a sense suggested by Lawvere and Schanuel whereby finite sets are objective natural numbers. At the objective level, the axioms defining abstract Mackey and Tambara functors are categorically familiar. The first step was taken by Harald Lindner in 1976 when he recognized that Mackey functors, defined as pairs of fun

Hua Huang, Tianshi Xu, Yuanzhe Xi, Edmond Chow

Gaussian Processes (GPs) are flexible, nonparametric Bayesian models widely used for regression and classification because of their ability to capture complex data patterns and quantify predictive uncertainty. However, the O(n^3) computational cost of kernel matrix operations poses a major obstacle to applying GPs at scale. HiGP is a high-performance Python

Tram Thi Minh Tran

Digital technologies are reshaping how people experience their surroundings, often pulling focus toward virtual spaces and making it harder to stay present and engaged. Wearable augmented reality (AR), by embedding digital information into the physical world, may further immerse users in digital layers. Yet paradoxically, it also holds the potential to suppo

Abdusamatjan Iskandar, Xiaofeng Wang, Ali Esamdin, Xiangyun Zeng

We present extensive optical observations of a nearby Type Ia supernova (SN Ia), SN 2021hpr, located in the spiral galaxy NGC 3147 at a distance of $\sim$ 45 Mpc. Our observations cover a phase within $\sim 1-2$ days to $\sim 290$ days after the explosion. SN 2021hpr is found to be a spectroscopically normal SN Ia, with an absolute B-band peak magnitude of $

Kenta Tsukahara, Kanji Tanaka, Daiki Iwata, Jonathan Tay Yu Liang

In emerging multi-robot societies, heterogeneous agents must continually extract and integrate local knowledge from one another through communication, even when their internal models are completely opaque. Existing approaches to continual or collaborative learning for visual place recognition (VPR) largely assume white-box access to model parameters or share

Fanyu Wang, Hangyu Zhu, Zhenping Xie

Deep learning has shown promising performance on various machine learning tasks. Nevertheless, the uninterpretability of deep learning models severely restricts the usage domains that require feature explanations, such as text correction. Therefore, a novel interpretable deep learning model (named AxBERT) is proposed for Chinese spelling correction by aligni

Ziyang Liu, Yurui Hu, Yihan Deng

This paper introduces a novel multi-stage decision-making model that integrates hypothesis testing and dynamic programming algorithms to address complex decision-making scenarios.Initially,we develop a sampling inspection scheme that controls for both Type I and Type II errors using a simple random sampling method without replacement,ensuring the randomness

Michael Levinov, Yosef Yomdin, Dmitry Batenkov

We investigate the problem of reconstructing a 2D piecewise smooth function from its bandlimited Fourier measurements. This is a well known and well studied problem with many real world implications, in particular in medical imaging. While many techniques have been proposed over the years to solve the problem, very few consider the accurate reconstruction of

Abigale Snortland, Katherine Van Ness, Jennifer A. Franck, Ari Athair

Cross-flow turbine power is a net sum of power generation from rotating blades and power loss from rotating support structures. While the aggregate forces and torques at the turbine level are important for end use, these can inhibit a deeper understanding of fluid-structure interactions. Identification of blade-level forces and torques allows for specific in

Ji Zhou, Kainan Wu, Haide Wang, Jinyang Yang

Driven by the ever-increasing capacity demands, the 50G passive optical network (PON) is maturing gradually. One of the main challenges for the 50G PON is implementing burst-mode digital signal processing (BM-DSP) for the burst upstream signal. In this paper, we demonstrate a real-time BM-DSP for burst reception of 25Gbit/s on-off keying signal to meet the a

Da Li, Keping Bi, Jiafeng Guo, Xueqi Cheng

Table retrieval, essential for accessing information through tabular data, is less explored compared to text retrieval. The row/column structure and distinct fields of tables (including titles, headers, and cells) present unique challenges. For example, different table fields have varying matching preferences: cells may favor finer-grained (word/phrase level

