March 2025 arXiv papers — page 122

S Balasubramanian, Yedu Krishna P, Talasu Sai Sriram, M Sai Subramaniam

Feature Distillation (FD) strategies are proven to be effective in mitigating Catastrophic Forgetting (CF) seen in Class Incremental Learning (CIL). However, current FD approaches enforce strict alignment of feature magnitudes and directions across incremental steps, limiting the model's ability to adapt to new knowledge. In this paper we propose Structurall

Sivana Hamer, Jacob Bowen, Md Nazmul Haque, Robert Hines

Software supply chain frameworks, such as the US NIST Secure Software Development Framework (SSDF), detail what tasks software development organizations are recommended or mandated to adopt to reduce security risk. However, to further reduce the risk of similar attacks occurring, software organizations benefit from knowing what tasks mitigate attack techniqu

Ying Zang, Yuncan Gao, Jiangi Zhang, Yuangi Hu

This work advances zero-shot interactive segmentation for remote sensing imagery through three key contributions. First, we propose a novel sketch-based prompting method, enabling users to intuitively outline objects, surpassing traditional point or box prompts. Second, we introduce LTL-Sensing, the first dataset pairing human sketches with remote sensing im

Behrooz Mirzaii, Bruno Reis Ramos, Thiago Verissimo

In this article, we explore the second integral homology, or Schur multiplier, of the special linear group ${\rm SL}_2(\mathbb{Z}[1/n])$ for a positive integer $n$. We definitively calculate the group structure of $H_2({\rm SL}_2(\mathbb{Z}[1/n]),\mathbb{Z})$ when $n$ is divisible by one of the primes $2$, $3$, $5$, $7$ or $13$. For a general $n > 1$, we off

Harold Triedman, Rishi Jha, Vitaly Shmatikov

Multi-agent systems coordinate LLM-based agents to perform tasks on users' behalf. In real-world applications, multi-agent systems will inevitably interact with untrusted inputs, such as malicious Web content, files, email attachments, and more. Using several recently proposed multi-agent frameworks as concrete examples, we demonstrate that adversarial conte

Allan R. P. Moreira, Shi-Hai Dong, Emmanuel N. Saridakis

We investigate the fermion localization in extra-dimensional $f(Q,\mathcal{T})$ gravity with cuscuton dynamics. This modified gravity theory is based on the nonmetricity scalar $Q$ and on the trace of the energy-momentum tensor $\mathcal{T}$. Using the first-order formalism we construct the complete brane system for three well-known superpotentials, namely t

Hari Paudyal, Yogendra Limbu, Michael E. Flatté, Durga Paudyal

Rare-earth containing wide band gap oxides, which provide spin-photon interface and narrow linewidth optical emission, are getting significant attention as the most promising candidate materials in advancing quantum transduction and memories. Here, from $ab~initio$ calculations, we identify antiferromagnetic ground states in structurally preferred monoclinic

Sándor Battaglini-Fischer, Nishanthi Srinivasan, Bálint László Szarvas, Xiaoyu Chu

Large Language Model (LLM) services such as ChatGPT, DALLE, and Cursor have quickly become essential for society, businesses, and individuals, empowering applications such as chatbots, image generation, and code assistance. The complexity of LLM systems makes them prone to failures and affects their reliability and availability, yet their failure patterns ar

Siddharth Malviy, Vipul Kakkar, Swapnil Srivastava

The cyclic subgroup graph ${\Gamma(G)}$ of a group $G$ is the simple undirected graph with cyclic subgroups as a vertex set and two distinct vertices $H_1$ and $H_2$ are adjacent if and only if $H_1 \leq H_2$ and there does not exist any cyclic subgroup $K$ such that $H_1 < K < H_2$. In this paper, we classify all the finite groups $G$ such that $\Gamma(G)$

Enze Liu, Bowen Zheng, Wayne Xin Zhao, Ji-Rong Wen

In recent years, substantial research efforts have been devoted to enhancing sequential recommender systems by integrating abundant side information with ID-based collaborative information. This study specifically focuses on leveraging the textual metadata (e.g., titles and brands) associated with items. While existing methods have achieved notable success b

Minh T. P. Nguyen, Maximilian Rimbach-Russ, Lieven M. K. Vandersypen, Stefano Bosco

Direct multi-qubit gates are becoming critical to facilitate quantum computations in near-term devices by reducing the gate counts and circuit depth. Here, we demonstrate that fast and high fidelity three-qubit gates can be realized in a single step by leveraging small anisotropic and chiral three-qubit interactions. These ingredients naturally arise in stat

