March 2025 arXiv papers — page 115

Kenny Tsu Wei Choo, Rajesh Krishna Balan, Youngki Lee

With mobile apps rapidly permeating all aspects of daily living with use by all segments of the population, it is crucial to support the evaluation of app usability for specific impaired users to improve app accessibility. In this work, we examine the effects of using our \textit{augmented virtuality} impairment simulation system--\textit{Empath-D}--to suppo

Alessandro Catania, Michele Bertozzi, Nikita J. Greenidge, Benjamin Calme

This paper presents the in vivo validation of an inductive wireless power transfer (WPT) system integrated for the first time into a magnetically controlled robotic capsule endoscopy platform. The proposed system enables continuous power delivery to the capsule without the need for onboard batteries, thus extending operational time and reducing size constrai

Ankur Pandey, Nijjwal Karak, Debarati Mondal

In this paper, we show that sets with zero Sobolev $p(\cdot)$-capacity have generalized Hausdorff $h(\cdot)$-measure zero, for some gauge function $h(\cdot).$ We also prove that sets with zero Musielak-Orlicz-Sobolev $\Phi(\cdot,\cdot)$-capacity, for a particular class of functions $\Phi(\cdot,\cdot),$ have generalized Hausdorff $h(\cdot)$-measure zero, for

Yuki Nagahama

Digital holography (DH) enables non-contact, noninvasive 3D imaging of transparent and moving microscopic samples by capturing amplitude and phase information in a single shot. In this work, we present a compact, low-cost, real-time smartphone-based DHM system accelerated by GPUs. The system comprises a 3D-printed optical system using readily available image

Chen Liu, Peike Li, Liying Yang, Dadong Wang

Accurately localizing audible objects based on audio-visual cues is the core objective of audio-visual segmentation. Most previous methods emphasize spatial or temporal multi-modal modeling, yet overlook challenges from ambiguous audio-visual correspondences such as nearby visually similar but acoustically different objects and frequent shifts in objects' so

Jia-Lin Pei, Guo-Liang Ma, Adam Bzdak

Symmetric cumulants can improve our understanding of the joint probability distribution function $ P\left ( v_{m},v_{n},v_{k}, \dots,\Psi _{m},\Psi _{n},\Psi _{k},\dots \right )$, potentially offering new insights into the nature of the fluctuations of the quark-gluon plasma produced in relativistic heavy-ion collisions. In this work, the four-particle symme

Daniel R. Johnston, Callum J. Shakespeare, Navid C. Constantinou

Whenever oceanic currents flow over rough topography, there is an associated stress that acts to modify the flow. In the deep ocean, this stress is predominantly a form drag due to pressure differentials across topography, caused by the formation of internal waves and other baroclinic motions: processes that act on such small scales most global ocean models

Junhyeok Kim, Jaewoo Park, Junhee Park, Sangeyl Lee

For people affected by blindness and low vision (BLV), safe and independent navigation remains a major challenge, impacting over 2.2 billion individuals worldwide. Although multimodal large language models (MLLMs) offer new opportunities for assistive navigation, progress has been limited by the scarcity of accessibility-aware datasets, because creating them

Haozhe Si, Yuxuan Wan, Minh Do, Deepak Vasisht

Geospatial raster data, such as that collected by satellite-based imaging systems at different times and spectral bands, hold immense potential for enabling a wide range of high-impact applications. This potential stems from the rich information that is spatially and temporally contextualized across multiple channels and sensing modalities. Recent work has a

Dimitrios G. Konstantinides, Charalampos D. Passalidis

In this paper we introduce and study several multivariate, heavy-tailed distribution classes, and we explore their closure properties and their applications. We consider the class of multivariate, positively decreasing distributions, and its intersection with other multivariate distribution classes.

Jingxian Wang, Moritz Kahlert, Tai Fei, Changxu Zhang

Digital modulation schemes such as PMCW have recently attracted increasing attention as possible replacements for FMCW modulation in future automotive radar systems. A significant obstacle to their widespread adoption is the expensive and power-consuming ADC required at gigahertz frequencies. To mitigate these challenges, employing low-resolution ADC, such a

Chen Liu, Liying Yang, Peike Li, Dadong Wang

Sound-guided object segmentation has drawn considerable attention for its potential to enhance multimodal perception. Previous methods primarily focus on developing advanced architectures to facilitate effective audio-visual interactions, without fully addressing the inherent challenges posed by audio natures, \emph{\ie}, (1) feature confusion due to the ove