Alexandra Olteanu, Solon Barocas, Su Lin Blodgett, Lisa Egede

There is a growing proliferation of AI systems designed to mimic people's behavior, work, abilities, likenesses, or humanness -- systems we dub AI automatons. Individuals, groups, or generic humans are being simulated to produce creative work in their styles, to respond to surveys in their places, to probe how they would use a new system before deployment, t

Changhe Chen, Xiaohao Xu, Xiangdong Wang, Xiaonan Huang

Designing soft robots is a complex and iterative process that demands cross-disciplinary expertise in materials science, mechanics, and control, often relying on intuition and extensive experimentation. While foundation models, especially Large Language Models (LLMs), have demonstrated impressive reasoning abilities, their capacity to conduct embodied design

Tong Liang, Jim Davis

Recent studies are leveraging advancements in large language models (LLMs) trained on extensive internet-crawled text data to generate textual descriptions of downstream classes in CLIP-based zero-shot image classification. While most of these approaches aim at improving accuracy, our work focuses on ``making better mistakes", of which the mistakes' severiti

Dujun Nie, Xianda Guo, Yiqun Duan, Ruijun Zhang

Object Goal Navigation-requiring an agent to locate a specific object in an unseen environment-remains a core challenge in embodied AI. Although recent progress in Vision-Language Model (VLM)-based agents has demonstrated promising perception and decision-making abilities through prompting, none has yet established a fully modular world model design that red

Junda He, Jieke Shi, Terry Yue Zhuo, Christoph Treude

Recently, Large Language Models (LLMs) have been increasingly used to automate SE tasks such as code generation and summarization. However, evaluating the quality of LLM-generated software artifacts remains challenging. Human evaluation, while effective, is very costly and time-consuming. Traditional automated metrics like BLEU rely on high-quality reference

Yixuan Huang, Jie Yang, Chao-Kai Wen, Shi Jin

Future wireless communication networks are expected to be smarter and more aware of their surroundings, enabling a wide range of context-aware applications. Reconfigurable intelligent surfaces (RISs) are set to play a critical role in supporting various sensing tasks, such as target recognition. However, current methods typically use RIS configurations optim

Naokant Deo, Sandeep Kumar

The present article intends to introduce the sequence of Baskakov-Durrmeyer type operators linked with the generating functions of Boas-Buck type polynomials. After calculating the moments, including the limiting case of central moments of the constructed sequence of operators, in the subsequent sections, we estimate the convergence rate using the Ditzian-To

Xidan Zhang, Yihan Zhuang, Qian Guo, Haodong Yang

Approaches for improving generative adversarial networks (GANs) training under a few samples have been explored for natural images. However, these methods have limited effectiveness for synthetic aperture radar (SAR) images, as they do not account for the unique electromagnetic scattering properties of SAR. To remedy this, we propose a physics-inspired regul

Kui Huang, Mengke Song, Shuo Ba, Ling An

Waste classification is crucial for improving processing efficiency and reducing environmental pollution. Supervised deep learning methods are commonly used for automated waste classification, but they rely heavily on large labeled datasets, which are costly and inefficient to obtain. Real-world waste data often exhibit category and style biases, such as var

Haoyang Li, Shang Wu, Xiaokang Zhang, Xinmei Huang

Text-to-SQL, the task of translating natural language questions into SQL queries, plays a crucial role in enabling non-experts to interact with databases. While recent advancements in large language models (LLMs) have significantly enhanced text-to-SQL performance, existing approaches face notable limitations in real-world text-to-SQL applications. Prompting

Keshu Wu, Pei Li, Yang Zhou, Rui Gan

The advancement of Connected and Automated Vehicles (CAVs) and Vehicle-to-Everything (V2X) offers significant potential for enhancing transportation safety, mobility, and sustainability. However, the integration and analysis of the diverse and voluminous V2X data, including Basic Safety Messages (BSMs) and Signal Phase and Timing (SPaT) data, present substan

Zirui Wu, Xiao Liu, Jiayi Li, Lingpeng Kong

While Large Language Model-based agents have demonstrated substantial progress in task completion, existing evaluation benchmarks tend to overemphasize single-task performance, with insufficient attention given to the crucial aspects of multitask planning and execution efficiency required in real-world scenarios. To bridge this gap, we present Recipe2Plan, a

