March 2025 arXiv papers — page 104

M. C. Crabb

Results of Macdonald and Dold from the 1960s and '70s expressing the Lefschetz numbers of symmetric powers of a self-map of a compact ENR in terms of the Lefschetz numbers of iterates of the map are extended using the notion of a Lefschetz-polynomial functor. Configuration spaces and Borsuk-Ulam symmetric products, as well as symmetric powers, are treated as

Philippe Jaming, Yann Bourroux, Aingeru Fernández-Bertolin

We investigate quantitative unique continuation properties for discrete magnetic Schr{\"o}dinger operators in certain periodic graphs. This unique continuation property will be quantified through what is known in the literature as a Three Balls Inequality. We are able to extend this inequality to another family of periodic graph which contains the Hexagonal

Cong Wang, Jinshan Pan, Liyan Wang, Wei Wang

We propose Intra and Inter Parser-Prompted Transformers (PPTformer) that explore useful features from visual foundation models for image restoration. Specifically, PPTformer contains two parts: an Image Restoration Network (IRNet) for restoring images from degraded observations and a Parser-Prompted Feature Generation Network (PPFGNet) for providing IRNet wi

Mingchi Hou, Ina Kodrasi

The generalizability of speech enhancement (SE) models across speaker conditions remains largely unexplored, despite its critical importance for broader applicability. This paper investigates the performance of the hybrid variational autoencoder (VAE)-non-negative matrix factorization (NMF) model for SE, focusing primarily on its generalizability to patholog

Seung Woo Ko, Joopyo Hong, Suyoung Kim, Seungjai Bang

Camouflaged object detection (COD) aims to generate a fine-grained segmentation map of camouflaged objects hidden in their background. Due to the hidden nature of camouflaged objects, it is essential for the decoder to be tailored to effectively extract proper features of camouflaged objects and extra-carefully generate their complex boundaries. In this pape

Xi Shen, Julian Gamboa, Tabassom Hamidfar, Shamima Mitu

The Polar Mellin Transform (PMT) is a well-known technique that converts images into shift, scale and rotation invariant signatures for object detection using opto-electronic correlators. However, this technique cannot be properly applied when there are multiple targets in a single input. Here, we propose a Segmented PMT (SPMT) that extends this methodology

Sergei Tomilov, Mykyta Redkin, Yicheng Wang, Anna Suzuki

We demonstrate peak power and pulse energy scaling of Kerr-lens modelocking (KLM) Ho:YAG thin-disk lasers (TDLs) emitting at 2.1-{\mu}m wavelength. We compare different laser configurations to reach a maximum pulse energy of 1.7 {\mu}J at an output power of 29 W with a pulse duration of 434 fs, corresponding to a peak power of 3.7 MW. This represents a 5-fol

Thien Duc Hua, Mohammadali Mohammadi, Hien Quoc Ngo, Michail Matthaiou

We investigate the integration of stacked intelligent metasurfaces (SIMs) into cell-free massive multiple input multiple output (CF-mMIMO) system to enhance the simultaneous wireless information and power transfer (SWIPT) performance. Closed-form expressions for the spectral efficiency (SE) of the information-decoding receivers (IRs) and the average sum of h

Julian Gamboa, Xi Shen, Tabassom Hamidfar, Shamima Mitu

Opto-electronic joint transform correlators (OJTCs) use a focal plane array (FPA) to detect the joint power spectrum (JPS) of two input images, projecting it onto a spatial light modulator (SLM) to be optically Fourier transformed. The JPS is composed of two self-intensities and two conjugate-products, where only the latter produce the cross-correlation. How

T. V. Smirnova, M. O. Toropov, S. A. Tyul'bashev

The analysis of variations in the emission intensity of the pulsar B0950+08 from 2014 to 2022 with scales from minutes to years was carried out. The observations were obtained in a round-the-clock daily survey conducted on the Large Phased Array (LPA) radio telescope. The high variability of emission is shown not only from pulse to pulse, but also at scales

Runsong Zhu, Shi Qiu, Zhengzhe Liu, Ka-Hei Hui

Lifting multi-view 2D instance segmentation to a radiance field has proven to be effective to enhance 3D understanding. Existing methods rely on direct matching for end-to-end lifting, yielding inferior results; or employ a two-stage solution constrained by complex pre- or post-processing. In this work, we design a new end-to-end object-aware lifting approac

Ruonan Guo, Cheng Li, Shuang Zhou, Niu Li

This is the second paper in a series that utilize IFS from MaNGA, NUV imaging from Swift/UVOT and NIR imaging from 2MASS to study dust attenuation properties on kpc scales in nearby galaxies. We apply the method developed in Paper I (Zhou et al. 2023) to the updated SWiM_v4.2 catalog, and measure the optical attenuation curve and the attenuation in three NUV

