March 2025 arXiv papers — page 149

Adel ElZemity, Budi Arief, Shujun Li

The integration of large language models (LLMs) into cyber security applications presents both opportunities and critical safety risks. We introduce CyberLLMInstruct, a dataset of 54,928 pseudo-malicious instruction-response pairs spanning cyber security tasks including malware analysis, phishing simulations, and zero-day vulnerabilities. Our comprehensive e

Bilal Canturk

The time evolution of the one-point probability vector of stochastic processes and quantum processes for $N$-level systems have been unified. Hence, quantum states and quantum operations can be regarded as generalizations of the one-point probability vectors and stochastic matrices, respectively. More essentially, based on the unification, it has been proven

Dai Sun, Huhao Guan, Kun Zhang, Xike Xie

Dynamic and static components in scenes often exhibit distinct properties, yet most 4D reconstruction methods treat them indiscriminately, leading to suboptimal performance in both cases. This work introduces SDD-4DGS, the first framework for static-dynamic decoupled 4D scene reconstruction based on Gaussian Splatting. Our approach is built upon a novel prob

Xi Cheng, Hao-kun Mao, Hong-wei Xu, Qiong Li

Privacy Amplification (PA) is indispensable in Quantum Key Distribution (QKD) post-processing, as it eliminates information leakage to eavesdroppers. Field-programmable gate arrays (FPGAs) are highly attractive for QKD systems due to their flexibility and high integration. However, due to limited resources, input and output sizes remain the primary bottlenec

Thomas De Min, Subhankar Roy, Stéphane Lathuilière, Elisa Ricci

Machine unlearning is an emerging paradigm to remove the influence of specific training data (i.e., the forget set) from a model while preserving its knowledge of the rest of the data (i.e., the retain set). Previous approaches assume the forget data to be uniformly distributed from all training datapoints. However, if the data to unlearn is dominant in one

Hannes Waclawek, Stefan Huber

This work explores an extension of machine learning-optimized piecewise polynomial approximation by incorporating energy optimization as an additional objective. Traditional closed-form solutions enable continuity and approximation targets but lack flexibility in accommodating complex optimization goals. By leveraging modern gradient descent optimizers withi

Henri Orland

A Schr\"odinger bridge is the most probable time-dependent probability distribution that connects an initial probability distribution $w_{i}$ to a final one $w_{f}$. The problem has been solved and widely used for the case of simple Brownian evolution (non-interacting particles). It is related to the problem of entropy-regularized Wasserstein optimal transpo

Mostafa Chegenizadeh, Sina Rafati Niya, Claudio J. Tessone

Blockchain technology has recently gained widespread popularity as a practical method of storing immutable data while preserving the privacy of users by anonymizing their real identities. This anonymization approach, however, significantly complicates the analysis of blockchain data. To address this problem, heuristic-based clustering algorithms as an effect

Xin Li, Zhuo Cai, Shoujin Wang, Kun Yu

Large language models (LLMs) have recently shown remarkable performance in language tasks and beyond. However, due to their limited inherent causal reasoning ability, LLMs still face challenges in handling tasks that require robust causal reasoning ability, such as health-care and economic analysis. As a result, a growing body of research has focused on enha

Donald L. Kreher, Shuxing Li, Douglas R. Stinson

We initiate the study of $\lambda$-fold near-factorizations of groups with $\lambda > 1$. While $\lambda$-fold near-factorizations of groups with $\lambda = 1$ have been studied in numerous papers, this is the first detailed treatment for $\lambda > 1$. We establish fundamental properties of $\lambda$-fold near-factorizations and introduce the notion of equi

C. Pan, Y. C. Yang, X. F. Jiang, X. H. Wu

Nuclear masses of exotic nuclei are important for both nuclear physics and astrophysics. The deformed relativistic Hartree-Bogoliubov theory in continuum (DRHBc) is capable of providing proper descriptions for exotic nuclei by simultaneously including deformation, pairing correlation and continuum effects, and a mass table of even-$Z$ nuclei with $8 \leqslan

Somnath Gandal

In the present paper, we establish a multiplicity result for a following class of nonlocal Neumann eigenvalue problems involving the fractional p-Laplacian. \begin{align} \begin{cases} (-\Delta)^{s}_{p}u + a(x) \abs{u}^{p-2}u =\lambda h(x,u) & \text {in } \Omega, \mathcal{N}_{s,p}u=0 & \text {in } \mathbb{R}^N \setminus \overline{\Omega}, \end{cases} \end{al

