March 2025 arXiv papers — page 203

Matthew Faust, Wencai Liu

As a corollary of our main results, we prove that for any connected $\mathbb{Z}^d$-periodic graph, when edge weights and potentials are treated as variables, the corresponding periodic graph operators generically (i.e., outside a proper algebraic subset of the variable space) do not have flat bands.

Haoyang Zhang, Lingwei Meng, Li Zhang, Benzhong Dai

The scale invariance of accretion processes (SIAP) is crucial for understanding the physical processes of black hole accretion systems at different scales. When applying this rule to high-frequency quasi-periodic oscillations (HFQPOs), there is an observation-theory confrontation in active galactic nuclei (AGNs). By compiling an updated X-ray HFQPO catalog,

Francesco Maddalena, Gianluca Orlando

We propose a model for frequency-dependent damping in the linear wave equation. After proving well-posedness of the problem, we study qualitative properties of the energy. In the one-dimensional case, we provide an explicit analysis for special choices of the damping operator. Finally, we show, in special cases, that solutions split into a dissipative and a

Haowei Sun, Xintao Yan, Zhijie Qiao, Haojie Zhu

Traffic simulation is essential for autonomous vehicle (AV) development, enabling comprehensive safety evaluation across diverse driving conditions. However, traditional rule-based simulators struggle to capture complex human interactions, while data-driven approaches often fail to maintain long-term behavioral realism or generate diverse safety-critical eve

Alexandra Blessing, Mazyar Ghani Varzaneh, Tim Seitz

We derive a Gronwall type inequality for mild solutions of non-autonomous parabolic rough partial differential equations (RPDEs). This inequality together with an analysis of the Cameron-Martin space associated to the noise, allows us to obtain the existence of moments of all order for the solution of the corresponding RPDE and its Jacobian when the random i

Yves Vollmeier

In this thesis, we study concepts in quantum computing using graphical languages, specifically using the ZX-calculus. The core of the research revolves around (graphical) stabilizer decompositions. The first major focus is on the decomposition of non-stabilizer states created from star edges. We discuss previous results and then present novel decompositions

Tilman Richter, Paolo Malgaretti, Jens Harting

Liquid films of nanometric thickness are prone to spinodal dewetting driven by disjoining pressure, meaning that a non-wetting liquid film of homogeneous thickness in the range of tens of nanometers will spontaneously break into droplets. The surface energy of the underlying solid substrate heavily influences the dynamics and resulting droplet configurations

Ovidiu Savin, Hui Yu

We study minimizing cones in the Alt-Phillips problem when the exponent {\gamma} is close to 1. When {\gamma} converges to 1, we show that the cones concentrate around symmetric solutions to the classical obstacle problem. To be precise, the limiting profiles are radial in a subspace and invariant in directions perpendicular to that subspace.

Anastasia Georgiou, Daniel Jungen, Luise Kaven, Verena Hunstig

Bayesian Optimization (BO) with Gaussian Processes relies on optimizing an acquisition function to determine sampling. We investigate the advantages and disadvantages of using a deterministic global solver (MAiNGO) compared to conventional local and stochastic global solvers (L-BFGS-B and multi-start, respectively) for the optimization of the acquisition fun

Juba Bouaziz, Takuya Nomoto, Ryotaro Arita

We present a multi-scale computational approach that combines atomistic spin models with the cluster multipole (CMP) method. The CMP method enables a systematic and accurate generation of complex non-collinear magnetic structures using symmetry-adapted representations. The parameters of the spin model are derived from density functional theory using the magn

Varatharaja Nallathambi, Philipp Gabriel, Xinren Chen, Ziyuan Rao

Laser powder bed fusion (PBF-LB/M) enables the near-net shape production of permanent magnets with complex geometry while reducing material waste. However, controlling the microstructure and optimizing magnetic properties remain challenging due to rapid solidification and intrinsic heat treatment effects occurring during both inter-layer and intra-layer proc

Arun Ganesh, Ryan McKenna, Brendan McMahan, Adam Smith

We initiate a study of algorithms for model training with user-level differential privacy (DP), where each example may be attributed to multiple users, which we call the multi-attribution model. We first provide a carefully chosen definition of user-level DP under the multi-attribution model. Training in the multi-attribution model is facilitated by solving

