March 2025 arXiv papers — page 111

Sören Schlichting, Clemens Werthmann

Exploiting the first measurements of the same ion species in O+O collisons at RHIC and LHC, we propose an experimentally accessible observable to distinguish whether collective behaviour builds up through a hydrodynamic expansion of a strongly interacting QGP or through few rescatterings in a non-equilibrated dilute medium. Our procedure allows to disentangl

Paul Gattinger, Andreas W. Schell, Sven Ramelow, Markus Brandstetter

Sensing with undetected photons has enabled new, unconventional approaches to Fourier transform infrared (FTIR) spectroscopy. Leveraging properties of non-degenerated entangled photon pairs, mid-IR information can be accessed in the near-IR spectral domain to perform mid-IR spectroscopy with silicon-based detection schemes. Here, we address practical aspects

Jake Clarkson, Konstantin Avrachenkov, Eitan Altman

Consider an M/M/1-type queue where joining attains a known reward, but a known waiting cost is paid per time unit spent queueing. In the 1960s, Naor showed that any arrival optimally joins the queue if its length is less than a known threshold. Yet acquiring knowledge of the queue length often brings an additional cost, e.g., website loading time or data roa

Olivier Vincent, Pierre Bergeron, Patrick Dufour

With the imminent data releases from next-generation spectroscopic surveys, hundreds of thousands of white dwarf spectra are expected to become available within the next few years, increasing the data volume by an order of magnitude. This surge in data has created a pressing need for automated tools to efficiently analyze and classify these spectra. Although

Jorge Buescu, Henrique M. Oliveira

This paper investigates the dynamical system governing the phase differences between three identical oscillators arranged symmetrically and coupled by burst interactions. By constructing a discrete Lyapunov function, we prove the existence of two asymptotically stable fixed points on the 2-torus T^2, which correspond to Huygens synchronisation of three clock

Yongkang Cheng, Shaoli Huang

Animating virtual characters with holistic co-speech gestures is a challenging but critical task. Previous systems have primarily focused on the weak correlation between audio and gestures, leading to physically unnatural outcomes that degrade the user experience. To address this problem, we introduce HoleGest, a novel neural network framework based on decou

Vincent Caudrelier, Nicholas J. Ossi, Barbara Prinari

The goal of this work is to obtain a complete characterization of soliton and breather interactions in the integrable discrete Manakov (IDM) system, a vector generalization of the Ablowitz-Ladik model. The IDM system, which in the continuous limit reduces to the Manakov system (i.e., a 2-component vector nonlinear Schrodinger equation), was shown to admit a

Yijie Liu, Xinyi Shang, Yiqun Zhang, Yang Lu

Federated Semi-Supervised Learning (FSSL) aims to leverage unlabeled data across clients with limited labeled data to train a global model with strong generalization ability. Most FSSL methods rely on consistency regularization with pseudo-labels, converting predictions from local or global models into hard pseudo-labels as supervisory signals. However, we d

Konstantinos Nikoletos, Vasilis Efthymiou, George Papadakis, Kostas Stefanidis

The same real-world entity (e.g., a movie, a restaurant, a person) may be described in various ways on different datasets. Entity Resolution (ER) aims to find such different descriptions of the same entity, this way improving data quality and, therefore, data value. However, an ER pipeline typically involves several steps (e.g., blocking, similarity estimati

Tong Zhou, Shijin Duan, Gaowen Liu, Charles Fleming

Pre-trained models are valuable intellectual property, capturing both domain-specific and domain-invariant features within their weight spaces. However, model extraction attacks threaten these assets by enabling unauthorized source-domain inference and facilitating cross-domain transfer via the exploitation of domain-invariant features. In this work, we intr

Allahkaram Shafiei, Hozefa Jesawada, Karl Friston, Giovanni Russo

Despite their groundbreaking performance, autonomous agents can misbehave when training and environmental conditions become inconsistent, with minor mismatches leading to undesirable behaviors or even catastrophic failures. Robustness towards these training-environment ambiguities is a core requirement for intelligent agents and its fulfillment is a long-sta

Chi Han, Xin Liu, Haodong Wang, Shiyang Li

Despite significant achievements in improving the instruction-following capabilities of large language models (LLMs), the ability to process multiple potentially entangled or conflicting instructions remains a considerable challenge. Real-world scenarios often require consistency across multiple instructions over time, such as secret privacy, personal prefer

H. Acharya, M. Aker, D. Batzler, A. Beglarian

Precision spectroscopy of the electron spectrum of the tritium $\beta$-decay near the kinematic endpoint is a direct method to determine the effective electron antineutrino mass. The KArlsruhe TRItium Neutrino (KATRIN) experiment aims to determine this quantity with a sensitivity of better than 0.3$\,$eV (90$\,$% C.L.). An inhomogeneous electric potential in

Jake Smith, Roberto Proietti

We model spontaneous Raman scattering noise in polarization-encoded quantum communication channels co-propagating with classical signals using the depolarization channel. Utilizing NetSquid simulations, we validate the model against demonstrations of qubit transmission, entanglement distribution, and teleportation.

