November 2025 arXiv papers — page 76

Xiaosong Ren, Zhanping Jin, Xiaotong Zou, Xiaole Zhang

The development of quantum internet demands on-chip quantum processor nodes and interconnection between the nodes. Path-encoded photonic qubits are suitable for on-chip quantum information processors, while time-bin encoded ones are good at long-distance communication. It is necessary to develop an on-chip converter between the two encodings to satisfy the n

Joni Vanherck, Steven Moonen, Brent Zoomers, Kobe Werner

Accurate camera localization is crucial for robotics and Extended Reality (XR), enabling reliable navigation and alignment of virtual and real content. Existing visual methods often suffer from drift, scale ambiguity, and depend on fiducials or loop closure. This work introduces a real-time method for localizing a camera within a pre-captured, highly accurat

Wen-Hao Jiang, Ruiwen Ouyang, Ye-Ling Zhou

This paper investigates the landscape of models based on modular $S_4$ symmetry that predicts the trimaximal TM$_1$ mixing pattern for leptonic flavor mixing, and explores their parameter spaces with constraints from the latest high-precision measurement on $\theta_{12}$ and $\Delta m^2_{21}$ given by JUNO experiment. We review on how the mixing pattern aris

Chunyang Li, Zifeng Kang, Junwei Zhang, Zhuo Ma

The adoption of Vision-Language Models (VLMs) in embodied AI agents, while being effective, brings safety concerns such as jailbreaking. Prior work have explored the possibility of directly jailbreaking the embodied agents through elaborated multi-modal prompts. However, no prior work has studied or even reported indirect jailbreaks in embodied AI, where a b

Deniz Kasap, Taraneh Aminosharieh Najafi, Jérôme Paul Rémy Thevenot, Jonathan Dan

We present VersaPants, the first loose-fitting, textile-based capacitive sensing system for lower-body motion capture, built on the open-hardware VersaSens platform. By integrating conductive textile patches and a compact acquisition unit into a pair of pants, the system reconstructs lower-body pose without compromising comfort. Unlike IMU-based systems that

Tianyu Liu, Weihao Xuan, Hao Wu, Peter Humphrey

Advances in AI have introduced several strong models in computational pathology to usher it into the era of multi-modal diagnosis, analysis, and interpretation. However, the current pathology-specific visual language models still lack capacities in making the diagnosis with rigorous reasoning paths as well as handling divergent tasks, and thus, challenges of

Blake Matheny, Phuong Minh Nguyen, Minh Le Nguyen, Stephanie Reynolds

Idiomatic and figurative language form a large portion of colloquial speech and writing. With social media, this informal language has become more easily observable to people and trainers of large language models (LLMs) alike. While the advantage of large corpora seems like the solution to all machine learning and Natural Language Processing (NLP) problems,

Mariella Bastian, Damian Trilling, Mykola Makhortykh

The growing adoption of algorithm-powered tools in journalism enables new possibilities and raises many concerns. One way of addressing these concerns is by integrating journalistic practices and values into the design of algorithms that facilitate different journalistic tasks, from automated content generation to news content distribution. In this chapter,

Chi-Han Chen, Chieh-Ming Chen, Wen-Huang Cheng, Ching-Chun Huang

The study of terrain and landform classification through UAV remote sensing diverges significantly from ground vehicle patrol tasks. Besides grappling with the complexity of data annotation and ensuring temporal consistency, it also confronts the scarcity of relevant data and the limitations imposed by the effective range of many technologies. This research

Tasuki Kinjo

We prove the multiplicative version of the dimensional reduction theorem in cohomological Donaldson--Thomas theory. More precisely, we show that the BPS cohomology associated with the loop stack of a $0$-shifted symplectic stack admits a description analogous to orbifold cohomology, even though our stacks are not necessarily Deligne--Mumford. As an applicati

Yi Ting Tsai, Yu Wei Chen, Hong-Han Shuai, Ching-Chun Huang

Face super-resolution (FSR) is a critical technique for enhancing low-resolution facial images and has significant implications for face-related tasks. However, existing FSR methods are limited by fixed up-sampling scales and sensitivity to input size variations. To address these limitations, this paper introduces an Arbitrary-Resolution and Arbitrary-Scale

Miquel Noguer i Alonso

This paper introduces a comprehensive framework for Financial Information Theory by applying information-theoretic concepts such as entropy, Kullback-Leibler divergence, mutual information, normalized mutual information, and transfer entropy to financial time series. We systematically derive these measures with complete mathematical proofs, establish their t