Hongjian Li, Pingzhi Yuan

Let $A=\begin{pmatrix} a & b \\ c & d \end{pmatrix}\in M_2\left(\mathbb{Z}\right)$ be a given matrix such that $bc\neq0$ and let $C(A)=\{B\in M_2(\mathbb{Z}): AB=BA\}$. In this paper, we give a necessary and sufficient condition for the solvability of the matrix equation $uX^i+vY^j=wZ^k,\, i,\, j,\, k\in\mathbb{N},\, X, \,Y,\, Z\in C(A)$, where $u,\, v,\, w$

Mengzhen Liu, Ming Li, Rang Liu, Qian Liu

Reconfigurable antennas possess the capability to dynamically adjust their fundamental operating characteristics, thereby enhancing system adaptability and performance. To fully exploit this flexibility in modern wireless communication systems, this paper considers a novel tri-hybrid beamforming architecture, which seamlessly integrates pattern-reconfigurabl

Cheng-Wei Lee, Kingsley Egbo, Emily Garrity, Matthew Jankousky

In this paper we investigate, computationally and experimentally, the phase stability, electronic structure properties, and the propensity for n-type doping of In$_{2}$X$_{2}$O$_{7}$ (X=Si, Ge) ternary oxides. This family of materials contains promising novel wide-gap semiconductors based on their estimated high $n$-type Baliga figures of merit and acceptabl

Noel Portillo

This document presents the design and implementation of a low-power IoT server cluster, based on Raspberry Pi 3 Model B and powered by solar energy. The proposed architecture integrates Kubernetes (K3s) and Docker, providing an efficient, scalable, and high-performance computing environment. The cluster is designed to optimize energy consumption, leveraging

Joongi Shin, Ankit Khatri, Michael A. Hedderich, Andrés Lucero

People can generate high-quality ideas by building on each other's ideas. By enabling individuals to contribute their ideas at their own comfortable time and method (i.e., asynchronous ideation), they can deeply engage in ideation and improve idea quality. However, running asynchronous ideation faces a practical constraint. Whereas trained human facilitators

Sandipan P. D. Borthakur, Mihkel Kama, Luca Fossati, Quentin Kral

Accretion from protoplanetary or debris disks can contaminate the stellar photosphere, which is detectable in stars with radiative envelopes due to relatively slower photospheric mixing. The contaminated photosphere reflects ongoing disk processes, detectable through stellar spectroscopy. We investigate the composition of six gas-rich debris disk-hosting A-t

Songlong Xing, Zhengyu Zhao, Nicu Sebe

Despite its prevalent use in image-text matching tasks in a zero-shot manner, CLIP has been shown to be highly vulnerable to adversarial perturbations added onto images. Recent studies propose to finetune the vision encoder of CLIP with adversarial samples generated on the fly, and show improved robustness against adversarial attacks on a spectrum of downstr

Kemal Kirtac, Guido Germano

Financial sentiment has become a crucial yet complex concept in finance, increasingly used in market forecasting and investment strategies. Despite its growing importance, there remains a need to define and understand what financial sentiment truly represents and how it can be effectively measured. We explore the nature of financial sentiment and investigate

Andrea Scharnhorst

This paper pays a tribute to Loet's work in a specific way. More than 20 years ago Loet Leydesdorff and myself designed a programme for future innovation studies 'Measuring the knowledge base - a programme of innovation studies'. Although, the funding programme we envisioned eventually did not materialise, the proposal text set out the main lines of our rese

Martina Karl, Paolo Padovani, Paolo Giommi

The IceCube Neutrino Observatory publishes "alert events", i.e. detections of high-energy neutrinos with a moderate-to-high probability of being of astrophysical origin. While some events are produced in the atmosphere, a fraction of alert events should point back to their astrophysical sources. We aim to identify multiple alert events possibly related to a

Lingli Cao, He Zhang, Shanshan Li, Danyang Li

Architectural tactics (ATs), as the concrete implementation of architectural decisions in code, address non-functional requirements of software systems. Due to the implicit nature of architectural knowledge in code implementation, developers may risk inadvertently altering or removing these tactics during code modifications or optimizations. Such unintended

Logan Howe, Andrea Giachero, Michael Vissers, Jordan Wheeler

Quantum computing systems and fundamental physics experiments using superconducting technologies frequently require signal amplification chains operating near the quantum limit of added noise. Both Josephson parametric amplifiers (JPAs) and traveling wave parametric amplifiers (TWPAs) have been used as first-stage amplifiers to enable readout chains operatin

Keqi Chen, Zekai Sun, Yuhua Wen, Huijun Lian

The in-context learning capabilities of large language models (LLMs) show great potential in mental health support. However, the lack of counseling datasets, particularly in Chinese corpora, restricts their application in this field. To address this, we constructed Psy-Insight, the first mental health-oriented explainable multi-task bilingual dataset. We col

