February 2025 arXiv papers — page 9

Yingbing Huang, Deming Chen, Abhishek K. Umrawal

While large language models (LLMs) have made significant strides in generating coherent and contextually relevant text, they often function as opaque black boxes, trained on vast unlabeled datasets with statistical objectives, lacking an interpretable framework for responsible control. In this paper, we introduce JAM (Just A Move), a novel framework that int

Didarul Alam, Jiuyu Sun, Carsten A. Ullrich

This paper studies the performance of time-dependent density-functional theory (TDDFT) for calculating the dielectric function of semiconductors and insulators at finite momentum transfer, comparing against the standard Bethe-Salpeter equation (BSE). Specifically, we consider a recently proposed hybrid approach that mixes dielectrically screened exact exchan

Gibson Nkhata, Susan Gauch, Usman Anjum, Justin Zhan

Sentiment Analysis (SA) is instrumental in understanding peoples viewpoints facilitating social media monitoring recognizing products and brands and gauging customer satisfaction. Consequently SA has evolved into an active research domain within Natural Language Processing (NLP). Many approaches outlined in the literature devise intricate frameworks aimed at

Fei Wei, Yaliang Li, Bolin Ding

Large language models (LLMs), due to their advanced natural language capabilities, have seen significant success in applications where the user interface is usually a conversational artificial intelligence (AI) agent and engages the user through multi-round conversations. However, many scenarios require the agents to exhibit stronger social and conversationa

Chenxu Dang, Zaipeng Duan, Pei An, Xinmin Zhang

Recent top-performing temporal 3D detectors based on Lidars have increasingly adopted region-based paradigms. They first generate coarse proposals, followed by encoding and fusing regional features. However, indiscriminate sampling and fusion often overlook the varying contributions of individual points and lead to exponentially increased complexity as the n

Anjiao Gu, Xiaojiang Zhang

In this work, we develop and rigorously analyze a new class of particle methods for the magnetized Vlasov--Poisson--Fokker--Planck system. The proposed approach addresses two fundamental challenges: (1) the curse of dimensionality, which we mitigate through particle methods while preserving the system's asymptotic properties, and (2) the temporal step size l

Jinfeng Wang, Yanhao Huang, Sifan Song, Boqian Wang

EEG emotion recognition faces significant hurdles due to noise interference, signal nonstationarity, and the inherent complexity of brain activity which make accurately emotion classification. In this study, we present the Fourier Adjacency Transformer, a novel framework that seamlessly integrates Fourier-based periodic analysis with graph-driven structural

Hanbang Liang, Zhen Wang, Weihui Deng

Diffusion models have demonstrated their powerful image generation capabilities, effectively fitting highly complex image distributions. These models can serve as strong priors for image restoration. Existing methods often utilize techniques like ControlNet to sample high quality images with low quality images from these priors. However, ControlNet typically

Aaryan Garg, Akash Kumar, Yogesh S Rawat

In this work we study Weakly Supervised Spatio-Temporal Video Grounding (WSTVG), a challenging task of localizing subjects spatio-temporally in videos using only textual queries and no bounding box supervision. Inspired by recent advances in vision-language foundation models, we investigate their utility for WSTVG, leveraging their zero-shot grounding capabi

Youbing Hu, Yun Cheng, Zimu Zhou, Anqi Lu

Continual adaptation to domain shifts at test time (CTTA) is crucial for enhancing the intelligence of deep learning enabled IoT applications. However, prevailing TTA methods, which typically update all batch normalization (BN) layers, exhibit two memory inefficiencies. First, the reliance on BN layers for adaptation necessitates large batch sizes, leading t

Shanshan Wan, Yingmei Wei, Lai Kang, Tianrui Shen

Visual Place Recognition (VPR) is a major challenge for robotics and autonomous systems, with the goal of predicting the location of an image based solely on its visual features. State-of-the-art (SOTA) models extract global descriptors using the powerful foundation model DINOv2 as backbone. These models either explore the cross-image correlation or propose

Si-yu Li, Zhongrui Wang, Yingzhuo Han, Shaoqing Xu

Nanoscale polar structures are significant for understanding polarization processes in low-dimensional systems and hold potential for developing high-performance electronics. Here, we demonstrate a polar vortex superstructure arising from the reconstructed moir\'e patterns in twisted bilayer graphene aligned with hexagonal boron nitride. Scanning tunneling m

