March 2026 arXiv papers — page 93

Joris Sturm, Andrei Benediktovitch, Nina Rohringer, Andreas Knorr

We propose a framework to explore the internal charge distribution of mesoscopic quasiparticles by inelastic x-ray scattering, while also accounting for the conventional scattering from electrons. Specifically, we investigate a new contribution of intrinsic and optically pumped excitons (bound electron-hole pairs) to the x-ray scattering spectrum of transiti

Kartheek Bondugula, Santiago Mazuelas, Aritz Pérez, Anqi Liu

Loss functions play a central role in supervised classification. Cross-entropy (CE) is widely used, whereas the mean absolute error (MAE) loss can offer robustness but is difficult to optimize. Interpolating between the CE and MAE losses, generalized cross-entropy (GCE) has recently been introduced to provide a trade-off between optimization difficulty and r

Serge Tabachnikov

We define and study a continuous version of 2-frieze patterns, a combinatorial structure closely related with frieze patterns of Coxeter and Conway. We describe the relation of continuous 2-friezes with the moduli space of projective curves and relate the (pre)symplectic structure on the space of closed 2-friezes, considered as a cluster variety, with the Ad

Tadashi Udagawa

Cecotti and Vafa introduced the topological anti-topological fusion (tt*)-equation, whose solutions describe massive deformations of supersymmetric conformal field theories. We provide a rigorous analytic formulation of the $ADE$ classification of tt*-structures. Under natural structural assumptions, a tt*-structure over $\mathbb{C}^*$ can be described via i

Shohei Kiryu, Yohji Chin, Masahiro Takeoka, Kosuke Fukui

Hybrid bosonic codes combining bosonic codes with photon states offer a promising pathway for fault-tolerant quantum computation. However, the efficient generation of such states in optical setups remains technically challenging due to the requirement for complex non-Gaussian resources. In this paper, we propose a novel scheme to efficiently generate hybrid

Gonzalo Martínez-Fernández, Jose F Quesada, Agustín Riscos-Núñez, Francisco José Salguero-Lamillar

Palaeohispanic languages are those spoken in the Iberian Peninsula before the arrival of the Romans in the 3rd Century B.C. Their study was really put on motion after G\'omez Moreno deciphered the Iberian Levantine script, one of the several semi-sillabaries used by these languages. Still, the Palaeohispanic languages have varying degrees of decipherment, an

Yash Deshpande, Samaresh Bera

The extended Berkeley Packet Filter (eBPF) is useful for faster packet processing and network monitoring in softwarized deployments. Similarly, softwarized deployments of 5G core network services adopted eBPF to meet the stringent latency and bandwidth requirements of underlying applications. While the existing studies focused on network performance, securit

Giorgio Stucchi, J. Ignacio Cirac, Rahul Trivedi, Georgios Styliaris

We develop a framework for Matrix Product Quantum Channels (MPQCs), a one-dimensional tensor-network description of completely positive, trace-preserving maps. We focus on translation-invariant channels, generated by a single repeated tensor, that admit a local purification. We show that their purifying isometry can always be implemented by a constant-depth

Simran Ketha, Venkatakrishnan Ramaswamy

Deep networks have been known to have extraordinary generalization abilities, via mechanisms that aren't yet well understood. It is also known that upon shuffling labels in the training data to varying degrees, deep networks, trained with standard methods, can still achieve perfect or high accuracy on this corrupted training data. This phenomenon is called m

Jiyao Liu, Junzhi Ning, Wanying Qu, Lihao Liu

Medical image quality assessment (Med-IQA) is a prerequisite for clinical AI deployment, yet multimodal large language models (MLLMs) still fall substantially short of human experts, particularly when required to provide descriptive assessments with clinical reasoning beyond simple quality scores. However, improving them is hindered by the high cost of acqui

Xin Xu, Kexin Zhang

This paper investigates the asymptotic behavior of the principal eigenvalue $\lambda(s)$, as $s\to+\infty$, for the following elliptic eigenvalue problem \begin{equation*}\label{E} -\Delta_{M}u-s\langle \nabla_M f, \nabla_M u\rangle_g +c u=\lambda(s)u, \end{equation*} defined on an orientable and closed Riemannian manifold $(M,g)$. Assuming $f$ is a Morse fu

Tomás Caraballo, Javier López-de-la-Cruz, Alexandre N. Oliveira-Sousa, Paulo N. Seminario-Huertas

We introduce a stochastic SIR-type partial differential equation model incorporating random diffusion, reinfection, vital dynamics, and a randomly varying transmission rate. For the associated random dynamical system, we prove the existence of both random and exponential attractors. We construct a non-stationary, random disease-free global solution, which se

