November 2025 arXiv papers — page 140

Xinpeng Li, Kai Ming Ting

The proliferation of complex, black-box AI models has intensified the need for techniques that can explain their decisions. Feature attribution methods have become a popular solution for providing post-hoc explanations, yet the field has historically lacked a formal problem definition. This paper addresses this gap by introducing a formal definition for the

Hai Tao Li, Yan-Qing Ma, Cheng-Tai Tan, Jian Wang

Compton scattering is a fundamental process in QED with broad applications, yet its theoretical description at high energies is challenged by substantial next-to-leading order (NLO) corrections arising from double-logarithmic enhancements. To address this, we report the first calculation of the next-to-next-to-leading order (NNLO) total cross section with fu

Andreas Konstantin Kruff, Christin Katharina Kreutz, Timo Breuer, Philipp Schaer

Validating user simulation is a difficult task due to the lack of established measures and benchmarks, which makes it challenging to assess whether a simulator accurately reflects real user behavior. As part of the Sim4IA Micro-Shared Task at the Sim4IA Workshop, SIGIR 2025, we present Sim4IA-Bench, a simulation benchmark suit for the prediction of the next

Aleksandra Drozd-Rzoska, Agata Angelika Sojecka, Sylwester J. Rzoska

This report provides insights into global population dynamics since the beginning of the Anthropocene, focusing on empirical data and minimizing a priori the impact of model assumptions. It explores the Relative Growth Rate concept, introduced recently to global population studies by Lehman et al. [PNAS 118, e2024150118 (2021)] and subsequently extended to i

Robert Denk, Franz Gmeineder, Matthias Hieber

We establish the existence of energy-driven solutions to the momentum balance equation in Hibler's sea ice model. As a main novelty and different from previous results, we deal with the singular limit and therefore cover the true unregularized Hibler stress. To this end, we introduce an energy-based notion of solution that is able to capture plasticity effec

Viola Rädle, Tilman Hartwig, Benjamin Oesen, Emily Alice Kröger

GAMMA_FLOW is an open-source Python package for real-time analysis of spectral data. It supports classification, denoising, decomposition, and outlier detection of both single- and multi-component spectra. Instead of relying on large, computationally intensive models, it employs a supervised approach to non-negative matrix factorization (NMF) for dimensional

Stine Beltoft, Lukas Galke

Artificial intelligence (AI) and large language models (LLM) are reshaping science, with most recent advances culminating in fully-automated scientific discovery pipelines. But qualitative research has been left behind. Researchers in qualitative methods are hesitant about AI adoption. Yet when they are willing to use AI at all, they have little choice but t

Lian He, Meng Liu, Qilang Ye, Yu Zhou

Understanding 3D scene-level affordances from natural language instructions is essential for enabling embodied agents to interact meaningfully in complex environments. However, this task remains challenging due to the need for semantic reasoning and spatial grounding. Existing methods mainly focus on object-level affordances or merely lift 2D predictions to

Abhinav Das, Stephan Schlüter

Accurate electricity price forecasting is critical for strategic decision-making in deregulated electricity markets, where volatility stems from complex supply-demand dynamics and external factors. Traditional point forecasts often fail to capture inherent uncertainties, limiting their utility for risk management. This work presents a framework for probabili

Mohsen Amiri, Konstantin Avrachenkov, Ibtihal El Mimouni, Sindri Magnússon

Restless Multi-Armed Bandits (RMABs) are powerful models for decision-making under uncertainty, yet classical formulations typically assume fixed dynamics, an assumption often violated in nonstationary environments. We introduce MARBLE (Multi-Armed Restless Bandits in a Latent Markovian Environment), which augments RMABs with a latent Markov state that induc

Tong Wu, Yutong He, Bin Wang, Kun Yuan

Large language models (LLMs) have demonstrated remarkable success across diverse artificial intelligence tasks, driven by scaling laws that correlate model size and training data with performance improvements. However, this scaling paradigm incurs substantial memory overhead, creating significant challenges for both training and inference. While existing res

James Brown, Tarini S Hardikar, Kenny Heitritter, Kanav Setia

Simulating molecular systems on quantum computers requires efficient mappings from Fermionic operators to qubit operators. Traditional mappings such as Jordan-Wigner or Bravyi-Kitaev often produce high-weight Pauli terms, increasing circuit depth and measurement complexity. Although several local qubit mappings have been proposed to address this challenge, m

Wenhao Gao, Yongheng Wang, Wei Chen, Xinwei Shen

The rapid integration of renewable energy resources, such as tidal and photovoltaic (PV) power, coupled with the growing deployment of electric vehicle (EV) charging infrastructure, necessitates coordinated planning for coastal urban distribution networks (DN). This paper presents a tri-layer distributionally robust optimization framework to jointly optimize

