March 2025 arXiv papers — page 65

Enrique Artal Bartolo

Superisolated surface singularities in $(\mathbb{C}^3,0)$ were introduced by I. Luengo to prove that the $\mu$-constant stratum may be singular. The main feature of this family is that it can bring information from the projective plane curves (global setting but smaller dimension) into surface singularities. They are simple enough to allow to retreive inform

Amanpreet Singh Saimbhi

The increasing complexity of modern software systems has led to a rise in vulnerabilities that malicious actors can exploit. Traditional methods of vulnerability detection, such as static and dynamic analysis, have limitations in scalability and automation. This paper proposes a novel approach to detecting software vulnerabilities using a combination of code

Weronika Łajewska, Krisztian Balog

Retrieval-augmented generation (RAG) faces challenges related to factual correctness, source attribution, and response completeness. To address them, we propose a modular pipeline for grounded response generation that operates on information nuggets-minimal, atomic units of relevant information extracted from retrieved documents. The multistage pipeline enco

Paolo Ceravolo, Ernesto Damiani, Maria Elisa D'Amico, Bianca de Teffe Erb

This paper introduces the HH4AI Methodology, a structured approach to assessing the impact of AI systems on human rights, focusing on compliance with the EU AI Act and addressing technical, ethical, and regulatory challenges. The paper highlights AIs transformative nature, driven by autonomy, data, and goal-oriented design, and how the EU AI Act promotes tra

Risto Vaarandi, Leonidas Tsiopoulos, Gabor Visky, Muaan Ur Rehman

In recent years, many cyber incidents have occurred in the maritime sector, targeting the information technology (IT) and operational technology (OT) infrastructure. One of the key approaches for handling cyber incidents is cyber security monitoring, which aims at timely detection of cyber attacks with automated methods. Although several literature review pa

Manfried Faber

It has been known for over 100 years that there is a discrepancy between Maxwell's electrodynamics and the idea of a classical electron as the ``atom'' of electricity. This incompatibility is known under the terms 4/3 problem of the classical electron and radiation reaction force and was circumvented in the currently most successful theories, the quantum fie

Ning-Yuan Georgia Liu, David R. Keith

Transforming a dynamic hypothesis into a causal loop diagram (CLD) is crucial for System Dynamics Modelling. Extracting key variables and causal relationships from text to build a CLD is often challenging and time-consuming for novice modelers, limiting SD tool adoption. This paper introduces and tests a method for automating the translation of dynamic hypot

Zixin Chen, Sicheng Song, Kashun Shum, Yanna Lin

Misleading visualizations, which manipulate chart representations to support specific claims, can distort perception and lead to incorrect conclusions. Despite decades of research, they remain a widespread issue, posing risks to public understanding and raising safety concerns for AI systems involved in data-driven communication. While recent multimodal larg

Shuyao Lin, Zhuo Chen, Rebecca Janknecht, Zaoli Zhang

Fracture toughness ($K_\mathrm{Ic}$) and fracture strength ($\sigma_\mathrm{f}$) are key criteria in the selection and design of reliable ceramics. However, their experimental characterization remains challenging -- especially for ceramic thin films, where size and interfacial effects hinder accurate and reproducible measurements. Here, machine-learning inte

Abderrachid Hamrani, Anuradha Godavarty

Producing high-quality segmentation masks for medical images is a fundamental challenge in biomedical image analysis. Recent research has explored large-scale supervised training to enable segmentation across various medical imaging modalities and unsupervised training to facilitate segmentation without dense annotations. However, constructing a model capabl

A. Jiménez-Vargas, Abraham Rueda Zoca

Let $X$ be a metric space with a base point $0$, and let $\mathrm{Lip}_0(X)$ be the Banach space of all Lipschitz functions $f:X\longrightarrow \mathbb R$ such that $f(0)=0$. Given a set of points $\left((x_i,y_i)\right)_{i\in I}$ in $X^2$ with $x_i\neq y_i$ for all $i\in I$, we study the following interpolation problem: when for each bounded set $\left(\alp

Xiang Li, Bing Luo, Jianwei Huang, Yuan Luo

The rapid growth of AI-generated content (AIGC) services has created an urgent need for effective prompt pricing strategies, yet current approaches overlook users' strategic two-step decision-making process in selecting and utilizing generative AI models. This oversight creates two key technical challenges: quantifying the relationship between user prompt ca

Suman Adhya, Avishek Lahiri, Debarshi Kumar Sanyal, Partha Pratim Das

Negative sampling has emerged as an effective technique that enables deep learning models to learn better representations by introducing the paradigm of learn-to-compare. The goal of this approach is to add robustness to deep learning models to learn better representation by comparing the positive samples against the negative ones. Despite its numerous demon