Shuaijun Li, Jie Tang, Beixiong Zheng, Xiaokai Song

Reconfigurable intelligent surface (RIS)-aided vehicle-to-everything (V2X) communication has emerged as a crucial solution for providing reliable data services to vehicles on the road. However, in delay-sensitive or high-mobility communications, the rapid movement of vehicles can lead to random scattering in the environment and time-selective fading in the c

Pegah Mohammadipour, Xiantao Li

Zero-noise extrapolation (ZNE) is a widely used quantum error mitigation technique that artificially amplifies circuit noise and then extrapolates the results to the noise-free circuit. A common ZNE approach is Richardson extrapolation, which relies on polynomial interpolation. Despite its simplicity, efficient implementations of Richardson extrapolation fac

Jin Lei

Nonelastic breakup (NEB) reactions induced by the halo nucleus $^{11}$Be on $^{64}$Zn at 28.7 MeV are investigated using the Ichimura-Austern-Vincent (IAV) model combined with the Continuum Discretized Coupled Channels (CDCC) method. NEB cross sections calculated with full CDCC wave functions (including continuum states), ground-state-only CDCC wave function

Sinya Aoki

I report recent developments on hadron interactions in lattice QCD by the HAL QCD method. As an introduction, I summarize the lattice community's consensus on the absence of the deeply bound dinucleons at heavier pion masses. We then present 4 results by the HAL QCD method using $2+1$ flavor QCD gauge configurations at $m_\pi \simeq 146$ MeV and $a\simeq 0.0

Arman Abgaryan, Utkarsh Sharma

We propose a mechanism embedded into the foundational infrastructure of a blockchain network, designed to improve the utility of idle network resources, whilst enhancing market microstructure efficiency during block production by leveraging both network-owned and external capital. By systematically seeking to use idle network resources for internally capture

John J. Han, Jie Ying Wu

The lack of labeled datasets in 3D vision for surgical scenes inhibits the development of robust 3D reconstruction algorithms in the medical domain. Despite the popularity of Neural Radiance Fields and 3D Gaussian Splatting in the general computer vision community, these systems have yet to find consistent success in surgical scenes due to challenges such as

Xiang Xiang, Zhuo Xu, Yao Deng, Qinhao Zhou

The advancement of remote sensing, including satellite systems, facilitates the continuous acquisition of remote sensing imagery globally, introducing novel challenges for achieving open-world tasks. Deployed models need to continuously adjust to a constant influx of new data, which frequently exhibits diverse shifts from the data encountered during the trai

Ojonugwa Oluwafemi Ejiga Peter, Md Mahmudur Rahman, Fahmi Khalifa

The MEDVQA-GI challenge addresses the integration of AI-driven text-to-image generative models in medical diagnostics, aiming to enhance diagnostic capabilities through synthetic image generation. Existing methods primarily focus on static image analysis and lack the dynamic generation of medical imagery from textual descriptions. This study intends to parti

C. A. Morales

Notes from a course on linear dynamics given by the author at the University of Da Nang in January 2024.

Kenneth H. Hinkle, Pranav Nagarajan, Francis C. Fekel, Joanna Mikołajewska

T CrB is among the brightest novae. It is recurrent with outbursts happening approximately every 80 years. The next outburst is imminent, expected in 2025. The T CrB binary consists of an M4 III red giant (RG) secondary and a white dwarf (WD) primary. A time series of spectra of the RG was obtained between 2022 and 2024. Radial velocities (RVs) from these da

Radhika Kapoor, Sang T. Truong, Nick Haber, Maria Araceli Ruiz-Primo

Prediction of item difficulty based on its text content is of substantial interest. In this paper, we focus on the related problem of recovering IRT-based difficulty when the data originally reported item p-value (percent correct responses). We model this item difficulty using a repository of reading passages and student data from US standardized tests from

Riz Fernando Noronha, Kim Sneppen, Kunihiko Kaneko

Soil is a complex, dynamic material, with physical properties that depend on its biological content. We propose a cellular automaton model for self-organizing soil structure, where soil aggregates and serves as food for microbial species. These, in turn, produce nutrients that facilitate self-amplification, establishing a cyclical dynamic of consumption and

Hyungi Lee, Chaeyun Jang, Dongbok Lee, Juho Lee

Meta-learning aims to train models that can generalize to new tasks with limited labeled data by extracting shared features across diverse task datasets. Additionally, it accounts for prediction uncertainty during both training and evaluation, a concept known as uncertainty-aware meta-learning. Neural Process(NP) is a well-known uncertainty-aware meta-learni