Wenjing Zhang, Xuejiao Lei, Zhaoxiang Liu, Limin Han

DeepSeek-R1, renowned for its exceptional reasoning capabilities and open-source strategy, is significantly influencing the global artificial intelligence landscape. However, it exhibits notable safety shortcomings. Recent research conducted by Robust Intelligence, a subsidiary of Cisco, in collaboration with the University of Pennsylvania, revealed that Dee

Kanji Mori, Tomoya Takiwaki, Kazunori Kohri, Hiroki Nagakura

Light sterile neutrinos, $\nu_s$, are often introduced to explain an anomalous deficit in the electron antineutrino flux from nuclear reactors. If they exist, sterile neutrinos would also be produced in collapsing massive stars through the active-sterile neutrino oscillation. In order to investigate the impacts of sterile neutrinos on supernova dynamics, we

Shamima Mitu, Xi Shen, Julian Gamboa, Tabassom Hamidfar

An analog automatic event recognition (AER) system can be realized by combining the technique of holographic image recognition with the process of temporal signal correlation employing stimulated photon echo in an ensemble of two-level atoms. For efficient operation of the AER system, the optical transition in the two-level system must have a large oscillato

Nicola Tomassetti, Bruna Bertucci, Emanuele Fiandrini, Behrouz Khiali

In this paper, we investigate the heliospheric modulation of cosmic rays in interplanetary space, focusing on their propagation times and energy losses over the solar cycle. To perform the calculations, we employed a data-driven model based on the stochastic method. Our model was calibrated using time-resolved and energy-resolved data from several missions i

Pingting Hao, Kunpeng Liu, Wanfu Gao

In recent years, multi-view multi-label learning (MVML) has gained popularity due to its close resemblance to real-world scenarios. However, the challenge of selecting informative features to ensure both performance and efficiency remains a significant question in MVML. Existing methods often extract information separately from the consistency part and the c

Mihai Nadas, Laura Diosan, Andreea Tomescu

This survey reviews how large language models (LLMs) are transforming synthetic training data generation in both natural language and code domains. By producing artificial but task-relevant examples, these models can significantly augment or even substitute for real-world datasets, particularly in scenarios where labeled data is scarce, expensive, or sensiti

Shuqian Cheng, Mingzu Zhang, Sun-Yuan Hsieh, Eddie Cheng

As the scale of data centers continues to grow, there is an increasing demand for interconnection networks to resist malicious attacks. Hence, it is necessary to evaluate the reliability of networks under various fault patterns. The family of generalized $K_4$-hypercubes serve as interconnection networks of data centers, characterized by topological structur

Ziwei Wang, Weizhi Chen, Leyang Yang, Sheng Zhou

Graphical user interface (GUI) has become integral to modern society, making it crucial to be understood for human-centric systems. However, unlike natural images or documents, GUIs comprise artificially designed graphical elements arranged to convey specific semantic meanings. Current multi-modal large language models (MLLMs) already proficient in processin

Simon Gravelle, Cecilia M. S. Alvares, Jacob R. Gissinger, Axel Kohlmeyer

The availability of open-source molecular simulation software packages allows scientists and engineers to focus on running and analyzing simulations without having to write, parallelize, and validate their own simulation software. While molecular simulations thus become accessible to a larger audience, the `black box' nature of such software packages and wid

Nello Blaser, Morten Brun, Odin Hoff Gardaa, Lars M. Salbu

We introduce the monoidal Rips filtration, a filtered simplicial set for weighted directed graphs and other lattice-valued networks. Our construction generalizes the Vietoris-Rips filtration for metric spaces by replacing the maximum operator, determining the filtration values, with a more general monoidal product. We establish interleaving guarantees for th

Toshihiro Nomura, Shunsuke Kitou, Junichi Komatsu, Kenichiro Koga

Ice Ih, the most stable phase of water at ambient pressure, is a stacking of the honeycomb network of water molecules H2O. What if one layer of ice is exfoliated and confined to a two-dimensional (2D) sheet? Martyite Zn3(V2O7)(OH)2 2H2O, a mineral with the honeycomb lattice of H2O in the porous framework, is an ideal system for studying such monolayer ice. D

Jiyuan Xu, Xin Liu, Li Ma, Guoke Li

This study investigates the influence of chemical bonds on the magnetic structure of materials, a less explored area compared to their effect on crystal stability. By analyzing the strength and directionality of chemical bonds using the electron localization function (ELF) and charge density difference (CDD) methods, we examine their impact on magnetic excha