Luca Pezzè, Andrea Santoni, Chiara Mazzinghi, Marco Fattori

Precise measurements in optical and atomic systems often rely on differential interferometry. This method allows to handle large and correlated phase noise contributions -- such as environmental vibrations, thermal fluctuations, or instrumental drifts -- preventing them from blurring the signal. To date, this approach has primarily focused on extracting the

Dario Crisci, Sebastian E. Ferrando, Konrad Gajewski

An agent-based modelling methodology for the joint price evolution of two stocks is put forward. The method models future multidimensional price trajectories reflecting how a class of agents rebalance their portfolios in an operational way by reacting to how stocks' charts unfold. Prices are expressed in units of a third stock that acts as numeraire. The met

Namrata Kundu, Yves Lucet

We compute the closest convex piecewise linear-quadratic (PLQ) function with minimal number of pieces to a given univariate piecewise linear-quadratic function. The Euclidean norm is used to measure the distance between functions. First, we assume that the number and positions of the breakpoints of the output function are fixed, and solve a convex optimizati

Florian Galliot, Jonas Sénizergues

We introduce achievement positional games, a convention for positional games which encompasses the Maker-Maker and Maker-Breaker conventions. We consider two hypergraphs, one red and one blue, on the same vertex set. Two players, Left and Right, take turns picking a previously unpicked vertex. Whoever first fills an edge of their color, blue for Left or red

Hui Xue, Sarah M. Hooper, Iain Pierce, Rhodri H. Davies

To develop and evaluate a new deep learning MR denoising method that leverages quantitative noise distribution information from the reconstruction process to improve denoising performance and generalization. This retrospective study trained 14 different transformer and convolutional models with two backbone architectures on a large dataset of 2,885,236 image

Seyyed Danial Nazemi, Mohsen A. Jafari, Andrea Matta

Active Inference (AIF) is emerging as a powerful framework for decision-making under uncertainty, yet its potential in engineering applications remains largely unexplored. In this work, we propose a novel dual-layer AIF architecture that addresses both building-level and community-level energy management. By leveraging the free energy principle, each layer a

R. Dey, P. K. Netrakanti, D. K. Mishra, S. P. Behera

The Indian Scintillator Matrix for Reactor Anti-Neutrinos (ISMRAN) is an above-ground, very short baseline reactor anti-neutrino (${\overline{\ensuremath{\nu}}}_{e}$) experiment, located inside the Dhruva research reactor facility, Mumbai, India. The primary goal of the ISMRAN experiment is the indirect detection of reactor ${\overline{\ensuremath{\nu}}}_{e}

Haoyang Li, Siyu Zhou, Liang Wang, Guodong Long

Though CLIP-based prompt tuning significantly enhances pre-trained Vision-Language Models, existing research focuses on reconstructing the model architecture, e.g., additional loss calculation and meta-networks. These approaches generally lead to increased complexity and extended training cost. To maintain the efficiency of the tuning process, we propose plu

Peng Chen, Xiaobao Wei, Ming Lu, Hui Chen

Real-time speech-driven 3D facial animation has been attractive in academia and industry. Traditional methods mainly focus on learning a deterministic mapping from speech to animation. Recent approaches start to consider the nondeterministic fact of speech-driven 3D face animation and employ the diffusion model for the task. Existing diffusion-based methods

J. Aalbers, D. S. Akerib, A. K. Al Musalhi, F. Alder

While dual-phase xenon time projection chambers (TPCs) have driven the sensitivity towards weakly interacting massive particles (WIMPs) at the GeV/c^2 to TeV/c^2 mass scale, the scope for sub-GeV/c^2 dark matter particles is hindered by a limited nuclear recoil energy detection threshold. One approach to probe for lighter candidates is to consider cases wher

Luigi Ambrosio, Federico Renzi, Federico Vitillaro

We prove that every one-dimensional locally normal metric current, intended in the sense of U. Lang and S. Wenger, admits a nice integral representation through currents associated to (possibly unbounded) curves with locally finite length, generalizing the result shown by E. Paolini and E. Stepanov in the special case of Ambrosio-Kirchheim normal currents. O

Bram Rijsbosch, Gijs van Dijck, Konrad Kollnig

AI-generated images have become so good in recent years that individuals often cannot distinguish them any more from "real" images. This development, combined with the rapid spread of AI-generated content online, creates a series of societal risks. Watermarking, a technique that involves embedding information within images and other content to indicate their