Bo-Yi Liu, Zhi-Xuan Liu, Kuan Lun Chen, Shih-Yu Tsai

Deep learning models are widely used in decision-making and recommendation systems, where they typically rely on the assumption of a static data distribution between training and deployment. However, real-world deployment environments often violate this assumption. Users who receive negative outcomes may adapt their features to meet model criteria, i.e., rec

Andreas Sykora

We calculate the weighted Bergman kernel on a complex domain with a weight of the form $\rho=e^{-\alpha\phi}\mu g$, where $\alpha$ is a positive real number, $\phi$ is a K\"ahler potential, g is the determinant of the corresponding K\"ahler metric and $\mu$ is a real-valued positive function. Several $\star$-products related to the Bergman kernel are determi

Gorjan Radevski, Teodora Popordanoska, Matthew B. Blaschko, Tinne Tuytelaars

Audio-visual understanding is a rapidly evolving field that seeks to integrate and interpret information from both auditory and visual modalities. Despite recent advances in multi-modal learning, existing benchmarks often suffer from strong visual bias -- when answers can be inferred from visual data alone -- and provide only aggregate scores that conflate m

Marvin Heidinger, Snehal Jauhri, Vignesh Prasad, Georgia Chalvatzaki

When interacting with objects, humans effectively reason about which regions of objects are viable for an intended action, i.e., the affordance regions of the object. They can also account for subtle differences in object regions based on the task to be performed and whether one or two hands need to be used. However, current vision-based affordance predictio

Nicolas Pascal Guido Müller, Roman Vetter

We present PolyMorph, a lightweight standalone C++ program that extends its predecessor PolyHoop by a finite-difference solver for multi-component reaction-advection-diffusion equations. PolyMorph simulates two integral parts of tissue morphogenesis in two dimensions: 1) the mechanics of cellular deformation, growth and proliferation, and 2) transport and re

Zeke Wang, Jie Zhang, Hongjing Huang, Yingtao Li

Modern data analytics requires a huge amount of computing power and processes a massive amount of data. At the same time, the underlying computing platform is becoming much more heterogeneous on both hardware and software. Even though specialized hardware, e.g., FPGA- or GPU- or TPU-based systems, often achieves better performance than a CPU-only system due

Keyu Zhang, Andrew Martin

Decentralized smart contracts enable trustless collaboration but suffer from limited privacy and scalability, which hinders broader adoption. Trusted Execution Environment (TEE) based off-chain execution frameworks offer a promising solution to both issues. Although TEE-based frameworks have made significant progress, prior work has yet to fully explore cont

J. J. Chebly, K. Poppenhäger, J. D. Alvarado-Gómez, B. E. Wood

Main sequence stars of spectral types F, G, and K with low to moderate activity levels exhibit a recognizable pattern known as the first ionization potential effect (FIP effect), where elements with lower first ionization potentials are more abundant in the stellar corona than in the photosphere. In contrast, high activity main sequence stars such as AB Dor

Xinghan Li, Yue Yu, Xue Song, Haijun Shan

With the rapid advancement of vision generation models, the potential security risks stemming from synthetic visual content have garnered increasing attention, posing significant challenges for AI-generated image detection. Existing methods suffer from inadequate generalization capabilities, resulting in unsatisfactory performance on emerging generative mode

Elio Musacchio, Lucia Siciliani, Pierpaolo Basile, Giovanni Semeraro

In the current literature, most embedding models are based on the encoder-only transformer architecture to extract a dense and meaningful representation of the given input, which can be a text, an image, and more. With the recent advances in language modeling thanks to the introduction of Large Language Models, the possibility of extracting embeddings from t

Robert Turnbull, Neil D. Young, Edoardo Tescari, Lee F. Skerratt

Repetitive DNA sequences underpin genome architecture and evolutionary processes, yet they remain challenging to classify accurately. Terrier is a deep learning model designed to overcome these challenges by classifying repetitive DNA sequences using a publicly available, curated repeat sequence library trained under the RepeatMasker schema. Poor representat

Leo Widmer, Jiawei Huang, Niao He

Incentive design is a popular framework for guiding agents' learning dynamics towards desired outcomes by providing additional payments beyond intrinsic rewards. However, most existing works focus on a finite, small set of agents or assume complete knowledge of the game, limiting their applicability to real-world scenarios involving large populations and mod

Zeyu Li, Yang Su, Lile Liu, Yongjing Chen

We implement the Fourier shape parametrization within the point-coupling covariant density functional theory to construct the collective space, potential energy surface (PES), and mass tensor, which serve as inputs for the time-dependent generator coordinate method to simulate the fission dynamics. Taking \(^{226}\)Th as a benchmark, we demonstrate the super