Gayathry Pradeep, Ambily Ambattu Asokan, Aparna Pradeep Vadakke Kovilakam

We prove the Dickson-Siegel-Eichler-Roy (DSER) elementary orthogonal group, which was introduced by Amit Roy in 1968 and the Eichler-Siegel-Dickson transvection group, which is in literature in the works of Dickson, Siegel and Eichler, are equal over a commutative ring in which $2$ is invertible. We prove the equality in the free case by considering the odd

Junjia Huang, Pengxiang Yan, Jinhang Cai, Jiyang Liu

Text-driven image generation using diffusion models has recently gained significant attention. To enable more flexible image manipulation and editing, recent research has expanded from single image generation to transparent layer generation and multi-layer compositions. However, existing approaches often fail to provide a thorough exploration of multi-layer

Jun Honda

Using a comprehensive matched employer-employee dataset for U.S. financial advisers from 2008 to 2018, we revisit established evidence on labor market penalties following financial misconduct. Prior studies report that female advisers are 20% more likely to exit their firms following misconduct and that similar disparities exist for non-white advisers. Howev

Sumin In, Youngdong Jang, Utae Jeong, MinHyuk Jang

As 3D Gaussian Splatting (3DGS) is increasingly adopted in various academic and commercial applications due to its high-quality and real-time rendering capabilities, the need for copyright protection is growing. At the same time, its large model size requires efficient compression for storage and transmission. However, compression techniques, especially quan

Soham Ghosh, Farbod Shokrieh

We construct a tropical analogue of the Poincar\'e bundle and prove a (cohomological) Fourier-Mukai transform for real tori with integral structures. We then prove a tropical analogue of Beauville's generalized Poincar\'e formula for polarized abelian varieties. Some consequences include a geometric Riemann-Roch theorem for tropical abelian varieties, as wel

Seunggwan Lee, Hwanhee Jung, Byoungsoo Koh, Qixing Huang

A fundamental challenge in conditional 3D shape generation is to minimize the information loss and maximize the intention of user input. Existing approaches have predominantly focused on two types of isolated conditional signals, i.e., user sketches and text descriptions, each of which does not offer flexible control of the generated shape. In this paper, we

Juhee Kim, Woohyuk Choi, Byoungyoung Lee

Large Language Models (LLMs) are combined with tools to create powerful LLM agents that provide a wide range of services. Unlike traditional software, LLM agent's behavior is determined at runtime by natural language prompts from either user or tool's data. This flexibility enables a new computing paradigm with unlimited capabilities and programmability, but

Yilong Wu, Yifan Duan, Yuxi Chen, Xinran Zhang

Large-scale scene point cloud registration with limited overlap is a challenging task due to computational load and constrained data acquisition. To tackle these issues, we propose a point cloud registration method, MT-PCR, based on Modality Transformation. MT-PCR leverages a BEV capturing the maximal overlap information to improve the accuracy and utilizes

Louis Mahon, Mark Johnson, Mark Steedman

This work develops a probabilistic child language acquisition model to learn a range of linguistic phenonmena, most notably long-range syntactic dependencies of the sort found in object wh-questions, among other constructions. The model is trained on a corpus of real child-directed speech, where each utterance is paired with a logical form as a meaning repre

Hussein Naeem Hasan

The main purpose of the paper is development, implementation, and testing of a low-cost portable system to assist partially paralyzed patients in their hand rehabilitation after strokes or some injures. Rehabilitation includes time consuming and repetitive exercises which are costly and demotivating as well as the requirements of clinic attending and direct

Jun Qian, Ross Murch, Khaled B. Letaief

Integrating cell-free massive multiple-input multiple-output (MIMO) with simultaneous transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) can provide ubiquitous connectivity and enhance coverage. This paper explores a STAR-RIS-assisted cell-free massive MIMO system featuring multi-antenna users, multi-antenna access points (APs), and

Binglei Lou, Ruilin Wu, Philip Leong

The deployment of deep neural networks (DNNs) on resource-constrained edge devices such as field-programmable gate arrays (FPGAs) requires a careful balance of latency, power, and resource usage while maintaining high accuracy. Existing Lookup Table (LUT)-based DNNs, including LogicNets, PolyLUT, PolyLUT-Add, and NeuraLUT, exploit native FPGA resources with

Quang Trung Truong, Wong Yuk Kwan, Duc Thanh Nguyen, Binh-Son Hua

Underwater video analysis, hampered by the dynamic marine environment and camera motion, remains a challenging task in computer vision. Existing training-free video generation techniques, learning motion dynamics on the frame-by-frame basis, often produce poor results with noticeable motion interruptions and misaligments. To address these issues, we propose