Anas Buhayh, Elizabeth McKinnie, Robin Burke

Recommender ecosystems are an emerging subject of research. Such research examines how the characteristics of algorithms, recommendation consumers, and item providers influence system dynamics and long-term outcomes. One architectural possibility that has not yet been widely explored in this line of research is the consequences of a configuration in which re

Christopher Bennett, Kerstin Eder

Microelectronic design verification remains a critical bottleneck in device development, traditionally mitigated by expanding verification teams and computational resources. Since the late 1990s, machine learning (ML) has been proposed to enhance verification efficiency, yet many techniques have not achieved mainstream adoption. This review, from the perspec

Hiroki Aoki, Hiraku Kawanoue

Root systems are sets with remarkable symmetries and therefore they appear in many situations in mathematics. Among others, denominator formulae of root systems are very beautiful and mysterious equations which have several meanings from a variety of disciplines in mathematics. In this paper, we show a converse statement of this phenomena. Namely, for a give

Bernard F. Schutz, Tsvi Piran, Patrick J. Sutton

We describe an unexpected anthropic fine-tuning of gravity: human cognition arose on Earth only because the laws of gravity included gravitational waves. Their link is the heat from decays of the radioactive isotopes U-238 and Th-232, which were synthesized mainly in rare explosive mergers of binary neutron stars, brought about by the loss of orbital energy

Matteo Becchetti, Christoph Dlapa, Simone Zoia

We present differential equations (DEs) in canonical form for a family of two-loop five-point Feynman integrals containing elliptic functions and nested square roots. This is the only family for which canonical DEs were not yet available among those required to compute the two-loop leading-colour amplitude for top-pair production in association with a jet at

Maximilian Schaefer

This article studies the change in the prediction accuracy of a response variable when the number of predictors increases, and all variables follow a multivariate normal distribution. Assuming that the correlations between variables are independently drawn, I show that adding variables leads to globally increasing returns to scale when the mean of the correl

Kristian Kuznetsov, Laida Kushnareva, Polina Druzhinina, Anton Razzhigaev

Artificial Text Detection (ATD) is becoming increasingly important with the rise of advanced Large Language Models (LLMs). Despite numerous efforts, no single algorithm performs consistently well across different types of unseen text or guarantees effective generalization to new LLMs. Interpretability plays a crucial role in achieving this goal. In this stud

Varun Upreti, Dorian Rudolph, Ulysse Chabaud

Bosonic quantum systems operate in an infinite-dimensional Hilbert space, unlike discrete-variable quantum systems. This distinct mathematical structure leads to fundamental differences in quantum information processing, such as an exponentially greater complexity of state tomography [MMB+24] or a factoring algorithm in constant space [BCCRK24]. Yet, it rema

Débora N. P. Oliveira, Joshua Knights, Sebastián Barbas Laina, Simon Boche

Loop closures are essential for correcting odometry drift and creating consistent maps, especially in the context of large-scale navigation. Current methods using dense point clouds for accurate place recognition do not scale well due to computationally expensive scan-to-scan comparisons. Alternative object-centric approaches are more efficient but often str

Mengzhen Liu, Ming Li, Rang Liu, Qian Liu

Cell-free massive multi-input multi-output (CF-mMIMO) systems have emerged as a promising paradigm for next-generation wireless communications, offering enhanced spectral efficiency and coverage through distributed antenna arrays. However, the non-linearity of power amplifiers (PAs) in these arrays introduce spatial distortion, which may significantly degrad

Qiang Gao, Haiyang Chen, Wen-shin Lu, Yang-hao Chan

Strong electron-electron interaction can induce Mott insulating state, which is believed to host unusual correlated phenomena such as quantum spin liquid when quantum fluctuation dominates and unconventional superconductivity through doping. Transition metal compounds as correlated materials provide a versatile platform to engineer the Mott insulating state.