Ruiwu Liu

Couples decide whether to have children before knowing the persistent career loss generated by parenthood. After the loss is realized, household resources are renegotiated and individual market earnings partly determine outside options. This limited-commitment friction places a disproportionate share of female earnings risk on the wife even when expected hou

Zihan Liu, Xinhao Luo, Junxian Guo, Wentao Ni

In this work, we design and implement VQ-LLM, an efficient fused Vector Quantization (VQ) kernel generation framework. We first introduce a software abstraction called codebook cache to optimize codebook access efficiency and support the integration of VQ with various computations. The codebook cache adaptively stores different entries across the GPU's memor

Gargi V. Pillai, Debashis Sen

Most videos, including those captured through aerial remote sensing, are usually non-stationary in nature having time-varying feature statistics. Although, sophisticated reconstruction and prediction models exist for video anomaly detection, effective handling of non-stationarity has seldom been considered explicitly. In this paper, we propose to perform pre

Hang Zheng, Hongshen Xu, Yuncong Liu, Lu Chen

Large language models (LLMs) are prone to hallucination stemming from misaligned self-awareness, particularly when processing queries exceeding their knowledge boundaries. While existing mitigation strategies employ uncertainty estimation or query rejection mechanisms, they suffer from computational efficiency and sacrificed helpfulness. To address these iss

Jincheng He, Zhongheng He

Developing software with the source code open to the public is prevalent; however, similar to its closed counter part, open-source has quality problems, which cause functional failures, such as program breakdowns, and non-functional, such as long response times. Previous researchers have revealed when, where, how and what developers contribute to projects an

Bo Cheng, Jueqing Lu, Yuan Tian, Haifeng Zhao

Semi-supervised learning (SSL) has garnered significant attention due to its ability to leverage limited labeled data and a large amount of unlabeled data to improve model generalization performance. Recent approaches achieve impressive successes by combining ideas from both consistency regularization and pseudo-labeling. However, these methods tend to under

Yingji Zhong, Kaichen Zhou, Zhihao Li, Lanqing Hong

Neural Radiance Fields (NeRF) have shown remarkable capabilities for photorealistic novel view synthesis. One major deficiency of NeRF is that dense inputs are typically required, and the rendering quality will drop drastically given sparse inputs. In this paper, we highlight the effectiveness of rendered semantics from dense novel views, and show that rende

Martin Landriau, Erin Mentuch Cooper, Dustin Davis, Karl Gebhardt

The Hobby-Eberly Dark Energy Experiment (HETDEX) is an untargeted spectroscopic galaxy survey that uses Ly$\alpha$ emitting galaxies (LAEs) as tracers of 1.9 < z < 3.5 large scale structure. Most detections consist of a single emission line, whose identity is inferred via a Bayesian analysis of ancillary data. To determine the accuracy of these line identifi

Zhijin Meng, Mohammed Althubyani, Shengyuan Xie, Imran Razzak

Traditional rule-based conversational robots, constrained by predefined scripts and static response mappings, fundamentally lack adaptability for personalized, long-term human interaction. While Large Language Models (LLMs) like GPT-4 have revolutionized conversational AI through open-domain capabilities, current social robots implementing LLMs still lack em

Seyed Amir Mousavi, Utku Ozbulak, Francesca Tozzi, Nikdokht Rashidian

Video object segmentation is an emerging technology that is well-suited for real-time surgical video segmentation, offering valuable clinical assistance in the operating room by ensuring consistent frame tracking. However, its adoption is limited by the need for manual intervention to select the tracked object, making it impractical in surgical settings. In

Joshua S. Speagle, Catherine Zucker, Angus Beane, Phillip A. Cargile

We present brutus, an open source Python package for quickly deriving stellar properties, distances, and reddenings to stars based on grids of stellar models constrained by photometric and astrometric data. We outline the statistical framework for deriving these quantities, its implementation, and various Galactic priors over the 3-D distribution of stars, s

