March 2026 arXiv papers — page 24

Di Wu, Yuman Bai, Shoupeng Ren, Xinyu Zhang

Centralized stablecoins such as USDT and USDC enforce sanctions through contract-layer blacklist functions. Yet on public blockchains, a freeze is still an ordinary transaction competing with the sanctioned party's transfer for priority. It exposes a gap between contract-layer authority and ordering-layer enforcement: when both race for the same block, the o

Kaikai Zhang, Xiang Wang, Haoluo Zhao, Nan Chen

Artificial intelligence (AI) has achieved breakthroughs comparable to traditional numerical models in data-driven weather forecasting, yet it remains essentially statistical fitting and struggles to uncover the physical causal mechanisms of the atmosphere. Physics-oriented mechanism research still heavily relies on domain knowledge and cumbersome engineering

Wenkai Zhao, Zipei Wang, Mengjie Fang, Di Dong

General Multimodal Large Language Models (MLLMs) often underperform in capturing domain-specific nuances in medical diagnosis, trailing behind fully supervised baselines. Although fine-tuning provides a remedy, the high costs of expert annotation and massive computational overhead limit its scalability. To bridge this gap without updating the weights of the

Nick Fischer

The APSP Hypothesis states that the All-Pairs Shortest Paths (APSP) problem requires time $n^{3-o(1)}$ on graphs with polynomially bounded integer edge weights. Two increasingly stronger assumptions are the Strong APSP Hypothesis and the Directed Unweighted APSP Hypothesis, which state that the fastest-known APSP algorithms on graphs with small weights and u

Bimpe Ayoola, Anielle Andrade, Ronnie de Souza Santos, Paul Ralph

Social sustainability in software development means creating and maintaining systems that promote pro-social values (e.g., human well-being, equity), both now and in the future. However, social sustainability lacks clear conceptual and methodological foundations, and often takes a back seat to speed and profit. This paper therefore reports a narrative review

Zeli Wang, Qingxuan Yang, Shuyin Xia, Yueming Wu

Deep neural networks (DNNs) have emerged as a prominent approach for detecting smart contract vulnerabilities, driven by the growing contract datasets and advanced deep learning techniques. However, DNNs typically require large-scale labeled datasets to model the relationships between contract features and vulnerability labels. In practice, the labeling proc

Jun Liu, Ying Chen, Ziqian Lu, Qinyue Tong

Multivariate Time Series Anomaly Detection (MTSAD) is critical for real-world monitoring scenarios such as industrial control and aerospace systems. Mainstream reconstruction-based anomaly detection methods suffer from two key limitations: first, overfitting to spurious correlations induced by an overemphasis on cross-variable modeling; second, the generatio

Amir Weiss, Alejandro Lancho

We study joint compression and detection in distributed sensing systems motivated by emerging applications such as IoT-based localization. Two spatially separated sensors observe noisy signals and can exchange only a $k$-bit message over a reliable one-way low-rate link. One sensor compresses its observation into a $k$-bit description to help the other decid

Jonas O. Brown, Maedeh Taheri, Nicholas R. Sesing, Tina T. Salguero

Controlling collective electronic phases in low-dimensional materials is a central challenge for developing technologies based on charge-density waves. Here, we report that perpendicular electric and magnetic fields can be used to tune charge-density-wave transport in the quasi-two-dimensional material 1T-TaS2. Using h-BN-encapsulated thin-film heterostructu

Mahmood Sohrabi

In this paper, we study arbitrary models of the first-order theory of a ring $A$ where the additive group $A$ is a finitely generated abelian group. Following an earlier paper by this author, Alexei G. Myasnikov and Francis Oger, we call these rings the FDZ-rings or FDZ-algebras. The rings considered are not necessarily unitary, commutative, or associative.

Michael V. Klibanov, Jingzhi Li, Tian Niu, Vladimir G. Romanov

The first globally convergent numerical method is developed for a coefficient inverse problem (CIP) for the $n-$d, $n\geq 2$ wave equation with the unknown potential in the most challenging case when the $\delta -$ function is present in the initial condition with a single location of the point source. In fact, an approximate mathematical model for that CIP

Angelot Behajaina, Joachim König, Danny Neftin

We solve the problem of Davenport--Lewis--Schinzel (DLS), originating in the 1950s, regarding the reducibility of $f(X)-g(Y)\in\mathbb C[X,Y]$. This yields an almost-complete solution to the Hilbert--Siegel problem: For a polynomial map $f$ whose composition factors avoid only very specific low-degree polynomials, we explicitly describe over which integers t