Le Yi, Wei Huang, Lei Zhang, Kefu Zhao

The teacher-student paradigm has emerged as a canonical framework in semi-supervised learning. When applied to medical image segmentation, the paradigm faces challenges due to inherent image ambiguities, making it particularly vulnerable to erroneous supervision. Crucially, the student's iterative reconfirmation of these errors leads to self-reinforcing bias

Ia. A. Mogunov, A. Yu. Klokov, N. Yu. Frolov, A. V. Protasov

Laser-generated surface acoustic waves (SAW) facilitate efficient information processing in modern spintronics and magnonics. The ability to tune the SAW parameters such as amplitude is crucial to achieve acoustic control over magnonic properties. Such tunability can be achieved in phasechanging magnetic materials that accommodate both spin waves and SAWs. A

Jiahui Wang, Haiyue Zhu, Haoren Guo, Abdullah Al Mamun

Recent approaches for few-shot 3D point cloud semantic segmentation typically require a two-stage learning process, i.e., a pre-training stage followed by a few-shot training stage. While effective, these methods face overreliance on pre-training, which hinders model flexibility and adaptability. Some models tried to avoid pre-training yet failed to capture

Yibo Zhang

We investigate shrinking maps from a cusped hyperbolic surface into the moduli space of closed Riemann surfaces. For such a map and its lift to the Teichm\"uller space, we consider whether they are quasi-isometric embeddings with respect to natural metrics like the Teichm\"uller distance and the intrinsic distance. Under a mild condition, we prove that these

Zhuoqun Huang, Neil G. Marchant, Olga Ohrimenko, Benjamin I. P. Rubinstein

We consider the problem of certified robustness for sequence classification against edit distance perturbations. Naturally occurring inputs of varying lengths (e.g., sentences in natural language processing tasks) present a challenge to current methods that employ fixed-rate deletion mechanisms and lead to suboptimal performance. To this end, we introduce Ad

Changjian Xie, Cheng Wang

The convergence analysis of a third-order scheme for the highly nonlinear Landau-Lifshitz-Gilbert equation with a non-convex constraint is considered. In this paper, we first present a fully discrete semi-implicit method for solving the Landau-Lifshitz-Gilbert equation based on the third-order backward differentiation formula and the one-sided extrapolation

Andi Chen

It is challenging to reduce the complexity of neural networks while maintaining their generalization ability and robustness, especially for practical applications. Conventional solutions for this problem incorporate quantum-inspired neural networks with Kronecker products and hybrid tensor neural networks with MPO factorization and fully-connected layers. No

Tomáš Bezděk, Haomu Yuan, Vojtěch Novák, Silvie Illésová

This study systematically benchmarks classical optimization strategies for the Quantum Approximate Optimization Algorithm when applied to Generalized Mean-Variance Problems under near-term Noisy Intermediate-Scale Quantum conditions. We evaluate Dual Annealing, Constrained Optimization by Linear Approximation, and the Powell Method across noiseless, sampling

Marry Kong, Rina Buoy, Sovisal Chenda, Nguonly Taing

Khmer polarity classification is a fundamental natural language processing task that assigns a positive, negative, or neutral label to a given Khmer text input. Existing Khmer models typically predict the label without explaining the rationale behind the prediction. This paper proposes an explainable Khmer polarity classifier by fine-tuning an instruction-ba

S. Bellotti, P. I. Cristofari, J. R. Callingham, J. Morin

Radio observations at low frequencies are sensitive to the magnetic activity of stars and the plasma environment surrounding them. The accurate interpretation of the processes underlying the radio signatures requires a detailed characterisation of the stellar magnetism. We study two M dwarfs, StKM 1-1262 (M0 type, P$_\mathrm{rot}=1.24$ d) and V374 Peg (M4 ty

John Joon Young Chung, Vishakh Padmakumar, Melissa Roemmele, Yi Wang

People have different creative writing preferences, and large language models (LLMs) for these tasks can benefit from adapting to each user's preferences. However, these models are often trained over a dataset that considers varying personal tastes as a monolith. To facilitate developing personalized creative writing LLMs, we introduce LiteraryTaste, a datas

Yiwen Yin, Zhian Hu, Xiaoxi Xu, Chun Yu

Measuring GUI task difficulty is crucial for user behavior analysis and agent capability evaluation. Yet, existing benchmarks typically quantify difficulty based on motor actions (e.g., step counts), overlooking the cognitive demands underlying task completion. In this work, we propose Cognitive Chain, a novel framework that models task difficulty from a cog

