March 2025 arXiv papers — page 194

Guangze Chen, Anton Frisk Kockum

Quantum computation and quantum simulation require a versatile gate set to optimize circuit compilation for practical applications. However, existing platforms are often limited to specific gate types or rely on parametric couplers to extend their gate set, which compromises scalability. Here, we propose a scalable quantum simulator with an extended gate set

Mihai Fulger, Victor Lozovanu

We compute the generic infinitesimal Newton-Okounkov body at any point for some box-product polarizations on products of curves. This appears to be the first nontrivial description of such a body in arbitrary dimension.

Xing-Yu Zhong, Wen-Biao Han, Ling Sun

Identifying weak gravitational wave signals in noise and estimating the source properties require high-precision waveform templates. Numerical relativity (NR) simulations can provide the most accurate waveforms. However, it is challenging to compute waveform templates in high-dimensional parameter space using NR simulations due to high computational costs. I

Daniel Boyanovsky

In this article it is argued that synthetic axions, emergent collective excitations in topological insulators or Weyl semimetals hybridize with the cosmological axion, a compelling dark matter candidate via a common two photon decay channel since they both couple to electromagnetic fields via a Chern-Simons term. We point out an analogy to a V-type three lev

Ekansh Jauhari

The $n$-th symmetric product of a topological space $X$ is the orbit space of the natural action of the symmetric group $S_n$ on the product space $X^n$. In this paper, we compute the sequential topological complexities of (finite products of) the symmetric products of closed orientable surfaces, thereby verifying the rationality conjecture of Farber and Opr

Maël Voyer, Quentin Changeat, Pierre-Olivier Lagage, Pascal Tremblin

The study of the atmosphere of exoplanets orbiting white dwarfs is a largely unexplored field. With WD\,0806-661\,b, we present the first deep dive into the atmospheric physics and chemistry of a cold exoplanet around a white dwarf. We observed WD 0806-661 b using JWST's Mid-InfraRed Instrument Low-Resolution Spectrometer (MIRI-LRS), covering the wavelength

Chen Li, Yinyi Luo, Anudeep Bolimera, Uzair Ahmed

Large Language Models excel in reasoning yet often rely on Chain-of-Thought prompts, limiting performance on tasks demanding more nuanced topological structures. We present SOLAR (Scalable Optimization of Large-scale Architecture for Reasoning), a framework that dynamically optimizes Chain-of-Thought (CoT), Tree-of-Thought (ToT), and Graph-of-Thought (GoT) t

Alberto Morando

Objective: Many low-severity crashes are not reported due to sampling criteria, introducing missing not at random (MNAR) bias. If not addressed, MNAR bias can lead to inaccurate safety analyses. This paper illustrates a statistical method to address such bias. Methods: We defined a custom probability distribution for the observed data as a product of an expo

Thien Pham, Angelo Furno, Faïcel Chamroukhi, Latifa Oukhellou

This paper presents an advanced Federated Learning (FL) framework for forecasting complex spatiotemporal data, improving upon recent state-of-the-art models. In the proposed approach, the original Gated Recurrent Unit (GRU) module within previous Dynamic Spatial--Temporal Graph Convolutional Recurrent Network (DSTGCRN) modeling is first replaced with a Long

Bo Yuan, Yulin Chen, Zhen Tan, Wang Jinyan

Many text classification methods usually introduce external information (e.g., label descriptions and knowledge bases) to improve the classification performance. Compared to external information, some internal information generated by the model itself during training, like text embeddings and predicted label probability distributions, are exploited poorly wh

Baltazar Espinoza, Roger Sanchez, Jimmy Calvo-Monge, Fabio Sanchez

Epidemics exhibit interconnected processes that operate at multiple time and organizational scales, a hallmark of complex adaptive systems. Modern epidemiological modeling frameworks incorporate feedback between individual-level behavioral choices and centralized interventions. Nonetheless, the realistic operational course for disease detection, planning, an

Akshay Gaikwad, Manuel Sebastian Torres, Shahnawaz Ahmed, Anton Frisk Kockum

Quantum state tomography (QST) is a widely employed technique for characterizing the state of a quantum system. However, it is plagued by two fundamental challenges: computational and experimental complexity grows exponentially with the number of qubits, rendering experimental implementation and data post-processing arduous even for moderately sized systems.