Md Farhamdur Reza, Richeng Jin, Tianfu Wu, Huaiyu Dai

Existing score-based adversarial attacks mainly focus on crafting $top$-1 adversarial examples against classifiers with single-label classification. Their attack success rate and query efficiency are often less than satisfactory, particularly under small perturbation requirements; moreover, the vulnerability of classifiers with multi-label learning is yet to

Israel Quiros, Amit Kumar Rao

It has long been demonstrated that the vacuum scalar-tensor theory in the Jordan-frame Brans-Dicke parametrization is form-invariant under conformal transformations, provided that a suitable transformation of the coupling parameter $\omega$ is applied. Here, we generalize this framework to include the coupling of matter fields to gravity. We take into consid

Jian Zhai

We study the inverse boundary value problem for the linear elastic wave equation in three-dimensional isotropic medium. We show that both the Lam\'e parameters and the density can be uniquely recovered from the boundary measurements under the strictly convex foliation condition.

Varsha Chauhan, Anuradha Sharma

The purpose of this note is to rectify a typographical error in the statements of Theorems 5.5 and 5.6 of Sharma, Chauhan and Singh[3] and further analyze and discuss the significance of the results derived in Takieldin and Sol\'e [4]. In our opinion, several claims made by the authors in [4] are either factually incorrect or lack adequate substantiation, wh

Mehdi Makni, Kayhan Behdin, Gabriel Afriat, Zheng Xu

Differentially private stochastic gradient descent (DP-SGD) is broadly considered to be the gold standard for training and fine-tuning neural networks under differential privacy (DP). With the increasing availability of high-quality pre-trained model checkpoints (e.g., vision and language models), fine-tuning has become a popular strategy. However, despite r

Mingyang Song, Xiaoye Qu, Jiawei Zhou, Yu Cheng

Large Vision-Language Models (LVLMs) have achieved significant progress in combining visual comprehension with language generation. Despite this success, the training data of LVLMs still suffers from Long-Tail (LT) problems, where the data distribution is highly imbalanced. Previous works have mainly focused on traditional VLM architectures, i.e., CLIP or Vi

Kailin Li, Zhenxin Li, Shiyi Lan, Yuan Xie

Hydra-MDP++ introduces a novel teacher-student knowledge distillation framework with a multi-head decoder that learns from human demonstrations and rule-based experts. Using a lightweight ResNet-34 network without complex components, the framework incorporates expanded evaluation metrics, including traffic light compliance (TL), lane-keeping ability (LK), an

Joe Finney, Aaron L. Sharpe, Linsey K. Rodenbach, Jian Kang

Fractional Chern insulators (FCIs) -- the lattice analog of fractional quantum Hall states -- form as fractionalized quasiparticles emerge in a partially-filled Chern band. This fractionalization is driven by the interplay of electronic interaction and quantum geometry of the underlying wavefunctions. Bilayer graphene with an interlayer twist near the magic

Lingyi Wang, Wei Wu, Fuhui Zhou, Zhijin Qin

Different from traditional secure communication that focuses on symbolic protection at the physical layer, semantic secure communication requires further attention to semantic-level task performance at the application layer. There is a research gap on how to comprehensively evaluate and optimize the security performance of semantic communication. In order to

Seong Jang, Geon-Hyoung Park, Kenji Watanabe, Takashi Taniguchi

The observation of Josephson current in the quantum Hall regime has attracted considerable attention, revealing the coexistence of two seemingly incompatible phases: the quantum Hall and superconducting states. However, the mechanism underlying the Josephson current remains unclear because of the observed h/2e magnetic interference period and the lack of pre

Mangala Prasad

A standard finite element method discretizes the stochastic linear Schr\"{o}dinger equation driven by additive noise in the spatial variables. The weak convergence of the resulting approximate solution is analyzed, and it is established that the weak convergence rate is twice that of the strong convergence.

