March 2025 arXiv papers — page 196

Shen Zhang, Siyuan Liang, Yaning Tan, Zhaowei Chen

Diffusion transformers (DiTs) struggle to generate images at resolutions higher than their training resolutions. The primary obstacle is that the explicit positional encodings(PE), such as RoPE, need extrapolating to unseen positions which degrades performance when the inference resolution differs from training. In this paper, We propose a Length-Extrapolata

Alan Dix, Tommaso Turchi, Ben Wilson, Anna Monreale

While XAI focuses on providing AI explanations to humans, can the reverse - humans explaining their judgments to AI - foster richer, synergistic human-AI systems? This paper explores various forms of human inputs to AI and examines how human explanations can guide machine learning models toward automated judgments and explanations that align more closely wit

Ivan Oleksiyuk, Svyatoslav Voloshynovskiy, Tobias Golling

We introduce a new model for conditional and continuous data morphing called TRansport Adversarial Network for Smooth InTerpolation (TRANSIT). We apply it to create a background data template for weakly-supervised searches at the LHC. The method smoothly transforms sideband events to match signal region mass distributions. We demonstrate the performance of T

Thomas Shoesmith, James C. Knight, Balázs Mészáros, Jonathan Timcheck

Neuromorphic computing can reduce the energy requirements of neural networks and holds the promise to `repatriate' AI workloads back from the cloud to the edge. However, training neural networks on neuromorphic hardware has remained elusive. Here, we instead present a pipeline for training spiking neural networks on GPUs, using the efficient event-driven Eve

Halima Ibrahim Kure, Jishna Retnakumari, Lucian Nita, Saeed Sharif

Energy consumption in robotic arms is a significant concern in industrial automation due to rising operational costs and environmental impact. This study investigates the use of a local reduction method to optimize energy efficiency in robotic systems without compromising performance. The approach refines movement parameters, minimizing energy use while main

Josué-Antonio Nescolarde-Selva, José-Luis Usó-Doménech, Kristian Alonso-Stenberg

The ULEX model, in its present state, involves the study of the biomass and the population of the shrub Ulex parviflorus Pourret, but while being a dynamic model, it is static in the sense that it does not imply the appearance of new specimens of this plant. As a complement to the ULEX model in its two dynamic and spatial aspects, and with the idea of extend

Yingli Zhou, Yaodong Su, Youran Sun, Shu Wang

Graph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs), improving their factual accuracy, adaptability, interpretability, and trustworthiness. A number of graph-based RAG methods have been proposed in the literature. However, these methods have not been systematically and comp

Seyed Mohammad Hosseindokht

In this paper, a compressor system is analyzed in order to show its characteristics and design a control scheme to improve its efficiency. A mathematical technique has been created to forecast the onset of surge and instability in a compressor chart, drawing from the nonlinear Greitzer and Moore model. This approach employs the phase plane and Jacobian matri

Wendi Zhao, Shumin Guo, Tiancheng Ma, Zhengtao Liu

The discovery of near-room-temperature superconductivity in compressed hydrides has sparked intensive research efforts to identify superconducting hydrides stable at low or even ambient pressures. Herein, we demonstrate a new mechanism for achieving robust superconductivity in complex metal hydrides under moderate pressure, using Li3IrH9 as a paradigmatic ex

Minjeong Kim, Taeho Kim, Anna Galler, Dasol Kim

Two-dimensional hexagonal materials such as transition metal dichalcogenides exhibit valley degrees of freedom, offering fascinating potential for valley-based quantum computing and optoelectronics. In nonlinear optics, the K and K' valleys provide excitation resonances that can be used for ultrafast control of excitons, Bloch oscillations, and Floquet physi

Xunyang Hong, Yujie Yan, L. Martinelli, I. Biało

Emergent symmetry breakings in condensed matter systems are often intimately linked to collective excitations. For example, the intertwined spin-charge stripe order in cuprate superconductors is associated with spin and charge excitations. While the collective behavior of spin excitations is well established, the nature of charge excitations remains to be un

Inseo Lee, Youngyoon Choi, Joonseok Lee

Implicit Neural Representation for Videos (NeRV) has introduced a novel paradigm for video representation and compression, outperforming traditional codecs. As model size grows, however, slow encoding and decoding speed and high memory consumption hinder its application in practice. To address these limitations, we propose a new video representation and comp

