May 2025 arXiv papers — page 90

Kun-Yu Lin, Hongjun Wang, Weining Ren, Kai Han

This work introduces panoptic captioning, a novel task striving to seek the minimum text equivalent of images, which has broad potential applications. We take the first step towards panoptic captioning by formulating it as a task of generating a comprehensive textual description for an image, which encapsulates all entities, their respective locations and at

Chaerin Kong, Jiho Jang, Nojun Kwak

Differential Transformer has recently gained significant attention for its impressive empirical performance, often attributed to its ability to perform noise canceled attention. However, precisely how differential attention achieves its empirical benefits remains poorly understood. Moreover, Differential Transformer architecture demands large-scale training

Zhehui Wang, Benjamin Chen Ming Choong, Tian Huang, Daniel Gerlinghoff

Quantum optimization is the most mature quantum computing technology to date, providing a promising approach towards efficiently solving complex combinatorial problems. Methods such as adiabatic quantum computing (AQC) have been employed in recent years on important optimization problems across various domains. In deep learning, deep neural networks (DNN) ha

Jiayu Qiu, Hai Zhang

This paper establishes a rigorous mathematical framework for a generalized bulk-interface correspondence (BIC) in electronic systems with possibly nonconserved spin charge, where the Hamiltonian and spin operator do not commute. We first introduce the bulk spin conductance as a character of the bulk medium, which is defined as a potential-current correlation

Anton Hummel, Håkan Burden, Susanne Stenberg, Jan-Philipp Steghöfer

The EU AI Act makes explainability urgent for high-risk AI systems, yet most XAI research focuses on technical metrics rather than regulatory compliance. Understanding how legal requirements reshape XAI method design is challenging: the AI Act regulates organizational relationships (providers, deployers) using legal terminology, specifies obligations without

Wenqing Wu, Chengzhi Zhang, Tong Bao, Yi Zhao

Novelty is a core component of academic papers, and there are multiple perspectives on the assessment of novelty. Existing methods often focus on word or entity combinations, which provide limited insights. The content related to a paper's novelty is typically distributed across different core sections, e.g., Introduction, Methodology and Results. Therefore,

Simone Bombari, Jialei Luo, Inbar Seroussi, Marco Mondelli

Differentially private (DP) linear regression has received significant attention in the recent theoretical literature, with several approaches proposed to improve error rates. Our work considers the popular high-dimensional regime with random data, where the number of training samples $n$ and the input dimension $d$ grow at a proportional rate $d / n \to \ga

Anupam Ghosh, Partha Konar, Tousik Samui, Ritesh K. Singh

The study of leptoquarks and their couplings to fermions with different chiralities provides a powerful tool for distinguishing among different leptoquark models. As a case study, we focus on two specific third-generation scalar leptoquark models, $S_3$ and $R_2$, which differ in their electroweak quantum numbers and chiral structures of couplings to the top

Mahmoud M. Salim, Suhail I. Al-Dharrab, Daniel Benevides Da Costa, Ali H. Muqaibel

The emerging demands of sixth-generation wireless networks, such as ultra-connectivity, native intelligence, and cross-domain convergence, are bringing renewed focus to cooperative non-orthogonal multiple access (C-NOMA) as a fundamental enabler of scalable, efficient, and intelligent communication systems. C-NOMA builds on the core benefits of NOMA by lever

Qian Tan, Dongzhan Zhou, Peng Xia, Wanhao Liu

Multimodal large language models (MLLMs) have made impressive progress in many applications in recent years. However, chemical MLLMs that can handle cross-modal understanding and generation remain underexplored. To fill this gap, we propose ChemMLLM, a unified chemical multimodal large language model for molecule understanding and generation. Also, we design

Yuyang Jiang, Chacha Chen, Shengyuan Wang, Feng Li

Existing metrics often lack the granularity and interpretability to capture nuanced clinical differences between candidate and ground-truth radiology reports, resulting in suboptimal evaluation. We introduce a Clinically-grounded tabular framework with Expert-curated labels and Attribute-level comparison for Radiology report evaluation (CLEAR). CLEAR not onl

Yu Wang, Cailing Cai, Zhihua Xiao, Peifung E. Lam

Large language models (LLMs) are increasingly applied in fields such as finance, education, and governance due to their ability to generate human-like text and adapt to specialized tasks. However, their widespread adoption raises critical concerns about data privacy and security, including the risk of sensitive data exposure. In this paper, we propose a secu

