April 2026 arXiv papers — page 119

Yunhao Wang, Shiyu Zhu, Chensong Hua, Guojing Hu

Magnetic skyrmions are emerging as promising candidates for next-generation information technologies, while the realization of scalable skyrmion lattices with tailored configurations is essential for advancing fundamental skyrmion physics and developing future applications. Here we achieved the controllable generation and regulation of a large-area, highly o

Olga Y. Kushel

The concept of matrix $D$-stability, introduced in 1958 by Arrow and McManus is of major importance due to the variety of its applications. However, characterization of matrix $D$-stability for dimensions $n > 4$ is considered as a hard open problem. In this paper, we propose a recursive delete/zero algorithm for testing matrix $D$-stability. The algorithm g

L. River Spencer, Reagan A. Cardoza, Vijay K. Dubey, Collin E. Haese

Ultrasound imaging tasks such as calibration, inverse parameter estimation, and acquisition design require models that are physically grounded, efficient, and differentiable with respect to meaningful material and system parameters. While full-wave solvers offer high fidelity, they are often too expensive for iterative optimization, and existing ray-based me

Jingyuan Wang, Zhihao Jia, Chenyu Liu, Xinliang Zhou

EEG foundation models (EFMs) achieve strong cross-subject and cross-task generalization through large-scale pretraining and downstream fine-tuning. Through empirical analysis, we observe that (i) task-adapted EFMs provide strong decoding performance but incur substantial overhead when retained as inference backbones, making knowledge distillation a natural r

Juan-Juan Niu, Xu-Chang Zheng, Hong-Hao Ma

In this paper, we analyze the $1 \rightarrow 4$ decay channel for the production of doubly heavy quarkonium, $(b\bar{c})$ or $(c\bar{c})$, via top-quark decays, $t \to (b\bar{c}) + c + c + \bar{s}$ and $t \to (c\bar{c}) + b + c + \bar{s}$, within the framework of nonrelativistic QCD (NRQCD). The dominant contributions are considered in color-singlet S-wave s

Joonho Koh, Haechan Jung, Nayoung Kim, Wook Ko

Data-driven dexterous hand manipulation requires large-scale, physically consistent demonstration data. Simulation and video-based methods suffer from sim-to-real gaps and retargeting problems, while MoCap glove-based teleoperation systems require per-operator calibration and lack portability, as the robot hand is typically fixed to a stationary arm. Portabl

Yeqi Zhou, Songbai Yao, Jingtian Yu, Weijia Qian

This study performs a three-dimensional numerical simulation of the coupled flow field in a hydrogen-air rotating detonation combustor (RDC)-turbine system to evaluate the effectiveness of different film cooling strategies for the turbine blades. The results demonstrate that combining the endwall cooling with leading-edge film cooling effectively reduces bla

Aihua Li

Flow matching retains the generation quality of diffusion models while enabling substantially faster inference, making it a compelling paradigm for generative modeling. However, when applied to language modeling, it exhibits fundamental limitations in representing complex latent distributions with irregular geometries, such as anisotropy and multimodality. T

Blake K Winter, Amanda Taylor Lipnicki

We give a method for constructing an interactive art piece which illustrates two different definitions of the braid groups, along with their faithful action on the free group. The box also demonstrates how all motions of points in the plane can be realized by motions in a single T-shaped subspace of the plane. This helps students and those who are not specia

Tejas Iyer

Consider a generalised preferential attachment tree with attachment function $f$, that is a random tree, where at each time-step a node connects to an existing node $v$ with probability proportional to $f(\mathrm{deg}(v))$, where $\mathrm{deg}(v)$ denotes the degree of the node in the existing tree. We provide a counter-example to a conjecture of the author

Xin-Chen Duan, Yue-Lin Sming Tsai, Ziwei Wang

We study the linear cosmological evolution of inelastic self-interacting dark matter in a two-component dark sector with a small mass splitting, assuming thermal initial conditions for the two species. We derive the coupled background and perturbation equations for inelastic conversion between the two species, considering both power-law and low-velocity satu

Akiyoshi Tsuchiya

It is known that if a Gorenstein simplex of dimension \(d\) and degree \(s\) is not a lattice pyramid, then \(d \leq 2s-1\). In this paper, we study the extremal case \(d=2s-1\). More precisely, we characterize Gorenstein simplices of dimension \(2s-1\) and degree \(s\) which are not lattice pyramids in terms of even binary self-complementary codes. As an ap

Yuchao Li, Fei Chen, Yingke Li, Chuchu Fan

We consider deterministic finite-horizon optimal control problems with a fixed initial state. We introduce an on-line policy iteration method, which, starting from a given policy, however obtained, generates a sequence of cost-improving policies and corresponding trajectories. Each policy produces a trajectory, which is used in turn to generate data for trai