Yuhang Peng, Sidong Wang, Jihaoyu Yang, Shilong Li

Most current end-to-end (E2E) autonomous driving algorithms are built on standard vehicles in structured transportation scenarios, lacking exploration of robot navigation for unstructured scenarios such as auxiliary roads, campus roads, and indoor settings. This paper investigates E2E robot navigation in unstructured road environments. First, we introduce tw

Tuhin G. M. Al Mamun, Rahim Md. Abdur, Md. Sharif Hassan, Mohammad Bin Amin

The research investigates the effects of public spending on health and education in shaping the human development in south Asian three countries: India, Pakistan and Bangladesh. The study uses the VAR (Vector Auto regression) model to estimate the effects on government spending on these sectors to evaluate the human development. The findings state that there

Tetsuya Iye, Masaya Sakamoto, Shohei Takaya, Eisaku Sato

This study presents an end-to-end wireless digital twin platform constructed using open-source software and open data to enhance the evaluation of mobile communication systems. The proposed open wireless digital twin (OWDT) integrates OpenAirInterface (OAI) for Fifth-Generation New Radio (5G NR) protocol stack emulation and NVIDIA Sionna RT for high-resoluti

Deren Han, Min Tao, Zihao Xia

In this paper, we investigate a class of nonconvex and nonsmooth fractional programming problems, where the numerator composed of two parts: a convex, nonsmooth function and a differentiable, nonconvex function, and the denominator consists of a convex, nonsmooth function composed of a linear operator. These structured fractional programming problems have br

Massimo Giovannini

The maximal frequency domain of the cosmic gravitons falls in the THz region where, without conflicting with the existing phenomenological bounds, only few particles with opposite (comoving) three-momenta are produced. Although any reliable scrutiny of the ultra-high frequency spikes should include all the sources of late-time suppression at lower and interm

Jijiang Li, Qingyue Deng, Haibin Ling, Bingyao Huang

Projector-camera systems (ProCams) simulation aims to model the physical project-and-capture process and associated scene parameters of a ProCams, and is crucial for spatial augmented reality (SAR) applications such as ProCams relighting and projector compensation. Recent advances use an end-to-end neural network to learn the project-and-capture process. How

Yuchen Deng, Haibin Ling, Bingyao Huang

We propose LAPIG, a language guided projector image generation method with surface adaptation and stylization. LAPIG consists of a projector-camera system and a target textured projection surface. LAPIG takes the user text prompt as input and aims to transform the surface style using the projector. LAPIG's key challenge is that due to the projector's physica

Tian Long, Jieyun Pan, Edoardo Cipriano, Matteo Bucci

In this paper, a phase-change model based on a geometric Volume-of-Fluid (VOF) framework is extended to simulate nucleate boiling with a resolved microlayer and conjugate heat transfer. Heat conduction in both the fluid and solid domains is simultaneously solved, with Interfacial Heat-Transfer Resistance (IHTR) imposed. The present model is implemented in th

Tao Wang, Cong Zhang, Xingguang Qu, Kun Li

End-to-end autonomous driving (E2E-AD) has rapidly emerged as a promising approach toward achieving full autonomy. However, existing E2E-AD systems typically adopt a traditional multi-task framework, addressing perception, prediction, and planning tasks through separate task-specific heads. Despite being trained in a fully differentiable manner, they still e

Sutapa Ghosh, Alexey Gorlach, Chen Mechel, Maria V. Chekhova

Strong optical nonlinearities are key to a range of technologies, particularly in the generation of photonic quantum states. The strongest nonlinearity in hot atomic vapors originates from electromagnetically induced transparency (EIT), which, while effective, often lacks tunability and suffers from significant losses due to atomic absorption. We propose and

Feixiang He, Jiangbei Yue, Jialin Zhu, Armin Seyfried

Video-based high-density crowd analysis and prediction has been a long-standing topic in computer vision. It is notoriously difficult due to, but not limited to, the lack of high-quality data and complex crowd dynamics. Consequently, it has been relatively under studied. In this paper, we propose a new approach that aims to learn from in-the-wild videos, oft

Cheng Deng, Luoyang Sun, Jiwen Jiang, Yongcheng Zeng

While scaling laws have been continuously validated in large language models (LLMs) with increasing model parameters, the inherent tension between the inference demands of LLMs and the limited resources of edge devices poses a critical challenge to the development of edge intelligence. Recently, numerous small language models have emerged, aiming to distill

Taichi Murayama, Dongwoo Lim, Akira Matsui, Tsukasa Tanihara

Conspiracy theories present significant societal challenges, shaping political behavior, eroding public trust, and disrupting social cohesion. Addressing their impact requires recognizing that conspiracy engagement is not a singular act but a multi-stage process involving distinct cognitive and behavioral transitions. In this study, we investigate this seque