Bohan Jin, Shuhan Qi, Kehai Chen, Xinyi Guo

The widespread use of Large Multimodal Models (LMMs) has raised concerns about model toxicity. However, current research mainly focuses on explicit toxicity, with less attention to some more implicit toxicity regarding prejudice and discrimination. To address this limitation, we introduce a subtler type of toxicity named dual-implicit toxicity and a novel to

Cheng Cheng, Lin Song, Di An, Yicheng Xiao

Autoregressive (AR) image generators offer a language-model-friendly approach to image generation by predicting discrete image tokens in a causal sequence. However, unlike diffusion models, AR models lack a mechanism to refine previous predictions, limiting their generation quality. In this paper, we introduce TensorAR, a new AR paradigm that reformulates im

J. M. Amira, Ya-Qing Hu

We prove that certain classical groups $G\subseteq {\rm GL}(d,\mathbb{R}^d)$ serve to characterize ordinary polynomials in $d$ real variables as elements of finite-dimensional subspaces of $C(\mathbb{R}^d)$ that are invariant by changes of variables induced by translations and elements of $G$. We also show that, if the field $\mathbb{K}$ has characteristic $

Wanghan Xu, Wenlong Zhang, Fenghua Ling, Ben Fei

Meta-analysis is a systematic research methodology that synthesizes data from multiple existing studies to derive comprehensive conclusions. This approach not only mitigates limitations inherent in individual studies but also facilitates novel discoveries through integrated data analysis. Traditional meta-analysis involves a complex multi-stage pipeline incl

Woosung Koh, Wonbeen Oh, Jaein Jang, MinHyung Lee

Self-Taught Reasoners (STaR), synonymously known as Rejection sampling Fine-Tuning (RFT), is an integral part of the training pipeline of self-improving reasoning Language Models (LMs). The self-improving mechanism often employs random observation (data) sampling. However, this results in trained observation imbalance; inefficiently over-training on solved e

I. E. Ezeibe, S. O. Okide, D. C. Asogwa

Evaluating the performance of a lecturer has been essential for enhancing teaching quality, improving student learning outcomes, and strengthening the institution's reputation. The absence of such a system brings about lecturer performance evaluation which was neither comprehensive nor holistic. This system was designed using a web-based platform, created a

Jie Zhao, Xin Chen, Yongsheng Yuan, Michael Felsberg

Due to the challenges of processing temporal information, most trackers depend solely on visual discriminability and overlook the unique temporal coherence of video data. In this paper, we propose a lightweight and plug-and-play motion prompt tracking method. It can be easily integrated into existing vision-based trackers to build a joint tracking framework

P. Huijse, J. De Ridder, L. Eyer, L. Rimoldini

Gaia Data Release 3 (DR3) published for the first time epoch photometry, BP/RP (XP) low-resolution mean spectra, and supervised classification results for millions of variable sources. This extensive dataset offers a unique opportunity to study their variability by combining multiple Gaia data products. In preparation for DR4, we propose and evaluate a machi

Hossein Khalili, Seongbin Park, Venkat Bollapragada, Nader Sehatbakhsh

As vision-based machine learning models are increasingly integrated into autonomous and cyber-physical systems, concerns about (physical) adversarial patch attacks are growing. While state-of-the-art defenses can achieve certified robustness with minimal impact on utility against highly-concentrated localized patch attacks, they fall short in two important a

Qingru Hu, Yukun Huang, Brett Gladman, Wei Zhu

Sedna-like objects (a.k.a. sednoids) are transneptunian objects (TNOs) characterized by large semimajor axes and exceptionally high perihelia. Their high-$q$ orbits are detached from the influence of the four giant planets and need extra perturbation to form. One hypothesis posits that close stellar flybys could have perturbed objects from the primordial sca

Azhar Iqbal, Ishan Honhaga, Eyoel Teffera, Anthony Perry

This paper examines the tactical interaction between drones and tanks in modern warfare through game theory, particularly focusing on Stackelberg equilibrium and backward induction. It describes a high-stakes conflict between two teams: one using advanced drones for attack, and the other defending using tanks. The paper conceptualizes this as a sequential ga

Rajat Kumar Mishra, Arnab Saha

Let $K$ be a field of characteristic zero with a fixed derivation $\partial$ on it. In the case when $A$ is an abelian scheme, Buium considered the group scheme $K(A)$ which is the kernel of differential characters (also known as Manin characters) on the jet space of $A$. Then $K(A)$ naturally inherits a $D$-group scheme structure. Using the theory of univer

