November 2025 arXiv papers — page 12

Miguel Casasnovas, Francesc Wilhelmi, Richard Combes, Maksymilian Wojnar

The adoption of dynamic, self-learning solutions for real-time wireless network optimization has recently gained significant attention due to the limited adaptability of existing protocols. This paper investigates multi-armed bandit (MAB) strategies as a data-driven approach for decentralized, online channel access optimization in Wi-Fi, targeting dynamic ch

Edilberto Aguilar-Ruiz, Ramandeep Gill, Paz Beniamini, Jonathan Granot

The detection of a very-high-energy TeV spectral component in the afterglow emission of gamma-ray bursts (GRBs) has opened a new probe into the energetics of ultra-relativistic blast waves and the nature of the circumburst environment in which they propagate. The afterglow emission is well understood as the synchrotron radiation from the shock-accelerated el

I. Gallardo Cava, J. Alcolea, H. Van Winckel, V. Bujarrabal

There is a group of post-AGB stars that are part of a binary system and that show a significant NIR excess. These systems are known to host disks with Keplerian or quasi-Keplerian dynamics and to drive outflows of gas escaping from the rotating disk. These binary post-AGB stars can be categorized into two subclasses depending on the predominance of specific

Jorge F. Soriano, Eugene M. Chudnovsky

We demonstrate that moving edge dislocations can induce the reversal of magnetization in a ferromagnetic film due to the Barnett effect. The dynamics of magnetization is studied numerically within a discretized Landau-Lifshitz equation on a hexagonal lattice containing over $10^5$ sites. Local coordinate frames coupled to the crystallographic axes for each s

A. H. Ghaemi, Z. Yang, A. Huang, V. Sanjay

When a bubble rises to a free surface, its bursting dynamics in Newtonian fluids are governed by the interplay between viscous, capillary, and gravitational forces. In this work, we extend this classical problem to Herschel-Bulkley fluids, elucidating the role of viscoplasticity and non-Newtonian rheology in bubble bursting. Using direct numerical simulation

Valentin Wössner, Falko Ziebert, Ulrich S. Schwarz

Cellular actin structures are continuously turned over while keeping similar sizes. Since they all compete for a shared pool of actin monomers, the question arises how they can coexist in these dynamic steady states. Recently, the coexistence of branched actin networks with different densities growing in a shared and finite pool of purified proteins has been

Adi Glücksam, Leticia Pardo-Simón

We show that any bounded, simply connected domain with analytic boundary can be realised as a wandering domain of an entire function of any prescribed order in $(0, 1)$. Extending results of Boc Thaler, our construction simultaneously prescribes the domain and the exact order of the map. In particular, we produce the first examples of entire functions with b

Xinxi Zhang, Shiwei Tan, Quang Nguyen, Quan Dao

MeanFlow offers a promising framework for one-step generative modeling by directly learning a mean-velocity field, bypassing expensive numerical integration. However, we find that the highly curved generative trajectories of existing models induce a noisy loss landscape, severely bottlenecking convergence and model quality. We leverage a fundamental geometri

Peter Allen, Julia Böttcher, Jasmin Katz

A graph $\Gamma$ is said to be universal for a class of graphs $\mathcal{H}$ if $\Gamma$ contains a copy of every $H \in \mathcal{H}$ as a subgraph. The number of edges required for a host graph $\Gamma$ to be universal for the class of $D$-degenerate graphs on $n$ vertices has been shown to be $O(n^{2-1/D}(\log n)^{2/D}(\log\log n)^{5})$. We generalise this

Bin Sun, Jingyi Zhou, Jianan Mu, Zhiteng Chao

In traditional EDA flows, layout-level performance metrics are only obtainable after placement and routing, hindering global optimization at earlier stages. Although some neural-network-based solutions predict layout-level performance directly from netlists, they often face generalization challenges due to the black-box heuristics of commercial placement-and

Veronika Brooks, Joshua Fragoso García, Lin Zheng, Viktor Paul Müller

This study assesses the global hydrogen import potential in 2050 by looking at the renewable hydrogen production potential in prospective import-oriented countries. Renewable energy potentials calculated with a GIS based model and 2050 primary energy consumption projections are used to identify candidate importers by comparing it with expected demand. Two ap

Oscar Loaiza-Brito, J. L. López-Picón, Octavio Obregón

We use diffeomorphic mappings to connect black hole metrics with complex solutions allowed by the Kontsevich-Segal criterion. By swapping radial and time-like coordinates and applying complex mappings, we derive dynamic metrics suitable for a Quantum Field Theory. This is shown for static and rotating black holes, mapping their interiors into the Kantowski-S