Jihoon Ok, Kyeong Song

We consider a broad class of nonlinear integro-differential equations with a kernel whose differentiability order is described by a general function $\phi$. This class includes not only the fractional $p$-Laplace equations, but also borderline cases when the fractional order approaches $1$. Under mild assumptions on $\phi$, we establish sharp Sobolev-Poincar

Kathleen Anderson, Thomas Martinetz

We evaluate the information that can unintentionally leak into the low dimensional output of a neural network, by reconstructing an input image from a 40- or 32-element feature vector that intends to only describe abstract attributes of a facial portrait. The reconstruction uses blackbox-access to the image encoder which generates the feature vector. Other t

Yang Huang, Timothy C. Beers

We present an updated catalog of stellar parameters, including effective temperature, luminosity classification, and metallicity, for over fifty million stars from the SkyMapper Southern Survey (SMSS) DR4 and Gaia DR3. The accuracy of the derived parameters remains consistent with those achieved with SMSS DR2 using the same methods. Thanks to the advancement

Mohammad Siavashi, Faezeh Keshmiri Dindarloo, Dejan Kostic, Marco Chiesa

Large Language Models have revolutionized natural language processing, yet serving them efficiently in data centers remains challenging due to mixed workloads comprising latency-sensitive (LS) and best-effort (BE) jobs. Existing inference systems employ iteration-level first-come-first-served scheduling, causing head-of-line blocking when BE jobs delay LS jo

F. Ustuner, A. C. Mullins, S. Eisenhardt, M. Kocian

This contribution introduces a novel test system developed to evaluate the signal transmission quality in high-speed data links for the 2026 Inner Tracker (ITk) upgrade of the ATLAS experiment. Using an FPGA-based data acquisition (DAQ) framework, the setup can run simultaneous Bit Error Rate (BER) tests for up to 64 channels and generate virtual eye diagram

Halima I. Kure, Pradipta Sarkar, Ahmed B. Ndanusa, Augustine O. Nwajana

This paper investigates the critical issue of data poisoning attacks on AI models, a growing concern in the ever-evolving landscape of artificial intelligence and cybersecurity. As advanced technology systems become increasingly prevalent across various sectors, the need for robust defence mechanisms against adversarial attacks becomes paramount. The study a

Guanchen Li, Yixing Xu, Zeping Li, Ji Liu

Structural pruning enhances hardware-agnostic inference efficiency for large language models (LLMs) yet often fails to maintain comparable performance. Local pruning performs efficient layer-by-layer compression but ignores global topology. Although global pruning aims to identify an optimal sparse model, intuitive methods typically adopt a two-stage paradig

Álvaro Torras-Casas, Ka Man Yim, Ulrich Pennig

Given a poset-graded chain complex of vector spaces, a Conley complex is the minimal chain-homotopic reduction of the initial complex that respects the poset grading. A connection matrix is a matrix representing the differential of the Conley complex. In this work, we give an algebraic derivation of the Conley complex and its connection matrix using homologi

Eduard Muslimov, Simona Lombardo, Thibault Behaghel, Jiawei Liu

We describe a practical implementation of the anamorphically curved detector concept. In order to demonstrate its advantages, a telescope lab prototype was developed, built, and tested. It is based on a 4-mirror all-spherical unobscured design, similar to that proposed by D. Shafer. The telescope is open at F/#=5.5 and its extended field of view is 10.6x8 de

Olga Klopp, Fedor Noskov

We study low-rank estimation of an unknown sparse graphon from sampled network data under operator-norm loss, motivated by targeted interventions in graphon games. Starting from the observed adjacency matrix, we construct low-rank surrogates by singular value thresholding and, for smooth graphons, by block averaging followed by thresholding. We obtain non-as

Philippe Bouafia

We introduce a class of flat currents with fractal properties, called fractional currents, which satisfy a compactness theorem and remain stable under pushforwards by H\"older continuous maps. In top dimension, fractional currents are the currents represented by functions belonging to a fractional Sobolev space. The space of $\alpha$-fractional currents is i

Wojciech Broniowski, Enrique Ruiz Arriola

We analyze the gravitational form factors and mechanical properties of the nucleon, focusing on both some general issues as well as on modeling with meson dominance. We show that the lattice QCD results for the nucleon gravitational form factors at $m_\pi=170$~MeV, available for space-like momentum transfer squared up to 2GeV, are explained in a natural way

Jialiang Tang, Shuo Chen, Chen Gong, Jing Zhang

Time Series Forecasting (TSF) is critical in many real-world domains like financial planning and health monitoring. Recent studies have revealed that Large Language Models (LLMs), with their powerful in-contextual modeling capabilities, hold significant potential for TSF. However, existing LLM-based methods usually perform suboptimally because they neglect t