Xiaoxue Cheng, Junyi Li, Zhenduo Zhang, Xinyu Tang

Large reasoning models (LRMs) have demonstrated strong performance on complex reasoning tasks, but often suffer from overthinking, generating redundant content regardless of task difficulty. Inspired by the dual process theory in cognitive science, we propose Adaptive Cognition Policy Optimization (ACPO), a reinforcement learning framework that enables LRMs

Shuhao Han, Haotian Fan, Fangyuan Kong, Wenjie Liao

This paper reports on the NTIRE 2025 challenge on Text to Image (T2I) generation model quality assessment, which will be held in conjunction with the New Trends in Image Restoration and Enhancement Workshop (NTIRE) at CVPR 2025. The aim of this challenge is to address the fine-grained quality assessment of text-to-image generation models. This challenge eval

Arjhun Swaminathan, Mete Akgün

Deep neural networks for image classification remain vulnerable to adversarial examples -- small, imperceptible perturbations that induce misclassifications. In black-box settings, where only the final prediction is accessible, crafting targeted attacks that aim to misclassify into a specific target class is particularly challenging due to narrow decision re

Jingyu Li, Tiehua Zhang, Jinze Wang, Yi Zhang

Accurate classification of sleep stages based on bio-signals is fundamental not only for automatic sleep stage annotation, but also for clinical health management and continuous sleep monitoring. Traditionally, this task relies on experienced clinicians to manually annotate data, a process that is both time-consuming and labor-intensive. In recent years, dee

Gaurav Kumar, Soham Satyadharma, Harpreet Singh

Image to image translation is an active area of research in the field of computer vision, enabling the generation of new images with different styles, textures, or resolutions while preserving their characteristic properties. Recent architectures leverage Generative Adversarial Networks (GANs) to transform input images from one domain to another. In this wor

Rania Ahmed, Eman Ahmed, Ahmed Elbarbary, Ashraf Darwish

The fashion industry is an extremely profitable market that generates trillions of dollars in revenue by producing and distributing apparel, footwear, and accessories. This systematic literature review (SLR) seeks to systematically review and analyze the research landscape about the Generative Artificial Intelligence (GAI) and metaverse in the fashion indust

Zonghan Wu, Congyuan Zou, Junlin Wang, Chenhan Wang

Generative AI, particularly large language models (LLMs), is beginning to transform the financial industry by automating tasks and helping to make sense of complex financial information. One especially promising use case is the automatic creation of fundamental analysis reports, which are essential for making informed investment decisions, evaluating credit

C. Fairoos, Chiranjeeb Singha

In this paper, we derive the entropy of Reissner-Nordstr\"om (RN) and Kerr black holes using the Hawking-Gibbons path integral method. We determine the periodicity of the Euclidean time coordinate using two approaches: first, by analyzing the near-horizon geometry, and second, by applying the Chern-Gauss-Bonnet (CGB) theorem. For non-extremal cases, both the

Xingyu Zhang, Hanyun Du, Zeen Song, Siyu Zhao

Most existing multivariate time series forecasting methods adopt an all-to-all paradigm that feeds all variable histories into a unified model to predict their future values without distinguishing their individual roles. However, this undifferentiated paradigm makes it difficult to identify variable-specific causal influences and often entangles causally rel

Chenzhuo Zhao, Ziqian Liu, Xinda Wang, Junting Lu

Prompt optimization is a practical and widely applicable alternative to fine tuning for improving large language model performance. Yet many existing methods evaluate candidate prompts by sampling full outputs, often coupled with self critique or human annotated preferences, which limits scalability, especially for smaller models or models that are not instr

Yizhi Zhou, Haina Zhu, Hangting Chen

Recently, pre-trained models for music information retrieval based on self-supervised learning (SSL) are becoming popular, showing success in various downstream tasks. However, there is limited research on the specific meanings of the encoded information and their applicability. Exploring these aspects can help us better understand their capabilities and lim

Bingyang Cheng, Zhongtao Chen, Yichen Jin, Hao Zhang

While CANDECOMP/PARAFAC (CP) decomposition (CPD) is fundamental for tensor reconstruction, Bayesian CPD often scales poorly because variational updates require repeated matrix inversions. We develop CP generalized approximate message passing (CP-GAMP) for incomplete noisy Bayesian CPD. The algorithm uses Gaussian message approximations to avoid high-dimensio

Guohao Huo, Ruiting Dai, Ling Shao, Hao Tang

To address complex pathological feature extraction in automated cardiac MRI segmentation, we propose SAMba-UNet, a novel dual-encoder architecture that synergistically combines the vision foundation model SAM2, the linear-complexity state-space model Mamba, and the classical UNet to achieve cross-modal collaborative feature learning; to overcome domain shift

