May 2025 arXiv papers — page 43

Adilet Otemissov, Alina Abdikarimova

This paper proposes a novel approach for solving linear programs. We reformulate a primal-dual linear program as an unconstrained minimization of a convex and twice continuously differentiable merit function. When the optimal set of the primal-dual pair is nonempty, its optimal set is equal to the optimal set of the proposed merit function. Minimizing this m

Lintao Xu, Yinghao Wang, Chaohui Wang

Occlusion Boundary Estimation (OBE) identifies boundaries arising from both inter-object occlusions and self-occlusion within individual objects. This task is closely related to Monocular Depth Estimation (MDE), which infers depth from a single image, as Occlusion Boundaries (OBs) provide critical geometric cues for resolving depth ambiguities, while depth c

Nima Sedghiyeh, Sara Sadeghi, Reza Khodadadi, Farzin Kashani

Although Automatic Speech Recognition (ASR) systems have become an integral part of modern technology, their evaluation remains challenging, particularly for low-resource languages such as Persian. This paper introduces Persian Speech Recognition Benchmark(PSRB), a comprehensive benchmark designed to address this gap by incorporating diverse linguistic and a

José Rodríguez, David Manlove

In the {\sc Course Allocation} problem, there are a set of students and a set of courses at a given university. University courses may have different numbers of credits, typically related to different numbers of learning hours, and there may be other constraints such as courses running concurrently. Our goal is to allocate the students to the courses such th

Alvaro Gonzalez-Jimenez, Simone Lionetti, Ludovic Amruthalingam, Philippe Gottfrois

Medical anomaly detection has emerged as a promising solution to challenges in data availability and labeling constraints. Traditional methods extract features from different layers of pre-trained networks in Euclidean space; however, Euclidean representations fail to effectively capture the hierarchical relationships within these features, leading to subopt

Hong-Yun Yu, Ya-Feng Jiao, Jie Wang, Feng Li

We propose a molecular optomechanical platform to generate robust entanglement among bosonic modes-photons, phonons, and plasmons-under ambient conditions. The system integrates an ultrahigh-Q whispering-gallery-mode (WGM) optical resonator with a plasmonic nanocavity formed by a metallic nanoparticle and a single molecule. This hybrid architecture offers tw

Zijing Wang, Xingle Xu, Yongkang Liu, Yiqun Zhang

Model merging dramatically reduces storage and computational resources by combining multiple expert models into a single multi-task model. Although recent model merging methods have shown promising results, they struggle to maintain performance gains as the number of merged models increases. In this paper, we investigate the key obstacles that limit the scal

Constantine Theocharis, Edwin Brady

Inductive families provide a convenient way of programming with dependent types. Yet, when it comes to compilation, their default linked-tree runtime representations, as well as the need to convert between different indexed views of the same data, can lead to unsatisfactory runtime performance. In this paper, we introduce a language with dependent types, and

Abderrahmane Issam, Yusuf Can Semerci, Jan Scholtes, Gerasimos Spanakis

Understanding robustness is essential for building reliable NLP systems. Unfortunately, in the context of machine translation, previous work mainly focused on documenting robustness failures or improving robustness. In contrast, we study robustness from a model representation perspective by looking at internal model representations of ungrammatical inputs an

D Power, M V Umansky, V A Soukhanovskii

Using a reduced MHD model, extended to include field-aligned thermal conduction, we present numerical simulations of the churning mode (CM): a toroidally symmetric, non-linear plasma vortex in the vicinity of the null points in a snowflake (SF) divertor (Ryutov et al., Phys. Scr. 89 088002, 2014). Simulations are carried out across a range of inter-null sepa

Francesca Lisi, Luca Sabatini

Let $G$ be a finite group and let $(P_i)_{i=1}^n$ be Sylow subgroups for distinct primes $p_1,\ldots,p_n$. We conjecture that there exists $x \in G$ such that $P_i \cap P_i^x$ is inclusion-minimal in $\{ P_i \cap P_i^g : g \in G\}$ for all $i$. As a first step in this direction, we show that a finite group cannot be covered by (proper) Sylow normalizers for

Ellen Devereux, Animesh Datta

We present four quantum algorithms for solving a multidimensional drift-diffusion equation. They rely on a quantum linear system solver, a quantum Hamiltonian simulation, a quantum random walk, and the quantum Fourier transform. We compare the complexities of these methods to their classical counterparts, finding that diagonalization via the quantum Fourier

