October 2025 arXiv papers — page 43

Jian-Hua Liang, Yue You, Xi-Hua Guan, Xiao-Jing Du

Here, we demonstrate that quantum and classical descriptions generally yield different results for the spontaneous emission in nanophotonic cavities. Starting from the quantized single-mode field in a general context of dispersive and lossy cavities, we derive the expression for emission rate enhancement as well as key relevant parameters such as mode volume

Jan Steinheimer, Tom Reichert, Marcus Bleicher

Nuclear experiments near and below the threshold of hyperon production have shown that the production of Kaons is a sensitive probe for the dense QCD equation of state. At beam energies up to 1.5AGeV, strangeness production can probe the equation of state for densities up to approximately twice nuclear saturation. In this paper we will discuss the possibilit

Maísa Beraldo Bandeira, Alexander Engelmann, Timm Faulwasser

With the increasing number of flexible energy devices in distribution grids, coordination between Transmission System Operators (TSOs) and Distribution System Operators (DSOs) becomes critical for optimal system operation. One form of coordination is to solve the overall system operation problem in a hierarchical way, computing Feasible Operational Regions (

Léonie Gasteiner, Alyona Glazyrina, Naomi Murdoch, Olfa D'Angelo

Regolith simulants are essential for space research and technology development. Yet, their physical properties often differ from those of true planetary soil, particularly when compared to regolith properties in-situ, that experience notably reduced gravity. We focus on lunar regolith simulants and explore various techniques to modify existing simulants to r

A. O. Barvinsky, A. E. Kalugin, W. Wachowski

We consider expansions for the kernels of operator functions of second-order minimal operators on a curved background. We show that the terms of these expansions originate in the ultraviolet or infrared regions. We propose a systematic approach to obtaining ultraviolet terms using term-by-term integration of the DeWitt expansion of the heat kernel. We discus

Alcino Cunha, Nuno Macedo

Validation is a central activity when developing formal specifications. Similarly to coding, a possible validation technique is to define upfront test cases or scenarios that a future specification should satisfy or not. Unfortunately, specifying such test cases is burdensome and error prone, which could cause users to skip this validation task. This paper r

Dennis Müller

The mathematisation of the socio-economic sphere, where mathematics actively constructs social reality, presents a challenge for studies on ethics in mathematics and its education. While existing scholarship on ethics in mathematics offers insights, it often remains philosophically driven and disconnected from other relevant disciplines. This paper addresses

Shovon Sengupta, Sunny Kumar Singh, Tanujit Chakraborty

Accurate macroeconomic forecasting has become harder amid geopolitical disruptions, policy reversals, and volatile financial markets. Conventional vector autoregressions (VARs) overfit in high dimensional settings, while threshold VARs struggle with time varying interdependencies and complex parameter structures. We address these limitations by extending the

Xinyu Wang, Jonas M. Kübler, Kailash Budhathoki, Yida Wang

When serving a single base LLM with several different LoRA adapters simultaneously, the adapters cannot simply be merged with the base model's weights as the adapter swapping would create overhead and requests using different adapters could not be batched. Rather, the LoRA computations have to be separated from the base LLM computations, and in a multi-devic

Zhen Cao, F. Aharonian, Y. X. Bai, Y. W. Bao

We report the first detection of gamma-ray emission up to ultra-high-energy (UHE; $>$100 TeV) emission from the prototypical gamma-ray binary system LS I +61 303 using data from the Large High Altitude Air Shower Observatory (LHAASO). It is detected with significances of 9.2$\sigma$ in WCDA (1.4--30.5 TeV) and 6.2$\sigma$ in KM2A (25--267 TeV); in KM2A alone

Th. Karakostas, Ph. Komninou, V. Pontikis

The strain states of the components of pseudomorphic superlattices can be accurately modeled analytically through the application of linear elasticity. In this particular case of GaN/AlN 'free-standing' superlattices, the predictions derived from elastic modeling have been compared with total energy calculations of several systems made of components with var

Yossi Oren, Viraj Pandya, Rachel S. Somerville, Shy Genel

We measure and analyze the inflows and outflows of mass, energy, and metals through the interstellar medium (ISM) and circumgalactic medium (CGM) of galaxies in the IllustrisTNG100 simulations. We identify the dominant feedback mechanism in bins of halo virial mass and redshift by computing the integrated energy input from SNe and the ``kinetic'' and ``therm

Alex S. Taylor, Noortje Marres, Mercedes Bunz, Thao Phan

The street has emerged as a primary site where everyday publics are confronted with AI as an infrastructural phenomenon, as machine learning-based systems are now commonly deployed in this setting in the form of automated cars, facial recognition, smart billboards and the like. While these deployments of AI in the street have attracted significant media atte