R. O'Flanagan

I report the existence of exactly one non-trivial solution to the equation $i(A,B)+i(A,\neg B)+i(\neg A,B)+i(\neg A,\neg B)= 0$, where $i(A,B)=\log\frac{P(A\text{ and }B)}{P(A)P(B)}$, and $P(A)$ is the probability of the proposition $A$. The equation specifies an information balance condition between two logical propositions, which is satisfied only by indep

Kelly O. Marshall, Omid Poursaeed, Sergiu Oprea, Amit Kumar

3D indoor scene generation is an important problem for the design of digital and real-world environments. To automate this process, a scene generation model should be able to not only generate plausible scene layouts, but also take into consideration visual features and style preferences. Existing methods for this task exhibit very limited control over these

Saisai He, Yang Liu, Bin Xi, Hong-Gang Luo

Motivated by a recent experiment that realizes nearest-neighbor dipolar couplings in an optical lattice [C. Lagoin, $\textit{et al.}$, Nature $\textbf{609}$, 485 (2022)], we study a one-dimensional version of the two-component extended Bose-Hubbard model via the density-matrix renormalization group method. By using the nearest-neighbor and on-site interactio

V. D. Burkert, A. Camsonne, P. Chatagnon, K. Cichy

This article summarizes the main ideas behind creating an open database proposed for use in the exploration of generalized parton distributions (GPDs). This lightweight database is well suited for GPD phenomenology and is designed to store both experimental and lattice-QCD data. It can also aid in benchmarking GPD-related developments, such as GPD models. Th

Siwon Kim, Wooyung Yun, Jeongbin Oh, Soomok Lee

Deep learning has emerged as the predominant solution for classifying medical images. We intend to apply these developments to the ultra-widefield (UWF) retinal imaging dataset. Since UWF images can accurately diagnose various retina diseases, it is very important to clas sify them accurately and prevent them with early treatment. However, processing images

Zhuoling Li, Hossein Rahmani, Qiuhong Ke, Jun Liu

Video diffusion models have recently achieved remarkable results in video generation. Despite their encouraging performance, most of these models are mainly designed and trained for short video generation, leading to challenges in maintaining temporal consistency and visual details in long video generation. In this paper, we propose LongDiff, a novel trainin

Mimi Dai, Hassan Babaei

Due to the singular nonlinear Hall term, the non-resistive electron magnetohydrodynamics (MHD) is not known to be locally well-posed in general. In this paper we consider the $2\frac12$D electron MHD with either horizontal or vertical resistivity and show local well-posedness in Sobolev spaces.

Atheeth. S, Chandrashekar L N, Isha Munjal, Swathi Padmanabhan

In this paper, we describe the complete fabrication process of a 1D piezoelectric Micromachined Ultrasound Transducer (pMUT) array operating at 16 MHz underwater. We demonstrate the applicability of this pMUT Array in medical imaging using photoacoustic imaging (PAI) and ultrasound imaging (USI) experiments. There are 16 individual pMUT devices in the array,

Esmail Gumaan

ExpertRAG is a novel theoretical framework that integrates Mixture-of-Experts (MoE) architectures with Retrieval Augmented Generation (RAG) to advance the efficiency and accuracy of knowledge-intensive language modeling. We propose a dynamic retrieval gating mechanism coupled with expert routing, enabling the model to selectively consult an external knowledg

Ke Niu, Yuwen Chen, Haiyang Yu, Zhuofan Chen

Computer-Aided Design (CAD) plays a pivotal role in industrial manufacturing, yet 2D Parametric Primitive Analysis (PPA) remains underexplored due to two key challenges: structural constraint reasoning and advanced semantic understanding. To tackle these challenges, we first propose an Efficient Hybrid Parametrization (EHP) for better representing 2D enginee

Lukas Grund, Hendrik Süß

We study linear operators preserving the property of being a volume polynomial. More, precisely we show that a linear operator preserves this property if the associated symbol is itself a volume polynomial. This can be seen as an analogue to theorems by Borcea-Br\"and\'en and Br\"and\'en-Huh for stable polynomials and Lorentzian polynomials, respectively.