M. Mebratu, W. L. K. Wu

Extragalactic foregrounds in cosmic microwave background (CMB) observations are both a source of cosmological and astrophysical information and a nuisance to the CMB. Effective field-level modeling that captures their non-Gaussian statistical distributions is increasingly important for optimal information extraction, particularly given the precise and low-no

Qinjun Fei, Nuria Rodríguez-Barroso, María Victoria Luzón, Zhongliang Zhang

In cross-silo Federated Learning (FL), client selection is critical to ensure high model performance, yet it remains challenging due to data quality decompensation, budget constraints, and incentive compatibility. As training progresses, these factors exacerbate client heterogeneity and degrade global performance. Most existing approaches treat these challen

Roi Cohen, Omri Fahn, Gerard de Melo

Large Language Models are known to capture real-world knowledge, allowing them to excel in many downstream tasks. Despite recent advances, these models are still prone to what are commonly known as hallucinations, causing them to emit unwanted and factually incorrect text. In this work, we study how well LLMs capture uncertainty, without explicitly being tra

Kenneth J. Falconer, Shuqin Zhang

We examine Frostman-type characterisations and other extremal measure criteria for a range of fractal dimensions of sets. In particular we derive properties of the less familiar modified lower box dimension and upper correlation dimension. We also express a number of fractal dimensions in terms of Fourier properties of measures.

Cunyi Yin, Chenwei Wang, Jing Chen, Hao Jiang

Accurate indoor positioning for unmanned aerial vehicles (UAVs) is critical for logistics, surveillance, and emergency response applications, particularly in GPS-denied environments. Existing indoor localization methods, including optical tracking, ultra-wideband, and Bluetooth-based systems, face cost, accuracy, and robustness trade-offs, limiting their pra

Alex A. Saoulis, Davide Piras, Niall Jeffrey, Alessio Spurio Mancini

Simulation-based inference (SBI) enables cosmological parameter estimation when closed-form likelihoods or models are unavailable. However, SBI relies on machine learning for neural compression and density estimation. This requires large training datasets which are prohibitively expensive for high-quality simulations. We overcome this limitation with multifi

Jukka Kiukas, Andreas Ruschhaupt

We formulate a quantum arrival time measurement process for a Bosonic many-particle system, with the aim of extracting statistical information on single-particle properties. The arrival time is based on a dynamical multi-particle absorption model in the Fock space, and we consider systems in coherent and incoherent mixtures of $N$-particle states. We find th

Ludgero Glorias, Federico Martellosio, J. M. C. Santos Silva

We consider two nonparametric approaches to ensure that linear instrumental variables estimators satisfy the rich-covariates condition emphasized by Blandhol et al. (2025), even when the instrument is not unconditionally randomly assigned and the model is not saturated. Both approaches start with a nonparametric estimate of the expectation of the instrument

Martin C. Cooper, Imane Bousdira, Clément Carbonnel

A classifier is considered interpretable if each of its decisions has an explanation which is small enough to be easily understood by a human user. A DNF formula can be seen as a binary classifier $\kappa$ over boolean domains. The size of an explanation of a positive decision taken by a DNF $\kappa$ is bounded by the size of the terms in $\kappa$, since we

C. Walz, F. -C. Weber, S. -P. Zeuschner, K. Dumesnil

We investigate strain-induced contributions to the transient polar magneto-optical Kerr effect response in laser-excited terfenol. The tr-MOKE signals obtained from TbFe$_{2}$ films with and without glass capping exhibit distinct signatures associated with transient strain. We experimentally observe the arrival of strain pulses via the reflectivity change. T

Max O. Hansen, Julia von Rothkirch, Peter Kopietz

We study the interplay between critical isotropic elasticity and classical Ising criticality using a functional renormalization group (FRG) approach which is implemented such that the volume is fixed during the entire renormalization group flow. For dimensions slightly smaller than four we use a simple truncation of the FRG flow equations to recover the fixe

Zirui Niu, Daniele Astolfi, Giordano Scarciotti

We address the output regulation problem of linear systems with non-smooth and non-periodic exogenous signals. Specifically, we first formulate and solve the full-information problem by designing a state-feedback controller. We study the solvability of the regulator equations, providing a new non-resonance condition. We then focus on the error-feedback probl

Thomas Deschatre, Xavier Warin

This article presents an input convex neural network architecture using Kolmogorov-Arnold networks (ICKAN). Two specific networks are presented: the first is based on a low-order, linear-by-part, representation of functions, and a universal approximation theorem is provided. The second is based on cubic splines, for which only numerical results support conve