Giacomo Polesello

The production of a dark photon $A^{\prime}$ at the proposed CERN FCC-ee collider is investigated. The study addresses the associated production $e^+e^-\rightarrow\gamma A^{\prime}$ followed by the decay $A^{\prime}\rightarrow\mu^+\mu^-$. The 95% CL sensitivity on the mixing between the photon and the dark photon is evaluated in the $m_{A^{\prime}}$ mass ran

Marco Roschkowski, Hannes Gernandt

We develop two local energy methods for distributed parameter port-Hamiltonian (pH) systems on one-dimensional spatial domains. The methods are applied to derive a characterization of exponential stability directly in terms of the energy passing through the boundary over a given time horizon. The resulting condition is verified for a network of vibrating str

Shuqi Liu, Han Wu, Guanzhi Deng, Jianshu Chen

Knowledge-enhanced text generation aims to enhance the quality of generated text by utilizing internal or external knowledge sources. While language models have demonstrated impressive capabilities in generating coherent and fluent text, the lack of interpretability presents a substantial obstacle. The limited interpretability of generated text significantly

Yu Zhang, Jingyi Liu, Yiwei Shi, Qi Zhang

Visual AutoRegressive modeling (VAR) based on next-scale prediction has revitalized autoregressive visual generation. Although its full-context dependency, i.e., modeling all previous scales for next-scale prediction, facilitates more stable and comprehensive representation learning by leveraging complete information flow, the resulting computational ineffic

Francis Lörler

We study a class of self-repelling diffusions on compact Riemannian manifolds whose drift is the gradient of a potential accumulated along their trajectory. When the interaction potential admits a suitable spectral decomposition, the dynamics and its environment are equivalent to a finite-dimensional degenerate diffusion. We show that this diffusion is a sec

Shuo Ni, Di Wang, He Chen, Haonan Guo

Instruction-driven segmentation in remote sensing generates masks from guidance, offering great potential for accessible and generalizable applications. However, existing methods suffer from fragmented task formulations and limited instruction data, hindering effective understanding and generalization. To address these issues, we introduce GeoSeg-1M, the fir

Brayan Alves, Thanh Phong Lê, Gianna Nossa, Tan Toi Phan

Proton magnetic resonance spectroscopic imaging (1H-MRSI) at ultra-high field has seen an increase in usage in the preclinical field. Challenges related to long acquisition time and low concentration of brain metabolites in the rodent brain have led to the development and application of acceleration schemes for 3D-1H-MRSI, such the undersampling technique Co

Xiang-Dong Li, Qi Yan

In this paper, we prove a Li-Yau-Hamilton type Harnack estimate for the $f$-mean curvature flow in Euclidean space, which can be viewed as a gradient flow of the weighed area functional with the measure density function $e^{-f}$.

Rodrigo Palma-Amestoy, Edoardo Provenzi, Marcelo Bertalmío, Vicent Caselles

Basic phenomenology of human color vision has been widely taken as an inspiration to devise explicit color correction algorithms. The behavior of these models in terms of significative image features (such as contrast and dispersion) can be difficult to characterize. To cope with this, we propose to use a variational formulation of color contrast enhancement

S. Hariharan, R. Suresh, V. K. Chandrasekar

This study investigates the dynamics of a globally coupled network of heterogeneous FitzHugh Nagumo (FHN) oscillators under stochastic influences, with particular emphasis on the emergence of extreme events (EE). While previous studies explored FHN networks subjected to homogeneous noise, revealing behaviors such as noise-induced synchronization, stochastic

Pengyu Li

The government's effort to alleviate HIV stigma has been justified by the suppression effect of stigma on the HIV testing rate. Nevertheless, the deterrence effect of stigma on undesirable sexual behaviours has long been overlooked. This study adapts the existing framework on HIV stigma with an additional stage that formally models people's choices on whethe

Xin Li

Contemporary ML separates the static structure of parameters from the dynamic flow of inference, yielding systems that lack the sample efficiency and thermodynamic frugality of biological cognition. In this theoretical work, we propose \textbf{Memory-Amortized Inference (MAI)}, a formal framework rooted in algebraic topology that unifies learning and memory

Ahmad Adnan Qidan, Taisir El-Gorashi, Majid Safari, Harald Haas

OWC has been considered as a key enabling technology to unlock unprecedented speeds of communication, supporting high demands of data traffic. In this paper, infrared lasers are used as optical transmitters operating in an indoor environment under eye safety regulations due to their high modulation speed. To provide efficient multiple access service, NOMA-ba