Alejandro D. Mousist, Pedro Delgado de Robles Martín, Raquel Lladró Climent, Julian Cobos Aparicio

Rapid identification of hazardous events is essential for next-generation Earth Observation (EO) missions supporting disaster response. However, current monitoring pipelines remain largely ground-centric, introducing latency due to downlink limitations, multi-source data fusion constraints, and the computational cost of exhaustive scene analysis. This work p

You Li, Dewei Zhou, Fan Ma, Fu Li

Recent Video-to-Audio (V2A) methods have achieved remarkable progress, enabling the synthesis of realistic, high-quality audio. However, they struggle with fine-grained temporal control in multi-event scenarios or when visual cues are insufficient, such as small regions, off-screen sounds, or occluded or partially visible objects. In this paper, we propose F

Jiahui Xu, Emmet Murphy, Lana Josipovic

When the MLIR project was first introduced, it promised to address the issues that the HLS community had with the LLVM project. But is this really the case, and is MLIR the "right"/"best" compiler infrastructure for HLS? We here share our experiences based on the development of Dynamatic (github.com/EPFL-LAP/dynamatic).

Peng Kuang, Emma Söderberg, April Yi Wang, Martin Höst

Program comprehension is an essential activity in software engineering. Not only does it often challenge professionals, but it can also hinder novices from advancing their programming skills. Gaze, an emerging modality in developer tools, has so far primarily been utilized to improve our understanding of programmers' visual attention and as a means to reason

Alexander Corner, Nick Gurski

Operads were originally defined by May to have right actions of the symmetric groups, but later formulations have also used no groups actions at all or group actions by such families as the braid groups. We call such families action operads, as they are the algebraic objects that encode parametrized group actions on operads. In Part I of this paper, we study

Javier López-de-la-Cruz, Felipe Rivero, Carlos R. Takaessu

We investigate some chemostat models incorporating wall growth, competition, random fluctuations on the dilution rate, and different consumption functions (Monod and Haldane). We analyze the asymptotic behavior of the solutions of the corresponding random differential systems to establish conditions on the model parameters under which the microbes persist in

Michael Hubbertz, Qi Han, Tobias Meisen

Deep learning-based online mapping has emerged as a cornerstone of autonomous driving, yet these models frequently fail to generalize beyond familiar environments. We propose a framework to identify and measure the underlying failure modes by disentangling two effects: Memorization of input features and overfitting to known map geometries. We propose measure

Chirantan Hebballi, Akash Poptani, Amrutha Benny, Rajshekar Kalayappan

A Runtime Verification (RV) framework that supports online, at-speed verification of properties that can change dynamically (during in-field operations) will benefit a large variety of applications. Several state-of-the-art RV frameworks propose to implement monitors on FPGAs. While this approach can support changes to the property being monitored during in-

Kota Mitsumoto, Shuji Ishihara

Cellular metabolic networks exhibit scale-free topologies with power-law degree distributions across diverse organisms. Although such topologies are often linked to mutational robustness and evolutionary advantage, their role in metabolic dynamics remains unclear. Using dynamical mean-field theory, we derive an exact solution for an intracellular catalytic r

Riccardo Scantamburlo, Mauro Mezzanzana, Giacomo Buonanno, Francesco Bertolotti

Do LLMs talk like us? This question intrigues a multitude of scholar and it is relevant in many fields, from education to academia. This work presents an interpretable statistical feature for distinguishing human written and LLMs generated dialogue. We introduce a lightweight metric derived from semantic categories distribution. Using the Empath lexical anal

Muhammad Muneeb, David B. Ascher

Human genetics offers a promising route to therapeutic discovery, yet practical frameworks translating genotype-derived signal into ranked target and drug hypotheses remain limited, particularly when matched disease transcriptomics are unavailable. Here we present G2DR, a genotype-first prioritization framework propagating inherited variation through genetic

Panna Gehér, Dömötör Pálvölgyi, Dániel G. Simon, Géza Tóth

A graph is called a $k$-planar unit distance graph if it can be drawn in the plane such that every edge is a unit line segment and is involved in at most $k$ crossings. We investigate $u_k(n)$, the maximum number of edges of such graphs on $n$ vertices. For $k=1$, we improve the best known upper bound, by showing that $u_1(n) \leq 3n - c\sqrt{n}$ for some co

Gesa Sarnighausen, Anne Wald, Andreas Hauptmann

We study the concept of including the causality principle as regularizer into the solution of linear time-dependent inverse problems. This is achieved by combining transformer-based predictions with classical variational regularization, resulting in what we call transformer causality regularization (TCR). The causality principle states that an object at time