Krzysztof P. Zamarski, Charly Beulenkamp, Yi Zeng, Manuele Landini

We report the creation of ultracold samples of $^{39}$K$^{133}$Cs molecules in their rovibrational ground state. By investigating potentially suitable excited states using one- and two-photon spectroscopy, we have identified a pathway to the ground state via an exceptionally narrow intermediate state. Using Stimulated Raman Adiabatic Passage (STIRAP), we cre

CMS Collaboration

A search for the violation of the charge-parity ($CP$) symmetry in the production of top quarks in association with Z bosons is presented, using events with at least three charged leptons and additional jets. The search is performed in a sample of proton-proton collision data collected by the CMS experiment at the CERN LHC in 2016$-$2018 at a center-of-mass

Liuhan Chen, Xiaodong Cun, Xiaoyu Li, Xianyi He

Video inbetweening aims to synthesize intermediate video sequences conditioned on the given start and end frames. Current state-of-the-art methods primarily extend large-scale pre-trained Image-to-Video Diffusion Models (I2V-DMs) by incorporating the end-frame condition via direct fine-tuning or temporally bidirectional sampling. However, the former results

Marie Steinacker, Yuri Kheifetz, Markus Scholz

Hematotoxicity, drug-induced damage to the blood-forming system, is a frequent side effect of cytotoxic chemotherapy and poses a significant challenge in clinical practice due to its high inter-patient variability and limited predictability. Current mechanistic models often struggle to accurately forecast outcomes for patients with irregular or atypical traj

Yinhui Kan, Paul C. V. Thrane, Xujing Liu, Shailesh Kumar

The development of high-quality solid-state photon sources is essential to nano optics, quantum photonics, and related fields. A key objective of this research area is to develop tunable photon sources that not only enhance the performance but also offer dynamic functionalities. However, the realization of compact and robust photon sources with precise and w

Steven Sam, Silima Marshal DAbreo

Agriculture constitutes a primary source of food production, economic growth and employment in India, but the sector is confronted with low farm productivity and yields aggravated by increased pressure on natural resources and adverse climate change variability. Efforts involving green revolution, land irrigations, improved seeds and organic farming have yie

Xudong Tan, Yaoxin Yang, Peng Ye, Jialin Zheng

Vision-Language-Action (VLA) models have emerged as a powerful paradigm for general-purpose robot control through natural language instructions. However, their high inference cost-stemming from large-scale token computation and autoregressive decoding-poses significant challenges for real-time deployment and edge applications. While prior work has primarily

Natalie Carl, Trever Schirmer, Niklas Kowallik, Joshua Adamek

Function-as-a-Service (FaaS) is an event-driven serverless cloud computing model in which small, stateless functions are invoked in response to events, such as HTTP requests, new database entries, or messages. Current FaaS platform assume that each function invocation corresponds to a single event. However, from an application perspective, it is desirable to

Rong Chao, Rauf Nasretdinov, Yu-Chiang Frank Wang, Ante Jukić

The Interspeech 2025 URGENT Challenge aimed to advance universal, robust, and generalizable speech enhancement by unifying speech enhancement tasks across a wide variety of conditions, including seven different distortion types and five languages. We present Universal Speech Enhancement Mamba (USEMamba), a state-space speech enhancement model designed to han

Stefano Frixione

During the course of an ongoing work on the small-$x$ behaviour of parton distribution functions, some identities have been found which involve Stirling numbers of the first and the second kind, as well as binomial coefficients. Without any claim of originality I report them in this note.

Ibrahim Shoer, Engin Erzin

In affective computing, datasets often contain multiple annotations from different annotators, which may lack full agreement. Typically, these annotations are merged into a single gold standard label, potentially losing valuable inter-rater variability. We propose a multi-annotator training approach for continuous emotion recognition (CER) that seeks a conse

Illia Donhauzer, Nikolai Leonenko, Andriy Olenko

The paper introduces a new class of random fields, supCAR fields, which are constructed as superpositions of continuous autoregressive random fields. These supCAR fields possess infinitely divisible marginal distributions. Their second-order properties are characterised by a novel family of covariance functions which can exhibit short- and long-range spatial

Sreeharsha Udayashankar, Samer Al-Kiswany

Data deduplication has gained wide acclaim as a mechanism to improve storage efficiency and conserve network bandwidth. Its most critical phase, data chunking, is responsible for the overall space savings achieved via the deduplication process. However, modern data chunking algorithms are slow and compute-intensive because they scan large amounts of data whi

