April 2026 arXiv papers — page 53

Leonardo Arrighi, Julia Eva Belloni, Aurélie Gallet, Ivan Gentile

Most Neural Networks (NNs) for classification are trained using Cross-Entropy as a loss function. This approach requires the model to have an explicit classification layer. However, there exist alternative approaches, such as Contrastive Learning (CL). Instead of explicitly operating a classification, CL has the NN produce an embedding space where projection

Zhiwei Wei, Chenxi Song, Tazhu Wang, Fan Wu

Thematic maps play a central role in academic communication, yet their large-scale design evolution has rarely been examined empirically. This study presents a longitudinal and multilingual analysis of thematic map design practices in academic cartography from 1990 to 2020. We compile a corpus of 45,732 research articles from sixteen authoritative Chinese- a

Badri N. Patro, Vijay S. Agneeswaran

State space models (SSMs) achieve linear-time complexity but struggle with multi-channel physiological signals due to three limitations: fixed kernels cannot capture multi-scale temporal dynamics (motor preparation over hundreds of milliseconds vs. execution transients in tens of milliseconds), Markovian state updates restrict global context for periodic osc

Mikhail Egorov

This work describes a few-body dynamics method based on the Faddeev integral equations in momentum space for determining the total cross sections of fusion and breakup reactions with two- and three-body final channels in the continuum, employing a cluster representation of the colliding nuclei. Total cross sections were obtained for the reactions $^3\text{He

Ana L. N. Dias, Ronald Dickman, Tiago Venzel Rosembach

We develop a mean-field description including spatial structure for a simplified version of the three-state active matter model studied by Venzel et al. (Phys. Rev. E 110, 014109 (2024)). The resulting triangular lattice of coupled nonlinear differential equations are integrated numerically using a fourth-order Runge-Kutta scheme. Starting from various order

Isaac Tosin Adisa

Objective: To propose and retrospectively validate an integrated framework addressing three barriers to clinical translation of readmission prediction: lack of explainability, absence of deployment reliability infrastructure, and inadequate demographic fairness evaluation. Materials and Methods: We constructed a cohort of 415231 adult admissions from the MIM

Zewen Li, Shuo Ye, Zitong Yu, Weicheng Xie

Industrial anomaly detection based on RGB-3D multimodal data has emerged as a mainstream paradigm for intelligent quality inspection. However, existing unsupervised methods suffer from two critical limitations: ambiguous cross-modal alignment caused by the lack of high-level semantic guidance and insufficient geometric modeling for RGB-to-3D feature mapping.

Hojjat Karami, David Atienza, Jean-Philippe Thiran, Anisoara Ionescu

Feature engineering for Electronic Health Records (EHR) is complicated by irregular observation intervals, variable measurement frequencies, and structural sparsity inherent to clinical time series. Existing automated methods either lack clinical domain awareness or assume clean, regularly sampled inputs, limiting their applicability to real-world EHR data.

Marcelo Fernandez - TraslaIA

Autonomous systems increasingly operate under partial observability where execution-relevant state is never fully accessible. Existing governance mechanisms -- trusted execution environments, oracle-signed state proofs, cryptographic attestation -- enforce the integrity of computation and state projections. We show this is structurally insufficient: an authe

Amirhossein Keshavarzchafjiri, Janith K. Dassanayake, Gayan A. Aruma Baduge, Mojtaba Vaezi

A novel Gamma-distributed geometric constellation design framework for integrated sensing and communication (ISAC) is proposed in this paper. In this framework, constellation points are modeled as samples drawn from a parameterized two-dimensional distribution, with a Gamma distribution for the amplitude and a uniform distribution for the phase. End-task per

Julius Owusu, Monika Avila Márquez

Empirical researchers routinely invoke the no-interference or \textit{individualistic treatment response} (ITR) assumption to identify causal effects in observational studies, despite concerns that interference across units may arise in many economic settings. This paper studies the causal content of standard ITR-based identification formulas when arbitrary

Lukas Gerlach

The chase is a sound, complete, but possibly non-terminating algorithm for reasoning with existential rules (aka. tuple-generating dependencies), a highly expressive knowledge representation language. Although the procedure appears simple, research on theoretical properties and optimization for practical implementations has grown to a point where verifying c

Chen Peng

DEKL 2.0 is a dependent type-theoretic framework for trace-indexed knowledge evolution. Its central claim is that the proof calculus remains monotone under standard structural rules, while non-monotonic behavior arises semantically from trace extension. Finite and infinite traces are first-class objects in the computational universe; knowledge is interpreted