Yusheng Li, Yichao Yao, Minyu Feng, Tina P. Benko

The structure of heterogeneous networks and human mobility patterns profoundly influence the spreading of endemic diseases. In small-scale communities, individuals engage in social interactions within confined environments, such as homes and workplaces, where daily routines facilitate virus transmission through predictable mobility pathways. Here, we introdu

Hannah M. Bellenbaum, Maximilian P. Böhme, Michael Bonitz, Tilo Döppner

Warm dense matter (WDM) is an active field of research, with applications ranging from astrophysics to inertial confinement fusion. Ionization degree and continuum lowering are important quantities to understand how materials behave under these conditions, but can be difficult to diagnose since experimental campaigns are limited and often require model-depen

Pengcheng Zhou, Lantian Zhang, Wei Li

Semi-supervised learning is of great significance in medical image segmentation by exploiting unlabeled data. Among its strategies, the co-training framework is prominent. However, previous co-training studies predominantly concentrate on network initialization variances and pseudo-label generation, while overlooking the equilibrium between information inter

Wei Lu, Si-Bao Chen, Hui-Dong Li, Qing-Ling Shu

Remote sensing object detection (RSOD) often suffers from degradations such as low spatial resolution, sensor noise, motion blur, and adverse illumination. These factors diminish feature distinctiveness, leading to ambiguous object representations and inadequate foreground-background separation. Existing RSOD methods exhibit limitations in robust detection o

Mariusz Dzwonkowski, Adrian Bekasiewicz, Slawomir Koziel

The process of developing antenna structures typically involves prototype measurements. While accurate validation of far-field performance can be performed in dedicated facilities like anechoic chambers, high cost of construction and maintenance might not justify their use for teaching, or low-budget research scenarios. Non-anechoic experiments provide a cos

Asim Ullah, Jameel-Un Nabi

Recently, a list of the top 50 most important electron capture (EC) and $\beta$-decay (BD) nuclei, averaged throughout the stellar trajectory for $0.500 > Y_e > 0.400$, was published. The current study presents the calculation of EC and BD rates, from the published list with $A = 65\text{--}80$, on a detailed temperature-density grid. The EC and BD rates wer

Adrita Ghosh, Parthasakha Das, Tanujit Chakraborty, Pritha Das

Cholera, an acute diarrheal disease, is a serious concern in developing and underdeveloped areas. A qualitative understanding of cholera epidemics aims to foresee transmission patterns based on reported data and mechanistic models. The mechanistic model is a crucial tool for capturing the dynamics of disease transmission and population spread. However, using

Helffer Bernard, Nicoleau Francois

In this paper, we analyze the magnetic Dirichlet-to-Neumann operator (D-to-N map) $\check \Lambda(b,\nu)$ on the exterior of the disk with respect to a magnetic potential $A_{b, \nu}=A^b + A_\nu$ where, for $b\in \mathbb R$ and $\nu \in \mathbb R$, $A^b (x,y)= b\, (-y, x)$ and $A_\nu (x,y)$ is the Aharonov-Bohm potential centered at the origin of flux $2\pi

Lifan Guan, Chengyang Wu

Let $\Gamma$ be a torsion-free subgroup of $SL_3(R)$ commensurable with $SL_3(Z)$, and $Y=SO_3(R)\backslash SL_3(R)/\Gamma$ be endowed with the natural locally symmetric space structure. We prove that for any point y in Y, the set of directions in which the geodesic ray starting from y is bounded in Y, is hyperplane absolute winning.

Yuchen Niu, Siew-Kei Lam

Automated Insulin Delivery (AID) systems represent a significant advancement in diabetes care and wearable physiological closed-loop control technologies, integrating continuous glucose monitoring, control algorithms, and insulin pumps to improve blood glucose level control and reduce the burden of patient self-management. However, their increasing dependenc

Qiao Wang, Pengfei Li, Yushou Song, Jalu Li

Currently, the X-ray detectors are widely used in medical imaging, industrial inspection, aerospace, and other fields, as the market demand for high-efficiency, flexible, and low-power detectors is increased. Although the traditional inorganic X-ray detection materials have achieved great success and effectiveness, they have their own limitations and let alo

Eyal Marantz, Ori Plonsky

Predicting human decision-making under risk and uncertainty is a long-standing challenge in cognitive science, economics, and AI. While prior research has focused on numerically described lotteries, real-world decisions often rely on textual descriptions. This study conducts the first large-scale exploration of human decision-making in such tasks using a lar

Róbert Trényi, Simeon Ball, David G. Glynn, Marcos Curty

Purity testing protocols (PTPs), i.e., protocols that decide with high probability whether or not a distributed bipartite quantum state is maximally entangled, have been proven to be a useful tool in many quantum communication applications. In this paper, we provide geometrical constructions for such protocols that originate directly from classical linear er