Teng Lin

The scarcity of high-quality knowledge graphs (KGs) remains a critical bottleneck for downstream AI applications, as existing extraction methods rely heavily on error-prone pattern-matching techniques or resource-intensive large language models (LLMs). While recent tools leverage LLMs to generate KGs, their computational demands limit accessibility for low-r

Jonas G. Kühn, Ruben Tandon, Lucas Marquis, Liurong Lin

The Programmable Liquid-crystal Active Coronagraphic Imager for the DAG telescope (PLACID) instrument is a novel high-contrast direct imaging facility that was recently installed on the new Turkish 4-m DAG telescope. In brief, PLACID consists in a fore-optics coronagraphic intermediate stage platform, installed in-between the TROIA XAO system and the DIRAC H

J. A. Molina-Calzada, J. Maíz Apellániz

The fourth part of the Alma Luminous Star catalogue (ALS IV) aims to create the most comprehensive sample of massive stars in the Magellanic Clouds (MCs). By combining Gaia DR3 with Simbad and complementing this information with other photometric and spectroscopic catalogues, we select the massive stars in this region. To achieve this, we apply filters in ph

Siyuan Zheng, Pai Liu, Xi Chen, Jizheng Dong

Human-like virtual characters are crucial for games, storytelling, and virtual reality, yet current methods rely heavily on annotated data or handcrafted persona prompts, making it difficult to scale up and generate realistic, contextually coherent personas. We create the first QA dataset for BaZi-based persona reasoning, where real human experiences categor

Agustin Brusco, Bruno Sivilotti, Ana M. Botti, Brenda Cervantes

Skipper Charge-Coupled Devices (Skipper-CCDs) are ultra-low-threshold detectors capable of detecting energy deposits in silicon at the eV scale. Increasingly used in rare-event searches, one of the major challenges in these experiments is mitigating low-energy backgrounds. In this work, we present results on trap characterization in a silicon Skipper-CCD pro

Dhairya Kantawala

We study a class of infinite-horizon average-cost Markov Decision Processes (MDPs) whose reward and transition structures are nearly separable. For the totally separable baseline (that is, with no perturbation), we derive an explicit stationary decision rule that is exactly average-optimal. We then show that under an epsilon-perturbation of the separable str

Mohammad Atif Quamar, Mohammad Areeb, Nishant Sharma, Ananth Shreekumar

LLM alignment remains a critical challenge. Inference-time methods provide a flexible alternative to fine-tuning, but their uniform computational effort often yields suboptimal alignment. We hypothesize that for many alignment tasks, the initial tokens of a response are disproportionately more critical. To leverage this principle, we introduce AdaSearch, a n

Duc-Manh Nguyen

We compute the Siegel-Veech constants associated to saddle connections with distinct endpoints on Prym eigenforms for real quadratic orders with non-square discriminant in $\Omega \mathcal{M}_3(2,2)^{\rm odd}$.

Ruyi Teng, Dan Wang, Wei Chen, Yulong Gao

Distributional linear quadratic regulator (LQR) is a new framework that integrates the distributional reinforcement learning and classical LQR, which offers a new way to study the random return instead of the expected cost. Unlike iterative approximation using dynamic programming in the DRL, a closed-form expression for the random return can be exactly chara

Avyay Kodali, Priyanshi Singh, Pranay Pandey, Krishna Bhatia

This study compares Quantum Reservoir Computing (QRC) with classical models such as Echo State Networks (ESNs) and Long Short-Term Memory networks (LSTMs), as well as hybrid quantum-classical architectures (QLSTM), for the nonlinear autoregressive moving average task (NARMA-10). We evaluate forecasting accuracy (NRMSE), computational cost, and evaluation tim

Meng Gao, Yu Tian, Changxu Yan, Hongbao Zhang

In this paper, we employ, for the first time, the holographic gravity approach to investigate the dynamical stability of solitons in spherical superfluids. Transverse perturbations are applied to the background of spherical soliton configurations, and the collective excitation modes of the solitons are examined within the framework of linear analysis. Our st

Keiya Hirashima, Michiko S. Fujii, Takayuki R. Saitoh, Naoto Harada

A major goal of computational astrophysics is to simulate the Milky Way Galaxy with sufficient resolution down to individual stars. However, the scaling fails due to some small-scale, short-timescale phenomena, such as supernova explosions. We have developed a novel integration scheme of $N$-body/hydrodynamics simulations working with machine learning. This