Rodrigo Lecaros, Ariel A. Pérez, Manuel F. Prado

This paper considers a semi-discrete forward stochastic parabolic operator with homogeneous Dirichlet conditions in arbitrary dimensions. We show the lack of null controllability for a spatial semi-discretization of a null-controllable stochastic parabolic system from any initial datum. However, by proving a new Carleman estimate for its semi-discrete backwa

Rui Lu, Runzhe Wang, Kaifeng Lyu, Xitai Jiang

Score-based diffusion models have achieved incredible performance in generating realistic images, audio, and video data. While these models produce high-quality samples with impressive details, they often introduce unrealistic artifacts, such as distorted fingers or hallucinated texts with no meaning. This paper focuses on textual hallucinations, where diffu

Haoran Fan, Bin Li, Yixuan Weng, Shoujun Zhou

While LLMs have demonstrated remarkable potential in time series forecasting, their practical deployment remains constrained by excessive computational demands and memory footprints. Existing LLM-based approaches typically suffer from three critical limitations: Inefficient parameter utilization in handling numerical time series patterns; Modality misalignme

Arnout Roebben, Toon van Waterschoot, Marc Moonen

Two algorithms for combined acoustic echo cancellation (AEC) and noise reduction (NR) are analysed, namely the generalised echo and interference canceller (GEIC) and the extended multichannel Wiener filter (MWFext). Previously, these algorithms have been examined for linear echo paths, and assuming access to voice activity detectors (VADs) that separately de

Karl Audun Borgersen, Morten Goodwin

For consumer usage of locally deployed LLMs, the GGUF format and k\_quantization are invaluable tools for maintaining the performance of the original model while reducing it to sizes deployable with consumer-grade hardware. The number of bits dedicated to each weight from the original model is reduced based on how important they are thought to be during mode

Grzegorz Pastuszak

The aim of this survey is to present applications of covering techniques in the theory of Krull-Gabriel dimension. We start with recalling fundamental facts of the classical covering theory of quivers and locally bounded categories. Then we present some recent results on covering theory of functor categories. These are interesting themselves, but also allow

Supat Roongpraiwan, Zongdian Li, Tao Yu, Kei Sakaguchi

Millimeter wave (mmWave) technology in vehicle-to-everything (V2X) communication offers unprecedented data rates and low latency, but faces significant reliability challenges due to signal blockages and limited range. This paper introduces a novel system for managing dynamic multi-hop mmWave V2X communications in complex blocking environments. We present a s

Giuseppe di Donato, Luigi Pilo

The dynamical realisation of the equation of state $p +\rho =0$ is studied. A non-pathological dynamics for the perturbations of such a system mimicking a dynamical cosmological constant (DCC) requires to go beyond the perfect fluid paradigm. It is shown that an anisotropic stress must be always present. The Hamiltonian of the system in isolation resembles t

Lida Chen, Dong Xu, Chenxin An, Xintao Wang

Large Language Models (LLMs) face efficiency bottlenecks due to the quadratic complexity of the attention mechanism when processing long contexts. Sparse attention methods offer a promising solution, but existing approaches often suffer from incomplete effective context and/or require complex implementation of pipeline. We present a comprehensive analysis of

Vincent Freiberger, Arthur Fleig, Erik Buchmann

Protecting online privacy requires users to engage with and comprehend website privacy policies, but many policies are difficult and tedious to read. We present the first qualitative user study on Large Language Model (LLM)-driven privacy policy assessment. To this end, we build and evaluate an LLM-based privacy policy assessment browser extension, which hel

Alperen Yildiz, Sin G. Teo, Yiling Lou, Yebo Feng

Large Language Models (LLMs) have shown promise in software vulnerability detection, particularly on function-level benchmarks like Devign and BigVul. However, real-world detection requires interprocedural analysis, as vulnerabilities often emerge through multi-hop function calls rather than isolated functions. While repository-level benchmarks like ReposVul

Kristian Toccacelo, Ulrik Lund Andersen, Jonatan Bohr Brask

We establish limitations and bounds on the transmission of quantum states between gravitationally interacting mechanical oscillators under different models of gravity. This provides benchmarks that can enable tests for quantum features of gravity. Our proposal does not require the measurement of gravitationally induced entanglement and only requires final me

R. Jafari, J. Naji, A. Langari, Vahid Karimipour

We study the impact of noise on the dynamics of entanglement in the transverse-field Ising chain, with the field quenched linearly across one or both of the quantum critical points of the model. Taking concurrence as a measure of entanglement, we find that a quench generates entanglement between nearest- and next-nearest-neighbor spins, with noise reducing t

Nir Nechushtan, Hanzhong Zhang, Yosef London, Mallachi Meller

Detection of signals buried in noise is the major challenge for sensing. Classically, the optimal detector is a matched filter, whose sensitivity meets the classical limit of correlation between the filter target and the measured signal within the noise. For classical signals, the correlation is limited by the separability criterion in frequency-time. Quantu