Damian Boborzi, Phillip Mueller, Jonas Emrich, Dominik Schmid

Generative models have recently made remarkable progress in the field of 3D objects. However, their practical application in fields like engineering remains limited since they fail to deliver the accuracy, quality, and controllability needed for domain-specific tasks. Fine-tuning large generative models is a promising perspective for making these models avai

Wenbo Xiao, Qiannan Han, Gang Shu, Guiping Liang

Accurate body dimension and weight measurements are critical for optimizing poultry management, health assessment, and economic efficiency. This study introduces an innovative deep learning-based model leveraging multimodal data-2D RGB images from different views, depth images, and 3D point clouds-for the non-invasive estimation of duck body dimensions and w

Runlin Liu, Zhe Zhang, Yunge Hu, Yuhang Lin

Automated regression test generation has been extensively explored, yet generating high-quality tests for Python programs remains particularly challenging. Because of the Python's dynamic typing features, existing approaches, ranging from search-based software testing (SBST) to recent LLM-driven techniques, are often prone to type errors. Hence, existing met

Mohaddeseh Chegini, Ali Mahloojifar

The BI_RADS score is a probabilistic reporting tool used by radiologists to express the level of uncertainty in predicting breast cancer based on some morphological features in mammography images. There is a significant variability in describing masses which sometimes leads to BI_RADS misclassification. Using a BI_RADS prediction system is required to suppor

Davide Fucci, Massimiliano Di Penta, Simone Romano, Giuseppe Scanniello

Software Bills of Material (SBOMs) are becoming a consolidated, often enforced by governmental regulations, way to describe software composition. However, based on recent studies, SBOMs suffer from limited support for their consumption and lack information beyond simple dependencies, especially regarding software vulnerabilities. This paper reports the resul

Svetlana Karpova, Federico Sánchez, Dana Douqa

We introduce a novel approach that utilizes neutrino events from the off-axis near detector to investigate the beam profile in long-baseline neutrino experiments. Understanding the dynamics of the neutrino beam is crucial for improving the precision of neutrino oscillation measurements. We demonstrate that certain observables related to the azimuthal angle o

Jinyang Dong, Shizhen Wu, Rui Liu, Xiao Liang

In this paper, the safety-critical control problem for uncertain systems under multiple control barrier function (CBF) constraints and input constraints is investigated. A novel framework is proposed to generate a safety filter that minimizes changes to reference inputs when safety risks arise, ensuring a balance between safety and performance. A nonlinear d

Nguyen-Thi Dang, Frédéric Paulin, Rafael Sayous

Given a local field $\widehat K$ with positive characteristic, we study the dynamics of the diagonal subgroup of the linear group $\operatorname{GL}_n(\widehat K)$ on homogeneous spaces of discrete lattices in ${\widehat K}^{\,n}$. We first give a function field version of results by Margulis and Tomanov-Weiss, characterizing the divergent diagonal orbits. W

Kaixin Shen, Ruijie Quan, Jiaxu Miao, Jun Xiao

The rapid advancement of image editing techniques has raised concerns about their misuse for generating Not-Safe-for-Work (NSFW) content. This necessitates a targeted protection mechanism that blocks malicious edits while preserving normal editability. However, existing protection methods fail to achieve this balance, as they indiscriminately disrupt all edi

Temenoujka P. Peneva, Tatiana L. Todorova

Let $\alpha\in \mathbb{R}\setminus\mathbb{Q}$ and $\beta\in \mathbb{R}$ be given. Suppose that $a_1,\ldots,a_s$ are distinct positive integers that do not contain a reduced residue system modulo $p^2$ for any prime $p$. We prove that there exist infinitely many primes $p$ such that the inequality $||\alpha p+\beta||<p^{-1/10}$ holds and all the numbers $p+a_

Ori Yoran, Kunhao Zheng, Fabian Gloeckle, Jonas Gehring

Compression is at the heart of intelligence. A theoretically optimal way to compress any sequence of data is to find the shortest program that outputs that sequence and then halts. However, such 'Kolmogorov compression' is uncomputable, and code generating LLMs struggle to approximate this theoretical ideal, as it requires reasoning, planning and search capa

Bo Peng, Jintao Chen, Mufeng Yao, Chenhao Zhang

Texture recognition is a fundamental problem in computer vision and pattern recognition. Recent progress leverages feature aggregation into discriminative descriptions based on convolutional neural networks (CNNs). However, modeling non-local context relations through visual primitives remains challenging due to the variability and randomness of texture prim