Zoltan Bajnok, Apor Roth

We compare the spectrum of the elliptic Ruijsenaars-Schneider model with the finite-size spectrum of the sine-Gordon model, highlighting both their similarities and differences. Our analysis focuses on the two-particle sector in the center-of-mass frame. At the free point, we carry out an analytic comparison, while at generic couplings we employ non-perturba

Ilmo Räisänen, Ilari Maasilta

The Simmons model is a well-known and widely used model for the elastic tunneling current of a metallic tunnel junction, and fitting it to electrical measurements can be used to estimate thicknesses and heights of the tunnel barriers. We present here an improvement of the Simmons model, deriving new more accurate analytical formulas for the tunneling current

G. C. Bento, J. X. Cruz Neto, J. O. Lopes, B. S. Mordukhovich

The paper is devoted to the study of regularized versions of multiobjective optimization problems described by directionally Lipschitzian functions. Such regularizations appear in proximal-type algorithms of multiobjective optimization, various models of machine learning, medical physics, etc. We investigate and illustrate several useful properties of direct

Daniela Ferrero, H. Tracy Hall, Leslie Hogben, Mark Hunnell

Zero forcing in a graph refers to the evolution of vertex states under repeated application of a color change rule. Typically the states are chosen to be blue and white, and a forcing set is an initial set of blue vertices such that all of the vertices are blue at the end of the process. In this context, the propagation time of a set in a graph is the number

Kaichen Zhang, Keming Wu, Zuhao Yang, Bo Li

Recent advancements in large reasoning models have fueled growing interest in extending such capabilities to multimodal domains. However, despite notable progress in visual reasoning, the lack of transparent and reproducible data curation and training strategies remains a major barrier to scalable research. In this work, we introduce OpenMMReasoner, a fully

Mohammad Areeb Qazi, Maryam Nadeem, Mohammad Yaqub

Healthcare requires AI that is predictive, reliable, and data-efficient. However, recent generative models lack physical foundation and temporal reasoning required for clinical decision support. As scaling language models show diminishing returns for grounded clinical reasoning, world models are gaining traction because they learn multimodal, temporally cohe

Li-Wen Liao, Andrew Cooper

The galaxy size-luminosity and size-stellar mass relations are important constraints on the galactic baryon cycle of gas accretion, star formation, and feedback. There are conflicting claims in the literature regarding how environment influences size: both direct transformative effects and `assembly bias' may contribute to observed variations with environmen

Jiashu Yao, Heyan Huang, Shuang Zeng, Chuwei Luo

Through reinforcement learning (RL) with outcome correctness rewards, large reasoning models (LRMs) with scaled inference computation have demonstrated substantial success on complex reasoning tasks. However, the one-sided reward, focused solely on final correctness, limits its ability to provide detailed supervision over internal reasoning process. This def

Shreyas Kumar, Ravi Prakash

Reinforcement learning (RL) offers a powerful approach for robots to learn complex, collaborative skills by combining Dynamic Movement Primitives (DMPs) for motion and Variable Impedance Control (VIC) for compliant interaction. However, this model-free paradigm often risks instability and unsafe exploration due to the time-varying nature of impedance gains.

Mélanie Bertelson, Pranav Chakravarthy, Sheila Sandon

Using generating functions quadratic at infinity for Lagrangian submanifolds of twisted cotangent bundles, we define spectral selectors for compactly supported lcs Hamiltonian diffeomorphisms of the locally conformal symplectizations $S^1 \times \mathbb{R}^{2n+1}$ and $S^1 \times \mathbb{R}^{2n} \times S^1$ of $\mathbb{R}^{2n+1}$ and $\mathbb{R}^{2n} \times

Daniel Sela, Nan Cheng, Kai Sun

Hyperbolic lattices extend crystallinity into curved space, where negative curvature and exponentially large boundaries reshape collective excitations beyond Euclidean intuition. In this Letter, we push the study beyond scalar fields by exploring vector fields on hyperbolic lattices. Using phonons as an example, we show that the Goldstone theorem breaks down

Deqiao Gan, Xiaoxia Xu, Xiaohu Ge, Yuanwei Liu

A joint communication and computation (JCC) framework using segmented pinching antenna system (PASS) is proposed, where both the communication bit streams and computation data are simultaneously transmitted via uplink communications. The segmented PASS design is used to yield the tractable uplink transmission, and to mitigate large-scale path loss and in-wav

Hang Ding, Jiawei Zhou, Haiyun Jiang

Retrieval-Augmented Generation (RAG) has emerged as a powerful framework for knowledge-intensive tasks, yet its effectiveness in long-context scenarios is often bottlenecked by the retriever's inability to distinguish sparse yet crucial evidence. Standard retrievers, optimized for query-document similarity, frequently fail to align with the downstream goal o