I. Kanowski, J. T. Mendel, E. Wisnioski, N. M. Förster Schreiber

Mergers and interactions can significantly affect the morphological and dynamical properties of galaxies, however the impact of mergers on turbulence at $z > 1$ has not been observationally constrained. In this work we use the interaction strength parameter $Q_P$ to identify likely interacting and isolated galaxies at cosmic noon ($z \sim 1-2$) within the KM

Heng Ping, Peiyu Zhang, Shixuan Li, Wei Yang

LLM-based RTL code generation methods increasingly target both functional correctness and PPA quality, yet existing approaches universally decouple the two objectives, optimizing PPA only after correctness is fully achieved. Whether through sequential multi-agent pipelines, evolutionary search with binary correctness gates, or hierarchical reward dependencie

Hao Wang, Li Zhao, Shuai Deng, Yu-Han Ma

While the geometry of equilibrium states and driven non-equilibrium processes is clearly understood, a geometric description for relaxation towards equilibrium is still lacking. Here, we propose a thermo-geometric measure based on the Rayleigh quotient, reformulating relaxation as a fundamental competition between entropic stiffness and frictional dissipatio

Bekir Can Lütfüoğlu, Javlon Rayimbaev, Sardor Murodov, Jakhongir Kurbanov

We construct an analytic geodesic-optics description of quasinormal ringing, black-hole shadows, strong lensing, and grey-body factors for the static spherical metric introduced in [Bakopoulos, et. al. arXiv:2310.11919]. Working in a weak-hair regime, we derive closed first-order formulas for the photon-sphere radius, orbital frequency, and Lyapunov exponent

Sajedeh Shahbazi, Alexander Pachl, Kathrin Schwer, Patrick Maier

Mechanically isolated defect centers in hexagonal boron nitride are promising coherent quantum emitters, yet spectral instabilities persist, and their spin-related nature remains unclear. Here we investigate a single mechanically isolated quantum emitter in hBN integrated onto a coplanar waveguide. The emitter exhibits exceptionally bright resonant fluoresce

Billel Guelmame, Taoufik Hmidi, Haroune Houamed, Frédéric Rousset

We consider the Euler-Poisson system for ions where the electrons are given by a Maxwell-Boltzmann distribution, and we investigate the existence of one-dimensional periodic traveling waves. More precisely, we first establish the existence of a smooth global branch of bifurcation emanating from a constant equilibrium. We then construct a singular traveling w

Ofir Cohen, Ron Bitton, Asaf Shabtai, Rami Puzis

The incidence of cybersecurity attacks utilizing social engineering techniques has increased. Such attacks exploit the fact that in every secure system, there is at least one individual with the means to access sensitive information. Since it is easier to deceive a person than it is to bypass the defense mechanisms in place, these types of attacks have gaine

Timon Kopka, Sara Tamburello, Luca Oneto, Lindert van Biert

Hydrogen-based zero-emission ships are a key element in the decarbonization of the maritime sector. To strengthen these their economic competitiveness, it is key to drive their costs to a minimum. Current literature mainly focuses on fuel consumption minimization, but there is a lack of explicit consideration of costs arising from cell degradation and optimi

Tingyang Sun, Ting He, I-Hong Hou

As a current trend in Artificial Intelligence (AI), large foundation models are increasingly employed as the core of AI services. However, even after training, serving such models at scale remains a challenging task due to their heavy resource footprints, particularly in terms of GPU memory. While recent works revealed unique characteristics of systems servi

Arda Gulucu, Emre Ozan Polat

Near-infrared (NIR) emitters operating in the 650-900 nm range are highly attractive for imaging and sensing in turbid media; however, cadmium-free InP-based quantum dots (QDs) often suffer from limited brightness due to nonradiative pathways and inefficient photon outcoupling. In particular, heterostructured InP QDs can exhibit band alignments that induce p

Haoran Li, Lihao Mai, Chenhan Xiao, Erik Blasch

As power systems transition toward renewable-rich and inverter-dominated operations, accurate time-domain dynamic analysis becomes increasingly critical. Such analysis supports key operational tasks, including transient stability assessment, dynamic security analysis, contingency screening, and post-fault trajectory evaluation. In practice, these tasks may o

Wenji Fang, Yao Lu, Shang Liu, Jing Wang

Recent advances in large language models (LLMs) have sparked growing interest in automatic RTL optimization for better performance, power, and area (PPA). However, existing methods are still far from realistic RTL optimization. Their evaluation settings are often unrealistic: they are tested on manually degraded, small-scale RTL designs and rely on weak open