Kumataro Yazawa, Keita Kume, Isao Yamada

We propose a variable smoothing algorithm for minimizing a nonsmooth and nonconvex cost function. The cost function is the sum of a smooth function and a composition of a difference-of-convex (DC) function with a smooth mapping. At each step of our algorithm, we generate a smooth surrogate function by using the Moreau envelope of each weakly convex function

Zixuan Zheng, Yilei Shi, Chunlei Li, Jingliang Hu

Cell counting in microscopy images is vital in medicine and biology but extremely tedious and time-consuming to perform manually. While automated methods have advanced in recent years, state-of-the-art approaches tend to increasingly complex model designs. In this paper, we propose a conceptually simple yet effective decoupled learning scheme for automated c

Guowei Wang, Changxing Ding

Long-term test-time adaptation (TTA) is a challenging task due to error accumulation. Recent approaches tackle this issue by actively labeling a small proportion of samples in each batch, yet the annotation burden quickly grows as the batch number increases. In this paper, we investigate how to achieve effortless active labeling so that a maximum of one samp

Suzana Veljanovska, Hans Dermot Doran

The development and deployment of safe and dependable AI models is crucial in applications where functional safety is a key concern. Given the rapid advancement in AI research and the relative novelty of the safe-AI domain, there is an increasing need for a workflow that balances stability with adaptability. This work proposes a transparent, complete, yet fl

Mykyta Syromiatnikov, Victoria Ruvinskaya, Nataliia Komleva

Leading large language models have demonstrated impressive capabilities in reasoning-intensive tasks, such as standardized educational testing. However, they often require extensive training in low-resource settings with inaccessible infrastructure. Small or compact models, though more efficient, frequently lack sufficient support for underrepresented langua

Yaxiong Chen, Yujie Wang, Zixuan Zheng, Jingliang Hu

Medical ultrasound imaging is ubiquitous, but manual analysis struggles to keep pace. Automated segmentation can help but requires large labeled datasets, which are scarce. Semi-supervised learning leveraging both unlabeled and limited labeled data is a promising approach. State-of-the-art methods use consistency regularization or pseudo-labeling but grow in

Pengfei Tian, Fan Yang, Peng Ding

The stratified linear permutation statistic arises in various statistics problems, including stratified and post-stratified survey sampling, stratified and post-stratified experiments, conditional permutation tests, etc. Although we can derive the Berry--Esseen bounds for the stratified linear permutation statistic based on existing bounds for the non-strati

Jaewoo Song, Daemin Park, Kanghyun Baek, Sangyub Lee

Developing effective visual inspection models remains challenging due to the scarcity of defect data. While image generation models have been used to synthesize defect images, producing highly realistic defects remains difficult. We propose DefectFill, a novel method for realistic defect generation that requires only a few reference defect images. It leverag

P. S. Ardra, Jasine Babu, R. Krithika, Deepak Rajendraprasad

Given a multigraph $G$ whose edges are colored from the set $[q]:=\{1,2,\ldots,q\}$ (\emph{$q$-colored graph}), and a vector $\alpha=(\alpha_1,\ldots,\alpha_{q}) \in \mathbb{N}^{q}$ (\emph{color-constraint}), a subgraph $H$ of $G$ is called \emph{$\alpha$-colored}, if $H$ has exactly $\alpha_i$ edges of color $i$ for each $i \in[q]$. In this paper, we focus

Jiankang Wang, Zhihan Zhang, Zhihang Liu, Yang Li

Multimodal large language models (MLLMs) have made remarkable progress in either temporal or spatial localization. However, they struggle to perform spatio-temporal video grounding. This limitation stems from two major challenges. Firstly, it is difficult to extract accurate spatio-temporal information of each frame in the video. Secondly, the substantial nu

Huy-Hoang Bui, Bach-Thuan Bui, Quang-Vinh Tran, Yasuyuki Fujii

Visual localization is considered to be one of the crucial parts in many robotic and vision systems. While state-of-the art methods that relies on feature matching have proven to be accurate for visual localization, its requirements for storage and compute are burdens. Scene coordinate regression (SCR) is an alternative approach that remove the barrier for s

A. I. Medvedeva, V. V. Bakutkin

Subject of research: is the study of methods for analyzing perimetric images for the diagnosis and control of glaucoma diseases. Objects of research: is a dataset collected on the ophthalmological perimeter with the results of various patient pathologies, since the ophthalmological community is acutely aware of the issue of disease control and import substit

Bita Shariatpanahi, Erfan Nozari, Soroush Daftarian, Fahimeh Arab

Understanding how neural dynamics shape cognitive experiences remains a central challenge in neuroscience and psychiatry. Here, we present a novel framework leveraging state-to-output controllability from dynamical systems theory to model the interplay between cognitive perturbations, neural activity, and subjective experience. We demonstrate that large-scal

