March 2025 arXiv papers — page 216

Lingkai Meng, Yu Shao, Long Yuan, Longbin Lai

The rise of graph analytics platforms has led to the development of various benchmarks for evaluating and comparing platform performance. However, existing benchmarks often fall short of fully assessing performance due to limitations in core algorithm selection, data generation processes (and the corresponding synthetic datasets), as well as the neglect of A

Serkan Akkoyun

In quantum circuits, qubits and the quantum gates acting on them have traditionally been analysed using matrix algebra and Dirac notation. While powerful, these can be unintuitive for conceptual understanding and rapid problem solving. In this work, a new schematic representation method is developed that visualizes the effects of quantum gates on qubits with

Guo Chen, Ling Lin, Chengfeng Zhang, Jie Zhang

Based on first-principles calculations and ab initio molecular dynamics simulations, the polymerisation of the unsaturated cis dinitrogen-difluoride (cis-N2F2) molecular compound is investigated. The thermodynamic, dynamical and thermal stabilities of the nitrogen fluorine NF system are investigated at conditions of 0-3000 K and 0-200 GPa. The cis-N2F2 molec

Zilin Zhao, Chishui Chen, Haotian Shi, Jiale Chen

Efficient path planning for unmanned aerial vehicles (UAVs) is crucial in remote sensing and information collection. As task scales expand, the cooperative deployment of multiple UAVs significantly improves information collection efficiency. However, collaborative communication and decision-making for multiple UAVs remain major challenges in path planning, e

Jiarui Yang, Songpengcheng Xia, Zengyuan Lai, Lan Sun

Millimeter-wave (mmWave) radar offers robust sensing capabilities in diverse environments, making it a highly promising solution for human body reconstruction due to its privacy-friendly and non-intrusive nature. However, the significant sparsity of mmWave point clouds limits the estimation accuracy. To overcome this challenge, we propose a two-stage deep le

Haoan Jin, Jiacheng Shi, Hanhui Xu, Kenny Q. Zhu

Large language models (LLMs) demonstrate significant potential in advancing medical applications, yet their capabilities in addressing medical ethics challenges remain underexplored. This paper introduces MedEthicEval, a novel benchmark designed to systematically evaluate LLMs in the domain of medical ethics. Our framework encompasses two key components: kno

Maria Barbati, Salvatore Greco, José Rui Figueira

We present a multi-objective portfolio decision model that involves selecting both a portfolio of projects and a set of elements to allocate to each project. Our model includes a defined set of objectives to optimize, with projects contributing to these objectives in various ways. The elements included in the portfolios are assessed based on both qualitative

Ahmet Selim Çanakçı, Niclas Vödisch, Kürsat Petek, Wolfram Burgard

A main bottleneck of learning-based robotic scene understanding methods is the heavy reliance on extensive annotated training data, which often limits their generalization ability. In LiDAR panoptic segmentation, this challenge becomes even more pronounced due to the need to simultaneously address both semantic and instance segmentation from complex, high-di

Jiaqi Xie

We extend the asymptotic formula for counting integral matrices with a given irreducible characteristic polynomial by Eskin, Mozes and Shah in 1996 to the case of counting elements in a maximal order of certain central simple algebra with a given irreducible characteristic polynomial.

Yassine Nabou, Ion Necoara

In this paper, we develop a regularized higher-order Taylor based method for solving composite (e.g., nonlinear least-squares) problems. At each iteration, we replace each smooth component of the objective function by a higher-order Taylor approximation with an appropriate regularization, leading to a regularized higher-order Taylor approximation (RHOTA) alg

Yixuan Fan, Haotian Xu, Mengqiao Liu, Qing Zhuo

The Entrance Dependent Vehicle Routing Problem (EDVRP) is a variant of the Vehicle Routing Problem (VRP) where the scale of cities influences routing outcomes, necessitating consideration of their entrances. This paper addresses EDVRP in agriculture, focusing on multi-parameter vehicle planning for irregularly shaped fields. To address the limitations of tra

Aida Abiad, Benjamin Jany

The quantum chromatic number, a generalization of the chromatic number, was first defined in relation to the non-local quantum coloring game. We generalize the former by defining the quantum $k$-distance chromatic number $\chi_{kq}(G)$ of a graph $G$, which can be seen as the quantum chromatic number of the $k$-th power graph, $G^k$, and as generalization of

D. S. Gireesh, HemanthKumar B

In this study, we explore the arithmetic properties of $b_{7^k}(n)$ for any $k\geq1$, which enumerates the partitions of $n$ where no part is divisible by $7^k$. By constructing generating functions for $b_{7^k}(n)$ over specific arithmetic progressions, we establish a collection of Ramanujan-type congruences.