Wen-Wan He, Mao Song, Jian-You Guo, Xuan Luo

Resonances are ubiquitous phenomena in nature, and physicists have developed many methods to explore resonant states. Of particular note is the complex momentum representation(CMR) method, which has been developed and widely used in the study of resonant states in atomic, molecular and nuclear physics. Here, for the first time, we have developed this novel m

Yuan-Hao Wei

This tutorial focuses on the fundamental architectures of Variational Autoencoders (VAE) and Generative Adversarial Networks (GAN), disregarding their numerous variations, to highlight their core principles. Both VAE and GAN utilize simple distributions, such as Gaussians, as a basis and leverage the powerful nonlinear transformation capabilities of neural n

Anthony Christiana, Ben Clingenpeel, Huizheng Guo, Jinseok Oh

In this paper, we analyze the homology of the Yang-Baxter Operators $R_{(m)}$ yielding the HOMFLYPT polynomial, reducing the computation of the $n$-th homology of $R_{(m)}$ for arbitrary $m$ to the computation of $n+1$ initial conditions. We then produce the explicit formulas for the third and fourth homology.

Jiaying "Lizzy" Liu, Yunlong Wang, Allen Jue, Yao Lyu

Individuals with severe mental illnesses (SMI), particularly schizophrenia, experience complex and intense emotions frequently. They increasingly turn to vlogging as an authentic medium for emotional disclosure and online support-seeking. While previous research has primarily focused on text-based disclosure, little is known about how people construct narrat

Yingqi Gao, Zhiling Luo

In the context of the Text-to-SQL task, table and column descriptions are crucial for bridging the gap between natural language and database schema. This report proposes a method for automatically generating effective database descriptions when explicit descriptions are unavailable. The proposed method employs a dual-process approach: a coarse-to-fine proces

Julius Fergy Tiongson Rabago

This paper introduces a method for estimating the shape and location of an embedded tumor. The approach utilizes shape optimization techniques, applying the coupled complex boundary method. By rewriting the problem -- characterized by a measured temperature profile and corresponding flux (e.g., from infrared thermography) -- into a complex boundary value pro

Xun Tang, Lexing Ying

We introduce the functional hierarchical tensor under a wavelet basis (FHT-W) ansatz for high-dimensional density estimation in lattice models. Recently, the functional tensor network has emerged as a suitable candidate for density estimation due to its ability to calculate the normalization constant exactly, a defining feature not enjoyed by neural network

Seongyeol Kim, Juan Pablo Gonzalez-Aguilera, Ryan Roussel, Gyujin Kim

Understanding the 6-dimensional phase space distribution of particle beams is essential for optimizing accelerator performance. Conventional diagnostics such as use of transverse deflecting cavities offer detailed characterization but require dedicated hardware and space. Generative phase space reconstruction (GPSR) methods have shown promise in beam diagnos

Shaobo Wang, Yicun Yang, Zhiyuan Liu, Chenghao Sun

Dataset distillation has emerged as a powerful approach for reducing data requirements in deep learning. Among various methods, distribution matching-based approaches stand out for their balance of computational efficiency and strong performance. However, existing distance metrics used in distribution matching often fail to accurately capture distributional

Jaques Darné, Naoya Enomoto, Takao Satoh

In this short paper, we show that the McCool group does not satisfy the Andreadakis equality from degree $7$, and we give a lower bound for the size of the difference between the two relevant filtrations. As a consequence, we see that the Andreadakis problem for the McCool group does not stabilize.

Rishi Mukherjee, Sakshi Singh, Jack McWilliams, Junaed Sattar

We introduce COU: Common Objects Underwater, an instance-segmented image dataset of commonly found man-made objects in multiple aquatic and marine environments. COU contains approximately 10K segmented images, annotated from images collected during a number of underwater robot field trials in diverse locations. COU has been created to address the lack of dat

Lei Zhang, Yu Pan, Bingrong Dai, Lin Wang

Diffusion Models (DMs) have achieved remarkable success in image generation, yet recent studies reveal their vulnerability to backdoor attacks, where adversaries manipulate outputs via covert triggers embedded in inputs. Existing defenses, such as backdoor detection and trigger inversion, are largely effective because prior attacks rely on limited input spac

Ranjana Mehta, Joydip Saha

In this paper, we study the Apery tables for the numerical semigroups given by Bresinsky and Arslan. Using the Apery tables we write the tangent cones of the Bresinsky and Arsalan curves at the origin. Further, we calculate Hilbert series of the tangent cone of the Bresinsky and Arslan curves. We prove that both classes of the curve have Cohen- Macaulay tang

