April 2026 arXiv papers — page 130

Li Chen

The next generation of autonomous AI systems will be constrained not only by model capability, but by how intelligence is structured across heterogeneous hardware. Current paradigms -- cloud-centric AI, on-device inference, and edge-cloud pipelines -- treat planning, reasoning, and execution as a monolithic process, leading to unnecessary latency, energy con

Zhijie Bao, Fangke Chen, Licheng Bao, Chenhui Zhang

The potential of Multimodal Large Language Models (MLLMs) in domain of medical imaging raise the demands of systematic and rigorous evaluation frameworks that are aligned with the real-world medical imaging practice. Existing practices that report single or coarse-grained metrics are lack the granularity required for specialized clinical support and fail to

F. Miguel Marqués

The interaction of neutrons and nuclei at low energies may potentially lead to scattering lengths several orders of magnitude larger than the effective range of the interaction, well beyond the nuclear scale. If such cases existed, they could lead to the observation of the Efimov effect in nuclei, a remarkable universal phenomenon that has been observed only

Ema Češek, Jan Grošelj, Andrej Kolar-Požun, Maruša Lekše

The paper is concerned with three types of cubic splines over a triangulation that are characterized by three degrees of freedom associated with each vertex of the triangulation. The splines differ in computational complexity, polynomial reproduction properties, and smoothness. With the aim to make them a versatile tool for numerical analysis, a unified repr

Laura Grazioli, Yukuan Hu, Tommaso Nottoli, Filippo Lipparini

The computation of excited states within the Complete Active Space Self-Consistent Field (CASSCF) framework remains a significant challenge in quantum chemistry, both theoretically and algorithmically. In this work, we extend the K\"ahler manifold formalism introduced in Part I of this series to the CASSCF theory, and draw a geometrical connection from the t

Hongjin Xiong, Teng Ma

The physical properties of matter are typically described by coefficient matrices governed by crystal symmetry. Applying spatial operations, such as rotation, inversion, and mirror, to these matrices provides an effective approach for investigating material properties. However, the diversity of coefficient matrix types complicates their transformation via si

Federico Pisani, Leonie Spitz, Libor Vojáček, Flaviano José dos Santos

On the textbook example of an isolated antiferromagnetic Heisenberg dimer, we demonstrate that the magnetic form factor and the magnetic electron density distribution can be extracted from the momentum-dependence of the inelastic neutron scattering (INS) intensity of a magnetic excitation. We measure the three-dimensional (3D) magnetic structure factor of th

Geoffrey Powell

The purpose of this paper is to investigate the relationship between hairy graph complexes associated to cyclic operads and their counterparts for operads (and, more generally, dioperads). This is based on the author's interpretation of these as Koszul complexes for the associated modules over the respective appropriate twisted downward (walled) Brauer categ

Ziling Ma, Ángel López Oriona, Hernando Ombao, Ying Sun

We study adaptive pooling under predictive heterogeneity in high-dimensional multivariate time series forecasting, where global models improve statistical efficiency but may fail to capture heterogeneous predictive structure, while naive specialization can induce negative transfer. We formulate adaptive pooling as a statistical decision problem and propose a

Zhaoxiang Wu, Miao He, Wuming Luo, Ziyan Deng

JUNO is designed to determine the neutrino mass ordering with an energy resolution of 3% at 1 MeV. In the real detector, however, deformations of the central stainless-steel structure during installation lead to deviations of the photomultiplier tube (PMT) positions from their design values. Based on the limited survey data of the PMTs and the stainless-stee

Jie Liang, Jiahao Wu, Chao Wang, Jiayu Yang

Dynamic 3D scene reconstruction is essential for immersive media such as VR, MR, and XR, yet remains challenging for long multi-view sequences with large-scale motion. Existing dynamic Gaussian approaches are either Frame-Stream, offering scalability but poor temporal stability, or Clip, achieving local consistency at the cost of high memory and limited sequ

Özlem Tuğfe Demir, Emil Björnson

Network-controlled repeaters (NCRs) are a low-cost means to extend coverage and strengthen macro diversity in wireless networks. They operate in real time by amplifying and re-transmitting the incoming signal with only hardware-level delays, without requiring any channel state information (CSI) at the repeater itself. However, their power amplifiers (PAs) ge

Kexin Zhang, Tao Chen, Jinlong Ma, Puqing Jiang

Multilayered thin-film structures are frequently encountered in industrial applications, where accurate thermal property characterization is essential for performance optimization. These films, typically ranging from nanometers to micrometers in thickness, often exhibit anisotropic thermal conductivity and non-bulk heat capacity, which are challenging to mea