Yuqi Chen, Weihan Zhang, Xin Huang

Public-private graph, where a public network is visible to everyone and every user is also associated with its own small private graph accessed by itself only, widely exists in real-world applications of social networks and financial networks. Most existing work on community search, finding a query-dependent community containing a given query, only studies o

Abdelrahman Emad Abdelazim, Alan Rodrigo Diaz-Rizo, Hassan Aboushady, Haralampos-G. Stratigopoulos

Covert channels (CCs) in wireless chips pose a serious security threat, as they enable the exfiltration of sensitive information from the chip to an external attacker. In this work, we propose an AI-based defense mechanism deployed at the RF receiver, where the model directly monitors raw I/Q samples to detect, in real time, the presence of a CC embedded wit

Yuting Zeng, Zhiwen Zheng, Jingya Wang, You Zhou

Safe and efficient assistive planning for visually impaired scenarios remains challenging, since existing methods struggle with multi-objective optimization, generalization, and interpretability. In response, this paper proposes a Momentum-Constrained Hybrid Heuristic Trajectory Optimization Framework (MHHTOF). To balance multiple objectives of comfort and s

Oliver Thim, Andreas Ekström, Christian Forssén

We predict ground-state energies of $^3$H, $^4$He, and $^6$Li in chiral effective field theory up to next-to-next-to-next-to-leading-order (N$^3$LO) using a power counting guided by renormalization-group invariance. Subleading two-nucleon interactions are treated perturbatively, and for $^4$He and $^6$Li, we calculate the perturbative corrections from numeri

Yomna Elsayed, Cecily Jones

As companies enter the race for agentic AI adoption, fears surface around agentic autonomy and its subsequent risks. These fears compound as companies scale their agentic AI adoption with low-code applications, without a comparable scaling in their governance processes and expertise resulting in a phenomenon known as "Agent Sprawl". While shadow AI tools can

Rushi Chambyal, Tapender, Labh Singh, Surender Verma

In the present work we investigate the phenomenological implications of a vanishing effective Majorana neutrino mass within a $3+1$ neutrino framework adding a eV-scale sterile neutrino beside three active neutrino states in light of latest cosmology driven bounds on sum of neutrino masses ($\sum_{i}m_i$). We explore the parameter space where the destructive

Ranran Shen, Xiaoyi Zhu, Pan Peng, Zengfeng Huang

We study the problem of designing \emph{sublinear spectral clustering oracles} for well-clusterable graphs. Such an oracle is an algorithm that, given query access to the adjacency list of a graph $G$, first constructs a compact data structure $\mathcal{D}$ that captures the clustering structure of $G$. Once built, $\mathcal{D}$ enables sublinear time respon

Debodeep Banerjee, Burcu Sayin, Stefano Teso, Andrea Passerini

Building on recent advances in AI, hybrid decision making (HDM) holds the promise of improving human decision quality and reducing cognitive load. We work in the context of learning to guide (LtG), a recently proposed HDM framework in which the human is always responsible for the final decision: rather than suggesting decisions, in LtG the AI supplies (textu

Matthias Keller, Daniel Lenz, Marcel Schmidt

We survey recent results on graphs and their Laplacians related to the behavior of the graph at large. In particular, we focus on Liouville theorems, recurrence and characterizations of Dirichlet forms via boundary terms.

Felix Joos, Xinyue Xie

Suppose $G$ is a $k$-uniform hypergraph on $n$ vertices such that every $(k-1)$-subset $S$ of $V(G)$ belongs to at least $\delta n$ edges, where $\delta> 1/2$. Let $\Psi(G)$ denote the number of tight Hamilton cycles in $G$, that is, cyclic orderings of $V(G)$ in which every $k$ consecutive vertices form an edge. We prove that $\log\Psi(G)\ge kh(G)-n\log{n\c

Luca Claude Gino Lebon, Johan Lindberg, Claudio Altafini

In this paper, we identify the smallest set of control input nodes and an associated output feedback law that achieves complete disturbance decoupling for a class of coupled oscillator networks. The focus is specifically on systems linearized around a stable phase-locked synchronized state. The proposed theoretical framework is applied to the linearized swin

Mani Lokamani, Gustav Bihlmayer, Gregor Michalicek, Daniel Wortmann

Non-van der Waals two-dimensional (2D) materials derived from strongly bonded non-layered crystals have recently emerged as a novel and rising platform for nanoscale research. While uncovering and tuning their (opto-)electronic, catalytic, and magnetic properties has been the focus of intense research, the impact of spin-orbit coupling (SOC) onto their elect