Jabez Magomere, Scott Hale

The adoption of crowdsourced election monitoring as a complementary alternative to traditional election monitoring is on the rise. Yet, its reliance on digital response volunteers to manually process incoming election reports poses a significant scaling bottleneck. In this paper, we address the challenge of scaling crowdsourced election monitoring by advanci

Victor Truong Thinh Lam, Mircea Lazar

This paper presents a new fast active-set quadratic programming (QP) solver based on inverse matrix updates, which is suitable for real-time model predictive control (MPC). This QP solver, called imuQP (inverse matrix update QP), is based on Karush-Kuhn-Tucker (KKT) conditions and is inspired by Hildreth's QP solver. An extensive convergence and optimality a

Yan-Xia Ren, Renming Song, Yaping Zhu

In this paper, we investigate the asymptotic behaviors of the survival probability and maximal displacement of a subcritical branching killed L\'{e}vy process $X$ in $\mathbb{R}$. Let $\zeta$ denote the extinction time, $M_t$ be the maximal position of all the particles alive at time $t$, and $M:=\sup_{t\ge 0}M_t$ be the all-time maximum. Under the assumptio

Rima Benhammadi, PAtrice Meunier, Juan J. Hidalgo

We investigate the convective instability resulting from the dissolution of carbon dioxide (CO$_{2}$) into water in a heterogeneous Hele-Shaw cell utilizing both experimental and numerical approaches. Experiments are conducted in a Hele-Shaw cell with a variable gap width corresponding to a log-normally distributed permeability of variance $\sigma_{\log K}^2

Yara Kyrychenko, Jon Roozenbeek, Brandon Davidson, Sander van der Linden

As large language models (LLMs) enter the mainstream, aligning them to foster constructive dialogue rather than exacerbate societal divisions is critical. Using an individualized and multicultural alignment dataset of over 7,500 conversations of individuals from 74 countries engaging with 21 LLMs, we examined how linguistic attributes linked to constructive

William Boone Samuels

Achieving scalable, fault-tolerant quantum computation requires quantum memory architectures that minimize error correction overhead while preserving coherence. This work presents a framework for high-dimensional qudit memory in 153Eu:Y2SiO5, integrating three core mechanisms: (i) non-destructive syndrome extraction, using spin-echo sequences to encode error

Patricia Bachmann, Anna Brötzner, Miriam Goetze, Philipp Kindermann

We investigate saturated geometric drawings of graphs with geometric thickness $k$, where no edge can be added without increasing $k$. We establish lower and upper bounds on the number of edges in such drawings if the vertices lie in convex position. We also study the more restricted version where edges are precolored, and for $k=2$ the case for vertices in

Juha Harviainen, Frank Sommer, Manuel Sorge, Stefan Szeider

We present a comprehensive classical and parameterized complexity analysis of decision tree pruning operations, extending recent research on the complexity of learning small decision trees. Thereby, we offer new insights into the computational challenges of decision tree simplification, a crucial aspect of developing interpretable and efficient machine learn

Zhengke Lu, Long Feng

We address the problem of robust sparse estimation of the precision matrix for heavy-tailed distributions in high-dimensional settings. In such high-dimensional contexts, we observe that the covariance matrix can be approximated by a spatial-sign covariance matrix, scaled by a constant. Based on this insight, we introduce two new procedures, the Spatial-Sign

Jørgen Anker Olsen, Grzegorz Malczyk, Kostas Alexis

Exploring planetary bodies with lower gravity, such as the moon and Mars, allows legged robots to utilize jumping as an efficient form of locomotion thus giving them a valuable advantage over traditional rovers for exploration. Motivated by this fact, this paper presents the design, simulation, and learning-based "in-flight" attitude control of Olympus, a ju

Jonas Dube, Julius Kühn, Chen Wang, Sonal Mistry

The development of high-performance photocathodes is essential for generating high-brightness electron beams required by existing and future accelerators. This work introduces a state-of-the-art triple evaporation growth system designed for bialkali antimonide photocathodes. By enabling the simultaneous deposition of all three materials, this system signific

Pietro Luigi Muzzeddu, Davide Venturelli, Andrea Gambassi

We study the static and dynamical properties of a harmonically confined Rouse polymer coupled to a fluctuating correlated medium, which affect each other reciprocally during their stochastic evolution. The medium is modeled by a scalar Gaussian field which can feature modes with slow relaxation and long-range spatial correlations. We show that these modes af