Arsene Yerle, Pierre Gaspard, Joel Mabillard

The spectral function of density fluctuations, also known as the dynamic structure factor, of a monatomic cubic crystal with vacancies is derived from the macroscopic equations describing transport in crystalline solids. The resonances of the spectral function are identified as a Brillouin doublet of sound propagation, a central Rayleigh peak of heat diffusi

Christophe Biscio, Frédéric Lavancier

We propose a random forest estimator for the intensity of spatial point processes, applicable with or without covariates. It retains the well-known advantages of a random forest approach, including the ability to handle a large number of covariates, out-of-bag cross-validation, and variable importance assessment. Importantly, even in the absence of covariate

Ryan Martin, Naomi Singer, Jonathan Williams

A crucial step in fitting a regression model to data is determining the model's structure, i.e., the subset of explanatory variables to be included. However, the uncertainty in this step is often overlooked due to a lack of satisfactory methods. Frequentists have no broadly applicable confidence set constructions for a model's structure, and Bayesian posteri

Zhang XueGuang

In this manuscript, variations in optical spectral index $\alpha_{5100}$ are applied for detecting central sub-parsec binary black hole systems (sub-pc BBHs) in broad line active galactic nuclei (BLAGN), due to apparent effects of obscurations on central two BH accreting systems. For sub-pc BBHs in BLAGN, two main characteristics on $\alpha_{5100}$ can be ex

Alaa Awad Abdellatif, Sergio Silva, Eduardo Baltazar, Bruno Oliveira

This paper proposes an optimized Reconfigurable Internet of Things (RIoT) framework that integrates optical and radio wireless technologies with a focus on energy efficiency, scalability, and adaptability. To address the inherent complexity of hybrid optical-radio environments, a high-fidelity Digital Twin (DT) is developed within the Network Simulator 3 (NS

Yan Huang, Shoujie Li, Xingting Li, Wenbo Ding

Data-driven robotic learning faces an obvious dilemma: robust policies demand large-scale, high-quality demonstration data, yet collecting such data remains a major challenge owing to high operational costs, dependence on specialized hardware, and the limited spatial generalization capability of current methods. The Universal Manipulation Interface (UMI) rel

Bo-Xi Li, Peng Ye

Building on the infinite-component Chern--Simons theory of three-dimensional fracton phases by Ma et al. [Phys. Rev. B 105, 195124 (2022)] and the Toeplitz braiding of anyons by Li et al.~[Phys. Rev B 110, 205108 (2024)], we show that stacking $(3+1)$D $BF$ topological field theories, which serve as low-energy effective descriptions of a class of three-dimen

Yanbin Zhu, Xiaomeng Jiang, Yong Li

In this paper, we derive the Onsager-Machlup functional for stochastic differential equations driven by time-varying fractional noise of the form X(t) = x0 + integral from 0 to t b_s(X(s)) ds + integral from 0 to t sigma_s dB^H(s), where B^H denotes fractional Brownian motion with Hurst parameter H. Our main results are established for H in (1/4, 1) by exten

Helena Monke, Benjamin Fresz, Marco Bernreuther, Yilin Chen

Although neural networks are a powerful tool, their widespread use is hindered by the opacity of their decisions and their black-box nature, which result in a lack of trustworthiness. To alleviate this problem, methods in the field of explainable Artificial Intelligence try to unveil how such automated decisions are made. But explainable AI methods are often

Shengqi Dang, Fu Chai, Jiaxin Li, Chao Yuan

The rise of 3D generative models has enabled automatic 3D geometry and texture synthesis from multimodal inputs (e.g., text or images). However, these methods often ignore physical constraints and manufacturability considerations. In this work, we address the challenge of producing 3D designs that are both lightweight and self-supporting. We present DensiCra

Eric Goles, Pedro Montealegre, Martín Ríos-Wilson, Guillaume Theyssier

An automata network is a graph of entities, each holding a state from a finite set and evolving according to a local update rule which depends only on its neighbors in the network's graph. It is freezing if there is an order on the states such that the state evolution of any node is non-decreasing in any orbit. They are commonly used to model epidemic propag

David C. Konijn, Harish K. Vedantham, Cyril Tasse, Timothy W. Shimwell

Coronal mass ejections (CMEs) are major drivers of space weather in the Solar System, but their occurrence rate on other stars is unknown. A characteristic (deca-)metric radio burst with a time-frequency drift, known as a Type II radio burst, is a key observational signature of CMEs. We searched a total of 107 years of stellar data using time-frequency spect

Olivier Destaing, Bertrand Fourcade

Protein nanoclustering is a characteristic feature of their activated state and is essential for forming numerous subcellular structures. The formation of these nanoclusters is highly dependent on a series of post-translational modifications, such as mono-and multi-phosphorylation and dephosphorylation of residues. We theoretically simulate how a protein can

Yanli Li, Yanan Zhou, Zhongliang Guo, Nan Yang

Federated learning (FL) enables privacy-preserving collaborative model training but remains vulnerable to adversarial behaviors that compromise model utility or fairness across sensitive groups. While extensive studies have examined attacks targeting either objective, strategies that simultaneously degrade both utility and fairness remain largely unexplored.