Bingzheng Jiang, Jiayuan Wang, Han Ding, Lijun Zhu

This paper presents a robust monocular visual SLAM system that simultaneously utilizes point, line, and vanishing point features for accurate camera pose estimation and mapping. To address the critical challenge of achieving reliable localization in low-texture environments, where traditional point-based systems often fail due to insufficient visual features

Sang Pyo Kim

Astrophysical compact objects, such as magnetars, neutron star mergers, etc, have strong electromagnetic fields beyond the Schwinger field ($B_c = 4.4 \times 10^{13}\, {\rm G}$). In strong electric fields, electron-positron pairs are produced from the vacuum, gamma rays create electron-positron pairs in strong magnetic fields, and propagating photons experie

Peng Hu, Chunming He, Lei Xu, Jingduo Tian

Blind Face Restoration (BFR) addresses the challenge of reconstructing degraded low-quality (LQ) facial images into high-quality (HQ) outputs. Conventional approaches predominantly rely on learning feature representations from ground-truth (GT) data; however, inherent imperfections in GT datasets constrain restoration performance to the mean quality level of

Arthur Moreau, Mohammed Brahimi, Richard Shaw, Athanasios Papaioannou

We present Better Together, a method that simultaneously solves the human pose estimation problem while reconstructing a photorealistic 3D human avatar from multi-view videos. While prior art usually solves these problems separately, we argue that joint optimization of skeletal motion with a 3D renderable body model brings synergistic effects, i.e. yields mo

Marcus Meschede, Ludwig Mathey

In the pursuit of robust quantum computing, we put forth a platform based on photonic qubits in a circuit-QED environment. Specifically, we propose a versatile two-qubit gate based on two cavities coupled via a transmon, constituting a selective number-dependent phase gate operating on the in-phase eigenmodes of the two cavities, the Eigen-SNAP gate. This ga

Xinjian Luo, Ting Yu, Xiaokui Xiao

The inference process of modern large language models (LLMs) demands prohibitive computational resources, rendering them infeasible for deployment on consumer-grade devices. To address this limitation, recent studies propose distributed LLM inference frameworks, which employ split learning principles to enable collaborative LLM inference on resource-constrai

Hamza Djelouat, Reijo Leinonen, Mikko J. Sillanpää, Bhaskar D. Rao

This letter addresses the problem of estimating block sparse signal with unknown group partitions in a multiple measurement vector (MMV) setup. We propose a Bayesian framework by applying an adaptive total variation (TV) penalty on the hyper-parameter space of the sparse signal. The main contributions are two-fold. 1) We extend the TV penalty beyond the imme

Somsubhra De, Advait Vats

The rise of Generative AI has led to a surge in AI-generated reviews, often posing a serious threat to the credibility of online platforms. Reviews serve as the primary source of information about products and services. Authentic reviews play a vital role in consumer decision-making. The presence of fabricated content misleads consumers, undermines trust and

Chuan Qin, Xin Chen, Chengrui Wang, Pengmin Wu

In recent years, the rapid advancement of Artificial Intelligence (AI) technologies, particularly Large Language Models (LLMs), has revolutionized the paradigm of scientific discovery, establishing AI-for-Science (AI4Science) as a dynamic and evolving field. However, there is still a lack of an effective framework for the overall assessment of AI4Science, pa

Ibrahim Habli, Richard Hawkins, Colin Paterson, Philippa Ryan

We present our Balanced, Integrated and Grounded (BIG) argument for assuring the safety of AI systems. The BIG argument adopts a whole-system approach to constructing a safety case for AI systems of varying capability, autonomy and criticality. Firstly, it is balanced by addressing safety alongside other critical ethical issues such as privacy and equity, ac

Mitsuharu Yashima, Nina Seto, Yujiro Oshita, Masataka Kakoi

The intrinsic electronic properties of La$_3$Ni$_2$O$_{7}$ have been selectively investigated by nuclear quadrupole resonance (NQR) at the La(2) site outside the NiO$_2$ bilayers. The La(2)$_{\rm a}$ site of the ideal La$_3$Ni$_2$O$_{7}$ is clearly distinguished from the La(2)$_{\rm b}$ site close to the local defects. Below 150K, almost half of the intrinsi

Francis X. Diebold, Aaron Mora, Minchul Shin

We study the properties of macroeconomic survey forecast response averages as the number of survey respondents grows. Such averages are ``portfolios" of forecasts. We characterize the speed and pattern of the gains from diversification as a function of portfolio size (the number of survey respondents) in both (1) the key real-world data-based environment of

Samuel L. Rudge, Christoph Kaspar, Rudolf Smorka, Riley J. Preston

The connection between molecular vibrations and spin polarization in charge transport through molecular junctions is currently a topic of high interest, with important consequences for a variety of phenomena, such as chirality-induced spin selectivity (CISS). In this work, we follow this theme by exploring the relationship between vibronic dynamics and the c

Ziyu Liu

These notes present an alternative approach to the asymptotic stability of stochastic partial differential equations driven by multiplicative noise, applicable to a wide range of dissipative systems. The method builds on general criteria established in \cite{GLLL2024b,L2023}, utilizing the eventual continuity and generalized coupling techniques.