Lama Moukheiber, Mira Moukheiber, Dana Moukheiiber, Jae-Woo Ju

We introduce a novel question-answering (QA) dataset using echocardiogram reports sourced from the Medical Information Mart for Intensive Care database. This dataset is specifically designed to enhance QA systems in cardiology, consisting of 771,244 QA pairs addressing a wide array of cardiac abnormalities and their severity. We compare large language models

Andrei Marin, Adrian Stefan Carstea

Blending Painlev\'e property with singularity confinement for a general arbitrary order Sawada-Kotera differential-difference equation, we find a proliferation of ``tau-functions'' (coming from strictly confined patterns). However only one of these function enters into the Hirota bilinear form (the others give multi-linear expressions) but has specific relat

Lang Mei, Chong Chen, Jiaxin Mao

As large language models (LLMs) gain widespread attention in both academia and industry, it becomes increasingly critical and challenging to effectively evaluate their capabilities. Existing evaluation methods can be broadly categorized into two types: manual evaluation and automatic evaluation. Manual evaluation, while comprehensive, is often costly and res

Michael Zwilich, Carsten Fallnich

This study explores the excitation of transverse laser modes through spatial gain shaping, while focusing on the boundary between selective single-mode and multi-mode lasing. By deliberately reducing the similarity between intensity distributions of pump and laser mode, it was studied, if and which other modes are excited besides the target mode, and how the

Jianhao M. Yang

A variational framework is developed here to quantize fermionic fields based on the extended stationary action principle. From the first principle, we successfully derive the well-known Floreanini-Jackiw representation of the Schr\"{o}dinger equation for the wave functional of fermionic fields - an equation typically introduced as a postulate in standard can

J. P. S. Sandhu, M. Bhardwaj, N. Ananthkrishnan, A. Sharma

The notion of sub-optimal Oswatitsch solutions is introduced in order to systematically conduct a tradeoff between total pressure recovery (TPR) and intake drag coefficient (CDi) for supersonic intakes. It is shown that the Oswatitsch-optimal TPR for a biconic intake may be enhanced by adding a conical flare which modifies the terminal normal shock into a no

Mohsen Masoudi, Davod Khojasteh Salkuyeh

This article introduces an iterative method for solving nonsingular positive semidefinite systems of linear equations. To construct the iteration process, the coefficient matrix is split into two positive semidefinite matrices along with an arbitrary Hermitian positive definite shift matrix. Several conditions are established to guarantee the convergence of

Husne Ara Rubaiyeat, Njayou Youssouf, Md Kamrul Hasan, Hasan Mahmud

Sign language recognition (SLR) for low-resource languages like Bangla suffers from signer variability, viewpoint variations, and limited annotated datasets. In this paper, we present BdSLW401, a large-scale, multi-view, word-level Bangla Sign Language (BdSL) dataset with 401 signs and 102,176 video samples from 18 signers in front and lateral views. To impr

Wan Nor Arifin, Najib Majdi Yaacob

Machine learning (ML) methods are being increasingly used across various domains of medicine research. However, despite advancements in the use of ML in medicine, clear and definitive guidelines for determining sample sizes in medical ML research are lacking. This article proposes a method for determining sample sizes for medical research utilizing ML method

Zhuo Li, Yuhao Du, Xiaoqi Jiao, Yiwen Guo

Selecting high-quality and diverse training samples from extensive datasets plays a crucial role in reducing training overhead and enhancing the performance of Large Language Models (LLMs). However, existing studies fall short in assessing the overall value of selected data, focusing primarily on individual quality, and struggle to strike an effective balanc

Xinyu Wang, Bohan Zhuang, Qi Wu

Large Vision Language Models (LVLMs) have demonstrated remarkable abilities in understanding and reasoning about both visual and textual information. However, existing evaluation methods for LVLMs, primarily based on benchmarks like Visual Question Answering and image captioning, often fail to capture the full scope of LVLMs' capabilities. These benchmarks a

Zicheng Zhang, Tengchuan Kou, Shushi Wang, Chunyi Li

Evaluating text-to-vision content hinges on two crucial aspects: visual quality and alignment. While significant progress has been made in developing objective models to assess these dimensions, the performance of such models heavily relies on the scale and quality of human annotations. According to Scaling Law, increasing the number of human-labeled instanc

Xiulong Yuan, Hongtao Xu, Wenting Shen, Ang Wang

Long context fine-tuning of large language models(LLMs) involves training on datasets that are predominantly composed of short sequences and a small proportion of longer sequences. However, existing approaches overlook this long-tail distribution and employ training strategies designed specifically for long sequences. Moreover, these approaches also fail to