Xuelin Xie, Xiliang Lu

To obtain more accurate model parameters and improve prediction accuracy, we proposed a regularized Kriging model that penalizes the hyperparameter theta in the Gaussian stochastic process, termed the Theta-regularized Kriging. We derived the optimization problem for this model from a maximum likelihood perspective. Additionally, we presented specific implem

Jean-Bastien Grill, Michal Valko, Rémi Munos

You are a robot and you live in a Markov decision process (MDP) with a finite or an infinite number of transitions from state-action to next states. You got brains and so you plan before you act. Luckily, your roboparents equipped you with a generative model to do some Monte-Carlo planning. The world is waiting for you and you have no time to waste. You want

Hongbin Liu, Zhengyuan Jiang, Cheng Hong, Neil Zhenqiang Gong

A vision foundation model outputs an embedding vector for an image, which can be affected by common editing operations (e.g., JPEG compression, brightness, contrast adjustments). These common perturbations alter embedding vectors and may impact the performance of downstream tasks using these embeddings. In this work, we present the first systematic study on

Zhen Tao, Riwei Lai, Chenyun Yu, Weixin Chen

Large language model (LLM) based recommendation agents personalize what they know through evolving per-user semantic memory, yet how they reason remains a universal, static system prompt shared identically across all users. This asymmetry is a fundamental bottleneck: when a recommendation fails, the agent updates its memory of user preferences but never inte

Sahoko Ishida, Mohammed Osman, Ziyao Cui, Uchenna Agu

Inadequate dietary micronutrient intake is a significant risk factor for deficiency and remains a major global health challenge. Nutrition programmes and interventions are most effective when targeted to populations at greatest risk. Household Consumption and Expenditure Surveys (HCES) are a widely available source of dietary data; however, they are often no

Jason Liartis, Eirini Kaldeli, Lambrini Gyftokosta, Eleftherios Chelioudakis

Hate, derogatory, and offensive speech remains a persistent challenge in online platforms and public discourse. While automated detection systems are widely used, most focus on censorship or removal, raising concerns for transparency and freedom of expression, and limiting opportunities to explain why content is harmful. To address these issues, explanatory

Andrew Dai, Boris Meinardus, Ciaran Regan, Yingtao Tian

Frontier model developers aim to train models continually to possess emergent, diverse capabilities. To extend capabilities, the current pre-training and post-training paradigm requires manually starting training runs with static datasets or reward functions every time. Addressing this limitation, our work pursues the insight that open-endedness (via the coe

Nicolas Zalot, Ekaterina Sokolova-Lapa, Aafia Zainab, Philipp Thalhammer

The X-ray spectra of neutron stars with moderate magnetic fields ($B\sim 10^{12}$ G) in high-mass X-ray binaries (HMXBs) at low X-ray luminosities ($L_\mathrm{X}\lesssim 10^{35}$ erg/s) are characterized by a double humped shape. This shape has been explained either as the radiation from a two-temperature magnetized atmosphere, where thermal radiation domina

Philipp Hacker, Matthias Holweg

The current advancement in and deployment of agentic AI systems has created a set of key challenges for the legal frameworks that govern their use. We cover two central components: first, the regulatory classification of agents under the EU AI Act, and second, the legal status and validity of autonomous actions within the established framework of EU contract

Jun Wang, Shuo Tan, Zelong Sun, Tiancheng Gu

Retrieval-Augmented Generation (RAG) extends Large Vision-Language Models (LVLMs) with external visual knowledge. However, existing visual RAG systems typically rely on generic retrieval signals that overlook the fine-grained visual semantics essential for complex reasoning. To address this limitation, we propose UniDoc-RL, a unified reinforcement learning f

Attila Egri-Nagy, Chrystopher L. Nehaniv

Computational power can be measured by assigning an algebraic structure to a computational device. Here, we convert a small patch of Conway's Game of Life into a transformation semigroup. The conversion captures not only time evolution but also interactive operations. In this way, the cellular automaton becomes directly programmable. Once this measurement is

Yongbo Chen, Hesheng Wang, Shoudong Huang, Hanna Kurniawati

Efficiently locating target objects in complex indoor environments with diverse furniture, such as shelves, tables, and beds, is a significant challenge for mobile robots. This difficulty arises from factors like localization errors, limited fields of view, and visual occlusion. We address this by framing the object-search task as a highdimensional Partially

Cunyi Nan

In this paper, we investigate induced and nonlinear fiber topological pressure for random dynamical systems. We define a non-averaged induced fiber pressure via spanning and separated sets, characterize it as the pseudo-inverse of the classical fiber topological pressure studied previously, and establish the corresponding variational principle. We also defin