Amarilton Lopes Magalhães, André Lima Férrer de Almeida, Gilderlan Tavares de Araújo

We consider the data-aided channel estimation (CE) problem in a reconfigurable intelligent surface (RIS)-assisted wireless communication system, where the channel and information symbols are estimated jointly during the CE phase, differently from pure pilot-aided methods. We propose a two-stage semi-blind receiver that jointly estimates the combined channel

Colleen Gilhuly, Haleh Shahzad

Text summarizing is a critical Natural Language Processing (NLP) task with applications ranging from information retrieval to content generation. Large Language Models (LLMs) have shown remarkable promise in generating fluent abstractive summaries but they can produce hallucinated details not grounded in the source text. Regardless of the method of generatin

Victor Mendoza-Estrada, Rafael González-Hernández, Bernardo Uribe, Libor Šmejkal

We study the electronic, magnetic, and spin transport properties of the orthorhombic Mn$_{5}$Si$_{3}$ compound in the $AF2$ phase using symmetry analysis and ab-initio calculations. Our ground state energy calculations align with experimental observations, demonstrating that the collinear antiferromagnetic (AFM) order, with N\'{e}el vector in the [010] direc

George Boxer, Frank Calegari, Toby Gee, Vincent Pilloni

We prove the modularity of a positive proportion of abelian surfaces over $\mathbf{Q}$. More precisely, we prove the modularity of abelian surfaces which are ordinary at $3$ and are $3$-distinguished, subject to some assumptions on the $3$-torsion representation (a "big image" hypothesis, and a technical hypothesis on the action of a decomposition group at $

Matthieu Cadiot, Jean-Philippe Lessard

This paper presents a novel approach to rigorously solving initial value problems for semilinear parabolic partial differential equations (PDEs) using fully spectral Fourier-Chebyshev expansions. By reformulating the PDE as a system of nonlinear ordinary differential equations and leveraging Chebyshev series in time, we reduce the problem to a zero-finding t

Pengyu Zhang, Xieyuanli Chen, Yuwei Chen, Beizhen Bi

Ground penetrating radar (GPR) based localization has gained significant recognition in robotics due to its ability to detect stable subsurface features, offering advantages in environments where traditional sensors like cameras and LiDAR may struggle. However, existing methods are primarily focused on small-scale place recognition (PR), leaving the challeng

Toshiharu Kawasaki

In this paper, we show the new fixed point theorem in metric spaces. Furthermore, for this fixed point theorem, we apply to the Collatz conjecture.

Akiko Tei, Stanislav Gunar, Takenori J. Okamoto

Solar quiet regions are divided into coronal hole regions (CH) and quiet-Sun regions (QS). The global magnetic field in CH is considered open to interplanetary space, while that in QS is closed. To constrain the solar atmosphere and solar wind model, we statistically compared CH and QS in the chromosphere by quantitatively analyzing all available high-resolu

Haitao Li, Yifan Chen, Yiran Hu, Qingyao Ai

Retrieval-augmented generation (RAG) has proven highly effective in improving large language models (LLMs) across various domains. However, there is no benchmark specifically designed to assess the effectiveness of RAG in the legal domain, which restricts progress in this area. To fill this gap, we propose LexRAG, the first benchmark to evaluate RAG systems

Leming Shen, Qiang Yang, Kaiyan Cui, Yuanqing Zheng

Federated Learning (FL) facilitates collaborative training of a shared global model without exposing clients' private data. In practical FL systems, clients (e.g., edge servers, smartphones, and wearables) typically have disparate system resources. Conventional FL, however, adopts a one-size-fits-all solution, where a homogeneous large global model is transm

Cong Liu, Xin-Ze Song, Zhi-Xi Wu, Guo-Yong Yuan

The phenomenon of stochastic resonance, wherein the stimulus-response of a system can be maximized by an intermediate level of noise, has been extensively investigated through linear response theory. As yet a unified response-noise or response-frequency formula embracing diverse factors, such as noise color, damping coefficients, and coupling, is still lacki

Yuqian Chen, Leo Zekelman, Yui Lo, Suheyla Cetin-Karayumak

Tractography parcellation classifies streamlines reconstructed from diffusion MRI into anatomically defined fiber tracts for clinical and research applications. However, clinical scans often have incomplete fields of view (FOV) where brain regions are partially imaged, leading to partial or truncated fiber tracts. To address this challenge, we introduce Trac