Anna Dall'Acqua, Manuel Schlierf

We study the length-preserving elastic flow of curves in arbitrary codimension with free boundary on hypersurfaces. This constrained gradient flow is given by a nonlocal evolution equation with nonlinear higher-order boundary conditions. We prove global existence and subconvergence to critical points. The proof strategy involves a careful treatment of short-

Yaxin Gao, Sha Li, Menghao Wu

Transition metal molybdenum oxides A2Mo3O8 (A= Mn, Fe, Co) are known to be polar magnets where A ions are located in either octahedrally or tetrahedrally coordinated sites. In this paper we predict that their polarizations can be reversed via swapping of two coordinations for A ions, giving rise to robust vertical ferroelectricity. Such unique ferroelectrici

Shahmar Mirishli

This article examines the evolving landscape of artificial intelligence (AI) regulation in financial services, detailing the legal frameworks and compliance challenges posed by rapid technological adoption. By reviewing current legislation, industry guidelines, and real-world use cases, it highlights how AI-driven processes, from fraud detection to algorithm

Yue Su, Xinyu Zhan, Hongjie Fang, Han Xue

Mainstream visuomotor policies predominantly rely on generative models for holistic action prediction, while current autoregressive policies, predicting the next token or chunk, have shown suboptimal results. This motivates a search for more effective learning methods to unleash the potential of autoregressive policies for robotic manipulation. This paper in

Kaye Jiale Li, Jane SiNan Long, Kinwah Wu, Albert K. H. Kong

Determining the mass of the neutron stars (NSs) accurately improves our understanding of the NS interior and complicated binary evolution. However, the masses of the systems are degenerate with orbital inclination angle when using solely gravitational waves (GWs) or electromagnetic measurements, especially for face-on binaries. Taking advantages of both GWs

Umang Bhatt, Sanyam Kapoor, Mihir Upadhyay, Ilia Sucholutsky

Strategies for orchestrating the interactions between multiple agents, both human and artificial, can wildly overestimate performance and underestimate the cost of orchestration. We design a framework to orchestrate agents under realistic conditions, such as inference costs or availability constraints. We show theoretically that orchestration is only effecti

Alan A. Coley, Nicholas T. Layden, Diego F. Lopez

We first present an overview of the Schwarzschild vacuum spacetime within general relativity, with particular emphasis on the role of scalar polynomial invariants and the null frame approach (and the related Cartan invariants), that justifies the conventional interpretation of the Schwarzschild geometry as a black hole spacetime admitting a horizon (at $r=2M

Jie Huang, Haorui Chen, Jiaxuan Ren, Siran Peng

Currently, deep learning-based methods for remote sensing pansharpening have advanced rapidly. However, many existing methods struggle to fully leverage feature heterogeneity and redundancy, thereby limiting their effectiveness. We use the covariance matrix to model the feature heterogeneity and redundancy and propose Correlation-Aware Covariance Weighting (

Xumin Chang, Feng Liu, Fan Xu, Cheng Xu

Two-dimensional moire materials present unprecedented opportunities to explore quantum phases of matter arising from the interplay of band topology and strong correlations.One of the most striking examples is the recent observation of fractional quantum anomalous Hall (FQAH) effect in twisted bilayer MoTe$_2$ (tMoTe2) with relatively large twist angles(~3.7d

Marvin Seyfarth, Salman Ul Hassan Dar, Isabelle Ayx, Matthias Alexander Fink

Advancements in AI for medical imaging offer significant potential. However, their applications are constrained by the limited availability of data and the reluctance of medical centers to share it due to patient privacy concerns. Generative models present a promising solution by creating synthetic data as a substitute for real patient data. However, medical

S. B. Zhang, G. Hobbs, S. Johnston, S. Dai

Fast radio bursts (FRBs) are energetic, short-duration radio pulses of unclear origin. To explore effective survey strategies for detecting FRBs from nearby globular clusters (GCs), we investigate the burst energy distribution, which has a strong influence on the detection rate. We re-analyze FRBs and pulsars exhibiting broad energy distributions by fitting