Horacio Thompson, Marcelo Errecalde

Gambling disorder is a complex behavioral addiction that is challenging to understand and address, with severe physical, psychological, and social consequences. Early Risk Detection (ERD) on the Web has become a key task in the scientific community for identifying early signs of mental health behaviors based on social media activity. This work presents our p

Yuri Markin, Zoja Kun'kova

The theory of the method of magnetic circular dichroism spectroscopy in reflected light (RMCD) has been developed using a simplified scheme without an analyzer, with the use of an additional mirror in the optical system and with the application of the method of phase modulation of the light wave using a photoelastic modulator. An additional mirror reflector,

Xin Li

Computation fundamentally separates time from space: nondeterministic search is exponential in time but polynomially simulable in space (Savitch's Theorem). We propose that the brain physically instantiates a biological variant of this theorem through Memory-Amortized Inference (MAI), creating a geometry of certainty from the chaos of exploration. We formali

Gianluca Faraco, Arnaud Maret

We construct analogues of Fenchel-Nielsen coordinates on an open and dense subset of the space of holonomies of branched hyperbolic structures on a closed genus-2 surface. We show that these coordinates satisfy an analogue of Wolpert's magic formula, and thus provide Darboux charts for the Goldman symplectic form. To this end, we revisit the parametrization

Shuqi Liu, Bowei He, Chen Ma, Linqi Song

Large language models (LLMs) typically enhance their performance through either the retrieval of semantically similar information or the improvement of their reasoning capabilities. However, a significant challenge remains in effectively integrating both retrieval and reasoning strategies to optimize LLM performance. In this paper, we introduce a reasoning-a

Yifei Wang, Jacky Keung, Zhenyu Mao, Jingyu Zhang

Chart-to-code generation is a critical task in automated data visualization, translating complex chart structures into executable programs. While recent Multi-modal Large Language Models (MLLMs) improve chart representation, existing approaches still struggle to achieve cross-type generalization, memory efficiency, and modular design. To address these challe

Guy Tchuente

Many public services are produced in networked systems where quality depends on local effort and on how higher-level authorities monitor providers. We develop a simple model in which monitoring is a public good on a network with strategic complementarities. A regulator chooses between decentralized monitoring (cheaper, local oversight) and centralized monito

Xiang Hu, Zhanchao Zhou, Ruiqi Liang, Zehuan Li

This work explores the challenge of building ``Machines that Can Remember'', framing long-term memory as the problem of efficient ultra-long context modeling. We argue that this requires three key properties: \textbf{sparsity}, \textbf{random-access flexibility}, and \textbf{length generalization}. To address ultra-long-context modeling, we leverage Hierarch

Huiguang Zhang

This paper addresses the challenging problem of parameter estimation for multicomponent complex exponential signals, commonly known as sums of cisoids. Traditional approaches that estimate individual component parameters face significant difficulties when the number of components is large, including permutation ambiguity, computational complexity from high-d

Matteo D'Achille, Jan Grebík, Ali Khezeli, Konstantin Recke

We study Poisson--Voronoi percolation and its discrete analogue Bernoulli--Voronoi percolation in spaces with a non-amenable product structure. We develop a new method of proving smallness of the uniqueness threshold $p_u(\lambda)$ at small intensities $\lambda>0$ based on the unbounded borders phenomenon of their underlining ideal Poisson--Voronoi tessellat

Yaoling Yang, Andrew Tanggara, Tobias Haug, Kishor Bharti

Bosonic codes utilize the infinite-dimensional Hilbert space of harmonic oscillators to encode quantum information, offering a hardware-efficient approach to quantum error correction. Designing these codes requires precise geometric arrangements of quantum states in the phase space. Here, we introduce Quantum Cubature Codes (QCCs), a powerful and generalized

Azusa Yamaguchi

A clearer understanding of when coordination emerges, fluctuates, or collapses in decentralized multi-agent reinforcement learning (MARL) is increasingly sought in order to characterize the dynamics of multi-agent learning systems. We revisit fully independent Q-learning (IQL) as a minimal decentralized testbed and run large-scale experiments across environm

Joanna D. Sakowska, David Martínez-Delgado, Sarah Pearson, Francisco J. Riquel-Castilla

While mergers between massive galaxies and their dwarf satellites are well studied, the properties of dwarf - dwarf satellite mergers are not well constrained. Stellar streams trace satellite disruption and, in the dwarf galaxy regime, are predicted to provide novel constraints on low-mass galaxy evolution and dark matter. However, the mass ratios required t