Johannes Düreth, Philipp Gagel, David Laibacher, Oleg A. Egorov

Exciton-polariton III-V semiconductor microcavities provide a robust platform for emulating complex Hamiltonians, enabling topological photonics and quantum simulation for advanced photonic functionalities. Here, we introduce two novel fabrication techniques - etch-and-oversputter and deposit-and-oversputter - that overcome limitations of traditional photoni

Jiashun Suo, Xiaojian Liao, Limin Xiao, Li Ruan

Large language models like GPT-4 are resource-intensive, but recent advancements suggest that smaller, specialized experts can outperform the monolithic models on specific tasks. The Collaboration-of-Experts (CoE) approach integrates multiple expert models, improving the accuracy of generated results and offering great potential for precision-critical applic

Luobin Wang, Hongzhan Yu, Chenning Yu, Sicun Gao

Diffusion models have recently gained significant attention in robotics due to their ability to generate multi-modal distributions of system states and behaviors. However, a key challenge remains: ensuring precise control over the generated outcomes without compromising realism. This is crucial for applications such as motion planning or trajectory forecasti

Dorit Hochbaum, Torpong Nitayanont

We consider here a classification method that balances two objectives: large similarity within the samples in the cluster, and large dissimilarity between the cluster and its complement. The method, referred to as HNC or SNC, requires seed nodes, or labeled samples, at least one of which is in the cluster and at least one in the complement. Other than that,

Guangyin Bao, Qi Zhang, Zixuan Gong, Zhuojia Wu

Concept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with particular concepts. Precisely localizing these regions stands as a crucial long-term goal in neuroscience to grasp essential brain functions and mechanisms. Conventional experiment-driven approaches hinge on manual

Xuan Cai, Xuesong Bai, Zhiyong Cui, Danmu Xie

Autonomous driving (AD) testing constitutes a critical methodology for assessing performance benchmarks prior to product deployment. The creation of segmented scenarios within a simulated environment is acknowledged as a robust and effective strategy; however, the process of tailoring these scenarios often necessitates laborious and time-consuming manual eff

Payel Sarkar

In this paper, I propose a static wormhole model within modified $f(R,T)$ gravity where $f(R,T)=R+2\lambda T$. The wormhole solutions have been evolved in four cases: three different shape function along with redshift $\phi=\frac{\phi_0}{r}$ and a variable EoS parameter $\omega(r)$ with constant redshift function. I also have explored the energy conditions a

Yajun Liu, Beth Andrews

Sequential change-point detection for time series is widely used in data monitoring in practice. In this work, we focus on sequential change-point detection on high-order compositional time series models. Under the regularity conditions, we prove that a process following the generalized Beta AR(p) model with exogenous variables is stationary and ergodic. We

Keondo Park, You Rim Choi, Inhoe Lee, Hyung-Sin Kim

Running deep learning models on resource-constrained edge devices has drawn significant attention due to its fast response, privacy preservation, and robust operation regardless of Internet connectivity. While these devices already cope with various intelligent tasks, the latest edge devices that are equipped with multiple types of low-power accelerators (i.

Lin Huang, Yujuan Tan, Weisheng Li, Shitai Shan

This paper addresses the inherent limitations of conventional bottleneck structures (diminished instance discriminability due to overemphasis on batch statistics) and decoupled heads (computational redundancy) in object detection frameworks by proposing two novel modules: the Instance-Specific Bottleneck with full-channel global self-attention (ISB) and the

Xianqiang Li

In this paper, we prove the equivalence between sofic $p$-metric mean dimension and sofic metric mean dimension. This answers a question of Hayes in \cite{HB }. Furthermore, we establish the product formula for the sofic $p$-metric mean dimension.

Minh Le

It is shown in this paper that blow-up does not occur in the following chemotaxis system under homogeneous Neumann boundary conditions in a smooth, open, bounded domain \(\Omega \subset \mathbb{R}^2\): \begin{equation*} \begin{cases} u_t = \Delta u - \chi \nabla \cdot \left( \frac{u}{v^k} \nabla v \right) + ru - \mu u^2, \qquad &\text{in } \Omega \times (0,T

Mominul Islam, Mohammad Junayed Hasan, M. R. C. Mahdy

The detection of Alzheimer disease (AD) from clinical MRI data is an active area of research in medical imaging. Recent advances in quantum computing, particularly the integration of parameterized quantum circuits (PQCs) with classical machine learning architectures, offer new opportunities to develop models that may outperform traditional methods. However,