Prince Mathew, Vincent Penelle, A. V. Sreejith

We give an active learning algorithm for deterministic one-counter automata (DOCAs) where the learner can ask the teacher membership and minimal equivalence queries. The algorithm called OL* learns a DOCA in time polynomial in the size of the smallest DOCA, recognising the target language. All existing algorithms for learning DOCAs, even for the subclasses o

Prachi Chavan, Bin Yang, Miroslav Brož, Josef Hanuš

The Pallas collisional family of asteroids, named after (2) Pallas, is notable for its high orbital inclination and the distinct blue color of Pallas and a few larger B-type family members. While Pallas itself, as one of the largest asteroids, has been studied in detail, most of its smaller family members still remain unexplored. This study aims to character

Matias Cosarinsky, Ramiro Billot, Lucas Mansilla, Gabriel Jimenez

Assessing the quality of automatic image segmentation is crucial in clinical practice, but often very challenging due to the limited availability of ground truth annotations. Reverse Classification Accuracy (RCA) is an approach that estimates the quality of new predictions on unseen samples by training a segmenter on those predictions, and then evaluating it

Songyuan Li, Jia Hu, Geyong Min, Haojun Huang

The convergence of edge computing and Artificial Intelligence (AI) gives rise to Edge-AI, which enables the deployment of real-time AI applications at the network edge. A key research challenge in Edge-AI is edge inference acceleration, which aims to realize low-latency high-accuracy Deep Neural Network (DNN) inference by offloading partitioned inference tas

Jonas Faltinath, Fabian Mohn, Fynn Foerger, Martin Möddel

The precise derivation of physical quantities like temperature or pressure at arbitrary locations is useful in numerous contexts, e.g. medical procedures or industrial process engineering. The novel sensor technology of magneto-mechanical resonators (MMR), based on the interaction of a rotor and stator permanent magnet, allows for the combined tracking of th

A. Hahlin, O. Kochukhov, P. Chaturvedi, E. Guenther

Magnetic field investigations of Sun-like stars, using Zeeman splitting of non-polarised spectra, in the optical and H-band have found significantly different magnetic field strengths for the same stars, the cause of which is currently unknown. We aim to further investigate this issue by systematically analysing the magnetic field of $\xi$ Boo A, a magnetica

Sumit Vashishtha, Odalric-Ambrym Maillard

We introduce Dirichlet Process Posterior Sampling (DPPS), a Bayesian non-parametric algorithm for multi-arm bandits based on Dirichlet Process (DP) priors. Like Thompson-sampling, DPPS is a probability-matching algorithm, i.e., it plays an arm based on its posterior-probability of being optimal. Instead of assuming a parametric class for the reward generatin

Honghao Fu, Kieran Mastel, Xingjian Zhang

In their recent breakthrough result, Slofstra and the second author show that there is a two-player one-round perfect zero-knowledge MIP* protocol for RE (STOC'24). We build on their result to show that there exists a succinct two-player one-round perfect zero-knowledge MIP* protocol for RE against dishonest verifiers with polylog question size and O(1) answ

Chenhao Yang, Siwei Huang, Chuan Hu

In the field of conditional autonomous driving technology, driver perceived risk prediction plays a crucial role in reducing traffic risks and ensuring passenger safety. This study introduces an innovative perceived risk prediction model for human-machine interaction in intelligent driving systems. The model aims to enhance prediction accuracy and, thereby,

Atsushi Higuchi, Vasileios A. Letsios

It is commonly believed that a unitary supersymmetric quantum field theory (QFT) involving graviton and gravitino fields on fixed 4-dimensional de Sitter spacetime ($dS_{4}$) cannot exist due to known challenges associated with supersymmetry (SUSY) in $dS_{4}$. In this paper, we contradict this expectation by presenting a new unitary supersymmetric QFT on $d

Hakan Johansson

This paper considers the reconstruction of digital complex baseband signals from M-periodically nonuniformly sampled real bandpass signals. With such a sampling, bandpass signals with arbitrary frequency locations can be sampled and reconstructed, as opposed to uniform sampling which requires the signal to be within one of the Nyquist bands. It is shown how

Jiageng Zhong, Qi Zhou, Ming Li, Armin Gruen

Low-overlap aerial imagery poses significant challenges to traditional photogrammetric methods, which rely heavily on high image overlap to produce accurate and complete mapping products. In this study, we propose a novel workflow based on monocular depth estimation to address the limitations of conventional techniques. Our method leverages tie points obtain

Kwing Hei Li, Alejandro Aguirre, Simon Oddershede Gregersen, Philipp G. Haselwarter