Sungho Moon, Daegyu Han, Hera Koo, Sangeun Chae

Disaggregated storage systems improve resource utilization and enable independent scaling of storage and compute resources by separating storage resources from computing resources in data centers. NVMe over fabrics (NVMeoF) is a key technology that underpins the functionality and benefits of disaggregated storage systems. While NVMeoF inherently possesses su

Tao Chen, Puqing Jiang

Accurate characterization of heat transport across solid-liquid interfaces is essential for thermal management in micro and nanoscale systems. Yet existing techniques often require prior knowledge of liquid properties, which complicates the simultaneous resolution of interfacial and bulk behaviors, and lose sensitivity once interfacial conductance exceeds 10

Tao Chen, Puqing Jiang

Solid-liquid interfacial thermal conductance (ITC) critically influences heat transport in microfluidic, electronic, and energy systems, yet most optical thermometry techniques are limited to specific metal-liquid interfaces. In this work, we introduce a universal broadband square-pulsed thermometry method that enables simultaneous quantification of ITC acro

Tomáš Kocák, Gergely Neu, Michal Valko

We propose a new partial-observability model for online learning problems where the learner, besides its own loss, also observes some noisy feedback about the other actions, depending on the underlying structure of the problem. We represent this structure by a weighted directed graph, where the edge weights are related to the quality of the feedback shared b

Tomas Kocak, Michal Valko, Remi Munos, Shipra Agrawal

Thompson Sampling (TS) has attracted a lot of interest due to its good empirical performance, in particular in the computational advertising. Though successful, the tools for its performance analysis appeared only recently. In this paper, we describe and analyze SpectralTS algorithm for a bandit problem, where the payoffs of the choices are smooth given an u

Pierre Perrault, Vianney Perchet, Michal Valko

We investigate stochastic combinatorial semi-bandits, where the entire joint distribution of outcomes impacts the complexity of the problem instance (unlike in the standard bandits). Typical distributions considered depend on specific parameter values, whose prior knowledge is required in theory but quite difficult to estimate in practice; an example is the

Yifeng Zhou, Yuehong Hu, Zhixiang Feng, Junwei Pan

Recommender systems have historically developed along two largely independent paradigms: feature interaction models for modeling correlations among multi-field categorical features, and sequential models for capturing user behavior dynamics from historical interaction sequences. Although recent trends attempt to bridge these paradigms within shared backbones

Yun-Tak Oh, Dongsoo Lee, Jungyoul Park, Kyung Chul Jeong

This work identifies a necessary condition for any variational quantum approach to reach the exact ground state. Briefly, the norms of the projections of the input and the ground state onto each group module must match, implying that module weights of the solution state have to be known in advance in order to reach the exact ground state. An exemplary case i

Sara Albert-Niclòs, Esther Cabezas-Rivas

We study area- and length-preserving curvature flows for embedded closed curves on pinched Hadamard surfaces. In the variable-curvature setting, the evolution equations contain additional lower-order terms, so the PDE analysis requires refined comparison arguments and delicate curvature estimates. For smooth convex initial curves, we prove preservation and i

Petteri Harjulehto, Ritva Hurri-Syrjänen

We study Poincare-Sobolev type inequalities for compactly supported smooth functions which are defined in the Euclidean $n$-space and whose absolute value of gradient are Choquet $\delta /n$-integrable with respect to the $\delta$-dimensional Hausdorff content, $n\geq 2$, $\delta\in (0,n]$. In particular, our results imply a new Sobolev inequality for quasic

Yuanlei Zheng, Pei Fu, Hang Li, Ziyang Wang

Multi-page Document Visual Question Answering requires reasoning over semantics, layouts, and visual elements in long, visually dense documents. Existing OCR-free methods face a trade-off between capacity and precision: end-to-end models scale poorly with document length, while visual retrieval-based pipelines are brittle and passive. We propose Doc-$V^*$, a

Megan Smith, Venkatesh Thirugnana Sambandham, Florian Richter, Laura Crompton

Text-to-Image (T2I) generation models have been widely adopted across various industries, yet are criticized for frequently exhibiting societal stereotypes. While a growing body of research has emerged to evaluate and mitigate these biases, the field at present contends with conceptual ambiguity, for example terms like "bias" and "fairness" are not always cl

Muhammad Ahmed Ullah Khan, Muhammad Haris Bin Amir, Didier Stricker, Muhammad Zeshan Afzal

Continual learning enables models to acquire new knowledge over time while retaining previously learned capabilities. However, its application to text-to-3D generation remains unexplored. We present ReConText3D, the first framework for continual text-to-3D generation. We first demonstrate that existing text-to-3D models suffer from catastrophic forgetting un