Shahmar Mirishli

This article examines the evolving role of legal frameworks in shaping ethical artificial intelligence (AI) use in corporate governance. As AI systems become increasingly prevalent in business operations and decision-making, there is a growing need for robust governance structures to ensure their responsible development and deployment. Through analysis of re

Rin Ota, Nanako Nakajima, Ryuto Takemasa, Hiroya Tamaru

The use of a wire probe is a robust method for beam profile measurement, but it can only provide a 1D projection of the beam profile. In this study, we developed a novel method for measuring a beam projected from a 360{\deg} angle by a dual-axis rotation of a wire probe and obtaining a complete 2D profile via image reconstruction. We conducted a proof-of-pri

Sinan Fan, Liang Xie, Chen Shen, Ge Teng

Prompt-tuning (PT) for large language models (LLMs) can facilitate the performance on various conventional NLP tasks with significantly fewer trainable parameters. However, our investigation reveals that PT provides limited improvement and may even degrade the primitive performance of LLMs on complex reasoning tasks. Such a phenomenon suggests that soft prom

Francesco Anna Mele, Giovanni Barbarino, Vittorio Giovannetti, Marco Fanizza

Non-asymptotic quantum Shannon theory analyses how to transmit quantum information across a quantum channel as efficiently as possible within a specified error tolerance, given access to a finite, fixed, number of channel uses. In a recent work, we derived computable lower bounds on the non-asymptotic capacities of memoryless bosonic Gaussian channels. In th

Shahmar Mirishli

This article examines the ethical and legal implications of artificial intelligence (AI) driven data collection, focusing on developments from 2023 to 2024. It analyzes recent advancements in AI technologies and their impact on data collection practices across various sectors. The study compares regulatory approaches in the European Union, the United States,

Zhi-Da Zhang, Yao Song, Wen-Zheng Dong, Xiu-Hao Deng

We introduce a generalized filter-function framework that treats noise coupling strength as a tunable control parameter, enabling target noise suppression across user-defined frequency bands. By optimizing this generalized filter function, we design band-selective control pulses that achieve $0.9999$ fidelity of single- and two-qubit gates under strong noise

Zhen Chen, Zhihao Peng, Xusheng Liang, Cheng Wang

Inpatient pathways demand complex clinical decision-making based on comprehensive patient information, posing critical challenges for clinicians. Despite advancements in large language models (LLMs) in medical applications, limited research focused on artificial intelligence (AI) inpatient pathways systems, due to the lack of large-scale inpatient datasets.

Jiayi Zhong, Yuxin Deng

ZZ crosstalk and decoherence hinder superconducting quantum computing. To enhance parallelism in mitigating ZZ crosstalk, we formulate the problem by integrating quantum cycles and two forms of qubit interference. We then propose CYCO, a CYcle-aware ZZ Crosstalk Optimization algorithm, which uses a timing-based greedy strategy to schedule gates through cycle

Roan Schellingerhout, Francesco Barile, Nava Tintarev

The use of recommender systems in the recruitment domain has been labeled as 'high-risk' in recent legislation. As a result, strict requirements regarding explainability and fairness have been put in place to ensure proper treatment of all involved stakeholders. To allow for stakeholder-specific explainability, while also handling highly heterogeneous recrui

Corentin Sautier, Gilles Puy, Alexandre Boulch, Renaud Marlet

Panoptic segmentation of LiDAR point clouds is fundamental to outdoor scene understanding, with autonomous driving being a primary application. While state-of-the-art approaches typically rely on end-to-end deep learning architectures and extensive manual annotations of instances, the significant cost and time investment required for labeling large-scale poi

Zhibing Zhang

In this paper, we study Liouville type results for the three-dimensional stationary incompressible MHD equations and Hall-MHD equations. Using the energy method and an iteration argument, we establish Liouville type theorems if Lebesgue norms of the velocity and magnetic field on the annulus satisfy certain growth conditions. Furthermore, by establishing new

Fabio Natali

The purpose of this paper is to establish the existence and spectral stability, with respect to perturbations of the same period, of double-periodic standing waves for the nonlinear focusing Schr\"odinger equation posed on the bi-dimensional torus. We first show that such double-periodic solutions can be constructed via local and global bifurcation theory, u

Ananya Ganapthy, Praveen Shastry, Naveen Kumarasami, Anandakumar D

Background: This study introduces a Vision-Language Model (VLM) leveraging SIGLIP and Gemma-3b architectures for automated acute tuberculosis (TB) screening. By integrating chest X-ray images and clinical notes, the model aims to enhance diagnostic accuracy and efficiency, particularly in resource-limited settings. Methods: The VLM combines visual data from