Yong Li, David Sauzin, Shanzhong Sun

We propose a systematic analysis of Alim-Yau-Zhou's double scaling limit and Couso-Santamar\'{i}a's large radius limit for the perturbative free energies in B-model topological string theory based on \'Ecalle's Resurgence Theory. Taking advantage of the known resurgent properties of the formal solutions to the Airy equation and of the stability of resurgent

Jinseok Bae, Jungdam Won, Donggeun Lim, Inwoo Hwang

We present a versatile latent representation that enables physically simulated character to efficiently utilize motion priors. To build a powerful motion embedding that is shared across multiple tasks, the physics controller should employ rich latent space that is easily explored and capable of generating high-quality motion. We propose integrating continuou

Mousa Alizadeh, Mohammad Hossein Samaei, Azam Seilsepour, Alireza Monavarian

Effective epidemic modeling is essential for managing public health crises, requiring robust methods to predict disease spread and optimize resource allocation. This study introduces a novel deep learning framework that advances time series forecasting for infectious diseases, with its application to COVID 19 data as a critical case study. Our hybrid approac

Zhi-Wei Lu, Hanxu Zhang, Tao Li, Mamutjan Ababekri

The existing intense laser-based approaches for nuclear excitation offer ultrafast temporal resolution and high efficiency compared to traditional accelerator probes. However, controlling nuclear properties such as spin and magnetic moment remains an unprecedented challenge. Here, we put forward a novel method for nuclear excitation and control induced by in

Kairong Luo, Haodong Wen, Shengding Hu, Zhenbo Sun

Training large models is both resource-intensive and time-consuming, making it crucial to understand the quantitative relationship between model performance and hyperparameters. In this paper, we present an empirical law that describes how the pretraining loss of large language models evolves under different learning rate schedules, such as constant, cosine,

Zachary Olkin, Aaron D. Ames

Computing the receding horizon optimal control of nonlinear hybrid systems is typically prohibitively slow, limiting real-time implementation. To address this challenge, we propose a layered Model Predictive Control (MPC) architecture for robust stabilization of hybrid systems. A high level "hybrid" MPC is solved at a slow rate to produce a stabilizing hybri

Warat Kongkitkul, Sompote Youwai, Warut Sakulpojworachai

This research introduces a novel dual-attention transformer architecture for predicting soil electrical resistivity, a critical parameter for high-voltage substation construction. Our model employs attention mechanisms operating across both features and data batches, enhanced by feature embedding layers that project inputs into higher-dimensional spaces. We

Jun Li, Qifeng Xu, Yifan Lin, Nan Xie

The insufficient stability and reliability of Optical Voltage Sensor is primarily caused by thermal stress induced birefringence. In this paper, a method based on arbitrary electric field direction modulation and isomerism electrodes is proposed to suppress or regulate it. With the aid of multi-physics Finite Element Method, Jones Matrix and the theory of ph

Milind Nakul, Vidya Muthukumar, Ashwin Pananjady

Suppose we observe a trajectory of length $n$ from an exponentially $\alpha$-mixing stochastic process over a finite but potentially large state space. We consider the problem of estimating the probability mass placed by the stationary distribution of any such process on elements that occur with a certain frequency in the observed sequence. We estimate this

Ayano Komaki, Naoki Yoshida

We study the effect of stellar evolution on the dispersal of protoplanatary disks by performing one-dimensional simulations of long-term disk evolution. Our simulations include viscous disk accretion, magnetohydrodynamic winds, and photoevaporation as important disk dispersal processes. We consider a wide range of stellar mass of $0.1$ - $7M_{\odot}$, and in

Hadam Baek, Hannie Shin, Jiyoung Seo, Chanwoo Kim

Accurately modeling sound propagation with complex real-world environments is essential for Novel View Acoustic Synthesis (NVAS). While previous studies have leveraged visual perception to estimate spatial acoustics, the combined use of surface normal and structural details from 3D representations in acoustic modeling has been underexplored. Given their dire

Kunlun Qi, Lian Shen, Li Wang

The central object in wave turbulence theory is the wave kinetic equation (WKE), which is an evolution equation for wave action density and acts as the wave analog of the Boltzmann kinetic equations for particle interactions. Despite recent exciting progress in the theoretical aspects of the WKE, numerical developments have lagged behind. In this paper, we i

Changyou Wang

For a ball $B_R(0)\subset\mathbb{R}^2$, we provide sufficient conditions such that a harmonic map $u\in C^\infty(B_R(0)\setminus\{0\}, N)$, with a self-similar bound on its gradient, belongs to $C^\infty(B_R(0))$. Those conditions also guarantee the triviality of such harmonic maps when $R=\infty$.