Jason Hung

Artificial intelligence (AI) control protocols assume that trusted large language model (LLM) monitors reliably assess proposed actions across all deployment contexts. This paper tests that assumption in the geographic dimension. We audit Claude Opus 4.6-the monitor specified in Apart Research's AI Control Hackathon Track 3 benchmark-for systematic gaps in i

Anant Jain, Ayush Tripathi

Electroencephalography (EEG) - based air-writing recognition offers a human-computer interaction paradigm by decoding neural activity associated with handwriting movements. Despite its potential, reliable EEG-based air-writing recognition remains challenging due to low signal-to-noise ratio and pronounced inter-subject variability. In this study, we examine

María Pereda

Understanding the formation of social ties requires disentangling the roles of individual traits and local network structure. We analyse signed social relationships among 3,395 students using an interpretable machine learning model -- the Explainable Boosting Machine (EBM) -- to predict link polarity from individual attributes (prosociality, cognitive reflec

Lokendra Kumar, Shubham Aggarwal

We present the first study of Hyper-Connections (HC) for volumetric multi-modal brain tumor segmentation, integrating them as a drop-in replacement for fixed residual connections across five architectures: nnU-Net, SwinUNETR, VT-UNet, U-Net, and U-Netpp. Dynamic HC consistently improves all 3D models on the BraTS 2021 dataset, yielding up to +1.03 percent me

Anders Giovanni Møller, Elisa Bassignana, Francesco Pierri, Luca Maria Aiello

The ubiquity of multimedia content is reshaping online information spaces, particularly in social media environments. At the same time, search is being rapidly transformed by generative AI, with large language models (LLMs) routinely deployed as intermediaries between users and multimedia content to retrieve and summarize information. Despite their growing i

Adrian Dacko, Lahcen Oussi

We investigate the vacuum distribution of a family of partial sums of nonsymmetric position operators, depending on a real parameter $\lambda$, and acting on the discrete Fock space in the framework of V-monotone independence. We analyze the combinatorics of the moments of this distribution, and using its Cauchy--Stieltjes transform, we determine its exact f

Priyabrat Dash, Mathis Bode, Konduri Aditya

Large-eddy simulations (LES) require closures for filtered production rates because the resolved fields do not contain all correlations that govern chemical source terms. We develop a graph neural network (GNN) that predicts filtered species production rates on non-uniform meshes from inputs of filtered mass fractions and temperature. Direct numerical simula

Federico Maria Quetti, Elena Ballante, Silvia Figini, Paolo Giudici

A major limitation of clustering approaches is their lack of explainability: methods rarely provide insight into which features drive the grouping of similar observations. To address this limitation, we propose an ensemble-based clustering framework that integrates bagging and feature dropout to generate feature importance scores, in analogy with feature imp

Bavo Tistaert, Stan Servaes, Alejandro Gonzalez-Garcia, Ibrahim Ibrahim

This paper presents a novel hybrid motion planning method for holonomic multi-agent systems. The proposed decentralised model predictive control (MPC) framework tackles the intractability of classical centralised MPC for a growing number of agents while providing safety guarantees. This is achieved by combining a decentralised version of the alternating dire

Xuan Wei, Jie Tang, Yu Tao

Variability is one of the classic features of active galactic nuclei (AGNs). The normalized structure function was applied to distinguish variability samples from OVRO, ASAS-SN and Fermi. A power-law function model was selected to fit the structure functions of samples of three bands. We present the available samples of three bands, and by integrating two pa

Evanthia Papadopoulou, Zeyu Wang

We consider the Voronoi diagram of lines in $\mathbb{R}^3$ under the Euclidean metric, and give a full classification of its structure in the base case of four lines in general position. We first show that the number of vertices in the Voronoi diagram of four lines in general position is always even, between 0 and 8, and all such numbers can be realized. We

Chiyu Ma, Shuo Yang, Kexin Huang, Jinda Lu

We present Future-KL Influenced Policy Optimization (FIPO), a reinforcement learning algorithm designed to overcome reasoning bottlenecks in large language models. While GRPO style training scales effectively, it typically relies on outcome-based rewards (ORM) that distribute a global advantage uniformly across every token in a trajectory. We argue that this

Alejandro Bris Cuerpo, Arturo Arroyo-Castro, Alexey Vladimirov

We derive the factorization theorem for the quasi-transverse-momentum-dependent (quasi-TMD) correlator, including kinematic power corrections to all orders. The resulting expression involves only twist-two TMD distributions and is frame invariant. As in the leading-power approximation, the reduced soft factor factorizes multiplicatively; however, in contrast