Nele Tornow, Paul Herrmann, Clemens Schneider, Ferdinand Evers

Light-matter interactions are governed by conservation laws of energy and momentum. For harmonic generation in crystalline solids, energy conservation imposes that $m$ incoming photons with energy $\hbar \omega_0$ are combined to form one photon at energy $m\hbar \omega_0$. Linear momentum conservation governs phase matching, whereas angular momentum conserv

Edoardo Pona, Milad Kazemi, Mehran Hosseini, Yali Du

Monitoring LLM safety at scale requires balancing cost and accuracy: a cheap latent-space probe can screen every input, but hard cases should be escalated to a more expensive expert. Existing cascades delegate based on probe uncertainty, but uncertainty is a poor proxy for the utility of an expert call, as it ignores whether the expert would actually improve

Harishkumar Kishorkumar Prajapati

We present a hybrid retrieval system for COVID-19 scientific literature, evaluated on the TREC-COVID benchmark (171,332 papers, 50 expert queries). The system implements six retrieval configurations spanning sparse (SPLADE), dense (BGE), rank-level fusion (RRF), and a projection-based vector fusion (B5) approach. RRF fusion achieves the best relevance (nDCG@

Michael Richter

AI leaders and safety reports increasingly warn that advances in model reasoning may enable biological misuse, including by low-expertise users, while major labs describe safeguards as expanding but still evolving rather than settled. This study benchmarks ChatGPT 5.2 Auto, Gemini 3 Pro Thinking, Claude Opus 4.5 and Meta's Muse Spark Thinking on 73 novice-fr

Huiqiu Lin, Hongliang Lu, Feihong Yuan, Xiaonan Zhao

Let $\gamma$ be a constant such that $0 < \gamma < 1$, and let $n$ be a sufficiently large integer. Consider a $3$-uniform hypergraph $H$ on $n$ vertices. In 2013, K\"{u}hn, Osthus, and Treglown, along with Khan independently, proved that for large enough $n$ with $n\equiv 0\pmod{3}$, if $\delta_1(H)\geq\binom{2n/3}{2}$, then $H$ admits a perfect matching. F

Jia Feng, Zhanyue Qin, Cuiyun Gao, Ruiqi Wang

Repository-level code intelligence tasks require large language models (LLMs) to process long, multi-file contexts. Such inputs introduce three challenges: crucial context can be obscured by noise, truncated due to limited windows, and increased inference latency. Context compression mitigates these risks by condensing inputs. While studied in NLP, its appli

Jun-Lin Zhou, Mamutjan Ababekri, Yong-Zheng Ren, Yu Wang

Vortex $\gamma$ photons in superposition states have important applications in photonuclear, high-energy, and strong-field physics. However, their controlled generation in the $\gamma$-ray regime remains a great challenge. Here, we put forward a novel method for the generation of vortex $\gamma$ photon in superposition states, with controllable orbital angul

Kentaro Hoshisashi, Carolyn E Phelan, Paolo Barucca

Physics-Informed Neural Networks (PINNs) recast PDE solving as an optimisation problem in function space by minimising a residual-based objective, yet many applications require additional derivative-based relations that are just as fundamental as the governing equations. In this paper, we present Derivative-Constrained PINNs (DC-PINNs), a general framework t

Pankaj Deoli, Atef Tej, Anmol Ashri, Anandatirtha JS

We address the challenge of synthetic-to-real transfer in forestry perception where real data have only coarse Tree labels while synthetic data provide fine-grained trunk/crown annotations. We introduce MGTD, a mixed-granularity dataset with 53k synthetic and 3.6k real images, and a four-stage protocol isolating domain shift and granularity mismatch. Our cor

Santiago Paramés-Estévez, Nicolás Filloy-Montesino, Jorge Fernández-Fabeiro, José Carlos Mouriño-Gallego

The technical support team of a supercomputing centre accumulates, over the course of decades, a large volume of resolved incidents that constitute critical operational knowledge. At the Galician Supercomputing Center (CESGA) this history has been managed for over twenty years with Request Tracker (RT), whose built-in search engine has significant limitation

Franz Weißer, Amar Kasibovic, Wolfgang Utschick

Indoor wireless communication environments are strongly influenced by dynamic conditions, which affect channel state information (CSI) and, consequently, the precoding strategy and the selection of the access point (AP). Device-free sensing and localization functionalities can provide information about these conditions, including, for example, the user's pos

İhsan Ertuğrul Karakaş, Özden Özel, İlkay Ulusoy, Orhan Murat Koçak

Self-sustained neural activity in the absence of ongoing external input is a fundamental feature of nervous system dynamics, yet the conditions under which it can emerge in biophysically grounded network models remain incompletely understood. We studied whether a recurrent network of Hodgkin-Huxley neurons with spike-timing-dependent plasticity and intrinsic