Hamid Ohadi

We demonstrate unconventional photon blockade in a symmetric Kerr dimer driven with equal-amplitude fields at a $90^\circ$ phase difference. The minimum inter-cavity coupling is $J_{\min} = γ/4$ at a Kerr nonlinearity $U \ll γ$ achievable in standard photonic molecules. The quadrature-driven site emits strongly antibunched light with a smooth, oscillation-fr

Jian Gao, Lufeng Zhang, Ping Fang, Pu Ke

Fluctuation theorems show how coarse graining transforms microscopic symmetry into observable irreversibility. Here we ask whether an analogous symmetrybased diagnostic can be constructed for financial markets. At the microscopic level, each transaction pairs a buyer and a seller, whereas trading decisions are typically made from coarse-grained price histori

Maksim Pershin, Ivan Golovanov, Pavel Baltabaev, Natalia Trankova

Contextual bandit algorithms suffer from high regret during cold-start, when the learner has insufficient data to distinguish good arms from bad. We propose augmenting Disjoint LinUCB with LLM pseudo-observations: after each round, a large language model predicts counterfactual rewards for the unplayed arms, and these predictions are injected into the learne

E. M. M., Kivits, Paul M. J. Van den Hof

Dynamic networks consist of interconnected dynamical systems. The subsystems can be viewed as transformations of input signals into output signals, where signals flow from one system into another through interconnections. The signal flows represent directions of information flow, thus a dynamic network can be visualised by a directed graph. In contrast, natu

Takumi Suzuki, Takaya Hoshi, Akito Kawasaki, Shotaro Oki

Light's intrinsic carrier frequency of hundreds of terahertz theoretically enables information processing at terahertz clock rates. In optical quantum computing, continuous-variable quantum teleportation is the fundamental building block for deterministic logic operations. This protocol transfers unknown quantum states between nodes using quantum entanglemen

Meijia Wang, Guochao Wang, Haozhen Chu, Bin Yao

Few-shot fine-grained image classification aims to recognize subcategories with high visual similarity using only a limited number of annotated samples. Existing metric learning-based methods typically rely solely on spatial domain features. Confined to this single perspective, models inevitably suffer from inherent texture biases, entangling essential struc

Pablo Benlloch, Oscar Romero, Antonio Leon, Jaime Lloret

Network security is a critical concern in the digital landscape of today, with users demanding secure browsing experiences and protection of their personal data. This study explores the dynamic integration of Machine Learning (ML) algorithms with Software-Defined Networking (SDN) controllers to enhance network security through adaptive decision mechanisms. T

Wenhao Wang, Haoting Shi, Mengying Yuan, Yiquan Lin

Training GUI agents with traditional centralized methods faces significant cost and scalability challenges. Federated learning (FL) offers a promising solution, yet its potential is hindered by the lack of benchmarks that capture real-world, cross-platform heterogeneity. To bridge this gap, we introduce FedGUI, the first comprehensive benchmark for developin

Xudong Gao, Chuanneng Luo, Mengxue Tao, Haijiang Yu

This work presents a compact, low-crosstalk CWDM4 MUX/DEMUX utilizing cascaded multimode waveguide grating filters. The individual filters are designed with finite Gaussian apodization and positive dispersion, enabling strong unilateral sidelobe suppression while maintaining a minimum feature size compatible with UV lithography. By cascading these filters, w

Hassan Ali, Doreen Jirak, Luca Müller, Stefan Wermter

Gesture recognition research, unlike NLP, continues to face acute data scarcity, with progress constrained by the need for costly human recordings or image processing approaches that cannot generate authentic variability in the gestures themselves. Recent advancements in image-to-video foundation models have enabled the generation of photorealistic, semantic

W. Knafo, T. Thebault, K. Somesh, G. Lapertot

The question of a possible quantum critical point lying inside of a superconducting phase is central for understanding unconventional superconductivity. In various unconventional superconductors, non-Fermi-liquid/'strange-metal' $T^{n}$ variations, with $n<2$, of the electrical resistivity have been identified as the signature of magnetic quantum criticality

Gabriele Mattioli, Evelyn Turri, Sara Sarto, Lorenzo Baraldi

Tool learning with foundation models aims to endow AI systems with the ability to invoke external resources -- such as APIs, computational utilities, and specialized models -- to solve complex tasks beyond the reach of standalone language generation. While recent advances in Large Language Models (LLMs) and Multimodal Large Language Models (MLLMs) have expan