Junle Li, Zhaoxiang Wu, Guanda Gong, Zhaohan Li

Liquid scintillator detectors are widely used in neutrino experiments due to their low energy threshold and high energy resolution. Despite the tiny abundance of $^{14}$C in LS, the photons induced by the $\beta$ decay of the $^{14}$C isotope inevitably contaminate the signal, degrading the energy resolution. In this work, we propose three models to tag $^{1

Ruiyun Zhang, Zhaolin Wang, Zhiqing Wei, Yuanwei Liu

To advance integrated sensing and communications (ISAC) in sixth-generation (6G) extremely large-scale multiple-input multiple-output (XL-MIMO) networks, a low-complexity compressed sensing (CS)-based dictionary design is proposed for wideband near-field (WB-NF) target localization. Currently, the massive signal dimensions in the WB-NF regime impose severe c

Shashwat Singh, Sheri Zhang, Spencer Matonis, Zeynep Temel

Mudskippers are unique amphibious fish capable of locomotion in diverse environments, including terrestrial surfaces, aquatic habitats, and highly viscous substrates such as mud. This versatile locomotion is largely enabled by their powerful tail, which stores and rapidly releases energy to produce impulsive jumps. Inspired by this biological mechanism, we p

Ertian Chen, Lichao Chen, Lars Nesheim

The European Union Emissions Trading System is set to substantially increase the effective carbon price faced by airlines. To quantify the impact of this carbon regulation on the European airline industry, we estimate a two-stage model of airline competition with endogenous route entry, flight frequencies, and pricing using European data on market shares and

Mansi Nema, Kuntal Deka, Sanjeev Sharma, Tharmalingam Ratnarajah

This paper investigates the physical layer security of a non-orthogonal multiple access (NOMA) system assisted by a tertiary-mode simultaneously transmitting and reflecting reconfigurable intelligent surface (STAR-RIS), which can perform transmission, reflection, and jamming simultaneously. The system comprises a base station (BS) serving two users located o

Alessandro Rovetta

This paper develops an interpretive framework for divergence P-values and S-values within a descriptive frequentist perspective. Statistical analysis is framed as operating within idealized worlds defined by a set of assumptions and a target hypothesis, where probabilities describe the behavior of data under the model but do not assign truth values to hypoth

Lingyu Liu, Yaxiong Wang, Li Zhu, Lizi Liao

This work introduces a new approach to automatic oil painting that emphasizes the creation of dynamic and expressive brushstrokes. A pivotal challenge lies in mitigating the duplicate and common-place strokes, which often lead to less aesthetic outcomes. Inspired by the human painting process, \ie, observing, comparing, and drawing, we incorporate differenti

Heather Battey, Nancy Reid

We study the assessment of semiparametric and other highly-parametrised models from the perspective of foundational principles of parametric statistical inference. In doing so, we highlight the possibility of avoiding the usual semiparametric considerations, which typically require estimation of nuisance components through kernel smoothing or basis expansion

Wishnu Badrawani, Elsa Dyahpitaloka, Ahmad F. F. Alanshori, Imam Mukhlis

This study investigates the relationship between coin demand, payment innovation, COVID-19, and a coin's purchasing power, particularly in emerging countries like Indonesia. The rapid advancement of payment platforms, combined with high adoption during the pandemic, has positioned non-cash payments as a complement or substitute for coin money for transaction

Grace M. Hwang, Jessica D. Falcone, Joseph D. Monaco, Courtney R. Pinard

Neuromorphic engineering has matured over the past four decades and is currently experiencing explosive growth with the potential to transform biomedical engineering and neurotechnologies. Participants at the Neuromorphic Principles in Biomedicine and Healthcare (NPBH) Workshop (October 2024) -- representing a broad cross-section of the community, including

Beichen Zheng

This work reformulates Chiba's affine-order prescription as a polynomial-moment problem for a transformed positive measure, and develops an alternative finite-precision construction route based on this reformulation. The proposed construction proceeds through discrete-measure realization, symmetric Lanczos reduction, and Golub--Welsch extraction, replacing t

B. Krishna Das, Poornendu Kumar, Haripada Sau

We introduce the notion of Cayley--Hamilton tuples: these are commuting operator tuples that are annihilated by a non-zero polynomial and such that its Taylor joint spectrum coincides with the algebraic variety determined by its annihilating ideal. Commuting matrix tuples are Cayley--Hamilton tuples. We provide two families of Cayley--Hamilton tuples in the