Lokesh Kumar, Nirmesh Shah, Ashishkumar P. Gudmalwar, Pankaj Wasnik

Human communication seamlessly integrates speech and bodily motion, where hand gestures naturally complement vocal prosody to express intent, emotion, and emphasis. While recent text-to-speech (TTS) systems have begun incorporating multimodal cues such as facial expressions or lip movements, the role of hand gestures in shaping prosody remains largely undere

Guanliang Li, Pedro Espinosa-Angulo, David Perez-Saura, Santiago Tapia-Fernandez

Efficient structural perception is essential for mapping and autonomous navigation on resource-constrained robots. Existing 3D methods are computationally prohibitive, while traditional 2D geometric approaches lack robustness. This paper presents a lightweight, real-time framework that projects 3D LiDAR data into 2D Bird's-Eye-View (BEV) images to enable eff

A. Vazquez-Palomo, C. Betegón, J. Weickenmeier, E. Martínez-Pañeda

Alzheimer's disease is characterised by the spreading of misfolded proteins and progressive structural changes in the brain. Despite significant clinical research, understanding how microscopic protein dynamics translate into macroscopic tissue degeneration remains a major challenge. In this work, we present a three-dimensional, finite element-based computat

Vaibhav Gupta, Florian Grensing, Beyza Cinar, Louisa van den Boom

Hypoglycemia is a severe condition of decreased blood glucose, specifically below 70 mg/dL (3.9 mmol/L). This condition can often be asymptomatic and challenging to predict in individuals with type 1 diabetes (T1D). Research on hypoglycemic prediction typically uses a combination of blood glucose readings and heart rate data to predict hypoglycemic events. G

Jürgen Richter-Gebert

We consider the incidence structure formed by the twelve pentagons given by the vertex neighborhoods of the icosahedron. Interpreting this structure purely in terms of coplanarity conditions, we show that -- up to projective equivalence -- it admits exactly two realizations. Both realizations coincide with the vertex set of the regular icosahedron and interp

Caterina Garofalo, Horst Lenske, Francesco Cappuzzello, Manuela Cavallaro

The Majorana Double Charge Exchange nuclear reaction mechanism represents a powerful and novel scenario to probe the dynamics of the neutrinoless double beta decay. Recent studies presented in this manuscript have highlighted a key feature of this mechanism, namely its independence with respect to the nuclei involved in the reaction.

Jonathan Richard Shewchuk

The restricted Delaunay triangulation of a closed surface $\Sigma$ and a finite point set $V \subset \Sigma$ is a subcomplex of the Delaunay tetrahedralization of $V$ whose triangles approximate $\Sigma$. It is well known that if $V$ is a sufficiently dense sample of a smooth $\Sigma$, then the union of the restricted Delaunay triangles is homeomorphic to $\

Van-Duy Ngo, Stergos Afantenos, Emiliano Lorini, Miguel Couceiro

In this paper, we adopt a relational view of analogies applied to Semantic Role Classification in FrameNet. We define analogies as formal relations over the Cartesian product of frame evoking lexical units (LUs) and frame element (FEs) pairs, which we use to construct a new dataset. Each element of this binary relation is labelled as a valid analogical insta

Maximiliano Rivera Figueroa, Jannis Held, Pradyumna Kumar Bishoyi, Marina Petrova

Indoor positioning faces ongoing challenges due to complex propagation conditions, such as multipath propagation, signal blockages, and intrinsic target characteristics that substantially impact measurement reliability and positioning accuracy. Existing methods, in particular Least Squares (LS), frequently struggle to maintain robustness when confronted with

Elvio G. Amparore

Range-Based Set Reconciliation (RBSR) synchronizes ordered sets by recursively comparing summaries of contiguous ranges and refining only the mismatching parts. While its communication complexity is well understood, its local computational cost fundamentally depends on the storage backend that must answer repeated range-summary, rank, and enumeration queries

Tomoki Katayama, Hiroki Matsui, Yuri Michinobu, Fumiya Okamatsu

We investigate a concrete realization of the Dark Dimension scenario, where a single large extra dimension is set at sub-millimeter scales. In this framework, the Casimir energy of bulk fields accounts for the observed dark energy. Working in a 5-dimensional setup with the Standard Model confined to a 4-dimensional brane, we derive the effective action for t

Dohyun Bu, Chanho Kim, Seokun Choi, Jong-Seok Lee

Data assimilation (DA) for systems governed by partial differential equations (PDE) aims to reconstruct full spatiotemporal fields from sparse high-fidelity (HF) observations while respecting physical constraints. While full-grid low-fidelity (LF) simulations provide informative priors in multi-fidelity settings, recovering an HF field consistent with both s

Kazunori Nakamoto, Shingo Okuyama, Yasuhiro Omoda

We give the classification of thick representations and dense representations of the symmetric group over a field of characteristic zero.