Haochen Shi, Tianshi Zheng, Weiqi Wang, Baixuan Xu

Large Language Model (LLM) routing is a pivotal technique for navigating a diverse landscape of LLMs, aiming to select the best-performing LLMs tailored to the domains of user queries, while managing computational resources. However, current routing approaches often face limitations in scalability when dealing with a large pool of specialized LLMs, or in the

Olivier Besson

We consider estimating the population covariance matrix when the number of available samples is less than the size of the observations. The sample covariance matrix (SCM) being singular, regularization is mandatory in this case. For this purpose we consider minimizing Stein's loss function and we investigate a method based on augmenting the partial Cholesky

Fuchun Ge, Yuxinxin Chen, Pavlo O. Dral

Molecular dynamics (MD) is a powerful tool for exploring the behavior of atomistic systems, but its reliance on sequential numerical integration limits simulation efficiency. We present a novel neural network architecture, MDtrajNet, and a pre-trained foundational model, MDtrajNet-1, that directly generates MD trajectories across chemical space, bypassing fo

Sotiris Michaelides, Daniel Eguiguren Chavez, Martin Henze

With the ongoing adoption of 5G for communication in industrial systems and critical infrastructure, the security of industrial UEs such as 5G-enabled industrial robots becomes an increasingly important topic. Most notably, to meet the stringent security requirements of industrial deployments, industrial UEs not only have to fully comply with the 5G specific

Yunlang Guo, Bo Wang, Xiangdong Li, Dongdong Liu

Eclipsing millisecond pulsars (MSPs) are a type of pulsar binaries with close orbits ($\lesssim1.0\,$d). They are important objects for studying the accretion history of neutron stars (NSs), pulsar winds, and the origin of isolated MSPs, etc. Recently, a new eclipsing MSP, PSR J$1928+1815$, was discovered by the Five-hundred-meter Aperture Spherical radio Te

Feng Liu, Lixin Zou, Xiangyu Zhao, Min Tang

Generative models, particularly diffusion model, have emerged as powerful tools for sequential recommendation. However, accurately modeling user preferences remains challenging due to the noise perturbations inherent in the forward and reverse processes of diffusion-based methods. Towards this end, this study introduces FMRec, a Flow Matching based model tha

Seongryong Jung, Suwan Yoon, DongGeon Kim, Hwanhee Lee

Large language models (LLMs) offer impressive performance but are impractical for resource-constrained deployment due to high latency and energy consumption. Knowledge distillation (KD) addresses this by transferring knowledge from a large teacher to a smaller student model. However, conventional KD, notably approaches like Forward KL (FKL) and Reverse KL (R

Jan Habscheid, Satyvir Singh, Lambert Theisen, Stefanie Braun

In this study, we present a finite element solver for a thermodynamically consistent electrolyte model that accurately captures multicomponent ionic transport by incorporating key physical phenomena such as steric effects, solvation, and pressure coupling. The model is rooted in the principles of non-equilibrium thermodynamics and strictly enforces mass cons

Ambily A. A., Aparna Pradeep V. K

In 1976, L.N. Vaserstein used a construction analogous to the Gram-Schmidt orthogonalisation, for obtaining a set of symplectic matrices from a set of elementary matrices. We have a similar construction for Petrov's odd unitary group. Here, we prove that the elementary matrices in the odd unitary analogue of the Gram-Schmidt process form a set of generators

Essa Jan, Moiz Ali, Muhammad Saram Hassan, Fareed Zaffar

As the knowledge of large language models (LLMs) becomes outdated over time, there is a growing need for efficient methods to update them, especially when injecting proprietary information. Our study reveals that comprehension-intensive fine-tuning tasks (e.g., question answering and blanks) achieve substantially higher knowledge retention rates (48%) compar

Wanghan Xu, Xiangyu Zhao, Yuhao Zhou, Xiaoyu Yue

Advancements in Large Language Models (LLMs) drive interest in scientific applications, necessitating specialized benchmarks such as Earth science. Existing benchmarks either present a general science focus devoid of Earth science specificity or cover isolated subdomains, lacking holistic evaluation. Furthermore, current benchmarks typically neglect the asse

Yufei Yin, Lechao Cheng, Wengang Zhou, Jiajun Deng

In recent years, weakly supervised object detection (WSOD) has attracted much attention due to its low labeling cost. The success of recent WSOD models is often ascribed to the two-stage multi-class classification (MCC) task, i.e., multiple instance learning and online classification refinement. Despite achieving non-trivial progresses, these methods overloo