Ziqing Yang, Yixin Wu, Yun Shen, Wei Dai

The tremendous commercial potential of large language models (LLMs) has heightened concerns about their unauthorized use. Third parties can customize LLMs through fine-tuning and offer only black-box API access, effectively concealing unauthorized usage and complicating external auditing processes. This practice not only exacerbates unfair competition, but a

Masayoshi Sato

We analyze the correlation function in JT gravity using three approaches: by summing over all geodesics connecting boundary operators, integrating over the region of moduli space determined by the ``no-shortcut condition'' introduced by D.Stanford and Z.Yang, and using the formula for the universal spectral density correlation in the $\tau$-scaling limit. We

Román Salmerón Gómez, Catalina García García

This paper shows that the degree of approximate multicollinearity in a linear regression model increases simply by including independent variables, even if these are not highly linearly related. In the current situation where it is relatively easy to find linear models with a large number of independent variables, it is shown that this issue can lead to the

Giulio Binosi

We study some harmonic properties of slice regular functions in one and several Clifford variables and give explicit formulas of the iterated Laplacian applied to slice regular functions and to their spherical derivative, which are new also in the one variable context. We propose several Almansi-type decompositions for slice functions in several Clifford var

Tadej Škvorc, Marko Robnik-Šikonja

Many less-resourced languages struggle with a lack of large, task-specific datasets that are required for solving relevant tasks with modern transformer-based large language models (LLMs). On the other hand, many linguistic resources, such as dictionaries, are rarely used in this context despite their large information contents. We show how LLMs can be used

Kristýna Bukvišová, Radek Kalousek, Marek Patočka, Jakub Zlámal

Casimir effect, explained by Hendrik Casimir in 1948, is a macroscopic manifestation of quantum electrodynamics. Symmetry breaking due to space confinement of vacuum fluctuations in between two planar mirrors results in an attractive force acting between the two mirrors. Here, we show that spontaneous self-assembly of two-dimensional (2D) layered materials g

Oskar Eklund, Annika Lang, Moritz Schauer

The smoothing distribution is the conditional distribution of the diffusion process in the space of trajectories given noisy observations made continuously in time. It is generally difficult to sample from this distribution. We use the theory of enlargement of filtrations to show that the conditional process has an additional drift term derived from the back

Cecilia Diana-Albelda, Roberto Alcover-Couso, Álvaro García-Martín, Jesus Bescos

Gliomas are aggressive brain tumors that require accurate imaging-based diagnosis, with segmentation playing a critical role in evaluating morphology and treatment decisions. Manual delineation of gliomas is time-consuming and prone to variability, motivating the use of deep learning to improve consistency and alleviate clinical workload. However, existing m

Robin Haunschild, Lutz Bornmann

In this study, we investigated a phenomenon that one intuitively would assume does not exist: self-citations on the paper basis. Actually, papers citing themselves do exist in the Web of Science (WoS) database. In total, we obtained 44,857 papers that have self-citation relations in the WoS raw dataset. In part, they are database artefacts but in part they a

Eduardo Abi Jaber, Alessandro Bondi, Nathan De Carvalho, Eyal Neuman

We formulate and solve an optimal trading problem with alpha signals, where transactions induce a nonlinear transient price impact described by a general propagator model, including power-law decay. Using a variational approach, we demonstrate that the optimal trading strategy satisfies a nonlinear stochastic Fredholm equation with both forward and backward

Lars Bredereke, Yale Hartmann, Tanja Schultz

Object tracking is a key challenge of computer vision with various applications that all require different architectures. Most tracking systems have limitations such as constraining all movement to a 2D plane and they often track only one object. In this paper, we present a new modular pipeline that calculates 3D trajectories of multiple objects. It is adapt

Kalyanbrata Pal, Souvik Ghose, Shilpa Sarkar, Tapas K. Das

We investigate the axially symmetric accretion of low angular momentum hydrodynamic matter onto a rotating black hole. The gravitational field under consideration is assumed to be described by a pseudo-Newtonian Kerr potential. The accreting matter consists of different species defined by a relativistic equation of state with a variable adiabatic index.We co

Yann Bourreau, Sebastian Brandt, Alexandre Nolin

Brooks' theorem states that all connected graphs but odd cycles and cliques can be colored with $\Delta$ colors, where $\Delta$ is the maximum degree of the graph. Such colorings have been shown to admit non-trivial distributed algorithms [Panconesi and Srinivasan, Combinatorica 1995] and have been studied intensively in the distributed literature. In partic