Qingyang Liu, Xiangyu Jiang, Ying Chen, Chunjiang Shi

The charm quark density-density correlation is calculated for $1S$ and $1P$ conventional charmonia and $J^{PC}=1^{-+},0^{+-}$ charmoniumlike states from lattice QCD and are interpreted as spatial wave functions of these states with some approximations. The angular distributions of $c\bar{c}$ in conventional charmonia are found to be in accordance with the ex

Xingpao Suo

A Hamiltonian with eigenenergy \( E_n = \rho_n(1 - \rho_n) \) has been constructed, where \( \rho_n \) denotes the \( n \)-th non-trivial zero of the Riemann zeta function. To construct such a Hamiltonian, we generalize the Berry-Keating paradigm and encode number-theoretic information into the Hamiltonian using modular forms.Although our construction does n

Maosen Zhao, Pengtao Chen, Chong Yu, Yan Wen

Model quantization reduces the bit-width of weights and activations, improving memory efficiency and inference speed in diffusion models. However, achieving 4-bit quantization remains challenging. Existing methods, primarily based on integer quantization and post-training quantization fine-tuning, struggle with inconsistent performance. Inspired by the succe

Junyan Zhang, Yubo Gao, Yibo Yan, Jungang Li

The finetuning of Large Language Models (LLMs) has significantly advanced their instruction-following capabilities, yet the underlying computational mechanisms driving these improvements remain poorly understood. This study systematically examines how fine-tuning reconfigures LLM computations by isolating and analyzing instruction-specific sparse components,

Jong Hak Moon, Geon Choi, Paloma Rabaey, Min Gwan Kim

Radiology reports convey detailed clinical observations and capture diagnostic reasoning that evolves over time. However, existing evaluation methods are limited to single-report settings and rely on coarse metrics that fail to capture fine-grained clinical semantics and temporal dependencies. We introduce LUNGUAGE, a benchmark dataset for structured radiolo

Gleb Mezentsev, Ivan Oseledets

A recent study showed that large language models (LLMs) can reconstruct surprisingly long texts - up to thousands of tokens - via autoregressive generation from just one trained input embedding. In this work, we explore whether autoregressive decoding is essential for such reconstruction. We show that frozen LLMs can generate hundreds of accurate tokens in j

Adriel I. Santoso, Le Bin Ho

Dark matter (DM) remains one of the most compelling unresolved problems in fundamental physics, motivating the search for new detection approaches. We propose a network-based quantum sensor architecture to enhance sensitivity to ultralight DM fields. Each node in the network is a superconducting qubit, interconnected via controlled-Z gates in symmetric topol

Hesam Araghi, Jan van Gemert, Nergis Tomen

Event cameras offer high temporal resolution and power efficiency, making them well-suited for edge AI applications. However, their high event rates present challenges for data transmission and processing. Subsampling methods provide a practical solution, but their effect on downstream visual tasks remains underexplored. In this work, we systematically evalu

Miklos Eper, Szilard Szabo

We give explicit cubic equations for the wild character varieties corresponding to the rank 3 representations of Painlev\'e equations, and compare them to the ones of their classical rank 2 representations.

Johanna Senk, Anno C. Kurth, Steve Furber, Tobias Gemmeke

The local circuitry of the mammalian brain is a focus of the search for generic computational principles because it is largely conserved across species and modalities. In 2014 a model was proposed representing all neurons and synapses of the stereotypical cortical microcircuit below $1\,\text{mm}^2$ of brain surface. The model reproduces fundamental features

Yu Yan, Sheng Sun, Mingfeng Li, Yunlong Song

To prevent the misuse of Large Language Models (LLMs) for malicious purposes, numerous efforts have been made to develop the safety alignment mechanisms of LLMs. However, as multiple LLMs become readily accessible through various Model-as-a-Service (MaaS) platforms, attackers can strategically exploit LLMs' heterogeneous safety policies to fulfill malicious

Francesco Urso, Federica Baffigi, Esther Ciarrocchi, Leonida Antonio Gizzi

This study explores the production of radionuclides in a PMMA (Polymethylmethacrylate) phantom irradiated with Very High Energy Electrons (VHEE) beams as a novel method for dose verification. In this work, Monte Carlo simulation studies using the Geant4 toolkit and experimental measurements with a VHEE beam produced by a laser-plasma accelerator were conduct

Huy Hoang, Tien Mai, Pradeep Varakantham, Tanvi Verma

Offline imitation learning typically learns from expert and unlabeled demonstrations, yet often overlooks the valuable signal in explicitly undesirable behaviors. In this work, we study offline imitation learning from contrasting behaviors, where the dataset contains both expert and undesirable demonstrations. We propose a novel formulation that optimizes a