Weijia Zhao, Linlin Li, Kaiqi Kuang, Yang Lin

Objective: Time-domain diffuse optical imaging (DOI) requires accurate forward models for photon propagation in scattering media. However, existing simulators lack comprehensive experimental validation, especially for non-contact configurations with oblique illumination. This study rigorously evaluates three widely used open-source simulators, including MMC,

Setareh Hasanabadi, Mohammad Saber Azimi, Mehrdad Bakhshayesh Karam, Hossein Arabi

To evaluate how partial volume correction (PVC) affects the reproducibility of 18F-FDG PET radiomic features in lymphoma lesions, with respect to lesion volume and tissue type. This single-center retrospective study included 131 newly diagnosed lymphoma patients who underwent baseline 18F-FDG PET/CT. In total, 1,603 lesions (1,302 lymph nodes, 117 spleen/liv

Chen Yang, Ran Le, Yun Xing, Zhenwei An

Large Language Model (LLM) agents have developed rapidly in recent years to solve complex real-world problems using external tools. However, the scarcity of high-quality trajectories still hinders the development of stronger LLM agents. Most existing works on multi-turn dialogue synthesis validate correctness only at the trajectory level, which may overlook

Yu Li, Zhe Yang, Yi Huang, Xin Liu

Recent advancements in large language models (LLMs) have demonstrated remarkable text generation capabilities. However, controlling specific attributes of generated text remains challenging without architectural modifications or extensive fine-tuning. Current methods typically toggle a single, basic attribute but struggle with precise multi-attribute control

Aviv Ratzon, Omri Barak

Predictive learning has emerged as a central paradigm for training models across diverse data domains and is increasingly viewed as a foundation for modern artificial intelligence. A common intuition for this success is that accurate prediction requires models to capture the underlying dynamics of the environment, leading to the emergence of structured world

Yuchen Huang, Sijia Li, Minghao Liu, Wei Liu

LLM-based agents can autonomously accomplish complex tasks across various domains. However, to further cultivate capabilities such as adaptive behavior and long-term decision-making, training on static datasets built from human-level knowledge is insufficient. These datasets are costly to construct and lack both dynamism and realism. A growing consensus is t

J. R. Callingham, C. Tasse, R. Keers, R. D. Kavanagh

Coronal mass ejections (CMEs) are massive expulsions of magnetised plasma from a star, and are the largest contributors to space weather in the Solar System. CMEs are theorized to play a key role in planetary atmospheric erosion, especially for planets that are close to their host star. However, such a conclusion remains controversial as there has not been a

Qipeng Huang, Shanmei Zeng, Jingxian Cui, Lin Htein

In this study, we propose a microfiber waveguide for temperature and air pressure measurement. To improve mechanical strength of the sensor, a C-shaped fiber is sandwiches between two single mode fibers (SMFs) by fusion splice. The microfiber waveguide is 3D printed between two SMFs to connect two fiber cores by two-photon polymerization technology. Due to m

Roland Aydin, Christian Cyron, Steve Bachelor, Ashton Anderson

Current AI training methods align models with human values only after their core capabilities have been established, resulting in models that are easily misaligned and lack deep-rooted value systems. We propose a paradigm shift from "model training" to "model raising", in which alignment is woven into a model's development from the start. We identify several

Amir M. Mansourian, Amir Mohammad Babaei, Shohreh Kasaei

Multi-teacher knowledge distillation (KD), a more effective technique than traditional single-teacher methods, transfers knowledge from expert teachers to a compact student model using logit or feature matching. However, most existing approaches lack knowledge diversity, as they rely solely on unimodal visual information, overlooking the potential of cross-m

Guangze Gu, Ziwei Li, Michael Ruzhansky, Zhipeng Yang

In this paper, we study the existence and multiplicity of solutions to the following class of nonlinear Dirac equations (NLDE) on noncompact quantum graphs: \[ -i\,\varepsilon c\,\sigma_1\,\partial_x u + m c^2 \sigma_3 u + V(x)\,u = f(|u|)\,u, \quad x\in \mathcal{G}, \tag{P} \] where \(V:\mathcal{G}\to\mathbb{R}\) and \(f:\mathbb{R}\to\mathbb{R}\) are contin