Andrew Nugent, Susana N. Gomes, Marie-Therese Wolfram

We extend a classical model of continuous opinion formation to explicitly include an age-structured population. We begin by considering a stochastic differential equation model which incorporates ageing dynamics and birth/death processes, in a bounded confidence type opinion formation model. We then derive and analyse the corresponding mean field partial dif

Tim Maurer, Abdulrahman Mohamed Selim, Hasan Md Tusfiqur Alam, Matthias Eiletz

This paper presents InFL-UX, an interactive, proof-of-concept browser-based Federated Learning (FL) toolkit designed to integrate user contributions seamlessly into the machine learning (ML) workflow. InFL-UX enables users across multiple devices to upload datasets, define classes, and collaboratively train classification models directly in the browser using

Ryuya Hora

This paper introduces the notion of complete connectedness of a Grothendieck topos, defined as the existence of a left adjoint to a left adjoint to a left adjoint to the global sections functor, and provides many examples. Typical examples include presheaf topoi over a category with an initial object, such as the topos of sets, the Sierpi\'nski topos, the to

Robert Jankowski, Roya Aliakbarisani, M. Ángeles Serrano, Marián Boguñá

Bipartite networks appear in many real-world contexts, linking entities across two distinct sets. They are often analyzed via one-mode projections, but such projections can introduce artificial correlations and inflated clustering, obscuring the true underlying structure. In this paper, we propose a geometric model for bipartite networks that leverages the h

Shuang Liu, Yihan Wang, Yifan Zhu, Yibo Miao

Wasserstein distributionally robust optimization (WDRO) optimizes against worst-case distributional shifts within a specified uncertainty set, leading to enhanced generalization on unseen adversarial examples, compared to standard adversarial training which focuses on pointwise adversarial perturbations. However, WDRO still suffers fundamentally from the rob

Yecong Wan, Mingwen Shao, Yuanshuo Cheng, Wangmeng Zuo

In this paper, we aim ambitiously for a realistic yet challenging problem, namely, how to reconstruct high-quality 3D scenes from sparse low-resolution views that simultaneously suffer from deficient perspectives and clarity. Whereas existing methods only deal with either sparse views or low-resolution observations, they fail to handle such hybrid and compli

Mikhail G. Katz

Infinitesimals have seen ups and downs in their tumultuous history. In the 18th century, d'Alembert set the tone by describing infinitesimals as chimeras. Some adversaries of infinitesimals, including Moigno and Connes, picked up on the term. We highlight the work of Cauchy, No\"el, Poisson and Riemann. We also chronicle reactions by Moigno, Lamarle and Cant

Jesse Laeuchli, Rolando Trujillo Rasua

In this article we uncover flaws and pitfalls of a quantum-based remote memory attestation procedure for Internet-of-Things devices. We also show limitations of quantum memory that suggests the attestation problem for quantum memory is fundamentally different to the attestation problem for classical memory, even when the devices can perform quantum computati

José Carlos Bellido, Guillermo García-Sáez

Bessel potential spaces, introduced in the 1960s, are derived through complex interpolation between Lebesgue and Sobolev spaces, making them intermediate spaces of fractional differentiability order. Bessel potential spaces have recently gained attention due to their identification with the Riesz fractional gradient. This paper explores Bessel potential spac

Minh Tuan Luu, Christopher Linderälv, Zsolt Benedek, Ádám Ganyecz

The negatively charged nitrogen-vacancy center in diamond is a prototype photoluminescent point defect spin qubit with promising quantum technology applications, enabled by its efficient optical spin polarization and readout. Its low-lying electronic states and optical spin polarization cycle have been well characterized over decades, establishing it as a be

Lukáš Gajdošech, Hassan Ali, Jan-Gerrit Habekost, Martin Madaras

Datasets for object detection often do not account for enough variety of glasses, due to their transparent and reflective properties. Specifically, open-vocabulary object detectors, widely used in embodied robotic agents, fail to distinguish subclasses of glasses. This scientific gap poses an issue for robotic applications that suffer from accumulating error

Miguel Lloret-Climent, Josué-Antonio Nescolarde-Selva, Kristian Alonso-Stenberg, Andrés Montoyo

The framework of the present study was the destination life cycle model, a classical model that describes the development of tourist destinations. We examined mass tourism in Benidorm based on tourist accommodation supply and demand statistics over the January 2016 - October 2018 period, provided by Spain's National Institute for Statistics. The objective wa