Shreya Santra, Thomas Robbins, Kazuya Yoshida

Autonomous navigation in unstructured environments is essential for field and planetary robotics, where robots must efficiently reach goals while avoiding obstacles under uncertain conditions. Conventional algorithmic approaches often require extensive environment-specific tuning, limiting scalability to new domains. Deep Reinforcement Learning (DRL) provide

Samuel Mattoso, Jing Yang, Florian Deißenbeck, Ahmed Abdelkawy

Introducing electric fields into density functional theory (DFT) calculations is essential for understanding electrochemical processes, interfacial phenomena, and the behavior of materials under applied bias. However, applying user-defined electrostatic potentials in DFT is nontrivial and often requires direct modification to the specific DFT code. In this w

Mohammad Hossein Jafari Naeimi, Ali Norouzi, Athena Abdi

This paper introduces GRAD, a real-time anomaly detection method for autonomous vehicle sensors that integrates statistical analysis and deep learning to ensure the reliability of sensor data. The proposed approach combines the Reinforced Exponential Moving Average (REMA), which adapts smoothing factors and thresholding for outlier detection, with the Multi-

Cristian Simionescu

This thesis works to address a pivotal challenge in medical image analysis: the reliance on extensive labeled datasets, which are often limited due to the need for expert annotation and constrained by privacy and legal issues. By focusing on the development of self-supervised learning techniques and domain adaptation methods, this research aims to circumvent

Sebastian Maier, Manuel Schneider, Stefan Feuerriegel

Large language models (LLMs) show strong potential to support creative tasks, but the role of the interface design is poorly understood. In particular, the effect of different modes of collaboration between humans and LLMs on co-creation outcomes is unclear. To test this, we conducted a randomized controlled experiment ($N = 486$) comparing: (a) two variants

Edouard Lansiaux, Antoine Simonet, Eric Wiel

We present SwiftEmbed, a production-oriented serving system for static token embeddings that achieves 1.12\,ms p50 latency for single-text requests while maintaining a 60.6 MTEB average score across 8 representative tasks. Built around the open-source Potion-base-8M distilled model from MinishLab and implemented in Rust, the system delivers 50,000 requests p

Alfonso Piscitelli, Cristina David, Mattia De Rosa, Ali Mohammed

We introduce _transparent documents_, interactive web-based scholarly articles which allow readers to explore the relationship to the underlying data by hovering over fragments of text, and present an LLM-based tool for authoring transparent documents, building on recent developments in data provenance for general-purpose programming languages. As a target p

Itamar Avitan, Tal Golan

Linearly transforming stimulus representations of deep neural networks yields high-performing models of behavioral and neural responses to complex stimuli. But does the test accuracy of such predictions identify genuine representational alignment? We addressed this question through a large-scale model-recovery study. Twenty diverse vision models were linearl

Mouhand Alkadri, Dania Desouki, Khloud Al Jallad

The performance of Artificial Intelligence (AI) systems fundamentally depends on high-quality training data. However, low-resource languages like Arabic suffer from severe data scarcity. Moreover, the absence of child-specific speech corpora is an essential gap that poses significant challenges. To address this gap, we present our created dataset, Arabic Lit

Dominik Kirstein, Christian Kremer

We introduce the notion of a semifree isovariant $G$-Poincar\'e space, a homotopical notion interpolating between semifree closed smooth $G$-manifolds and the equivariant Poincar\'e spaces of [HKK24b]. It carries the additional structure of an equivariant Poincar\'e embedding of the fixed points of a semifree $G$-Poincar\'e space. Under suitable gap conditio

Dirk Elias Schut, Adriaan Graas, Robert van Liere, Tristan van Leeuwen

Deep learning has shown impressive results in reducing noise and artifacts in X-ray computed tomography (CT) reconstruction. Self-supervised CT reconstruction methods are especially appealing for real-world applications because they require no ground truth training examples. However, these methods involve a simplified X-ray physics model during training, whi

Jie Li, Xiaohu Tang

In this paper, we present two constructions of degraded read friendly (DRF) MDS array codes with two parity nodes and a sub-packetization level of 2 over small finite fields, applicable for any arbitrary code length. The first construction achieves the smallest repair bandwidth among all existing constructions with the same parameters, and is asymptotically

Yushen Lin, Zihan Chen, Zhiguo Ding

Federated learning (FL) in wireless networks is limited by straggler delays from unpredictable channel conditions. In this paper, we investigate the pinching-antenna system (PASS), which dynamically 'pinches' the radiator along a dielectric waveguide to shorten the worst links. In synchronous FL (SFL), we prove that PASS shortens the worst-link distance, and