Wei Xia, Zhi-Hong Deng

With the rapid advancement of large language models (LLMs), their deployment in real-world applications has become increasingly widespread. LLMs are expected to deliver robust performance across diverse tasks, user preferences, and practical scenarios. However, as demands grow, ensuring that LLMs produce responses aligned with human intent remains a foundati

G. González-Torà, A. A. C. Sander, E. Egorova, O. Egorov

Classical Wolf-Rayet (WR) stars are hot, massive stars with depleted hydrogen. At low metallicities (Z), WN3-type WR stars have relatively thin winds and are major sources of ionizing flux. The detection of high-ionization emission lines in high-redshift ($z$) galaxies as well as nearby low-Z dwarf galaxies raises questions about the origin of He II ionizing

Ching-Heng Cheng, Chih-Chung Hsu

Remote sensing change detection (RSCD) aims to identify surface changes from co-registered bi-temporal images. However, many deep learning-based RSCD methods rely solely on change-map annotations and underuse the semantic information in non-changing regions, which limits robustness under illumination variation, off-nadir views, and scarce labels. This articl

Ching-Heng Cheng, Jen-Wei Lee, Chia-Ming Lee, Chih-Chung Hsu

Underwater Image Enhancement (UIE) aims to restore visibility and correct color distortions caused by wavelength-dependent absorption and scattering. Recent hybrid approaches, which couple domain priors with modern deep neural architectures, have achieved strong performance but incur high computational cost, limiting their practicality in real-time scenarios

Rudrakshala Kavya Sri, Piotr Bartłomiejczyk, Sishu Shankar Muni

This research investigates the dynamic behavior of one dimensional discrete systems using two computational algorithms, the numerical transitivity and the numerical locally eventually onto (LEO) tests. Both algorithms are systematically applied to a variety of interval maps, including classical examples such as beta transformations and expanding Lorenz maps,

Amir Kavoosi

This study introduces the Quantitative Geometric Market Structuralist (QGMS) framework a hybrid analytical methodology integrating geometric pattern recognition with quantitative mathematical modeling to identify terminal zones of large-scale market movements. Unlike conventional econometric or signal-based models, the QGMS framework conceptualizes market dy

Tommaso Milanese, Baris Can Efe, Claudio Bruschini, Nobukazu Teranishi

CMOS-compatible single-photon avalanche diodes (SPADs) have emerged in many systems as the solution of choice for cameras with photon-number resolution and photon counting capabilities. Being natively digital optical interfaces, SPADs are naturally drawn to in situ logic processing and event-driven computation; they are usually coupled to discrete FPGAs to e

Hao Shu

This work introduces a learning-enhanced observer (LEO) for linear time-invariant systems with uncertain dynamics. Rather than relying solely on nominal models, the proposed framework treats the system matrices as optimizable variables and refines them through gradient-based minimization of a steady-state output discrepancy loss. The resulting data-informed

Zeqiang Lai, Yunfei Zhao, Zibo Zhao, Xin Yang

We present NaTex, a native texture generation framework that predicts texture color directly in 3D space. In contrast to previous approaches that rely on baking 2D multi-view images synthesized by geometry-conditioned Multi-View Diffusion models (MVDs), NaTex avoids several inherent limitations of the MVD pipeline. These include difficulties in handling occl

Samuel Stevens

ImageNet-1K linear-probe transfer accuracy remains the default proxy for visual representation quality, yet it no longer predicts performance on scientific imagery. Across 46 modern vision model checkpoints, ImageNet top-1 accuracy explains only 34% of variance on ecology tasks and mis-ranks 30% of models above 75% accuracy. We present BioBench, an open ecol

Shigenobu Hirose

Analogous to the HR diagram for stars, the thermal equilibrium curve encodes the thermodynamics of accretion disks by expressing the local balance between heating -- primarily via viscous dissipation -- and cooling -- typically through radiative transfer. These curves are commonly plotted as surface density versus effective temperature. When an S-shaped locu

Raul Quintero-Monsebaiz, Per Hyldgaard

We compute and track the impact of truly nonlocal-correlation effects on the quasi-particle (QP) band-structure of hexagonal boron-nitride (h-BN) systems. To that end, we start with the consistent-exchange vdW-DF-cx version [PRB 89, 035412 (2014)] of the van der Waals density functional (vdW-DF) method [JPCM 39, 390001 (2020)] for exchange-correlation (XC) f

Xiao-Min Zhang, Run-Qing Zhao, Yun-Cai Feng, Peng-Cheng Chu

This paper presents and investigates non-Gaussian perturbations for the warm k-inflation model that is driven by pure kinetic energy. The two complementary components of the overall non-Gaussianity are the three-point and four-point correlations. The intrinsic non-Gaussian component, denoted as the nonlinear parameter f_{NL}^{int}, is rooted in the three-poi