Konstantinos Alexis, Giorgos Giannopoulos, Dimitrios Gunopulos

Self-supervised learning has achieved remarkable success in learning visual representations from clean data, yet remains challenging when clean observations are sparse or not available at all. In this paper, we demonstrate that pretrained vision models can be leveraged to learn distortion-robust representations, which can then be effectively applied to downs

Eduardo Abi Jaber, Clément Rey, Dimitri Sotnikov

Malliavin calculus is a powerful and general framework for the analysis of square-integrable random variables, but it often suffers from a lack of tractability and explicit representations. To address this limitation, we focus on a subclass of random variables given by finite linear combinations of time-extended Brownian motion signatures. The class remains

Lourdes Camblor-Navarro, Iago Pardo, Oriol Arteaga

Mueller matrix (MM) polarimetry is an effective, non-invasive tool for retrieving information from complex media. However, the finite dynamic range of optical detectors poses a significant challenge when measurements involve strong intensity contrasts, where bright regions risk saturation while dark regions suffer from poor signal-to-noise ratio. To address

Wenxuan Xie, Yuelin Zhang, Qingpeng Ding, Jianghua Chen

Wireless localization of permanent magnets enables occlusion-free guidance for medical interventions, yet its practical accuracy is fundamentally limited by two coupled challenges: the poor observability of conventional planar sensor arrays and the simulation-to-reality (Sim-to-Real) gap of learning-based estimators. To address these issues, this article pre

Yi Geng, Pan Cao

This paper proposes a grouped pattern (GP) for sensing signals and a corresponding multi-periodogram algorithm for range estimation in integrated sensing and communications (ISAC) systems. GP partitions subcarriers into groups with an identical intra-group configuration replicated across groups, producing range profiles with periodic peaks and a structured m

I. Polichtchouk, M. C. A. Clare, M. Chantry, E. Gascón

A hybrid approach to numerical weather prediction is investigated, in which the unperturbed physics-based ECMWF Integrated Forecasting System (IFS) is spectrally nudged toward forecasts from a machine-learned weather forecast model, trained to forecast on model levels. Nudging is applied only to the large scales of virtual temperature and vorticity, with the

Keisuke Kataoka, Yoshihito Kuno, Takahiro Orito, Ikuo Ichinose

We study how the color code under decoherence gives rise to an intrinsic mixed-state topological order (imTO), which has no counterpart in pure ground states of local gapped Hamiltonians. For decoherence induced by XX-type operators on red links of the honeycomb lattice, we show that the resulting mixed state inherits half of the topological properties of th

Yingfeng Luo, Hongyu Liu, Dingyang Lin, Kaiyan Chang

Large Language Models (LLMs) have achieved remarkable performance in Machine Translation (MT), but deploying them at scale remains prohibitively expensive. A widely adopted remedy is the hybrid system paradigm, which balances cost and quality by serving most requests with a small model and selectively routing a fraction to a large model. However, existing ro

Zhengqin Fan, Simon DeDeo

We present the results from Meno, a simple autoformalizer that proves theorems in Lean by systematically exploring the space of both formal and informal proofs, and tactic ablation, a new method for exploring mathematical creativity under constraint. We show these tools in action on simple theorems found in Terrence Tao's Analysis I, selectively ablating sol

Seong Hun Lee, Patrick Vandewalle, Javier Civera

We introduce NONSAC (Non-Minimal Sampling and Consensus), a general framework for robust and scalable model estimation from arbitrarily large datasets contaminated with noise and outliers. NONSAC repeatedly samples non-minimal subsets of data and generates model hypotheses using a robust estimator, producing multiple candidate models. The final model is sele

Wataru Hirota, Tomoki Taniguchi, Tomoko Ohkuma, Kosuke Takahashi

Evaluating LLM-generated business ideas is often harder to scale than generating them. Unlike standard NLP benchmarks, business idea evaluation relies on multi-dimensional criteria such as feasibility, novelty, differentiation, user need, and market size, and expert judgments often disagree. This paper studies a methodological question raised by such disagre

F. J. Artola, A. Redl, S. N. Gerasimov, R. A. Pitts

Predicting three-dimensional thermal loads during tokamak disruptions is essential for ITER yet remains weakly developed. We present a physics-based workflow that couples MHD simulations of vertical displacement events with field line tracing on a realistic 3D first wall model and a transient wall thermal response. The approach is validated against JET disch

Hamza A. Abushahla, Ariel Justine N. Panopio, Layth Al-Khairulla, Mohamed I. AlHajri

Handwritten Arabic manuscripts preserve the Arab world's intellectual and cultural heritage, and writer identification supports provenance, authenticity verification, and historical analysis. Using the Muharaf dataset of historical Arabic manuscripts, we evaluate writer identification from individual line images and, to the best of our knowledge, provide the