Xiangbin Wei, Yuanfeng Wang, Ao XU, Lingyu Zhu

Building on recent advances in Bayesian statistics and image denoising, we propose Noise2Score3D, a fully unsupervised framework for point cloud denoising. Noise2Score3D learns the score function of the underlying point cloud distribution directly from noisy data, eliminating the need for clean data during training. Using Tweedie's formula, our method perfor

Akira Hasegawa, Ryuta Kambe, Toshiaki Aoki, Yuuki Takano

In operating system development, concurrency poses significant challenges. It is difficult for humans to manually review concurrent behaviors or to write test cases covering all possible executions, often resulting in critical bugs. Preemption in schedulers serves as a typical example. This paper proposes a development method for concurrent software, such as

Yu Bu, Yulin Zhu, Kai Zhou

Accurate graph annotation typically requires substantial labeled data, which is often challenging and resource-intensive to obtain. In this paper, we present Crowdsourced Homophily Ties Based Graph Annotation via Large Language Model (CSA-LLM), a novel approach that combines the strengths of crowdsourced annotations with the capabilities of large language mo

Hisakazu Minakata

An excess observed in the accelerator neutrino experiments in the $\nu_{\mu} \rightarrow \nu_{e}$ channel at high confidence level (CL) has been interpreted as due to eV-scale sterile neutrino(s). But, it has been suffered from the problem of ``appearance-disappearance tension'' at the similarly high CL because the measurements of the $\nu_{\mu} \rightarrow

Luozheng Qin, Zhiyu Tan, Mengping Yang, Xiaomeng Yang

Video Detailed Captioning (VDC) is a crucial task for vision-language bridging, enabling fine-grained descriptions of complex video content. In this paper, we first comprehensively benchmark current state-of-the-art approaches and systematically identified two critical limitations: biased capability towards specific captioning aspect and misalignment with hu

Xin-Jia Zhou, Feng Yang, Xiao-Dong Yang, Lin Ma

Theoretical challenges in understanding the nature of glass and the glass transition remain significant open questions in statistical and condensed matter physics. As a prototypical example of complex physical systems, glasses and the vitrification process have been central research topics, consistently attracting broad scientific interest. This focus has dr

Haoxuan Wang, Jinlong Peng, Qingdong He, Hao Yang

With the rapid development of diffusion models in image generation, the demand for more powerful and flexible controllable frameworks is increasing. Although existing methods can guide generation beyond text prompts, the challenge of effectively combining multiple conditional inputs while maintaining consistency with all of them remains unsolved. To address

Linzhao Jia, Changyong Qi, Yuang Wei, Han Sun

Effective lesson planning is crucial in education process, serving as the cornerstone for high-quality teaching and the cultivation of a conducive learning atmosphere. This study investigates how large language models (LLMs) can enhance teacher preparation by incorporating them with Gagne's Nine Events of Instruction, especially in the field of mathematics e

R. Ferrini, S. V. Koniakhin

The present paper is devoted to comprehensive theoretical studies of exction-polariton quantum fluids specificities in the optics of their utilization for quantum turbulence research. We show that a non-trivial implementation of time-varying potential for excitation of quantum fluid (injection of quantized vortices) via the stirring procedure can be efficien

Sylvain Lannebère, Nader Engheta, Mário G. Silveirinha

In recent years, there has been growing interest in non-Hermitian phenomena in low-symmetry conductors, particularly optical gain driven by electro-optic effects. Conventional semiclassical treatments typically attribute these effects to nonlinear interactions associated with the anomalous velocity of Bloch electrons. Here, we present a phenomenological micr

Francesco Marzioni, Riccardo Natali, Nicola Malossi, David Vitali

Optomechanics with semi-transparent membrane multi-oscillators in a high-finesse cavity is an established solution for designing the dispersive interaction, and reaching many achievements, such as the study of non-linear dynamics, heat transfer, and so on. The multi-oscillators are dielectric slabs, usually with low reflectivity, constituting an etalon. Here