Jiabao Liu, Hiroki Nagakura, Masamichi Zaizen, Lucas Johns

There has been growing evidence that mu and tau neutrinos are noticeably different due to the emergence of muons in core-collapse supernovae (CCSNe) and binary neutron star mergers (BNSMs). Recent theoretical studies also suggest that all flavors of neutrinos and antineutrinos inevitably experience some flavor mixing instabilities including fast neutrino fla

Will Sandholtz, Andrew Tai

Top trading cycles with fixed tie-breaking (TTC) has been suggested to deal with indifferences in object allocation problems. Unfortunately, under general indifferences, TTC is neither Pareto efficient nor group strategy-proof. Furthermore, it may not select an allocation in the core of the market, even when the core is non-empty. However, when indifferences

Yassine Tahraoui

We study the singular limit of Fokker-Planck equation of polymers density as the dominant time-scale of small scale component of turbulent flow goes to zero. Here, we complete the study of Flandoli-Tahraoui[arXiv:2410.00520] about scaling limit as the space-scale of small scale component of turbulent flow goes to zero by using stochastic modeling of turbulen

Zhangyu Wang, Zeping Liu, Jielu Zhang, Zhongliang Zhou

Image geolocalization is a fundamental yet challenging task, aiming at inferring the geolocation on Earth where an image is taken. State-of-the-art methods employ either grid-based classification or gallery-based image-location retrieval, whose spatial generalizability significantly suffers if the spatial distribution of test images does not align with the c

Diwei Wang, Cédric Bobenrieth, Hyewon Seo

Assessing gait impairment plays an important role in early diagnosis, disease monitoring, and treatment evaluation for neurodegenerative diseases. Despite its widespread use in clinical practice, it is limited by subjectivity and a lack of precision. While recent deep learning-based approaches have consistently improved classification accuracies, they often

Archit Patke, Christian Pinto, Saurabh Jha, Haoran Qiu

Hardware memory disaggregation (HMD) is an emerging technology that enables access to remote memory, thereby creating expansive memory pools and reducing memory underutilization in datacenters. However, a significant challenge arises when accessing remote memory over a network: increased contention that can lead to severe application performance degradation.

Jinjin Hu, Xujun Zhang

We show that a smooth bounded domain in $\mathbb{C}^n$ admitting partial pseudoconvex exhaustion remains partial pseudoconvex. The main ingredient of the proof is based on a new characterization of hyper-$q$-convex domains. Furthermore, we get several convex analogies.

Mariia Slobodian, Mykola Kozlenko

This paper presents the machine learning approach to the automated classification of a dog's emotional state based on the processing and recognition of audio signals. It offers helpful information for improving human-machine interfaces and developing more precise tools for classifying emotions from acoustic data. The presented model demonstrates an overall a

Mingi Kwon, Shin seong Kim, Jaeseok Jeong. Yi Ting Hsiao, Youngjung Uh

Diffusion models have achieved remarkable success in text-to-image synthesis, largely attributed to the use of classifier-free guidance (CFG), which enables high-quality, condition-aligned image generation. CFG combines the conditional score (e.g., text-conditioned) with the unconditional score to control the output. However, the unconditional score is in ch

Markus J. Aschwanden, Carolus, J. Schrijver

The observed size distributions of solar and stellar flares is found to be consistent with the predictions of the fractal-diffusive self-organized criticality (FD-SOC) model, which predicts power law slopes with universal constants of $\alpha_F=(9/5)=1.80$ for the flux, and $\alpha_E=(5/3)\approx 1.67$ for the fluence, respectively. In this Letter we explore

Ruoxi Cheng, Haoxuan Ma, Weixin Wang, Ranjie Duan

Alignment is vital for safely deploying large language models (LLMs). Existing techniques are either reward-based (training a reward model on preference pairs and optimizing with reinforcement learning) or reward-free (directly fine-tuning on ranked outputs). Recent research shows that well-tuned reward-based pipelines remain the most robust, and single-resp

Jiaxin Huang, Runnan Chen, Ziwen Li, Zhengqing Gao

Reasoning segmentation aims to segment target objects in complex scenes based on human intent and spatial reasoning. While recent multimodal large language models (MLLMs) have demonstrated impressive 2D image reasoning segmentation, adapting these capabilities to 3D scenes remains underexplored. In this paper, we introduce MLLM-For3D, a simple yet effective

Xiaofei Hui, Haoxuan Qu, Hossein Rahmani, Jun Liu

Human-object interaction (HOI) detection often faces high levels of ambiguity and indeterminacy, as the same interaction can appear vastly different across different human-object pairs. Additionally, the indeterminacy can be further exacerbated by issues such as occlusions and cluttered backgrounds. To handle such a challenging task, in this work, we begin w