Pacôme Van Overschelde

We investigate quantitative properties of exact locally conformally symplectic (LCS) manifolds, namely the homotheties of the Lee form that still produce an exact LCS form. This gives the notion of elasticity of an exact LCS pair. Using this, we characterize LCS manifolds of the first kind. We then generalize a result of Bazzoni and Marrero on the latter, by

Shireen Hilal, Azat O. Ismagilov, Anton N. Tsypkin, Maksim V. Melnik

We present a theoretical study of how linear chirp controls plasma density in a water jet using a two-stage framework. Stage I solves carrier-population and current equations at a single point, driven by a chirped super-Gaussian pulse. By fixing bandwidth and normalizing for intensity, we isolate a chirp-only response of plasma density, which exceeds unity a

Pingping Zhu, Dun Li, Xiang Yu, Zheng Zhong

Soft solids exhibit instability and develop surface undulations due to surface effects, a phenomenon known as the elastic Plateau-Rayleigh (PR) instability, driven by the interplay of surface and bulk elasticity. Previous studies on the PR instability in solids mainly focused on the case of constant surface tension and ignored the effect of surface elasticit

A. V. Bobrova, G. V. Osipov

This study investigates the collective dynamics (phase synchronization, instantaneous frequencies synchronization and mean frequencies synchronization) in an ensemble of four nonidentical phase oscillators with repulsive coupling. We use the Kuramoto-Sakaguchi system of ordinary differential equations as our mathematical model. Depending on the coupling stre

Marta Lewicka

A classical result by Poznyak asserts that any smooth $2$-dimensional Riemannian metric $g$, posed on the closure of a simply connected domain $ω\subset\mathbb{R}^2$, has a smooth isometric immersion into $\mathbb{R}^4$. Using techniques of convex integration, we prove that for any $2$-dimensional $g\in{C}^{r,β}$, an isometric immersion of regularity ${C}^{1

Marek Wazny, Lehel Csillag, Miguel A. S. Pinto, Tiberiu Harko

The non-conservation of the energy-momentum tensor in $f(R,T)$ gravity can be interpreted as an effective manifestation of dissipation. Motivated by this, we propose a new formulation of $f(R,T)$ gravity based on the Herglotz variational principle, which extends the usual {Hamilton} variational principle to dissipative systems by allowing the Lagrangian to d

Otto Savola, Alexandru Paler

Neutral-atom quantum computers encode qubits in individually trapped atoms arranged in optical lattices. Achieving defect-free atom configurations is essential for high-fidelity quantum gates and scalable error correction, yet stochastic loading and atom loss during rearrangement hinder reliable large-scale assembly. This work presents ATLAS, an open-source

Yusheng Hu, Huaiyi Pan, Shaobo Zhong, Liying Zhang

Wildland-Urban Interface (WUI) fires represent a compound disaster resulting from the interactions between natural ecosystems and human settlements, characterized by significantly dynamic evolving risks. However, most current risk assessment studies are based on static frameworks, which struggle to effectively capture the dynamic changes in risk over time. T

Minseok Seo, Mark Hamilton, Changick Kim

We present \textbf{Upsample Anything}, a lightweight test-time optimization (TTO) framework that restores low-resolution features to high-resolution, pixel-wise outputs without any training. Although Vision Foundation Models demonstrate strong generalization across diverse downstream tasks, their representations are typically downsampled by 14x/16x (e.g., Vi

Qian Qin, JinJing Jiao, Zhiguo Wang, Hua Nie

This paper investigates the dynamics of a reaction-diffusion system with two free boundaries, modeling the invasion of two cooperative species, where the free boundaries represent expanding fronts. We first analyze the long-term behavior of the system, showing that it follows a spreading-vanishing dichotomy: the two species either spread across the entire re

Idris Delsol, Omar Fawzi, Li Gao, Mizanur Rahaman

We study the optimal rates of emulation (also called interconversion) between quantum channels. When the source and the target channels are idempotent, we give a single-letter expression for the zero-error emulation capacity in terms of structural properties of the range of the two channels. This expression shows that channel emulation is not reversible for

Md Musfiqur Rahman Sanim, Zhihao Shu, Bahram Afsharmanesh, AmirAli Mirian

Image-based 3D scene reconstruction, which transforms multi-view images into a structured 3D representation of the surrounding environment, is a common task across many modern applications. 3D Gaussian Splatting (3DGS) is a new paradigm to address this problem and offers considerable efficiency as compared to the previous methods. Motivated by this, and cons