Haolin Wang, Xueyan Li, Yazhe Niu, Shuai Hu

Large Language Models (LLMs) have exhibited impressive capabilities across numerous domains, yet they often struggle with complex reasoning and decision-making tasks. Decision-making games, which inherently require multifaceted reasoning logic, serve as ideal sandboxes for evaluating and enhancing the reasoning abilities of LLMs. In this work, we first explo

Andrea Zerio, Maya Bechler-Speicher, Tine Jess, Aleksejs Sazonovs

Routinely collected clinical blood tests are an emerging molecular data source for large-scale biomedical research but inherently feature irregular sampling and informative observation. Traditional approaches rely on imputation, which can distort learning signals and bias predictions while lacking biological interpretability. We propose a novel methodology u

Chang-Yan Wang

We investigate the geometric quantum complexity of Bose-Einstein condensate (BEC) at finite temperature. Specifically, we use the Bures and Sj\"oqvist metrics -- generalizations of the Fubini-Study metric for mixed quantum states, as well as the Nielsen geometric complexity approach based on purification of mixed states. Starting from the Bogoliubov Hamilton

Jorge Baeza-Ballesteros, Pilar Hernández, Fernando Romero-López

We study the scaling of meson-meson scattering amplitudes with the number of colors, $N_\text{c}$. We use lattice calculations in a theory with $N_\text{f}=4$ degenerate flavors, with $N_\text{c}=3-6$ and pion mass $M_\pi\approx 560$ MeV. We focus on three different scattering channels, two of which have the same quantum numbers as some tetraquark candidates

Wang Yicao

The paper presents a new functional model for completely non-unitary contractions on a Hilbert space. This model is based on the observation that the theory of contractions shares a common geometric basis with the extension theory of symmetric operators recently developed by the author in \cite{wang2024complex}. Compared with the now classical Sz.-Nagy-Foias

Nipuni Ginige, Nandana Rajatheva, Matti Latva-aho

Reconfigurable intelligent surface (RIS) is an emerging technology that is used to improve the system performance in beyond 5G systems. In this letter, we propose a novel convolutional neural network (CNN)-based autoencoder to jointly optimize the transmitter, the receiver, and the RIS of a RIS-assisted communication system. The proposed system jointly optim

Omar Shaikh, Hussein Mozannar, Gagan Bansal, Adam Fourney

Language models excel at following instructions but often struggle with the collaborative aspects of conversation that humans naturally employ. This limitation in grounding -- the process by which conversation participants establish mutual understanding -- can lead to outcomes ranging from frustrated users to serious consequences in high-stakes scenarios. To

Weikang Zheng, Luc Dessart, Alexei V. Filippenko, Yi Yang

We present photometric and spectroscopic observations of SN 2023ixf covering from day one to 442 days after explosion. SN 2023ixf reached a peak $V$-band absolute magnitude of $-18.2 \pm 0.07$, and light curves show that it is in the fast-decliner (IIL) subclass with a relatively short ``plateau'' phase (fewer than $\sim 70$ days). Early-time spectra of SN 2

Yanxin Zhang, Chengpu Yu, Filippo Fabiani

We consider the identification of non-causal systems with random switching modes (NCSRSM), a class of models essential for describing typical power load management and department store inventory dynamics. The simultaneous identification of causal-andanticausal subsystems, along with the presence of random switching sequences, however, make the overall identi

Elizabeth A. Silber

Infrasound sensing offers critical capabilities for detecting and geolocating bolide events globally. However, the observed back azimuths, directions from which infrasound signals arrive at stations, often differ from the theoretical expectations based on the bolide's peak brightness location. For objects with shallow entry angles, which traverse longer atmo

Neha S., Saurabh

The variability of Young Stellar Objects (YSOs) is a crucial tool for understanding the mechanisms driving flux changes. In this study, we present an infrared variability analysis of a large sample of over 20,000 candidate YSOs, using data from the ALLWISE and NEOWISE surveys, which span around a decade with a 6-month cadence. We applied Lomb-Scargle Periodo

Heng Ping, Shixuan Li, Peiyu Zhang, Anzhe Cheng

Recent advances in large language models (LLMs) have demonstrated remarkable capabilities in code generation tasks. However, when applied to hardware description languages (HDL), these models exhibit significant limitations due to data scarcity, resulting in hallucinations and incorrect code generation. To address these challenges, we propose HDLCoRe, a trai