Eva Hackmann, Moritz Huckfeldt, Claus Lämmerzahl, Dennis Philipp

Mass redistribution on Earth due to dynamic processes such as ice melting and sea level rise leads to a changing gravitational field, observable by geodetic techniques. Monitoring this change over time allows us to learn more about our planet and its dynamic evolution. In this paper, we highlight the impact of General Relativity (GR) on geodesy: it provides

Chiara Cappellino, Gianluca Mancusi, Matteo Mosconi, Angelo Porrello

Open-Vocabulary object detectors can generalize to an unrestricted set of categories through simple textual prompting. However, adapting these models to rare classes or reinforcing their abilities on multiple specialized domains remains essential. While recent methods rely on monolithic adaptation strategies with a single set of weights, we embrace modular d

Martin Tappler, Ignacio D. Lopez-Miguel, Sebastian Tschiatschek, Ezio Bartocci

We consider the challenging problem of using domain knowledge to improve deep reinforcement learning policies. To this end, we propose LEGIBLE, a novel approach, following a multi-step process, which starts by mining rules from a deep RL policy, constituting a partially symbolic representation. These rules describe which decisions the RL policy makes and whi

Leandro C. Souza, Renato Portugal

This paper proposes a single-qudit quantum neural network for multiclass classification, by using the enhanced representational capacity of high-dimensional qudit states. Our design employs an $d$-dimensional unitary operator, where $d$ corresponds to the number of classes, constructed using the Cayley transform of a skew-symmetric matrix, to efficiently enc

Abhishek Baruah, Bidyut Hazarika, Prabwal Phukon

This paper seeks to explore the holographic optical appearance of an AdS black hole with higher derivative corrections in the presence of a string cloud, drawing on the AdS/CFT correspondence and wave optics. We introduce a Gaussian wave source that oscillates at the AdS boundary and propagates through the bulk. The resulting response function is then analyz

E. Pérez-Montero, J. A. Fernández-Ontiveros, B. Pérez-Díaz, J. M. Vílchez

After exploring the infrared softness diagram to characterize the hardness of the incident ionizing radiation in star-forming regions, we exploit the availability of high-excitation lines in the same spectral regime to explore its use for studying the narrow-line regions in AGN. We adapted the IR softness diagram to consider very high-excitation lines, such

Sidhartha Patnaik, Kumarasamy Sakthivel

In this study, we investigate the optimal control of the Landau-Lifshitz-Bloch equation within confined domains in $\mathbb R^n$ for $n= 2, 3.$ We establish the existence of strong solutions for dimensions $n=1, 2, 3$ under suitable growth conditions on the control, and analyze the existence and uniqueness of regular solutions. We formulate the control probl

Sijme-Jan Paardekooper, Hossam Aly

Dust grains embedded in gas flow give rise to a class of hydrodynamic instabilities, called resonant drag instabilities. These instabilities have predominantly been studied for single grain sizes, in which case they are found to grow fast. Nonlinear simulations indicate that strong dust overdensities can form, aiding the formation of planetesimals. In realit

Olivier Saidi

NP hard optimization problems like the Traveling Salesman Problem (TSP) defy efficient solutions in the worst case, yet real-world instances often exhibit exploitable patterns. We propose a novel patternaware complexity framework that quantifies and leverages structural regularities e.g., clustering, symmetry to reduce effective computational complexity acro

Julian Feuerpfeil

Let $p$ be a prime. An oriented pro-$p$ group $(G,\theta)$ is said to have the Bogomolov--Positselski property if it is Kummerian and if $I_\theta(G)$ is a free pro-$p$ group. In this paper, we provide a new criterion for an oriented pro-$p$ group to satisfy the Bogomolov--Positselski property. This criterion builds on earlier work of Positselski (arXiv:1405

Di Zhao, Longhui Ma, Siwei Wang, Miao Wang

With the rapid advancements in Large Language Models (LLMs), an increasing number of studies have leveraged LLMs as the cognitive core of agents to address complex task decision-making challenges. Specially, recent research has demonstrated the potential of LLM-based agents on automating Windows GUI operations. However, existing methodologies exhibit two cri

Luigi Appolloni, Riccardo Molle

We consider the problem $-\Delta u+\lambda u=u^{p-1}$, where $u\in H^1_0(\Omega)$ verifies $\|u\|_{L^2}=m>0$, and $\lambda\in [0,+\infty)$. Here, $\mathbb{R}^N\setminus\Omega$ is nonempty and compact. We prove the existence of a solution with a constrained Morse index lower than or equal to $N+1$, both in the case $m$ fixed and $\mathbb{R}^N\setminus\Omega$