Waqar Muhammad Ashraf, Vivek Dua, Ramit Debnath

Machine learning and optimisation techniques (MLOPT) hold significant potential to accelerate the decarbonisation of industrial systems by enabling data-driven operational improvements. However, the practical application of MLOPT in industrial settings is often hindered by a lack of domain compliance and system-specific consistency, resulting in suboptimal s

Musaab H. Hamed-Ahmed, Diego Ramil-López, Paula Fraga-Lamas, Tiago M. Fernández-Caramés

Traditional XR and Metaverse applications prioritize user experience (UX) for adoption and success but often overlook a crucial aspect of user interaction: emotions. This article addresses this gap by presenting an emotion-aware Metaverse application: a Virtual Reality (VR) fire drill simulator designed to prepare crews for shipboard emergencies. The simulat

Maren Brauner, Thomas Masseron, Marco Pignatari, D. Aníbal García-Hernández

We provide an overview of the latest advances in the study of phosphorus-rich stars, covering their detailed chemical abundance analyses and innovative mining approaches. Following the discovery of 16 low-mass and low-metallicity stars rich in P, we expanded this sample by demonstrating that a recently identified group of Si-rich giants is also P-rich. A det

Riccardo Dondi, Manuel Lafond

We consider two variants, (s,z,l)-Temporal Separator and (s,z,l)-Temporal Cut, respectively, of the vertex separator and the edge cut problem in temporal graphs. The goal is to remove the minimum number of vertices (temporal edges, respectively) in order to delete all the temporal paths that have time travel at most l between a source vertex s and target ver

Timothée Mathieu

We propose a new statistical hypothesis testing framework which decides visually, using confidence intervals, whether the means of two samples are equal or if one is larger than the other. With our method, the user can at the same time visualize the confidence region of the means and do a test to decide if the means of the two populations are significantly d

Akihiko Monnai, Grégoire Pihan, Björn Schenke, Chun Shen

Exploration of the QCD phase diagram is pivotal in particle and nuclear physics. We construct a full four-dimensional equation of state of QCD with net baryon, electric charge, and strangeness by extending the NEOS model beyond the conventional two-dimensional approximation. Lattice QCD calculations based on the Taylor expansion method and the hadron resonan

Ernesto Garcia, Paola Bermolen, Matthieu Jonckheere, Seva Shneer

We analyze the efficiency of parallelization and restart mechanisms for stochastic simulations in model-free settings, where the underlying system dynamics are unknown. Such settings are common in Reinforcement Learning (RL) and rare event estimation, where standard variance-reduction techniques like importance sampling are inapplicable. Focusing on the chal

Enkhtogtokh Togootogtokh, Christian Klasen

This research introduces a novel AI techniques as Mixture-of-Experts Transformers with Group Relative Policy Optimization (GRPO) for voice health care applications on voice pathology detection. With the architectural innovations, we adopt advanced training paradigms inspired by reinforcement learning, namely Proximal Policy Optimization (PPO) and Group-wise

Guglielmo Lami, Mario Collura, Nishan Ranabhat

Aimed at introducing readers to the physics of strongly correlated many-body systems, these notes focus on numerical methods, with detailed discussions on implementing working code for exact diagonalization. A brief introduction to tensor network methods is also included. Prepared for the Summer School Quantumandu, held at Tribhuvan University (Kathmandu, Ne

Florian Plötzky, Katarina Britz, Wolf-Tilo Balke

The use of narratives as a means of fusing information from knowledge graphs (KGs) into a coherent line of argumentation has been the subject of recent investigation. Narratives are especially useful in event-centric knowledge graphs in that they provide a means to connect different real-world events and categorize them by well-known narrations. However, spe

Thilo Reinold, Suman Ghosh, Guillermo Gallego

Robot manipulation is a common task in fields like industrial manufacturing. Detecting when objects slip from a robot's grasp is crucial for safe and reliable operation. Event cameras, which register pixel-level brightness changes at high temporal resolution (called ``events''), offer an elegant feature when mounted on a robot's end effector: since they only

Ziyuan Yang, Yingyu Chen, Chengrui Gao, Andrew Beng Jin Teoh

Current deep learning (DL)-based palmprint verification models rely on centralized training with large datasets, which raises significant privacy concerns due to biometric data's sensitive and immutable nature. Federated learning~(FL), a privacy-preserving distributed learning paradigm, offers a compelling alternative by enabling collaborative model training

Wenqiao Li, Yao Gu, Xintao Chen, Xiaohao Xu

Humans detect real-world object anomalies by perceiving, interacting, and reasoning based on object-conditioned physical knowledge. The long-term goal of Industrial Anomaly Detection (IAD) is to enable machines to autonomously replicate this skill. However, current IAD algorithms are largely developed and tested on static, semantically simple datasets, which