Jishnu Goswami, Yasumichi Aoki, Hidenori Fukaya, Shoji Hashimoto

We calculate second- and selected fourth-order conserved-charge fluctuations in $(2+1)$-flavor QCD using M\"obius domain-wall fermions (MDWF) along a line of constant physics. Gauge ensembles were generated for two light-to-strange quark-mass ratios, $m_l/m_s=1/10$ and $1/27.4$, corresponding to heavier-than-physical and physical pion masses, respectively. F

Ioannis Protogeros, Rufat Asadli, Benjamin Hoffman, Laurent Vanbever

There is a rapidly growing interest in using Large Language Models (LLMs) to automate complex network operations, but their reliable adoption requires rigorous assessment of their effectiveness and safety. Existing benchmarks do not address whether LLMs can successfully resolve errors in large-scale, interdependent network configurations without introducing

Dawid Kotowski, Mario Ohlberger

This contribution proposes novel data-driven surrogate modeling approaches for parameterized parabolic PDEs, where the parameter dependence can be split into two parts with different decay behavior of the Kolmogorov $N$-width. Such problems naturally arise in many industrial flow processes with dominant advection or traveling fronts in the solution trajector

Wei Hong, Shanshan Hu, Wei Liu, Shiyuan Yang

In this work, we establish the small-noise asymptotic behaviour (namely, the functional law of large numbers and the large deviation principle) for multi-scale McKean--Vlasov diffusions with super-linear kernels. In this setting, the interaction depends on the laws of both the slow component and the fast oscillating process. Consequently, the frozen (paramet

Tejas Harith, Antoine Kaufmann

Public clouds increasingly expose heterogeneous hardware, but their allocation interface remains built around rigid on-demand and spot service classes. This makes it hard to satisfy time-varying tenant objectives and operator constraints in oversubscribed, heterogeneous clusters without exposing internal application or infrastructure state. We present Laisse

Giuseppe Lerner

This lecture reviews the principles of particle-matter interactions, providing the essential physics background required to understand beam loss mechanisms in high-energy accelerators and their associated implications. The main interaction processes of photons and charged particles are introduced, together with an overview of nuclear reactions. The lecture t

Annika Bätz, Pavel Klasek, Seo-Young Ham, Philipp Neumaier

Automated train operation on existing railway infrastructure requires robust camera-based perception, yet the railway domain lacks public benchmark suites with standardized evaluation protocols that would enable reproducible comparison of approaches. We present RAIL-BENCH, the first perception benchmark suite for the railway domain. It comprises five challen

Rayson Laroca, Valfride Nascimento, Donggun Kim, Sanghyeok Chung

Low-Resolution License Plate Recognition (LRLPR) remains a challenging problem in real-world surveillance scenarios, where long capture distances, compression artifacts, and adverse imaging conditions can severely degrade license plate legibility. To promote progress in this area, we organized the ICPR 2026 Competition on Low-Resolution License Plate Recogni

Pengzhen Ke, Yuxuan Qin, Liang Feng Zhang

Private Information Retrieval (PIR) allows clients to retrieve database entries without leaking retrieval indices, yet malicious servers seriously compromise retrieval correctness. Existing Authenticated PIR (APIR) schemes resist selective-failure attacks but rely on computational hardness assumptions. In contrast, information-theoretic PIR with Result Verif

Wentao Shi, Qifan Wang, Chen Chen, Fei Liu

Reinforcement learning (RL) effectively optimizes Large Language Model (LLM)-based recommenders by contrasting positive and negative items. Empirically, training with beam-search negatives consistently outperforms random negatives, yet the mechanism is not well understood. We address this gap by analyzing the induced optimization objective and show that: (i)

Yueqi Wang, Wing Tat Leung, Guanglian Li

We propose a novel numerical homogenization method based on the edge multiscale approach for solving indefinite time-harmonic Maxwell equations in heterogeneous media with large wavenumber. Numerical methods for these equations in homogeneous media with high wavenumber are particularly challenging due to the so-called pollution effect: the mesh size must be

Davide Mattiolo, Pietro Negrini, Silvia M. C. Pagani

Snarks are $2$-connected cubic graphs that do not admit a proper $3$-edge-coloring. For a cubic graph $G$, its resistance $r(G)$ is the minimum number of edges whose removal results in a $3$-edge-colorable graph, while its flow resistance $r_f(G)$ is the minimum number of edges whose removal results in a graph admitting a nowhere-zero $\mathbb{Z}_2 \times \m