Yiman Bao, Jie Gao, Jinke He, Frans A. Oliehoek

Efficient timing in ride-matching is crucial for improving the performance of ride-hailing and ride-pooling services, as it determines the number of drivers and passengers considered in each matching process. Traditional batched matching methods often use fixed time intervals to accumulate ride requests before assigning matches. While this approach increases

Sebastian Jung, Tim Janz, Vahid Aref, Stephan ten Brink

In coherent optical communication systems the laser phase noise is commonly modeled as a Wiener process. We propose a sliding-window based linearization of the phase noise, enabling a novel description. We show that, by stochastically modeling the residual error introduced by this approximation, equalization-enhanced phase noise (EEPN) can be described and d

Maria Berti, Emilio Bellini, Camille Bonvin, Martin Kunz

In light of the evidence for dynamical dark energy (DE) found from the most recent Dark Energy Spectroscopic Instrument (DESI) baryon acoustic oscillation (BAO) measurements, we perform a non-parametric, model-independent reconstruction of the DE density evolution. To do so, we develop and validate a new framework that reconstructs the DE density through a t

Lei Shi, Ying Luo, Wei Wu, Yunwei Zhang

The recent discovery of pressure-induced superconductivity in La$_3$Ni$_2$O$_7$ has established a novel platform for studying unconventional superconductors. However, achieving superconductivity in this system currently requires relatively high pressures. In this study, we propose a chemical pressure strategy via Sr substitution to stabilize high-Tc supercon

Daniel Ketels

We analyze the effects of a scale-dependent suppression function $\Omega(k, \Lambda)$ on the functional space geometry in renormalization theory. By introducing a dynamical cutoff scale $\Lambda$, the suppression function smoothly regulates high-momentum contributions without requiring a hard cutoff. We show that $\Omega(k, \Lambda)$ induces a modified metri

Haoqi Huang, Ping Wang, Jianhua Pei, Jiacheng Wang

The rapid expansion of data from diverse sources has made anomaly detection (AD) increasingly essential for identifying unexpected observations that may signal system failures, security breaches, or fraud. As datasets become more complex and high-dimensional, traditional detection methods struggle to effectively capture intricate patterns. Advances in deep l

Corina Catarau-Cotutiu, Esther Mondragon, Eduardo Alonso

The ability of artificial intelligence agents to make optimal decisions and generalise them to different domains and tasks is compromised in complex scenarios. One way to address this issue has focused on learning efficient representations of the world and on how the actions of agents affect them in state-action transitions. Whereas such representations are

Kristoffer Andersson, Adam Andersson, Cornelis W. Oosterlee

We introduce the deep multi-FBSDE method for robust approximation of coupled forward-backward stochastic differential equations (FBSDEs), focusing on cases where the deep BSDE method of Han, Jentzen, and E (2018) fails to converge. To overcome the convergence issues, we consider a family of FBSDEs that are equivalent to the original problem in the sense that

Tian-Ci Liu, Ben-Yang Zhu, Yun-Feng Liang, Xiao-Song Hu

The DArk Matter Particle Explorer (DAMPE) is a space-based instrument for detecting GeV-TeV cosmic rays and gamma rays. High-energy cosmic rays could be emitted from several dark matter candidates theoretically, such as the heavy dark matter (HDM) and the primordial black holes (PBHs). HDM particles with a mass of $>100\,{\rm TeV}$ could decay into $\gtrsim

Adrian Fischer, Gesine Reinert, Wenkai Xu

Providing theoretical guarantees for parameter estimation in exponential random graph models is a largely open problem. While maximum likelihood estimation has theoretical guarantees in principle, verifying the assumptions for these guarantees to hold can be very difficult. Moreover, in complex networks, numerical maximum likelihood estimation is computer-in

Dominique Bourn

Starting from the varietal notion of syntactic equivalence relation, we generalized it to a categorical concept; namely Equ-saturating category. We produce various examples and focuse our attention on the protomodular context in which any equivalence relation is then shown to have a centralizer.