We present Coneris, the first higher-order concurrent separation logic for reasoning about error probability bounds of higher-order concurrent probabilistic programs with higher-order state. To support modular reasoning about concurrent (non-probabilistic) program modules, state-of-the-art program logics internalize the classic notion of linearizability with

Pierre Fraigniaud, Hovhannes A. Harutyunyan

This paper revisits the study of (minimum) broadcast graphs, i.e., graphs enabling fast information dissemination from every source node to all the other nodes (and having minimum number of edges for this property). This study is performed in the framework of compact distributed data structures, that is, when the broadcast protocols are bounded to be encoded

Alex Turzillo, Naren Manjunath, Jose Garre-Rubio

A method of using partial symmetries to distinguish two dimensional symmetry protected topological (SPT) phases of on-site, unitary symmetries is proposed. This novel order parameter takes a wavefunction, such as a ground state of a lattice model, and detects its SPT invariants as expectation values of finitely supported operators, without the need for flux

Saif Anwar, Nathan Griffiths, Thomas Popham, Abhir Bhalerao

Recent improvements in the expressive power of spatio-temporal models have led to performance gains in many real-world applications, such as traffic forecasting and social network modelling. However, understanding the predictions from a model is crucial to ensure reliability and trustworthiness, particularly for high-risk applications, such as healthcare and

Shoshauna Gauvin

We give physical gap certificates for regulated Yang-Mills Hamiltonians and conditional continuum-transfer criteria. At fixed spatial cutoff, the physical $SU(3)$ Hamiltonian has a volume-independent gap of at least $8a/3$ for magnetic/electric ratio $b/a\le1/648$, with a thermodynamic ground state and a surviving finite-energy Wilson excitation. Its centere

Liat Nemirovsky-Levy, Amit Kam, Meir Lederman, Meir Orenstein

Quantum nanophotonics merges the precision of nanoscale light manipulation with the capabilities of quantum technologies, offering a pathway for enhanced light-matter interaction and compact realization of quantum devices. Here, we show how a recently-demonstrated nonlinear nanophotonic process can be employed to selectively create photonic high-dimensional

Nenad Petrovic, Yurui Zhang, Moaad Maaroufi, Kuo-Yi Chao

Multimodal summarization integrating information from diverse data modalities presents a promising solution to aid the understanding of information within various processes. However, the application and advantages of multimodal summarization have not received much attention in model-based engineering (MBE), where it has become a cornerstone in the design and

Shuang Liu, Na Su, Huixia Fu, Yan Liu

The properties of metal-organic frameworks (MOFs) are expected to be sensitive to external pressures because of their inherently flexible structures. Although pressure-driven structural transitions have been intensively studied, the influence of pressure on magnetism has been less exploited for MOFs. Especially, the efficiency of applied pressure may strongl

Sunghyun Ahn, Youngwan Jo, Kijung Lee, Sein Kwon

Video anomaly detection (VAD) is crucial for video analysis and surveillance in computer vision. However, existing VAD models rely on learned normal patterns, which makes them difficult to apply to diverse environments. Consequently, users should retrain models or develop separate AI models for new environments, which requires expertise in machine learning,

Yinshan Chang, Yichao Huang, Dang-Zheng Liu, Xiaolin Zeng

The goal of this note is twofold: first, we explain the relation between the isomorphism theorems in the context of vertex reinforced jump process discovered in [BHS19, BHS21] and the standard Markovian isomorphism theorems for Markovian jump processes; second, we introduce the vertex reinforced counterpart of the standard Poissonian loop soup developed by L

Osnat Mokryn, Teddy Lazebnik, Hagit Ben Shoshan

The analysis of high-dimensional timeline data and the identification of outliers and anomalies is critical across diverse domains, including sensor readings, biological and medical data, historical records, and global statistics. However, conventional analysis techniques often struggle with challenges such as high dimensionality, complex distributions, and

Zhihao Shi, Dong Huo, Yuhongze Zhou, Kejia Yin

Current 3D inpainting and object removal methods are largely limited to front-facing scenes, facing substantial challenges when applied to diverse, "unconstrained" scenes where the camera orientation and trajectory are unrestricted. To bridge this gap, we introduce a novel approach that produces inpainted 3D scenes with consistent visual quality and coherent

Yu-Hsi Chen, Ching-Kai Lin, PingKong Huang, Chin-Tien Wu

Video understanding has largely relied on deep spatiotemporal architectures, including 3D convolutional networks and optical flow (OF) based models. While effective, these methods are often computationally expensive and depend on heuristic motion representations that are sensitive to illumination, scale, and structural changes. To address these limitations,