Jun Ji, Joseph G Thomas, Zichen Xi, Ruxuan Liu

Microwave-frequency acoustic waves in solids have emerged as a versatile platform for both classical and quantum applications. While phononic integrated devices and circuits are being developed on various material platforms, an ideal phononic integrated circuit (PnIC) platform should simultaneously support low-loss waveguide structures, high-quality-factor r

Kiyoshi Igusa, Ray Maresca

For a finite dimensional algebra $\Lambda$, we consider a torsion class $G$ in $mod$-$\Lambda$, which is not necessarily finitely generated. We construct a wall-and-chamber structure for $G$ where the chambers are the connected components of the complement of the union of walls. We also consider ``infinitesimal chambers". To each chamber we associate a ``pse

Tao Chen, Andreu Cecilia, Lei Wang, Daniele Astolfi

Replay attacks comprise replaying previously recorded sensor measurements and injecting malicious signals into a physical plant, causing great damage to cyber-physical systems. Replay attack detection has been widely studied for linear systems, whereas limited research has been reported for nonlinear cases. In this paper, the replay attack is studied in the

Ashish Pandey

Sequential fine-tuning of pretrained language encoders often overwrites previously acquired capabilities, but the forgetting behavior of parameter-efficient updates remains under-characterized. We present a controlled empirical study of Low-Rank Adaptation (LoRA) in sequential transformer encoder fine-tuning with companion representation probes that test a f

Zhihao Mao, Bangpu Chen

Few-shot medical image segmentation (FSMIS) has achieved notable progress, yet most existing methods mainly rely on semantic correspondences from scarce annotations while under-utilizing a key property of medical imagery: anatomical targets exhibit repeatable high-frequency morphology (e.g., boundary geometry and spatial layout) across patients and acquisiti

Xiang Ao, Jingxuan Zhang, Xinyu Zhao

Accurately predicting stock repurchases is crucial for quantitative investment and risk management, yet traditional static models fail to capture the complex temporal dependencies of corporate financial conditions. This paper proposes a dynamic early warning system integrating economic theory with deep temporal networks. Using Chinese A-share panel data (201

Chunmei Wang, Shangyou Zhang

This paper presents an auto-stabilized weak Galerkin (WG) finite element method for the Biot's consolidation model within the classical displacement-pressure two-field formulation. Unlike traditional WG approaches, the proposed scheme achieves numerical stability without the requirement of traditional stabilizers. Spatial discretization is performed using we

Fengxiang Li, Han Zhang, Haoyang Huang, Jinghui Wang

We present KAT-Coder-V2, an agentic coding model developed by the KwaiKAT team at Kuaishou. KAT-Coder-V2 adopts a "Specialize-then-Unify" paradigm that decomposes agentic coding into five expert domains - SWE, WebCoding, Terminal, WebSearch, and General - each undergoing independent supervised fine-tuning and reinforcement learning, before being consolidated

Florian Bönsel, Flore K. Kunst

We present a generic wave equation for surface plasmon polaritons on any macroscopically curved metal-dielectric interface, with isotropic and anisotropic geometric potentials linear in curvature. Remarkably, the anisotropic birefringence vanishes if the metal-to-dielectric permittivity ratio equals the golden ratio squared, mimicking isotropy at linear orde

Maya Chhetri, Pavel Drabek, Ratnasingham Shivaji

We consider an eigenvalue problem of the form \begin{equation*} \left\{\begin{array}{rclll} -\Delta_{p} u -\Delta_{q} u&=& \lambda K(x)|u|^{p-2}u & \mbox{ in } \Omega^e u&=&0\qquad \quad &\mbox{ on } \partial \Omega u(x) &\to& 0 &\mbox{ as } |x| \to \infty\,, \end{array}\right. \end{equation*} where $\Omega^e$ is the exterior of a simply connected, bounded d

Tamer Tlas

We utilize the concentration of measure phenomenon to study the large $N$ limit of the $O(N)$ principal chiral model. The partition function in this limit is demonstrated to be that of a free massive theory.

Miriam Simm, Corinna Hoose, Tom Beucler

Conformal prediction can yield statistically valid prediction intervals for any regression model, with no model modifications and small computational costs. To assess its practical value, we apply conformal methods to quantify uncertainty in machine learning emulators of six microphysical process rates. Microphysical process rates describe small-scale proces

Samik Some, Vinay P. Namboodiri

Present-day deep neural networks for video semantic segmentation require a large number of fine-grained pixel-level annotations to achieve the best possible results. Obtaining such annotations, however, is very expensive. On the other hand, raw, unannotated video frames are practically free to obtain. Similarly, coarse annotations, which do not require preci