Mandar R. Nalavade, Ravindra S. Tomar, Gaurav S. Kasbekar

We address the problem of beam scheduling for downlink transmissions in a single-cell millimeter wave (mmWave) network. The cell contains a mmWave base station (mBS) and its associated users. At the end of each time slot, a packet arrives into the queue of a user at the mBS with a certain probability. A holding cost is incurred for the packets stored in a us

Yibo Yan, Shen Wang, Jiahao Huo, Philip S. Yu

Mathematical error detection in educational settings presents a significant challenge for Multimodal Large Language Models (MLLMs), requiring a sophisticated understanding of both visual and textual mathematical content along with complex reasoning capabilities. Though effective in mathematical problem-solving, MLLMs often struggle with the nuanced task of i

Juntao Dai, Taiye Chen, Yaodong Yang, Qian Zheng

Reinforcement learning from human feedback (RLHF) is an effective method for aligning large language models (LLMs) with human values. However, reward over-optimization remains an open challenge leading to discrepancies between the performance of LLMs under the reward model and the true human objectives. A primary contributor to reward over-optimization is th

Varvara Krechetova, Denis Kochedykov

This paper establishes a benchmark for evaluating tool-calling capabilities of large language models (LLMs) on multi-step geospatial tasks relevant to commercial GIS practitioners. We assess eight commercial LLMs (Claude Sonnet 3.5 and 4, Claude Haiku 3.5, Gemini 2.0 Flash, Gemini 2.5 Pro Preview, GPT-4o, GPT-4.1 and o4-mini) using a simple tool-calling agen

P. Hardy, P. Rousselot, C. Richard, V. Boudon

Cyanogen ($\mathrm{C_2N_2}$) is suspected for a long time to be present in comets and to contribute to the creation of the CN radical. So far no observations with ground-based facilities have managed to detect this species but the Rosetta mission, thanks to in situ observations with the ROSINA mass spectrometer detected this species in the coma of 67P/Churyu

Max Sommer, Mark Wyatt, Yinuo Han

Recent JWST observations of the Fomalhaut debris disk have revealed a significant abundance of dust interior to the outer planetesimal belt, raising questions about its origin and maintenance. In this study, we apply an analytical model to the Fomalhaut system, that simulates the dust distribution interior to a planetesimal belt, as collisional fragments acr

Xuanzhao Gao, Qi Zhou, Zecheng Gan, Jiuyang Liang

Dielectrically confined Coulomb systems are widely employed in molecular dynamics (MD) simulations. Despite extensive efforts in developing efficient and accurate algorithms for these systems, rigorous and accurate error estimates, which are crucial for optimal parameter selection for simulations, is still lacking. In this work, we present a rigorous error a

Md Farhan Shahriyar, Gazi Tanbhir

Quantum technologies are rapidly advancing as image classification tasks grow more complex due to large image volumes and extensive parameter updates required by traditional machine learning models. Quantum Machine Learning (QML) offers a promising solution for medical image classification. The parallelization of quantum computing can significantly improve s

Dharana Joshi, Tanay Nag

We explore the topological properties of extended SSH models, considering four sub-lattices in a unit cell and second-nearest-neighbor intercell hopping for SSH4 and SSH long-range (SSHLR) models, respectively. The additional tuning parameters cause the SSH4 (SSHLR) model to host chiral symmetry protected two (two and four) zero-energy modes producing a rich

Igor V. Ovchinnikov

Non-integrability in the sense of dynamical systems, also known as dynamical chaos, is a strongly nonlinear qualitative phenomenon. Its most promising theoretical descriptions are likely to emerge from non-perturbative approaches, with symmetry-based methods being particularly reliable. One such symmetry-based framework is supersymmetric theory of stochastic

Alexander Gielisse, Jan van Gemert

Implicit neural representations (INRs) such as NeRF and SIREN encode a signal in neural network parameters and show excellent results for signal reconstruction. Using INRs for downstream tasks, such as classification, is however not straightforward. Inherent symmetries in the parameters pose challenges and current works primarily focus on designing architect

Annisa Sarah, Rosario G. Garroppo, Gianfranco Nencioni

Multi-access edge computing (MEC) is a promising technology that provides low-latency processing capabilities. To optimize the network performance in a MEC system, an efficient routing path between a user and a MEC host is essential. The network performance is characterized by multiple attributes, including packet-loss probability, latency, and jitter. A use

Antonios Kyriazis, Fengwei Yang

Light bosons can form a gravitational atom (GA) around a spinning black hole through the superradiance process. Considering the black hole to be part of a binary system, the tidal potential of the companion periodically perturbs the GA such that an ``atomic'' transition occurs between two of its energy eigenstates. The resonant transition is modeled by the L