Benjamin Billot, Ramya Muthukrishnan, Esra Abaci-Turk, P. Ellen Grant

Unsupervised registration strategies bypass requirements in ground truth transforms or segmentations by optimising similarity metrics between fixed and moved volumes. Among these methods, a recent subclass of approaches based on unsupervised keypoint detection stand out as very promising for interpretability. Specifically, these methods train a network to pr

Mingyu Deng, Shengqian Han

Weighted sum rate maximization (WSRM) for precoder optimization effectively balances performance and fairness among users. Recent studies have demonstrated the potential of deep learning in precoder optimization for sum rate maximization. However, the WSRM problem necessitates a redesign of neural network architectures to incorporate user weights into the in

Vincent C. Müller

In this paper I want to propose an argument to support Jerry Fodor's thesis (Fodor 1983) that input systems are modular and thus informationally encapsulated. The argument starts with the suggestion that there is a "grounding problem" in perception, i. e. that there is a problem in explaining how perception that can yield a visual experience is possible, how

Adrian Chang, Kai Wang, Yuanbo Li, Manolis Savva

In this work we study indoor scene object placement. Given a 3D indoor scene and an object, the task is to predict placement locations within the scene. Empirical observations of data-driven approaches to the problem show their tendency to miss placement modes. We introduce a system which helps to address this flaw. We design a Domain Specific Language (DSL)

P. Bydžovský, D. Denisova, F. Knapp, P. Veselý

The DWIA formalism for computing the cross sections in electroproduction of hypernuclei used before for lighter (p-shell and sd-shell) systems is utilized in studying electroproduction on heavier targets such as $^{52}$Cr, and $^{208}$Pb. First, the effects from kaon distortion and kinematics are re-examined and then results for the heavy targets are discuss

Jiawang Zhang, Xing Ji, Kun Xu

The boundary layer represents a fundamental structure in fluid dynamics, where accurate boundary discretization significantly enhances computational efficiency. This paper presents a third-order boundary discretization for compact gas-kinetic scheme (GKS). Wide stencils and curved boundaries pose challenges in the boundary treatment for high-order schemes, p

Joohwi Lee, Kaito Miyamoto

In the rapidly advancing field of materials informatics, nonlinear machine learning models have demonstrated exceptional predictive capabilities for material properties. However, their black-box nature limits interpretability, and they may incorporate features that do not contribute to -- or even deteriorate -- model performance. This study employs explainab

Zhenyu Wang, Zikang Wang, Jiyue Jiang, Pengan Chen

Large Language Models (LLMs) are revolutionizing bioinformatics, enabling advanced analysis of DNA, RNA, proteins, and single-cell data. This survey provides a systematic review of recent advancements, focusing on genomic sequence modeling, RNA structure prediction, protein function inference, and single-cell transcriptomics. Meanwhile, we also discuss sever

M. T. Guaglianone, E. Sorrentino, E. Cardillo, M. T. Chiaravalloti

The realization of digital preservation in compliance with legislation is extremely important, especially in a sensitive domain like healthcare, where guaranteeing document reliability, authenticity, integrity and readability over time is essential to have an immediate return in terms of efficiency of the whole care setting. In this perspective, the present

Kaede Hanazawa

This paper studies the welfare effects of self-preferencing by Airbnb, a practice where Airbnb utilizes its pricing algorithm to prioritize maximizing platform-wide commission revenue rather than optimizing individual host revenues. To examine this welfare implication, I construct a Bertrand competition model with differentiated products between Airbnb hosts

Manuel Mancini, Federica Piazza, Corentin Vienne

Working in the setting of ideally exact categories, we investigate the representability of actions of unital non-associative algebras over a field. We show that, in general, such categories fail to be action representable: for instance, the category of all unital algebras is not even action accessible. We then consider this problem in the context of operadic

Weishu Zhan, Zheng Liang, Hongyu Song, Wei Pan

Quadrupedal robots exhibit remarkable adaptability in unstructured environments, making them well-suited for formation control in real-world applications. However, keeping stable formations while ensuring collision-free navigation presents significant challenges due to dynamic obstacles, communication constraints, and the complexity of legged locomotion. Thi