Xiao-Wen Huo, Jun-Hong An, Peng-Bo Li

Levitated mesoscopic particles hold the promise of revolutionizing gravity sensing by using quantum effects. However, conventional quantum gravimeters based on such systems fail to harness the intrinsic large-mass advantage of the particles, because their commonly utilized auxiliary quantum systems counteract the role of mass as a resource. To overcome this

Diego Berti, Davide Polimeni, Susanna Terracini

We investigate expansive solutions of the $N$-body problem in $\mathbb{R}^d$ ($d\ge2$) driven by homogeneous Newtonian potentials of degree $-\alpha$. We establish the existence of half-entire expansive motions with prescribed initial configuration and asymptotic direction for a wide range of homogeneity exponents $\alpha$. Our approach is variational and re

Gakuto Watanabe, Soichiro Yamane, Ryotaro Maki, Atsutoshi Ikeda

Altermagnets are a new class of magnets accompanying global time-reversal symmetry breaking (TRSB) without net magnetization. The TRSB results in formation of novel altermagnetic domains. Features of altermagnetic domains, in particular their responses to external stimuli, are essentially important but yet unexplored. Here, we report visualization of bulk al

Wen-Hui Nie, Mei-Yu Zhang, Lin-Cheng Wang, Chong Li

Floquet dynamical quantum phase transitions (FDQPTs) reveal many nonequilibrium critical phenomena in periodically driven quantum systems, and their underlying mechanisms have attracted deep attention in recent years. In this paper, we consider an extended XY spin chain under a periodic flux-quench protocol, and demonstrate the effect of the flux difference

Corentin Correia, Vincent Dumoncel

We focus on halo products, a class of groups introduced by Genevois and Tessera, and whose geometry mimics lamplighters. Famous examples are lampshufflers. Motivated by their work on the classifications up to quasi-isometry of these groups, we initiate a more quantitative study of their geometry. Indeed, it follows from the work of Delabie, Koivisto, Le Ma\^

Sinan Wang, Qi Zhang, Jiacheng Li, Lili Wei

Android apps are built on APIs that abstract core Android system functionalities. These APIs are officially documented in multiple files distributed with the Android source code or SDK, which we collectively refer to as Android API Lists (AALs). Prior Android research has relied on specific AALs, often treating them as interchangeable ground truth. However,

Attila Egri-Nagy

Programming is about automation in a wide variety of domains. Developing itself is one of those. As a side-effect, progress in automated coding may make people less willing to learn computer programming. This could become an issue, if the skill of computational problem solving is not only for the immediate economic benefit, but an important part of our knowl

Shivank Garg, Sankalp Mittal, Manish Gupta

Communicating complex system designs or scientific processes through text alone is inefficient and prone to ambiguity. A system that automatically generates scientific architecture diagrams from text with high semantic fidelity can be useful in multiple applications like enterprise architecture visualization, AI-driven software design, and educational conten

Enrique Otarola, Abner J. Salgado

We analyze an optimal control problem with pointwise tracking for a fractional semilinear elliptic partial differential equation. The diffusion is characterized by the spectral fractional Laplacian $(-\Delta)^s$ with $s \in (1/2,1)$, a range that guarantees the well-posedness of point evaluations of the state. In addition to the nonconvexity of the control p

Jianning Fu

Let $\mu_{\lambda}$ be the Bernoulli convolution measure with parameter $\lambda\in(0,1)$. We study the regularity of the function %We prove that $h=h_{\phi}:\lambda\mapsto \int_{\mathbb{R}}\phi(x)\,d\mu_{\lambda}(x)$ for H\"older observables $\phi$. We describe sufficient conditions for both smoothness and non smoothness of this function. In particular, we

Bo-Rui Li, Song He, Yu-Xiao Liu

Quasi-primary correlators in two-dimensional conformal field theories deformed simultaneously by $T\bar T$ and root-$T\bar T$ are studied. A path-integral formulation motivated by the geometric realization of the combined deformation is used to develop a geometric framework for evaluating the deformed correlators. Within this framework, the two-point functio

Jan Kren, Bojan Ničeno, Yohei Sato

Unstructured meshes are among the most versatile approaches for capturing non-canonical geometries in fluid dynamics simulations. Despite this, most high-fidelity first-principles phase-change models are developed and applied on structured meshes. We present a phase-change simulation method for unstructured meshes that combines the algebraic Volume-of-Fluid

Jacek Krajczok, Piotr. M. Sołtan

We continue the study of the braided compact quantum group $\mathrm{SU}_q(2)$ for complex $q$ satisfying $0<|q|<1$ introduced by Kasprzak, Meyer, Roy and Woronowicz (J. Noncommut. Geom. 10(4):1611-1625, 2016). We address such aspects as existence of the Haar measure, construct the scaling group, the antipode and its polar decomposition and describe the relat