A. Walton Green, Ljupcho Petrov, Brett D. Wick

We present a proof of the one-sided $A_2$ theorem in dimension one, with a logarithmic loss. This theorem concerns one-sided Calder\'on-Zygmund operators (CZOs) whose kernels $K(x,y)$ vanish whenever $x < y$. These operators are bounded on $L^2(w)$ provided that the weight $w$ belongs to the one-sided class $A_2^{\uparrow}$. The argument reduces the norm est

Gustavo Penha, Aleksandr V. Petrov, Claudia Hauff, Enrico Palumbo

The Cranfield paradigm has long provided reliable, reproducible evaluation in ad hoc retrieval, and recent work has begun extending this framework to recommender systems. A recent development in IR is the use of Large Language Models (LLMs) as automatic relevance judges, showing promising agreement with human assessors. Whether this LLM-judge paradigm---stud

Haruki Sakajo, Hiroshi Takato, Hiroshi Tsutsui, Komei Soda

Large-scale Vision Language Models (LVLMs) exhibit advanced capabilities in tasks that require visual information, including object detection. These capabilities have promising applications in various industrial domains, such as autonomous driving. For example, LVLMs can generate safety-oriented descriptions of videos captured by road-facing cameras. However

Alexandr V. Kostochka, Zishen Qu, Maddy Ritter, Douglas B. West

The $\textit{$m$-deck}$ of an $n$-vertex graph is the multiset of unlabeled induced subgraphs with $m$ vertices. Caterpillars are trees in which all nonleaf vertices lie on a single path. We prove for $n\ge48$ that any $n$-vertex caterpillar is reconstructible (up to isomorphism) from its $m$-deck when $m>n/2$. The result is sharp, since for $n\ge6$ there ar

Jiaqi Wang, Rong Ma

Let $p$ be an odd prime, Jianqiang Zhao has established a curious congruence, which is $$ \sum_{i+j+k=p \atop i,j,k > 0} \frac{1}{ijk} \equiv -2B_{p-3}\pmod p , $$ where $B_{n}$ denotes the $n$-th Bernoulli number. In this paper, we will generalize this kind of sums and prove a family of similar congruences modulo prime powers $p^r$.

Enzo Nicolás Spotorno, Josafat Leal Filho, Antônio Augusto Fröhlich

This paper presents a framework for physics-informed learning in complex cyber-physical systems governed by differential equations with both unknown dynamics and algebraic invariants. First, we formalize the Hybrid Recurrent Physics-Informed Neural Network (HRPINN), a general-purpose architecture that embeds known physics as a hard structural constraint with

Sven Enns, Julian Hering-Stratemeier, Georg von Freymann

Additive manufacturing using 2-Photon Polymerization (2PP, aka direct laser writing DLW) enables the fabrication of almost arbitrary complex 3D structures from the meso to the submicron scale. However, deviations between the anticipated target structure and the actual print often occur due to physico-chemical processes, limiting the accuracy and reliability

A. A. Hakobyan, M. H. Gevorgyan, A. G. Karapetyan, G. A. Mamon

Photospheric and high-velocity features (PVFs and HVFs) of Si II $\lambda$6355 and Ca II IR3 lines in supernova Ia (SN Ia) spectra provide insights into ejecta structure, energetics, and circumstellar interaction, yet their interplay remains poorly understood. We analyse a representative sample of 145 nearby SNe Ia observed within $\pm$5 days of B-band maxim

Angelika Ghazale, Oliver F. Piattella

We investigate the gravitational lensing properties and the formation of relativistic images associated with black holes modeled by the Reissner-Nordstr\"om and Kerr space-time geometries. In particular, we perform numerical computations of the deflection angles, image angular positions, magnification factors (including demagnification), and time delays betw

Jinmay Kalita, Sumit Kumar Mehta, Suraj Panja, Pranab Kumar Mondal

We investigate the flow dynamics of nutrient solution through the xylem vessels of Brassica juncea under drought stress. To this end, we perform experiments to obtain morphological traits of xylem vessels under drought-stressed conditions, and develop a mathematical framework to model the underlying flow through the xylem, considering several features releva

Minh Tri LÊ, Ali Ait-Bachir

Prioritizing service tickets in IT Service Management (ITSM) is critical for operational efficiency but remains challenging due to noisy textual inputs, subjective writing styles, and pronounced class imbalance. We evaluate two families of approaches for ticket prioritization: embedding-based pipelines that combine dimensionality reduction, clustering, and c

Hengyuan Liu, Zheng Li, Donghua Wang, Yankai Wu

Mutation-based Fault Localization (MBFL) has been widely explored for automated software debugging, leveraging artificial mutants to identify faulty code entities. However, MBFL faces significant challenges due to interference mutants generated from non-faulty code entities but can be killed by failing tests. These mutants mimic the test sensitivity behavior