Dean Brandner, Sergio Lucia

Optimal operation of chemical processes is vital for energy, resource, and cost savings in chemical engineering. The problem of optimal operation can be tackled with reinforcement learning, but traditional reinforcement learning methods face challenges due to hard constraints related to quality and safety that must be strictly satisfied, and the large amount

Mariem Zayene, Oussama Habachi, Gerard Chalhoub

Energy efficiency is shaping up to be one of the most challenging issues for 6G networks. The reason is fairly straightforward: Networks will need to meet extreme service demands while remaining sustainable and traditional optimization techniques are too limited. With users moving, traffic swinging unpredictably and services pulling in different directions,

Abishek Karthik, Pandiyaraju V, Sreya Mynampati

The task of weed detection is an essential element of precision agriculture since accurate species identification allows a farmer to selectively apply herbicides and fits into sustainable agriculture crop management. This paper proposes a hybrid deep learning framework recipe for weed detection that utilizes Convolutional Neural Networks (CNNs), Vision Trans

S. Bouarroudj, A. N. Zubkov

We describe certain almost-simple algebraic supergroups over an algebraically closed field of odd or zero characteristic. In addition to supergroups with simple Lie superalgebras from Kac's theorem, we construct new supergroups whose Lie superalgebra is either non-simple or simple but is not part of Kac's list.

Daniel Vaughan, Kateřina Vaughan

This paper presents a proof-of-concept demonstration of agent-to-agent communication across distributed systems, using only natural-language messages and without shared identifiers, structured schemas, or centralised data exchange. The prototype explores how multiple organisations (represented here as a Clinic, Insurer, and Specialist Network) can cooperate

Anguohao Yang, Bohdan Novosyadlyj, Bohdan Melekh, Gennadii Milinevsky

We construct two self-consistent analytic approximations to the neutral hydrogen fraction, $x_{\rm HI}(z)$, and the helium ionization fractions, $x_{\rm HeI}(z)$, $x_{\rm HeII}(z)$, and $x_{\rm HeIII}(z)$, that are consistent with current constraints inferred from quasar spectra, galaxy surveys, and CMB polarization measurements. These approximations describ

Yan Niu, Anqing Wang, Xiangxiang Ren, Dong Liu

Cosmic-ray muons, owing to their high penetration power and abundance, have been widely employed as a natural probe in experimental particle physics. We developed a meter-scale cosmic-ray muon telescope, consisting of two parallel super-layers (1 m $\times$ 1 m) separated vertically by one meter. A super-layer is composed of two orthogonal detection layers,

William Hao-Cheng Huang

Linear probes are widely used to interpret and evaluate neural representations, yet their reliability remains unclear, as probes may appear accurate in some regimes but collapse unpredictably in others. We uncover a spectral mechanism behind this phenomenon and formalize it as the Spectral Identifiability Principle (SIP), a verifiable Fisher-inspired conditi

David Bechtoldt, Sidney Bender

Machine learning models that operate on graph-structured data, such as molecular graphs or social networks, often make accurate predictions but offer little insight into why certain predictions are made. Counterfactual explanations address this challenge by seeking the closest alternative scenario where the model's prediction would change. Although counterfa

Nico Hahn, Lars Öhrström, R. Matthias Geilhufe

We develop a framework to describe collective buckling in metal-organic frameworks (MOFs). Starting from the microscopic structure of a single organic linker, we define a buckling coordinate governed by an effective double-well potential. Coupling between linkers is introduced within a dipole-dipole approximation, resulting in an effective lattice Hamiltonia

Dasom Kim, Maxime Dherbécourt, Sae R. Endo, Geon Lee

Optical cavities provide a powerful means to engineer light-matter hybrid states by coupling confined electromagnetic fields with matter excitations. Achieving in situ control of the coupling strength is essential for investigating how such hybridization evolves with the coupling strength. In this work, we use a symmetry-changing structural phase transition

Lin-Gang Wei, Yun-Mei Li

The Comment [Y.-M. Li, B. Wei, and K. Chang, Phys. Rev. Lett. 132, 219601 (2024)] pointed out that it is incorrect to predict the temperature-driven topological phase transition of Dirac magnons in honeycomb ferromagnets with Dzyaloshinskii-Moriya interactions based on the theory in Lu et al. [Y.-S. Lu, J.-L. Li, and C.-T. Wu, Phys. Rev. Lett. 127, 217202 (2

Dongjian Yang, Xiaoyuan Li, Chuanmei Xi, Ye Sun

Dendrite-inspired neurons have been widely used in tasks such as image classification due to low computational complexity and fast inference speed. Temporal data prediction, as a key machine learning task, plays a key role in real-time scenarios such as sensor data analysis, financial forecasting, and urban traffic management. However, existing dendrite-insp