Savinien Kreczman, Sébastien Labbé, Manon Stipulanti

Introduced in 2001 by Lecomte and Rigo, abstract numeration systems provide a way of expressing natural numbers with words from a language $L$ accepted by a finite automaton. As it turns out, these numeration systems are not necessarily positional, i.e., we cannot always find a sequence $U=(U_i)_{i\ge 0}$ of integers such that the value of every word in the

Teodor Rotaru, Panagiotis Patrinos, François Glineur

We investigate a difference-of-convex (DC) formulation where the second term is allowed to be weakly convex. We examine the precise behavior of a single iteration of the difference-of-convex algorithm (DCA), providing a tight characterization of the objective function decrease, distinguishing between six distinct parameter regimes. Our proofs, inspired by th

Matthew G. Hennessy, Tom Shearer, Axel C. Moore

Fibre-reinforced hydrogels are promising materials for biomedical applications due to their strength, toughness, and tunability. However, it remains unclear how to design fibre-reinforced hydrogels for use in specific applications due to the lack of a flexible modelling framework that can predict and hence optimise their behaviour. In this paper, we present

Soichiro Morisaki, Jun'ya Kume, Takumi Fujimori, Yuta Michimura

In recent years, numerous experiments have been proposed and conducted to search for ultralight bosonic dark matter (ULBDM). Signals from ULBDM in such experiments are characterized by extremely narrow spectral widths. A near-optimal detection strategy is to divide the data based on the signal coherence time and sum the power across these segments. However,

Tianyu Cui, Song-Jun Xu, Artem Moskalev, Shuwei Li

Inferring Gene Regulatory Networks (GRNs) from gene expression data is crucial for understanding biological processes. While supervised models are reported to achieve high performance for this task, they rely on costly ground truth (GT) labels and risk learning gene-specific biases, such as class imbalances of GT interactions, rather than true regulatory mec

Dimitri von Rütte, Janis Fluri, Yuhui Ding, Antonio Orvieto

While state-of-the-art language models achieve impressive results through next-token prediction, they have inherent limitations such as the inability to revise already generated tokens. This has prompted exploration of alternative approaches such as discrete diffusion. However, masked diffusion, which has emerged as a popular choice due to its simplicity and

The CUPID Collaboration, K. Alfonso, A. Armatol, C. Augier

Cryogenic calorimeters, also known as bolometers, are among the leading technologies for searching for rare events. The CUPID experiment is exploiting this technology to deploy a tonne-scale detector to search for neutrinoless double-beta decay of $^{100}$Mo. The CUPID collaboration proposed an innovative approach to assembling bolometers in a stacked config

Matthieu Carreau, Roi Naveiro, William N. Caballero

Research in adversarial machine learning (AML) has shown that statistical models are vulnerable to maliciously altered data. However, despite advances in Bayesian machine learning models, most AML research remains concentrated on classical techniques. Therefore, we focus on extending the white-box model poisoning paradigm to attack generic Bayesian inference

Ivan Milev, Mislav Balunović, Maximilian Baader, Martin Vechev

Large Language Model (LLM) Agents leverage the advanced reasoning capabilities of LLMs in real-world applications. To interface with an environment, these agents often rely on tools, such as web search or database APIs. As the agent provides the LLM with tool documentation along the user query, the completeness and correctness of this documentation is critic

Maxime Di Folco, Emily Chan, Marta Hasny, Cosmin I. Bercea

General-purpose AI models, particularly those designed for text and vision, demonstrate impressive versatility across a wide range of deep-learning tasks. However, they often underperform in specialised domains like medical imaging, where domain-specific solutions or alternative knowledge transfer approaches are typically required. Recent studies have noted

Changqian Rao, David Waxman, Wei Lin, Zhuoyi Song

In biochemical reaction networks, the first passage time (FPT) of a reaction quantifies the time it takes for the reaction to first occur, from the initial state. While the mean FPT historically served as a summary metric, a far more comprehensive characterization of the dynamics of the network is contained within the complete FPT distribution. The relativel

Viktor Stojkoski, César A. Hidalgo

Efforts to apply economic complexity to identify diversification opportunities often rely on diagrams comparing the relatedness and complexity or products, technologies, or industries. Yer, the use of these diagrams is not based on empirical or theoretidal evidence supporting some notion of optimality. Here, we introduce an optimization-based framework that

Yanqing Shen, Turcan Tuna, Marco Hutter, Cesar Cadena

Place recognition is essential to maintain global consistency in large-scale localization systems. While research in urban environments has progressed significantly using LiDARs or cameras, applications in natural forest-like environments remain largely under-explored. Furthermore, forests present particular challenges due to high self-similarity and substan