Chen Li, Debo Cheng, Yasuhiko Morimoto

Aspect-based sentiment analysis seeks to determine sentiment with a high level of detail. While graph convolutional networks (GCNs) are commonly used for extracting sentiment features, their straightforward use in syntactic feature extraction can lead to a loss of crucial information. This paper presents a novel edge-enhanced GCN, called EEGCN, which improve

Rihito Nagase, Takahiro Sagawa

In recent advances in finite-time thermodynamics, optimization of entropy production required for finite-time information processing is an important issue. In this work, we consider finite-time feedback processes in classical discrete systems described by Markov jump processes, and derive achievable bounds on entropy production for feedback processes control

Mohammad Wahiduzzaman Khan, Sheng Chen, Ilya Mironov, Leizhen Zhang

Model memorization has implications for both the generalization capacity of machine learning models and the privacy of their training data. This paper investigates label memorization in binary classification models through two novel passive label inference attacks (BLIA). These attacks operate passively, relying solely on the outputs of pre-trained models, s

Jialu Zhou, Dianxi Shi, Shaowu Yang, Chunping Qiu

With fully leveraging the value of unlabeled data, semi-supervised medical image segmentation algorithms significantly reduces the limitation of limited labeled data, achieving a significant improvement in accuracy. However, the distributional shift between labeled and unlabeled data weakens the utilization of information from the labeled data. To alleviate

Qiong Wu, Xiangcong Yang, Yiyi Zhou, Chenxin Fang

Despite great progress, existing multimodal large language models (MLLMs) are prone to visual hallucination, greatly impeding their trustworthy applications. In this paper, we study this problem from the perspective of visual-spatial reasoning, and propose a new learning task for MLLMs, termed Grounded Chain-of-Thought (GCoT). Different from recent visual Co

Zhicheng Jiang, Zhengtai Liu, Chenqiang Hua, Xiangqi Liu

Chiral multifold fermions in solids exhibit unique band structures and topological properties, making them ideal for exploring fundamental physical phenomena related to nontrivial topology, chirality, and symmetry breaking. However, the challenge of obtaining clean, flat surfaces through cleavage has hindered the investigation of their unique electronic stat

Xinyu Ma, Ziyang Ding, Zhicong Luo, Chi Chen

Knowledge-Intensive Visual Grounding (KVG) requires models to localize objects using fine-grained, domain-specific entity names rather than generic referring expressions. Although Multimodal Large Language Models (MLLMs) possess rich entity knowledge and strong generic grounding capabilities, they often fail to effectively utilize such knowledge when groundi

Chen Li, Huidong Tang, Ye Zhu, Yoshihiro Yamanishi

Generating molecules with desired chemical properties presents a critical challenge in fields such as chemical synthesis and drug discovery. Recent advancements in artificial intelligence (AI) and deep learning have significantly contributed to data-driven molecular generation. However, challenges persist due to the inherent sensitivity of simplified molecul

Yong-Ju Hai, Shihang Zhang, Haoyu Guan, Peihao Huang

We demonstrate a robust quantum control framework that enables high-fidelity gate operations in semiconductor spin qubit systems with always-on couplings. Always-on interactions between qubits pose a fundamental challenge for quantum processors by inducing correlated errors that can trigger chaotic dynamics. Our approach suppresses both static coupling noise

Kazushi Aoyama

Effects of a charge-density-wave (CDW) order on the Ruderman-Kittel-Kasuya-Yosida (RKKY) interaction has been theoretically investigated. Assuming that the CDW with an incommensurate ordering vector ${\bf Q}_c$ is induced by a Fermi surface nesting, we show that the CDW order suppresses the conventional RKKY interaction and that it can induce a mode coupling

Yechao Zhang, Yingzhe Xu, Junyu Shi, Leo Yu Zhang

Deep neural networks (DNNs) are susceptible to universal adversarial perturbations (UAPs). These perturbations are meticulously designed to fool the target model universally across all sample classes. Unlike instance-specific adversarial examples (AEs), generating UAPs is more complex because they must be generalized across a wide range of data samples and m

Leonardo A. Lessa, Shengqi Sang, Tsung-Cheng Lu, Timothy H. Hsieh

In open quantum systems, we directly relate anomalies of higher-form symmetries to the long-range entanglement of any mixed state with such symmetries. First, we define equivalence classes of long-range entanglement in mixed states via stochastic local channels (SLCs), which effectively ``mod out'' any classical correlations and thus distinguish phases by di

Xiantong Chen, Xuanting Ji, Ya-Wen Sun

The occurrence of a topological phase transition can be demonstrated by a direct observation of a change in the topological invariant. For holographic topological semimetals, a topological Hamiltonian method needs to be employed to calculate the topological invariants due to the strong coupling nature of the system. We calculate the topological invariants fo