Animesh, Plaban Kumar Bhowmick, Pralay Mitra

Accurate prediction of binding sites of a given protein, to which ligands can bind, is a critical step in structure-based computational drug discovery. Recently, Equivariant Graph Neural Networks (GNNs) have emerged as a powerful paradigm for binding site identification methods due to the large-scale availability of 3D structures of proteins via protein data

Matthieu Bourguignon, Gustavo A. Rosales-Sosa, Yoshinari Kato, Sergio Sao-Joao

Electron irradiation enables quantitative control over the plastic flow dynamics of silicate glasses, even far below the glass transition temperature. Through stress-relaxation experiments spanning ambient to near-glass-transition temperatures, we uncover a time-current equivalence that grants direct access to steady-state plastic flow over five decades in s

Jaime Alonso-Hernández, Carmen Sánchez Contreras, Raghvendra Sahai, Jorge Sanz-Forcada

Aims. In a first study, we characterised the properties of the gas component in the circumstellar envelopes surrounding a sample of 29 AGB stars with UV excesses. Now we intend to complement this information with an analysis of the dust component and compare the estimated parameters with those previously inferred from larger samples of AGB stars. Methods. We

Luce Breuil

Recent biological evidence suggests the presence of a two-phase ageing process in several species. We introduce a system of two age-structured partial differential equations (PDE) representing two phases of ageing of a wild population. The model includes a coupling of both equations through birth and transition between phases and non-linearities due to compe

Inkyu Jang, Chams E. Mballo, Claire J. Tomlin, H. Jin Kim

Stochastic control barrier functions (SCBFs) provide a safety-critical control framework for systems subject to stochastic disturbances by bounding the probability of remaining within a safe set. However, synthesizing a valid SCBF that explicitly reflects the true safety probability of the system, which is the most natural measure of safety, remains a challe

F. Acernese, A. Agapito, D. Agarwal, I. -L. Ahrend

This document presents an overview of the design, implementation, and expected performance of the Advanced Virgo Plus (AdV+) upgrades in view of the O5 observing run. Following the experience gained during the O4 commissioning and operations, the Virgo Collaboration has revised the upgrade strategy to address limitations associated with marginally stable rec

Dmytro Petryk, Ievgen Kabin, Peter Langendoerfer, Zoya Dyka

Devices employing cryptographic approaches have to be resistant to physical attacks. Side-Channel Analysis (SCA) and Fault Injection (FI) attacks are frequently used to reveal cryptographic keys. In this paper, we present a combined SCA and laser illumination attack against an Elliptic Curve Scalar Multiplication accelerator using a differential probe from T

Yijie Ding, Zitian Guo, Jiacheng Li, Letian Peng

A widely held hypothesis for why generative recommendation (GR) models outperform conventional item ID-based models is that they generalize better. However, there is few systematic way to verify this hypothesis beyond a superficial comparison of overall performance. To address this gap, we categorize each data instance based on the specific capability requir

Giacomo Borghi, Hyesung Im, Lorenzo Pareschi

Population-based learning paradigms, including evolutionary strategies, Population-Based Training (PBT), and recent model-merging methods, combine fast within-model optimisation with slower population-level adaptation. Despite their empirical success, a general mathematical description of the resulting collective training dynamics remains incomplete. We intr

Manuru Nithin Padiyar, Priyabrat Dash, Konduri Aditya

Direct numerical simulations of turbulent reacting flows involving millions of grid points and detailed chemical mechanisms with hundreds of species and thousands of reactions are computationally prohibitive. To address this challenge, we present two data-driven chemical mechanism reduction formulations based on graph neural networks (GNNs) with message-pass

Jiyeong Kim, Yerim So, Hyesong Choi, Uiwon Hwang

Unified Multimodal Models (UMMs) have emerged as a promising paradigm that integrates multimodal understanding and generation within a unified modeling framework. However, current generative training paradigms suffer from inherent limitations. We present Semantically-Grounded Supervision (SeGroS), a fine-tuning framework designed to resolve the granularity m

Rüdiger Ehlers

Chains of co-B\"uchi automata (COCOA) have recently been introduced as a new canonical model for representing arbitrary omega-regular languages. They can be minimized in polynomial time and are hence an attractive language representation for applications in which normally, deterministic omega-automata are used. While it is known how to build COCOA from deter