Xing Su, Jian-Jian Cheng, Lin Zhang

We present a comprehensive analysis of the dynamic diversity associated with superradiant phase transitions within a one-dimensional tight-binding electronic chain that is intrinsically coupled to a single-mode optical cavity. By employing the quantized electromagnetic vector potential through the Peierls substitution, the gauge-invariant coupled Bose-Fermi

Dimitris Oikonomou, Nicolas Loizou

Sharpness-Aware Minimization (SAM) has emerged as a powerful method for improving generalization in machine learning models by minimizing the sharpness of the loss landscape. However, despite its success, several important questions regarding the convergence properties of SAM in non-convex settings are still open, including the benefits of using normalizatio

Sara Rahmani, Wei Liang, Yu-Wen Peng, Yu Lu

In present work, we study the reactions $D^{0} \rightarrow K^{+} K^{-} \eta$ and $D^0 \rightarrow \pi^{+} \pi^{-} \eta$, and find that the $a_0(980)$ state plays a dominant role. At the quark level, the external and internal $W$-emission mechanisms are taken into account, which can hadronize into the final states, and then the $a_0(980)$ state is generated f

Haoyuan Li, Ziqin Ye, Yue Hao, Weiyang Lin

Accurate object perception is essential for robotic applications such as object navigation. In this paper, we propose DQO-MAP, a novel object-SLAM system that seamlessly integrates object pose estimation and reconstruction. We employ 3D Gaussian Splatting for high-fidelity object reconstruction and leverage quadrics for precise object pose estimation. Both o

Yu Ning, Fei Shi, Tao Luo, Xiande Zhang

The existence of $k$-uniform states has been a widely studied problem due to their applications in several quantum information tasks and their close relation to combinatorial objects like Latin squares and orthogonal arrays. With the machinery of quantum enumerators and linear programming, we establish several improved non-existence results and bounds on $k$

Yusheng Zhao, Junyu Luo, Xiao Luo, Jinsheng Huang

Test-time adaptation aims to adapt a well-trained model to potential distribution shifts at test time using only unlabeled test data, without access to the original training data. While previous efforts mainly focus on a single modality, test-time distribution shift in the multi-modal setting is more complex and calls for new solutions. This paper tackles th

Stephen Chung, Wenyu Du, Jie Fu

Recent advancements in reinforcement learning (RL) for large language models (LLMs), exemplified by DeepSeek R1, have shown that even a simple question-answering task can substantially improve an LLM's reasoning capabilities. In this work, we extend this approach by modifying the task into a multi-attempt setting. Instead of generating a single response per

Zhihua Shen, Siyang Chen, Han Wang, Tongsu Zhang

Multi-frame infrared small target detection (IRSTD) plays a crucial role in low-altitude and maritime surveillance. The hybrid architecture combining CNNs and Transformers shows great promise for enhancing multi-frame IRSTD performance. In this paper, we propose LVNet, a simple yet powerful hybrid architecture that redefines low-level feature learning in hyb

Min Bao, Yanmei Chen, Qiusheng Gu, Huiyuan Wang

Using the integral field unit data from the Mapping Nearby Galaxies at Apache Point Observatory (MaNGA) survey, we build a sample of gas-star misaligned galaxies. The large-scale environment of misaligned galaxies is dominated by filaments and clusters, while is less dense relative to the gas-star aligned control galaxies. The direction of the large-scale st

Shuo Wang, Tong Ren, Nan Cheng, Rong Wang

Purpose: This study proposes a novel anatomically-driven dynamic modeling framework for coronary arteries using skeletal skinning weights computation, aiming to achieve precise control over vessel deformation while maintaining real-time performance for surgical simulation applications. Methods: We developed a computational framework based on biharmonic energ

Chew Lian Chua, David Gunawan, Sandy Suardi

This paper advances the local projections (LP) method by addressing its inefficiency in high-frequency economic and financial data with volatility clustering. We incorporate a generalized autoregressive conditional heteroskedasticity (GARCH) process to resolve serial correlation issues and extend the model with GARCH-X and GARCH-HAR structures. Monte Carlo s

Huiqian Luo

Room temperature superconductivity, as one of the famous jewels on the crown of physics, has attracted continuous attention and unremitting efforts from numerous scientists. In recent years, more and more reports on room temperature superconductivity evoke many anticipations, but results remain controversial. Here, we introduce the characteristics of superco