Xin Zhou, Francesco Massel

In our work, we show how, for a network of bosonic modes, canonical commutation relations constrain the coefficients relating input and internal modes. Based on these constraints, we derive a lower bound on the total steady-state squeezing achievable in reservoir-engineered (dissipative) squeezing schemes, quantified by the sum of mode-optimal quadrature var

Martin Colot, Cédric Simar, Guy Cheron, Ana Maria Cebolla Alvarez

Continuous estimation of high-dimensional finger kinematics from forearm surface electromyography (EMG) could enable natural control for hand prostheses, AR/XR interfaces, and teleoperation. However, the complexity of human hand gestures and the entanglement of forearm muscles make accurate recognition intrinsically challenging. Existing approaches typically

Lihao Zheng, Zhenwei Shao, Yu Zhou, Yan Yang

Although Multimodal Large Language Models (MLLMs) have advanced rapidly, they still face notable challenges in fine-grained multi-image understanding, often exhibiting spatial hallucination, attention leakage, and failures in object constancy. In addition, existing approaches typically rely on expensive human annotations or large-scale chain-of-thought (CoT)

Daria Fokina, Marco Baldan, Constantin Romankiewicz, Wolfgang Laudensack

In this study, deep learning is used to estimate kinetic parameters for modeling itaconic acid production based on real batch experiments conducted at different agitation speeds and reactor scales. Two deep learning strategies, namely direct deep learning (DDL) and generative conditional flow matching (CFM) are compared and benchmarked against nonlinear regr

Jesse Elliott, Constantin Ickstadt, Thorsten Theobald, Elias Tsigaridas

By results of Dantzig (1951) and Adler (2013), computing the optimal solutions of a linear program is equivalent to finding optimal strategies in zero-sum bimatrix games. Dantzig's original result was incomplete, in the sense that the reduction of a linear program to a zero-sum game did not work for all possible linear programs. We show that, under a natural

Thibault Pautrel, Florent Bouchard, Ammar Mian, Guillaume Ginolhac

We introduce two federated learning frameworks for the classical SPDnet model operating on symmetric positive definite (SPD) matrices with Stiefel-constrained parameters. Unlike standard Euclidean averaging, which violates orthogonality, our approach preserves geometric structure through two efficient aggregation strategies: ProjAvg, projecting arithmetic me

Jan Jedelský

The first-order (FO) model checking problem asks, given an FO sentence $\phi$ and a graph $G$, whether $G$ is a model of $\phi$. This problem is known to be $\mathsf{AW[*]}$-hard when parameterized by the quantifier rank of the formula. A classical algorithm decides this problem in XP-time parameterized by the number of variables in the formula. Due to $\mat

Yunquan Chen, Haoyu Chen

Understanding social dominance in animal behavior is critical for neuroscience and behavioral studies. In this work, we explore the capability of Multimodal Large Language Models(MLLMs) to analyze raw behavioral video of mice and predict their dominance hierarchy. We introduce MTT-Bench, a novel benchmark comprising annotated videos of pairwise mouse interac

Xuejing Luo, Hee-Seung Moon, Christian Holz, Antti Oulasvirta

Selecting out-of-reach objects is a fundamental task in mixed reality (MR). Existing methods rely on a single cue or deterministically fuse multiple cues, leading to performance degradation when the dominant cue becomes unreliable. In this work, we introduce a probabilistic cue integration framework that enables flexible combination of multiple user-generate

Giuseppe Lerner

The operation of high-energy and high-intensity particle accelerators inevitably leads to the loss of a fraction of beam particles, either through controlled processes or accidental events. This article builds on a first lecture on particle-matter interactions to review the main beam loss mechanisms in high-energy and high-intensity accelerators and their im

Younes Djemmal, You Zuo, Kim Gerdes, Kirian Guiller

Patent retrieval underpins critical decisions in innovation, examination, and IP strategy, yet progress has been hampered by the absence of benchmarks that reflect the diversity of real world search scenarios. We address this gap with two contributions. First, we introduce Sophiabench, a large-scale patent retrieval benchmark comprising 10,000 queries and 75

Matthias Günther, Lutz Klotz

The notions of infimum and maximal lower bounds of a set $\mathfrak M$ of bounded self-adjoint operators were mainly studied for a set $\mathfrak M$ of two elements. The present paper deals with more general sets $\mathfrak M$, where it is required that $\mathfrak M$ is nonempty and bounded from below. Kadison's theorem on the existence of the infimum of a t