David Isele, Alexandre Miranda Anon, Faizan M. Tariq, Goro Yeh

Reliable automated driving technology is challenged by various sources of uncertainties, in particular, behavioral uncertainties of traffic agents. It is common for traffic agents to have intentions that are unknown to others, leaving an automated driving car to reason over multiple possible behaviors. This paper formalizes a behavior planning scheme in the

Bo Wang, Yiqiao Li, Jianlong Zhou, Fang Chen

EXplainable machine learning (XML) has recently emerged to address the mystery mechanisms of machine learning (ML) systems by interpreting their 'black box' results. Despite the development of various explanation methods, determining the most suitable XML method for specific ML contexts remains unclear, highlighting the need for effective evaluation of expla

Chin-Chia Michael Yeh, Xiran Fan, Zhimeng Jiang, Yujie Fan

Spatio-temporal data, prevalent in real-world applications such as traffic monitoring, financial transactions, and ride-share demands, represents a specialized case of multivariate time series characterized by high dimensionality. This high dimensionality necessitates computationally efficient models and benefits from applying univariate forecasting approach

Vaishnavi Pulavarthi, Deeksha Nandal, Soham Dan, Debjit Pal

Assertions have been the de facto collateral for simulation-based and formal verification of hardware designs for over a decade. The quality of hardware verification, i.e., detection and diagnosis of corner-case design bugs, is critically dependent on the quality of the assertions. With the onset of generative AI such as Transformers and Large-Language Model

Jianqiao Chen, Nan Ma, Wenkai Liu, Xiaodong Xu

Channel reconstruction and generalization capability are of equal importance for developing channel estimation schemes within deep learning (DL) framework. In this paper, we exploit a novel DL-based scheme for efficient OFDM channel estimation where the neural networks for channel reconstruction and generalization are respectively designed. For the former, w

Shoummo Ahsan Khandoker, Estelle M. Inack, Mohamed Hibat-Allah

Understanding the principles of protein folding is a cornerstone of computational biology, with implications for drug design, bioengineering, and the understanding of fundamental biological processes. Lattice protein folding models offer a simplified yet powerful framework for studying the complexities of protein folding, enabling the exploration of energeti

José Edson Sampaio

The renowned Theorem of Nobile, proved by Nobile in 1975, states that a pure dimensional complex analytic set $X$ is analytically smooth if and only if its Nash transformation $\eta: \mathcal{N}(X) \to X$ is an analytic isomorphism. While the Theorem of Nobile was fundamental in complex geometry, it remained an open question for 50 years whether the theorem

Changyeon Kim, Minho Heo, Doohyun Lee, Jinwoo Shin

Reinforcement Learning (RL) agents have demonstrated their potential across various robotic tasks. However, they still heavily rely on human-engineered reward functions, requiring extensive trial-and-error and access to target behavior information, often unavailable in real-world settings. This paper introduces REDS: REward learning from Demonstration with S

Griffin Higgins, Roozbeh Razavi-Far, Xichen Zhang, Amir David

This work aims to provide both privacy and utility within a split learning framework while considering both forward attribute inference and backward reconstruction attacks. To address this, a novel approach has been proposed, which makes use of class activation maps and autoencoders as a plug-in strategy aiming to increase the user's privacy and destabilize

Martín Matamala, Juan Pablo Peña, José Zamora

In this work we show that any connected locally connected graph defines a metric space having at least as many lines as vertices with only three exception: the complete multipartite graphs $K_{1,2,2}$, $K_{2,2,2}$ and $K_{2,2,2,2}$. This proves that this class fulfills a conjecture, proposed by Chen and Chv\'atal, saying that any metric space on n points has

Leonardo Berti, Flavio Giorgi, Gjergji Kasneci

Large Language Models (LLMs) are leading a new technological revolution as one of the most promising research streams toward artificial general intelligence. The scaling of these models, accomplished by increasing the number of parameters and the magnitude of the training datasets, has been linked to various so-called emergent abilities that were previously

Li Yang, Shimaa Naser, Abdallah Shami, Sami Muhaidat

The transition from 5G to 6G mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal

T. B. Pawar, A. Miszuda, K. G. Hełminiak, F. Marcadon

Eclipsing double-lined spectroscopic binaries hosting $\delta$ Scuti-type pulsators offer a unique laboratory for simultaneously constraining stellar geometry and interior structure. In this study, we present a comprehensive analysis of five oscillating eclipsing Algol binaries. By combining high-precision, short-cadence TESS photometry with multi-epoch high