Terence R. Smith

Rational wave numbers are periodic sequences ${\mathbf \omega}={\bf A}{\bf w}(f,g)$ in which amplitude ${\bf A}$ a product of powers of trigonometric sequences and ${\bf w}(f,g)=\exp({\bf {i2}\pi ( f {\mathbf \xi} \oplus g{\bf 1})})$ is a sequence with $ \xi \epsilon \mathbb{ Z}$ and $f,g$ rational. They generalize the cyclic groups of the $n$th roots of uni

Yichen Jin, Qingfeng Lin, Yang Li, Hancheng Zhu

The recently emerged movable antenna (MA) shows great promise in leveraging spatial degrees of freedom to enhance the performance of wireless systems. However, resource allocation in MA-aided systems faces challenges due to the nonconvex and coupled constraints on antenna positions. This paper systematically reveals the challenges posed by the minimum antenn

Yunzhen He, Yusuke Takase, Yoichi Ishibashi, Hidetoshi Shimodaira

Large Language Models (LLMs) are increasingly being used in real-world applications. However, concerns about the reliability of the content they generate persist, as it frequently deviates from factual correctness or exhibits deficiencies in logical reasoning. This paper proposes a novel decoding strategy aimed at enhancing both factual accuracy and inferent

Detlev Buchholz, Fabio Ciolli, Giuseppe Ruzzi, Ezio Vasselli

The creation of electrically charged states and the resulting electromagnetic fields are considered in space-time regions in which such experiments can actually be carried out, namely in future-directed light cones. Under the simplifying assumption of external charges, charged states are formed from neutral pairs of opposite charges, with one charge being sh

Jisoo Hong, Youngjin Jung, Jihwan Bae, Seungho Song

This study developed an algorithm capable of detecting a reference line (a 0.2 mm thick piano wire) to accurately determine the position of an automated installation robot within an elevator shaft. A total of 3,245 images were collected from the experimental tower of H Company, the leading elevator manufacturer in South Korea, and the detection performance w

Zhun Mou, Bin Xia, Zhengchao Huang, Wenming Yang

Recent great advances in video generation models have demonstrated their potential to produce high-quality videos, bringing challenges to effective evaluation. Unlike human evaluation, existing automated evaluation metrics lack highlevel semantic understanding and reasoning capabilities for video, thus making them infeasible and unexplainable. To fill this g

Gemei Liu, Yi Ru-Ya Zhang

When $u$ is close to a single Talenti bubble $v$ of the $p$-Sobolev inequality, we show that \begin{equation*} \|Du-Dv\|_{L^p(\mathbb{R}^n)}^{\max\{1,p-1\}}\le C \|-{\rm div}(|Du|^{p-2}Du)-|u|^{p^*-2}u\|_{W^{-1,q}(\mathbb{R}^n)}, \end{equation*} where $C=C(n,p)>0$. This estimate provides a sharp stability estimate for the Struwe-type decomposition in the sin

Blas Fernández, Roghayeh Maleki, Štefko Miklavič, Giusy Monzillo

Let $\Gamma$ denote a finite, connected graph with vertex set $X$. Fix $x \in X$ and let $\varepsilon \ge 3$ denote the eccentricity of $x$. For mutually distinct scalars $\{\theta^*_i\}_{i=0}^\varepsilon$ define a diagonal matrix $A^*=A^*(\theta^*_0, \theta^*_1, \ldots, \theta^*_{\varepsilon}) \in M_X(\mathbb{R})$ as follows: for $y \in X$ we let $(A^*)_{yy

Jisoo Hong, Yongmin Hong, Jung-Woo Baek, Sung-Woo Kang

The injection molding process is a traditional technique for making products in various industries such as electronics and automobiles via solidifying liquid resin into certain molds. Although the process is not related to creating the main part of engines or semiconductors, this manufacturing methodology sets the final form of the products. Re-cently, resea

Indu K. Dihingia, Yosuke Mizuno

Quasi-periodic oscillations (QPOs) are very common in black hole accretion systems that are seen from the modulations in luminosity. Many supermassive black hole sources (e.g., RE J1034+396, 1H~0707-495, MCG-6-30-15, 1ES~1927+654, Sgr~A$^*$) have been observed to exhibit QPO-like variability in the range of mHz in different energy bands (e.g., radio, NIR, X-

Günter Rote

We present a program for enumerating all pseudoline arrangements with a small number of pseudolines and abstract order types of small point sets. This program supports computer experiments with these structures, and it complements the order-type database of Aichholzer, Aurenhammer, and Krasser. This system makes it practical to explore the abstract order typ