Shuai-Qing Jiang, Dong Xu, Agnes P. C. van Hoof, Wei-Hua Lei

We present multiband observations and analysis of EP240801a, a low-energy, extremely soft gamma-ray burst (GRB) discovered on August 1, 2024 by the Einstein Probe (EP) satellite, with a weak contemporaneous signal also detected by Fermi/GBM. Optical spectroscopy of the afterglow, obtained by GTC and Keck, identified the redshift of $z = 1.6734$. EP240801a ex

Dilek Küçük, Fazli Can

Recent developments in computer science and artificial intelligence have also contributed to the legal domain, as revealed by the number and range of related publications and applications. Machine and deep learning models require considerable amount of domain-specific data for training and comparison purposes, in order to attain high-performance in the legal

Shiheng Zhao, Pierre A. Haas

Euler buckling epitomises mechanical instabilities: An inextensible straight elastic line buckles under compression when the compressive force reaches a critical value $F_\ast>0$. Here, we extend this classical, planar instability to the buckling under compression of an inextensible relaxed elastic line on a curved surface. By weakly nonlinear analysis of an

Christian Rondanini, Barbara Carminati, Elena Ferrari, Antonio Gaudiano

The rapid evolution of malware attacks calls for the development of innovative detection methods, especially in resource-constrained edge computing. Traditional detection techniques struggle to keep up with modern malware's sophistication and adaptability, prompting a shift towards advanced methodologies like those leveraging Large Language Models (LLMs) for

Ziyang Zhang, Yang Zhao, Ming-Ching Chang, Changyao Lin

Deep neural network (DNN) models are increasingly popular in edge video analytic applications. However, the compute-intensive nature of DNN models pose challenges for energy-efficient inference on resource-constrained edge devices. Most existing solutions focus on optimizing DNN inference latency and accuracy, often overlooking energy efficiency. They also f

Julian Aron Prenner, Romain Robbes

Traditional spectrum-based fault localization (SBFL) exploits differences in a program's coverage spectrum when run on passing and failing test cases. However, such runs can provide a wealth of additional information beyond mere coverage. Working with thousands of execution traces of short programs submitted to competitive programming contests and leveraging

Rolando Gonzales Martinez, Mariza Cooray

This study leverages spatial machine learning (SML) to enhance the accuracy of Proxy Means Testing (PMT) for poverty targeting in Indonesia. Conventional PMT methodologies are prone to exclusion and inclusion errors due to their inability to account for spatial dependencies and regional heterogeneity. By integrating spatial contiguity matrices, SML models mi

Malcolm Murray, Henry Papadatos, Otter Quarks, Pierre-François Gimenez

The literature and multiple experts point to many potential risks from large language models (LLMs), but there are still very few direct measurements of the actual harms posed. AI risk assessment has so far focused on measuring the models' capabilities, but the capabilities of models are only indicators of risk, not measures of risk. Better modeling and quan

Etienne Granet, Henrik Dreyer

We introduce AppQSim, a benchmarking suite for quantum computers focused on applications of Hamiltonian simulation. We consider five different settings for which we define a precise task and score: condensed matter and material simulation (dynamic and static properties), nuclear magnetic resonance simulation, chemistry ground state preparation, and classical

Filippo Cantucci, Marco Marini, Rino Falcone

Human trust in social robots is a complex attitude based on cognitive and emotional evaluations, as well as a behavior, like task delegation. While previous research explored the features of robots that influence overall trust attitude, it remains unclear whether these features affect behavioral trust. Additionally, there is limited investigation into which

Connie Miao, Sébastien Léger, Ziqian Li, Gideon Lee

The implementation of a quantum router capable of performing both quantum signal routing and quantum addressing (a Q2-router) represents a key step toward building quantum networks and quantum random access memories. We realize a Q2-router that uses fixed-frequency transmon qubits to implement a routing protocol based on two native controlled-iSWAP gates. Th

Ioannis Emmanouil, Wei Ren

In this paper, we introduce the cofibrant derived category of a group algebra $kG$ and study its relation to the derived category of $kG$. We also define the cofibrant singularity category of $kG$, whose triviality characterizes the regularity of $kG$ with respect to the cofibrant dimension, and examine its significance as a measure of the obstruction to the

Raha Asadi, Bodil Biering, Vincent van Dijk, Oksana Kulyk

Software developing small and medium enterprises (SMEs) play a crucial role as suppliers to larger corporations and public administration. It is therefore necessary for them to be able to demonstrate that their products meet certain security criteria, both to gain trust of their customers and to comply to standards that demand such a demonstration. In this s