Francesco Fazzini, Kristina Giesel, Eric Rullit

We extend the investigation into the formation of shell-crossing singularites (SCS) in effective polymerized LTB models to the LQG-inspired asymmetric bounce model, as well as to effective LTB models based on the solutions of Bardeen and Hayward, in which no bounce occurs. While the asymmetric bouncing model belongs to the class of bounded polymerization fun

Yanfeng Shi, Pengfei Cai, Jun Liu, Qing Gu

Large Audio-Language Models (LALMs) enable general audio understanding and demonstrate remarkable performance across various audio tasks. However, these models still face challenges in temporal perception (e.g., inferring event onset and offset), leading to limited utility in fine-grained scenarios. To address this issue, we propose Audio-Side Time Prompt an

Hang Fan, Ying Lu, Weican Liu, Dunnan Liu

Accurate building load forecasting plays a critical role in facilitating demand response aggregation and optimizing energy management. However, the complex temporal dependencies and high volatility of building loads limit the improvement of prediction accuracy. To this end, we propose a novel end-to-end building load forecasting framework. Specifically, the

Angshul Majumdar

Can scientific discovery be made arbitrarily easy by choosing the right representation, collecting enough data, and deploying sufficiently powerful algorithms? This paper argues that the answer is fundamentally negative. We introduce the Existential Theory of Research (ETR), a formal framework that models discovery as the recovery of structured explanations

Sinan Kurtyigit, Sabine Schulte im Walde, Alexander Fraser

Metaphor detection models achieve strong benchmark performance, yet it remains unclear whether this reflects transferable generalization or lexical memorization. To address this, we analyze generalization in metaphor detection through RoBERTa, the shared backbone of many state-of-the-art systems, focusing on English verbs using the VU Amsterdam Metaphor Corp

Haoran Lou, Ziyan Liu, Chunxiao Fan, Yuexin Wu

Multimodal Large Language Models (MLLMs) possess intrinsic reasoning and world-knowledge capabilities, yet adapting them for dense retrieval remains challenging. Existing approaches rely on invasive parameter updates, such as full fine-tuning and LoRA, which may disrupt the pre-trained semantic space and impair the structured knowledge essential for reasonin

Skerdi Haviari

Planning empirical experiments such as clinical trials or A/B tests requires sample size determination, which in many interesting cases has no closed-form solution (e.g. factorial or adaptive designs). adsasi is a new R package that enables simulations-first sample size calculations for any trial that can be simulated in short compute time. First, the user s

Mohammed Irshadh Ismaaeel Sathyamangalam Imran, Lasse Peters, Michael Khayyat, Stefano Arrigoni

We address the multi-agent motion planning problem where interactions, collisions, and congestion co-exist. Conventional game-theoretic planners capture interactions among agents but often converge to conservative, congested equilibria. Homotopy planners, on the other hand, can explore topologically distinct paths, but lack mechanisms to account for the inte

Yitao Yan, Shuangyu Han, Jie Bao, Biao Huang

In process operations, it is desirable to manage the sensitivity of the system output against external disturbance in the form of finite $\mathcal{L}_2$-gain stabilization. This matter is, however, nonsensical for stochastic systems because the stochastic uncertainties in the control input almost always lead to an unbounded $\mathcal{L}_2$ gain from the dist

Dhruv Sahnan, Subhabrata Dutta, Tanmoy Chakraborty, Preslav Nakov

Professional fact-checkers rely on domain knowledge and deep contextual understanding to verify claims. Large language models (LLMs) and large reasoning models (LRMs) lack such grounding and primarily reason from available evidence alone, creating a mismatch between expert-led and fully automated claim verification. To mitigate this gap, we posit human-AI co

Sayan Kumar Chaki, Antoine Gourru, Julien Velcin

Fairness in language models is typically studied as a property of a single, centrally optimized model. As large language models become increasingly agentic, we propose that fairness emerges through interaction and exchange. We study this via a controlled hospital triage framework in which two agents negotiate over three structured debate rounds. One agent is

Adam Burgess, Nicholas Werren, Erik M. Gauger

Accurately modelling many-body quantum transport systems poses a challenge both conceptually and computationally due to the growth of the Hilbert space and the multi-scale nature of the geometries and couplings present in most naturally occurring networks. A compounding complexity of such systems is that the environment typically plays a key role in the tran

Yanchao Li, Luobin Qiu, Mingying Zhong

The pointwise space-time behavior of the Green's function of the three-dimensional modified Vlasov-Poisson-Boltzmann system is studied in this paper. It is shown that the Green's function has a decomposition of the macroscopic diffusive waves and Huygens waves with the speed $\sqrt{\frac{8}{3}}$ at low-frequency, the singular kinetic wave and the remainder t

Malo Jézéquel

We improve the best known upper bounds on the number of Ruelle resonances in disks of large radius for Gevrey uniformly hyperbolic flows. The proof is based on Rugh's approach of dynamical determinants that replaces the study of the flow itself by the analysis of a system of open hyperbolic maps.