Shanli Ye, Qisong Zheng

In this paper, we compute the exact value of the norm of the Hilbert matrix operator $\mathcal{H}$ acting from the classical Bloch space $\mathcal{B}$ into the logarithmically weighted Bloch space $\mathcal{B}_{\log}$, and show that it equals $\frac{3}{2}$; we also find that the norm from the space of bounded analytic functions $H^\infty$ into the logarithmi

Matthew Morris, Ian Horrocks

Graph neural networks (GNNs) are frequently used for knowledge graph completion. Their black-box nature has motivated work that uses sound logical rules to explain predictions and characterise their expressivity. However, despite the prevalence of GNNs that use mean as an aggregation function, explainability and expressivity results are lacking for them. We

Yaokai Feng, Kouichi Sakurai

This survey systematizes the evolution of network intrusion detection systems (NIDS), from conventional methods such as signature-based and neural network (NN)-based approaches to recent integrations with large language models (LLMs). It clearly and concisely summarizes the current status, strengths, and limitations of conventional techniques, and explores t

Kamil Wojcicki, Yusuf Ziya Isik, Laura Lechler, Mansur Yesilbursa

While recent neural audio codecs deliver superior speech quality at ultralow bitrates over traditional methods, their practical adoption is hindered by obstacles related to low-resource operation and robustness to acoustic distortions. Edge deployment scenarios demand codecs that operate under stringent compute constraints while maintaining low latency and b

Raúl Baier-Soto, Yara Jaffé, Alexis Finoguenov, P. Christopher Haines

In a hierarchical $\Lambda$CDM Universe, cosmic filaments serve as the primary channels for matter accretion into galaxy clusters, influencing the shape of their dark matter halos. We investigate whether the elongation of galaxy clusters correlates with the orientation of surrounding filaments, providing the first observational test of this relationship in l

Keita Takahashi, Tetsuro Abe, Yasuhito Nakamura, Ryo Hidaka

The portfolio optimization problem is a critical issue in asset management and has long been studied. Markowitz's mean-variance model has fundamental limitations, such as the assumption of a normal distribution for returns and sensitivity to estimation errors in input parameters. In this research, we propose a novel graph theory-based approach, the cardinali

Miloš Japundžić, Danijela Rajter-Ćirić

We consider the Cauchy problem for stochastic fractional evolution equations with Caputo time fractional derivative of order $1<\alpha<2$ and space variable coefficients on an unbounded domain. The space derivatives that appear in the equations are of integer or fractional order such as the left and the right Liouville fractional derivative as well as the Ri

Jiayan Guo, Wenming Hong

Let $T$ be the random family tree associated with the critical Galton--Watson process $\{Z_{n}\}_{n\geq0}$. Geiger (1999) provided an explicit representation of the law of $T$ conditioned on $\{Z_{n}>0\}$ by inductively constructing the conditioned tree $\tilde{T}_n$ along the line of descent of the left--most particle. Intuitively, $\tilde{T}_n$ is composed

Jonathan Bauermann, David R. Nelson

We study the genetic interfaces between two species of an expanding colony that consists of individual microorganisms that reproduce and undergo diffusion, both at the frontier and in the interior. Within the bulk of the colony, the genetic interface is controlled in a simple way via interspecies interactions. However, at the frontier of the colony, the gene

Jiahao Chang, Chongjie Ye, Yushuang Wu, Yuantao Chen

Existing multi-view 3D object reconstruction methods heavily rely on sufficient overlap between input views, where occlusions and sparse coverage in practice frequently yield severe reconstruction incompleteness. Recent advancements in diffusion-based 3D generative techniques offer the potential to address these limitations by leveraging learned generative p

F. Bado, P. de Laverny, Z. Kam, A. Recio-Blanco

Stellar rotation is a fundamental parameter in stellar studies. However, large homogeneous catalogues of rotational velocities derived from high-resolution stellar spectra are still lacking. The main objective of this work is to determine the line-broadening parameter (Vbroad), a proxy for the stellar rotational velocity, in a large sample of FGKM stars base

Riccardo Romanello, Daniele Lizzio Bosco, Jacopo Cossio, Dusan Sutulovic

CNOT gates are fundamental to quantum computing, as they facilitate entanglement, a crucial resource for quantum algorithms. Certain classes of quantum circuits are constructed exclusively from CNOT gates. Given their widespread use, it is imperative to minimise the number of CNOT gates employed. This problem, known as CNOT minimisation, remains an open chal