Yayin Zheng, Chen Wan, Zihong Guo, Hailing Kuang

Adversarial attacks have become a significant challenge in the security of machine learning models, particularly in the context of black-box defense strategies. Existing methods for enhancing adversarial transferability primarily focus on the spatial domain. This paper presents Frequency-Space Attack (FSA), a new adversarial attack framework that effectively

ShuNing Sun, YinSong Xiong, Yu Zhang, Zhuoran Zheng

Although \textbf{L}abel \textbf{D}istribution \textbf{L}earning (LDL) has promising representation capabilities for characterizing the polysemy of an instance, the complexity and high cost of the label distribution annotation lead to inexact in the construction of the label space. The existence of a large number of inexact labels generates a label space with

Dar-Yen Chen, Hmrishav Bandyopadhyay, Kai Zou, Yi-Zhe Song

Negative guidance -- explicitly suppressing unwanted attributes -- remains a fundamental challenge in diffusion models, particularly in few-step sampling regimes. While Classifier-Free Guidance (CFG) works well in standard settings, it fails under aggressive sampling step compression due to divergent predictions between positive and negative branches. We pre

Mingyang Song, Mao Zheng

As test-time scaling becomes a pivotal research frontier in Large Language Models (LLMs) development, contemporary and advanced post-training methodologies increasingly focus on extending the generation length of long Chain-of-Thought (CoT) responses to enhance reasoning capabilities toward DeepSeek R1-like performance. However, recent studies reveal a persi

Peijie Wang, Chao Yang, Zhong-Zhi Li, Fei Yin

Geometry is a fundamental branch of mathematics and plays a crucial role in evaluating the reasoning capabilities of multimodal large language models (MLLMs). However, existing multimodal mathematics benchmarks mainly focus on plane geometry and largely ignore solid geometry, which requires spatial reasoning and is more challenging than plane geometry. To ad

E. Di Salvo, P. Cosme, L. Fritz

In this paper, we derive the Euler and Navier-Stokes equations for electronic two-band systems in arbitrary dimension and with generic power-law dispersion relations. We focus on the hydrodynamic transport regime, where such systems offer a unique tunability between a Fermi-liquid type regime at high doping and the inherent two-band physics of the low-densit

Muzzamil Ahmad Eatoo, Himanshu Mishra

Despite the mounting evidence proving that the air-water interface or the microdroplet geometry has nothing to do with the spontaneous formation of hydrogen peroxide (H2O2), the myth persists. Three recent studies by George and co-workers give credence to the myth by showing connections between the spontaneous formation of hydroxyl (HO) radicals and hydrogen

Ivan Tolkachev, Daniel R. Mason, Max Boleininger, Pui-Wai Ma

Nanocrystalline materials have been proposed for use in future fusion reactors due to their high grain boundary density that may act as a sink for irradiation-induced defects. We use molecular dynamics to model collision cascades in nanocrystalline iron and compare the damage evolution to that observed in initially perfect, single crystalline iron. The nanoc

Zhiwang Yu

Topological data analysis (TDA) offers novel mathematical tools for deep learning. Inspired by Carlsson et al., this study designs topology-aware convolutional kernels that significantly improve speech recognition networks. Theoretically, by investigating orthogonal group actions on kernels, we establish a fiber-bundle decomposition of matrix spaces, enablin

Zheng Li, Mao Zheng, Mingyang Song, Wenjie Yang

Recently, deep reasoning large language models(LLMs) like DeepSeek-R1 have made significant progress in tasks such as mathematics and coding. Inspired by this, several studies have employed reinforcement learning(RL) to enhance models' deep reasoning capabilities and improve machine translation(MT) quality. However, the terminology translation, an essential

Rochelle Choenni, Ivan Titov

Multilingual LLM performance is often critically dependent on model size. With an eye on efficiency, this has led to a surge in interest in one-shot pruning methods that retain the benefits of large-scale pretraining while shrinking the model size. However, as pruning tends to come with performance loss, it is important to understand the trade-offs between m

Elija Perrier

AIXI is a widely studied model of artificial general intelligence (AGI) based upon principles of induction and reinforcement learning. However, AIXI is fundamentally classical in nature - as are the environments in which it is modelled. Given the universe is quantum mechanical in nature and the exponential overhead required to simulate quantum mechanical sys

D. K. He, Z. Song

We revisit the phase diagram of an anisotropic Dicke model by revealing the non-analyticity induced by underlying exceptional points. We find that, from a dynamical perspective, the conventional superradiant phase can be further separated into three regions, in which the systems are characterized by different effective Hamiltonians, including the harmonic os