Francisco Eiras, Eliott Zemour, Eric Lin, Vaikkunth Mugunthan

Large Language Model (LLM) based judges form the underpinnings of key safety evaluation processes such as offline benchmarking, automated red-teaming, and online guardrailing. This widespread requirement raises the crucial question: can we trust the evaluations of these evaluators? In this paper, we highlight two critical challenges that are typically overlo

Xiyue Zhang, Xiaoyong Xue, Xiaoning Du, Xiaofei Xie

Federated learning (FL), as a powerful learning paradigm, trains a shared model by aggregating model updates from distributed clients. However, the decoupling of model learning from local data makes FL highly vulnerable to backdoor attacks, where a single compromised client can poison the shared model. While recent progress has been made in backdoor detectio

Yi Shen, Jian Zhang, Jieyun Huang, Shuming Shi

Recent advancements in slow thinking reasoning models have shown exceptional performance in complex reasoning tasks. However, these models often exhibit overthinking (generating redundant reasoning steps for simple problems), leading to excessive computational resource usage. While current mitigation strategies uniformly reduce reasoning tokens, they risk de

Tristan van der Vlugt

We give a survey of cardinal charcteristics of the higher Cicho\'n diagram defined on the higher Baire space ${}^\kappa\kappa$ for $\kappa$ regular with $2^{<\kappa}=\kappa$. Specifically, we will compare consistency proofs from the classical Cicho\'n diagram with various well-known forcing notions to similar constructions generalised to the higher Cicho\'n

Edoardo Bianchi, Oswald Lanz

This paper introduces Gate-Shift-Pose, an enhanced version of Gate-Shift-Fuse networks, designed for athlete fall classification in figure skating by integrating skeleton pose data alongside RGB frames. We evaluate two fusion strategies: early-fusion, which combines RGB frames with Gaussian heatmaps of pose keypoints at the input stage, and late-fusion, whic

Meijin Lin, Lin Guo, Dicheng Chen, Jianshu Chen

Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Phili

Weichen Fan, Furkan Ayhan, Thibault Wildi, Mikhail Volkov

Optical frequency combs and their spectra of evenly spaced discrete laser lines are essential to modern time and frequency metrology. Recent advances in integrated photonic waveguides enable efficient nonlinear broadening of an initially narrowband frequency comb to multi-octave bandwidth. Here, we study the nonlinear dynamics in the generation of such ultra

Xiang Zhang, Shuhan Xie, Yule Sun

In this paper, we establish the existence and uniqueness of solutions to the two-dimensional Burgers equation using the framework of infinite-dimensional dynamical systems. The two-dimensional Burgers equation, which models the interplay between nonlinear advection and viscous dissipation, is given by: $$ u_{t} + u \cdot \nabla u = \nu \Delta u + f, $$ where

Michael Konopik, Sigrid Leyendecker, Sofya Maslovskaya, Sina Ober-Blöbaum

In this work, we propose and study a new approach to formulate the optimal control problem of second-order differential equations, with a particular interest in those derived from force-controlled Lagrangian systems. The formulation results in a new hyperregular control Langrangian and, thus, a new control Hamiltonian whose equations of motion provide necess

Jon Asier Bárcena-Petisco, Fouad Et-Tahri

This paper is devoted to the averaged controllability of the random Schr\"odinger equation, with diffusivity as a random variable drawn from a general probability distribution. First, we show that the solutions to these random Schr\"odinger equations are null averaged controllable with an open-loop control independent of randomness from any arbitrary subset

Kazunori Kohri, Partha Kumar Paul, Narendra Sahu

The recent observation of the ultrahigh-energy neutrino event KM3-230213A by the KM3NeT experiment offers a compelling avenue to explore physics beyond the Standard Model (SM). In this paper, we explore a simplest possibility that this event originates from the decay of a super-heavy dark matter (SHDM). We consider a minimal scenario where the SHDM decays to

Van Bach Nguyen, Christin Seifert, Jörg Schlötterer

The need for interpretability in deep learning has driven interest in counterfactual explanations, which identify minimal changes to an instance that change a model's prediction. Current counterfactual (CF) generation methods require task-specific fine-tuning and produce low-quality text. Large Language Models (LLMs), though effective for high-quality text g

Xiangyu Miao, Jun Sun, Hang Lai, Xinpeng Di

With the rapid development of embodied intelligence, locomotion control of quadruped robots on complex terrains has become a research hotspot. Unlike traditional locomotion control approaches focusing solely on velocity tracking, we pursue to balance the agility and robustness of quadruped robots on diverse and complex terrains. To this end, we propose an en