Xiaofei Kong, Lei Li, Zhaoyun Chen, Cheng Xue

Low-Rank Adaptation (LoRA) enables efficient fine-tuning of pre-trained language models through low-rank matrix approximation, achieving effectiveness in many scenarios. However, its representation capacity is constrained in complex tasks or high-rank dependency settings, potentially limiting model adaptability. To overcome the expressive bottleneck in class

Edward Farhi, Sam Gutmann, Daniel Ranard, Benjamin Villalonga

We study MaxCut on 3-regular graphs of minimum girth $g$ for various $g$'s. We obtain new lower bounds on the maximum cut achievable in such graphs by analyzing the Quantum Approximate Optimization Algorithm (QAOA). For $g \geq 16$, at depth $p \geq 7$, the QAOA improves on previously known lower bounds. Our bounds are established through classical numerical

Chryssis Georgiou, Manaswini Piduguralla, Sathya Peri

In this work, we formalize a novel shared memory model inspired by the popular GPU architecture. Within this model, we develop algorithmic solutions to the Byzantine Consensus problem and analyze their fault-resilience.

Takumi Ito, Riku Funada, Mitsuji Sampei, Gennaro Notomista

This work proposes a novel multi-robot task allocation framework for robots that can switch between multiple modes, e.g., flying, driving, or walking. We first provide a method to encode the multi-mode property of robots as a graph, where the mode of each robot is represented by a node. Next, we formulate a constrained optimization problem to decide both the

Peirong Zhang, Yuliang Liu, Songxuan Lai, Hongliang Li

Handwriting verification has stood as a steadfast identity authentication method for decades. However, this technique risks potential privacy breaches due to the inclusion of personal information in handwritten biometrics such as signatures. To address this concern, we propose using the Random Digit String (RDS) for privacy-preserving handwriting verificatio

Zhiyan Liu, Kaibin Huang

The sixth-generation (6G) mobile network is envisioned to incorporate sensing and edge artificial intelligence (AI) as two key functions. Their natural convergence leads to the emergence of Integrated Sensing and Edge AI (ISEA), a novel paradigm enabling real-time acquisition and understanding of sensory information at the network edge. However, ISEA faces a

Jingzhou Huang, Jiuyao Lu, Alexander Williams Tolbert

Variable selection poses a significant challenge in causal modeling, particularly within the social sciences, where constructs often rely on inter-related factors such as age, socioeconomic status, gender, and race. Indeed, it has been argued that such attributes must be modeled as macro-level abstractions of lower-level manipulable features, in order to pre

Jianan Li, Huan Chen, Wangcai Zhao, Rui Chen

Hyperspectral Images (HSIs) are crucial across numerous fields but are hindered by the long acquisition times associated with traditional spectrometers. The Coded Aperture Snapshot Spectral Imaging (CASSI) system mitigates this issue through a compression technique that accelerates the acquisition process. However, reconstructing HSIs from compressed data pr

Qiming Wang, Yulong Gao, Yang Wang, Xiongwei Zhao

Conventional algorithms in autonomous exploration face challenges due to their inability to accurately and efficiently identify the spatial distribution of convex regions in the real-time map. These methods often prioritize navigation toward the nearest or information-rich frontiers -- the boundaries between known and unknown areas -- resulting in incomplete

Zhang Jiaxing, Tang Hao

Despite significant advances in deep learning for image and video segmentation, existing models continue to face challenges in cross-domain adaptability and generalization. Image and video segmentation are fundamental tasks in computer vision with wide-ranging applications in healthcare, agriculture, industrial inspection, and autonomous driving. With the ad

Chang Liu, Bavesh Balaji, Saad Hossain, C Thomas

Unsupervised domain adaptation for semantic segmentation (DASS) aims to transfer knowledge from a label-rich source domain to a target domain with no labels. Two key approaches in DASS are (1) vision-only approaches using masking or multi-resolution crops, and (2) language-based approaches that use generic class-wise prompts informed by target domain (e.g. "

Haoxiao Wang, Kaichen Zhou, Binrui Gu, Zhiyuan Feng

Manipulating transparent objects presents significant challenges due to the complexities introduced by their reflection and refraction properties, which considerably hinder the accurate estimation of their 3D shapes. To address these challenges, we propose a single-view RGB-D-based depth completion framework, TransDiff, that leverages the Denoising Diffusion

Gul Sheeraz, Qun Chen, Liu Feiyu, Zhou Fengjin

Breast cancer remains a leading cause of cancer-related mortality worldwide. Early detection is critical, yet manual histopathology analysis is complex and subject to inter-observer variability. While deep neural network-based diagnostic systems have advanced binary tasks, they struggle with multiclass subtype prediction due to inter-class similarity, class