Zijian He, Yuwei Ning, Yipeng Qin, Guangrun Wang

Virtual Try-On (VTON) is a transformative technology in e-commerce and fashion design, enabling realistic digital visualization of clothing on individuals. In this work, we propose VTON 360, a novel 3D VTON method that addresses the open challenge of achieving high-fidelity VTON that supports any-view rendering. Specifically, we leverage the equivalence betw

Yacob Alemiye Mamo, Berdinkov N. Leonid, Pastukhova N. Elena

We investigate the period changes of 13 short-period Type II Cepheids using the O-C method over a century-long data baseline. The O-C diagrams for these stars exhibit a parabolic trend, indicating both increasing and decreasing period changes over time. These observed period changes are consistent with recent theoretical models based on horizontal branch evo

Ruwei Pan, Hongyu Zhang, Zhonghao Jiang, Ran Hou

With the increasing prevalence of fraudulent Android applications such as fake and malicious applications, it is crucial to detect them with high accuracy and adaptability. We present AgentDroid, a novel tool for Android fraudulent application detection based on multi-modal analysis and multi-agent systems. AgentDroid overcomes the limitations of traditional

Milan Papež, Martin Rektoris, Václav Šmídl, Tomáš Pevný

Deep generative models (DGMs) have recently demonstrated remarkable success in capturing complex probability distributions over graphs. Although their excellent performance is attributed to powerful and scalable deep neural networks, it is, at the same time, exactly the presence of these highly non-linear transformations that makes DGMs intractable. Indeed,

Antonis C. Kakas

Aristotle is generally accepted as the father of logic. The ideas that he raised in his study of logical reasoning carried the development of science over the centuries. Today, in the era of AI, this title of the fatherhood of logic has a renewed significance. Behind it lies his original idea that human reasoning could be studied as a process and that perhap

Vinícius S. Silva, Cássio Pigozzo, Luciano M. Abreu

We study the thermal behavior of quarkonia and fully-heavy tetraquark states associated to the charmonium, bottomonium and bottom-charmonium mass spectra. The starting point is the Schr\"odinger formalism with a vacuum Cornell-like potential. The spin-spin, spin-orbit and tensor contributions are also considered to describe the structure of the vacuum quarko

Yuanyuan Wang, Minda Ma, Nan Zhou, Zhili Ma

In the context of increasing global climate change, decarbonizing the residential building sector is crucial for sustainable development. This study aims to analyze the role of various influencing factors in carbon intensity changes using the decomposing structural decomposition (DSD) to assess and compare the potential and effectiveness of electrifying end-

Bowen He, Juan Li, Zhanxin Li

The objective of this paper is to weaken the Lipschitz condition to a monotonicity condition and to study the corresponding Pontryagin stochastic maximum principle (SMP) for a mean-field optimal control problem under monotonicity conditions.The dynamics of the controlled state process is governed by a mean-field stochastic differential equation (SDE) whose c

Tingting Wang, Jiaxin Su, Haobing Liu, Ruobing Jiang

Node classification in graphs aims to predict the categories of unlabeled nodes by utilizing a small set of labeled nodes. However, weighted graphs often contain noisy edges and anomalous edge weights, which can distort fine-grained relationships between nodes and hinder accurate classification. We propose the Edge Weight-aware Graph Structure Learning (EWGS

Yunbo Long, Liming Xu, Alexandra Brintrup

Link prediction is crucial for uncovering hidden connections within complex networks, enabling applications such as identifying potential customers and products. However, this research faces significant challenges, including concerns about data privacy, as well as high computational and storage costs, especially when dealing with large-scale networks. Conden

Joaquim Reizi Barreto

This paper provides an overview of Lawvere's Fixed-Point Theorem in category theory and aims to detail the universal framework underlying self-reference and recursive structures. First, we rigorously define fundamental concepts - such as terminal objects, products, Cartesian Closed Categories, exponential objects, evaluation maps, currying, and point-surject

Buxin Su, Jiaoyang Huang, Jin Jin, Bingxin Zhao

Prediction model training is often hindered by limited access to individual-level data due to privacy concerns and logistical challenges, particularly in biomedical research. Resampling-based self-training presents a promising approach for building prediction models using only summary-level data. These methods leverage summary statistics to sample pseudo dat