Zin Mar Myint, Avikal Srivastava

Let \( G \) be a finite simple undirected graph. Four graph parameters related to network monitoring are the \emph{geodetic set}, \emph{edge geodetic set}, \emph{strong edge geodetic set}, and \emph{monitoring edge geodetic set}, with corresponding minimum sizes, denoted by \( g(G), eg(G), seg(G) \), and \( meg(G) \), respectively. These parameters quantify

Ricardo Pedro Querido Andrade Silva, Nassim Bouarour, Dina Fettache, Sarab Boussouar

Quiz design is a tedious process that teachers undertake to evaluate the acquisition of knowledge by students. Our goal in this paper is to automate quiz composition from a set of multiple choice questions (MCQs). We formalize a generic sequential decision-making problem with the goal of training an agent to compose a quiz that meets the desired topic covera

Yuxuan Gu, Lunjun Liu, Xiaocheng Feng, Kun Zhu

An essential problem in artificial intelligence is whether LLMs can simulate human cognition or merely imitate surface-level behaviors, while existing datasets suffer from either synthetic reasoning traces or population-level aggregation, failing to capture authentic individual cognitive patterns. We introduce a benchmark grounded in the longitudinal researc

Shentong Mo, Sukmin Yun

Unified multimodal pretraining has emerged as a promising paradigm for jointly modeling language and vision within a single foundation model. However, existing approaches largely rely on implicit or indirect alignment signals and remain suboptimal for simultaneously supporting multimodal understanding and generation, particularly in settings that require fin

Aimé Matheron, Doug Storey, Max F. Gilljohann, Sheldon Rego

Extreme beams of charged particles and photons, reaching ultrahigh densities or producing intense gamma-ray bursts, are central to accelerator physics, laboratory astrophysics, and strong-field quantum electrodynamics research. Yet their generation is hindered by conventional focusing methods at multi-GeV energies that rely on massive magnetic assemblies, li

Simon Jörz, Felix Schuhknecht

In the database community, we typically evaluate new methods based on experimental results, which we produce by integrating the proposed method along with a set of baselines in a single benchmarking codebase and measuring the individual runtimes. If we are unhappy with the performance of our method, we gradually improve it while repeatedly comparing to the b

Wei-Hua Li, Cheng Sun, Chu-Song Chen

Multimodal story customization aims to generate coherent story flows conditioned on textual descriptions, reference identity images, and shot types. While recent progress in story generation has shown promising results, most approaches rely on text-only inputs. A few studies incorporate character identity cues (e.g., facial ID), but lack broader multimodal c

Tim Alderson, Simeon Ball

Let $\mathcal{X}$ be a set of $(h-1)$-dimensional subspaces of $\mathrm{PG}(kh-1,q)$ with the property that every hyperplane contains at most $t$ elements of $\mathcal{X}$. We prove the upper bound $|\mathcal{X}| \leq (t-k+2)q^h + t$, and characterise the structure of $\mathcal{X}$ in the case of equality. We call sets attaining this bound \emph{length-maxim

Daniel Galviz

We establish the equivalence between $U(1)$ Chern-Simons and Reshetikhin-Turaev TQFTs associated with finite quadratic modules. For gauge group $U(1)$ and even level $k$, we prove that the corresponding Chern-Simons TQFT is naturally isomorphic to the Reshetikhin-Turaev TQFT determined by the pointed modular category $C(\mathbb Z_k,q_k)$. The equivalence hol

Fateme Shojaei Arani, Brahim Lamine, Jiro Soda

We develop a quantum description of the resonant interaction between electromagnetic (EM) and gravitational waves (GW). We first show that Lorentz invariance together with polarization selection rules forbids any photon-graviton mixing in free space. We demonstrate that confining the EM field within a cavity quantum electrodynamics (cavity-QED) environment b

Matthieu Astorg, Fabrizio Bianchi

We study parabolic implosion in a general non-autonomous setting. Let $f(w)=w+w^2+O(w^3)$ be a holomorphic germ tangent to the identity. We consider the iteration of non-autonomous perturbations of the form \[ w_{j+1}=f(w_j)+\varepsilon_{j,n}^2. \] We show that, when the $\varepsilon_{j,n}^2$'s satisfy a Lavaurs-type condition, the element $w_n$ can be descr

Xuanze Chen, Jiajun Zhou, Yadong Li, Shanqing Yu

Heterogeneous graph representation learning (HGRL) is essential for modeling complex systems with diverse node and edge types. However, most existing methods are limited to closed-world settings with shared schemas and feature spaces, hindering cross-domain generalization. While recent graph foundation models improve transferability, they often target homoge