Yifei Qian, Zhongliang Guo, Bowen Deng, Chun Tong Lei

Zero-shot object counting aims to count instances of arbitrary object categories specified by text descriptions. Existing methods typically rely on vision-language models like CLIP, but often exhibit limited sensitivity to text prompts. We present T2ICount, a diffusion-based framework that leverages rich prior knowledge and fine-grained visual understanding

Genta Furuya, Kazumasa Hattori

We investigate the impact of order parameter fluctuations on magnetoelectric effects in metallic systems using classical Monte Carlo simulations. We focus on a a chiral quadrupole order in a distorted kagome lattice in a model incorporating conduction electrons and classical orbital moments. The ordered orbital moments break mirror symmetry and couple with t

Yuepeng Hu, Zhengyuan Jiang, Neil Zhenqiang Gong

Text-to-image models can generate harmful images when presented with unsafe prompts, posing significant safety and societal risks. Alignment methods aim to modify these models to ensure they generate only non-harmful images, even when exposed to unsafe prompts. A typical text-to-image model comprises two main components: 1) a text encoder and 2) a diffusion

Chi Ruan, Jiying Zhao, Wenhu Chen

Although open-vocabulary object detectors can generalize to unseen categories, they still rely on predefined textual prompts or classifier heads during inference. Recent generative object detectors address this limitation by coupling an autoregressive language model with a detector backbone, enabling direct category name generation for each detected object.

Wei Kang, Nan Wang, Jang Seung, Shuo Wang

Phishing attacks, typically carried out by email, remain a significant cybersecurity threat with attackers creating legitimate-looking websites to deceive recipients into revealing sensitive information or executing harmful actions. In this paper, we propose {\bf EPhishCADE}, the first {\em privacy-aware}, {\em multi-dimensional} framework for {\bf E}mail {\

Ayana Niwa, Masahiro Kaneko, Kentaro Inui

Large language models (LLMs) can exhibit advanced reasoning yet still generate incorrect answers. We hypothesize that such errors frequently stem from spurious beliefs, propositions the model internally considers true but are incorrect. To address this, we propose a method to rectify the belief space by suppressing these spurious beliefs while simultaneously

Zhiqiang Shen, Peng Cao, Jinzhu Yang, Osmar R. Zaiane

Due to domain shifts across diverse medical imaging modalities, learned segmentation models often suffer significant performance degradation during deployment. We posit that these domain shifts can generally be categorized into two main components: 1) "style" shifts, referring to global disparities in image properties such as illumination, contrast, and colo

Burt Totaro

Rost defined the Chow group of algebraic cycles with coefficients in a locally constant torsion etale sheaf. We generalize the definition to allow non-torsion coefficients. Chow groups with twisted coefficients are related to Serre's notion of "negligible cohomology" for finite groups. We generalize a computation by Merkurjev and Scavia of negligible cohomol

Yue Chen, Haoran Chen, Xi Shen, Weizhao Chen

Using first-principles electronic structure calculation, we demonstrate the spin dissipation process in bulk Fe by orbital excitations within the energy bands of pure spin character. The variation of orbitals in the intraband transitions provides an efficient channel to convert spin to orbital angular momentum with spin-orbit interaction. This mechanism domi

Juntao Tan, Liangwei Yang, Zuxin Liu, Zhiwei Liu

Personalization is critical in AI assistants, particularly in the context of private AI models that work with individual users. A key scenario in this domain involves enabling AI models to access and interpret a user's private data (e.g., conversation history, user-AI interactions, app usage) to understand personal details such as biographical information, p

Efren Morales Amaya

Let $M$ be a convex body and let $K$ be a closed convex surface $K$ both contained in the Euclidean space $\mathbb{E}^3$. What can we say about $M$ if $K$ encloses $M$ and if from all the points in $K$ the body $M$ looks the same? In this work we are going to present a result which claims that if for every two support cones $C_x$, $C_y$ of $M$, with apexes $

Ting-Yang Hsiao, Yun-Feng Lo, Winnie Wang

In this paper, we consider an $N$-oscillators complexified Kuramoto model. We first observe that there are solutions exhibiting finite-time blow-up behavior in all coupling regimes. When the coupling strength $\lambda>\lambda_c$, sufficient conditions for various types of synchronization are established for general $N \geq 2$. On the other hand, we analyze t