Simone Colli, Emiliano Maresi, Vincenzo Bonnici

The identification of homologous gene families across multiple genomes is a central task in bacterial pangenomics traditionally requiring computationally demanding all-against-all comparisons. PanDelos addresses this challenge with an alignment-free and parameter-free approach based on k-mer profiles, combining high speed, ease of use, and competitive accura

Bhavik Kumar, Daniel Malz

Cold atoms in optical lattices are a versatile and highly controllable platform for quantum simulation, capable of realizing a broad family of Hubbard models, and allowing site-resolved readout via quantum gas microscopes. In principle, arbitrary site-dependent potentials can also be implemented; however, since lattice spacings are typically below the diffra

Yingying Feng, Jie Li, Jie Hu, Yukang Zhang

Real-world object re-identification (ReID) systems often face modality inconsistencies, where query and gallery images come from different sensors (e.g., RGB, NIR, TIR). However, most existing methods assume modality-matched conditions, which limits their robustness and scalability in practical applications. To address this challenge, we propose MDReID, a fl

Ravit Helled, Simon Müller, Henrik Knierim

The evolution of gaseous planets is a complex process influenced by various physical parameters and processes. In this study, we present critical modifications to the Modules for Experiments in Stellar Astrophysics (MESA) code to enhance its applicability to giant planet modelling. We introduce an equation of state specifically tailored for materials at plan

Harald Monsuur

Backward parabolic equations, such as the backward heat equation, are classical examples of ill-posed problems where solutions may not exist or depend continuously on the data. In this work, we study a least squares finite element method to numerically approximate solutions to such problems. We derive conditional stability estimates for the weak formulation

Haochen Zhao, Yuyao Kong, Yongxiu Xu, Gaopeng Gou

Despite progress in multimodal sarcasm detection, existing datasets and methods predominantly focus on single-image scenarios, overlooking potential semantic and affective relations across multiple images. This leaves a gap in modeling cases where sarcasm is triggered by multi-image cues in real-world settings. To bridge this gap, we introduce MMSD3.0, a new

Xavier Caruso, Florian Fürnsinn, Daniel Vargas-Montoya, Wadim Zudilin

We compute the Galois groups of the reductions modulo the prime numbers $p$ of the generating series of Ap\'ery numbers, Domb numbers and Almkvist--Zudilin numbers. We observe in particular that their behavior is governed by congruence conditions on p.

R. S. Walker, G. Ramos-Fernandez, D. Boyer, S. E. Smith-Aguilar

Collective systems that self-organise to maximise the group's ability to collect and distribute information can be successful in environments with high spatial and temporal variation. Such organisations are abundant in nature, as sharing information is a key benefit of many biological collective systems, and have been influential in the design of many artifi

Hai Yu, Zhichao Yang, Wei He, Jianda Han

Cable-suspended aerial transportation systems are employed extensively across various industries. The capability to flexibly adjust the relative position between the multirotor and the payload has spurred growing interest in the system equipped with variable-length cable, promising broader application potential. Compared to systems with fixed-length cables,

Yakup Emre Şahin, Niki Kilbertus, Sören Becker

We introduce MIO, a transformer-based model for inferring symbolic ordinary differential equations (ODEs) from multiple observed trajectories of a dynamical system. By combining multiple instance learning with transformer-based symbolic regression, the model effectively leverages repeated observations of the same system to learn more generalizable representa

Junya Endo, Hiroyasu Matsuura, Manfred Sigrist, Masao Ogata

We propose a general strategy for enhancing the anomalous Nernst coefficient based on the Sommerfeld-Bethe relation. This approach provides a systematic framework for understanding the small anomalous Nernst coefficients typically observed in ferromagnets and identifies conditions under which substantial enhancements can be realized. We further introduce sim

Uday Chand De, Hülya Bağdatli Yilmaz

In this work, a detailed examination of a specific case of a generalized quasi-Einstein manifold (GQE)n is provided. It begins by exploring generalized quasi-Einstein spacetimes under certain conditions. The analysis then focuses on cases that admit a parallel time-like vector field. Among the findings, it is demonstrated that such spacetimes can be categori

Ho-Yun YuChih, Ye Shen

Many efforts were made in order to better understand the energy extraction via magnetic reconnection from a rotating black hole, following the work of Comisso and Asenjo in 2021. We also tried to make some progress in our previous works, in which we discussed differences between bulk plasma with different streamlines and also defined the covering factor as a

Alexandros Konstantinou

We investigate variations of Selmer ranks under quadratic twists satisfying the Heegner hypothesis. In particular, starting with an elliptic curve $E/\mathbb{Q}$ with partial $2$-torsion and a common relaxed Selmer group, we derive explicit formulae describing the effect of twisting on Selmer ranks in terms of matrices over $\mathbb{F}_{2}$. As an applicatio