Thomas Ligon

We investigate Hill's lunar equations, series and the motion of the perigee, and we use computers to go farther than has previously been known, calculating the coefficients of Hill's series up to order 24 in m, and the coefficients that do not depend on a_0 up to order 30. Numerical calculations indicate that the radius of convergence of Hill's series is som

Yara Mahmoud, Jeffrin Sam, Nguyen Khang, Marcelino Fernando

Safe and trustworthy Human Robot Interaction (HRI) requires robots not only to complete tasks but also to regulate impedance and speed according to scene context and human proximity. We present SafeHumanoid, an egocentric vision pipeline that links Vision Language Models (VLMs) with Retrieval-Augmented Generation (RAG) to schedule impedance and velocity para

Mitsunori Araki, Miguel Sanz-Novo, Christian P. Endres, Paola Caselli

Molecules harbouring sulfur are thought to have played a key role in the biological processes of life on Earth, and thus, they are of much interest when found in space. Here we report on the astronomical detection of a six-membered sulfur-bearing cyclic hydrocarbon in the interstellar medium. Observations of the Galactic Centre molecular cloud G+0.693-0.027

Arman Marti-Shahandeh, Yue Ren, Victoria Schleis

We present algorithms for computing zero-dimensional tropical varieties as implemented in OscarZerodimensionalTropicalization.jl. The algorithms include a mathematical workaround for a common practical issue arising when working with polynomials over inexact fields in existing software systems.

Yi-Jun Chang, Lyuting Chen, Haoran Zhou

The content-oblivious model, introduced by Censor-Hillel, Cohen, Gelles, and Sel (PODC 2022; Distributed Computing 2023), captures an extremely weak form of communication where nodes can only send asynchronous, content-less pulses. Censor-Hillel, Cohen, Gelles, and Sel showed that no non-constant function $f(x,y)$ can be computed correctly by two parties usi

Thierry Monteil, Khaydar Nurligareev

This paper is devoted to the structure of the complete asymptotic expansion of the probability that a large combinatorial object is irreducible or consists of a given number of irreducible parts, where irreducibility is understood in terms of combinatorial construction SEQ, labeled or unlabeled. We show that for rapidly growing (i.e. gargantuan) combinatoria

Eduardo Abi Jaber, Donatien Hainaut, Edouard Motte

We introduce a novel signature approach for pricing and hedging path-dependent options with instantaneous and permanent market impact under a mean-quadratic variation criterion. Leveraging the expressive power of signatures, we recast an inherently nonlinear and non-Markovian stochastic control problem into a tractable form, yielding hedging strategies in (p

Chi-Ming Chang, Haoyu Zhang

We investigate finite-$N$ BPS cohomology in the D1--D5 CFT, focusing on the sector of fortuitous classes. Analyzing the supercharge cochain complexes in the $N=2$ and $N=3$ theories, we construct several explicit fortuitous classes. We study the decomposition of these cohomology classes into ${\rm SU}(2)_a\times {\rm SU}(2)_b$ representations and conjecture

Pei Li, Kai-Jia Sun, Bo Zhou, Guo-Liang Ma

Protons and neutrons within atomic nuclei undergo intense and fleeting encounters driven by the strong force at short distances. These interactions generate close-proximity pairs, a phenomenon known as short-range correlations (SRCs). While the properties of SRCs have been extensively studied in cold nuclear matter, their behavior under extremely hot and den

Tianhao Xie, Linlian Jiang, Xinxin Zuo, Yang Wang

Realistic scene appearance modeling has advanced rapidly with Gaussian Splatting, which enables real-time, high-quality rendering. Recent advances introduced per-primitive textures that incorporate spatial color variations within each Gaussian, improving their expressiveness. However, texture-based Gaussians parameterize appearance with a uniform per-Gaussia

Louis Moureaux, Aleksandra Lelek, Francesco Hautmann, Laurent Favart

Experimental measurements of the transverse momentum of Drell-Yan lepton pairs are sensitive to non-perturbative physics associated with the intrinsic parton transverse momentum $k_T$. We discuss recent determinations of intrinsic $k_T$ in the context of transverse momentum dependent (TMD) parton branching calculations and collinear parton-shower Monte Carlo

Hamid Gadirov

Scientific simulations and experimental measurements produce vast amounts of spatio-temporal data, yet extracting meaningful insights remains challenging due to high dimensionality, complex structures, and missing information. Traditional analysis methods often struggle with these issues, motivating the need for more robust, data-driven approaches. This diss