Michele Giusfredi, Stefano Iubini, Antonio Politi, Paolo Politi

Some lattice models having two conservation laws may display an equilibrium phase transition from a homogeneous (positive temperature - PT) to a condensed (negative temperature) phase, where a finite fraction of the energy is localized in a few sites. We study one such stochastic model in an out-of-equilibrium setup, where the ends of the lattice chain are a

Tiago Massoni, Ricardo Duarte, Ruan Oliveira

Background. Career abandonment, the process in which professionals leave the activity, assuming positions in another area, among software developers involves frustration with the lost investment and emotional and financial costs, even though being beneficial for the human being, depending on personal context. Previous studies have identified work-related mot

Hongyeob Kim, Inyoung Jung, Dayoon Suh, Youjia Zhang

Audio-Visual Question Answering (AVQA) requires not only question-based multimodal reasoning but also precise temporal grounding to capture subtle dynamics for accurate prediction. However, existing methods mainly use question information implicitly, limiting focus on question-specific details. Furthermore, most studies rely on uniform frame sampling, which

N. Graham, H. Weigel

The vacuum polarization energy is the leading quantum correction to the classical energy of a soliton. We study this energy for two-component solitons in one space dimension as a function of the soliton's topological charge. We find that both the classical and the vacuum polarization energies are linear functions of the topological charge with a small offset

Chao Wang, Weiwei Fu, Yang Zhou

Vision-language models (VLMs) have achieved remarkable advancements, capitalizing on the impressive capabilities of large language models (LLMs) across diverse tasks. Despite this, a critical challenge known as hallucination occurs when models overconfidently describe objects or attributes absent from the image, a problem exacerbated by the tendency of VLMs

Phil Pützstück

We answer a question of Schwede on the existence of global Picard spectra associated to his ultra-commutative global ring spectra; given an ultra-commutative global ring spectrum $R$, we show there exists a global spectrum $\mathrm{pic}_\mathrm{eq}(R)$ assembling the Picard spectra of all underlying $G$-equivariant ring spectra $\mathrm{res}_G R$ of $R$ into

Viktoria Kraxberger, Marcus Bumbar, Angela Gligorova, Claude Amsler

A study of antiproton annihilations at rest on thin solid targets is underway at the ASACUSA facility, which now features a dedicated beam line for slow extraction at 250 eV. The experiment will employ new technologies, such as the Timepix4 ASICs coupled to silicon sensors, to measure the total multiplicity, energy, and angular distribution of various prongs

Peggy Varniere, Raphaël Mignon-Risse, Fabien Casse

The detection of gravitational waves from binary black holes (BBHs) started the hunt for their pre-merger electromagnetic emission. In that respect, numerical simulations have been looking for the "smoking gun" signal that could help identify pre-merger systems. Here we study if any of the expected features of circumbinary discs, such as the periodic modulat

Andrea Alessandrelli, Adriano Barra, Andrea Ladiana, Andrea Lepre

By leveraging tools from the statistical mechanics of complex systems, in these short notes we extend the architecture of a neural network for hetero-associative memory (called three-directional associative memories, TAM) to explore supervised and unsupervised learning protocols. In particular, by providing entropic-heterogeneous datasets to its various laye

Runhan Chen, Meijin Lin, Jianshu Chen, Liangjie Lin

Given the need to elucidate the mechanisms underlying illnesses and their treatment, as well as the lack of harmonization of acquisition and post-processing protocols among different magnetic resonance system vendors, this work is to determine if metabolite concentrations obtained from different sessions, machine models and even different vendors of 3 T scan

Xuerui Zhang

Small targets are particularly difficult to detect due to their low pixel count, complex backgrounds, and varying shooting angles, which make it hard for models to extract effective features. While some large-scale models offer high accuracy, their long inference times make them unsuitable for real-time deployment on edge devices. On the other hand, models d

Marco Arazzi, Mert Cihangiroglu, Antonino Nocera

Federated Averaging remains the most widely used aggregation strategy in federated learning due to its simplicity and scalability. However, its performance degrades significantly in non-IID data settings, where client distributions are highly imbalanced or skewed. Additionally, it relies on clients transmitting metadata, specifically the number of training s