A. Sytov, K. Cassou, V. Kubytskyi, M. Lenivenko

We present the development and integration of a Machine Learning (ML)-based surrogate model, trained on Particle-In-Cell (PIC) simulations of laser-driven plasma wakefield acceleration source of electrons, into Geant4 simulation toolkit. Our model enables the generation and tracking of plasma-accelerated beams within complete experimental setups, unifying pl

Dongyan Sui, Haitian Zheng, Siyang Leng, Stefan Vlaski

Social learning strategies enable agents to infer the underlying true state of nature in a distributed manner by receiving private environmental signals and exchanging beliefs with their neighbors. Previous studies have extensively focused on static environments, where the underlying true state remains unchanged over time. In this paper, we consider a dynami

Bin Liu, Xinglin Lyu, Junhui Li, Daimeng Wei

Recent studies in prompting large language model (LLM) for document-level machine translation (DMT) primarily focus on the inter-sentence context by flatting the source document into a long sequence. This approach relies solely on the sequence of sentences within the document. However, the complexity of document-level sequences is greater than that of shorte

Matieyendou Lamboni

This paper proposes new ANOVA-based approximations of functions and emulators of high-dimensional models using either available derivatives or local stochastic evaluations of such models. Our approach makes use of sensitivity indices to design adequate structures of emulators. For high-dimensional models with available derivatives, our derivative-based emula

Wonjun Yi, Yong-Hwa Park

This study presents a novel multi-output classification (MOC) framework designed for domain adaptation in fault diagnosis, addressing challenges posed by partially labeled (PL) target domain dataset and coexisting faults in rotating machinery. Unlike conventional multi-class classification (MCC) approaches, the MOC framework independently classifies the seve

Hongyu Sun, Qiuhong Ke, Ming Cheng, Yongcai Wang

This paper proposes a general solution to enable point cloud recognition models to handle distribution shifts at test time. Unlike prior methods, which rely heavily on training data (often inaccessible during online inference) and are limited to recognizing a fixed set of point cloud classes predefined during training, we explore a more practical and challen

Junjie Chen, Xuyang Liu, Subin Huang, Linfeng Zhang

With the advent of large vision-language models (LVLMs) demonstrating increasingly human-like abilities, a pivotal question emerges: do different LVLMs interpret multimodal sarcasm differently, and can a single model grasp sarcasm from multiple perspectives like humans? To explore this, we introduce an analytical framework using systematically designed promp

Pierre Le Doussal

We study the probability distribution function $P(\lambda)$ of the largest eigenvalue $\lambda_{\rm max}$ of $N \times N$ random matrices of the form $H + V$, where $H$ belongs to the GOE/GUE ensemble and $V$ is a full rank deterministic diagonal perturbation. This model is related to spherical spin glasses and semi-discrete directed polymers. In the large $

Patrick Letendre

Let $\mathcal{D}_{n} \subset \mathbb{N}$ denote the set of the $\tau(n)$ divisors of $n$. We study the function $$ D_{n}(X,Y):=|\{d \in \mathcal{D}_{n}:\ X \le d \le X+Y\}| $$ for $Y \le X$.

Joaquim Reizi Barreto

This paper develops a categorical framework to clarify the relationship between the completeness and compactness theorems in classical first-order logic. Rather than claiming that different model constructions yield naturally isomorphic results-a statement that generally fails without strong assumptions-we focus on a precise and provable reformulation. Speci

Maryam Daniali, Shivaram Karandikar, Dabriel Zimmerman, J. Eric Schmitt

Clinically acquired brain MRIs and radiology reports are valuable but underutilized resources due to the challenges of manual analysis and data heterogeneity. We developed fine-tuned language models (LMs) to classify brain MRI reports as normal (reports with limited pathology) or abnormal, fine-tuning BERT, BioBERT, ClinicalBERT, and RadBERT on 44,661 report

Sumin Lim, Arzhang Ardavan

The implementation of practical error correction protocols is essential for deployment of quantum information technologies. Ways of exploiting high-spin nuclei, which have multi-level quantum resources, have attracted interest in this context because they offer additional Hilbert space dimensions in a spatially compact and theoretically efficient structure.