Jan Skolimowski, Nguyen Minh Nguyen, Giuseppe Cuono, Carmine Autieri

We formulate the tight-binding model for cubic $\alpha$-Sn based on the DFT calculations. In the model, we incorporate a variable bond angle, which allows us to simulate the effect of the in-plane strain. In the bulk, we demonstrate the presence of the $\mathbb{Z}_2$ topological invariant and a non-zero mirror Chern number, making $\alpha$-Sn one of the rare

David Marzocca, Francesco Montagno, Manuel Morales-Alvarado, Andrea Wulzer

A high energy muon collider naturally produces a collimated beam of neutrinos for a fixed-target experiment at a dedicated far-forward facility. The high intensity and energy of the beam makes it ideally suited for astonishingly precise measurements of neutrino scattering on nucleons in the deeply inelastic regime, enabling the determination of the Cabibbo--

Ariful Islam, Tanvir Mahmud, Md Rifat Hossen

The expansion of the Internet and social networks has led to an explosion of user-generated content. Author intent understanding plays a crucial role in interpreting social media content. This paper addresses author intent classification in Bangla social media posts by leveraging both textual and visual data. Recognizing limitations in previous unimodal appr

Rostislav-Paul Wilhelm, Fabio Bacchini

Validity of fluid models breaks down for non-thermal or weakly collisional plasmas which often occur e.g. in the solar wind. In these regimes one has to resort to modelling through the first-principle Vlasov-Maxwell system, but its six-dimensional phase-space dynamics, strong filamentation, and multi-scale structure make direct numerical simulation extremely

Mark Martinez, Anna O'Grady, Katelyn Breivik, Gina Chen

Core collapse supernovae (CCSNe) impact many areas of astrophysics, including compact object formation and gravitational waves, but many uncertainties remain in our understanding of the evolution of their progenitors. We use the binary population synthesis code COSMIC to simulate populations of CCSNe across a wide range of metallicities and binary evolution

Enzio M'sihid, Etienne Parizot, Matteo Battisti, Sylvie Blin

We present a comprehensive study addressing pile-up effects in single photoelectron counting with R-11265 Hamamatsu multi-anode photomultiplier tubes (MAPMTs) equipped with the SPACIROC-3 ASIC. Extended dead time in the electronics causes saturation and quenching of the counting rate, an effect we counter by inverting the pile-up plot once the double pulse r

Alexandre Moine, Sam Westrick, Joseph Tassarotti

Nondeterminism makes parallel programs challenging to write and reason about. To avoid these challenges, researchers have developed techniques for internally deterministic parallel programming, in which the steps of a parallel computation proceed in a deterministic way. Internal determinism is useful because it lets a programmer reason about a program as if

Yueer Zhou, Yichen Wu, Ying Wei

Low-Rank Adaptation (LoRA) enables efficient Continual Learning but often suffers from catastrophic forgetting due to destructive interference between tasks. Our analysis reveals that this degradation is primarily driven by antagonistic directional updates where new task gradients directly oppose the historical weight trajectory. To address this, we propose

Xinnong Du, Zhonghao Lyu, Xiaowen Cao, Chunyang Wen

Federated edge learning (FEEL) provides a promising foundation for edge artificial intelligence (AI) by enabling collaborative model training while preserving data privacy. However, limited and heterogeneous local datasets, as well as resource-constrained deployment, severely degrade both model generalization and resource utilization, leading to a compromise

Aaron Steiner, Ralph Peeters, Christian Bizer

Large language model agents are increasingly used to automate web tasks such as product search, offer comparison, and checkout. Current research explores different interfaces through which these agents interact with websites, including traditional HTML browsing, retrieval-augmented generation (RAG) over pre-crawled content, communication via Web APIs using t

Ze Long Liu, Pier Francesco Monni

We present the first computation of the complete two-loop, fully-differential soft function describing the production of a heavy-quark pair in association with a color-singlet system at hadron colliders. This result constitutes one of the most complex soft functions known to date and it is obtained in closed analytic form for generic multi-dimensional kinema

Mees M. Flapper, Detlef Lohse, Sander G. Huisman

This work investigates chiral particles, which break mirror symmetry, in turbulent Taylor--Couette flow. These particles generally display a translation-rotation coupling moving through a quiescent fluid. Here we performed experiments using large chiral particles (typical size \unit{5}{mm}) in turbulent Taylor--Couette flow, for Reynolds numbers $9\cdot10^3