Bhagyarathi Sahoo, Captain R. Singh, Raghunath Sahoo

The study of spin polarization of $\Lambda$ hyperons in ultrarelativistic heavy-ion collisions provides insights into the angular momentum and vortical structure of the possible existence of QGP. The present study examines the global spin polarization of $\Lambda$ hyperons using a second-order relativistic viscous hydrodynamic framework that incorporates med

Raphael Steiner

Given a digraph $D$, let $c(D)$ denote the largest integer $k$ such that there are $k$ openly disjoint cycles through a vertex, i.e., a collection of directed cycles $C_1,\ldots,C_k$ through a common vertex $v$ such that $C_1-v,\ldots,C_k-v$ are pairwise vertex-disjoint. The famous Caccetta-H\"aggkvist conjecture and its regular variant due to Behzad, Chartr

Geonhee Ahn, Donghyun Lee, Hayoung Doo, Jonggeol Na

Large language models (LLMs) have enabled agentic AI systems for scientific discovery, but most approaches remain limited to textbased reasoning without automated experimental verification. We propose MIND, an LLM-driven framework for automated hypothesis validation in materials research. MIND organizes the scientific discovery process into hypothesis refine

Panagiotis Kostas

We introduce a notion of global dimension for a triangulated category relative to a compact silting object. We prove that the finiteness of this dimension is an intrinsic property of the triangulated category itself and, therefore, independent of the choice of the silting object. Focusing on the setup of connective differential graded (dg) algebras, we analy

Giuseppe Gaetano Luciano, Jaume Gin\' e, Daniel Chemisana

The Heisenberg position-momentum uncertainty relation is a cornerstone of quantum mechanics. However, its standard formulation is not fully consistent with special relativity. While partial understanding has been achieved in the ultra-relativistic regime, a comprehensive description is still lacking, particularly in the intermediate velocity domain, where pa

Andrea Giuliani

Neutrinoless double beta decay is a hypothetical nuclear transition whose observation would demonstrate that neutrinos are their own antiparticles and that lepton number is not conserved, with far-reaching implications for the origin of neutrino mass and the matter-antimatter imbalance in the Universe. This review examines the theoretical foundations of this

Pirzada Suhail, Aditya Anand, Amit Sethi

Reliable and interpretable decision-making is essential in medical imaging, where diagnostic outcomes directly influence patient care. Despite advances in deep learning, most medical AI systems operate as opaque black boxes, providing little insight into why a particular diagnosis was reached. In this paper, we introduce Med-CAM, a framework for generating m

Chenghao Sun, Chengsheng Zhang, Guanzheng Qin, Rui Dai

Mechanistic interpretability seeks to localize model behavior to the internal components that causally realize it. Prior work has advanced activation-space localization and causal tracing, but modules that appear important in activation space may merely aggregate or amplify upstream signals rather than encode the target capability in their own parameters. To

Ruiying Zeng, Shuyao Jiang, Wenxuan Zhao, Yangfan Zhou

Performance debugging in WebAssembly (Wasm) runtimes is essential for ensuring the robustness of Wasm, especially since performance issues have frequently occurred in Wasm runtimes, which can significantly degrade the capabilities of hosted services. Many performance issues in Wasm runtimes result from suboptimal compilation of input Wasm programs, for which

Xiao Pu, Zepeng Cheng, Lin Yuan, Yu Wu

As large language models (LLMs) generate text that increasingly resembles human writing, the subtle cues that distinguish AI-generated content from human-written content become increasingly challenging to capture. Reliance on generator-specific artifacts is inherently unstable, since new models emerge rapidly and reduce the robustness of such shortcuts. This

Zirui Zheng, Yingyang Chen, Xinyue Pei, Xingwei Wang

To address the high mobility impacts and the ultra-reliable and low-latency communication (URLLC) requirements in autonomous driving scenarios, rate-splitting multiple access (RSMA) combined with short-packet communication (SPC) emerges as a promising solution.Autonomous vehicles rely on real-time information exchange to ensure safety and coordination, makin

Eike Schulte, Jan Sören Schwarz, Malte Stomberg, Sharaf Alsharif

In a mosaic, a tessera is a single stone. We introduce tesserae for the co-simulation framework mosaik, where they are sets of entities. They allow for a visual, intuitive, and yet systematic description of simulation scenarios by allowing their entities to be created together and the entities of two tesserae to be connected simultaneously, while ensuring th

Wojciech Żuławiński, Agnieszka Wyłomańska

This article introduces new methods for the analysis of cyclostationary time series with infinite variance. Traditional cyclostationary analysis, based on periodically correlated (PC) processes, relies on the autocovariance function (ACVF). However, the ACVF is not suitable for data exhibiting a heavy-tailed distribution, particularly with infinite variance.