Shayan Rokhva, Babak Teimourpour, Romina Babaei

This research presents an advanced sentiment analysis framework studied on Iranian restaurant reviews, combining fuzzy logic with conventional sentiment analysis techniques to assess both sentiment polarity and intensity. A dataset of 1266 reviews, alongside corresponding star ratings, was compiled and preprocessed for analysis. Initial sentiment analysis wa

Kazumasa Fujiwara, Vladimir Georgiev

We study the decay properties of non-negative solutions to the one-dimensional defocusing damped wave equation in the Fujita subcritical case under a specific initial condition. Specifically, we assume that the initial data are positive, satisfy a condition ensuring the positiveness of solutions, and exhibit polynomial decay at infinity. To show the decay pr

Jiang Xiaobo, Yongru Chen

Open-world link prediction supports the knowledge representation and link prediction of new entities, enhancing the practical value of knowledge graphs in real-world applications. However, as research deepens, the performance improvements in open-world link prediction have gradually reached a bottleneck. Understanding its intrinsic impact mechanisms is cruci

Mikhail A. Anisimov, Sergey V. Buldyrev, Frédéric Caupin, Thomas J. Longo

Fluid polyamorphism is the existence of multiple fluid-fluid phase transitions in a single-component substance. It can occur due to interconversion between two alternative molecular or supramolecular states. In this work, we investigate a special (``degenerate'') case of fluid polyamorphism, in which all three characteristic parameters of the interconversion

Ertuğrul Keçeci, Müjde Güzelkaya, Tufan Kumbasar

This paper presents FedAlign, a Federated Learning (FL) framework particularly designed for System Identification (SYSID) tasks by aligning state representations. Local workers can learn State-Space Models (SSMs) with equivalent representations but different dynamics. We demonstrate that directly aggregating these local SSMs via FedAvg results in a global mo

Sameer Salunkhe, Ramananda Santra, Ruta Kale

We report the discovery of a radio megahalo in the merging cluster PLCKG287.0+32.9, based on upgraded Giant Metrewave Radio telescope (uGMRT) observations at frequencies 300-850 MHz. The sensitive radio observations provide a new window to study the complex physics occurring in this system. Apart from significant detections of the known diffuse radio emissio

Adam Bjorndahl

We introduce a logical framework for reasoning about "uncertain interpretations" and investigate two key applications: a new semantics for implication capturing a kind of "meaning entailment", and a conservative notion of "evidentially supported" belief that takes the form of a Dempster-Shafer belief function.

Wei Xie, Wei-Hua Lei

Outflows/jets are ubiquitous in a wide range of astrophysical objects, yet the mechanisms responsible for their generation remain elusive. One hypothesis is that they are magnetically driven. Based on general relativistic MHD equations, we establish a formulation to describe the outflows driven by large-scale magnetic fields from the accretion disk in Schwar

Shachar Carmeli, Kiran Luecke

We define a projective variant of classical complex orientation theory. Using this, we construct a map of spectra which lifts the total Chern class, providing an alternative answer to an old question of Segal \cite{segal}, previously answered by Lawson et al \cite{lawsonetal}. We also lift and generalize the ``sharp'' construction of Ando-French-Ganter \cite

Zengqian Wang, Yuanfang Yue, Z. Y. Xie, Fengjie Ma

A wide variety of applications has inspired great interest in designing new materials and investigating fundamental physics in the ferroelectric field. In the concept of ferroelectricity, the spontaneous polarization is traditionally considered a core property, but we find that the `spontaneous' seems unnecessary. Based on this understanding, we suggest a ne

Zheng Ma, Meirong Tang, Zhaoyi Xu

Quasi-Periodic Oscillations (QPOs) are an important phenomenon commonly observed in the X-ray radiation of black holes and neutron stars, closely related to the dynamics of accretion disks around compact objects and general relativistic effects. The objective of this study is to use the QPO phenomenon to distinguish between dark matter-black hole systems and

Xuezao Wang, Yijun Xu, Wei Gu, Kai Liu

The increasing penetration of renewables is replacing traditional synchronous generation in modern power systems with low-inertia asynchronous converter-interfaced generators (CIGs). This penetration threatens the dynamic stability of the modern power system. To assess the latter, we resort to the critical clearing time (CCT) as a stability index, which is t

Shentong Mo, Zehua Chen, Fan Bao, Jun Zhu

Recent works in cross-modal understanding and generation, notably through models like CLAP (Contrastive Language-Audio Pretraining) and CAVP (Contrastive Audio-Visual Pretraining), have significantly enhanced the alignment of text, video, and audio embeddings via a single contrastive loss. However, these methods often overlook the bidirectional interactions

Zhidan Yan, Wei Wang

For an $n$-vertex graph $G$, the walk matrix of $G$, denoted by $W(G)$, is the matrix $[e,A(G)e,\ldots,(A(G))^{n-1}e]$, where $A(G)$ is the adjacency matrix of $G$ and $e$ is the all-ones vector. For two integers $m$ and $\ell$ with $1\le \ell\le (m+1)/2$, let $G\circ P_m^{(\ell)}$ be the rooted product of $G$ and the path $P_m$ taking the $\ell$-th vertex o