Jhonatan Tavori, Anat Bremler-Barr, Hanoch Levy, Ofek Lavi

Modern cloud applications are built from independent microservices, offering scalability and usage-based billing. However, their reliance on independently-operating auto-scalers introduces coordination challenges. Default retry patterns can trigger &#34;retry storms&#34; during service miscoordination or adversarial overload, amplifying load, latency, and re

Alexander Willmes, Patrick Bethke, M. Mohamed El Kordy Shehata, George Simion

A quantitative description of the exchange interaction in quantum dots is relevant for modeling gate operations of spin qubits. By measuring the amplitude and frequency of exchange-driven qubit state oscillations, we measure the detuning dependence of the exchange coupling in a GaAs double quantum dot over three orders of magnitude. Both 1D and 3D full confi

Georgia Kanli, Daniele Perlo, Selma Boudissa, Radovan Jirik

MR data are acquired in the frequency domain, known as k-space. Acquiring high-quality and high-resolution MR images can be time-consuming, posing a significant challenge when multiple sequences providing complementary contrast information are needed or when the patient is unable to remain in the scanner for an extended period of time. Reducing k-space measu

Paolo Andreetto, Nazar Bartosik, Andrea Bersani, Daniele Calzolari

This work presents a proof of concept for MUSIC, a multi-purpose detector conceived for high-precision and ultra-high-energy physics studies in the challenging environment of $\sqrt{s}=10$ TeV muon-antimuon collisions. The detector features a central tracking system, electromagnetic and hadronic calorimeters, and dedicated muon detectors. This paper outlines

Jiancheng Dong, Pengyue Jia, Jingyu Peng, Maolin Wang

Carefully engineered system prompts play a critical role in guiding the behavior of LLM agents, but their considerable length introduces significant drawbacks, including increased inference latency, higher computational cost, and reduced effective context length. This raises the question of whether such lengthy prompts can be replaced by a drastically reduce

Wenhui Shi, Maria G. Westdickenberg, Michael Westdickenberg

We revisit the HED Method for the Mullins-Sekerka evolution in the plane. We identify a natural notion of distance, intrinsic to the interface itself. Using this distance, the energy, and the dissipation, we develop natural assumptions on the flow and, assuming existence of a solution satisfying these conditions, establish not just the algebraic rate (previo

Timothy Ossowski, Sheng Zhang, Qianchu Liu, Guanghui Qin

High-quality and carefully curated data is a cornerstone of training medical large language models, as it directly impacts both generalization and robustness to unseen clinical tasks. We investigate strategies for training and data curation to develop a robust multimodal reasoning model in the medical domain. Our work focuses on supervised fine-tuning (SFT)

El Mehdi Achour, Umberto L. Hryniewicz, Michael Westdickenberg

It is an old idea to use gradient flows or time-discretized variants thereof as methods for solving minimization problems. In some applications, for example in machine learning contexts, it is important to know that for generic initial data, gradient flow trajectories do not get stuck at saddle points. There are classical results concerned with the non-degen

Boris D. Andrews, Patrick E. Farrell

Ordinary and partial differential equations describing thermodynamically isolated systems typically possess conserved quantities (like mass, momentum, and energy) and dissipated quantities (like entropy). Preserving these conservation and dissipation laws on discretisation in time can yield vastly better approximations for the same computational effort, comp

Arunkumar Bupathy, Darian Hall, Ivan I. Smalyukh, Gerardo Campos-Villalobos

Knotted fields in classical and quantum systems have long been recognized for their non-trivial topologies and particle-like behavior, but practical applications have been limited by the difficulty of stabilizing them. Recently, stable knotted solitonic textures--heliknotons--were discovered in chiral liquid crystals, forming adaptive crystal assemblies via

Ariful Islam, Md Rifat Hossen, Tanvir Mahmud

Multi-aspect sentiment analysis of Bangla e-commerce reviews remains challenging due to limited annotated datasets, morphological complexity, code-mixing phenomena, and domain shift issues, affecting 300 million Bangla-speaking users. Existing approaches lack explainability and cross-domain generalization capabilities crucial for practical deployment. We pre

Oren Bell, Harun Teper, Mario Günzel, Chris Gill

This paper addresses limitations of current scheduling methods in the Robot Operating System (ROS)2, focusing on scheduling tasks beyond simple chains and analyzing arbitrary Directed Acyclic Graphs (DAGs). While previous research has focused mostly on chain-based scheduling with ad-hoc response time analyses, we propose a novel approach using the events exe

Siyu Jiang, Aidi Yang, Fa Peng Huang

The nature of dark matter (DM) remains one of the most challenges in modern physics. Composite or macroscopic DM present a compelling alternative to conventional particle DM, yet their terrestrial search is notoriously challenging due to low number density. This Letter presents a unified, multimessenger search strategy for composite DM, dramatically improvin