Issatay Tokmurziyev, Miguel Altamirano Cabrera, Muhammad Haris Khan, Yara Mahmoud

LLM-Glasses is a wearable navigation system which assists visually impaired people by utilizing YOLO-World object detection, GPT-4o-based reasoning, and haptic feedback for real-time guidance. The device translates visual scene understanding into intuitive tactile feedback on the temples, allowing hands-free navigation. Three studies evaluate the system: rec

Carlos Albors, Jianan Canal Li, Gonzalo Benegas, Chengzhong Ye

Genomic language models (gLMs) have shown mostly modest success in identifying evolutionarily constrained elements in mammalian genomes. To address this issue, we introduce a novel framework for training gLMs that explicitly models nucleotide evolution on phylogenetic trees using multispecies whole-genome alignments. Our approach integrates an alignment into

Renshuang Jiang, Pan Dong, Zhenling Duan, Yu Shi

To provide flexibility and low-level interaction capabilities, the unsafe tag in Rust is essential in many projects, but undermines memory safety and introduces Undefined Behaviors (UBs) that reduce safety. Eliminating these UBs requires a deep understanding of Rust's safety rules and strong typing. Traditional methods require depth analysis of code, which i

Sonnet Xu, Joseph Janizek, Yixing Jiang, Roxana Daneshjou

Vision language models (VLMs) show promise in medical diagnosis, but their performance across demographic subgroups when using in-context learning (ICL) remains poorly understood. We examine how the demographic composition of demonstration examples affects VLM performance in two medical imaging tasks: skin lesion malignancy prediction and pneumothorax detect

Sarvesh Arora, Sarthak Arora, Deepika Kumar, Vallari Agrawal

Social media has significantly reshaped interpersonal communication, fostering connectivity while also enabling the proliferation of misinformation. The unchecked spread of false narratives has profound effects on mental health, contributing to increased stress, anxiety, and misinformation-driven paranoia. This study presents a hybrid transformer-based appro

Gen Shi, Hui Zhang, Jie Tian

Accurate segmentation of 3D vascular structures is essential for various medical imaging applications. The dispersed nature of vascular structures leads to inherent spatial uncertainty and necessitates location awareness, yet most current 3D medical segmentation models rely on the patch-wise training strategy that usually loses this spatial context. In this

Hamed Nozari, Hoessein Abdi, Agnieszka Szmelter-Jarosz

This paper presents the Goat Optimization Algorithm (GOA), a novel bio-inspired metaheuristic optimization technique inspired by goats' adaptive foraging, strategic movement, and parasite avoidance behaviors.GOA is designed to balance exploration and exploitation effectively by incorporating three key mechanisms, adaptive foraging for global search, movement

Guotao Shen, Ziheng Yan, Xin Jin, Longhai Wu

In the research of video quality assessment (VQA), two-branch network has emerged as a promising solution. It decouples VQA with separate technical and aesthetic branches to measure the perception of low-level distortions and high-level semantics respectively. However, we argue that while technical and aesthetic perspectives are complementary, the technical

S. K. Ocker, M. Chen, S. P. Oh, P. Sharma

The circumgalactic medium (CGM) is poorly constrained at the sub-parsec scales relevant to turbulent energy dissipation and regulation of multi-phase structure. Fast radio bursts (FRBs) are sensitive to small-scale plasma density fluctuations, which can induce multipath propagation (scattering). The amount of scattering depends on the density fluctuation spe

Eun Cheol Choi, Ashwin Balasubramanian, Jinhu Qi, Emilio Ferrara

Misinformation surrounding emerging outbreaks poses a serious societal threat, making robust countermeasures essential. One promising approach is stance detection (SD), which identifies whether social media posts support or oppose misleading claims. In this work, we finetune classifiers on COVID-19 misinformation SD datasets consisting of claims and correspo

Yunian Pan, Jun Li, Lifan Xu, Shunqiao Sun

Nonlinear frequency hopping has emerged as a promising approach for mitigating interference and enhancing range resolution in automotive FMCW radar systems. Achieving an optimal balance between high range-resolution and effective interference mitigation remains challenging, especially without centralized frequency scheduling. This paper presents a game-theor

Ramkrishna Joshi, Aniruddha Joshi

Ethics play an important role in determining the behavior of an individual under certain circumstances. Ethical or unethical behavior can be treated as a strategy of a player in a pay-off game. In this paper, we present two analytical solutions to studying time evolution of behavior of an individual from ethics perspective. We also present the effect of a th