Jitendra Singh, Banda Naveen, Suraj Srivastava, Aditya K. Jagannatham

A hybrid beamformer (HBF) is designed for integrated sensing and communication (ISAC)-aided millimeter wave (mmWave) systems. The ISAC base station (BS), relying on a limited number of radio frequency (RF) chains, supports multiple communication users (CUs) and simultaneously detects the radar target (RT). To maximize the probability of detection (PD) of the

Jie-Ming Wang

In this paper, the two-sided Dirichlet heat kernel estimates are obtained for a class of discontinuous isotropic Levy processes with Gaussian components in Lipschitz open sets. Furthermore, the necessary and sufficient conditions for the Varopoulos-type Dirichlet heat kernel estimates holding for such processes in Lipschitz open sets are derived.

Tobia Poppi, Tejaswi Kasarla, Pascal Mettes, Lorenzo Baraldi

Addressing the retrieval of unsafe content from vision-language models such as CLIP is an important step towards real-world integration. Current efforts have relied on unlearning techniques that try to erase the model's knowledge of unsafe concepts. While effective in reducing unwanted outputs, unlearning limits the model's capacity to discern between safe a

Seref Kalem

The silicon suboxide SiOx (x<2.0) offers promising industrial application possibilities ranging from electrodes in lithium-ion batteries, which are used widely in electrical vehicles and portable devices to sensing applications. Therefore, a low cost, environmental friendly and high performance silicon oxide materials are required for an appropriate operatio

Hun Kang, Kyoungok Kim

Isolation Forest (iForest) is an unsupervised anomaly detection algorithm designed to effectively detect anomalies under the assumption that anomalies are ``few and different." Various studies have aimed to enhance iForest, but the resulting algorithms often exhibited significant performance disparities across datasets. Additionally, the challenge of isolati

Yingying Deng, Xiangyu He, Fan Tang, Weiming Dong

The customization of multiple attributes has gained popularity with the rising demand for personalized content creation. Despite promising empirical results, the contextual coherence between different attributes has been largely overlooked. In this paper, we argue that subsequent attributes should follow the multivariable conditional distribution introduced

Zhaopeng Feng, Jiahan Ren, Jiayuan Su, Jiamei Zheng

Process reward models (PRMs) have shown success in complex reasoning tasks for large language models (LLMs). However, their application to machine translation (MT) remains underexplored due to the lack of systematic methodologies and evaluation benchmarks. To address this gap, we introduce \textbf{MT-RewardTree}, a comprehensive framework for constructing, e

Yoshiki Yano, Kazuki Shibata, Maarten Kokshoorn, Takamitsu Matsubara

Recent advances in Large Language Models (LLMs) have permitted the development of language-guided multi-robot systems, which allow robots to execute tasks based on natural language instructions. However, achieving effective coordination in distributed multi-agent environments remains challenging due to (1) misalignment between instructions and task requireme

Monit Sharma, Hoong Chuin Lau

Quantum optimization holds promise for addressing classically intractable combinatorial problems, yet a standardized framework for benchmarking its performance, particularly in terms of solution quality, computational speed, and scalability is still lacking. In this work, we introduce a comprehensive benchmarking framework designed to systematically evaluate

Naresh Tiwari

This paper presents a comprehensive analysis of the strategic imperative for healthcare organizations to develop proprietary foundation models rather than relying exclusively on commercial alternatives. We examine four fundamental considerations driving this imperative: the domain-specific requirements of healthcare data representation, critical data soverei

Seref Kalem

Black silicon (b-Si) has been receiving a great deal of interest for its potential to be used in applications ranging from sensors to solar cells and electrodes in batteries due to its promising optical, electronic and structural properties. Several approaches have been used to demonstrate the possibility of producing application quality b-Si, which also exh

Jin Zhang, Kun Yang, Jianxin Yu, Jia Zhang

The photostriction effect, a light-induced mechanical deformation in materials, originates from the intricate interplay between lattice structure and electronic excitation. In photovoltaic semiconductors, this effect plays a crucial role in shaping non-equilibrium structural responses, yet its fundamental mechanism remains elusive. Here, we uncover lattice e

Varinder Singh, Euijoon Kwon, G J Milburn

We consider a periodic quantum clock based on cooperative resonance fluorescence at zero temperature. In the quantum case, this system has an exact steady state and the limit cycle appears in conditional quantum dynamics under homodyne detection. We show that the intrinsic quantum phase diffusion on the limit cycle leads to fluctuations in the period. By sim