Suyang Zhong, Boying Huang, Pengwei Xu, Fanjie Xu

Resolving transient atomic configurations in non-crystalline or dynamic environments remains a fundamental bottleneck in the physical sciences. While X-ray absorption spectroscopy (XAS) is a premier probe of local structure, inverting spectra into structural descriptors is a notoriously ill-posed problem due to inherent many-to-one mapping. Here, we present

Alessandro Beolchi, Mauro Pontani, Kathleen Howell, Chiara Pozzi

Near-Earth Objects (NEOs) are attractive exploration targets due to their accessibility, scientific value, and resources. Although trajectory design has revealed efficient pathways to these bodies, systematic strategies for Earth-NEO transfers, especially with low thrust, remain limited. This work presents a streamlined methodology that blends the Sun-Earth

Chang-Chao Liu, Ji-Yong Liu, Jing Li, Hua-Xun Li

Correlated electron systems with topological flat bands show great promise in exploring exotic quantum phenomena. However, such crystalline materials remain rare. Here we report the discovery of a novel material, Cs$_3$V$_9$Te$_{13}$, which unexpectedly exhibits magnetism and significant electron correlations. The crystal structure features two interpenetrat

Sanjeev Kumar Verma

Neutrino disappearance measurements using binned reconstructed-energy spectra exhibit a regime in which small mass-squared splittings become unidentifiable at quadratic order when smooth spectral shape uncertainties are represented by profiled nuisance parameters in the fit. In the small-phase limit, the oscillation-induced modification of the detected spect

Ingrid Dæhlen, Nils Lid Hjort, Ingrid Hobæk Haff

Tuning parameters are parameters involved in an estimating procedure for the purpose of reducing the risk of some other estimator. Examples include the degree of penalization in penalized regression and likelihood problems, as well as the balance parameter in hybrid methods. Typically tuning parameters are set to the minimizers of some estimator of the risk,

Canran Xiao, Liwei Hou

Continual web personalization is essential for engagement, yet real-world non-stationarity and privacy constraints make it hard to adapt quickly without forgetting long-term preferences. We target this gap by seeking a privacy-conscious, parameter-efficient interface that controls stability-plasticity at the user/session level while tying user memory to a sh

Panagiotis Kounatidis, Andreas A. Malikopoulos

In this paper, we present a framework for solving continuous optimal control problems when the true system dynamics are approximated through an imperfect model. We derive a control strategy by applying Pontryagin's Minimum Principle to the model-based Hamiltonian functional, which includes an additional penalty term that captures the deviation between the mo

Anant Talasikar, Marina Matthaiou, Michael Gauding, Heinz Pitsch

This work applies Resolvent Analysis (RA) to study the dynamics of a hydrogen-air slot flame with a Reynolds number of 5500, a Karlovitz number of 20, and an equivalence ratio of 0.4. Direct Numerical Simulations (DNS) data are analyzed using shifted Spectral Proper Orthogonal Decomposition (SPOD), and the resulting structures are compared with optimal resol

Ayako Ohmura, Kazuki Ichikawa, Kyohei Tanaka, Takashi Naka

Transition metal ditellurides with the CdI2-type structure are materials with intriguing superconducting and electronic properties as demonstrated by PdTe2. Gold substituted PdTe2, AuxPd1-xTe2, adopts the CdI2-type structure for a Pd content larger than 43 at.% at room temperature, and in this range enhanced superconductivity with a critical temperature (Tc)

Yang Yang, Chunlin Ji, Haoyang Li, Ke Deng

Quantifying predictive uncertainty is essential for real world machine learning applications, especially in scenarios requiring reliable and interpretable predictions. Many common parametric approaches rely on neural networks to estimate distribution parameters by optimizing the negative log likelihood. However, these methods often encounter challenges like

Martyna Czuba, Patrick Holzer, Hein Zay Yar Oo

Quantum Neural Networks (QNNs) offer a promising framework for integrating quantum computing principles into machine learning, yet their practical capabilities and limitations remain insufficiently studied. In this work, we systematically investigate the trainability and approximation properties of QNNs by benchmarking diverse circuit architectures and encod

Hongyu Yan, Qiwei Li, Jiaolong Yang, Yadong Mu

Most existing vision-language-action (VLA) models for robotic manipulation lack progress awareness, typically relying on hand-crafted heuristics for task termination. This limitation is particularly severe in long-horizon tasks involving cascaded sub-goals. In this work, we investigate the estimation and integration of task progress, proposing a novel model

Seamus Brady

Non-Axiomatic Reasoning Systems (NARS) provide a framework for building adaptive agents that operate under insufficient knowledge and resources. However, the standard input language, Narsese, poses a usability barrier: its dense symbolic notation, overloaded punctuation, and implicit conventions make programs difficult to read, write, and maintain. We presen