Dong Wang, Sophie Bodek, Nicholas T. Ouellette, Mark D. Shattuck

Interactions between fluids and granular materials are prevalent on the Earth's surface. In the case of fluid flow over a sediment bed, the fluid imparts a shear stress to the granular materials. When the applied shear stress is above a critical value, the grains become entrained in the fluid flow. Prior experimental studies have shown that granular beds sub

Priyanka Sharma, Manoj K Mishra, Devendra Kumar Mishra

In a quantum LiDAR system, to achieve a better resolution and sensitivity, detection scheme plays an important role. We propose a novel detection scheme in which the photo detector considers only the $4n$ number of photons, where $n \in \mathbb{N}$, as a click and the rest of them as a no-click. Similar to the $Z$-detection scheme, where we get a click for a

Jingxuan Liu, Zhi Qu, Jin Tei, Hidetaka Kamigaito

Automatic evaluation metrics are essential for building multilingual translation systems. The common practice of evaluating these systems is averaging metric scores across languages, yet this is suspicious since metrics may suffer from cross-lingual scoring bias, where translations of equal quality receive different scores across languages. This problem has

Xu Dong, Wanqing Li, Anthony Adeyemi-Ejeye, Andrew Gilbert

Skeleton-based human action recognition is a powerful approach for understanding human behaviour from pose data, but collecting large-scale, diverse, and well-annotated 3D skeleton datasets is both expensive and labor-intensive. To address this challenge, we propose a conditional generative pipeline for data augmentation in skeleton action recognition. Our m

Yifu Chen, Shengpeng Ji, Qian Chen, Tianle Liang

End-to-end spoken dialogue models have garnered significant attention because they offer a higher potential ceiling in expressiveness and perceptual ability than cascaded systems. However, the intelligence and expressiveness of current open-source spoken dialogue models often remain below expectations. Motivated by the success of online reinforcement learnin

Nico Meyer, Christopher Mutschler, Dominik Seuß, Andreas Maier

Concatenating quantum error correction codes scales error correction capability by driving logical error rates down double-exponentially across levels. However, the noise structure shifts under concatenation, making it hard to choose an optimal code sequence. We automate this choice by estimating the effective noise channel after each level and selecting the

Hang Lv, Sheng Liang, Hongchao Gu, Wei Guo

Information Extraction aims to distill structured, decision-relevant information from unstructured text, serving as a foundation for downstream understanding and reasoning. However, it is traditionally treated merely as a terminal objective: once extracted, the resulting structure is often consumed in isolation rather than maintained and reused during multi-

Jeremy Brazas, Atish Mitra

In this paper, we study homotopical analogues of the Cantor-Bendixson derivative. For each $n\geq 0$, the "$\pi_n$-wild set" $\mathbf{w}_n(X)$ of a topological space $X$ is the subspace of $X$ consisting of the points at which there exists a shrinking sequence of essential based maps $S^n\to X$. Since the operator $\mathbf{w}_n$ permits iteration, every give

G. Aytug Akarlar

We present causal evidence that hallucination in autoregressive language models is an early trajectory commitment governed by asymmetric attractor dynamics. Using same-prompt bifurcation, in which we repeatedly sample identical inputs to observe spontaneous divergence, we isolate trajectory dynamics from prompt-level confounds. On Qwen2.5-1.5B across 61 prom

Subhra Mondal, Amitava Choudhuri

In this article, our goal is to investigate the cosmological dynamics and structure formation in a modified cosmological framework inspired by a generalized mass-to-horizon entropy relation and consistent with the Clausius relation. Invoking the gravity-thermodynamics conjecture leads to alterations in the Friedmann equations as well as Hubble parameter evol

Neel Kelkar, Simon Niedermayr, Klaus Engel, Rüdiger Westermann

We introduce a hybrid Gaussian-hash-grid radiance representation for reconstructing 2D Gaussian scene models from multi-view images. Similar to NeST splatting, our approach reduces the entanglement between geometry and appearance common in NeRF-based models, but adds per-Gaussian latent features alongside hash-grid features to bias the optimizer toward a sep

Shen Fan, Mikołaj Kida, Przemyslaw Musialski

Many CAD learning pipelines discretize Boundary Representations (B-Reps) into triangle meshes, discarding analytic surface structure and topological adjacency and thereby weakening consistent instance-level analysis. We present STEP-Parts, a deterministic CAD-to-supervision toolchain that extracts geometric instance partitions directly from raw STEP B-Reps a