Irched Chafaa, Giacomo Bacci, Luca Sanguinetti

Power allocation is an important task in wireless communication networks. Classical optimization algorithms and deep learning methods, while effective in small and static scenarios, become either computationally demanding or unsuitable for large and dynamic networks with varying user loads. This letter explores the potential of transformer-based deep learnin

Eva Kogler, Dominik Spath, Roman Lucrezi, Hitoshi Mori

This paper introduces the Julia package IsoME, an easy-to-use yet accurate and robust computational tool designed to calculate superconducting properties. Multiple levels of approximation are supported, ranging from the basic McMillan-Allen-Dynes formula and its machine learning-enhanced variant to Eliashberg theory including static Coulomb interactions deri

Yuna Kato, Mariko Isogawa, Shohei Mori, Hideo Saito

Occlusion-free video generation is challenging due to surgeons' obstructions in the camera field of view. Prior work has addressed this issue by installing multiple cameras on a surgical light, hoping some cameras will observe the surgical field with less occlusion. However, this special camera setup poses a new imaging challenge since camera configurations

Keling Wang, Chang Wei, Jeremy A. Labrecque

Clinical practice guidelines are designed to guide clinical practice and involve causal language. Sometimes guidelines make or require stronger causal claims than those in the references they rely on, a phenomenon we refer to as 'causal language jump'. We evaluated the strength of expressed causation in diabetes guidelines and the evidence they reference to

Na Li, Ye Xing, Jing-Rui Shi

In this work, we investigate the production and decay of the molecular states $cc\bar c\bar q$ with $J^P=1^+$ using the phenomenological analysis and effective Lagrangian approach. Based on an SU(3) flavor symmetry analysis to identify golden channels, we further explore the dynamics of these processes under the molecular assumptions of ${\eta_c D^*}$ and ${

Jesse Beisegel, Katharina Klost, Kristin Knorr, Fabienne Ratajczak

We consider the problem of finding a Hamiltonian path with precedence constraints in the form of a partial order on the vertex set. This problem is known as Partially Ordered Hamiltonian Path Problem (POHPP). Here, we study the complexity for graph width parameters for which the ordinary Hamiltonian Path problem is in $\mathsf{FPT}$. We show that POHPP is $\

Stella Spadoni

The present work is devoted to Computability Logic (CoL), the young and volcanic research-project developed by Giorgi Japaridze. Our main goal is to provide the reader with a clear panoramic view of this vast new land, starting from its core knots and making our way towards the outer threads, in a somewhat three-dimensional, spacial gait. Furthermore, throug

Abraham Loeb, Richard Cloete

Recently, Seligman et. al. (2024) identified a population of near-Earth objects (NEOs) that exhibit statistically significant non-gravitational accelerations with no coma, and labeled them dark comets. Here, we show that one of these objects, 2005 VL1, was at closest approach to Earth in November 1965 when the Venera 2 spacecraft was launched to explore Venu

Yanfei Li, Teng Yin, Wenyi Shang, Jingyu Liu

Missing modalities pose a major issue in Alzheimer's Disease (AD) diagnosis, as many subjects lack full imaging data due to cost and clinical constraints. While multi-modal learning leverages complementary information, most existing methods train only on complete data, ignoring the large proportion of incomplete samples in real-world datasets like ADNI. This

Ross Willard

This is the second of three papers motivated by the author's desire to understand and explain "algebraically" one aspect of Dmitriy Zhuk's proof of the CSP Dichotomy Theorem. In this paper we extend Zhuk's "bridge" construction to arbitrary meet-irreducible congruences of finite algebras in locally finite varieties with a Taylor term. We then connect bridges

Rajesh Selukar

Modeling of growth (or decay) curves arises in many fields such as microbiology, epidemiology, marketing, and econometrics. Parametric forms like Logistic and Gompertz are often used for modeling such monotonic patterns. While useful for compact description, the real-life growth curves rarely follow these parametric forms perfectly. Therefore, the curve esti

Fridtjof Betz, Felix Binkowski, Jan David Fischbach, Nick Feldman

The points where diffraction orders emerge or vanish in the propagating spectrum of periodic non-Hermitian systems are referred to as scattering thresholds. Close to these branch points, resonances from different Riemann sheets can tremendously impact the optical response. However, these resonances are so far elusive for two reasons. First, their contributio