Emre Esenturk, Atef Sahli, Valeriia Haberland, Aleksandra Ziuboniewicz

Cancer progression involves the sequential accumulation of genetic alterations that cumulatively shape the tumour phenotype. In prostate cancer, tumours can follow divergent evolutionary trajectories that lead to distinct subtypes, but the causes of this divergence remain unclear. While causal inference could elucidate the factors involved, conventional meth

Matteo Sodano, Federico Magistri, Elias Marks, Fares Hosn

Crop yield estimation is a relevant problem in agriculture, because an accurate yield estimate can support farmers' decisions on harvesting or precision intervention. Robots can help to automate this process. To do so, they need to be able to perceive the surrounding environment to identify target objects such as trees and plants. In this paper, we introduce

Youness Hadouch, Daoud Mezzane, M barek Amjoud, Hana Ursic

Magnetoelectric composites integrate the coupling between magnetic and piezoelectric materials to create new functionalities for potential technological applications. This coupling is typically achieved through the exchange of magnetic, electric, or elastic energy across the interfaces between the different constituent materials. Tailoring the strength of th

Dingning Liu, Cheng Wang, Peng Gao, Renrui Zhang

Multimodal Large Language Models (MLLMs) exhibit impressive capabilities across a variety of tasks, especially when equipped with carefully designed visual prompts. However, existing studies primarily focus on logical reasoning and visual understanding, while the capability of MLLMs to operate effectively in 3D vision remains an ongoing area of exploration.

Sven Günther, Lennart Balkenhol, Christian Fidler, Ali Rida Khalife

In this work, we present OL\'E, a new online learning emulator for use in cosmological inference. The emulator relies on Gaussian Processes and Principal Component Analysis for efficient data compression and fast evaluation. Moreover, OL\'E features an automatic error estimation for optimal active sampling and online learning. All training data is computed o

G. R. Gladstone, J. M. Shull, W. R. Pryor, J. Slavin

During September 2023 the Alice ultraviolet spectrograph on the New Horizons (NH) spacecraft was used to map diffuse Lyman alpha (Lya) emission over most of the sky, at a range of 56.9 AU from the Sun. At that distance, models predict that the interplanetary medium Lya emissions result from comparable amounts of resonant backscattering of the solar Lya line

Liewen Liao, Weihao Yan, Wang Xu, Ming Yang

Learning-based 3D reconstruction has emerged as a transformative technique in autonomous driving, enabling precise modeling of environments through advanced neural representations. It has inspired pioneering solutions for vital tasks in autonomous driving, such as dense mapping and closed-loop simulation, as well as comprehensive scene feature for driving sc

Günther Rüdiger, Manfred Schultz

The Tayler instability of an azimuthal magnetic field with one or two ``rings'' along the radius is studied for an axially unbounded Taylor-Couette flow. The rotation law of the conducting fluid is a quasi-Keplerian one. Without rotation all toroidal fields are the more destabilized the more the radial profiles differ from the uniformity. For medium Reynolds

Jungwon Seo, Ferhat Ozgur Catak, Chunming Rong, Kibeom Hong

Federated Learning (FL) enables privacy-preserving multi-source information fusion (MSIF) but is challenged by client drift in highly heterogeneous data settings. Many existing drift-mitigation strategies rely on reference-based techniques--such as gradient adjustments or proximal loss--that use historical snapshots (e.g., past gradients or previous global m

Yi Zhang

This study proposes a lightweight method for building image super-resolution using a Dilated Contextual Feature Modulation Network (DCFMN). The process includes obtaining high-resolution images, down-sampling them to low-resolution, enhancing the low-resolution images, constructing and training a lightweight network model, and generating super-resolution out

Zhanggen Jin, Haobin Duan, Zhiyang Hang

Games have played a pivotal role in advancing artificial intelligence, with AI agents using sophisticated techniques to compete. Despite the success of neural network based game AIs, their performance often requires significant computational resources. In this paper, we present Rapfi, an efficient Gomoku agent that outperforms CNN-based agents in limited com

Thorben Pieper-Sethmacher, Frank van der Meulen, Aad van der Vaart

Let X be the mild solution to a semilinear stochastic partial differential equation. In this article, we develop methodology to sample from the infinite-dimensional diffusion bridge that arises from conditioning X on a linear transformation LXT of the final state XT at some time T > 0. This solves a problem that has so far not been attended to in the literat

Rui Wang, Quentin Lohmeyer, Mirko Meboldt, Siyu Tang

Reconstructing clean, distractor-free 3D scenes from real-world captures remains a significant challenge, particularly in highly dynamic and cluttered settings such as egocentric videos. To tackle this problem, we introduce DeGauss, a simple and robust self-supervised framework for dynamic scene reconstruction based on a decoupled dynamic-static Gaussian Spl

Alvin Combrink, Sabino Francesco Roselli, Martin Fabian

Multi-agent Path Finding (MAPF) is the problem of planning collision-free movements of agents so that they get from where they are to where they need to be. Commonly, agents are located on a graph and can traverse edges. This problem has many variations and has been studied for decades. Two such variations are the continuous-time and the lifelong MAPF proble