Hoang Nhan Luu, Yu-Cheng Qiu, S. -H. Henry Tye

Recently the Dark Energy Survey (DES) Collaboration presented evidence that the equation of state $w$ of the dark energy is varying, or $w \simeq -0.948$ if it is constant. In either case, the dark energy cannot be due to a cosmological constant alone. Here we study an ultralight axion (or axion-like particle) with mass $m_\phi \simeq 2 \times 10^{-33}$ eV t

Duanya Lyu, Luyu Liu, Catherine Campbell, Yuxuan Zhang

Understanding food acquisition is crucial for developing strategies to combat food insecurity, a major public health concern. The emergence of large-scale mobile location data (typically exemplified by GPS data), which captures people's movement over time at high spatiotemporal resolutions, offer a new approach to study this topic. This paper evaluates the p

Xiangyu Cui, Ki-Hong Park, Mohamed-Slim Alouini

One of the most important technologies in the fifth generation (5G) and the sixth generation (6G) is massive multiple input multiple outputs (MIMO) or extremely large-scale MIMO (XL-MIMO). With the evolving high-frequency technologies in millimeter band or tereHz band, the communication scene is changing into near-field rather than the conventional far-field

Muhidin A. Mohamed, Shuab D. Ahmed, Yahye A. Isse, Hanad M. Mohamed

The fact that everyone with a social media account can create and share content, and the increasing public reliance on social media platforms as a news and information source bring about significant challenges such as misinformation, fake news, harmful content, etc. Although human content moderation may be useful to an extent and used by these platforms to f

Matteo Bonato, Leonardo Trobbiani, Ivano Baronchelli, Gianfranco De Zotti

This paper presents a novel approach to the use of Planck telescope data for the systematic search of ultra-bright high-redshift strongly lensed galaxies. These galaxies provide crucial insights into the early universe, particularly during phases of intense star formation. The Planck mission, despite its limited angular resolution, offers a unique opportunit

Chi-Ning Chou, Hang Le, Yichen Wang, SueYeon Chung

Integrating task-relevant information into neural representations is a fundamental ability of both biological and artificial intelligence systems. Recent theories have categorized learning into two regimes: the rich regime, where neural networks actively learn task-relevant features, and the lazy regime, where networks behave like random feature models. Yet

Yuanchen Deng, Dalton Anderson, Xingyu Du, Will Roberts

Guided phonons have become an increasingly important platform for classical and quantum information processing. While conventional surface acoustic wave systems are typically only guided in the vertical direction, two-dimensionally confined waveguide systems offer significant advantages in terms of density of phononic circuit components and much higher inten

S. V. Demidov

In a supersymmetric model with low scale supersymmetry breaking light sgoldstinos can appear in decays of mesons abundantly produced in atmospheric showers. We obtain bounds on parameter space of such a scenario from the Super-Kamiokande atmospheric neutrino oscillation data and estimate future sensitivity of the Hyper-Kamiokande project. We find that the bo

Chandrashekhar Rai, Debarati Sen

Spatial and temporal delays in a wireless multi-antenna system, paired with an orthogonal frequency division multiplexing (OFDM) waveform, can be utilized to estimate the Angle of Arrival (AoA) and Time of Arrival (ToA) of scatterers in the radio channel through spectral estimation techniques. However, in millimeter-wave (mmWave) and TeraHertz (THz) systems,

Bingli Jiao, Chenbo Wang, Zijian Zhou

This study is grounded in the concept of spatial symmetry, which allows two co-phase RF sources to jointly radiate harmonic electromagnetic (EM) waves, even in presence of electromagnetic couplings between them. The superposition law is directly applied to the two waves owing to their sources, including the EM coupling effects. Our research uncovers a confli

Milad Jafarypouria, Stepan Lomov, Sergey Abaimov

This research explores the influence of interfacial debonding between a broken fiber and matrix on stress redistribution surrounding a fiber break within a unidirectional (UD) impregnated fiber bundle, accounting for misalignment of fibers and fiber diameter distribution in randomly packed fiber configurations. Finite-element modelling is conducted on carbon

Boming Zhang, Yunguo Jiang

This paper numerically investigates the dynamical properties of kink and antikink collisions in the Christ_Lee model in the regime of epsilon approaching the phi4 theory. With given epsilon and the initial velocity Vin, we exhibiting the formation of bion, scattering, and n_bounce states. Additionally, we show the self_similar fractal structures in the plot