Zhiyuan Li, Haisheng Yu, Guangchuan Guo, Nan Zhou

Complex scientific questions often entail multiple intents, such as identifying gene mutations and linking them to related diseases. These tasks require evidence from diverse sources and multi-hop reasoning, while conventional retrieval-augmented generation (RAG) systems are usually single-intent oriented, leading to incomplete evidence coverage. To assess t

Gergely Dinya, Péter Halász, András Lőrincz, Kristóf Karacs

We present a fast, spatio-temporal scene understanding framework based on Visual Geometry Grounded Transformer (VGGT). The proposed pipeline is designed to enable efficient, close to real-time performance, supporting applications including assistive navigation. To achieve continuous updates of the 3D scene representation, we process the image flow with a sli

Yu-Feng Wu

Given $\beta\in\mathbb{Z}[i]$ with $|\beta|>1$ and a finite set $D\subset\mathbb{Q}(i)$, let \[K_{\beta, D}=\left\{\sum_{j=1}^{\infty}\frac{d_j}{\beta^j}: d_j\in D, \forall j\geq 1\right\}.\] Let $\mathcal{S}$ be a finite set of non-associate prime elements in $\mathbb{Z}[i]$ not dividing $\beta$. We prove that if the Hausdorff dimension of $K_{\beta,D}$ is

Ziyue Li, Guanglun Zhang, Grant Ruan, Haiwang Zhong

Preventive control is a crucial strategy for power system operation against impending natural hazards, and its effectiveness fundamentally relies on the realism of scenario generation. While most existing studies employ sequential Monte Carlo simulation and assume independent sampling of component failures, this oversimplification neglects the spatial correl

Zhen Sun, Zongmin Zhang, Deqi Liang, Han Sun

As LLMs become more common, non-expert users can pose risks, prompting extensive research into jailbreak attacks. However, most existing black-box jailbreak attacks rely on hand-crafted heuristics or narrow search spaces, which limit scalability. Compared with prior attacks, we propose Game-Theory Attack (GTA), an scalable black-box jailbreak framework. Conc

Manjun Cui, Ziqi Yan, Yangfan He, Zhichao Zhang

Dynamic graph signal processing provides a principled framework for analyzing time-varying data defined on irregular graph domains. However, existing joint time-vertex transforms such as the joint time-vertex fractional Fourier transform assign only one fractional order to the spatial domain and another one to the temporal domain, thereby restricting their c

Riccardo Grazi, Dario Ferraro, Niccolò Traverso Ziani

Quantum batteries are quantum mechanical systems able to store and release energy in a controlled fashion. Among them, a special role is played by quantum structures defined as networks of two-level systems. In this context, it has recently been shown that the energy stored in free fermion quantum batteries is sensitive to the quantum phase diagram of the ba

Ioana Moflic, Alexandru Paler, Akash Kundu

Quantum computing faces a key challenge: balancing the need for low circuit depth (crucial for fault tolerance) with the high accuracy required for complex computations like quantum chemistry and error correction, which typically require deeper circuits. We overcome this trade-off by introducing a novel reinforcement learning approach featuring engineered re

Francesco Paolo Maiale, Anastasiia Trofimova, Nicola Guglielmi

We investigate joint spectral characteristics of a family of matrices $\mathcal F $, associated with products in the semigroup generated by $\mathcal F$. In the literature, extremal measures such as the well-known joint spectral radius and the lower spectral radius have been extensively studied. However, these measures fail to capture the typical growth rate

Hand Yue, Ma Chenning, Liu Jinxia, Zhou Zixian

Plate structures are widely used in large-scale engineering fields such as aerospace, hull manufacturing, and construction. However, the plate structure is easily damaged during long-term service or when it is impacted by foreign objects. Such a damage may lead to serious safety accidents. Beamforming and L-shaped sensor cluster (LSSC) localization method ca

Erwan Dereure, Robin Louiset, Laura Parkkinen, David A Menassa

Parkinson's disease (PD) is a neurodegenerative disorder associated with the accumulation of misfolded alpha-synuclein aggregates, forming Lewy bodies and neuritic shape used for pathology diagnostics. Automatic analysis of immunohistochemistry histopathological images with Deep Learning provides a promising tool for better understanding the spatial organiza

Fatma Almaz

In this study, the geometric properties of null helices on a totally umbilical submanifold within a three-dimensional semi-Riemannian manifold are investigated. The pseudo-Riemannian metric structure of semi-Riemannian manifolds and the fact that the submanifold is totally umbilical complicate the differential geometry of null helices. The study uses the giv