Oscar Eriksson, Anders Ågren Thuné, Johannes Borgström, David Broman

In recent years, there has been extensive research on how to extend general-purpose programming language semantics with domain-specific modeling constructs. Two areas of particular interest are (i) universal probabilistic programming where Bayesian probabilistic models are encoded as programs, and (ii) differentiable programming where differentiation operato

Kamran Behnia, Shiyan Li, Johnpierre Paglione, Louis Taillefer

Recently, Wang et al. [1] reported on an unusual violation of Wiedemann-Franz law in three semimetals. We compare their observations to our observations in a variety of systems, where the apparent WF law violations in the same temperature range arise as a consequence of electron-phonon decoupling. Given the empirical similarity of their data with these cases

Manas Thakur, Nakka Nishika, Bommiditha Jyothsnavi, Surkanti Sai Sahasra

With the ever-increasing threat of ballistic impact, it is crucial to provide a solution that is not only effective but also economical. A majority of studies contribute toward alternatives to monolithic structures by incorporating sandwiched cores, which are often prone to crushing and delamination. In recent years, sand-based composites have emerged as a p

Siqi Zhang, Yanyuan Qiao, Qunbo Wang, Longteng Guo

The aspiration of the Vision-and-Language Navigation (VLN) task has long been to develop an embodied agent with robust adaptability, capable of seamlessly transferring its navigation capabilities across various tasks. Despite remarkable advancements in recent years, most methods necessitate dataset-specific training, thereby lacking the capability to general

Siwei Han, Peng Xia, Ruiyi Zhang, Tong Sun

Document Question Answering (DocQA) is a very common task. Existing methods using Large Language Models (LLMs) or Large Vision Language Models (LVLMs) and Retrieval Augmented Generation (RAG) often prioritize information from a single modal, failing to effectively integrate textual and visual cues. These approaches struggle with complex multi-modal reasoning

Hajime Togashi, Debashree Sen, Hana Gil, Chang Ho Hyun

Significance of the chiral symmetry restoration is studied by considering the role of the modification of the nucleon mass in nuclear medium at finite density and temperature. Using the Korea-IBS-Daegu-SKKU density functional theory, we can create models that have an identical nuclear matter equation of state but different isoscalar and isovector effective m

Chengze Jiang, Zhuangzhuang Wang, Minjing Dong, Jie Gui

Multimodal Large Language Models (MLLMs) have demonstrated exceptional performance in artificial intelligence by facilitating integrated understanding across diverse modalities, including text, images, video, audio, and speech. However, their deployment in real-world applications raises significant concerns about adversarial vulnerabilities that could compro

Minye Wu, Haizhao Dai, Kaixin Yao, Tinne Tuytelaars

Differentiable rendering enables efficient optimization by allowing gradients to be computed through the rendering process, facilitating 3D reconstruction, inverse rendering and neural scene representation learning. To ensure differentiability, existing solutions approximate or re-formulate traditional rendering operations using smooth, probabilistic proxies

B Zhang, M. Zhang, D. Y. Sun, X. G. Gong

Using molecular dynamics simulations, we systematically investigate supercooled liquids formed at cooling rates below and above the critical cooling rate (CCR). By analyzing the distribution of short-time averaged potential energies (DoPE) and crystallization behaviors, we identify two distinct dynamical regimes in supercooled liquids: the glass-forming regi

Vijay Kumar, S. P. Ram, S. Singh, Kavish Bhardwaj

Two-photon Raman excitation between the ground hyperfine states $|5 \ ^2S_{1/2}, F = 2\rangle$ and $|5 \ ^2S_{1/2}, F = 1\rangle$ of $^{87}$Rb atom has been experimentally studied. The Rabi coupling strengths of various transition involved have been calculated in presence of a weak magnetic field. A density matrix formalism has been developed to understand t

Hamid Jahanian

In the process industry, the configuration of Safety Instrumented Systems (SIS) must comply with a defined set of safety requirements, typically documented in the Safety Requirements Specification (SRS). The functional safety standard IEC 61511 outlines the necessary content and quality criteria for the SRS. However, developing an effective SRS can be challe

Mu Chen, Liulei Li, Wenguan Wang, Yi Yang

Top-leading solutions for Video Scene Graph Generation (VSGG) typically adopt an offline pipeline. Though demonstrating promising performance, they remain unable to handle real-time video streams and consume large GPU memory. Moreover, these approaches fall short in temporal reasoning, merely aggregating frame-level predictions over a temporal context. In re

Yixuan Li, Changli Tang, Jimin Zhuang, Yudong Yang

Human vision is dynamic and continuous. However, in video understanding with multimodal large language models (LLMs), existing methods primarily rely on static features extracted from images sampled at a fixed low frame rate of frame-per-second (FPS) $\leqslant$2, leading to critical visual information loss. In this paper, we introduce F-16, the first multim