Yang Li, Zhiyuan He, Yuxuan Huang, Zhuhanling Xiao

Recent Vision-Language Models (VLMs) exhibit strong perceptual reasoning abilities, yet they often struggle to adapt efficiently when encountering novel tasks at test time. In contrast, humans leverage the metacognitive model with memory, enabling continuous strategy refinement through metacognitive control when faced with new challenges. To bridge this gap,

George Tyler, Luca Zanetti

We revisit the theoretical performances of Spectral Clustering, a classical algorithm for graph partitioning that relies on the eigenvectors of a matrix representation of the graph. Informally, we show that Spectral Clustering works well as long as the smallest eigenvalues appear in groups well separated from the rest of the matrix representation's spectrum.

Yuyang Li, Swasthik Muloor, Jack Laudati, Nickolas Dematteis

Miniature imaging systems are essential for space-constrained applications but are limited by memory and power constraints. While machine learning can reduce data size by extracting key features, its high energy demands often exceed the capacity of small batteries. This paper presents a CNN hardware accelerator optimized for object classification in miniatur

Linghao Kong, Xiaopeng Hong

Deep neural network-based time series prediction models have recently demonstrated superior capabilities in capturing complex temporal dependencies. However, it is challenging for these models to account for uncertainty associated with their predictions, because they directly output scalar values at each time step. To address such a challenge, we propose a n

Yunpeng Qu, Yazhou Sun, Bingyu Hui, Jian Wang

Automatic modulation recognition (AMR) is a crucial step in wireless communication systems, which identifies the modulation scheme from detected signals to provide key information for further processing. However, previous work has mainly focused on the identification of a single signal, overlooking the phenomenon of multiple signal superposition in practical

Alain Connes, Walter D. van Suijlekom

For a real distribution $\mathcal{D}$ on the interval $[0,L]$ with $\tilde{\mathcal{ D}}$ the associated even distribution on the interval $[-L, L]$, we prove that if the associated quadratic form with Schwartz kernel $\tilde{\mathcal{D}}(x - y)$ defines a lower-bounded selfadjoint operator on $L^2([-\frac{L}{2}, \frac{L}{2}])$, whose lowest spectral value $

Guozheng Sun, Lei Wang, Yanhao Wang, Jie Wang

Radar automatic target recognition (RATR) based on high-resolution range profile (HRRP) has attracted increasing attention due to its ability to capture fine-grained structural features. However, recognizing targets under electronic countermeasures (ECM), especially the mainstream interrupted-sampling repeater jamming (ISRJ), remains a significant challenge,

Hidekazu Furusho, David Jarossay

We present a concise method for deriving an explicit formula for $p$-adic multiple zeta values. The formula features a variant of multiple harmonic sums, termed binomial multiple harmonic sums.

Ben Amies-King, Marco Lucamarini

White Rabbit (WR) technology provides a commercially-available off-the-shelf solution for time synchronisation with sub-nanosecond accuracy and picosecond-level precision over optical fibre links typically spanning tens of kilometres. Such high-performance time dissemination can support a variety of applications, including position, navigation and timing (PN

Imraul Emmaka, Tran Viet Xuan Phuong

Federated Learning (FL) offers a promising approach to collaboratively train machine learning models without centralizing raw data, yet its scalability is often throttled by excessive communication overhead. This challenge is magnified in Internet of Things (IoT) environments, where devices face stringent bandwidth, latency, and energy constraints. Conventio

Artyom Tsanda, Sarah Reiss, Konrad Scheffler, Marija Boberg

Magnetic particle imaging reconstructs tracer distributions using a system matrix obtained through time-consuming, noise-prone calibration measurements. Methods for addressing imperfections in measured system matrices increasingly rely on deep neural networks, yet curated training data remain scarce. This study evaluates whether physics-based simulated syste

Dilara Abdel, Alain Blaustein, Claire Chainais-Hillairet, Maxime Herda

We consider a stationary drift-diffusion system with ionic charge carriers and external generation of electron and hole charge carriers. This system arises, among other applications, in the context of semiconductor modeling for perovskite solar cells. Thanks to truncation techniques and iterative energy estimates, we show the existence and uniform upper and

Silvia Zuffi

Forests play a critical role in global ecosystems by supporting biodiversity and mitigating climate change via carbon sequestration. Accurate aboveground biomass (AGB) estimation is essential for assessing carbon storage and wildfire fuel loads, yet traditional methods rely on labor-intensive field measurements or remote sensing approaches with significant l