Yizhao Xu, Hongyuan Zhu, Caiyun Liu, Tianfu Wang

3D editing refers to the ability to apply local or global modifications to 3D assets. Effective 3D editing requires maintaining semantic consistency by performing localized changes according to prompts, while also preserving local invariance so that unchanged regions remain consistent with the original. However, existing approaches have significant limitatio

Tiago Fernandes, Francesco Di Renzo, Antonio Onofre, Alejandro Torres-Forné

Glitches frequently contaminate data in gravitational-wave detectors, complicating the observation and analysis of astrophysical signals. This work introduces VIGILant, an automatic pipeline for classification and visualization of glitches in the Virgo detector. Using a curated dataset of Virgo O3b glitches, two machine learning approaches are evaluated: tre

Aviral Dawar, Roshan Karanth, Vikram Goyal, Dhruv Kumar

While Large Language Models (LLMs) have significantly advanced Text-to-SQL performance, existing benchmarks predominantly focus on Western contexts and simplified schemas, leaving a gap in real-world, non-Western applications. We present IndicDB, a multilingual Text-to-SQL benchmark for evaluating cross-lingual semantic parsing across diverse Indic languages

Boxuan Jiang, Chenyun Dai, Can Han

Deep learning-based surface electromyography (sEMG) gesture recognition is frequently bottlenecked by data scarcity and limited subject diversity. While synthetic data generation via Generative Adversarial Networks (GANs) and diffusion models has emerged as a promising augmentation strategy, these approaches often face challenges regarding training stability

Bakhtinur Juraev, Arman Tursunov, Zdeněk Stuchlík, Martin Kološ

We investigate the consistency and precision of approximate analytical expressions for the electromagnetic self-force acting on a charged particle in Schwarzschild spacetime endowed with weak electromagnetic fields. A fundamental requirement of relativistic particle dynamics is the preservation of the four-velocity normalization ($u^\mu u_\mu=-1$), which imp

Giovanna Kobus Conrado, Andreas Pavlogiannis

The verification of concurrent programs under weak-memory models is a burgeoning effort, owing to the increasing adoption of weak memory in concurrent software and hardware. Release/Acquire has become the standard model for high-performance concurrent programming, adopted by common mainstream languages and computer architectures. In a surprising result, Abdu

Luca Avena, Gianmarco Bet, Lars Schroeder, Clara Stegehuis

The node2vec random walk is a non-Markovian random walk on the vertex set of a graph, widely used for network embedding and exploration. This random walk model is defined in terms of three parameters which control the probability of, respectively, backtracking moves, moves within triangles, and moves to the remaining neighboring nodes. From a mathematical st

Yifan Fang, Bile Peng, Yingyang Chen, Qiang Li

In large-scale reconfigurable intelligent surface (RIS) communication systems, the precise acquisition of channel state information (CSI) is challenging. Consider a practical RIS configuration where only a few reflective elements serve as anchors to estimate CSI, which are termed partial CSI. To improve the robustness against partial CSI and the scalability

H. Arlow, D. J. Pisano, M. A. Bershady, L. R. Hunt

We study the evolution of Luminous Compact Blue Galaxies (LCBGs) by making use of H I emission line data provided by the full 856 h COSMOS H I Large Extragalactic Survey (CHILES), which spans a redshift range of $0\leq z\leq 0.48$ within the COSMOS field. We report the results on a cubelet stacking analysis, which we use to estimate the average H I mass evol

Jianfeng Cai, Huiping Li, Jiayi Li

This paper explores the problem of generalized phase retrieval, which involves reconstructing a length-$n$ signal $\bm{x}$ from its $m$ phaseless samples $y_k = \left|\langle \bm{a}_k,\bm{x}\rangle\right|^2$, where $k = 1,2,...,m$, and $\bm{a}_k$ are the measurement vectors. This problem can be reformulated into recovering a positive semidefinite rank-$1$ ma