Piotr Z. Stasiak, Andrew Baggaley, Carlo. F. Barenghi, Giorgio Krstulovic

Vortex reconnections play a fundamental role in fluids.They increase the complexity of flow and develop small-scale motions.In this work, we report that in superfluids, they can also excite large scales. We numerically illustrate that during a superfluid vortex reconnection energy is injected into the thermal (normal) component of helium~II at small length s

Michał Dolina, Jakub Dec, Stanisław Drożdż, Jarosław Kwapień

Recent research shows that punctuation patterns in texts exhibit universal features across languages. Analysis of Western classical literature reveals that the distribution of spaces between punctuation marks aligns with a discrete Weibull distribution, typically used in survival analysis. By extending this analysis to Chinese literature represented here by

Tim Engels, Ivo Adan, Onno Boxma, Jacques Resing

In this paper, we analyze a polling system on a circle with. Random batches of customers arrive at a circle, where each customer, independently, obtains a location according to a general distribution. A single server cyclically travels over the circle to serve all customers. We analyze the experienced delay of batches for two service policies: globally gated

Wei Liu, Xin Liu, Michael K. Ng, Zaikun Zhang

Semi-supervised clustering is a basic problem in various applications. Most existing methods require knowledge of the ideal cluster number, which is often difficult to obtain in practice. Besides, satisfying the must-link constraints is another major challenge for these methods. In this work, we view the semi-supervised clustering task as a partitioning prob

Lakhadive Mehulkumar R, Anshu Sharma, Basuraj Bhowmik

Structural health monitoring (SHM) is an essential engineering field aimed at ensuring the safety and reliability of civil infrastructures. This study proposes a methodology using multivariate variational mode decomposition (MVMD) for damage detection and modal identification. MVMD decomposes multi-sensor vibration responses into intrinsic modal components,

Yijie Xu, Bolun Zheng, Wei Zhu, Hangjia Pan

Social media popularity prediction task aims to predict the popularity of posts on social media platforms, which has a positive driving effect on application scenarios such as content optimization, digital marketing and online advertising. Though many studies have made significant progress, few of them pay much attention to the integration between popularity

Demin Wang, Zhaoyong Huang, Yu-Zhe Liu

We provide a method for computing the global dimension and self-injective dimension of almost gentle algebras,and prove that an almost gentle algebra is Gorenstein if it satisfies the Auslander condition.

Vittorio Pippi, Matthieu Guillaumin, Silvia Cascianelli, Rita Cucchiara

Large Multimodal Models (LMMs) are powerful tools that are capable of reasoning and understanding multimodal information beyond text and language. Despite their entrenched impact, the development of LMMs is hindered by the higher computational requirements compared to their unimodal counterparts. One of the main causes of this is the large amount of tokens n

A. S. Makarov, G. V. Afonin, R. A. Konchakov, J. C. Qiao

We performed parallel study of calorimetric and high-frequency shear modulus behavior of Zr-based metallic glasses after deep relaxation just below the glass transition. It is shown that deep relaxation results in the appearance of a strong peak of the excess heat capacity while the shear modulus is moderately affected. A theory assuming high-frequency shear

Tanzir Hossain, Ar-Rafi Islam, Md. Sabbir Hossain, Annajiat Alim Rasel

This study presents a cascaded architecture for extractive summarization of multimedia content via audio-to-text alignment. The proposed framework addresses the challenge of extracting key insights from multimedia sources like YouTube videos. It integrates audio-to-text conversion using Microsoft Azure Speech with advanced extractive summarization models, in

Nanda Rea, Davide De Grandis

Magnetars are the most magnetic objects in the Universe, serving as unique laboratories to test physics under extreme magnetic conditions that cannot be replicated on Earth. They were discovered in the late 1970s through their powerful X-ray flares, and were subsequently identified as neutron stars characterized by steady and transient emission across the ra

Rohit Menon, Nils Dengler, Sicong Pan, Gokul Krishna Chenchani

For scene understanding in unstructured environments, an accurate and uncertainty-aware metric-semantic mapping is required to enable informed action selection by autonomous systems. Existing mapping methods often suffer from overconfident semantic predictions, and sparse and noisy depth sensing, leading to inconsistent map representations. In this paper, we

Michael Blondin, Alain Finkel, Piotr Hofman, Filip Mazowiecki

Workflow nets are a well-established variant of Petri nets for the modeling of process activities such as business processes. The standard correctness notion of workflow nets is soundness, which comes in several variants. Their decidability was shown decades ago, but their complexity was only identified recently. In this work, we are primarily interested in