Chaoxin Huang, Lisi Li, Peiyue Ma, Xing Huang

Theoretically, the relative change of the Heisenberg-type nearest-neighbor coupling $J_1$ and next-nearest-neighbor coupling $J_2$ in the face-centered-cubic lattice can give rise to three main antiferromagnetic orderings of type-I, type-II, and type-III. However, it is difficult to tune the $J_2/J_1$ ratio in real materials. Here, we report studies on the i

Rico Pohle, Nic Shannon

The search for spin liquids, magnetic phases which lie outside the Landau paradigm, remains one of the central challenges for modern condensed matter physics. For a long time, the prime candidates were thought to be spin-1/2 magnets, but recently examples have been identified in many spin-1 materials, including the pyrochlore NaCaNi$_2$F$_7$. Here we use num

Akihiro Narimatsu, Tomoki Yamagami

Random walks (RWs) are fundamental stochastic processes with applications across physics, computer science, and information processing. A recent extension, the laser chaos decision-maker, employs chaotic time series from semiconductor lasers to solve multi-armed bandit (MAB) problems at ultrafast speeds, and its threshold adjustment mechanism has been modele

Jiakun Yan, Hartmut Kaiser, Marc Snir

Asynchronous Many-Task (AMT) systems offer a potential solution for efficiently programming complicated scientific applications on extreme-scale heterogeneous architectures. However, they exhibit different communication needs from traditional bulk-synchronous parallel (BSP) applications, posing new challenges for underlying communication libraries. This work

John Cenker, Jordan Fonseca, Mai Nguyen, Chaowei Hu

Atomically thin van der Waals materials provide a highly tunable platform for exploring emergent quantum phenomena in solid state systems. Due to their remarkable mechanical strength, one enticing tuning knob is strain. However, the weak strain transfer of graphite and hBN, which are standard components of high-qualityvdW devices, poses fundamental challenge

Sung-Yeon Park, Can Cui, Yunsheng Ma, Ahmadreza Moradipari

Recent advances in multi-modal large language models (MLLMs) have demonstrated strong performance across various domains; however, their ability to comprehend driving scenes remains less proven. The complexity of driving scenarios, which includes multi-view information, poses significant challenges for existing MLLMs. In this paper, we introduce NuPlanQA-Eva

Edward Witten

Quantum mechanics requires a hermitian inner product <~,~> -- linear in one variable, antilinear in the other -- while the inner product (~,~) that comes most naturally from Euclidean path integrals is linear in each variable. Here we discuss the relation between the two inner products. In a theory with no time-reversal or reflection symmetry, they differ by

Linjian Meng, Tianpei Yang, Youzhi Zhang, Zhenxing Ge

Counterfactual Regret Minimization (CFR) algorithms are widely used to compute a Nash equilibrium (NE) in two-player zero-sum imperfect-information extensive-form games (IIGs). Among them, Predictive CFR$^+$ (PCFR$^+$) is particularly powerful, achieving an exceptionally fast empirical convergence rate via the prediction in many games.However, the empirical

Shenghao Fu, Qize Yang, Yuan-Ming Li, Yi-Xing Peng

Recent advances in Large Multi-modal Models (LMMs) are primarily focused on offline video understanding. Instead, streaming video understanding poses great challenges to recent models due to its time-sensitive, omni-modal and interactive characteristics. In this work, we aim to extend the streaming video understanding from a new perspective and propose a nov

Tatsuro Sakai, Kanji Tanaka, Yuki Minase, Jonathan Tay Yu Liang

In robot vision, thermal cameras hold great potential for recognizing humans even in complete darkness. However, their application to multi-person tracking (MPT) has been limited due to data scarcity and the inherent difficulty of distinguishing individuals. In this study, we propose a cooperative MPT system that utilizes co-located RGB and thermal cameras,

Hamza Jnane, Adam Siegel, M. Fernando Gonzalez-Zalba

Silicon spin qubits are promising candidates for building scalable quantum computers due to their nanometre scale features. However, delivering microwave control signals locally to each qubit poses a challenge and instead methods that utilise global control fields have been proposed. These require tuning the frequency of selected qubits into resonance with a

Zhengmao He

In this paper, we investigate the sobriety of weakly first-countable spaces and give some sufficient conditions that the Scott topologies of the open set lattices are sober. The main results are: (1) Let $P$ and $Q$ be two posets. If $\Sigma P\times \Sigma Q$ is a Fr\'{e}chet space, then $\Sigma (P\times Q)=\Sigma P \times \Sigma Q$. (2) For every $\omega$-w