Michelle Hallmann, Michael Stern, Juliane Henning, Ute Franke

The maintenance of rail vehicles and infrastructure plays a critical role in reducing delays, preventing malfunctions, and ensuring the economic efficiency of rail transportation companies. Predictive maintenance systems powered by supervised machine learning offer a promising approach by detecting failures before they occur, reducing unscheduled downtime, a

Haru Fukatsu, Ryoji Yasuda, Yuki Funabora, Shinji Doki

This paper presents Funabot-Upper, a wearable haptic suit that enables users to perceive 14 upper-body motions, including those of the trunk, shoulder, elbow, and wrist. Inducing kinesthetic perception through wearable haptic devices has attracted attention, and various devices have been developed in the past. However, these have been limited to verification

Sinan Mutlu, Georgios F. Angelis, Savas Ozkan, Paul Wisbey

Realistic and smooth full-body tracking is crucial for immersive AR/VR applications. Existing systems primarily track head and hands via Head Mounted Devices (HMDs) and controllers, making the 3D full-body reconstruction in-complete. One potential approach is to generate the full-body motions from sparse inputs collected from limited sensors using a Neural N

G. Tejaswini, Anjana E Sudheer, Amrendra Kumar, M. Vallinayagam

Janus derivatives of 2H MX2 (M = Mo/W; X = S/Se), namely MSSe, have already been experimentally realized and explored for applications in photocatalysis, photovoltaics, and optoelectronics. Focusing on the photocatalytic properties of these layers, we investigate the adsorption of atomic hydrogen on the MSSe layers in the presence of metalloid dopants B, Si,

Anton Reinhard, Simeon Ehrig, René Widera, Michael Bussmann

Complex computational problems in science often consist of smaller parts that can have largely distinct compute requirements from one another. For optimal efficiency, analyzing each subtask and scheduling it on the best-suited hardware would be necessary. Other considerations must be taken into account, too, such as parallelism, dependencies between differen

Oliver Bimber, Karl Dietrich von Ellenrieder, Michael Haller, Rakesh John Amala Arokia Nathan

In vegetated environments, such as forests, exploratory robots play a vital role in navigating complex, cluttered environments where human access is limited and traditional equipment struggles. Visual occlusion from obstacles, such as foliage, can severely obstruct a robot's sensors, impairing scene understanding. We show that "peering", a characteristic sid

Sarunas Kaubrys

This paper studies the Cohomological Donaldson-Thomas theory of loop stacks of $0$-shifted symplectic stacks. In particular, we compare $(-1)$-shifted tangent stacks of these moduli problems, which we view as additive, to loop stacks, which we view as multiplicative, via an exponential map that preserves induced $(-1)$-shifted symplectic structures. As an ap

Hao Wu, Shanchi Wu, Xinyuan Yao, Rui Ni

Rydberg atomic quantum receivers have been seen as novel radio frequency measurements and the high sensitivity to a large range of frequencies makes it attractive for communications reception. However, current implementations of Rydberg array antennas predominantly rely on simple stacking of multiple single-antenna units. While conceptually straightforward,

Chiara Rubaltelli, Marcello Morini, Eugenio Moro, Ilario Filippini

Highly dynamic and mobile applications, such as vehicular networks, require stable connectivity, which is often challenging to achieve. Network densification is a key approach to address this issue and can be achieved cost-effectively through mobile base stations and wireless relaying. However, existing solutions rely on rigid and complex architectures that

Yang Xu, Zuliang Yang, Kai Ming Ting

Trajectory similarity retrieval is an important part of spatiotemporal data mining, however, existing methods have the following limitations: traditional metrics are computationally expensive, while learning-based methods suffer from substantial training costs and potential instability. This paper addresses these problems by proposing Geometric Prototype Tra

Saptarshi Sarkar, Tirthankar Roy Choudhury

Constraining the Epoch of Reionization (EoR) with physically motivated simulations is hampered by the high cost of conventional parameter inference. We present an efficient emulator-based framework that dramatically reduces this bottleneck for the photon-conserving semi-numerical code SCRIPT. Our approach combines (i) a reliable coarse-resolution MCMC to loc

Enrico Pasqualetto

We study the predual of the space of functions of bounded variation defined over a metric measure space $({\rm X},{\sf d},\mathfrak m)$ with $\mathfrak m$ finite. More specifically, for any exponent $p\in(1,\infty)$ we construct an isometric predual of the space ${\rm BV}_p({\rm X})$ of $p$-integrable functions of bounded variation, which we equip with the n

Francisco Alegría, Matías Morales, Claudio Muñoz, Felipe Poblete

Two evolution models based on the generalized Collatz operator are introduced. These models are characterized by coefficients $\alpha$ and $\beta$ in the Collatz dynamics, and are suitably defined. Here, $\alpha=\beta=1$, and $\alpha=3$, $\beta=1$ correspond to the Nollatz and classical Collatz operators, respectively. In general, the first evolution model i

Tarek M. Elgindi

We will discuss various aspects of the incompressible Euler equation. We will discuss, in particular, problems related to the least action principle, the existence of special solutions, the problem of solvability, singularity formation, and asymptotic behavior.