William Cook

We study the trapezoidal rule for periodic functions on uniform grids and show that the quadrature error exhibits a rich deterministic structure, beyond traditional asymptotic or statistical interpretations. Focusing on the prototype function f(x) = sin^2(2 pi k x), we derive an analytical expression for the error governed by a resonance function chi_P(y), c

Mohammed K. Alqedra, Chiao-Tzu Huang, Wen-Hao Chang, Sofiane Haffouz

On-demand single-photon sources operating at telecom wavelengths are crucial for quantum communication and photonic quantum technologies. In this work, we demonstrate high-purity, indistinguishable single-photon generation in the telecom O-band from an InAsP/InP nanowire quantum dot. We measured a single-photon purity of $g^2(0)=0.006(3)$ under aboveband exc

Xue Jiang, Xiulian Peng, Yuan Zhang, Yan Lu

Current large speech language models are mainly based on semantic tokens from discretization of self-supervised learned representations and acoustic tokens from a neural codec, following a semantic-modeling and acoustic-synthesis paradigm. However, semantic tokens discard paralinguistic attributes of speakers that is important for natural spoken communicatio

Buddhadev Hajra, Rajib Sarkar

For a simple graph $G$, let $J_G$ denote the corresponding binomial edge ideal. This article considers the binomial edge ideal of the corona product of two connected graphs $G$ and $H$. The corona product of $G$ and $H$, denoted by $G\circ H$, is a construction where each vertex of $G$ is connected (via the coning-off) to an entire copy of $H$. This is a dir

Emin Moghadas, Matthias Reitner, Tim Wehling, Giorgio Sangiovanni

We present a dynamical mean-field study of the nonperturbative electronic mechanisms, which may lead to significant enhancements of the electron-phonon coupling in correlated electron systems. Analyzing the effects of electronic correlations on the lowest-order electron-phonon processes, we show that in the proximity of the Mott metal-to-insulator transition

Lizhuo Liu, Clive Cenxin Aw

In both classical and quantum physics, irreversible processes are described by maps that contract the space of states. The change in volume has often been taken as a natural quantifier of the amount of irreversibility. In Bayesian inference, loss of information results in the retrodiction for the initial state becoming increasingly influenced by the choice o

Rajiv Kumar, Rajib Sarkar

Let $G$ be a simple graph and $I_3(G)$ be its $3$-path ideal in the corresponding polynomial ring $R$. In this article, we prove that for an arbitrary graph $G$, $reg(R/I_3(G))$ is bounded below by $2\nu_3(G)$, where $\nu_3(G)$ denotes the $3$-path induced matching number of $G$. We give a class of graphs, namely, trees for which the lower bound is attained.

Ondřej Kincl, Ilya Peshkov, Walter Boscheri

In this work, we use a moving Voronoi and sharp interface approach for simulating two-phase flows. At every time step, the mesh is generated anew from Voronoi seeds that behave as material points. The paper is a continuation of our previous works on moving Voronoi meshes where we have considered single phase incompressible and compressible flows. In the cont

Stephan von Malottki, Moritz A. Goerzen, Hendrik Schrautzer, Pavel F. Bessarab

We present an eigenmode following method (EMF) that has been developed for the numerical scanning of the potential energy surface and the direct calculation of entropy and pre-exponential factors of Arrhenius-like transition rates in the framework of transition state theory. In contrast to other methods, we do not use EMF to move "uphill" or "downhill" in en

André Schamschurko, Nenad Petrovic, Alois Christian Knoll

The latest research on Large Language Models (LLMs) has demonstrated significant advancement in the field of Natural Language Processing (NLP). However, despite this progress, there is still a lack of reliability in these models. This is due to the stochastic architecture of LLMs, which presents a challenge for users attempting to ascertain the reliability o

Sebastian Pineda Arango, Pedro Mercado, Shubham Kapoor, Abdul Fatir Ansari

Covariates provide valuable information on external factors that influence time series and are critical in many real-world time series forecasting tasks. For example, in retail, covariates may indicate promotions or peak dates such as holiday seasons that heavily influence demand forecasts. Recent advances in pretraining large language model architectures fo

Chengjie Wu, Yi Ding, Yiying Yan, Yuguo Su

A deep understanding of the correlation between electronic and mechanical degrees of freedom is crucial to the development of quantum devices in a nanoelectromechanical system (NEMS). In this work, we first establish a fully quantum mechanical approach for transport through a NEMS device, which is valid for arbitrary bias voltages, temperatures, and electro-

F. P. Callan, A. Holas, J. Morán-Fraile, S. A. Sim

We carry out NLTE (non local thermodynamic equilibrium) radiative transfer simulations to determine whether explosion during the merger of a carbon-oxygen (CO) white dwarf (WD) with a helium (He) WD can reproduce the characteristic Ca II/[Ca II] and He I lines observed in Ca-rich transients. Our study is based on a 1D representation of a hydrodynamic simulat