Ivan A. Godino, Eva D. Z. Groenendijk, Tanjona R. Rabemananjara

In global PDF analyses, parton distribution functions (PDFs) are parametrised at a fixed input scale $Q_0$ and evolved to higher scales using the DGLAP equations. Since QCD evolution is fully determined within perturbation theory, the fitted PDFs should, in principle, be independent of the arbitrary choice of $Q_0$. In this work, we test this within the NNPD

Jiliang Hu, Zuchao Li, Baoyuan Qi, Liu Guoming

Significant progress has been made in spoken question answering (SQA) in recent years. However, many existing methods, including large audio language models, struggle with processing long audio. Follow the success of retrieval augmented generation, a speech-related retriever shows promising in help preprocessing long-form speech. But the performance of exist

J. Haddad

We establish several sufficient conditions under which a locally integrable function $f:\mathbb R^n \to \mathbb R$ represents a positive-definite distribution. In particular we consider functions of the form $f(\|x\|)$ where $\|\cdot\|$ is a fixed norm in $\mathbb R^n$.

Sébastien Ott, Yvan Velenik

Let $h:[0,1]\to\mathbb{R}$ be $C^2$ and such that $\sup_{[0,1]} h''<0$. For a (large) positive integer $n$, set $h_n(k) = n h(k/n)$ for any $k\in\{0,\dots,n\}$. We consider a random walk $(S_k)_{k\geq 0}$ with i.i.d.\ centred increments having some finite exponential moments. We are interested in the event $\{S\geq h_n\} = \{S_k\geq h_n(k)\;\forall k\in\{0,\

Tong Li, Kai Ma, Man Yuan

The laser of an intense electromagnetic field promotes the studies of strong-field particle physics in high-intensity frontier. Particle accelerator facilities in the world produce high-quality muon and proton beams. In this work, we propose the nonlinear Compton scattering to light dark particles through the collision between intense laser pulse and muon or

Jian-Min Wang, Yi-Lin Wang, Yong-Jie Chen, Jun-Rong Liu

As an unprecedented large population in the early universe, the JWST-discovered little red dots (LRDs) have garnered much attention for formation of massive black holes and galaxies, but their nature remains a mystery. The LRDs appearing as ``Chimeras" like both active galactic nuclei (AGNs) and galaxies have stimulated renewed interest in the roadmap of cen

Sriganapathy Raghav, Boris Malomed, Utpal Roy

We propose atom interferometers based on quantum droplet (QD), which is also being reported as a superior platform for interferometry. The emphasis has been given to harmonic-oscillator (HO) or ring-shaped potentials. In the HO trap, a Gaussian barrier induces coherent splitting; in the ring, one or two barriers guide the splitting and subsequent recombinati

Sarvenaz Babakhani, David Remy, Alina Roitberg

Energy expenditure estimation aims to infer human metabolic rate from physiological signals such as heart rate, respiration, or accelerometer data, and has been studied primarily with classical regression methods. The few existing deep learning approaches rarely disentangle the role of neural architecture from that of signal choice. In this work, we systemat

Minlan Shao, Zijian Zhang, Yili Wang, Yiwei Dai

Accurate traffic forecasting plays a vital role in intelligent transportation systems, enabling applications such as congestion control, route planning, and urban mobility optimization. However, traffic forecasting remains challenging due to two key factors: (1) complex spatial dependencies arising from dynamic interactions between road segments and traffic

Meixia He, Peican Zhu, Le Cheng, Yangming Guo

Recent studies have demonstrated that hypergraph neural networks (HGNNs) are susceptible to adversarial attacks. However, existing methods rely on the specific information mechanisms of target HGNNs, overlooking the common vulnerability caused by the significant differences in hyperedge pivotality along aggregation paths in most HGNNs, thereby limiting the t

Sébastien Ott, Yvan Velenik

We consider integer-valued random walks with independent but not identically distributed increments, and extend to this context several classical estimates, including a local limit theorem, precise small-ball estimates (both conditional on the final point and unconditional), and bounds on the probability that the random walk trajectory remains positive up to

Mouhammed Achhab, Pierre Jehel, Fabrice Gatuingt

Integrating uncertainties in the design process of reinforced concrete rail bridges, in a fully probabilistic framework, makes their design more complex and challenging. To propagate these uncertainties and convey their influence on the performance of the engineering system, a high-dimensional design space is supposed to be explored. A great challenge to be

Rui Wan, Qi Zheng, Ruoyu Zhang, Bu Chen

The Animation-based Generative Codec (AGC) is an emerging paradigm for talking-face video compression. However, deploying its intricate decoder on resource and power-constrained edge devices presents challenges due to numerous parameters, the inflexibility to adapt to dynamically evolving algorithms, and the high power consumption induced by extensive comput