Valentin Mouton, Adrien Mélot

Designing frictional interfaces to exhibit prescribed macroscopic behavior is a challenging inverse problem, made difficult by the non-uniqueness of solutions and the computational cost of contact simulations. Traditional approaches rely on heuristic search over low-dimensional parameterizations, which limits their applicability to more complex or nonlinear

Taiga Inoue, Naofumi Otsuru, Akimasa Hirata

This study evaluated transcranial temporal interference stimulation (tTIS) for focal targeting of the insula and hippocampus, which are clinically relevant yet anatomically difficult to stimulate. Individualized and group-level electrode optimizations were compared to determine whether generalized montages can provide reliable targeting with reduced modeling

Lukas Ostrowski, Christian Rohde

We consider a Navier--Stokes--Allen--Cahn (NSAC) system that governs the compressible motion of a viscous, immiscible two-phase fluid at constant temperature. Weak solutions of the NSAC system dissipate an appropriate energy functional. Based on an equivalent re-formulation of the NSAC system we propose a fully-discrete discontinuous Galerkin (dG) discretiza

Daiyuan Li, Shreya Arya, Robert Ghrist

Most equivariant neural networks rely on a single global symmetry, limiting their use in domains where symmetries are instead local. We introduce Torsor CNNs, a framework for learning on graphs with local symmetries encoded as edge potentials -- group-valued transformations between neighboring coordinate frames. We establish that this geometric construction

The KM3NeT collaboration

On the 13th February 2023 the KM3NeT/ARCA telescope observed a track-like event compatible with a ultra-high-energy muon with an estimated energy of 120 PeV, produced by a neutrino with an even higher energy, making it the most energetic neutrino event ever detected. The reported equivalent flux suggest the possible existence of a new diffuse component. A di

Jin Huang, Yingqiang Wang, Haoda Li, Zichen Liu

In multisensor systems, time synchronization is particularly challenging for underwater integrated navigation systems (INSs) incorporating acoustic positioning, where time delays can significantly degrade accuracy when measurement and fusion epochs are misaligned. This article introduces a tightly coupled navigation framework that integrates a passive invert

Ruoyu Wang, Beier Zhu, Junzhi Li, Liangyu Yuan

Diffusion-based generative processes, formulated as differential equation solving, frequently balance computational speed with sample quality. Our theoretical investigation of ODE- and SDE-based solvers reveals complementary weaknesses: ODE solvers accumulate irreducible gradient error along deterministic trajectories, while SDE methods suffer from amplified

Yuanzhen Xie, Liu Ye, Jiqun Chu, Mochi Gao

Text-to-SQL tasks have gained attractive improvements since the release of ChatGPT. Among them, agent-based frameworks have been widely used in this field. However, the impact of data-centric strategies on text-to-SQL tasks has rarely been explored. In this paper, we systemically design a fully automated data-centric pipeline for text-to-SQL tasks, including

Youssif Abuzied, Hassan AbdEltawab, Abdelrhman Gaber, Tamer ElBatt

This paper presents ECGXtract, a deep learning-based approach for interpretable ECG feature extraction, addressing the limitations of traditional signal processing and black-box machine learning methods. In particular, we develop convolutional neural network models capable of extracting both temporal and morphological features with strong correlations to a c

Federico Cacciafesta, Elena Danesi, Eric Séré

In this paper we prove generalized Strichartz estimates for the massive Dirac equation in the case of two critical potential perturbations, namely the $2d$ Aharonov-Bohm magnetic potential and the $3d$ Coulomb potential. The proof makes use of the relativistic Hankel transform introduced in previous works of Cacciafesta, S\'er\'e and Cacciafesta, Fanelli for

Andrew P. Allan, Aline A. Vidotto, Jorge Sanz-Forcada, Carolina Villarreal D'Angelo

The HeI 1083nm transit signature is commonly used in tracing escaping planetary atmospheres. However, it can be affected by stellar activity, complicating detections and interpretations of atmospheric escape. We model how stellar activity cycles affect the atmospheric escape and HeI 1083nm signatures of four types of highly irradiated exoplanets, at 0.025 an

Yingjie Zhou

Hypernuclei are bound states of hyperons (Y) and nucleons (N). Measurements on their yields can help us investigate their production mechanisms. In particular, the ${}^5_{\Lambda}$He and $^{4}_{\Lambda}$H(e) are substantially tighter bound compared to the $^{3}_{\Lambda}$H. The large radius of the $^{3}_{\Lambda}$H leads to suppression in coalescence models,

Amandine Favre

In this article, we give a geometric model for non-homogeneous tubes of the cluster category of the affine type $D$. This model is given in terms of homotopy classes of unoriented arcs in the twice punctured disk. In particular, we extend the geometric model for the tube of rank $n-2$ given in arXiv:2407.11232 to the two tubes of rank $2$.