Yongyun Chen, Qiusheng Gu, Junhui Fan, Xiaotong Guo

Accretion supermassive black holes in the center of active galaxies usually produce ``jets''-collimated bipolar outflows of relativistic particles. Magnetic fields near the black hole event horizon may play a crucial role in the formation of jets/outflows. Both theory and observation indicate that jets/outflows driven by centrally active supermassive black h

Xueliang Zhao, Wei Wu, Jian Guan, Lingpeng Kong

The ability of large language models to solve complex mathematical problems has progressed significantly, particularly for tasks requiring advanced reasoning. However, the scarcity of sufficiently challenging problems, particularly at the Olympiad level, hinders further advancements. In this work, we introduce PromptCoT, a novel approach for automatically ge

Majid Behravan, Denis Gracanin

This paper presents Matrix, an advanced AI-powered framework designed for real-time 3D object generation in Augmented Reality (AR) environments. By integrating a cutting-edge text-to-3D generative AI model, multilingual speech-to-text translation, and large language models (LLMs), the system enables seamless user interactions through spoken commands. The fra

Hai-Yang Zhang, Ya-Peng Hu, Yu-Sen An

In this paper, we investigate the motion of charged particles around the weakly magnetized Schwarzschild-like bumblebee black hole which has Lorentz symmetry breaking. Charged particles have curled orbits around the black hole which can only appear in the presence of external magnetic field. We investigate the effect of Lorentz violation factor on the curled

Jiahui Luo, Kai Feng, Haijin Zeng, Yongyong Chen

As a crucial part of the spectral filter array (SFA)-based multispectral imaging process, spectral demosaicing has exploded with the proliferation of deep learning techniques. However, (1) bothering by the difficulty of capturing corresponding labels for real data or simulating the practical spectral imaging process, end-to-end networks trained in a supervis

Pengchen Liang, Leijun Shi, Huiping Yao, Bin Pu

Rapid bone scintigraphy is crucial for diagnosing skeletal disorders and detecting tumor metastases in children, as it shortens scan duration and reduces discomfort. However, accelerated acquisition often degrades image quality, impairing the visibility of fine anatomical details and potentially compromising diagnosis. To overcome this limitation, we introdu

Takahisa Igata

In static, spherically symmetric spacetimes, the deflection angle of photons in the strong deflection limit exhibits a logarithmic divergence. We introduce an analytical framework that clarifies the physical origin of this divergence by employing local, coordinate-invariant geometric quantities alongside the properties of the matter distribution. In contrast

Puhan Yang, Guchan Li

The rectilinear Steiner minimum tree (RSMT) problem computes the shortest network connecting a given set of points using only horizontal and vertical lines, possibly adding extra points (Steiner points) to minimize the total length. RSMT solvers seek to balance speed and accuracy. In this work, we design a framework to boost existing RSMT solvers, extending

Zhifei Xie, Mingbao Lin, Zihang Liu, Pengcheng Wu

Recent advancements in multimodal reasoning have largely overlooked the audio modality. We introduce Audio-Reasoner, a large-scale audio language model for deep reasoning in audio tasks. We meticulously curated a large-scale and diverse multi-task audio dataset with simple annotations. Then, we leverage closed-source models to conduct secondary labeling, QA

Yuhi Kamio

Paul Erd\H{o}s posed a problem on the asymptotic estimation of decomposing 1 into a sum of infinitely many unit fractions in \cite{Erd80}. We point out that this problem can be solved in the same way as the finite case, as shown in \cite{Sou05}.

Wenqi Guo, Yiyang Du, Shan Du

Gas leakage poses a significant hazard that requires prevention. Traditionally, human inspection has been used for detection, a slow and labour-intensive process. Recent research has applied machine learning techniques to this problem, yet there remains a shortage of high-quality, publicly available datasets. This paper introduces a synthetic dataset, SimGas

Xin Jin, Longhai Wu, Jie Chen, Ilhyun Cho

Video frame interpolation and prediction aim to synthesize frames in-between and subsequent to existing frames, respectively. Despite being closely-related, these two tasks are traditionally studied with different model architectures, or same architecture but individually trained weights. Furthermore, while arbitrary-time interpolation has been extensively s

Yuxun Ma, Toru Seo

Route choice models are one of the most important foundations for transportation research. Traditionally, theory-based models have been utilized for their great interpretability, such as logit models and Recursive logit models. More recently, machine learning approaches have gained attentions for their better prediction accuracy. In this study, we propose no