Rishabh Maheshwary, Masoud Hashemi, Khyati Mahajan, Shiva Krishna Reddy Malay

Iterative RAG for multi-hop question answering faces challenges with lengthy contexts and the buildup of irrelevant information. This hinders a model's capacity to process and reason over retrieved content and limits performance. While recent methods focus on compressing retrieved information, they are either restricted to single-round RAG, require finetunin

Hiromichi Nakazato, Tohru Ozawa

The time-dependent free Schr\"odinger operator is shown to be characterized as the only linear partial differential operator of the second order that is invariant under the Galilei group in the Euclidean space-time $\mathbb R\times\mathbb R^n$. The method of proof depends on the analysis of the invariance of polynomials given by the application of the linear

Ronen Gradwohl, Eilam Shapira, Moshe Tennenholtz

Algorithmic fairness has emerged as a central issue in ML, and it has become standard practice to adjust ML algorithms so that they will satisfy fairness requirements such as Equal Opportunity. In this paper we consider the effects of adopting such fair classifiers on the overall level of ecosystem fairness. Specifically, we introduce the study of fairness w

Mohammad Rubyet Islam, Peter Sandborn

This research explores the application of Multimodal Generative AI to enhance story point estimation in Agile software development. By integrating text, image, and categorical data using advanced models like BERT, CNN, and XGBoost, our approach surpasses the limitations of traditional single-modal estimation methods. The results demonstrate strong accuracy f

Yan Zhao, Yang Li, Zhengxue Cheng, Hengdi Zhang

Though robotic dexterous manipulation has progressed substantially recently, challenges like in-hand occlusion still necessitate fine-grained tactile perception, leading to the integration of more tactile sensors into robotic hands. Consequently, the increased data volume imposes substantial bandwidth pressure on signal transmission from the hand's controlle

Xiaoxue Han, Pengfei Hu, Jun-En Ding, Chang Lu

Deep learning models trained on extensive Electronic Health Records (EHR) data have achieved high accuracy in diagnosis prediction, offering the potential to assist clinicians in decision-making and treatment planning. However, these models lack two crucial features that clinicians highly value: interpretability and interactivity. The ``black-box'' nature of

Sung Une Lee, Liming Dong, Zhenchang Xing, Muhammad Ejaz Ahmed

The escalating complexity of modern software development environments has heightened concerns around supply chain security. However, existing frameworks often fall short in translating abstract security principles into concrete, actionable practices. This paper introduces the Software Security Mapping Framework, a structured solution designed to operationali

Bekir Danış

We focus on the new type perturbed metric spaces and introduce a contraction mapping namely new type perturbed Kannan mappings. For these mappings, we show that Banach's fixed point theorem holds. Moreover, this new generalization of Banach's contraction principle does not depend on the continuity of the operator.

Abdullah Karasan, Ozge Sezgin Alp, Gerhard-Wilhelm Weber

In this study, we propose a novel machine-learning-based measure for stock price crash risk, utilizing the minimum covariance determinant methodology. Employing this newly introduced dependent variable, we predict stock price crash risk through cross-sectional regression analysis. The findings confirm that the proposed method effectively captures stock price

Kui Zhao, Ziting Wang, Yu Liu, Gui - Han Liang

Quantum simulation has emerged as a powerful framework for investigating complex many - body phenomena. A key requirement for emulating these dynamics is the realization of fully controllable quantum systems enabling various spin interactions. Yet, quantum simulators remain constrained in the types of attainable interactions. Here we demonstrate experimental

Christoforos Neofytidis, Hongbin Sun, Ye Tian, Shicheng Wang

Let $E_i$ be an oriented circle bundle over a closed oriented aspherical $n$-manifold $M_i$ with Euler class $e_i\in H^2(M_i;\mathbb{Z})$, $i=1,2$. We prove the following: (i) If every finite-index subgroup of $\pi_1(M_2)$ has trivial center, then any non-zero degree map from $E_1$ to $E_2$ is homotopic to a fiber-preserving map. (ii) The mapping degree set

Josh Alman, Zhao Song

Attention mechanisms lie at the heart of modern large language models (LLMs). Straightforward algorithms for forward and backward (gradient) computation take quadratic time, and a line of work initiated by [Alman and Song NeurIPS 2023] and [Alman and Song NeurIPS 2024] has shown that quadratic time is necessary unless the model weights are small, in which ca