Annalisa Conversano

We develop a general ring theory in the o-minimal setting culminating in a description of all the definable rings in an arbitrary o-minimal structure. We show that every definably connected ring with non-trivial multiplication defines an infinite field and it is essentially semialgebraic. A surprisingly strong correspondence between definably connected rings

Chaoran Yin, Tianqi Wang, Linjie Yan, Chengpeng Hao

This paper addresses the adaptive radar target detection problem in the presence of Gaussian interference with unknown statistical properties. To this end, the problem is first formulated as a binary hypothesis test, and then we derive a detection architecture grounded on the hybrid of Maximum Likelihood (ML) and Maximum A Posterior (MAP) approach. Specifica

Zhentao Wang, Jiawen Xie, Xuhang Zhang

M. Kapranov and V. Schechtman introduced the contingency metamatrix for a finite Coxeter group and conjectured that the contingency metamatrix is totally positive. For the Coxeter groups of type $A$, this conjecture has been proved by P. Etingof. In this article, we prove this conjecture for the Coxeter groups of type $B$ and exceptional types.

Dinushi Munasinghe, Ben Webster

It's known that many different blocks of $\mathbb{F}_pS_n$ for different values of $n$ are equivalent as categories, though the corresponding block algebras are almost never isomorphic. Thus, it is a challenging problem to give one particularly nice representative of this Morita equivalence class of algebras. This has been accomplished for the case of RoCK b

Xuemei Li, Yechi Liu

In this paper, we investigate the dynamical behaviors of a delayed lateral vibration model of footbridges proposed based on the facts that pedestrians will reduce their walking speed or stop walking when the response of the footbridge becomes sufficiently large, and that the bridge velocity can not be changed at once when the pedestrians begin to walk on the

Hang Xu, Tailong Xiao, Jingzheng Huang, Ming He

Critical ground states of quantum many-body systems have emerged as vital resources for quantum-enhanced sensing. Traditional methods to prepare these states often rely on adiabatic evolution, which may diminish the quantum sensing advantage. In this work, we propose a quantum reinforcement learning (QRL)-enhanced critical sensing protocol for quantum many-b

Yusei Ito, Tatsunori Taniai, Ryo Igarashi, Yoshitaka Ushiku

Crystal structure modeling with graph neural networks is essential for various applications in materials informatics, and capturing SE(3)-invariant geometric features is a fundamental requirement for these networks. A straightforward approach is to model with orientation-standardized structures through structure-aligned coordinate systems, or"frames." Howeve

Anusha Srikanthan, Yifan Xue, Vijay Kumar, Nikolai Matni

We consider the problem of safe real-time navigation of a robot in a dynamic environment with moving obstacles of arbitrary smooth geometries and input saturation constraints. We assume that the robot detects and models nearby obstacle boundaries with a short-range sensor and that this detection is error-free. This problem presents three main challenges: i)

Arjan Cornelissen, Simon Apers, Sander Gribling

Estimating the volume of a convex body is a canonical problem in theoretical computer science. Its study has led to major advances in randomized algorithms, Markov chain theory, and computational geometry. In particular, determining the query complexity of volume estimation to a membership oracle has been a longstanding open question. Most of the previous wo

Zhichao Yang, Leida Li, Pengfei Chen, Jinjian Wu

Contrastive vision-language models, such as CLIP, have demonstrated excellent zero-shot capability across semantic recognition tasks, mainly attributed to the training on a large-scale I&1T (one Image with one Text) dataset. This kind of multimodal representations often blend semantic and perceptual elements, placing a particular emphasis on semantics. Howev

Rui Luo, Zhixin Zhou

We introduce Volume-Sorted Prediction Set (VSPS), a novel method for uncertainty quantification in multi-target regression that uses conditional normalizing flows with conformal calibration. This approach constructs flexible, non-convex predictive regions with guaranteed coverage probabilities, overcoming limitations of traditional methods. By learning a tra