Tianyi Pan, Wei Wang, Jianliang Zhai, Tusheng Zhang

The purpose of this paper is to establish the well-posedness of the stochastic Stefan problem on moving hypersurfaces. Through a specially designed transformation, it turns out we need to solve stochastic partial differential equations on a fixed hypersurface with a new kind of nonhomogeneous monotonicity involving a family of time-dependent operators. This

Kürşad Metehan Gül, Selahattin Burak Sarsılmaz

This article proposes a simple, graph-independent perspective on partitioning the node set of a graph and provides multi-agent systems (MASs) with objectives beyond cooperation and bipartition. Specifically, we first introduce the notion of $k$-partition transformation to achieve any desired partition of the nodes. Then, we use this notion to formulate the m

Aviv Shamsian, Eitan Shaar, Aviv Navon, Gal Chechik

Machine unlearning aims to remove the influence of problematic training data after a model has been trained. The primary challenge in machine unlearning is ensuring that the process effectively removes specified data without compromising the model's overall performance on the remaining dataset. Many existing machine unlearning methods address this challenge

Shenyu Zhang, Jiaguo Tian, Zhengbang Zhu, Shan Huang

Microscopic traffic simulation has become an important tool for autonomous driving training and testing. Although recent data-driven approaches advance realistic behavior generation, their learning still relies primarily on a single real-world dataset, which limits their diversity and thereby hinders downstream algorithm optimization. In this paper, we propo

Kensuke Tatematsu, Akifumi Wachi

Achieving autonomous agents with robust generalization capabilities across diverse games and tasks remains one of the ultimate goals in AI research. Recent advancements in transformer-based offline reinforcement learning, exemplified by the MultiGame Decision Transformer [Lee et al., 2022], have shown remarkable performance across various games or tasks. How

Wenxuan Song, Jiayi Chen, Pengxiang Ding, Han Zhao

Vision-Language-Action (VLA) models demonstrate remarkable potential for generalizable robotic manipulation. The performance of VLA models can be improved by integrating with action chunking, a critical technique for effective control. However, action chunking linearly scales up action dimensions in VLA models with increased chunking sizes. This reduces the

Jiwan Kim, Mingyu Han, Ian Oakley

Wireless earbuds are an appealing platform for wearable computing on-the-go. However, their small size and out-of-view location mean they support limited different inputs. We propose finger identification input on earbuds as a novel technique to resolve these problems. This technique involves associating touches by different fingers with different responses.

Jiwan Kim, Jiwan Son, Ian Oakley

Smartwatches offer powerful features, but their small touchscreens limit the expressiveness of the input that can be achieved. To address this issue, we present, and open-source, the first sonar-based around-device input on an unmodified consumer smartwatch. We achieve this using a fine-grained, one-dimensional sonar-based finger-tracking system. In addition

Eduardo Guzman, Francisco Ortega, Ramon G. Rubio

Rhamnolipids are very promising sugar-based biosurfactants, generally produced by bacteria, with a wide range of properties that can be exploited at an industrial and technological level, e.g. in cosmetics, food science, or oil recovery, to provide benefits for human health and the environment. This has led to intensive research into optimizing their product

Richard Chow, James Bremer

It is well known that phase function methods allow for the numerical solution of a large class of oscillatory second order linear ordinary differential equations in time independent of frequency. Unfortunately, these methods break down in the commonly-occurring case in which the equation has turning points. Here, we resolve this difficulty by introducing a g

Hai-Xing Lin, Jie Ren, Jian Tang

Recent reactor neutrino oscillation experiments reported precision measurements of $\sin^2 2\theta_{13}$ and $\Delta m^2_{ee}$ under the standard 3$\nu$ oscillation framework. However, inter-experiment consistency checks through the parameter goodness-of-fit test reveal proximity to the tension boundary, with the Double Chooz, RENO, and Daya Bay ensemble yie

Tongkun Guan, Zining Wang, Pei Fu, Zhengtao Guo

In recent years, general visual foundation models (VFMs) have witnessed increasing adoption, particularly as image encoders for popular multi-modal large language models (MLLMs). However, without semantically fine-grained supervision, these models still encounter fundamental prediction errors in the context of downstream text-image-related tasks, i.e., perce

Yicong Zheng, Nora Wolf, Charan Ranganath, Randall C. O'Reilly

Many tasks require flexibly modifying perception and behavior based on current goals. Humans can retrieve episodic memories from days to years ago, using them to contextualize and generalize behaviors across novel but structurally related situations. The brain's ability to control episodic memories based on task demands is often attributed to interactions be