Weiming Xu, Tao Yang, Chang Liu, Kun Wu

The scramjet engine is a key propulsion system for hypersonic vehicles, leveraging supersonic airflow to achieve high specific impulse, making it a promising technology for aerospace applications. Understanding and controlling the complex interactions between fuel injection, turbulent combustion, and aerodynamic effects of compressible flows are crucial for

Yidi Liu, Dong Li, Yuxin Ma, Jie Huang

Ultra-high-definition (UHD) image restoration often faces computational bottlenecks and information loss due to its extremely high resolution. Existing studies based on Variational Autoencoders (VAE) improve efficiency by transferring the image restoration process from pixel space to latent space. However, degraded components are inherently coupled with back

Kewei Sui, Anindita Ghosh, Inwoo Hwang, Bing Zhou

Humans inhabit a world defined by interactions -- with other humans, objects, and environments. These interactive movements not only convey our relationships with our surroundings but also demonstrate how we perceive and communicate with the real world. Therefore, replicating these interaction behaviors in digital systems has emerged as an important topic fo

Tao Yang, Weiming Xu, Liangliang Xu, Peng Zhang

Thermoacoustic instability in annular combustors, essential to aero engines and modern gas turbines, can severely impair operational stability and efficiency, accurately recognizing and understanding various combustion modes is the prerequisite for understanding and controlling combustion instabilities. However, the high-dimensional spatial-temporal dynamics

Bhawana Chhaglani, Alan Seefeldt

Tech neck, a growing musculoskeletal concern caused by prolonged poor posture during device use, has significant health implications. This study investigates the relationship between head posture and muscular activity in the upper trapezius muscle to predict muscle strain by leveraging data from EMG sensors and head trackers. We train a regression model to p

Yuebing Liang, Shenhao Wang, Jiangbo Yu, Zhan Zhao

Travel demand modeling has shifted from aggregated trip-based models to behavior-oriented activity-based models because daily trips are essentially driven by human activities. To analyze the sequential activity-travel decisions, deep inverse reinforcement learning (DIRL) has proven effective in learning the decision mechanisms by approximating a reward funct

Brian Cho, Ana-Roxana Pop, Ariel Evnine, Nathan Kallus

To design effective digital interventions, experimenters face the challenge of learning decision policies that balance multiple objectives using offline data. Often, they aim to develop policies that maximize goal outcomes, while ensuring there are no undesirable changes in guardrail outcomes. To provide credible recommendations, experimenters must not only

Jerry Huang, Siddarth Madala, Risham Sidhu, Cheng Niu

Retrieval-augmented generation (RAG) systems rely on retrieval models for identifying relevant contexts and answer generation models for utilizing those contexts. However, retrievers exhibit imperfect recall and precision, limiting downstream performance. We introduce RAG-RL, an answer generation model trained not only to produce answers but also to identify

Zhifeng Wang, Renjiao Yi, Xin Wen, Chenyang Zhu

Angiography imaging is a medical imaging technique that enhances the visibility of blood vessels within the body by using contrast agents. Angiographic images can effectively assist in the diagnosis of vascular diseases. However, contrast agents may bring extra radiation exposure which is harmful to patients with health risks. To mitigate these concerns, in

Christine Lee, Jihye Choi, Bilge Mutlu

The widespread adoption of Large Language Models (LLMs) and LLM-powered agents in multi-user settings underscores the need for reliable, usable methods to accommodate diverse preferences and resolve conflicting directives. Drawing on conflict resolution theory, we introduce a user-centered workflow for multi-user personalization comprising three stages: Refl

Sarah Frei, Katrina Honigs, John Voight

We explain the linear algebraic framework provided by Tate modules of isogenous abelian varieties in a category-theoretic way.

Gang Meng, Bin Jiang

Energy transfer during molecular collisions on metal surfaces plays a pivotal role in a host of critical interfacial processes. Despite significant efforts, our understanding of relevant energy transfer mechanisms, even in an extensively-studied benchmark like NO scattering from Au(111), remains far from complete. To fully disentangle different energy transf

Ahmad M. Nagib, Hatem Abou-Zeid, Hossam S. Hassanein

Deep reinforcement learning (DRL)-based slicing policies have shown significant success in simulated environments but face challenges in physical systems such as open radio access networks (O-RANs) due to simulation-to-reality gaps. These policies often lack safety guarantees to ensure compliance with service level agreements (SLAs), such as the strict laten