Ram Karan Choudhary

Let $G$ be a finite group and let $\Irr(G)$ denote the set of irreducible complex characters of $G$. For a normal subgroup $N \trianglelefteq G$ and $\chi \in \Irr(G)$, we say that $\chi$ is \emph{fully ramified} over $N$ if $\chi(g)=0$ for all $g \in G \setminus N$. A group $G$ is said to be of \emph{central type} if there exists $\chi \in \Irr(G)$ that is

Christian Bernert, Loïs Faisant, Jakob Glas

We prove the "all-the-heights'' version of the Batyrev--Manin--Peyre conjecture for split quintic del Pezzo surfaces, both for counting rational points over global function fields in positive characteristic and for the motivic version over a general base field.

Yuhe Liu, Zhenxiong Tan, Yujia Hu, Songhua Liu

Recent advances in diffusion-based controllable visual generation have led to remarkable improvements in image quality. However, these powerful models are typically deployed on cloud servers due to their large computational demands, raising serious concerns about user data privacy. To enable secure and efficient on-device generation, we explore in this paper

Minh-Tuan Tran, Xuan-May Le, Quan Hung Tran, Mehrtash Harandi

Existing generative models, such as diffusion and auto-regressive networks, are inherently static, relying on a fixed set of pretrained parameters to handle all inputs. In contrast, humans flexibly adapt their internal generative representations to each perceptual or imaginative context. Inspired by this capability, we introduce Composer, a new paradigm for

Bayan Abdullah Aldahlawi, A. B. M. Ashikur Rahman, Irfan Ahmad

Large language models (LLMs) have achieved strong performance across a wide range of tasks, but they are also prone to sycophancy, the tendency to agree with user statements regardless of validity. Previous research has outlined both the extent and the underlying causes of sycophancy in earlier models, such as ChatGPT-3.5 and Davinci. Newer models have since

Abdullah Khanfor, Chaima Zaghouani, Hakim Ghazzai, Ahmad Alsharoa

Light Detection and Ranging (LiDAR) technology offers significant advantages for effective crowd management. This article presents LiDAR technology and highlights its primary advantages over other monitoring technologies, including enhanced privacy, performance in various weather conditions, and precise 3D mapping. We present a general taxonomy of four key t

David Miranda Paredes, Jose M. Saavedra, Marcelo Pizarro

News videos are among the most prevalent content types produced by television stations and online streaming platforms, yet generating textual descriptions to facilitate indexing and retrieval largely remains a manual process. Video Large Language Models (VidLLMs) offer significant potential to automate this task, but a comprehensive evaluation in the news do

Yuhan Gao, Xinqing Li, Xin He, Bing Li

Edge detection is a fundamental image analysis task that underpins numerous high-level vision applications. Recent advances in Transformer architectures have significantly improved edge quality by capturing long-range dependencies, but this often comes with computational overhead. Achieving higher pixel-level accuracy requires increased input resolution, fur

He Gao

Gravitational wave astronomy has revealed that close binaries with compact companions are widespread. Long GRBs (LGRBs) from massive star collapse face persistent challenges in achieving the rapid core rotation required by the collapsar model. Binary interaction via tidal spin-up offers a natural solution; recent population synthesis studies suggest a substa

Benoît Collins, Wangjun Yuan

We investigate the extremal values of partial traces of matrix tensors under operator norm constraints. To evaluate these multi-linear quantities, we develop a comprehensive graphical formalism that encodes multi-leg partial traces, partial permutations, and their moments using colored directed graphs. With this graphical framework, we establish optimal, sha

Tuan Minh Pham, Deepak Gupta

Networks that characterise the interactions between units composing complex systems are typically treated as fixed. Yet, such networks often stochastically evolve over time, shaping the collective behavior of complex systems. To date, we lack a general non-equilibrium thermodynamic treatment of such time-dependent networks. In this Letter, to address this pr

Dean Kraizberg

We investigate the structural relationship between prefix-free codes over the binary alphabet and a class of unlabeled rooted trees, which we call \emph{symmetric} trees. We establish a canonical correspondence between prefix-free codes and symmetric trees, preserving not only the lengths of codewords but also some additional commutative structure. Using thi

Alcides Garat

New symmetries have been found in Einstein-Maxwell spacetimes. New symmetries have also been found in imperfect fluid curved spacetimes. We will prove in this paper that we can extend these symmetries to spacetimes with higher curvature terms. Higher curvature theories are in many cases associated to dark energy for instance. We provide further justification

Sheikh Shakil Akhtar, Geevarghese Philip

We study two related problems on simple, un-directed graphs: Edge Deletion to Cactus and Spanning Tree to Cactus. Edge Deletion to Cactus has been known to be NP-hard on general graphs at least since 1988. We show improved exact algorithms for the former and a polynomial time algorithm for the latter.