Junhao Ge, Zuhong Liu, Longteng Fan, Yifan Jiang

End-to-end (E2E) autonomous driving (AD) models require diverse, high-quality data to perform well across various driving scenarios. However, collecting large-scale real-world data is expensive and time-consuming, making high-fidelity synthetic data essential for enhancing data diversity and model robustness. Existing driving simulators for synthetic data ge

Hongjia Zhai, Hai Li, Zhenzhe Li, Xiaokun Pan

Recently, 3D Gaussian Splatting (3DGS) has shown encouraging performance for open vocabulary scene understanding tasks. However, previous methods cannot distinguish 3D instance-level information, which usually predicts a heatmap between the scene feature and text query. In this paper, we propose PanoGS, a novel and effective 3D panoptic open vocabulary scene

Ivan So

By generalizing the argument of Pavelescu \cite{Pav12}, we show that every transverse link $ K $ in a compact contact 3-manifold can be transversely isotoped to a braid with respect to a rational open book decomposition.

W. Porod

Spin-1 resonances are among the states predicted by composite Higgs models and one expects them the have masses in the range of a few TeV. We focus here on models based on an underlying gauge-fermion description which predict QCD-coloured vector and axial-vector states as well as states charged under the electroweak gauge groups. The former can come as tripl

Ze Zhang, Enyuan Zhao, Yi Jiang, Jie Nie

The Remote Sensing Copy-Move Question Answering (RSCMQA) task focuses on interpreting complex tampering scenarios and inferring the relationships between objects. Currently, publicly available datasets often use randomly generated tampered images, which lack spatial logic and do not meet the practical needs of defense security and land resource monitoring. T

Tianyu Wang, Hui Tong, Chencan Wang, Xiaoying Qu

The in-medium nucleon-nucleon scattering cross section is a pivotal quantity for studying the medium effects of strong interaction, and its precise knowledge is critical for understanding the equation of state for dense matter, intermediate-energy heavy-ion collision dynamics, and related phenomena. In this work, we perform a microscopic investigation of in-

Samuel Schmidgall, Michael Moor

Progress in scientific discovery is rarely the result of a single "Eureka" moment, but is rather the product of hundreds of scientists incrementally working together toward a common goal. While existing agent workflows are capable of producing research autonomously, they do so in isolation, without the ability to continuously improve upon prior research resu

Simone Costa, Stefano Della Fiore, Eva R. Engel

A famous conjecture of Graham asserts that every set $A \subseteq \mathbb{Z}_p \setminus \{0\}$ can be ordered so that all partial sums are distinct. Bedert and Kravitz proved that this statement holds whenever $|A| \leq e^{c(\log p)^{1/4}}$. In this paper, we will use a similar procedure to obtain an upper bound of the same type in the case of semidirect pr

Xuesong Chen, Shaoshuai Shi, Tao Ma, Jingqiu Zhou

The perception system for autonomous driving generally requires to handle multiple diverse sub-tasks. However, current algorithms typically tackle individual sub-tasks separately, which leads to low efficiency when aiming at obtaining full-perception results. Some multi-task learning methods try to unify multiple tasks with one model, but do not solve the co

Surabhi Jaiswal, Prithwiraj Maity, Snigdha Thakur, Marisol Ripoll

Polar polymer activity is a fundamental mechanism behind a large number of cellular dynamical processes. The number and location of the active sites on the polymer backbone play a central role in their dynamics and conformational properties. Globular conformations for high motor densities change to stretched ones for the more realistic moderate or low densit

Konstantinos Oikonomidis, Emanuel Laude, Panagiotis Patrinos

In this paper we present a unifying framework for continuous optimization methods grounded in the concept of generalized convexity. Utilizing the powerful theory of $\Phi$-convexity, we propose a conceptual algorithm that extends the classical difference-of-convex method, encompassing a broad spectrum of optimization algorithms. Relying exclusively on the to

Victor Blanco, Miguel A. Pozo, Justo Puerto, Alberto Torrejon

In this paper we address a unified mathematical optimization framework to compute a wide range of measures used in most operations research and data science contexts. The goal is to embed such metrics within general optimization models allowing their efficient computation. We assess the usefulness of this approach applying it to three different families of m

Filip Stefaniuk, Robert Ślepaczuk

The article investigates the usage of Informer architecture for building automated trading strategies for high frequency Bitcoin data. Three strategies using Informer model with different loss functions: Root Mean Squared Error (RMSE), Generalized Mean Absolute Directional Loss (GMADL) and Quantile loss, are proposed and evaluated against the Buy and Hold be