Shreya Singh, Gaurav Varshney, Tarun Kumar Singh, Vidhi Mishra

Browser extensions are additional tools developed by third parties that integrate with web browsers to extend their functionality beyond standard capabilities. However, the browser extension platform is increasingly being exploited by hackers to launch sophisticated cyber threats. These threats encompass a wide range of malicious activities, including but no

Ioannis Emmanouil, Wei Ren

In this paper, we examine the relation between certain subclasses of the classes of Gorenstein projective, Gorenstein flat and Gorenstein injective modules over a group algebra, which consist of the cofibrant, cofibrant-flat and fibrant modules respectively. These subclasses have all structural properties that the Gorenstein classes are known to have, regard

Tianyang Zhang, Zhuoxuan Jiang, Haotian Zhang, Lin Lin

We propose a novel system, MathMistake Checker, designed to automate step-by-step mistake finding in mathematical problems with lengthy answers through a two-stage process. The system aims to simplify grading, increase efficiency, and enhance learning experiences from a pedagogical perspective. It integrates advanced technologies, including computer vision a

Jeanette Falk, Yiyi Chen, Janet Rafner, Mike Zhang

Hackathons have become popular collaborative events for accelerating the development of creative ideas and prototypes. There are several case studies showcasing creative outcomes across domains such as industry, education, and research. However, there are no large-scale studies on creativity in hackathons which can advance theory on how hackathon formats lea

Matthias Komm

This note presents an overview of current and potential future applications of machine-learning-based techniques in the study of the top quark. The research community has developed a diverse set of ideas and tools, including algorithms for the efficient reconstruction of recorded collision events and innovative methods for statistical inference. Recent appli

Andrea Gallo Rosso, Jan Conrad, Junu Jeong

Axions are well-motivated dark matter particles. Many experiments are looking for their experimental evidence. For haloscopes, the problem reduces to the identification of a peak above a noisy baseline. Its modeling, however, may problematic. State-of-the-art analysis rely on the Savitzky-Golay (SG) filtering, which is intrinsically affected by any possible

Chiara Coviello, Luis Lehner, Vania Vellucci

In this work, we investigate the tidal deformability of regular black holes (RBHs). Employing different phenomenological models, we analyze their response to both test fields and gravitational perturbations, interpreting the latter within the framework of Einstein's field equations in the presence of an appropriate exotic matter distribution. Numerical and a

Isaac Skog, Manon Kok, Gustaf Hendeby, Chuan Huang

Magnetic-field simultaneous localization and mapping (SLAM) using consumer-grade inertial and magnetometer sensors offers a scalable, cost-effective solution for indoor localization. However, the rapid error accumulation in the inertial navigation process limits the feasible exploratory phases of these systems. Advances in magnetometer array processing have

Marco Di Marco, Sebastiano Don, Davide Vittone

We introduce the space SBV$_X$ of special functions with bounded $X$-variation in Carnot-Carath\'eodory spaces and study its main properties. Our main outcome is an approximation result, with respect to the BV$_X$ topology, for SBV$_X$ functions.

Ioannis Emmanouil, Wei Ren

In this paper, we study the class of cofibrant modules over a group algebra $kG$, that were introduced by Benson. We prove that this class is always the left-hand side of a complete hereditary and projective cotorsion pair. We also examine the relation between cofibrant and Gorenstein projective modules and the behaviour of cofibrant modules with respect to

Fabio Michele Russo, Carlo Metta, Anna Monreale, Salvatore Rinzivillo

As predictive machine learning models become increasingly adopted and advanced, their role has evolved from merely predicting outcomes to actively shaping them. This evolution has underscored the importance of Trustworthy AI, highlighting the necessity to extend our focus beyond mere accuracy and toward a comprehensive understanding of these models' behavior

Ioannis Hadjifrangiskou, Sumesh P. Thampi, Rahil N. Valani

We show that a suspension of non-interacting deformable particles subjected to an oscillatory shear flow leads to development of nematic order that arises from the phenomenon of phase synchronization. The synchronized state corresponds to a unique, stable limit cycle confined in the toroidal state space. The limit cycle exists since, unlike rigid particles,

Yuxiang Gao, Xiaodong Cao, Zhicheng Zhong

Ionic conductivity is a critical property of solid ionic conductors, directly influencing the performance of energy storage devices such as batteries. However, accurately calculating ionic conductivity or diffusion coefficient remains challenging due to the complex, dynamic nature of ionic motion, which often yield significant deviations, especially at room