Calum A. Brown, Katarzyna Macieszczak, Robert L. Jack

Quantum trajectories of a Markovian open quantum system arise from the back-action of measurements performed in the environment with which the system interacts. In this work, we consider counting measurements of quantum jumps, corresponding to different representations of the same quantum master equation. We derive necessary and sufficient conditions under w

Wei He, Shangzhi Zhang, Chun-Guang Li, Xianbiao Qi

Spectral clustering, as a popular tool for data clustering, requires an eigen-decomposition step on a given affinity to obtain the spectral embedding. Nevertheless, such a step suffers from the lack of generalizability and scalability. Moreover, the obtained spectral embeddings can hardly provide a good approximation to the ground-truth partition and thus a

Urmimala Dey, Jordan A. R. Cowell, Nicholas C. Bristowe

Halide perovskites have emerged as promising candidates for next generation photovoltaic applications, attracting significant attention in recent years. Through first-principles calculations combined with group-theoretical analyses, we investigate the structural phase diagram of Pb-free Jahn-Teller-active $A_2$Au$_2X_6$ ($A$: Cs, Rb; $X$: Cl, Br, I) double p

Guilherme Feitosa de Almeida

In this paper, we derive Open WDVV equations starting from any Hurwitz Dubrovin Frobenius manifold. The WDVV equations play a crucial role in the structure of Frobenius manifolds, quantum cohomology, and integrable systems. Extending these ideas, Open WDVV equations provide a framework to incorporate boundary conditions, making them fundamental in Open Gromo

Faezeh Sarlakifar, Mohammadreza Mohammadzadeh Asl, Sajjad Rezvani Khaledi, Armin Salimi-Badr

Traditional Long Short-Term Memory (LSTM) networks are effective for handling sequential data but have limitations such as gradient vanishing and difficulty in capturing long-term dependencies, which can impact their performance in dynamic and risky environments like stock trading. To address these limitations, this study explores the usage of the newly intr

Haixing Gong, Hui Zou, Xingzhou Liang, Shiyuan Meng

In the rapidly evolving field of artificial intelligence (AI), mapping innovation patterns and understanding effective technology transfer from research to applications are essential for economic growth. However, existing data infrastructures suffer from fragmentation, incomplete coverage, and insufficient evaluative capacity. Here, we present DeepInnovation

Mikhail Rudenko, Artem Eliseev, Artem Mitrofanov, Stepan Kalmykov

The kinetic isotope effect (KIE) is essential in various chemical applications from reaction mechanism studies to tritium removal from water. Traditional KIE evaluation relies on experimental measurements or computational approaches like density functional theory (DFT), which are often costly and inaccurate. Here, we present a novel semi-empirical method for

Aleš Paták, Martin Zouhar, Ivo Konvalina, Eliška Materna Mikmeková

We present ab initio theory for electron reflection spectroscopy of few-layer graphene for arbitrary angles of incidence. The inelastic effects are included in a consistent way using the optical potential retrieved from ab initio simulations of electron energy-loss spectra. We demonstrate a significant impact of inelastic effects even for single-layer graphe

Qiang Li, Jin Niu, Qin Luo, Lina Yu

In the context of global urbanization and motorization, traffic congestion has become a significant issue, severely affecting the quality of life, environment, and economy. This paper puts forward a single-agent reinforcement learning (RL)-based regional traffic signal control (TSC) model. Different from multi - agent systems, this model can coordinate traff

Michael Mackey

We review known results concerning powers and products of $(m,p)$-isometries with a view to providing elementary proofs based on properties of polynomials. We consider also the situation when $p=\infty$ where we find elements of graph theory and combinatorics arise naturally.

Yigang Chen, Xiang Ji, Ziyue Zhang, Yuming Zhou

Deep learning-based drug-target interaction (DTI) prediction methods have demonstrated strong performance; however, real-world applicability remains constrained by limited data diversity and modeling complexity. To address these challenges, we propose SCOPE-DTI, a unified framework combining a large-scale, balanced semi-inductive human DTI dataset with advan

Fengliu Li, Giusi Vaira, Juncheng Wei, Yuanze Wu

In this paper, we consider the Brezis-Nirenberg problem $$ -\Delta u=\lambda u+|u|^{\frac{4}{N-2}}u,\quad\mbox{in}\,\, \Omega,\quad u=0,\quad\mbox{on}\,\, \partial\Omega, $$ where $\lambda\in\mathbb{R}$, $\Omega\subset\mathbb R^N$ is a bounded domain with smooth boundary $\partial\Omega$ and $N\geq3$. We prove that every eigenvalue of the Laplacian operator