Lorena G Barberia, Belinda Lombard, Norton Trevisan Roman, Tatiane C. M. Sousa

Background Advances in machine learning (ML) models have increased the capability of researchers to detect vaccine hesitancy in social media using Natural Language Processing (NLP). A considerable volume of research has identified the persistence of COVID-19 vaccine hesitancy in discourse shared on various social media platforms. Methods Our objective in thi

Fei Li, Wenxuan Liu, Jingjing Chen, Ruixu Zhang

Open Vocabulary Video Anomaly Detection (OVVAD) seeks to detect and classify both base and novel anomalies. However, existing methods face two specific challenges related to novel anomalies. The first challenge is detection ambiguity, where the model struggles to assign accurate anomaly scores to unfamiliar anomalies. The second challenge is categorization c

Franco Bagnoli, Sara Dridi, Nazim Fates

Controllability, one of the fundamental concepts in control theory, consists in guiding a system from an initial state to a desired one within a limited (and possibly minimum) time interval. When the objective is limited to a specific sub-region of the system's domain, the concept is referred to as regional controllability. We examine this notion in the cont

M. R. Siavash Katebzadeh, Antonios Katsarakis, Boris Grot

Today's datacenter applications rely on datastores that are required to provide high availability, consistency, and performance. To achieve high availability, these datastores replicate data across several nodes. Such replication is managed through a reliable protocol designed to keep the replicas consistent using a consistency model, even in the presence of

Oliver Jenkinson, Xiaoran Li, Yuexin Liao, Yiwei Zhang

We study the optimization of ergodic averages for multi-valued dynamical systems, i.e. where points may have multiple different forward orbits. Under upper semi-continuity assumptions, we show that the maximum space average with respect to invariant probability measures for such systems can be characterised in terms of maximum time averages on an auxiliary s

Qiang Yang, Yayun Hu, Zhe Hou, Peiqing Tong

Universal conductance fluctuations (UCF) are a hallmark of quantum interference in mesoscopic devices. According to the Altshuler-Lee-Stone theory, the amplitude of UCF remains independent of system parameters such as Fermi energy and disorder strength. However, recent experiments have demonstrated a significant variation in UCF with respect to Fermi energy

Sabyasachi Maulik, Soumen Pari

Growth of entanglement entropy in time-dependent states formed due to a global quench in holographic conformal field theories which admit an Einstein-Gauss-Bonnet dual gravity description is studied. The global quench in the bulk is modelled by an AdS Vaidya solution with an electric charge. It is observed that the Gauss-Bonnet correction parameter leads to

Javad SeraJ, Mohammad Mahdi Mohajeri, Mohammad Javad Dousti

Tuning large language models is essential for optimizing their performance across diverse applications, particularly in scenarios with limited data availability. Tuning large language models in scarce data scenarios is crucial, particularly given that the convergence speed of the LoRA method is lower than that of full fine-tuning. In this paper, we present a

Massimiliano Ghiotto

This paper introduces HyperNOs, a PyTorch library designed to streamline and automate the process of exploring neural operators, with a special focus on hyperparameter optimization for comprehensive and exhaustive exploration. Indeed, HyperNOs takes advantage of state-of-the-art optimization algorithms and parallel computing implemented in the Ray-tune libra

Noah M. MacKay

In Ref. arXiv:2502.08816, Hawking radiation was analyzed through a statistical mechanics framework, revealing a structured microstate description of black hole horizons and information transfer into the radiation background. This study extends that approach by formulating Hawking radiation and black hole evaporation in the language of stochastic mechanics, e

Rochana Chaturvedi, Peyman Baghershahi, Sourav Medya, Barbara Di Eugenio

Temporal information extraction from unstructured text is essential for contextualizing events and deriving actionable insights, particularly in the medical domain. We address the task of extracting clinical events and their temporal relations using the well-studied I2B2 2012 Temporal Relations Challenge corpus. This task is inherently challenging due to com

Yang Yang, Tong Zhang, Jian Wu, Lijie Su

With the rapid advancement of large language models, academic topic identification and topic evolution analysis are crucial for enhancing AI's understanding capabilities. Dynamic topic analysis provides a powerful approach to capturing and understanding the temporal evolution of topics in large-scale datasets. This paper presents a two-stage dynamic topic an

Chen-Ran Hu, Yong-Feng Huang, Jin-Jun Geng, Chen Deng

Fast radio bursts (FRBs) are millisecond-duration radio flashes of extragalactic origin, with magnetars implicated as viable central engines. Yet their triggering and radiation mechanisms remain unknown. Radio telescopes inevitably record bursts incompletely, as limited sensitivity and finite bandwidth lead to observational truncation. Here we establish a ge