Niccolò Turcato, Matteo Iovino, Aris Synodinos, Alberto Dalla Libera

Recent advancements in Large Language Models (LLMs) and Visual Language Models (VLMs) have significantly impacted robotics, enabling high-level semantic motion planning applications. Reinforcement Learning (RL), a complementary paradigm, enables agents to autonomously optimize complex behaviors through interaction and reward signals. However, designing effec

Muhammad Amien Ibrahim, Faisal, Tora Sangputra Yopie Winarto, Zefanya Delvin Sulistiya

Detecting gender-based hate speech in Indonesian social media remains challenging due to limited labeled datasets. While binary hate speech classification has advanced, a more granular category like gender-targeted hate speech is understudied because of class imbalance issues. This paper addresses this gap by comparing three data augmentation techniques for

Giovanni Di Gennaro, Amedeo Buonanno, Gianmarco Romano, Stefano Buzzi

This study addresses the challenge of access point (AP) and user equipment (UE) association in cell-free massive MIMO networks. It introduces a deep learning algorithm leveraging Bidirectional Long Short-Term Memory cells and a hybrid probabilistic methodology for weight updating. This approach enhances scalability by adapting to variations in the number of

Yuhan Sun, Igor Uljarevic, Umut Varolgunes

We prove contact big fiber theorems, analogous to the symplectic big fiber theorem by Entov and Polterovich, using symplectic cohomology with support. Unlike in the symplectic case, the validity of the statements requires conditions on the closed contact manifold. One such condition is to admit a Liouville filling with non-zero symplectic cohomology. In the

Julius Reinhard, Felix Wiesner, Themistoklis Sidiropoulos, Martin Hennecke

High-harmonic generation (HHG) is used as a source for various imaging applications in the extreme ultraviolet spectral range. It offers spatially coherent radiation and unique elemental contrast with the potential for attosecond time resolution. The unfavorable efficiency scaling to higher photon energies prevented the imaging application in the soft X-ray

Shubhrangshu Ghosh, Souvik Ghose, Kalyanbrata Pal, Arunabha Bhadra

The velocity-dependent Newtonian analogous potentials (NAPs) corresponding to general relativistic (GR) spacetimes accurately capture most of the relativistic features, including all classical tests of GR, effectively representing spacetime geometries in Newtonian terms. The NAP formulated by Tejeda \& Rosswog (TR13) for Schwarzschild spacetime has been appl

Daniela Pöhn, Heiner Lüken

Security situational awareness refers to identifying, mitigating, and preventing digital cyber threats by gathering information to understand the current situation. With awareness, the basis for decisions is present, particularly in complex situations. However, while logging can track the successful login into a system, it typically cannot determine if the l

Joseph P Johnson, H. K. Jassal

In this work, we discuss model-independent reconstruction of the expansion history of the late Universe. We use Gaussian Process Regression (GPR) to reconstruct the evolution of various cosmological parameters such as Hubble parameter $H(z)$ and deceleration parameter $q(z)$ using observational data to train the GPR model. We look at the GP reconstruction of

Jixing Ye, Maurizio Boscardin, Matteo Centis Vignali, Francesco Ficorella

Future high-luminosity hadron collider experiments feature unprecedented levels of event pile-up and extreme radiation environments, calling for sensors capable of 4D tracking, even after significant radiation damage. To this purpose, 3D sensors represent a viable solution, since they provide excellent radiation tolerance and very good temporal resolution. I

Yana van de Sande, Gunes Açar, Thabo van Woudenberg, Martha Larson

We study LLM judgments of misinformation expressed with uncertainty. Our experiments study the response of three widely used LLMs (GPT-4o, LlaMA3, DeepSeek-v2) to misinformation propositions that have been verified false and then are transformed into uncertain statements according to an uncertainty typology. Our results show that after transformation, LLMs c

Kousuke Kumasaki, Toshihiro Yada, Ken Funo, Takahiro Sagawa

Feedback cooling plays a critical role in stabilizing quantum systems and achieving low temperatures, where a key question is to determine the fundamental thermodynamic limits on cooling performance. We establish a fundamental bound on quantum feedback cooling in Gaussian systems, by deriving a generalized second law of thermodynamics involving the kinetic t

Andrea Cosso, Laura Perelli

We study a specific class of finite-horizon mean field optimal stopping problems by means of the dynamic programming approach. In particular, we consider problems where the state process is not affected by the stopping time. Such problems arise, for instance, in the pricing of American options when the underlying asset follows a McKean-Vlasov dynamics. Due t