Alireza Jafari, Hong-Son Nguyen, Yen-Chen Liu

Mobile robots joining public spaces like sidewalks must care for pedestrian comfort. Many studies consider pedestrians' objective safety, for example, by developing collision avoidance algorithms, but not enough studies take the pedestrian's subjective safety or comfort into consideration. Quantifying comfort is a major challenge that hinders mobile robots f

Himanshu Mishra, Anubhab Roy

We investigate the orientation dynamics of a settling spheroid in simple shear flow, combining a deterministic dynamical-systems analysis with a stochastic Fokker-Planck treatment. The dynamics is governed by the competition between the Jeffery torque from the background shear and the inertial torque from settling. For configurations in which gravity lies in

Gregory Morse, Tamás Kozsik

This paper studies obfuscation techniques for Erlang programs at the source, abstract syntax tree, BEAM assembly, and BEAM bytecode levels. We focus on transformations that complicate reverse engineering, decompilation, and recompilation while remaining grounded in the actual behavior of the Erlang compiler, validator, loader, and virtual machine. The paper

Kai-Ge Zhang, Jian-Feng He, Chengjie Fu, Zong-Kuan Guo

We derive a one-loop effective description of axion inflation by integrating out a heavy Dirac fermion with an inflaton-dependent complex mass undergoing a smooth localized threshold transition. The threshold induces correlated corrections to the inflaton and gauge sectors, including a Coleman-Weinberg term, a vacuum-polarization correction, and an anomaly-i

Yitao Yan, Yu Tong, Jie Bao, Wei Wang

The abundance of process operating data in modern industries, along with the rapid advancement of learning techniques, has led to a paradigm shift towards data-centric analysis and control. However, integrating machine learning with control theory for big data-driven control of nonlinear systems remains a challenging open problem. This is because the state-b

Mohammad Raeisi Gahrouei, Pedram Ramin, Vincenzo A. Riggio, Carlos Domingo-Felez

Model-based solutions for nitrous oxide (N2O) emissions from wastewater treatment plants (WWTP) are informed by operational datasets designed to control nutrient levels in liquid waste, coupled with dedicated campaigns for N2O measurements. We analysed how machine learning (ML) models predict disturbances to WWT operation and spatially variable N2O emissions

Thomas Bartz-Beielstein

The spotoptim package implements surrogate-model-based optimization of expensive black-box functions in Python. Building on two decades of Sequential Parameter Optimization (SPO) methodology, it provides a Kriging-based optimization loop with Expected Improvement, support for continuous, integer, and categorical variables, noise-aware evaluation via Optimal

Yunfan Jiao, Tie Liu, Wenyu Jiao, Fengwei Xu

We present a detailed investigation of a multipolar episodic molecular outflow in the mini-starburst region W49N, which hosts the most luminous water maser in the Galaxy. Using high-resolution ($\sim$0.3 arcsec) Atacama Large Millimeter/submillimeter Array (ALMA) observations of the $\mathrm{^{12}CO}$ emission as part of the ALMA-QUARKS survey, we analyze th

Yu-Chen Liu, De-Qing Kong, Song Tan, Zi-Han Zhao

During the first superior conjunction of the Tianwen-1 Mars probe in October 2021, its downlink signal received by the Wuqing 70-m radio telescope passed within 4.53 solar radii of the Sun. The signal was significantly perturbed by the solar wind, providing a mechanism to probe coronal activity. We analyze the Doppler frequency scintillation spectrum of the

José Alfredo Cañizo, Alejandro Gárriz, Diego Alfonso Marín

In this work we study the large-time behaviour of solutions of the Heat Equation in the hyperbolic space $\mathbb{H}^d$, providing precise speeds of convergence in $L^1$ and $L^\infty$ to their asymptotic profiles by means of an adaptation of entropy estimates. For $L^1$ initial conditions we are able to identify the asymptotic profile in $L^1$, which is not

Cihan Ruan, Lebin Zhou, Bingqing Zhao, Rongduo Han

DNA-based storage has emerged as a promising approach to the global data crisis, offering molecular-scale density and millennial-scale stability at low maintenance cost. Over the past decade, substantial progress has been made in storing text, images, and files in DNA -- yet video remains an open challenge. The difficulty is not merely technical: effective v

Ziv Fenigstein, Kobi Gal, Avi Segal, Osama Swidan

Assessing teachers' geometric content knowledge is essential for geometry instructional quality and student learning, but difficult to scale. The Van Hiele model characterizes geometric reasoning through five hierarchical levels. Traditional Van Hiele assessment relies on manual expert analysis of open-ended responses. This process is time-consuming, costly,