F. Rodríguez-Díaz, D. Gutiérrez-Avilés, A. Troncoso, F. Martínez-Álvarez

Quantum machine learning offers promising advantages for classification tasks, but noise, decoherence, and connectivity constraints in current devices continue to limit the efficient execution of feature map-based circuits. Gate Assessment and Threshold Evaluation (GATE) is presented as a circuit optimization methodology that reduces quantum feature maps usi

Sanjay Chaudhuri, Dean Dustin, Bertrand Clarke

We use the law of total variance to generate multiple expansions for the posterior predictive variance. These expansions are sums of terms involving conditional expectations and conditional variances and provide a quantification of the sources of predictive uncertainty. Since the posterior predictive variance is fixed given the model, it represents a constan

Shang-Min Tsai, Piero Ferrari, Mats Kuipers, Jacob Lustig-Yaeger

Recent transmission spectra of the temperate sub-Neptune K2-18 b obtained with JWST have attracted significant attention. Debates have quickly arisen over the interpretation of the spectral data, particularly the recent MIRI observation where dimethyl sulfide (DMS) and dimethyl disulfide (DMDS) are claimed. Here we revisit K2-18 b as a case study to examine

Yanpeng Li, Zhi Liu, Jiahui Xie, Wang Zhou

Let $\mathbf{R}$ be the sample correlation matrix constructed from $\mathbf{X}\in \mathbb{R}^{p\times n}$, whose entries are independent and identically distributed random variables with mean zero and tail probability condition $\lim_{x\rightarrow \infty}x^3\mathbb{P}(|\xi|>x)=0$. We derive the universal logarithmic law for $\log \det \mathbf{R}$, \begin{equ

Uche Mbaka, Michelle Carey

Traditional Functional Principal Component Analysis typically focuses on densely observed univariate functional data, yet many applications, particularly in longitudinal studies, involve multivariate functional data observed sparsely and irregularly across subjects. A common approach for extracting multivariate functional principal components in such setting

Xingchen Song, Di Wu, Dinghao Zhou, Pengyu Cheng

Most existing text-to-speech (TTS) systems either synthesize speech sentence by sentence and stitch the results together, or drive synthesis from plain-text dialogues alone. Both approaches leave models with little understanding of global context or paralinguistic cues, making it hard to capture real-world phenomena such as multi-speaker interactions (interr

Yunqiao Xu, Uktamjon Uktamov, Pierros Ntelis, Ahmadjon Abdujabbarov

In this paper, we show that in weak field limit Schwarzschild Bertotti-Robinson black hole (Schwarzschild-BR BH) turns into Schwarzschild black hole immersed in external uniform magnetic field which is given in 1. The dynamics of both magnetized and electrically charged particles in the vicinity of a Schwarzschild-BR black hole are investigated. The innermos

Franek Stark, Jakob Middelberg, Shubham Vyas

Binary on/off thrusters are commonly used for spacecraft attitude and position control during proximity operations. However, their discrete nature poses challenges for conventional continuous control methods. The control of these discrete actuators is either explicitly formulated as a mixed-integer optimization problem or handled in a two-layer approach, whe

Minyue Dai, Ke Fan, Anyi Rao, Jingbo Wang

Text-to-motion (T2M) generation is becoming a practical tool for animation and interactive avatars. However, modifying specific body parts while maintaining overall motion coherence remains challenging. Existing methods typically rely on cumbersome, high-dimensional joint constraints (e.g., trajectories), which hinder user-friendly, iterative refinement. To

Alberto Caprara, Fabio Furini, Claudio Gentile, Leo Liberti

We prove the \textbf{NP}-hardness, using Karp reductions, of some problems related to the correlation polytope and its corresponding cone, spanned by all of the $n\times n$ rank-one matrices over $\{0,1\}$. The problems are: membership, rank of the decomposition, and a ``relaxed rank'' obtained from relaxing the zero-norm expression for the rank to an $\ell_

Yao Yao, David Howard, Perla Maiolino

Task-driven design of soft robots requires models that are physically accurate and computationally efficient, while remaining transferable across actuator designs and task scenarios. However, existing modeling approaches typically face a fundamental trade-off between physical fidelity and computational efficiency, which limits model reuse across design and t

Salma Rachidi, Aso Bozorgpanah, Eric Fey, Alexander Jung

Machine learning (ML) promises better clinical decision-making, yet opaque model behavior limits the adoption in healthcare. We propose two novel regularization techniques for ensuring the interpretability of ML models trained on real-world data. In particular, we consider the prediction of five-year survival for multiple myeloma patients using clinical data