Ziqiang Li, Jun Li, Lizhi Xiong, Zhangjie Fu

Text-to-image diffusion models have made significant advancements in generating high-quality, diverse images from text prompts. However, the inherent limitations of textual signals often prevent these models from fully capturing specific concepts, thereby reducing their controllability. To address this issue, several approaches have incorporated personalizat

Matt Gottsacker, Mengyu Chen, David Saffo, Feiyu Lu

Head-worn augmented reality (AR) allows audiences to be immersed and engaged in stories told by live presenters. While presenters may also be in AR to have the same level of immersion and awareness as their audience, this symmetric presentation style may diminish important social cues such as eye contact. In this work, we examine the effects this (a)symmetry

Ori Peleg, Natalie Lang, Dan Ben Ami, Stefano Rini

Federated learning (FL) enables multiple edge devices to collaboratively train a machine learning model without the need to share potentially private data. Federated learning proceeds through iterative exchanges of model updates, which pose two key challenges: First, the accumulation of privacy leakage over time, and second, communication latency. These two

Yingyu Yang

We compute the half-wormhole contribution in a complex SYK model with one time point. When the chemical potential is zero, the result is similar to two decoupled Majorana SYK models. There's a disk contribution in a single copy of the model, which is a bit subdominant to the unlinked half-wormhole. After removing out the disk we find out the linked half-worm

Wensheng Wang, Ning Tan

The acquisition of large-scale and diverse demonstration data are essential for improving robotic imitation learning generalization. However, generating such data for complex manipulations is challenging in real-world settings. We introduce HybridGen, an automated framework that integrates Vision-Language Model (VLM) and hybrid planning. HybridGen uses a two

Praveen Shastry, Sowmya Chowdary Muthulur, Naveen Kumarasami, Anandakumar D

Background: This study proposes a Vision-Language Model (VLM) leveraging the SIGLIP encoder and Gemma-3b transformer decoder to enhance automated chronic tuberculosis (TB) screening. By integrating chest X-ray images with clinical data, the model addresses the challenges of manual interpretation, improving diagnostic consistency and accessibility, particular

Fernando Gaspoz, Christian Kreuzer, Andreas Veeser, Winnifried Wollner

We consider finite element solutions to optimization problems, where the state depends on the possibly constrained control through a linear partial differential equation. Basing upon a reduced and rescaled optimality system, we derive a posteriori bounds capturing the approximation of the state, the adjoint state, the control and the observation. The upper a

Ruoyan Avery Yin, Zhichu Ren, Zongyou Yin, Zhen Zhang

The Copilot for Real-world Experimental Scientist (CRESt) system empowers researchers to control autonomous laboratories through conversational AI, providing a seamless interface for managing complex experimental workflows. We have enhanced CRESt by integrating a multi-agent collaboration mechanism that utilizes the complementary strengths of the ChatGPT and

Francesco Serafini, Francesco Battista, Paolo Gualtieri, Carlo Massimo Casciola

Provided a sufficient concentration of long-chain polymers in a Newtonian solvent, turbulent wall-bounded flows exhibit a universal state known as Maximum Drag Reduction (MDR). Through direct numerical simulations, we show that the wall-normal kinetic energy flux characterising Newtonian wall-bounded turbulence is suppressed at MDR, and that the polymers mai

Barbara Franci, Filippo Fabiani, Alberto Bemporad

We develop a scheme based on active learning to compute equilibria in a generalized Nash equilibrium problem (GNEP). Specifically, an external observer (or entity), with little knowledge on the multi-agent process at hand, collects sensible data by probing the agents' best-response (BR) mappings, which are then used to recursively update local parametric est

Sepideh Masoudi

Data is a valuable asset, and sharing it as a product across organizations is key to building comprehensive and useful insights in fields such as science and industry. Before sharing, data often requires transformation to comply with governance policies and meet the requirements of recipient organizations. By leveraging pipelines, these transformations can b

Kai Tong, Kang Pan, Xiao Zhang, Erli Meng

Large Language Models (LLMs) possess encompassing capabilities that can process diverse language-related tasks. However, finetuning on LLMs will diminish this general skills and continual finetuning will further cause severe degradation on accumulated knowledge. Recently, Continual Learning (CL) in Large Language Models (LLMs) arises which aims to continuall

Chen Zhao, Zhizhou Chen, Yunzhe Xu, Enxuan Gu

Ultra-high-definition (UHD) image restoration faces significant challenges due to its high resolution, complex content, and intricate details. To cope with these challenges, we analyze the restoration process in depth through a progressive spectral perspective, and deconstruct the complex UHD restoration problem into three progressive stages: zero-frequency