Xiao-Bin Sui, Jing Liu, Rong-Gen Cai

We investigate the properties of gravitational waves generated by heating induced phase transitions in warm inflation. In this scenario, the heating phase of inflation followed by subsequent cosmological cooling can trigger two associated first-order phase transitions and generate characteristic gravitational waves. The correlated gravitational wave spectral

Veska Tsenkova, Peter Stanchev, Daniel Petrov, Deyan Lazarov

Current convolution neural network (CNN) classification methods are predominantly focused on flat classification which aims solely to identify a specified object within an image. However, real-world objects often possess a natural hierarchical organization that can significantly help classification tasks. Capturing the presence of relations between objects e

Arghyadeep Paul, Antoine Strugarek

Star-planet magnetic interactions (SPMI) occurring in the sub-Alfvenic regime can, in principle, induce stellar chromospheric hotspots. Currently, estimates of the power generated by SPMI primarily rely on analytical scaling laws that relate stellar and planetary parameters to the interaction energetics. The existing scaling laws published in the literature

Thai-Binh Nguyen, Katerina Zmolikova, Pingchuan Ma, Ngoc Quan Pham

We introduce the task of Multi-Modal Context-Aware Recognition (MCoRec) in the ninth CHiME Challenge, which addresses the cocktail-party problem of overlapping conversations in a single-room setting using audio, visual, and contextual cues. MCoRec captures natural multi-party conversations where the recordings focus on unscripted, casual group chats, leading

Weixuan Chen, Qianqian Yang, Shuo Shao, Shunpu Tang

While semantic communication (SemCom) improves transmission efficiency by focusing on task-relevant information, it also raises critical privacy concerns. Many existing secure SemCom approaches rely on restrictive or impractical assumptions, such as favorable channel conditions for the legitimate user or prior knowledge of the eavesdropper's model. To addres

Runjie Zheng, Zhen Wang, Anjie Qiao, Jiancong Xie

Accurate protein function prediction requires integrating heterogeneous intrinsic signals (e.g., sequence and structure) with noisy extrinsic contexts (e.g., protein-protein interactions and GO term annotations). However, two key challenges hinder effective fusion: (i) cross-modal distributional mismatch among embeddings produced by pre-trained intrinsic enc

Bang Xiao, Lingjie Jiang, Shaohan Huang, Tengchao Lv

Large Language Models (LLMs) have become valuable assistants for developers in code-related tasks. While LLMs excel at traditional programming tasks such as code generation and bug fixing, they struggle with visually-oriented coding tasks, often producing suboptimal aesthetics. In this paper, we introduce a new pipeline to enhance the aesthetic quality of LL

Mohammed Aljafari, Ismail Alturki, Ahmed Mori, Yehya Kadumi

Mubeen is a proprietary Arabic language model developed by MASARAT SA, optimized for deep understanding of Arabic linguistics, Islamic studies, and cultural heritage. Trained on an extensive collection of authentic Arabic sources significantly expanded by digitizing historical manuscripts via a proprietary Arabic OCR engine, the model incorporates seminal sc

Rishi Kumar, Indranil Saha Dalal, K. Muralidhar

Platelet-enriched plasma and red blood cells (RBC) are needed in the treatment of blood-related diseases, including anaemia and blood cancer. These essential components must be separated from blood in well-designed experimental setups. If active techniques are used, the blood components are likely to be damaged or contaminated while handling. Passive techniq

Long Zhang, Guangxin Ni, Xuehao Wu, Guoying Gao

Emerging altermagnets with zero net magnetic moment and moment-dependent spin splitting offer a promising avenue for antiferromagnetic spintronic devices, yet their integration into magnetic tunnel junctions has been hindered by reliance on ferromagnetic electrodes (introducing stray fields) or by limited functionality (non-tunable magnetoresistance without

Mateusz Wiśniewski, Jakub Spiechowicz

Nowadays a bit is no longer a mere abstraction but a physical quantity whose manipulation governs both operation of modern technologies and theoretical frontiers of fundamental science. In this work we propose a setup in which the memory time can be utilized to control the generation and storage of binary information. In particular, we consider a nonequilibr