Sofya Maslovskaya, Sina Ober-Blöbaum, Boris Wembe

Optimal control problems with symmetries often admit a non stationary turnpike property called trim turnpike, which characterizes the convergence of optimal solutions to certain symmetry induced trajectories called trim primitives. In this paper we establish an exponential trim turnpike property for a class of optimal control problems with structural propert

Deepesh Bhati, Sakshi Khandelwal

The Pareto distribution plays a crucial role in various disciplines, necessitating robust goodness-of-fit tests for its validation. This article introduces a novel tests based on Stein's characterization and the Laplace transform, offering a fresh perspective on model assessment. We establish the asymptotic properties of the proposed test and evaluate its em

C. M. Lewis

Searches for neutrino electromagnetic interactions at reactor sites require an unusual combination of ultra-low thresholds and a stable low-background environment. It is shown here that cryogenic undoped cesium iodide (CsI) naturally satisfies these conditions in a way prior detectors have not. Although suppression of nuclear recoil ionization efficiency at

Thorbjørn Skovhus, Thomas Olsen

The magnetic excitation spectrum of itinerant magnets exhibits rich and complex spectral features that often complicate interpretation of the underlying physics. For perturbations in the long wavelength limit, one obtains a well defined pole at zero frequency in the spectral function, the Goldstone magnon. However, for optical modes and finite wavevectors, t

Cristian Tabares, Alberto Muñoz de las Heras, Jan T. Schneider, Alejandro González-Tudela

Long-range interactions are the source of many equilibrium and out-of-equilibrium quantum many-body phenomena. Analog simulators based on ionic, atomic, superconducting, and molecular systems provide a natural platform to obtain these interactions using vibration- and photon-mediated processes. Recent experimental advances, such as their integration in multi

Vlatko Vedral

We present the standard double slit experiment with non-relativistic particles in the Heisenberg Picture of quantum mechanics. Our motivation is threefold. First and foremost, and contrary to some claims in the literature, we show that there is no need to talk about non-locality when explaining the interference fringes. Secondly, we emphasise the fact that e

Siyi Li, Yue Jiang, Bowen Jing, Liuyuxin Yang

Advancements in 3D modeling,digital display technologies,and the growing availability of digital cultural heritage data have significantly improved the accuracy of heritage depictions and expanded opportunities for analysis.However,while many studies focus on presenting specific cultural heritage figurines,an often overlooked aspect is the visualization of t

Gökdeniz Ersoy, Mehmet Alper Tatar, Eray Tonbul, Serap Kırbız

Driver drowsiness is a major cause of traffic accidents worldwide, posing a serious threat to public safety. Vision-based driver monitoring systems often rely on fixed Eye Aspect Ratio (EAR) and Mouth Aspect Ratio (MAR) thresholds; however, such fixed values frequently fail to generalize across individuals due to variations in facial structure, illumination,

Aadarsh Devanand, Praful D. Mankar

We present a novel framework for line-of-sight (LoS) delay-Doppler (DD) estimation in dense scattering propagation environments. We present two time-frequency (TF) domain pilot sequences inspired by the Zadoff-Chu sequence that exhibit desirable autocorrelation properties. Further, we present a twisted convolution-based approach for LoS DD estimation directl

Oussama Bouanani, Jim Berend, Wojciech Samek, Sebastian Lapuschkin

Neuron labeling assigns textual descriptions to internal units of deep networks. Existing approaches typically rely on highly activating examples, often yielding broad or misleading labels by focusing on dominant but incidental visual factors. Prior work such as FALCON introduced contrastive examples -- inputs that are semantically similar to activating exam

Meike Weiß, Alice C. Niemeyer

Deforming fundamental domains of wallpaper groups provides a systematic way to generate non-convex blocks which admit topological interlocking assemblies (TIAs). We use this approach to construct TIAs that fully occupy the space between two parallel planes and incorporate multiple block types. In addition to wallpaper groups, semiregular tessellations are em

Tuomas Hytönen

Schatten class properties of commutators $[b,T]$ of pointwise multipliers $b$ and singular integral operators $T$ have been characterised in a variety of settings. An abstract framework, covering many of these results as special cases, was proposed by the author [arXiv:2411.02613]. However, recent results about commutators of the concrete Bessel-Riesz transf

Eduard Paul Enoiu, Robert Feldt

AI assistants can increasingly generate and evolve test cases. The challenge is no longer merely to produce them, but also to help engineers understand why a generated artefact exists and what supports it. Existing work has focused on classifying testing techniques, linking requirements to tests and structuring system assurance arguments, but it does not exp

Kaveh Eftekharinasab

We investigate the integrability of Fr\'{e}chet tangent distributions on Fr\'{e}chet manifolds. We introduce the local well-posedness Condition W for split tangent subbundles, which reduces the local integrability problem to solving initial value problems with parameters whose solutions define curves tangent to the distribution. By applying a variational app