Lijian Li, Yuanpeng He, Chi-Man Pun

Recently, prototype learning has emerged in semi-supervised medical image segmentation and achieved remarkable performance. However, the scarcity of labeled data limits the expressiveness of prototypes in previous methods, potentially hindering the complete representation of prototypes for class embedding. To overcome this issue, we propose an efficient prot

Fanbin Lu, Zhisheng Zhong, Shu Liu, Chi-Wing Fu

Training large language models (LLMs) as interactive agents for controlling graphical user interfaces (GUIs) presents a unique challenge to optimize long-horizon action sequences with multimodal feedback from complex environments. While recent works have advanced multi-turn reinforcement learning (RL) for reasoning and tool-using capabilities in LLMs, their

Shijie Zhang, Renhao Li, Songsheng Wang, Philipp Koehn

The advancement of Large Language Models (LLMs) enables flexible and interpretable automatic evaluations. In the field of machine translation evaluation, utilizing LLMs with translation error annotations based on Multidimensional Quality Metrics (MQM) yields more human-aligned judgments. However, current LLM-based evaluation methods still face challenges in

Yuhao Li, Xuanhua Shi, Yunfei Zhao, Yongluan Zhou

This paper proposes Redox, a training data management system designed to achieve high I/O efficiency. The key insight is a new observation of file redirection: for model training, when training data in one file is requested, the system has the flexibility to return the data of another file. Based on this property, Redox starts with a bold design principle th

Junjie Zheng, Zihao Chen, Chaofan Ding, Yunming Liang

Current movie dubbing technology can produce the desired speech using a reference voice and input video, maintaining perfect synchronization with the visuals while effectively conveying the intended emotions. However, crucial aspects of movie dubbing, including adaptation to various dubbing styles, effective handling of dialogue, narration, and monologues, a

Sheng-Fu Wang, Laurent Prevot, Jou-an Chi, Ri-Sheng Huang

The achievements of Large Language Models in Natural Language Processing, especially for high-resource languages, call for a better understanding of their characteristics from a cognitive perspective. Researchers have attempted to evaluate artificial models by testing their ability to predict behavioral (e.g., eye-tracking fixations) and physiological (e.g.,

Desiree Heim, Lars-Peter Meyer, Markus Schröder, Johannes Frey

When using Large Language Models (LLMs) to support Knowledge Graph Engineering (KGE), one of the first indications when searching for an appropriate model is its size. According to the scaling laws, larger models typically show higher capabilities. However, in practice, resource costs are also an important factor and thus it makes sense to consider the ratio

Wei Meng

This study investigates the emotional rhythms and behavioral mechanisms of dominant political leaders in strategic decision-making. Using the Trump administration's 125 percent tariff hike on China as a case, it adopts a Multimodal Cognitive Behavioral Modeling framework. This includes micro-expression tracking, acoustic intonation analysis, semantic flow mo

Ofer Neufeld

Photoelectron circular dichroism (PECD) is a method whereby randomly oriented chiral molecules are irradiated by circularly-polarized light, photoionizing electrons, which are measured in a momentum-resolved manner. This scheme permits chiral light-matter interactions within the electric-dipole approximation (avoiding weak magnetic-dipole interactions), yiel

Guy Moshel, Omer Rabinowitz, Eliya Blumenthal, Shay Hacohen-Gourgy

Cosmic rays and background radioactive decay can deposit significant energy into superconducting quantum circuits on planar chips. This energy converts into pair-breaking phonons that travel across the substrate and generate quasiparticles, leading to correlated energy and phase errors in nearby qubits. To mitigate this, we fabricated two separate dies and p

A. Chilingarian

At least two mechanisms effectively transfer interplanetary magnetic field (IMF) disturbances into the atmosphere. First, the inflow of solar wind into the ionosphere at low latitudes significantly enhances the total vertical electron content, increasing atmospheric conductivity. Second, Forbush decreases (FD) reduce the cosmic ray flux by a few percent, low

Ibrahim Al Azher, Miftahul Jannat Mokarrama, Zhishuai Guo, Sagnik Ray Choudhury

In scientific research, ``limitations'' refer to the shortcomings, constraints, or weaknesses of a study. A transparent reporting of such limitations can enhance the quality and reproducibility of research and improve public trust in science. However, authors often underreport limitations in their papers and rely on hedging strategies to meet editorial requi

Jiaru Zou, Yikun Ban, Zihao Li, Yunzhe Qi

Large language models are typically adapted to downstream tasks through supervised fine-tuning on domain-specific data. While standard fine-tuning focuses on minimizing generation loss to optimize model parameters, we take a deeper step by retaining and leveraging the model's own learning signals, analogous to how human learners reflect on past mistakes to i