Seungah Son, Andrez Saurez, Dongsoo Har

While pre-trained language models excel at semantic understanding, they often struggle to capture nuanced affective information critical for affective recognition tasks. To address these limitations, we propose a novel framework for enhancing emotion-aware embeddings in transformer-based models. Our approach introduces a continuous valence-arousal labeling s

Vicente Balmaseda, Bokun Wang, Ching-Long Lin, Tianbao Yang

In self-supervised contrastive learning, negative pairs are typically constructed using an anchor image and a sample drawn from the entire dataset, excluding the anchor. However, this approach can result in the creation of negative pairs with similar semantics, referred to as "false negatives", leading to their embeddings being falsely pushed apart. To addre

Raphaël Beuzart-Plessis, Michael Harris, Jack Thorne

Genestier--Lafforgue and Fargues--Scholze have constructed a semisimple local Langlands paramterization for reductive groups over equicharacteristic local fields. Assuming a version of the stable twisted trace formula for function fields, we prove the surjectivity of this parameterization for split groups in sufficiently large characteristic.

Jesse Lu, David Qu, Jim Qu, Ryan Fong

The exponential growth of artificial intelligence has fueled the development of high-bandwidth photonic interconnect fabrics as a critical component of modern AI supercomputers. As the demand for ever-increasing AI compute and connectivity continues to grow, the need for high-throughput photonic simulation engines to accelerate and even revolutionize photoni

Jędrzej Warczyński, Mateusz Lango, Ondrej Dusek

We introduce a simple approach that uses a large language model (LLM) to automatically implement a fully interpretable rule-based data-to-text system in pure Python. Experimental evaluation on the WebNLG dataset showed that such a constructed system produces text of better quality (according to the BLEU and BLEURT metrics) than the same LLM prompted to direc

Xinyuan Chen, Yiwei Li, Qian M. Zhou

The study of times to nonterminal events of different types and their interrelation is a compelling area of interest. The primary challenge in analyzing such multivariate event times is the presence of informative censoring by the terminal event. While numerous statistical methods have been proposed for a single nonterminal event, i.e., semi-competing risks

Zhefan Xu, Haoyu Shen, Xinming Han, Hanyu Jin

Accurate perception of dynamic obstacles is essential for autonomous robot navigation in indoor environments. Although sophisticated 3D object detection and tracking methods have been investigated and developed thoroughly in the fields of computer vision and autonomous driving, their demands on expensive and high-accuracy sensor setups and substantial comput

Joao Marcos Correia Marques, Nils Dengler, Tobias Zaenker, Jesper Mucke

Searching for objects in cluttered environments requires selecting efficient viewpoints and manipulation actions to remove occlusions and reduce uncertainty in object locations, shapes, and categories. In this work, we address the problem of manipulation-enhanced semantic mapping, where a robot has to efficiently identify all objects in a cluttered shelf. Al

Jingxin Deng, Bin Chen, Zhiwei Liang, Yi Lei

We derive a heuristic nonlinear interference model for 4D probabilistic shaping considering the polarization and time correlation of the 4D symbols. We demonstrate an average SNR prediction gap from split-step Fourier simulations of 0.15~dB.

Hao Xuan, Bokai Yang, Xingyu Li

The softmax function is a fundamental component in deep learning. This study delves into the often-overlooked parameter within the softmax function, known as "temperature," providing novel insights into the practical and theoretical aspects of temperature scaling for image classification. Our empirical studies, adopting convolutional neural networks and tran

Ira J. S. Shokar, Peter H. Haynes, Rich R. Kerswell

We demonstrate that a deep learning emulator for chaotic systems can forecast phenomena absent from training data. Using the Kuramoto-Sivashinsky and beta-plane turbulence models, we evaluate the emulator through scenarios probing the fundamental phenomena of both systems: forecasting spontaneous relaminarisation, capturing initialisation of arbitrary chaoti

Sourabh Gapate, Royan Kamyar, Benjamin Smarr

Chronotypes allow for comparisons of one individual's daily rhythms to that of others and the environment. Mismatch between an individual's chronotype and the timing constraints of their social environment create social jet lag, which is correlated with mental and physical health risks. The concept of chronotype implicitly supposes that a single phase applie

Saman Khamesian, Asiful Arefeen, Stephanie M. Carpenter, Hassan Ghasemzadeh

Maintaining a balanced diet is essential for overall health, yet many individuals struggle with meal planning due to nutritional complexity, time constraints, and lack of dietary knowledge. Personalized food recommendations can help address these challenges by tailoring meal plans to individual preferences, habits, and dietary restrictions. However, existing