Sanidhya Gupta, Sanidhay Bhambay, Narges Alavisamani, Neil Walton

Future quantum architectures are expected to be modular, with quantum processors connecting multiple quantum processing units (QPUs) via photonic interconnects. In topological quantum error correction, such as color codes, this creates seam boundaries where parity checks require remote CNOT operations using heralded Bell pairs. These non-local checks are slo

Łukasz Płociniczak, Hubert Woszczek

We establish uniform error bounds of the L1 discretization of the Caputo fractional derivative of the function from the weighted Sobolev space with weight belonging to the Mucknenhoupt class. We present how our framework works for several examples of weight, which belong to the Muckenhoupt class. As and application, we show the convergence of the L1 scheme f

Ignacio Santamaria, Carlos Beltrán, Eduard Jorswieck, Mohammad Soleymani

This paper proposes and analyzes Riemannian optimization algorithms on the manifold of unitary and symmetric matrices, denoted ${\cal {U}}_s$, which naturally models the scattering matrices of passive and reciprocal devices such as beyond-diagonal reconfigurable intelligent surfaces (BD-RISs). Despite its relevance, the geometry of ${\cal {U}}_s$ has remaine

Marcus Johan Schytt, John Bagterp Jørgensen

We present a modular and thermodynamically consistent modeling framework for simulating steady-state and transient behavior in fixed-bed reactors. Accurate simulation of dynamic reactor behavior is essential for enabling flexible operation in Power-to-X (P2X) applications, such as Power-to-Ammonia and Power-to-Methanol, where fluctuating renewable energy inp

Li Li, Ming Cheng, Weixin Zhu, Yannan Wang

Multi-speaker automatic speech recognition (ASR) aims to transcribe conversational speech involving multiple speakers, requiring the model to capture not only what was said, but also who said it and sometimes when it was spoken. Recent Speech-LLM approaches have shown the potential of unified modeling for this task, but jointly learning speaker attribution,

A. A. Averianov, A. O. Barvinsky, I. L. Buchbinder, V. A. Krykhtin

We consider a general reducible gauge theory deformed by mass or/and interaction terms violating gauge invariance. It is shown that in the Abelian case, by using the Stueckelberg-type procedure, this theory with broken gauge symmetry can be converted into exactly gauge-invariant theory which under a suitable choice of gauge conditions can be treated within t

George H. Hitching, Alfonso Zamora

For a vector bundle $V$ over a curve $X$, the Segre invariant $s_n (V)$ encodes the maximal degree attained by rank $n$ subbundles of $V$. The functions $s_n$ define stratifications on moduli of $V$ which are well studied. Let $G$ be a connected reductive algebraic group, and $E \to X$ a principal $G$-bundle. For each parabolic subgroup $P \subset G$ there i

Haiyun Qiu, Xingyu Wu, Kay Chen Tan

Continual Model Merging (CMM) sequentially integrates task-specific models into a unified architecture without intensive retraining. However, existing CMM methods are hindered by a fundamental saturation-redundancy dilemma: backbone-centric approaches face parameter saturation and representation interference within fixed capacities, whereas Mixture-of-Expert

Ram Sewak Dubey, Maysam Rabbani, Rodrigo Pinto

We evaluate subsidy mechanisms in the FCC's Rural Health Care program using administrative data covering the full population of participants. The original price-cap mechanism removes cost-containment incentives for health care providers. An ad valorem mechanism introduced in 2014 addresses this flaw by making providers bear 35% of costs. However, allowing co

Sara Rimoldi, Arianna De Vecchi, Hazem Hesham Yousef Shalby, Federica Villa

Wearable devices running Human Activity Recognition(HAR) on Inertial Measurement Units~(IMUs) waste energy by performing continuous classification for each window, even during long periods of unchanged activity. We address this with a lightweight change-detection gate: a non-parametric algorithm based on dynamic template matching that runs continuously at on

Yong Liu, Bin Tang, Rangrang Zhang

We establish the well-posedness of stationary solutions for a class of SPDEs with locally monotone coefficients, and prove the Freidlin--Wentzell large deviation principle (LDP) for these stationary solutions. The LDP for the associated invariant measures then follows via the contraction principle, avoiding the need to construct the quasi-potential and verif

Arjan Bik, Javier Álvarez-Márquez, Alejandro Crespo Gómez, Luis Colina

Studying the interstellar medium (ISM) in merging high-redshift galaxies is crucial for understanding early galaxy assembly, star formation, and black hole growth, predicted by hierarchical $\Lambda$CDM models. Deep imaging and spatially resolved spectroscopy with JWST enable unprecedented insight into these processes, even for galaxies in the Epoch of Reion