Tze-Kean Ng, Joshua Teng-Khing Khoo, Aixin Sun

A good number of toolkits have been developed in Recommender Systems (RecSys) research to promote fair evaluation and reproducibility. However, recent critical examinations of RecSys evaluation protocols have raised concerns regarding the validity of existing evaluation pipelines. In this demonstration, we present RecNextEval, a reference implementation of a

Gregory Morse, Tamás Kozsik

Loop nesting forests (LNFs) are a fundamental abstraction for reasoning about control-flow structure, enabling applications such as compiler optimizations, program analysis, and dominator computation. While efficient static algorithms for constructing LNFs are well understood, maintaining them under dynamic graph updates has remained largely unexplored due t

Patrick Schmidt, Stefan Streif

Solving optimal control problems to determine a stabilizing controller involves a significant computational effort. Time-varying optimal control provides a remedy by designing a tracking system, given as an ordinary differential equation, to track the solution of the optimal control problem. To improve the applicability of the method, measurement errors are

Bruno Petrungaro, Anthony C. Constantinou

Causal Bayesian Networks (CBNs) are a powerful tool for reasoning under uncertainty about complex real-world problems. Such problems evolve over time, responding to external shocks as they occur. To support decision-making, CBNs require a cause-and-effect map of the variables under consideration, known as the network's structure. Learning the graphical struc

N. L. Matsko, Mahmoud A. Salem

Photoabsorption in the low-energy region for C$_{60}$ and PTCDA molecular complexes is studied within linear response TDDFT. For the PBE, B3LYP and HSE exchange-correlation (xc) functionals the dependence of the accuracy of the exciton energy on the electron-hole separation is analyzed. Particular attention is paid to the charge-transfer (CT) excitons. The i

Hui Han, Shunli Wang, Yandan Zhao, Taiping Yao

In Deepfake Detection (DFD) tasks, researchers proposed two types of MLLM-based methods: complementary combination with small DFD detectors, or static forgery knowledge injection. The lack of professional forgery knowledge hinders the performance of these DFD-MLLMs. To solve this, we deeply considered two insightful issues: How to provide high-quality associ

Yinsong Chen, Samson S. Yu, Kashem M. Muttaqi

Deep learning classifiers achieve high accuracy in power quality disturbance (PQD) recognition, but existing explanation methods return a single deterministic attribution map and provide no measure of its reliability. This paper develops a post-hoc Bayesian explanation (B-explanation) method for trained PQD classifiers. A computationally efficient Laplace ap

Brieuc Benvegnen, Nikolaos Ntarakas, Tilen Potisk, Ignacio Pagonabarraga

Ultrasound-guided drug and gene delivery (USDG) is a promising non-invasive approach for targeted therapeutic applications. Mechanical properties of encapsulated microbubbles (EMBs), which serve as contrast agents, strongly affect their specific interactions with ultrasound and are thus critical to the success and efficiency of USDG. Accurate calibration of

Xiaojun Tan, Yuchen Zhao

The statistical essence of the Transformer architecture has long remained elusive: Is it a universal approximator, or a neural network version of known computational algorithms? Through rigorous algebraic proof, we show that the latter better describes Transformer's basic nature: Ordinary Least Squares (OLS) is a special case of the single-layer Linear Trans

Hanxuan Chen, Jie Zheng, Siqi Yang, Tianle Zeng

Vision-and-Language Navigation for Unmanned Aerial Vehicles (UAV-VLN) represents a pivotal challenge in embodied artificial intelligence, focused on enabling UAVs to interpret high-level human commands and execute long-horizon tasks in complex 3D environments. This paper provides a comprehensive and structured survey of the field, from its formal task defini

Aleksandra Deptuch, Anna Drzewicz, Marcin Piwowarczyk, Michał Czerwiński

A ternary liquid crystalline mixture was designed to obtain a tilted hexatic smectic phase in the glassy state. Structural, electro-optic, and dielectric properties of the mixture are investigated, and selected measurements are also performed for its pure components. In particular, the electron density profile perpendicular to smectic layers is determined fr

Fatemeh Marzani, Thijs van Ede, Geert Heijenk, Maarten van Steen

An important aspect of crowd monitoring is knowing how many people we are dealing with. Sometimes, knowing the size of a crowd in a single location and at a specific moment is enough. Matters become problematic when counting the same people across dif ferent locations or counting them over longer periods of time. In those cases, we need to identify and later

Yijia Zeng

The lowest lying charmonium system $\eta_c$ has been observed for more than four decades. Studies of its production and decay properties provide an unique platform to investigate the inner structure of charmonium systems, hence improve our understanding of strong interaction in the charm sector. BESIII detector at the $e^+e^-$ BEPCII collider has already col