Donghyeok Shin, HeeSun Bae, Gyuwon Sim, Wanmo Kang

Utilizing a large-scale dataset is essential for training high-performance deep learning models, but it also comes with substantial computation and storage costs. To overcome these challenges, dataset distillation has emerged as a promising solution by compressing the large-scale dataset into a smaller synthetic dataset that retains the essential information

Hyeonjun Kim, Kanghoon Lee, Junho Park, Jiachen Li

Multi-Agent Reinforcement Learning (MARL) has shown promise in solving complex problems involving cooperation and competition among agents, such as an Unmanned Surface Vehicle (USV) swarm used in search and rescue, surveillance, and vessel protection. However, aligning system behavior with user preferences is challenging due to the difficulty of encoding exp

Milin Patel, Rolf Jung

Safety of the Intended Functionality (SOTIF) addresses sensor performance limitations and deep learning-based object detection insufficiencies to ensure the intended functionality of Automated Driving Systems (ADS). This paper presents a methodology examining the adaptability and performance evaluation of the 3D object detection methods on a LiDAR point clou

P. A. C. Cunha, A. Humphrey, J. Brinchmann, A. Paulino-Afonso

Active Galactic Nuclei (AGN) significantly influence galaxy evolution. Specific sources such as obscured AGNs, especially Type II quasars (QSO2), still remain understudied. We characterise 366 QSO2 candidates in the redshift desert (median z~1.1) identified via machine learning from SDSS/WISE photometry, analysing their spectral energy distributions (SEDs) a

Pengwu Song, Zhiping Wang, Peng Yao, Liang Xu

The chorioallantoic membrane (CAM) model is a widely used in vivo platform for studying angiogenesis, especially in relation to tumor growth, drug delivery, and vascular biology.Since the topology and morphology of developing blood vessels is a key evaluation metric, accurate vessel segmentation is essential for quantitative analysis of angiogenesis. However

Ngoc Luyen Le, Marie-Hélène Abel

Group recommender systems aim to generate recommendations that align with the collective preferences of a group, introducing challenges that differ significantly from those in individual recommendation scenarios. This paper presents Joint Group Profiling and Recommendation via Deep Neural Network-based Multi-Task Learning, a framework that unifies group prof

Devon Jarvis, Verena Klar, Richard Klein, Benjamin Rosman

Patients with semantic dementia (SD) present with remarkably consistent atrophy of neurons in the anterior temporal lobe and behavioural impairments, such as graded loss of category knowledge. While relearning of lost knowledge has been shown in acute brain injuries such as stroke, it has not been widely supported in chronic cognitive diseases such as SD. Pr

Yiguan Lin, Bin Xu, Yinghao Li, Yang Gao

Large Language Models (LLMs) require instruction fine-tuning to perform different downstream tasks. However, the instruction fine-tuning phase still demands significant computational resources and labeled data, lacking a paradigm that can improve model performance without additional computational power and data. Model merging aims to enhance performance by c

Xiaorang Guo, Tigran Bunarjyan, Dai Liu, Benjamin Lienhard

Superconducting qubits are among the most promising candidates for building quantum information processors. Yet, they are often limited by slow and error-prone qubit readout -- a critical factor in achieving high-fidelity operations. While current methods, including deep neural networks, enhance readout accuracy, they typically lack support for mid-circuit m

Dragos Costea, Alina Marcu, Marius Leordeanu

Generating novel views from recorded videos is crucial for enabling autonomous UAV navigation. Recent advancements in neural rendering have facilitated the rapid development of methods capable of rendering new trajectories. However, these methods often fail to generalize well to regions far from the training data without an optimized flight path, leading to

A. Camposeo, T. Virgili, F. Lombardi, G. Cerullo

In the context of quantum thermodynamics, quantum batteries have emerged as promising devices for energy storage and manipulation. Over the past decade, substantial progress has been made in understanding the fundamental properties of quantum batteries, with several experimental implementations showing great promise. This Perspective provides an overview of

Maria J. Esteban

The fundamental role of mathematics as an inspiration for artists, but also as a tool for art creation, is presented in this paper following different art fields, like architecture, sculpture, painting, photography, literature and poetry, movie making and music. The historical viewpoint is completed with recent applications of mathematics to create art in th

Jacopo Borsotti

We introduce a two-timescale SIRS-type model in which a fraction $\theta$ of infected individuals experiences a severe course of the disease, requiring hospitalization. During hospitalization, these individuals do not contribute to further infections. We analyze the model's equilibria, perform a bifurcation analysis, and explore its two-timescale nature (usi