Lei Li, Yunxiao Liu, Chenchen Wan

We propose a quasi-random operator splitting method for evolution equations driven by multiple mechanisms. The method uses a low-discrepancy sequence to generate the ordering of the subflows, while requiring only one application of each subflow per time step. In particular, for a decomposition into \(p\) operators, the classical multi-operator Strang splitti

Adi Cohen, Yuval Pinter

Diacritics are orthographic marks that clarify pronunciation, distinguish similar words, or alter meaning. They play a central role in many writing systems, yet their impact on language technology has not been systematically quantified across scripts. While prior work has examined diacritics in individual languages, there's no cross-linguistic, data-driven f

Lina Wang, Bin Wang

In this work, we present a novel family of explicit relaxation Particle-in-Cell (ER-PIC) methods for the Vlasov-Poisson equation with a strong magnetic field. These schemes achieve exact energy conservation by combining a splitting framework with the dynamic updating of a relaxation parameter at each time step. Using an averaging technique, we rigorously est

Tewodros Kederalah Idris, Roald Eiselen, Prasenjit Mitra

Cross-lingual transfer learning enables NLP for low-resource languages by leveraging labeled data from higher-resource sources, yet existing comparisons of source language selection strategies do not control for total training data, confounding language selection effects with data quantity effects. We introduce Budget-Xfer, a framework that formulates multi-

Xinying Lin, Xuyang Liu, Yiyu Wang, Teng Ma

Video large language models (VideoLLMs) show strong capability in video understanding, yet long-context inference is still dominated by massive redundant visual tokens in the prefill stage. We revisit token compression for VideoLLMs under a tight budget and identify a key bottleneck, namely insufficient spatio-temporal information coverage. Existing methods

Sindhunil Barman Roy

Classical Marxism and the algebra of revolution were formulated within the ontological constraints of 19th-century Newtonian materialism-a world of discrete, predictable, billiard-ball interactions. However, the 20th-century transitions in physics, from Thomas Kuhn's paradigm shifts to Phil Anderson's philosophy of emergence, have dismantled the reductionist

C. E. Kofinas

Let $F_{3}$ be the free group of rank $3$ and let $G_{3} = F_{3}/[F_{3}^{\prime\prime}, F_{3}, F_{3}]$, that is, $G_{3}$ is a free centre-by-centre-by-metabelian group of rank $3$. We show that ${\rm Aut}(G_{3})$ contains a proper finitely generated subgroup that is dense with respect to the formal power series topology.

Isaac Harris, Andreas Kleefeld

We consider an inverse shape problem for recovering an unknown simply supported obstacle in two dimensions from near--field point--source measurements for the biharmonic Helmholtz equation. The measured data consist of the scattered field and its Laplacian on a closed measurement curve surrounding the obstacle. By exploiting an operator splitting of the biha

Shi Qiu, Junyi Deng, Yiwei Deng, Haoran Dong

AI agents powered by large language models exhibit strong reasoning and problem-solving capabilities, enabling them to assist scientific research tasks such as formula derivation and code generation. However, whether these agents can reliably perform end-to-end reproduction from real scientific papers remains an open question. We introduce PRBench, a benchma

Barry Djibrina, Jiajia Li

Understanding how the human brain instantiates distinct emotional states is a key challenge in affective neuroscience. While network-based approaches have advanced emotion processing research,they remain largely descriptive,leaving the dynamical stability of emotional brain states unquantified.This study introduces a novel framework to quantify this stabilit

Hasan Mahmud Rhidoy, Mahdi H. Miraz, Iftekhar Salam

The convergence of blockchain and the Internet of Things (IoT) enables secure, decentralised, and verifiable data exchange across distributed smart environments. However, traditional blockchain frameworks suffer from inherent scalability constraints, limited throughput, and high latency, which conflict with the stringent real-time requirements of IoT applica

Michaël Darche, Raphaël Assier, Sébastien Guenneau, Bruno Lombard

We consider wave propagation through a 1D periodic network of slowly time-modulated interfaces. Each interface is modelled by time-dependent spring-mass jump conditions, where mass and rigidity interface parameters are modulated in time. Low-frequency homogenisation yields a leading-order model described by an effective time-dependent wave equation, i.e.\ a

Jörg Neunhäuserer

In \cite{[NE]} we introduce $\alpha$-expansions a real numbers in $(0,1]$, given by \[ \sum_{i=1}^{\infty}(\alpha-1)^{i-1}\alpha^{-(d_{1}+\dots+d_{i})}\] with $\alpha>1$ and $d_{i}\in\mathbb{N}$ and discuss ergodic theoretical and dimension theoretical aspects of this expansions. In this sequel we study mutifractal aspects of this expansions.