Anna Abalkina, Marie Kunešová, Yagmur Ozturk, Solal Pirelli

Paper mills are a growing threat to the integrity of science, yet their penetration in conference proceedings remains underexplored despite conferences being more important than journals in some scientific subfields. This study aims to identify papers in conference proceedings whose titles have been offered for sale on social media platforms. We collected pu

Qiandong Sun, Rui He, Shilin Hou, Jiyan Dai

Row column addressed (RCA) transducers present a promising solution for ultrafast volumetric imaging with a reduced channel count and a large field of view. However, RCA-based 3D imaging is fundamentally limited by severe sidelobe artifacts and a low signal-to-noise ratio (SNR), primarily due to weak transmit focusing inherent in RCA based ultrafast imaging

Dmitry Babichev, Sergey Babichev

We study the exact counting problem for all lattice rectangles contained in the square $[0,n)\times[0,n)$, including non-axis-parallel ones. Starting from the standard parametrization by a primitive direction $(u,v)$ and two side lengths, we derive several exact algorithms: the classical $O(n^2)$ sweep, decompositions of complexity $O(n^{3/2}\log n)$ and $O(

Xiaozhou Li

Microservice is a popular software architecture that relies on decentralized teams and clear service ownership to support modularity and scalability. However, in practice, developers frequently contribute across multiple services, creating organizational coupling (OC) that gradually erodes architectural boundaries and increases coordination overhead. This st

Xirui Li, Ming Li, Yunze Xiao, Ryan Wong

Collective intelligence refers to the ability of a group to achieve outcomes beyond what any individual member can accomplish alone. As large language model agents scale to populations of millions, a key question arises: Does collective intelligence emerge spontaneously from scale? We present the first empirical evaluation of this question in a large-scale a

A. Alexander, A. Carey, G. Levitina, A. Rennie

We prove an integral formula for the spectral flow of differentiable loops of unitaries of the form ${\rm Id}+$Schatten. Our formula is in terms of a regularised winding number, expressed in terms of exact differential forms, and we show how the formula extends to non-closed paths. Applying these ideas to the scattering operator of Schr\"{o}dinger scattering

Rajdeep Mazumdar, Kalyan Malakar, Kalyan Bhuyan

We investigate the late-time cosmological viability of Rényi entropic cosmology (REC) by confronting its modified Friedmann dynamics with DESI DR2 BAO, cosmic chronometers, gravitational-wave standard sirens, redshift-space distortions, and two independent Type Ia supernova compilations, PantheonPlus and Union3. Unlike earlier works, we obtain a stringent co

Abbas Ali Saberi, Roderich Moessner

Motivated by the importance ascribed to correlations in random matrices used to model phenomena in various scientific disciplines, we report how algebraic correlations between matrix elements affect the eigenvalue statistics and spectral density of random matrices. These correlations, introduced through a long-range correlated percolation model, decay as a p

Zhengxu Yu, Yu Fu, Zhiyuan He, Yuxuan Huang

Individual agent capabilities have advanced rapidly through modular skills and tool integrations, yet multi-agent systems remain constrained by fixed team structures, tightly coupled coordination logic, and session-bound learning. We argue that this reflects a deeper absence: a principled organisational layer that governs how a workforce of agents is assembl

Agostino Occhicone, Alberto Sinibaldi, Paola Di Matteo, Daniele Chiappetta

Surface functionalization plays a decisive role in the performance of biosensors, as it governs the efficiency and stability of biomolecule immobilization at the sensor interface and, consequently, the overall performance of the biosensing platforms. In this work, we present a comparative study of three organosilane chemistries - APTES, APDMS, and CPTES - ap

L. Bengel, H. Peng, B. de Rijk, C. Koos

We investigate a bi-directionally coupled system consisting of a Kerr-nonlinear microresonator and a continuous-wave single-mode semiconductor laser. Inside the resonator, a forward-propagating and a backscattered field interact nonlinearly, while a fraction of the backscattered field is fed back into the laser cavity. We show in this paper that the interact

Abhinaba Basu

We introduce HubRouter, a pluggable module that replaces O(n^2) attention layers with O(nM) hub-mediated routing, where M << n is a small number of learned hub tokens. We demonstrate it in two from-scratch architectures: a Jamba-style hybrid and a 12-layer Transformer; retrofit into pretrained models is a tested negative case. HubRouter implements an encode-