Rafael B. Frigori

The rapid evolution and global impact of coronaviruses, notably SARS-CoV-1 and SARS-CoV-2, underscore the importance of understanding their molecular mechanisms in detail. This study focuses on the receptor-binding motif (RBM) within the Spike protein of these viruses, a critical element for viral entry through interaction with the ACE2 receptor. We investig

Ana C. M. Ciqueira, Geanderson A. Carvalho, Paulo H. Faccin, Fabrício T. Dalmolin

In this work, we propose a modified Newton dynamics (MOND) model to study the rotation curves of galaxies. The model is described by an arctangent interpolating function and it fits the rotation curves of several galaxies without invoking the presence of dark matter. We took from the literature the rotation curve data of fifteen spiral galaxies, and used it

Mohammadreza Fakhraei, Chris A. Kieslich, Michael P. Howard

The interaction between two particles with shape or interaction anisotropy can be modeled using a pairwise potential energy function that depends on their relative position and orientation; however, this function is often challenging to mathematically formulate. Data-driven approaches for approximating anisotropic pair potentials have gained significant inte

Yiping Wang, Jeongheon Choe, Eric Anderson, Weijie Li

The fractional quantum anomalous Hall (FQAH) effect was recently discovered in twisted MoTe2 bilayers (tMoTe2). Experiments to date have revealed Chern insulators from hole doping at v = -1, -2/3, -3/5, and -4/7 (per moiré unit cell). In parallel, theories predict that, between v = -1 and -3, there exist exotic quantum phases, such as the coveted fractional

Przemysław Sekuła, Michał Romaszewski, Przemysław Głomb, Michał Cholewa

Entanglement is a fundamental feature of quantum mechanics, playing a crucial role in quantum information processing. However, classifying entangled states, particularly in the mixed-state regime, remains a challenging problem, especially as system dimensions increase. In this work, we focus on bipartite quantum states and present a data-driven approach to e

Ana Djurdjevac, Xiaohao Ji, Nicolas Perkowski

We consider the weak-error rate of the SPDE approximation by regularized Dean-Kawasaki equation with Itô noise for particle systems with mean-field interactions both on the drift and the noise. The global existence and uniqueness of the corresponding SPDEs are established using the variational approach to SPDEs, and the weak-error rate is estimated using the

Maxime Méloux, Silviu Maniu, François Portet, Maxime Peyrard

As AI systems are used in high-stakes applications, ensuring interpretability is crucial. Mechanistic Interpretability (MI) aims to reverse-engineer neural networks by extracting human-understandable algorithms to explain their behavior. This work examines a key question: for a given behavior, and under MI's criteria, does a unique explanation exist? Dra

Qiuyu Ren

We note an adjunction inequality in $k\overline{\mathbb{CP}^2}$ for the $s$-version of the $Sq^1$-refinement of Rasmussen's $s$-invariant. This does not hold for general spatial refinements of $s$-invariants.

Avinash Bhardwaj, Animesh Bhandari

Inspired by the work of Bemrose et al. \cite{Be16}, we delve into the study of weaving frames in Krein spaces. This paper presents a comprehensive exploration of various properties and characterizations of Krein space weaving frames. In support of our findings, several examples and counter examples are provided, illustrating the applicability of the theoreti

Salvatore Capozziello, Anupam Mazumdar, Giuseppe Meluccio

We show how a nonlocal gravitational interaction can circumvent the Weinberg no-go theorem on cosmological constant, which forbids the existence of any solution to the cosmological constant problem within the context of local field theories unless some fine-tuning is assumed. In particular, Infinite Derivative Gravity theories hint at a possible understandin

Changyou Geng, Dezhi Ren, Enkai Mao, Changfu Zou

Reconfigurable battery systems (RBSs) are emerging as a promising solution to improving fault tolerance, charge and thermal balance, energy delivery, etc. To optimize these performance metrics of RBSs, high-dimensional nonlinear integer programming problems need to be formulated and solved. To accomplish this, it is necessary to address several critical chal

David T. -B. G. Lilienfeldt

We consider an algebraic cycle on the triple product of the prime level modular curve $X_0(p)$ with origins in work of Darmon and Rotger. It is defined over the quadratic extension of $\mathbb{Q}$ ramified only at $p$ whose associated quadratic character $χ$ is the Legendre symbol at $p$. We prove that it is null-homologous and describe actions of various gr