Stefano Della Fiore

We derive refined entropy upper bounds for $q$-ary $B_2$ codes by exploiting the Fourier structure of the i.i.d. difference distribution $D=X-Y$. Since the pmf of $D$ is an autocorrelation, its Fourier series is a nonnegative trigonometric polynomial of degree at most $q-1$. This leads to a natural convex relaxation over candidate difference distributions, e

Rohit Kumar Mishra, Chandni Thakkar

A central objective in inverse problems arising in integral geometry is to understand the kernel characterization, inversion formulas, stability estimates, range characterization, and unique continuation properties of integral transforms. In this paper, we study all these aspects for Radon transforms acting on symmetric $m$-tensor fields in $\mathbb{R}^n$. O

Sanghyeon Lee, Minwoo Lee, Euijin Shin, Kangyeol Kim

We introduce a parameter-efficient adaptation method for panel-aware in-context image generation with pre-trained diffusion transformers. The key idea is to compose learnable, panel-specific orthogonal operators onto the backbone's frozen positional encodings. This design provides two desirable properties: (1) isometry, which preserves the geometry of intern

Tania Robens

In this proceeding contribution, I give a short overview on selected topics regarding extended scalar sector phenomenology. After a short overview on extended scalar sectors with light scalars at Higgs factories, I concentrate on the Inert Doublet model and recent studies exploring its discovery potential at lepton colliders.

Andreea Catalina Chitu, Gabriela Ileana Sebe, Dan Lascu

We establish sharp bounds for the Hausdorff dimension of sets of irrational numbers in $(0,1)$ whose digits in the $N$-expansion are either uniformly bounded or tend to infinity. For sets with digits bounded by an integer $M \ge N$, we obtain improved Jarn\'ik-type bounds that generalize and refine classical results for regular continued fractions, with expl

Paula Harder, Johannes Flemming, Mihai Alexe, Gert Mertes

We introduce AIFS-COMPO, a skilful medium-range data-driven global forecasting system for aerosols and reactive gases. Building on the ECMWF Artificial Intelligence Forecast System (AIFS), AIFS-COMPO employs a transformer-based encoder-processor-decoder architecture to jointly model meteorological and atmospheric composition variables. The model is trained o

Temjensangba, Hemant Kumar Mishra

Let $A = \begin{bmatrix} E & F \\ F^T & G \end{bmatrix}$ be a $2n \times 2n$ real positive definite matrix, where $E, F,$ and $G$ are $n \times n$ blocks. It is shown that $\ d(E \oplus G) \prec^w d(A)$. Here $d(A)$ denotes the $n$-vector consisting of the symplectic eigenvalues of $A$ arranged in the non-decreasing order. We also observe the following weak

Yu Mei, Ziyao Zhang, Qingyang Wan, Shiyi Wang

Parent-AI collaboration to support real-time conversations with children is challenging due to the sensitivity and open-ended nature of such interactions. Existing systems often simplify collaboration into static modes, providing limited support for adapting AI to continuously evolving conversational contexts. To address this gap, we systematically investiga

Chi Cuong Le, Weiming Zhi

Reliable robot perception requires not only predicting scene structure, but also identifying where predictions should be treated as unreliable due to sparse or missing observations. We present ContraMap, a contrastive continuous mapping method that augments kernel-based discriminative maps with an explicit uncertainty class trained using synthetic noise samp

Xinyu Zhang, Zhiteng Chao, Yonghao Wang, Bin Sun

Register Transfer Level (RTL) design translates high-level specifications into hardware using HDLs such as Verilog. Although LLM-based RTL generation is promising, the scarcity of functionally verifiable high-quality data limits both accuracy and diversity. Existing post-training typically produces a single HDL implementation per specification, lacking aware

G. G. L. Nashed, A. Eid

We investigate the realization of a nonsingular cosmological bounce in metric $f(R)$ gravity using a controlled exponential deformation of the Starobinsky $R^{2}$ model. Adopting a smooth Gaussian-type bouncing scale factor, we first demonstrate a no-go result showing that a positive-curvature vacuum bounce cannot be supported by the model $f(R)=R+\alpha R^{