Devesh Giri, Suvodip Mukherjee

Gravitational-wave (GW) observations of compact binary coalescences (CBCs) are traditionally interpreted under the assumption that the binary evolves in isolation. However, in realistic astrophysical environments, brief three-body encounters may perturb the binary's orbital evolution and imprint deviations on the emitted GWs. We develop a physically motivate

Yun Wang, Peng Xie, Shaogang Fan, Zhibo Zhou

Cathaya argyrophylla is an endangered paleoendemic gymnosperm characterized by restricted ecological adaptability and high pathogen susceptibility. To elucidate its genomic architecture and evolutionary history, a de novo chromosome-level genome assembly was constructed using PacBio High-Fidelity long reads and Hi-C scaffolding. The resulting 22.73 Gb assemb

Zaiyan Yang, Xinpeng Liu, Heng Guo, Jinglei Shi

We propose a neural regularization method that refines the noisy 3D semantic field produced by lifting multi-view inconsistent 2D features, in order to obtain an accurate and robust 3D semantic Gaussian Splatting. The 2D features extracted from vision foundation models suffer from multi-view inconsistency due to a lack of cross-view constraints. Lifting thes

Chenxi Gu, Xiaoning Du, John Grundy

Watermarking has emerged as a promising technique for tracing the authorship of content generated by large language models (LLMs). Among existing approaches, the KGW scheme is particularly attractive due to its versatility, efficiency, and effectiveness in natural language generation. However, KGW's effectiveness degrades significantly under low-entropy sett

Jia-Mian Wu, Jun Liu, Siqi Li, Xiaoya Wang

Computed tomography (CT)-based attenuation and scatter correction improves quantitative PET but adds radiation exposure that is particularly undesirable in pediatric imaging. Existing CT-free methods are commonly trained in homogeneous settings and often degrade under scanner or radiotracer shifts, which limits their clinical utility. We propose the Generali

Bin Wu, Arastun Mammadli, Xiaoyu Zhang, Emine Yilmaz

The rapid growth of AI agent ecosystems is transforming how complex tasks are delegated and executed, creating a new challenge of identifying suitable agents for a given task. Unlike traditional tools, agent capabilities are often compositional and execution-dependent, making them difficult to assess from textual descriptions alone. However, existing researc

Grigory Solomadin, Antoine Touzé

In this paper, we establish formality (over $\mathbb{Q}$) for diagrams of Eilenberg-MacLane spaces of any height $n\geq 1$. This implies spectral sequence (over $\mathbb{Q}$) collapse at page $2$ for any diagram of EML spaces over any small category. We prove by functor calculus argument that formality does not hold over any fixed commutative ring $\mathbf{k

Yifei Wang, Jacky Keung, Xiaoxue Ma, Zhenyu Mao

Accurate requirement-to-code traceability is crucial for software maintenance. However, existing IR- and embedding-based methods are heavily dependent on lexical similarity, often yielding incomplete or inconsistent links across projects and languages and incurring high cost from long-context retrieval and prompting. This paper presents R2Code, an LLM-based

Erik Hermansson, Lynn Dunsire, David Svensson, Thomas Jaki

We introduce Robust Bayesian Sequential Borrowing (RBSB), a framework for extrapolating evidence across adjacent subgroups in multi-population clinical programmes where studies are conducted in sequence and populations are ordered by clinical proximity. Conventional approaches weight all historical sources uniformly or exclude distant populations entirely, f

Jorge Villarrubia, Luis Costero, Francisco D. Igual, Katzalin Olcoz

To mitigate the increasingly common underutilization of computational resources in modern GPUs, spatial sharing methods enable multiple applications to use them simultaneously. This work presents a comprehensive evaluation of NVIDIA's primary technologies to achieve that goal: Multi-Process Service (MPS) and Multi-Instance GPU (MIG). Our findings reveal a cr

Gerald Isheanesu Matungamire, Alkistis Aikaterini Sigourou, Gerrit Schrock, Zoya Dyka

Scalar multiplication kP is the operation most frequently targeted in Elliptic Curve (EC) cryptosystems. To protect against single-trace Side-Channel Analysis (SCA) attacks, the atomicity principle and various atomic block patterns have been proposed in the past. In this work we use our software and hardware implementations to demonstrate that binary right-t

Bulent Soykan, Gulsah Hancerliogullari Koksalmis, Hsin-Hsiung Huang, Laura J. Brattain

Predicting individual cognitive decline in Alzheimer's disease (AD) is difficult due to the heterogeneity of disease progression. Reliable clinical tools require not only high accuracy but also fairness across demographics and robustness to missing data. We present CognitiveTwin, a digital twin framework that predicts patient-specific cognitive trajectories.