Boseong Jeon

In this report, I present an inpainting framework named \textit{ControlFill}, which involves training two distinct prompts: one for generating plausible objects within a designated mask (\textit{creation}) and another for filling the region by extending the background (\textit{removal}). During the inference stage, these learned embeddings guide a diffusion

Victor-Alexandru Pădurean, Paul Denny, Alkis Gotovos, Adish Singla

Computing students increasingly rely on generative AI tools for programming assistance, often without formal instruction or guidance. This highlights a need to teach students how to effectively interact with AI models, particularly through natural language prompts, to generate and critically evaluate code for solving computational tasks. To address this, we

Inkyu Kim, Sangkeum Lee, Haechan Jeong, Sarvar Hussain Nengroo

Satellite communication systems (SCSs) used for tactical purposes require robust security and anti-jamming capabilities, making frequency hopping (FH) a powerful option. However, the current FH systems face challenges due to significant interference from other devices and the considerable path loss inherent in satellite communication. This misalignment leads

J. Eduardo Ferreira Ribeiro, Mário Zenha-Rela, João Gabriel Silva

Agile software development is becoming increasingly popular in the aerospace industry because of its capability to accommodate requirement changes. However, safety-critical domains require compliance with strict regulations such as the DO-178C avionics standard, which demands thorough documentation. The main challenge of this constraint is not the content it

Ru Xu, Lu Meng, Hai-Xiang Zhu, Ning Li

The hadronic $\Sigma_c^{(*)}\bar{D}^{(*)}$ and $\Sigma_c^{(*)}{D}^{(*)}$ interactions are revisited, with a focus on their short-range parts, motivated by a tension between the interpretations of $P_{c\bar{c}}(4312)$, $P_{c\bar{c}}(4440)$, and $P_{c\bar{c}}(4457)$ in effective field theory (EFT) frameworks and the one-boson-exchange (OBE) model. While the th

Vasilios Mavroudis, Gregory Palmer, Sara Farmer, Kez Smithson Whitehead

Reinforcement Learning (RL) and Multi-Agent Reinforcement Learning (MARL) have emerged as promising methodologies for addressing challenges in automated cyber defence (ACD). These techniques offer adaptive decision-making capabilities in high-dimensional, adversarial environments. This report provides a structured set of guidelines for cybersecurity professi

K K Singh, A Singh, A Tolamatti, P J Meintjes

A high degree of linear polarization measured in the optical emission is an important observational feature of blazars. It provides strong evidence of the presence of relativistic particles and magnetic field ordering in the non-thermal emission regions of blazars owing to the synchrotron nature of low energy radiation. Thus, the polarization studies of blaz

Georgios Makridis, Vasileios Koukos, Georgios Fatouros, Dimosthenis Kyriazis

In today's data-driven era, computational systems generate vast amounts of data that drive the digital transformation of industries, where Artificial Intelligence (AI) plays a key role. Currently, the demand for eXplainable AI (XAI) has increased to enhance the interpretability, transparency, and trustworthiness of AI models. However, evaluating XAI methods

Mohsen Minaei, Pedro Moreno-Sanchez, Zhiyong Fang, Srinivasan Raghuraman

We propose Data Tumbling Layer (DTL), a cryptographic scheme for non-interactive data tumbling. The core concept is to enable users to commit to specific data and subsequently re-use to the encrypted version of these data across different applications while removing the link to the previous data commit action. We define the following security and privacy not

Daniela Pöhn, Nils Gruschka

The European General Data Protection Regulation (GDPR) grants European users the right to access their data processed and stored by organizations. Although the GDPR contains requirements for data processing organizations (e.g., understandable data provided within a month), it leaves much flexibility. In-depth research on how online services handle data subje

Jialong Xue, Wei Gao, Yu Wang, Chao Ji

High-precision tiny object alignment remains a common and critical challenge for humanoid robots in real-world. To address this problem, this paper proposes a vision-based framework for precisely estimating and controlling the relative position between a handheld tool and a target object for humanoid robots, e.g., a screwdriver tip and a screw head slot. By

Yingfei Sun, Xu Gu, Wei Ji, Hanbin Zhao

Many studies combine text and audio to capture multi-modal information but they overlook the model's generalization ability on new datasets. Introducing new datasets may affect the feature space of the original dataset, leading to catastrophic forgetting. Meanwhile, large model parameters can significantly impact training performance. To address these limita