Kaito Nitani, Seisuke Kyochi

This paper introduces a design method for densergraph-frequency graph Fourier frames (DGFFs) to enhance graph signal processing and analysis. The graph Fourier transform (GFT) enables us to analyze graph signals in the graph spectral domain and facilitates various graph signal processing tasks, such as filtering, sampling and reconstruction, denoising, and s

Shani Gamrian, Hila Barel, Feiran Li, Masakazu Yoshimura

Object detection models are typically applied to standard RGB images processed through Image Signal Processing (ISP) pipelines, which are designed to enhance sensor-captured RAW images for human vision. However, these ISP functions can lead to a loss of critical information that may be essential in optimizing for computer vision tasks, such as object detecti

Nicolas Espinosa-Dice, Sanjiban Choudhury, Wen Sun, Gokul Swamy

We consider the problem of imitation learning under misspecification: settings where the learner is fundamentally unable to replicate expert behavior everywhere. This is often true in practice due to differences in observation space and action space expressiveness (e.g. perceptual or morphological differences between robots and humans). Given the learner mus

M. Jarzyna, L. Kunz, W. Zwolinski, M. Jachura

Deep-space optical communication links operate under severely limited signal power, approaching the photon-starved regime which requires a receiver capable of measuring individual incoming photons. This makes the photon information efficiency (PIE), i.e. the number of bits that can be retrieved from a single received photon, a relevant figure of merit to cha

Zihao Liu, Xiaoyu Wu, Jianqin Wu, Xuxu Wang

Video anomaly detection (VAD) aims to detect anomalies that deviate from what is expected. In open-world scenarios, the expected events may change as requirements change. For example, not wearing a mask may be considered abnormal during a flu outbreak but normal otherwise. However, existing methods assume that the definition of anomalies is invariable, and t

Xuechen Wang, Yaxin Gao, Menghao Wu

The ion conductivity of a solid-state ion conductor generally increases exponentially upon reduction in ion migration barrier. For prevalent cathode material LiCoO2, the room-temperature ion conductivity and migration barrier are respectively around 10-4 S/cm and 0.3 eV. In this paper, through first-principles calculations we predict the existence of 1D supe

Bernd Zimmering, Cecília Coelho, Vaibhav Gupta, Maria Maleshkova

Modelling forced dynamical systems - where an external input drives the system state - is critical across diverse domains such as engineering, finance, and the natural sciences. In this work, we propose Laplace-Net, a decoupled, solver-free neural framework for learning forced and delay-aware systems. It leverages a Laplace transform-based approach to decomp

Zakariae Zrimek, Youssef Mourchid, Mohammed El Hassouni

Gait disorder recognition plays a crucial role in the early diagnosis and monitoring of movement disorders. Existing approaches, including spatio-temporal graph convolutional networks (ST-GCNs), often face high memory demands and struggle to capture complex spatio-temporal dependencies, limiting their efficiency in clinical applications. To address these cha

Krzysztof A. Meissner, Hermann Nicolai

We clarify and extend our earlier work (K.A.Meissner and H.Nicolai, Phys. Rev. D91 (2015) 065029 and Phys. Rev. Lett. 121 (2018) 091601) where it was shown how to amend a scheme originally proposed by M. Gell-Mann to identify the three families of quarks and leptons of the Standard Model with the 48 spin 1/2 fermions of N=8 supergravity that remain after abs

Amaury Lambert, Hélène Leman, Hélène Morlon, Josué Tchouanti

We consider a metapopulation made up of $K$ demes, each containing $N$ individuals bearing a heritable quantitative trait. Demes are connected by migration and undergo independent Moran processes with mutation and selection based on trait values. Mutation and migration rates are tuned so that each deme receives a migrant or a mutant in the same slow timescal

Jose M. Obrero-Perez, Fernando Nunez-Galvez, Lidia Contreras-Bernal, Javier Castillo-Seoane

Halide perovskite solar cells (PSCs) offer high efficiency and low costs, making them key for future photovoltaics. Optimizing charge transport layers is crucial, with porous TiO$_2$ widely used as electron transport layers (ETL) due to its energy alignment, transparency, and abundance. However, its efficiency relies on crystallinity requiring high-temperatu

Simona Olmi, Antonio Politi

Many dynamical systems operate in a fluctuating environment. However, even in low-dimensional setups, transitions and bifurcations have not yet been fully understood. In this Letter we focus on crises, a sudden flooding of the phase space due to the crossing of the boundary of the basin of attraction. We find that crises occur also in non-autonomous systems