Sanjay Oruganti, Sergei Nirenburg, Marjorie McShane, Jesse English

Deploying embodied AI agents in the physical world demands cognitive capabilities for long-horizon planning that execute reliably, deterministically, and transparently. We present HARMONIC, a cognitive-robotic architecture that pairs OntoAgent, a content-centric cognitive architecture providing metacognitive self-monitoring, domain-grounded diagnosis, and co

Caterina Garofalo, Horst Lenske, Francesco Cappuzzello, Manuela Cavallaro

The Majorana Double Charge Exchange (MDCE) provides a suitable environment for studying the dynamics of the neutrinoless double beta ($0\nu\beta\beta$) decay, particularly short-range correlations among nucleons. The study of the pion potential is essential in this respect, as it represents the strong interaction counterpart of the neutrino potential, drivin

Zeyu Ding, Katja Ickstadt, Nadja Klein, Alexander Munteanu

Efficient and scalable non-parametric or semi-parametric regression analysis and density estimation are of crucial importance to the fields of statistics and machine learning. However, available methods are limited in their ability to handle large-scale data. We address this issue by developing a novel coreset construction for multivariate conditional transf

Kassem Fawaz, Ren Yi, Octavian Suciu, Rishabh Khandelwal

The ability to simulate human privacy decisions has significant implications for aligning autonomous agents with individual intent and conducting cost-effective, large-scale privacy-centric user studies. Prior approaches prompt Large Language Models (LLMs) with natural language user statements, data-sharing histories, or demographic attributes to simulate pr

Zijian Lu, Yiping Zuo, Yupeng Nie, Xin He

Self-generated skills for web agents are often unstable and can even hurt performance relative to direct acting. We argue that the key bottleneck is not only skill generation quality, but the fact that web skills remain implicit and therefore cannot be checked or locally repaired. To address this, we present ContractSkill, a framework that converts a draft s

Alessandro De Rosis

We introduce a distribution-free lattice Boltzmann formulation for general compartmental reaction--diffusion systems arising in mathematical epidemiology. The proposed scheme, termed a single-step simplified lattice Boltzmann method (SSLBM), evolves directly macroscopic compartment densities, eliminating the need for particle distribution functions and expli

Yifei Zhao, Fanyu Zhao, Zhongyuan Zhang, Shengtang Wu

Generalized few-shot 3D point cloud segmentation aims to adapt to novel classes from only a few annotations while maintaining strong performance on base classes, but this remains challenging due to the inherent stability-plasticity trade-off: adapting to novel classes can interfere with shared representations and cause base-class forgetting. We present HOP3D

Wei Shao, Khaled Khasawneh, Setareh Rafatirad, Houman Homayoun

Serverless computing abstracts infrastructure management but also obscures system-level behaviors that can introduce security risks. Prior work has shown that serverless platforms are vulnerable to attacks exploiting shared execution environments, including attacker--victim co-location and denial-of-service through resource contention, yet analyzing these ri

Koki Okura

This paper investigates expansions of distal structures by a unary subset that arises as the image of a projection map. We first provide a sufficient condition for such an expansion to remain distal. Based on this criterion, we establish the distality of three kinds of expansions involving the integers or the $p$-adic fields. Let $R$ be an almost sparse sequ

Chun-yao Liu, Zheng-wen Long, Qi-liang He

Constrained by the complexity of theoretical calculations, current research on genuine tripartite nonlocality (GTN) within the relativistic framework concentrates mainly on Greenberger-Horne-Zeilinger-like states, with few studies addressing W states or even general tripartite states. In this paper, we apply numerical methods to investigate how environmental

Tanishk Shrimal, Sara Collins, Priyajit Jana, M. Padmanath

We report on our lattice QCD study of coupled $DD_s^* - D^*D_s$ scattering in the $J^P=1^+$ channel and elastic $DD_s$ scattering in the $J^P=0^+$ channel, aimed at investigating the possible existence of $cc\bar{u}\bar{s}$ tetraquarks near threshold. The calculation uses CLS ensembles with $m_\pi \approx 280$ MeV, lattice spacing $a \approx 0.09$ fm, and sp

Priyank Vasu

We present a method for constructing harmonic immersions in $\mathbb{R}^3$, known as the Enneper-type representation. We also prove that any harmonic immersion in $\mathbb{R}^3$ can be obtained using this approach. Furthermore, we determine the number of non-planar rotational harmonic immersions in $\mathbb{R}^3$ that connect two coaxial circles in parallel

Xiang Zhuang, Chenyi Zhou, Kehua Feng, Zhihui Zhu

Artificial intelligence has demonstrated remarkable capability in predicting scientific properties, yet scientific discovery remains an inherently physical, long-horizon pursuit governed by experimental cycles. Most current computational approaches are misaligned with this reality, framing discovery as isolated, task-specific predictions rather than continuo