Wonkwang Lee, Jongwon Jeong, Taehong Moon, Hyeon-Jong Kim

Motion synthesis for diverse object categories holds great potential for 3D content creation but remains underexplored due to two key challenges: (1) the lack of comprehensive motion datasets that include a wide range of high-quality motions and annotations, and (2) the absence of methods capable of handling heterogeneous skeletal templates from diverse obje

Y A Rouzoumka, E Terreaux, C Morisseau, J. -P Ovarlez

This paper presents a novel approach to radar target detection using Variational AutoEncoders (VAEs). Known for their ability to learn complex distributions and identify out-ofdistribution samples, the proposed VAE architecture effectively distinguishes radar targets from various noise types, including correlated Gaussian and compound Gaussian clutter, often

Yixiang Sun, Haotian Fu, Michael Littman, George Konidaris

We propose DRAGO, a novel approach for continual model-based reinforcement learning aimed at improving the incremental development of world models across a sequence of tasks that differ in their reward functions but not the state space or dynamics. DRAGO comprises two key components: Synthetic Experience Rehearsal, which leverages generative models to create

Hicham Tarif, Nadir Maaroufi

The main contribution of this paper is a constructive method for building separable multivariate vector-valued wavelet bases in the general framework of \( L^2(\mathbb{R}^d, \mathbb{R}^m) \) for any \( d, m \geq 1 \). While separable wavelet bases in \( L^2(\mathbb{R}^d, \mathbb{R}) \) are well-established and widely applied, the explicit construction of tru

Yang Hu, Vladyslav Turlo

Experiments reveal negative (non-Laplacian) surface stresses in metal oxide nanoparticles, partly associated with humidity during fabrication and annealing. Using a neural network interatomic potential for MgO, we prove that water adsorption induces surface hydroxylation, shifting facets from {100} to {110} to {111} and switching the average surface stress f

Junsoo Kim, Hunjong Lee, Geonwoo Ko, Gyubin Choi

The growing adoption of Large Language Models (LLMs) across various domains has driven the demand for efficient and scalable AI-serving solutions. Deploying LLMs requires optimizations to manage their significant computational and data demands. The prefill stage processes large numbers of input tokens in parallel, increasing computational load, while the dec

Biao Ouyang, Yingying Zhang, Hanyin Cheng, Yang Shu

With the continued migration of storage to cloud database systems,the impact of slow queries in such systems on services and user experience is increasing. Root-cause diagnosis plays an indispensable role in facilitating slow-query detection and revision. This paper proposes a method capable of both identifying possible root cause types for slow queries and

Yifei Huang, Jilan Xu, Baoqi Pei, Yuping He

We present Vinci, a vision-language system designed to provide real-time, comprehensive AI assistance on portable devices. At its core, Vinci leverages EgoVideo-VL, a novel model that integrates an egocentric vision foundation model with a large language model (LLM), enabling advanced functionalities such as scene understanding, temporal grounding, video sum

Junhao Shi, Qinyuan Cheng, Zhaoye Fei, Yining Zheng

Aligning powerful AI models on tasks that surpass human evaluation capabilities is the central problem of \textbf{superalignment}. To address this problem, weak-to-strong generalization aims to elicit the capabilities of strong models through weak supervisors and ensure that the behavior of strong models aligns with the intentions of weak supervisors without

Max van Haren, Lennart Blanken, Tom Oomen

Frequency-domain performance analysis of intersample behavior in sampled-data and multirate systems is challenging due to the lack of a frequency-separation principle, and systematic identification techniques are lacking. The aim of this \manuscript is to develop an efficient technique for identifying the full intersample performance in the frequency-domain

Frédéric Chapoton

Starting from the data of an arbor, which is a rooted tree with vertices decorated by disjoint sets, we introduce a lattice polytope and a partial order on its lattice points. We give recursive algorithms for various classical invariants of these polytopes and posets, using the tree structure. For linear arbors, we propose a conjecture exchanging the Ehrhart

Aoxiang Chen, David J. Nott, Linda S. L. Tan

Bayesian inference has many advantages for complex models, but standard Monte Carlo methods for summarizing the posterior can be computationally demanding, and it is attractive to consider optimization-based variational methods. Our work considers Gaussian approximations with sparse precision matrices which are tractable to optimize in high-dimensions. The o

Olivier Guichard

Gromov Hyperbolic groups have remarkable finiteness properties;for example those that are torsion-free are fundamental groups of finitecomplexes whose universal cover iscontractible (property~$F$). In this talk we will show thattheir subgroups can have exotic finiteness properties:there are hyperbolic groups containing finitely generated subgroups withinterm