Erik Jung, Helge Gehring, Frank Brueckerhoff-Plueckelmann, Linus Kraemer

Photonic Integrated Circuits (PICs) offer transformative potential for computing systems, enabling high-bandwidth neuromorphic processors and facilitating low decoherence quantum computing on a chip scale platform. However, the development of robust and scalable optical packaging solutions remains a major challenge. Efficient fiber-to-chip coupling is essent

Martin Ravn Christiansen

We prove that the eigenvalues of a 2-body operator $\gamma_{2}^{\Psi}$ associated to a fermionic $N$-particle state $\Psi$ are highly constrained by the structure of the corresponding eigenvectors: If $\Phi=\sum_{k=1}^{\infty}\lambda_{k}u_{k}\wedge v_{k}$ is the canonical form of an eigenvector $\Phi$ with eigenvalue $\Lambda$, then $\Lambda\leq(1+\frac{N-2}

Thiago Araujo

Connections between classical and quantum integrable systems are analyzed from the viewpoint of Slavnov products of Bethe states. It is well known that, modulo model dependent aspects, the functional structure of Slavnov products generally takes the form of determinants. Building on recent results on the structure of rational and trigonometric models, we sho

Yansen Zhang, Bowei He, Xiaokun Zhang, Haolun Wu

Existing recommender systems tend to prioritize items closely aligned with users' historical interactions, inevitably trapping users in the dilemma of ``filter bubble''. Recent efforts are dedicated to improving the diversity of recommendations. However, they mainly suffer from two major issues: 1) a lack of explainability, making it difficult for the system

I. A. Pyrkh, A. E. Rudnev, D. A. Kumpilov, I. S. Cojocaru

Lanthanides are nowadays extensively used to investigate the properties of strongly correlated matter. Nevertheless, exploiting the Zeeman manifold of a lanthanide atom ground state is challenging due to the unavoidable presence of depolarization collisions. Here we demonstrate that in the case of the thulium atom, it is possible to suppress this depolarizat

Feiyang Pan, Shenghe Zheng, Chunyan Yin, Guangbin Dou

MEMS gyroscopes play a critical role in inertial navigation and motion control applications but typically suffer from a fundamental trade-off between measurement range and noise performance. Existing hardware-based solutions aimed at mitigating this issue introduce additional complexity, cost, and scalability challenges. Deep-learning methods primarily focus

M. Ferraro, F. Mangini, K. Stefanska, W. A. Gemechu

Statistical mechanics permits to connect the macroscopic properties of matter with the laws governing the evolution of its microscopic constituents. Such an approach has been very successful for systems of particles governed by either classical or quantum mechanics. In a classical gas, different thermodynamic laws apply to the weakly or strongly interacting

Davi A. Bezerra, Filipi N. Silva, Diego R. Amancio

Citations play a fundamental role in the scientific ecosystem, serving as a foundation for tracking the flow of knowledge, acknowledging prior work, and assessing scholarly influence. In scientometrics, they are also central to the construction of quantitative indicators. Not all citations, however, serve the same function: some provide background, others in

Leon Tolksdorf, Arturo Tejada, Christian Birkner, Nathan van de Wouw

Many motion planning algorithms for automated driving require estimating the probability of collision (POC) to account for uncertainties in the measurement and estimation of the motion of road users. Common POC estimation techniques often utilize sampling-based methods that suffer from computational inefficiency and a non-deterministic estimation, i.e., each

Michael Stenger, Robert Leppich, André Bauer, Samuel Kounev

The growing need for synthetic time series, due to data augmentation or privacy regulations, has led to numerous generative models, frameworks, and evaluation measures alike. Objectively comparing these measures on a large scale remains an open challenge. We propose the Synthetic Time series Evaluation Benchmark (STEB) -- the first benchmark framework that e

Matheus Brito, Adriano Moura

Motivated by the limitations of cluster algebra techniques in detecting imaginary modules, we build on the representation-theoretic framework developed by the first author and Chari to extend the construction of such modules beyond previously known cases, which arise from the tensor product of a higher-order Kirillov--Reshetikhin module and its dual. Our fir

Yuki Adachi, C. S. Lim, Nobuhito Maru

We discuss the 't Hooft-Polyakov (TP) monopole and then dyon in the framework of higher dimensional gauge theories, such as gauge-Higgs unification models. First, we point out that the Bogomol'nyi-Prasad-Sommerfield (BPS) monopole is nothing but a self-dual gauge field in the 4-dimensional (4D) space including the extra dimension, which is argued to lead to