Abbas Tariverdi

Most control systems run on digital hardware with limited communication resources. This work develops self-triggered control for linear systems where sensors update independently (asynchronous measurements). The controller computes an optimal horizon at each sampling instant, selecting which sensor to read over the next several time steps to maximize inter-s

Tommaso Trabocchi, Raffaella Burioni, Lucilla de Arcangelis, Duccio Fanelli

A generalized version of the Wilson-Cowan (WC) model is proposed which accounts for the evolution of the synaptic resources. Adiabatic elimination of the fast variables is performed to yield a simplified framework for the coupled interaction between active excitatory and inhibitory neurons. The latter model is shown to smoothly converge to the benchmark WC m

Ludovic Sacchelli, Alessandro Scagliotti

We discuss the problem of input design for uncertainty reduction in a parameter estimation procedure. Assuming a linear continuous-time control system with noisy measurements, we formulate an objective of variance reduction in a Bayesian Gaussian setting as an optimal control problem and analyze it from a geometric control perspective. The resulting cost fun

Jesus Anaya-Galeana, Orlando Luongo, Hernando Quevedo

We explore and compare two distinct temperature definitions for scalar field inflation in the context of small- and large-field potentials. The first is based on a real gas, fluid-like temperature, $T_{RG}$, while the second corresponds to a relativistic species-like temperature, $T_{RS}$. We derive the fundamental thermodynamic relations for both and analyz

Zihao Liu, Zunnan Xu, Shi Shu, Jun Zhou

This work presents Controllable Layer Decomposition (CLD), a method for achieving fine-grained and controllable multi-layer separation of raster images. In practical workflows, designers typically generate and edit each RGBA layer independently before compositing them into a final raster image. However, this process is irreversible: once composited, layer-le

Yun Lu, Xiaoyu Shi, Hong Xie, Chongjun Xia

This paper revisits fairness-aware interactive recommendation (e.g., TikTok, KuaiShou) by introducing a novel control knob, i.e., the lifecycle of items. We make threefold contributions. First, we conduct a comprehensive empirical analysis and uncover that item lifecycles in short-video platforms follow a compressed three-phase pattern, i.e., rapid growth, t

Goldy Ahuja, Shashikiran Ganesh

Comet 3I/ATLAS, also known as C/2025 N1, was discovered on 2025 July 1 UT by NASA Asteroid Terrestrial-impact Last Alert System (ATLAS), with a v$_{\infty}$ $\sim$ 58 kms$^{-1}$. This is the fastest among the three interstellar objects discovered so far. In this work, we study the interaction of the 3I/ATLAS with Mars, pre-perihelion, and Jupiter post-perihe

Sixuan Zhang, Tsunefumi Mizuno, Tomohisa Kawashima, Chris Done

Cygnus X-1 (Cyg X-1) exhibited a low hard state in 2022, observed by several missions. The IXPE reported that the polarization angle is aligned with the radio jet and gave a polarization degree approximately 4 times higher than the general expectations of $1\%$ through the analysis of the time-integrated data with a simple spectral model, indicating that the

Qing Chang, Zhiming Hu

Comprehensively interpreting human behavior is a core challenge in human-aware artificial intelligence. However, prior works typically focused on body behavior, neglecting the crucial role of eye gaze and its synergy with body motion. We present GazeInterpreter - a novel large language model-based (LLM-based) approach that parses eye gaze data to generate ey

Zhen-Hong Lyu, Rong-Gen Cai, Shao-Jiang Wang, Xiang-Xi Zeng

The recent preference for a dynamical dark energy (DE) from the Dark Energy Spectroscopic Instrument seems to call for interactions between DE and dark matter (DM), either from direct DE-DM interaction or indirect interaction induced by modified gravity. Therefore, an independent probe for these kinds of DE-DM interactions would be appealing from observation

H. R. Paz

High dropout rates in engineering programmes are conventionally attributed to student deficits: lack of academic preparation or motivation. However, this view neglects the causal role of "normative friction": the complex system of administrative rules, exam validity windows, and prerequisite chains that constrain student progression. This paper introduces "T