Bing-Sui Lu

We investigate the behavior of the nonresonant Casimir-Polder force acting on a metastable polarized state of a two-level atomic system with a right-circularly polarized electric dipole transition in the presence of a monolayer topological insulator inhabiting an anomalous quantum Hall insulator state with a negative Chern number, finding that the force can

Tianhao Ni, Bingjie Li, Zhigang Yao

To address the dual challenges of the curse of dimensionality and the difficulty in separating intra-cluster and inter-cluster structures in high-dimensional manifold embedding, we proposes an Adaptive Multi-Scale Manifold Embedding (AMSME) algorithm. By introducing ordinal distance to replace traditional Euclidean distances, we theoretically demonstrate tha

Ziqian Li, Eesh Gupta, Fang Zhao, Riju Banerjee

Dynamic random access memory (DRAM) is critical to classical computing but notably absent in current superconducting quantum processors. Integrating high-coherence memory units would enable resource-efficient control of logical qubits and allow the separate optimization of logic and storage subsystems. Here, we realize an 8-bit cascaded random access quantum

Xinqing Li, Ruiqi Song, Qingyu Xie, Ye Wu

With the rapid advancement of autonomous driving technology, a lack of data has become a major obstacle to enhancing perception model accuracy. Researchers are now exploring controllable data generation using world models to diversify datasets. However, previous work has been limited to studying image generation quality on specific public datasets. There is

Lili Yang, Mengshuai Chang, Xiao Guo, Yuxin Feng

To address the issues of the existing frustum-based methods' underutilization of image information in road three-dimensional object detection as well as the lack of research on agricultural scenes, we constructed an object detection dataset using an 80-line Light Detection And Ranging (LiDAR) and a camera in a complex tractor road scene and proposed a new ne

Koichiro Moriya, Akihiko Noda

We study the asymptotic properties of the GLS estimator in multivariate regression with heteroskedastic and autocorrelated errors. We derive Wald statistics for linear restrictions and assess their performance. The statistics remains robust to heteroskedasticity and autocorrelation.

Bao-Yun Dong, Yanhua Zhou, Wei Wang, Tao Wang

The anisotropic Dicke model reveals the important role that counter-rotating wave terms play in the coupling between light and two level atoms. It is intriguing to generate the model to the strongly correlated many body case, where the competition between atomic interaction and light-atom coupling will induce exotic phenomenon. In this paper, we provide a pe

Mufan Liu, Qi Yang, He Huang, Wenjie Huang

3D Gaussian Splatting (3DGS) has emerged as an efficient and high-fidelity paradigm for novel view synthesis. To adapt 3DGS for dynamic content, deformable 3DGS incorporates temporally deformable primitives with learnable latent embeddings to capture complex motions. Despite its impressive performance, the high-dimensional embeddings and vast number of primi

Sayak Nag, Udita Ghosh, Calvin-Khang Ta, Sarosij Bose

Scene Graph Generation (SGG) aims to represent visual scenes by identifying objects and their pairwise relationships, providing a structured understanding of image content. However, inherent challenges like long-tailed class distributions and prediction variability necessitate uncertainty quantification in SGG for its practical viability. In this paper, we i

Kang Yang, Tianci Bu, Lantao Li, Chunxu Li

Collaborative perception in multi-agent system enhances overall perceptual capabilities by facilitating the exchange of complementary information among agents. Current mainstream collaborative perception methods rely on discretized feature maps to conduct fusion, which however, lacks flexibility in extracting and transmitting the informative features and can

Long Tang, Dengpan Ye, Sirun Chen, Xiuwen Shi

The fine-tuning technique for text-to-image diffusion models facilitates image customization but risks privacy breaches and opinion manipulation. Current research focuses on prompt- or image-level adversarial attacks for anti-customization, yet it overlooks the correlation between these two levels and the relationship between internal modules and inputs. Thi

Taotao Wang, Yuxin Jin, Qing Yang, Yihan Xia

Federated Learning (FL) has emerged as a promising paradigm in distributed machine learning, enabling collaborative model training while preserving data privacy. However, despite its many advantages, FL still contends with significant challenges -- most notably regarding security and trust. Zero-Knowledge Proofs (ZKPs) offer a potential solution by establish

Laura Aymerich-Franch, Tarek Taha, Hiroshi Ishiguro, Takahiro Miyashita

Robot avatars for customer service are gaining traction in Japan. However, their acceptance in other societal contexts remains underexplored, complicating efforts to design robot avatars suitable for diverse cultural environments. To address this, we interviewed key stakeholders in Dubai's service sector to gain insights into their experiences deploying soci