Komal Negi, Mahender Singh

In this paper, we introduce twisted virtual doodles, defined as stable equivalence classes of immersed circles on closed surfaces that may be non-orientable. These objects admit planar representative diagrams, considered up to a suitable set of Reidemeister-type moves. To develop the associated braid-theoretic framework, we define twisted virtual twin groups

Tommaso Zaccherini, Siyuan Liu, Dimos V. Dimarogonas

We propose decentralized k-hop Prescribed Performance State and Input Observers for heterogeneous multi-agent systems subject to bounded external disturbances. In the proposed input/state observer, each agent estimates the state and input of agents located two or more hops away using only local information exchanged with 1-hop neighbors, while guaranteeing t

Xingqi Lin, Liangyu Chen, Min Wu, Min Zhang

Robustness verification is a promising technique for rigorously proving Recurrent Neural Networks (RNNs) robustly. A key challenge is to over-approximate the nonlinear activation functions with linear constraints, which can transform the verification problem into an efficiently solvable linear programming problem. Existing methods over-approximate the nonlin

Igor Klep, Jacob Levenson, Scott McCullough

We prove a Fej\'er-Riesz type factorization for positive matrix-valued noncommutative trigonometric polynomials on $\mathscr{W}\times\mathfrak{Y}$, where $\mathscr{W}$ is either the free semigroup $\langle x \rangle_g$ or the free product group $\mathbb{Z}_2^{g}$, and $\mathfrak{Y}$ is a discrete group. More precisely, using the shortlex order, if $A$ has de

Seunghyeon Lee, Hyunmin Seo, Hwanjo Heo, Anduo Wang

Modern enterprise networks comprise diverse and heterogeneous systems that support a wide range of services, making it challenging for administrators to track and analyze sophisticated attacks such as advanced persistent threats (APTs), which often exploit multiple vectors. To address this challenge, we introduce the concept of network-level security provena

Dawei Jiao, Mahdi Bayanifar, Alexei Ashikhmin, Olav Tirkkonen

We study the transversality of the Toffoli gate in a hybrid-code system that employs two quantum error correction codes with special structure. We find that a system using a triorthogonal code with its paired code supports a fully transversal implementation of the Toffoli gate. Through circuit-level analysis, we prove the transversality of the Toffoli operat

Zhihang Chen, Junwu Tu

Let $X$ be a quasi-compact separated scheme over a base field. Keller proved a theorem stating that the cyclic homology of $X$ is canonically isomorphic to the cyclic homology of the dg category ${\sf Perf}(X)$ consisting of perfect complexes over $X$. This theorem shows the categorical nature of the cyclic homology. In this note, we generalize Keller's theo

Stefano Balietti, Pietro Saggese, Stefan Kitzler, Bernhard Haslhofer

This chapter explores how Decentralized Autonomous Organizations (DAOs), a novel institutional form based on blockchain technology, challenge traditional centralized governance structures. DAOs govern projects ranging from finance to science and digital communities. They aim to redistribute decision-making power through programmable, transparent, and partici

Jiaping Cao, Ting Sun, Man Lung Yiu, Xiao Yan

Spatial data analytics systems are widely studied in both the academia and industry. However, existing systems are limited when handling a large number of moving objects and real time spatial queries. In this work, we architect a scalable and efficient system CheetahGIS to process streaming spatial queries over massive moving objects. In particular, CheetahG

Yuyao Long

In recent years, graph neural networks (GNNs) have been widely applied in tackling combinatorial optimization problems. However, existing methods still suffer from limited accuracy when addressing that on complex graphs and exhibit poor scalability, since full training requires loading the whole adjacent matrix and all embeddings at a time, the it may result

Eleonora Brandimarti

Higher education often requires choosing a bachelor's and a master's degree, yet the returns of these combined choices and the role of courses in different disciplines remain understudied. This paper addresses this gap using detailed data on Italian graduates and university programs. I study the labor market returns to combinations of bachelor's and master's

Evgeny Andronov

Studies of the phase diagram of strongly interacting matter created in nuclear collisions are typically carried out using event-by-event fluctuations. Well-known way to disentangle statistical and dynamical fluctuations is to construct special observables named strongly intensive which are free from trivial volume fluctuations. Within the color string model

Mirko Degli Esposti, Armando Bazzani, Chiara Dellacasa, Matteo Falcioni

This paper introduces a scalable methodology for the objective analysis of quality metrics across six major Italian metropolitan areas: Rome, Bologna, Florence, Milan, Naples, and Palermo. Leveraging georeferenced Street View imagery and an advanced Urban Vision Intelligence system, we systematically classify the visual environment, focusing on key metrics s