Mohammad R. Garousi

The classical effective action in string theory is background-independent, and its invariance under the Buscher rules constrains its form up to a few parameters. This work investigates how this picture changes at the quantum level, where loop corrections introduce an inherent background dependence. We propose a T-duality map for the loop-level effective acti

Clément Foucart, Olivier Hénard

We revisit certain decompositions of continuous-state branching processes (CSBPs), commonly referred to as skeletal decompositions, through the lens of intertwining of semi-groups. Precisely, we associate to a CSBP $X$ with branching mechanism $\psi$ a family of $\mathbb{R}_+\times \mathbb{Z}_+$-valued branching processes $(X^\lambda, L^\lambda)$, indexed by

Paula Andrea Pérez-Toro, Tomás Arias-Vergara, Fangxu Xing, Xiaofeng Liu

Understanding the relationship between vocal tract motion during speech and the resulting acoustic signal is crucial for aided clinical assessment and developing personalized treatment and rehabilitation strategies. Toward this goal, we introduce an audio-to-video generation framework for creating Real Time/cine-Magnetic Resonance Imaging (RT-/cine-MRI) visu

Ylenia Nisticò, João Carlos Virgolino Soares, Lorenzo Amatucci, Geoff Fink

This paper introduces an innovative state estimator, MUSE (MUlti-sensor State Estimator), designed to enhance state estimation's accuracy and real-time performance in quadruped robot navigation. The proposed state estimator builds upon our previous work presented in [1]. It integrates data from a range of onboard sensors, including IMUs, encoders, cameras, a

Arkadiusz Bryłkowski, Jakub Klikowski

Large language models (LLMs) are among the best methods for processing natural language, partly due to their versatility. At the same time, domain-specific LLMs are more practical in real-life applications. This work introduces a novel natural language dataset created by acquired data from official legislative authorities' websites. The study focuses on form

Huan-Hsuan Kung, Chen-Yu Liu, Qian-Rui Lee, Chiang-Yuan Hu

Accurate determination of qubit parameters is critical for the successful implementation of quantum information and computation applications. In solid state systems, the parameters of individual qubits vary across the entire system, requiring time consuming measurements and manual fitting processes for characterization. Recent developed superconducting qubit

Wuzhou Sun, Siyi Li, Qingxiang Zou, Zixing Liao

In various game scenarios, selecting a fixed number of targets from multiple enemy units is an extremely challenging task. This difficulty stems from the complex relationship between the threat levels of enemy units and their feature characteristics, which complicates the design of rule-based evaluators. Moreover, traditional supervised learning methods face

Pallavi Garg, Jaibir Singh, Ankit Kumar Gaur, Sugumar Venkataramani

Strong light-matter interactions are gaining importance in controlling the photoswitching behavior of molecules exhibiting bistability. Here, we used the strategy of electronic strong coupling of both the E and Z-isomers of azopyrrole photoswitches to a single cavity mode, thereby tuning the conditions from strong to weak coupling in an on-the-go photoisomer

Ashshak Sharifdeen, Muhammad Akhtar Munir, Sanoojan Baliah, Salman Khan

Test-time prompt tuning for vision-language models (VLMs) is getting attention because of their ability to learn with unlabeled data without fine-tuning. Although test-time prompt tuning methods for VLMs can boost accuracy, the resulting models tend to demonstrate poor calibration, which casts doubts on the reliability and trustworthiness of these models. No

Walter Zimmer, Ross Greer, Daniel Lehmberg, Marc Pavel

Even though a significant amount of work has been done to increase the safety of transportation networks, accidents still occur regularly. They must be understood as unavoidable and sporadic outcomes of traffic networks. No public dataset contains 3D annotations of real-world accidents recorded from roadside camera and LiDAR sensors. We present the TUM Traff

Weiming Zhang, Dingwen Xiao, Lei Chen, Lin Wang

Entity Segmentation (ES) aims at identifying and segmenting distinct entities within an image without the need for predefined class labels. This characteristic makes ES well-suited to open-world applications with adaptation to diverse and dynamically changing environments, where new and previously unseen entities may appear frequently. Existing ES methods ei

Oren Shrout, Ayellet Tal

We propose SFMNet, a novel 3D sparse detector that combines the efficiency of sparse convolutions with the ability to model long-range dependencies. While traditional sparse convolution techniques efficiently capture local structures, they struggle with modeling long-range relationships. However, capturing long-range dependencies is fundamental for 3D object