Xiao-Meng Zhang, Guang-Chen He, Zhao-Xian Chen, Ze-Guo Chen

Unlike classical bits that can only occupy one of two discrete states, quantum bits (qubits) can exist in arbitrary coherent superpositions of the ground and excited states. This fundamental distinction grants qubits enhanced capabilities for information storage and processing. The Bloch sphere provides an intuitive and powerful geometric framework for visua

Saisamarth Rajesh Phaye, Milos Cernak, Andrew Harper

Conventional methods for speech enhancement rely on handcrafted loss functions (e.g., time or frequency domain losses) or deep feature losses (e.g., using WavLM or wav2vec), which often fail to capture subtle signal properties essential for optimal performance. To address this, we propose Model as Loss, a novel training paradigm that utilizes the encoder fro

Spiros Patsourakos, Vasilis Archontis

We search for signatures of magnetic flux cancellation in a 3D resistive MHD flux-emergence simulation of coronal jets and eruptions in a coronal-hole-like environment. To do this, we analysed the output from a 3D MHD simulation of an emerging twisted horizontal flux tube from the convection zone into the solar atmosphere. The simulation considered the impac

Hailin Zhong, Hanlin Wang, Yujun Ye, Meiyi Zhang

Current personalized recommender systems predominantly rely on static offline data for algorithm design and evaluation, significantly limiting their ability to capture long-term user preference evolution and social influence dynamics in real-world scenarios. To address this fundamental challenge, we propose a high-fidelity social simulation platform integrat

Yifan Shan, Bo Liu, Sebastian Bidegain, Thijs Roumen

What if digital fabrication waste could observe the world? What would they see? What would they say? "THE WASTIVE" reimagines digital fabrication waste as sentient observers, giving them a poetic voice through interactive art. As viewers approach, the installation awakens, mimicking the rhythmic ebb and flow of ocean waves - a silent dialogue where discarded

Lorraine Saju, Tobias Holtdirk, Meetkumar Pravinbhai Mangroliya, Arnim Bleier

Computational reproducibility is fundamental to scientific research, yet many published code supplements lack the necessary documentation to recreate their computational environments. While researchers increasingly share code alongside publications, the actual reproducibility of these materials remains poorly understood. In this work, we assess the computati

Xurui Li, Zhonesheng Jiang, Tingxuan Ai, Yu Zhou

Robust unsupervised anomaly detection (AD) in real-world scenarios is an important task. Current methods exhibit severe performance degradation on the MVTec AD 2 benchmark due to its complex real-world challenges. To solve this problem, we propose a robust framework RoBiS, which consists of three core modules: (1) Swin-Cropping, a high-resolution image pre-p

Shivaraj Mulleria Babu

The Short Baseline Near Detector (SBND) is a LArTPC detector, located 110 meters from the Fermilab's Booster Neutrino Beam (BNB). It is designed to measure neutrino cross-section and aid in searches for excess electron like neutrino events. The electric field inside the SBND-TPC may have distortions due to a number of reasons, such as the space charge effect

Lasse Asikainen, Yu Chen, Risto Korhonen

We show that for a vanishing period difference operator of a meromorphic function \( f \), there exist the following estimates regarding proximity functions, \[ \lim_{\eta \to 0} m_\eta\left(r, \frac{\Delta_\eta f - a\eta}{f' - a} \right) = 0 \] and \[ \lim_{r \to \infty} m_\eta\left(r, \frac{\Delta_\eta f - a\eta}{f' - a} \right) = 0, \] where \( \Delta_\et

Arnaud Durand, Juha Kontinen, Werner Mérian, Jouko Väänänen

We propose a systematic study of the so-called flattening operator in team semantics. This operator was first introduced by Hodges in 1997, and has not been studied in more detail since. We begin a systematic study of the expressive power this operator adds to the most well-known team-based logics, such as dependence logic, anonymity logic, inclusion logic a

Rao Ma, Mengjie Qian, Siyuan Tang, Stefano Bannò

The growing population of L2 English speakers has increased the demand for developing automatic graders for spoken language assessment (SLA). Historically, statistical models, text encoders, and self-supervised speech models have been utilised for this task. However, cascaded systems suffer from the loss of information, while E2E graders also have limitation

Xuanning Zhou, Zihao Shi, Hao Zeng, Xiaobo Xia

Conformal prediction (CP) is a powerful framework for uncertainty quantification, generating prediction sets with coverage guarantees. Split conformal prediction relies on labeled data in the calibration procedure. However, the labeled data is often limited in real-world scenarios, leading to unstable coverage performance in different runs. To address this i