Shun Iwase, Shuya Takahashi, Nakamasa Inoue, Rio Yokota

The double descent phenomenon, which deviates from the traditional bias-variance trade-off theory, attracts considerable research attention; however, the mechanism of its occurrence is not fully understood. On the other hand, in the study of convolutional neural networks (CNNs) for image recognition, methods are proposed to quantify the bias on shape feature

Xie Li, Zhaoyue Yuan, Zhenduo Zhang, Youcheng Sun

Direct kernel fuzzing is a targeted approach that focuses on specific areas of the kernel, effectively addressing the challenges of frequent updates and the inherent complexity of operating systems, which are critical infrastructure. This paper introduces SyzAgent, a framework that integrates LLMs with the state-of-the-art kernel fuzzer Syzkaller, where the

Ruixin Wu, Zihan Li, Jin Wang, Xiangyu Xu

Millimeter-wave (mmWave) radar has attracted significant attention in robotics and autonomous driving. However, despite the perception stability in harsh environments, the point cloud generated by mmWave radar is relatively sparse while containing significant noise, which limits its further development. Traditional mmWave radar enhancement approaches often s

Vu Tung Lam, Do Hai Son, Tran Thi Thuy Quynh, Le Trung Thanh

Near-field channel estimation is a fundamental challenge in the sixth-generation (6G) wireless communication, where extremely large antenna arrays (ELAA) enable near-field communication (NFC) but introduce significant signal processing complexity. Traditional model-based methods suffer from high computational costs and limited scalability in large-scale ELAA

Ziyang Zeng, Dongyuan Li, Yuqing Yang

Online medical service provides patients convenient access to doctors, but effectively ranking doctors based on specific medical needs remains challenging. Current ranking approaches typically lack the interpretability crucial for patient trust and informed decision-making. Additionally, the scarcity of standardized benchmarks and labeled data for supervised

Arun Kumar Yadav, Partha Pratim Bhaduri, Subhasis Chattopadhyay

In this article, we reexamine the formulation for extraction of Knudsen number ($K$), the ratio of shear viscosity to entropy density ($\eta/s$), within the incomplete thermalization scenario, using eccentricity scaling of elliptic flow ($v_{2}$) of final state hadrons. Data on centrality dependence of charged hadron $v_{2}$ in Xe-Xe and Pb-Pb collisions, me

Lizhe Zhang, Wentao Chen, Li Zhong, Letian Peng

Large language models (LLMs) have recently demonstrated exceptional code generation capabilities. However, there is a growing debate whether LLMs are mostly doing memorization (i.e., replicating or reusing large parts of their training data) versus generalization (i.e., beyond training data). Existing evaluations largely proxy memorization with surface/struc

Ai-Chao Wang, Neng-Chang Wei, Fei Huang

A comprehensive analysis of all available data on cross sections and spin-dependent observables for the $\gamma p \to K^+ \Sigma^0(1385)$, $\gamma n \to K^+ \Sigma^-(1385)$, and $\pi^+ p \to K^+ \Sigma^+(1385)$ reactions is performed within an effective Lagrangian framework. In addition to the $s$-channel $N$ exchange, $t$-channel $K$ and $K^\ast$ exchanges,

Zecheng Shen, Chendi Xie, Wei-Chih Chen, Yao Wang

Spin-triplet superconductivity is a key platform for topological quantum computing, yet its experimental realization and control in solid-state materials remain a significant challenge. For this purpose, we propose an ultrafast optical strategy to manipulate spin-triplet superconductivity by leveraging $p$-wave pairing instabilities in the extended Hubbard m

Ngoc-Son Duong, Khac-Hoang Ngo, Thai-Mai Dinh, Van-Linh Nguyen

Accurate parameter estimation such as angle of arrival (AOA) is essential to enhance the performance of integrated sensing and communication (ISAC) in mmWave multiple-input multiple-output (MIMO) systems. This work presents a sensing-aided communication channel estimation mechanism, where the sensing channel shares the same AOA with the uplink communication

Siddharth Chandak, Isha Thapa, Nicholas Bambos, David Scheinker

Selecting the right monitoring level in Remote Patient Monitoring (RPM) systems for e-healthcare is crucial for balancing patient outcomes, various resources, and patient's quality of life. A prior work has used one-dimensional health representations, but patient health is inherently multidimensional and typically consists of many measurable physiological fa

Songting Li, Wenting Wang, Sergey E. Koposov, Ting S. Li

We present the SpecDis value added stellar distance catalog accompanying DESI DR1. SpecDis trains a feed-forward Neural Network (NN) with Gaia parallaxes and gets the distance estimates. To build up unbiased training sample, we do not apply selections on parallax error or signal-to-noise (S/N) of the stellar spectra, and instead we incorporate parallax error