M. A. Shishkin

We present a meshless numerical method for simulating the dynamics of axisymmetric vesicles in a viscous medium. Key innovations include: (1) adaptive reparameterization based on local length scales, reducing the number of required harmonics; (2) gauge dynamics for maintaining optimal parameterization; (3) error control near the symmetry axis; and (4) spectr

Dongsheng Wang, Jinsen Zhang, Dawei Su, Hui Huang

Recently, sparse autoencoders (SAEs) have emerged as a promising technique for interpreting activations in foundation models by disentangling features into a sparse set of concepts. However, identifying the optimal level of sparsity for each neuron remains challenging in practice: excessive sparsity can lead to poor reconstruction, whereas insufficient spars

Tomasz Służalec, Marcin Łoś, Askold Vilkha, Maciej Paszyński

We explore the possibility of solving Partial Differential Equations (PDEs) using discrete weak formulations. We propose a programming environment for defining a discrete computational domain, introducing discrete functions defined over a set of points, constructing discrete inner products, and introducing discrete weak formulations employing Kronecker delta

Yang Liu, Alexander Schied, Zhenyu Shen

In continuous-time portfolio selection for non-concave utility functions, the martingale duality approach is widely adopted in complete markets, while the dynamic programming approach may sometimes lead to singular solutions of the Hamilton-Jacobi-Bellman (HJB) equation. We propose "dynamic Lagrange multipliers" in a non-concave utility framework, bridging t

Bowen Ping, Zijun Chen, Tingfeng Hui, Qize Yu

Reinforcement Learning (RL) has emerged as a critical driver for enhancing the reasoning capabilities of Large Language Models (LLMs). While recent advancements have focused on reward engineering or data synthesis, few studies exploit the model's intrinsic representation characteristics to guide the training process. In this paper, we first observe the prese

Yifu Chen, Shengpeng Ji, Zhengqing Liu, Qian Chen

Achieving seamless, human-like interaction remains a key challenge for full-duplex spoken dialogue models (SDMs). Reinforcement learning (RL) has substantially enhanced text- and vision-language models, while well-designed reward signals are crucial for the performance of RL. We consider RL a promising strategy to address the key challenge for SDMs. However,

Annika Tjabben, Carolin Conrad, Hans D. Schotten

Reliable communication in confined environments, such as blood vessels or industrial pipelines, remain challenging due to signal attenuation and limited sensor accessibility. Therefore, this work investigates microbubbles as robust information carriers within the Internet of Bio-Nano Things (IoBNT) paradigm, leveraging their established use as ultrasound con

Jannis Fischer, Alejandro Jurado, Timo Betz

Recent advances in the field of active soft matter promise a lot. Both, experimental advances and theoretical understanding point towards new material classes in reach, for example self-healing materials that might switch their properties from elastic to solid easily or switch their macroscopic shapes. All these materials require an active force to propel pa

Julian Jeggle, Matthias Rüschenbaum, Adrian Paskert, Ivan Kalthoff

Active colloidal microparticles require reliable actuation to sustain directed motion. Light-based propulsion is particularly attractive as it provides persistent energy supply and enables direct spatiotemporal control. Here, we introduce 3D-printable particles with symmetry-broken refractive index profiles (SBRIP particles) that achieve propulsion through d

Nuno J. Alves, José Miguel Urbano

We consider stationary $p$-Schr\"odinger equations on the whole space with integrable data and potentials that are confining in measure. We introduce asymptotic energy solutions in an asymptotic $L^p$ framework and establish existence and uniqueness in the degenerate range $p\ge2$. The proof relies on a new Rellich$\unicode{x2013}$Kondrachov-type compactness

Luca Barletta, Alex Dytso

We study the amplitude-constrained additive white Gaussian noise (AWGN) channel from the perspective of near-optimal input distributions. While it is known that the capacity-achieving input is discrete with finitely many mass points, the precise scaling of its support size as a function of the amplitude constraint remains an open problem. In this work, we in

Markus Banagl

If a finite group $G$ acts on a rational homology manifold, then the orbit space is well-known to be a rational homology manifold again. We consider here actions on spaces that may be much more singular. If the $G$-space is a Witt pseudomanifold, which includes all arbitrarily singular complex pure-dimensional algebraic varieties, then we prove that the orbi

Yuxuan Xiao, Hao Shen, Junyu Guo, Dingkang Wang

We formalize the Wu-Ritt characteristic set method for the triangular decomposition of polynomial systems in the Lean 4 theorem prover. Our development includes the core algebraic notions of the method, such as polynomial initials, orders, pseudo-division, pseudo-remainders with respect to a polynomial or a triangular set, and standard and weak ascending set