Igor Haladjian

We construct a family of links we call torus necklaces for which the link groups are precisely the braid groups of generalised $J$-reflection groups. Moreover, this correspondence exhibits the meridians of the aforementioned link groups as braid reflections. In particular, this construction generalises to all irreducible rank two complex reflection groups a

Samuel Cameron, Elsen Tjhung

We consider a generic class of stochastic particle-based models whose state at an instant in time is described by a set of continuous degrees of freedom (e.g. positions), and the length of this set changes stochastically in time due to birth-death processes. Using a master equation formalism, we write down the dynamics of the corresponding (infinite) set of

Jasmin Wachter, Michael Radloff, Maja Smolej, Katharina Kinder-Kurlanda

We introduce an Item Response Theory (IRT)-based framework to detect and quantify socioeconomic bias in large language models (LLMs) without relying on subjective human judgments. Unlike traditional methods, IRT accounts for item difficulty, improving ideological bias estimation. We fine-tune two LLM families (Meta-LLaMa 3.2-1B-Instruct and Chat- GPT 3.5) to

Jasper Arends, Guanjie Lyu, Mhamed Mesfioui, Elisa Perrone

We propose an alternative formulation of Spearman's rho for zero-inflated count data. The formulation yields an estimator with explicitly attainable bounds, facilitating interpretation in settings where the standard range [-1,1] is no longer informative.

Jiayi Fu, Siyu Liu, Zikun Liu, Chun-Le Guo

We propose a novel Iterative Predictor-Critic Code Decoding framework for real-world image dehazing, abbreviated as IPC-Dehaze, which leverages the high-quality codebook prior encapsulated in a pre-trained VQGAN. Apart from previous codebook-based methods that rely on one-shot decoding, our method utilizes high-quality codes obtained in the previous iteratio

Witold Marciszewski, Roman Pol, Piotr Zakrzewski

The Hurewicz property is a classical generalization of $\sigma$-compactness and Sierpi\'nski sets (whose existence follows from CH) are standard examples of non-$\sigma$-compact Hurewicz spaces. We show, solving a problem stated by Szewczak and Tsaban, that for each Sierpi\'nski set S of cardinality at least $\mathfrak b$ there is a Hurewicz space H with $S\

Entao Yang, Xiaotian Zhang, Yue Shang, Ge Zhang

One of the central challenges in modern machine learning is understanding how neural networks generalize knowledge learned from training data to unseen test data. While numerous empirical techniques have been proposed to improve generalization, a theoretical understanding of the mechanism of generalization remains elusive. Here we introduce the concept of Bo

Roger Alimi, David J. Tannor

An efficient new approach to signal compression is presented based of a novel variation on the Gabor basis set. Following earlier work by Shimshovitz and Tannor, we convolve the conventional Gabor functions with Dirichlet functions to obtain a Periodic Gabor basis set (PG). The PG basis is exact for continuous functions that are periodic band-limited. Using

Mrinmoyee Saha, Luca Horray, Pedro Portugal, Christian Flindt

It has been predicted that the time-dependent current in a Fabry-P\'erot cavity can turn negative even if the applied voltage pulses are always positive. It has also been suggested that the negative currents are related to interfering paths, however, an analytic description of this surprising phenomenon has so far been missing. Here we make use of Floquet sc

Zhongzheng Yang, Fanbang Zheng, Dingsong Wu, Bin-Bin Zhang

The recently discovered heavy-fermion superconductor, UTe2, is an excellent candidate for spin-triplet superconductors where electrons form spin-triplet Cooper pairs with spin S = 1 and odd parity. Unconventional superconductivity often hosts unconventional vortices. Yet, the vortex core and lattice in UTe2 have not been directly visualized and characterized

Mohammad Ali Badragheh, Vanessa Miß, Bernhard Roling, Michael Vogel

7Li NMR diffusometry and relaxometry are combined with electrochemical impedance spectroscopy to compare the mechanisms for the dynamics and transport of lithium ions in disordered and crystalline electrolytes with argyrodite composition Li5.5PS4.5Cl1.5. The dc conductivity of a disordered sample prepared by ball milling amounts to 0.76 mScm-1 at room temper

Shane T. Jensen

I present a simple and transparent standard for career greatness in baseball: any major league player with H > 2500 or HR > 350 or K > 2800 or W > 240 makes my Hall of Fame Cut. Rate statistics are avoided due to small sample issues and to ensure the standard is permanent once achieved. Hits and home runs were chosen to represent the two extremes of batting