Juseon-Do, Jaesung Hwang, Jingun Kwon, Hidetaka Kamigaito

This study investigates retrieval-augmented summarization by specifically examining the impact of exemplar summary lengths under length constraints, not covered by previous work. We propose a Diverse Length-aware Maximal Marginal Relevance (DL-MMR) algorithm to better control summary lengths. This algorithm combines the query relevance with diverse target le

Lihua Zhou, Mao Ye, Shuaifeng Li, Nianxin Li

Test-time adaptation with pre-trained vision-language models, such as CLIP, aims to adapt the model to new, potentially out-of-distribution test data. Existing methods calculate the similarity between visual embedding and learnable class embeddings, which are initialized by text embeddings, for zero-shot image classification. In this work, we first analyze t

Shreecheta Chowdhury, Amit Chakraborty, Saunak Dutta

We propose an Unsupervised Learning Algorithm, Self-Organizing Maps (SOM), built on a neural network architecture, for the probe of a rare top decay, mediated by Flavor Changing Neutral Current (FCNC), to charm and the Higgs boson, with the Higgs boson further decaying to a pair of b-quarks or a pair of gauge bosons ($W^{\pm}/Z$) in a boosted regime. Ideally

Mauro Di Nasso, Lorenzo Luperi Baglini, Marcello Mamino, Rosario Mennuni

We introduce the notion of Ramsey partition regularity, a generalisation of partition regularity involving infinitary configurations. We provide characterisations of this notion in terms of certain ultrafilters related to tensor products and dubbed Ramsey's witnesses; and we also consider their nonstandard counterparts as pairs of hypernatural numbers, calle

Roman E. Gerasimov, Petr A. Krachkov, Roman N. Lee

We calculate the NNLO QED corrections to the $C$-even part of differential cross section of $e^+e^- \to \mu^+\mu^-$ process. We neglect power corrections in the electron mass and obtain the result in terms of Goncharov's polylogarithms.

Richard D. Paul, Johannes Seiffarth, David Rügamer, Hanno Scharr

Cell tracking is a key computational task in live-cell microscopy, but fully automated analysis of high-throughput imaging requires reliable and, thus, uncertainty-aware data analysis tools, as the amount of data recorded within a single experiment exceeds what humans are able to overlook. We here propose and benchmark various methods to reason about and qua

Ruihai Wu, Ziyu Zhu, Yuran Wang, Yue Chen

Cluttered garments manipulation poses significant challenges due to the complex, deformable nature of garments and intricate garment relations. Unlike single-garment manipulation, cluttered scenarios require managing complex garment entanglements and interactions, while maintaining garment cleanliness and manipulation stability. To address these demands, we

Yuhang Ma, Bo Cheng, Shanyuan Liu, Hongyi Zhou

Flow-based Transformer models have achieved state-of-the-art image generation performance, but often suffer from high inference latency and computational cost due to their large parameter sizes. To improve inference efficiency without compromising quality, we propose Bridged Progressive Rectified Flow Transformers (NAMI), which decompose the generation proce

Pei Yang, Hai Ci, Mike Zheng Shou

Computer agents powered by vision-language models (VLMs) have significantly advanced human-computer interaction, enabling users to perform complex tasks through natural language instructions. However, these agents are vulnerable to context deception attacks, an emerging threat where adversaries embed misleading content into the agent's operational environmen

Sven Jacobs, Henning Peters, Steffen Jaschke, Natalie Kiesler

Generative artificial intelligence (GenAI) offers new possibilities for generating personalized programming exercises, addressing the need for individual practice. However, the task quality along with the student perspective on such generated tasks remains largely unexplored. Therefore, this paper introduces and evaluates a new feature of the so-called Tutor

Timothy C. Miller

We present the first positive combinatorial rule for expanding the product of a permuted-basement Demazure atom and a Schur polynomial. Special cases of permuted-basement Demazure atoms include Demazure atoms and characters. These cases have known tableau formulas for their expansions when multiplied by a Schur polynomial, due to Haglund, Luoto, Mason and va

Di Zhao, Umar Salman, Zongjie Wang

This paper presents a comprehensive risk assessment model for power distribution networks with a focus on the influence of climate conditions and vegetation management on outage risks. Using a dataset comprising outage records, meteorological indicators, and vegetation metrics, this paper develops a logistic regression model that outperformed several alterna

Jonas Peter, Victor Luder, Leyla Rivero Davis, Lukas Schulthess

In recent years, zoological institutions have made significant strides to reimagine ex situ animal habitats, moving away from traditional single-species enclosures towards expansive multi-species environments, more closely resembling semi-natural ecosystems. This paradigm shift, driven by a commitment to animal welfare, encourages a broader range of natural