Peipei Xie, Siwei Chen, Zejun Xiang, Shasha Zhang

At SAC 2013, Berger et al. first proposed the Extended Generalized Feistel Networks (EGFN) structure for the design of block ciphers with efficient diffusion. Later, based on the Type-2 EGFN, they instantiated a new lightweight block cipher named Lilliput (published in IEEE Transactions on Computers, Vol. 65, Issue 7, 2016). According to published cryptanaly

Hantao Zheng, Ning Han, Yawen Zeng, Hao Chen

Recent weakly supervised video anomaly detection methods have achieved significant advances by employing unified frameworks for joint optimization. However, this paradigm is limited by a fundamental sensitivity-stability trade-off, as the conflicting objectives for detecting transient and sustained anomalies lead to either fragmented predictions or over-smoo

Wen Yin, Cencen Liu, Dingrui Liu, Bing Su

Unifying Image Quality Assessment (IQA) and Image Aesthetic Assessment (IAA) in a single multimodal large language model is appealing, yet existing methods adopt a task-agnostic recipe that applies the same reasoning strategy and reward to both tasks. We show this is fundamentally misaligned: IQA relies on low-level, objective perceptual cues and benefits fr

Miroslav Vořechovský, Jan Mašek

Space-filling experimental designs are widely used in engineering computer experiments, where only a limited number of expensive model evaluations can be afforded. Distance-based designs such as Maximin or Minimax ensure global space-filling, while Latin hypercube sampling enforces uniform one-dimensional projections, yet neither guarantees uniformity in low

Shuanghao Shu, Yichao Li, Wenxiu Yang, Jiaxin Wang

We present the HI galaxy observation results of the FATHOMER (FAst neuTral HydrOgen intensity Mapping ExpeRiment), a pilot drift scan survey by the Five-hundred-meter Aperture Spherical radio Telescope (FAST). The survey comprises 28 hours of observations over 7 nights in 2021, covering a $60\, \deg^2$ sky area in the frequency range 1.05-1.45 GHz. The HI ga

Qi Luo, Minghui Xu, Dongxiao Yu, Xiuzhen Cheng

Modern graph learning systems often combine links with text, as in citation networks with abstracts or social graphs with user posts. In such systems, text is usually easier to edit than graph structure, which creates a practical security risk: an attacker may hide a small malicious cue in training text and later use it to trigger incorrect predictions. This

Nabelanita Utami, Ryohei Sasano

The evolution of writing assistance tools from machine translation to large language models (LLMs) has changed how researchers write. This study investigates whether this shift is homogenizing research papers by analyzing native language identification (NLI) trends in ACL Anthology papers across three eras: pre-neural network (NN), pre-LLM, and post-LLM. We

Kyudan Jung, Jihwan Kim, Soyoon Kim, Jeonghoon Kim

As the paradigm of AI shifts from text-based LLMs to Speech Language Models (SLMs), there is a growing demand for full-duplex systems capable of real-time, natural human-computer interaction. However, the development of such models is constrained by the scarcity of high-quality, multi-speaker conversational data, as existing large-scale resources are predomi

Chengzhi Hong, Bijun Li

Monocular 3D lane detection remains challenging due to depth ambiguity and weak geometric constraints. Mainstream methods rely on depth guidance, BEV projection, and anchor- or curve-based heads with simplified physical assumptions, remapping high-dimensional image features while only weakly encoding road geometry. Lacking an invariant geometric-topological

Shiqi Gao, Zitong Xu, Kang Fu, Huiyu Duan

Evaluating text-guided image editing (TIE) methods remains a challenging problem, as reliable assessment should simultaneously consider perceptual quality, alignment with textual instructions, and preservation of original image content. Despite rapid progress in TIE models, existing evaluation benchmarks remain limited in scale and often show weak correlatio

Annika Brockhaus, Wioletta M. Ruszel, Cristian Spitoni

We study a discrete-time asynchronous midpoint dynamics on the circle in which, at each step, a uniformly chosen neighboring pair moves to the midpoint along the shortest arc. Although the update rule is locally contractive, we show that the global relaxation mechanism depends sharply on the boundary topology. Under open boundary conditions the system conver

Valentin Braeutigam, Matthias Stock, Bernhard Egger

Most currently used object detection methods are learning-based, and can detect objects under varying appearances. Those models require training and a training dataset. We focus on use cases with less data variation, but the requirement of being free of generation of training data and training. Such a setup is for example desired in automatic testing of grap