Yang Zhao, Yan Zhang, Xubo Yang

Existing human motion generation methods with trajectory and pose inputs operate global processing on both modalities, leading to suboptimal outputs. In this paper, we propose IKMo, an image-keyframed motion generation method based on the diffusion model with trajectory and pose being decoupled. The trajectory and pose inputs go through a two-stage condition

Takumi Asano

We consider conditions under which endomorphisms of varieties become automorphisms. For example, there is a remarkable theorem, called Ax-Grothendieck theorem, which states that any injective endomorphism of a variety is bijective. Over an algebraically closed field of characteristic zero, bijectivity of endomorphisms of varieties implies that the endomorphi

Sergey Karpukhin, Vadim Titov, Andrey Kuznetsov, Aibek Alanov

In latest years plethora of identity-preserving adapters for a personalized generation with diffusion models have been released. Their main disadvantage is that they are dominantly trained jointly with base diffusion models, which suffer from slow multi-step inference. This work aims to tackle the challenge of training-free adaptation of pretrained ID-adapte

Sam C. Scholten, Jakub Iwański, Kaijian Xing, Johannes Binder

Hexagonal boron nitride (hBN) has recently been shown to host native defects exhibiting optically detected magnetic resonance (ODMR) with applications in nanoscale magnetic sensing and imaging. To advance these applications, deposition methods to create wafer-scale hBN films with controlled thicknesses are desirable, but a systematic study of the ODMR proper

D. A. Zyuzin, A. V. Karpova, A. Yu. Kirichenko, Yu. A. Shibanov

Transitional millisecond pulsars (tMSPs) in tight binary systems represent an important evolutionary link between low-mass X-ray binaries and radio millisecond pulsars. To date, only three confirmed tMSPs and a few candidates have been discovered. Most of them are gamma-ray sources. For this reason, searching for multiwavelength counterparts to unassociated

Nancy C. Woods, Virtue Ene Agada, Adebola K. Ojo

Phishing is a semantic attack which targets the user rather than the computer. It is a new Internet crime in comparison with other forms such as virus and hacking. Considering the damage phishing websites has caused to various economies by collapsing organizations, stealing information and financial diversion, various researchers have embarked on different w

Honglin Gao, Xiang Li, Lan Zhao, Gaoxi Xiao

Heterogeneous graph neural networks (HGNNs) have recently drawn increasing attention for modeling complex multi-relational data in domains such as recommendation, finance, and social networks. While existing research has been largely focused on enhancing HGNNs' predictive performance, their robustness and security, especially under backdoor attacks, remain u

Subhagata Chattopadhyay, Amit K Chattopadhyay

The COVID-19 pandemic has significantly increased the incidence of post-infection cardiovascular events, particularly myocardial infarction, in individuals over 40. While the underlying mechanisms remain elusive, this study employs a hybrid machine learning approach to analyze epidemiological data in assessing 13 key heart attack risk factors and their susce

Tianyi Xu, Hongjie Chen, Wang Qing, Lv Hang

Large-scale training corpora have significantly improved the performance of ASR models. Unfortunately, due to the relative scarcity of data, Chinese accents and dialects remain a challenge for most ASR models. Recent advancements in self-supervised learning have shown that self-supervised pre-training, combined with large language models (LLM), can effective

Mengjie Qian, Rao Ma, Stefano Bannò, Kate M. Knill

Spoken Grammatical Error Correction (SGEC) and Feedback (SGECF) are crucial for second language learners, teachers and test takers. Traditional SGEC systems rely on a cascaded pipeline consisting of an ASR, a module for disfluency detection (DD) and removal and one for GEC. With the rise of end-to-end (E2E) speech foundation models, we investigate their effe

Jintao Zhang, Xiaoming Xu, Jia Wei, Haofeng Huang

The efficiency of attention is critical because its time complexity grows quadratically with sequence length. SageAttention2 addresses this by utilizing quantization to accelerate matrix multiplications (Matmul) in attention. To further accelerate SageAttention2, we propose to utilize the faster instruction of FP8 Matmul accumulated in FP16. The instruction

Anqi Tang, Youming Chen, Shuchen Xue, Zhaoqiang Liu

Diffusion models (DMs) have demonstrated remarkable ability to generate diverse and high-quality images by efficiently modeling complex data distributions. They have also been explored as powerful generative priors for signal recovery, resulting in a substantial improvement in the quality of reconstructed signals. However, existing research on signal recover