A. Chilingarian

We investigate the energy spectra of secondary cosmic ray particles associated with two distinct solar events: the magnetospheric effect (ME) of 5 November 2023 and ground-level enhancement (GLE 74) of 11 May 2024. Using data from the SEVAN and Neutron Monitor networks and energy release histograms from particle spectrometers, we reconstruct spectra and iden

Xu-Dan Xie, Zheng-Yuan Xue, Dan-Bo Zhang

In open quantum systems, the Liouvillian gap characterizes the relaxation time toward the steady state. However, accurately computing this quantity is notoriously difficult due to the exponential growth of the Hilbert space and the non-Hermitian nature of the Liouvillian superoperator. In this work, we propose a variational quantum algorithm for efficiently

Yongjun Kim, Jihong Park, Mehdi Bennis, Junil Choi

Neural network-based medium access control (MAC) protocol models (NPMs) improve goodput through site-specific operations but are vulnerable to shifts from their training network environments, such as changes in the number of user equipments (UEs) severely degrade goodput. To enhance resilience against such environmental shifts, we propose three novel semanti

Daehan Choi, Woo-Joo Kim, Young-Ik Sohn

We experimentally demonstrate a compact and broadband rapid adiabatic couplers (RACs) with arbitrary power split ratios, achieved through the combination of translational offset and waveguide width control. Fabricated RACs of four different target split ratios show power splitting within $\pm$3% of the design target over a 160 nm wavelength range. Using thes

Vikramaditya Mondal, Shinji Mukohyama

We quantize a Bianchi IX universe in Ho\v{r}ava-Lifshitz theory. For analytical tractability, we consider the small anisotropy limit of the Bianchi IX, that is, a perturbative anisotropic deformation of a closed, homogeneous and isotropic universe. In the case of the projectable theory we further set the ``dark matter as integration constant'' to zero by ass

Ilgee Hong, Changlong Yu, Liang Qiu, Weixiang Yan

Reinforcement learning from human feedback (RLHF) has become a powerful post-training paradigm for aligning large language models with human preferences. A core challenge in RLHF is constructing accurate reward signals, where the conventional Bradley-Terry reward models (BT RMs) often suffer from sensitivity to data size and coverage, as well as vulnerabilit

Sebastian Janampa, Marios Pattichis

Line detection is a basic digital image processing operation used by higher-level processing methods. Recently, transformer-based methods for line detection have proven to be more accurate than methods based on CNNs, at the expense of significantly lower inference speeds. As a result, video analysis methods that require low latencies cannot benefit from curr

Sampanna Yashwant Kahu, Naman Ahuja

Social media and online forums are increasingly becoming popular. Unfortunately, these platforms are being used for spreading hate speech. In this paper, we design black-box techniques to protect users from hate-speech on online platforms by generating perturbations that can fool state of the art deep learning based hate speech detection models thereby decre

Ayaki Sunaga, Tibor Győri, Gábor Czakó, Edit Matyus

The methanol molecule is a sensitive probe of astrochemistry, astrophysics, and fundamental physics. The first-principles elucidation and prediction of its rotation-torsional-vibrational motions are enabled in this work by the computation of a full-dimensional, \emph{ab initio} potential energy surface (PES) and numerically exact quantum dynamics. An active-

Kalindi Singh, Aayush Kashyap, Aswani Kumar Cherukuri

The widespread adoption of encrypted communication protocols such as HTTPS and TLS has enhanced data privacy but also rendered traditional anomaly detection techniques less effective, as they often rely on inspecting unencrypted payloads. This study aims to develop an interpretable machine learning-based framework for anomaly detection in encrypted network t

Alaa Khaddaj, Logan Engstrom, Aleksander Madry

Choice of training data distribution greatly influences model behavior. Yet, in large-scale settings, precisely characterizing how changes in training data affects predictions is often difficult due to model training costs. Current practice is to instead extrapolate from scaled down, inexpensive-to-train proxy models. However, changes in data do not influenc

Hayeon Bang, Taegyun Kwon, Juhan Nam

This paper presents <Dialogue in Resonance>, an interactive music piece for a human pianist and a computer-controlled piano that integrates real-time automatic music transcription into a score-driven framework. Unlike previous approaches that primarily focus on improvisation-based interactions, our work establishes a balanced framework that combines composed