Wentao Huang, Li Wang

In this paper we study the following zero mass Schr\"{o}dinger-Bopp-Podolsky system with critical growth \[ \begin{cases} -\Delta u +q^2\phi u=\mu|u|^{p-2}u+|u|^4u\\ -\Delta \phi+a^2\Delta^2\phi=4\pi u^2, \end{cases} \] where $a>0$, $q\neq0$, $\mu>0$ is a parameter and $p\in(3,6)$. By introducing a new functional framework developed by Caponio et al. \cite{C

Edmund Karasiewicz, Shuichiro Takeda

We construct some Iwahori types, in the sense of Bushnell-Kutzko, for the double cover of an almost simple simply-laced simply-connected Chevalley group $\widetilde{G}$ over any $2$-adic field. These types capture the covering group analog of the Bernstein block of unramified principal series. We also prove that the associated Hecke algebra essentially admit

Xinhai Wang, Shu Yang, Liangyu Wang, Lin Zhang

Circuit discovery, which involves identifying sparse and task-relevant subnetworks in pre-trained language models, is a cornerstone of mechanistic interpretability. Automated Circuit Discovery (ACDC) has emerged as a pivotal methodology in circuit discovery, but its application to large language models is severely limited by computational inefficiency and pr

Teresa Arias-Marco, José-Manuel Fernández-Barroso

Naturally reductive manifolds are an important class of Riemannian manifolds because they provide examples that generalize the locally symmetric ones. A property is said to be inaudible if there exists a unitary operator which intertwines the Laplace-Beltrami operator of two Riemannian manifolds such that one of them satisfies the property and the other does

Lukas Gehrke, Leonie Terfurth, Klaus Gramann

Virtual reality (VR) can create compelling experiences that evoke presence, the sense of ``being there.'' However, problems in rendering can create sensorimotor disruptions that undermine presence and task performance. Presence is typically assessed with post-hoc questionnaires, but their coarse temporal resolution limits insight into how sensorimotor disrup

Félix Chavelli, Paul Boniol, Michaël Thomazo

Time series segmentation is a fundamental task in analyzing temporal data across various domains, from human activity recognition to energy monitoring. While numerous state-of-the-art methods have been developed to tackle this problem, the evaluation of their performance remains critically limited. Existing measures predominantly focus on change point accura

Nelvy Choque-Challapa, Rory Smith, Iván Lacerna, J. Alfonso L. Aguerri

The Virgo cluster is one of the closest clusters to us where we can further study the evolution of galaxies, with several infalling substructures and several filaments around it have been reported. Therefore, it makes this cluster and its surrounding an interesting place to study the spatial distribution of the population of dwarf and bright giant galaxies.

Qi Li, Jun Wang

Traditional clustering algorithms often struggle with high-dimensional and non-uniformly distributed data, where low-density boundary samples are easily disturbed by neighboring clusters, leading to unstable and distorted clustering results. To address this issue, we propose a Group-driven Clustering via Gravitational Attraction and Optimization (GCAO) algor

Riko Yokozawa, Kentaro Fujii, Yuta Nomura, Shingo Murata

Autonomous robotic navigation in real-world environments requires exploration to acquire environmental information as well as goal-directed navigation in order to reach specified targets. Active inference (AIF) based on the free-energy principle provides a unified framework for these behaviors by minimizing the expected free energy (EFE), thereby combining e

Qing-Hong Cao, Jian-Nan Ding, Yandong Liu, Jin-Long Yuan

We investigate CP-violating effects in electroweak interactions at future high-energy muon colliders within the Standard Model Effective Field Theory (SMEFT) framework. Focusing on four dimension-six CP-odd operators -- $ \mathcal{O}_{\widetilde{W}}, \mathcal{O}_{H\widetilde{W}}, \mathcal{O}_{H\widetilde{W}B}, \mathcal{O}_{H\widetilde{B}}$ -- we analyze vect

Inhyeok Choi, Dongryul M. Kim

For a non-elementary subgroup of the mapping class group of a surface, we study its invariant Radon measures on the space of measured laminations, by classifying them on the recurrent measured laminations. In particular, given a divergence-type subgroup, we show the uniquely ergodic by explicitly constructing the ergodic measure. This generalizes Lindenstrau

Tianyi Ma, Tengyao Wang, Richard J. Samworth

We study in-context learning problems where a Transformer is pretrained on tasks drawn from a mixture distribution $\pi=\sum_{\alpha\in\mathcal{A}} \lambda_{\alpha} \pi_{\alpha}$, called the pretraining prior, in which each mixture component $\pi_{\alpha}$ is a distribution on tasks of a specific difficulty level indexed by $\alpha$. Our goal is to understan