Aleksandr Kaplun, Boris Katsnelson

This study develops a theoretical framework for modeling acoustic pulse propagation in a non-ideal shallow-water waveguide. We derive an {\epsilon}-pseudodifferential operator ({\epsilon}-PDO) formulation from the general three-dimensional wave equation, that accounts for vertical stratification, bottom interaction, and slow horizontal inhomogeneity. Using t

Mohammed Srati

In this paper, we introduce and study a new class of fractional modular function spaces, called \emph{Fractional Anisotropic Musielak--Sobolev Spaces}, which generalize both the fractional Anisotropic Orlicz--Sobolev spaces and the Anisotropic fractional Sobolev spaces with variable exponent. These spaces are designed to handle anisotropic and heterogeneous

Junjie Miao, Minghui Xu

Moran sets are a non-autonomous generalization of self-similar sets. In this paper, we study the quasi-Assouad and Assouad dimensions of Moran sets in $\mathbb{R}^{d}$. First we provide quasi-Assouad dimension formulae for Moran sets satisfying $c_*>0$. Then, we provide the upper and lower bounds for quasi-Assouad dimension formulae for Moran sets without as

Ioannis Gavras, Panagiotis Gavriilidis, George C. Alexandropoulos

This paper presents a physics-consistent framework for bistatic sensing incorporating a 2-Dimensional (2D) waveguide-fed metasurface antenna array capable of realizing eXtremely-Large Multiple-Input Multiple-Output (XL MIMO) apertures. A coupled-dipole model is presented that captures the array's mutual coupling due to both waveguide and free-space interacti

I. Leyva, Irene Sendiña-Nadal, Christophe Letellier, J. R. Sevilla-Escoboza

Although synchronization has been extensively studied, important processes underlying its emergence have remained hidden by the use of global order parameters. Here, we uncover how the route unfolds through a sequential transition between two well-known but previously unconnected phenomena: chaotic itinerancy (CI) and intermittent synchronization (IS). Using

Jian Wang, Hong Shen, Chan-Tong Lam

Traditional distributed backdoor attacks (DBA) in federated learning improve stealthiness by decomposing global triggers into sub-triggers, which however requires more poisoned data to maintian the attck strength and hence increases the exposure risk. To overcome this defect, This paper proposes a novel method, namely Fractal-Triggerred Distributed Backdoor

Lan Ma, Qifu Tyler Sun, Shaoteng Liu, Liyang Zhou

A $(k+r,k,l)$ binary array code of length $k+r$, dimension $k$, and sub-packetization $l$ is composed of $l\times(k+r)$ matrices over $\mathbb{F}_2$, with every column of the matrix stored on a separate node in the distributed storage system and viewed as a coordinate of the codeword. It is said to be maximum distance separable (MDS) if any $k$ out of $k+r$

Chenghao Liu, Taha Aksu, Juncheng Liu, Xu Liu

We introduce Moirai 2.0, a decoder-only time-series foundation model trained on a new corpus of 36M series. The model adopts quantile forecasting and multi-token prediction, improving both probabilistic accuracy and inference efficiency. On the Gift-Eval benchmark, it ranks among the top pretrained models while achieving a strong trade-off between accuracy,

Jiyuan Wang, Li Zhang, Haipeng Lin, Qile Liu

Recent advances in brain-inspired artificial intelligence have sought to align neural signals with visual semantics using multimodal models such as CLIP. However, existing methods often treat CLIP as a static feature extractor, overlooking its adaptability to neural representations and the inherent physiological-symbolic gap in EEG-image alignment. To addres

Rustam Ibragimov, Jihyun Kim, Anton Skrobotov

This paper develops robust inference methods for predictive regressions that address key challenges posed by endogenously persistent or heavy-tailed regressors, as well as persistent volatility in errors. Building on the Cauchy estimation framework, we propose two novel tests: one based on $t$-statistic group inference and the other employing a hybrid approa

Tim Wittenborg, Niklas Stehr, Oliver Karras, Sören Auer

Videos and Podcasts have established themselves as the medium of choice for civic dissemination, but also as carriers of misinformation. The emerging Science Communication Knowledge Infrastructure (SciCom KI), which curates these increasingly non-textual media, remains fragmented and inadequately equipped to scale against the content flood. Our work sets out

Yi-Hsien Hsieh, Ta-Jung Chien, Chun-Kai Huang, Shao-Hua Sun

Clinical time series derived from electronic health records (EHRs) are inherently irregular, with asynchronous sampling, missing values, and heterogeneous feature dynamics. While numerical laboratory measurements are highly informative, existing embedding strategies usually combine feature identity and value embeddings through additive operations, which cons