Aashish Anantha Ramakrishnan, Aadarsh Anantha Ramakrishnan, Dongwon Lee

Interpreting figurative language such as sarcasm across multi-modal inputs presents unique challenges, often requiring task-specific fine-tuning and extensive reasoning steps. However, current Chain-of-Thought approaches do not efficiently leverage the same cognitive processes that enable humans to identify sarcasm. We present IRONIC, an in-context learning

Masanari Kimura

This study develops a higher-order asymptotic framework for test-time adaptation (TTA) of Batch Normalization (BN) statistics under distribution shift by integrating classical Edgeworth expansion and saddlepoint approximation techniques with a novel one-step M-estimation perspective. By analyzing the statistical discrepancy between training and test distribu

Yan Zhao, Zhengxue Cheng, Junxuan Zhang, Qunshan Gu

Most learning-based lossless compressors are designed for a single modality, requiring separate models for multi-modal data and lacking flexibility. However, different modalities vary significantly in format and statistical properties, making it ineffective to use compressors that lack modality-specific adaptations. While multi-modal large language models (M

K. -Y. Huang, E. Behrens, M. Bouvier, S. Viti

Formaldehyde (H$_2$CO) and methanol (CH$_3$OH) have served as traditional tracers of the star formation process for decades. Studies of the environments which produce these species, though, have pointed to significant differences in the physical environments within which each molecule resides. In this paper we investigate the physical and chemical conditions

Daisuke Yukita, Tim Miller, Joel Mackenzie

In this paper, we conduct a critical review of existing theories and frameworks on human-human collaborative writing to assess their relevance to the current human-AI paradigm in organizational workplace settings, and draw seven insights along with design implications for human-AI collaborative writing tools. Our main finding was that, as we delegate more wr

Preeti Mehta, Aman Sagar, Suchi Kumari

This study aims to address the growing challenge of distinguishing computer-generated imagery (CGI) from authentic digital images across three different color spaces; RGB, YCbCr, and HSV. Given the limitations of existing classification methods in handling the complexity and variability of CGI, this research proposes a Swin Transformer based model for accura

Hwiyeong Lee, Uiji Hwang, Hyelim Lim, Taeuk Kim

Large language models often retain unintended content, prompting growing interest in knowledge unlearning. Recent approaches emphasize localized unlearning, restricting parameter updates to specific regions in an effort to remove target knowledge while preserving unrelated general knowledge. However, their effectiveness remains uncertain due to the lack of r

Masanari Kimura

Label shift adaptation aims to recover target class priors when the labelled source distribution $P$ and the unlabelled target distribution $Q$ share $P(X \mid Y) = Q(X \mid Y)$ but $P(Y) \neq Q(Y)$. Classical black-box shift estimators invert an empirical confusion matrix of a frozen classifier, producing a brittle point estimate that ignores sampling noise

Kristin Qi, Youxiang Zhu, Caroline Summerour, John A. Batsis

Early detection of cognitive decline is crucial for enabling interventions that can slow neurodegenerative disease progression. Traditional diagnostic approaches rely on labor-intensive clinical assessments, which are impractical for frequent monitoring. Our pilot study investigates voice assistant systems (VAS) as non-invasive tools for detecting cognitive

Jian Zhai

We consider an inverse boundary problem for the dynamical Maxwell's equations. We show that the electric permittivity, conductivity, and magnetic permeability can be uniquely determined locally if there is a strictly convex foliation with respect to the wave speed.

Zhen Zhang, Xiangyu Chu, Yunxi Tang, Lulu Zhao

Manipulating elasto-plastic objects remains a significant challenge due to severe self-occlusion, difficulties of representation, and complicated dynamics. This work proposes a novel framework for elasto-plastic object manipulation with a quasi-static assumption for motions, leveraging 3D occupancy to represent such objects, a learned dynamics model trained

Wenxuan Zhu, Qiyuan Wu, Tengda Tang, Renzi Meng

This paper addresses the limitations of multi-node perception and delayed scheduling response in distributed systems by proposing a GNN-based multi-node collaborative perception mechanism. The system is modeled as a graph structure. Message-passing and state-update modules are introduced. A multi-layer graph neural network is constructed to enable efficient

Roman Karasev

We note that an argument by Rogers (1958) gives a proof of Vaaler's theorem (1979) about sections of the cube and allows certain generalizations of the theorem.

Hyukjun Kwon, Chenglin Fan

Differential Privacy (DP) is a rigorous privacy standard widely adopted in data analysis and machine learning. However, its guarantees rely on correctly introducing randomized noise--an assumption that may not hold if the implementation is faulty or manipulated by an untrusted analyst. To address this concern, we propose the first verifiable implementation o