Xuejuan Yang, Aigen Li

The gas-phase abundances of deuterium (D) in the local interstellar medium (ISM) exhibit considerable regional variations. Particularly, in some regions the gas-phase D abundances are substantially lower than the primordial D abundance generated in the Big Bang, after subtracting the astration reduction caused by the Galactic chemical evolution. Deuterated p

Yonghwi Kim, Kai-Kit Wong, Jianzhong, Zhang

Nonlinear self-interference (SI) cancellation is essential for mitigating the impact of transmitter-side nonlinearity on overall SI cancellation performance in flexible duplex systems, including in-band full-duplex (IBFD) and sub-band full-duplex (SBFD). Digital SI cancellation (SIC) must address the nonlinearity in the power amplifier (PA) and the in-phase/

Fong Yew Leong, Wei-Bin Ewe, Tran Si Bui Quang, Zhongyuan Zhang

This study benchmarks hybrid quantum physics-informed neural network (HQPINN) to model high-speed flows, compared against classical physics-informed neural networks (PINNs) and fully quantum neural networks (QNNs). The HQPINN architecture integrates a parameterized quantum circuit (PQC) with a classical neural network in parallel, trained via a physics-infor

Ahmed El-Dawy, Amr El-Zawawi, Mohamed El-Habrouk

Reliable perception of the environment plays a crucial role in enabling efficient self-driving vehicles. Therefore, the perception system necessitates the acquisition of comprehensive 3D data regarding the surrounding objects within a specific time constrain, including their dimensions, spatial location and orientation. Deep learning has gained significant p

Joshua S. Speagle, Catherine Zucker, Ana Bonaca, Phillip A. Cargile

We present "augustus", a catalog of distance, extinction, and stellar parameter estimates to 170 million stars from $14\,{\rm mag} < r < 20\,{\rm mag}$ and with $|b| > 10^\circ$ drawing on a combination of optical to near-IR photometry from Pan-STARRS, 2MASS, UKIDSS, and unWISE along with parallax measurements from \textit{Gaia} DR2 and 3-D dust extinction m

Sebastian Barros

This theoretical work examines 'hallucinations' in both human cognition and large language models, comparing how each system can produce perceptions or outputs that deviate from reality. Drawing on neuroscience and machine learning research, we highlight the predictive processes that underlie human and artificial thought. In humans, complex neural mechanisms

Ailin Deng, Tri Cao, Zhirui Chen, Bryan Hooi

Vision-Language Models (VLMs) excel in integrating visual and textual information for vision-centric tasks, but their handling of inconsistencies between modalities is underexplored. We investigate VLMs' modality preferences when faced with visual data and varied textual inputs in vision-centered settings. By introducing textual variations to four vision-cen

Yan Miao, Will Shen, Sayan Mitra

We present a novel framework demonstrating zero-shot sim-to-real transfer of visual control policies learned in a Neural Radiance Field (NeRF) environment for quadrotors to fly through racing gates. Robust transfer from simulation to real flight poses a major challenge, as standard simulators often lack sufficient visual fidelity. To address this, we constru

Zhixun Chen, Ming Li, Yuxuan Huang, Yali Du

Large Language Model (LLM) agents have demonstrated remarkable generalization capabilities across multi-domain tasks. Existing agent tuning approaches typically employ supervised finetuning on entire expert trajectories. However, behavior-cloning of full trajectories can introduce expert bias and weaken generalization to states not covered by the expert data

Ramón H. Ruiz-Medina

A set with a group action is referred to as a $G$-set, and the set of functions that commute with this action forms a monoid under function composition. This paper examines the case where the $G$-set is finite, which implies that the monoid of $G$-equivariant functions is also finite. The document provides formulas for calculating the cardinality of this mon

BESIII Collaboration, M. Ablikim, M. N. Achasov, P. Adlarson

Using $e^+e^-$ data taken at the center-of-mass energy of 3.773 GeV with the BESIII detector, corresponding to an integrated luminosity of 20.3 fb$^{-1}$, we report the first measurement of the decay dynamics of the semileptonic decays $D^{+(0)}\to K^-\pi^+\pi^{0(-)} e^+\nu_e$. The amplitude analysis gives the hadronic form factors of the semileptonic $D$ tr