Amelia Bielby, Arushi Chauhan, Cassia Pearce, Yue Ren

A Laman graph $G$ is a minimally rigid graph in dimension two, and its realization number is its number of distinct embeddings with fixed generic edge lengths. While conjectured to grow exponentially in the number of vertices of $G$, the best proven lower bound is merely $2$. Motivated by the fact that the realization number can be expressed as a tropical in

Eric Kolor, Edoardo Magnone, Muhammad Harussani Moklis, Md. Rubel

We present a forward prediction material screening framework designed to discover Pd-Cu alloys with improved B2 phase stability, thereby unlocking simultaneous $H_2$ generation and utilization. First, we trained CatBoost models with literature-derived Pd alloy data to predict $H_2$ permeability from composition and testing conditions. We evaluated fractional

Kuranage Roche Rayan Ranasinghe, Zhaolin Wang, Giuseppe Thadeu Freitas de Abreu, Emil Björnson

A novel electromagnetic (EM) structure termed flexible continuous aperture array (FCAPA) is proposed, which incorporates inherent surface flexibility into typical continuous aperture array (CAPA) systems, thereby enhancing the degrees-of-freedom (DoF) of multiple-input multiple-output (MIMO) systems equipped with this technology. By formulating and solving a

Helena Bordini de Lucas, Leonardo Dalla Porta, Alain Destexhe, Maria V. Sanchez-Vives

Characterizing the brain dynamics during different cortical states can reveal valuable information about its patterns across various cognitive processes. In particular, studying the differences between awake and sleep stages can shed light on the understanding of brain processes essential for physical and mental well-being, such as memory consolidation, info

Shreyas Bharadwaj, Bamdev Mishra, Cyrus Mostajeran, Alberto Padoan

The paper studies a geometrically robust least-squares problem that extends classical and norm-based robust formulations. Rather than minimizing residual error for fixed or perturbed data, we interpret least-squares as enforcing approximate subspace inclusion between measured and true data spaces. The uncertainty in this geometric relation is modeled as a me

Yuxi Wei, Zirui Wang, Kangning Yin, Yue Hu

Data scaling has long remained a critical bottleneck in robot learning. For humanoid robots, human videos and motion data are abundant and widely available, offering a free and large-scale data source. Besides, the semantics related to the motions enable modality alignment and high-level robot control learning. However, how to effectively mine raw video, ext

Jinghao Huang, Siqi Yao, Yu Zhang

The problem of Motion Sickness (MS) among passengers significantly impacts the comfort and efficiency of In-Vehicle Infotainment Systems (IVIS) use. In this study, we innovatively designed SimPath, a visual design to effectively mitigate passengers' MS and boost their efficiency of using IVIS during driving. The study focuses on the problem of irregular moti

Kaixiang Shu, Kai Meng, Junqin Luo

Deep neural networks typically learn spatially entangled representations that conflate discriminative foreground features with spurious background correlations, thereby undermining model interpretability and robustness. We propose a novel understanding framework for gradient-based attribution from an information-theoretic perspective. We prove that, under mi

Joschka Haltaufderheide, Florian Funer, Esther Braun, Hans-Jörg Ehni

Multimodal digital biomarkers (MDBs) integrate diverse physiological, behavioral, and contextual data to provide continuous representations of health. This paper argues that MDBs expand the concept of digital biomarkers along the dimensions of variability, complexity and abstraction, producing an ontological shift that datafies health and an epistemic shift

Anna Veverková, Kristína Bartha, Jozef Veselý, Pere Barriobero-Vila

This study examines the phase transitions occurring during linear heating of the Zr15Nb alloy through a comprehensive, multi-technique methodology comprising in-situ high-energy synchrotron X-ray diffraction (HEXRD), electrical resistance measurements, differential scanning calorimetry (DSC), and thermal expansion analysis, supplemented by ex-situ transmissi

Iva Radecic, Bozidar Filipovic-Grcic, Paul Akiki, Alain Xemard

HVDC networks offer several advantages over traditional HVAC systems, particularly for long-distance power transmission and integration of renewable energy sources, such as reduced losses and enhanced stability and control, but also increase the risk of oscillations. This study investigates electrical resonant phenomena associated with HVDC stations through

Thrassos K. Oikonomou, Dimitrios Tyrovolas, Sotiris A. Tegos, Panagiotis D. Diamantoulakis

This paper presents a maximum-likelihood detection framework that jointly mitigates hardware (HW) impairments in both amplitude and phase. By modeling transceiver distortions as residual amplitude and phase noise, we introduce the approximate phase-and-amplitude distortion detector (PAD-D), which operates in the polar domain and effectively mitigates both di